WorldWideScience

Sample records for prognostic meteorological model

  1. Verification of a prognostic meteorological and air pollution model for year-long predictions in the Kwinana industrial region of Western Australia

    International Nuclear Information System (INIS)

    Hurley, P.J.; Blockley, A.; Rayner, K.

    2001-01-01

    A prognostic air pollution model (TAPM) has been used to predict meteorology and sulphur dioxide concentration in the Kwinana industrial region of Western Australia for 1997, with a view to verifying TAPM for use in environmental impact assessments and associated air pollution studies. The regulatory plume model, DISPMOD, developed for the Kwinana region has also been run using both an observationally based meteorological file (denoted DISPMOD-O) and using a TAPM-based meteorological file (denoted DISPMOD-T). TAPM predictions of the meteorology for 1997 compare well with the observed values at each of the five monitoring sites. Root mean square error and index of agreement values for temperature and winds indicate that TAPM performs well at predicting the meteorology, compared to the performance of similar models from other studies. The yearly average, 99.9 percentile, maximum and mean of the top 10 ground-level sulphur dioxide concentrations for 1997 were predicted well by all of the model runs, although DISPMOD-O and DISPMOD-T tended to overpredict extreme statistics at sites furthest from the sources. Overall, TAPM performed better than DISPMOD-O, which in turn performed better than DISPMOD-T, for all statistics considered, but we consider that all three sets of results are sufficiently accurate for regulatory applications. The mean of the top ten concentrations is generally considered to be a robust performance statistic for air pollution applications, and we show that compared to the site-averaged observed value of 95μgm -3 , TAPM predicted 94μgm -3 , DISPMOD-O predicted 111μgm -3 and DISPMOD-T predicted 125μgm -3 . The results indicate that the prognostic meteorological and air pollution approach to regulatory modelling used by TAPM, gives comparable or better results than the current regulatory approach used in the Kwinana region (DISPMOD), and also indicates that the approach of using a currently accepted regulatory model with a prognostically

  2. Evaluation of meteorological fields generated by a prognostic mesoscale model using data collected during the 1993 GMAQS/COAST field study

    International Nuclear Information System (INIS)

    Lolk, N.K.; Douglas, S.G.

    1996-01-01

    In 1993, the US Interior Department's Minerals Management Service (MMS) sponsored the Gulf of Mexico Air Quality Study (GMAQS). Its purpose was to assess potential impacts of offshore petrochemical development on ozone concentrations in nonattainment areas in the Texas/Louisiana Gulf Coast region as mandated by the 1990 Clean Air Act Amendments. The GMAQS comprised data collection, data analysis, and applications of an advanced photochemical air quality model, the variable-grid Urban Airshed Model (UAM-V), and a prognostic mesoscale meteorological model (SAIMM -- Systems Applications International Mesoscale Model) to simulate two ozone episodes that were captured during the summer field study. The primary purpose of this paper is to evaluate the SAIMM-simulated meteorological fields using graphical analysis that utilize the comprehensive GMAQS/COAST (Gulf of Mexico Air Quality Study/Coastal Oxidant Assessment for Southeast Texas) database and to demonstrate the ability of the SAIMM to simulate the day-to-day variations in the evolution and structure of the gulf breeze and the mixed layer

  3. Use of data assimilation procedures in the meteorological pre-processors of decision support systems to improve the meteorological input of atmospheric dispersion models

    International Nuclear Information System (INIS)

    Kovalets, I.; Andronopoulos, S.; Bartzis, J.G.

    2003-01-01

    Full text: The Atmospheric Dispersion Models (ADMs) play a key role in decision support systems for nuclear emergency management, as they are used to determine the current, and predict the future spatial distribution of radionuclides after an accidental release of radioactivity to the atmosphere. Meteorological pre-processors (MPPs), usually act as interface between the ADMs and the incoming meteorological data. Therefore the quality of the results of the ADMs crucially depends on the input that they receive from the MPPs. The meteorological data are measurements from one or more stations in the vicinity of the nuclear power plant and/or prognostic data from Numerical Weather Prediction (NWP) models of National Weather Services. The measurements are representative of the past and current local conditions, while the NWP data cover a wider range in space and future time, where no measurements exist. In this respect, the simultaneous use of both by an MPP immediately poses the questions of consistency and of the appropriate methodology for reconciliation of the two kinds of meteorological data. The main objective of the work presented in this paper is the introduction of data assimilation (DA) techniques in the MPP of the RODOS (Real-time On-line Decision Support) system for nuclear emergency management in Europe, developed under the European Project 'RODOS-Migration', to reconcile the NWP data with the local observations coming from the meteorological stations. More specifically, in this paper: the methodological approach for simultaneous use of both meteorological measurements and NWP data in the MPP is presented; the method is validated by comparing results of calculations with experimental data; future ways of improvement of the meteorological input for the calculations of the atmospheric dispersion in the RODOS system are discussed. The methodological approach for solving the DA problem developed in this work is based on the method of optimal interpolation (OI

  4. Application of Prognostic Mesoscale Modeling in the Southeast United States

    International Nuclear Information System (INIS)

    Buckley, R.L.

    1999-01-01

    A prognostic model is being used to provide regional forecasts for a variety of applications at the Savannah River Site (SRS). Emergency response dispersion models available at SRS use the space and time-dependent meteorological data provided by this model to supplement local and regional observations. Output from the model is also used locally to aid in forecasting at SRS, and regionally in providing forecasts of the potential time and location of hurricane landfall within the southeast United States

  5. Elements of a unified prognostic model for secondary air contamination by resuspension

    International Nuclear Information System (INIS)

    Besnus, F.; Garger, E.; Gordeev, S.; Hollaender, W.; Kashparov, V.; Martinez-Serrano, J.; Mironov, V.; Nicholson, K.; Tschiersch, J.; Vintersved, I.

    1996-01-01

    Based on results of several joint experimental campaigns and an extensive literature survey, a prognostic model was constructed capable of predicting airborne activity concentrations and size distributions as well as soil surface activity concentrations as a function of time and meteorological conditions. Example scenario calculations show that agricultural practices are of lesser importance to secondary air contamination than dust storms immediately after primary deposition and forest fires

  6. Evaluation of an atmospheric model with surface and ABL meteorological data for energy applications in structured areas

    Science.gov (United States)

    Triantafyllou, A. G.; Kalogiros, J.; Krestou, A.; Leivaditou, E.; Zoumakis, N.; Bouris, D.; Garas, S.; Konstantinidis, E.; Wang, Q.

    2018-03-01

    This paper provides the performance evaluation of the meteorological component of The Air Pollution Model (TAPM), a nestable prognostic model, in predicting meteorological variables in urban areas, for both its surface layer and atmospheric boundary layer (ABL) turbulence parameterizations. The model was modified by incorporating four urban land surface types, replacing the existing single urban surface. Control runs were carried out over the wider area of Kozani, an urban area in NW Greece. The model was evaluated for both surface and ABL meteorological variables by using measurements of near-surface and vertical profiles of wind and temperature. The data were collected by using monitoring surface stations in selected sites as well as an acoustic sounder (SOnic Detection And Ranging (SODAR), up to 300 m above ground) and a radiometer profiler (up to 600 m above ground). The results showed the model demonstrated good performance in predicting the near-surface meteorology in the Kozani region for both a winter and a summer month. In the ABL, the comparison showed that the model's forecasts generally performed well with respect to the thermal structure (temperature profiles and ABL height) but overestimated wind speed at the heights of comparison (mostly below 200 m) up to 3-4 ms-1.

  7. Mathematical problems in meteorological modelling

    CERN Document Server

    Csomós, Petra; Faragó, István; Horányi, András; Szépszó, Gabriella

    2016-01-01

    This book deals with mathematical problems arising in the context of meteorological modelling. It gathers and presents some of the most interesting and important issues from the interaction of mathematics and meteorology. It is unique in that it features contributions on topics like data assimilation, ensemble prediction, numerical methods, and transport modelling, from both mathematical and meteorological perspectives. The derivation and solution of all kinds of numerical prediction models require the application of results from various mathematical fields. The present volume is divided into three parts, moving from mathematical and numerical problems through air quality modelling, to advanced applications in data assimilation and probabilistic forecasting. The book arose from the workshop “Mathematical Problems in Meteorological Modelling” held in Budapest in May 2014 and organized by the ECMI Special Interest Group on Numerical Weather Prediction. Its main objective is to highlight the beauty of the de...

  8. Utilization of mesoscale atmospheric dynamic model PHYSIC as a meteorological forecast model in nuclear emergency response system

    International Nuclear Information System (INIS)

    Nagai, Haruyasu; Yamazawa, Hiromi

    1997-01-01

    It is advantageous for an emergency response system to have a forecast function to provide a time margin for countermeasures in case of a nuclear accident. We propose to apply an atmospheric dynamic model PHYSIC (Prognostic HYdroStatic model Including turbulence Closure model) as a meteorological forecast model in the emergency system. The model uses GPV data which are the output of the numerical weather forecast model of Japan Meteorological Agency as the initial and boundary conditions. The roles of PHYSIC are the interface between GPV data and the emergency response system and the forecast of local atmospheric phenomena within the model domain. This paper presents a scheme to use PHYSIC to forecast local wind and its performance. Horizontal grid number of PHYSIC is fixed to 50 x 50, whereas the mesh and domain sizes are determined in consideration of topography causing local winds at an objective area. The model performance was examined for the introduction of GPV data through initial and boundary conditions and the predictability of local wind field and atmospheric stability. The model performance was on an acceptable level as the forecast model. It was also recognized that improvement of cloud calculation was necessary in simulating atmospheric stability. (author)

  9. Meteorological uncertainty of atmospheric dispersion model results (MUD)

    International Nuclear Information System (INIS)

    Havskov Soerensen, J.; Amstrup, B.; Feddersen, H.

    2013-08-01

    The MUD project addresses assessment of uncertainties of atmospheric dispersion model predictions, as well as possibilities for optimum presentation to decision makers. Previously, it has not been possible to estimate such uncertainties quantitatively, but merely to calculate the 'most likely' dispersion scenario. However, recent developments in numerical weather prediction (NWP) include probabilistic forecasting techniques, which can be utilised also for long-range atmospheric dispersion models. The ensemble statistical methods developed and applied to NWP models aim at describing the inherent uncertainties of the meteorological model results. These uncertainties stem from e.g. limits in meteorological observations used to initialise meteorological forecast series. By perturbing e.g. the initial state of an NWP model run in agreement with the available observational data, an ensemble of meteorological forecasts is produced from which uncertainties in the various meteorological parameters are estimated, e.g. probabilities for rain. Corresponding ensembles of atmospheric dispersion can now be computed from which uncertainties of predicted radionuclide concentration and deposition patterns can be derived. (Author)

  10. Coupling meteorological and hydrological models for flood forecasting

    Directory of Open Access Journals (Sweden)

    Bartholmes

    2005-01-01

    Full Text Available This paper deals with the problem of analysing the coupling of meteorological meso-scale quantitative precipitation forecasts with distributed rainfall-runoff models to extend the forecasting horizon. Traditionally, semi-distributed rainfall-runoff models have been used for real time flood forecasting. More recently, increased computer capabilities allow the utilisation of distributed hydrological models with mesh sizes from tenths of metres to a few kilometres. On the other hand, meteorological models, providing the quantitative precipitation forecast, tend to produce average values on meshes ranging from slightly less than 10 to 200 kilometres. Therefore, to improve the quality of flood forecasts, the effects of coupling the meteorological and the hydrological models at different scales were analysed. A distributed hydrological model (TOPKAPI was developed and calibrated using a 1x1 km mesh for the case of the river Po closed at Ponte Spessa (catchment area c. 37000 km2. The model was then coupled with several other European meteorological models ranging from the Limited Area Models (provided by DMI and DWD with resolutions from 0.0625° * 0.0625°, to the ECMWF ensemble predictions with a resolution of 1.85° * 1.85°. Interesting results, describing the coupled model behaviour, are available for a meteorological extreme event in Northern Italy (Nov. 1994. The results demonstrate the poor reliability of the quantitative precipitation forecasts produced by meteorological models presently available; this is not resolved using the Ensemble Forecasting technique, when compared with results obtainable with measured rainfall.

  11. Meteorological uncertainty of atmospheric dispersion model results (MUD)

    Energy Technology Data Exchange (ETDEWEB)

    Havskov Soerensen, J.; Amstrup, B.; Feddersen, H. [Danish Meteorological Institute, Copenhagen (Denmark)] [and others

    2013-08-15

    The MUD project addresses assessment of uncertainties of atmospheric dispersion model predictions, as well as possibilities for optimum presentation to decision makers. Previously, it has not been possible to estimate such uncertainties quantitatively, but merely to calculate the 'most likely' dispersion scenario. However, recent developments in numerical weather prediction (NWP) include probabilistic forecasting techniques, which can be utilised also for long-range atmospheric dispersion models. The ensemble statistical methods developed and applied to NWP models aim at describing the inherent uncertainties of the meteorological model results. These uncertainties stem from e.g. limits in meteorological observations used to initialise meteorological forecast series. By perturbing e.g. the initial state of an NWP model run in agreement with the available observational data, an ensemble of meteorological forecasts is produced from which uncertainties in the various meteorological parameters are estimated, e.g. probabilities for rain. Corresponding ensembles of atmospheric dispersion can now be computed from which uncertainties of predicted radionuclide concentration and deposition patterns can be derived. (Author)

  12. Concordance for prognostic models with competing risks

    DEFF Research Database (Denmark)

    Wolbers, Marcel; Blanche, Paul; Koller, Michael T

    2014-01-01

    The concordance probability is a widely used measure to assess discrimination of prognostic models with binary and survival endpoints. We formally define the concordance probability for a prognostic model of the absolute risk of an event of interest in the presence of competing risks and relate i...

  13. Distributed Prognostics Based on Structural Model Decomposition

    Data.gov (United States)

    National Aeronautics and Space Administration — Within systems health management, prognostics focuses on predicting the remaining useful life of a system. In the model-based prognostics paradigm, physics-based...

  14. Diagnostic and Prognostic Models for Generator Step-Up Transformers

    Energy Technology Data Exchange (ETDEWEB)

    Vivek Agarwal; Nancy J. Lybeck; Binh T. Pham

    2014-09-01

    In 2014, the online monitoring (OLM) of active components project under the Light Water Reactor Sustainability program at Idaho National Laboratory (INL) focused on diagnostic and prognostic capabilities for generator step-up transformers. INL worked with subject matter experts from the Electric Power Research Institute (EPRI) to augment and revise the GSU fault signatures previously implemented in the Electric Power Research Institute’s (EPRI’s) Fleet-Wide Prognostic and Health Management (FW-PHM) Suite software. Two prognostic models were identified and implemented for GSUs in the FW-PHM Suite software. INL and EPRI demonstrated the use of prognostic capabilities for GSUs. The complete set of fault signatures developed for GSUs in the Asset Fault Signature Database of the FW-PHM Suite for GSUs is presented in this report. Two prognostic models are described for paper insulation: the Chendong model for degree of polymerization, and an IEEE model that uses a loading profile to calculates life consumption based on hot spot winding temperatures. Both models are life consumption models, which are examples of type II prognostic models. Use of the models in the FW-PHM Suite was successfully demonstrated at the 2014 August Utility Working Group Meeting, Idaho Falls, Idaho, to representatives from different utilities, EPRI, and the Halden Research Project.

  15. A Meteorological Distribution System for High Resolution Terrestrial Modeling (MicroMet)

    Science.gov (United States)

    Liston, G. E.; Elder, K.

    2004-12-01

    Spatially distributed terrestrial models generally require atmospheric forcing data on horizontal grids that are of higher resolution than available meteorological data. Furthermore, the meteorological data collected may not necessarily represent the area of interest's meteorological variability. To address these deficiencies, computationally efficient and physically realistic methods must be developed to take available meteorological data sets (e.g., meteorological tower observations) and generate high-resolution atmospheric-forcing distributions. This poster describes MicroMet, a quasi-physically-based, but simple meteorological distribution model designed to produce high-resolution (e.g., 5-m to 1-km horizontal grid increments) meteorological data distributions required to run spatially distributed terrestrial models over a wide variety of landscapes. The model produces distributions of the seven fundamental atmospheric forcing variables required to run most terrestrial models: air temperature, relative humidity, wind speed, wind direction, incoming solar radiation, incoming longwave radiation, and precipitation. MicroMet includes a preprocessor that analyzes meteorological station data and identifies and repairs potential data deficiencies. The model uses known relationships between meteorological variables and the surrounding area (primarily topography) to distribute those variables over any given landscape. MicroMet performs two kinds of adjustments to available meteorological data: 1) when there are data at more than one location, at a given time, the data are spatially interpolated over the domain using a Barnes objective analysis scheme, and 2) physical sub-models are applied to each MicroMet variable to improve its realism at a given point in space and time with respect to the terrain. The three, 25-km by 25-km, Cold Land Processes Experiment (CLPX) mesoscale study areas (MSAs: Fraser, North Park, and Rabbit Ears) will be used as example Micro

  16. Comparison of two prognostic models for acute pulmonary embolism

    Directory of Open Access Journals (Sweden)

    Abd-ElRahim Ibrahim Youssef

    2016-10-01

    Conclusion: (1 There is an agreement to great extent in risk stratification of APE patients by PESI and ESC prognostic models, where mortality rate is increased among high risk classes of both models, (2 ESC prognostic model is more accurate than PESI model in mortality prediction of APE patients especially in the high risk class, (3 echocardiographic evidence of RVD and elevated plasma BNP can help to identify APE patients at increased risk of adverse short-term outcome and (4 integration of RVD assessment by echocardiography and BNP to clinical findings improves the prognostic value of ESC model.

  17. Intercomparisons of Prognostic, Diagnostic, and Inversion Modeling Approaches for Estimation of Net Ecosystem Exchange over the Pacific Northwest Region

    Science.gov (United States)

    Turner, D. P.; Jacobson, A. R.; Nemani, R. R.

    2013-12-01

    The recent development of large spatially-explicit datasets for multiple variables relevant to monitoring terrestrial carbon flux offers the opportunity to estimate the terrestrial land flux using several alternative, potentially complimentary, approaches. Here we developed and compared regional estimates of net ecosystem exchange (NEE) over the Pacific Northwest region of the U.S. using three approaches. In the prognostic modeling approach, the process-based Biome-BGC model was driven by distributed meteorological station data and was informed by Landsat-based coverages of forest stand age and disturbance regime. In the diagnostic modeling approach, the quasi-mechanistic CFLUX model estimated net ecosystem production (NEP) by upscaling eddy covariance flux tower observations. The model was driven by distributed climate data and MODIS FPAR (the fraction of incident PAR that is absorbed by the vegetation canopy). It was informed by coarse resolution (1 km) data about forest stand age. In both the prognostic and diagnostic modeling approaches, emissions estimates for biomass burning, harvested products, and river/stream evasion were added to model-based NEP to get NEE. The inversion model (CarbonTracker) relied on observations of atmospheric CO2 concentration to optimize prior surface carbon flux estimates. The Pacific Northwest is heterogeneous with respect to land cover and forest management, and repeated surveys of forest inventory plots support the presence of a strong regional carbon sink. The diagnostic model suggested a stronger carbon sink than the prognostic model, and a much larger sink that the inversion model. The introduction of Landsat data on disturbance history served to reduce uncertainty with respect to regional NEE in the diagnostic and prognostic modeling approaches. The FPAR data was particularly helpful in capturing the seasonality of the carbon flux using the diagnostic modeling approach. The inversion approach took advantage of a global

  18. A Model-Based Prognostics Approach Applied to Pneumatic Valves

    Data.gov (United States)

    National Aeronautics and Space Administration — Within the area of systems health management, the task of prognostics centers on predicting when components will fail. Model-based prognostics exploits domain...

  19. A Model-based Prognostics Approach Applied to Pneumatic Valves

    Data.gov (United States)

    National Aeronautics and Space Administration — Within the area of systems health management, the task of prognostics centers on predicting when components will fail. Model-based prognostics exploits domain...

  20. Effects of Meteorological Data Quality on Snowpack Modeling

    Science.gov (United States)

    Havens, S.; Marks, D. G.; Robertson, M.; Hedrick, A. R.; Johnson, M.

    2017-12-01

    Detailed quality control of meteorological inputs is the most time-intensive component of running the distributed, physically-based iSnobal snow model, and the effect of data quality of the inputs on the model is unknown. The iSnobal model has been run operationally since WY2013, and is currently run in several basins in Idaho and California. The largest amount of user input during modeling is for the quality control of precipitation, temperature, relative humidity, solar radiation, wind speed and wind direction inputs. Precipitation inputs require detailed user input and are crucial to correctly model the snowpack mass. This research applies a range of quality control methods to meteorological input, from raw input with minimal cleaning, to complete user-applied quality control. The meteorological input cleaning generally falls into two categories. The first is global minimum/maximum and missing value correction that could be corrected and/or interpolated with automated processing. The second category is quality control for inputs that are not globally erroneous, yet are still unreasonable and generally indicate malfunctioning measurement equipment, such as temperature or relative humidity that remains constant, or does not correlate with daily trends observed at nearby stations. This research will determine how sensitive model outputs are to different levels of quality control and guide future operational applications.

  1. Development and validation of logistic prognostic models by predefined SAS-macros

    Directory of Open Access Journals (Sweden)

    Ziegler, Christoph

    2006-02-01

    Full Text Available In medical decision making about therapies or diagnostic procedures in the treatment of patients the prognoses of the course or of the magnitude of diseases plays a relevant role. Beside of the subjective attitude of the clinician mathematical models can help in providing such prognoses. Such models are mostly multivariate regression models. In the case of a dichotomous outcome the logistic model will be applied as the standard model. In this paper we will describe SAS-macros for the development of such a model, for examination of the prognostic performance, and for model validation. The rational for this developmental approach of a prognostic modelling and the description of the macros can only given briefly in this paper. Much more details are given in. These 14 SAS-macros are a tool for setting up the whole process of deriving a prognostic model. Especially the possibility of validating the model by a standardized software tool gives an opportunity, which is not used in general in published prognostic models. Therefore, this can help to develop new models with good prognostic performance for use in medical applications.

  2. Applications of complex terrain meteorological models to emergency response management

    International Nuclear Information System (INIS)

    Yamada, Tetsuji; Leone, J.M. Jr.; Rao, K.S.; Dickerson, M.H.; Bader, D.C.; Williams, M.D.

    1989-01-01

    The Office of Health and Environmental Research (OHER), US Department of Energy (DOE), has supported the development of mesoscale transport and diffusion and meteorological models for several decades. The model development activities are closely tied to the OHER field measurement program which has generated a large amount of meteorological and tracer gas data that have been used extensively to test and improve both meteorological and dispersion models. This paper briefly discusses the history of the model development activities associated with the OHER atmospheric science program. The discussion will then focus on how results from this program have made their way into the emergency response community in the past, and what activities are presently being pursued to improve real-time emergency response capabilities. Finally, fruitful areas of research for improving real-time emergency response modeling capabilities are suggested. 35 refs., 5 figs

  3. Optimizing Time Intervals of Meteorological Data Used with Atmospheric Dose Modeling at SRS

    International Nuclear Information System (INIS)

    Simpkins, A.A.

    1999-01-01

    Measured tritium oxide concentrations in air have been compared with calculated values using routine release Gaussian plume models for different time intervals of meteorological data. These comparisons determined an optimum time interval of meteorological data used with atmospheric dose models at the Savannah River Site (SRS). Meteorological data of varying time intervals (1-yr to 10-yr) were used for the comparison. Insignificant differences are seen in using a one-year database as opposed to a five-year database. Use of a ten-year database results in slightly more conservative results. For meteorological databases of length one to five years the mean ratio of predicted to measured tritium oxide concentrations is approximately 1.25 whereas for the ten-year meteorological database the ration is closer to 1.35. Currently at the Savannah River Site a meteorological database of five years duration is used for all dose models. This study suggests no substantially improved accuracy using meteorological files of shorter or longer time intervals

  4. A framework for quantifying net benefits of alternative prognostic models

    DEFF Research Database (Denmark)

    Rapsomaniki, Eleni; White, Ian R; Wood, Angela M

    2012-01-01

    New prognostic models are traditionally evaluated using measures of discrimination and risk reclassification, but these do not take full account of the clinical and health economic context. We propose a framework for comparing prognostic models by quantifying the public health impact (net benefit......) of the treatment decisions they support, assuming a set of predetermined clinical treatment guidelines. The change in net benefit is more clinically interpretable than changes in traditional measures and can be used in full health economic evaluations of prognostic models used for screening and allocating risk...... reduction interventions. We extend previous work in this area by quantifying net benefits in life years, thus linking prognostic performance to health economic measures; by taking full account of the occurrence of events over time; and by considering estimation and cross-validation in a multiple...

  5. A framework for quantifying net benefits of alternative prognostic models

    NARCIS (Netherlands)

    Rapsomaniki, E.; White, I.R.; Wood, A.M.; Thompson, S.G.; Feskens, E.J.M.; Kromhout, D.

    2012-01-01

    New prognostic models are traditionally evaluated using measures of discrimination and risk reclassification, but these do not take full account of the clinical and health economic context. We propose a framework for comparing prognostic models by quantifying the public health impact (net benefit)

  6. Astronomijos ir meteorologijos jungtys Arato Reiškiniuose. The Connections Between Astronomy and Meteorology in Aratus’ Phaenomena

    Directory of Open Access Journals (Sweden)

    Naglis Kardelis

    2010-01-01

    Full Text Available The author of the article focuses on the connections between astronomy and meteorology in the Phaenome­na of Aratus of Soli (fl. 276 BC. Firstly, the attention is drawn to such connections that are apparent in the structure of the poem which has two structural parts, the astronomical and the meteorological. It is shown that the astronomical part does not end abruptly, but merges gradually into the meteorological one. Such effect is achieved by way of methodical downward transition from the North pole and the upper sky, via the lower sky, to the atmosphere and then to the very surface of earth. Therefore, Aratus first of all describes the celestial phenomena as such, simply as marvels of the sky, without any reference to their prognostic (that is, meteorological function. Then he speaks of them in relation to their prognostic function. After that the poet descends from the level of celestial phenomena to the level of earth’s atmosphere, that is, from astronomical to meteorological level, and focuses on meteorologi­cal phenomena proper, such as the rain, the storm, the wind, the snow, the rainbow, and so on, drawing attention to their prognostic function. Then the poet descends even lower, from the level of atmosphere to the level of earth’s surface, and describes various earthly phenomena, such as prognostically relevant physical and chemical properties of various common substances and strange behaviour of animals which also has significant prognostic value. In the process of the overall gradual descent from the North pole to the ground level, the poet, when it serves his artistic purpose, sometimes quickly and unexpectedly changes the scale from large to small (and vice versa, or alti­tude from high to low (and vice versa, or even speaks of various phenomena that simultaneously appear on different scale, large and small, and on different levels, astronomical and meteorological.Secondly, the author of the article analyses the connections

  7. Sensitivity of hydrological modeling to meteorological data and implications for climate change studies

    International Nuclear Information System (INIS)

    Roy, L.G.; Roy, R.; Desrochers, G.E.; Vaillancourt, C.; Chartier, I.

    2008-01-01

    There are uncertainties associated with the use of hydrological models. This study aims to analyse one source of uncertainty associated with hydrological modeling, particularly in the context of climate change studies on water resources. Additional intent of this study is to compare the ability of some meteorological data sources, used in conjunction with an hydrological model, to reproduce the hydrologic regime of a watershed. A case study on a watershed of south-western Quebec, Canada using five different sources of meteorological data as input to an offline hydrological model are presented in this paper. Data used came from weather stations, NCEP reanalysis, ERA40 reanalysis and two Canadian Regional Climate Model (CRCM) runs driven by NCEP and ERA40 reanalysis, providing atmospheric driving boundary conditions to this limited-area climate model. To investigate the sensitivity of simulated streamflow to different sources of meteorological data, we first calibrated the hydrological model with each of the meteorological data sets over the 1961-1980 period. The five different sets of parameters of the hydrological model were then used to simulate streamflow of the 1981-2000 validation period with the five meteorological data sets as inputs. The 25 simulated streamflow series have been compared to the observed streamflow of the watershed. The five meteorological data sets do not have the same ability, when used with the hydrological model, to reproduce streamflow. Our results show also that the hydrological model parameters used may have an important influence on results such as water balance, but it is linked with the differences that may have in the characteristics of the meteorological data used. For climate change impacts assessments on water resources, we have found that there is an uncertainty associated with the meteorological data used to calibrate the model. For expected changes on mean annual flows of the Chateauguay River, our results vary from a small

  8. Application of nonlinear forecasting techniques for meteorological modeling

    Directory of Open Access Journals (Sweden)

    V. Pérez-Muñuzuri

    2000-10-01

    Full Text Available A nonlinear forecasting method was used to predict the behavior of a cloud coverage time series several hours in advance. The method is based on the reconstruction of a chaotic strange attractor using four years of cloud absorption data obtained from half-hourly Meteosat infrared images from Northwestern Spain. An exhaustive nonlinear analysis of the time series was carried out to reconstruct the phase space of the underlying chaotic attractor. The forecast values are used by a non-hydrostatic meteorological model ARPS for daily weather prediction and their results compared with surface temperature measurements from a meteorological station and a vertical sounding. The effect of noise in the time series is analyzed in terms of the prediction results.Key words: Meterology and atmospheric dynamics (mesoscale meteorology; general – General (new fields

  9. Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

    OpenAIRE

    M. Bocquet; H. Elbern; H. Eskes; M. Hirtl; R. Žabkar; G. R. Carmichael; J. Flemming; A. Inness; M. Pagowski; J. L. Pérez Camaño; P. E. Saide; R. San Jose; M. Sofiev; J. Vira; A. Baklanov

    2015-01-01

    Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of three-dimensional chemical (including aerosol) concentrations and perform inverse modeling of input variables or model parameters (e.g., emissions). Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. They offer the possibility to assimilate both meteorologica...

  10. Model-based Prognostics with Concurrent Damage Progression Processes

    Data.gov (United States)

    National Aeronautics and Space Administration — Model-based prognostics approaches rely on physics-based models that describe the behavior of systems and their components. These models must account for the several...

  11. A prognostic pollen emissions model for climate models (PECM1.0

    Directory of Open Access Journals (Sweden)

    M. C. Wozniak

    2017-11-01

    Full Text Available We develop a prognostic model called Pollen Emissions for Climate Models (PECM for use within regional and global climate models to simulate pollen counts over the seasonal cycle based on geography, vegetation type, and meteorological parameters. Using modern surface pollen count data, empirical relationships between prior-year annual average temperature and pollen season start dates and end dates are developed for deciduous broadleaf trees (Acer, Alnus, Betula, Fraxinus, Morus, Platanus, Populus, Quercus, Ulmus, evergreen needleleaf trees (Cupressaceae, Pinaceae, grasses (Poaceae; C3, C4, and ragweed (Ambrosia. This regression model explains as much as 57 % of the variance in pollen phenological dates, and it is used to create a climate-flexible phenology that can be used to study the response of wind-driven pollen emissions to climate change. The emissions model is evaluated in the Regional Climate Model version 4 (RegCM4 over the continental United States by prescribing an emission potential from PECM and transporting pollen as aerosol tracers. We evaluate two different pollen emissions scenarios in the model using (1 a taxa-specific land cover database, phenology, and emission potential, and (2 a plant functional type (PFT land cover, phenology, and emission potential. The simulated surface pollen concentrations for both simulations are evaluated against observed surface pollen counts in five climatic subregions. Given prescribed pollen emissions, the RegCM4 simulates observed concentrations within an order of magnitude, although the performance of the simulations in any subregion is strongly related to the land cover representation and the number of observation sites used to create the empirical phenological relationship. The taxa-based model provides a better representation of the phenology of tree-based pollen counts than the PFT-based model; however, we note that the PFT-based version provides a useful and climate-flexible emissions

  12. Model Adaptation for Prognostics in a Particle Filtering Framework

    Directory of Open Access Journals (Sweden)

    Bhaskar Saha

    2011-01-01

    Full Text Available One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the “curse of dimensionality”, i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for “well-designed” particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion and Li-Polymer batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well-designed prognostic model that can take advantage of the model adaptation properties of a particle filter.

  13. Model Adaptation for Prognostics in a Particle Filtering Framework

    Science.gov (United States)

    Saha, Bhaskar; Goebel, Kai Frank

    2011-01-01

    One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the "curse of dimensionality", i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for "well-designed" particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well-designed prognostic model that can take advantage of the model adaptation properties of a particle filter.

  14. One multi-media environmental system with linkage between meteorology/ hydrology/ air quality models and water quality model

    Science.gov (United States)

    Tang, C.; Lynch, J. A.; Dennis, R. L.

    2016-12-01

    The biogeochemical processing of nitrogen and associated pollutants is driven by meteorological and hydrological processes in conjunction with pollutant loading. There are feedbacks between meteorology and hydrology that will be affected by land-use change and climate change. Changes in meteorology will affect pollutant deposition. It is important to account for those feedbacks and produce internally consistent simulations of meteorology, hydrology, and pollutant loading to drive the (watershed/water quality) biogeochemical models. In this study, the ecological response to emission reductions in streams in the Potomac watershed was evaluated. Firstly, we simulated the deposition by using the fully coupled Weather Research & Forecasting (WRF) model and the Community Multiscale Air Quality (CAMQ) model; secondly, we created the hydrological data by the offline linked Variable Infiltration Capacity (VIC) model and the WRF model. Lastly, we investigated the water quality by one comprehensive/environment model, namely the linkage of CMAQ, WRF, VIC and the Model of Acidification of Groundwater In Catchment (MAGIC) model from 2002 to 2010.The simulated results (such as NO3, SO4, and SBC) fit well to the observed values. The linkage provides a generally accurate, well-tested tool for evaluating sensitivities to varying meteorology and environmental changes on acidification and other biogeochemical processes, with capability to comprehensively explore strategic policy and management design.

  15. Application of nonlinear forecasting techniques for meteorological modeling

    Directory of Open Access Journals (Sweden)

    V. Pérez-Muñuzuri

    Full Text Available A nonlinear forecasting method was used to predict the behavior of a cloud coverage time series several hours in advance. The method is based on the reconstruction of a chaotic strange attractor using four years of cloud absorption data obtained from half-hourly Meteosat infrared images from Northwestern Spain. An exhaustive nonlinear analysis of the time series was carried out to reconstruct the phase space of the underlying chaotic attractor. The forecast values are used by a non-hydrostatic meteorological model ARPS for daily weather prediction and their results compared with surface temperature measurements from a meteorological station and a vertical sounding. The effect of noise in the time series is analyzed in terms of the prediction results.

    Key words: Meterology and atmospheric dynamics (mesoscale meteorology; general – General (new fields

  16. Online-coupled meteorology and chemistry models: history, current status, and outlook

    Directory of Open Access Journals (Sweden)

    Y. Zhang

    2008-06-01

    Full Text Available The climate-chemistry-aerosol-cloud-radiation feedbacks are important processes occurring in the atmosphere. Accurately simulating those feedbacks requires fully-coupled meteorology, climate, and chemistry models and presents significant challenges in terms of both scientific understanding and computational demand. This paper reviews the history and current status of the development and application of online-coupled meteorology and chemistry models, with a focus on five representative models developed in the US including GATOR-GCMOM, WRF/Chem, CAM3, MIRAGE, and Caltech unified GCM. These models represent the current status and/or the state-of-the science treatments of online-coupled models worldwide. Their major model features, typical applications, and physical/chemical treatments are compared with a focus on model treatments of aerosol and cloud microphysics and aerosol-cloud interactions. Aerosol feedbacks to planetary boundary layer meteorology and aerosol indirect effects are illustrated with case studies for some of these models. Future research needs for model development, improvement, application, as well as major challenges for online-coupled models are discussed.

  17. New prognostic model for extranodal natural killer/T cell lymphoma, nasal type.

    Science.gov (United States)

    Cai, Qingqing; Luo, Xiaolin; Zhang, Guanrong; Huang, Huiqiang; Huang, Hui; Lin, Tongyu; Jiang, Wenqi; Xia, Zhongjun; Young, Ken H

    2014-09-01

    Extranodal natural killer/T cell lymphoma, nasal type (ENKTL) is an aggressive disease with a poor prognosis, requiring risk stratification in affected patients. We designed a new prognostic model specifically for ENKTL to identify high-risk patients who need more aggressive therapy. We retrospectively reviewed 158 patients who were newly diagnosed with ENKTL. The estimated 5-year overall survival rate was 39.4 %. Independent prognostic factors included total protein (TP) 100 mg/dL, and Korean Prognostic Index (KPI) score ≥2. We constructed a new prognostic model by combining these prognostic factors: group 1 (64 cases (41.0 %)), no adverse factors; group 2 (58 cases (37.2 %)), one adverse factor; and group 3 (34 cases (21.8 %)), two or three adverse factors. The 5-year overall survival (OS) rates of these groups were 66.7, 23.0, and 5.9 %, respectively (p KPI model alone (p KPI model alone.

  18. Prognostic modelling options for remaining useful life estimation by industry

    Science.gov (United States)

    Sikorska, J. Z.; Hodkiewicz, M.; Ma, L.

    2011-07-01

    Over recent years a significant amount of research has been undertaken to develop prognostic models that can be used to predict the remaining useful life of engineering assets. Implementations by industry have only had limited success. By design, models are subject to specific assumptions and approximations, some of which are mathematical, while others relate to practical implementation issues such as the amount of data required to validate and verify a proposed model. Therefore, appropriate model selection for successful practical implementation requires not only a mathematical understanding of each model type, but also an appreciation of how a particular business intends to utilise a model and its outputs. This paper discusses business issues that need to be considered when selecting an appropriate modelling approach for trial. It also presents classification tables and process flow diagrams to assist industry and research personnel select appropriate prognostic models for predicting the remaining useful life of engineering assets within their specific business environment. The paper then explores the strengths and weaknesses of the main prognostics model classes to establish what makes them better suited to certain applications than to others and summarises how each have been applied to engineering prognostics. Consequently, this paper should provide a starting point for young researchers first considering options for remaining useful life prediction. The models described in this paper are Knowledge-based (expert and fuzzy), Life expectancy (stochastic and statistical), Artificial Neural Networks, and Physical models.

  19. Random forest meteorological normalisation models for Swiss PM10 trend analysis

    Science.gov (United States)

    Grange, Stuart K.; Carslaw, David C.; Lewis, Alastair C.; Boleti, Eirini; Hueglin, Christoph

    2018-05-01

    Meteorological normalisation is a technique which accounts for changes in meteorology over time in an air quality time series. Controlling for such changes helps support robust trend analysis because there is more certainty that the observed trends are due to changes in emissions or chemistry, not changes in meteorology. Predictive random forest models (RF; a decision tree machine learning technique) were grown for 31 air quality monitoring sites in Switzerland using surface meteorological, synoptic scale, boundary layer height, and time variables to explain daily PM10 concentrations. The RF models were used to calculate meteorologically normalised trends which were formally tested and evaluated using the Theil-Sen estimator. Between 1997 and 2016, significantly decreasing normalised PM10 trends ranged between -0.09 and -1.16 µg m-3 yr-1 with urban traffic sites experiencing the greatest mean decrease in PM10 concentrations at -0.77 µg m-3 yr-1. Similar magnitudes have been reported for normalised PM10 trends for earlier time periods in Switzerland which indicates PM10 concentrations are continuing to decrease at similar rates as in the past. The ability for RF models to be interpreted was leveraged using partial dependence plots to explain the observed trends and relevant physical and chemical processes influencing PM10 concentrations. Notably, two regimes were suggested by the models which cause elevated PM10 concentrations in Switzerland: one related to poor dispersion conditions and a second resulting from high rates of secondary PM generation in deep, photochemically active boundary layers. The RF meteorological normalisation process was found to be robust, user friendly and simple to implement, and readily interpretable which suggests the technique could be useful in many air quality exploratory data analysis situations.

  20. Comparative analysis of meteorological performance of coupled chemistry-meteorology models in the context of AQMEII phase 2

    Science.gov (United States)

    Air pollution simulations critically depend on the quality of the underlying meteorology. In phase 2 of the Air Quality Model Evaluation International Initiative (AQMEII-2), thirteen modeling groups from Europe and four groups from North America operating eight different regional...

  1. [A prognostic model of a cholera epidemic].

    Science.gov (United States)

    Boev, B V; Bondarenko, V M; Prokop'eva, N V; San Román, R T; Raygoza-Anaya, M; García de Alba, R

    1994-01-01

    A new model for the prognostication of cholera epidemic on the territory of a large city is proposed. This model reflects the characteristic feature of contacting infection by sensitive individuals due to the preservation of Vibrio cholerae in their water habitat. The mathematical model of the epidemic quantitatively reflects the processes of the spread of infection by kinetic equations describing the interaction of the streams of infected persons, the causative agents and susceptible persons. The functions and parameters of the model are linked with the distribution of individuals according to the duration of the incubation period and infectious process, as well as the period of asymptomatic carrier state. The computer realization of the model by means of IBM PC/AT made it possible to study the cholera epidemic which took place in Mexico in 1833. The verified model of the cholera epidemic was used for the prognostication of the possible spread of this infection in Guadalajara, taking into account changes in the epidemiological situation and the size of the population, as well as improvements in sanitary and hygienic conditions, in the city.

  2. Modelling the meteorological forest fire niche in heterogeneous pyrologic conditions.

    Science.gov (United States)

    De Angelis, Antonella; Ricotta, Carlo; Conedera, Marco; Pezzatti, Gianni Boris

    2015-01-01

    Fire regimes are strongly related to weather conditions that directly and indirectly influence fire ignition and propagation. Identifying the most important meteorological fire drivers is thus fundamental for daily fire risk forecasting. In this context, several fire weather indices have been developed focussing mainly on fire-related local weather conditions and fuel characteristics. The specificity of the conditions for which fire danger indices are developed makes its direct transfer and applicability problematic in different areas or with other fuel types. In this paper we used the low-to-intermediate fire-prone region of Canton Ticino as a case study to develop a new daily fire danger index by implementing a niche modelling approach (Maxent). In order to identify the most suitable weather conditions for fires, different combinations of input variables were tested (meteorological variables, existing fire danger indices or a combination of both). Our findings demonstrate that such combinations of input variables increase the predictive power of the resulting index and surprisingly even using meteorological variables only allows similar or better performances than using the complex Canadian Fire Weather Index (FWI). Furthermore, the niche modelling approach based on Maxent resulted in slightly improved model performance and in a reduced number of selected variables with respect to the classical logistic approach. Factors influencing final model robustness were the number of fire events considered and the specificity of the meteorological conditions leading to fire ignition.

  3. A molecular prognostic model predicts esophageal squamous cell carcinoma prognosis.

    Directory of Open Access Journals (Sweden)

    Hui-Hui Cao

    Full Text Available Esophageal squamous cell carcinoma (ESCC has the highest mortality rates in China. The 5-year survival rate of ESCC remains dismal despite improvements in treatments such as surgical resection and adjuvant chemoradiation, and current clinical staging approaches are limited in their ability to effectively stratify patients for treatment options. The aim of the present study, therefore, was to develop an immunohistochemistry-based prognostic model to improve clinical risk assessment for patients with ESCC.We developed a molecular prognostic model based on the combined expression of axis of epidermal growth factor receptor (EGFR, phosphorylated Specificity protein 1 (p-Sp1, and Fascin proteins. The presence of this prognostic model and associated clinical outcomes were analyzed for 130 formalin-fixed, paraffin-embedded esophageal curative resection specimens (generation dataset and validated using an independent cohort of 185 specimens (validation dataset.The expression of these three genes at the protein level was used to build a molecular prognostic model that was highly predictive of ESCC survival in both generation and validation datasets (P = 0.001. Regression analysis showed that this molecular prognostic model was strongly and independently predictive of overall survival (hazard ratio = 2.358 [95% CI, 1.391-3.996], P = 0.001 in generation dataset; hazard ratio = 1.990 [95% CI, 1.256-3.154], P = 0.003 in validation dataset. Furthermore, the predictive ability of these 3 biomarkers in combination was more robust than that of each individual biomarker.This technically simple immunohistochemistry-based molecular model accurately predicts ESCC patient survival and thus could serve as a complement to current clinical risk stratification approaches.

  4. A Model-based Avionic Prognostic Reasoner (MAPR)

    Data.gov (United States)

    National Aeronautics and Space Administration — The Model-based Avionic Prognostic Reasoner (MAPR) presented in this paper is an innovative solution for non-intrusively monitoring the state of health (SoH) and...

  5. Degradations analysis and aging modeling for health assessment and prognostics of PEMFC

    International Nuclear Information System (INIS)

    Jouin, Marine; Gouriveau, Rafael; Hissel, Daniel; Péra, Marie-Cécile; Zerhouni, Noureddine

    2016-01-01

    Applying prognostics to Proton Exchange Membrane Fuel Cell (PEMFC) stacks is a good solution to help taking actions extending their lifetime. However, it requires a great understanding of the degradation mechanisms and failures occurring within the stack. This task is not simple when applied to a PEMFC due to the different levels (stack - cells - components), the different scales and the multiple causes that lead to degradation. To overcome this problem, this work proposes a methodology dedicated to the setting of a framework and a modeling of the aging for prognostics. This methodology is based on a deep literature review and degradation analyses of PEMFC stacks. This analysis allows defining a proper vocabulary dedicated to PEMFC's prognostics and health management and a clear limited framework to perform prognostics. Then the degradations review is used to select critical components within the stack, and to define their critical failure mechanisms thanks the proposal of new fault trees. The impact of these critical components and mechanisms on the power loss during aging is included to the model for prognostics. This model is finally validated on four datasets with different mission profiles both for health assessment and prognostics. - Highlights: • A proper framework to perform PHM, particularly prognostics, of PEMFC is proposed. • A degradation analysis is performed. • A completely new model of PEMFC degradation is proposed. • SOH estimation is performed with very high coefficients of determination.

  6. Urban meteorological modelling for nuclear emergency preparedness

    International Nuclear Information System (INIS)

    Baklanov, Alexander; Sorensen, Jens Havskov; Hoe, Steen Cordt; Amstrup, Bjarne

    2006-01-01

    The main objectives of the current EU project 'Integrated Systems for Forecasting Urban Meteorology, Air Pollution and Population Exposure' (FUMAPEX) are the improvement of meteorological forecasts for urban areas, the connection of numerical weather prediction (NWP) models to urban air pollution and population dose models, the building of improved urban air quality information and forecasting systems, and their application in cities in various European climates. In addition to the forecast of the worst air-pollution episodes in large cities, the potential use of improved weather forecasts for nuclear emergency management in urban areas, in case of hazardous releases from nuclear accidents or terror acts, is considered. Such use of NWP data is tested for the Copenhagen metropolitan area and the Oresund region. The Danish Meteorological Institute (DMI) is running an experimental version of the HIRLAM NWP model over Zealand including the Copenhagen metropolitan area with a horizontal resolution of 1.4 km, thus approaching the city-scale. This involves 1-km resolution physiographic data with implications for the urban surface parameters, e.g. surface fluxes, roughness length and albedo. For the city of Copenhagen, the enhanced high-resolution NWP forecasting will be provided to demonstrate the improved dispersion forecasting capabilities of the Danish nuclear emergency preparedness decision-support system, the Accident Reporting and Guidance Operational System (ARGOS), used by the Danish Emergency Management Agency (DEMA). Recently, ARGOS has been extended with a capability of real-time calculation of regional-scale atmospheric dispersion of radioactive material from accidental releases. This is effectuated through on-line interfacing with the Danish Emergency Response Model of the Atmosphere (DERMA), which is run at DMI. For local-scale modelling of atmospheric dispersion, ARGOS utilises the Local-Scale Model Chain (LSMC), which makes use of high-resolution DMI

  7. Updating and prospective validation of a prognostic model for high sickness absence

    NARCIS (Netherlands)

    Roelen, C.A.M.; Heymans, M.W.; Twisk, J.W.R.; van Rhenen, W.; Pallesen, S.; Bjorvatn, B.; Moen, B.E.; Mageroy, N.

    2015-01-01

    Objectives To further develop and validate a Dutch prognostic model for high sickness absence (SA). Methods Three-wave longitudinal cohort study of 2,059 Norwegian nurses. The Dutch prognostic model was used to predict high SA among Norwegian nurses at wave 2. Subsequently, the model was updated by

  8. Modeling for Battery Prognostics

    Science.gov (United States)

    Kulkarni, Chetan S.; Goebel, Kai; Khasin, Michael; Hogge, Edward; Quach, Patrick

    2017-01-01

    For any battery-powered vehicles (be it unmanned aerial vehicles, small passenger aircraft, or assets in exoplanetary operations) to operate at maximum efficiency and reliability, it is critical to monitor battery health as well performance and to predict end of discharge (EOD) and end of useful life (EOL). To fulfil these needs, it is important to capture the battery's inherent characteristics as well as operational knowledge in the form of models that can be used by monitoring, diagnostic, and prognostic algorithms. Several battery modeling methodologies have been developed in last few years as the understanding of underlying electrochemical mechanics has been advancing. The models can generally be classified as empirical models, electrochemical engineering models, multi-physics models, and molecular/atomist. Empirical models are based on fitting certain functions to past experimental data, without making use of any physicochemical principles. Electrical circuit equivalent models are an example of such empirical models. Electrochemical engineering models are typically continuum models that include electrochemical kinetics and transport phenomena. Each model has its advantages and disadvantages. The former type of model has the advantage of being computationally efficient, but has limited accuracy and robustness, due to the approximations used in developed model, and as a result of such approximations, cannot represent aging well. The latter type of model has the advantage of being very accurate, but is often computationally inefficient, having to solve complex sets of partial differential equations, and thus not suited well for online prognostic applications. In addition both multi-physics and atomist models are computationally expensive hence are even less suited to online application An electrochemistry-based model of Li-ion batteries has been developed, that captures crucial electrochemical processes, captures effects of aging, is computationally efficient

  9. Meteorological fluid dynamics asymptotic modelling, stability and chaotic atmospheric motion

    CERN Document Server

    Zeytounian, Radyadour K

    1991-01-01

    The author considers meteorology as a part of fluid dynamics. He tries to derive the properties of atmospheric flows from a rational analysis of the Navier-Stokes equations, at the same time analyzing various types of initial and boundary problems. This approach to simulate nature by models from fluid dynamics will be of interest to both scientists and students of physics and theoretical meteorology.

  10. A framework for quantifying net benefits of alternative prognostic models

    OpenAIRE

    Rapsomaniki, E.; White, I.R.; Wood, A.M.; Thompson, S.G.; Ford, I.

    2012-01-01

    New prognostic models are traditionally evaluated using measures of discrimination and risk reclassification, but these do not take full account of the clinical and health economic context. We propose a framework for comparing prognostic models by quantifying the public health impact (net benefit) of the treatment decisions they support, assuming a set of predetermined clinical treatment guidelines. The change in net benefit is more clinically interpretable than changes in traditional measure...

  11. Coupling of high-resolution meteorological and wave models over southern Italy

    Directory of Open Access Journals (Sweden)

    L. Bertotti

    2009-07-01

    Full Text Available In the framework of RISKMED project, three different high-resolution limited area meteorological models (BOLAM, MOLOCH and WRF have been run over southern Italy for the retrospective analysis of three case studies characterized by strong winds and severe wave conditions in the Ionian, southern Adriatic and southern Tyrrhenian seas. All the models were able to reproduce the main meteorological features of each event.

    The wind fields simulated by the meteorological models and those provided by the ECMWF analysis have been ingested into a wave model (WAM for the hindcast of the main wave parameters. The results have been compared with the observations of three buoys whose measurements were available in the area of interest.

    A remarkable improvement in the representation of the significant wave height came out using the limited area model data with respect to the simulations where the ECMWF analyses were used as forcing. Among the limited area models, the BOLAM-MOLOCH modelling system provided slightly better performances. From the limited set of simulations, the different model predictions came out closer to each other and more skilful in areas where the waves approach the coastline perpendicularly from the open sea.

  12. Prognostics for Steam Generator Tube Rupture using Markov Chain model

    International Nuclear Information System (INIS)

    Kim, Gibeom; Heo, Gyunyoung; Kim, Hyeonmin

    2016-01-01

    This paper will describe the prognostics method for evaluating and forecasting the ageing effect and demonstrate the procedure of prognostics for the Steam Generator Tube Rupture (SGTR) accident. Authors will propose the data-driven method so called MCMC (Markov Chain Monte Carlo) which is preferred to the physical-model method in terms of flexibility and availability. Degradation data is represented as growth of burst probability over time. Markov chain model is performed based on transition probability of state. And the state must be discrete variable. Therefore, burst probability that is continuous variable have to be changed into discrete variable to apply Markov chain model to the degradation data. The Markov chain model which is one of prognostics methods was described and the pilot demonstration for a SGTR accident was performed as a case study. The Markov chain model is strong since it is possible to be performed without physical models as long as enough data are available. However, in the case of the discrete Markov chain used in this study, there must be loss of information while the given data is discretized and assigned to the finite number of states. In this process, original information might not be reflected on prediction sufficiently. This should be noted as the limitation of discrete models. Now we will be studying on other prognostics methods such as GPM (General Path Model) which is also data-driven method as well as the particle filer which belongs to physical-model method and conducting comparison analysis

  13. A framework for quantifying net benefits of alternative prognostic models.

    Science.gov (United States)

    Rapsomaniki, Eleni; White, Ian R; Wood, Angela M; Thompson, Simon G

    2012-01-30

    New prognostic models are traditionally evaluated using measures of discrimination and risk reclassification, but these do not take full account of the clinical and health economic context. We propose a framework for comparing prognostic models by quantifying the public health impact (net benefit) of the treatment decisions they support, assuming a set of predetermined clinical treatment guidelines. The change in net benefit is more clinically interpretable than changes in traditional measures and can be used in full health economic evaluations of prognostic models used for screening and allocating risk reduction interventions. We extend previous work in this area by quantifying net benefits in life years, thus linking prognostic performance to health economic measures; by taking full account of the occurrence of events over time; and by considering estimation and cross-validation in a multiple-study setting. The method is illustrated in the context of cardiovascular disease risk prediction using an individual participant data meta-analysis. We estimate the number of cardiovascular-disease-free life years gained when statin treatment is allocated based on a risk prediction model with five established risk factors instead of a model with just age, gender and region. We explore methodological issues associated with the multistudy design and show that cost-effectiveness comparisons based on the proposed methodology are robust against a range of modelling assumptions, including adjusting for competing risks. Copyright © 2011 John Wiley & Sons, Ltd.

  14. Prognostics 101: A tutorial for particle filter-based prognostics algorithm using Matlab

    International Nuclear Information System (INIS)

    An, Dawn; Choi, Joo-Ho; Kim, Nam Ho

    2013-01-01

    This paper presents a Matlab-based tutorial for model-based prognostics, which combines a physical model with observed data to identify model parameters, from which the remaining useful life (RUL) can be predicted. Among many model-based prognostics algorithms, the particle filter is used in this tutorial for parameter estimation of damage or a degradation model. The tutorial is presented using a Matlab script with 62 lines, including detailed explanations. As examples, a battery degradation model and a crack growth model are used to explain the updating process of model parameters, damage progression, and RUL prediction. In order to illustrate the results, the RUL at an arbitrary cycle are predicted in the form of distribution along with the median and 90% prediction interval. This tutorial will be helpful for the beginners in prognostics to understand and use the prognostics method, and we hope it provides a standard of particle filter based prognostics. -- Highlights: ► Matlab-based tutorial for model-based prognostics is presented. ► A battery degradation model and a crack growth model are used as examples. ► The RUL at an arbitrary cycle are predicted using the particle filter

  15. Evaluating the performance of ENVI-met model in diurnal cycles for different meteorological conditions

    Science.gov (United States)

    Acero, Juan A.; Arrizabalaga, Jon

    2018-01-01

    Urban areas are known to modify meteorological variables producing important differences in small spatial scales (i.e. microscale). These affect human thermal comfort conditions and the dispersion of pollutants, especially those emitted inside the urban area, which finally influence quality of life and the use of public open spaces. In this study, the diurnal evolution of meteorological variables measured in four urban spaces is compared with the results provided by ENVI-met (v 4.0). Measurements were carried out during 3 days with different meteorological conditions in Bilbao in the north of the Iberian Peninsula. The evaluation of the model accuracy (i.e. the degree to which modelled values approach measured values) was carried out with several quantitative difference metrics. The results for air temperature and humidity show a good agreement of measured and modelled values independently of the regional meteorological conditions. However, in the case of mean radiant temperature and wind speed, relevant differences are encountered highlighting the limitation of the model to estimate these meteorological variables precisely during diurnal cycles, in the considered evaluation conditions (sites and weather).

  16. Systematic review of prognostic models in traumatic brain injury

    Directory of Open Access Journals (Sweden)

    Roberts Ian

    2006-11-01

    Full Text Available Abstract Background Traumatic brain injury (TBI is a leading cause of death and disability world-wide. The ability to accurately predict patient outcome after TBI has an important role in clinical practice and research. Prognostic models are statistical models that combine two or more items of patient data to predict clinical outcome. They may improve predictions in TBI patients. Multiple prognostic models for TBI have accumulated for decades but none of them is widely used in clinical practice. The objective of this systematic review is to critically assess existing prognostic models for TBI Methods Studies that combine at least two variables to predict any outcome in patients with TBI were searched in PUBMED and EMBASE. Two reviewers independently examined titles, abstracts and assessed whether each met the pre-defined inclusion criteria. Results A total of 53 reports including 102 models were identified. Almost half (47% were derived from adult patients. Three quarters of the models included less than 500 patients. Most of the models (93% were from high income countries populations. Logistic regression was the most common analytical strategy to derived models (47%. In relation to the quality of the derivation models (n:66, only 15% reported less than 10% pf loss to follow-up, 68% did not justify the rationale to include the predictors, 11% conducted an external validation and only 19% of the logistic models presented the results in a clinically user-friendly way Conclusion Prognostic models are frequently published but they are developed from small samples of patients, their methodological quality is poor and they are rarely validated on external populations. Furthermore, they are not clinically practical as they are not presented to physicians in a user-friendly way. Finally because only a few are developed using populations from low and middle income countries, where most of trauma occurs, the generalizability to these setting is limited.

  17. A Discussion on Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms based on Kalman Filter Estimation Applied to Prognostics of Electronics Components

    Science.gov (United States)

    Celaya, Jose R.; Saxen, Abhinav; Goebel, Kai

    2012-01-01

    This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process and how it relates to uncertainty representation, management, and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function and the true remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for the two while considering prognostics in making critical decisions.

  18. Propagation of hydro-meteorological uncertainty in a model cascade framework to inundation prediction

    Science.gov (United States)

    Rodríguez-Rincón, J. P.; Pedrozo-Acuña, A.; Breña-Naranjo, J. A.

    2015-07-01

    This investigation aims to study the propagation of meteorological uncertainty within a cascade modelling approach to flood prediction. The methodology was comprised of a numerical weather prediction (NWP) model, a distributed rainfall-runoff model and a 2-D hydrodynamic model. The uncertainty evaluation was carried out at the meteorological and hydrological levels of the model chain, which enabled the investigation of how errors that originated in the rainfall prediction interact at a catchment level and propagate to an estimated inundation area and depth. For this, a hindcast scenario is utilised removing non-behavioural ensemble members at each stage, based on the fit with observed data. At the hydrodynamic level, an uncertainty assessment was not incorporated; instead, the model was setup following guidelines for the best possible representation of the case study. The selected extreme event corresponds to a flood that took place in the southeast of Mexico during November 2009, for which field data (e.g. rain gauges; discharge) and satellite imagery were available. Uncertainty in the meteorological model was estimated by means of a multi-physics ensemble technique, which is designed to represent errors from our limited knowledge of the processes generating precipitation. In the hydrological model, a multi-response validation was implemented through the definition of six sets of plausible parameters from past flood events. Precipitation fields from the meteorological model were employed as input in a distributed hydrological model, and resulting flood hydrographs were used as forcing conditions in the 2-D hydrodynamic model. The evolution of skill within the model cascade shows a complex aggregation of errors between models, suggesting that in valley-filling events hydro-meteorological uncertainty has a larger effect on inundation depths than that observed in estimated flood inundation extents.

  19. Impact of inherent meteorology uncertainty on air quality model predictions

    Science.gov (United States)

    It is well established that there are a number of different classifications and sources of uncertainties in environmental modeling systems. Air quality models rely on two key inputs, namely, meteorology and emissions. When using air quality models for decision making, it is impor...

  20. Model-based Prognostics with Fixed-lag Particle Filters

    Data.gov (United States)

    National Aeronautics and Space Administration — Model-based prognostics exploits domain knowl- edge of the system, its components, and how they fail by casting the underlying physical phenom- ena in a...

  1. Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms Based on Kalman Filter Estimation

    Science.gov (United States)

    Galvan, Jose Ramon; Saxena, Abhinav; Goebel, Kai Frank

    2012-01-01

    This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process, and how it relates to uncertainty representation, management and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for two while considering prognostics in making critical decisions.

  2. Atmospheric Boundary Layer Modeling for Combined Meteorology and Air Quality Systems

    Science.gov (United States)

    Atmospheric Eulerian grid models for mesoscale and larger applications require sub-grid models for turbulent vertical exchange processes, particularly within the Planetary Boundary Layer (PSL). In combined meteorology and air quality modeling systems consistent PSL modeling of wi...

  3. Measures to assess the prognostic ability of the stratified Cox proportional hazards model

    DEFF Research Database (Denmark)

    (Tybjaerg-Hansen, A.) The Fibrinogen Studies Collaboration.The Copenhagen City Heart Study; Tybjærg-Hansen, Anne

    2009-01-01

    Many measures have been proposed to summarize the prognostic ability of the Cox proportional hazards (CPH) survival model, although none is universally accepted for general use. By contrast, little work has been done to summarize the prognostic ability of the stratified CPH model; such measures...

  4. Application of WRF/Chem-MADRID and WRF/Polyphemus in Europe - Part 1: Model description, evaluation of meteorological predictions, and aerosol-meteorology interactions

    Science.gov (United States)

    Zhang, Y.; Sartelet, K.; Wu, S.-Y.; Seigneur, C.

    2013-07-01

    Comprehensive model evaluation and comparison of two 3-D air quality modeling systems (i.e., the Weather Research and Forecast model (WRF)/Polyphemus and WRF with chemistry and the Model of Aerosol Dynamics, Reaction, Ionization, and Dissolution (MADRID) (WRF/Chem-MADRID)) are conducted over Western Europe. Part 1 describes the background information for the model comparison and simulation design, the application of WRF for January and July 2001 over triple-nested domains in Western Europe at three horizontal grid resolutions: 0.5°, 0.125°, and 0.025°, and the effect of aerosol/meteorology interactions on meteorological predictions. Nine simulated meteorological variables (i.e., downward shortwave and longwave radiation fluxes (SWDOWN and LWDOWN), outgoing longwave radiation flux (OLR), temperature at 2 m (T2), specific humidity at 2 m (Q2), relative humidity at 2 m (RH2), wind speed at 10 m (WS10), wind direction at 10 m (WD10), and precipitation (Precip)) are evaluated using available observations in terms of spatial distribution, domainwide daily and site-specific hourly variations, and domainwide performance statistics. The vertical profiles of temperature, dew points, and wind speed/direction are also evaluated using sounding data. WRF demonstrates its capability in capturing diurnal/seasonal variations and spatial gradients and vertical profiles of major meteorological variables. While the domainwide performance of LWDOWN, OLR, T2, Q2, and RH2 at all three grid resolutions is satisfactory overall, large positive or negative biases occur in SWDOWN, WS10, and Precip even at 0.125° or 0.025° in both months and in WD10 in January. In addition, discrepancies between simulations and observations exist in T2, Q2, WS10, and Precip at mountain/high altitude sites and large urban center sites in both months, in particular, during snow events or thunderstorms. These results indicate the model's difficulty in capturing meteorological variables in complex terrain and

  5. Bridging the Radiative Transfer Models for Meteorology and Solar Energy Applications

    Science.gov (United States)

    Xie, Y.; Sengupta, M.

    2017-12-01

    Radiative transfer models are used to compute solar radiation reaching the earth surface and play an important role in both meteorology and solar energy studies. Therefore, they are designed to meet the needs of specialized applications. For instance, radiative transfer models for meteorology seek to provide more accurate cloudy-sky radiation compared to models used in solar energy that are geared towards accuracy in clear-sky conditions associated with the maximum solar resource. However, models for solar energy applications are often computationally faster, as the complex solution of the radiative transfer equation is parameterized by atmospheric properties that can be acquired from surface- or satellite-based observations. This study introduces the National Renewable Energy Laboratory's (NREL's) recent efforts to combine the advantages of radiative transfer models designed for meteorology and solar energy applictions. A fast all-sky radiation model, FARMS-NIT, was developed to efficiently compute narrowband all-sky irradiances over inclined photovoltaic (PV) panels. This new model utilizes the optical preperties from a solar energy model, SMARTS, to computes surface radiation by considering all possible paths of photon transmission and the relevent scattering and absorption attenuation. For cloudy-sky conditions, cloud bidirectional transmittance functions (BTDFs) are provided by a precomputed lookup table (LUT) by LibRadtran. Our initial results indicate that FARMS-NIT has an accuracy that is similar to LibRadtran, a highly accurate multi-stream model, but is significantly more efficient. The development and validation of this model will be presented.

  6. An intercomparison of several diagnostic meteorological processors used in mesoscale air quality modeling

    Energy Technology Data Exchange (ETDEWEB)

    Vimont, J.C. [National Park Service, Lakewood, CO (United States); Scire, J.S. [Sigma Research Corp., Concord, MA (United States)

    1994-12-31

    A major component, and area of uncertainty, in mesoscale air quality modeling, is the specification of the meteorological fields which affect the transport and dispersion of pollutants. Various options are available for estimating the wind and mixing depth fields over a mesoscale domain. Estimates of the wind field can be obtained from spatial and temporal interpolation of available observations or from diagnostic meteorological models, which estimate a meteorological field from available data and adjust those fields based on parameterizations of physical processes. A major weakness of these processors is their dependence on spatially and temporally sparse input data, particularly upper air data. These problems are exacerbated in regions of complex terrain and along the shorelines of large bodies of water. Similarly, the estimation of mixing depth is also reliant upon sparse observations and the parameterization of the convective and mechanical processes. The meteorological processors examined in this analysis were developed to drive different Lagrangian puff models. This paper describes the algorithms these processors use to estimate the wind fields and mixing depth fields.

  7. Using prognostic models in CLL to personalize approach to clinical care: Are we there yet?

    Science.gov (United States)

    Mina, Alain; Sandoval Sus, Jose; Sleiman, Elsa; Pinilla-Ibarz, Javier; Awan, Farrukh T; Kharfan-Dabaja, Mohamed A

    2018-03-01

    Four decades ago, two staging systems were developed to help stratify CLL into different prognostic categories. These systems, the Rai and the Binet staging, depended entirely on abnormal exam findings and evidence of anemia and thrombocytopenia. Better understanding of biologic, genetic, and molecular characteristics of CLL have contributed to better appreciating its clinical heterogeneity. New prognostic models, the GCLLSG prognostic index and the CLL-IPI, emerged. They incorporate biologic and genetic information related to CLL and are capable of predicting survival outcomes and cases anticipated to need therapy earlier in the disease course. Accordingly, these newer models are helping develop better informed surveillance strategies and ultimately tailor treatment intensity according to presence (or lack thereof) of certain prognostic markers. This represents a step towards personalizing care of CLL patients. We anticipate that as more prognostic factors continue to be identified, the GCLLSG prognostic index and CLL-IPI models will undergo further revisions. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. Meteorological monitoring for dose assessment and emergency response modeling - how much is enough?

    International Nuclear Information System (INIS)

    Glantz, C.S.

    1990-01-01

    Individuals responsible for emergency response or environmental/dose assessment routinely ask if there are enough meteorological data to adequately support their objectives. The answer requires detailed consideration of the intended applications, capabilities of the atmospheric dispersion model data, pollutant release characteristics, terrain in the modeling region, and size and distribution of the human population in the modeling domain. The meteorologist's detailed knowledge of, and experience in, studying atmospheric transport and diffusion can assist in determining the appropriate level of meteorological monitoring

  9. Mesoscale meteorological model based on radioactive explosion cloud simulation

    International Nuclear Information System (INIS)

    Zheng Yi; Zhang Yan; Ying Chuntong

    2008-01-01

    In order to simulate nuclear explosion and dirty bomb radioactive cloud movement and concentration distribution, mesoscale meteorological model RAMS was used. Particles-size, size-active distribution and gravitational fallout in the cloud were considered. The results show that the model can simulate the 'mushroom' clouds of explosion. Three-dimension fluid field and radioactive concentration field were received. (authors)

  10. Application of WRF/Chem-MADRID and WRF/Polyphemus in Europe – Part 1: Model description, evaluation of meteorological predictions, and aerosol–meteorology interactions

    Directory of Open Access Journals (Sweden)

    Y. Zhang

    2013-07-01

    Full Text Available Comprehensive model evaluation and comparison of two 3-D air quality modeling systems (i.e., the Weather Research and Forecast model (WRF/Polyphemus and WRF with chemistry and the Model of Aerosol Dynamics, Reaction, Ionization, and Dissolution (MADRID (WRF/Chem-MADRID are conducted over Western Europe. Part 1 describes the background information for the model comparison and simulation design, the application of WRF for January and July 2001 over triple-nested domains in Western Europe at three horizontal grid resolutions: 0.5°, 0.125°, and 0.025°, and the effect of aerosol/meteorology interactions on meteorological predictions. Nine simulated meteorological variables (i.e., downward shortwave and longwave radiation fluxes (SWDOWN and LWDOWN, outgoing longwave radiation flux (OLR, temperature at 2 m (T2, specific humidity at 2 m (Q2, relative humidity at 2 m (RH2, wind speed at 10 m (WS10, wind direction at 10 m (WD10, and precipitation (Precip are evaluated using available observations in terms of spatial distribution, domainwide daily and site-specific hourly variations, and domainwide performance statistics. The vertical profiles of temperature, dew points, and wind speed/direction are also evaluated using sounding data. WRF demonstrates its capability in capturing diurnal/seasonal variations and spatial gradients and vertical profiles of major meteorological variables. While the domainwide performance of LWDOWN, OLR, T2, Q2, and RH2 at all three grid resolutions is satisfactory overall, large positive or negative biases occur in SWDOWN, WS10, and Precip even at 0.125° or 0.025° in both months and in WD10 in January. In addition, discrepancies between simulations and observations exist in T2, Q2, WS10, and Precip at mountain/high altitude sites and large urban center sites in both months, in particular, during snow events or thunderstorms. These results indicate the model's difficulty in capturing meteorological variables in complex

  11. Simplified prognostic model for critically ill patients in resource limited settings in South Asia

    NARCIS (Netherlands)

    Haniffa, Rashan; Mukaka, Mavuto; Munasinghe, Sithum Bandara; de Silva, Ambepitiyawaduge Pubudu; Jayasinghe, Kosala Saroj Amarasiri; Beane, Abi; de Keizer, Nicolette; Dondorp, Arjen M.

    2017-01-01

    Background: Current critical care prognostic models are predominantly developed in high-income countries (HICs) and may not be feasible in intensive care units (ICUs) in lower-and middle-income countries (LMICs). Existing prognostic models cannot be applied without validation in LMICs as the

  12. Air quality modeling: evaluation of chemical and meteorological parameterizations

    International Nuclear Information System (INIS)

    Kim, Youngseob

    2011-01-01

    The influence of chemical mechanisms and meteorological parameterizations on pollutant concentrations calculated with an air quality model is studied. The influence of the differences between two gas-phase chemical mechanisms on the formation of ozone and aerosols in Europe is low on average. For ozone, the large local differences are mainly due to the uncertainty associated with the kinetics of nitrogen monoxide (NO) oxidation reactions on the one hand and the representation of different pathways for the oxidation of aromatic compounds on the other hand. The aerosol concentrations are mainly influenced by the selection of all major precursors of secondary aerosols and the explicit treatment of chemical regimes corresponding to the nitrogen oxides (NO x ) levels. The influence of the meteorological parameterizations on the concentrations of aerosols and their vertical distribution is evaluated over the Paris region in France by comparison to lidar data. The influence of the parameterization of the dynamics in the atmospheric boundary layer is important; however, it is the use of an urban canopy model that improves significantly the modeling of the pollutant vertical distribution (author) [fr

  13. Evaluation of prognostic models developed using standardised image features from different PET automated segmentation methods.

    Science.gov (United States)

    Parkinson, Craig; Foley, Kieran; Whybra, Philip; Hills, Robert; Roberts, Ashley; Marshall, Chris; Staffurth, John; Spezi, Emiliano

    2018-04-11

    Prognosis in oesophageal cancer (OC) is poor. The 5-year overall survival (OS) rate is approximately 15%. Personalised medicine is hoped to increase the 5- and 10-year OS rates. Quantitative analysis of PET is gaining substantial interest in prognostic research but requires the accurate definition of the metabolic tumour volume. This study compares prognostic models developed in the same patient cohort using individual PET segmentation algorithms and assesses the impact on patient risk stratification. Consecutive patients (n = 427) with biopsy-proven OC were included in final analysis. All patients were staged with PET/CT between September 2010 and July 2016. Nine automatic PET segmentation methods were studied. All tumour contours were subjectively analysed for accuracy, and segmentation methods with segmentation methods studied, clustering means (KM2), general clustering means (GCM3), adaptive thresholding (AT) and watershed thresholding (WT) methods were included for analysis. Known clinical prognostic factors (age, treatment and staging) were significant in all of the developed prognostic models. AT and KM2 segmentation methods developed identical prognostic models. Patient risk stratification was dependent on the segmentation method used to develop the prognostic model with up to 73 patients (17.1%) changing risk stratification group. Prognostic models incorporating quantitative image features are dependent on the method used to delineate the primary tumour. This has a subsequent effect on risk stratification, with patients changing groups depending on the image segmentation method used.

  14. Improvement of disease prediction and modeling through the use of meteorological ensembles: human plague in Uganda.

    Directory of Open Access Journals (Sweden)

    Sean M Moore

    Full Text Available Climate and weather influence the occurrence, distribution, and incidence of infectious diseases, particularly those caused by vector-borne or zoonotic pathogens. Thus, models based on meteorological data have helped predict when and where human cases are most likely to occur. Such knowledge aids in targeting limited prevention and control resources and may ultimately reduce the burden of diseases. Paradoxically, localities where such models could yield the greatest benefits, such as tropical regions where morbidity and mortality caused by vector-borne diseases is greatest, often lack high-quality in situ local meteorological data. Satellite- and model-based gridded climate datasets can be used to approximate local meteorological conditions in data-sparse regions, however their accuracy varies. Here we investigate how the selection of a particular dataset can influence the outcomes of disease forecasting models. Our model system focuses on plague (Yersinia pestis infection in the West Nile region of Uganda. The majority of recent human cases have been reported from East Africa and Madagascar, where meteorological observations are sparse and topography yields complex weather patterns. Using an ensemble of meteorological datasets and model-averaging techniques we find that the number of suspected cases in the West Nile region was negatively associated with dry season rainfall (December-February and positively with rainfall prior to the plague season. We demonstrate that ensembles of available meteorological datasets can be used to quantify climatic uncertainty and minimize its impacts on infectious disease models. These methods are particularly valuable in regions with sparse observational networks and high morbidity and mortality from vector-borne diseases.

  15. Improvement of Disease Prediction and Modeling through the Use of Meteorological Ensembles: Human Plague in Uganda

    Science.gov (United States)

    Moore, Sean M.; Monaghan, Andrew; Griffith, Kevin S.; Apangu, Titus; Mead, Paul S.; Eisen, Rebecca J.

    2012-01-01

    Climate and weather influence the occurrence, distribution, and incidence of infectious diseases, particularly those caused by vector-borne or zoonotic pathogens. Thus, models based on meteorological data have helped predict when and where human cases are most likely to occur. Such knowledge aids in targeting limited prevention and control resources and may ultimately reduce the burden of diseases. Paradoxically, localities where such models could yield the greatest benefits, such as tropical regions where morbidity and mortality caused by vector-borne diseases is greatest, often lack high-quality in situ local meteorological data. Satellite- and model-based gridded climate datasets can be used to approximate local meteorological conditions in data-sparse regions, however their accuracy varies. Here we investigate how the selection of a particular dataset can influence the outcomes of disease forecasting models. Our model system focuses on plague (Yersinia pestis infection) in the West Nile region of Uganda. The majority of recent human cases have been reported from East Africa and Madagascar, where meteorological observations are sparse and topography yields complex weather patterns. Using an ensemble of meteorological datasets and model-averaging techniques we find that the number of suspected cases in the West Nile region was negatively associated with dry season rainfall (December-February) and positively with rainfall prior to the plague season. We demonstrate that ensembles of available meteorological datasets can be used to quantify climatic uncertainty and minimize its impacts on infectious disease models. These methods are particularly valuable in regions with sparse observational networks and high morbidity and mortality from vector-borne diseases. PMID:23024750

  16. Investigating the Effect of Damage Progression Model Choice on Prognostics Performance

    Data.gov (United States)

    National Aeronautics and Space Administration — The success of model-based approaches to systems health management depends largely on the quality of the underly- ing models. In model-based prognostics, it is...

  17. Evaluation of data assimilation techniques for a mesoscale meteorological model and their effects on air quality model results

    Science.gov (United States)

    Amicarelli, A.; Gariazzo, C.; Finardi, S.; Pelliccioni, A.; Silibello, C.

    2008-05-01

    Data assimilation techniques are methods to limit the growth of errors in a dynamical model by allowing observations distributed in space and time to force (nudge) model solutions. They have become common for meteorological model applications in recent years, especially to enhance weather forecast and to support air-quality studies. In order to investigate the influence of different data assimilation techniques on the meteorological fields produced by RAMS model, and to evaluate their effects on the ozone and PM10 concentrations predicted by FARM model, several numeric experiments were conducted over the urban area of Rome, Italy, during a summer episode.

  18. Evaluation of data assimilation techniques for a mesoscale meteorological model and their effects on air quality model results

    Energy Technology Data Exchange (ETDEWEB)

    Amicarelli, A; Pelliccioni, A [ISPESL - Dipartimento Insediamenti Produttivi e Interazione con l' Ambiente, Via Fontana Candida, 1 00040 Monteporzio Catone (RM) Italy (Italy); Finardi, S; Silibello, C [ARIANET, via Gilino 9, 20128 Milano (Italy); Gariazzo, C

    2008-05-01

    Data assimilation techniques are methods to limit the growth of errors in a dynamical model by allowing observations distributed in space and time to force (nudge) model solutions. They have become common for meteorological model applications in recent years, especially to enhance weather forecast and to support air-quality studies. In order to investigate the influence of different data assimilation techniques on the meteorological fields produced by RAMS model, and to evaluate their effects on the ozone and PM{sub 10} concentrations predicted by FARM model, several numeric experiments were conducted over the urban area of Rome, Italy, during a summer episode.

  19. Evaluation of data assimilation techniques for a mesoscale meteorological model and their effects on air quality model results

    International Nuclear Information System (INIS)

    Amicarelli, A; Pelliccioni, A; Finardi, S; Silibello, C; Gariazzo, C

    2008-01-01

    Data assimilation techniques are methods to limit the growth of errors in a dynamical model by allowing observations distributed in space and time to force (nudge) model solutions. They have become common for meteorological model applications in recent years, especially to enhance weather forecast and to support air-quality studies. In order to investigate the influence of different data assimilation techniques on the meteorological fields produced by RAMS model, and to evaluate their effects on the ozone and PM 10 concentrations predicted by FARM model, several numeric experiments were conducted over the urban area of Rome, Italy, during a summer episode

  20. Model Adaptation for Prognostics in a Particle Filtering Framework

    Data.gov (United States)

    National Aeronautics and Space Administration — One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated....

  1. Online coupled regional meteorology chemistry models in Europe : Current status and prospects

    NARCIS (Netherlands)

    Baklanov, A.; Schlünzen, K.; Suppan, P.; Baldasano, J.; Brunner, D.; Aksoyoglu, S.; Carmichael, G.; Douros, J.; Flemming, J.; Forkel, R.; Galmarini, S.; Gauss, M.; Grell, G.; Hirtl, M.; Joffre, S.; Jorba, O.; Kaas, E.; Kaasik, M.; Kallos, G.; Kong, X.; Korsholm, U.; Kurganskiy, A.; Kushta, J.; Lohmann, U.; Mahura, A.; Manders-Groot, A.; Maurizi, A.; Moussiopoulos, N.; Rao, S.T.; Savage, N.; Seigneur, C.; Sokhi, R.S.; Solazzo, E.; Solomos, S.; Sørensen, B.; Tsegas, G.; Vignati, E.; Vogel, B.; Zhang, Y.

    2014-01-01

    Online coupled mesoscale meteorology atmospheric chemistry models have undergone a rapid evolution in recent years. Although mainly developed by the air quality modelling community, these models are also of interest for numerical weather prediction and regional climate modelling as they can consider

  2. A NEW COMBINED LOCAL AND NON-LOCAL PBL MODEL FOR METEOROLOGY AND AIR QUALITY MODELING

    Science.gov (United States)

    A new version of the Asymmetric Convective Model (ACM) has been developed to describe sub-grid vertical turbulent transport in both meteorology models and air quality models. The new version (ACM2) combines the non-local convective mixing of the original ACM with local eddy diff...

  3. Meteorological monitoring for environmental/dose assessment and emergency response modeling: How much is enough?

    International Nuclear Information System (INIS)

    Glantz, C.S.

    1989-01-01

    In evaluation the effectiveness and appropriateness of meteorological monitoring programs, managers responsible for planning and operating emergency response or environmental/dose assessment systems must routinely question whether enough meteorological data are being obtained to adequately support system applications. There is no simple answer or cookbook procedure that can be followed in generating an appropriate answer to this question. The answer must be developed through detailed consideration of the intended applications for the data, the capabilities of the models that would use the data, pollutant release characteristics, terrain in the modeling region, the size of the modeling domain, and the distribution of human population in the modeling domain. It is recommended that manager consult meteorologists when assessing these factors; the meteorologist's detailed knowledge of, and experience in, studying atmospheric transport and diffusion should assist the manager in determining the appropriate level of meteorological monitoring. 1 ref

  4. A meteorological distribution system for high-resolution terrestrial modeling (MicroMet)

    Science.gov (United States)

    Glen E. Liston; Kelly Elder

    2006-01-01

    An intermediate-complexity, quasi-physically based, meteorological model (MicroMet) has been developed to produce high-resolution (e.g., 30-m to 1-km horizontal grid increment) atmospheric forcings required to run spatially distributed terrestrial models over a wide variety of landscapes. The following eight variables, required to run most terrestrial models, are...

  5. METRODOS: Meteorological preprocessor chain

    DEFF Research Database (Denmark)

    Astrup, P.; Mikkelsen, T.; Deme, S.

    2001-01-01

    The METRODOS meteorological preprocessor chain combines measured tower data and coarse grid numerical weather prediction (NWP) data with local scale flow models and similarity scaling to give high resolution approximations of the meteorological situation. Based on available wind velocity and dire...

  6. Forecasting rain events - Meteorological models or collective intelligence?

    Science.gov (United States)

    Arazy, Ofer; Halfon, Noam; Malkinson, Dan

    2015-04-01

    Collective intelligence is shared (or group) intelligence that emerges from the collective efforts of many individuals. Collective intelligence is the aggregate of individual contributions: from simple collective decision making to more sophisticated aggregations such as in crowdsourcing and peer-production systems. In particular, collective intelligence could be used in making predictions about future events, for example by using prediction markets to forecast election results, stock prices, or the outcomes of sport events. To date, there is little research regarding the use of collective intelligence for prediction of weather forecasting. The objective of this study is to investigate the extent to which collective intelligence could be utilized to accurately predict weather events, and in particular rainfall. Our analyses employ metrics of group intelligence, as well as compare the accuracy of groups' predictions against the predictions of the standard model used by the National Meteorological Services. We report on preliminary results from a study conducted over the 2013-2014 and 2014-2015 winters. We have built a web site that allows people to make predictions on precipitation levels on certain locations. During each competition participants were allowed to enter their precipitation forecasts (i.e. 'bets') at three locations and these locations changed between competitions. A precipitation competition was defined as a 48-96 hour period (depending on the expected weather conditions), bets were open 24-48 hours prior to the competition, and during betting period participants were allowed to change their bets with no limitation. In order to explore the effect of transparency, betting mechanisms varied across study's sites: full transparency (participants able to see each other's bets); partial transparency (participants see the group's average bet); and no transparency (no information of others' bets is made available). Several interesting findings emerged from

  7. Cairo city air quality research initiative part-i: A meteorological modelling

    International Nuclear Information System (INIS)

    Abdel-AAl, M.M.

    2001-01-01

    The modified meteorological model Hotmac (Higher order turbulence model for atmospheric circulation) is a three-dimensional and finite grid model developed primarily for simospheric motions and based on solving the conservation equations of mass momentum, energy and turbulent kinetic energy. The model is used for studying air quality of cairo cty and its surrounding to treat a domain that includes an urbanized area for understanding problems of air pollution. The acquired terrain (elevation) data for Egypt was obtained. The local and upper level geostrophic data were provided by rawinsonde of wind speed and direction, temperature,relative humidity, water vapour, and pressure The potential temperature was obtained by a computer program. The meteorological data was obtained for helwan site, about 20 kilometer south of cairo city. Three mested grids were used, with grids resolutions of 2 6 and 18 kilometers to cover a domain of approximately 360 km that extended from the red Sea to the mediterranean Sea

  8. The Impacts of Different Meteorology Data Sets on Nitrogen Fate and Transport in the SWAT Watershed Model

    Science.gov (United States)

    In this study, we investigated how different meteorology data sets impacts nitrogen fate and transport responses in the Soil and Water Assessment Tool (SWAT) model. We used two meteorology data sets: National Climatic Data Center (observed) and Mesoscale Model 5/Weather Research ...

  9. Application of a mesoscale forecasting model (NMM) coupled to the CALMET to develop forecast meteorology to use with the CALPUFF air dispersion model

    International Nuclear Information System (INIS)

    Radonjic, Z.; Telenta, B.; Kirklady, J.; Chambers, D.; Kleb, H.

    2006-01-01

    An air quality assessment was undertaken as part of the Environmental Assessment for the Port Hope Area Initiative. The assessment predicted potential effects associated with the remediation efforts for historic low-level radioactive wastes and construction of Long-Term Waste Management Facilities (LTWMFs) for both the Port Hope and Port Granby Projects. A necessary element of air dispersion modelling is the development of suitable meteorological data. For the Port Hope and Port Granby Projects, a meteorological station was installed in close proximity to the location of the recommended LTWMF in Port Hope. The recommended location for the Port Granby LTWMF is approximately 10 km west of the Port Hope LTWMF. Concerns were raised regarding the applicability of data collected for the Port Hope meteorological station to the Port Granby Site. To address this concern, a new method for processing meteorological data, which coupled mesoscale meteorological forecasting data the U.S. EPA CALMET meteorological data processor, was applied. This methodology is possible because a new and advanced mesoscale forecasting modelling system enables extensive numerical calculations on personal computers. As a result of this advancement, mesoscale forecasting systems can now be coupled with the CALMET meteorological data processor and the CALPUFF air dispersion modelling system to facilitate wind field estimations and air dispersion analysis. (author)

  10. [Prediction model of meteorological grade of wheat stripe rust in winter-reproductive area, Sichuan Basin, China].

    Science.gov (United States)

    Guo, Xiang; Wang, Ming Tian; Zhang, Guo Zhi

    2017-12-01

    The winter reproductive areas of Puccinia striiformis var. striiformis in Sichuan Basin are often the places mostly affected by wheat stripe rust. With data on the meteorological condition and stripe rust situation at typical stations in the winter reproductive area in Sichuan Basin from 1999 to 2016, this paper classified the meteorological conditions inducing wheat stripe rust into 5 grades, based on the incidence area ratio of the disease. The meteorological factors which were biologically related to wheat stripe rust were determined through multiple analytical methods, and a meteorological grade model for forecasting wheat stripe rust was created. The result showed that wheat stripe rust in Sichuan Basin was significantly correlated with many meteorological factors, such as the ave-rage (maximum and minimum) temperature, precipitation and its anomaly percentage, relative humidity and its anomaly percentage, average wind speed and sunshine duration. Among these, the average temperature and the anomaly percentage of relative humidity were the determining factors. According to a historical retrospective test, the accuracy of the forecast based on the model was 64% for samples in the county-level test, and 89% for samples in the municipal-level test. In a meteorological grade forecast of wheat stripe rust in the winter reproductive areas in Sichuan Basin in 2017, the prediction was accurate for 62.8% of the samples, with 27.9% error by one grade and only 9.3% error by two or more grades. As a result, the model could deliver satisfactory forecast results, and predicate future wheat stripe rust from a meteorological point of view.

  11. Prognostic methods in medicine

    NARCIS (Netherlands)

    Lucas, P. J.; Abu-Hanna, A.

    1999-01-01

    Prognosis--the prediction of the course and outcome of disease processes--plays an important role in patient management tasks like diagnosis and treatment planning. As a result, prognostic models form an integral part of a number of systems supporting these tasks. Furthermore, prognostic models

  12. Building prognostic models for breast cancer patients using clinical variables and hundreds of gene expression signatures

    Directory of Open Access Journals (Sweden)

    Liu Yufeng

    2011-01-01

    Full Text Available Abstract Background Multiple breast cancer gene expression profiles have been developed that appear to provide similar abilities to predict outcome and may outperform clinical-pathologic criteria; however, the extent to which seemingly disparate profiles provide additive prognostic information is not known, nor do we know whether prognostic profiles perform equally across clinically defined breast cancer subtypes. We evaluated whether combining the prognostic powers of standard breast cancer clinical variables with a large set of gene expression signatures could improve on our ability to predict patient outcomes. Methods Using clinical-pathological variables and a collection of 323 gene expression "modules", including 115 previously published signatures, we build multivariate Cox proportional hazards models using a dataset of 550 node-negative systemically untreated breast cancer patients. Models predictive of pathological complete response (pCR to neoadjuvant chemotherapy were also built using this approach. Results We identified statistically significant prognostic models for relapse-free survival (RFS at 7 years for the entire population, and for the subgroups of patients with ER-positive, or Luminal tumors. Furthermore, we found that combined models that included both clinical and genomic parameters improved prognostication compared with models with either clinical or genomic variables alone. Finally, we were able to build statistically significant combined models for pathological complete response (pCR predictions for the entire population. Conclusions Integration of gene expression signatures and clinical-pathological factors is an improved method over either variable type alone. Highly prognostic models could be created when using all patients, and for the subset of patients with lymph node-negative and ER-positive breast cancers. Other variables beyond gene expression and clinical-pathological variables, like gene mutation status or DNA

  13. Accounting for treatment use when validating a prognostic model: a simulation study.

    Science.gov (United States)

    Pajouheshnia, Romin; Peelen, Linda M; Moons, Karel G M; Reitsma, Johannes B; Groenwold, Rolf H H

    2017-07-14

    Prognostic models often show poor performance when applied to independent validation data sets. We illustrate how treatment use in a validation set can affect measures of model performance and present the uses and limitations of available analytical methods to account for this using simulated data. We outline how the use of risk-lowering treatments in a validation set can lead to an apparent overestimation of risk by a prognostic model that was developed in a treatment-naïve cohort to make predictions of risk without treatment. Potential methods to correct for the effects of treatment use when testing or validating a prognostic model are discussed from a theoretical perspective.. Subsequently, we assess, in simulated data sets, the impact of excluding treated individuals and the use of inverse probability weighting (IPW) on the estimated model discrimination (c-index) and calibration (observed:expected ratio and calibration plots) in scenarios with different patterns and effects of treatment use. Ignoring the use of effective treatments in a validation data set leads to poorer model discrimination and calibration than would be observed in the untreated target population for the model. Excluding treated individuals provided correct estimates of model performance only when treatment was randomly allocated, although this reduced the precision of the estimates. IPW followed by exclusion of the treated individuals provided correct estimates of model performance in data sets where treatment use was either random or moderately associated with an individual's risk when the assumptions of IPW were met, but yielded incorrect estimates in the presence of non-positivity or an unobserved confounder. When validating a prognostic model developed to make predictions of risk without treatment, treatment use in the validation set can bias estimates of the performance of the model in future targeted individuals, and should not be ignored. When treatment use is random, treated

  14. Study of solar radiation prediction and modeling of relationships between solar radiation and meteorological variables

    International Nuclear Information System (INIS)

    Sun, Huaiwei; Zhao, Na; Zeng, Xiaofan; Yan, Dong

    2015-01-01

    Highlights: • We investigate relationships between solar radiation and meteorological variables. • A strong relationship exists between solar radiation and sunshine duration. • Daily global radiation can be estimated accurately with ARMAX–GARCH models. • MGARCH model was applied to investigate time-varying relationships. - Abstract: The traditional approaches that employ the correlations between solar radiation and other measured meteorological variables are commonly utilized in studies. It is important to investigate the time-varying relationships between meteorological variables and solar radiation to determine which variables have the strongest correlations with solar radiation. In this study, the nonlinear autoregressive moving average with exogenous variable–generalized autoregressive conditional heteroscedasticity (ARMAX–GARCH) and multivariate GARCH (MGARCH) time-series approaches were applied to investigate the associations between solar radiation and several meteorological variables. For these investigations, the long-term daily global solar radiation series measured at three stations from January 1, 2004 until December 31, 2007 were used in this study. Stronger relationships were observed to exist between global solar radiation and sunshine duration than between solar radiation and temperature difference. The results show that 82–88% of the temporal variations of the global solar radiation were captured by the sunshine-duration-based ARMAX–GARCH models and 55–68% of daily variations were captured by the temperature-difference-based ARMAX–GARCH models. The advantages of the ARMAX–GARCH models were also confirmed by comparison of Auto-Regressive and Moving Average (ARMA) and neutral network (ANN) models in the estimation of daily global solar radiation. The strong heteroscedastic persistency of the global solar radiation series was revealed by the AutoRegressive Conditional Heteroscedasticity (ARCH) and Generalized Auto

  15. Assessment of NASA's Physiographic and Meteorological Datasets as Input to HSPF and SWAT Hydrological Models

    Science.gov (United States)

    Alacron, Vladimir J.; Nigro, Joseph D.; McAnally, William H.; OHara, Charles G.; Engman, Edwin Ted; Toll, David

    2011-01-01

    This paper documents the use of simulated Moderate Resolution Imaging Spectroradiometer land use/land cover (MODIS-LULC), NASA-LIS generated precipitation and evapo-transpiration (ET), and Shuttle Radar Topography Mission (SRTM) datasets (in conjunction with standard land use, topographical and meteorological datasets) as input to hydrological models routinely used by the watershed hydrology modeling community. The study is focused in coastal watersheds in the Mississippi Gulf Coast although one of the test cases focuses in an inland watershed located in northeastern State of Mississippi, USA. The decision support tools (DSTs) into which the NASA datasets were assimilated were the Soil Water & Assessment Tool (SWAT) and the Hydrological Simulation Program FORTRAN (HSPF). These DSTs are endorsed by several US government agencies (EPA, FEMA, USGS) for water resources management strategies. These models use physiographic and meteorological data extensively. Precipitation gages and USGS gage stations in the region were used to calibrate several HSPF and SWAT model applications. Land use and topographical datasets were swapped to assess model output sensitivities. NASA-LIS meteorological data were introduced in the calibrated model applications for simulation of watershed hydrology for a time period in which no weather data were available (1997-2006). The performance of the NASA datasets in the context of hydrological modeling was assessed through comparison of measured and model-simulated hydrographs. Overall, NASA datasets were as useful as standard land use, topographical , and meteorological datasets. Moreover, NASA datasets were used for performing analyses that the standard datasets could not made possible, e.g., introduction of land use dynamics into hydrological simulations

  16. Prognostics Health Management and Physics based failure Models for Electrolytic Capacitors

    Data.gov (United States)

    National Aeronautics and Space Administration — This paper proposes first principles based modeling and prognostics approach for electrolytic capacitors. Electrolytic capacitors and MOSFETs are the two major...

  17. Selection of some meteorological fluctuations to create forecasting models of NO2 in Jinamar (Gran Canarias)

    International Nuclear Information System (INIS)

    Vera Castellano, A.; Lopez Cancio, J.; Corujo Jimenez, J.

    1997-01-01

    The study of meteorological fluctuations that have been reported in urban and semi urban zones has reached in the last years an increasing importance to environmental pollution researches because its knowledge permits the elaboration of empirical models able to predict periods of potential pollution in these zones. In this work, it has been made use of the data on concentrations of NO 2 supplied by an chemiluminescent analyser and the meteorological data provided by a meteorological station located in the surroundings of the analyser, in order to determine the variables that have taken part in the elaboration of a forecasting model of this pollutant in Jinamar Valley. (Author) 15 refs

  18. Model variations in predicting incidence of Plasmodium falciparum malaria using 1998-2007 morbidity and meteorological data from south Ethiopia.

    Science.gov (United States)

    Loha, Eskindir; Lindtjørn, Bernt

    2010-06-16

    Malaria transmission is complex and is believed to be associated with local climate changes. However, simple attempts to extrapolate malaria incidence rates from averaged regional meteorological conditions have proven unsuccessful. Therefore, the objective of this study was to determine if variations in specific meteorological factors are able to consistently predict P. falciparum malaria incidence at different locations in south Ethiopia. Retrospective data from 42 locations were collected including P. falciparum malaria incidence for the period of 1998-2007 and meteorological variables such as monthly rainfall (all locations), temperature (17 locations), and relative humidity (three locations). Thirty-five data sets qualified for the analysis. Ljung-Box Q statistics was used for model diagnosis, and R squared or stationary R squared was taken as goodness of fit measure. Time series modelling was carried out using Transfer Function (TF) models and univariate auto-regressive integrated moving average (ARIMA) when there was no significant predictor meteorological variable. Of 35 models, five were discarded because of the significant value of Ljung-Box Q statistics. Past P. falciparum malaria incidence alone (17 locations) or when coupled with meteorological variables (four locations) was able to predict P. falciparum malaria incidence within statistical significance. All seasonal AIRMA orders were from locations at altitudes above 1742 m. Monthly rainfall, minimum and maximum temperature was able to predict incidence at four, five and two locations, respectively. In contrast, relative humidity was not able to predict P. falciparum malaria incidence. The R squared values for the models ranged from 16% to 97%, with the exception of one model which had a negative value. Models with seasonal ARIMA orders were found to perform better. However, the models for predicting P. falciparum malaria incidence varied from location to location, and among lagged effects, data

  19. Model variations in predicting incidence of Plasmodium falciparum malaria using 1998-2007 morbidity and meteorological data from south Ethiopia

    Directory of Open Access Journals (Sweden)

    Loha Eskindir

    2010-06-01

    Full Text Available Abstract Background Malaria transmission is complex and is believed to be associated with local climate changes. However, simple attempts to extrapolate malaria incidence rates from averaged regional meteorological conditions have proven unsuccessful. Therefore, the objective of this study was to determine if variations in specific meteorological factors are able to consistently predict P. falciparum malaria incidence at different locations in south Ethiopia. Methods Retrospective data from 42 locations were collected including P. falciparum malaria incidence for the period of 1998-2007 and meteorological variables such as monthly rainfall (all locations, temperature (17 locations, and relative humidity (three locations. Thirty-five data sets qualified for the analysis. Ljung-Box Q statistics was used for model diagnosis, and R squared or stationary R squared was taken as goodness of fit measure. Time series modelling was carried out using Transfer Function (TF models and univariate auto-regressive integrated moving average (ARIMA when there was no significant predictor meteorological variable. Results Of 35 models, five were discarded because of the significant value of Ljung-Box Q statistics. Past P. falciparum malaria incidence alone (17 locations or when coupled with meteorological variables (four locations was able to predict P. falciparum malaria incidence within statistical significance. All seasonal AIRMA orders were from locations at altitudes above 1742 m. Monthly rainfall, minimum and maximum temperature was able to predict incidence at four, five and two locations, respectively. In contrast, relative humidity was not able to predict P. falciparum malaria incidence. The R squared values for the models ranged from 16% to 97%, with the exception of one model which had a negative value. Models with seasonal ARIMA orders were found to perform better. However, the models for predicting P. falciparum malaria incidence varied from location

  20. Updating and prospective validation of a prognostic model for high sickness absence.

    Science.gov (United States)

    Roelen, C A M; Heymans, M W; Twisk, J W R; van Rhenen, W; Pallesen, S; Bjorvatn, B; Moen, B E; Magerøy, N

    2015-01-01

    To further develop and validate a Dutch prognostic model for high sickness absence (SA). Three-wave longitudinal cohort study of 2,059 Norwegian nurses. The Dutch prognostic model was used to predict high SA among Norwegian nurses at wave 2. Subsequently, the model was updated by adding person-related (age, gender, marital status, children at home, and coping strategies), health-related (BMI, physical activity, smoking, and caffeine and alcohol intake), and work-related (job satisfaction, job demands, decision latitude, social support at work, and both work-to-family and family-to-work spillover) variables. The updated model was then prospectively validated for predictions at wave 3. 1,557 (77 %) nurses had complete data at wave 2 and 1,342 (65 %) at wave 3. The risk of high SA was under-estimated by the Dutch model, but discrimination between high-risk and low-risk nurses was fair after re-calibration to the Norwegian data. Gender, marital status, BMI, physical activity, smoking, alcohol intake, job satisfaction, job demands, decision latitude, support at the workplace, and work-to-family spillover were identified as potential predictors of high SA. However, these predictors did not improve the model's discriminative ability, which remained fair at wave 3. The prognostic model correctly identifies 73 % of Norwegian nurses at risk of high SA, although additional predictors are needed before the model can be used to screen working populations for risk of high SA.

  1. Sensitivity and uncertainty studies of the CRAC2 code for selected meteorological models and parameters

    International Nuclear Information System (INIS)

    Ward, R.C.; Kocher, D.C.; Hicks, B.B.; Hosker, R.P. Jr.; Ku, J.Y.; Rao, K.S.

    1985-01-01

    We have studied the sensitivity of results from the CRAC2 computer code, which predicts health impacts from a reactor-accident scenario, to uncertainties in selected meteorological models and parameters. The sources of uncertainty examined include the models for plume rise and wet deposition and the meteorological bin-sampling procedure. An alternative plume-rise model usually had little effect on predicted health impacts. In an alternative wet-deposition model, the scavenging rate depends only on storm type, rather than on rainfall rate and atmospheric stability class as in the CRAC2 model. Use of the alternative wet-deposition model in meteorological bin-sampling runs decreased predicted mean early injuries by as much as a factor of 2-3 and, for large release heights and sensible heat rates, decreased mean early fatalities by nearly an order of magnitude. The bin-sampling procedure in CRAC2 was expanded by dividing each rain bin into four bins that depend on rainfall rate. Use of the modified bin structure in conjunction with the CRAC2 wet-deposition model changed all predicted health impacts by less than a factor of 2. 9 references

  2. Enhancement of Physics-of-Failure Prognostic Models with System Level Features

    National Research Council Canada - National Science Library

    Kacprzynski, Gregory

    2002-01-01

    .... The novelty in the current prognostic tool development is that predictions are made through the fusion of stochastic physics-of-failure models, relevant system or component level health monitoring...

  3. A Physics-Based Modeling Framework for Prognostic Studies

    Science.gov (United States)

    Kulkarni, Chetan S.

    2014-01-01

    Prognostics and Health Management (PHM) methodologies have emerged as one of the key enablers for achieving efficient system level maintenance as part of a busy operations schedule, and lowering overall life cycle costs. PHM is also emerging as a high-priority issue in critical applications, where the focus is on conducting fundamental research in the field of integrated systems health management. The term diagnostics relates to the ability to detect and isolate faults or failures in a system. Prognostics on the other hand is the process of predicting health condition and remaining useful life based on current state, previous conditions and future operating conditions. PHM methods combine sensing, data collection, interpretation of environmental, operational, and performance related parameters to indicate systems health under its actual application conditions. The development of prognostics methodologies for the electronics field has become more important as more electrical systems are being used to replace traditional systems in several applications in the aeronautics, maritime, and automotive fields. The development of prognostics methods for electronics presents several challenges due to the great variety of components used in a system, a continuous development of new electronics technologies, and a general lack of understanding of how electronics fail. Similarly with electric unmanned aerial vehicles, electrichybrid cars, and commercial passenger aircraft, we are witnessing a drastic increase in the usage of batteries to power vehicles. However, for battery-powered vehicles to operate at maximum efficiency and reliability, it becomes crucial to both monitor battery health and performance and to predict end of discharge (EOD) and end of useful life (EOL) events. We develop an electrochemistry-based model of Li-ion batteries that capture the significant electrochemical processes, are computationally efficient, capture the effects of aging, and are of suitable

  4. Simulating Storm Surge Impacts with a Coupled Atmosphere-Inundation Model with Varying Meteorological Forcing

    Directory of Open Access Journals (Sweden)

    Alexandra N. Ramos Valle

    2018-04-01

    Full Text Available Storm surge events have the potential to cause devastating damage to coastal communities. The magnitude of their impacts highlights the need for increased accuracy and real-time forecasting and predictability of storm surge. In this study, we assess two meteorological forcing configurations to hindcast the storm surge of Hurricane Sandy, and ultimately support the improvement of storm surge forecasts. The Weather Research and Forecasting (WRF model is coupled to the ADvanced CIRCulation Model (ADCIRC to determine water elevations. We perform four coupled simulations and compare storm surge estimates resulting from the use of a parametric vortex model and a full-physics atmospheric model. One simulation is forced with track-based meteorological data calculated from WRF, while three simulations are forced with the full wind and pressure field outputs from WRF simulations of varying resolutions. Experiments were compared to an ADCIRC simulation forced by National Hurricane Center best track data, as well as to station observations. Our results indicated that given accurate meteorological best track data, a parametric vortex model can accurately forecast maximum water elevations, improving upon the use of a full-physics coupled atmospheric-surge model. In the absence of a best track, atmospheric forcing in the form of full wind and pressure field from a high-resolution atmospheric model simulation prove reliable for storm surge forecasting.

  5. Configuring the HYSPLIT Model for National Weather Service Forecast Office and Spaceflight Meteorology Group Applications

    Science.gov (United States)

    Dreher, Joseph G.

    2009-01-01

    For expedience in delivering dispersion guidance in the diversity of operational situations, National Weather Service Melbourne (MLB) and Spaceflight Meteorology Group (SMG) are becoming increasingly reliant on the PC-based version of the HYSPLIT model run through a graphical user interface (GUI). While the GUI offers unique advantages when compared to traditional methods, it is difficult for forecasters to run and manage in an operational environment. To alleviate the difficulty in providing scheduled real-time trajectory and concentration guidance, the Applied Meteorology Unit (AMU) configured a Linux version of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) (HYSPLIT) model that ingests the National Centers for Environmental Prediction (NCEP) guidance, such as the North American Mesoscale (NAM) and the Rapid Update Cycle (RUC) models. The AMU configured the HYSPLIT system to automatically download the NCEP model products, convert the meteorological grids into HYSPLIT binary format, run the model from several pre-selected latitude/longitude sites, and post-process the data to create output graphics. In addition, the AMU configured several software programs to convert local Weather Research and Forecast (WRF) model output into HYSPLIT format.

  6. Accounting for treatment use when validating a prognostic model: a simulation study

    Directory of Open Access Journals (Sweden)

    Romin Pajouheshnia

    2017-07-01

    Full Text Available Abstract Background Prognostic models often show poor performance when applied to independent validation data sets. We illustrate how treatment use in a validation set can affect measures of model performance and present the uses and limitations of available analytical methods to account for this using simulated data. Methods We outline how the use of risk-lowering treatments in a validation set can lead to an apparent overestimation of risk by a prognostic model that was developed in a treatment-naïve cohort to make predictions of risk without treatment. Potential methods to correct for the effects of treatment use when testing or validating a prognostic model are discussed from a theoretical perspective.. Subsequently, we assess, in simulated data sets, the impact of excluding treated individuals and the use of inverse probability weighting (IPW on the estimated model discrimination (c-index and calibration (observed:expected ratio and calibration plots in scenarios with different patterns and effects of treatment use. Results Ignoring the use of effective treatments in a validation data set leads to poorer model discrimination and calibration than would be observed in the untreated target population for the model. Excluding treated individuals provided correct estimates of model performance only when treatment was randomly allocated, although this reduced the precision of the estimates. IPW followed by exclusion of the treated individuals provided correct estimates of model performance in data sets where treatment use was either random or moderately associated with an individual's risk when the assumptions of IPW were met, but yielded incorrect estimates in the presence of non-positivity or an unobserved confounder. Conclusions When validating a prognostic model developed to make predictions of risk without treatment, treatment use in the validation set can bias estimates of the performance of the model in future targeted individuals, and

  7. Land surface modelling in hydrology and meteorology – lessons learned from the Baltic Basin

    Directory of Open Access Journals (Sweden)

    L. P. Graham

    2000-01-01

    Full Text Available By both tradition and purpose, the land parameterization schemes of hydrological and meteorological models differ greatly. Meteorologists are concerned primarily with solving the energy balance, whereas hydrologists are most interested in the water balance. Meteorological climate models typically have multi-layered soil parameterisation that solves temperature fluxes numerically with diffusive equations. The same approach is carried over to a similar treatment of water transport. Hydrological models are not usually so interested in soil temperatures, but must provide a reasonable representation of soil moisture to get runoff right. To treat the heterogeneity of the soil, many hydrological models use only one layer with a statistical representation of soil variability. Such a hydrological model can be used on large scales while taking subgrid variability into account. Hydrological models also include lateral transport of water – an imperative if' river discharge is to be estimated. The concept of a complexity chain for coupled modelling systems is introduced, together with considerations for mixing model components. Under BALTEX (Baltic Sea Experiment and SWECLIM (Swedish Regional Climate Modelling Programme, a large-scale hydrological model of runoff in the Baltic Basin is used to review atmospheric climate model simulations. This incorporates both the runoff record and hydrological modelling experience into atmospheric model development. Results from two models are shown. A conclusion is that the key to improved models may be less complexity. Perhaps the meteorological models should keep their multi-layered approach for modelling soil temperature, but add a simpler, yet physically consistent, hydrological approach for modelling snow processes and water transport in the soil. Keywords: land surface modelling; hydrological modelling; atmospheric climate models; subgrid variability; Baltic Basin

  8. Machine learning modeling of plant phenology based on coupling satellite and gridded meteorological dataset

    Science.gov (United States)

    Czernecki, Bartosz; Nowosad, Jakub; Jabłońska, Katarzyna

    2018-04-01

    Changes in the timing of plant phenological phases are important proxies in contemporary climate research. However, most of the commonly used traditional phenological observations do not give any coherent spatial information. While consistent spatial data can be obtained from airborne sensors and preprocessed gridded meteorological data, not many studies robustly benefit from these data sources. Therefore, the main aim of this study is to create and evaluate different statistical models for reconstructing, predicting, and improving quality of phenological phases monitoring with the use of satellite and meteorological products. A quality-controlled dataset of the 13 BBCH plant phenophases in Poland was collected for the period 2007-2014. For each phenophase, statistical models were built using the most commonly applied regression-based machine learning techniques, such as multiple linear regression, lasso, principal component regression, generalized boosted models, and random forest. The quality of the models was estimated using a k-fold cross-validation. The obtained results showed varying potential for coupling meteorological derived indices with remote sensing products in terms of phenological modeling; however, application of both data sources improves models' accuracy from 0.6 to 4.6 day in terms of obtained RMSE. It is shown that a robust prediction of early phenological phases is mostly related to meteorological indices, whereas for autumn phenophases, there is a stronger information signal provided by satellite-derived vegetation metrics. Choosing a specific set of predictors and applying a robust preprocessing procedures is more important for final results than the selection of a particular statistical model. The average RMSE for the best models of all phenophases is 6.3, while the individual RMSE vary seasonally from 3.5 to 10 days. Models give reliable proxy for ground observations with RMSE below 5 days for early spring and late spring phenophases. For

  9. Aircraft Anomaly Prognostics, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — Ridgetop Group will leverage its proven Electromechanical Actuator (EMA) prognostics methodology to develop an advanced model-based actuator prognostic reasoner...

  10. Sensitivity of modeled estuarine circulation to spatial and temporal resolution of input meteorological forcing of a cold frontal passage

    Science.gov (United States)

    Weaver, Robert J.; Taeb, Peyman; Lazarus, Steven; Splitt, Michael; Holman, Bryan P.; Colvin, Jeffrey

    2016-12-01

    In this study, a four member ensemble of meteorological forcing is generated using the Weather Research and Forecasting (WRF) model in order to simulate a frontal passage event that impacted the Indian River Lagoon (IRL) during March 2015. The WRF model is run to provide high and low, spatial (0.005° and 0.1°) and temporal (30 min and 6 h) input wind and pressure fields. The four member ensemble is used to force the Advanced Circulation model (ADCIRC) coupled with Simulating Waves Nearshore (SWAN) and compute the hydrodynamic and wave response. Results indicate that increasing the spatial resolution of the meteorological forcing has a greater impact on the results than increasing the temporal resolution in coastal systems like the IRL where the length scales are smaller than the resolution of the operational meteorological model being used to generate the forecast. Changes in predicted water elevations are due in part to the upwind and downwind behavior of the input wind forcing. The significant wave height is more sensitive to the meteorological forcing, exhibited by greater ensemble spread throughout the simulation. It is important that the land mask, seen by the meteorological model, is representative of the geography of the coastal estuary as resolved by the hydrodynamic model. As long as the temporal resolution of the wind field captures the bulk characteristics of the frontal passage, computational resources should be focused so as to ensure that the meteorological model resolves the spatial complexities, such as the land-water interface, that drive the land use responsible for dynamic downscaling of the winds.

  11. Testing the importance of accurate meteorological input fields and parameterizations in atmospheric transport modelling using DREAM - Validation against ETEX-1

    DEFF Research Database (Denmark)

    Brandt, J.; Bastrup-Birk, A.; Christensen, J.H.

    1998-01-01

    A tracer model, the DREAM, which is based on a combination of a near-range Lagrangian model and a long-range Eulerian model, has been developed. The meteorological meso-scale model, MM5V1, is implemented as a meteorological driver for the tracer model. The model system is used for studying...

  12. Modelling regional scale surface fluxes, meteorology and CO2 mixing ratios for the Cabauw tower in the Netherlands

    NARCIS (Netherlands)

    Tolk, L.F.; Peters, W.; Meesters, A.G.C.A.; Groenendijk, M.; Vermeulen, A.T.; Steeneveld, G.J.; Dolman, A.J.

    2009-01-01

    We simulated meteorology and atmospheric CO2 transport over the Netherlands with the mesoscale model RAMS-Leaf3 coupled to the biospheric CO2 flux model 5PM. The results were compared with meteorological and CO2 observations, with emphasis on the tall tower of Cabauw. An analysis of the coupled

  13. Multistream sensor fusion-based prognostics model for systems with single failure modes

    International Nuclear Information System (INIS)

    Fang, Xiaolei; Paynabar, Kamran; Gebraeel, Nagi

    2017-01-01

    Advances in sensor technology have facilitated the capability of monitoring the degradation of complex engineering systems through the analysis of multistream degradation signals. However, the varying levels of correlation with physical degradation process for different sensors, high-dimensionality of the degradation signals and cross-correlation among different signal streams pose significant challenges in monitoring and prognostics of such systems. To address the foregoing challenges, we develop a three-step multi-sensor prognostic methodology that utilizes multistream signals to predict residual useful lifetimes of partially degraded systems. We first identify the informative sensors via the penalized (log)-location-scale regression. Then, we fuse the degradation signals of the informative sensors using multivariate functional principal component analysis, which is capable of modeling the cross-correlation of signal streams. Finally, the third step focuses on utilizing the fused signal features for prognostics via adaptive penalized (log)-location-scale regression. We validate our multi-sensor prognostic methodology using simulation study as well as a case study of aircraft turbofan engines available from NASA repository.

  14. Prognostics for Microgrid Components

    Science.gov (United States)

    Saxena, Abhinav

    2012-01-01

    Prognostics is the science of predicting future performance and potential failures based on targeted condition monitoring. Moving away from the traditional reliability centric view, prognostics aims at detecting and quantifying the time to impending failures. This advance warning provides the opportunity to take actions that can preserve uptime, reduce cost of damage, or extend the life of the component. The talk will focus on the concepts and basics of prognostics from the viewpoint of condition-based systems health management. Differences with other techniques used in systems health management and philosophies of prognostics used in other domains will be shown. Examples relevant to micro grid systems and subsystems will be used to illustrate various types of prediction scenarios and the resources it take to set up a desired prognostic system. Specifically, the implementation results for power storage and power semiconductor components will demonstrate specific solution approaches of prognostics. The role of constituent elements of prognostics, such as model, prediction algorithms, failure threshold, run-to-failure data, requirements and specifications, and post-prognostic reasoning will be explained. A discussion on performance evaluation and performance metrics will conclude the technical discussion followed by general comments on open research problems and challenges in prognostics.

  15. Evaluating the effects of model structure and meteorological input data on runoff modelling in an alpine headwater basin

    Science.gov (United States)

    Schattan, Paul; Bellinger, Johannes; Förster, Kristian; Schöber, Johannes; Huttenlau, Matthias; Kirnbauer, Robert; Achleitner, Stefan

    2017-04-01

    Modelling water resources in snow-dominated mountainous catchments is challenging due to both, short concentration times and a highly variable contribution of snow melt in space and time from complex terrain. A number of model setups exist ranging from physically based models to conceptional models which do not attempt to represent the natural processes in a physically meaningful way. Within the flood forecasting system for the Tyrolean Inn River two serially linked hydrological models with differing process representation are used. Non- glacierized catchments are modelled by a semi-distributed, water balance model (HQsim) based on the HRU-approach. A fully-distributed energy and mass balance model (SES), purpose-built for snow- and icemelt, is used for highly glacierized headwater catchments. Previous work revealed uncertainties and limitations within the models' structures regarding (i) the representation of snow processes in HQsim, (ii) the runoff routing of SES, and (iii) the spatial resolution of the meteorological input data in both models. To overcome these limitations, a "strengths driven" model coupling is applied. Instead of linking the models serially, a vertical one-way coupling of models has been implemented. The fully-distributed snow modelling of SES is combined with the semi-distributed HQsim structure, allowing to benefit from soil and runoff routing schemes in HQsim. A monte-carlo based modelling experiment was set up to evaluate the resulting differences in the runoff prediction due to the improved model coupling and a refined spatial resolution of the meteorological forcing. The experiment design follows a gradient of spatial discretisation of hydrological processes and meteorological forcing data with a total of six different model setups for the alpine headwater basin of the Fagge River in the Tyrolean Alps. In general, all setups show a good performance for this particular basin. It is therefore planned to include other basins with differing

  16. Modeling of meteorology, chemistry and aerosol for the 2017 Utah Winter Fine Particle Study

    Science.gov (United States)

    McKeen, S. A.; Angevine, W. M.; McDonald, B.; Ahmadov, R.; Franchin, A.; Middlebrook, A. M.; Fibiger, D. L.; McDuffie, E. E.; Womack, C.; Brown, S. S.; Moravek, A.; Murphy, J. G.; Trainer, M.

    2017-12-01

    The Utah Winter Fine Particle Study (UWFPS-17) field project took place during January and February of 2017 within the populated region of the Great Salt Lake, Utah. The study focused on understanding the meteorology and chemistry associated with high particulate matter (PM) levels often observed near Salt Lake City during stable wintertime conditions. Detailed composition and meteorological observations were taken from the NOAA Twin-Otter aircraft and several surface sites during the study period, and extremely high aerosol conditions were encountered for two cold-pool episodes occurring in the last 2 weeks of January. A clear understanding of the photochemical and aerosol processes leading to these high PM events is still lacking. Here we present high spatiotemporal resolution simulations of meteorology, PM and chemistry over Utah from January 13 to February 1, 2017 using the WRF/Chem photochemical model. Correctly characterizing the meteorology is difficult due to the complex terrain and shallow inversion layers. We discuss the approach and limitations of the simulated meteorology, and evaluate low-level pollutant mixing using vertical profiles from missed airport approaches by the NOAA Twin-Otter performed routinely during each flight. Full photochemical simulations are calculated using NOx, ammonia and VOC emissions from the U.S. EPA NEI-2011 emissions inventory. Comparisons of the observed vertical column amounts of NOx, ammonia, aerosol nitrate and ammonium with model results shows the inventory estimates for ammonia emissions are low by a factor of four and NOx emissions are low by nearly a factor of two. The partitioning of both nitrate and NH3 between gas and particle phase depends strongly on the NH3 and NOx emissions to the model and calculated NOx to nitrate conversion rates. These rates are underestimated by gas-phase chemistry alone, even though surface snow albedo increases photolysis rates by nearly a factor of two. Several additional conversion

  17. Instantaneous Linkages between Clouds and Large-Scale Meteorology over the Southern Ocean in Observations and a Climate Model

    Energy Technology Data Exchange (ETDEWEB)

    Wall, Casey J. [Department of Atmospheric Sciences, University of Washington, Seattle, Washington; Hartmann, Dennis L. [Department of Atmospheric Sciences, University of Washington, Seattle, Washington; Ma, Po-Lun [Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington

    2017-12-01

    Instantaneous, coincident, footprint-level satellite observations of cloud properties and radiation taken during austral summer over the Southern Ocean are used to study relationships between clouds and large-scale meteorology. Cloud properties are very sensitive to the strength of vertical motion in the middle-troposphere, and low-cloud properties are sensitive to estimated inversion strength, low-level temperature advection, and sea surface temperature. These relationships are quantified. An index for the meteorological anomalies associated with midlatitude cyclones is presented, and it is used to reveal the sensitivity of clouds to the meteorology within the warm- and cold-sector of cyclones. The observed relationships between clouds and meteorology are compared to those in the Community Atmosphere Model version 5 (CAM5) using satellite simulators. Low-clouds simulated by CAM5 are too few, too bright, and contain too much ice, and low-clouds located in the cold-sector of cyclones are too sensitive to variations in the meteorology. The latter two biases are dramatically reduced when CAM5 is coupled with an updated boundary layer parameterization know as Cloud Layers Unified by Binormals (CLUBB). More generally, this study demonstrates that examining the instantaneous timescale is a powerful approach to understanding the physical processes that control clouds and how they are represented in climate models. Such an evaluation goes beyond the cloud climatology and exposes model bias under various meteorological conditions.

  18. An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation.

    Science.gov (United States)

    Candido Dos Reis, Francisco J; Wishart, Gordon C; Dicks, Ed M; Greenberg, David; Rashbass, Jem; Schmidt, Marjanka K; van den Broek, Alexandra J; Ellis, Ian O; Green, Andrew; Rakha, Emad; Maishman, Tom; Eccles, Diana M; Pharoah, Paul D P

    2017-05-22

    PREDICT is a breast cancer prognostic and treatment benefit model implemented online. The overall fit of the model has been good in multiple independent case series, but PREDICT has been shown to underestimate breast cancer specific mortality in women diagnosed under the age of 40. Another limitation is the use of discrete categories for tumour size and node status resulting in 'step' changes in risk estimates on moving between categories. We have refitted the PREDICT prognostic model using the original cohort of cases from East Anglia with updated survival time in order to take into account age at diagnosis and to smooth out the survival function for tumour size and node status. Multivariable Cox regression models were used to fit separate models for ER negative and ER positive disease. Continuous variables were fitted using fractional polynomials and a smoothed baseline hazard was obtained by regressing the baseline cumulative hazard for each patients against time using fractional polynomials. The fit of the prognostic models were then tested in three independent data sets that had also been used to validate the original version of PREDICT. In the model fitting data, after adjusting for other prognostic variables, there is an increase in risk of breast cancer specific mortality in younger and older patients with ER positive disease, with a substantial increase in risk for women diagnosed before the age of 35. In ER negative disease the risk increases slightly with age. The association between breast cancer specific mortality and both tumour size and number of positive nodes was non-linear with a more marked increase in risk with increasing size and increasing number of nodes in ER positive disease. The overall calibration and discrimination of the new version of PREDICT (v2) was good and comparable to that of the previous version in both model development and validation data sets. However, the calibration of v2 improved over v1 in patients diagnosed under the age

  19. Improved meteorology from an updated WRF/CMAQ modeling system with MODIS vegetation and albedo

    Science.gov (United States)

    Realistic vegetation characteristics and phenology from the Moderate Resolution Imaging Spectroradiometer (MODIS) products improve the simulation for the meteorology and air quality modeling system WRF/CMAQ (Weather Research and Forecasting model and Community Multiscale Air Qual...

  20. Generic Software Architecture for Prognostics (GSAP) User Guide

    Science.gov (United States)

    Teubert, Christopher Allen; Daigle, Matthew John; Watkins, Jason; Sankararaman, Shankar; Goebel, Kai

    2016-01-01

    The Generic Software Architecture for Prognostics (GSAP) is a framework for applying prognostics. It makes applying prognostics easier by implementing many of the common elements across prognostic applications. The standard interface enables reuse of prognostic algorithms and models across systems using the GSAP framework.

  1. Coupling X-band dual-polarized mini-radars and hydro-meteorological forecast models: the HYDRORAD project

    Directory of Open Access Journals (Sweden)

    E. Picciotti

    2013-05-01

    Full Text Available Hydro-meteorological hazards like convective outbreaks leading to torrential rain and floods are among the most critical environmental issues world-wide. In that context weather radar observations have proven to be very useful in providing information on the spatial distribution of rainfall that can support early warning of floods. However, quantitative precipitation estimation by radar is subjected to many limitations and uncertainties. The use of dual-polarization at high frequency (i.e. X-band has proven particularly useful for mitigating some of the limitation of operational systems, by exploiting the benefit of easiness to transport and deploy and the high spatial and temporal resolution achievable at small antenna sizes. New developments on X-band dual-polarization technology in recent years have received the interest of scientific and operational communities in these systems. New enterprises are focusing on the advancement of cost-efficient mini-radar network technology, based on high-frequency (mainly X-band and low-power weather radar systems for weather monitoring and hydro-meteorological forecasting. Within the above context, the main objective of the HYDRORAD project was the development of an innovative mbox{integrated} decision support tool for weather monitoring and hydro-meteorological applications. The integrated system tool is based on a polarimetric X-band mini-radar network which is the core of the decision support tool, a novel radar products generator and a hydro-meteorological forecast modelling system that ingests mini-radar rainfall products to forecast precipitation and floods. The radar products generator includes algorithms for attenuation correction, hydrometeor classification, a vertical profile reflectivity correction, a new polarimetric rainfall estimators developed for mini-radar observations, and short-term nowcasting of convective cells. The hydro-meteorological modelling system includes the Mesoscale Model 5

  2. Performance assessment of retrospective meteorological inputs for use in air quality modeling during TexAQS 2006

    Science.gov (United States)

    Ngan, Fong; Byun, Daewon; Kim, Hyuncheol; Lee, Daegyun; Rappenglück, Bernhard; Pour-Biazar, Arastoo

    2012-07-01

    To achieve more accurate meteorological inputs than was used in the daily forecast for studying the TexAQS 2006 air quality, retrospective simulations were conducted using objective analysis and 3D/surface analysis nudging with surface and upper observations. Model ozone using the assimilated meteorological fields with improved wind fields shows better agreement with the observation compared to the forecasting results. In the post-frontal conditions, important factors for ozone modeling in terms of wind patterns are the weak easterlies in the morning for bringing in industrial emissions to the city and the subsequent clockwise turning of the wind direction induced by the Coriolis force superimposing the sea breeze, which keeps pollutants in the urban area. Objective analysis and nudging employed in the retrospective simulation minimize the wind bias but are not able to compensate for the general flow pattern biases inherited from large scale inputs. By using an alternative analyses data for initializing the meteorological simulation, the model can re-produce the flow pattern and generate the ozone peak location closer to the reality. The inaccurate simulation of precipitation and cloudiness cause over-prediction of ozone occasionally. Since there are limitations in the meteorological model to simulate precipitation and cloudiness in the fine scale domain (less than 4-km grid), the satellite-based cloud is an alternative way to provide necessary inputs for the retrospective study of air quality.

  3. Providing Meteorological Information for Controlled Burns at the Savannah River Site

    International Nuclear Information System (INIS)

    Buckley, R.

    1999-01-01

    Regional and local weather information are important for a variety of applications at the Savannah River Site (SRS), a Department of Energy (DOE) facility covering approximately 800 square kilometers of southwest South Carolina east of the Savannah River. For example, meteorological observations and forecasts are used to assess the consequences of an accidental radiological or chemical release. Traditionally, hazards posed by SRS operations have been associated with nuclear reactors, chemical reprocessing plants, fuel fabrication, or waste-vitrification facilities. However, recent events have shown site-specific meteorology to be a valuable tool to the United States Forest Service (USFS) in mitigating potential hazards from controlled burns that are conducted at the SRS. Prescribed burns at the SRS are important for a variety of reasons. The removal of thick undergrowth allows wildlife to more easily feed and migrate, accelerates the growth of young pine stands, and controls certain diseases that affect local pine forests (e.g. Adams et al. 1973). In addition, the removal of twigs, pine needles, or leaves (a fuel source) reduces the chance of serious wildfire damage. However, the threat of smoke inhalation and reduced visibility requires careful planning on the part of the fire professionals. At the SRS, approximately 100 square kilometers of land per year are burned in a controlled manner, mainly in the spring.To reduce the potentially harmful effects to any onsite activity, it is important that USFS personnel understand current and predicted weather patterns within the area. This paper discusses two sources of meteorological information that are provided to SRS-USFS personnel for use in planning forest burns: (1) a meteorological tower system which provides current data from a series of onsite locations, and (2) an operational prognostic mesoscale model used to generate forecast information. The forecast data supplements the basic National Weather Service (NWS

  4. Risk factors and prognostic models for perinatal asphyxia at term

    NARCIS (Netherlands)

    Ensing, S.

    2015-01-01

    This thesis will focus on the risk factors and prognostic models for adverse perinatal outcome at term, with a special focus on perinatal asphyxia and obstetric interventions during labor to reduce adverse pregnancy outcomes. For the majority of the studies in this thesis we were allowed to use data

  5. A Meteorological Information Mining-Based Wind Speed Model for Adequacy Assessment of Power Systems With Wind Power

    DEFF Research Database (Denmark)

    Guo, Yifei; Gao, Houlei; Wu, Qiuwei

    2017-01-01

    Accurate wind speed simulation is an essential prerequisite to analyze the power systems with wind power. A wind speed model considering meteorological conditions and seasonal variations is proposed in this paper. Firstly, using the path analysis method, the influence weights of meteorological...... systems with wind power. The assessment results of the modified IEEE-RTS79 and IEEE-RTS96 demonstrated the effectiveness and accuracy of the proposed model....

  6. Modeling PM2.5 Urban Pollution Using Machine Learning and Selected Meteorological Parameters

    Directory of Open Access Journals (Sweden)

    Jan Kleine Deters

    2017-01-01

    Full Text Available Outdoor air pollution costs millions of premature deaths annually, mostly due to anthropogenic fine particulate matter (or PM2.5. Quito, the capital city of Ecuador, is no exception in exceeding the healthy levels of pollution. In addition to the impact of urbanization, motorization, and rapid population growth, particulate pollution is modulated by meteorological factors and geophysical characteristics, which complicate the implementation of the most advanced models of weather forecast. Thus, this paper proposes a machine learning approach based on six years of meteorological and pollution data analyses to predict the concentrations of PM2.5 from wind (speed and direction and precipitation levels. The results of the classification model show a high reliability in the classification of low (25 µg/m3 and low (<10 µg/m3 versus moderate (10–25 µg/m3 concentrations of PM2.5. A regression analysis suggests a better prediction of PM2.5 when the climatic conditions are getting more extreme (strong winds or high levels of precipitation. The high correlation between estimated and real data for a time series analysis during the wet season confirms this finding. The study demonstrates that the use of statistical models based on machine learning is relevant to predict PM2.5 concentrations from meteorological data.

  7. Large-scale external validation and comparison of prognostic models: an application to chronic obstructive pulmonary disease

    NARCIS (Netherlands)

    Guerra, Beniamino; Haile, Sarah R.; Lamprecht, Bernd; Ramírez, Ana S.; Martinez-Camblor, Pablo; Kaiser, Bernhard; Alfageme, Inmaculada; Almagro, Pere; Casanova, Ciro; Esteban-González, Cristóbal; Soler-Cataluña, Juan J.; de-Torres, Juan P.; Miravitlles, Marc; Celli, Bartolome R.; Marin, Jose M.; ter Riet, Gerben; Sobradillo, Patricia; Lange, Peter; Garcia-Aymerich, Judith; Antó, Josep M.; Turner, Alice M.; Han, MeiLan K.; Langhammer, Arnulf; Leivseth, Linda; Bakke, Per; Johannessen, Ane; Oga, Toru; Cosio, Borja; Ancochea-Bermúdez, Julio; Echazarreta, Andres; Roche, Nicolas; Burgel, Pierre-Régis; Sin, Don D.; Soriano, Joan B.; Puhan, Milo A.

    2018-01-01

    External validations and comparisons of prognostic models or scores are a prerequisite for their use in routine clinical care but are lacking in most medical fields including chronic obstructive pulmonary disease (COPD). Our aim was to externally validate and concurrently compare prognostic scores

  8. Implementation of Remaining Useful Lifetime Transformer Models in the Fleet-Wide Prognostic and Health Management Suite

    International Nuclear Information System (INIS)

    Agarwal, Vivek; Lybeck, Nancy J.; Pham, Binh; Rusaw, Richard; Bickford, Randall

    2015-01-01

    Research and development efforts are required to address aging and reliability concerns of the existing fleet of nuclear power plants. As most plants continue to operate beyond the license life (i.e., towards 60 or 80 years), plant components are more likely to incur age-related degradation mechanisms. To assess and manage the health of aging plant assets across the nuclear industry, the Electric Power Research Institute has developed a web-based Fleet-Wide Prognostic and Health Management (FW-PHM) Suite for diagnosis and prognosis. FW-PHM is a set of web-based diagnostic and prognostic tools and databases, comprised of the Diagnostic Advisor, the Asset Fault Signature Database, the Remaining Useful Life Advisor, and the Remaining Useful Life Database, that serves as an integrated health monitoring architecture. The main focus of this paper is the implementation of prognostic models for generator step-up transformers in the FW-PHM Suite. One prognostic model discussed is based on the functional relationship between degree of polymerization, (the most commonly used metrics to assess the health of the winding insulation in a transformer) and furfural concentration in the insulating oil. The other model is based on thermal-induced degradation of the transformer insulation. By utilizing transformer loading information, established thermal models are used to estimate the hot spot temperature inside the transformer winding. Both models are implemented in the Remaining Useful Life Database of the FW-PHM Suite. The Remaining Useful Life Advisor utilizes the implemented prognostic models to estimate the remaining useful life of the paper winding insulation in the transformer based on actual oil testing and operational data.

  9. A Method for Evaluation of Model-Generated Vertical Profiles of Meteorological Variables

    Science.gov (United States)

    2016-03-01

    evaluated WRF output for the boundary layer over Svalbard in the Arctic in terms of height above ground compared to tower and tethered balloon ...Valparaiso, Chile; 2011. Dutsch ML. Evaluation of the WRF model based on observations made by controlled meteorological balloons in the atmospheric

  10. Prognostic stratification of patients with advanced renal cell carcinoma treated with sunitinib: comparison with the Memorial Sloan-Kettering prognostic factors model

    International Nuclear Information System (INIS)

    Bamias, Aristotelis; Anastasiou, Ioannis; Stravodimos, Kostas; Xanthakis, Ioannis; Skolarikos, Andreas; Christodoulou, Christos; Syrigos, Kostas; Papandreou, Christos; Razi, Evangelia; Dafni, Urania; Fountzilas, George; Karadimou, Alexandra; Dimopoulos, Meletios A; Lampaki, Sofia; Lainakis, George; Malettou, Lia; Timotheadou, Eleni; Papazisis, Kostas; Andreadis, Charalambos; Kontovinis, Loukas

    2010-01-01

    The treatment paradigm in advanced renal cell carcinoma (RCC) has changed in the recent years. Sunitinib has been established as a new standard for first-line therapy. We studied the prognostic significance of baseline characteristics and we compared the risk stratification with the established Memorial Sloan Kettering Cancer Center (MSKCC) model. This is a retrospective analysis of patients treated in six Greek Oncology Units of HECOG. Inclusion criteria were: advanced renal cell carcinoma not amenable to surgery and treatment with Sunitinib. Previous cytokine therapy but no targeted agents were allowed. Overall survival (OS) was the major end point. Significance of prognostic factors was evaluated with multivariate cox regression analysis. A model was developed to stratify patients according to risk. One hundred and nine patients were included. Median follow up has been 15.8 months and median OS 17.1 months (95% CI: 13.7-20.6). Time from diagnosis to the start of Sunitinib (<= 12 months vs. >12 months, p = 0.001), number of metastatic sites (1 vs. >1, p = 0.003) and performance status (PS) (<= 1 vs >1, p = 0.001) were independently associated with OS. Stratification in two risk groups ('low' risk: 0 or 1 risk factors; 'high' risk: 2 or 3 risk factors) resulted in distinctly different OS (median not reached [NR] vs. 10.8 [95% confidence interval (CI): 8.3-13.3], p < 0.001). The application of the MSKCC risk criteria resulted in stratification into 3 groups (low and intermediate and poor risk) with distinctly different prognosis underlying its validity. Nevertheless, MSKCC model did not show an improved prognostic performance over the model developed by this analysis. Studies on risk stratification of patients with advanced RCC treated with targeted therapies are warranted. Our results suggest that a simpler than the MSKCC model can be developed. Such models should be further validated

  11. Investigations of the adequacy of the meteorological transport model developed for the reactor safety study

    International Nuclear Information System (INIS)

    Spring, J.L.; Brown, W.D.; Church, H.W.; McGrath, P.E.; Ritchie, L.T.; Russo, A.J.; Steck, G.P.; Wayland, J.R.; Blond, R.M.; Wall, I.B.

    1978-01-01

    A computer model (CRAC) was developed for the Reactor Safety Study (WASH-1400) [1] to estimate the consequences of postulated accidents at U.S. commercial nuclear power plants. One hundred reactors at 68 sites were included in the study. The 68 sites were divided into 6 classes according to their geographic location and meteorology. For each site class, a composite population distribution was constructed from the true population distributions at each of the sites comprising that class, and a reference site was chosen for which a full year of meteorological data (wind speed, atmospheric stability, occurrence of rain) was obtained. Given data about a postulated accident (probability, amounts of the released radionuclides, etc.) and the reference reactor site (meteorology, composite population, land usage), CRAC was used to calculate the atmospheric dispersion and ground deposition of the released radionuclides (Gaussian plume submodel) and the health effects (dosimetric and dose response submodels) and costs (land interdiction and decontamination submodel) resulting from their release. The Gaussian plume model used in CRAC either did not treat or treated simplistically a number of meteorological phenomena. Simplified models were used to treat plume rise, inversion layers, and rainstorms, while wind shear, wind direction, and correlations between wind fields and population distributions were not treated at all. Some of the effects of all of these phenomena on predictions of accident consequences as calculated using CRAC have been or are being investigated. The results of these studies are summarized

  12. A Distributed Approach to System-Level Prognostics

    Science.gov (United States)

    Daigle, Matthew J.; Bregon, Anibal; Roychoudhury, Indranil

    2012-01-01

    Prognostics, which deals with predicting remaining useful life of components, subsystems, and systems, is a key technology for systems health management that leads to improved safety and reliability with reduced costs. The prognostics problem is often approached from a component-centric view. However, in most cases, it is not specifically component lifetimes that are important, but, rather, the lifetimes of the systems in which these components reside. The system-level prognostics problem can be quite difficult due to the increased scale and scope of the prognostics problem and the relative Jack of scalability and efficiency of typical prognostics approaches. In order to address these is ues, we develop a distributed solution to the system-level prognostics problem, based on the concept of structural model decomposition. The system model is decomposed into independent submodels. Independent local prognostics subproblems are then formed based on these local submodels, resul ting in a scalable, efficient, and flexible distributed approach to the system-level prognostics problem. We provide a formulation of the system-level prognostics problem and demonstrate the approach on a four-wheeled rover simulation testbed. The results show that the system-level prognostics problem can be accurately and efficiently solved in a distributed fashion.

  13. LES-based generation of high-frequency fluctuation in wind turbulence obtained by meteorological model

    Science.gov (United States)

    Tamura, Tetsuro; Kawaguchi, Masaharu; Kawai, Hidenori; Tao, Tao

    2017-11-01

    The connection between a meso-scale model and a micro-scale large eddy simulation (LES) is significant to simulate the micro-scale meteorological problem such as strong convective events due to the typhoon or the tornado using LES. In these problems the mean velocity profiles and the mean wind directions change with time according to the movement of the typhoons or tornadoes. Although, a fine grid micro-scale LES could not be connected to a coarse grid meso-scale WRF directly. In LES when the grid is suddenly refined at the interface of nested grids which is normal to the mean advection the resolved shear stresses decrease due to the interpolation errors and the delay of the generation of smaller scale turbulence that can be resolved on the finer mesh. For the estimation of wind gust disaster the peak wind acting on buildings and structures has to be correctly predicted. In the case of meteorological model the velocity fluctuations have a tendency of diffusive variation without the high frequency component due to the numerically filtering effects. In order to predict the peak value of wind velocity with good accuracy, this paper proposes a LES-based method for generating the higher frequency components of velocity and temperature fields obtained by meteorological model.

  14. A prognostic model for soft tissue sarcoma of the extremities and trunk wall based on size, vascular invasion, necrosis, and growth pattern

    DEFF Research Database (Denmark)

    Carneiro, Ana; Bendahl, Par-Ola; Engellau, Jacob

    2011-01-01

    type, necrosis, and grade. METHODS:: Whole-tumor sections from 239 soft tissue sarcomas of the extremities were reviewed for the following prognostic factors: size, vascular invasion, necrosis, and growth pattern. A new prognostic model, referred to as SING (Size, Invasion, Necrosis, Growth......), was established and compared with other clinically applied systems. RESULTS:: Size, vascular invasion, necrosis, and peripheral tumor growth pattern provided independent prognostic information with hazard ratios of 2.2-2.6 for development of metastases in multivariate analysis. When these factors were combined...... into the prognostic model SING, high risk of metastasis was predicted with a sensitivity of 74% and a specificity of 85%. Moreover, the prognostic performance of SING compared favorably with other widely used systems. CONCLUSIONS:: SING represents a promising prognostic model, and vascular invasion and tumor growth...

  15. Numerical Weather Prediction Models on Linux Boxes as tools in meteorological education in Hungary

    Science.gov (United States)

    Gyongyosi, A. Z.; Andre, K.; Salavec, P.; Horanyi, A.; Szepszo, G.; Mille, M.; Tasnadi, P.; Weidiger, T.

    2012-04-01

    Education of Meteorologist in Hungary - according to the Bologna Process - has three stages: BSc, MSc and PhD, and students graduating at each stage get the respective degree (BSc, MSc and PhD). The three year long base BSc course in Meteorology can be chosen by undergraduate students in the fields of Geosciences, Environmental Sciences and Physics. BasicsFundamentals in Mathematics (Calculus), Physics (General and Theoretical) Physics and Informatics are emphasized during their elementary education. The two year long MSc course - in which about 15 to 25 students are admitted each year - can be studied only at our the Eötvös Loránd uUniversity in the our country. Our aim is to give a basic education in all fields of Meteorology. Main topics are: Climatology, Atmospheric Physics, Atmospheric Chemistry, Dynamic and Synoptic Meteorology, Numerical Weather Prediction, modeling Modeling of surfaceSurface-atmosphere Iinteractions and Cclimate change. Education is performed in two branches: Climate Researcher and Forecaster. Education of Meteorologist in Hungary - according to the Bologna Process - has three stages: BSc, MSc and PhD, and students graduating at each stage get the respective degree. The three year long BSc course in Meteorology can be chosen by undergraduate students in the fields of Geosciences, Environmental Sciences and Physics. Fundamentals in Mathematics (Calculus), (General and Theoretical) Physics and Informatics are emphasized during their elementary education. The two year long MSc course - in which about 15 to 25 students are admitted each year - can be studied only at the Eötvös Loránd University in our country. Our aim is to give a basic education in all fields of Meteorology: Climatology, Atmospheric Physics, Atmospheric Chemistry, Dynamic and Synoptic Meteorology, Numerical Weather Prediction, Modeling of Surface-atmosphere Interactions and Climate change. Education is performed in two branches: Climate Researcher and Forecaster

  16. A real case simulation of the air-borne effluent dispersion on a typical summer day under CDA scenario for PFBR using an advanced meteorological and dispersion model

    International Nuclear Information System (INIS)

    Srinivas, C.V; Venkatesan, R.; Bagavath Singh, A.; Somayaji, K.M.

    2003-11-01

    Environmental concentrations and radioactive doses within and beyond the site boundary for the CDA situation of PFBR have been estimated using an Advanced Radiological Impact Prediction system for a real atmospheric situation on a typical summer day in the month of May 2003. The system consists of a meso-scale atmospheric prognostic model MM5 coupled with a random walk Lagrangian particle dispersion model FLEXPART for the simulation of transport, diffusion and deposition of radio nuclides. The details of the modeling system, its capabilities and various features are presented. The model has been validated for the simulated coastal atmospheric features of land-sea breeze, development of TIBL etc., with site and regional meteorological observations from IMD. Analysis of the dose distribution in a situation that corresponds to the atmospheric conditions on the chosen day shows that the doses for CDA through different pathways are 8 times less than the earlier estimations made according to regulatory requirements using the Gaussian Plume Model (GPM) approach. However for stack releases a higher dose than was reported earlier occurred beyond the site boundary at 2-4 km range under stable and fumigation conditions. The doses due to stack releases under these conditions maintained almost the same value in 3 to 10 km range and decreased there after. Deposition velocities computed from radionuclide species, wind speed, surface properties were 2 orders lower than the values used earlier and hence gave more realistic estimates of ground deposited activity. The study has enabled to simulate the more complex meteorological situation that actually is present at the site of interest and the associated spatial distribution of radiological impact around Kalpakkam. In order to draw meaningful conclusion that can be compared with regulatory estimates future study would be undertaken to simulate the dispersion under extreme meteorological situations which could possibly be worse than

  17. Physics based Degradation Modeling and Prognostics of Electrolytic Capacitors under Electrical Overstress Conditions

    Data.gov (United States)

    National Aeronautics and Space Administration — This paper proposes a physics based degradation modeling and prognostics approach for electrolytic capacitors. Electrolytic capacitors are critical components in...

  18. A hybrid prognostic model for multistep ahead prediction of machine condition

    Science.gov (United States)

    Roulias, D.; Loutas, T. H.; Kostopoulos, V.

    2012-05-01

    Prognostics are the future trend in condition based maintenance. In the current framework a data driven prognostic model is developed. The typical procedure of developing such a model comprises a) the selection of features which correlate well with the gradual degradation of the machine and b) the training of a mathematical tool. In this work the data are taken from a laboratory scale single stage gearbox under multi-sensor monitoring. Tests monitoring the condition of the gear pair from healthy state until total brake down following several days of continuous operation were conducted. After basic pre-processing of the derived data, an indicator that correlated well with the gearbox condition was obtained. Consecutively the time series is split in few distinguishable time regions via an intelligent data clustering scheme. Each operating region is modelled with a feed-forward artificial neural network (FFANN) scheme. The performance of the proposed model is tested by applying the system to predict the machine degradation level on unseen data. The results show the plausibility and effectiveness of the model in following the trend of the timeseries even in the case that a sudden change occurs. Moreover the model shows ability to generalise for application in similar mechanical assets.

  19. A risk evaluation model using on-site meteorological data

    International Nuclear Information System (INIS)

    Kang, C.S.

    1979-01-01

    A model is considered in order to evaluate the potential risk from a nuclear facility directly combining the on site meteorological data. The model is utilized to evaluate the environmental consequences from the routine releases during normal plant operation as well as following postulated accidental releases. The doses to individual and risks to the population-at-large are also analyzed in conjunction with design of rad-waste management and safety systems. It is observed that the conventional analysis, which is done in two separate unaffiliated phases of releases and atmospheric dispersion tends to result in unnecessary over-design of the systems because of high resultant doses calculated by multiplication of two extreme values. (author)

  20. Modeling and roles of meteorological factors in outbreaks of highly pathogenic avian influenza H5N1.

    Directory of Open Access Journals (Sweden)

    Paritosh K Biswas

    Full Text Available The highly pathogenic avian influenza A virus subtype H5N1 (HPAI H5N1 is a deadly zoonotic pathogen. Its persistence in poultry in several countries is a potential threat: a mutant or genetically reassorted progenitor might cause a human pandemic. Its world-wide eradication from poultry is important to protect public health. The global trend of outbreaks of influenza attributable to HPAI H5N1 shows a clear seasonality. Meteorological factors might be associated with such trend but have not been studied. For the first time, we analyze the role of meteorological factors in the occurrences of HPAI outbreaks in Bangladesh. We employed autoregressive integrated moving average (ARIMA and multiplicative seasonal autoregressive integrated moving average (SARIMA to assess the roles of different meteorological factors in outbreaks of HPAI. Outbreaks were modeled best when multiplicative seasonality was incorporated. Incorporation of any meteorological variable(s as inputs did not improve the performance of any multivariable models, but relative humidity (RH was a significant covariate in several ARIMA and SARIMA models with different autoregressive and moving average orders. The variable cloud cover was also a significant covariate in two SARIMA models, but air temperature along with RH might be a predictor when moving average (MA order at lag 1 month is considered.

  1. Physics Based Electrolytic Capacitor Degradation Models for Prognostic Studies under Thermal Overstress

    Science.gov (United States)

    Kulkarni, Chetan S.; Celaya, Jose R.; Goebel, Kai; Biswas, Gautam

    2012-01-01

    Electrolytic capacitors are used in several applications ranging from power supplies on safety critical avionics equipment to power drivers for electro-mechanical actuators. This makes them good candidates for prognostics and health management research. Prognostics provides a way to assess remaining useful life of components or systems based on their current state of health and their anticipated future use and operational conditions. Past experiences show that capacitors tend to degrade and fail faster under high electrical and thermal stress conditions that they are often subjected to during operations. In this work, we study the effects of accelerated aging due to thermal stress on different sets of capacitors under different conditions. Our focus is on deriving first principles degradation models for thermal stress conditions. Data collected from simultaneous experiments are used to validate the desired models. Our overall goal is to derive accurate models of capacitor degradation, and use them to predict performance changes in DC-DC converters.

  2. Cross-national validation of prognostic models predicting sickness absence and the added value of work environment variables.

    Science.gov (United States)

    Roelen, Corné A M; Stapelfeldt, Christina M; Heymans, Martijn W; van Rhenen, Willem; Labriola, Merete; Nielsen, Claus V; Bültmann, Ute; Jensen, Chris

    2015-06-01

    To validate Dutch prognostic models including age, self-rated health and prior sickness absence (SA) for ability to predict high SA in Danish eldercare. The added value of work environment variables to the models' risk discrimination was also investigated. 2,562 municipal eldercare workers (95% women) participated in the Working in Eldercare Survey. Predictor variables were measured by questionnaire at baseline in 2005. Prognostic models were validated for predictions of high (≥30) SA days and high (≥3) SA episodes retrieved from employer records during 1-year follow-up. The accuracy of predictions was assessed by calibration graphs and the ability of the models to discriminate between high- and low-risk workers was investigated by ROC-analysis. The added value of work environment variables was measured with Integrated Discrimination Improvement (IDI). 1,930 workers had complete data for analysis. The models underestimated the risk of high SA in eldercare workers and the SA episodes model had to be re-calibrated to the Danish data. Discrimination was practically useful for the re-calibrated SA episodes model, but not the SA days model. Physical workload improved the SA days model (IDI = 0.40; 95% CI 0.19-0.60) and psychosocial work factors, particularly the quality of leadership (IDI = 0.70; 95% CI 053-0.86) improved the SA episodes model. The prognostic model predicting high SA days showed poor performance even after physical workload was added. The prognostic model predicting high SA episodes could be used to identify high-risk workers, especially when psychosocial work factors are added as predictor variables.

  3. Prognostic model based on nailfold capillaroscopy for identifying Raynaud's phenomenon patients at high risk for the development of a scleroderma spectrum disorder: PRINCE (prognostic index for nailfold capillaroscopic examination).

    Science.gov (United States)

    Ingegnoli, Francesca; Boracchi, Patrizia; Gualtierotti, Roberta; Lubatti, Chiara; Meani, Laura; Zahalkova, Lenka; Zeni, Silvana; Fantini, Flavio

    2008-07-01

    To construct a prognostic index based on nailfold capillaroscopic examinations that is capable of predicting the 5-year transition from isolated Raynaud's phenomenon (RP) to RP secondary to scleroderma spectrum disorders (SSDs). The study involved 104 consecutive adult patients with a clinical history of isolated RP, and the index was externally validated in another cohort of 100 patients with the same characteristics. Both groups were followed up for 1-8 years. Six variables were examined because of their potential prognostic relevance (branching, enlarged and giant loops, capillary disorganization, microhemorrhages, and the number of capillaries). The only factors that played a significant prognostic role were the presence of giant loops (hazard ratio [HR] 2.64, P = 0.008) and microhemorrhages (HR 2.33, P = 0.01), and the number of capillaries (analyzed as a continuous variable). The adjusted prognostic role of these factors was evaluated by means of multivariate regression analysis, and the results were used to construct an algorithm-based prognostic index. The model was internally and externally validated. Our prognostic capillaroscopic index identifies RP patients in whom the risk of developing SSDs is high. This model is a weighted combination of different capillaroscopy parameters that allows physicians to stratify RP patients easily, using a relatively simple diagram to deduce the prognosis. Our results suggest that this index could be used in clinical practice, and its further inclusion in prospective studies will undoubtedly help in exploring its potential in predicting treatment response.

  4. Model-based Prognostics under Limited Sensing

    Data.gov (United States)

    National Aeronautics and Space Administration — Prognostics is crucial to providing reliable condition-based maintenance decisions. To obtain accurate predictions of component life, a variety of sensors are often...

  5. Prognostic models for predicting posttraumatic seizures during acute hospitalization, and at 1 and 2 years following traumatic brain injury.

    Science.gov (United States)

    Ritter, Anne C; Wagner, Amy K; Szaflarski, Jerzy P; Brooks, Maria M; Zafonte, Ross D; Pugh, Mary Jo V; Fabio, Anthony; Hammond, Flora M; Dreer, Laura E; Bushnik, Tamara; Walker, William C; Brown, Allen W; Johnson-Greene, Doug; Shea, Timothy; Krellman, Jason W; Rosenthal, Joseph A

    2016-09-01

    Posttraumatic seizures (PTS) are well-recognized acute and chronic complications of traumatic brain injury (TBI). Risk factors have been identified, but considerable variability in who develops PTS remains. Existing PTS prognostic models are not widely adopted for clinical use and do not reflect current trends in injury, diagnosis, or care. We aimed to develop and internally validate preliminary prognostic regression models to predict PTS during acute care hospitalization, and at year 1 and year 2 postinjury. Prognostic models predicting PTS during acute care hospitalization and year 1 and year 2 post-injury were developed using a recent (2011-2014) cohort from the TBI Model Systems National Database. Potential PTS predictors were selected based on previous literature and biologic plausibility. Bivariable logistic regression identified variables with a p-value models. Multivariable logistic regression modeling with backward-stepwise elimination was used to determine reduced prognostic models and to internally validate using 1,000 bootstrap samples. Fit statistics were calculated, correcting for overfitting (optimism). The prognostic models identified sex, craniotomy, contusion load, and pre-injury limitation in learning/remembering/concentrating as significant PTS predictors during acute hospitalization. Significant predictors of PTS at year 1 were subdural hematoma (SDH), contusion load, craniotomy, craniectomy, seizure during acute hospitalization, duration of posttraumatic amnesia, preinjury mental health treatment/psychiatric hospitalization, and preinjury incarceration. Year 2 significant predictors were similar to those of year 1: SDH, intraparenchymal fragment, craniotomy, craniectomy, seizure during acute hospitalization, and preinjury incarceration. Corrected concordance (C) statistics were 0.599, 0.747, and 0.716 for acute hospitalization, year 1, and year 2 models, respectively. The prognostic model for PTS during acute hospitalization did not

  6. External validation of prognostic models to predict risk of gestational diabetes mellitus in one Dutch cohort: prospective multicentre cohort study.

    Science.gov (United States)

    Lamain-de Ruiter, Marije; Kwee, Anneke; Naaktgeboren, Christiana A; de Groot, Inge; Evers, Inge M; Groenendaal, Floris; Hering, Yolanda R; Huisjes, Anjoke J M; Kirpestein, Cornel; Monincx, Wilma M; Siljee, Jacqueline E; Van 't Zelfde, Annewil; van Oirschot, Charlotte M; Vankan-Buitelaar, Simone A; Vonk, Mariska A A W; Wiegers, Therese A; Zwart, Joost J; Franx, Arie; Moons, Karel G M; Koster, Maria P H

    2016-08-30

     To perform an external validation and direct comparison of published prognostic models for early prediction of the risk of gestational diabetes mellitus, including predictors applicable in the first trimester of pregnancy.  External validation of all published prognostic models in large scale, prospective, multicentre cohort study.  31 independent midwifery practices and six hospitals in the Netherlands.  Women recruited in their first trimester (diabetes mellitus of any type were excluded.  Discrimination of the prognostic models was assessed by the C statistic, and calibration assessed by calibration plots.  3723 women were included for analysis, of whom 181 (4.9%) developed gestational diabetes mellitus in pregnancy. 12 prognostic models for the disorder could be validated in the cohort. C statistics ranged from 0.67 to 0.78. Calibration plots showed that eight of the 12 models were well calibrated. The four models with the highest C statistics included almost all of the following predictors: maternal age, maternal body mass index, history of gestational diabetes mellitus, ethnicity, and family history of diabetes. Prognostic models had a similar performance in a subgroup of nulliparous women only. Decision curve analysis showed that the use of these four models always had a positive net benefit.  In this external validation study, most of the published prognostic models for gestational diabetes mellitus show acceptable discrimination and calibration. The four models with the highest discriminative abilities in this study cohort, which also perform well in a subgroup of nulliparous women, are easy models to apply in clinical practice and therefore deserve further evaluation regarding their clinical impact. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  7. Integrating meteorology into research on migration.

    Science.gov (United States)

    Shamoun-Baranes, Judy; Bouten, Willem; van Loon, E Emiel

    2010-09-01

    Atmospheric dynamics strongly influence the migration of flying organisms. They affect, among others, the onset, duration and cost of migration, migratory routes, stop-over decisions, and flight speeds en-route. Animals move through a heterogeneous environment and have to react to atmospheric dynamics at different spatial and temporal scales. Integrating meteorology into research on migration is not only challenging but it is also important, especially when trying to understand the variability of the various aspects of migratory behavior observed in nature. In this article, we give an overview of some different modeling approaches and we show how these have been incorporated into migration research. We provide a more detailed description of the development and application of two dynamic, individual-based models, one for waders and one for soaring migrants, as examples of how and why to integrate meteorology into research on migration. We use these models to help understand underlying mechanisms of individual response to atmospheric conditions en-route and to explain emergent patterns. This type of models can be used to study the impact of variability in atmospheric dynamics on migration along a migratory trajectory, between seasons and between years. We conclude by providing some basic guidelines to help researchers towards finding the right modeling approach and the meteorological data needed to integrate meteorology into their own research. © The Author 2010. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved.

  8. Bayesian based Prognostic Model for Predictive Maintenance of Offshore Wind Farms

    DEFF Research Database (Denmark)

    Asgarpour, Masoud; Sørensen, John Dalsgaard

    2018-01-01

    The operation and maintenance costs of offshore wind farms can be significantly reduced if existing corrective actions are performed as efficient as possible and if future corrective actions are avoided by performing sufficient preventive actions. In this paper a prognostic model for degradation...... monitoring, fault prediction and predictive maintenance of offshore wind components is defined. The diagnostic model defined in this paper is based on degradation, remaining useful lifetime and hybrid inspection threshold models. The defined degradation model is based on an exponential distribution...

  9. Bayesian based Prognostic Model for Predictive Maintenance of Offshore Wind Farms

    DEFF Research Database (Denmark)

    Asgarpour, Masoud; Sørensen, John Dalsgaard

    2018-01-01

    monitoring, fault prediction and predictive maintenance of offshore wind components is defined. The diagnostic model defined in this paper is based on degradation, remaining useful lifetime and hybrid inspection threshold models. The defined degradation model is based on an exponential distribution......The operation and maintenance costs of offshore wind farms can be significantly reduced if existing corrective actions are performed as efficient as possible and if future corrective actions are avoided by performing sufficient preventive actions. In this paper a prognostic model for degradation...

  10. Bayesian based Prognostic Model for Predictive Maintenance of Offshore Wind Farms

    DEFF Research Database (Denmark)

    Asgarpour, Masoud

    2017-01-01

    monitoring, fault detection and predictive maintenance of offshore wind components is defined. The diagnostic model defined in this paper is based on degradation, remaining useful lifetime and hybrid inspection threshold models. The defined degradation model is based on an exponential distribution......The operation and maintenance costs of offshore wind farms can be significantly reduced if existing corrective actions are performed as efficient as possible and if future corrective actions are avoided by performing sufficient preventive actions. In this paper a prognostic model for degradation...

  11. Numerical experiments with assimilation of the mean and unresolved meteorological conditions into large-eddy simulation model

    OpenAIRE

    Esau, Igor

    2010-01-01

    Micrometeorology, city comfort, land use management and air quality monitoring increasingly become important environmental issues. To serve the needs, meteorology needs to achieve a serious advance in representation and forecast on micro-scales (meters to 100 km) called meteorological terra incognita. There is a suitable numerical tool, namely, the large-eddy simulation modelling (LES) to support the development. However, at present, the LES is of limited utility for applications. The study a...

  12. Effects of meteorological models on the solution of the surface energy balance and soil temperature variations in bare soils

    Science.gov (United States)

    Saito, Hirotaka; Šimůnek, Jiri

    2009-07-01

    SummaryA complete evaluation of the soil thermal regime can be obtained by evaluating the movement of liquid water, water vapor, and thermal energy in the subsurface. Such an evaluation requires the simultaneous solution of the system of equations for the surface water and energy balance, and subsurface heat transport and water flow. When only daily climatic data is available, one needs not only to estimate diurnal cycles of climatic data, but to calculate the continuous values of various components in the energy balance equation, using different parameterization methods. The objective of this study is to quantify the impact of the choice of different estimation and parameterization methods, referred together to as meteorological models in this paper, on soil temperature predictions in bare soils. A variety of widely accepted meteorological models were tested on the dataset collected at a proposed low-level radioactive-waste disposal site in the Chihuahua Desert in West Texas. As the soil surface was kept bare during the study, no vegetation effects were evaluated. A coupled liquid water, water vapor, and heat transport model, implemented in the HYDRUS-1D program, was used to simulate diurnal and seasonal soil temperature changes in the engineered cover installed at the site. The modified version of HYDRUS provides a flexible means for using various types of information and different models to evaluate surface mass and energy balance. Different meteorological models were compared in terms of their prediction errors for soil temperatures at seven observation depths. The results obtained indicate that although many available meteorological models can be used to solve the energy balance equation at the soil-atmosphere interface in coupled water, vapor, and heat transport models, their impact on overall simulation results varies. For example, using daily average climatic data led to greater prediction errors, while relatively simple meteorological models may

  13. Development and validation of a prognostic model for recurrent glioblastoma patients treated with bevacizumab and irinotecan

    DEFF Research Database (Denmark)

    Urup, Thomas; Dahlrot, Rikke Hedegaard; Grunnet, Kirsten

    2016-01-01

    Background Predictive markers and prognostic models are required in order to individualize treatment of recurrent glioblastoma (GBM) patients. Here, we sought to identify clinical factors able to predict response and survival in recurrent GBM patients treated with bevacizumab (BEV) and irinotecan....... Material and methods A total of 219 recurrent GBM patients treated with BEV plus irinotecan according to a previously published treatment protocol were included in the initial population. Prognostic models were generated by means of multivariate logistic and Cox regression analysis. Results In multivariate...

  14. On the influence of meteorological input on photochemical modelling of a severe episode over a coastal area

    Science.gov (United States)

    Pirovano, G.; Coll, I.; Bedogni, M.; Alessandrini, S.; Costa, M. P.; Gabusi, V.; Lasry, F.; Menut, L.; Vautard, R.

    The modelling reconstruction of the processes determining the transport and mixing of ozone and its precursors in complex terrain areas is a challenging task, particularly when local-scale circulations, such as sea breeze, take place. Within this frame, the ESCOMPTE European campaign took place in the vicinity of Marseille (south-east of France) in summer 2001. The main objectives of the field campaign were to document several photochemical episodes, as well as to constitute a detailed database for chemistry transport models intercomparison. CAMx model has been applied on the largest intense observation periods (IOP) (June 21-26, 2001) in order to evaluate the impacts of two state-of-the-art meteorological models, RAMS and MM5, on chemical model outputs. The meteorological models have been used as best as possible in analysis mode, thus allowing to identify the spread arising in pollutant concentrations as an indication of the intrinsic uncertainty associated to the meteorological input. Simulations have been deeply investigated and compared with a considerable subset of observations both at ground level and along vertical profiles. The analysis has shown that both models were able to reproduce the main circulation features of the IOP. The strongest discrepancies are confined to the Planetary Boundary Layer, consisting of a clear tendency to underestimate or overestimate wind speed over the whole domain. The photochemical simulations showed that variability in circulation intensity was crucial mainly for the representation of the ozone peaks and of the shape of ozone plumes at the ground that have been affected in the same way over the whole domain and all along the simulated period. As a consequence, such differences can be thought of as a possible indicator for the uncertainty related to the definition of meteorological fields in a complex terrain area.

  15. Evaluation of Savannah River Plant emergency response models using standard and nonstandard meteorological data

    International Nuclear Information System (INIS)

    Hoel, D.D.

    1984-01-01

    Two computer codes have been developed for operational use in performing real time evaluations of atmospheric releases from the Savannah River Plant (SRP) in South Carolina. These codes, based on mathematical models, are part of the SRP WIND (Weather Information and Display) automated emergency response system. Accuracy of ground level concentrations from a Gaussian puff-plume model and a two-dimensional sequential puff model are being evaluated with data from a series of short range diffusion experiments using sulfur hexafluoride as a tracer. The models use meteorological data collected from 7 towers on SRP and at the 300 m WJBF-TV tower about 15 km northwest of SRP. The winds and the stability, which is based on turbulence measurements, are measured at the 60 m stack heights. These results are compared to downwind concentrations using only standard meteorological data, i.e., adjusted 10 m winds and stability determined by the Pasquill-Turner stability classification method. Scattergrams and simple statistics were used for model evaluations. Results indicate predictions within accepted limits for the puff-plume code and a bias in the sequential puff model predictions using the meteorologist-adjusted nonstandard data. 5 references, 4 figures, 2 tables

  16. Predicting stabilizing treatment outcomes for complex posttraumatic stress disorder and dissociative identity disorder: an expertise-based prognostic model.

    Science.gov (United States)

    Baars, Erik W; van der Hart, Onno; Nijenhuis, Ellert R S; Chu, James A; Glas, Gerrit; Draijer, Nel

    2011-01-01

    The purpose of this study was to develop an expertise-based prognostic model for the treatment of complex posttraumatic stress disorder (PTSD) and dissociative identity disorder (DID). We developed a survey in 2 rounds: In the first round we surveyed 42 experienced therapists (22 DID and 20 complex PTSD therapists), and in the second round we surveyed a subset of 22 of the 42 therapists (13 DID and 9 complex PTSD therapists). First, we drew on therapists' knowledge of prognostic factors for stabilization-oriented treatment of complex PTSD and DID. Second, therapists prioritized a list of prognostic factors by estimating the size of each variable's prognostic effect; we clustered these factors according to content and named the clusters. Next, concept mapping methodology and statistical analyses (including principal components analyses) were used to transform individual judgments into weighted group judgments for clusters of items. A prognostic model, based on consensually determined estimates of effect sizes, of 8 clusters containing 51 factors for both complex PTSD and DID was formed. It includes the clusters lack of motivation, lack of healthy relationships, lack of healthy therapeutic relationships, lack of other internal and external resources, serious Axis I comorbidity, serious Axis II comorbidity, poor attachment, and self-destruction. In addition, a set of 5 DID-specific items was constructed. The model is supportive of the current phase-oriented treatment model, emphasizing the strengthening of the therapeutic relationship and the patient's resources in the initial stabilization phase. Further research is needed to test the model's statistical and clinical validity.

  17. Meteorological considerations in emergency response capability at nuclear power plant

    International Nuclear Information System (INIS)

    Fairobent, J.E.

    1985-01-01

    Meteorological considerations in emergency response at nuclear power plants are discussed through examination of current regulations and guidance documents, including discussion of the rationale for current regulatory requirements related to meteorological information for emergency response. Areas discussed include: major meteorological features important to emergency response; onsite meteorological measurements programs, including redundant and backup measurements; access to offsite sources of meteorological information; consideration of real-time and forecast conditions and atmospheric dispersion modeling

  18. Few promising multivariable prognostic models exist for recovery of people with non-specific neck pain in musculoskeletal primary care: A systematic review

    NARCIS (Netherlands)

    R.W. Wingbermühle (Roel); E. van Trijffel (Emiel); Nelissen, P.M. (Paul M.); B.W. Koes (Bart); A.P. Verhagen (Arianne)

    2017-01-01

    markdownabstractQuestion: Which multivariable prognostic model(s) for recovery in people with neck pain can be used in primary care? Design: Systematic review of studies evaluating multivariable prognostic models. Participants: People with non-specific neck pain presenting at primary care.

  19. No prognostic value added by vitamin D pathway SNPs to current prognostic system for melanoma survival.

    Directory of Open Access Journals (Sweden)

    Li Luo

    Full Text Available The prognostic improvement attributed to genetic markers over current prognostic system has not been well studied for melanoma. The goal of this study is to evaluate the added prognostic value of Vitamin D Pathway (VitD SNPs to currently known clinical and demographic factors such as age, sex, Breslow thickness, mitosis and ulceration (CDF. We utilized two large independent well-characterized melanoma studies: the Genes, Environment, and Melanoma (GEM and MD Anderson studies, and performed variable selection of VitD pathway SNPs and CDF using Random Survival Forest (RSF method in addition to Cox proportional hazards models. The Harrell's C-index was used to compare the performance of model predictability. The population-based GEM study enrolled 3,578 incident cases of cutaneous melanoma (CM, and the hospital-based MD Anderson study consisted of 1,804 CM patients. Including both VitD SNPs and CDF yielded C-index of 0.85, which provided slight but not significant improvement by CDF alone (C-index = 0.83 in the GEM study. Similar results were observed in the independent MD Anderson study (C-index = 0.84 and 0.83, respectively. The Cox model identified no significant associations after adjusting for multiplicity. Our results do not support clinically significant prognostic improvements attributable to VitD pathway SNPs over current prognostic system for melanoma survival.

  20. Air pollution meteorology

    Energy Technology Data Exchange (ETDEWEB)

    Shirvaikar, V V; Daoo, V J [Environmental Assessment Div., Bhabha Atomic Research Centre, Mumbai (India)

    2002-06-01

    This report is intended as a training cum reference document for scientists posted at the Environmental Laboratories at the Nuclear Power Station Sites and other sites of the Department of Atomic Energy with installations emitting air pollutants, radioactive or otherwise. Since a manual already exists for the computation of doses from radioactive air pollutants, a general approach is take here i.e. air pollutants in general are considered. The first chapter presents a brief introduction to the need and scope of air pollution dispersion modelling. The second chapter is a very important chapter discussing the aspects of meteorology relevant to air pollution and dispersion modelling. This chapter is important because without this information one really does not understand the phenomena affecting dispersion, the scope and applicability of various models or their limitations under various weather and site conditions. The third chapter discusses the air pollution models in detail. These models are applicable to distances of a few tens of kilometres. The fourth chapter discusses the various aspects of meteorological measurements relevant to air pollution. The chapters are followed by two appendices. Apendix A discusses the reliability of air pollution estimates. Apendix B gives some practical examples relevant to general air pollution. It is hoped that the document will prove very useful to the users. (author)

  1. Interim report on the meteorological database

    International Nuclear Information System (INIS)

    Stage, S.A.; Ramsdell, J.V.; Simonen, C.A.; Burk, K.W.

    1993-01-01

    The Hanford Environmental Dose Reconstruction (HEDR) Project is estimating radiation doses that individuals may have received from operations at Hanford from 1944 to the present. An independent Technical Steering Panel (TSP) directs the project, which is being conducted by the Battelle, Pacific Northwest Laboratories in Richland, Washington. The goals of HEDR, as approved by the TSP, include dose estimates and determination of confidence ranges for these estimates. This letter report describes the current status of the meteorological database. The report defines the meteorological data available for use in climate model calculations, describes the data collection procedures and the preparation and control of the meteorological database. This report also provides an initial assessment of the data quality. The available meteorological data are adequate for atmospheric calculations. Initial checks of the data indicate the data entry accuracy meets the data quality objectives

  2. Airline meteorological requirements

    Science.gov (United States)

    Chandler, C. L.; Pappas, J.

    1985-01-01

    A brief review of airline meteorological/flight planning is presented. The effects of variations in meteorological parameters upon flight and operational costs are reviewed. Flight path planning through the use of meteorological information is briefly discussed.

  3. The value of using seasonality and meteorological variables to model intra-urban PM2.5 variation

    Science.gov (United States)

    Olvera Alvarez, Hector A.; Myers, Orrin B.; Weigel, Margaret; Armijos, Rodrigo X.

    2018-06-01

    A yearlong air monitoring campaign was conducted to assess the impact of local temperature, relative humidity, and wind speed on the temporal and spatial variability of PM2.5 in El Paso, Texas. Monitoring was conducted at four sites purposely selected to capture the local traffic variability. Effects of meteorological events on seasonal PM2.5 variability were identified. For instance, in winter low-wind and low-temperature conditions were associated with high PM2.5 events that contributed to elevated seasonal PM2.5 levels. Similarly, in spring, high PM2.5 events were associated with high-wind and low-relative humidity conditions. Correlation coefficients between meteorological variables and PM2.5 fluctuated drastically across seasons. Specifically, it was observed that for most sites correlations between PM2.5 and meteorological variables either changed from positive to negative or dissolved depending on the season. Overall, the results suggest that mixed effects analysis with season and site as fixed factors and meteorological variables as covariates could increase the explanatory value of LUR models for PM2.5.

  4. Multicollinearity in prognostic factor analyses using the EORTC QLQ-C30: identification and impact on model selection.

    Science.gov (United States)

    Van Steen, Kristel; Curran, Desmond; Kramer, Jocelyn; Molenberghs, Geert; Van Vreckem, Ann; Bottomley, Andrew; Sylvester, Richard

    2002-12-30

    Clinical and quality of life (QL) variables from an EORTC clinical trial of first line chemotherapy in advanced breast cancer were used in a prognostic factor analysis of survival and response to chemotherapy. For response, different final multivariate models were obtained from forward and backward selection methods, suggesting a disconcerting instability. Quality of life was measured using the EORTC QLQ-C30 questionnaire completed by patients. Subscales on the questionnaire are known to be highly correlated, and therefore it was hypothesized that multicollinearity contributed to model instability. A correlation matrix indicated that global QL was highly correlated with 7 out of 11 variables. In a first attempt to explore multicollinearity, we used global QL as dependent variable in a regression model with other QL subscales as predictors. Afterwards, standard diagnostic tests for multicollinearity were performed. An exploratory principal components analysis and factor analysis of the QL subscales identified at most three important components and indicated that inclusion of global QL made minimal difference to the loadings on each component, suggesting that it is redundant in the model. In a second approach, we advocate a bootstrap technique to assess the stability of the models. Based on these analyses and since global QL exacerbates problems of multicollinearity, we therefore recommend that global QL be excluded from prognostic factor analyses using the QLQ-C30. The prognostic factor analysis was rerun without global QL in the model, and selected the same significant prognostic factors as before. Copyright 2002 John Wiley & Sons, Ltd.

  5. Eighth joint conference on applications of air pollution meteorology with A & WMA

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1994-12-31

    The eighth Joint Conference on Applications of Air Pollution Meteorology, held January 23-28, 1994, again brings together the American Meteorological Society and Air and Waste Management Association with a broader scientific community to examine the role of the atmosphere on current air quality issues. The CAA Amendments non-attainment title has brought renewed interest in the pairing of complex dynamical meteorological models with photochemical air quality models. Requirements that future attainment to regulations be demonstrated with these models invite a new look at model evaluation. The CAAA titles addressing air toxics have brought renewed interest in near-source dispersion and deposition of toxic chemicals. Consequently, this conference is divided into sessions focusing on topics related to these issues. They include: The Dispersion Environment; Meteorology in Emissions Determination; Long-Range and Mesoscale Pollutant Transport and Fate; Meteorology and Photochemistry; Advanced Dispersion Models and Modeling Systems; Topics in Model Evaluation; Complex Flow Affecting Dispersion Near Structures; and Coastal and Complex Terrain Issues Evaluation.

  6. Impact of inherent meteorology uncertainty on air quality ...

    Science.gov (United States)

    It is well established that there are a number of different classifications and sources of uncertainties in environmental modeling systems. Air quality models rely on two key inputs, namely, meteorology and emissions. When using air quality models for decision making, it is important to understand how uncertainties in these inputs affect the simulated concentrations. Ensembles are one method to explore how uncertainty in meteorology affects air pollution concentrations. Most studies explore this uncertainty by running different meteorological models or the same model with different physics options and in some cases combinations of different meteorological and air quality models. While these have been shown to be useful techniques in some cases, we present a technique that leverages the initial condition perturbations of a weather forecast ensemble, namely, the Short-Range Ensemble Forecast system to drive the four-dimensional data assimilation in the Weather Research and Forecasting (WRF)-Community Multiscale Air Quality (CMAQ) model with a key focus being the response of ozone chemistry and transport. Results confirm that a sizable spread in WRF solutions, including common weather variables of temperature, wind, boundary layer depth, clouds, and radiation, can cause a relatively large range of ozone-mixing ratios. Pollutant transport can be altered by hundreds of kilometers over several days. Ozone-mixing ratios of the ensemble can vary as much as 10–20 ppb

  7. Cytogenetic prognostication within medulloblastoma subgroups.

    Science.gov (United States)

    Shih, David J H; Northcott, Paul A; Remke, Marc; Korshunov, Andrey; Ramaswamy, Vijay; Kool, Marcel; Luu, Betty; Yao, Yuan; Wang, Xin; Dubuc, Adrian M; Garzia, Livia; Peacock, John; Mack, Stephen C; Wu, Xiaochong; Rolider, Adi; Morrissy, A Sorana; Cavalli, Florence M G; Jones, David T W; Zitterbart, Karel; Faria, Claudia C; Schüller, Ulrich; Kren, Leos; Kumabe, Toshihiro; Tominaga, Teiji; Shin Ra, Young; Garami, Miklós; Hauser, Peter; Chan, Jennifer A; Robinson, Shenandoah; Bognár, László; Klekner, Almos; Saad, Ali G; Liau, Linda M; Albrecht, Steffen; Fontebasso, Adam; Cinalli, Giuseppe; De Antonellis, Pasqualino; Zollo, Massimo; Cooper, Michael K; Thompson, Reid C; Bailey, Simon; Lindsey, Janet C; Di Rocco, Concezio; Massimi, Luca; Michiels, Erna M C; Scherer, Stephen W; Phillips, Joanna J; Gupta, Nalin; Fan, Xing; Muraszko, Karin M; Vibhakar, Rajeev; Eberhart, Charles G; Fouladi, Maryam; Lach, Boleslaw; Jung, Shin; Wechsler-Reya, Robert J; Fèvre-Montange, Michelle; Jouvet, Anne; Jabado, Nada; Pollack, Ian F; Weiss, William A; Lee, Ji-Yeoun; Cho, Byung-Kyu; Kim, Seung-Ki; Wang, Kyu-Chang; Leonard, Jeffrey R; Rubin, Joshua B; de Torres, Carmen; Lavarino, Cinzia; Mora, Jaume; Cho, Yoon-Jae; Tabori, Uri; Olson, James M; Gajjar, Amar; Packer, Roger J; Rutkowski, Stefan; Pomeroy, Scott L; French, Pim J; Kloosterhof, Nanne K; Kros, Johan M; Van Meir, Erwin G; Clifford, Steven C; Bourdeaut, Franck; Delattre, Olivier; Doz, François F; Hawkins, Cynthia E; Malkin, David; Grajkowska, Wieslawa A; Perek-Polnik, Marta; Bouffet, Eric; Rutka, James T; Pfister, Stefan M; Taylor, Michael D

    2014-03-20

    Medulloblastoma comprises four distinct molecular subgroups: WNT, SHH, Group 3, and Group 4. Current medulloblastoma protocols stratify patients based on clinical features: patient age, metastatic stage, extent of resection, and histologic variant. Stark prognostic and genetic differences among the four subgroups suggest that subgroup-specific molecular biomarkers could improve patient prognostication. Molecular biomarkers were identified from a discovery set of 673 medulloblastomas from 43 cities around the world. Combined risk stratification models were designed based on clinical and cytogenetic biomarkers identified by multivariable Cox proportional hazards analyses. Identified biomarkers were tested using fluorescent in situ hybridization (FISH) on a nonoverlapping medulloblastoma tissue microarray (n = 453), with subsequent validation of the risk stratification models. Subgroup information improves the predictive accuracy of a multivariable survival model compared with clinical biomarkers alone. Most previously published cytogenetic biomarkers are only prognostic within a single medulloblastoma subgroup. Profiling six FISH biomarkers (GLI2, MYC, chromosome 11 [chr11], chr14, 17p, and 17q) on formalin-fixed paraffin-embedded tissues, we can reliably and reproducibly identify very low-risk and very high-risk patients within SHH, Group 3, and Group 4 medulloblastomas. Combining subgroup and cytogenetic biomarkers with established clinical biomarkers substantially improves patient prognostication, even in the context of heterogeneous clinical therapies. The prognostic significance of most molecular biomarkers is restricted to a specific subgroup. We have identified a small panel of cytogenetic biomarkers that reliably identifies very high-risk and very low-risk groups of patients, making it an excellent tool for selecting patients for therapy intensification and therapy de-escalation in future clinical trials.

  8. Future directions of meteorology related to air-quality research.

    Science.gov (United States)

    Seaman, Nelson L

    2003-06-01

    Meteorology is one of the major factors contributing to air-pollution episodes. More accurate representation of meteorological fields has been possible in recent years through the use of remote sensing systems, high-speed computers and fine-mesh meteorological models. Over the next 5-20 years, better meteorological inputs for air quality studies will depend on making better use of a wealth of new remotely sensed observations in more advanced data assimilation systems. However, for fine mesh models to be successful, parameterizations used to represent physical processes must be redesigned to be more precise and better adapted for the scales at which they will be applied. Candidates for significant overhaul include schemes to represent turbulence, deep convection, shallow clouds, and land-surface processes. Improvements in the meteorological observing systems, data assimilation and modeling, coupled with advancements in air-chemistry modeling, will soon lead to operational forecasting of air quality in the US. Predictive capabilities can be expected to grow rapidly over the next decade. This will open the way for a number of valuable new services and strategies, including better warnings of unhealthy atmospheric conditions, event-dependent emissions restrictions, and now casting support for homeland security in the event of toxic releases into the atmosphere.

  9. Annual report of the Dynamic Meteorology Laboratory, 1986

    International Nuclear Information System (INIS)

    1987-01-01

    Research on climate simulation; data assimilation and forecasting; nonlinear dynamics and atmospheric turbulence; wave dynamics in the middle atmosphere; African and tropical meteorology and climatology; spectroscopy and modeling of atmospheric radiation; satellite meteorology and climatology; and active lidar remote sensing is presented [fr

  10. Quantifying PM2.5-Meteorology Sensitivities in a Global Climate Model

    Science.gov (United States)

    Westervelt, D. M.; Horowitz, L. W.; Naik, V.; Tai, A. P. K.; Fiore, A. M.; Mauzerall, D. L.

    2016-01-01

    Climate change can influence fine particulate matter concentrations (PM2.5) through changes in air pollution meteorology. Knowledge of the extent to which climate change can exacerbate or alleviate air pollution in the future is needed for robust climate and air pollution policy decision-making. To examine the influence of climate on PM2.5, we use the Geophysical Fluid Dynamics Laboratory Coupled Model version 3 (GFDL CM3), a fully-coupled chemistry-climate model, combined with future emissions and concentrations provided by the four Representative Concentration Pathways (RCPs). For each of the RCPs, we conduct future simulations in which emissions of aerosols and their precursors are held at 2005 levels while other climate forcing agents evolve in time, such that only climate (and thus meteorology) can influence PM2.5 surface concentrations. We find a small increase in global, annual mean PM2.5 of about 0.21 micro-g/cu m3 (5%) for RCP8.5, a scenario with maximum warming. Changes in global mean PM2.5 are at a maximum in the fall and are mainly controlled by sulfate followed by organic aerosol with minimal influence of black carbon. RCP2.6 is the only scenario that projects a decrease in global PM2.5 with future climate changes, albeit only by -0.06 micro-g/cu m (1.5%) by the end of the 21st century. Regional and local changes in PM2.5 are larger, reaching upwards of 2 micro-g/cu m for polluted (eastern China) and dusty (western Africa) locations on an annually averaged basis in RCP8.5. Using multiple linear regression, we find that future PM2.5 concentrations are most sensitive to local temperature, followed by surface wind and precipitation. PM2.5 concentrations are robustly positively associated with temperature, while negatively related with precipitation and wind speed. Present-day (2006-2015) modeled sensitivities of PM2.5 to meteorological variables are evaluated against observations and found to agree reasonably well with observed sensitivities (within 10e50

  11. Applied Meteorology Unit (AMU)

    Science.gov (United States)

    Bauman, William; Crawford, Winifred; Barrett, Joe; Watson, Leela; Wheeler, Mark

    2010-01-01

    This report summarizes the Applied Meteorology Unit (AMU) activities for the first quarter of Fiscal Year 2010 (October - December 2009). A detailed project schedule is included in the Appendix. Included tasks are: (1) Peak Wind Tool for User Launch Commit Criteria (LCC), (2) Objective Lightning Probability Tool, Phase III, (3) Peak Wind Tool for General Forecasting, Phase II, (4) Upgrade Summer Severe Weather Tool in Meteorological Interactive Data Display System (MIDDS), (5) Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS) Update and Maintainability, (5) Verify 12-km resolution North American Model (MesoNAM) Performance, and (5) Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) Graphical User Interface.

  12. A prognostic model of triple-negative breast cancer based on miR-27b-3p and node status.

    Directory of Open Access Journals (Sweden)

    Songjie Shen

    Full Text Available Triple-negative breast cancer (TNBC is an aggressive but heterogeneous subtype of breast cancer. This study aimed to identify and validate a prognostic signature for TNBC patients to improve prognostic capability and to guide individualized treatment.We retrospectively analyzed the prognostic performance of clinicopathological characteristics and miRNAs in a training set of 58 patients with invasive ductal TNBC diagnosed between 2002 and 2012. A prediction model was developed based on independent clinicopathological and miRNA covariates. The prognostic value of the model was further validated in a separate set of 41 TNBC patients diagnosed between 2007 and 2008.Only lymph node status was marginally significantly associated with poor prognosis of TNBC (P = 0.054, whereas other clinicopathological factors, including age, tumor size, histological grade, lymphovascular invasion, P53 status, Ki-67 index, and type of surgery, were not. The expression levels of miR-27b-3p, miR-107, and miR-103a-3p were significantly elevated in the metastatic group compared with the disease-free group (P value: 0.008, 0.005, and 0.050, respectively. The Cox proportional hazards regression analysis revealed that lymph node status and miR-27b-3p were independent predictors of poor prognosis (P value: 0.012 and 0.027, respectively. A logistic regression model was developed based on these two independent covariates, and the prognostic value of the model was subsequently confirmed in a separate validation set. The two different risk groups, which were stratified according to the model, showed significant differences in the rates of distant metastasis and breast cancer-related death not only in the training set (P value: 0.001 and 0.040, respectively but also in the validation set (P value: 0.013 and 0.012, respectively.This model based on miRNA and node status covariates may be used to stratify TNBC patients into different prognostic subgroups for potentially

  13. Assessment of a surface-layer parameterization scheme in an atmospheric model for varying meteorological conditions

    Directory of Open Access Journals (Sweden)

    T. J. Anurose

    2014-06-01

    Full Text Available The performance of a surface-layer parameterization scheme in a high-resolution regional model (HRM is carried out by comparing the model-simulated sensible heat flux (H with the concurrent in situ measurements recorded at Thiruvananthapuram (8.5° N, 76.9° E, a coastal station in India. With a view to examining the role of atmospheric stability in conjunction with the roughness lengths in the determination of heat exchange coefficient (CH and H for varying meteorological conditions, the model simulations are repeated by assigning different values to the ratio of momentum and thermal roughness lengths (i.e. z0m/z0h in three distinct configurations of the surface-layer scheme designed for the present study. These three configurations resulted in differential behaviour for the varying meteorological conditions, which is attributed to the sensitivity of CH to the bulk Richardson number (RiB under extremely unstable, near-neutral and stable stratification of the atmosphere.

  14. Meteorological safeguarding of nuclear power plant operation in Czechoslovakia

    International Nuclear Information System (INIS)

    Rak, J.; Skulec, S.

    1976-01-01

    A meteorological tower 200 m high has to be built for meteorological control of the operation of the A-1 nuclear power plant at Jaslovske Bohunice. This meteorological station will measure the physical properties of the lower layers of the atmosphere, carry out experimental verifications of the models of air pollution, investigate the effects of waste heat and waste water from the nuclear power plant on the microclimate, provide the theoretical processing of measured data with the aim of selecting the most favourable model for conditions prevailing in the Czechoslovak Socialist Republic, perform basic research of the physical properties of the ground and boundary layers of the atmosphere and the coordination of state-wide plans in the field of securing the operation of nuclear power plants with regard to meteorology. (Z.M.)

  15. Prediction Model for Demands of the Health Meteorological Information Using a Decision Tree Method

    Directory of Open Access Journals (Sweden)

    Jina Oh, RN, PhD

    2010-09-01

    Conclusions: It can be effectively used as a reference model for future studies and is a suggested direction in health meteorological information service and policy development. We suggest health forecasting as a nursing service and a primary health care network for healthier and more comfortable life.

  16. Motivational Meteorology.

    Science.gov (United States)

    Benjamin, Lee

    1993-01-01

    Describes an introductory meteorology course for nonacademic high school students. The course is made hands-on by the use of an educational software program offered by Accu-Weather. The program contains a meteorology database and instructional modules. (PR)

  17. Maintenance-based prognostics of nuclear plant equipment for long-term operation

    Energy Technology Data Exchange (ETDEWEB)

    Welz, Zachary; Coble, Jamie; Upadhyaya, Belle; Hines, Wes [University of Tennessee, Knoxville (United States)

    2017-08-15

    While industry understands the importance of keeping equipment operational and well maintained, the importance of tracking maintenance information in reliability models is often overlooked. Prognostic models can be used to predict the failure times of critical equipment, but more often than not, these models assume that all maintenance actions are the same or do not consider maintenance at all. This study investigates the influence of integrating maintenance information on prognostic model prediction accuracy. By incorporating maintenance information to develop maintenance-dependent prognostic models, prediction accuracy was improved by more than 40% compared with traditional maintenance-independent models. This study acts as a proof of concept, showing the importance of utilizing maintenance information in modern prognostics for industrial equipment.

  18. Meteorological satellite systems

    CERN Document Server

    Tan, Su-Yin

    2014-01-01

    Meteorological Satellite Systems” is a primer on weather satellites and their Earth applications. This book reviews historic developments and recent technological advancements in GEO and polar orbiting meteorological satellites. It explores the evolution of these remote sensing technologies and their capabilities to monitor short- and long-term changes in weather patterns in response to climate change. Satellites developed by various countries, such as U.S. meteorological satellites, EUMETSAT, and Russian, Chinese, Japanese and Indian satellite platforms are reviewed. This book also discusses international efforts to coordinate meteorological remote sensing data collection and sharing. This title provides a ready and quick reference for information about meteorological satellites. It serves as a useful tool for a broad audience that includes students, academics, private consultants, engineers, scientists, and teachers.

  19. Wind Power Meteorology

    DEFF Research Database (Denmark)

    Lundtang Petersen, Erik; Mortensen, Niels Gylling; Landberg, Lars

    Wind power meteorology has evolved as an applied science, firmly founded on boundary-layer meteorology, but with strong links to climatology and geography. It concerns itself with three main areas: siting of wind turbines, regional wind resource assessment, and short-term prediction of the wind...... resource. The history, status and perspectives of wind power meteorology are presented, with emphasis on physical considerations and on its practical application. Following a global view of the wind resource, the elements of boundary layer meteorology which are most important for wind energy are reviewed......: wind profiles and shear, turbulence and gust, and extreme winds. The data used in wind power meteorology stem mainly from three sources: onsite wind measurements, the synoptic networks, and the re-analysis projects. Wind climate analysis, wind resource estimation and siting further require a detailed...

  20. Moderate Traumatic Brain Injury: Clinical Characteristics and a Prognostic Model of 12-Month Outcome.

    Science.gov (United States)

    Einarsen, Cathrine Elisabeth; van der Naalt, Joukje; Jacobs, Bram; Follestad, Turid; Moen, Kent Gøran; Vik, Anne; Håberg, Asta Kristine; Skandsen, Toril

    2018-03-31

    Patients with moderate traumatic brain injury (TBI) often are studied together with patients with severe TBI, even though the expected outcome of the former is better. Therefore, we aimed to describe patient characteristics and 12-month outcomes, and to develop a prognostic model based on admission data, specifically for patients with moderate TBI. Patients with Glasgow Coma Scale scores of 9-13 and age ≥16 years were prospectively enrolled in 2 level I trauma centers in Europe. Glasgow Outcome Scale Extended (GOSE) score was assessed at 12 months. A prognostic model predicting moderate disability or worse (GOSE score ≤6), as opposed to a good recovery, was fitted by penalized regression. Model performance was evaluated by area under the curve of the receiver operating characteristics curves. Of the 395 enrolled patients, 81% had intracranial lesions on head computed tomography, and 71% were admitted to an intensive care unit. At 12 months, 44% were moderately disabled or worse (GOSE score ≤6), whereas 8% were severely disabled and 6% died (GOSE score ≤4). Older age, lower Glasgow Coma Scale score, no day-of-injury alcohol intoxication, presence of a subdural hematoma, occurrence of hypoxia and/or hypotension, and preinjury disability were significant predictors of GOSE score ≤6 (area under the curve = 0.80). Patients with moderate TBI exhibit characteristics of significant brain injury. Although few patients died or experienced severe disability, 44% did not experience good recovery, indicating that follow-up is needed. The model is a first step in development of prognostic models for moderate TBI that are valid across centers. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

  1. Model study of meteorology and photochemical air pollution over un urban area in south-eastern France (ESCOMPTE campaign).

    Science.gov (United States)

    Taghavi, M.; Cautenet, S.

    2003-04-01

    The ESCOMPTE Campaign has been conducted over Southern France (Provence region including the Marseille, Aix and Toulon cities and the Fos-Berre industrial center) in June and July of 2001. In order to study the redistribution of the pollutants emitted by anthropic and biogenic emissions and their impact on the atmospheric chemistry, we used meso-scale modeling (RAMS model, paralleled version 4.3, coupled on line with chemical modules : MOCA2.2 (Poulet et al, 2002) including 29 gaseous species). The hourly high resolution emissions were obtained from ESCOMPTE database (Ponche et al, 2002). The model was coupled with the dry deposition scheme (Walmsley and Weseley,1996). In this particular case of complex circulation (sea breeze associated with topography), the processes involving peaks of pollution were strongly non linear, and the meso scale modeling coupled on line with chemistry module was an essential step for a realistic redistribution of chemical species. Two nested grids satisfactorily describe the synoptic dynamics and the sea breeze circulations. The ECMWF meteorological fields provide the initial and boundary conditions. Different events characterized by various meteorological situations were simulated. Meteorological fields retrieved by modeling, also Modeled ozone, NOx, CO and SO2 concentrations, were compared with balloons, lidars, aircrafts and surface stations measurements. The chemistry regimes were explained according to the distribution of plumes. The stratified layers were examined.

  2. Exploring the Utility of Model-based Meteorology Data for Heat-Related Health Research and Surveillance

    Science.gov (United States)

    Vaidyanathan, A.; Yip, F.

    2017-12-01

    Context: Studies that have explored the impacts of environmental exposure on human health have mostly relied on data from weather stations, which can be limited in geographic scope. For this assessment, we: (1) evaluated the performance of the meteorological data from the North American Land Data Assimilation System Phase 2 (NLDAS) model with measurements from weather stations for public health and specifically for CDC's Environmental Public Health Tracking Program, and (2) conducted a health assessment to explore the relationship between heat exposure and mortality, and examined region-specific differences in heat-mortality (H-M) relationships when using model-based estimates in place of measurements from weather stations.Methods: Meteorological data from the NLDAS Phase 2 model was evaluated against measurements from weather stations. A time-series analysis was conducted, using both station- and model-based data, to generate H-M relationships for counties in the U.S. The county-specific risk information was pooled to characterize regional relationships for both station- and model-based data, which were then compared to identify degrees of overlap and discrepancies between results generated using the two data sources. Results: NLDAS-based heat metrics were in agreement with those generated using weather station data. In general, the H-M relationship tended to be non-linear and varied by region, particularly the heat index value at which the health risks become positively significant. However, there was a high degree of overlap between region-specific H-M relationships generated from weather stations and the NLDAS model.Interpretation: Heat metrics from NLDAS model are available for all counties in the coterminous U.S. from 1979-2015. These data can facilitate health research and surveillance activities exploring health impacts associated with long-term heat exposures at finer geographic scales.Conclusion: High spatiotemporal coverage of environmental health data

  3. External validation of prognostic models to predict risk of gestational diabetes mellitus in one Dutch cohort: prospective multicentre cohort study.

    NARCIS (Netherlands)

    Lamain-de Ruiter, M.; Kwee, A.; Naaktgeboren, C.A.; Groot, I. de; Evers, I.M.; Groenendaal, F.; Hering, Y.R.; Huisjes, A.J.M.; Kirpestein, C.; Monincx, W.M.; Siljee, J.E.; Zelfde, A. van't; Oirschot, C.M. van; Vankan-Buitelaar, S.A.; Vonk, M.A.A.W.; Wiegers, T.A.; Zwart, J.J.; Franx, A.; Moons, K.G.M.; Koster, M.P.H.

    2016-01-01

    Objective: To perform an external validation and direct comparison of published prognostic models for early prediction of the risk of gestational diabetes mellitus, including predictors applicable in the first trimester of pregnancy. Design: External validation of all published prognostic models in

  4. Predicting residential air exchange rates from questionnaires and meteorology: model evaluation in central North Carolina.

    Science.gov (United States)

    Breen, Michael S; Breen, Miyuki; Williams, Ronald W; Schultz, Bradley D

    2010-12-15

    A critical aspect of air pollution exposure models is the estimation of the air exchange rate (AER) of individual homes, where people spend most of their time. The AER, which is the airflow into and out of a building, is a primary mechanism for entry of outdoor air pollutants and removal of indoor source emissions. The mechanistic Lawrence Berkeley Laboratory (LBL) AER model was linked to a leakage area model to predict AER from questionnaires and meteorology. The LBL model was also extended to include natural ventilation (LBLX). Using literature-reported parameter values, AER predictions from LBL and LBLX models were compared to data from 642 daily AER measurements across 31 detached homes in central North Carolina, with corresponding questionnaires and meteorological observations. Data was collected on seven consecutive days during each of four consecutive seasons. For the individual model-predicted and measured AER, the median absolute difference was 43% (0.17 h(-1)) and 40% (0.17 h(-1)) for the LBL and LBLX models, respectively. Additionally, a literature-reported empirical scale factor (SF) AER model was evaluated, which showed a median absolute difference of 50% (0.25 h(-1)). The capability of the LBL, LBLX, and SF models could help reduce the AER uncertainty in air pollution exposure models used to develop exposure metrics for health studies.

  5. Scrub Typhus Incidence Modeling with Meteorological Factors in South Korea.

    Science.gov (United States)

    Kwak, Jaewon; Kim, Soojun; Kim, Gilho; Singh, Vijay P; Hong, Seungjin; Kim, Hung Soo

    2015-06-29

    Since its recurrence in 1986, scrub typhus has been occurring annually and it is considered as one of the most prevalent diseases in Korea. Scrub typhus is a 3rd grade nationally notifiable disease that has greatly increased in Korea since 2000. The objective of this study is to construct a disease incidence model for prediction and quantification of the incidences of scrub typhus. Using data from 2001 to 2010, the incidence Artificial Neural Network (ANN) model, which considers the time-lag between scrub typhus and minimum temperature, precipitation and average wind speed based on the Granger causality and spectral analysis, is constructed and tested for 2011 to 2012. Results show reliable simulation of scrub typhus incidences with selected predictors, and indicate that the seasonality in meteorological data should be considered.

  6. Scrub Typhus Incidence Modeling with Meteorological Factors in South Korea

    Directory of Open Access Journals (Sweden)

    Jaewon Kwak

    2015-06-01

    Full Text Available Since its recurrence in 1986, scrub typhus has been occurring annually and it is considered as one of the most prevalent diseases in Korea. Scrub typhus is a 3rd grade nationally notifiable disease that has greatly increased in Korea since 2000. The objective of this study is to construct a disease incidence model for prediction and quantification of the incidences of scrub typhus. Using data from 2001 to 2010, the incidence Artificial Neural Network (ANN model, which considers the time-lag between scrub typhus and minimum temperature, precipitation and average wind speed based on the Granger causality and spectral analysis, is constructed and tested for 2011 to 2012. Results show reliable simulation of scrub typhus incidences with selected predictors, and indicate that the seasonality in meteorological data should be considered.

  7. Air quality modeling in the Valley of Mexico: meteorology, emissions and forecasting

    Science.gov (United States)

    Garcia-Reynoso, A.; Jazcilevich, A. D.; Diaz-Nigenda, E.; Vazquez-Morales, W.; Torres-Jardon, R.; Ruiz-Suarez, G.; Tatarko, J.; Bornstein, R.

    2007-12-01

    The Valley of Mexico presents important challenges for air quality modeling: complex terrain, a great variety of anthropogenic and natural emissions sources, and high altitude and low latitude increasing the amount of radiation flux. The modeling group at the CCA-UNAM is using and merging state of the art models to study the different aspects that influence the air quality phenomenon in the Valley of Mexico. The air quality model MCCM that uses MM5 as its meteorological input has been a valuable tool to study important features of the complex and intricate atmospheric flows on the valley, such as local confluences and vertical fumigation. Air quality modeling has allowed studying the interaction between the atmospheres of the valleys surrounding the Valley of Mexico, prompting the location of measurement stations during the MILAGRO campaign. These measurements confirmed the modeling results and expanded our knowledge of the transport of pollutants between the Valleys of Cuernavaca, Puebla and Mexico. The urban landscape of Mexico City complicates meteorological modeling. Urban-MM5, a model that explicitly takes into account the influence of buildings, houses, streets, parks and anthropogenic heat, is being implemented. Preliminary results of urban-MM5 on a small area of the city have been obtained. The current emissions inventory uses traffic database that includes hourly vehicular activity in more than 11,000 street segments, includes 23 area emissions categories, more than 1,000 industrial sources and biogenic emissions. To improve mobile sources emissions a system consisting of a traffic model and a car simulator is underway. This system will allow for high time and space resolution and takes into account motor stress due to different driving regimes. An important source of emissions in the Valley of Mexico is erosion dust. The erosion model WEPS has been integrated with MM5 and preliminary results showing dust episodes over Mexico City have been obtained. A

  8. THE VALUE OF NUDGING IN THE METEOROLOGY MODEL FOR RETROSPECTIVE CMAQ SIMULATIONS

    Science.gov (United States)

    Using a nudging-based data assimilation approach throughout a meteorology simulation (i.e., as a "dynamic analysis") is considered valuable because it can provide a better overall representation of the meteorology than a pure forecast. Dynamic analysis is often used in...

  9. A Prognostic Model for Development of Profound Shock among Children Presenting with Dengue Shock Syndrome.

    Directory of Open Access Journals (Sweden)

    Phung Khanh Lam

    Full Text Available To identify risk factors and develop a prediction model for the development of profound and recurrent shock amongst children presenting with dengue shock syndrome (DSS.We analyzed data from a prospective cohort of children with DSS recruited at the Paediatric Intensive Care Unit of the Hospital for Tropical Disease in Ho Chi Minh City, Vietnam. The primary endpoint was "profound DSS", defined as ≥2 recurrent shock episodes (for subjects presenting in compensated shock, or ≥1 recurrent shock episodes (for subjects presenting initially with decompensated/hypotensive shock, and/or requirement for inotropic support. Recurrent shock was evaluated as a secondary endpoint. Risk factors were pre-defined clinical and laboratory variables collected at the time of presentation with shock. Prognostic model development was based on logistic regression and compared to several alternative approaches.The analysis population included 1207 children of whom 222 (18% progressed to "profound DSS" and 433 (36% had recurrent shock. Independent risk factors for both endpoints included younger age, earlier presentation, higher pulse rate, higher temperature, higher haematocrit and, for females, worse hemodynamic status at presentation. The final prognostic model for "profound DSS" showed acceptable discrimination (AUC=0.69 for internal validation and calibration and is presented as a simple score-chart.Several risk factors for development of profound or recurrent shock among children presenting with DSS were identified. The score-chart derived from the prognostic models should improve triage and management of children presenting with DSS in dengue-endemic areas.

  10. Monitoring, modeling and mitigating impacts of wind farms on local meteorology

    Science.gov (United States)

    Baidya Roy, Somnath; Traiteur, Justin; Kelley, Neil

    2010-05-01

    Wind power is one of the fastest growing sources of energy. Most of the growth is in the industrial sector comprising of large utility-scale wind farms. Recent modeling studies have suggested that such wind farms can significantly affect local and regional weather and climate. In this work, we present observational evidence of the impact of wind farms on near-surface air temperatures. Data from perhaps the only meteorological field campaign in an operational wind farm shows that downwind temperatures are lower during the daytime and higher at night compared to the upwind environment. Corresponding radiosonde profiles at the nearby Edwards Air Force Base WMO meteorological station show that the diurnal environment is unstable while the nocturnal environment is stable during the field campaign. This behavior is consistent with the hypothesis proposed by Baidya Roy et al. (JGR 2004) that states that turbulence generated in the wake of rotors enhance vertical mixing leading to a warming/cooling under positive/negative potential temperature lapse rates. We conducted a set of 306 simulations with the Regional Atmospheric Modeling System (RAMS) to test if regional climate models can capture the thermal effects of wind farms. We represented wind turbines with a subgrid parameterization that assumes rotors to be sinks of momentum and sources of turbulence. The simulated wind farms consistently generated a localized warming/cooling under positive/negative lapse rates as hypothesized. We found that these impacts are inversely correlated with background atmospheric boundary layer turbulence. Thus, if the background turbulence is high due to natural processes, the effects of additional turbulence generated by wind turbine rotors are likely to be small. We propose the following strategies to minimize impacts of wind farms: • Engineering solution: design rotors that generate less turbulence in their wakes. Sensitivity simulations show that these turbines also increase the

  11. Reduction of thermal models of buildings: improvement of techniques using meteorological influence models; Reduction de modeles thermiques de batiments: amelioration des techniques par modelisation des sollicitations meteorologiques

    Energy Technology Data Exchange (ETDEWEB)

    Dautin, S.

    1997-04-01

    This work concerns the modeling of thermal phenomena inside buildings for the evaluation of energy exploitation costs of thermal installations and for the modeling of thermal and aeraulic transient phenomena. This thesis comprises 7 chapters dealing with: (1) the thermal phenomena inside buildings and the CLIM2000 calculation code, (2) the ETNA and GENEC experimental cells and their modeling, (3) the techniques of model reduction tested (Marshall`s truncature, Michailesco aggregation method and Moore truncature) with their algorithms and their encoding in the MATRED software, (4) the application of model reduction methods to the GENEC and ETNA cells and to a medium size dual-zone building, (5) the modeling of meteorological influences classically applied to buildings (external temperature and solar flux), (6) the analytical expression of these modeled meteorological influences. The last chapter presents the results of these improved methods on the GENEC and ETNA cells and on a lower inertia building. These new methods are compared to classical methods. (J.S.) 69 refs.

  12. Comparison of HYSPLIT-4 model simulations of the ETEX data, using meteorological input data of differing spatial and temporal resolution

    International Nuclear Information System (INIS)

    Hess, G.D.; Mills, G.A.; Draxler, R.R.

    1997-01-01

    Model simulations of air concentrations during ETEX-1 using the HYSPLIT-4 (HYbrid Single-Particle Lagrangian Integrated Trajectories, version 4) code and analysed meteorological data fields provided by ECMWF and the Australian Bureau of Meteorology are presented here. The HYSPLIT-4 model is a complete system for computing simple trajectories to complex dispersion and deposition simulations using either puff or particle approaches. A mixed dispersion algorithm is employed in this study: puffs in the horizontal and particles in the vertical

  13. Modeling the impacts of green infrastructure land use changes on air quality and meteorology case study and sensitivity analysis in Kansas City

    Science.gov (United States)

    Changes in vegetation cover associated with urban planning efforts may affect regional meteorology and air quality. Here we use a comprehensive coupled meteorology-air quality model (WRF-CMAQ) to simulate the influence of planned land use changes from green infrastructure impleme...

  14. Developing the Model for the GIS Applications in National Hydro-Meteorological Service in Poland

    Science.gov (United States)

    Kubacka, D.; Barszczynska, M.; Madej, P.

    2003-04-01

    Institute of Meteorology and Water Management (IMWM) manages the national hydrological-meteorological service, the task of which is to maintain the network of stations, process data, as well issue warnings, reports and announcements. There are 5 divisions of IMWM scattered all over Poland. Each division includes numerous stations and the scientific-research departments. The data gathered, processed and analysed in IMWM are space-related, therefore spatial information systems are indispensable for its processing and visualisation. The project of GIS application in (IMWM) will be discussed in the presentation. With the divisions being so dispersed, numerous and heterogeneous in structure, GIS implementation is very complicated. On the one hand GIS should enable advanced spatial analyses to be carried out by the research, as well as data processing departments. On the other hand, it should provide passive access to a limited scope of information (e.g. for outside customers). Need analysis was carried out first. It resulted in proposals concerning the content of shared resources of geometrical data and connections with attribute data, as well as in proposals of GIS use in routine works. A model was prepared using various types of GIS software depending on the requirements of each division. It is based on standard solutions involving professional GIS, desktop GIS and simple tools for data presentation. In some departments the specialised software had to be taken into account (e.g. satellite data processing). It is necessary to develop and implement dedicated research methods for some individual tasks. The analysis of mapping requirements showed that there is a need to prepare thematic maps at least at two levels of detail. Presently, the works are concentrated on assembling thematic layers for a general map (at 1: 500000 scale) sufficient for many applications, including data visualisation in the Internet and IMWM publications, as well as the tool for measurements and

  15. The 1989 progress report: dynamic meteorology

    International Nuclear Information System (INIS)

    Sadourny, R.

    1989-01-01

    The 1989 progress report of the laboratory of Dynamic Meteorology of the Polytechnic School (France) is presented. The aim of the research programs is the dynamic study of climate and environment in relationship with the global athmospheric behavior. The investigations reported were performed in the fields of: climate modelling, dynamic study of Turbulence, analysis of atmospheric radiation and nebulosity, tropical meteorology and climate, Earth radioactive balance, lidar measurements, middle atmosphere studies. The published papers, the conferences and Laboratory staff are listed [fr

  16. Prognostic Modeling in Pathologic N1 Breast Cancer Without Elective Nodal Irradiation After Current Standard Systemic Management.

    Science.gov (United States)

    Yu, Jeong Il; Park, Won; Choi, Doo Ho; Huh, Seung Jae; Nam, Seok Jin; Kim, Seok Won; Lee, Jeong Eon; Kil, Won Ho; Im, Young-Hyuck; Ahn, Jin Seok; Park, Yeon Hee; Cho, Eun Yoon

    2015-08-01

    This study was conducted to establish a prognostic model in patients with pathologic N1 (pN1) breast cancer who have not undergone elective nodal irradiation (ENI) under the current standard management and to suggest possible indications for ENI. We performed a retrospective study with patients with pN1 breast cancer who received the standard local and preferred adjuvant chemotherapy treatment without neoadjuvant chemotherapy and ENI from January 2005 to June 2011. Most of the indicated patients received endocrine and trastuzumab therapy. In 735 enrolled patients, the median follow-up period was 58.4 months (range, 7.2-111.3 months). Overall, 55 recurrences (7.4%) developed, and locoregional recurrence was present in 27 patients (3.8%). Recurrence-free survival was significantly related to lymphovascular invasion (P = .04, hazard ratio [HR], 1.83; 95% confidence interval [CI], 1.03-2.88), histologic grade (P = .03, HR, 2.57; 95% CI, 1.05-6.26), and nonluminal A subtype (P = .02, HR, 3.04; 95% CI, 1.23-7.49) in multivariate analysis. The prognostic model was established by these 3 prognostic factors. Recurrence-free survival was less than 90% at 5 years in cases with 2 or 3 factors. The prognostic model has stratified risk groups in pN1 breast cancer without ENI. Patients with 2 or more factors should be considered for ENI. Copyright © 2015 Elsevier Inc. All rights reserved.

  17. PREDICT: a new UK prognostic model that predicts survival following surgery for invasive breast cancer.

    Science.gov (United States)

    Wishart, Gordon C; Azzato, Elizabeth M; Greenberg, David C; Rashbass, Jem; Kearins, Olive; Lawrence, Gill; Caldas, Carlos; Pharoah, Paul D P

    2010-01-01

    The aim of this study was to develop and validate a prognostication model to predict overall and breast cancer specific survival for women treated for early breast cancer in the UK. Using the Eastern Cancer Registration and Information Centre (ECRIC) dataset, information was collated for 5,694 women who had surgery for invasive breast cancer in East Anglia from 1999 to 2003. Breast cancer mortality models for oestrogen receptor (ER) positive and ER negative tumours were derived from these data using Cox proportional hazards, adjusting for prognostic factors and mode of cancer detection (symptomatic versus screen-detected). An external dataset of 5,468 patients from the West Midlands Cancer Intelligence Unit (WMCIU) was used for validation. Differences in overall actual and predicted mortality were detection for the first time. The model is well calibrated, provides a high degree of discrimination and has been validated in a second UK patient cohort.

  18. A Low-order Coupled Chemistry Meteorology Model for Testing Online and Offline Advanced Data Assimilation Schemes

    Science.gov (United States)

    Bocquet, M.; Haussaire, J. M.

    2015-12-01

    Bocquet and Sakov have recently introduced a low-order model based on the coupling of thechaotic Lorenz-95 model which simulates winds along a mid-latitude circle, with thetransport of a tracer species advected by this wind field. It has been used to testadvanced data assimilation methods with an online model that couples meteorology andtracer transport. In the present study, the tracer subsystem of the model is replacedwith a reduced photochemistry module meant to emulate reactive air pollution. Thiscoupled chemistry meteorology model, the L95-GRS model, mimics continental andtranscontinental transport and photochemistry of ozone, volatile organic compounds andnitrogen dioxides.The L95-GRS is specially useful in testing advanced data assimilation schemes, such as theiterative ensemble Kalman smoother (IEnKS) that combines the best of ensemble andvariational methods. The model provides useful insights prior to any implementation ofthe data assimilation method on larger models. For instance, online and offline dataassimilation strategies based on the ensemble Kalman filter or the IEnKS can easily beevaluated with it. It allows to document the impact of species concentration observationson the wind estimation. The model also illustrates a long standing issue in atmosphericchemistry forecasting: the impact of the wind chaotic dynamics and of the chemical speciesnon-chaotic but highly nonlinear dynamics on the selected data assimilation approach.

  19. Survey on Prognostics Techniques for Updating Initiating Event Frequency in PSA

    International Nuclear Information System (INIS)

    Kim, Hyeonmin; Heo, Gyunyoung

    2015-01-01

    One of the applications using PSA is a risk monito. The risk monitoring is real-time analysis tool to decide real-time risk based on real state of components and systems. In order to utilize more effective, the methodologies that manipulate the data from Prognostics was suggested. Generally, Prognostic comprehensively includes not only prognostic but also monitoring and diagnostic. The prognostic method must need condition monitoring. In case of applying PHM to a PSA model, the latest condition of NPPs can be identified more clearly. For reducing the conservatism and uncertainties, we suggested the concept that updates the initiating event frequency in a PSA model by using Bayesian approach which is one of the prognostics techniques before. From previous research, the possibility that PSA is updated by using data more correctly was found. In reliability theory, the Bathtub curve divides three parts (infant failure, constant and random failure, wareout failure). In this paper, in order to investigate the applicability of prognostic methods in updating quantitative data in a PSA model, the OLM acceptance criteria from NUREG, the concept of how to using prognostic in PSA, and the enabling prognostic techniques are suggested. The prognostic has the motivation that improved the predictive capabilities using existing monitoring systems, data, and information will enable more accurate equipment risk assessment for improved decision-making

  20. Survey on Prognostics Techniques for Updating Initiating Event Frequency in PSA

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Hyeonmin; Heo, Gyunyoung [Kyung Hee University, Yongin (Korea, Republic of)

    2015-05-15

    One of the applications using PSA is a risk monito. The risk monitoring is real-time analysis tool to decide real-time risk based on real state of components and systems. In order to utilize more effective, the methodologies that manipulate the data from Prognostics was suggested. Generally, Prognostic comprehensively includes not only prognostic but also monitoring and diagnostic. The prognostic method must need condition monitoring. In case of applying PHM to a PSA model, the latest condition of NPPs can be identified more clearly. For reducing the conservatism and uncertainties, we suggested the concept that updates the initiating event frequency in a PSA model by using Bayesian approach which is one of the prognostics techniques before. From previous research, the possibility that PSA is updated by using data more correctly was found. In reliability theory, the Bathtub curve divides three parts (infant failure, constant and random failure, wareout failure). In this paper, in order to investigate the applicability of prognostic methods in updating quantitative data in a PSA model, the OLM acceptance criteria from NUREG, the concept of how to using prognostic in PSA, and the enabling prognostic techniques are suggested. The prognostic has the motivation that improved the predictive capabilities using existing monitoring systems, data, and information will enable more accurate equipment risk assessment for improved decision-making.

  1. Using Constraints from Satellite Gravimetry to Study Meteorological Excitations of the Chandler Wobble for an Earth Model with Frequency-dependent Responses

    Science.gov (United States)

    Chen, W.; Li, J.; Ray, J.; Cheng, M.; Chen, J.; Wilson, C. R.

    2015-12-01

    What maintain(s) the damping Chandler wobble (CW) is still under debate though meteorological excitations are now more preferred. However, controversial results have been obtained: Gross [2000] and Gross et al. [2003] suggested oceanic processes are more efficient to excite the CW than atmospheric ones during 1980 - 2000. Brzezinski and Nastula [2002] concluded that their contributions are almost the same, and they can only provide ~80% of the power needed to maintain the CW observed during 1985 - 1996. Polar motion excitations involve not only the perturbations within the Earth system (namely, mass redistributions and motions of relative to the mantle), but also the Earth's responses to those perturbations (namely, the rheology of the Earth). Chen et al. [2013a] developed an improved theory for polar motion excitation taking into account the Earth's frequency-dependent responses, of which the polar motion transfer functions are ~10% higher than those of previous theories around the CW band. Chen et al. [2013b] compared the geophysical excitations derived from various global atmospheric, oceanic and hydrological models (NCEP, ECCO, ERA40, ERAinterim and ECMWF operational products), and found significant and broad-band discrepancies for models released by different institutes. In addition, the atmosphere, ocean and hydrology models are usually developed in a somewhat independent manner and thus the global (atmospheric, oceanic and hydrological) mass is not conserved [e.g., Yan and Chao, 2012]. Therefore, the matter-term excitations estimated from those models are problematic. In one word, it is unlikely to obtain reliable conclusions on meteorological excitations of CW on the basis of the original meteorological models. Satellite gravimetry can measure mass transportations caused by atmospheric, oceanic and hydrological processes much more accurately than those provided by the original meteorological models, and can force the global (atmospheric, oceanic and

  2. Software library of meteorological routines for air quality models; Libreria de software de procedimientos meteorologicos para modelos de dispersion de contaminantes

    Energy Technology Data Exchange (ETDEWEB)

    Galindo Garcia, Ivan Francisco

    1999-04-01

    Air quality models are an essential tool for most air pollution studies. The models require, however, certain meteorological information about the model domain. Some of the required meteorological parameters can be measured directly, but others must be estimated from available measured data. Therefore, a set of procedures, routines and computational programs to obtain all the meteorological and micrometeorological input data is required. The objective in this study is the identification and implementation of several relationships and methods for the determination of all the meteorological parameters required as input data by US-EPA recommended air pollution models. To accomplish this, a study about air pollution models was conducted, focusing, particularly, on the model meteorological input data. Also, the meteorological stations from the Servicio Meteorologico Nacional (SMN) were analyzed. The type and quality of the meteorological data produced was obtained. The routines and methods developed were based, particularly, on the data produced by SMN stations. Routines were organized in a software library, which allows one to build the specific meteorological processor needed, independently of the model used. Methods were validated against data obtained from an advanced meteorological station owned and operated by the Electrical Research Institute (Instituto de Investigaciones Electricas (IIE)). The results from the validation show that the estimation of the parameters required by air pollution models from routinely available data from Mexico meteorological stations is feasible and therefore let us take full advantage of the use of air pollution models. As an application example of the software library developed, the building of a meteorological processor for a specific air pollution model (CALPUFF) is described. The big advantage the library represents is evident from this example. [Espanol] Los modelos de dispersion de contaminantes constituyen una herramienta

  3. Development Of A Multivariate Prognostic Model For Pain And Activity Limitation In People With Low Back Disorders Receiving Physiotherapy.

    Science.gov (United States)

    Ford, Jon J; Richards BPhysio, Matt C; Surkitt BPhysio, Luke D; Chan BPhysio, Alexander Yp; Slater, Sarah L; Taylor, Nicholas F; Hahne, Andrew J

    2018-05-28

    To identify predictors for back pain, leg pain and activity limitation in patients with early persistent low back disorders. Prospective inception cohort study; Setting: primary care private physiotherapy clinics in Melbourne, Australia. 300 adults aged 18-65 years with low back and/or referred leg pain of ≥6-weeks and ≤6-months duration. Not applicable. Numerical rating scales for back pain and leg pain as well as the Oswestry Disability Scale. Prognostic factors included sociodemographics, treatment related factors, subjective/physical examination, subgrouping factors and standardized questionnaires. Univariate analysis followed by generalized estimating equations were used to develop a multivariate prognostic model for back pain, leg pain and activity limitation. Fifty-eight prognostic factors progressed to the multivariate stage where 15 showed significant (pduration, high multifidus tone, clinically determined inflammation, higher back and leg pain severity, lower lifting capacity, lower work capacity and higher pain drawing percentage coverage). The preliminary model identifying predictors of low back disorders explained up to 37% of the variance in outcome. This study evaluated a comprehensive range of prognostic factors reflective of both the biomedical and psychosocial domains of low back disorders. The preliminary multivariate model requires further validation before being considered for clinical use. Copyright © 2018. Published by Elsevier Inc.

  4. Application of WRF/Chem-MADRID and WRF/Polyphemus in Europe - Part 1: Model description and evaluation of meteorological predictions

    Science.gov (United States)

    Zhang, Y.; Sartelet, K.; Wu, S.-Y.; Seigneur, C.

    2013-02-01

    Comprehensive model evaluation and comparison of two 3-D air quality modeling systems (i.e. the Weather Research and Forecast model (WRF)/Polyphemus and WRF with chemistry and the Model of Aerosol Dynamics, Reaction, Ionization, and Dissolution (MADRID) (WRF/Chem-MADRID) are conducted over western Europe. Part 1 describes the background information for the model comparison and simulation design, as well as the application of WRF for January and July 2001 over triple-nested domains in western Europe at three horizontal grid resolutions: 0.5°, 0.125°, and 0.025°. Six simulated meteorological variables (i.e. temperature at 2 m (T2), specific humidity at 2 m (Q2), relative humidity at 2 m (RH2), wind speed at 10 m (WS10), wind direction at 10 m (WD10), and precipitation (Precip)) are evaluated using available observations in terms of spatial distribution, domainwide daily and site-specific hourly variations, and domainwide performance statistics. WRF demonstrates its capability in capturing diurnal/seasonal variations and spatial gradients of major meteorological variables. While the domainwide performance of T2, Q2, RH2, and WD10 at all three grid resolutions is satisfactory overall, large positive or negative biases occur in WS10 and Precip even at 0.025°. In addition, discrepancies between simulations and observations exist in T2, Q2, WS10, and Precip at mountain/high altitude sites and large urban center sites in both months, in particular, during snow events or thunderstorms. These results indicate the model's difficulty in capturing meteorological variables in complex terrain and subgrid-scale meteorological phenomena, due to inaccuracies in model initialization parameterization (e.g. lack of soil temperature and moisture nudging), limitations in the physical parameterizations of the planetary boundary layer (e.g. cloud microphysics, cumulus parameterizations, and ice nucleation treatments) as well as limitations in surface heat and moisture budget

  5. State of the art and taxonomy of prognostics approaches, trends of prognostics applications and open issues towards maturity at different technology readiness levels

    Science.gov (United States)

    Javed, Kamran; Gouriveau, Rafael; Zerhouni, Noureddine

    2017-09-01

    Integrating prognostics to a real application requires a certain maturity level and for this reason there is a lack of success stories about development of a complete Prognostics and Health Management system. In fact, the maturity of prognostics is closely linked to data and domain specific entities like modeling. Basically, prognostics task aims at predicting the degradation of engineering assets. However, practically it is not possible to precisely predict the impending failure, which requires a thorough understanding to encounter different sources of uncertainty that affect prognostics. Therefore, different aspects crucial to the prognostics framework, i.e., from monitoring data to remaining useful life of equipment need to be addressed. To this aim, the paper contributes to state of the art and taxonomy of prognostics approaches and their application perspectives. In addition, factors for prognostics approach selection are identified, and new case studies from component-system level are discussed. Moreover, open challenges toward maturity of the prognostics under uncertainty are highlighted and scheme for an efficient prognostics approach is presented. Finally, the existing challenges for verification and validation of prognostics at different technology readiness levels are discussed with respect to open challenges.

  6. Meteorological data assimilation for real-time emergency response

    International Nuclear Information System (INIS)

    Sugiyama, G.; Chan, S.T.

    1996-11-01

    The US Department of Energy's Atmospheric Release Advisory Capability (ARAC) provides real-time dose assessments of airborne pollutant releases. Diverse data assimilation techniques are required to meet the needs of a new generation of ARAC models and to take advantage of the rapidly expanding availability of meteorological data. We are developing a hierarchy of algorithms to provide gridded meteorological fields which can be used to drive dispersion codes or to provide initial fields for mesoscale models. Data to be processed include winds, temperature, moisture, and turbulence

  7. Coupled simulation of meteorological parameters and sound intensity in a narrow valley

    Energy Technology Data Exchange (ETDEWEB)

    Heimann, D. [Deutsche Forschungsanstalt fuer Luft- und Raumfahrt e.V. (DLR), Wessling (Germany). Inst. fuer Physik der Atmosphaere; Gross, G. [Hannover Univ. (Germany). Inst. fuer Meteorologie und Klimatologie

    1997-07-01

    A meteorological mesoscale model is used to simulate the inhomogeneous distribution of temperature and the appertaining development of thermal wind systems in a narrow two-dimensional valley during the course of a cloud-free day. A simple sound particle model takes up the simulated meteorological fields and calculates the propagation of noise which originates from a line source at one of the slopes of this valley. The coupled modeling system ensures consistency of topography, meteorological parameters and the sound field. The temporal behaviour of the sound intensity level across the valley is examined. It is only governed by the time-dependent meteorology. The results show remarkable variations of the sound intensity during the course of a day depending on the location in the valley. (orig.) 23 refs.

  8. Fine resolution atmospheric sulfate model driven by operational meteorological data: Comparison with observations

    International Nuclear Information System (INIS)

    Benkovitz, C.M.; Schwartz, S.E.; Berkowitz, C.M.; Easter, R.C.

    1993-09-01

    The hypothesis that anthropogenic sulfur aerosol influences clear-sky and cloud albedo and can thus influence climate has been advanced by several investigators; current global-average climate forcing is estimated to be of comparable magnitude, but opposite sign, to longwave forcing by anthropogenic greenhouse gases. The high space and time variability of sulfate concentrations and column aerosol burdens have been established by observational data; however, geographic and time coverage provided by data from surface monitoring networks is very limited. Consistent regional and global estimates of sulfate aerosol loading, and the contributions to this loading from different sources can be obtained only by modeling studies. Here we describe a sub-hemispheric to global-scale Eulerian transport and transformation model for atmospheric sulfate and its precursors, driven by operational meteorological data, and report results of calculations for October, 1986 for the North Atlantic and adjacent continental regions. The model, which is based on the Global Chemistry Model uses meteorological data from the 6-hour forecast model of the European Center for Medium-Range Weather Forecast to calculate transport and transformation of sulfur emissions. Time- and location-dependent dry deposition velocities were estimated using the methodology of Wesely and colleagues. Chemical reactions includes gaseous oxidation of SO 2 and DMS by OH, and aqueous oxidation of SO 2 by H 2 O 2 and O 3 . Anthropogenic emissions were from the NAPAP and EMEP 1985 inventories and biogenic emissions based on Bates et al. Calculated sulfate concentrations and column burdens exhibit high variability on spatial scale of hundreds of km and temporal scale of days. Calculated daily average sulfate concentrations closely reproduce observed concentrations at locations widespread over the model domain

  9. Meteorology Products - Naval Oceanography Portal

    Science.gov (United States)

    section Advanced Search... Sections Home Time Earth Orientation Astronomy Meteorology Oceanography Ice You are here: Home › FNMOC › Meteorology Products FNMOC Logo FNMOC Navigation Meteorology Products Oceanography Products Tropical Applications Climatology and Archived Data Info Meteorology Products Global

  10. Meteorological Monitoring Program

    International Nuclear Information System (INIS)

    Hancock, H.A. Jr.; Parker, M.J.; Addis, R.P.

    1994-01-01

    The purpose of this technical report is to provide a comprehensive, detailed overview of the meteorological monitoring program at the Savannah River Site (SRS) near Aiken, South Carolina. The principle function of the program is to provide current, accurate meteorological data as input for calculating the transport and diffusion of any unplanned release of an atmospheric pollutant. The report is recommended for meteorologists, technicians, or any personnel who require an in-depth understanding of the meteorological monitoring program

  11. Meteorological Monitoring Program

    Energy Technology Data Exchange (ETDEWEB)

    Hancock, H.A. Jr. [ed.; Parker, M.J.; Addis, R.P.

    1994-09-01

    The purpose of this technical report is to provide a comprehensive, detailed overview of the meteorological monitoring program at the Savannah River Site (SRS) near Aiken, South Carolina. The principle function of the program is to provide current, accurate meteorological data as input for calculating the transport and diffusion of any unplanned release of an atmospheric pollutant. The report is recommended for meteorologists, technicians, or any personnel who require an in-depth understanding of the meteorological monitoring program.

  12. MODELING OF RELATIONSHIP BETWEEN GROUNDWATER FLOW AND OTHER METEOROLOGICAL VARIABLES USING FUZZY LOGIC

    Directory of Open Access Journals (Sweden)

    Şaban YURTÇU

    2006-02-01

    Full Text Available In this study, modeling of the effect of rainfall, flow and evaporation as independent variables on the change of underground water levels as dependent variables were investigated by fuzzy logic (FL. In the study, total 396 values taken from six observation stations belong to Afyon inferior basin in Akarçay from 1977 to 1989 years were used. Using the monthly average values of stations, the change of underground water level was modeled by FL. It is observed that the results obtained from FL and the observations are compatible with each other. This shows FL modeling can be used to estimate groundwater levels from the appropriate meteorological value.

  13. On prognostic models, artificial intelligence and censored observations.

    Science.gov (United States)

    Anand, S S; Hamilton, P W; Hughes, J G; Bell, D A

    2001-03-01

    The development of prognostic models for assisting medical practitioners with decision making is not a trivial task. Models need to possess a number of desirable characteristics and few, if any, current modelling approaches based on statistical or artificial intelligence can produce models that display all these characteristics. The inability of modelling techniques to provide truly useful models has led to interest in these models being purely academic in nature. This in turn has resulted in only a very small percentage of models that have been developed being deployed in practice. On the other hand, new modelling paradigms are being proposed continuously within the machine learning and statistical community and claims, often based on inadequate evaluation, being made on their superiority over traditional modelling methods. We believe that for new modelling approaches to deliver true net benefits over traditional techniques, an evaluation centric approach to their development is essential. In this paper we present such an evaluation centric approach to developing extensions to the basic k-nearest neighbour (k-NN) paradigm. We use standard statistical techniques to enhance the distance metric used and a framework based on evidence theory to obtain a prediction for the target example from the outcome of the retrieved exemplars. We refer to this new k-NN algorithm as Censored k-NN (Ck-NN). This reflects the enhancements made to k-NN that are aimed at providing a means for handling censored observations within k-NN.

  14. Uncertainty in dispersion forecasts using meteorological ensembles

    International Nuclear Information System (INIS)

    Chin, H N; Leach, M J

    1999-01-01

    The usefulness of dispersion forecasts depends on proper interpretation of results. Understanding the uncertainty in model predictions and the range of possible outcomes is critical for determining the optimal course of action in response to terrorist attacks. One of the objectives for the Modeling and Prediction initiative is creating tools for emergency planning for special events such as the upcoming the Olympics. Meteorological forecasts hours to days in advance are used to estimate the dispersion at the time of the event. However, there is uncertainty in any meteorological forecast, arising from both errors in the data (both initial conditions and boundary conditions) and from errors in the model. We use ensemble forecasts to estimate the uncertainty in the forecasts and the range of possible outcomes

  15. Hyperion technology enables unified meteorological and radiological monitoring

    International Nuclear Information System (INIS)

    Zigic, A.; Saponjic, D.; Arandjelovic, V.; Zunic, Z. . E-mail address of corresponding author: alex@vin.bg.ac.yu; Zigic, A.)

    2005-01-01

    The present state of meteorological and radiological measurement and monitoring are quite localized to smaller areas which implies the difficulties in knowing the measurement results in the wider region instantly. The need for establishing a distributed, flexible, modular and centralized measurement system for both meteorological and radiological parameters of environment is arising. The measurement and monitoring of radiological parameters of environment are not sufficient since there is a strong correlation between radiological and meteorological parameters which implies a unified distributed automatic monitoring system. The unified monitoring system makes it possible to transfer, process and store measured data in local and central databases. Central database gives a possibility of easy access to all measured data for authorized personnel and institutions. Stored measured data in central database gives a new opportunity to create a base for meteorological and radiological modelling and studies. (author)

  16. Gene Expression of the EGF System-a Prognostic Model in Non-Small Cell Lung Cancer Patients Without Activating EGFR Mutations

    DEFF Research Database (Denmark)

    Sandfeld-Paulsen, Birgitte; Folkersen, Birgitte Holst; Rasmussen, Torben Riis

    2016-01-01

    OBJECTIVES: Contradicting results have been demonstrated for the expression of the epidermal growth factor receptor (EGFR) as a prognostic marker in non-small cell lung cancer (NSCLC). The complexity of the EGF system with four interacting receptors and more than a dozen activating ligands is a l.......17-6.47], P model that takes the complexity of the EGF system into account and shows that this model is a strong prognostic marker in NSCLC patients.......OBJECTIVES: Contradicting results have been demonstrated for the expression of the epidermal growth factor receptor (EGFR) as a prognostic marker in non-small cell lung cancer (NSCLC). The complexity of the EGF system with four interacting receptors and more than a dozen activating ligands...... is a likely explanation. The aim of this study is to demonstrate that the combined network of receptors and ligands from the EGF system is a prognostic marker. MATERIAL AND METHODS: Gene expression of the receptors EGFR, HER2, HER3, HER4, and the ligands AREG, HB-EGF, EPI, TGF-α, and EGF was measured...

  17. Meteorological Modeling Using the WRF-ARW Model for Grand Bay Intensive Studies of Atmospheric Mercury

    Directory of Open Access Journals (Sweden)

    Fong Ngan

    2015-02-01

    Full Text Available Measurements at the Grand Bay National Estuarine Research Reserve support a range of research activities aimed at improving the understanding of the atmospheric fate and transport of mercury. Routine monitoring was enhanced by two intensive measurement periods conducted at the site in summer 2010 and spring 2011. Detailed meteorological data are required to properly represent the weather conditions, to determine the transport and dispersion of plumes and to understand the wet and dry deposition of mercury. To describe the mesoscale features that might influence future plume calculations for mercury episodes during the Grand Bay Intensive campaigns, fine-resolution meteorological simulations using the Weather Research and Forecasting (WRF model were conducted with various initialization and nudging configurations. The WRF simulations with nudging generated reasonable results in comparison with conventional observations in the region and measurements obtained at the Grand Bay site, including surface and sounding data. The grid nudging, together with observational nudging, had a positive effect on wind prediction. However, the nudging of mass fields (temperature and moisture led to overestimates of precipitation, which may introduce significant inaccuracies if the data were to be used for subsequent atmospheric mercury modeling. The regional flow prediction was also influenced by the reanalysis data used to initialize the WRF simulations. Even with observational nudging, the summer case simulation results in the fine resolution domain inherited features of the reanalysis data, resulting in different regional wind patterns. By contrast, the spring intensive period showed less influence from the reanalysis data.

  18. Modeling the wind-fields of accidental releases with an operational regional forecast model

    International Nuclear Information System (INIS)

    Albritton, J.R.; Lee, R.L.; Sugiyama, G.

    1995-01-01

    The Atmospheric Release Advisory Capability (ARAC) is an operational emergency preparedness and response organization supported primarily by the Departments of Energy and Defense. ARAC can provide real-time assessments of atmospheric releases of radioactive materials at any location in the world. ARAC uses robust three-dimensional atmospheric transport and dispersion models, extensive geophysical and dose-factor databases, meteorological data-acquisition systems, and an experienced staff. Although it was originally conceived and developed as an emergency response and assessment service for nuclear accidents, the ARAC system has been adapted to also simulate non-radiological hazardous releases. For example, in 1991 ARAC responded to three major events: the oil fires in Kuwait, the eruption of Mt. Pinatubo in the Philippines, and the herbicide spill into the upper Sacramento River in California. ARAC's operational simulation system, includes two three-dimensional finite-difference models: a diagnostic wind-field scheme, and a Lagrangian particle-in-cell transport and dispersion scheme. The meteorological component of ARAC's real-time response system employs models using real-time data from all available stations near the accident site to generate a wind-field for input to the transport and dispersion model. Here we report on simulation studies of past and potential release sites to show that even in the absence of local meteorological observational data, readily available gridded analysis and forecast data and a prognostic model, the Navy Operational Regional Atmospheric Prediction System, applied at an appropriate grid resolution can successfully simulate complex local flows

  19. A prognostic model for soft tissue sarcoma of the extremities and trunk wall based on size, vascular invasion, necrosis, and growth pattern

    DEFF Research Database (Denmark)

    Carneiro, Ana; Bendahl, Par-Ola; Engellau, Jacob

    2011-01-01

    type, necrosis, and grade. METHODS:: Whole-tumor sections from 239 soft tissue sarcomas of the extremities were reviewed for the following prognostic factors: size, vascular invasion, necrosis, and growth pattern. A new prognostic model, referred to as SING (Size, Invasion, Necrosis, Growth...

  20. EARTH, WIND AND FIRE: BUILDING METEOROLOGICALLY-SENSITIVE BIOGENIC AND WILDLAND FIRE EMISSION ESTIMATES FOR AIR QUALITY MODELS

    Science.gov (United States)

    Emission estimates are important for ensuring the accuracy of atmospheric chemical transport models. Estimates of biogenic and wildland fire emissions, because of their sensitivity to meteorological conditions, need to be carefully constructed and closely linked with a meteorolo...

  1. Modeling for pollution dispersion and air quality. 3.: meteorological data and emissions

    International Nuclear Information System (INIS)

    Bertagna, Silvia

    2005-01-01

    To better and correctly choose the suitable modeling system to use, it is necessary previously to define with objective criteria the characteristic of the problem to be studied and to gather together a great amount of input data and information, needed by the model, regarding, namely, the meteorological diffusive conditions of the atmosphere, the characteristic of the emission source (type, number, site etc.) and the characteristic of the area of interest (as land use and orography). In this work, the main different typologies of input data, which occur to simulate the air pollutant dispersion, are described, together with the instruments to obtain them: they include the consultation and the elaboration of information coming from databases and inventories appositely built and often also the use of other models or dedicated SW programs [it

  2. Lectures in Micro Meteorology

    DEFF Research Database (Denmark)

    Larsen, Søren Ejling

    This report contains the notes from my lectures on Micro scale meteorology at the Geophysics Department of the Niels Bohr Institute of Copenhagen University. In the period 1993-2012, I was responsible for this course at the University. At the start of the course, I decided that the text books...... available in meteorology at that time did not include enough of the special flavor of micro meteorology that characterized the work of the meteorology group at Risø (presently of the Institute of wind energy of the Danish Technical University). This work was focused on Boundary layer flows and turbulence...

  3. Physics Based Modeling and Prognostics of Electrolytic Capacitors

    Science.gov (United States)

    Kulkarni, Chetan; Ceyla, Jose R.; Biswas, Gautam; Goebel, Kai

    2012-01-01

    This paper proposes first principles based modeling and prognostics approach for electrolytic capacitors. Electrolytic capacitors have become critical components in electronics systems in aeronautics and other domains. Degradations and faults in DC-DC converter unit propagates to the GPS and navigation subsystems and affects the overall solution. Capacitors and MOSFETs are the two major components, which cause degradations and failures in DC-DC converters. This type of capacitors are known for its low reliability and frequent breakdown on critical systems like power supplies of avionics equipment and electrical drivers of electromechanical actuators of control surfaces. Some of the more prevalent fault effects, such as a ripple voltage surge at the power supply output can cause glitches in the GPS position and velocity output, and this, in turn, if not corrected will propagate and distort the navigation solution. In this work, we study the effects of accelerated aging due to thermal stress on different sets of capacitors under different conditions. Our focus is on deriving first principles degradation models for thermal stress conditions. Data collected from simultaneous experiments are used to validate the desired models. Our overall goal is to derive accurate models of capacitor degradation, and use them to predict performance changes in DC-DC converters.

  4. The role of the Finnish Meteorological Institute

    International Nuclear Information System (INIS)

    Savolainen, A.L.; Valkama, I.

    1993-01-01

    The Finnish Meteorological Institute is responsible for the dispersion forecasts for the radiation control in Finland. In addition to the normal weather forecasts the duty forecaster has the work station based three dimensional trajectory model and the short range dispersion model YDINO at his disposal. For expert use, dispersion and dose model TRADOS is available. The TRADOS, developed by the Finnish Meteorological Institute and by the Technical Research Centre of Finland, includes a meteorological data base that utilizes the numerical forecasts of the High Resolution Limited Area Model (HIRLAM) weather prediction model. The transport is described by three-dimensional air-parcel trajectories. For each time step the integrated air concentrations as well as dry and wet deposition for selected groups of radionuclides are computed. In the operational emergency application only external dose rates are computed. In the statistical version also individual and population dose estimates via several external and internal pathways can be made. The TRADOS is currently run under two separate user interfaces. The trajectory and dispersion model interface includes ready-made lists of the nuclear power plants and other installations. The dose model has a set of release terms for several groups of radionuclides. There is also a graphical module that enables the computed results to be presented in grid or also isolines. A new graphical user interface and presentation lay-outs redesigned as visual and end-user friendly as possible and with the aim of possible and with the aim of possible adoption as a Nordic standard will be installed in the near future. (orig.)

  5. Mayo Alliance Prognostic Model for Myelodysplastic Syndromes: Integration of Genetic and Clinical Information.

    Science.gov (United States)

    Tefferi, Ayalew; Gangat, Naseema; Mudireddy, Mythri; Lasho, Terra L; Finke, Christy; Begna, Kebede H; Elliott, Michelle A; Al-Kali, Aref; Litzow, Mark R; Hook, C Christopher; Wolanskyj, Alexandra P; Hogan, William J; Patnaik, Mrinal M; Pardanani, Animesh; Zblewski, Darci L; He, Rong; Viswanatha, David; Hanson, Curtis A; Ketterling, Rhett P; Tang, Jih-Luh; Chou, Wen-Chien; Lin, Chien-Chin; Tsai, Cheng-Hong; Tien, Hwei-Fang; Hou, Hsin-An

    2018-06-01

    To develop a new risk model for primary myelodysplastic syndromes (MDS) that integrates information on mutations, karyotype, and clinical variables. Patients with World Health Organization-defined primary MDS seen at Mayo Clinic (MC) from December 28, 1994, through December 19, 2017, constituted the core study group. The National Taiwan University Hospital (NTUH) provided the validation cohort. Model performance, compared with the revised International Prognostic Scoring System, was assessed by Akaike information criterion and area under the curve estimates. The study group consisted of 685 molecularly annotated patients from MC (357) and NTUH (328). Multivariate analysis of the MC cohort identified monosomal karyotype (hazard ratio [HR], 5.2; 95% CI, 3.1-8.6), "non-MK abnormalities other than single/double del(5q)" (HR, 1.8; 95% CI, 1.3-2.6), RUNX1 (HR, 2.0; 95% CI, 1.2-3.1) and ASXL1 (HR, 1.7; 95% CI, 1.2-2.3) mutations, absence of SF3B1 mutations (HR, 1.6; 95% CI, 1.1-2.4), age greater than 70 years (HR, 2.2; 95% CI, 1.6-3.1), hemoglobin level less than 8 g/dL in women or less than 9 g/dL in men (HR, 2.3; 95% CI, 1.7-3.1), platelet count less than 75 × 10 9 /L (HR, 1.5; 95% CI, 1.1-2.1), and 10% or more bone marrow blasts (HR, 1.7; 95% CI, 1.1-2.8) as predictors of inferior overall survival. Based on HR-weighted risk scores, a 4-tiered Mayo alliance prognostic model for MDS was devised: low (89 patients), intermediate-1 (104), intermediate-2 (95), and high (69); respective median survivals (5-year overall survival rates) were 85 (73%), 42 (34%), 22 (7%), and 9 months (0%). The Mayo alliance model was subsequently validated by using the external NTUH cohort and, compared with the revised International Prognostic Scoring System, displayed favorable Akaike information criterion (1865 vs 1943) and area under the curve (0.87 vs 0.76) values. We propose a simple and contemporary risk model for MDS that is based on a limited set of genetic and clinical variables

  6. Prognostics of Power MOSFET

    Science.gov (United States)

    Celaya, Jose Ramon; Saxena, Abhinav; Vashchenko, Vladislay; Saha, Sankalita; Goebel, Kai Frank

    2011-01-01

    This paper demonstrates how to apply prognostics to power MOSFETs (metal oxide field effect transistor). The methodology uses thermal cycling to age devices and Gaussian process regression to perform prognostics. The approach is validated with experiments on 100V power MOSFETs. The failure mechanism for the stress conditions is determined to be die-attachment degradation. Change in ON-state resistance is used as a precursor of failure due to its dependence on junction temperature. The experimental data is augmented with a finite element analysis simulation that is based on a two-transistor model. The simulation assists in the interpretation of the degradation phenomena and SOA (safe operation area) change.

  7. Prognostics and health management of engineering systems an introduction

    CERN Document Server

    Kim, Nam-Ho; Choi, Joo-Ho

    2017-01-01

    This book introduces the methods for predicting the future behavior of a system’s health and the remaining useful life to determine an appropriate maintenance schedule. The authors introduce the history, industrial applications, algorithms, and benefits and challenges of PHM (Prognostics and Health Management) to help readers understand this highly interdisciplinary engineering approach that incorporates sensing technologies, physics of failure, machine learning, modern statistics, and reliability engineering. It is ideal for beginners because it introduces various prognostics algorithms and explains their attributes, pros and cons in terms of model definition, model parameter estimation, and ability to handle noise and bias in data, allowing readers to select the appropriate methods for their fields of application. Among the many topics discussed in-depth are: • Prognostics tutorials using least-squares • Bayesian inference and parameter estimation • Physics-based prognostics algorithms including non...

  8. Meteorological and sulphur dioxide dispersion modelling for an industrial complex near Mexico city metropolitan area

    International Nuclear Information System (INIS)

    Mora, V.R.; Sosa, G.; Molina, M.M.; Palmerin-ruiz, M.E.; Melgarejo-flores, L.E.

    2009-01-01

    Major sulphur dioxide emissions in Mexico are due largely to fuel of oil refining and coal combustion. In Tula-Vito-Apasco industrial corridor (TVA) are located two important sources of SO/sub 2/: the 'Miguel Hidalgo' refinery and the 'Francisco Perez Rios' power plant. Due to from March 25 to April 22 of 2006 a major field campaign took place as part of a collaborative research program called MILAGRO. Data collected around the Industrial Complex were used to: a) evaluate the air quality to local and regional scale; b) study the structure of the atmospheric boundary layer (BL); and c) validate meteorological and dispersion models. In this study we presented the behaviour of daytime BL, and the results of meteorological and dispersion modelling for selected episodes of high sulfur dioxide (SO/sub 2/). The Regional Atmospheric Modeling System (RAMS) and the Hybrid and Particle Concentration Transport Model (HYPACT) were used to evaluate the impact of SO/sub 2/ emissions to regional scale. For modelling, we selected the days where higher mean daily levels of SO/sub 2 /surface concentrations were observed, these corresponded to March 31 and April 6. The results indicate that: The daytime BL in TVA, exhibited a normal behavior, a stable layer or thermal inversion close to surface was observed at 0800 LST (up to 80% of the cases), then the mixing height (MH) growths, with a growth rate of 313 m h-1 (between 0800 to 1200 LST). The most rapid MH growth happened between 1200 to 1500 LST;. The maximum MH was observed at 1500 LST (90% of the cases); the mean maximum MH was close to 2794 m AGL; Potential temperature and humidity profiles showed a normal behavior; High persistence in wind direction (> 0.6) close to surface up to 500 m AGL, was observed at 1500, and 1800 LST, at the same time, a low level jet, penetrating from the NE, with wind speed between 6 to 8 m s/sup -1/ was observed. Meteorological modelling was used to determine the circulation patterns in the region

  9. Evaluation of Simulated Marine Aerosol Production Using the WaveWatchIII Prognostic Wave Model Coupled to the Community Atmosphere Model within the Community Earth System Model

    Energy Technology Data Exchange (ETDEWEB)

    Long, M. S. [Harvard Univ., Cambridge, MA (United States). School of Engineering and Applied Sciences; Keene, William C. [Univ. of Virginia, Charlottesville, VA (United States). Dept. of Environmental Sciences; Zhang, J. [Univ. of North Dakota, Grand Forks, ND (United States). Dept. of Atmospheric Sciences; Reichl, B. [Univ. of Rhode Island, Narragansett, RI (United States). Graduate School of Oceanography; Shi, Y. [Univ. of North Dakota, Grand Forks, ND (United States). Dept. of Atmospheric Sciences; Hara, T. [Univ. of Rhode Island, Narragansett, RI (United States). Graduate School of Oceanography; Reid, J. S. [Naval Research Lab. (NRL), Monterey, CA (United States); Fox-Kemper, B. [Brown Univ., Providence, RI (United States). Earth, Environmental and Planetary Sciences; Craig, A. P. [National Center for Atmospheric Research, Boulder, CO (United States); Erickson, D. J. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computer Science and Mathematics Division; Ginis, I. [Univ. of Rhode Island, Narragansett, RI (United States). Graduate School of Oceanography; Webb, A. [Univ. of Tokyo (Japan). Dept. of Ocean Technology, Policy, and Environment

    2016-11-08

    Primary marine aerosol (PMA) is emitted into the atmosphere via breaking wind waves on the ocean surface. Most parameterizations of PMA emissions use 10-meter wind speed as a proxy for wave action. This investigation coupled the 3rd generation prognostic WAVEWATCH-III wind-wave model within a coupled Earth system model (ESM) to drive PMA production using wave energy dissipation rate – analogous to whitecapping – in place of 10-meter wind speed. The wind speed parameterization did not capture basin-scale variability in relations between wind and wave fields. Overall, the wave parameterization did not improve comparison between simulated versus measured AOD or Na+, thus highlighting large remaining uncertainties in model physics. Results confirm the efficacy of prognostic wind-wave models for air-sea exchange studies coupled with laboratory- and field-based characterizations of the primary physical drivers of PMA production. No discernible correlations were evident between simulated PMA fields and observed chlorophyll or sea surface temperature.

  10. Cross-National Validation of Prognostic Models Predicting Sickness Absence and the Added Value of Work Environment Variables

    NARCIS (Netherlands)

    Roelen, Corne A. M.; Stapelfeldt, Christina M.; Heymans, Martijn W.; van Rhenen, Willem; Labriola, Merete; Nielsen, Claus V.; Bultmann, Ute; Jensen, Chris

    Purpose To validate Dutch prognostic models including age, self-rated health and prior sickness absence (SA) for ability to predict high SA in Danish eldercare. The added value of work environment variables to the models' risk discrimination was also investigated. Methods 2,562 municipal eldercare

  11. Accelerated Aging with Electrical Overstress and Prognostics for Power MOSFETs

    Science.gov (United States)

    Saha, Sankalita; Celaya, Jose Ramon; Vashchenko, Vladislav; Mahiuddin, Shompa; Goebel, Kai F.

    2011-01-01

    Power electronics play an increasingly important role in energy applications as part of their power converter circuits. Understanding the behavior of these devices, especially their failure modes as they age with nominal usage or sudden fault development is critical in ensuring efficiency. In this paper, a prognostics based health management of power MOSFETs undergoing accelerated aging through electrical overstress at the gate area is presented. Details of the accelerated aging methodology, modeling of the degradation process of the device and prognostics algorithm for prediction of the future state of health of the device are presented. Experiments with multiple devices demonstrate the performance of the model and the prognostics algorithm as well as the scope of application. Index Terms Power MOSFET, accelerated aging, prognostics

  12. Surface meteorology and Solar Energy

    Science.gov (United States)

    Stackhouse, Paul W. (Principal Investigator)

    The Release 5.1 Surface meteorology and Solar Energy (SSE) data contains parameters formulated for assessing and designing renewable energy systems. Parameters fall under 11 categories including: Solar cooking, solar thermal applications, solar geometry, tilted solar panels, energy storage systems, surplus product storage systems, cloud information, temperature, wind, other meteorological factors, and supporting information. This latest release contains new parameters based on recommendations by the renewable energy industry and it is more accurate than previous releases. On-line plotting capabilities allow quick evaluation of potential renewable energy projects for any region of the world. The SSE data set is formulated from NASA satellite- and reanalysis-derived insolation and meteorological data for the 10-year period July 1983 through June 1993. Results are provided for 1 degree latitude by 1 degree longitude grid cells over the globe. Average daily and monthly measurements for 1195 World Radiation Data Centre ground sites are also available. [Mission Objectives] The SSE project contains insolation and meteorology data intended to aid in the development of renewable energy systems. Collaboration between SSE and technology industries such as the Hybrid Optimization Model for Electric Renewables ( HOMER ) may aid in designing electric power systems that employ some combination of wind turbines, photovoltaic panels, or diesel generators to produce electricity. [Temporal_Coverage: Start_Date=1983-07-01; Stop_Date=1993-06-30] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180].

  13. Errors and improvements in the use of archived meteorological data for chemical transport modeling: an analysis using GEOS-Chem v11-01 driven by GEOS-5 meteorology

    Directory of Open Access Journals (Sweden)

    K. Yu

    2018-01-01

    Full Text Available Global simulations of atmospheric chemistry are commonly conducted with off-line chemical transport models (CTMs driven by archived meteorological data from general circulation models (GCMs. The off-line approach has the advantages of simplicity and expediency, but it incurs errors due to temporal averaging in the meteorological archive and the inability to reproduce the GCM transport algorithms exactly. The CTM simulation is also often conducted at coarser grid resolution than the parent GCM. Here we investigate this cascade of CTM errors by using 222Rn–210Pb–7Be chemical tracer simulations off-line in the GEOS-Chem CTM at rectilinear 0.25°  ×  0.3125° (≈ 25 km and 2°  ×  2.5° (≈ 200 km resolutions and online in the parent GEOS-5 GCM at cubed-sphere c360 (≈ 25 km and c48 (≈ 200 km horizontal resolutions. The c360 GEOS-5 GCM meteorological archive, updated every 3 h and remapped to 0.25°  ×  0.3125°, is the standard operational product generated by the NASA Global Modeling and Assimilation Office (GMAO and used as input by GEOS-Chem. We find that the GEOS-Chem 222Rn simulation at native 0.25°  ×  0.3125° resolution is affected by vertical transport errors of up to 20 % relative to the GEOS-5 c360 online simulation, in part due to loss of transient organized vertical motions in the GCM (resolved convection that are temporally averaged out in the 3 h meteorological archive. There is also significant error caused by operational remapping of the meteorological archive from a cubed-sphere to a rectilinear grid. Decreasing the GEOS-Chem resolution from 0.25°  ×  0.3125° to 2°  ×  2.5° induces further weakening of vertical transport as transient vertical motions are averaged out spatially and temporally. The resulting 222Rn concentrations simulated by the coarse-resolution GEOS-Chem are overestimated by up to 40 % in surface air relative to the

  14. Reference crop evapotranspiration estimate using high-resolution meteorological network's data

    Directory of Open Access Journals (Sweden)

    C. Lussana

    2009-10-01

    Full Text Available Water management authorities need detailed information about each component of the hydrological balance. This document presents a method to estimate the evapotranspiration rate, initialized in order to obtain the reference crop evapotranspiration rate (ET0. By using an Optimal Interpolation (OI scheme, the hourly observations of several meteorological variables, measured by a high-resolution local meteorological network, are interpolated over a regular grid. The analysed meteorological fields, containing detailed meteorological information, enter a model for turbulent heat fluxes estimation based on Monin-Obukhov surface layer similarity theory. The obtained ET0 fields are then post-processed and disseminated to the users.

  15. PREDICTIONS OF DISPERSION AND DEPOSITION OF FALLOUT FROM NUCLEAR TESTING USING THE NOAA-HYSPLIT METEOROLOGICAL MODEL

    Science.gov (United States)

    Moroz, Brian E.; Beck, Harold L.; Bouville, André; Simon, Steven L.

    2013-01-01

    The NOAA Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT) was evaluated as a research tool to simulate the dispersion and deposition of radioactive fallout from nuclear tests. Model-based estimates of fallout can be valuable for use in the reconstruction of past exposures from nuclear testing, particularly, where little historical fallout monitoring data is available. The ability to make reliable predictions about fallout deposition could also have significant importance for nuclear events in the future. We evaluated the accuracy of the HYSPLIT-predicted geographic patterns of deposition by comparing those predictions against known deposition patterns following specific nuclear tests with an emphasis on nuclear weapons tests conducted in the Marshall Islands. We evaluated the ability of the computer code to quantitatively predict the proportion of fallout particles of specific sizes deposited at specific locations as well as their time of transport. In our simulations of fallout from past nuclear tests, historical meteorological data were used from a reanalysis conducted jointly by the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR). We used a systematic approach in testing the HYSPLIT model by simulating the release of a range of particles sizes from a range of altitudes and evaluating the number and location of particles deposited. Our findings suggest that the quantity and quality of meteorological data are the most important factors for accurate fallout predictions and that when satisfactory meteorological input data are used, HYSPLIT can produce relatively accurate deposition patterns and fallout arrival times. Furthermore, when no other measurement data are available, HYSPLIT can be used to indicate whether or not fallout might have occurred at a given location and provide, at minimum, crude quantitative estimates of the magnitude of the deposited activity. A variety of

  16. Meteorology observations in the Athabasca oil sands region

    International Nuclear Information System (INIS)

    1996-01-01

    Meteorological data was collected in the Athabasca oil sands area of Alberta in support of Syncrude' application for approval to develop and operate the Aurora Mine. Meteorology controls the transport and dispersion of gaseous and particulate emissions which are vented into the atmosphere. Several meteorological monitoring stations have been set up in the Fort McMurray and Fort McKay area. The study was part of Suncor's commitment to Alberta Environmental Protection to substantially reduce SO 2 emissions by July 1996. Also, as a condition of approval of the proposed Aurora Mine, the company was required to develop additional ambient air quality, sulphur deposition and biomonitoring programs. Background reports were prepared for: (1) source characterization, (2) ambient air quality observations, (3) meteorology observations, and (4) air quality monitoring. The following factors were incorporated into dispersion modelling: terrain, wind, turbulence, temperature, net radiation and mixing height, relative humidity and precipitation. 15 refs., 9 tabs., 40 figs

  17. Configuring the HYSPLIT Model for National Weather Service Forecast Office and Spaceflight Meteorology Group Applications

    Science.gov (United States)

    Dreher, Joseph; Blottman, Peter F.; Sharp, David W.; Hoeth, Brian; Van Speybroeck, Kurt

    2009-01-01

    The National Weather Service Forecast Office in Melbourne, FL (NWS MLB) is responsible for providing meteorological support to state and county emergency management agencies across East Central Florida in the event of incidents involving the significant release of harmful chemicals, radiation, and smoke from fires and/or toxic plumes into the atmosphere. NWS MLB uses the National Oceanic and Atmospheric Administration Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to provide trajectory, concentration, and deposition guidance during such events. Accurate and timely guidance is critical for decision makers charged with protecting the health and well-being of populations at risk. Information that can describe the geographic extent of areas possibly affected by a hazardous release, as well as to indicate locations of primary concern, offer better opportunity for prompt and decisive action. In addition, forecasters at the NWS Spaceflight Meteorology Group (SMG) have expressed interest in using the HYSPLIT model to assist with Weather Flight Rules during Space Shuttle landing operations. In particular, SMG would provide low and mid-level HYSPLIT trajectory forecasts for cumulus clouds associated with smoke plumes, and high-level trajectory forecasts for thunderstorm anvils. Another potential benefit for both NWS MLB and SMG is using the HYSPLIT model concentration and deposition guidance in fog situations.

  18. The use of prognostic factors in metastatic renal cell carcinoma.

    Science.gov (United States)

    Li, Haoran; Samawi, Haider; Heng, Daniel Y C

    2015-12-01

    Over the last decade, the treatment landscape of metastatic renal cell carcinoma (mRCC) has evolved tremendously. The outcome of patients with mRCC has been improved since the advent of targeted therapy. In this review, we address the use of prognostic schema in the era of targeted treatment. This article summarizes the current available prognostic models and the evidence to support their use in clinical settings. Prognostic models can help guide clinicians in their decision making, as they have been validated in the first- and second-line targeted therapy settings as well as in non-clear cell mRCC. Prognostic factors are important in patient counseling, clinical trial stratification, and therapy planning. Very selected favorable-risk patients with minimal bulk and slow-growing disease could potentially be observed before needing treatment. Patients with poor-risk disease may be eligible for treatment with temsirolimus. Patients with a very poor prognosis may not be suitable candidates for cytoreductive nephrectomy. New biomarkers are on the horizon, though their roles need to be validated and their additive contribution to improve existing prognostic models examined. Copyright © 2015 Elsevier Inc. All rights reserved.

  19. Prognostic meta-signature of breast cancer developed by two-stage mixture modeling of microarray data

    Directory of Open Access Journals (Sweden)

    Ghosh Debashis

    2004-12-01

    Full Text Available Abstract Background An increasing number of studies have profiled tumor specimens using distinct microarray platforms and analysis techniques. With the accumulating amount of microarray data, one of the most intriguing yet challenging tasks is to develop robust statistical models to integrate the findings. Results By applying a two-stage Bayesian mixture modeling strategy, we were able to assimilate and analyze four independent microarray studies to derive an inter-study validated "meta-signature" associated with breast cancer prognosis. Combining multiple studies (n = 305 samples on a common probability scale, we developed a 90-gene meta-signature, which strongly associated with survival in breast cancer patients. Given the set of independent studies using different microarray platforms which included spotted cDNAs, Affymetrix GeneChip, and inkjet oligonucleotides, the individually identified classifiers yielded gene sets predictive of survival in each study cohort. The study-specific gene signatures, however, had minimal overlap with each other, and performed poorly in pairwise cross-validation. The meta-signature, on the other hand, accommodated such heterogeneity and achieved comparable or better prognostic performance when compared with the individual signatures. Further by comparing to a global standardization method, the mixture model based data transformation demonstrated superior properties for data integration and provided solid basis for building classifiers at the second stage. Functional annotation revealed that genes involved in cell cycle and signal transduction activities were over-represented in the meta-signature. Conclusion The mixture modeling approach unifies disparate gene expression data on a common probability scale allowing for robust, inter-study validated prognostic signatures to be obtained. With the emerging utility of microarrays for cancer prognosis, it will be important to establish paradigms to meta

  20. Prognostic Value of Quantitative Stress Perfusion Cardiac Magnetic Resonance.

    Science.gov (United States)

    Sammut, Eva C; Villa, Adriana D M; Di Giovine, Gabriella; Dancy, Luke; Bosio, Filippo; Gibbs, Thomas; Jeyabraba, Swarna; Schwenke, Susanne; Williams, Steven E; Marber, Michael; Alfakih, Khaled; Ismail, Tevfik F; Razavi, Reza; Chiribiri, Amedeo

    2018-05-01

    This study sought to evaluate the prognostic usefulness of visual and quantitative perfusion cardiac magnetic resonance (CMR) ischemic burden in an unselected group of patients and to assess the validity of consensus-based ischemic burden thresholds extrapolated from nuclear studies. There are limited data on the prognostic value of assessing myocardial ischemic burden by CMR, and there are none using quantitative perfusion analysis. Patients with suspected coronary artery disease referred for adenosine-stress perfusion CMR were included (n = 395; 70% male; age 58 ± 13 years). The primary endpoint was a composite of cardiovascular death, nonfatal myocardial infarction, aborted sudden death, and revascularization after 90 days. Perfusion scans were assessed visually and with quantitative analysis. Cross-validated Cox regression analysis and net reclassification improvement were used to assess the incremental prognostic value of visual or quantitative perfusion analysis over a baseline clinical model, initially as continuous covariates, then using accepted thresholds of ≥2 segments or ≥10% myocardium. After a median 460 days (interquartile range: 190 to 869 days) follow-up, 52 patients reached the primary endpoint. At 2 years, the addition of ischemic burden was found to increase prognostic value over a baseline model of age, sex, and late gadolinium enhancement (baseline model area under the curve [AUC]: 0.75; visual AUC: 0.84; quantitative AUC: 0.85). Dichotomized quantitative ischemic burden performed better than visual assessment (net reclassification improvement 0.043 vs. 0.003 against baseline model). This study was the first to address the prognostic benefit of quantitative analysis of perfusion CMR and to support the use of consensus-based ischemic burden thresholds by perfusion CMR for prognostic evaluation of patients with suspected coronary artery disease. Quantitative analysis provided incremental prognostic value to visual assessment and

  1. Meteorological Data Visualization in Multi-User Virtual Reality

    Science.gov (United States)

    Appleton, R.; van Maanen, P. P.; Fisher, W. I.; Krijnen, R.

    2017-12-01

    Due to their complexity and size, visualization of meteorological data is important. It enables the precise examining and reviewing of meteorological details and is used as a communication tool for reporting, education and to demonstrate the importance of the data to policy makers. Specifically for the UCAR community it is important to explore all of such possibilities.Virtual Reality (VR) technology enhances the visualization of volumetric and dynamical data in a more natural way as compared to a standard desktop, keyboard mouse setup. The use of VR for data visualization is not new but recent developments has made expensive hardware and complex setups unnecessary. The availability of consumer of the shelf VR hardware enabled us to create a very intuitive and low cost way to visualize meteorological data. A VR viewer has been implemented using multiple HTC Vive head sets and allows visualization and analysis of meteorological data in NetCDF format (e.g. of NCEP North America Model (NAM), see figure). Sources of atmospheric/meteorological data include radar and satellite as well as traditional weather stations. The data includes typical meteorological information such as temperature, humidity, air pressure, as well as those data described by the climate forecast (CF) model conventions (http://cfconventions.org). Other data such as lightning-strike data and ultra-high-resolution satellite data are also becoming available. The users can navigate freely around the data which is presented in a virtual room at a scale of up to 3.5 X 3.5 meters. The multiple users can manipulate the model simultaneously. Possible mutations include scaling/translating, filtering by value and using a slicing tool to cut-off specific sections of the data to get a closer look. The slicing can be done in any direction using the concept of a `virtual knife' in real-time. The users can also scoop out parts of the data and walk though successive states of the model. Future plans are (a.o.) to

  2. Estimating water equivalent snow depth from related meteorological variables

    International Nuclear Information System (INIS)

    Steyaert, L.T.; LeDuc, S.K.; Strommen, N.D.; Nicodemus, M.L.; Guttman, N.B.

    1980-05-01

    Engineering design must take into consideration natural loads and stresses caused by meteorological elements, such as, wind, snow, precipitation and temperature. The purpose of this study was to determine a relationship of water equivalent snow depth measurements to meteorological variables. Several predictor models were evaluated for use in estimating water equivalent values. These models include linear regression, principal component regression, and non-linear regression models. Linear, non-linear and Scandanavian models are used to generate annual water equivalent estimates for approximately 1100 cooperative data stations where predictor variables are available, but which have no water equivalent measurements. These estimates are used to develop probability estimates of snow load for each station. Map analyses for 3 probability levels are presented

  3. Statistical methods and regression analysis of stratospheric ozone and meteorological variables in Isfahan

    Science.gov (United States)

    Hassanzadeh, S.; Hosseinibalam, F.; Omidvari, M.

    2008-04-01

    Data of seven meteorological variables (relative humidity, wet temperature, dry temperature, maximum temperature, minimum temperature, ground temperature and sun radiation time) and ozone values have been used for statistical analysis. Meteorological variables and ozone values were analyzed using both multiple linear regression and principal component methods. Data for the period 1999-2004 are analyzed jointly using both methods. For all periods, temperature dependent variables were highly correlated, but were all negatively correlated with relative humidity. Multiple regression analysis was used to fit the meteorological variables using the meteorological variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to obtain subsets of the predictor variables to be included in the linear regression model of the meteorological variables. In 1999, 2001 and 2002 one of the meteorological variables was weakly influenced predominantly by the ozone concentrations. However, the model did not predict that the meteorological variables for the year 2000 were not influenced predominantly by the ozone concentrations that point to variation in sun radiation. This could be due to other factors that were not explicitly considered in this study.

  4. Assessing the impact of local meteorological variables on surface ozone in Hong Kong during 2000-2015 using quantile and multiple line regression models

    Science.gov (United States)

    Zhao, Wei; Fan, Shaojia; Guo, Hai; Gao, Bo; Sun, Jiaren; Chen, Laiguo

    2016-11-01

    The quantile regression (QR) method has been increasingly introduced to atmospheric environmental studies to explore the non-linear relationship between local meteorological conditions and ozone mixing ratios. In this study, we applied QR for the first time, together with multiple linear regression (MLR), to analyze the dominant meteorological parameters influencing the mean, 10th percentile, 90th percentile and 99th percentile of maximum daily 8-h average (MDA8) ozone concentrations in 2000-2015 in Hong Kong. The dominance analysis (DA) was used to assess the relative importance of meteorological variables in the regression models. Results showed that the MLR models worked better at suburban and rural sites than at urban sites, and worked better in winter than in summer. QR models performed better in summer for 99th and 90th percentiles and performed better in autumn and winter for 10th percentile. And QR models also performed better in suburban and rural areas for 10th percentile. The top 3 dominant variables associated with MDA8 ozone concentrations, changing with seasons and regions, were frequently associated with the six meteorological parameters: boundary layer height, humidity, wind direction, surface solar radiation, total cloud cover and sea level pressure. Temperature rarely became a significant variable in any season, which could partly explain the peak of monthly average ozone concentrations in October in Hong Kong. And we found the effect of solar radiation would be enhanced during extremely ozone pollution episodes (i.e., the 99th percentile). Finally, meteorological effects on MDA8 ozone had no significant changes before and after the 2010 Asian Games.

  5. A METEOROLOGICAL RISK ASSESSMENT METHOD FOR POWER LINES BASED ON GIS AND MULTI-SENSOR INTEGRATION

    Directory of Open Access Journals (Sweden)

    Z. Lin

    2016-06-01

    Full Text Available Power lines, exposed in the natural environment, are vulnerable to various kinds of meteorological factors. Traditional research mainly deals with the influence of a single meteorological condition on the power line, which lacks of comprehensive effects evaluation and analysis of the multiple meteorological factors. In this paper, we use multiple meteorological monitoring data obtained by multi-sensors to implement the meteorological risk assessment and early warning of power lines. Firstly, we generate meteorological raster map from discrete meteorological monitoring data using spatial interpolation. Secondly, the expert scoring based analytic hierarchy process is used to compute the power line risk index of all kinds of meteorological conditions and establish the mathematical model of meteorological risk. By adopting this model in raster calculator of ArcGIS, we will have a raster map showing overall meteorological risks for power line. Finally, by overlaying the power line buffer layer to that raster map, we will get to know the exact risk index around a certain part of power line, which will provide significant guidance for power line risk management. In the experiment, based on five kinds of observation data gathered from meteorological stations in Guizhou Province of China, including wind, lightning, rain, ice, temperature, we carry on the meteorological risk analysis for the real power lines, and experimental results have proved the feasibility and validity of our proposed method.

  6. Transitions in Prognostic Awareness Among Terminally Ill Cancer Patients in Their Last 6 Months of Life Examined by Multi-State Markov Modeling.

    Science.gov (United States)

    Hsiu Chen, Chen; Wen, Fur-Hsing; Hou, Ming-Mo; Hsieh, Chia-Hsun; Chou, Wen-Chi; Chen, Jen-Shi; Chang, Wen-Cheng; Tang, Siew Tzuh

    2017-09-01

    Developing accurate prognostic awareness, a cornerstone of preference-based end-of-life (EOL) care decision-making, is a dynamic process involving more prognostic-awareness states than knowing or not knowing. Understanding the transition probabilities and time spent in each prognostic-awareness state can help clinicians identify trigger points for facilitating transitions toward accurate prognostic awareness. We examined transition probabilities in distinct prognostic-awareness states between consecutive time points in 247 cancer patients' last 6 months and estimated the time spent in each state. Prognostic awareness was categorized into four states: (a) unknown and not wanting to know, state 1; (b) unknown but wanting to know, state 2; (c) inaccurate awareness, state 3; and (d) accurate awareness, state 4. Transitional probabilities were examined by multistate Markov modeling. Initially, 59.5% of patients had accurate prognostic awareness, whereas the probabilities of being in states 1-3 were 8.1%, 17.4%, and 15.0%, respectively. Patients' prognostic awareness generally remained unchanged (probabilities of remaining in the same state: 45.5%-92.9%). If prognostic awareness changed, it tended to shift toward higher prognostic-awareness states (probabilities of shifting to state 4 were 23.2%-36.6% for patients initially in states 1-3, followed by probabilities of shifting to state 3 for those in states 1 and 2 [9.8%-10.1%]). Patients were estimated to spend 1.29, 0.42, 0.68, and 3.61 months in states 1-4, respectively, in their last 6 months. Terminally ill cancer patients' prognostic awareness generally remained unchanged, with a tendency to become more aware of their prognosis. Health care professionals should facilitate patients' transitions toward accurate prognostic awareness in a timely manner to promote preference-based EOL decisions. Terminally ill Taiwanese cancer patients' prognostic awareness generally remained stable, with a tendency toward developing

  7. Cumulative Intracranial Tumor Volume Augments the Prognostic Value of Diagnosis-Specific Graded Prognostic Assessment Model for Survival in Patients with Melanoma Cerebral Metastases

    DEFF Research Database (Denmark)

    Hirshman, Brian R; Wilson, Bayard R; Ali, Mir Amaan

    2018-01-01

    BACKGROUND: The diagnosis-specific graded prognostic assessment scale (ds-GPA) for patients with melanoma brain metastasis (BM) utilizes only 2 key prognostic variables: Karnofsky performance status and the number of intracranial metastases. We wished to determine whether inclusion of cumulative ...

  8. Techniques for Improved Retrospective Fine-scale Meteorology

    Science.gov (United States)

    Pleim-Xiu Land-Surface model (PX LSM) was developed for retrospective meteorological simulations to drive chemical transport models. One of the key features of the PX LSM is the indirect soil moisture and temperature nudging. The idea is to provide a three hourly 2-m temperature ...

  9. Quality Assurance Guidance for the Collection of Meteorological Data Using Passive Radiometers

    Science.gov (United States)

    This document augments the February 2000 guidance entitled Meteorological Monitoring Guidance for Regulatory Modeling Applications and the March 2008 guidance entitled Quality Assurance Handbook for Air Pollution Measurement Systems Volume IV: Meteorological Measurements Version ...

  10. Analysis of the effects of meteorology on aircraft exhaust dispersion and deposition using a Lagrangian particle model

    Energy Technology Data Exchange (ETDEWEB)

    Pecorari, Eliana, E-mail: eliana.pecorari@unive.it [Department of Environmental Science, Informatics and Statistics, University Ca’ Foscari Venice, Calle Larga Santa Marta 2137, Dorsoduro, 30123 Venezia (Italy); Mantovani, Alice [OSMOTECH S.r.l., via Francesco Sforza, 15, 20122 Milano (Italy); Franceschini, Chiara [Department of Environmental Science, Informatics and Statistics, University Ca’ Foscari Venice, Calle Larga Santa Marta 2137, Dorsoduro, 30123 Venezia (Italy); Bassano, Davide [SAVE S.p.A., Marco Polo Venice airport viale G. Galilei 30/1, 30173 Tessera-Venezia (Italy); Palmeri, Luca [Department of Industrial Engineering, University of Padova, v. Marzolo 9, 35131 Padova (Italy); Rampazzo, Giancarlo [Department of Environmental Science, Informatics and Statistics, University Ca’ Foscari Venice, Calle Larga Santa Marta 2137, Dorsoduro, 30123 Venezia (Italy)

    2016-01-15

    The risk of air quality degradation is of considerable concern particularly for those airports that are located near urban areas. The ability to quantitatively predict the effects of air pollutants originated by airport operations is important for assessing air quality and the related impacts on human health. Current emission regulations have focused on local air quality in the proximity of airports. However, an integrated study should consider the effects of meteorological events, at both regional and local level, that can affect the dispersion and the deposition of exhausts. Rigorous scientific studies and extensive experimental data could contribute to the analysis of the impacts of airports expansion plans. This paper is focused on the analysis of the effects of meteorology on aircraft emission for the Marco Polo Airport in Venice. This is the most important international airport in the eastern part of the Po’ Valley, one of the most polluted area in Europe. Air pollution is exacerbated by meteorology that is a combination of large and local scale effects that do not allow significant dispersion. Moreover, the airport is located near Venice, a city of noteworthy cultural and architectural relevance, and nearby the lagoon that hosts several areas of outstanding ecological importance at European level (Natura 2000 sites). Dispersion and deposit of the main aircraft exhausts (NOx, HC and CO) have been evaluated by using a Lagrangian particle model. Spatial and temporal aircraft exhaust dispersion has been analyzed for LTO cycle. Aircraft taxiing resulted to be the most impacting aircraft operation especially for the airport working area and its surroundings, however occasionally peaks may be observed even at high altitudes when cruise mode starts. Mixing height can affect concentrations more significantly than the concentrations in the exhausts themselves. An increase of HC and CO concentrations (15–50%) has been observed during specific meteorological events

  11. Analysis of the effects of meteorology on aircraft exhaust dispersion and deposition using a Lagrangian particle model

    International Nuclear Information System (INIS)

    Pecorari, Eliana; Mantovani, Alice; Franceschini, Chiara; Bassano, Davide; Palmeri, Luca; Rampazzo, Giancarlo

    2016-01-01

    The risk of air quality degradation is of considerable concern particularly for those airports that are located near urban areas. The ability to quantitatively predict the effects of air pollutants originated by airport operations is important for assessing air quality and the related impacts on human health. Current emission regulations have focused on local air quality in the proximity of airports. However, an integrated study should consider the effects of meteorological events, at both regional and local level, that can affect the dispersion and the deposition of exhausts. Rigorous scientific studies and extensive experimental data could contribute to the analysis of the impacts of airports expansion plans. This paper is focused on the analysis of the effects of meteorology on aircraft emission for the Marco Polo Airport in Venice. This is the most important international airport in the eastern part of the Po’ Valley, one of the most polluted area in Europe. Air pollution is exacerbated by meteorology that is a combination of large and local scale effects that do not allow significant dispersion. Moreover, the airport is located near Venice, a city of noteworthy cultural and architectural relevance, and nearby the lagoon that hosts several areas of outstanding ecological importance at European level (Natura 2000 sites). Dispersion and deposit of the main aircraft exhausts (NOx, HC and CO) have been evaluated by using a Lagrangian particle model. Spatial and temporal aircraft exhaust dispersion has been analyzed for LTO cycle. Aircraft taxiing resulted to be the most impacting aircraft operation especially for the airport working area and its surroundings, however occasionally peaks may be observed even at high altitudes when cruise mode starts. Mixing height can affect concentrations more significantly than the concentrations in the exhausts themselves. An increase of HC and CO concentrations (15–50%) has been observed during specific meteorological events

  12. Enhanced Prognostic Model for Lithium Ion Batteries Based on Particle Filter State Transition Model Modification

    Directory of Open Access Journals (Sweden)

    Buddhi Arachchige

    2017-11-01

    Full Text Available This paper focuses on predicting the End of Life and End of Discharge of Lithium ion batteries using a battery capacity fade model and a battery discharge model. The proposed framework will be able to estimate the Remaining Useful Life (RUL and the Remaining charge through capacity fade and discharge models. A particle filter is implemented that estimates the battery’s State of Charge (SOC and State of Life (SOL by utilizing the battery’s physical data such as voltage, temperature, and current measurements. The accuracy of the prognostic framework has been improved by enhancing the particle filter state transition model to incorporate different environmental and loading conditions without retuning the model parameters. The effect of capacity fade in the reduction of the EOD (End of Discharge time with cycling has also been included, integrating both EOL (End of Life and EOD prediction models in order to get more accuracy in the estimations.

  13. The trajectory model tranco as applied to the Chernobyl accident using E.C.M.W.F. meteorological data

    International Nuclear Information System (INIS)

    Zarimpas, N.

    1989-01-01

    This report presents the TRANCO (trajectory analysis) code and discusses its application to model atmospheric transport during and after the Chernobyl accident. The archived-processed meteorological information from the ECMWF, which is used for the purposes of this study, is also described. Finally, results are discussed and compared with those produced by similar models

  14. Impact of meteorological changes on the incidence of scarlet fever in Hefei City, China

    Science.gov (United States)

    Duan, Yu; Huang, Xiao-lei; Wang, Yu-jie; Zhang, Jun-qing; Zhang, Qi; Dang, Yue-wen; Wang, Jing

    2016-10-01

    Studies on scarlet fever with meteorological factors included were few. We aimed to illustrate meteorological factors' effects on monthly incidence of scarlet fever. Cases of scarlet fever were collected from the report of legal infectious disease in Hefei City from 1985 to 2006; the meteorological data were obtained from the weather bureau of Hefei City. Monthly incidence and corresponding meteorological data in these 22 years were used to develop the model. The model of auto regressive integrated moving average with covariates was used in statistical analyses. There was a highest peak from March to June and a small peak from November to January. The incidence of scarlet fever ranges from 0 to 0.71502 (per 105 population). SARIMAX (1,0,0)(1,0,0)12 model was fitted with monthly incidence and meteorological data optimally. It was shown that relative humidity ( β = -0.002, p = 0.020), mean temperature ( β = 0.006, p = 0.004), and 1 month lag minimum temperature ( β = -0.007, p ARIMA model could be useful not only for prediction but also for the analysis of multiple correlations.

  15. Quantitative modeling of clinical, cellular, and extracellular matrix variables suggest prognostic indicators in cancer: a model in neuroblastoma.

    Science.gov (United States)

    Tadeo, Irene; Piqueras, Marta; Montaner, David; Villamón, Eva; Berbegall, Ana P; Cañete, Adela; Navarro, Samuel; Noguera, Rosa

    2014-02-01

    Risk classification and treatment stratification for cancer patients is restricted by our incomplete picture of the complex and unknown interactions between the patient's organism and tumor tissues (transformed cells supported by tumor stroma). Moreover, all clinical factors and laboratory studies used to indicate treatment effectiveness and outcomes are by their nature a simplification of the biological system of cancer, and cannot yet incorporate all possible prognostic indicators. A multiparametric analysis on 184 tumor cylinders was performed. To highlight the benefit of integrating digitized medical imaging into this field, we present the results of computational studies carried out on quantitative measurements, taken from stromal and cancer cells and various extracellular matrix fibers interpenetrated by glycosaminoglycans, and eight current approaches to risk stratification systems in patients with primary and nonprimary neuroblastoma. New tumor tissue indicators from both fields, the cellular and the extracellular elements, emerge as reliable prognostic markers for risk stratification and could be used as molecular targets of specific therapies. The key to dealing with personalized therapy lies in the mathematical modeling. The use of bioinformatics in patient-tumor-microenvironment data management allows a predictive model in neuroblastoma.

  16. Meteorology/Oceanography Help - Naval Oceanography Portal

    Science.gov (United States)

    section Advanced Search... Sections Home Time Earth Orientation Astronomy Meteorology Oceanography Ice You are here: Home › Help › Meteorology/Oceanography Help USNO Logo USNO Info Meteorology/Oceanography Help Send an e-mail regarding meteorology or oceanography products. Privacy Advisory Your E-Mail

  17. A Distributed Approach to System-Level Prognostics

    Science.gov (United States)

    2012-09-01

    the end of (useful) life ( EOL ) and/or the remaining useful life (RUL) of components, subsystems, or systems. The prognostics problem itself can be...system state estimate, computes EOL and/or RUL. In this paper, we focus on a model-based prognostics approach (Orchard & Vachtse- vanos, 2009; Daigle...been focused on individual components, and determining their EOL and RUL, e.g., (Orchard & Vachtsevanos, 2009; Saha & Goebel, 2009; Daigle & Goebel

  18. Nottingham Prognostic Index in Triple-Negative Breast Cancer: a reliable prognostic tool?

    International Nuclear Information System (INIS)

    Albergaria, André; Ricardo, Sara; Milanezi, Fernanda; Carneiro, Vítor; Amendoeira, Isabel; Vieira, Daniella; Cameselle-Teijeiro, Jorge; Schmitt, Fernando

    2011-01-01

    A breast cancer prognostic tool should ideally be applicable to all types of invasive breast lesions. A number of studies have shown histopathological grade to be an independent prognostic factor in breast cancer, adding prognostic power to nodal stage and tumour size. The Nottingham Prognostic Index has been shown to accurately predict patient outcome in stratified groups with a follow-up period of 15 years after primary diagnosis of breast cancer. Clinically, breast tumours that lack the expression of Oestrogen Receptor, Progesterone Receptor and Human Epidermal growth factor Receptor 2 (HER2) are identified as presenting a 'triple-negative' phenotype or as triple-negative breast cancers. These poor outcome tumours represent an easily recognisable prognostic group of breast cancer with aggressive behaviour that currently lack the benefit of available systemic therapy. There are conflicting results on the prevalence of lymph node metastasis at the time of diagnosis in triple-negative breast cancer patients but it is currently accepted that triple-negative breast cancer does not metastasize to axillary nodes and bones as frequently as the non-triple-negative carcinomas, favouring instead, a preferentially haematogenous spread. Hypothetically, this particular tumour dissemination pattern would impair the reliability of using Nottingham Prognostic Index as a tool for triple-negative breast cancer prognostication. The present study tested the effectiveness of the Nottingham Prognostic Index in stratifying breast cancer patients of different subtypes with special emphasis in a triple-negative breast cancer patient subset versus non- triple-negative breast cancer. We demonstrated that besides the fact that TNBC disseminate to axillary lymph nodes as frequently as luminal or HER2 tumours, we also showed that TNBC are larger in size compared with other subtypes and almost all grade 3. Additionally, survival curves demonstrated that these prognostic factors are

  19. Development and validation of a prognostic model incorporating texture analysis derived from standardised segmentation of PET in patients with oesophageal cancer

    Energy Technology Data Exchange (ETDEWEB)

    Foley, Kieran G. [Cardiff University, Division of Cancer and Genetics, Cardiff (United Kingdom); Hills, Robert K. [Cardiff University, Haematology Clinical Trials Unit, Cardiff (United Kingdom); Berthon, Beatrice; Marshall, Christopher [Wales Research and Diagnostic PET Imaging Centre, Cardiff (United Kingdom); Parkinson, Craig; Spezi, Emiliano [Cardiff University, School of Engineering, Cardiff (United Kingdom); Lewis, Wyn G. [University Hospital of Wales, Department of Upper GI Surgery, Cardiff (United Kingdom); Crosby, Tom D.L. [Department of Oncology, Velindre Cancer Centre, Cardiff (United Kingdom); Roberts, Stuart Ashley [University Hospital of Wales, Department of Clinical Radiology, Cardiff (United Kingdom)

    2018-01-15

    This retrospective cohort study developed a prognostic model incorporating PET texture analysis in patients with oesophageal cancer (OC). Internal validation of the model was performed. Consecutive OC patients (n = 403) were chronologically separated into development (n = 302, September 2010-September 2014, median age = 67.0, males = 227, adenocarcinomas = 237) and validation cohorts (n = 101, September 2014-July 2015, median age = 69.0, males = 78, adenocarcinomas = 79). Texture metrics were obtained using a machine-learning algorithm for automatic PET segmentation. A Cox regression model including age, radiological stage, treatment and 16 texture metrics was developed. Patients were stratified into quartiles according to a prognostic score derived from the model. A p-value < 0.05 was considered statistically significant. Primary outcome was overall survival (OS). Six variables were significantly and independently associated with OS: age [HR =1.02 (95% CI 1.01-1.04), p < 0.001], radiological stage [1.49 (1.20-1.84), p < 0.001], treatment [0.34 (0.24-0.47), p < 0.001], log(TLG) [5.74 (1.44-22.83), p = 0.013], log(Histogram Energy) [0.27 (0.10-0.74), p = 0.011] and Histogram Kurtosis [1.22 (1.04-1.44), p = 0.017]. The prognostic score demonstrated significant differences in OS between quartiles in both the development (X{sup 2} 143.14, df 3, p < 0.001) and validation cohorts (X{sup 2} 20.621, df 3, p < 0.001). This prognostic model can risk stratify patients and demonstrates the additional benefit of PET texture analysis in OC staging. (orig.)

  20. Machine learning methods reveal the temporal pattern of dengue incidence using meteorological factors in metropolitan Manila, Philippines.

    Science.gov (United States)

    Carvajal, Thaddeus M; Viacrusis, Katherine M; Hernandez, Lara Fides T; Ho, Howell T; Amalin, Divina M; Watanabe, Kozo

    2018-04-17

    Several studies have applied ecological factors such as meteorological variables to develop models and accurately predict the temporal pattern of dengue incidence or occurrence. With the vast amount of studies that investigated this premise, the modeling approaches differ from each study and only use a single statistical technique. It raises the question of whether which technique would be robust and reliable. Hence, our study aims to compare the predictive accuracy of the temporal pattern of Dengue incidence in Metropolitan Manila as influenced by meteorological factors from four modeling techniques, (a) General Additive Modeling, (b) Seasonal Autoregressive Integrated Moving Average with exogenous variables (c) Random Forest and (d) Gradient Boosting. Dengue incidence and meteorological data (flood, precipitation, temperature, southern oscillation index, relative humidity, wind speed and direction) of Metropolitan Manila from January 1, 2009 - December 31, 2013 were obtained from respective government agencies. Two types of datasets were used in the analysis; observed meteorological factors (MF) and its corresponding delayed or lagged effect (LG). After which, these datasets were subjected to the four modeling techniques. The predictive accuracy and variable importance of each modeling technique were calculated and evaluated. Among the statistical modeling techniques, Random Forest showed the best predictive accuracy. Moreover, the delayed or lag effects of the meteorological variables was shown to be the best dataset to use for such purpose. Thus, the model of Random Forest with delayed meteorological effects (RF-LG) was deemed the best among all assessed models. Relative humidity was shown to be the top-most important meteorological factor in the best model. The study exhibited that there are indeed different predictive outcomes generated from each statistical modeling technique and it further revealed that the Random forest model with delayed meteorological

  1. Hydrological and meteorological aspects of floods in the Alps: an overview

    Directory of Open Access Journals (Sweden)

    Baldassare Bacchi

    2003-01-01

    Full Text Available This introductory paper presents and summarises recent research on meteorological and hydrological aspects of floods in the Alps. The research activities were part of the international research project RAPHAEL (Runoff and Atmospheric Processes for flood HAzard forEcasting and controL together with experiments within the Special Observing Period-SOP conducted in autumn 1999 for the Mesoscale Alpine Programme —MAP. The investigations were based on both field experiments and numerical simulations, using meteorological and hydrological models, of ten major floods that occurred in the past decade in the European Alps. The two basins investigated were the Ticino (6599 km2 at the Lago Maggiore outlet on the southern side of the Alps and the Ammer catchment (709 km2 in the Bavarian Alps. These catchments and their sub-catchments cover an appropriate range of spatial scales with which to investigate and test in an operational context the potential of both mesoscale meteorological and distributed hydrological models for flood forecasting. From the data analyses and model simulations described in this Special Issue, the major sources of uncertainties for flood forecasts in mid-size mountain basins are outlined and the accuracy flood forecasts is assessed. Keywords: floods, mountain hydrology, meteorological models, Alps

  2. Meteorology and atomic energy

    International Nuclear Information System (INIS)

    Anon.

    1986-01-01

    The science of meteorology is useful in providing information that will be of assistance in the choice of favorable plant locations and in the evaluation of significant relations between meteorology and the design, construction, and operation of plant and facilities, especially those from which radioactive or toxic products could be released to the atmosphere. Under a continuing contract with the Atomic Energy Commission, the Weather Bureau has carried out this study. Some of the meteorological techniques that are available are summarized, and their applications to the possible atmospheric pollution deriving from the use of atomic energy are described. Methods and suggestions for the collection, analysis, and use of meteorological data are presented. Separate abstracts are included of 12 chapters in this publication for inclusion in the Energy Data Base

  3. Prognostic model for chronic hypertension in women with a history of hypertensive pregnancy disorders at term

    NARCIS (Netherlands)

    van der Velde-Visser, S.D.; Hermes, W.; Twisk, J; Franx, A.; Pampus, M.G.; Koopmans, C.; Mol, B. W J; de Groot, J.C.M.J.

    2017-01-01

    Introduction The association between hypertensive pregnancy disorders and cardiovascular disease later in life is well described. In this study we aim to develop a prognostic model from patients characteristics known before, early in, during and after pregnancy to identify women at increased risk of

  4. Meteorology and Wind Energy Department annual report 1996

    Energy Technology Data Exchange (ETDEWEB)

    Hauge Madsen, P.; Dannemand Andersen, P.; Skrumsager, B. [eds.

    1997-07-01

    In 1996 the Meteorology and Wind Energy Department has performed research within the programme areas: (1) wind energy and (2) atmospheric processes. The objectives are through research in boundary layer meteorology, fluid dynamics, aerodynamics and structural mechanics to contribute with new knowledge within (1) wind energy in relation to development, manufacturing, operation and export as well as testing and certification of wind turbines, and (2) aspects of boundary-layer meteorology related to environmental and energy problems of society. The work is supported by the research programs of the Ministry of Environment and Energy, the Nordic Council of Ministers, EU as well as by industry. Through our research and development work we develop and provide methodologies including computer models for use by industry, institutions, and governmental authorities. In the long view we are developing facilities and programs enabling us to serve as a national and European centre for wind-energy and boundary-layer meteorological research. A summary of our activities in 1996 is presented. (au) 4 tabs., 5 ills.

  5. Droning on about the Weather: Meteorological Science on a School-Friendly Scale

    Science.gov (United States)

    Murphy, Phil; O'Neill, Ashley; Brown, Abby

    2016-01-01

    Meteorology is an important branch of science that offers exciting career opportunities and yet is not usually included in school curricula. The availability of multi-rotor model aircraft (drones) offers an exciting opportunity to bring meteorology into school science.

  6. Plaque Brachytherapy for Uveal Melanoma: A Vision Prognostication Model

    International Nuclear Information System (INIS)

    Khan, Niloufer; Khan, Mohammad K.; Bena, James; Macklis, Roger; Singh, Arun D.

    2012-01-01

    Purpose: To generate a vision prognostication model after plaque brachytherapy for uveal melanoma. Methods and Materials: All patients with primary single ciliary body or choroidal melanoma treated with iodine-125 or ruthenium-106 plaque brachytherapy between January 1, 2005, and June 30, 2010, were included. The primary endpoint was loss of visual acuity. Only patients with initial visual acuity better than or equal to 20/50 were used to evaluate visual acuity worse than 20/50 at the end of the study, and only patients with initial visual acuity better than or equal to 20/200 were used to evaluate visual acuity worse than 20/200 at the end of the study. Factors analyzed were sex, age, cataracts, diabetes, tumor size (basal dimension and apical height), tumor location, and radiation dose to the tumor apex, fovea, and optic disc. Univariate and multivariable Cox proportional hazards were used to determine the influence of baseline patient factors on vision loss. Kaplan-Meier curves (log rank analysis) were used to estimate freedom from vision loss. Results: Of 189 patients, 92% (174) were alive as of February 1, 2011. At presentation, visual acuity was better than or equal to 20/50 and better than or equal to 20/200 in 108 and 173 patients, respectively. Of these patients, 44.4% (48) had post-treatment visual acuity of worse than 20/50 and 25.4% (44) had post-treatment visual acuity worse than 20/200. By multivariable analysis, increased age (hazard ratio [HR] of 1.01 [1.00-1.03], P=.05), increase in tumor height (HR of 1.35 [1.22-1.48], P<.001), and a greater total dose to the fovea (HR of 1.01 [1.00-1.01], P<.001) were predictive of vision loss. This information was used to develop a nomogram predictive of vision loss. Conclusions: By providing a means to predict vision loss at 3 years after treatment, our vision prognostication model can be an important tool for patient selection and treatment counseling.

  7. Plaque Brachytherapy for Uveal Melanoma: A Vision Prognostication Model

    Energy Technology Data Exchange (ETDEWEB)

    Khan, Niloufer [Department of Radiation Oncology, Taussig Cancer Center, Cleveland Clinic, Cleveland, Ohio (United States); Khan, Mohammad K. [Department of Radiation Oncology, Emory University School of Medicine, Atlanta, Georgia (United States); Bena, James [Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio (United States); Macklis, Roger [Department of Radiation Oncology, Taussig Cancer Center, Cleveland Clinic, Cleveland, Ohio (United States); Singh, Arun D., E-mail: singha@ccf.org [Department of Ophthalmic Oncology, Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio (United States)

    2012-11-01

    Purpose: To generate a vision prognostication model after plaque brachytherapy for uveal melanoma. Methods and Materials: All patients with primary single ciliary body or choroidal melanoma treated with iodine-125 or ruthenium-106 plaque brachytherapy between January 1, 2005, and June 30, 2010, were included. The primary endpoint was loss of visual acuity. Only patients with initial visual acuity better than or equal to 20/50 were used to evaluate visual acuity worse than 20/50 at the end of the study, and only patients with initial visual acuity better than or equal to 20/200 were used to evaluate visual acuity worse than 20/200 at the end of the study. Factors analyzed were sex, age, cataracts, diabetes, tumor size (basal dimension and apical height), tumor location, and radiation dose to the tumor apex, fovea, and optic disc. Univariate and multivariable Cox proportional hazards were used to determine the influence of baseline patient factors on vision loss. Kaplan-Meier curves (log rank analysis) were used to estimate freedom from vision loss. Results: Of 189 patients, 92% (174) were alive as of February 1, 2011. At presentation, visual acuity was better than or equal to 20/50 and better than or equal to 20/200 in 108 and 173 patients, respectively. Of these patients, 44.4% (48) had post-treatment visual acuity of worse than 20/50 and 25.4% (44) had post-treatment visual acuity worse than 20/200. By multivariable analysis, increased age (hazard ratio [HR] of 1.01 [1.00-1.03], P=.05), increase in tumor height (HR of 1.35 [1.22-1.48], P<.001), and a greater total dose to the fovea (HR of 1.01 [1.00-1.01], P<.001) were predictive of vision loss. This information was used to develop a nomogram predictive of vision loss. Conclusions: By providing a means to predict vision loss at 3 years after treatment, our vision prognostication model can be an important tool for patient selection and treatment counseling.

  8. Meteorological instrumentation for nuclear facilities

    International Nuclear Information System (INIS)

    Costa, A.C.L. da.

    1983-01-01

    The main requirements of regulatory agencies, concerning the meteorological instrumentation needed for the licensing of nuclear facilities are discussed. A description is made of the operational principles of sensors for the various meteorological parameters and associated electronic systems. An analysis of the problems associated with grounding of a typical meteorological station is presented. (Author) [pt

  9. The strong prognostic value of KELIM, a model-based parameter from CA 125 kinetics in ovarian cancer

    DEFF Research Database (Denmark)

    You, Benoit; Colomban, Olivier; Heywood, Mark

    2013-01-01

    Unexpected results were recently reported about the poor surrogacy of Gynecologic Cancer Intergroup (GCIG) defined CA-125 response in recurrent ovarian cancer (ROC) patients. Mathematical modeling may help describe CA-125 decline dynamically and discriminate prognostic kinetic parameters....

  10. A study of the dengue epidemic and meteorological factors in Guangzhou, China, by using a zero-inflated Poisson regression model.

    Science.gov (United States)

    Wang, Chenggang; Jiang, Baofa; Fan, Jingchun; Wang, Furong; Liu, Qiyong

    2014-01-01

    The aim of this study is to develop a model that correctly identifies and quantifies the relationship between dengue and meteorological factors in Guangzhou, China. By cross-correlation analysis, meteorological variables and their lag effects were determined. According to the epidemic characteristics of dengue in Guangzhou, those statistically significant variables were modeled by a zero-inflated Poisson regression model. The number of dengue cases and minimum temperature at 1-month lag, along with average relative humidity at 0- to 1-month lag were all positively correlated with the prevalence of dengue fever, whereas wind velocity and temperature in the same month along with rainfall at 2 months' lag showed negative association with dengue incidence. Minimum temperature at 1-month lag and wind velocity in the same month had a greater impact on the dengue epidemic than other variables in Guangzhou.

  11. Modeling the contributions of emission, meteorology, and chemistry to high PM2.5 levels over China

    Science.gov (United States)

    Wang, Y.; Zhang, Q.; Jia, B.; Jiang, J.; Zhou, W.; Wang, B.

    2014-12-01

    PM2.5 is known to harm health and public welfare. In recent years, regional haze with PM2.5 levels exceeding ten folds of WHO's air quality guideline has become the largest air quality concern in China. To better protect the health of millions of people, the key question is whether we understand the formation mechanism of high PM2.5 episodes well enough to guide the formation of effective control strategies. Here we present a modeling analysis in conjunction of observational constraints to estimate the contribution of emissions, meteorology, and secondary chemical formation to changes in PM2.5 levels over China. Certain meteorological conditions are found particularly conducive to trigger fast increases of secondary PM under current emissions mixtures in China. While the nested-grid GEOS-Chem model reproduces the distribution of PM2.5 and simulates up to ~400 μg/m3 of daily maximum PM2.5, it fails to capture the large sulfate enhancement during haze. We propose heterogeneous oxidation of SO2 on deliquesced aerosols as an additional source of sulfate under high relative humidity conditions. Parameterizing this process in the model improves the simulated spatial distribution and results in significant increases of sulfate enhancement ratio and sulfate fraction in PM2.5 during haze episodes. Implications of our modeling analysis for PM2.5 pollution control policies will also be discussed.

  12. Meteorological instrumentation for nuclear installations

    International Nuclear Information System (INIS)

    Costa, A.C.L. da.

    1983-01-01

    The main requirements of regulatory agencies, concerning the meteorological instrumentation needed for the licensing of nuclear facilities are discussed. A description is made of the operational principles of sensors for the various meteorological parameters and associated electronic systems. Finally, it is presented an analysis of the problems associated with grounding of a typical meteorological station. (Author) [pt

  13. Meteorological radar services: a brief discussion and a solution in practice

    Science.gov (United States)

    Nicolaides, K. A.

    2014-08-01

    The Department of Meteorology is the organization designated by the Civil Aviation Department and by the National Supervisory Authority of the Republic of Cyprus, as an air navigation service provider, based on the regulations of the Single European Sky. Department of Meteorology holds and maintains also an ISO: 9001/2008, Quality System, for the provision of meteorological and climatological services to aeronautic and maritime community, but also to the general public. In order to fulfill its obligations the Department of Meteorology customs the rather dense meteorological stations network, with long historical data series, installed and maintained by the Department, in parallel with modelling and Numerical Weather Prediction (NWP), along with training and gaining of expertise. Among the available instruments in the community of meteorologists is the meteorological radar, a basic tool for the needs of very short/short range forecasting (nowcasting). The Department of Meteorology installed in the mid 90's a C-band radar over «Throni» location and expanded its horizons in nowcasting, aviation safety and warnings issuance. The radar has undergone several upgrades but today technology has over passed its rather old technology. At the present the Department of Meteorology is in the process of buying Meteorological Radar Services as a result of a public procurement procedure. Two networked X-band meteorological radar will be installed (the project now is in the phase of infrastructure establishment while the hardware is in the process of assemble), and maintained from Space Hellas (the contractor) for a 13 years' time period. The present article must be faced as a review article of the efforts of the Department of Meteorology to support its weather forecasters with data from meteorological radar.

  14. Integration of Ground, Buoys, Satellite and Model data to map the Changes in Meteorological Parameters Associated with Harvey Hurricane

    Science.gov (United States)

    Chauhan, A.; Sarkar, S.; Singh, R. P.

    2017-12-01

    The coastal areas have dense onshore and marine observation network and are also routinely monitored by constellation of satellites. The monitoring of ocean, land and atmosphere through a range of meteorological parameters, provides information about the land and ocean surface. Satellite data also provide information at different pressure levels that help to access the development of tropical storms and formation of hurricanes at different categories. Integration of ground, buoys, satellite and model data showing the changes in meteorological parameters during the landfall stages of hurricane Harvey will be discussed. Hurricane Harvey was one of the deadliest hurricanes at the Gulf coast which caused intense flooding from the precipitation. The various observation networks helped city administrators to evacuate the coastal areas, that minimized the loss of lives compared to the Galveston hurricane of 1900 which took 10,000 lives. Comparison of meteorological parameters derived from buoys, ground stations and satellites associated with Harvey and 2005 Katrina hurricane present some of the interesting features of the two hurricanes.

  15. Space based inverse modeling of seasonal variations of anthropogenic and natural emissions of nitrogen oxides over China and effects of uncertainties in model meteorology and chemistry

    Science.gov (United States)

    Lin, J.

    2011-12-01

    Nitrogen oxides (NOx ≡ NO + NO2) are important atmospheric constituents affecting the tropospheric chemistry, surface air quality and climatic forcing. They are emitted both from anthropogenic and from natural (soil, lightning, biomass burning, etc.) sources, which can be estimated inversely from satellite remote sensing of the vertical column densities (VCDs) of nitrogen dioxide (NO2) in the troposphere. Based on VCDs of NO2 retrieved from OMI, a novel approach is developed in this study to separate anthropogenic emissions of NOx from natural sources over East China for 2006. It exploits the fact that anthropogenic and natural emissions vary with seasons with distinctive patterns. The global chemical transport model (CTM) GEOS-Chem is used to establish the relationship between VCDs of NO2 and emissions of NOx for individual sources. Derived soil emissions are compared to results from a newly developed bottom-up approach. Effects of uncertainties in model meteorology and chemistry over China, an important source of errors in the emission inversion, are evaluated systematically for the first time. Meteorological measurements from space and the ground are used to analyze errors in meteorological parameters driving the CTM.

  16. Numerical simulation of a meteorological regime of Pontic region

    Science.gov (United States)

    Toropov, P.; Silvestrova, K.

    2012-04-01

    The Black Sea Coast of Caucasus is one of priority in sense of meteorological researches. It is caused both strategic and economic importance of coast, and current development of an infrastructure for the winter Olympic Games «Sochi-2014». During the winter period at the Black Sea Coast of Caucasus often there are the synoptic conditions leading to occurrence of the dangerous phenomena of weather: «northeast», ice-storms, strong rains, etc. The Department of Meteorology (Moscow State University) throughout 8 years spends regular measurements on the basis of Southern Department of Institute of Oenology of the Russian Academy of Sciences in July and February. They include automatically measurements with the time resolution of 5 minutes in three points characterizing landscape or region (coast, steppe plain, top of the Markothsky ridge), measurements of flux of solar radiation, measurements an atmospheric precipitation in 8 points, which remoteness from each other - 2-3 km. The saved up material has allowed to reveal some features of a meteorological mode of coast. But an overall objective of measurements - an estimation of quality of the numerical forecast by means of «meso scale» models (for example - model WRF). The first of numerical experiments by WRF model were leaded in 2007 year and were devoted reproduction of a meteorological mode of the Black Sea coast. The second phase of experiments has been directed on reproduction the storm phenomena (Novorossiysk nord-ost). For estimation of the modeling data was choused area witch limited by coordinates 44,1 - 44,75 (latitude) and 37,6 - 39 (longitude). Estimations are spent for the basic meteorological parameters - for pressure, temperature, speed of a wind. As earlier it was marked, 8 meteorological stations are located in this territory. Their values are accepted for the standard. Errors are calculated for February 2005, 2006, 2008, 2011 years, because in these periods was marked a strong winds. As the

  17. Pantex Plant meteorological monitoring program

    International Nuclear Information System (INIS)

    Snyder, S.F.

    1993-07-01

    The current meteorological monitoring program of the US Department of Energy's Pantex Plant, Amarillo, Texas, is described in detail. Instrumentation, meteorological data collection and management, and program management are reviewed. In addition, primary contacts are noted for instrumentation, calibration, data processing, and alternative databases. The quality assurance steps implemented during each portion of the meteorological monitoring program are also indicated

  18. Large-scale Meteorological Patterns Associated with Extreme Precipitation Events over Portland, OR

    Science.gov (United States)

    Aragon, C.; Loikith, P. C.; Lintner, B. R.; Pike, M.

    2017-12-01

    Extreme precipitation events can have profound impacts on human life and infrastructure, with broad implications across a range of stakeholders. Changes to extreme precipitation events are a projected outcome of climate change that warrants further study, especially at regional- to local-scales. While global climate models are generally capable of simulating mean climate at global-to-regional scales with reasonable skill, resiliency and adaptation decisions are made at local-scales where most state-of-the-art climate models are limited by coarse resolution. Characterization of large-scale meteorological patterns associated with extreme precipitation events at local-scales can provide climatic information without this scale limitation, thus facilitating stakeholder decision-making. This research will use synoptic climatology as a tool by which to characterize the key large-scale meteorological patterns associated with extreme precipitation events in the Portland, Oregon metro region. Composite analysis of meteorological patterns associated with extreme precipitation days, and associated watershed-specific flooding, is employed to enhance understanding of the climatic drivers behind such events. The self-organizing maps approach is then used to characterize the within-composite variability of the large-scale meteorological patterns associated with extreme precipitation events, allowing us to better understand the different types of meteorological conditions that lead to high-impact precipitation events and associated hydrologic impacts. A more comprehensive understanding of the meteorological drivers of extremes will aid in evaluation of the ability of climate models to capture key patterns associated with extreme precipitation over Portland and to better interpret projections of future climate at impact-relevant scales.

  19. Performance and evaluation of a coupled prognostic model TAPM over a mountainous complex terrain industrial area

    Science.gov (United States)

    Matthaios, Vasileios N.; Triantafyllou, Athanasios G.; Albanis, Triantafyllos A.; Sakkas, Vasileios; Garas, Stelios

    2018-05-01

    Atmospheric modeling is considered an important tool with several applications such as prediction of air pollution levels, air quality management, and environmental impact assessment studies. Therefore, evaluation studies must be continuously made, in order to improve the accuracy and the approaches of the air quality models. In the present work, an attempt is made to examine the air pollution model (TAPM) efficiency in simulating the surface meteorology, as well as the SO2 concentrations in a mountainous complex terrain industrial area. Three configurations under different circumstances, firstly with default datasets, secondly with data assimilation, and thirdly with updated land use, ran in order to investigate the surface meteorology for a 3-year period (2009-2011) and one configuration applied to predict SO2 concentration levels for the year of 2011.The modeled hourly averaged meteorological and SO2 concentration values were statistically compared with those from five monitoring stations across the domain to evaluate the model's performance. Statistical measures showed that the surface temperature and relative humidity are predicted well in all three simulations, with index of agreement (IOA) higher than 0.94 and 0.70 correspondingly, in all monitoring sites, while an overprediction of extreme low temperature values is noted, with mountain altitudes to have an important role. However, the results also showed that the model's performance is related to the configuration regarding the wind. TAPM default dataset predicted better the wind variables in the center of the simulation than in the boundaries, while improvement in the boundary horizontal winds implied the performance of TAPM with updated land use. TAPM assimilation predicted the wind variables fairly good in the whole domain with IOA higher than 0.83 for the wind speed and higher than 0.85 for the horizontal wind components. Finally, the SO2 concentrations were assessed by the model with IOA varied from 0

  20. Meteorological Drivers of Extreme Air Pollution Events

    Science.gov (United States)

    Horton, D. E.; Schnell, J.; Callahan, C. W.; Suo, Y.

    2017-12-01

    The accumulation of pollutants in the near-surface atmosphere has been shown to have deleterious consequences for public health, agricultural productivity, and economic vitality. Natural and anthropogenic emissions of ozone and particulate matter can accumulate to hazardous concentrations when atmospheric conditions are favorable, and can reach extreme levels when such conditions persist. Favorable atmospheric conditions for pollutant accumulation include optimal temperatures for photochemical reaction rates, circulation patterns conducive to pollutant advection, and a lack of ventilation, dispersion, and scavenging in the local environment. Given our changing climate system and the dual ingredients of poor air quality - pollutants and the atmospheric conditions favorable to their accumulation - it is important to characterize recent changes in favorable meteorological conditions, and quantify their potential contribution to recent extreme air pollution events. To facilitate our characterization, this study employs the recently updated Schnell et al (2015) 1°×1° gridded observed surface ozone and particulate matter datasets for the period of 1998 to 2015, in conjunction with reanalysis and climate model simulation data. We identify extreme air pollution episodes in the observational record and assess the meteorological factors of primary support at local and synoptic scales. We then assess (i) the contribution of observed meteorological trends (if extant) to the magnitude of the event, (ii) the return interval of the meteorological event in the observational record, simulated historical climate, and simulated pre-industrial climate, as well as (iii) the probability of the observed meteorological trend in historical and pre-industrial climates.

  1. Cutaneous Lymphoma International Consortium Study of Outcome in Advanced Stages of Mycosis Fungoides and Sézary Syndrome: Effect of Specific Prognostic Markers on Survival and Development of a Prognostic Model

    Science.gov (United States)

    Scarisbrick, Julia J.; Prince, H. Miles; Vermeer, Maarten H.; Quaglino, Pietro; Horwitz, Steven; Porcu, Pierluigi; Stadler, Rudolf; Wood, Gary S.; Beylot-Barry, Marie; Pham-Ledard, Anne; Foss, Francine; Girardi, Michael; Bagot, Martine; Michel, Laurence; Battistella, Maxime; Guitart, Joan; Kuzel, Timothy M.; Martinez-Escala, Maria Estela; Estrach, Teresa; Papadavid, Evangelia; Antoniou, Christina; Rigopoulos, Dimitis; Nikolaou, Vassilki; Sugaya, Makoto; Miyagaki, Tomomitsu; Gniadecki, Robert; Sanches, José Antonio; Cury-Martins, Jade; Miyashiro, Denis; Servitje, Octavio; Muniesa, Cristina; Berti, Emilio; Onida, Francesco; Corti, Laura; Hodak, Emilia; Amitay-Laish, Iris; Ortiz-Romero, Pablo L.; Rodríguez-Peralto, Jose L.; Knobler, Robert; Porkert, Stefanie; Bauer, Wolfgang; Pimpinelli, Nicola; Grandi, Vieri; Cowan, Richard; Rook, Alain; Kim, Ellen; Pileri, Alessandro; Patrizi, Annalisa; Pujol, Ramon M.; Wong, Henry; Tyler, Kelly; Stranzenbach, Rene; Querfeld, Christiane; Fava, Paolo; Maule, Milena; Willemze, Rein; Evison, Felicity; Morris, Stephen; Twigger, Robert; Talpur, Rakhshandra; Kim, Jinah; Ognibene, Grant; Li, Shufeng; Tavallaee, Mahkam; Hoppe, Richard T.; Duvic, Madeleine; Whittaker, Sean J.; Kim, Youn H.

    2015-01-01

    Purpose Advanced-stage mycosis fungoides (MF; stage IIB to IV) and Sézary syndrome (SS) are aggressive lymphomas with a median survival of 1 to 5 years. Clinical management is stage based; however, there is wide range of outcome within stages. Published prognostic studies in MF/SS have been single-center trials. Because of the rarity of MF/SS, only a large collaboration would power a study to identify independent prognostic markers. Patients and Methods Literature review identified the following 10 candidate markers: stage, age, sex, cutaneous histologic features of folliculotropism, CD30 positivity, proliferation index, large-cell transformation, WBC/lymphocyte count, serum lactate dehydrogenase, and identical T-cell clone in blood and skin. Data were collected at specialist centers on patients diagnosed with advanced-stage MF/SS from 2007. Each parameter recorded at diagnosis was tested against overall survival (OS). Results Staging data on 1,275 patients with advanced MF/SS from 29 international sites were included for survival analysis. The median OS was 63 months, with 2- and 5-year survival rates of 77% and 52%, respectively. The median OS for patients with stage IIB disease was 68 months, but patients diagnosed with stage III disease had slightly improved survival compared with patients with stage IIB, although patients diagnosed with stage IV disease had significantly worse survival (48 months for stage IVA and 33 months for stage IVB). Of the 10 variables tested, four (stage IV, age > 60 years, large-cell transformation, and increased lactate dehydrogenase) were independent prognostic markers for a worse survival. Combining these four factors in a prognostic index model identified the following three risk groups across stages with significantly different 5-year survival rates: low risk (68%), intermediate risk (44%), and high risk (28%). Conclusion To our knowledge, this study includes the largest cohort of patients with advanced-stage MF/SS and

  2. Surface Prognostic Charts

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Surface Prognostic Charts are historical surface prognostic (forecast) charts created by the United States Weather Bureau. They include fronts, isobars, cloud, and...

  3. Climate and meteorology

    Energy Technology Data Exchange (ETDEWEB)

    Hoitink, D.J.

    1995-06-01

    This section of the 1994 Hanford Site Environmental Report summarizes the significant activities conducted in 1994 to monitor the meteorology and climatology of the site. Meteorological measurements are taken to support Hanford Site emergency preparedness and response, Hanford Site operations, and atmospheric dispersion calculations. Climatological data are collected to help plan weather-dependent activities and are used as a resource to assess the environmental effects of Hanford Site operations.

  4. Climate and meteorology

    International Nuclear Information System (INIS)

    Hoitink, D.J.

    1995-01-01

    This section of the 1994 Hanford Site Environmental Report summarizes the significant activities conducted in 1994 to monitor the meteorology and climatology of the site. Meteorological measurements are taken to support Hanford Site emergency preparedness and response, Hanford Site operations, and atmospheric dispersion calculations. Climatological data are collected to help plan weather-dependent activities and are used as a resource to assess the environmental effects of Hanford Site operations

  5. Prognostic model for patients treated for colorectal adenomas with regard to development of recurrent adenomas and carcinoma

    DEFF Research Database (Denmark)

    Jensen, P; Krogsgaard, M R; Christiansen, J

    1996-01-01

    -80. INTERVENTIONS: All patients were followed up by rectoscopy and double contrast barium enema. The survival data were analysed by Cox's proportional hazards model. MAIN OUTCOME MEASURES: Variables of significant prognostic importance for recurrence of adenomas and the development of cancer were identified...

  6. Linear Multivariable Regression Models for Prediction of Eddy Dissipation Rate from Available Meteorological Data

    Science.gov (United States)

    MCKissick, Burnell T. (Technical Monitor); Plassman, Gerald E.; Mall, Gerald H.; Quagliano, John R.

    2005-01-01

    Linear multivariable regression models for predicting day and night Eddy Dissipation Rate (EDR) from available meteorological data sources are defined and validated. Model definition is based on a combination of 1997-2000 Dallas/Fort Worth (DFW) data sources, EDR from Aircraft Vortex Spacing System (AVOSS) deployment data, and regression variables primarily from corresponding Automated Surface Observation System (ASOS) data. Model validation is accomplished through EDR predictions on a similar combination of 1994-1995 Memphis (MEM) AVOSS and ASOS data. Model forms include an intercept plus a single term of fixed optimal power for each of these regression variables; 30-minute forward averaged mean and variance of near-surface wind speed and temperature, variance of wind direction, and a discrete cloud cover metric. Distinct day and night models, regressing on EDR and the natural log of EDR respectively, yield best performance and avoid model discontinuity over day/night data boundaries.

  7. Few promising multivariable prognostic models exist for recovery of people with non-specific neck pain in musculoskeletal primary care: a systematic review.

    Science.gov (United States)

    Wingbermühle, Roel W; van Trijffel, Emiel; Nelissen, Paul M; Koes, Bart; Verhagen, Arianne P

    2018-01-01

    Which multivariable prognostic model(s) for recovery in people with neck pain can be used in primary care? Systematic review of studies evaluating multivariable prognostic models. People with non-specific neck pain presenting at primary care. Baseline characteristics of the participants. Recovery measured as pain reduction, reduced disability, or perceived recovery at short-term and long-term follow-up. Fifty-three publications were included, of which 46 were derivation studies, four were validation studies, and three concerned combined studies. The derivation studies presented 99 multivariate models, all of which were at high risk of bias. Three externally validated models generated usable models in low risk of bias studies. One predicted recovery in non-specific neck pain, while two concerned participants with whiplash-associated disorders (WAD). Discriminative ability of the non-specific neck pain model was area under the curve (AUC) 0.65 (95% CI 0.59 to 0.71). For the first WAD model, discriminative ability was AUC 0.85 (95% CI 0.79 to 0.91). For the second WAD model, specificity was 99% (95% CI 93 to 100) and sensitivity was 44% (95% CI 23 to 65) for prediction of non-recovery, and 86% (95% CI 73 to 94) and 55% (95% CI 41 to 69) for prediction of recovery, respectively. Initial Neck Disability Index scores and age were identified as consistent prognostic factors in these three models. Three externally validated models were found to be usable and to have low risk of bias, of which two showed acceptable discriminative properties for predicting recovery in people with neck pain. These three models need further validation and evaluation of their clinical impact before their broad clinical use can be advocated. PROSPERO CRD42016042204. [Wingbermühle RW, van Trijffel E, Nelissen PM, Koes B, Verhagen AP (2018) Few promising multivariable prognostic models exist for recovery of people with non-specific neck pain in musculoskeletal primary care: a systematic review

  8. On the predictability of land surface fluxes from meteorological variables

    Science.gov (United States)

    Haughton, Ned; Abramowitz, Gab; Pitman, Andy J.

    2018-01-01

    Previous research has shown that land surface models (LSMs) are performing poorly when compared with relatively simple empirical models over a wide range of metrics and environments. Atmospheric driving data appear to provide information about land surface fluxes that LSMs are not fully utilising. Here, we further quantify the information available in the meteorological forcing data that are used by LSMs for predicting land surface fluxes, by interrogating FLUXNET data, and extending the benchmarking methodology used in previous experiments. We show that substantial performance improvement is possible for empirical models using meteorological data alone, with no explicit vegetation or soil properties, thus setting lower bounds on a priori expectations on LSM performance. The process also identifies key meteorological variables that provide predictive power. We provide an ensemble of empirical benchmarks that are simple to reproduce and provide a range of behaviours and predictive performance, acting as a baseline benchmark set for future studies. We reanalyse previously published LSM simulations and show that there is more diversity between LSMs than previously indicated, although it remains unclear why LSMs are broadly performing so much worse than simple empirical models.

  9. Prognostic model for chronic hypertension in women with a history of hypertensive pregnancy disorders at term.

    Science.gov (United States)

    Visser, V S; Hermes, W; Twisk, J; Franx, A; van Pampus, M G; Koopmans, C; Mol, B W J; de Groot, C J M

    2017-10-01

    The association between hypertensive pregnancy disorders and cardiovascular disease later in life is well described. In this study we aim to develop a prognostic model from patients characteristics known before, early in, during and after pregnancy to identify women at increased risk of cardiovascular disease e.g. chronic hypertension years after pregnancy complicated by hypertension at term. We included women with a history of singleton pregnancy complicated by hypertension at term. Women using antihypertensive medication before pregnancy were excluded. We measured hypertension in these women more than 2years postpartum. Different patients characteristics before, early in, during and after pregnancy were considered to develop a prognostic model of chronic hypertension at 2-years. These included amongst others maternal age, blood pressure at pregnancy intake and blood pressure six weeks post-partum. Univariable analyses followed by a multivariable logistic regression analysis was performed to determine which combination of predictors best predicted chronic hypertension. Model performance was assessed by calibration (graphical plot) and discrimination (area under the receiver operating characteristic (AUC)). Of the 305 women in who blood pressure 2.5years after pregnancy was assessed, 105 women (34%) had chronic hypertension. The following patient characteristics were significant associated with chronic hypertension: higher maternal age, lower education, negative family history on hypertensive pregnancy disorders, higher BMI at booking, higher diastolic blood pressure at pregnancy intake, higher systolic blood pressure during pregnancy and higher diastolic blood pressure at six weeks post-partum. These characteristics were included in the prognostic model for chronic hypertension. Model performance was good as indicated by good calibration and good discrimination (AUC; 0.83 (95% CI 0.75 - 0.92). Chronic hypertension can be expected from patient characteristics

  10. Assessment of Meteorological Drought Hazard Area using GIS in ...

    African Journals Online (AJOL)

    Michael Horsfall

    The purpose of this study was to make a model of the meteorological drought hazard area using GIS. ... overlaying different hazard indicator maps in the GIS, deploying the new model. The final ..... Northeast Thailand Project Bangkok. Min. of.

  11. Pembangunan Aplikasi Pengolahan Data Unsur Cuaca Pada Stasiun Meteorologi Kota Tegal Berbasis Model Waterfall

    Directory of Open Access Journals (Sweden)

    Mohammad Khambali

    2017-01-01

    Full Text Available Untuk mendapatkan suatu pengolahan data yang cepat dan akurat serta dikelola dengan baik tentunya harus mempunyai sebuah sistem pengelolaan yang baik. Aplikasi pengolahan data unsur cuaca disusulkan pada penelitian ini. Dengan menggunakan model waterfall menjadikan tahapan dalam pengembangan sistem yang dibuat menjadi terstruktur dengan baik tahapannya. Dengan dibangunnya aplikasi pengolahan data unsur cuaca, maka kendala yang dihadapi oleh stasiun meteorologi Tegal khususnya dibagian pengamatan yaitu dalam penghitungan unsur cuaca dapat ditanggulangi sehingga dapat mempermudah dalam memperoleh laporan tentang data unsur cuaca.

  12. Journal of Meteorology and Climate Science

    African Journals Online (AJOL)

    The Journal of Meteorology and Climate Science publishes rigorous theoretical reasoning and advanced empirical research in all areas of Meteorology and Climate Sciences. We welcome articles or proposals from all perspectives and on all subjects pertaining to Meteorology, Agriculture, Humanity, Physics, Geography, ...

  13. European meteorological data: contribution to research, development, and policy support

    Science.gov (United States)

    Biavetti, Irene; Karetsos, Sotiris; Ceglar, Andrej; Toreti, Andrea; Panagos, Panos

    2014-08-01

    The Joint Research Centre of the European Commission has developed Interpolated Meteorological Datasets available on a regular 25x25km grid both to the scientific community and the general public. Among others, the Interpolated Meteorological Datasets include daily maximum/minimum temperature, cumulated daily precipitation, evapotranspiration and wind speed. These datasets can be accessed through a web interface after a simple registration procedure. The Interpolated Meteorological Datasets also serve the Crop Growth Monitoring System (CGMS) at European level. The temporal coverage of the datasets is more than 30 years and the spatial coverage includes EU Member States, neighboring European countries, and the Mediterranean countries. The meteorological data are highly relevant for the development, implementation and assessment of a number of European Union (EU) policy areas: agriculture, soil protection, environment, agriculture, food security, energy, climate change. An online user survey has been carried out in order to assess the impact of the Interpolated Meteorological Datasets on research developments. More than 70% of the users have used the meteorological datasets for research purposes and more than 50% of the users have used those sources as main input for their models. The usefulness of the data scored more than 70% and it is interesting to note that around 25% of the users have published their scientific outputs based on the Interpolated Meteorological Datasets. Finally, the user feedback focuses mostly on improving the data distribution process as well as the visibility of the web platform.

  14. Cross-National Validation of Prognostic Models Predicting Sickness Absence and the Added Value of Work Environment Variables

    NARCIS (Netherlands)

    Roelen, C.A.M.; Stapelfeldt, C.M.; Heijmans, M.W.; van Rhenen, W.; Labriola, M.; Nielsen, C.V.; Bultmann, U.; Jensen, C.

    2015-01-01

    Purpose To validate Dutch prognostic models including age, self-rated health and prior sickness absence (SA) for ability to predict high SA in Danish eldercare. The added value of work environment variables to the models’ risk discrimination was also investigated. Methods 2,562 municipal eldercare

  15. A novel approach towards fatigue damage prognostics of composite materials utilizing SHM data and stochastic degradation modeling

    NARCIS (Netherlands)

    Loutas, T.; Eleftheroglou, N.

    2016-01-01

    A prognostic framework is proposed in order to estimate the remaining useful life of composite materials under fatigue loading based on acoustic emission data and a sophisticated Non Homogenous Hidden Semi Markov Model. Bayesian neural networks are also utilized as an alternative machine learning

  16. Improvement of Meteorological Inputs for TexAQS-II Air Quality Simulations

    Science.gov (United States)

    Ngan, F.; Byun, D.; Kim, H.; Cheng, F.; Kim, S.; Lee, D.

    2008-12-01

    An air quality forecasting system (UH-AQF) for Eastern Texas, which is in operation by the Institute for Multidimensional Air Quality Studies (IMAQS) at the University of Houston, uses the Fifth-Generation PSU/NCAR Mesoscale Model MM5 model as the meteorological driver for modeling air quality with the Community Multiscale Air Quality (CMAQ) model. While the forecasting system was successfully used for the planning and implementation of various measurement activities, evaluations of the forecasting results revealed a few systematic problems in the numerical simulations. From comparison with observations, we observe some times over-prediction of northerly winds caused by inaccurate synoptic inputs and other times too strong southerly winds caused by local sea breeze development. Discrepancies in maximum and minimum temperature are also seen for certain days. Precipitation events, as well as clouds, are simulated at the incorrect locations and times occasionally. Model simulatednrealistic thunderstorms are simulated, causing sometimes cause unrealistically strong outflows. To understand physical and chemical processes influencing air quality measures, a proper description of real world meteorological conditions is essential. The objective of this study is to generate better meteorological inputs than the AQF results to support the chemistry modeling. We utilized existing objective analysis and nudging tools in the MM5 system to develop the MUltiscale Nest-down Data Assimilation System (MUNDAS), which incorporates extensive meteorological observations available in the simulated domain for the retrospective simulation of the TexAQS-II period. With the re-simulated meteorological input, we are able to better predict ozone events during TexAQS-II period. In addition, base datasets in MM5 such as land use/land cover, vegetation fraction, soil type and sea surface temperature are updated by satellite data to represent the surface features more accurately. They are key

  17. Modelling the future distribution of ammonium nitrate concentrations in The Netherlands for 2020: The sensitivity to meteorological parameters

    Science.gov (United States)

    Williams, J. E.; van der Swaluw, E.; de Vries, W. J.; Sauter, F. J.; van Pul, W. A. J.; Hoogerbrugge, R.

    2015-08-01

    We present a parameterization developed to simulate Ammonium particle (NH4+) concentrations in the Operational Priority Substances (OPS) source-receptor model, without the necessity of using a detailed chemical scheme. By using the ratios of the main pre-cursor gases SO2, NO2 and NH3, and utilising calculations performed using a chemical box-model, we show that the parameterization can simulate annual mean NH4+ concentration fields to within ∼15% of measured values at locations throughout the Netherlands. Performing simulations for different decades, we find a strong correlation of simulated NH4+ distributions for both past (1993-1995) and present (2009-2012) time periods. Although the total concentration of NH4+ has decreased over the period, we find that the fraction of NH4+ transported into the Netherlands has increased from around 40% in the past to 50% for present-day. This is due to the variable efficiency of mitigation practises across economic sectors. Performing simulations for the year 2020 using associated emission estimates, we show that there are generally decreases of ∼8-25% compared to present day concentrations. By altering the meteorological fields applied in the future simulations, we show that a significant uncertainty of between ∼50 and 100% exists on this estimated NH4+ distribution as a result of variability in the temperature dependent emission terms and relative humidity. Therefore, any projections of future NH4+ distributions should be performed using well chosen meteorological fields representing recent meteorological situations.

  18. Added Value of uncertainty Estimates of SOurce term and Meteorology (AVESOME)

    DEFF Research Database (Denmark)

    Sørensen, Jens Havskov; Schönfeldt, Fredrik; Sigg, Robert

    In the early phase of a nuclear accident, two large sources of uncertainty exist: one related to the source term and one associated with the meteorological data. Operational methods are being developed in AVESOME for quantitative estimation of uncertainties in atmospheric dispersion prediction.......g. at national meteorological services, the proposed methodology is feasible for real-time use, thereby adding value to decision support. In the recent NKS-B projects MUD, FAUNA and MESO, the implications of meteorological uncertainties for nuclear emergency preparedness and management have been studied...... uncertainty in atmospheric dispersion model forecasting stemming from both the source term and the meteorological data is examined. Ways to implement the uncertainties of forecasting in DSSs, and the impacts on real-time emergency management are described. The proposed methodology allows for efficient real...

  19. A framework for quantifying net benefits of alternative prognostic models‡

    Science.gov (United States)

    Rapsomaniki, Eleni; White, Ian R; Wood, Angela M; Thompson, Simon G

    2012-01-01

    New prognostic models are traditionally evaluated using measures of discrimination and risk reclassification, but these do not take full account of the clinical and health economic context. We propose a framework for comparing prognostic models by quantifying the public health impact (net benefit) of the treatment decisions they support, assuming a set of predetermined clinical treatment guidelines. The change in net benefit is more clinically interpretable than changes in traditional measures and can be used in full health economic evaluations of prognostic models used for screening and allocating risk reduction interventions. We extend previous work in this area by quantifying net benefits in life years, thus linking prognostic performance to health economic measures; by taking full account of the occurrence of events over time; and by considering estimation and cross-validation in a multiple-study setting. The method is illustrated in the context of cardiovascular disease risk prediction using an individual participant data meta-analysis. We estimate the number of cardiovascular-disease-free life years gained when statin treatment is allocated based on a risk prediction model with five established risk factors instead of a model with just age, gender and region. We explore methodological issues associated with the multistudy design and show that cost-effectiveness comparisons based on the proposed methodology are robust against a range of modelling assumptions, including adjusting for competing risks. Copyright © 2011 John Wiley & Sons, Ltd. PMID:21905066

  20. Syllabi for Instruction in Agricultural Meteorology.

    Science.gov (United States)

    De Villiers, G. D. B.; And Others

    A working group of the Commission for Agricultural Meteorology has prepared this report to fill a need for detailed syllabi for instruction in agricultural meteorology required by different levels of personnel. Agrometeorological personnel are classified in three categories: (1) professional meteorological personnel (graduates with basic training…

  1. WJBF TV tower meteorological database for the ERAD Code-1993

    International Nuclear Information System (INIS)

    Weber, A.H.

    1996-07-01

    The Explosive Release Atmospheric Dispersion (ERAD) model (Boughton and DeLaurentis 1992) is a three-dimensional numerical model for simulating atmospheric transport and dispersion. The ERAD code is particularly adept at handling explosive releases into the atmosphere and is being used by the Materials and Accountability Department at the Savannah River Site (SRS) to provide risk estimates. The Environmental Technology Section (ETS) was asked to provide meteorological data to be used for applying ERAD to some site facilities. The ERAD model requires a vertical profile of meteorological measurements. The 1993 data from the WJBF-TV tower has been processed and provided for this purpose. This document describes the steps taken to prepare and format the database

  2. Mapping the Martian Meteorology

    Science.gov (United States)

    Allison, M.; Ross, J. D.; Solomon, N.

    1999-01-01

    The Mars-adapted version of the NASA/GISS general circulation model (GCM) has been applied to the hourly/daily simulation of the planet's meteorology over several seasonal orbits. The current running version of the model includes a diurnal solar cycle, CO2 sublimation, and a mature parameterization of upper level wave drag with a vertical domain extending from the surface up to the 6microb level. The benchmark simulations provide a four-dimensional archive for the comparative evaluation of various schemes for the retrieval of winds from anticipated polar orbiter measurements of temperatures by the Pressure Modulator Infrared Radiometer. Additional information is contained in the original extended abstract.

  3. Fear of knowledge: Clinical hypotheses in diagnostic and prognostic reasoning.

    Science.gov (United States)

    Chiffi, Daniele; Zanotti, Renzo

    2017-10-01

    Patients are interested in receiving accurate diagnostic and prognostic information. Models and reasoning about diagnoses have been extensively investigated from a foundational perspective; however, for all its importance, prognosis has yet to receive a comparable degree of philosophical and methodological attention, and this may be due to the difficulties inherent in accurate prognostics. In the light of these considerations, we discuss a considerable body of critical thinking on the topic of prognostication and its strict relations with diagnostic reasoning, pointing out the distinction between nosographic and pathophysiological types of diagnosis and prognosis, underlying the importance of the explication and explanation processes. We then distinguish between various forms of hypothetical reasoning applied to reach diagnostic and prognostic judgments, comparing them with specific forms of abductive reasoning. The main thesis is that creative abduction regarding clinical hypotheses in diagnostic process is very unlikely to occur, whereas this seems to be often the case for prognostic judgments. The reasons behind this distinction are due to the different types of uncertainty involved in diagnostic and prognostic judgments. © 2016 John Wiley & Sons, Ltd.

  4. Surface Meteorology and Solar Energy

    Data.gov (United States)

    National Aeronautics and Space Administration — Surface Meteorology and Solar Energy data - over 200 satellite-derived meteorology and solar energy parameters, monthly averaged from 22 years of data, global solar...

  5. THE MODEL OF UNCLEAR EXPERT SYSTEM OF PROGNOSTICATION THE CONTENT OF EDUCATION

    Directory of Open Access Journals (Sweden)

    Ivan M. Tsidylo

    2012-12-01

    Full Text Available The article deals with the problem of development of the expert system of prognostication of the educational content by means of fuzzy logic. It was the model of making decision by the group of experts in accordance to meaningfulness of the theme in the educational programme on the base of the hierarchical system that combines in itself the use of both unclear and stochastic data. The structure of the unclear system, function and mechanisms of construction of separate blocks of the model are described. The surface of review of the unclear system represents dependence of estimation of the theme meaningfulness on the level of competence of group of experts and size to the point at the permanent value of level’s variation. The testing of the controller on a test selection proves the functional fitness of the developed model.

  6. Effects of short-term variability of meteorological variables on soil temperature in permafrost regions

    Science.gov (United States)

    Beer, Christian; Porada, Philipp; Ekici, Altug; Brakebusch, Matthias

    2018-03-01

    Effects of the short-term temporal variability of meteorological variables on soil temperature in northern high-latitude regions have been investigated. For this, a process-oriented land surface model has been driven using an artificially manipulated climate dataset. Short-term climate variability mainly impacts snow depth, and the thermal diffusivity of lichens and bryophytes. These impacts of climate variability on insulating surface layers together substantially alter the heat exchange between atmosphere and soil. As a result, soil temperature is 0.1 to 0.8 °C higher when climate variability is reduced. Earth system models project warming of the Arctic region but also increasing variability of meteorological variables and more often extreme meteorological events. Therefore, our results show that projected future increases in permafrost temperature and active-layer thickness in response to climate change will be lower (i) when taking into account future changes in short-term variability of meteorological variables and (ii) when representing dynamic snow and lichen and bryophyte functions in land surface models.

  7. Spatial clustering and meteorological drivers of summer ozone in Europe

    Science.gov (United States)

    Carro-Calvo, Leopoldo; Ordóñez, Carlos; García-Herrera, Ricardo; Schnell, Jordan L.

    2017-10-01

    We have applied the k-means clustering technique on a maximum daily 8-h running average near-surface ozone (MDA8 O3) gridded dataset over Europe at 1° × 1° resolution for summer 1998-2012. This has resulted in a spatial division of nine regions where ozone presents coherent spatiotemporal patterns. The role of meteorology in the variability of ozone at different time scales has been investigated by using daily meteorological fields from the NCEP-NCAR meteorological reanalysis. In the five regions of central-southern Europe ozone extremes (exceedances of the summer 95th percentile) occur mostly under anticyclonic circulation or weak sea level pressure gradients which trigger elevated temperatures and the recirculation of air masses. In the four northern regions extremes are associated with high-latitude anticyclones that divert the typical westerly flow at those latitudes and cause the advection of aged air masses from the south. The impact of meteorology on the day-to-day variability of ozone has been assessed by means of two different types of multiple linear models. These include as predictors meteorological fields averaged within the regions (;region-based; approach) or synoptic indices indicating the degree of resemblance between the daily meteorological fields over a large domain (25°-70° N, 35° W - 35° E) and their corresponding composites for extreme ozone days (;index-based; approach). With the first approach, a reduced set of variables, always including daily maximum temperature within the region, explains 47-66% of the variability (adjusted R2) in central-southern Europe, while more complex models are needed to explain 27-49% of the variability in the northern regions. The index-based approach yields better results for the regions of northern Europe, with adjusted R2 = 40-57%. Finally, both methodologies have also been applied to reproduce the interannual variability of ozone, with the best models explaining 66-88% of the variance in central

  8. Exploring the relationship between meteorology and surface PM2.5 in Northern India

    Science.gov (United States)

    Schnell, J.; Naik, V.; Horowitz, L. W.; Paulot, F.; Ginoux, P. A.

    2017-12-01

    Northern India is one of the most polluted and densely populated regions in world. Accurately modeling pollution in the region is difficult due to the extreme conditions with respect to emissions, meteorology, and topography, but it is paramount in order to understand how future changes in emissions and climate may alter the region's pollution regime. We evaluate a developmental version of the new-generation NOAA GFDL Atmospheric Model, version 4 (AM4) in its ability to simulate observed wintertime PM2.5 and its relationship to meteorology over the Northern India (23°N-31°N, 68°E-90°E). We perform two simulations of the GFDL-AM4 nudged to observed meteorology for the period (1980-2016) with two emission inventories developed for CMIP5 and CMIP6 and compare results with observations from India's Central Pollution Control Board (CPCB) for the period 1 October 2015 - 31 March 2016. Overall, our results indicate that the simulation with CMIP6 emissions has substantially reduced the low model bias in the region. The AM4, albeit biased low, generally simulates the magnitude and daily variability in observed total PM2.5. Ammonium nitrate and ammonium sulfate are the primary components of PM2.5 in the model, and although not directly observed, correlations of total observed PM2.5 and meteorology with the modeled individual PM2.5 components suggest the same for the observations. The model correctly reproduces the shape and magnitude of the seasonal cycle of PM2.5; but for the diurnal cycle, it misses the early evening rise and secondary maximum found in the observations. Observed PM2.5 abundances within the densely populated Indo-Gangetic Plain are by far the highest and are closely related to boundary layer meteorology, specifically relative humidity, wind speed, boundary layer height, and inversion strength. The GFDL-AM4 reproduces the observed pollution gradient over Northern India as well as the strength of the meteorology-PM2.5 relationship in most locations.

  9. Meteorological Controls on Local and Regional Volcanic Ash Dispersal.

    Science.gov (United States)

    Poulidis, Alexandros P; Phillips, Jeremy C; Renfrew, Ian A; Barclay, Jenni; Hogg, Andrew; Jenkins, Susanna F; Robertson, Richard; Pyle, David M

    2018-05-02

    Volcanic ash has the capacity to impact human health, livestock, crops and infrastructure, including international air traffic. For recent major eruptions, information on the volcanic ash plume has been combined with relatively coarse-resolution meteorological model output to provide simulations of regional ash dispersal, with reasonable success on the scale of hundreds of kilometres. However, to predict and mitigate these impacts locally, significant improvements in modelling capability are required. Here, we present results from a dynamic meteorological-ash-dispersion model configured with sufficient resolution to represent local topographic and convectively-forced flows. We focus on an archetypal volcanic setting, Soufrière, St Vincent, and use the exceptional historical records of the 1902 and 1979 eruptions to challenge our simulations. We find that the evolution and characteristics of ash deposition on St Vincent and nearby islands can be accurately simulated when the wind shear associated with the trade wind inversion and topographically-forced flows are represented. The wind shear plays a primary role and topographic flows a secondary role on ash distribution on local to regional scales. We propose a new explanation for the downwind ash deposition maxima, commonly observed in volcanic eruptions, as resulting from the detailed forcing of mesoscale meteorology on the ash plume.

  10. Prognostic value of proliferation in pleomorphic soft tissue sarcomas

    DEFF Research Database (Denmark)

    Seinen, Jojanneke M; Jönsson, Mats; Bendahl, Pär-Ola O

    2012-01-01

    = 1.6-12.1), Top2a (hazard ratio = 2.2, CI = 1.2-3.5) and high S-phase fraction (hazard ratio = 1.8, CI = 1.2-3.7) significantly correlated with risk for metastasis. When combined with currently used prognostic factors, Ki-67, S-phase fraction and Top2a fraction contributed to refined identification...... of prognostic risk groups. Proliferation, as assessed by expression of Ki-67 and Top2a and evaluation of S-phase fraction and applied to statistical decision-tree models, provides prognostic information in soft tissue sarcomas of the extremity and trunk wall. Though proliferation contributes independently...... to currently applied prognosticators, its role is particularly strong when few other factors are available, which suggests a role in preoperative decision-making related to identification of high-risk individuals who would benefit from neoadjuvant therapy....

  11. Fire and forest meteorology

    Science.gov (United States)

    SA Ferguson; T.J. Brown; M. Flannigan

    2005-01-01

    The American Meteorological Society symposia series on Fire and Forest Meteorology provides biennial forums for atmospheric and fire scientists to introduce and discuss the latest and most relevant research on weather, climate and fire. This special issue highlights significant work that was presented at the Fifth Symposium in Orlando, Florida during 16-20 November...

  12. A prognostic scoring model for survival after locoregional therapy in de novo stage IV breast cancer.

    Science.gov (United States)

    Kommalapati, Anuhya; Tella, Sri Harsha; Goyal, Gaurav; Ganti, Apar Kishor; Krishnamurthy, Jairam; Tandra, Pavan Kumar

    2018-05-02

    The role of locoregional treatment (LRT) remains controversial in de novo stage IV breast cancer (BC). We sought to analyze the role of LRT and prognostic factors of overall survival (OS) in de novo stage IV BC patients treated with LRT utilizing the National Cancer Data Base (NCDB). The objective of the current study is to create and internally validate a prognostic scoring model to predict the long-term OS for de novo stage IV BC patients treated with LRT. We included de novo stage IV BC patients reported to NCDB between 2004 and 2015. Patients were divided into LRT and no-LRT subsets. We randomized LRT subset to training and validation cohorts. In the training cohort, a seventeen-point prognostic scoring system was developed based on the hazard ratios calculated using Cox-proportional method. We stratified both training and validation cohorts into two "groups" [group 1 (0-7 points) and group 2 (7-17 points)]. Kaplan-Meier method and log-rank test were used to compare OS between the two groups. Our prognostic score was validated internally by comparing the OS between the respective groups in both the training and validation cohorts. Among 67,978 patients, LRT subset (21,200) had better median OS as compared to that of no-LRT (45 vs. 24 months; p < 0.0001). The group 1 and group 2 in the training cohort showed a significant difference in the 3-year OS (p < 0.0001) (68 vs. 26%). On internal validation, comparable OS was seen between the respective groups in each cohort (p = 0.77). Our prognostic scoring system will help oncologists to predict the prognosis in de novo stage IV BC patients treated with LRT. Although firm treatment-related conclusions cannot be made due to the retrospective nature of the study, LRT appears to be associated with a better OS in specific subgroups.

  13. Ensemble atmospheric dispersion modeling for emergency response consequence assessments

    International Nuclear Information System (INIS)

    Addis, R.P.; Buckley, R.L.

    2003-01-01

    Full text: Prognostic atmospheric dispersion models are used to generate consequence assessments, which assist decision-makers in the event of a release from a nuclear facility. Differences in the forecast wind fields generated by various meteorological agencies, differences in the transport and diffusion models themselves, as well as differences in the way these models treat the release source term, all may result in differences in the simulated plumes. This talk will address the U.S. participation in the European ENSEMBLE project, and present a perspective an how ensemble techniques may be used to enable atmospheric modelers to provide decision-makers with a more realistic understanding of how both the atmosphere and the models behave. Meteorological forecasts generated by numerical models from national and multinational meteorological agencies provide individual realizations of three-dimensional, time dependent atmospheric wind fields. These wind fields may be used to drive atmospheric dispersion (transport and diffusion) models, or they may be used to initiate other, finer resolution meteorological models, which in turn drive dispersion models. Many modeling agencies now utilize ensemble-modeling techniques to determine how sensitive the prognostic fields are to minor perturbations in the model parameters. However, the European Union programs RTMOD and ENSEMBLE are the first projects to utilize a WEB based ensemble approach to interpret the output from atmospheric dispersion models. The ensembles produced are different from those generated by meteorological forecasting centers in that they are ensembles of dispersion model outputs from many different atmospheric transport and diffusion models utilizing prognostic atmospheric fields from several different forecast centers. As such, they enable a decision-maker to consider the uncertainty in the plume transport and growth as a result of the differences in the forecast wind fields as well as the differences in the

  14. DESCARTES AND THE METEOROLOGY OF THE WORLD

    Directory of Open Access Journals (Sweden)

    Patrick BRISSEY

    2012-11-01

    Full Text Available Descartes claimed that he thought he could deduce the assumptions of his Meteorology by the contents of the Discourse. He actually began the Meteorology with assumptions. The content of the Discourse, moreover, does not indicate how he deduced the assumptions of the Meteorology. We seem to be left in a precarious position. We can examine the text as it was published, independent of Descartes’ claims, which suggests that he incorporated a presumptive or hypothetical method. On the other hand, we can take Descartes’ claims as our guide and search for the epistemic foundations of the Meteorology independent of the Discourse. In this paper, I will pursue the latter route. My aim is to explain why, and how, Descartes thought that he had deduced the assumptions of the Meteorology. My interest, in this case, is solely Descartes’ physical foundation for the Meteorology, in the physics and physiology that resulted in Descartes’ explanation. With this aim, I provide an interpretation of Descartes’ World where many of its conclusions serve as evidence for the assumptions of the Meteorology. I provisionally conclude that Descartes thought that his World was the epistemic foundation for his Meteorology.

  15. Lloyd Berkner: Catalyst for Meteorology's Fabulous Fifties

    Science.gov (United States)

    Lewis, J. M.

    2002-05-01

    In the long sweep of meteorological history - from Aristotle's Meteorologica to the threshold of the third millennium - the 1950s will surely be recognized as a defining decade. The contributions of many individuals were responsible for the combination of vision and institution building that marked this decade and set the stage for explosive development during the subsequent forty years. In the minds of many individuals who were active during those early years, however, one name stands out as a prime mover par excellence: Lloyd Viel Berkner. On May 1, 1957, Berkner addressed the National Press Club. The address was entitled, "Horizons of Meteorology". It reveals Berkner's insights into meteorology from his position as Chairman of the Committee on Meteorology of the National Academy of Sciences, soon to release the path-breaking report, Research and Education in Meteorology (1958). The address also reflects the viewpoint of an individual deeply involved in the International Geophysical Year (IGY). It is an important footnote to meteorological history. We welcome this opportunity to profile Berkner and to discuss "Horizons of Meteorology" in light of meteorology's state-of-affairs in the 1950s and the possible relevance to Berkner's ideas to contemporary issues.

  16. Systematic review of renal carcinoma prognostic factors.

    Science.gov (United States)

    Lorente, D; Trilla, E; Meseguer, A; Planas, J; Placer, J; Celma, A; Salvador, C; Regis, L; Morote, J

    2017-05-01

    The natural history of renal cell carcinoma is heterogeneous. Some scenarios can be found in terms of clinical presentation, clinical evolution or type of recurrence (local/metastatic). The aim of this publication is to analyze the most important prognostic factors published in the literature. A literature review ob published papers was performed using the Pubmed, from first Motzer's classification published in 1999 to 2015, according to PRISMA declaration. Search was done using the following keywords: kidney neoplasm, kidney cancer, renal cell carcinoma, prognostic factors, mortality, survival and disease progression. Papers were classified according to level of evidence, the number of patients included and the type of study performed. The evolution in the knowledge of molecular pathways related to renal oncogenesis and the new targeted therapies has left to remain obsolete the old prognostic models. It's necessary to perform a continuous review to actualize nomograms and to adapt them to the new scenarios. Is necessary to perform a proper external validation of existing prognostic factors using prospective and multicentric studies to add them into the daily urologist clinical practice. Copyright © 2016 AEU. Publicado por Elsevier España, S.L.U. All rights reserved.

  17. Numerical simulation of meteorological conditions for peak pollution in Paris

    Energy Technology Data Exchange (ETDEWEB)

    Carissimo, B. [Electricite de France (EDF), 78 - Chatou (France). Direction des Etudes et Recherches

    1997-06-01

    Results obtained on the simulation of meteorological conditions during two episodes of peak pollution in Paris are presented, one in the winter, the other in the summer. The A3UR air quality modelling system is first described followed by the MERCURE mesoscale meteorological model. The conditions of simulation are described. The results obtained on these two causes show satisfactory agreement, for example on the magnitude of the urban heat island which is correctly reproduced. In this study, several areas of progress have been identified: improvement of the altitude measurement network around cities, the simulation of light wind conditions and the simulation of formation and dissipation of fog. (author) 24 refs.

  18. Instantaneous-to-daily GPP upscaling schemes based on a coupled photosynthesis-stomatal conductance model: correcting the overestimation of GPP by directly using daily average meteorological inputs.

    Science.gov (United States)

    Wang, Fumin; Gonsamo, Alemu; Chen, Jing M; Black, T Andrew; Zhou, Bin

    2014-11-01

    Daily canopy photosynthesis is usually temporally upscaled from instantaneous (i.e., seconds) photosynthesis rate. The nonlinear response of photosynthesis to meteorological variables makes the temporal scaling a significant challenge. In this study, two temporal upscaling schemes of daily photosynthesis, the integrated daily model (IDM) and the segmented daily model (SDM), are presented by considering the diurnal variations of meteorological variables based on a coupled photosynthesis-stomatal conductance model. The two models, as well as a simple average daily model (SADM) with daily average meteorological inputs, were validated using the tower-derived gross primary production (GPP) to assess their abilities in simulating daily photosynthesis. The results showed IDM closely followed the seasonal trend of the tower-derived GPP with an average RMSE of 1.63 g C m(-2) day(-1), and an average Nash-Sutcliffe model efficiency coefficient (E) of 0.87. SDM performed similarly to IDM in GPP simulation but decreased the computation time by >66%. SADM overestimated daily GPP by about 15% during the growing season compared to IDM. Both IDM and SDM greatly decreased the overestimation by SADM, and improved the simulation of daily GPP by reducing the RMSE by 34 and 30%, respectively. The results indicated that IDM and SDM are useful temporal upscaling approaches, and both are superior to SADM in daily GPP simulation because they take into account the diurnally varying responses of photosynthesis to meteorological variables. SDM is computationally more efficient, and therefore more suitable for long-term and large-scale GPP simulations.

  19. Prognostic factors of breast cancer

    International Nuclear Information System (INIS)

    Gonzalez Ortega, Jose Maria; Morales Wong, Mario Miguel; Lopez Cuevas, Zoraida; Diaz Valdez, Marilin

    2011-01-01

    The prognostic factors must to be differentiated of the predictive ones. A prognostic factor is any measurement used at moment of the surgery correlated with the free interval of disease or global survival in the absence of the systemic adjuvant treatment and as result is able to correlate with the natural history of the disease. In contrast, a predictive factor is any measurement associated with the response to a given treatment. Among the prognostic factors of the breast cancer are included the clinical, histological, biological, genetic and psychosocial factors. In present review of psychosocial prognostic factors has been demonstrated that the stress and the depression are negative prognostic factors in patients presenting with breast cancer. It is essential to remember that the assessment of just one prognostic parameter is a help but it is not useful to clinical and therapeutic management of the patient.(author)

  20. Description of the University of Auckland Global Mars Mesoscale Meteorological Model (GM4)

    Science.gov (United States)

    Wing, D. R.; Austin, G. L.

    2005-08-01

    The University of Auckland Global Mars Mesoscale Meteorological Model (GM4) is a numerical weather prediction model of the Martian atmosphere that has been developed through the conversion of the Penn State University / National Center for Atmospheric Research fifth generation mesoscale model (MM5). The global aspect of this model is self consistent, overlapping, and forms a continuous domain around the entire planet, removing the need to provide boundary conditions other than at initialisation, yielding independence from the constraint of a Mars general circulation model. The brief overview of the model will be given, outlining the key physical processes and setup of the model. Comparison between data collected from Mars Pathfinder during its 1997 mission and simulated conditions using GM4 have been performed. Diurnal temperature variation as predicted by the model shows very good correspondence with the surface truth data, to within 5 K for the majority of the diurnal cycle. Mars Viking Data is also compared with the model, with good agreement. As a further means of validation for the model, various seasonal comparisons of surface and vertical atmospheric structure are conducted with the European Space Agency AOPP/LMD Mars Climate Database. Selected simulations over regions of interest will also be presented.

  1. The meteorological data acquisition system

    International Nuclear Information System (INIS)

    Bouharrour, S.; Thomas, P.

    1975-07-01

    The 200 m meteorological tower of the Karlsruhe Nuclear Research Center has been equipped with 45 instruments measuring the meteorological parameters near the ground level. Frequent inquiry of the instruments implies data acquisition with on-line data reduction. This task is fulfilled by some peripheral units controlled by a PDP-8/I. This report presents details of the hardware configuration and a short description of the software configuration of the meteorological data acquisition system. The report also serves as an instruction for maintenance and repair work to be carried out at the system. (orig.) [de

  2. Towards A Model-based Prognostics Methodology for Electrolytic Capacitors: A Case Study Based on Electrical Overstress Accelerated Aging

    Directory of Open Access Journals (Sweden)

    Gautam Biswas

    2012-12-01

    Full Text Available This paper presents a model-driven methodology for predict- ing the remaining useful life of electrolytic capacitors. This methodology adopts a Kalman filter approach in conjunction with an empirical state-based degradation model to predict the degradation of capacitor parameters through the life of the capacitor. Electrolytic capacitors are important components of systems that range from power supplies on critical avion- ics equipment to power drivers for electro-mechanical actuators. These devices are known for their comparatively low reliability and given their critical role in the system, they are good candidates for component level prognostics and health management. Prognostics provides a way to assess remain- ing useful life of a capacitor based on its current state of health and its anticipated future usage and operational conditions. This paper proposes and empirical degradation model and discusses experimental results for an accelerated aging test performed on a set of identical capacitors subjected to electrical stress. The data forms the basis for developing the Kalman-filter based remaining life prediction algorithm.

  3. Improvement of PSA Models Using Monitoring and Prognostics

    Energy Technology Data Exchange (ETDEWEB)

    Heo, Gyun Young; Chang, Yoon Suk; Kim, Hyun Dae [Kyung Hee University, Yongin (Korea, Republic of)

    2014-08-15

    Probabilistic Safety Assessment (PSA) has performed a significant role for quantitative decision-making by finding design and operational vulnerability and evaluating cost-benefit in improving such weak points. Especially, it has been widely used as the core methodology for Risk-Informed Applications (RIAs). Even though the nature of PSA seeks realistic results, there are still 'conservative' aspects. The sources for the conservatism come from the assumption of safety analysis and the estimation of failure frequency. Surveillance, Diagnosis, and Prognosis (SDP) utilizing massive database and information technology is worthwhile to be highlighted in terms of the capability of alleviating the conservatism in the conventional PSA. This paper provides enabling techniques to concretize the method to provide time- and condition-dependent risk by integrating a conventional PSA model with condition monitoring and prognostics techniques. We will discuss how to integrate the results with frequency of initiating events (IEs) and failure probability of basic events (BEs). Two illustrative examples will be introduced: how the failure probability of a passive system can be evaluated under different plant conditions and how the IE frequency for Steam Generator Tube Rupture (SGTR) can be updated in terms of operating time. We expect that the proposed PSA model can take a role of annunciator to show the variation of Core Damage Frequency (CDF) in terms of time and operational conditions.

  4. Integrating Tenascin-C protein expression and 1q25 copy number status in pediatric intracranial ependymoma prognostication: A new model for risk stratification.

    Science.gov (United States)

    Andreiuolo, Felipe; Le Teuff, Gwénaël; Bayar, Mohamed Amine; Kilday, John-Paul; Pietsch, Torsten; von Bueren, André O; Witt, Hendrik; Korshunov, Andrey; Modena, Piergiorgio; Pfister, Stefan M; Pagès, Mélanie; Castel, David; Giangaspero, Felice; Chimelli, Leila; Varlet, Pascale; Rutkowski, Stefan; Frappaz, Didier; Massimino, Maura; Grundy, Richard; Grill, Jacques

    2017-01-01

    Despite multimodal therapy, prognosis of pediatric intracranial ependymomas remains poor with a 5-year survival rate below 70% and frequent late deaths. This multicentric European study evaluated putative prognostic biomarkers. Tenascin-C (TNC) immunohistochemical expression and copy number status of 1q25 were retained for a pooled analysis of 5 independent cohorts. The prognostic value of TNC and 1q25 on the overall survival (OS) was assessed using a Cox model adjusted to age at diagnosis, tumor location, WHO grade, extent of resection, radiotherapy and stratified by cohort. Stratification on a predictor that did not satisfy the proportional hazards assumption was considered. Model performance was evaluated and an internal-external cross validation was performed. Among complete cases with 5-year median follow-up (n = 470; 131 deaths), TNC and 1q25 gain were significantly associated with age at diagnosis and posterior fossa tumor location. 1q25 status added independent prognostic value for death beyond the classical variables with a hazard ratio (HR) = 2.19 95%CI = [1.29; 3.76] (p = 0.004), while TNC prognostic relation was tumor location-dependent with HR = 2.19 95%CI = [1.29; 3.76] (p = 0.004) in posterior fossa and HR = 0.64 [0.28; 1.48] (p = 0.295) in supratentorial (interaction p value = 0.015). The derived prognostic score identified 3 different robust risk groups. The omission of upfront RT was not associated with OS for good and intermediate prognostic groups while the absence of upfront RT was negatively associated with OS in the poor risk group. Integrated TNC expression and 1q25 status are useful to better stratify patients and to eventually adapt treatment regimens in pediatric intracranial ependymoma.

  5. Integrating Tenascin-C protein expression and 1q25 copy number status in pediatric intracranial ependymoma prognostication: A new model for risk stratification.

    Directory of Open Access Journals (Sweden)

    Felipe Andreiuolo

    Full Text Available Despite multimodal therapy, prognosis of pediatric intracranial ependymomas remains poor with a 5-year survival rate below 70% and frequent late deaths.This multicentric European study evaluated putative prognostic biomarkers. Tenascin-C (TNC immunohistochemical expression and copy number status of 1q25 were retained for a pooled analysis of 5 independent cohorts. The prognostic value of TNC and 1q25 on the overall survival (OS was assessed using a Cox model adjusted to age at diagnosis, tumor location, WHO grade, extent of resection, radiotherapy and stratified by cohort. Stratification on a predictor that did not satisfy the proportional hazards assumption was considered. Model performance was evaluated and an internal-external cross validation was performed.Among complete cases with 5-year median follow-up (n = 470; 131 deaths, TNC and 1q25 gain were significantly associated with age at diagnosis and posterior fossa tumor location. 1q25 status added independent prognostic value for death beyond the classical variables with a hazard ratio (HR = 2.19 95%CI = [1.29; 3.76] (p = 0.004, while TNC prognostic relation was tumor location-dependent with HR = 2.19 95%CI = [1.29; 3.76] (p = 0.004 in posterior fossa and HR = 0.64 [0.28; 1.48] (p = 0.295 in supratentorial (interaction p value = 0.015. The derived prognostic score identified 3 different robust risk groups. The omission of upfront RT was not associated with OS for good and intermediate prognostic groups while the absence of upfront RT was negatively associated with OS in the poor risk group.Integrated TNC expression and 1q25 status are useful to better stratify patients and to eventually adapt treatment regimens in pediatric intracranial ependymoma.

  6. Prognostic cloud water in the Los Alamos general circulation model

    International Nuclear Information System (INIS)

    Kristjansson, J.E.; Kao, C.Y.J.

    1994-01-01

    Most of today's general circulation models (GCMs) have a greatly simplified treatment of condensation and clouds. Recent observational studies of the earth's radiation budget have suggested cloud-related feedback mechanisms to be of tremendous importance for the issue of global change. Thus, an urgent need for improvements in the treatment of clouds in GCMs has arisen, especially as the clouds relate to radiation. In this paper, we investigate the effects of introducing prognostic cloud water into the Los Alamos GCM. The cloud water field, produced by both stratiform and convective condensation, is subject to 3-dimensional advection and vertical diffusion. The cloud water enters the radiation calculations through the longwave emissivity calculations. Results from several sensitivity simulations show that realistic water and precipitation fields can be obtained with the applied method. Comparisons with observations show that the most realistic results are obtained when more sophisticated schemes for moist convection are introduced at the same time. The model's cold bias is reduced and the zonal winds becomes stronger because of more realistic tropical convection

  7. Diagnostic Reasoning using Prognostic Information for Unmanned Aerial Systems

    Science.gov (United States)

    Schumann, Johann; Roychoudhury, Indranil; Kulkarni, Chetan

    2015-01-01

    With increasing popularity of unmanned aircraft, continuous monitoring of their systems, software, and health status is becoming more and more important to ensure safe, correct, and efficient operation and fulfillment of missions. The paper presents integration of prognosis models and prognostic information with the R2U2 (REALIZABLE, RESPONSIVE, and UNOBTRUSIVE Unit) monitoring and diagnosis framework. This integration makes available statistically reliable health information predictions of the future at a much earlier time to enable autonomous decision making. The prognostic information can be used in the R2U2 model to improve diagnostic accuracy and enable decisions to be made at the present time to deal with events in the future. This will be an advancement over the current state of the art, where temporal logic observers can only do such valuation at the end of the time interval. Usefulness and effectiveness of this integrated diagnostics and prognostics framework was demonstrated using simulation experiments with the NASA Dragon Eye electric unmanned aircraft.

  8. Predictions of dispersion and deposition of fallout from nuclear testing using the NOAA-HYSPLIT meteorological model.

    Science.gov (United States)

    Moroz, Brian E; Beck, Harold L; Bouville, André; Simon, Steven L

    2010-08-01

    The NOAA Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT) was evaluated as a research tool to simulate the dispersion and deposition of radioactive fallout from nuclear tests. Model-based estimates of fallout can be valuable for use in the reconstruction of past exposures from nuclear testing, particularly where little historical fallout monitoring data are available. The ability to make reliable predictions about fallout deposition could also have significant importance for nuclear events in the future. We evaluated the accuracy of the HYSPLIT-predicted geographic patterns of deposition by comparing those predictions against known deposition patterns following specific nuclear tests with an emphasis on nuclear weapons tests conducted in the Marshall Islands. We evaluated the ability of the computer code to quantitatively predict the proportion of fallout particles of specific sizes deposited at specific locations as well as their time of transport. In our simulations of fallout from past nuclear tests, historical meteorological data were used from a reanalysis conducted jointly by the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR). We used a systematic approach in testing the HYSPLIT model by simulating the release of a range of particle sizes from a range of altitudes and evaluating the number and location of particles deposited. Our findings suggest that the quantity and quality of meteorological data are the most important factors for accurate fallout predictions and that, when satisfactory meteorological input data are used, HYSPLIT can produce relatively accurate deposition patterns and fallout arrival times. Furthermore, when no other measurement data are available, HYSPLIT can be used to indicate whether or not fallout might have occurred at a given location and provide, at minimum, crude quantitative estimates of the magnitude of the deposited activity. A variety of

  9. CROP YIELD AND CO2 FIXATION MONITORING IN ASIA USING A PHOTOSYNTHETICSTERILITY MODEL WITH SATELLITES AND METEOROLOGICAL DATA

    Energy Technology Data Exchange (ETDEWEB)

    Daijiro Kaneko [Department of Civil and Environmental Engineering, Matsue National College of Technology, Matsue (Japan); Toshiro Kumakura [Department of Civil and Environmental Engineering, Nagaoka University of Technology, Nagaoka (Japan); Peng Yang [Laboratory of Resources Remote Sensing and Digital Agriculture, Ministry of Agriculture, Beijing (China)

    2008-09-30

    This study is intended to develop a model for estimating carbon dioxide (CO{sub 2}) fixation in the carbon cycle and for monitoring grain yields using a photosynthetic-sterility model, which integrates solar radiation and air temperature effects on photosynthesis, along with grain-filling from heading to ripening. Grain production monitoring would support orderly crisis management to maintain food security in Asia, which is facing climate fluctuation through this century of global warming. The author improved a photosynthesis-and-sterility model to compute both the crop yield and crop situation index CSI, which gives a percentage of rice yields compared to normal annual production. The model calculates photosynthesis rates including biomass effects, lowtemperature sterility, and high-temperature injury by incorporating solar radiation, effective air temperature, the normalized difference vegetation index NDVI, and the effect of temperature on photosynthesis by grain plant leaves. A decision-tree method classifies the distribution of crop fields in Asia using MODIS fundamental landcover and SPOT VEGETATION data, which include the Normalized Vegetation index (NDVI) and Land Surface Water Index (LSWI). This study provides daily distributions of the photosynthesis rate, which is the CO2 fixation in Asian areas combined with the land-cover distribution, the Japanese geostationary meteorological satellite (GMS), and meteorological re-analysis data by National Centers for Environmental Prediction (NCEP). The method is based on routine observation data, enabling automated monitoring of crop yields.

  10. Comparison of surface meteorological data representativeness for the Weldon Spring transport and dispersion modeling analysis

    International Nuclear Information System (INIS)

    Lazaro, M.

    1989-06-01

    The US Department of Energy is conducting the Weldon Spring Site Remedial Action Project under the Surplus Facilities Management Program (SFMP). The major goals of the SFMP are to eliminate potential hazards to the public and the environment that associated with contamination at SFMP sites and to make surplus property available for other uses to the extent possible. This report presents the results of analysis of available meteorological data from stations near the Weldon Spring site. Data that are most representative of site conditions are needed to accurately model the transport and dispersion of air pollutants associated with remedial activities. Such modeling will assist the development of mitigative measures. 17 refs., 12 figs., 6 tabs

  11. Exploring Stage I non-small-cell lung cancer: development of a prognostic model predicting 5-year survival after surgical resection†.

    Science.gov (United States)

    Guerrera, Francesco; Errico, Luca; Evangelista, Andrea; Filosso, Pier Luigi; Ruffini, Enrico; Lisi, Elena; Bora, Giulia; Asteggiano, Elena; Olivetti, Stefania; Lausi, Paolo; Ardissone, Francesco; Oliaro, Alberto

    2015-06-01

    Despite impressive results in diagnosis and treatment of non-small-cell lung cancer (NSCLC), more than 30% of patients with Stage I NSCLC die within 5 years after surgical treatment. Identification of prognostic factors to select patients with a poor prognosis and development of tailored treatment strategies are then advisable. The aim of our study was to design a model able to define prognosis in patients with Stage I NSCLC, submitted to surgery with curative intent. A retrospective analysis of two surgical registries was performed. Predictors of survival were investigated using the Cox model with shared frailty (accounting for the within-centre correlation). Candidate predictors were: age, gender, smoking habit, morbidity, previous malignancy, Eastern Cooperative Oncology Group performance status, clinical N stage, maximum standardized uptake value (SUV(max)), forced expiratory volume in 1 s, carbon monoxide lung diffusion capacity (DLCO), extent of surgical resection, systematic lymphadenectomy, vascular invasion, pathological T stage, histology and histological grading. The final model included predictors with P model demonstrated that mortality was significantly associated with age, male sex, presence of cardiac comorbidities, DLCO (%), SUV(max), systematic nodal dissection, presence of microscopic vascular invasion, pTNM stage and histological grading. The final model showed a fair discrimination ability (C-statistic = 0.69): the calibration of the model indicated a good agreement between observed and predicted survival. We designed an effective prognostic model based on clinical, pathological and surgical covariates. Our preliminary results need to be refined and validated in a larger patient population, in order to provide an easy-to-use prognostic tool for Stage I NSCLC patients. © The Author 2014. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

  12. Validation of CALMET/CALPUFF models simulations around a large power plant stack

    Energy Technology Data Exchange (ETDEWEB)

    Hernandez-Garces, A.; Souto, J. A.; Rodriguez, A.; Saavedra, S.; Casares, J. J.

    2015-07-01

    Calmest/CALPUFF modeling system is frequently used in the study of atmospheric processes and pollution, and several validation tests were performed until now; nevertheless, most of them were based on experiments with a large compilation of surface and aloft meteorological measurements, rarely available. At the same time, the use of a large operational smokestack as tracer/pollutant source is not usual. In this work, first CALMET meteorological diagnostic model is nested to WRF meteorological prognostic model simulations (3x3 km{sup 2} horizontal resolution) over a complex terrain and coastal domain at NW Spain, covering 100x100 km{sup 2}, with a coal-fired power plant emitting SO{sub 2}. Simulations were performed during three different periods when SO{sub 2} hourly glc peaks were observed. NCEP reanalysis were applied as initial and boundary conditions. Yong Sei University-Pleim-Chang (YSU) PBL scheme was selected in the WRF model to provide the best input to three different CALMET horizontal resolutions, 1x1 km{sup 2}, 0.5x0.5 km{sup 2}, and 0.2x0.2 km{sup 2}. The best results, very similar between them, were achieved using the last two resolutions; therefore, the 0.5x0.5 km{sup 2} resolution was selected to test different CALMET meteorological inputs, using several combinations of WRF outputs and/or surface and upper-air measurements available in the simulation domain. With respect to meteorological aloft models output, CALMET PBL depth estimations are very similar to PBL depth estimations using upper-air measurements (rawinsondes), and significantly better than WRF PBL depth results. Regarding surface models surface output, the available meteorological sites were divided in two groups, one to provide meteorological input to CALMET (when applied), and another to models validation. Comparing WRF and CALMET outputs against surface measurements (from sites for models validation) the lowest RMSE was achieved using as CALMET input dataset WRF output combined with

  13. Validation of CALMET/CALPUFF models simulations around a large power plant stack

    Energy Technology Data Exchange (ETDEWEB)

    Hernandez-Garces, A.; Souto Rodriguez, J.A.; Saavedra, S.; Casares, J.J.

    2015-07-01

    CALMET/CALPUFF modeling system is frequently used in the study of atmospheric processes and pollution, and several validation tests were performed until now; nevertheless, most of them were based on experiments with a large compilation of surface and aloft meteorological measurements, rarely available. At the same time, the use of a large operational smokestack as tracer/pollutant source is not usual. In this work, first CALMET meteorological diagnostic model is nested to WRF meteorological prognostic model simulations (3x3 km2 horizontal resolution) over a complex terrain and coastal domain at NW Spain, covering 100x100 km2 , with a coal-fired power plant emitting SO2. Simulations were performed during three different periods when SO2 hourly glc peaks were observed. NCEP reanalysis were applied as initial and boundary conditions. Yong Sei University-Pleim-Chang (YSU) PBL scheme was selected in the WRF model to provide the best input to three different CALMET horizontal resolutions, 1x1 km2 , 0.5x0.5 km2 , and 0.2x0.2 km2. The best results, very similar between them, were achieved using the last two resolutions; therefore, the 0.5x0.5 km2 resolution was selected to test different CALMET meteorological inputs, using several combinations of WRF outputs and/or surface and upper-air measurements available in the simulation domain. With respect to meteorological aloft models output, CALMET PBL depth estimations are very similar to PBL depth estimations using upper-air measurements (rawinsondes), and significantly better than WRF PBL depth results. Regarding surface models surface output, the available meteorological sites were divided in two groups, one to provide meteorological input to CALMET (when applied), and another to models validation. Comparing WRF and CALMET outputs against surface measurements (from sites for models validation) the lowest RMSE was achieved using as CALMET input dataset WRF output combined with surface measurements (from sites for CALMET model

  14. Validation of CALMET/CALPUFF models simulations around a large power plant stack

    International Nuclear Information System (INIS)

    Hernandez-Garces, A.; Souto, J. A.; Rodriguez, A.; Saavedra, S.; Casares, J. J.

    2015-01-01

    Calmest/CALPUFF modeling system is frequently used in the study of atmospheric processes and pollution, and several validation tests were performed until now; nevertheless, most of them were based on experiments with a large compilation of surface and aloft meteorological measurements, rarely available. At the same time, the use of a large operational smokestack as tracer/pollutant source is not usual. In this work, first CALMET meteorological diagnostic model is nested to WRF meteorological prognostic model simulations (3x3 km 2 horizontal resolution) over a complex terrain and coastal domain at NW Spain, covering 100x100 km 2 , with a coal-fired power plant emitting SO 2 . Simulations were performed during three different periods when SO 2 hourly glc peaks were observed. NCEP reanalysis were applied as initial and boundary conditions. Yong Sei University-Pleim-Chang (YSU) PBL scheme was selected in the WRF model to provide the best input to three different CALMET horizontal resolutions, 1x1 km 2 , 0.5x0.5 km 2 , and 0.2x0.2 km 2 . The best results, very similar between them, were achieved using the last two resolutions; therefore, the 0.5x0.5 km 2 resolution was selected to test different CALMET meteorological inputs, using several combinations of WRF outputs and/or surface and upper-air measurements available in the simulation domain. With respect to meteorological aloft models output, CALMET PBL depth estimations are very similar to PBL depth estimations using upper-air measurements (rawinsondes), and significantly better than WRF PBL depth results. Regarding surface models surface output, the available meteorological sites were divided in two groups, one to provide meteorological input to CALMET (when applied), and another to models validation. Comparing WRF and CALMET outputs against surface measurements (from sites for models validation) the lowest RMSE was achieved using as CALMET input dataset WRF output combined with surface measurements (from sites for

  15. Predicting stabilizing treatment outcomes for complex posttraumatic stress disorder and dissociative identity disorder: an expertise-based prognostic model

    NARCIS (Netherlands)

    Baars, E.W.; van der Hart, O.; Nijenhuis, E.R.S.; Chu, J.A.; Glas, G.; Draaijer, N.

    2011-01-01

    The purpose of this study was to develop an expertise-based prognostic model for the treatment of complex posttraumatic stress disorder (PTSD) and dissociative identity disorder (DID).We developed a survey in 2 rounds: In the first round we surveyed 42 experienced therapists (22 DID and 20 complex

  16. The first mathematical models of dynamic meteorology: The Berlin prize contest of 1746

    Energy Technology Data Exchange (ETDEWEB)

    Egger, J.; Pelkowski, J. [Muenchen Univ. (Germany). Meteorologisches Inst.

    2008-02-15

    The first models of dynamic meteorology were published in 1747 as a result of a prize contest of the Academy of Prussia. The topic of the contest concerned the determination of the winds 'if the Earth were surrounded everywhere by an ocean'. D'Alembert formulated a shallow water model for the first time in his prize-winning contribution and attempted to calculate tidal motions within the fluid layers. Daniel Bernoulli viewed the atmosphere as a boundary layer wherein the winds rotating with the earth at low elevations have to adjust their motion to a solar atmosphere at large heights. He is first in applying the principle of angular momentum conservation in continuum geophysics when calculating the zonal wind profile. An account of the historical background of the contest is given, as well as some later reactions to d'Alembert's solution. (orig.)

  17. Meteorology Online.

    Science.gov (United States)

    Kahl, Jonathan D. W.

    2001-01-01

    Describes an activity to learn about meteorology and weather using the internet. Discusses the National Weather Service (NWS) internet site www.weather.gov. Students examine maximum and minimum daily temperatures, wind speed, and direction. (SAH)

  18. Uncertainty analysis of hydro-meteorological forecasts

    OpenAIRE

    Grythe, Karl Kristian; Gao, Yukun

    2010-01-01

    Masteroppgave i informasjons- og kommunikasjonsteknologi 2010 – Universitetet i Agder, Grimstad Meteorological and hydrological forecasts are very important to human’s life which concerns agriculture, industry, transport, etc. The Nordic hydropower industry use and develop hydrological forecasting models to make predictions of rivers steam flow. The quantity of incoming stream flow is important to the electricity production because excessive water in reservoir will cause flood ...

  19. Epicurean Meteorology: Sources, method, scope and organization

    NARCIS (Netherlands)

    Bakker, F.A.

    2016-01-01

    In Epicurean Meteorology Frederik Bakker discusses the meteorology as laid out by Epicurus (341-270 BCE) and Lucretius (1st century BCE). Although in scope and organization their ideas are clearly rooted in the Peripatetic tradition, their meteorology sets itself apart from this tradition by its

  20. Prognostic Biomarker Identification Through Integrating the Gene Signatures of Hepatocellular Carcinoma Properties

    Directory of Open Access Journals (Sweden)

    Jialin Cai

    2017-05-01

    Full Text Available Many molecular classification and prognostic gene signatures for hepatocellular carcinoma (HCC patients have been established based on genome-wide gene expression profiling; however, their generalizability is unclear. Herein, we systematically assessed the prognostic effects of these gene signatures and identified valuable prognostic biomarkers by integrating these gene signatures. With two independent HCC datasets (GSE14520, N = 242 and GSE54236, N = 78, 30 published gene signatures were evaluated, and 11 were significantly associated with the overall survival (OS of postoperative HCC patients in both datasets. The random survival forest models suggested that the gene signatures were superior to clinical characteristics for predicting the prognosis of the patients. Based on the 11 gene signatures, a functional protein-protein interaction (PPI network with 1406 nodes and 10,135 edges was established. With tissue microarrays of HCC patients (N = 60, we determined the prognostic values of the core genes in the network and found that RAD21, CDK1, and HDAC2 expression levels were negatively associated with OS for HCC patients. The multivariate Cox regression analyses suggested that CDK1 was an independent prognostic factor, which was validated in an independent case cohort (N = 78. In cellular models, inhibition of CDK1 by siRNA or a specific inhibitor, RO-3306, reduced cellular proliferation and viability for HCC cells. These results suggest that the prognostic predictive capacities of these gene signatures are reproducible and that CDK1 is a potential prognostic biomarker or therapeutic target for HCC patients.

  1. Meteorological Summaries

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Multi-year summaries of one or more meteorological elements at a station or in a state. Primarily includes Form 1078, a United States Weather Bureau form designed...

  2. Medical Meteorology: the Relationship between Meteorological Parameters (Humidity, Rainfall, Wind, and Temperature and Brucellosis in Zanjan Province

    Directory of Open Access Journals (Sweden)

    Yousefali Abedini

    2016-06-01

    Full Text Available Background: Brucellosis (Malta fever is a major contagious zoonotic disease, with economic and public health importance. Methods To assess the effect of meteorological (temperature, rainfall, humidity, and wind and climate parameters on incidence of brucellosis, brucellosis distribution and meteorological zoning maps of Zanjan Province were prepared using Inverse Distance Weighting (IDW and Kriging technique in Arc GIS medium. Zoning maps of mean temperature, rainfall, humidity, and wind were compared to brucellosis distribution maps. Results: Correlation test showed no relationship between the mean number of patients with brucellosis and any of the four meteorological parameters. Conclusion: It seems that in Zanjan province there is no correlation between brucellosis and meteorological parameters.

  3. Technology and Meteorology. An Action Research Paper.

    Science.gov (United States)

    Taggart, Raymond F.

    Meteorology, the science of weather and weather conditions, has traditionally been taught via textbook and rote demonstration. This study was intended to determine to what degree utilizing technology in the study of meteorology improves students' attitudes towards science and to measure to what extent technology in meteorology increases…

  4. Real-Time Prognostics of a Rotary Valve Actuator

    Science.gov (United States)

    Daigle, Matthew

    2015-01-01

    Valves are used in many domains and often have system-critical functions. As such, it is important to monitor the health of valves and their actuators and predict remaining useful life. In this work, we develop a model-based prognostics approach for a rotary valve actuator. Due to limited observability of the component with multiple failure modes, a lumped damage approach is proposed for estimation and prediction of damage progression. In order to support the goal of real-time prognostics, an approach to prediction is developed that does not require online simulation to compute remaining life, rather, a function mapping the damage state to remaining useful life is found offline so that predictions can be made quickly online with a single function evaluation. Simulation results demonstrate the overall methodology, validating the lumped damage approach and demonstrating real-time prognostics.

  5. A Hybrid Prognostic Approach for Remaining Useful Life Prediction of Lithium-Ion Batteries

    Directory of Open Access Journals (Sweden)

    Wen-An Yang

    2016-01-01

    Full Text Available Lithium-ion battery is a core component of many systems such as satellite, spacecraft, and electric vehicles and its failure can lead to reduced capability, downtime, and even catastrophic breakdowns. Remaining useful life (RUL prediction of lithium-ion batteries before the future failure event is extremely crucial for proactive maintenance/safety actions. This study proposes a hybrid prognostic approach that can predict the RUL of degraded lithium-ion batteries using physical laws and data-driven modeling simultaneously. In this hybrid prognostic approach, the relevant vectors obtained with the selective kernel ensemble-based relevance vector machine (RVM learning algorithm are fitted to the physical degradation model, which is then extrapolated to failure threshold for estimating the RUL of the lithium-ion battery of interest. The experimental results indicated that the proposed hybrid prognostic approach can accurately predict the RUL of degraded lithium-ion batteries. Empirical comparisons show that the proposed hybrid prognostic approach using the selective kernel ensemble-based RVM learning algorithm performs better than the hybrid prognostic approaches using the popular learning algorithms of feedforward artificial neural networks (ANNs like the conventional backpropagation (BP algorithm and support vector machines (SVMs. In addition, an investigation is also conducted to identify the effects of RVM learning algorithm on the proposed hybrid prognostic approach.

  6. Improved Meteorological Input for Atmospheric Release Decision support Systems and an Integrated LES Modeling System for Atmospheric Dispersion of Toxic Agents: Homeland Security Applications

    Energy Technology Data Exchange (ETDEWEB)

    Arnold, E; Simpson, M; Larsen, S; Gash, J; Aluzzi, F; Lundquist, J; Sugiyama, G

    2010-04-26

    When hazardous material is accidently or intentionally released into the atmosphere, emergency response organizations look to decision support systems (DSSs) to translate contaminant information provided by atmospheric models into effective decisions to protect the public and emergency responders and to mitigate subsequent consequences. The Department of Homeland Security (DHS)-led Interagency Modeling and Atmospheric Assessment Center (IMAAC) is one of the primary DSSs utilized by emergency management organizations. IMAAC is responsible for providing 'a single piont for the coordination and dissemination of Federal dispersion modeling and hazard prediction products that represent the Federal position' during actual or potential incidents under the National Response Plan. The Department of Energy's (DOE) National Atmospheric Release Advisory Center (NARAC), locatec at the Lawrence Livermore National Laboratory (LLNL), serves as the primary operations center of the IMAAC. A key component of atmospheric release decision support systems is meteorological information - models and data of winds, turbulence, and other atmospheric boundary-layer parameters. The accuracy of contaminant predictions is strongly dependent on the quality of this information. Therefore, the effectiveness of DSSs can be enhanced by improving the meteorological options available to drive atmospheric transport and fate models. The overall goal of this project was to develop and evaluate new meteorological modeling capabilities for DSSs based on the use of NASA Earth-science data sets in order to enhance the atmospheric-hazard information provided to emergency managers and responders. The final report describes the LLNL contributions to this multi-institutional effort. LLNL developed an approach to utilize NCAR meteorological predictions using NASA MODIS data for the New York City (NYC) region and demonstrated the potential impact of the use of different data sources and data

  7. Development and analysis of prognostic equations for mesoscale kinetic energy and mesoscale (subgrid scale) fluxes for large-scale atmospheric models

    Science.gov (United States)

    Avissar, Roni; Chen, Fei

    1993-01-01

    Generated by landscape discontinuities (e.g., sea breezes) mesoscale circulation processes are not represented in large-scale atmospheric models (e.g., general circulation models), which have an inappropiate grid-scale resolution. With the assumption that atmospheric variables can be separated into large scale, mesoscale, and turbulent scale, a set of prognostic equations applicable in large-scale atmospheric models for momentum, temperature, moisture, and any other gaseous or aerosol material, which includes both mesoscale and turbulent fluxes is developed. Prognostic equations are also developed for these mesoscale fluxes, which indicate a closure problem and, therefore, require a parameterization. For this purpose, the mean mesoscale kinetic energy (MKE) per unit of mass is used, defined as E-tilde = 0.5 (the mean value of u'(sub i exp 2), where u'(sub i) represents the three Cartesian components of a mesoscale circulation (the angle bracket symbol is the grid-scale, horizontal averaging operator in the large-scale model, and a tilde indicates a corresponding large-scale mean value). A prognostic equation is developed for E-tilde, and an analysis of the different terms of this equation indicates that the mesoscale vertical heat flux, the mesoscale pressure correlation, and the interaction between turbulence and mesoscale perturbations are the major terms that affect the time tendency of E-tilde. A-state-of-the-art mesoscale atmospheric model is used to investigate the relationship between MKE, landscape discontinuities (as characterized by the spatial distribution of heat fluxes at the earth's surface), and mesoscale sensible and latent heat fluxes in the atmosphere. MKE is compared with turbulence kinetic energy to illustrate the importance of mesoscale processes as compared to turbulent processes. This analysis emphasizes the potential use of MKE to bridge between landscape discontinuities and mesoscale fluxes and, therefore, to parameterize mesoscale fluxes

  8. Model-based prognostics for batteries which estimates useful life and uses a probability density function

    Science.gov (United States)

    Saha, Bhaskar (Inventor); Goebel, Kai F. (Inventor)

    2012-01-01

    This invention develops a mathematical model to describe battery behavior during individual discharge cycles as well as over its cycle life. The basis for the form of the model has been linked to the internal processes of the battery and validated using experimental data. Effects of temperature and load current have also been incorporated into the model. Subsequently, the model has been used in a Particle Filtering framework to make predictions of remaining useful life for individual discharge cycles as well as for cycle life. The prediction performance was found to be satisfactory as measured by performance metrics customized for prognostics for a sample case. The work presented here provides initial steps towards a comprehensive health management solution for energy storage devices.

  9. Proposal of a clinical typing system and generation of a prognostic model in patients with nasopharyngeal carcinoma from Southern China.

    Science.gov (United States)

    Sun, Peng; Chen, Cui; Chen, Xin-Lin; Cheng, Yi-Kan; Zeng, Lei; Zeng, Zhi-Jian; Liu, Li-Zhi; Su, Yong; Gu, Mo-Fa

    2014-01-01

    To propose a novel clinical typing classification focusing on the distinct progression patterns of nasopharyngeal carcinoma (NPC), to supplement our knowledge of the clinical-biological behavior, to provide useful knowledge for treatment planning, and to contribute to basic research in NPC. 632 consecutive patients were retrospectively reviewed and analyzed according to the novel typing system. We considered that NPC can be divided into 5 types as follows: limited (L), ascending (A), descending (D) ascending- descending (mixed) (AD), and distant metastasis types (M). The distribution of these clinical types, their association with Epstein-Barr virus (EBV) serology and prognostic value were explored. 55 (8.70%), 59 (9.34%), 177 (28.01%), 321 (50.79%) and 20 (3.16%) patients were classified as Type L, A, D, AD and M, respectively. EBV (VCA)-IgA titers, EBV early antigen (EA)-IgA serum titers, and capsid antigen lg(EBV DNA) were positively associated with the clinical typing (pTypes L, A, D, AD and M were 100, 100, 95.10, 88.20 and 59.30%, respectively (ptype, which were independent predictors of OS (multivariate Cox proportional model). The prognostic model stratified patients into 4 risk subgroups. The 3-year OS rates of the low, intermediate, high and extremely high risk groups were 99.5, 90.0, 85.5 and 53.2%, respectively (ptyping system and prognostic model can supplement TNM classification, and may help design novel treatment strategies, evaluate risk stratification and investigate the varied biological characteristics of NPC.

  10. Defense Meteorological Satellite Program (DMSP) Film

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The United States Air Force Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) is a polar orbiting meteorological sensor with two...

  11. A comparative study of the response of modeled non-drizzling stratocumulus to meteorological and aerosol perturbations

    Directory of Open Access Journals (Sweden)

    J. L. Petters

    2013-03-01

    Full Text Available The impact of changes in aerosol and cloud droplet concentration (Na and Nd on the radiative forcing of stratocumulus-topped boundary layers (STBLs has been widely studied. How these impacts compare to those due to variations in meteorological context has not been investigated in a systematic fashion for non-drizzling overcast stratocumulus. In this study we examine the impact of observed variations in meteorological context and aerosol state on daytime, non-drizzling overcast stratiform evolution, and determine how resulting changes in cloud properties compare. Using large-eddy simulation (LES we create a model base case of daytime southeast Pacific coastal stratocumulus, spanning a portion of the diurnal cycle (early morning to near noon and constrained by observations taken during the VOCALS (VAMOS Ocean-Atmosphere-Land Study field campaign. We perturb aerosol and meteorological properties around this base case to investigate the stratocumulus response. We determine perturbations in the cloud top jumps in potential temperature θ and total water mixing ratio qt from ECMWF Re-analysis Interim data, and use a set of Nd values spanning the observable range. To determine the cloud response to these meteorological and aerosol perturbations, we compute changes in liquid water path (LWP, bulk optical depth (τ and cloud radiative forcing (CRF. We find that realistic variations in the thermodynamic jump properties can elicit a response in the cloud properties of τ and shortwave (SW CRF that are on the same order of magnitude as the response found due to realistic changes in aerosol state (i.e Nd. In response to increases in Nd, the cloud layer in the base case thinned due to increases in evaporative cooling and entrainment rate. This cloud thinning somewhat mitigates the increase in τ resulting from increases in Nd. On the other hand, variations in θ and qt jumps did not substantially modify Nd. The cloud layer thickens in response to an increase

  12. Applications of the Regional Atmospheric Modeling System (RAMS) to provide input to photochemical grid models for the Lake Michigan Ozone Study (LMOS)

    Energy Technology Data Exchange (ETDEWEB)

    Lyons, W.A.; Tremback, C.J.; Pielke, R.A. [ASTeR, Inc., Ft. Collins, CO (United States); Eastman, J.L. [Colorado State Univ., Ft. Collins, CO (United States)

    1994-12-31

    In spite of stringent emission controls, numerous exceedances of the US ozone air quality standard have continued in the Lake Michigan region, especially during the very hot summers of 1987 and 1988. Analyses revealed that exceedances of the 120 PPB hourly standard were 400% more likely at monitors located within 20 km of the lakeshore. While the role of Lake Michigan in exacerbating regional air quality problems has been investigated for almost 20 years, the relative impacts of various phenomena upon regional photochemical air quality have yet to be quantified. In order to design a defensible regional emission control policy, LMOS sponsored the development of a comprehensive regional photochemical modeling system. This is comprised of an emission model, an advanced regional photochemical model, and a prognostic meteorological model.

  13. Development and Validation of a Lifecycle-based Prognostics Architecture with Test Bed Validation

    Energy Technology Data Exchange (ETDEWEB)

    Hines, J. Wesley [Univ. of Tennessee, Knoxville, TN (United States); Upadhyaya, Belle [Univ. of Tennessee, Knoxville, TN (United States); Sharp, Michael [Univ. of Tennessee, Knoxville, TN (United States); Ramuhalli, Pradeep [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Jeffries, Brien [Univ. of Tennessee, Knoxville, TN (United States); Nam, Alan [Univ. of Tennessee, Knoxville, TN (United States); Strong, Eric [Univ. of Tennessee, Knoxville, TN (United States); Tong, Matthew [Univ. of Tennessee, Knoxville, TN (United States); Welz, Zachary [Univ. of Tennessee, Knoxville, TN (United States); Barbieri, Federico [Univ. of Tennessee, Knoxville, TN (United States); Langford, Seth [Univ. of Tennessee, Knoxville, TN (United States); Meinweiser, Gregory [Univ. of Tennessee, Knoxville, TN (United States); Weeks, Matthew [Univ. of Tennessee, Knoxville, TN (United States)

    2014-11-06

    On-line monitoring and tracking of nuclear plant system and component degradation is being investigated as a method for improving the safety, reliability, and maintainability of aging nuclear power plants. Accurate prediction of the current degradation state of system components and structures is important for accurate estimates of their remaining useful life (RUL). The correct quantification and propagation of both the measurement uncertainty and model uncertainty is necessary for quantifying the uncertainty of the RUL prediction. This research project developed and validated methods to perform RUL estimation throughout the lifecycle of plant components. Prognostic methods should seamlessly operate from beginning of component life (BOL) to end of component life (EOL). We term this "Lifecycle Prognostics." When a component is put into use, the only information available may be past failure times of similar components used in similar conditions, and the predicted failure distribution can be estimated with reliability methods such as Weibull Analysis (Type I Prognostics). As the component operates, it begins to degrade and consume its available life. This life consumption may be a function of system stresses, and the failure distribution should be updated to account for the system operational stress levels (Type II Prognostics). When degradation becomes apparent, this information can be used to again improve the RUL estimate (Type III Prognostics). This research focused on developing prognostics algorithms for the three types of prognostics, developing uncertainty quantification methods for each of the algorithms, and, most importantly, developing a framework using Bayesian methods to transition between prognostic model types and update failure distribution estimates as new information becomes available. The developed methods were then validated on a range of accelerated degradation test beds. The ultimate goal of prognostics is to provide an accurate assessment for

  14. Extreme meteorological conditions

    International Nuclear Information System (INIS)

    Altinger de Schwarzkopf, M.L.

    1983-01-01

    Different meteorological variables which may reach significant extreme values, such as the windspeed and, in particular, its occurrence through tornadoes and hurricanes that necesarily incide and wich must be taken into account at the time of nuclear power plants' installation, are analyzed. For this kind of study, it is necessary to determine the basic phenomenum of design. Two criteria are applied to define the basic values of design for extreme meteorological variables. The first one determines the expected extreme value: it is obtained from analyzing the recurence of the phenomenum in a convened period of time, wich may be generally of 50 years. The second one determines the extreme value of low probability, taking into account the nuclear power plant's operating life -f.ex. 25 years- and considering, during said lapse, the occurrence probabilities of extreme meteorological phenomena. The values may be determined either by the deterministic method, which is based on the acknowledgement of the fundamental physical characteristics of the phenomena or by the probabilistic method, that aims to the analysis of historical statistical data. Brief comments are made on the subject in relation to the Argentine Republic area. (R.J.S.) [es

  15. A model-based prognostic approach to predict interconnect failure using impedance analysis

    Energy Technology Data Exchange (ETDEWEB)

    Kwon, Dae Il; Yoon, Jeong Ah [Dept. of System Design and Control Engineering. Ulsan National Institute of Science and Technology, Ulsan (Korea, Republic of)

    2016-10-15

    The reliability of electronic assemblies is largely affected by the health of interconnects, such as solder joints, which provide mechanical, electrical and thermal connections between circuit components. During field lifecycle conditions, interconnects are often subjected to a DC open circuit, one of the most common interconnect failure modes, due to cracking. An interconnect damaged by cracking is sometimes extremely hard to detect when it is a part of a daisy-chain structure, neighboring with other healthy interconnects that have not yet cracked. This cracked interconnect may seem to provide a good electrical contact due to the compressive load applied by the neighboring healthy interconnects, but it can cause the occasional loss of electrical continuity under operational and environmental loading conditions in field applications. Thus, cracked interconnects can lead to the intermittent failure of electronic assemblies and eventually to permanent failure of the product or the system. This paper introduces a model-based prognostic approach to quantitatively detect and predict interconnect failure using impedance analysis and particle filtering. Impedance analysis was previously reported as a sensitive means of detecting incipient changes at the surface of interconnects, such as cracking, based on the continuous monitoring of RF impedance. To predict the time to failure, particle filtering was used as a prognostic approach using the Paris model to address the fatigue crack growth. To validate this approach, mechanical fatigue tests were conducted with continuous monitoring of RF impedance while degrading the solder joints under test due to fatigue cracking. The test results showed the RF impedance consistently increased as the solder joints were degraded due to the growth of cracks, and particle filtering predicted the time to failure of the interconnects similarly to their actual timesto- failure based on the early sensitivity of RF impedance.

  16. Prognostics

    Data.gov (United States)

    National Aeronautics and Space Administration — Prognostics has received considerable attention recently as an emerging sub-discipline within SHM. Prognosis is here strictly defined as “predicting the time at...

  17. Modern history of meteorological services with pictures for a century

    International Nuclear Information System (INIS)

    2004-07-01

    This book deals with modern history of meteorological services with pictures for a century. It is divided into twelve chapters, which mention meteorological services before the Joseon Dynasty period, meteorological observation about surface weather observation, aero logical observation, meteorological satellite, seismometry, observation on yellow dust, and observation on the falling of thunderbolt, weather forecast, meteorological telecommunication, education for weather, research for weather, promotion on weather, international cooperation, main events, special aid on meteorological services, meteorological disaster and the list of the offices for meteorological services.

  18. Development of adequate meteorological monitoring standards for safety analysis of nuclear facilities

    International Nuclear Information System (INIS)

    Alp, E.; Lewis, P.J.

    1985-09-01

    The aim of this report is to identify what constitutes adequate meteorological information for airborne dispersion calculations in case of releases from nuclear facilities during 'normal operation', 'design postulated accidents', and 'emergency situations'. The models used for estimating downwind dispersion are reviewed, including short-range simple terrain, short-range complex terrain and medium to long range models with emphasis on Lagrangian models. The meteorogolical input parameters required for running these models are identified. The methods by which these parameters may be obtained from raw meteorological data are then considered. Emphasis is placed on well-tried and recommended methods rather than those which are currently being developed and lack long-term field tests. The meteorological data required to calculate the parameters that are in turn input to dispersion calculation methods can be obtained mainly from tower measurements. Recommended tower height is 50 m, with two levels of instruments (10 and 50 m) for wind speed, wind direction and temperature. Data for precipitation and solar radiation, that may be required under certain conditions and for special calculations, may be estimated from nearby representative weather stations (if available). For simple terrain, a single tower is sufficient. For complex terrain, such as coastal regions, two towers are desirable for accurate characterization of the turbulence regime in the vicinity of a release site. The report provides the necessary accuracy specifications for instruments required for the meteorological measurements. Data monitoring and recording, maintenance, quality control and assurance are also discussed. Error propagation analyses are recommended to determine the full implications of instrument accuracies on the accuracy of dispersion model predictions. 82 refs

  19. COMPARISON OF CONSEQUENCE ANALYSIS RESULTS FROM TWO METHODS OF PROCESSING SITE METEOROLOGICAL DATA

    International Nuclear Information System (INIS)

    , D

    2007-01-01

    Consequence analysis to support documented safety analysis requires the use of one or more years of representative meteorological data for atmospheric transport and dispersion calculations. At minimum, the needed meteorological data for most atmospheric transport and dispersion models consist of hourly samples of wind speed and atmospheric stability class. Atmospheric stability is inferred from measured and/or observed meteorological data. Several methods exist to convert measured and observed meteorological data into atmospheric stability class data. In this paper, one year of meteorological data from a western Department of Energy (DOE) site is processed to determine atmospheric stability class using two methods. The method that is prescribed by the U.S. Nuclear Regulatory Commission (NRC) for supporting licensing of nuclear power plants makes use of measurements of vertical temperature difference to determine atmospheric stability. Another method that is preferred by the U.S. Environmental Protection Agency (EPA) relies upon measurements of incoming solar radiation, vertical temperature gradient, and wind speed. Consequences are calculated and compared using the two sets of processed meteorological data from these two methods as input data into the MELCOR Accident Consequence Code System 2 (MACCS2) code

  20. Meteorological interpretation of transient LOD changes

    Science.gov (United States)

    Masaki, Y.

    2008-04-01

    The Earth’s spin rate is mainly changed by zonal winds. For example, seasonal changes in global atmospheric circulation and episodic changes accompanied with El Nĩ os are clearly detected n in the Length-of-day (LOD). Sub-global to regional meteorological phenomena can also change the wind field, however, their effects on the LOD are uncertain because such LOD signals are expected to be subtle and transient. In our previous study (Masaki, 2006), we introduced atmospheric pressure gradients in the upper atmosphere in order to obtain a rough picture of the meteorological features that can change the LOD. In this presentation, we compare one-year LOD data with meteorological elements (winds, temperature, pressure, etc.) and make an attempt to link transient LOD changes with sub-global meteorological phenomena.

  1. Application of zero-inflated poisson mixed models in prognostic factors of hepatitis C.

    Science.gov (United States)

    Akbarzadeh Baghban, Alireza; Pourhoseingholi, Asma; Zayeri, Farid; Jafari, Ali Akbar; Alavian, Seyed Moayed

    2013-01-01

    In recent years, hepatitis C virus (HCV) infection represents a major public health problem. Evaluation of risk factors is one of the solutions which help protect people from the infection. This study aims to employ zero-inflated Poisson mixed models to evaluate prognostic factors of hepatitis C. The data was collected from a longitudinal study during 2005-2010. First, mixed Poisson regression (PR) model was fitted to the data. Then, a mixed zero-inflated Poisson model was fitted with compound Poisson random effects. For evaluating the performance of the proposed mixed model, standard errors of estimators were compared. The results obtained from mixed PR showed that genotype 3 and treatment protocol were statistically significant. Results of zero-inflated Poisson mixed model showed that age, sex, genotypes 2 and 3, the treatment protocol, and having risk factors had significant effects on viral load of HCV patients. Of these two models, the estimators of zero-inflated Poisson mixed model had the minimum standard errors. The results showed that a mixed zero-inflated Poisson model was the almost best fit. The proposed model can capture serial dependence, additional overdispersion, and excess zeros in the longitudinal count data.

  2. Coupling of WRF meteorological model to WAM spectral wave model through sea surface roughness at the Balearic Sea: impact on wind and wave forecasts

    Science.gov (United States)

    Tolosana-Delgado, R.; Soret, A.; Jorba, O.; Baldasano, J. M.; Sánchez-Arcilla, A.

    2012-04-01

    Meteorological models, like WRF, usually describe the earth surface characteristics by tables that are function of land-use. The roughness length (z0) is an example of such approach. However, over sea z0 is modeled by the Charnock (1955) relation, linking the surface friction velocity u*2 with the roughness length z0 of turbulent air flow, z0 = α-u2* g The Charnock coefficient α may be considered a measure of roughness. For the sea surface, WRF considers a constant roughness α = 0.0185. However, there is evidence that sea surface roughness should depend on wave energy (Donelan, 1982). Spectral wave models like WAM, model the evolution and propagation of wave energy as a function of wind, and include a richer sea surface roughness description. Coupling WRF and WAM is thus a common way to improve the sea surface roughness description of WRF. WAM is a third generation wave model, solving the equation of advection of wave energy subject to input/output terms of: wind growth, energy dissipation and resonant non-linear wave-wave interactions. Third generation models work on the spectral domain. WAM considers the Charnock coefficient α a complex yet known function of the total wind input term, which depends on the wind velocity and on the Charnock coefficient again. This is solved iteratively (Janssen et al., 1990). Coupling of meteorological and wave models through a common Charnock coefficient is operationally done in medium-range met forecasting systems (e.g., at ECMWF) though the impact of coupling for smaller domains is not yet clearly assessed (Warner et al, 2010). It is unclear to which extent the additional effort of coupling improves the local wind and wave fields, in comparison to the effects of other factors, like e.g. a better bathymetry and relief resolution, or a better circulation information which might have its influence on local-scale meteorological processes (local wind jets, local convection, daily marine wind regimes, etc.). This work, within the

  3. EVALUATION OF METEOROLOGICAL ALERT CHAIN IN CASTILLA Y LEÓN (SPAIN): How can the meteorological risk managers help researchers?

    Science.gov (United States)

    López, Laura; Guerrero-Higueras, Ángel Manuel; Sánchez, José Luis; Matía, Pedro; Ortiz de Galisteo, José Pablo; Rodríguez, Vicente; Lorente, José Manuel; Merino, Andrés; Hermida, Lucía; García-Ortega, Eduardo; Fernández-Manso, Oscar

    2013-04-01

    Evaluating the meteorological alert chain, or, how information is transmitted from the meteorological forecasters to the final users, passing through risk managers, is a useful tool that benefits all the links of the chain, especially the meteorology researchers and forecasters. In fact, the risk managers can help significantly to improve meteorological forecasts in different ways. Firstly, by pointing out the most appropriate type of meteorological format, and its characteristics when representing the meteorological information, consequently improving the interpretation of the already-existing forecasts. Secondly, by pointing out the specific predictive needs in their workplaces related to the type of significant meteorological parameters, temporal or spatial range necessary, meteorological products "custom-made" for each type of risk manager, etc. In order to carry out an evaluation of the alert chain in Castilla y León, we opted for the creation of a Panel of Experts made up of personnel specialized in risk management (Responsible for Protection Civil, Responsible for Alert Services and Hydrological Planning of Hydrographical Confederations, Responsible for highway maintenance, and management of fires, fundamentally). In creating this panel, a total of twenty online questions were evaluated, and the majority of the questions were multiple choice or open-ended. Some of the results show how the risk managers think that it would be interesting, or very interesting, to carry out environmental educational campaigns about the meteorological risks in Castilla y León. Another result is the elevated importance that the risk managers provide to the observation data in real-time (real-time of wind, lightning, relative humidity, combined indices of risk of avalanches, snowslides, index of fires due to convective activity, etc.) Acknowledgements The authors would like to thank the Junta de Castilla y León for its financial support through the project LE220A11-2.

  4. Analysis of the effects of meteorology on aircraft exhaust dispersion and deposition using a Lagrangian particle model.

    Science.gov (United States)

    Pecorari, Eliana; Mantovani, Alice; Franceschini, Chiara; Bassano, Davide; Palmeri, Luca; Rampazzo, Giancarlo

    2016-01-15

    The risk of air quality degradation is of considerable concern particularly for those airports that are located near urban areas. The ability to quantitatively predict the effects of air pollutants originated by airport operations is important for assessing air quality and the related impacts on human health. Current emission regulations have focused on local air quality in the proximity of airports. However, an integrated study should consider the effects of meteorological events, at both regional and local level, that can affect the dispersion and the deposition of exhausts. Rigorous scientific studies and extensive experimental data could contribute to the analysis of the impacts of airports expansion plans. This paper is focused on the analysis of the effects of meteorology on aircraft emission for the Marco Polo Airport in Venice. This is the most important international airport in the eastern part of the Po' Valley, one of the most polluted area in Europe. Air pollution is exacerbated by meteorology that is a combination of large and local scale effects that do not allow significant dispersion. Moreover, the airport is located near Venice, a city of noteworthy cultural and architectural relevance, and nearby the lagoon that hosts several areas of outstanding ecological importance at European level (Natura 2000 sites). Dispersion and deposit of the main aircraft exhausts (NOx, HC and CO) have been evaluated by using a Lagrangian particle model. Spatial and temporal aircraft exhaust dispersion has been analyzed for LTO cycle. Aircraft taxiing resulted to be the most impacting aircraft operation especially for the airport working area and its surroundings, however occasionally peaks may be observed even at high altitudes when cruise mode starts. Mixing height can affect concentrations more significantly than the concentrations in the exhausts themselves. An increase of HC and CO concentrations (15-50%) has been observed during specific meteorological events

  5. HYDRO-METEOROLOGICAL CHARACTERISTICS FOR SUSTAINABLE LAND MANAGEMENT IN THE SINGKARAK BASIN, WEST SUMATRA

    Directory of Open Access Journals (Sweden)

    Kasdi Subagyono

    2008-11-01

    Full Text Available Studi tentang karakteristik hidro-meteorologi telah dilakukan di wilayah danau Singkarak pada 2006-2007 dengan melibatkan partisipasi masyarakat. Stasiun iklim otomatis dan pengukur tinggi muka air otomatis dipasang untuk memonitor data hidrologi dan meteorologi di wilayah cekungan Singkarak. Data meteorologi dianalisa untuk mengetahui karakteristik iklim di wilayah sekitar danau. Model hidrologi GR4J dan H2U diaplikasikan untuk simulasi discharge dan untuk mengkarakterisasi proses hidrologi di wilayah danau. Simulasi model aliran divalidasi pada musim hujan. Alternatif pengelolaan lahan diformulasikan berdasarkan karakteristik hidrologi daerah aliran sungai di sekitar cekungan Singkarak. Hasil penelitian menunjukkan bahwa daerah tangkapan di sekitar danau Singkarak memiliki respon yang tinggi terhadap jumlah dan intensitas hujan. Hidrograp menunjukkan peningkatan yang tajam dari discharge segera setelah curah hujan mulai dan menurun relative lamban ketika curah hujan berhenti. Untuk pengelolaan lahan secara berkelanjutan di wilayah danau Singkarak, konservasi lahan dan air harus menjadi prioritas utama. Wanatani dapat diimplementasikan sebagai alternatif sistem pertanaman oleh penduduk lokal. Karena potensi kelangkaan air bisa terjadi pada periode kering, panen air dan konservasi air dapat diterapkan sebagai opsi yang dapat dikombinasikan dalam sistem pengelolaan lahan.   Hydro-meteorological processes of the Singkarak basin has been studied involving participatory of local community in 2006-2007. Automatic weather station (AWS and automatic water level recorder (AWLR were installed to record meteorological and hydrological data within the Singkarak Basin. Meteorological data was analyzed to understand the meteorological characteristic surrounding the Basin area. Model of GR4J and H2U were used to simulated discharge and to understand the hydrological processes within the basin. The validation of simulated discharge was done in the wet season

  6. Variations in environmental tritium doses due to meteorological data averaging and uncertainties in pathway model parameters

    Energy Technology Data Exchange (ETDEWEB)

    Kock, A.

    1996-05-01

    The objectives of this research are: (1) to calculate and compare off site doses from atmospheric tritium releases at the Savannah River Site using monthly versus 5 year meteorological data and annual source terms, including additional seasonal and site specific parameters not included in present annual assessments; and (2) to calculate the range of the above dose estimates based on distributions in model parameters given by uncertainty estimates found in the literature. Consideration will be given to the sensitivity of parameters given in former studies.

  7. Variations in environmental tritium doses due to meteorological data averaging and uncertainties in pathway model parameters

    International Nuclear Information System (INIS)

    Kock, A.

    1996-05-01

    The objectives of this research are: (1) to calculate and compare off site doses from atmospheric tritium releases at the Savannah River Site using monthly versus 5 year meteorological data and annual source terms, including additional seasonal and site specific parameters not included in present annual assessments; and (2) to calculate the range of the above dose estimates based on distributions in model parameters given by uncertainty estimates found in the literature. Consideration will be given to the sensitivity of parameters given in former studies

  8. Modeling Occurrence of Urban Mosquitos Based on Land Use Types and Meteorological Factors in Korea

    Directory of Open Access Journals (Sweden)

    Yong-Su Kwon

    2015-10-01

    Full Text Available Mosquitoes are a public health concern because they are vectors of pathogen, which cause human-related diseases. It is well known that the occurrence of mosquitoes is highly influenced by meteorological conditions (e.g., temperature and precipitation and land use, but there are insufficient studies quantifying their impacts. Therefore, three analytical methods were applied to determine the relationships between urban mosquito occurrence, land use type, and meteorological factors: cluster analysis based on land use types; principal component analysis (PCA based on mosquito occurrence; and three prediction models, support vector machine (SVM, classification and regression tree (CART, and random forest (RF. We used mosquito data collected at 12 sites from 2011 to 2012. Mosquito abundance was highest from August to September in both years. The monitoring sites were differentiated into three clusters based on differences in land use type such as culture and sport areas, inland water, artificial grasslands, and traffic areas. These clusters were well reflected in PCA ordinations, indicating that mosquito occurrence was highly influenced by land use types. Lastly, the RF represented the highest predictive power for mosquito occurrence and temperature-related factors were the most influential. Our study will contribute to effective control and management of mosquito occurrences.

  9. Factors Affecting Physicians' Intentions to Communicate Personalized Prognostic Information to Cancer Patients at the End of Life: An Experimental Vignette Study.

    Science.gov (United States)

    Han, Paul K J; Dieckmann, Nathan F; Holt, Christina; Gutheil, Caitlin; Peters, Ellen

    2016-08-01

    To explore the effects of personalized prognostic information on physicians' intentions to communicate prognosis to cancer patients at the end of life, and to identify factors that moderate these effects. A factorial experiment was conducted in which 93 family medicine physicians were presented with a hypothetical vignette depicting an end-stage gastric cancer patient seeking prognostic information. Physicians' intentions to communicate prognosis were assessed before and after provision of personalized prognostic information, while emotional distress of the patient and ambiguity (imprecision) of the prognostic estimate were varied between subjects. General linear models were used to test the effects of personalized prognostic information, patient distress, and ambiguity on prognostic communication intentions, and potential moderating effects of 1) perceived patient distress, 2) perceived credibility of prognostic models, 3) physician numeracy (objective and subjective), and 4) physician aversion to risk and ambiguity. Provision of personalized prognostic information increased prognostic communication intentions (P < 0.001, η(2) = 0.38), although experimentally manipulated patient distress and prognostic ambiguity had no effects. Greater change in communication intentions was positively associated with higher perceived credibility of prognostic models (P = 0.007, η(2) = 0.10), higher objective numeracy (P = 0.01, η(2) = 0.09), female sex (P = 0.01, η(2) = 0.08), and lower perceived patient distress (P = 0.02, η(2) = 0.07). Intentions to communicate available personalized prognostic information were positively associated with higher perceived credibility of prognostic models (P = 0.02, η(2) = 0.09), higher subjective numeracy (P = 0.02, η(2) = 0.08), and lower ambiguity aversion (P = 0.06, η(2) = 0.04). Provision of personalized prognostic information increases physicians' prognostic communication intentions to a hypothetical end-stage cancer patient, and

  10. Capitalizing on Citizen Science Data for Validating Models and Generating Hypotheses Describing Meteorological Drivers of Mosquito-Borne Disease Risk

    Science.gov (United States)

    Boger, R. A.; Low, R.; Paull, S.; Anyamba, A.; Soebiyanto, R. P.

    2017-12-01

    Temperature and precipitation are important drivers of mosquito population dynamics, and a growing set of models have been proposed to characterize these relationships. Validation of these models, and development of broader theories across mosquito species and regions could nonetheless be improved by comparing observations from a global dataset of mosquito larvae with satellite-based measurements of meteorological variables. Citizen science data can be particularly useful for two such aspects of research into the meteorological drivers of mosquito populations: i) Broad-scale validation of mosquito distribution models and ii) Generation of quantitative hypotheses regarding changes to mosquito abundance and phenology across scales. The recently released GLOBE Observer Mosquito Habitat Mapper (GO-MHM) app engages citizen scientists in identifying vector taxa, mapping breeding sites and decommissioning non-natural habitats, and provides a potentially useful new tool for validating mosquito ubiquity projections based on the analysis of remotely sensed environmental data. Our early work with GO-MHM data focuses on two objectives: validating citizen science reports of Aedes aegypti distribution through comparison with accepted scientific data sources, and exploring the relationship between extreme temperature and precipitation events and subsequent observations of mosquito larvae. Ultimately the goal is to develop testable hypotheses regarding the shape and character of this relationship between mosquito species and regions.

  11. The Fleet Numerical Meteorology and Oceanography Center (FNMOC) - Naval

    Science.gov (United States)

    Meteorology Oceanography Ice You are here: Home › FNMOC FNMOC Logo FNMOC Navigation Meteorology Products Oceanography Products Tropical Applications Climatology and Archived Data Info The Fleet Numerical Meteorology and Oceanography Center (FNMOC) The Fleet Numerical Meteorology and Oceanography Center (FNMOC

  12. A hydro-meteorological model chain to assess the influence of natural variability and impacts of climate change on extreme events and propose optimal water management

    Science.gov (United States)

    von Trentini, F.; Willkofer, F.; Wood, R. R.; Schmid, F. J.; Ludwig, R.

    2017-12-01

    The ClimEx project (Climate change and hydrological extreme events - risks and perspectives for water management in Bavaria and Québec) focuses on the effects of climate change on hydro-meteorological extreme events and their implications for water management in Bavaria and Québec. Therefore, a hydro-meteorological model chain is applied. It employs high performance computing capacity of the Leibniz Supercomputing Centre facility SuperMUC to dynamically downscale 50 members of the Global Circulation Model CanESM2 over European and Eastern North American domains using the Canadian Regional Climate Model (RCM) CRCM5. Over Europe, the unique single model ensemble is conjointly analyzed with the latest information provided through the CORDEX-initiative, to better assess the influence of natural climate variability and climatic change in the dynamics of extreme events. Furthermore, these 50 members of a single RCM will enhance extreme value statistics (extreme return periods) by exploiting the available 1500 model years for the reference period from 1981 to 2010. Hence, the RCM output is applied to drive the process based, fully distributed, and deterministic hydrological model WaSiM in high temporal (3h) and spatial (500m) resolution. WaSiM and the large ensemble are further used to derive a variety of hydro-meteorological patterns leading to severe flood events. A tool for virtual perfect prediction shall provide a combination of optimal lead time and management strategy to mitigate certain flood events following these patterns.

  13. A copula-based sampling method for data-driven prognostics

    International Nuclear Information System (INIS)

    Xi, Zhimin; Jing, Rong; Wang, Pingfeng; Hu, Chao

    2014-01-01

    This paper develops a Copula-based sampling method for data-driven prognostics. The method essentially consists of an offline training process and an online prediction process: (i) the offline training process builds a statistical relationship between the failure time and the time realizations at specified degradation levels on the basis of off-line training data sets; and (ii) the online prediction process identifies probable failure times for online testing units based on the statistical model constructed in the offline process and the online testing data. Our contributions in this paper are three-fold, namely the definition of a generic health index system to quantify the health degradation of an engineering system, the construction of a Copula-based statistical model to learn the statistical relationship between the failure time and the time realizations at specified degradation levels, and the development of a simulation-based approach for the prediction of remaining useful life (RUL). Two engineering case studies, namely the electric cooling fan health prognostics and the 2008 IEEE PHM challenge problem, are employed to demonstrate the effectiveness of the proposed methodology. - Highlights: • We develop a novel mechanism for data-driven prognostics. • A generic health index system quantifies health degradation of engineering systems. • Off-line training model is constructed based on the Bayesian Copula model. • Remaining useful life is predicted from a simulation-based approach

  14. Evaluating biomarkers for prognostic enrichment of clinical trials.

    Science.gov (United States)

    Kerr, Kathleen F; Roth, Jeremy; Zhu, Kehao; Thiessen-Philbrook, Heather; Meisner, Allison; Wilson, Francis Perry; Coca, Steven; Parikh, Chirag R

    2017-12-01

    A potential use of biomarkers is to assist in prognostic enrichment of clinical trials, where only patients at relatively higher risk for an outcome of interest are eligible for the trial. We investigated methods for evaluating biomarkers for prognostic enrichment. We identified five key considerations when considering a biomarker and a screening threshold for prognostic enrichment: (1) clinical trial sample size, (2) calendar time to enroll the trial, (3) total patient screening costs and the total per-patient trial costs, (4) generalizability of trial results, and (5) ethical evaluation of trial eligibility criteria. Items (1)-(3) are amenable to quantitative analysis. We developed the Biomarker Prognostic Enrichment Tool for evaluating biomarkers for prognostic enrichment at varying levels of screening stringency. We demonstrate that both modestly prognostic and strongly prognostic biomarkers can improve trial metrics using Biomarker Prognostic Enrichment Tool. Biomarker Prognostic Enrichment Tool is available as a webtool at http://prognosticenrichment.com and as a package for the R statistical computing platform. In some clinical settings, even biomarkers with modest prognostic performance can be useful for prognostic enrichment. In addition to the quantitative analysis provided by Biomarker Prognostic Enrichment Tool, investigators must consider the generalizability of trial results and evaluate the ethics of trial eligibility criteria.

  15. Development of the Next Generation Air Quality Modeling System (20th Joint Conference on the Applications of Air Pollution Meteorology with the A&WMA)

    Science.gov (United States)

    A next generation air quality modeling system is being developed at the U.S. EPA to enable modeling of air quality from global to regional to (eventually) local scales. We envision that the system will have three configurations: 1. Global meteorology with seamless mesh refinemen...

  16. Model for prognostication of population irradiation dose at the soil way of long-living radionuclides including in food chains

    International Nuclear Information System (INIS)

    Prister, B.S.; Vinogradskaya, V.D.

    2009-01-01

    On the basis of modern pictures of cesium and strontium ion absorption mechanisms a soil taking complex was build the kinetic model of radionuclide migration from soil to plants. Model parameter association with the agricultural chemistry properties of soil, represented by complex estimation of soil properties S e f. The example of model application for prognostication of population internal irradiation dose due to consumption of milk at the soil way of long-living radionuclides including in food chains

  17. Transport of radionuclides in the atmosphere during complex meteorological conditions

    International Nuclear Information System (INIS)

    Antic, D.; Telenta, B.

    1991-01-01

    Radionuclides from various sources (nuclear and fossil fuel power plants, nuclear facilities, medical facilities, etc.) are being released to the atmosphere. The meteorological conditions determine the atmospheric turbulence, dispersion, and removal processes of the radionuclides. A two-dimensional version of the cloud model based on the Klemp-Wilhelmson dynamic and Lin et al.'s microphysics and thermodynamics has been adapted and used to simulate the transport of radionuclides emitted from a power plant or other source to the atmosphere. Calculations of the trajectories and radii for a few puffs are included in this paper. These numerical investigations show that the presented model can be used for the transport simulation of radionuclides and for the assessment of the radiological impact of power plants and other sources in safety assessments and comparative studies. Because it can simulate puff trajectories, this model is especially valuable in the presence of complex meteorological conditions

  18. Weather or Not To Teach Junior High Meteorology.

    Science.gov (United States)

    Knorr, Thomas P.

    1984-01-01

    Presents a technique for teaching meteorology allowing students to observe and analyze consecutive weather maps and relate local conditions; a model illustrating the three-dimensional nature of the atmosphere is employed. Instructional methods based on studies of daily weather maps to trace systems sweeping across the United States are discussed.…

  19. Virtual Meteorological Center

    Directory of Open Access Journals (Sweden)

    Marius Brinzila

    2007-10-01

    Full Text Available A virtual meteorological center, computer based with Internet possibility transmission of the information is presented. Circumstance data is collected with logging field meteorological station. The station collects and automatically save data about the temperature in the air, relative humidity, pressure, wind speed and wind direction, rain gauge, solar radiation and air quality. Also can perform sensors test, analyze historical data and evaluate statistical information. The novelty of the system is that it can publish data over the Internet using LabVIEW Web Server capabilities and deliver a video signal to the School TV network. Also the system performs redundant measurement of temperature and humidity and was improved using new sensors and an original signal conditioning module.

  20. Meteorological Data Analysis Using MapReduce

    Directory of Open Access Journals (Sweden)

    Wei Fang

    2014-01-01

    Full Text Available In the atmospheric science, the scale of meteorological data is massive and growing rapidly. K-means is a fast and available cluster algorithm which has been used in many fields. However, for the large-scale meteorological data, the traditional K-means algorithm is not capable enough to satisfy the actual application needs efficiently. This paper proposes an improved MK-means algorithm (MK-means based on MapReduce according to characteristics of large meteorological datasets. The experimental results show that MK-means has more computing ability and scalability.

  1. Study on a new meteorological sampling scheme developed for the OSCAAR code system

    International Nuclear Information System (INIS)

    Liu Xinhe; Tomita, Kenichi; Homma, Toshimitsu

    2002-03-01

    One important step in Level-3 Probabilistic Safety Assessment is meteorological sequence sampling, on which the previous studies were mainly related to code systems using the straight-line plume model and more efforts are needed for those using the trajectory puff model such as the OSCAAR code system. This report describes the development of a new meteorological sampling scheme for the OSCAAR code system that explicitly considers population distribution. A group of principles set for the development of this new sampling scheme includes completeness, appropriate stratification, optimum allocation, practicability and so on. In this report, discussions are made about the procedures of the new sampling scheme and its application. The calculation results illustrate that although it is quite difficult to optimize stratification of meteorological sequences based on a few environmental parameters the new scheme do gather the most inverse conditions in a single subset of meteorological sequences. The size of this subset may be as small as a few dozens, so that the tail of a complementary cumulative distribution function is possible to remain relatively static in different trials of the probabilistic consequence assessment code. (author)

  2. Application of molecular biology of differentiated thyroid cancer for clinical prognostication.

    Science.gov (United States)

    Marotta, Vincenzo; Sciammarella, Concetta; Colao, Annamaria; Faggiano, Antongiulio

    2016-11-01

    Although cancer outcome results from the interplay between genetics and environment, researchers are making a great effort for applying molecular biology in the prognostication of differentiated thyroid cancer (DTC). Nevertheless, role of molecular characterisation in the prognostic setting of DTC is still nebulous. Among the most common and well-characterised genetic alterations related to DTC, including mutations of BRAF and RAS and RET rearrangements, BRAF V600E is the only mutation showing unequivocal association with clinical outcome. Unfortunately, its accuracy is strongly limited by low specificity. Recently, the introduction of next-generation sequencing techniques led to the identification of TERT promoter and TP53 mutations in DTC. These genetic abnormalities may identify a small subgroup of tumours with highly aggressive behaviour, thus improving specificity of molecular prognostication. Although knowledge of prognostic significance of TP53 mutations is still anecdotal, mutations of the TERT promoter have showed clear association with clinical outcome. Nevertheless, this genetic marker needs to be analysed according to a multigenetic model, as its prognostic effect becomes negligible when present in isolation. Given that any genetic alteration has demonstrated, taken alone, enough specificity, the co-occurrence of driving mutations is emerging as an independent genetic signature of aggressiveness, with possible future application in clinical practice. DTC prognostication may be empowered in the near future by non-tissue molecular prognosticators, including circulating BRAF V600E and miRNAs. Although promising, use of these markers needs to be refined by the technical sight, and the actual prognostic value is still yet to be validated. © 2016 Society for Endocrinology.

  3. Development and internal validation of a prognostic model to predict recurrence free survival in patients with adult granulosa cell tumors of the ovary

    NARCIS (Netherlands)

    van Meurs, Hannah S.; Schuit, Ewoud; Horlings, Hugo M.; van der Velden, Jacobus; van Driel, Willemien J.; Mol, Ben Willem J.; Kenter, Gemma G.; Buist, Marrije R.

    2014-01-01

    Models to predict the probability of recurrence free survival exist for various types of malignancies, but a model for recurrence free survival in individuals with an adult granulosa cell tumor (GCT) of the ovary is lacking. We aimed to develop and internally validate such a prognostic model. We

  4. Modeling the Effects of Meteorological Conditions on the Neutron Flux

    Science.gov (United States)

    2017-05-22

    about 2% between day and night on a given day [2]. In the 1960s, the launch of satellites allowed scientists to measure the sun’s cosmic rays outside...hour, a 20% variation, over five months of data collection with large variation between days . Meteorological data were collected with two commercially...contributes to the formation of the neutron flux. To account for the earth’s magnetic field, scientists have done extensive three-dimensional analysis

  5. Real-Time Adaptive Algorithms for Flight Control Diagnostics and Prognostics, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — Model-based machinery diagnostic and prognostic techniques depend upon high-quality mathematical models of the plant. Modeling uncertainties and errors decrease...

  6. Women in Meteorology.

    Science.gov (United States)

    Lemone, Margaret A.; Waukau, Patricia L.

    1982-11-01

    The names of 927 women who are or have been active in meteorology or closely related fields have been obtained from various sources. Of these women, at least 500 are presently active. An estimated 4-5% of the total number of Ph.D.s in meteorology are awarded to women. About 10% of those receiving B.S. and M.S. degrees are women.The work patterns, accomplishments, and salaries of employed women meteorologists have been summarized from 330 responses to questionnaires, as functions of age, family status, part- or full-time working status, and employing institutions. It was found that women meteorologists holding Ph.D.s are more likely than their male counterparts to be employed by universities. As increasing number of women were employed in operational meteorology, although few of them were married and fewer still responsible for children. Several women were employed by private industry and some had advanced into managerial positions, although at the present time, such positions remain out of the reach of most women.The subjective and objective effects of several gender-related factors have been summarized from the comments and responses to the questionnaires. The primary obstacles to advancement were found to be part-time work and the responsibility for children. Part-time work was found to have a clearly negative effect on salary increase as a function of age. prejudicated discrimination and rules negatively affecting women remain important, especially to the older women, and affirmative action programs are generally seen as beneficial.Surprisingly, in contrast to the experience of women in other fields of science, women Ph.D.s in meteorology earn salaries comparable of their employment in government or large corporations and universities where there are strong affirmative action programs and above-average salaries. Based on the responses to the questionnaire, the small size of the meteorological community is also a factor, enabling women to become recognized

  7. Serum prognostic biomarkers in head and neck cancer patients.

    Science.gov (United States)

    Lin, Ho-Sheng; Siddiq, Fauzia; Talwar, Harvinder S; Chen, Wei; Voichita, Calin; Draghici, Sorin; Jeyapalan, Gerald; Chatterjee, Madhumita; Fribley, Andrew; Yoo, George H; Sethi, Seema; Kim, Harold; Sukari, Ammar; Folbe, Adam J; Tainsky, Michael A

    2014-08-01

    A reliable estimate of survival is important as it may impact treatment choice. The objective of this study is to identify serum autoantibody biomarkers that can be used to improve prognostication for patients affected with head and neck squamous cell carcinoma (HNSCC). Prospective cohort study. A panel of 130 serum biomarkers, previously selected for cancer detection using microarray-based serological profiling and specialized bioinformatics, were evaluated for their potential as prognostic biomarkers in a cohort of 119 HNSCC patients followed for up to 12.7 years. A biomarker was considered positive if its reactivity to the particular patient's serum was greater than one standard deviation above the mean reactivity to sera from the other 118 patients, using a leave-one-out cross-validation model. Survival curves were estimated according to the Kaplan-Meier method, and statistically significant differences in survival were examined using the log rank test. Independent prognostic biomarkers were identified following analysis using multivariate Cox proportional hazards models. Poor overall survival was associated with African Americans (hazard ratio [HR] for death = 2.61; 95% confidence interval [CI]: 1.58-4.33; P = .000), advanced stage (HR = 2.79; 95% CI: 1.40-5.57; P = .004), and recurrent disease (HR = 6.66; 95% CI: 2.54-17.44; P = .000). On multivariable Cox analysis adjusted for covariates (race and stage), six of the 130 markers evaluated were found to be independent prognosticators of overall survival. The results shown here are promising and demonstrate the potential use of serum biomarkers for prognostication in HNSCC patients. Further clinical trials to include larger samples of patients across multiple centers may be warranted. © 2014 The American Laryngological, Rhinological and Otological Society, Inc.

  8. The Impact of Model and Rainfall Forcing Errors on Characterizing Soil Moisture Uncertainty in Land Surface Modeling

    Science.gov (United States)

    Maggioni, V.; Anagnostou, E. N.; Reichle, R. H.

    2013-01-01

    The contribution of rainfall forcing errors relative to model (structural and parameter) uncertainty in the prediction of soil moisture is investigated by integrating the NASA Catchment Land Surface Model (CLSM), forced with hydro-meteorological data, in the Oklahoma region. Rainfall-forcing uncertainty is introduced using a stochastic error model that generates ensemble rainfall fields from satellite rainfall products. The ensemble satellite rain fields are propagated through CLSM to produce soil moisture ensembles. Errors in CLSM are modeled with two different approaches: either by perturbing model parameters (representing model parameter uncertainty) or by adding randomly generated noise (representing model structure and parameter uncertainty) to the model prognostic variables. Our findings highlight that the method currently used in the NASA GEOS-5 Land Data Assimilation System to perturb CLSM variables poorly describes the uncertainty in the predicted soil moisture, even when combined with rainfall model perturbations. On the other hand, by adding model parameter perturbations to rainfall forcing perturbations, a better characterization of uncertainty in soil moisture simulations is observed. Specifically, an analysis of the rank histograms shows that the most consistent ensemble of soil moisture is obtained by combining rainfall and model parameter perturbations. When rainfall forcing and model prognostic perturbations are added, the rank histogram shows a U-shape at the domain average scale, which corresponds to a lack of variability in the forecast ensemble. The more accurate estimation of the soil moisture prediction uncertainty obtained by combining rainfall and parameter perturbations is encouraging for the application of this approach in ensemble data assimilation systems.

  9. Modeling the Short-Term Effect of Traffic and Meteorology on Air Pollution in Turin with Generalized Additive Models

    Directory of Open Access Journals (Sweden)

    Pancrazio Bertaccini

    2012-01-01

    Full Text Available Vehicular traffic plays an important role in atmospheric pollution and can be used as one of the key predictors in air-quality forecasting models. The models that can account for the role of traffic are especially valuable in urban areas, where high pollutant concentrations are often observed during particular times of day (rush hour and year (winter. In this paper, we develop a generalized additive models approach to analyze the behavior of concentrations of nitrogen dioxide (NO2, and particulate matter (PM10, collected at the environmental monitoring stations distributed throughout the city of Turin, Italy, from December 2003 to April 2005. We describe nonlinear relationships between predictors and pollutants, that are adjusted for unobserved time-varying confounders. We examine several functional forms for the traffic variable and find that a simple form can often provide adequate modeling power. Our analysis shows that there is a saturation effect of traffic on NO2, while such saturation is less evident in models linking traffic to PM10 behavior, having adjusted for meteorological covariates. Moreover, we consider the proposed models separately by seasons and highlight similarities and differences in the predictors’ partial effects. Finally, we show how forecasting can help in evaluating traffic regulation policies.

  10. Uncertainty Management for Diagnostics and Prognostics of Batteries using Bayesian Techniques

    Science.gov (United States)

    Saha, Bhaskar; Goebel, kai

    2007-01-01

    Uncertainty management has always been the key hurdle faced by diagnostics and prognostics algorithms. A Bayesian treatment of this problem provides an elegant and theoretically sound approach to the modern Condition- Based Maintenance (CBM)/Prognostic Health Management (PHM) paradigm. The application of the Bayesian techniques to regression and classification in the form of Relevance Vector Machine (RVM), and to state estimation as in Particle Filters (PF), provides a powerful tool to integrate the diagnosis and prognosis of battery health. The RVM, which is a Bayesian treatment of the Support Vector Machine (SVM), is used for model identification, while the PF framework uses the learnt model, statistical estimates of noise and anticipated operational conditions to provide estimates of remaining useful life (RUL) in the form of a probability density function (PDF). This type of prognostics generates a significant value addition to the management of any operation involving electrical systems.

  11. A globally calibrated scheme for generating daily meteorology from monthly statistics: Global-WGEN (GWGEN) v1.0

    Science.gov (United States)

    Sommer, Philipp S.; Kaplan, Jed O.

    2017-10-01

    While a wide range of Earth system processes occur at daily and even subdaily timescales, many global vegetation and other terrestrial dynamics models historically used monthly meteorological forcing both to reduce computational demand and because global datasets were lacking. Recently, dynamic land surface modeling has moved towards resolving daily and subdaily processes, and global datasets containing daily and subdaily meteorology have become available. These meteorological datasets, however, cover only the instrumental era of the last approximately 120 years at best, are subject to considerable uncertainty, and represent extremely large data files with associated computational costs of data input/output and file transfer. For periods before the recent past or in the future, global meteorological forcing can be provided by climate model output, but the quality of these data at high temporal resolution is low, particularly for daily precipitation frequency and amount. Here, we present GWGEN, a globally applicable statistical weather generator for the temporal downscaling of monthly climatology to daily meteorology. Our weather generator is parameterized using a global meteorological database and simulates daily values of five common variables: minimum and maximum temperature, precipitation, cloud cover, and wind speed. GWGEN is lightweight, modular, and requires a minimal set of monthly mean variables as input. The weather generator may be used in a range of applications, for example, in global vegetation, crop, soil erosion, or hydrological models. While GWGEN does not currently perform spatially autocorrelated multi-point downscaling of daily weather, this additional functionality could be implemented in future versions.

  12. Prognostics Approach for Power MOSFET Under Thermal-Stress

    Science.gov (United States)

    Galvan, Jose Ramon Celaya; Saxena, Abhinav; Kulkarni, Chetan S.; Saha, Sankalita; Goebel, Kai

    2012-01-01

    The prognostic technique for a power MOSFET presented in this paper is based on accelerated aging of MOSFET IRF520Npbf in a TO-220 package. The methodology utilizes thermal and power cycling to accelerate the life of the devices. The major failure mechanism for the stress conditions is dieattachment degradation, typical for discrete devices with leadfree solder die attachment. It has been determined that dieattach degradation results in an increase in ON-state resistance due to its dependence on junction temperature. Increasing resistance, thus, can be used as a precursor of failure for the die-attach failure mechanism under thermal stress. A feature based on normalized ON-resistance is computed from in-situ measurements of the electro-thermal response. An Extended Kalman filter is used as a model-based prognostics techniques based on the Bayesian tracking framework. The proposed prognostics technique reports on preliminary work that serves as a case study on the prediction of remaining life of power MOSFETs and builds upon the work presented in [1]. The algorithm considered in this study had been used as prognostics algorithm in different applications and is regarded as suitable candidate for component level prognostics. This work attempts to further the validation of such algorithm by presenting it with real degradation data including measurements from real sensors, which include all the complications (noise, bias, etc.) that are regularly not captured on simulated degradation data. The algorithm is developed and tested on the accelerated aging test timescale. In real world operation, the timescale of the degradation process and therefore the RUL predictions will be considerable larger. It is hypothesized that even though the timescale will be larger, it remains constant through the degradation process and the algorithm and model would still apply under the slower degradation process. By using accelerated aging data with actual device measurements and real

  13. Use of midlatitude soil moisture and meteorological observations to validate soil moisture simulations with biosphere and bucket models

    Science.gov (United States)

    Robock, Alan; Vinnikov, Konstantin YA.; Schlosser, C. Adam; Speranskaya, Nina A.; Xue, Yongkang

    1995-01-01

    Soil moisture observations in sites with natural vegetation were made for several decades in the former Soviet Union at hundreds of stations. In this paper, the authors use data from six of these stations from different climatic regimes, along with ancillary meteorological and actinometric data, to demonstrate a method to validate soil moisture simulations with biosphere and bucket models. Some early and current general circulation models (GCMs) use bucket models for soil hydrology calculations. More recently, the Simple Biosphere Model (SiB) was developed to incorporate the effects of vegetation on fluxes of moisture, momentum, and energy at the earth's surface into soil hydrology models. Until now, the bucket and SiB have been verified by comparison with actual soil moisture data only on a limited basis. In this study, a Simplified SiB (SSiB) soil hydrology model and a 15-cm bucket model are forced by observed meteorological and actinometric data every 3 h for 6-yr simulations at the six stations. The model calculations of soil moisture are compared to observations of soil moisture, literally 'ground truth,' snow cover, surface albedo, and net radiation, and with each other. For three of the stations, the SSiB and 15-cm bucket models produce good simulations of seasonal cycles and interannual variations of soil moisture. For the other three stations, there are large errors in the simulations by both models. Inconsistencies in specification of field capacity may be partly responsible. There is no evidence that the SSiB simulations are superior in simulating soil moisture variations. In fact, the models are quite similar since SSiB implicitly has a bucket embedded in it. One of the main differences between the models is in the treatment of runoff due to melting snow in the spring -- SSiB incorrectly puts all the snowmelt into runoff. While producing similar soil moisture simulations, the models produce very different surface latent and sensible heat fluxes, which

  14. Meteorology in site operations

    International Nuclear Information System (INIS)

    Anon.

    1986-01-01

    During the site selection and design phases of a plant, meteorological assistance must be based on past records, usually accumulated at stations not actually on the site. These preliminary atadvices will be averages and extremes that might be expected. After a location has been chosen and work has begun, current and forecast weather conditions become of immediate concern. On-site meteorological observations and forecasts have many applications to the operating program of an atomic energy site. Requirements may range from observations of the daily minimum temperatures to forecasts of radiation dosages from airborne clouds

  15. THE APPLICATION OF AN EVOLUTIONARY ALGORITHM TO THE OPTIMIZATION OF A MESOSCALE METEOROLOGICAL MODEL

    Energy Technology Data Exchange (ETDEWEB)

    Werth, D.; O' Steen, L.

    2008-02-11

    We show that a simple evolutionary algorithm can optimize a set of mesoscale atmospheric model parameters with respect to agreement between the mesoscale simulation and a limited set of synthetic observations. This is illustrated using the Regional Atmospheric Modeling System (RAMS). A set of 23 RAMS parameters is optimized by minimizing a cost function based on the root mean square (rms) error between the RAMS simulation and synthetic data (observations derived from a separate RAMS simulation). We find that the optimization can be efficient with relatively modest computer resources, thus operational implementation is possible. The optimization efficiency, however, is found to depend strongly on the procedure used to perturb the 'child' parameters relative to their 'parents' within the evolutionary algorithm. In addition, the meteorological variables included in the rms error and their weighting are found to be an important factor with respect to finding the global optimum.

  16. Meteorological conditions of the mudflow origin in the northern part of the French Alps

    Directory of Open Access Journals (Sweden)

    L. O. Pavlova

    2012-01-01

    Full Text Available A mudflow phenomena are at the top of the list of dangerous natural hazards in the mountains areas all over the world. Among factors resulting in a mudflow phenomena triggering, meteorological conditions are considered to be the most relevant. The general objective of this study was to identify meteorological parameters controlling the triggering of mudflow phenomena in one part of the French Alps over the last 40 years. Major factors are quite well explored at the global scale or contrariwise in very precise territory in particular catchment areas. However, for now we have a poor knowledge of those factors at the scale of a medium-sized region (including catchments with different geomorphic characteristics over several km² especially in the French Alps. In addition, in this region only a few studies focused on relationships with climate. To understand mudflow phenomena activity and their link with meteorological parameters in the north region of the French Alps, we used a multivariate statistical approach. Regional meteorological parameters (such as mean monthly temperature and precipitation were first computed from a Principal Component Analysis of observed meteorological data from four weather stations. A binomial monthly logistic regression probability model was then fitted between the main principal components and mudflow phenomena data base composed of 298 debris flow events triggered between 1971 and 2008. Results revealed that the most successful model including two meteorological predictors (minimal monthly temperature and the number of rainy days between May and September correctly explains more than 60% of the mudflow phenomena events.

  17. Tornado frequency in the USA - meteorological and non-meteorological factors of a downward trend

    Directory of Open Access Journals (Sweden)

    Mihajlović Jovan

    2015-01-01

    Full Text Available Citing numerical simulations, climate alarmists believe that global warming will lead to more frequent and more intensive tornadoes. Considering temperature increase data in the contiguous USA, this study has investigated the trend of strong tornadoes in F3+ category in the 1954-2012 period. Statistically significant decrease of tornadoes per year at an average rate of 0.44 has been recorded, that is, 4.4 tornadoes per decade. Tornado increase has been recorded with F0 and F1 categories and the cause of this increase lies in meteorological and non-meteorological factors. By using upper and lower standard deviation values, the stages of tornado activity have been singled out. The 1957-1974 period may be considered as an active stage and the 1978-2009 period as an inactive stage. Upward trend of air temperature increase does not correspond with the downward trend of the number of F3+ tornado category, while the correlation coefficient between these two variables is R = −0.14. This fact does not correspond with the simulation results and output data of various numerical models anticipating an increase in the number and intensity of tornado events in the conditions of surface air temperature growth.

  18. Assessing meteorological key factors influencing crop invasion by pollen beetle (

    Directory of Open Access Journals (Sweden)

    Jürgen Junk

    2016-09-01

    Full Text Available The pollen beetle, Meligethes aeneus F. (Coleoptera: Nitidulidae, is a severe pest of winter oilseed rape. A phenological model to forecast the first spring invasion of crops in Luxembourg by M. aeneus was developed in order to provide a tool for improving pest management and for assessing the potential effects of climate change on this pest. The model was derived using long-term, multi-site observational datasets of pollen beetle migration and meteorological data, as the timing of crop invasion is determined mainly by meteorological variables. Daily values of mean air and soil temperature, accumulated sunshine duration and precipitation were used to create a threshold-based model to forecast crop invasion. Minimising of the root mean squared error (RMSE of predicted versus observed migration dates was used as the quality criterion for selecting the optimum combination of threshold values for meteorological variables. We identified mean air temperature 8.0 °C, mean soil temperature 4.6 °C, and sunshine duration of 3.4 h as the best threshold values, with a cut-off of 1 mm precipitation and with no need for persistence of those conditions for more than one day (RMSE=9.3days$RMSE=9.3\\,\\text{days}$. Only in six out of 30 cases, differences between observed and predicted immigration dates were >5$>5$ days. In the future, crop invasion by pollen beetles will probably be strongly affected by changes in air temperature and precipitation related to climate change. We used a multi-model ensemble of 15 regional climate models driven by the A1B emission scenario to assess meteorological changes in two 30‑year future periods, near future (2021–2050 and far future (2069–2098 in comparison with the reference period (1971–2000. Air temperature and precipitation were predicted to increase in the first three months of each year, both in the near future and the far future. The pollen beetle migration model indicated that this change would

  19. Meteorological risks as drivers of innovation for agroecosystem management

    Science.gov (United States)

    Gobin, Anne; Van de Vyver, Hans; Zamani, Sepideh; Curnel, Yannick; Planchon, Viviane; Verspecht, Ann; Van Huylenbroeck, Guido

    2015-04-01

    Devastating weather-related events recorded in recent years have captured the interest of the general public in Belgium. The MERINOVA project research hypothesis is that meteorological risks act as drivers of environmental innovation in agro-ecosystem management which is being tested using a "chain of risk" approach. The major objectives are to (1) assess the probability of extreme meteorological events by means of probability density functions; (2) analyse the extreme events impact of on agro-ecosystems using process-based bio-physical modelling methods; (3) identify the most vulnerable agro-ecosystems using fuzzy multi-criteria and spatial analysis; (4) uncover innovative risk management and adaptation options using actor-network theory and economic modelling; and, (5) communicate to research, policy and practitioner communities using web-based techniques. Generalized Extreme Value (GEV) theory was used to model annual rainfall maxima based on location-, scale- and shape-parameters that determine the centre of the distribution, the deviation of the location-parameter and the upper tail decay, respectively. Likewise the distributions of consecutive rainy days, rainfall deficits and extreme 24-hour rainfall were modelled. Spatial interpolation of GEV-derived return levels resulted in maps of extreme precipitation, precipitation deficits and wet periods. The degree of temporal overlap between extreme weather conditions and sensitive periods in the agro-ecosystem was determined using a bio-physically based modelling framework that couples phenological models, a soil water balance, crop growth and environmental models. 20-year return values were derived for frost, heat stress, drought, waterlogging and field access during different sensitive stages for different arable crops. Extreme yield values were detected from detrended long term arable yields and relationships were found with soil moisture conditions, heat stress or other meteorological variables during the

  20. Distributed Prognostic Health Management with Gaussian Process Regression

    Science.gov (United States)

    Saha, Sankalita; Saha, Bhaskar; Saxena, Abhinav; Goebel, Kai Frank

    2010-01-01

    Distributed prognostics architecture design is an enabling step for efficient implementation of health management systems. A major challenge encountered in such design is formulation of optimal distributed prognostics algorithms. In this paper. we present a distributed GPR based prognostics algorithm whose target platform is a wireless sensor network. In addition to challenges encountered in a distributed implementation, a wireless network poses constraints on communication patterns, thereby making the problem more challenging. The prognostics application that was used to demonstrate our new algorithms is battery prognostics. In order to present trade-offs within different prognostic approaches, we present comparison with the distributed implementation of a particle filter based prognostics for the same battery data.

  1. Modeling monthly meteorological and agronomic frost days, based on minimum air temperature, in Center-Southern Brazil

    Science.gov (United States)

    Alvares, Clayton Alcarde; Sentelhas, Paulo César; Stape, José Luiz

    2017-09-01

    Although Brazil is predominantly a tropical country, frosts are observed with relative high frequency in the Center-Southern states of the country, affecting mainly agriculture, forestry, and human activities. Therefore, information about the frost climatology is of high importance for planning of these activities. Based on that, the aims of the present study were to develop monthly meteorological (F MET) and agronomic (F AGR) frost day models, based on minimum shelter air temperature (T MN), in order to characterize the temporal and spatial frost days variability in Center-Southern Brazil. Daily minimum air temperature data from 244 weather stations distributed across the study area were used, being 195 for developing the models and 49 for validating them. Multivariate regression models were obtained to estimate the monthly T MN, once the frost day models were based on this variable. All T MN regression models were statistically significant (p Brazilian region are the first zoning of these variables for the country.

  2. Development of a prognostic model for predicting spontaneous singleton preterm birth.

    Science.gov (United States)

    Schaaf, Jelle M; Ravelli, Anita C J; Mol, Ben Willem J; Abu-Hanna, Ameen

    2012-10-01

    To develop and validate a prognostic model for prediction of spontaneous preterm birth. Prospective cohort study using data of the nationwide perinatal registry in The Netherlands. We studied 1,524,058 singleton pregnancies between 1999 and 2007. We developed a multiple logistic regression model to estimate the risk of spontaneous preterm birth based on maternal and pregnancy characteristics. We used bootstrapping techniques to internally validate our model. Discrimination (AUC), accuracy (Brier score) and calibration (calibration graphs and Hosmer-Lemeshow C-statistic) were used to assess the model's predictive performance. Our primary outcome measure was spontaneous preterm birth at model included 13 variables for predicting preterm birth. The predicted probabilities ranged from 0.01 to 0.71 (IQR 0.02-0.04). The model had an area under the receiver operator characteristic curve (AUC) of 0.63 (95% CI 0.63-0.63), the Brier score was 0.04 (95% CI 0.04-0.04) and the Hosmer Lemeshow C-statistic was significant (pvalues of predicted probability. The positive predictive value was 26% (95% CI 20-33%) for the 0.4 probability cut-off point. The model's discrimination was fair and it had modest calibration. Previous preterm birth, drug abuse and vaginal bleeding in the first half of pregnancy were the most important predictors for spontaneous preterm birth. Although not applicable in clinical practice yet, this model is a next step towards early prediction of spontaneous preterm birth that enables caregivers to start preventive therapy in women at higher risk. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  3. Improving Computational Efficiency of Prediction in Model-Based Prognostics Using the Unscented Transform

    Science.gov (United States)

    Daigle, Matthew John; Goebel, Kai Frank

    2010-01-01

    Model-based prognostics captures system knowledge in the form of physics-based models of components, and how they fail, in order to obtain accurate predictions of end of life (EOL). EOL is predicted based on the estimated current state distribution of a component and expected profiles of future usage. In general, this requires simulations of the component using the underlying models. In this paper, we develop a simulation-based prediction methodology that achieves computational efficiency by performing only the minimal number of simulations needed in order to accurately approximate the mean and variance of the complete EOL distribution. This is performed through the use of the unscented transform, which predicts the means and covariances of a distribution passed through a nonlinear transformation. In this case, the EOL simulation acts as that nonlinear transformation. In this paper, we review the unscented transform, and describe how this concept is applied to efficient EOL prediction. As a case study, we develop a physics-based model of a solenoid valve, and perform simulation experiments to demonstrate improved computational efficiency without sacrificing prediction accuracy.

  4. Statistical methods for the assessment of prognostic biomarkers(part II): calibration and re-classification

    NARCIS (Netherlands)

    Tripepi, Giovanni; Jager, Kitty J.; Dekker, Friedo W.; Zoccali, Carmine

    2010-01-01

    Calibration is the ability of a prognostic model to correctly estimate the probability of a given event across the whole range of prognostic estimates (for example, 30% probability of death, 40% probability of myocardial infarction, etc.). The key difference between calibration and discrimination is

  5. Reliability analysis of meteorological data registered during nuclear power plant normal operation

    International Nuclear Information System (INIS)

    Amado, V.; Ulke, A.; Marino, B.; Thomas, L.

    2011-01-01

    The atmosphere is the environment in which gaseous radioactive discharges from nuclear power plants are transported. It is therefore essential to have reliable meteorological information to characterize the dispersion and feed evaluation models and radiological environmental impact during normal operation of the plant as well as accidental releases. In this way it is possible to determine the effects on the environment and in humans. The basic data needed to represent adequately the local weather include air temperature, wind speed and direction, rainfall, humidity and pressure. On the other hand, specific data consistent with the used model is required to determine the turbulence, for instance, radiation, cloud cover and vertical temperature gradient. It is important that the recorded data are representative of the local meteorology. This requires, first, properly placed instruments, that should be kept in operation and undergoing maintenance on a regular basis. Second, but equally substantial, a thorough analysis of its reliability must be performed prior to storage and/or data processing. In this paper we present the main criteria to consider choosing the location of a meteorological tower in the area of a nuclear power plant and propose a methodology for assessing the reliability of recorded data. The methodology was developed from the analysis of meteorological data registered in nuclear power plants in Argentina. (authors) [es

  6. Current status and advancement of SPEEDI network system

    International Nuclear Information System (INIS)

    Naoei Suda; Yuko Rintsu; Shigeru Serizawa; Nobuaki Umeyama; Tetsuo Yamazaki; Shigeru Moriuchi; Hiroyuki Handa; Masamichi Chino; Haruyasu Nagai; Hiromi Yamazawa

    2005-01-01

    The range and concentration of the radioactive plume discharged during an accident would depend on the local topography and the meteorological conditions, e.g., wind direction and velocity, precipitation, and atmospheric stability. Considering these situations, SPEEDI, a computational code system, predicts the concentration of radioactive materials in the atmosphere and on the ground surface, air absorbed dose rate, external exposure dose and internal dose by inhalation. We introduced advanced SPEEDI (A-SPEEDI) models to solve some problems that were included in the conventional SPEEDI. New models have been developed as successor models of the conventional SPEEDI by Japan Atomic Energy Research Institute. The major improvement is the introduction of a prognostic meteorological model PHYSIC to A-SPEEDI. PHYSIC performs the regional meteorological forecasts using nationwide meteorological observation data and meteorological forecast data RSM provided by the Japan Meteorological Agency (JMA) for the initial condition and the boundary conditions around a nuclear facility. Atmospheric dispersion and dose calculation models were upgraded to improve the prediction performance as well. By introducing A-SPEEDI models, a long-term meteorological forecast such as following 24 hours is possible, and the prediction performance is improved under various meteorological conditions. (authors)

  7. Synoptic and meteorological drivers of extreme ozone concentrations over Europe

    Science.gov (United States)

    Otero, Noelia Felipe; Sillmann, Jana; Schnell, Jordan L.; Rust, Henning W.; Butler, Tim

    2016-04-01

    The present work assesses the relationship between local and synoptic meteorological conditions and surface ozone concentration over Europe in spring and summer months, during the period 1998-2012 using a new interpolated data set of observed surface ozone concentrations over the European domain. Along with local meteorological conditions, the influence of large-scale atmospheric circulation on surface ozone is addressed through a set of airflow indices computed with a novel implementation of a grid-by-grid weather type classification across Europe. Drivers of surface ozone over the full distribution of maximum daily 8-hour average values are investigated, along with drivers of the extreme high percentiles and exceedances or air quality guideline thresholds. Three different regression techniques are applied: multiple linear regression to assess the drivers of maximum daily ozone, logistic regression to assess the probability of threshold exceedances and quantile regression to estimate the meteorological influence on extreme values, as represented by the 95th percentile. The relative importance of the input parameters (predictors) is assessed by a backward stepwise regression procedure that allows the identification of the most important predictors in each model. Spatial patterns of model performance exhibit distinct variations between regions. The inclusion of the ozone persistence is particularly relevant over Southern Europe. In general, the best model performance is found over Central Europe, where the maximum temperature plays an important role as a driver of maximum daily ozone as well as its extreme values, especially during warmer months.

  8. The prognostic value of FET PET at radiotherapy planning in newly diagnosed glioblastoma

    Energy Technology Data Exchange (ETDEWEB)

    Hoejklint Poulsen, Sidsel [The Finsen Center, Rigshospitalet, Department of Radiation Biology, Copenhagen (Denmark); Center of Diagnostic Investigation, Rigshospitalet, Department of Clinical Physiology, Nuclear Medicine and PET, Copenhagen (Denmark); Urup, Thomas; Grunnet, Kirsten; Skovgaard Poulsen, Hans [The Finsen Center, Rigshospitalet, Department of Radiation Biology, Copenhagen (Denmark); The Finsen Center, Rigshospitalet, Department of Oncology, Copenhagen (Denmark); Jarle Christensen, Ib [University of Copenhagen, Hvidovre Hospital, Laboratory of Gastroenterology, Copenhagen (Denmark); Larsen, Vibeke Andree [Center of Diagnostic Investigation, Rigshospitalet, Department of Radiology, Copenhagen (Denmark); Lundemann Jensen, Michael; Munck af Rosenschoeld, Per [The Finsen Center, Rigshospitalet, Department of Oncology, Copenhagen (Denmark); The Finsen Center, Rigshospitalet, Section of Radiotherapy, Copenhagen (Denmark); Law, Ian [Center of Diagnostic Investigation, Rigshospitalet, Department of Clinical Physiology, Nuclear Medicine and PET, Copenhagen (Denmark)

    2017-03-15

    Glioblastoma patients show a great variability in progression free survival (PFS) and overall survival (OS). To gain additional pretherapeutic information, we explored the potential of O-(2-{sup 18}F-fluoroethyl)-L-tyrosine (FET) PET as an independent prognostic biomarker. We retrospectively analyzed 146 consecutively treated, newly diagnosed glioblastoma patients. All patients were treated with temozolomide and radiation therapy (RT). CT/MR and FET PET scans were obtained postoperatively for RT planning. We used Cox proportional hazards models with OS and PFS as endpoints, to test the prognostic value of FET PET biological tumor volume (BTV). Median follow-up time was 14 months, and median OS and PFS were 16.5 and 6.5 months, respectively. In the multivariate analysis, increasing BTV (HR = 1.17, P < 0.001), poor performance status (HR = 2.35, P < 0.001), O(6)-methylguanine-DNA methyltransferase protein status (HR = 1.61, P = 0.024) and higher age (HR = 1.32, P = 0.013) were independent prognostic factors of poor OS. For poor PFS, only increasing BTV (HR = 1.18; P = 0.002) was prognostic. A prognostic index for OS was created based on the identified prognostic factors. Large BTV on FET PET is an independent prognostic factor of poor OS and PFS in glioblastoma patients. With the introduction of FET PET, we obtain a prognostic index that can help in glioblastoma treatment planning. (orig.)

  9. The Invigoration of Deep Convective Clouds Over the Atlantic: Aerosol Effect, Meteorology or Retrieval Artifact?

    Science.gov (United States)

    Koren, Ilan; Feingold, Graham; Remer, Lorraine A.

    2010-01-01

    Associations between cloud properties and aerosol loading are frequently observed in products derived from satellite measurements. These observed trends between clouds and aerosol optical depth suggest aerosol modification of cloud dynamics, yet there are uncertainties involved in satellite retrievals that have the potential to lead to incorrect conclusions. Two of the most challenging problems are addressed here: the potential for retrieved aerosol optical depth to be cloud-contaminated, and as a result, artificially correlated with cloud parameters; and the potential for correlations between aerosol and cloud parameters to be erroneously considered to be causal. Here these issues are tackled directly by studying the effects of the aerosol on convective clouds in the tropical Atlantic Ocean using satellite remote sensing, a chemical transport model, and a reanalysis of meteorological fields. Results show that there is a robust positive correlation between cloud fraction or cloud top height and the aerosol optical depth, regardless of whether a stringent filtering of aerosol measurements in the vicinity of clouds is applied, or not. These same positive correlations emerge when replacing the observed aerosol field with that derived from a chemical transport model. Model-reanalysis data is used to address the causality question by providing meteorological context for the satellite observations. A correlation exercise between the full suite of meteorological fields derived from model reanalysis and satellite-derived cloud fields shows that observed cloud top height and cloud fraction correlate best with model pressure updraft velocity and relative humidity. Observed aerosol optical depth does correlate with meteorological parameters but usually different parameters from those that correlate with observed cloud fields. The result is a near-orthogonal influence of aerosol and meteorological fields on cloud top height and cloud fraction. The results strengthen the case

  10. Jesuits' Contribution to Meteorology.

    Science.gov (United States)

    Udías, Agustín

    1996-10-01

    Starting in the middle of the nineteenth century, as part of their scientific tradition, Jesuits founded a considerable number of meteorological observatories throughout the world. In many countries, Jesuits established and maintained the first meteorological stations during the period from 1860 to 1950. The Jesuits' most important contribution to atmospheric science was their pioneer work related to the study and forecast of tropical hurricanes. That research was carried out at observatories of Belén (Cuba), Manila (Philippines), and Zikawei (China). B. Viñes, M. Decheyrens, J. Aigué, and C.E. Deppermann stood out in this movement.

  11. A hierarchical Bayesian model for improving short-term forecasting of hospital demand by including meteorological information

    OpenAIRE

    Sahu, Sujit K.; Baffour, Bernard; Minty, John; Harper, Paul; Sarran, Christophe

    2013-01-01

    The effect of weather on health has been widely researched, and the ability to forecast meteorological events is able to offer valuable insights into the impact on public health services. In addition, better predictions of hospital demand that are more sensitive to fluctuations in weather can allow hospital administrators to optimise resource allocation and service delivery. Using historical hospital admission data and several seasonal and meteorological variables for a site near the hospital...

  12. Site-specific meteorology identification for DOE facility accident analysis

    International Nuclear Information System (INIS)

    Rabin, S.B.

    1995-01-01

    Currently, chemical dispersion calculations performed for safety analysis of DOE facilities assume a Pasquill D-Stability Class with a 4.5 m/s windspeed. These meteorological conditions are assumed to conservatively address the source term generation mechanism as well as the dispersion mechanism thereby resulting in a net conservative downwind consequence. While choosing this Stability Class / Windspeed combination may result in an overall conservative consequence, the level of conservative can not be quantified. The intent of this paper is to document a methodology which incorporates site-specific meteorology to determine a quantifiable consequence of a chemical release. A five-year meteorological database, appropriate for the facility location, is utilized for these chemical consequence calculations, and is consistent with the approach used for radiological releases. The hourly averages of meteorological conditions have been binned into 21 groups for the chemical consequence calculations. These 21 cases each have a probability of occurrence based on the number of times each case has occurred over the five year sampling period. A code has been developed which automates the running of all the cases with a commercially available air modeling code. The 21 cases are sorted by concentration. A concentration may be selected by the user for a quantified level of conservatism. The methodology presented is intended to improve the technical accuracy and defensability of Chemical Source Term / Dispersion Safety Analysis work. The result improves the quality of safety analyses products without significantly increasing the cost

  13. Influence of the Meteorology Mast on a Cup Anemometer

    DEFF Research Database (Denmark)

    Hansen, Martin O. L.; Pedersen, B.M.

    1999-01-01

    The actuator disc model is applied on lattice-type meteorological masts to estimate the influence of the tower on the accuracy of the measured wind speed. Combining the results with corrections for the boom, on which the anemometer is mounted, good agreement is found for measurements made on the ...

  14. GPS IPW as a Meteorological Parameter and Climate Global Change Indicator

    Science.gov (United States)

    Kruczyk, M.; Liwosz, T.

    2011-12-01

    Paper focuses on comprehensive investigation of the GPS derived IPW (Integrated Precipitable Water, also IWV) as a geophysical tool. GPS meteorology is now widely acknowledged indirect method of atmosphere sensing. First we demonstrate GPS IPW quality. Most thorough inter-technique comparisons of directly measured IPW are attainable only for some observatories (note modest percentage of GPS stations equipped with meteorological devices). Nonetheless we have managed to compare IPW series derived from GPS tropospheric solutions (ZTD mostly from IGS and EPN solutions) and some independent techniques. IPW values from meteorological sources we used are: radiosoundings, sun photometer and input fields of numerical weather prediction model. We can treat operational NWP models as meteorological database within which we can calculate IWV for all GPS stations independently from network of direct measurements (COSMO-LM model maintained by Polish Institute of Meteorology and Water Management was tried). Sunphotometer (CIMEL-318, Central Geophysical Observatory IGF PAS, Belsk, Poland) data seems the most genuine source - so we decided for direct collocation of GPS measurements and sunphotometer placing permanent GPS receiver on the roof of Belsk Observatory. Next we analyse IPW as geophysical parameter: IPW demonstrates some physical effects evoked by station location (height and series correlation coefficient as a function of distance) and weather patterns like dominant wind directions (in case of neighbouring stations). Deficiency of surface humidity data to model IPW is presented for different climates. This inadequacy and poor humidity data representation in NWP model extremely encourages investigating information exchange potential between Numerical Model and GPS network. The second and most important aspect of this study concerns long series of IPW (daily averaged) which can serve as climatological information indicator (water vapour role in climate system is hard to

  15. Ionospheric irregularities in periods of meteorological disturbances

    Science.gov (United States)

    Borchevkina, O. P.; Karpov, I. V.

    2017-09-01

    The results of observations of the total electron content (TEC) in periods of storm disturbances of meteorological situation are presented in the paper. The observational results have shown that a passage of a meteorological storm is accompanied by a substantial decrease in values of TEC and critical frequencies of the ionospheric F2 region. The decreases in values of these ionospheric parameters reach 50% and up to 30% in TEC and critical frequency of the F2 layer, respectively, as compared to meteorologically quiet days. Based on qualitative analysis, it is found that the processes related to formation of local regions of thermospheric heating due to a dissipation of AGW coming into the upper atmosphere from the region of the meteorological disturbance in the lower atmosphere are a possible cause of these ionospheric disturbances.

  16. Serum C-reactive protein (CRP) as a simple and independent prognostic factor in extranodal natural killer/T-cell lymphoma, nasal type.

    Science.gov (United States)

    Li, Ya-Jun; Li, Zhi-Ming; Xia, Yi; Huang, Jia-Jia; Huang, Hui-Qiang; Xia, Zhong-Jun; Lin, Tong-Yu; Li, Su; Cai, Xiu-Yu; Wu-Xiao, Zhi-Jun; Jiang, Wen-Qi

    2013-01-01

    C-reactive protein (CRP) is a biomarker of the inflammatory response, and it shows significant prognostic value for several types of solid tumors. The prognostic significance of CRP for lymphoma has not been fully examined. We evaluated the prognostic role of baseline serum CRP levels in patients with extranodal natural killer (NK)/T-cell lymphoma (ENKTL). We retrospectively analyzed 185 patients with newly diagnosed ENKTL. The prognostic value of the serum CRP level was evaluated for the low-CRP group (CRP≤10 mg/L) versus the high-CRP group (CRP>10 mg/L). The prognostic value of the International Prognostic Index (IPI) and the Korean Prognostic Index (KPI) were evaluated and compared with the newly developed prognostic model. Patients in the high-CRP group tended to display increased adverse clinical characteristics, lower rates of complete remission (P60 years, hypoalbuminemia, and elevated lactate dehydrogenase levels were independent adverse predictors of OS. Based on these four independent predictors, we constructed a new prognostic model that identified 4 groups with varying OS: group 1, no adverse factors; group 2, 1 factor; group 3, 2 factors; and group 4, 3 or 4 factors (PKPI in distinguishing between the low- and intermediate-low-risk groups, the intermediate-low- and high-intermediate-risk groups, and the high-intermediate- and high-risk groups. Our results suggest that pretreatment serum CRP levels represent an independent predictor of clinical outcome for patients with ENKTL. The prognostic value of the new prognostic model is superior to both IPI and KPI.

  17. Practical options for selecting data-driven or physics-based prognostics algorithms with reviews

    International Nuclear Information System (INIS)

    An, Dawn; Kim, Nam H.; Choi, Joo-Ho

    2015-01-01

    This paper is to provide practical options for prognostics so that beginners can select appropriate methods for their fields of application. To achieve this goal, several popular algorithms are first reviewed in the data-driven and physics-based prognostics methods. Each algorithm’s attributes and pros and cons are analyzed in terms of model definition, model parameter estimation and ability to handle noise and bias in data. Fatigue crack growth examples are then used to illustrate the characteristics of different algorithms. In order to suggest a suitable algorithm, several studies are made based on the number of data sets, the level of noise and bias, availability of loading and physical models, and complexity of the damage growth behavior. Based on the study, it is concluded that the Gaussian process is easy and fast to implement, but works well only when the covariance function is properly defined. The neural network has the advantage in the case of large noise and complex models but only with many training data sets. The particle filter and Bayesian method are superior to the former methods because they are less affected by noise and model complexity, but work only when physical model and loading conditions are available. - Highlights: • Practical review of data-driven and physics-based prognostics are provided. • As common prognostics algorithms, NN, GP, PF and BM are introduced. • Algorithms’ attributes, pros and cons, and applicable conditions are discussed. • This will be helpful to choose the best algorithm for different applications

  18. Mesoscale atmospheric modelling technology as a tool for the long-term meteorological dataset development

    Science.gov (United States)

    Platonov, Vladimir; Kislov, Alexander; Rivin, Gdaly; Varentsov, Mikhail; Rozinkina, Inna; Nikitin, Mikhail; Chumakov, Mikhail

    2017-04-01

    The detailed hydrodynamic modelling of meteorological parameters during the last 30 years (1985 - 2014) was performed for the Okhotsk Sea and the Sakhalin island regions. The regional non-hydrostatic atmospheric model COSMO-CLM used for this long-term simulation with 13.2, 6.6 and 2.2 km horizontal resolutions. The main objective of creation this dataset was the outlook of the investigation of statistical characteristics and the physical mechanisms of extreme weather events (primarily, wind speed extremes) on the small spatio-temporal scales. COSMO-CLM is the climate version of the well-known mesoscale COSMO model, including some modifications and extensions adapting to the long-term numerical experiments. The downscaling technique was realized and developed for the long-term simulations with three consequent nesting domains. ERA-Interim reanalysis ( 0.75 degrees resolution) used as global forcing data for the starting domain ( 13.2 km horizontal resolution), then these simulation data used as initial and boundary conditions for the next model runs over the domain with 6.6 km resolution, and similarly, for the next step to 2.2 km domain. Besides, the COSMO-CLM model configuration for 13.2 km run included the spectral nudging technique, i.e. an additional assimilation of reanalysis data not only at boundaries, but also inside the whole domain. Practically, this computational scheme realized on the SGI Altix 4700 supercomputer system in the Main Computer Center of Roshydromet and used 2,400 hours of CPU time total. According to modelling results, the verification of the obtained dataset was performed on the observation data. Estimations showed the mean error -0.5 0C, up to 2 - 3 0C RMSE in temperature, and overestimation in wind speed (RMSE is up to 2 m/s). Overall, analysis showed that the used downscaling technique with applying the COSMO-CLM model reproduced the meteorological conditions, spatial distribution, seasonal and synoptic variability of temperature and

  19. Prognostic model for long-term survival of locally advanced non-small-cell lung cancer patients after neoadjuvant radiochemotherapy and resection integrating clinical and histopathologic factors

    International Nuclear Information System (INIS)

    Pöttgen, Christoph; Stuschke, Martin; Graupner, Britta; Theegarten, Dirk; Gauler, Thomas; Jendrossek, Verena; Freitag, Lutz; Jawad, Jehad Abu; Gkika, Eleni; Wohlschlaeger, Jeremias; Welter, Stefan; Hoiczyk, Matthias; Schuler, Martin; Stamatis, Georgios; Eberhardt, Wilfried

    2015-01-01

    Outcome of consecutive patients with locally advanced non-small cell lung cancer and histopathologically proven mediastional lymph node metastases treated with induction chemotherapy, neoadjuvant radiochemotherapy and thoracotomy at the West German Cancer Center between 08/2000 and 06/2012 was analysed. A clinico-pathological prognostic model for survival was built including partial or complete response according to computed tomography imaging (CT) as clinical parameters as well as pathologic complete remission (pCR) and mediastinal nodal clearance (MNC) as histopathologic factors. Proportional hazard analysis (PHA) and recursive partitioning analysis (RPA) were used to identify prognostic factors for survival. Long-term survival was defined as survival ≥ 36 months. A total of 157 patients were treated, median follow-up was 97 months. Among these patients, pCR and MNC were observed in 41 and 85 patients, respectively. Overall survival was 56 ± 4% and 36 ± 4% at 24 and 60 months, respectively. Sensitivities of pCR and MNC to detect long-term survivors were 38% and 61%, specificities were 84% and 52%, respectively. Multivariable survival analysis revealed pCR, cN3 category, and gender, as prognostic factors at a level of α < 0.05. Considering only preoperative available parameters, CT response became significant. Classifying patients with a predicted hazard above the median as high risk group and the remaining as low risk patients yielded better separation of the survival curves by the inclusion of histopathologic factors than by preoperative factors alone (p < 0.0001, log rank test). Using RPA, pCR was identified as the top prognostic factor above clinical factors (p = 0.0006). No long term survivors were observed in patients with cT3-4 cN3 tumors without pCR. pCR is the dominant histopathologic response parameter and improves prognostic classifiers, based on clinical parameters. The validated prognostic model can be used to estimate individual prognosis and

  20. Comparison of Two Probabilistic Fatigue Damage Assessment Approaches Using Prognostic Performance Metrics

    Directory of Open Access Journals (Sweden)

    Xuefei Guan

    2011-01-01

    Full Text Available In this paper, two probabilistic prognosis updating schemes are compared. One is based on the classical Bayesian approach and the other is based on newly developed maximum relative entropy (MRE approach. The algorithm performance of the two models is evaluated using a set of recently developed prognostics-based metrics. Various uncertainties from measurements, modeling, and parameter estimations are integrated into the prognosis framework as random input variables for fatigue damage of materials. Measures of response variables are then used to update the statistical distributions of random variables and the prognosis results are updated using posterior distributions. Markov Chain Monte Carlo (MCMC technique is employed to provide the posterior samples for model updating in the framework. Experimental data are used to demonstrate the operation of the proposed probabilistic prognosis methodology. A set of prognostics-based metrics are employed to quantitatively evaluate the prognosis performance and compare the proposed entropy method with the classical Bayesian updating algorithm. In particular, model accuracy, precision, robustness and convergence are rigorously evaluated in addition to the qualitative visual comparison. Following this, potential development and improvement for the prognostics-based metrics are discussed in detail.

  1. An adaptive functional regression-based prognostic model for applications with missing data

    International Nuclear Information System (INIS)

    Fang, Xiaolei; Zhou, Rensheng; Gebraeel, Nagi

    2015-01-01

    Most prognostic degradation models rely on a relatively accurate and comprehensive database of historical degradation signals. Typically, these signals are used to identify suitable degradation trends that are useful for predicting lifetime. In many real-world applications, these degradation signals are usually incomplete, i.e., contain missing observations. Often the amount of missing data compromises the ability to identify a suitable parametric degradation model. This paper addresses this problem by developing a semi-parametric approach that can be used to predict the remaining lifetime of partially degraded systems. First, key signal features are identified by applying Functional Principal Components Analysis (FPCA) to the available historical data. Next, an adaptive functional regression model is used to model the extracted signal features and the corresponding times-to-failure. The model is then used to predict remaining lifetimes and to update these predictions using real-time signals observed from fielded components. Results show that the proposed approach is relatively robust to significant levels of missing data. The performance of the model is evaluated and shown to provide significantly accurate predictions of residual lifetime using two case studies. - Highlights: • We model degradation signals with missing data with the goal of predicting remaining lifetime. • We examine two types of signal characteristics, fragmented and sparse. • We provide framework that updates remaining life predictions by incorporating real-time signal observations. • For the missing data, we show that the proposed model outperforms other benchmark models. • For the complete data, we show that the proposed model performs at least as good as a benchmark model

  2. Air Quality and Meteorological Boundary Conditions during the MCMA-2003 Field Campaign

    Science.gov (United States)

    Sosa, G.; Arriaga, J.; Vega, E.; Magaña, V.; Caetano, E.; de Foy, B.; Molina, L. T.; Molina, M. J.; Ramos, R.; Retama, A.; Zaragoza, J.; Martínez, A. P.; Márquez, C.; Cárdenas, B.; Lamb, B.; Velasco, E.; Allwine, E.; Pressley, S.; Westberg, H.; Reyes, R.

    2004-12-01

    A comprehensive field campaign to characterize photochemical smog in the Mexico City Metropolitan Area (MCMA) was conducted during April 2003. An important number of equipment was deployed all around the urban core and its surroundings to measure gas and particles composition from the various sources and receptor sites. In addition to air quality measurements, meteorology variables were also taken by regular weather meteorological stations, tethered balloons, radiosondes, sodars and lidars. One important issue with regard to the field campaign was the characterization of the boundary conditions in order to feed meteorological and air quality models. Four boundary sites were selected to measure continuously criteria pollutants, VOC and meteorological variables at surface level. Vertical meteorological profiles were measured at three other sites : radiosondes in Tacubaya site were launched every six hours daily; tethered balloons were launched at CENICA and FES-Cuautitlan sites according to the weather conditions, and one sodar was deployed at UNAM site in the south of the city. Additionally to these measurements, two fixed meteorological monitoring networks deployed along the city were available to complement these measurements. In general, we observed that transport of pollutants from the city to the boundary sites changes every day, according to the coupling between synoptic and local winds. This effect were less important at elevated sites such as Cerro de la Catedral and ININ, where synoptic wind were more dominant during the field campaign. Also, local sources nearby boundary sites hide the influence of pollution coming from the city some days, particularly at the La Reforma site.

  3. Daily river flow prediction based on Two-Phase Constructive Fuzzy Systems Modeling: A case of hydrological - meteorological measurements asymmetry

    Science.gov (United States)

    Bou-Fakhreddine, Bassam; Mougharbel, Imad; Faye, Alain; Abou Chakra, Sara; Pollet, Yann

    2018-03-01

    Accurate daily river flow forecast is essential in many applications of water resources such as hydropower operation, agricultural planning and flood control. This paper presents a forecasting approach to deal with a newly addressed situation where hydrological data exist for a period longer than that of meteorological data (measurements asymmetry). In fact, one of the potential solutions to resolve measurements asymmetry issue is data re-sampling. It is a matter of either considering only the hydrological data or the balanced part of the hydro-meteorological data set during the forecasting process. However, the main disadvantage is that we may lose potentially relevant information from the left-out data. In this research, the key output is a Two-Phase Constructive Fuzzy inference hybrid model that is implemented over the non re-sampled data. The introduced modeling approach must be capable of exploiting the available data efficiently with higher prediction efficiency relative to Constructive Fuzzy model trained over re-sampled data set. The study was applied to Litani River in the Bekaa Valley - Lebanon by using 4 years of rainfall and 24 years of river flow daily measurements. A Constructive Fuzzy System Model (C-FSM) and a Two-Phase Constructive Fuzzy System Model (TPC-FSM) are trained. Upon validating, the second model has shown a primarily competitive performance and accuracy with the ability to preserve a higher day-to-day variability for 1, 3 and 6 days ahead. In fact, for the longest lead period, the C-FSM and TPC-FSM were able of explaining respectively 84.6% and 86.5% of the actual river flow variation. Overall, the results indicate that TPC-FSM model has provided a better tool to capture extreme flows in the process of streamflow prediction.

  4. Prognostic factors and scoring system for survival in colonic perforation.

    Science.gov (United States)

    Komatsu, Shuhei; Shimomatsuya, Takumi; Nakajima, Masayuki; Amaya, Hirokazu; Kobuchi, Taketsune; Shiraishi, Susumu; Konishi, Sayuri; Ono, Susumu; Maruhashi, Kazuhiro

    2005-01-01

    No ideal and generally accepted prognostic factors and scoring systems exist to determine the prognosis of peritonitis associated with colonic perforation. This study was designed to investigate prognostic factors and evaluate the various scoring systems to allow identification of high-risk patients. Between 1996 and 2003, excluding iatrogenic and trauma cases, 26 consecutive patients underwent emergency operations for colorectal perforation and were selected for this retrospective study. Several clinical factors were analyzed as possible predictive factors, and APACHE II, SOFA, MPI, and MOF scores were calculated. The overall mortality was 26.9%. Compared with the survivors, non-survivors were found more frequently in Hinchey's stage III-IV, a low preoperative marker of pH, base excess (BE), and a low postoperative marker of white blood cell count, PaO2/FiO2 ratio, and renal output (24h). According to the logistic regression model, BE was a significant independent variable. Concerning the prognostic scoring systems, an APACHE II score of 19, a SOFA score of 8, an MPI score of 30, and an MOF score of 7 or more were significantly related to poor prognosis. Preoperative BE and postoperative white blood cell count were reliable prognostic factors and early classification using prognostic scoring systems at specific points in the disease process are useful to improve our understanding of the problems involved.

  5. Meteorological Integration for the Biological Warning and Incident Characterization (BWIC) System: General Guidance for BWIC Cities

    Energy Technology Data Exchange (ETDEWEB)

    Shaw, William J.; Wang, Weiguo; Rutz, Frederick C.; Chapman, Elaine G.; Rishel, Jeremy P.; Xie, YuLong; Seiple, Timothy E.; Allwine, K Jerry

    2007-02-16

    The U.S. Department of Homeland Security (DHS) is responsible for developing systems to detect the release of aerosolized bioagents in urban environments. The system that accomplishes this, known as BioWatch, is a robust first-generation monitoring system. In conjunction with the BioWatch detection network, DHS has also developed a software tool for cities to use to assist in their response when a bioagent is detected. This tool, the Biological Warning and Incident Characterization (BWIC) System, will eventually be deployed to all BioWatch cities to aid in the interpretation of the public health significance of indicators from the BioWatch networks. BWIC consists of a set of integrated modules, including meteorological models, that estimate the effect of a biological agent on a city’s population once it has been detected. For the meteorological models in BWIC to successfully calculate the distribution of biological material, they must have as input accurate meteorological data, and wind fields in particular. The purpose of this document is to provide guidance for cities to use in identifying sources of good-quality local meteorological data that BWIC needs to function properly. This process of finding sources of local meteorological data, evaluating the data quality and gaps in coverage, and getting the data into BWIC, referred to as meteorological integration, is described. The good news for many cities is that meteorological measurement networks are becoming increasingly common. Most of these networks allow their data to be distributed in real time via the internet. Thus, cities will often only need to evaluate the quality of available measurements and perhaps add a modest number of stations where coverage is poor.

  6. An Evaluation System for the Online Training Programs in Meteorology and Hydrology

    Science.gov (United States)

    Wang, Yong; Zhi, Xiefei

    2009-01-01

    This paper studies the current evaluation system for the online training program in meteorology and hydrology. CIPP model that includes context evaluation, input evaluation, process evaluation and product evaluation differs from Kirkpatrick model including reactions evaluation, learning evaluation, transfer evaluation and results evaluation in…

  7. Predicting Overall Survival After Stereotactic Ablative Radiation Therapy in Early-Stage Lung Cancer: Development and External Validation of the Amsterdam Prognostic Model

    Energy Technology Data Exchange (ETDEWEB)

    Louie, Alexander V., E-mail: Dr.alexlouie@gmail.com [Department of Radiation Oncology, VU University Medical Center, Amsterdam (Netherlands); Department of Radiation Oncology, London Regional Cancer Program, University of Western Ontario, London, Ontario (Canada); Department of Epidemiology, Harvard School of Public Health, Harvard University, Boston, Massachusetts (United States); Haasbeek, Cornelis J.A. [Department of Radiation Oncology, VU University Medical Center, Amsterdam (Netherlands); Mokhles, Sahar [Department of Cardio-Thoracic Surgery, Erasmus University Medical Center, Rotterdam (Netherlands); Rodrigues, George B. [Department of Radiation Oncology, London Regional Cancer Program, University of Western Ontario, London, Ontario (Canada); Stephans, Kevin L. [Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio (United States); Lagerwaard, Frank J. [Department of Radiation Oncology, VU University Medical Center, Amsterdam (Netherlands); Palma, David A. [Department of Radiation Oncology, London Regional Cancer Program, University of Western Ontario, London, Ontario (Canada); Videtic, Gregory M.M. [Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio (United States); Warner, Andrew [Department of Radiation Oncology, London Regional Cancer Program, University of Western Ontario, London, Ontario (Canada); Takkenberg, Johanna J.M. [Department of Cardio-Thoracic Surgery, Erasmus University Medical Center, Rotterdam (Netherlands); Reddy, Chandana A. [Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio (United States); Maat, Alex P.W.M. [Department of Cardio-Thoracic Surgery, Erasmus University Medical Center, Rotterdam (Netherlands); Woody, Neil M. [Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio (United States); Slotman, Ben J.; Senan, Suresh [Department of Radiation Oncology, VU University Medical Center, Amsterdam (Netherlands)

    2015-09-01

    Purpose: A prognostic model for 5-year overall survival (OS), consisting of recursive partitioning analysis (RPA) and a nomogram, was developed for patients with early-stage non-small cell lung cancer (ES-NSCLC) treated with stereotactic ablative radiation therapy (SABR). Methods and Materials: A primary dataset of 703 ES-NSCLC SABR patients was randomly divided into a training (67%) and an internal validation (33%) dataset. In the former group, 21 unique parameters consisting of patient, treatment, and tumor factors were entered into an RPA model to predict OS. Univariate and multivariate models were constructed for RPA-selected factors to evaluate their relationship with OS. A nomogram for OS was constructed based on factors significant in multivariate modeling and validated with calibration plots. Both the RPA and the nomogram were externally validated in independent surgical (n=193) and SABR (n=543) datasets. Results: RPA identified 2 distinct risk classes based on tumor diameter, age, World Health Organization performance status (PS) and Charlson comorbidity index. This RPA had moderate discrimination in SABR datasets (c-index range: 0.52-0.60) but was of limited value in the surgical validation cohort. The nomogram predicting OS included smoking history in addition to RPA-identified factors. In contrast to RPA, validation of the nomogram performed well in internal validation (r{sup 2}=0.97) and external SABR (r{sup 2}=0.79) and surgical cohorts (r{sup 2}=0.91). Conclusions: The Amsterdam prognostic model is the first externally validated prognostication tool for OS in ES-NSCLC treated with SABR available to individualize patient decision making. The nomogram retained strong performance across surgical and SABR external validation datasets. RPA performance was poor in surgical patients, suggesting that 2 different distinct patient populations are being treated with these 2 effective modalities.

  8. PM10 Pollution: Its Prediction and Meteorological Influence in PasirGudang, Johor

    International Nuclear Information System (INIS)

    Afzali, A; Ramli, M; Rashid, M; Sabariah, B

    2014-01-01

    Ambient PM 10 (i.e particulate diameter less than 10 um in size) pollution has negative impacts on human health and it is influenced by meteorological conditions. Although the correlation between meteorological parameters and PM 10 concentrations is significant in most cases, the linear relationship between them implies that the fraction of the variance, R2 rarely exceeds 25%. However, considering the previous day's concentration of pollutants to the multi-linear regression enhances the model performance and increases the value of R2. Alternatively, artificial neural networks (ANN) are used to capture the complex relationships among many factors considered which present a better prediction. Thus, this study presents the results of predicting ambient PM 10 concentration and the influence of meteorological parameters based on the data sampled from 2008 – 2010 in an industrial area of PasirGudang, Johor

  9. Development and validation of prognostic models in metastatic breast cancer: a GOCS study.

    Science.gov (United States)

    Rabinovich, M; Vallejo, C; Bianco, A; Perez, J; Machiavelli, M; Leone, B; Romero, A; Rodriguez, R; Cuevas, M; Dansky, C

    1992-01-01

    The significance of several prognostic factors and the magnitude of their influence on response rate and survival were assessed by means of uni- and multivariate analyses in 362 patients with stage IV (UICC) breast carcinoma receiving combination chemotherapy as first systemic treatment over an 8-year period. Univariate analyses identified performance status and prior adjuvant radiotherapy as predictors of objective regression (OR), whereas the performance status, prior chemotherapy and radiotherapy (adjuvants), white blood cells count, SGOT and SGPT levels, and metastatic pattern were significantly correlated to survival. In multivariate analyses favorable characteristics associated to OR were prior adjuvant radiotherapy, no prior chemotherapy and postmenopausal status. Regarding survival, the performance status and visceral involvement were selected by the Cox model. The predictive accuracy of the logistic and the proportional hazards models was retrospectively tested in the training sample, and prospectively in a new population of 126 patients also receiving combined chemotherapy as first treatment for metastatic breast cancer. A certain overfitting to data in the training sample was observed with the regression model for response. However, the discriminative ability of the Cox model for survival was clearly confirmed.

  10. A joint modelling exercise designed to assess the respective impact of emission changes and meteorological variability on the observed air quality trends in major urban hotspots.

    Science.gov (United States)

    Colette, Augustin; Bessagnet, Bertrand; Dangiola, Ariela; D'Isidoro, Massimo; Gauss, Michael; Granier, Claire; Hodnebrog, Øivind; Jakobs, Hermann; Kanakidou, Maria; Khokhar, Fahim; Law, Kathy; Maurizi, Alberto; Meleux, Frederik; Memmesheimer, Michael; Nyiri, Agnes; Rouil, Laurence; Stordal, Frode; Tampieri, Francesco

    2010-05-01

    With the growth of urban agglomerations, assessing the drivers of variability of air quality in and around the main anthropogenic emission hotspots has become a major societal concern as well as a scientific challenge. These drivers include emission changes and meteorological variability; both of them can be investigated by means of numerical modelling of trends over the past few years. A collaborative effort has been developed in the framework of the CityZen European project to address this question. Several chemistry and transport models (CTMs) are deployed in this activity: four regional models (BOLCHEM, CHIMERE, EMEP and EURAD) and three global models (CTM2, MOZART, and TM4). The period from 1998 to 2007 has been selected for the historic reconstruction. The focus for the present preliminary presentation is Europe. A consistent set of emissions is used by all partners (EMEP for the European domain and IPCC-AR5 beyond) while a variety of meteorological forcing is used to gain robustness in the ensemble spread amongst models. The results of this experiment will be investigated to address the following questions: - Is the envelope of models able to reproduce the observed trends of the key chemical constituents? - How the variability amongst models changes in time and space and what does it tell us about the processes driving the observed trends? - Did chemical regimes and aerosol formation processes changed in selected hotspots? Answering the above questions will contribute to fulfil the ultimate goal of the present study: distinguishing the respective contribution of meteorological variability and emissions changes on air quality trends in major anthropogenic emissions hotspots.

  11. Predicting Phenologic Response to Water Stress and Implications for Carbon Uptake across the Southeast U.S.

    Science.gov (United States)

    Lowman, L.; Barros, A. P.

    2016-12-01

    Representation of plant photosynthesis in modeling studies requires phenologic indicators to scale carbon assimilation by plants. These indicators are typically the fraction of photosynthetically active radiation (FPAR) and leaf area index (LAI) which represent plant responses to light and water availability, as well as temperature constraints. In this study, a prognostic phenology model based on the growing season index is adapted to determine the phenologic indicators of LAI and FPAR at the sub-daily scale based on meteorological and soil conditions. Specifically, we directly model vegetation green-up and die-off responses to temperature, vapor pressure deficit, soil water potential, and incoming solar radiation. The indices are based on the properties of individual plant functional types, driven by observational data and prior modeling applications. First, we describe and test the sensitivity of the carbon uptake response to predicted phenology for different vegetation types. Second, the prognostic phenology model is incorporated into a land-surface hydrology model, the Duke Coupled Hydrology Model with Prognostic Vegetation (DCHM-PV), to demonstrate the impact of dynamic phenology on modeled carbon assimilation rates and hydrologic feedbacks. Preliminary results show reduced carbon uptake rates when incorporating a prognostic phenology model that match well against the eddy-covariance flux tower observations. Additionally, grassland vegetation shows the most variability in LAI and FPAR tied to meteorological and soil conditions. These results highlight the need to incorporate vegetation-specific responses to water limitation in order to accurately estimate the terrestrial carbon storage component of the global carbon budget.

  12. Methods and strategy for modeling daily global solar radiation with measured meteorological data - A case study in Nanchang station, China

    International Nuclear Information System (INIS)

    Wu, Guofeng; Liu, Yaolin; Wang, Tiejun

    2007-01-01

    Solar radiation is a primary driver for many physical, chemical and biological processes on the earth's surface, and complete and accurate solar radiation data at a specific region are quite indispensable to the solar energy related researches. This study, with Nanchang station, China, as a case study, aimed to calibrate existing models and develop new models for estimating missing global solar radiation data using commonly measured meteorological data and to propose a strategy for selecting the optimal models under different situations of available meteorological data. Using daily global radiation, sunshine hours, temperature, total precipitation and dew point data covering the years from 1994 to 2005, we calibrated or developed and evaluated seven existing models and two new models. Validation criteria included intercept, slope, coefficient of determination, mean bias error and root mean square error. The best result (R 2 = 0.93) was derived from Chen model 2, which uses sunshine hours and temperature as predictors. The Bahel model, which only uses sunshine hours, was almost as good, explaining 92% of the solar radiation variance. Temperature based models (Bristow and Campbell, Allen, Hargreaves and Chen 1 models) provided less accurate results, of which the best one (R 2 = 0.69) is the Bristow and Campbell model. The temperature based models were improved by adding other variables (daily mean total precipitation and mean dew point). Two such models could explain 77% (Wu model 1) and 80% (Wu model 2) of the solar radiation variance. We, thus, propose a strategy for selecting an optimal method for calculating missing daily values of global solar radiation: (1) when sunshine hour and temperature data are available, use Chen model 2; (2) when only sunshine hour data are available, use Bahel model; (3) when temperature, total precipitation and dew point data are available but not sunshine hours, use Wu model 2; (4) when only temperature and total precipitation are

  13. Air pollutants, meteorology and plant injury

    Energy Technology Data Exchange (ETDEWEB)

    Mukammal, E I; Brandt, C S; Neuwirth, R; Pack, D H; Swinbank, W C

    1968-01-01

    The study of the effect of air pollutants on plant growth inevitably involves meteorological factors, and the World Meteorological Organization has therefore been giving much attention to this matter for some time. Within the Organization, responsibility for this work naturally fell to the Commission for Agricultural Meteorology (CAgM), and following the time-honored procedure in such cases, the Commission established in 1962 a small international group of acknowledged experts to study plant injury and reduction of yield by non-radioactive air pollutants, and charged it with the specific task of preparing a review of present knowledge of the subjects involved. After several years' work, the group fulfilled its appointed task and the resulting report is now published in this WMO Technical Note. 95 references.

  14. Optimization of a prognostic biosphere model for terrestrial biomass and atmospheric CO2 variability

    International Nuclear Information System (INIS)

    Saito, M.; Ito, A.; Maksyutov, S.

    2014-01-01

    This study investigates the capacity of a prognostic biosphere model to simulate global variability in atmospheric CO 2 concentrations and vegetation carbon dynamics under current environmental conditions. Global data sets of atmospheric CO 2 concentrations, above-ground biomass (AGB), and net primary productivity (NPP) in terrestrial vegetation were assimilated into the biosphere model using an inverse modeling method combined with an atmospheric transport model. In this process, the optimal physiological parameters of the biosphere model were estimated by minimizing the misfit between observed and modeled values, and parameters were generated to characterize various biome types. Results obtained using the model with the optimized parameters correspond to the observed seasonal variations in CO 2 concentration and their annual amplitudes in both the Northern and Southern Hemispheres. In simulating the mean annual AGB and NPP, the model shows improvements in estimating the mean magnitudes and probability distributions for each biome, as compared with results obtained using prior simulation parameters. However, the model is less efficient in its simulation of AGB for forest type biomes. This misfit suggests that more accurate values of input parameters, specifically, grid mean AGB values and seasonal variabilities in physiological parameters, are required to improve the performance of the simulation model. (authors)

  15. Meteorology, Macrophysics, Microphysics, Microwaves, and Mesoscale Modeling of Mediterranean Mountain Storms: The M8 Laboratory

    Science.gov (United States)

    Starr, David O. (Technical Monitor); Smith, Eric A.

    2002-01-01

    Comprehensive understanding of the microphysical nature of Mediterranean storms can be accomplished by a combination of in situ meteorological data analysis and radar-passive microwave data analysis, effectively integrated with numerical modeling studies at various scales, from synoptic scale down through the mesoscale, the cloud macrophysical scale, and ultimately the cloud microphysical scale. The microphysical properties of and their controls on severe storms are intrinsically related to meteorological processes under which storms have evolved, processes which eventually select and control the dominant microphysical properties themselves. This involves intense convective development, stratiform decay, orographic lifting, and sloped frontal lifting processes, as well as the associated vertical motions and thermodynamical instabilities governing physical processes that affect details of the size distributions and fall rates of the various types of hydrometeors found within the storm environment. Insofar as hazardous Mediterranean storms, highlighted in this study by three mountain storms producing damaging floods in northern Italy between 1992 and 2000, developing a comprehensive microphysical interpretation requires an understanding of the multiple phases of storm evolution and the heterogeneous nature of precipitation fields within a storm domain. This involves convective development, stratiform transition and decay, orographic lifting, and sloped frontal lifting processes. This also involves vertical motions and thermodynamical instabilities governing physical processes that determine details of the liquid/ice water contents, size disi:ributions, and fall rates of the various modes of hydrometeors found within hazardous storm environments.

  16. Prognostic factors in lupus nephritis

    DEFF Research Database (Denmark)

    Faurschou, Mikkel; Starklint, Henrik; Halberg, Poul

    2006-01-01

    To evaluate the prognostic significance of clinical and renal biopsy findings in an unselected cohort of patients with systemic lupus erythematosus (SLE) and nephritis.......To evaluate the prognostic significance of clinical and renal biopsy findings in an unselected cohort of patients with systemic lupus erythematosus (SLE) and nephritis....

  17. Development of regional meteorological and atmospheric diffusion simulation system

    International Nuclear Information System (INIS)

    Kubota, Ryuji; Iwashige, Kengo; Kasano, Toshio

    2002-01-01

    Regional atmospheric diffusion online network (RADON) with atmospheric diffusion analysis code (ADAC) : a simulation program of diffusion of radioactive materials, volcanic ash, pollen, NOx and SOx was developed. This system can be executed in personal computer (PC) and note PC on Windows. Emission data consists of online, offline and default data. It uses the meteorology data sources such as meteorological forecasting mesh data, automated meteorological data acquisition system (AMeDAS) data, meteorological observation data in site and municipality observation data. The meteorological forecasting mesh data shows forecasting value of temperature, wind speed, wind direction and humidity in about two days. The nuclear environmental monitoring center retains the online data (meteorological data, emission source data, monitoring station data) in its PC server and can run forecasting or repeating calculation using these data and store and print out the calculation results. About 30 emission materials can be calculated simultaneously. This system can simulate a series of weather from the past and real time to the future. (S.Y.)

  18. Hydro-meteorological evaluation of downscaled global ensemble rainfall forecasts

    Science.gov (United States)

    Gaborit, Étienne; Anctil, François; Fortin, Vincent; Pelletier, Geneviève

    2013-04-01

    Ensemble rainfall forecasts are of high interest for decision making, as they provide an explicit and dynamic assessment of the uncertainty in the forecast (Ruiz et al. 2009). However, for hydrological forecasting, their low resolution currently limits their use to large watersheds (Maraun et al. 2010). In order to bridge this gap, various implementations of the statistic-stochastic multi-fractal downscaling technique presented by Perica and Foufoula-Georgiou (1996) were compared, bringing Environment Canada's global ensemble rainfall forecasts from a 100 by 70-km resolution down to 6 by 4-km, while increasing each pixel's rainfall variance and preserving its original mean. For comparison purposes, simpler methods were also implemented such as the bi-linear interpolation, which disaggregates global forecasts without modifying their variance. The downscaled meteorological products were evaluated using different scores and diagrams, from both a meteorological and a hydrological view points. The meteorological evaluation was conducted comparing the forecasted rainfall depths against nine days of observed values taken from Québec City rain gauge database. These 9 days present strong precipitation events occurring during the summer of 2009. For the hydrologic evaluation, the hydrological models SWMM5 and (a modified version of) GR4J were implemented on a small 6 km2 urban catchment located in the Québec City region. Ensemble hydrologic forecasts with a time step of 3 hours were then performed over a 3-months period of the summer of 2010 using the original and downscaled ensemble rainfall forecasts. The most important conclusions of this work are that the overall quality of the forecasts was preserved during the disaggregation procedure and that the disaggregated products using this variance-enhancing method were of similar quality than bi-linear interpolation products. However, variance and dispersion of the different members were, of course, much improved for the

  19. Integrated modeling of the dynamic meteorological and sea surface conditions during the passage of Typhoon Morakot

    Science.gov (United States)

    Lee, Han Soo; Yamashita, Takao; Hsu, John R.-C.; Ding, Fei

    2013-01-01

    In August 2009, Typhoon Morakot caused massive flooding and devastating mudslides in the southern Taiwan triggered by extremely heavy rainfall (2777 mm in 4 days) which occurred during its passage. It was one of the deadliest typhoons that have ever attacked Taiwan in recent years. In this study, numerical simulations are performed for the storm surge and ocean surface waves, together with dynamic meteorological fields such as wind, pressure and precipitation induced by Typhoon Morakot, using an atmosphere-waves-ocean integrated modelling system. The wave-induced dissipation stress from breaking waves, whitecapping and depth-induced wave breaking, is parameterized and included in the wave-current interaction process, in addition to its influence on the storm surge level in shallow water along the coast of Taiwan. The simulated wind and pressure field captures the characteristics of the observed meteorological field. The spatial distribution of the accumulated rainfall within 4 days, from 00:00 UTC 6 August to 00:00 UTC 10 August 2009, shows similar patterns as the observed values. The 4-day accumulated rainfall of 2777 mm at the A-Li Shan mountain weather station for the same period depicted a high correlation with the observed value of 2780 mm/4 days. The effects of wave-induced dissipation stress in the wave-current interaction resulted in increased surge heights on the relatively shallow western coast of Taiwan, where the bottom slope of the bathymetry ranges from mild to moderate. The results also show that wave-breaking has to be considered for accurate storm surge prediction along the east coast of Taiwan over the narrow bank of surf zone with a high horizontal resolution of the model domain.

  20. Meteorological tracers in regional planning

    International Nuclear Information System (INIS)

    Mueller, K.H.

    1974-11-01

    Atmospheric tracers can be used as indicators to study both the ventilation of an urban region and its dispersion meteorology for air pollutants. A correlation analysis applied to the space-time dependent tracer concentrations is able to give transfer functions, the structure and characteristic parameters of which describe the meteorological and topographical situation of the urban region and its surroundings in an integral manner. To reduce the number of persons usually involved in a tracer experiment an automatic air sampling system had to be developed

  1. Accelerated Aging in Electrolytic Capacitors for Prognostics

    Science.gov (United States)

    Celaya, Jose R.; Kulkarni, Chetan; Saha, Sankalita; Biswas, Gautam; Goebel, Kai Frank

    2012-01-01

    The focus of this work is the analysis of different degradation phenomena based on thermal overstress and electrical overstress accelerated aging systems and the use of accelerated aging techniques for prognostics algorithm development. Results on thermal overstress and electrical overstress experiments are presented. In addition, preliminary results toward the development of physics-based degradation models are presented focusing on the electrolyte evaporation failure mechanism. An empirical degradation model based on percentage capacitance loss under electrical overstress is presented and used in: (i) a Bayesian-based implementation of model-based prognostics using a discrete Kalman filter for health state estimation, and (ii) a dynamic system representation of the degradation model for forecasting and remaining useful life (RUL) estimation. A leave-one-out validation methodology is used to assess the validity of the methodology under the small sample size constrain. The results observed on the RUL estimation are consistent through the validation tests comparing relative accuracy and prediction error. It has been observed that the inaccuracy of the model to represent the change in degradation behavior observed at the end of the test data is consistent throughout the validation tests, indicating the need of a more detailed degradation model or the use of an algorithm that could estimate model parameters on-line. Based on the observed degradation process under different stress intensity with rest periods, the need for more sophisticated degradation models is further supported. The current degradation model does not represent the capacitance recovery over rest periods following an accelerated aging stress period.

  2. Method to characterize local meteorology at nuclear facilities for application to emergency response needs

    International Nuclear Information System (INIS)

    Lindsey, C.G.; Glantz, C.S.

    1986-04-01

    Effluent dispersion is evaluated using computer codes that require various meteorological parameters such as wind and stability data. These data will be based on current conditions at the site in question, and on forecasts of the expected local meteorology for the time period to be simulated. To assist NRC personnel in preparing these forecasts, a weather-typing model was implemented to analyze the characteristic behavior of local meteorology as it responds to various synoptic-scale weather features (e.g., warm fronts, cold fronts, high pressure systems). Historical observations acquired by instrumented towers at several nuclear power plants were analyzed as a function of the prevailing synoptic weather feature, synoptic-scale pressure gradient, and time of year. This study focused on sites located in shoreline and complex terrain environments because of the occurrence of mesoscale circulations, which are the sea/lake-land breeze and valley wind systems. Such circulations produce diurnally changing wind and stability conditions that cannot be readily identified by synoptic-scale weather forecasts. The advantage in analyzing the climatological behavior of local meteorology as it responds to various synoptic weather systems is that certain weather systems will control the local meteorology and produce persistent conditions

  3. Correlation between the meteorological data acquisition systems of the Centro Experimental ARAMAR

    Energy Technology Data Exchange (ETDEWEB)

    Oliveira, Rando M.; Beu, Cássia M.L., E-mail: rando.oliveira@marinha.mil.br, E-mail: cassia.beu@marinha.mil.br [Centro Tecnológico da Marinha em São Paulo (CEA/CTMSP), Iperó, SP (Brazil). Centro Experimental ARAMAR

    2017-07-01

    Centro Experimental ARAMAR (CEA) is a Brazilian Navy Technological Center located in the rural area of lperó (São Paulo State), about 10-km distant from the nearest urban area. One of the most important activities at CEA is the nuclear fuel cycle research, as well as the development of a small-scale pressurized water reactor (PWR) land based prototype, The Laboratório Radioecológico (LAR E) is responsible for the meteorological observation program which relies on an automatic data collection system, The following variables are continuously measured: pressure, precipitation, wind speed, wind direction, temperature and relative humidity, The obtained data is refined and used in the annual reports to Comissão Nacional de Energia Nuclear (CNEN), and are an important input data for atmospheric dispersion models. Due to the construction of the Laboratório de Geraç!o Nucleoclétrica (LABGENE), it will be necessary to change tbe location of the towers and meteorological sensors, Thus, since 20 14, a new set of towers and sensors (Torre Nova) are in operation. The new location is 900 m distant from the old set (Torre Velha). Therefore, CEA has currently two meteorological data acquisition systems operating concurrently for approximately three years. The present work aims to compare the meteorological data of both systems in order to verify their agreement. The meteorological time series of both systems were submitted to a statistical analysis to evaluate their correlation. The results of this work confirm the compatibility of the two systems, showing that the Torre Velha can be deactivated without impairment to the meteorological time series. (author)

  4. Correlation between the meteorological data acquisition systems of the Centro Experimental ARAMAR

    International Nuclear Information System (INIS)

    Oliveira, Rando M.; Beu, Cássia M.L.

    2017-01-01

    Centro Experimental ARAMAR (CEA) is a Brazilian Navy Technological Center located in the rural area of lperó (São Paulo State), about 10-km distant from the nearest urban area. One of the most important activities at CEA is the nuclear fuel cycle research, as well as the development of a small-scale pressurized water reactor (PWR) land based prototype, The Laboratório Radioecológico (LAR E) is responsible for the meteorological observation program which relies on an automatic data collection system, The following variables are continuously measured: pressure, precipitation, wind speed, wind direction, temperature and relative humidity, The obtained data is refined and used in the annual reports to Comissão Nacional de Energia Nuclear (CNEN), and are an important input data for atmospheric dispersion models. Due to the construction of the Laboratório de Geraç!o Nucleoclétrica (LABGENE), it will be necessary to change tbe location of the towers and meteorological sensors, Thus, since 20 14, a new set of towers and sensors (Torre Nova) are in operation. The new location is 900 m distant from the old set (Torre Velha). Therefore, CEA has currently two meteorological data acquisition systems operating concurrently for approximately three years. The present work aims to compare the meteorological data of both systems in order to verify their agreement. The meteorological time series of both systems were submitted to a statistical analysis to evaluate their correlation. The results of this work confirm the compatibility of the two systems, showing that the Torre Velha can be deactivated without impairment to the meteorological time series. (author)

  5. Machine health prognostics using the Bayesian-inference-based probabilistic indication and high-order particle filtering framework

    Science.gov (United States)

    Yu, Jianbo

    2015-12-01

    Prognostics is much efficient to achieve zero-downtime performance, maximum productivity and proactive maintenance of machines. Prognostics intends to assess and predict the time evolution of machine health degradation so that machine failures can be predicted and prevented. A novel prognostics system is developed based on the data-model-fusion scheme using the Bayesian inference-based self-organizing map (SOM) and an integration of logistic regression (LR) and high-order particle filtering (HOPF). In this prognostics system, a baseline SOM is constructed to model the data distribution space of healthy machine under an assumption that predictable fault patterns are not available. Bayesian inference-based probability (BIP) derived from the baseline SOM is developed as a quantification indication of machine health degradation. BIP is capable of offering failure probability for the monitored machine, which has intuitionist explanation related to health degradation state. Based on those historic BIPs, the constructed LR and its modeling noise constitute a high-order Markov process (HOMP) to describe machine health propagation. HOPF is used to solve the HOMP estimation to predict the evolution of the machine health in the form of a probability density function (PDF). An on-line model update scheme is developed to adapt the Markov process changes to machine health dynamics quickly. The experimental results on a bearing test-bed illustrate the potential applications of the proposed system as an effective and simple tool for machine health prognostics.

  6. The introduction of horizontal inhomogeneity of meteorological conditions in the EOSTAR propagation model

    NARCIS (Netherlands)

    Eijk, A.M.J. van; Kunz, G.J.

    2006-01-01

    The effective field-of-view of an electro-optical sensor in a given meteorological scenario can be evaluated using a ray-tracer. The resulting ray trace diagram also provides information pertinent to the quality (distortion, mirages) of the image being viewed by the sensor. The EOSTAR (Electro

  7. A gap analysis of meteorological requirements for commercial space operators

    Science.gov (United States)

    Stapleton, Nicholas James

    Commercial space companies will soon be the primary method of launching people and supplies into orbit. Among the critical aspects of space launches are the meteorological concerns. Laws and regulations pertaining to meteorological considerations have been created to ensure the safety of the space industry and those living around spaceports; but, are they adequate? Perhaps the commercial space industry can turn to the commercial aviation industry to help answer that question. Throughout its history, the aviation industry has dealt with lessons learned from mishaps due to failures in understanding the significance of weather impacts on operations. Using lessons from the aviation industry, the commercial space industry can preempt such accidents and maintain viability as an industry. Using Lanicci's Strategic Planning Model, this study identified the weather needs of the commercial space industry by conducting three gap analyses. First, a comparative analysis was done between laws and regulations in commercial aviation and those in the commercial space industry pertaining to meteorological support, finding a "legislative gap" between the two industries, as no legal guarantee is in place to ensure weather products remain available to the commercial space industry. A second analysis was conducted between the meteorological services provided for the commercial aviation industry and commercial space industry, finding a gap at facilities not located at an established launch facility or airport. At such facilities, many weather observational technologies would not be present, and would need to be purchased by the company operating the spaceport facility. A third analysis was conducted between the meteorological products and regulations that are currently in existence, and those needed for safe operations within the commercial space industry, finding gaps in predicting lightning, electric field charge, and space weather. Recommendations to address these deficiencies have

  8. Meteorological Factors Affecting Evaporation Duct Height Climatologies.

    Science.gov (United States)

    1980-07-01

    Italy Maritime Meteorology Division Japan Meteorological Agency Ote-Machi 1-3-4 Chiyoda-Ku Tokyo, Japan Instituto De Geofisica U.N.A.M. Biblioteca ...Torre De Ciencias, 3ER Piso Ciudad Universitaria Mexico 20, D.F. Koninklijk Nederlands Meteorologisch Instituu. Postbus 201 3730 AE Debilt Netherlands

  9. A Meteorological Supersite for Aviation and Cold Weather Applications

    Science.gov (United States)

    Gultepe, Ismail; Agelin-Chaab, M.; Komar, J.; Elfstrom, G.; Boudala, F.; Zhou, B.

    2018-05-01

    The goal of this study is to better understand atmospheric boundary layer processes and parameters, and to evaluate physical processes for aviation applications using data from a supersite observing site. Various meteorological sensors, including a weather and environmental unmanned aerial vehicle (WE-UAV), and a fog and snow tower (FSOS) observations are part of the project. The PanAm University of Ontario Institute of Technology (UOIT) Meteorological Supersite (PUMS) observations are being collected from April 2015 to date. The FSOS tower gathers observations related to rain, snow, fog, and visibility, aerosols, solar radiation, and wind and turbulence, as well as surface and sky temperature. The FSOSs are located at three locations at about 450-800 m away from the PUMS supersite. The WE-UAV measurements representing aerosol, wind speed and direction, as well as temperature (T) and relative humidity (RH) are provided during clear weather conditions. Other measurements at the PUMS site include cloud backscattering profiles from CL51 ceilometer, MWR observations of liquid water content (LWC), T, and RH, and Microwave Rain Radar (MRR) reflectivity profile, as well as the present weather type, snow water depth, icing rate, 3D-ultrasonic wind and turbulence, and conventional meteorological observations from compact weather stations, e.g., WXTs. The results based on important weather event studies, representing fog, snow, rain, blowing snow, wind gust, planetary boundary layer (PBL) wind research for UAV, and icing conditions are given. The microphysical parameterizations and analysis processes for each event are provided, but the results should not be generalized for all weather events and be used cautiously. Results suggested that integrated observing systems based on data from a supersite as well as satellite sites can provide better information applicable to aviation meteorology, including PBL weather research, validation of numerical weather model predictions, and

  10. Research Ship Oceanus Underway Meteorological Data, Quality Controlled

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Research Ship Oceanus Underway Meteorological Data (delayed ~10 days for quality control) are from the Shipboard Automated Meteorological and Oceanographic System...

  11. Research Ship Melville Underway Meteorological Data, Quality Controlled

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Research Ship Melville Underway Meteorological Data (delayed ~10 days for quality control) are from the Shipboard Automated Meteorological and Oceanographic System...

  12. Research Ship Healy Underway Meteorological Data, Quality Controlled

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Research Ship Healy Underway Meteorological Data (delayed ~10 days for quality control) are from the Shipboard Automated Meteorological and Oceanographic System...

  13. Research Ship Knorr Underway Meteorological Data, Quality Controlled

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Research Ship Knorr Underway Meteorological Data (delayed ~10 days for quality control) are from the Shipboard Automated Meteorological and Oceanographic System...

  14. Research Ship Atlantis Underway Meteorological Data, Quality Controlled

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Research Ship Atlantis Underway Meteorological Data (delayed ~10 days for quality control) are from the Shipboard Automated Meteorological and Oceanographic System...

  15. NOAA Ship Fairweather Underway Meteorological Data, Quality Controlled

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NOAA Ship Fairweather Underway Meteorological Data (delayed ~10 days for quality control) are from the Shipboard Automated Meteorological and Oceanographic System...

  16. NOAA Ship Rainier Underway Meteorological Data, Quality Controlled

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NOAA Ship Rainier Underway Meteorological Data (delayed ~10 days for quality control) are from the Shipboard Automated Meteorological and Oceanographic System...

  17. Research Ship Tangaroa Underway Meteorological Data, Quality Controlled

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Research Ship Tangaroa Underway Meteorological Data (delayed ~10 days for quality control) are from the Shipboard Automated Meteorological and Oceanographic System...

  18. Medical Meteorology: the Relationship between Meteorological Parameters (Humidity, Rainfall, Wind, and Temperature) and Brucellosis in Zanjan Province

    OpenAIRE

    Yousefali Abedini; Nahideh Mohammadi; Koorosh Kamali; Mohsen Ahadnejad; Mehdi Azari

    2016-01-01

    Background: Brucellosis (Malta fever) is a major contagious zoonotic disease, with economic and public health importance. Methods To assess the effect of meteorological (temperature, rainfall, humidity, and wind) and climate parameters on incidence of brucellosis, brucellosis distribution and meteorological zoning maps of Zanjan Province were prepared using Inverse Distance Weighting (IDW) and Kriging technique in Arc GIS medium. Zoning maps of mean temperature, rainfall, humidity, and win...

  19. Thai venous stroke prognostic score: TV-SPSS.

    Science.gov (United States)

    Poungvarin, Niphon; Prayoonwiwat, Naraporn; Ratanakorn, Disya; Towanabut, Somchai; Tantirittisak, Tassanee; Suwanwela, Nijasri; Phanthumchinda, Kamman; Tiamkoa, Somsak; Chankrachang, Siwaporn; Nidhinandana, Samart; Laptikultham, Somsak; Limsoontarakul, Sansern; Udomphanthuruk, Suthipol

    2009-11-01

    Prognosis of cerebral venous sinus thrombosis (CVST) has never been studied in Thailand. A simple prognostic score to predict poor prognosis of CVST has also never been reported. The authors are aiming to establish a simple and reliable prognostic score for this condition. The medical records of CVST patients from eight neurological training centers in Thailand who received between April 1993 and September 2005 were reviewed as part of this retrospective study. Clinical features included headache, seizure, stroke risk factors, Glasgow coma scale (GCS), blood pressure on arrival, papilledema, hemiparesis, meningeal irritation sign, location of occluded venous sinuses, hemorrhagic infarction, cerebrospinal fluid opening pressure, treatment options, length of stay, and other complications were analyzed to determine the outcome using modified Rankin scale (mRS). Poor prognosis (defined as mRS of 3-6) was determined on the discharge date. One hundred ninety four patients' records, 127 females (65.5%) and mean age of 36.6 +/- 14.4 years, were analyzed Fifty-one patients (26.3%) were in the poor outcome group (mRS 3-6). Overall mortality was 8.4%. Univariate analysis and then multivariate analysis using SPSS version 11.5 revealed only four statistically significant predictors influencing outcome of CVST They were underlying malignancy, low GCS, presence of hemorrhagic infarction (for poor outcome), and involvement of lateral sinus (for good outcome). Thai venous stroke prognostic score (TV-SPSS) was derived from these four factors using a multiple logistic model. A simple and pragmatic prognostic score for CVST outcome has been developed with high sensitivity (93%), yet low specificity (33%). The next study should focus on the validation of this score in other prospective populations.

  20. A meteorological overview of the ARCTAS 2008 mission

    Directory of Open Access Journals (Sweden)

    H. E. Fuelberg

    2010-01-01

    Full Text Available The Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS mission was a multi-aircraft project whose major objective was to investigate the factors driving changes in the Arctic's atmospheric composition and climate. It was conducted during April and June–July 2008. The summer ARCTAS deployment was preceded by a week of flights over and around California to address state issues of air quality and climate forcing. This paper focuses on meteorological conditions during the ARCTAS Spring and Summer campaigns. We examine mission averaged large-scale flow patterns at the surface, 500 hPa, and 300 hPa and determine their departures from climatology. Results from runs of the Weather Research and Forecasting (WRF model are used to describe meteorological conditions on individual days. Our WRF configuration included a nested grid approach that provided horizontal spacing as small as 5 km. Trajectories calculated from the WRF output are used to determine transport pathways to the Arctic, including their origins and the altitudes at which they reach 70° N. We also present backward trajectories from selected legs of individual ARCTAS flights. Finally, the FLEXPART Lagrangian particle dispersion model, with the high resolution WRF data as input, is used to determine the paths of anthropogenic and biomass burning-derived CO. Results show that there was frequent and widespread transport to the Arctic during both phases of ARCTAS and that the three ARCTAS aircraft sampled air having a multitude of origins, following a myriad of paths, and experiencing many types of meteorological conditions.

  1. NOAA Ship Pisces Underway Meteorological Data, Quality Controlled

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — NOAA Ship Pisces Underway Meteorological Data (delayed ~10 days for quality control) are from the Shipboard Automated Meteorological and Oceanographic System (SAMOS)...

  2. Using climate derivatives for assessment of meteorological parameter relationships in RCM and observations

    Science.gov (United States)

    Timuhins, Andrejs; Bethers, Uldis; Bethers, Peteris; Klints, Ilze; Sennikovs, Juris; Frishfelds, Vilnis

    2017-04-01

    In a changing climate it is essential to estimate its impacts on different economic fields. In our study we tried to create a framework for climate change assessment and climate change impact estimation for the territory of Latvia and to create results which are also understandable for non-scientists (stakeholder, media and public). This approach allowed us to more carefully assess the presentation and interpretation of results and their validation, for public viewing. For the presentation of our work a website was created (www.modlab.lv/klimats) containing two types of documents in a unified framework, meteorological parameter analysis of different easily interpretable derivative values. Both of these include analysis of the current situation as well as illustrate the projection for future time periods. Derivate values are calculated using two data sources: the bias corrected regional climate data and meteorological observation data. Derivative documents contain description of derived value, some interesting facts and conclusions. Additionally, all results may be viewed in temporal and spatial graphs and maps, for different time periods as well as different seasons. Bias correction (Sennikovs and Bethers, 2009) for the control period 1961-1990 is applied to RCM data series. Meteorological observation data of the Latvian Environment, Geology, and Meteorology Agency and ENSEMBLES project daily data of 13 RCM runs for the period 1960-2100 are used. All the documents are prepared in python notebooks, which allow for flexible changes. At the moment following derivative values have been published: forest fire risk index, wind energy, phenology (Degree days), road condition (friction, ice conditions), daily minimal meteorological visibility, headache occurrence rate, firs snow date and meteorological parameter analysis: temperature, precipitation, wind speed, relative humidity, and cloudiness. While creating these products RCM ability to represent the actual climate was

  3. Meteorological measurements at nuclear power plants

    International Nuclear Information System (INIS)

    1995-01-01

    On-site meteorological measurements are necessary for evaluating atmospheric dispersion of gaseous effluents. Radiation doses in a plant's vicinity due to these effluents are calculated from the results of dispersion evaluations. The guide addresses the requirements for on-site meteorological measurement systems. Guide YVL 7.3 addresses atmospheric dispersion evaluations and calculation methods, Guide YVL 7.2 radiation dose calculations and Guide YVL 7.8 environmental data reporting. (5 refs.)

  4. Estimation of daily net radiation from synoptic meteorological data

    International Nuclear Information System (INIS)

    Lee, B.W.; Myung, E.J.; Kim, B.C.

    1991-01-01

    Five models for net radiation estimation reported by Linacre (1968), Berljand(1956), Nakayama et al. (1983), Chang (1970) and Doorenbos et al. (1977) were tested for the adaptability to Korea. A new model with effective longwave radiation term parameterized by air temperature, solar radiation and vapor pressure was formulated and tested for its accuracy. Above five models with original parameter values showed large absolute mean deviations ranging from 0.86 to 1.64 MJ/m 2 /day. The parameters of the above five models were reestimated by using net radiation and meteorological elements measured in Suwon, Korea

  5. An inflammation-based cumulative prognostic score system in patients with diffuse large B cell lymphoma in rituximab era.

    Science.gov (United States)

    Sun, Feifei; Zhu, Jia; Lu, Suying; Zhen, Zijun; Wang, Juan; Huang, Junting; Ding, Zonghui; Zeng, Musheng; Sun, Xiaofei

    2018-01-02

    Systemic inflammatory parameters are associated with poor outcomes in malignant patients. Several inflammation-based cumulative prognostic score systems were established for various solid tumors. However, there is few inflammation based cumulative prognostic score system for patients with diffuse large B cell lymphoma (DLBCL). We retrospectively reviewed 564 adult DLBCL patients who had received rituximab, cyclophosphamide, doxorubicin, vincristine and prednisolone (R-CHOP) therapy between Nov 1 2006 and Dec 30 2013 and assessed the prognostic significance of six systemic inflammatory parameters evaluated in previous studies by univariate and multivariate analysis:C-reactive protein(CRP), albumin levels, the lymphocyte-monocyte ratio (LMR), the neutrophil-lymphocyte ratio(NLR), the platelet-lymphocyte ratio(PLR)and fibrinogen levels. Multivariate analysis identified CRP, albumin levels and the LMR are three independent prognostic parameters for overall survival (OS). Based on these three factors, we constructed a novel inflammation-based cumulative prognostic score (ICPS) system. Four risk groups were formed: group ICPS = 0, ICPS = 1, ICPS = 2 and ICPS = 3. Advanced multivariate analysis indicated that the ICPS model is a prognostic score system independent of International Prognostic Index (IPI) for both progression-free survival (PFS) (p systemic inflammatory status was associated with clinical outcomes of patients with DLBCL in rituximab era. The ICPS model was shown to classify risk groups more accurately than any single inflammatory prognostic parameters. These findings may be useful for identifying candidates for further inflammation-related mechanism research or novel anti-inflammation target therapies.

  6. Modelling NOX concentrations through CFD-RANS in an urban hot-spot using high resolution traffic emissions and meteorology from a mesoscale model

    Science.gov (United States)

    Sanchez, Beatriz; Santiago, Jose Luis; Martilli, Alberto; Martin, Fernando; Borge, Rafael; Quaassdorff, Christina; de la Paz, David

    2017-08-01

    Air quality management requires more detailed studies about air pollution at urban and local scale over long periods of time. This work focuses on obtaining the spatial distribution of NOx concentration averaged over several days in a heavily trafficked urban area in Madrid (Spain) using a computational fluid dynamics (CFD) model. A methodology based on weighted average of CFD simulations is applied computing the time evolution of NOx dispersion as a sequence of steady-state scenarios taking into account the actual atmospheric conditions. The inputs of emissions are estimated from the traffic emission model and the meteorological information used is derived from a mesoscale model. Finally, the computed concentration map correlates well with 72 passive samplers deployed in the research area. This work reveals the potential of using urban mesoscale simulations together with detailed traffic emissions so as to provide accurate maps of pollutant concentration at microscale using CFD simulations.

  7. 10 CFR 960.5-2-3 - Meteorology.

    Science.gov (United States)

    2010-01-01

    ... 10 Energy 4 2010-01-01 2010-01-01 false Meteorology. 960.5-2-3 Section 960.5-2-3 Energy DEPARTMENT OF ENERGY GENERAL GUIDELINES FOR THE PRELIMINARY SCREENING OF POTENTIAL SITES FOR A NUCLEAR WASTE REPOSITORY Preclosure Guidelines Preclosure Radiological Safety § 960.5-2-3 Meteorology. (a) Qualifying...

  8. The impact of urban canopy meteorological forcing on summer photochemistry

    Science.gov (United States)

    Huszár, Peter; Karlický, Jan; Belda, Michal; Halenka, Tomáš; Pišoft, Petr

    2018-03-01

    The regional climate model RegCM4.4, including the surface model CLM4.5, was offline coupled to the chemistry transport model CAMx version 6.30 in order to investigate the impact of the urban canopy induced meteorological changes on the longterm summer photochemistry over central Europe for the 2001-2005 period. First, the urban canopy impact on the meteorological conditions was calculated performing a reference experiment without urban landsurface considered and an experiment with urban surfaces modeled with the urban parameterization within the CLM4.5 model. In accordance with expectations, strong increases of urban surface temperatures (up to 2-3 K), decreases of wind speed (up to -1 ms-1) and increases of vertical turbulent diffusion coefficient (up to 60-70 m2s-1) were found. For the impact on chemistry, these three components were considered. Additionally, we accounted for the effect of temperature enhanced biogenic emission increase. Several experiments were performed by adding these effects one-by-one to the total impact: i.e., first, only the urban temperature impact was considered driving the chemistry model; secondly, the wind impact was added and so on. We found that the impact on biogenic emission account for minor changes in the concentrations of ozone (O3), oxides of nitrogen NOx = NO + NO2 and nitric acid (HNO3). On the other hand, the dominating component acting is the increased vertical mixing, resulting in up to 5 ppbv increase of urban ozone concentrations while causing -2 to -3 ppbv decreases and around 1 ppbv increases of NOx and HNO3 surface concentrations, respectively. The temperature impact alone results in reduction of ozone, increase in NO, decrease in NO2 and increases of HNO3. The wind impact leads, over urban areas, to ozone decreases, increases of NOx and a slight increase in HNO3. The overall impact is similar to the impact of increased vertical mixing alone. The Process Analysis (PA) technique implemented in CAMx was adopted to

  9. The Cambridge Prognostic Groups for improved prediction of disease mortality at diagnosis in primary non-metastatic prostate cancer: a validation study.

    Science.gov (United States)

    Gnanapragasam, V J; Bratt, O; Muir, K; Lee, L S; Huang, H H; Stattin, P; Lophatananon, A

    2018-02-28

    The purpose of this study is to validate a new five-tiered prognostic classification system to better discriminate cancer-specific mortality in men diagnosed with primary non-metastatic prostate cancer. We applied a recently described five-strata model, the Cambridge Prognostic Groups (CPGs 1-5), in two international cohorts and tested prognostic performance against the current standard three-strata classification of low-, intermediate- or high-risk disease. Diagnostic clinico-pathological data for men obtained from the Prostate Cancer data Base Sweden (PCBaSe) and the Singapore Health Study were used. The main outcome measure was prostate cancer mortality (PCM) stratified by age group and treatment modality. The PCBaSe cohort included 72,337 men, of whom 7162 died of prostate cancer. The CPG model successfully classified men with different risks of PCM with competing risk regression confirming significant intergroup distinction (p study of nearly 75,000 men confirms that the CPG five-tiered prognostic model has superior discrimination compared to the three-tiered model in predicting prostate cancer death across different age and treatment groups. Crucially, it identifies distinct sub-groups of men within the old intermediate-risk and high-risk criteria who have very different prognostic outcomes. We therefore propose adoption of the CPG model as a simple-to-use but more accurate prognostic stratification tool to help guide management for men with newly diagnosed prostate cancer.

  10. A prognostic tool to identify adolescents at high risk of becoming daily smokers

    Directory of Open Access Journals (Sweden)

    Paradis Gilles

    2011-08-01

    Full Text Available Abstract Background The American Academy of Pediatrics advocates that pediatricians should be involved in tobacco counseling and has developed guidelines for counseling. We present a prognostic tool for use by health care practitioners in both clinical and non-clinical settings, to identify adolescents at risk of becoming daily smokers. Methods Data were drawn from the Nicotine Dependence in Teens (NDIT Study, a prospective investigation of 1293 adolescents, initially aged 12-13 years, recruited in 10 secondary schools in Montreal, Canada in 1999. Questionnaires were administered every three months for five years. The prognostic tool was developed using estimated coefficients from multivariable logistic models. Model overfitting was corrected using bootstrap cross-validation. Goodness-of-fit and predictive ability of the models were assessed by R2, the c-statistic, and the Hosmer-Lemeshow test. Results The 1-year and 2-year probability of initiating daily smoking was a joint function of seven individual characteristics: age; ever smoked; ever felt like you needed a cigarette; parent(s smoke; sibling(s smoke; friend(s smoke; and ever drank alcohol. The models were characterized by reasonably good fit and predictive ability. They were transformed into user-friendly tables such that the risk of daily smoking can be easily computed by summing points for responses to each item. The prognostic tool is also available on-line at http://episerve.chumontreal.qc.ca/calculation_risk/daily-risk/daily_smokingadd.php. Conclusions The prognostic tool to identify youth at high risk of daily smoking may eventually be an important component of a comprehensive tobacco control system.

  11. [Prognostic value of JAK2, MPL and CALR mutations in Chinese patients with primary myelofibrosis].

    Science.gov (United States)

    Xu, Z F; Li, B; Liu, J Q; Li, Y; Ai, X F; Zhang, P H; Qin, T J; Zhang, Y; Wang, J Y; Xu, J Q; Zhang, H L; Fang, L W; Pan, L J; Hu, N B; Qu, S Q; Xiao, Z J

    2016-07-01

    To evaluate the prognostic value of JAK2, MPL and CALR mutations in Chinese patients with primary myelofibrosis (PMF). Four hundred and two Chinese patients with PMF were retrospectively analyzed. The Kaplan-Meier method, the Log-rank test, the likelihood ratio test and the Cox proportional hazards regression model were used to evaluate the prognostic scoring system. This cohort of patients included 209 males and 193 females with a median age of 55 years (range: 15- 89). JAK2V617F mutations were detected in 189 subjects (47.0% ), MPLW515 mutations in 13 (3.2%) and CALR mutations in 81 (20.1%) [There were 30 (37.0%) type-1, 48 (59.3%) type-2 and 3 (3.7%) less common CALR mutations], respectively. 119 subjects (29.6%) had no detectable mutation in JAK2, MPL or CALR. Univariate analysis indicated that patients with CALR type-2 mutations or no detectable mutations had inferior survival compared to those with JAK2, MPL or CALR type- 1 or other less common CALR mutations (the median survival was 74vs 168 months, respectively [HR 2.990 (95% CI 1.935-4.619),P<0.001]. Therefore, patients were categorized into the high-risk with CALR type- 2 mutations or no detectable driver mutations and the low- risk without aforementioned mutations status. The DIPSS-Chinese molecular prognostic model was proposed by adopting mutation categories and DIPSS-Chinese risk group. The median survival of patients classified in low risk (132 subjects, 32.8% ), intermediate- 1 risk (143 subjects, 35.6%), intermediate- 2 risk (106 subjects, 26.4%) and high risk (21 subjects, 5.2%) were not reached, 156 (95% CI 117- 194), 60 (95% CI 28- 91) and 22 (95% CI 10- 33) months, respectively, and there was a statistically significant difference in overall survival among the four risk groups (P<0.001). There was significantly higher predictive power for survival according to the DIPSS-Chinese molecular prognostic model compared with the DIPSS-Chinese model (P=0.005, -2 log-likelihood ratios of 855.6 and 869

  12. PROMET - The Journal of Meteorological Education issued by DWD

    Science.gov (United States)

    Rapp, J.

    2009-09-01

    Promet is published by the German Meteorological Service (DWD) since 1971 to improve meteorologists and weather forecasters skills. The journal comprises mainly contributions to topics like biometeorology, the NAO, or meteorology and insurance business. The science-based articles should illustrate the special issue in an understandable and transparent way. In addition, the journal contains portraits of other national meteorological services and university departments, book reviews, list of university degrees, and other individual papers. Promet is published only in German language, but included English titles and abstracts. The journal is peer-reviewed by renowned external scientists. It is distributed free of charge by DWD to the own meteorological staff. On the other hand, DMG (the German Meteorological Society) hand it out to all members of the society. The current issues deal with "Modern procedures of weather forecasting in DWD” and "E-Learning in Meteorology”.

  13. Development of statistical analysis code for meteorological data (W-View)

    International Nuclear Information System (INIS)

    Tachibana, Haruo; Sekita, Tsutomu; Yamaguchi, Takenori

    2003-03-01

    A computer code (W-View: Weather View) was developed to analyze the meteorological data statistically based on 'the guideline of meteorological statistics for the safety analysis of nuclear power reactor' (Nuclear Safety Commission on January 28, 1982; revised on March 29, 2001). The code gives statistical meteorological data to assess the public dose in case of normal operation and severe accident to get the license of nuclear reactor operation. This code was revised from the original code used in a large office computer code to enable a personal computer user to analyze the meteorological data simply and conveniently and to make the statistical data tables and figures of meteorology. (author)

  14. On the early history of the Finnish Meteorological Institute

    Science.gov (United States)

    Nevanlinna, H.

    2014-03-01

    This article is a review of the foundation (in 1838) and later developments of the Helsinki (Finland) magnetic and meteorological observatory, today the Finnish Meteorological Institute (FMI). The main focus of the study is in the early history of the FMI up to the beginning of the 20th century. The first director of the observatory was Physics Professor Johan Jakob Nervander (1805-1848). He was a famous person of the Finnish scientific, academic and cultural community in the early decades of the 19th century. Finland was an autonomously part of the Russian Empire from 1809 to 1917, but the observatory remained organizationally under the University of Helsinki, independent of Russian scientific institutions, and funded by the Finnish Government. Throughout the late-19th century the Meteorological Institute was responsible of nationwide meteorological, hydrological and marine observations and research. The observatory was transferred to the Finnish Society of Sciences and Letters under the name the Central Meteorological Institute in 1881. The focus of the work carried out in the Institute was changed gradually towards meteorology. Magnetic measurements were still continued but in a lower level of importance. The culmination of Finnish geophysical achievements in the 19th century was the participation to the International Polar Year programme in 1882-1883 by setting up a full-scale meteorological and magnetic observatory in Sodankylä, Lapland.

  15. Prognostic Factors of Uterine Serous Carcinoma-A Multicenter Study.

    Science.gov (United States)

    Zhong, Xiaozhu; Wang, Jianliu; Kaku, Tengen; Wang, Zhiqi; Li, Xiaoping; Wei, Lihui

    2018-04-04

    The prognostic factors of uterine serous carcinoma (USC) vary among studies, and there is no report of Chinese USC patients. The aim of this study was to investigate the clinicopathological characteristics and prognostic factors in Chinese patients with USC. Patients with USC from 13 authoritative university hospitals in China and treated between 2004 and 2014 were retrospectively reviewed. Three-year disease-free survival rate (DFSR), cumulative recurrence, and cumulative mortality were estimated by Kaplan-Meier analyses and log-rank tests. Multivariate Cox regression analysis was used to model the association of potential prognostic factors with clinical outcomes. Data of a total of 241 patients were reviewed. The median follow-up was 26 months (range, 1-128 months). Median age was 60 years (range, 39-84 years), and 58.0% had stages I-II disease. The 3-year DFSR and cumulative recurrence were 46.8% and 27.7%. Advanced stage (III and IV) (P = 0.004), myometrial invasion (P = 0.001), adnexal involvement (P USC. Prospective studies are needed to confirm these results.

  16. A low-order coupled chemistry meteorology model for testing online and offline data assimilation schemes

    Science.gov (United States)

    Haussaire, J.-M.; Bocquet, M.

    2015-08-01

    Bocquet and Sakov (2013) have introduced a low-order model based on the coupling of the chaotic Lorenz-95 model which simulates winds along a mid-latitude circle, with the transport of a tracer species advected by this zonal wind field. This model, named L95-T, can serve as a playground for testing data assimilation schemes with an online model. Here, the tracer part of the model is extended to a reduced photochemistry module. This coupled chemistry meteorology model (CCMM), the L95-GRS model, mimics continental and transcontinental transport and the photochemistry of ozone, volatile organic compounds and nitrogen oxides. Its numerical implementation is described. The model is shown to reproduce the major physical and chemical processes being considered. L95-T and L95-GRS are specifically designed and useful for testing advanced data assimilation schemes, such as the iterative ensemble Kalman smoother (IEnKS) which combines the best of ensemble and variational methods. These models provide useful insights prior to the implementation of data assimilation methods on larger models. We illustrate their use with data assimilation schemes on preliminary, yet instructive numerical experiments. In particular, online and offline data assimilation strategies can be conveniently tested and discussed with this low-order CCMM. The impact of observed chemical species concentrations on the wind field can be quantitatively estimated. The impacts of the wind chaotic dynamics and of the chemical species non-chaotic but highly nonlinear dynamics on the data assimilation strategies are illustrated.

  17. US Marine Meteorological Journals

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This series consists of volumes entitled 'Meteorological Journal' (a regulation Navy-issue publication) which were to be completed by masters of merchant vessels...

  18. How well do meteorological indicators represent agricultural and forest drought across Europe?

    Science.gov (United States)

    Bachmair, S.; Tanguy, M.; Hannaford, J.; Stahl, K.

    2018-03-01

    Drought monitoring and early warning (M&EW) systems are an important component of agriculture/silviculture drought risk assessment. Many operational information systems rely mostly on meteorological indicators, and a few incorporate vegetation state information. However, the relationships between meteorological drought indicators and agricultural/silvicultural drought impacts vary across Europe. The details of this variability have not been elucidated sufficiently on a continental scale in Europe to inform drought risk management at administrative scales. The objective of this study is to fill this gap and evaluate how useful the variety of meteorological indicators are to assess agricultural/silvicultural drought across Europe. The first part of the analysis systematically linked meteorological drought indicators to remote sensing based vegetation indices (VIs) for Europe at NUTs3 administrative regions scale using correlation analysis for crops and forests. In a second step, a stepwise multiple linear regression model was deployed to identify variables explaining the spatial differences observed. Finally, corn crop yield in Germany was chosen as a case study to verify VIs’ representativeness of agricultural drought impacts. Results show that short accumulation periods of SPI and SPEI are best linked to crop vegetation stress in most cases, which further validates the use of SPI3 in existing operational drought monitors. However, large regional differences in correlations are also revealed. Climate (temperature and precipitation) explained the largest proportion of variance, suggesting that meteorological indices are less informative of agricultural/silvicultural drought in colder/wetter parts of Europe. These findings provide important context for interpreting meteorological indices on widely used national to continental M&EW systems, leading to a better understanding of where/when such M&EW tools can be indicative of likely agricultural stress and impacts.

  19. Results of development and field tests of a radar-tracer system providing meteorological support to modeling hazardous technological releases

    International Nuclear Information System (INIS)

    Shershakov, V.M.; Zukov, G.P.; Kosykh, V.S.

    2003-01-01

    Full text: Radar support to systems of automated radiation monitoring requires dealing with determination of geometric characteristics of air release of radionuclides. For doing this, an air release can be labeled by chaff propagating in the air similarly to particles of radioactive substance. Then, a chaff suspension can be treated as a spatially distributed radar target and thus be detected by a radar. For a number of years the Science and Production Association 'Typhoon' of Roshydromet, Obninsk has been developing a radar tracer system (RTS) for meteorological support of modeling hazardous technological releases. In September -December 2002 experiments were conducted to test the RTS in field. This presentation contains preliminary results of testing this system. A total of 9 experiments pursuing different goals were carried out. Of them 6 experiments were conducted approximately 6 km south-west of Obninsk in the vicinity of the village of Potresovo. The first three experiments were aimed at working out interaction between the MR and LDU and assessing the chaff cloud observation distance. In doing this, radar information was not transmitted from the MR to the CCS. In the last three experiments radar information was transmitted to the CCS by cell communication lines using telephones Siemens S35 with in-built modems. The CCS was deployed in building 4/25 of SPA 'Typhoon'. All information received in the CCS was put an a map. Three experiments were conducted in the area of the Kursk NPP as part of preparations for training exercises near the village of Makarovka about 7 km north-west of the city of Kurchatov. In the first two experiments radar information from the MR was passed by cell communication channels to the CCS deployed in the laboratory of external radiation monitoring of the Kursk nuclear power plant. Experiment 3 was a demonstration and arranged during the emergency response exercises at the Kursk NPP. The MR was based on the site of the external

  20. Prognostic breast cancer signature identified from 3D culture model accurately predicts clinical outcome across independent datasets

    Energy Technology Data Exchange (ETDEWEB)

    Martin, Katherine J.; Patrick, Denis R.; Bissell, Mina J.; Fournier, Marcia V.

    2008-10-20

    One of the major tenets in breast cancer research is that early detection is vital for patient survival by increasing treatment options. To that end, we have previously used a novel unsupervised approach to identify a set of genes whose expression predicts prognosis of breast cancer patients. The predictive genes were selected in a well-defined three dimensional (3D) cell culture model of non-malignant human mammary epithelial cell morphogenesis as down-regulated during breast epithelial cell acinar formation and cell cycle arrest. Here we examine the ability of this gene signature (3D-signature) to predict prognosis in three independent breast cancer microarray datasets having 295, 286, and 118 samples, respectively. Our results show that the 3D-signature accurately predicts prognosis in three unrelated patient datasets. At 10 years, the probability of positive outcome was 52, 51, and 47 percent in the group with a poor-prognosis signature and 91, 75, and 71 percent in the group with a good-prognosis signature for the three datasets, respectively (Kaplan-Meier survival analysis, p<0.05). Hazard ratios for poor outcome were 5.5 (95% CI 3.0 to 12.2, p<0.0001), 2.4 (95% CI 1.6 to 3.6, p<0.0001) and 1.9 (95% CI 1.1 to 3.2, p = 0.016) and remained significant for the two larger datasets when corrected for estrogen receptor (ER) status. Hence the 3D-signature accurately predicts breast cancer outcome in both ER-positive and ER-negative tumors, though individual genes differed in their prognostic ability in the two subtypes. Genes that were prognostic in ER+ patients are AURKA, CEP55, RRM2, EPHA2, FGFBP1, and VRK1, while genes prognostic in ER patients include ACTB, FOXM1 and SERPINE2 (Kaplan-Meier p<0.05). Multivariable Cox regression analysis in the largest dataset showed that the 3D-signature was a strong independent factor in predicting breast cancer outcome. The 3D-signature accurately predicts breast cancer outcome across multiple datasets and holds prognostic

  1. Application of data mining to the analysis of meteorological data for air quality prediction: A case study in Shenyang

    Science.gov (United States)

    Zhao, Chang; Song, Guojun

    2017-08-01

    Air pollution is one of the important reasons for restricting the current economic development. PM2.5 which is a vital factor in the measurement of air pollution is defined as a kind of suspended particulate matter with its equivalent diameter less than 25μm, which may enter the alveoli and therefore make a great impact on the human body. Meteorological factors are also one of the main factors affecting the production of PM2.5, therefore, it is essential to establish the model between meteorological factors and PM2.5 for the prediction. Data mining is a promising approach to model PM2.5 change, Shenyang which is one of the most important industrial city in Northeast China with severe air pollutions is set as the case city. Meteorological data (wind direction, wind speed, temperature, humidity, rainfall, etc.) from 2013 to 2015 and PM2.5 concentration data are used for this prediction. As to the requirements of the World Health Organization (WHO), three data mining models, whereby the predictions of PM2.5 are directly generated by the meteorological data. After assessment, the random forest model is appeared to offer better prediction performance than the other two. At last, the accuracy of the generated models are analysed.

  2. Prognostics and Health Management of Wind Turbines: Current Status and Future Opportunities

    Energy Technology Data Exchange (ETDEWEB)

    Sheng, Shuangwen

    2015-12-14

    Prognostics and health management is not a new concept. It has been used in relatively mature industries, such as aviation and electronics, to help improve operation and maintenance (O&M) practices. In the wind industry, prognostics and health management is relatively new. The level for both wind industry applications and research and development (R&D) has increased in recent years because of its potential for reducing O&M cost of wind power, especially for turbines installed offshore. The majority of wind industry application efforts has been focused on diagnosis based on various sensing and feature extraction techniques. For R&D, activities are being conducted in almost all areas of a typical prognostics and health management framework (i.e., sensing, data collection, feature extraction, diagnosis, prognosis, and maintenance scheduling). This presentation provides an overview of the current status of wind turbine prognostics and health management that focuses on drivetrain condition monitoring through vibration, oil debris, and oil condition analysis techniques. It also discusses turbine component health diagnosis through data mining and modeling based on supervisory control and data acquisition system data. Finally, it provides a brief survey of R&D activities for wind turbine prognostics and health management, along with future opportunities.

  3. Meteorological analysis of symptom data for people with seasonal affective disorder.

    Science.gov (United States)

    Sarran, Christophe; Albers, Casper; Sachon, Patrick; Meesters, Ybe

    2017-11-01

    It is thought that variation in natural light levels affect people with Seasonal Affective Disorder (SAD). Several meteorological factors related to luminance can be forecast but little is known about which factors are most indicative of worsening SAD symptoms. The aim of this meteorological analysis is to determine which factors are linked to SAD symptoms. The symptoms of 291 individuals with SAD in and near Groningen have been evaluated over the period 2003-2009. Meteorological factors linked to periods of low natural light (sunshine, global radiation, horizontal visibility, cloud cover and mist) and others (temperature, humidity and pressure) were obtained from weather observation stations. A Bayesian zero adjusted auto-correlated multilevel Poisson model was carried out to assess which variables influence the SAD symptom score BDI-II. The outcome of the study suggests that the variable sunshine duration, for both the current and previous week, and global radiation for the previous week, are significantly linked to SAD symptoms. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.

  4. Prediction of Basic Math Course Failure Rate in the Physics, Meteorology, Mathematics, Actuarial Sciences and Pharmacy Degree Programs

    Directory of Open Access Journals (Sweden)

    Luis Rojas-Torres

    2014-09-01

    Full Text Available This paper summarizes a study conducted in 2013 with the purpose of predicting the failure rate of math courses taken by Pharmacy, Mathematics, Actuarial Science, Physics and Meteorology students at Universidad de Costa Rica (UCR. Using the Logistics Regression statistical techniques applied to the 2010 cohort, failure rates were predicted of students in the aforementioned programs in one of their Math introductory courses (Calculus 101 for Physics and Meteorology, Math Principles for Mathematics and Actuarial Science and Applied Differential Equations for Pharmacy. For these models, the UCR admission average, the student’s genre, and the average correct answers in the Quantitative Skills Test were used as predictor variables. The most important variable for all models was the Quantitative Skills Test, and the model with the highest correct classification rate was the Logistics Regression. For the estimated Physics-Meteorology, Pharmacy and Mathematics-Actuarial Science models, correct classifications were 89.8%, 73.6%, and 93.9%, respectively.

  5. Monitoring Forsmark. Meteorological monitoring at Forsmark, January-December 2010

    Energy Technology Data Exchange (ETDEWEB)

    Andersson, Cari; Jones, Joergen (Swedish Meteorological and Hydrological Institute (SMHI), Norrkoeping (Sweden))

    2011-01-15

    In the Forsmark area, SKB's meteorological monitoring started in 2003 at the sites Storskaeret and Hoegmasten. However, since July 1, 2007 measurements are only performed at Hoegmasten. Measured and calculated parameters at Hoegmasten are precipitation and corrected precipitation, air temperature, barometric pressure, wind speed and direction, air humidity, global radiation and potential evapotranspiration. The Swedish Meteorological and Hydrological Institute, SMHI, has been responsible for planning and design, as well as for the operation of the stations used for meteorological monitoring. In general, the quality of the meteorological measurements during the period concerned, starting January 1, 2010, and ending December 31, 2010, has shown to be good

  6. Presentation of the EURODELTA III intercomparison exercise - evaluation of the chemistry transport models' performance on criteria pollutants and joint analysis with meteorology

    Science.gov (United States)

    Bessagnet, Bertrand; Pirovano, Guido; Mircea, Mihaela; Cuvelier, Cornelius; Aulinger, Armin; Calori, Giuseppe; Ciarelli, Giancarlo; Manders, Astrid; Stern, Rainer; Tsyro, Svetlana; García Vivanco, Marta; Thunis, Philippe; Pay, Maria-Teresa; Colette, Augustin; Couvidat, Florian; Meleux, Frédérik; Rouïl, Laurence; Ung, Anthony; Aksoyoglu, Sebnem; María Baldasano, José; Bieser, Johannes; Briganti, Gino; Cappelletti, Andrea; D'Isidoro, Massimo; Finardi, Sandro; Kranenburg, Richard; Silibello, Camillo; Carnevale, Claudio; Aas, Wenche; Dupont, Jean-Charles; Fagerli, Hilde; Gonzalez, Lucia; Menut, Laurent; Prévôt, André S. H.; Roberts, Pete; White, Les

    2016-10-01

    The EURODELTA III exercise has facilitated a comprehensive intercomparison and evaluation of chemistry transport model performances. Participating models performed calculations for four 1-month periods in different seasons in the years 2006 to 2009, allowing the influence of different meteorological conditions on model performances to be evaluated. The exercise was performed with strict requirements for the input data, with few exceptions. As a consequence, most of differences in the outputs will be attributed to the differences in model formulations of chemical and physical processes. The models were evaluated mainly for background rural stations in Europe. The performance was assessed in terms of bias, root mean square error and correlation with respect to the concentrations of air pollutants (NO2, O3, SO2, PM10 and PM2.5), as well as key meteorological variables. Though most of meteorological parameters were prescribed, some variables like the planetary boundary layer (PBL) height and the vertical diffusion coefficient were derived in the model preprocessors and can partly explain the spread in model results. In general, the daytime PBL height is underestimated by all models. The largest variability of predicted PBL is observed over the ocean and seas. For ozone, this study shows the importance of proper boundary conditions for accurate model calculations and then on the regime of the gas and particle chemistry. The models show similar and quite good performance for nitrogen dioxide, whereas they struggle to accurately reproduce measured sulfur dioxide concentrations (for which the agreement with observations is the poorest). In general, the models provide a close-to-observations map of particulate matter (PM2.5 and PM10) concentrations over Europe rather with correlations in the range 0.4-0.7 and a systematic underestimation reaching -10 µg m-3 for PM10. The highest concentrations are much more underestimated, particularly in wintertime. Further evaluation of

  7. Development of statistical analysis code for meteorological data (W-View)

    Energy Technology Data Exchange (ETDEWEB)

    Tachibana, Haruo; Sekita, Tsutomu; Yamaguchi, Takenori [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment

    2003-03-01

    A computer code (W-View: Weather View) was developed to analyze the meteorological data statistically based on 'the guideline of meteorological statistics for the safety analysis of nuclear power reactor' (Nuclear Safety Commission on January 28, 1982; revised on March 29, 2001). The code gives statistical meteorological data to assess the public dose in case of normal operation and severe accident to get the license of nuclear reactor operation. This code was revised from the original code used in a large office computer code to enable a personal computer user to analyze the meteorological data simply and conveniently and to make the statistical data tables and figures of meteorology. (author)

  8. Prognostic Factors for Persistent Leg-Pain in Patients Hospitalized With Acute Sciatica.

    Science.gov (United States)

    Fjeld, Olaf; Grotle, Margreth; Siewers, Vibeke; Pedersen, Linda M; Nilsen, Kristian Bernhard; Zwart, John-Anker

    2017-03-01

    Prospective cohort study. To identify potential prognostic factors for persistent leg-pain at 12 months among patients hospitalized with acute severe sciatica. The long-term outcome for patients admitted to hospital with sciatica is generally unfavorable. Results concerning prognostic factors for persistent sciatica are limited and conflicting. A total of 210 patients acutely admitted to hospital for either surgical or nonsurgical treatment of sciatica were consecutively recruited and received a thorough clinical and radiographic examination in addition to responding to a comprehensive questionnaire. Follow-up assessments were done at 6 weeks, 6 months, and 12 months. Potential prognostic factors were measured at baseline and at 6 weeks. The impact of these factors on leg-pain was analyzed by multiple linear regression modeling. A total of 151 patients completed the entire study, 93 receiving nonrandomized surgical treatment. The final multivariate models showed that the following factors were significantly associated with leg-pain at 12 months: high psychosocial risk according to the Örebro Musculosceletal Pain Questionnaire (unstandardized beta coefficient 1.55, 95% confidence interval [CI] 0.72-2.38, P sciatica. 2.

  9. The Impacts of Urbanization on Meteorology and Air Quality in the Los Angeles Basin

    Science.gov (United States)

    Li, Y.; Zhang, J.; Sailor, D.; Ban-Weiss, G. A.

    2017-12-01

    Urbanization has a profound influence on regional meteorology in mega cities like Los Angeles. This influence is driven by changes in land surface physical properties and urban processes, and their corresponding influence on surface-atmosphere coupling. Changes in meteorology from urbanization in turn influences air quality through weather-dependent chemical reaction, pollutant dispersion, etc. Hence, a real-world representation of the urban land surface properties and urban processes should be accurately resolved in regional climate-chemistry models for better understanding the role of urbanization on changing urban meteorology and associated pollutant dynamics. By incorporating high-resolution land surface data, previous research has improved model-observation comparisons of meteorology in urban areas including the Los Angeles basin, and indicated that historical urbanization has increased urban temperatures and altered wind flows significantly. However, the impact of urban expansion on air quality has been less studied. Thus, in this study, we aim to evaluate the effectiveness of resolving high-resolution heterogeneity in urban land surface properties and processes for regional weather and pollutant concentration predictions. We coupled the Weather Research and Forecasting model with Chemistry to the single-layer Urban Canopy Model to simulate a typical summer period in year 2012 for Southern California. Land cover type and urban fraction were determined from National Land Cover Data. MODIS observations were used to determine satellite-derived albedo, green vegetation fraction, and leaf area index. Urban morphology was determined from GIS datasets of 3D building geometries. An urban irrigation scheme was also implemented in the model. Our results show that the improved model captures the diurnal cycle of 2m air temperature (T2) and Ozone (O3) concentrations. However, it tends to overestimate wind speed and underestimate T2, which leads to an underestimation of O

  10. Meteorological circumstances during the 'Chernobyl-period'

    International Nuclear Information System (INIS)

    Ivens, R.; Lablans, W.N.; Wessels, H.R.A.

    1987-01-01

    The progress of the meteorological circumstances and air flows in Europe from 26th April up to 8th May 1986, which caused the spread of contaminated air originating from Chernobyl is outlined and mapped out. Furthermore a global survey is presented of the precipitation in the Netherlands during the period 2nd May to 10th May based on observations of various observation stations of the Royal Dutch Meteorologic Institute (KNMI). 11 figs.; 1 table (H.W.)

  11. Prognostic and survival analysis of 837 Chinese colorectal cancer patients.

    Science.gov (United States)

    Yuan, Ying; Li, Mo-Dan; Hu, Han-Guang; Dong, Cai-Xia; Chen, Jia-Qi; Li, Xiao-Fen; Li, Jing-Jing; Shen, Hong

    2013-05-07

    To develop a prognostic model to predict survival of patients with colorectal cancer (CRC). Survival data of 837 CRC patients undergoing surgery between 1996 and 2006 were collected and analyzed by univariate analysis and Cox proportional hazard regression model to reveal the prognostic factors for CRC. All data were recorded using a standard data form and analyzed using SPSS version 18.0 (SPSS, Chicago, IL, United States). Survival curves were calculated by the Kaplan-Meier method. The log rank test was used to assess differences in survival. Univariate hazard ratios and significant and independent predictors of disease-specific survival and were identified by Cox proportional hazard analysis. The stepwise procedure was set to a threshold of 0.05. Statistical significance was defined as P analysis suggested age, preoperative obstruction, serum carcinoembryonic antigen level at diagnosis, status of resection, tumor size, histological grade, pathological type, lymphovascular invasion, invasion of adjacent organs, and tumor node metastasis (TNM) staging were positive prognostic factors (P analysis showed a significant statistical difference in 3-year survival among these groups: LNR1, 73%; LNR2, 55%; and LNR3, 42% (P analysis results showed that histological grade, depth of bowel wall invasion, and number of metastatic lymph nodes were the most important prognostic factors for CRC if we did not consider the interaction of the TNM staging system (P < 0.05). When the TNM staging was taken into account, histological grade lost its statistical significance, while the specific TNM staging system showed a statistically significant difference (P < 0.0001). The overall survival of CRC patients has improved between 1996 and 2006. LNR is a powerful factor for estimating the survival of stage III CRC patients.

  12. Inconing solar radiation estimates at terrestrial surface using meteorological satellite

    International Nuclear Information System (INIS)

    Arai, N.; Almeida, F.C. de.

    1982-11-01

    By using the digital images of the visible channel of the GOES-5 meteorological satellite, and a simple radiative transfer model of the earth's atmosphere, the incoming solar radiation reaching ground is estimated. A model incorporating the effects of Rayleigh scattering and water vapor absorption, the latter parameterized using the surface dew point temperature value, is used. Comparisons with pyranometer observations, and parameterization versus radiosonde water vapor absorption calculation are presented. (Author) [pt

  13. Frequency modulator. Transmission of meteorological signals in LVC

    International Nuclear Information System (INIS)

    Rivero G, P.T.; Ramirez S, R.; Gonzalez M, J.L.; Rojas N, P.; Celis del Angel, L.

    2007-01-01

    The development of the frequency modulator and demodulator circuit for transmission of meteorological signals by means of fiber optics of the meteorology station to the nuclear reactor unit 1 in the Laguna Verde Central in Veracruz is described. (Author)

  14. Statistics of meteorological data at Tokai Research Establishment in JAERI

    International Nuclear Information System (INIS)

    Sekita, Tsutomu; Tachibana, Haruo; Matsuura, Kenichi; Yamaguchi, Takenori

    2003-12-01

    The meteorological observation data at Tokai site were analyzed statistically based on a 'Guideline of meteorological statistics for the safety analysis of nuclear power reactor' (Nuclear Safety Commission on January 28, 1982; revised on March 29, 2001). This report shows the meteorological analysis of wind direction, wind velocity and atmospheric stability etc. to assess the public dose around the Tokai site caused by the released gaseous radioactivity. The statistical period of meteorological data is every 5 years from 1981 to 1995. (author)

  15. Performance of critical care prognostic scoring systems in low and middle-income countries: a systematic review.

    Science.gov (United States)

    Haniffa, Rashan; Isaam, Ilhaam; De Silva, A Pubudu; Dondorp, Arjen M; De Keizer, Nicolette F

    2018-01-26

    Prognostic models-used in critical care medicine for mortality predictions, for benchmarking and for illness stratification in clinical trials-have been validated predominantly in high-income countries. These results may not be reproducible in low or middle-income countries (LMICs), not only because of different case-mix characteristics but also because of missing predictor variables. The study objective was to systematically review literature on the use of critical care prognostic models in LMICs and assess their ability to discriminate between survivors and non-survivors at hospital discharge of those admitted to intensive care units (ICUs), their calibration, their accuracy, and the manner in which missing values were handled. The PubMed database was searched in March 2017 to identify research articles reporting the use and performance of prognostic models in the evaluation of mortality in ICUs in LMICs. Studies carried out in ICUs in high-income countries or paediatric ICUs and studies that evaluated disease-specific scoring systems, were limited to a specific disease or single prognostic factor, were published only as abstracts, editorials, letters and systematic and narrative reviews or were not in English were excluded. Of the 2233 studies retrieved, 473 were searched and 50 articles reporting 119 models were included. Five articles described the development and evaluation of new models, whereas 114 articles externally validated Acute Physiology and Chronic Health Evaluation, the Simplified Acute Physiology Score and Mortality Probability Models or versions thereof. Missing values were only described in 34% of studies; exclusion and or imputation by normal values were used. Discrimination, calibration and accuracy were reported in 94.0%, 72.4% and 25% respectively. Good discrimination and calibration were reported in 88.9% and 58.3% respectively. However, only 10 evaluations that reported excellent discrimination also reported good calibration

  16. Wave Meteorology and Soaring

    Science.gov (United States)

    Wiley, Scott

    2008-01-01

    This viewgraph document reviews some mountain wave turbulence and operational hazards while soaring. Maps, photographs, and satellite images of the meteorological phenomena are included. Additionally, photographs of aircraft that sustained mountain wave damage are provided.

  17. A study on the uncertainty based on Meteorological fields on Source-receptor Relationships for Total Nitrate in the Northeast Asia

    Science.gov (United States)

    Sunwoo, Y.; Park, J.; Kim, S.; Ma, Y.; Chang, I.

    2010-12-01

    Northeast Asia hosts more than one third of world population and the emission of pollutants trends to increase rapidly, because of economic growth and the increase of the consumption in high energy intensity. In case of air pollutants, especially, its characteristics of emissions and transportation become issued nationally, in terms of not only environmental aspects, but also long-range transboundary transportation. In meteorological characteristics, westerlies area means what air pollutants that emitted from China can be delivered to South Korea. Therefore, considering meteorological factors can be important to understand air pollution phenomena. In this study, we used MM5(Fifth-Generation Mesoscale Model) and WRF(Weather Research and Forecasting Model) to produce the meteorological fields. We analyzed the feature of physics option in each model and the difference due to characteristic of WRF and MM5. We are trying to analyze the uncertainty of source-receptor relationships for total nitrate according to meteorological fields in the Northeast Asia. We produced the each meteorological fields that apply the same domain, same initial and boundary conditions, the best similar physics option. S-R relationships in terms of amount and fractional number for total nitrate (sum of N from HNO3, nitrate and PAN) were calculated by EMEP method 3.

  18. Reconstructing the prevailing meteorological and optical environment during the time of the Titanic disaster

    Science.gov (United States)

    Basu, Sukanta; Nunalee, Christopher G.; He, Ping; Fiorino, Steven T.; Vorontsov, Mikhail A.

    2014-10-01

    In this paper, we reconstruct the meteorological and optical environment during the time of Titanic's disaster utilizing a state-of-the-art meteorological model, a ray-tracing code, and a unique public-domain dataset called the Twentieth Century Global Reanalysis. With high fidelity, our simulation captured the occurrence of an unusually high Arctic pressure system over the disaster site with calm wind. It also reproduced the movement of a polar cold front through the region bringing a rapid drop in air temperature. The simulated results also suggest that unusual meteorological conditions persisted several hours prior to the Titanic disaster which contributed to super-refraction and intermittent optical turbulence. However, according to the simulations, such anomalous conditions were not present at the time of the collision of Titanic with an iceberg.

  19. Prognostic Performance Metrics

    Data.gov (United States)

    National Aeronautics and Space Administration — This chapter presents several performance metrics for offline evaluation of prognostics algorithms. A brief overview of different methods employed for performance...

  20. The effects of meteorological factors on the occurrence of Ganoderma sp. spores in the air

    Science.gov (United States)

    Grinn-Gofroń, Agnieszka; Strzelczak, Agnieszka

    2011-03-01

    Ganoderma sp. is an airborne fungal spore type known to trigger respiratory allergy symptoms in sensitive patients. Aiming to reduce the risk for allergic individuals, we analysed fungal spore circulation in Szczecin, Poland, and its dependence on meteorological conditions. Statistical models for the airborne spore concentrations of Ganoderma sp.—one of the most abundant fungal taxa in the area—were developed. Aerobiological sampling was conducted over 2004-2008 using a volumetric Lanzoni trap. Simultaneously, the following meteorological parameters were recorded: daily level of precipitation, maximum and average wind speed, relative humidity and maximum, minimum, average and dew point temperatures. These data were used as the explaining variables. Due to the non-linearity and non-normality of the data set, the applied modelling techniques were artificial neural networks (ANN) and mutlivariate regression trees (MRT). The obtained classification and MRT models predicted threshold conditions above which Ganoderma sp. appeared in the air. It turned out that dew point temperature was the main factor influencing the presence or absence of Ganoderma sp. spores. Further analysis of spore seasons revealed that the airborne fungal spore concentration depended only slightly on meteorological factors.