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. 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...

  4. 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

  5. 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

  6. 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...

  7. 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...

  8. 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

  9. 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

  10. 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...

  11. 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...

  12. 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.

  13. 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

  14. 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

  15. 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.

  16. [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.

  17. 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)

  18. 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)

  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. 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)

  1. 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...

  2. 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...

  3. 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

  4. 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

  5. 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.

  6. 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)

  7. 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...

  8. 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...

  9. 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.

  10. 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.

  11. 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...

  12. 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.

  13. 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)

  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 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...

  16. 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....

  17. 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...

  18. 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...

  19. 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...

  20. 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

  1. 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.

  2. 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.

  3. 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)

  4. 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.

  5. 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

  6. 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

  7. 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.

  8. 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...

  9. 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

  10. 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.

  11. 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...

  12. 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.

  13. 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

  14. 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.

  15. 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.

  16. 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

  17. 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...

  18. Electrochemistry-based Battery Modeling for Prognostics

    Science.gov (United States)

    Daigle, Matthew J.; Kulkarni, Chetan Shrikant

    2013-01-01

    Batteries are used in a wide variety of applications. In recent years, they have become popular as a source of power for electric vehicles such as cars, unmanned aerial vehicles, and commericial passenger aircraft. In such application domains, it becomes crucial to both monitor battery health and performance and to predict end of discharge (EOD) and end of useful life (EOL) events. To implement such technologies, it is crucial to understand how batteries work and to capture that knowledge in the form of models that can be used by monitoring, diagnosis, and prognosis algorithms. In this work, we develop electrochemistry-based models of lithium-ion batteries that capture the significant electrochemical processes, are computationally efficient, capture the effects of aging, and are of suitable accuracy for reliable EOD prediction in a variety of usage profiles. This paper reports on the progress of such a model, with results demonstrating the model validity and accurate EOD predictions.

  19. 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

  20. 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

  1. 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

  2. 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

  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 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

  5. 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

  6. 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...

  7. 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...

  8. 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...

  9. 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...

  10. 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

  11. 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.

  12. 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.

  13. 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

  14. 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).

  15. Key Issues for Seamless Integrated Chemistry–Meteorology Modeling

    Science.gov (United States)

    Online coupled meteorology–atmospheric chemistry models have greatly evolved in recent years. Although mainly developed by the air quality modeling community, these integrated models are also of interest for numerical weather prediction and climate modeling, as they can con...

  16. 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

  17. 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

  18. 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.

  19. 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.

  20. 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.

  1. 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...

  2. 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

  3. 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.

  4. 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...

  5. 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.

  6. Using Deep Learning Model for Meteorological Satellite Cloud Image Prediction

    Science.gov (United States)

    Su, X.

    2017-12-01

    A satellite cloud image contains much weather information such as precipitation information. Short-time cloud movement forecast is important for precipitation forecast and is the primary means for typhoon monitoring. The traditional methods are mostly using the cloud feature matching and linear extrapolation to predict the cloud movement, which makes that the nonstationary process such as inversion and deformation during the movement of the cloud is basically not considered. It is still a hard task to predict cloud movement timely and correctly. As deep learning model could perform well in learning spatiotemporal features, to meet this challenge, we could regard cloud image prediction as a spatiotemporal sequence forecasting problem and introduce deep learning model to solve this problem. In this research, we use a variant of Gated-Recurrent-Unit(GRU) that has convolutional structures to deal with spatiotemporal features and build an end-to-end model to solve this forecast problem. In this model, both the input and output are spatiotemporal sequences. Compared to Convolutional LSTM(ConvLSTM) model, this model has lower amount of parameters. We imply this model on GOES satellite data and the model perform well.

  7. 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.

  8. 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.

  9. 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.

  10. 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

  11. Meteorological Uncertainty of atmospheric Dispersion model results (MUD)

    DEFF Research Database (Denmark)

    Havskov Sørensen, Jens; Amstrup, Bjarne; Feddersen, Henrik

    The MUD project addresses assessment of uncertainties of atmospheric dispersion model predictions, as well as 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....

  12. Forecasting daily meteorological time series using ARIMA and regression models

    Science.gov (United States)

    Murat, Małgorzata; Malinowska, Iwona; Gos, Magdalena; Krzyszczak, Jaromir

    2018-04-01

    The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt- Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.

  13. 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.

  14. 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.

  15. Meteorological Uncertainty of atmospheric Dispersion model results (MUD)

    DEFF Research Database (Denmark)

    Havskov Sørensen, Jens; Amstrup, Bjarne; Feddersen, Henrik

    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’ di...

  16. 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.

  17. 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

  18. 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.

  19. 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.

  20. 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.

  1. 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

  2. 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 ...

  3. 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

  4. 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.

  5. 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

  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. 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

  9. 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.

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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.

  15. 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.

  16. 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

  17. 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.

  18. 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.

  19. 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...

  20. 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

  1. 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

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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

  7. 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.

  8. 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

  9. 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...

  10. 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

  11. 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

  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. 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....

  14. 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.

  15. 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.

  16. 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.

  17. 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

  18. 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)

  19. 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.

  20. 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

  1. 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.

  2. 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.

  3. 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...

  4. 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.

  5. 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

  6. 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...

  7. 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

  8. 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.

  9. 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

  10. 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

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.)

  16. 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

  17. 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.

  18. 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

  19. 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...

  20. 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

  1. 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

  2. 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

  3. [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.

  4. 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

  5. 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

  6. 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.

  7. 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...

  8. 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.

  9. 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

  10. 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

  11. 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

  12. 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...

  13. 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.

  14. 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)

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. Physical Modeling for Anomaly Diagnostics and Prognostics, Phase II

    Data.gov (United States)

    National Aeronautics and Space Administration — Ridgetop developed an innovative, model-driven anomaly diagnostic and fault characterization system for electromechanical actuator (EMA) systems to mitigate...

  1. 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.

  2. 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

  3. 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...

  4. 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)

  5. 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.

  6. 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

  7. 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

  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. 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....

  12. Watershed modeling tools and data for prognostic and diagnostic

    Science.gov (United States)

    Chambel-Leitao, P.; Brito, D.; Neves, R.

    2009-04-01

    When eutrophication is considered an important process to control it can be accomplished reducing nitrogen and phosphorus losses from both point and nonpoint sources and helping to assess the effectiveness of the pollution reduction strategy. HARP-NUT guidelines (Guidelines on Harmonized Quantification and Reporting Procedures for Nutrients) (Borgvang & Selvik, 2000) are presented by OSPAR as the best common quantification and reporting procedures for calculating the reduction of nutrient inputs. In 2000, OSPAR HARP-NUT guidelines on a trial basis. They were intended to serve as a tool for OSPAR Contracting Parties to report, in a harmonized manner, their different commitments, present or future, with regard to nutrients under the OSPAR Convention, in particular the "Strategy to Combat Eutrophication". HARP-NUT Guidelines (Borgvang and Selvik, 2000; Schoumans, 2003) were developed to quantify and report on the individual sources of nitrogen and phosphorus discharges/losses to surface waters (Source Orientated Approach). These results can be compared to nitrogen and phosphorus figures with the total riverine loads measured at downstream monitoring points (Load Orientated Approach), as load reconciliation. Nitrogen and phosphorus retention in river systems represents the connecting link between the "Source Orientated Approach" and the "Load Orientated Approach". Both approaches are necessary for verification purposes and both may be needed for providing the information required for the various commitments. Guidelines 2,3,4,5 are mainly concerned with the sources estimation. They present a set of simple calculations that allow the estimation of the origin of loads. Guideline 6 is a particular case where the application of a model is advised, in order to estimate the sources of nutrients from diffuse sources associated with land use/land cover. The model chosen for this was SWAT (Arnold & Fohrer, 2005) model because it is suggested in the guideline 6 and because it

  13. 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.

  14. 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.

  15. 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.

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

    International Nuclear Information System (INIS)

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

    1993-01-01

    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, there has arisen an urgent need for improvements in the treatment of clouds in GCMS, especially as the clouds relate to radiation. In the present paper, we investigate the effects of introducing pregnostic 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 long wave emissivity calculations. Results from several sensitivity simulations show that realistic cloud 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 become stronger, due to more realistic tropical convection

  17. Smart Irrigation From Soil Moisture Forecast Using Satellite And Hydro -Meteorological Modelling

    Science.gov (United States)

    Corbari, Chiara; Mancini, Marco; Ravazzani, Giovanni; Ceppi, Alessandro; Salerno, Raffaele; Sobrino, Josè

    2017-04-01

    Increased water demand and climate change impacts have recently enhanced the need to improve water resources management, even in those areas which traditionally have an abundant supply of water. The highest consumption of water is devoted to irrigation for agricultural production, and so it is in this area that efforts have to be focused to study possible interventions. The SIM project funded by EU in the framework of the WaterWorks2014 - Water Joint Programming Initiative aims at developing an operational tool for real-time forecast of crops irrigation water requirements to support parsimonious water management and to optimize irrigation scheduling providing real-time and forecasted soil moisture behavior at high spatial and temporal resolutions with forecast horizons from few up to thirty days. This study discusses advances in coupling satellite driven soil water balance model and meteorological forecast as support for precision irrigation use comparing different case studies in Italy, in the Netherlands, in China and Spain, characterized by different climatic conditions, water availability, crop types and irrigation techniques and water distribution rules. Herein, the applications in two operative farms in vegetables production in the South of Italy where semi-arid climatic conditions holds, two maize fields in Northern Italy in a more water reach environment with flood irrigation will be presented. This system combines state of the art mathematical models and new technologies for environmental monitoring, merging ground observed data with Earth observations. Discussion on the methodology approach is presented, comparing for a reanalysis periods the forecast system outputs with observed soil moisture and crop water needs proving the reliability of the forecasting system and its benefits. The real-time visualization of the implemented system is also presented through web-dashboards.

  18. 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

  19. 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

  20. 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...

  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. Sensitivity of ocean model simulation in the coastal ocean to the resolution of the meteorological forcing

    Science.gov (United States)

    Chen, Feng; Shapiro, Georgy; Thain, Richard

    2013-04-01

    The quality of ocean simulations depends on a number of factors such as approximations in governing equations, errors introduced by the numerical scheme, uncertainties in input parameters, and atmospheric forcing. The identification of relations between the uncertainties in input and output data is still a challenge for the development of numerical models. The impacts of ocean variables on ocean models are still not well known (e.g., Kara et al., 2009). Given the considerable importance of the atmospheric forcing to the air-sea interaction, it is essential that researchers in ocean modelling work need a good understanding about how sensitive the atmospheric forcing is to variations of model results, which is beneficial to the development of ocean models. Also, it provides a proper way to choose the atmospheric forcing in ocean modelling applications. Our previous study (Shapiro et al, 2011) has shown that the basin-wide circulation pattern and the temperature structure in the Black Sea produced by the same model is significantly dependent on the source of the meteorological input, giving remarkably different responses. For the purpose of this study we have chosen the Celtic Sea where high resolution meteo data are available from the UK Met office since 2006. The Celtic Sea is tidally dominated water basin, with the tidal stream amplitude varying from 0.25m/s in the southwest to 2 m/s in the Bristol Channel. It is also filled with mesoscale eddies which contribute to the formation of the residual (tidally averaged) circulation pattern (Young et al, 2003). The sea is strongly stratified from April to November, which adds to the formation of density driven currents. In this paper we analyse how sensitive the model output is to variations in the spatial resolution of meteorological using low (1.6°) and high (0.11°) resolution meteo forcing, giving the quantitative relation between variations of met forcing and the resulted differences of model results, as well as

  3. Impact of atmospheric release in stable night meteorological conditions; can emergency models predict dispersion

    Energy Technology Data Exchange (ETDEWEB)

    Connan, O.; Hebert, D.; Solier, L.; Voiseux, C.; Lamotte, M.; Laguionie, P.; Maro, D.; Thomas, L. [IRSN/PRP-ENV/SERIS/LRC (France)

    2014-07-01

    Atmospheric dispersion of pollutant or radionuclides in stratified meteorological condition, i.e. especially when weather conditions are very stable, mainly at night, is still poorly understood and not well apprehended by the operational atmospheric dispersion models. However, correctly predicting the dispersion of a radioactive plume, and estimating the radiological consequences for the population, following an unplanned atmospheric release of radionuclides are crucial steps in an emergency response. To better understand dispersion in these special weather conditions, IRSN performed a series of 22 air sampling campaigns between 2010 and 2013 in the vicinity of the La Hague nuclear reprocessing plant (AREVA - NC, France), at distances between 200 m and 3000 m from the facility. Krypton-85 ({sup 85}Kr), a b-and g-emitting radionuclide, released during the reprocessing of spent nuclear fuel was used as a non-reactive tracer of radioactive plumes. Experimental campaigns were realized in stability class stable or very stable (E or F according to Pasquill classification) 18 times, and in neutral conditions (D according to Pasquill classification) 4 times. During each campaign, Krypton-85 real time measurement were made to find the plume around the plant, and then integrated samples (30 min) were collected in bag perpendicularly to the assumed wind direction axis. After measurement by gamma spectrometry, we have, when it was possible, estimate the point of impact and the width of the plume. The objective was to estimate the horizontal dispersion (width) of the plume at ground level in function of the distance and be able to calculate atmospheric transfer coefficients. In a second step, objective was to conclude on the use of common model and on their uncertainties. The results will be presented in terms of impact on the near-field. They will be compared with data obtained in previous years in neutral atmospheric conditions, and finally the results will be confronted with

  4. Transitioning Enhanced Land Surface Initialization and Model Verification Capabilities to the Kenya Meteorological Department (KMD)

    Science.gov (United States)

    Case, Jonathan L.; Mungai, John; Sakwa, Vincent; Zavodsky, Bradley T.; Srikishen, Jayanthi; Limaye, Ashutosh; Blankenship, Clay B.

    2016-01-01

    Flooding, severe weather, and drought are key forecasting challenges for the Kenya Meteorological Department (KMD), based in Nairobi, Kenya. Atmospheric processes leading to convection, excessive precipitation and/or prolonged drought can be strongly influenced by land cover, vegetation, and soil moisture content, especially during anomalous conditions and dry/wet seasonal transitions. It is thus important to represent accurately land surface state variables (green vegetation fraction, soil moisture, and soil temperature) in Numerical Weather Prediction (NWP) models. The NASA SERVIR and the Short-term Prediction Research and Transition (SPoRT) programs in Huntsville, AL have established a working partnership with KMD to enhance its regional modeling capabilities. SPoRT and SERVIR are providing experimental land surface initialization datasets and model verification capabilities for capacity building at KMD. To support its forecasting operations, KMD is running experimental configurations of the Weather Research and Forecasting (WRF; Skamarock et al. 2008) model on a 12-km/4-km nested regional domain over eastern Africa, incorporating the land surface datasets provided by NASA SPoRT and SERVIR. SPoRT, SERVIR, and KMD participated in two training sessions in March 2014 and June 2015 to foster the collaboration and use of unique land surface datasets and model verification capabilities. Enhanced regional modeling capabilities have the potential to improve guidance in support of daily operations and high-impact weather and climate outlooks over Eastern Africa. For enhanced land-surface initialization, the NASA Land Information System (LIS) is run over Eastern Africa at 3-km resolution, providing real-time land surface initialization data in place of interpolated global model soil moisture and temperature data available at coarser resolutions. Additionally, real-time green vegetation fraction (GVF) composites from the Suomi-NPP VIIRS instrument is being incorporated

  5. 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.

  6. 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.

  7. 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

  8. 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.

  9. 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

  10. Micro-meteorological modelling in urban areas: pollutant dispersion and radiative effects modelling

    International Nuclear Information System (INIS)

    Milliez, Maya

    2006-01-01

    Atmospheric pollution and urban climate studies require to take into account the complex processes due to heterogeneity of urban areas and the interaction with the buildings. In order to estimate the impact of buildings on flow and pollutant dispersion, detailed numerical simulations were performed over an idealized urban area, with the three-dimensional model Mercure-Saturne, modelling both concentration means and their fluctuations. To take into account atmospheric radiation in built up areas and the thermal effects of the buildings, we implemented a three-dimensional radiative model adapted to complex geometry. This model, adapted from a scheme used for thermal radiation, solves the radiative transfer equation in a semi-transparent media, using the discrete ordinate method. The new scheme was validated with idealized cases and compared to a complete case. (author) [fr

  11. 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.

  12. 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.

  13. Modelling microphysical and meteorological controls on precipitation and cloud cellular structures in Southeast Pacific stratocumulus

    Directory of Open Access Journals (Sweden)

    H. Wang

    2010-07-01

    Full Text Available Microphysical and meteorological controls on the formation of open and closed cellular structures in the Southeast Pacific are explored using model simulations based on aircraft observations during the VAMOS Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx. The effectiveness of factors such as boundary-layer moisture and temperature perturbations, surface heat and moisture fluxes, large-scale vertical motion and solar heating in promoting drizzle and open cell formation for prescribed aerosol number concentrations is explored. For the case considered, drizzle and subsequent open cell formation over a broad region are more sensitive to the observed boundary-layer moisture and temperature perturbations (+0.9 g kg−1; −1 K than to a five-fold decrease in aerosol number concentration (150 vs. 30 mg−1. When embedding the perturbations in closed cells, local drizzle and pockets of open cell (POC formation respond faster to the aerosol reduction than to the moisture increase, but the latter generates stronger and more persistent drizzle. A local negative perturbation in temperature drives a mesoscale circulation that prevents local drizzle formation but promotes it in a remote area where lower-level horizontal transport of moisture is blocked and converges to enhance liquid water path. This represents a potential mechanism for POC formation in the Southeast Pacific stratocumulus region whereby the circulation is triggered by strong precipitation in adjacent broad regions of open cells. A simulation that attempts to mimic the influence of a coastally induced upsidence wave results in an increase in cloud water but this alone is insufficient to initiate drizzle. An increase of surface sensible heat flux is also effective in triggering local drizzle and POC formation.

    Both open and closed cells simulated with observed initial conditions exhibit distinct diurnal variations in cloud properties. A

  14. 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

  15. 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.

  16. 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.

  17. 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

  18. 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.

  19. 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

  20. 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

  1. 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

  2. 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.

  3. Mathematical models for atmospheric pollutants. Appendix A. Elements of meteorology. Final report

    International Nuclear Information System (INIS)

    Drake, R.L.; Laulainen, N.S.

    1979-08-01

    The meteorological elements that are important in the transport and fate of airborne pollutants are summarized. The summary consists of descriptions of the thermal, water, kinematic and dynamic structures of the atmosphere, the time and space scale classifications of atmospheric motions, and modifications of pollutant plumes as they pass over nonhomogeneous surface conditions and complex terrain. The discussions consider the atmospheric thermal layers and their stability, formation of clouds and the production of rain and snow, the movement of air masses and weather fronts, the horizontal winds in the planetary boundary layer, and wind profiles over transition zones and complex terrain

  4. 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

  5. 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.

  6. 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...

  7. 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

  8. 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

  9. 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.

  10. 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.

  11. 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

  12. 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.

  13. 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.

  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. 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.

  16. 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

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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

  2. 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

  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. 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

  5. Meso- and Micro-scale Modelling in China: Wind atlas analysis for 12 meteorological stations in NE China (Dongbei)

    DEFF Research Database (Denmark)

    Mortensen, Niels Gylling; Yang, Z.; Hansen, Jens Carsten

    As part of the “Meso-Scale and Micro-Scale Modelling in China” project, also known as the CMA component of the Sino-Danish Wind Energy Development Programme (WED), microscale modelling and analyses have been carried out for 12 meteorological stations in NE China. Wind speed and direction data from...... the twelve 70-m masts have been analysed using the Wind Atlas Analysis and Application Program (WAsP 10). The wind-climatological inputs are the observed wind climates derived from the WAsP Climate Analyst. Topographical inputs are elevation maps constructed from SRTM 3 data and roughness length maps...... constructed from Google Earth satellite imagery. The maps have been compared to Chinese topographical maps and adjusted accordingly. Summaries are given of the data measured at the 12 masts for the reference period 2009. The main result of the microscale modelling is an observational wind atlas for NE China...

  6. 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.

  7. 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

  8. 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

  9. 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...

  10. 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

  11. 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

  12. Meteorological conditions associated to high sublimation amounts in semiarid high-elevation Andes decrease the performance of empirical melt models

    Science.gov (United States)

    Ayala, Alvaro; Pellicciotti, Francesca; MacDonell, Shelley; McPhee, James; Burlando, Paolo

    2015-04-01

    Empirical melt (EM) models are often preferred to surface energy balance (SEB) models to calculate melt amounts of snow and ice in hydrological modelling of high-elevation catchments. The most common reasons to support this decision are that, in comparison to SEB models, EM models require lower levels of meteorological data, complexity and computational costs. However, EM models assume that melt can be characterized by means of a few index variables only, and their results strongly depend on the transferability in space and time of the calibrated empirical parameters. In addition, they are intrinsically limited in accounting for specific process components, the complexity of which cannot be easily reconciled with the empirical nature of the model. As an example of an EM model, in this study we use the Enhanced Temperature Index (ETI) model, which calculates melt amounts using air temperature and the shortwave radiation balance as index variables. We evaluate the performance of the ETI model on dry high-elevation sites where sublimation amounts - that are not explicitly accounted for the EM model - represent a relevant percentage of total ablation (1.1 to 8.7%). We analyse a data set of four Automatic Weather Stations (AWS), which were collected during the ablation season 2013-14, at elevations between 3466 and 4775 m asl, on the glaciers El Tapado, San Francisco, Bello and El Yeso, which are located in the semiarid Andes of central Chile. We complement our analysis using data from past studies in Juncal Norte Glacier (Chile) and Haut Glacier d'Arolla (Switzerland), during the ablation seasons 2008-09 and 2006, respectively. We use the results of a SEB model, applied to each study site, along the entire season, to calibrate the ETI model. The ETI model was not designed to calculate sublimation amounts, however, results show that their ability is low also to simulate melt amounts at sites where sublimation represents larger percentages of total ablation. In fact, we

  13. 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.

  14. 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.

  15. Green River air quality model development: meteorological and tracer data, July/August 1982 field study in Brush Valley, Colorado

    Energy Technology Data Exchange (ETDEWEB)

    Whiteman, C.D.; Lee, R.N.; Orgill, M.M.; Zak, B.D.

    1984-06-01

    Meteorological and atmospheric tracer studies were conducted during a 3-week period in July and August of 1982 in the Brush Creek Valley of northwestern Colorado. The objective of the field experiments was to obtain data to evaluate a model, called VALMET, developed at PNL to predict dispersion of air pollutants released from an elevated stack located within a deep mountain valley in the post-sunrise temperature inversion breakup period. Three tracer experiments were conducted in the valley during the 2-week period. In these experiments, sulfur hexafluoride (SF/sub 6/) was released from a height of approximately 100 m, beginning before sunrise and continuing until the nocturnal down-valley winds reversed several hours after sunrise. Dispersion of the sulfur hexafluoride after release was evaluated by measuring SF/sub 6/ concentrations in ambient air samples taken from sampling devices operated within the valley up to about 8 km down valley from the source. An instrumented research aircraft was also used to measure concentrations in and above the valley. Tracer samples were collected using a network of radio-controlled bag sampling stations, two manually operated gas chromatographs, a continuous SF/sub 6/ monitor, and a vertical SF/sub 6/ profiler. In addition, basic meteorological data were collected during the tracer experiments. Frequent profiles of vertical wind and temperature structure were obtained with tethered balloons operated at the release site and at a site 7.7 km down the valley from the release site. 10 references, 63 figures, 50 tables.

  16. 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

  17. 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.

  18. 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.

  19. 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.

  20. 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

  1. Meteorological instrumentation

    International Nuclear Information System (INIS)

    1982-06-01

    RFS or ''Regles Fondamentales de Surete'' (Basic Safety Rules) applicable to certain types of nuclear facilities lay down requirements with which compliance, for the type of facilities and within the scope of application covered by the RFS, is considered to be equivalent to compliance with technical French regulatory practice. The object of the RFS is to take advantage of standardization in the field of safety , while allowing for technical progress in that field. They are designed to enable the operating utility and contractors to know the rules pertaining to various subjects which are considered to be acceptable by the ''Service Central de Surete des Installations Nucleaires'' or the SCSIN (Central Department for the Safety of Nuclear Facilities). These RFS should make safety analysis easier and lead to better understanding between experts and individuals concerned with the problems of nuclear safety. The SCSIN reserves the right to modify, when considered necessary any RFS and specify, if need be, the terms under which a modification is deemed retroactive. The purpose of this RFS is to specify the meteorological instrumentation required at the site of each nuclear power plant equipped with at least one pressurized water reactor

  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. 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

  4. 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

  5. Experimental program for physics-of-failure modeling of electrolytic capacitors towards prognostics and health management

    International Nuclear Information System (INIS)

    Rana, Y.S.; Banerjee, Shantanab; Singh, Tej; Varde, P.V.

    2017-01-01

    Prognostics and Health Management (PHM) is a method used for predicting reliability of a component or system by assessing its current health and future operating conditions. A physics-of-failure (PoF)-based program on PHM for reliability prediction has been initiated at our institute. As part of the program, we aim at developing PoF-based models for degradation of electronic components and their experimental validation. In this direction, a database on existing PoF models for different electronic components has been prepared. We plan to experimentally determine the model constants and propose suitable methodology for PHM. Electrolytic capacitors are one of the most common passive components which find their applications in devices such as power supplies in aircrafts and printed circuit boards (PCBs) for regulation and protection of a nuclear reactor. Experimental studies have established that electrolytic capacitors degrade under electrical and thermal stress and tend to fail before their anticipated useful life at normal operating conditions. Equivalent series resistance (ESR) and capacitance (C) are the two main parameters used for monitoring health of such capacitors. In this paper, we present an experimental program for thermal and electrical overstress studies towards degradation models for electrolytic capacitors. (author)

  6. 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.

  7. 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.

  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. 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.

  10. 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 ...

  11. 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...

  12. 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.

  13. 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.

  14. 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...

  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. Analysis of acute myocardial infarction occurance in Saratov region using GIS-technologies and prognostic modeling

    Directory of Open Access Journals (Sweden)

    SokolovI.M.

    2012-09-01

    Full Text Available

     

    The research objective: To find estimation tools of incidence of acute myocardial infarction at the regional level and to optimize organization of medical assistance to patients with acute coronary pathology. Materials. With the use of statistics of territorial distribution of acute myocardial infarction incidence in the region and GIS-TECHNOLOGIES the statistical analysis and mathematical modelling of the spatially-organizational data has been carried out. Results. On the basis of the received results the prognostic model of development of acute coronary pathology has been generated. Measures on optimization of organization of medical assistance to patients with an acute coronary pathology have been stated. Conclusion. Methods of intellectual support of the doctor may become effective in formation of organizational structure of the system of stage-by-stage qualified and specialized aid to patients with acute coronary syndrome.

  17. A Modeling Framework for Prognostic Decision Making and its Application to UAV Mission Planning

    Data.gov (United States)

    National Aeronautics and Space Administration — The goal of prognostic decision making (PDM) is to utilize information on anticipated system health changes in selecting future actions. One of the key challenges in...

  18. Improving Clinical Risk Stratification at Diagnosis in Primary Prostate Cancer: A Prognostic Modelling Study.

    Directory of Open Access Journals (Sweden)

    Vincent J Gnanapragasam

    2016-08-01

    new five-stratum risk stratification system was produced, and its prognostic power was compared against the current system, with PCSM as the outcome. The results were analysed using a Cox hazards model, the log-rank test, Kaplan-Meier curves, competing-risks regression, and concordance indices. In the training set, the new risk stratification system identified distinct subgroups with different risks of PCSM in pair-wise comparison (p < 0.0001. Specifically, the new classification identified a very low-risk group (Group 1, a subgroup of intermediate-risk cancers with a low PCSM risk (Group 2, hazard ratio [HR] 1.62 [95% CI 0.96-2.75], and a subgroup of intermediate-risk cancers with an increased PCSM risk (Group 3, HR 3.35 [95% CI 2.04-5.49] (p < 0.0001. High-risk cancers were also sub-classified by the new system into subgroups with lower and higher PCSM risk: Group 4 (HR 5.03 [95% CI 3.25-7.80] and Group 5 (HR 17.28 [95% CI 11.2-26.67] (p < 0.0001, respectively. These results were recapitulated in the testing set and remained robust after inclusion of competing risks. In comparison to the current risk stratification system, the new system demonstrated improved prognostic performance, with a concordance index of 0.75 (95% CI 0.72-0.77 versus 0.69 (95% CI 0.66-0.71 (p < 0.0001. In an external cohort, the new system achieved a concordance index of 0.79 (95% CI 0.75-0.84 for predicting PCSM versus 0.66 (95% CI 0.63-0.69 (p < 0.0001 for the current NICE risk stratification system. The main limitations of the study were that it was registry based and that follow-up was relatively short.A novel and simple five-stratum risk stratification system outperforms the standard three-stratum risk stratification system in predicting the risk of PCSM at diagnosis in men with primary non-metastatic prostate cancer, even when accounting for competing risks. This model also allows delineation of new clinically relevant subgroups of men who might potentially receive more appropriate

  19. Near-Surface Meteorology During the Arctic Summer Cloud Ocean Study (ASCOS): Evaluation of Reanalyses and Global Climate Models.

    Science.gov (United States)

    De Boer, G.; Shupe, M.D.; Caldwell, P.M.; Bauer, Susanne E.; Persson, O.; Boyle, J.S.; Kelley, M.; Klein, S.A.; Tjernstrom, M.

    2014-01-01

    Atmospheric measurements from the Arctic Summer Cloud Ocean Study (ASCOS) are used to evaluate the performance of three atmospheric reanalyses (European Centre for Medium Range Weather Forecasting (ECMWF)- Interim reanalysis, National Center for Environmental Prediction (NCEP)-National Center for Atmospheric Research (NCAR) reanalysis, and NCEP-DOE (Department of Energy) reanalysis) and two global climate models (CAM5 (Community Atmosphere Model 5) and NASA GISS (Goddard Institute for Space Studies) ModelE2) in simulation of the high Arctic environment. Quantities analyzed include near surface meteorological variables such as temperature, pressure, humidity and winds, surface-based estimates of cloud and precipitation properties, the surface energy budget, and lower atmospheric temperature structure. In general, the models perform well in simulating large-scale dynamical quantities such as pressure and winds. Near-surface temperature and lower atmospheric stability, along with surface energy budget terms, are not as well represented due largely to errors in simulation of cloud occurrence, phase and altitude. Additionally, a development version of CAM5, which features improved handling of cloud macro physics, has demonstrated to improve simulation of cloud properties and liquid water amount. The ASCOS period additionally provides an excellent example of the benefits gained by evaluating individual budget terms, rather than simply evaluating the net end product, with large compensating errors between individual surface energy budget terms that result in the best net energy budget.

  20. Evaluating Ice Nucleating Particle Concentrations From Prognostic Dust Minerals in an Earth System Model

    Science.gov (United States)

    Perlwitz, J. P.; Knopf, D. A.; Fridlind, A. M.; Miller, R. L.; Pérez García-Pando, C.; DeMott, P. J.

    2016-12-01

    The effect of aerosol particles on the radiative properties of clouds, the so-called, indirect effect of aerosols, is recognized as one of the largest sources of uncertainty in climate prediction. The distribution of water vapor, precipitation, and ice cloud formation are influenced by the atmospheric ice formation, thereby modulating cloud albedo and thus climate. It is well known that different particle types possess different ice formation propensities with mineral dust being a superior ice nucleating particle (INP) compared to soot particles. Furthermore, some dust mineral types are more proficient INP than others, depending on temperature and relative humidity.In recent work, we have presented an improved dust aerosol module in the NASA GISS Earth System ModelE2 with prognostic mineral composition of the dust aerosols. Thus, there are regional variations in dust composition. We evaluated the predicted mineral fractions of dust aerosols by comparing them to measurements from a compilation of about 60 published literature references. Additionally, the capability of the model to reproduce the elemental composition of the simulated dusthas been tested at Izana Observatory at Tenerife, Canary Islands, which is located off-shore of Africa and where frequent dust events are observed. We have been able to show that the new approach delivers a robust improvement of the predicted mineral fractions and elemental composition of dust.In the current study, we use three-dimensional dust mineral fields and thermodynamic conditions, which are simulated using GISS ModelE, to calculate offline the INP concentrations derived using different ice nucleation parameterizations that are currently discussed. We evaluate the calculated INP concentrations from the different parameterizations by comparing them to INP concentrations from field measurements.

  1. Ground-based remote sensing profiling and numerical weather prediction model to manage nuclear power plants meteorological surveillance in Switzerland

    Directory of Open Access Journals (Sweden)

    B. Calpini

    2011-08-01

    Full Text Available The meteorological surveillance of the four nuclear power plants in Switzerland is of first importance in a densely populated area such as the Swiss Plateau. The project "Centrales Nucléaires et Météorologie" CN-MET aimed at providing a new security tool based on one hand on the development of a high resolution numerical weather prediction (NWP model. The latter is providing essential nowcasting information in case of a radioactive release from a nuclear power plant in Switzerland. On the other hand, the model input over the Swiss Plateau is generated by a dedicated network of surface and upper air observations including remote sensing instruments (wind profilers and temperature/humidity passive microwave radiometers. This network is built upon three main sites ideally located for measuring the inflow/outflow and central conditions of the main wind field in the planetary boundary layer over the Swiss Plateau, as well as a number of surface automatic weather stations (AWS. The network data are assimilated in real-time into the fine grid NWP model using a rapid update cycle of eight runs per day (one forecast every three hours. This high resolution NWP model has replaced the former security tool based on in situ observations (in particular one meteorological mast at each of the power plants and a local dispersion model. It is used to forecast the dynamics of the atmosphere in the planetary boundary layer (typically the first 4 km above ground layer and over a time scale of 24 h. This tool provides at any time (e.g. starting at the initial time of a nuclear power plant release the best picture of the 24-h evolution of the air mass over the Swiss Plateau and furthermore generates the input data (in the form of simulated values substituting in situ observations required for the local dispersion model used at each of the nuclear power plants locations. This paper is presenting the concept and two validation studies as well as the results of an

  2. Statistical modeling of phenological phases in Poland based on coupling satellite derived products and gridded meteorological data

    Science.gov (United States)

    Czernecki, Bartosz; Jabłońska, Katarzyna; Nowosad, Jakub

    2016-04-01

    The aim of the study was to create and evaluate different statistical models for reconstructing and predicting selected phenological phases. This issue is of particular importance in Poland where national-wide phenological monitoring was abandoned in the middle of 1990s and the reactivated network was established in 2006. Authors decided to evaluate possibilities of using a wide-range of statistical modeling techniques to create synthetic archive dataset. Additionally, a robust tool for predicting the most distinguishable phenophases using only free of charge data as predictors was created. Study period covers the years 2007-2014 and contains only quality-controlled dataset of 10 species and 14 phenophases. Phenological data used in this study originates from the manual observations network run by the Institute of Meteorology and Water Management - National Research Institute (IMGW-PIB). Three kind of data sources were used as predictors: (i) satellite derived products, (ii) preprocessed gridded meteorological data, and (iii) spatial properties (longitude, latitude, altitude) of the monitoring site. Moderate-Resolution Imaging Spectroradiometer (MODIS) level-3 vegetation products were used for detecting onset dates of particular phenophases. Following indices were used: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (fPAR). Additionally, Interactive Multisensor Snow and Ice Mapping System (IMS) products were chosen to detect occurrence of snow cover. Due to highly noisy data, authors decided to take into account pixel reliability information. Besides satellite derived products (NDVI, EVI, FPAR, LAI, Snow cover), a wide group of observational data and agrometeorological indices derived from the European Climate Assessment & Dataset (ECA&D) were used as a potential predictors: cumulative growing degree days (GDD), cumulative growing precipitation days (GPD

  3. A spatiotemporal dengue fever early warning model accounting for nonlinear associations with meteorological factors: a Bayesian maximum entropy approach

    Science.gov (United States)

    Lee, Chieh-Han; Yu, Hwa-Lung; Chien, Lung-Chang

    2014-05-01

    Dengue fever has been identified as one of the most widespread vector-borne diseases in tropical and sub-tropical. In the last decade, dengue is an emerging infectious disease epidemic in Taiwan especially in the southern area where have annually high incidences. For the purpose of disease prevention and control, an early warning system is urgently needed. Previous studies have showed significant relationships between climate variables, in particular, rainfall and temperature, and the temporal epidemic patterns of dengue cases. However, the transmission of the dengue fever is a complex interactive process that mostly understated the composite space-time effects of dengue fever. This study proposes developing a one-week ahead warning system of dengue fever epidemics in the southern Taiwan that considered nonlinear associations between weekly dengue cases and meteorological factors across space and time. The early warning system based on an integration of distributed lag nonlinear model (DLNM) and stochastic Bayesian Maximum Entropy (BME) analysis. The study identified the most significant meteorological measures including weekly minimum temperature and maximum 24-hour rainfall with continuous 15-week lagged time to dengue cases variation under condition of uncertainty. Subsequently, the combination of nonlinear lagged effects of climate variables and space-time dependence function is implemented via a Bayesian framework to predict dengue fever occurrences in the southern Taiwan during 2012. The result shows the early warning system is useful for providing potential outbreak spatio-temporal prediction of dengue fever distribution. In conclusion, the proposed approach can provide a practical disease control tool for environmental regulators seeking more effective strategies for dengue fever prevention.

  4. Drought Early Warning and Agro-Meteorological Risk Assessment using Earth Observation Rainfall Datasets and Crop Water Budget Modelling

    Science.gov (United States)

    Tarnavsky, E.

    2016-12-01

    The water resources satisfaction index (WRSI) model is widely used in drought early warning and food security analyses, as well as in agro-meteorological risk management through weather index-based insurance. Key driving data for the model is provided from satellite-based rainfall estimates such as ARC2 and TAMSAT over Africa and CHIRPS globally. We evaluate the performance of these rainfall datasets for detecting onset and cessation of rainfall and estimating crop production conditions for the WRSI model. We also examine the sensitivity of the WRSI model to different satellite-based rainfall products over maize growing regions in Tanzania. Our study considers planting scenarios for short-, medium-, and long-growing cycle maize, and we apply these for 'regular' and drought-resistant maize, as well as with two different methods for defining the start of season (SOS). Simulated maize production estimates are compared against available reported production figures at the national and sub-national (province) levels. Strengths and weaknesses of the driving rainfall data, insights into the role of the SOS definition method, and phenology-based crop yield coefficient and crop yield reduction functions are discussed in the context of space-time drought characteristics. We propose a way forward for selecting skilled rainfall datasets and discuss their implication for crop production monitoring and the design and structure of weather index-based insurance products as risk transfer mechanisms implemented across scales for smallholder farmers to national programmes.

  5. Simple prognostic model for patients with advanced cancer based on performance status.

    Science.gov (United States)

    Jang, Raymond W; Caraiscos, Valerie B; Swami, Nadia; Banerjee, Subrata; Mak, Ernie; Kaya, Ebru; Rodin, Gary; Bryson, John; Ridley, Julia Z; Le, Lisa W; Zimmermann, Camilla

    2014-09-01

    Providing survival estimates is important for decision making in oncology care. The purpose of this study was to provide survival estimates for outpatients with advanced cancer, using the Eastern Cooperative Oncology Group (ECOG), Palliative Performance Scale (PPS), and Karnofsky Performance Status (KPS) scales, and to compare their ability to predict survival. ECOG, PPS, and KPS were completed by physicians for each new patient attending the Princess Margaret Cancer Centre outpatient Oncology Palliative Care Clinic (OPCC) from April 2007 to February 2010. Survival analysis was performed using the Kaplan-Meier method. The log-rank test for trend was employed to test for differences in survival curves for each level of performance status (PS), and the concordance index (C-statistic) was used to test the predictive discriminatory ability of each PS measure. Measures were completed for 1,655 patients. PS delineated survival well for all three scales according to the log-rank test for trend (P statistic was similar for all three scales and ranged from 0.63 to 0.64. We present a simple tool that uses PS alone to prognosticate in advanced cancer, and has similar discriminatory ability to more complex models. Copyright © 2014 by American Society of Clinical Oncology.

  6. Mean atmospheric temperature model estimation for GNSS meteorology using AIRS and AMSU data

    Directory of Open Access Journals (Sweden)

    Rata Suwantong

    2017-03-01

    Full Text Available In this paper, the problem of modeling the relationship between the mean atmospheric and air surface temperatures is addressed. Particularly, the major goal is to estimate the model parameters at a regional scale in Thailand. To formulate the relationship between the mean atmospheric and air surface temperatures, a triply modulated cosine function was adopted to model the surface temperature as a periodic function. The surface temperature was then converted to mean atmospheric temperature using a linear function. The parameters of the model were estimated using an extended Kalman filter. Traditionally, radiosonde data is used. In this paper, satellite data from an atmospheric infrared sounder, and advanced microwave sounding unit sensors was used because it is open source data and has global coverage with high temporal resolution. The performance of the proposed model was tested against that of a global model via an accuracy assessment of the computed GNSS-derived PWV.

  7. Combining meteorological radar and network of rain gauges data for space–time model development

    OpenAIRE

    Pastoriza, Vicente; Núñez Fernández, Adolfo; Machado, Fernando; Mariño, Perfecto; Pérez Fontán, Fernando; Fiebig, Uwe-Carsten

    2009-01-01

    Technological developments and the trend to go higher and higher in frequency give rise to the need for true space–time rain field models for testing the dynamics of fade countermeasures. There are many models that capture the spatial correlation of rain fields. Worth mentioning are those models based on cell ensembles. However, the rain rate fields created in this way need the introduction of the time variable to reproduce their dynamics. In this paper, we have concentrated on ad...

  8. An internally validated prognostic model for success in revision stapes surgery for otosclerosis.

    Science.gov (United States)

    Wegner, Inge; Vincent, Robert; Derks, Laura S M; Rauh, Simone P; Heymans, Martijn W; Stegeman, Inge; Grolman, Wilko

    2018-03-09

    To develop a prediction model that can accurately predict the chance of success following revision stapes surgery in patients with recurrent or persistent otosclerosis at 2- to 6-months follow-up and to validate this model internally. A retrospective cohort study of prospectively gathered data in a tertiary referral center. The associations of 11 prognostic factors with treatment success were tested in 705 cases using multivariable logistic regression analysis with backward selection. Success was defined as a mean air-bone gap closure to 10 dB or less. The most relevant predictors were used to derive a clinical prediction rule to determine the probability of success. Internal validation by means of bootstrapping was performed. Model performance indices, including the Hosmer-Lemeshow test, the area under the receiver operating characteristics curve (AUC), and the explained variance were calculated. Success was achieved in 57.7% of cases at 2- to 6-months follow-up. Certain previous surgical techniques, primary causes of failure leading up to revision stapes surgery, and positions of the prosthesis placed during revision surgery were associated with higher success percentages. The clinical prediction rule performed moderately well in the original dataset (Hosmer-Lemeshow P = .78; AUC = 0.73; explained variance = 22%), which slightly decreased following internal validation by means of bootstrapping (AUC = 0.69; explained variance = 13%). Our study established the importance of previous surgical technique, primary cause of failure, and type of the prosthesis placed during the revision surgery in predicting the probability of success following stapes surgery at 2- to 6-months follow-up. 2b. Laryngoscope, 2018. © 2018 The American Laryngological, Rhinological and Otological Society, Inc.

  9. Incorporating Prognostic Marine Nitrogen Fixers and Related Bio-Physical Feedbacks in an Earth System Model

    Science.gov (United States)

    Paulsen, H.; Ilyina, T.; Six, K. D.

    2016-02-01

    Marine nitrogen fixers play a fundamental role in the oceanic nitrogen and carbon cycles by providing a major source of `new' nitrogen to the euphotic zone that supports biological carbon export and sequestration. Furthermore, nitrogen fixers may regionally have a direct impact on ocean physics and hence the climate system as they form extensive surface mats which can increase light absorption and surface albedo and reduce the momentum input by wind. Resulting alterations in temperature and stratification may feed back on nitrogen fixers' growth itself.We incorporate nitrogen fixers as a prognostic 3D tracer in the ocean biogeochemical component (HAMOCC) of the Max Planck Institute Earth system model and assess for the first time the impact of related bio-physical feedbacks on biogeochemistry and the climate system.The model successfully reproduces recent estimates of global nitrogen fixation rates, as well as the observed distribution of nitrogen fixers, covering large parts of the tropical and subtropical oceans. First results indicate that including bio-physical feedbacks has considerable effects on the upper ocean physics in this region. Light absorption by nitrogen fixers leads locally to surface heating, subsurface cooling, and mixed layer depth shoaling in the subtropical gyres. As a result, equatorial upwelling is increased, leading to surface cooling at the equator. This signal is damped by the effect of the reduced wind stress due to the presence of cyanobacteria mats, which causes a reduction in the wind-driven circulation, and hence a reduction in equatorial upwelling. The increase in surface albedo due to nitrogen fixers has only inconsiderable effects. The response of nitrogen fixers' growth to the alterations in temperature and stratification varies regionally. Simulations with the fully coupled Earth system model are in progress to assess the implications of the biologically induced changes in upper ocean physics for the global climate system.

  10. Seasonal modeling of hand, foot, and mouth disease as a function of meteorological variations in Chongqing, China

    Science.gov (United States)

    Wang, Pin; Zhao, Han; You, Fangxin; Zhou, Hailong; Goggins, William B.

    2017-08-01

    Hand, foot, and mouth disease (HFMD) is an enterovirus-induced infectious disease, mainly affecting children under 5 years old. Outbreaks of HFMD in recent years indicate the disease interacts with both the weather and season. This study aimed to investigate the seasonal association between HFMD and weather variation in Chongqing, China. Generalized additive models and distributed lag non-linear models based on a maximum lag of 14 days, with negative binomial distribution assumed to account for overdispersion, were constructed to model the association between reporting HFMD cases from 2009 to 2014 and daily mean temperature, relative humidity, total rainfall and sun duration, adjusting for trend, season, and day of the week. The year-round temperature and relative humidity, rainfall in summer, and sun duration in winter were all significantly associated with HFMD. An inverted-U relationship was found between mean temperature and HFMD above 19 °C in summer, with a maximum morbidity at 27 °C, while the risk increased linearly with the temperature in winter. A hockey-stick association was found for relative humidity in summer with increasing risks over 60%. Heavy rainfall, relative to no rain, was found to be associated with reduced HFMD risk in summer and 2 h of sunshine could decrease the risk by 21% in winter. The present study showed meteorological variables were differentially associated with HFMD incidence in two seasons. Short-term weather variation surveillance and forecasting could be employed as an early indicator for potential HFMD outbreaks.

  11. Smoke Dispersion Modeling Over Complex Terrain Using High-Resolution Meteorological Data and Satellite Observations: The FireHub Platform

    Science.gov (United States)

    Solomos, S.; Amiridis, V.; Zanis, P.; Gerasopoulos, E.; Sofiou, F. I.; Herekakis, T.; Brioude, J.; Stohl, A.; Kahn, R. A.; Kontoes, C.

    2015-01-01

    A total number of 20,212 fire hot spots were recorded by the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument over Greece during the period 2002e2013. The Fire Radiative Power (FRP) of these events ranged from 10 up to 6000 MW at 1 km resolution, and many of these fire episodes resulted in long-range transport of smoke over distances up to several hundred kilometers. Three different smoke episodes over Greece are analyzed here using real time hot-spot observations from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) satellite instrument as well as from MODIS hot-spots. Simulations of smoke dispersion are performed with the FLEXPART-WRF model and particulate matter emissions are calculated directly from the observed FRP. The modeled smoke plumes are compared with smoke stereo-heights from the Multiangle Imaging Spectroradiometer (MISR) instrument and the sensitivities to atmospheric and modeling parameters are examined. Driving the simulations with high resolution meteorology (4 4 km) and using geostationary satellite data to identify the hot spots allows the description of local scale features that govern smoke dispersion. The long-range transport of smoke is found to be favored over the complex coastline environment of Greece due to the abrupt changes between land and marine planetary boundary layers (PBL) and the decoupling of smoke layers from the surface.

  12. Prediction of minimum temperatures in an alpine region by linear and non-linear post-processing of meteorological models

    Directory of Open Access Journals (Sweden)

    R. Barbiero

    2007-05-01

    Full Text Available Model Output Statistics (MOS refers to a method of post-processing the direct outputs of numerical weather prediction (NWP models in order to reduce the biases introduced by a coarse horizontal resolution. This technique is especially useful in orographically complex regions, where large differences can be found between the NWP elevation model and the true orography. This study carries out a comparison of linear and non-linear MOS methods, aimed at the prediction of minimum temperatures in a fruit-growing region of the Italian Alps, based on the output of two different NWPs (ECMWF T511–L60 and LAMI-3. Temperature, of course, is a particularly important NWP output; among other roles it drives the local frost forecast, which is of great interest to agriculture. The mechanisms of cold air drainage, a distinctive aspect of mountain environments, are often unsatisfactorily captured by global circulation models. The simplest post-processing technique applied in this work was a correction for the mean bias, assessed at individual model grid points. We also implemented a multivariate linear regression on the output at the grid points surrounding the target area, and two non-linear models based on machine learning techniques: Neural Networks and Random Forest. We compare the performance of all these techniques on four different NWP data sets. Downscaling the temperatures clearly improved the temperature forecasts with respect to the raw NWP output, and also with respect to the basic mean bias correction. Multivariate methods generally yielded better results, but the advantage of using non-linear algorithms was small if not negligible. RF, the best performing method, was implemented on ECMWF prognostic output at 06:00 UTC over the 9 grid points surrounding the target area. Mean absolute errors in the prediction of 2 m temperature at 06:00 UTC were approximately 1.2°C, close to the natural variability inside the area itself.

  13. 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

  14. Introducing Subrid-scale Cloud Feedbacks to Radiation for Regional Meteorological and Cllimate Modeling

    Science.gov (United States)

    Convection systems and associated cloudiness directly influence regional and local radiation budgets, and dynamics and thermodynamics through feedbacks. However, most subgrid-scale convective parameterizations in regional weather and climate models do not consider cumulus cloud ...

  15. Sulphur simulations for East Asia using the MATCH model with meteorological data from ECMWF

    Energy Technology Data Exchange (ETDEWEB)

    Engardt, Magnuz

    2000-03-01

    As part of a model intercomparison exercise, with participants from a number of Asian, European and American institutes, sulphur transport and conversion calculations were conducted over an East Asian domain for 2 different months in 1993. All participants used the same emission inventory and simulated concentration and deposition at a number of prescribed geographic locations. The participants were asked to run their respective model both with standard parameters, and with a set of given parameters, in order to examine the different behaviour of the models. The study included comparison with measured data and model-to-model intercomparisons, notably source-receptor relationships. We hereby describe the MATCH model, used in the study, and report some typical results. We find that although the standard and the prescribed set of model parameters differed significantly in terms of sulphur conversion and wet scavenging rate, the resulting change in atmospheric concentrations and surface depositions only change marginally. We show that it is often more critical to choose a representative gridbox value than selecting a parameter from the suite available. The modelled, near-surface, atmospheric concentration of sulphur in eastern China is typically 5- 10 {mu}g S m{sup -3}, with large areas exceeding 20 {mu}g S m{sup -3}. In southern Japan the values range from 2-5 {mu}g S m{sup -3} . Atmospheric SO{sub 2} dominates over sulphate near the emission regions while sulphate concentrations are higher over e.g. the western Pacific. The sulphur deposition exceeds several g sulphur m{sup -2} year{sup -1} in large areas of China. Southern Japan receives 03-1 g S m{sup -2} year{sup -1}. In January, the total wet deposition roughly equals the dry deposition, in May - when it rains more in the domain - total wet deposition is ca. 50% larger than total dry deposition.

  16. Combination of Deterministic and Probabilistic Meteorological Models to enhance Wind Farm Power Forecasts

    International Nuclear Information System (INIS)

    Bremen, Lueder von

    2007-01-01

    Large-scale wind farms will play an important role in the future worldwide energy supply. However, with increasing wind power penetration all stakeholders on the electricity market will ask for more skilful wind power predictions regarding save grid integration and to increase the economic value of wind power. A Neural Network is used to calculate Model Output Statistics (MOS) for each individual forecast model (ECMWF and HIRLAM) and to model the aggregated power curve of the Middelgrunden offshore wind farm. We showed that the combination of two NWP models clearly outperforms the better single model. The normalized day-ahead RMSE forecast error for Middelgrunden can be reduced by 1% compared to single ECMWF. This is a relative improvement of 6%. For lead times >24h it is worthwhile to use a more sophisticated model combination approach than simple linear weighting. The investigated principle component regression is able to extract the uncorrelated information from two NWP forecasts. The spread of Ensemble Predictions is related to the skill of wind power forecasts. Simple contingency diagrams show that low spread corresponds is more often related to low forecast errors and high spread to large forecast errors

  17. Modeling detailed hydro-meteorological surfaces and runoff response in large diverse watersheds

    International Nuclear Information System (INIS)

    Byrne, J.; Kienzle, S.W.; MacDonald, R.J.

    2008-01-01

    An understanding of local variability in climatic conditions over complex terrain is imperative to making accurate assessments of impacts from climate change on fresh water ecosystems (Daly, 2006). The derivation of representative spatial data in diverse environments poses a significant challenge to the modelling community. This presentation describes the current status of a long term ongoing hydro-climate model development program. We are developing a gridded hydroclimate dataset for diverse watersheds using SimGrid (Larson, 2008; Lapp et al., 2005; Sheppard, 1996), a model that applies the Mountain Climate Model (MTCLIM; Hungerford et al., 1989) to simulate hydro-climatic conditions over diverse terrain. The model uses GIS based terrain categories (TC) classified by slope, aspect, elevation, and soil water storage. SimGrid provides daily estimates of solar radiation, air temperature, relative humidity, precipitation, snowpack and soil water storage over space. Earlier versions of the model have been applied in the St. Mary (Larson, 2008) and upper Oldman basins (Lapp et al., 2005), giving realistic estimates of hydro-climatic variables. The current study demonstrates improvements to the estimation of temperature, precipitation, snowpack, soil water storage and runoff from the basin. Soil water storage data for the upper drainage were derived with GIS and included in SimGrid to estimate soil water flux over the time period. These changes help improve the estimation of spatial climatic variability over the basin while accounting for topographical influence. In further work we will apply spatial hydro-climatic surfaces from the SimGrid model to assess the hydrologic response to environmental change for watersheds in Canada and beyond. (author)

  18. 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.

  19. Coupling Meteorological, Land Surface and Water Temperature Models in the Mississippi River Basin

    Science.gov (United States)

    Tang, C.; Cooter, E. J.

    2017-12-01

    Water temperature is a significant factor influencing of the stream ecosystem and water management especially under climate change. In this study, we demonstrate a physically based semi-Lagrangian water temperature model (RBM) coupled with the Variable Infiltration Capacity (VIC) hydrology model and Weather Research & Forecasting Model (WRF) in the Mississippi River Basin (MRB). The results of this coupling compare favorably with observed water temperature data at river gages throughout the MRB. Further sensitivity analysis shows that mean water temperatures increase by 1.3°C, 1.5°C, and 1.8°C in northern, central and southern MRB zones, respectively, under a hypothetical uniform air temperature increase of 3°C. If air temperatures increase uniformly by 6°C in this scenario, then water temperatures are projected to increase by 3.3°C, 3.5°C and 4.0°C. Lastly, downscaled air temperatures from a global climate model are used to drive the coupled VIC and RBM model from 2020 to 2099. Average stream temperatures from 2020 to 2099 increase by 1°C to 8°C above 1950 to 2010 average water temperatures, with non-uniform increases along the river. In some portions of the MRB, stream temperatures could increase above survival thresholds for several native fish species, which are critical components of the stream ecosystem. The increased water temperature accelerates harmful algal blooming which results in a larger dead zone in the Gulf of Mexico.

  20. 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...

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

    OpenAIRE

    Loha, Eskindir; Lindtj?rn, Bernt

    2010-01-01

    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 4...

  2. 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

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

    OpenAIRE

    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.

    2014-01-01

    The simulation of the coupled evolution of atmospheric dynamics, pollutant transport, chemical reactions and atmospheric composition is one of the most challenging tasks in environmental modelling, climate change studies, and weather forecasting for the next decades as they all involve strongly integrated processes. Weather strongly influences air quality (AQ) and atmospheric transport of hazardous materials, while atmospheric composition can influence both weather and climate by direc...

  4. Future intensification of hydro-meteorological extremes: downscaling using the weather research and forecasting model

    KAUST Repository

    El-Samra, R.

    2017-02-15

    A set of ten downscaling simulations at high spatial resolution (3 km horizontally) were performed using the Weather Research and Forecasting (WRF) model to generate future climate projections of annual and seasonal temperature and precipitation changes over the Eastern Mediterranean (with a focus on Lebanon). The model was driven with the High Resolution Atmospheric Model (HiRAM), running over the whole globe at a resolution of 25 km, under the conditions of two Representative Concentration Pathways (RCP) (4.5 and 8.5). Each downscaling simulation spanned one year. Two past years (2003 and 2008), also forced by HiRAM without data assimilation, were simulated to evaluate the model’s ability to capture the cold and wet (2003) and hot and dry (2008) extremes. The downscaled data were in the range of recent observed climatic variability, and therefore corrected for the cold bias of HiRAM. Eight future years were then selected based on an anomaly score that relies on the mean annual temperature and accumulated precipitation to identify the worst year per decade from a water resources perspective. One hot and dry year per decade, from 2011 to 2050, and per scenario was simulated and compared to the historic 2008 reference. The results indicate that hot and dry future extreme years will be exacerbated and the study area might be exposed to a significant decrease in annual precipitation (rain and snow), reaching up to 30% relative to the current extreme conditions.

  5. High-resolution numerical modeling of meteorological and hydrological conditions during May 2014 floods in Serbia

    Science.gov (United States)

    Vujadinovic, Mirjam; Vukovic, Ana; Cvetkovic, Bojan; Pejanovic, Goran; Nickovic, Slobodan; Djurdjevic, Vladimir; Rajkovic, Borivoj; Djordjevic, Marija

    2015-04-01

    In May 2014 west Balkan region was affected by catastrophic floods in Serbia, Bosnia and Herzegovina and eastern parts of Croatia. Observed precipitation amount were extremely high, on many stations largest ever recorded. In the period from 12th to 18th of May, most of Serbia received between 50 to 100 mm of rainfall, while western parts of the country, which were influenced the most, had over 200 mm of rainfall, locally even more than 300 mm. This very intense precipitation came when the soil was already saturated after a very wet period during the second half of April and beginning of May, when most of Serbia received between 120 i 170 mm of rainfall. New abundant precipitation on already saturated soil increased surface and underground water flow, caused floods, soil erosion and landslides. High water levels, most of them record breaking, were measured on the Sava, Drina, Dunav, Kolubara, Ljig, Ub, Toplica, Tamnava, Jadar, Zapadna Morava, Velika Morava, Mlava and Pek river. Overall, two cities and 17 municipals were severely affected by the floods, 32000 people were evacuated from their homes, while 51 died. Material damage to the infrastructure, energy power system, crops, livestock funds and houses is estimated to more than 2 billion euro. Although the operational numerical weather forecast gave in generally good precipitation prediction, flood forecasting in this case was mainly done through the expert judgment rather than relying on dynamic hydrological modeling. We applied an integrated atmospheric-hydrologic modelling system to some of the most impacted catchments in order to timely simulate hydrological response, and examine its potentials as a flood warning system. The system is based on the Non-hydrostatic Multiscale Model NMMB, which is a numerical weather prediction model that can be used on a broad range of spatial and temporal scales. Its non-hydrostatic module allows high horizontal resolution and resolving cloud systems as well as large

  6. Modeling actual evapotranspiration with routine meteorological variables in the data-scarce region of the Tibetan Plateau: Comparisons and implications

    Science.gov (United States)

    Ma, Ning; Zhang, Yinsheng; Xu, Chong-Yu; Szilagyi, Jozsef

    2015-08-01

    Quantitative estimation of actual evapotranspiration (ETa) by in situ measurements and mathematical modeling is a fundamental task for physical understanding of ETa as well as the feedback mechanisms between land and the ambient atmosphere. However, the ETa information in the Tibetan Plateau (TP) has been greatly impeded by the extremely sparse ground observation network in the region. Approaches for estimating ETa solely from routine meteorological variables are therefore important for investigating spatiotemporal variations of ETa in the data-scarce region of the TP. Motivated by this need, the complementary relationship (CR) and Penman-Monteith approaches were evaluated against in situ measurements of ETa on a daily basis in an alpine steppe region of the TP. The former includes the Nonlinear Complementary Relationship (Nonlinear-CR) as well as the Complementary Relationship Areal Evapotranspiration (CRAE) models, while the latter involves the Katerji-Perrier and the Todorovic models. Results indicate that the Nonlinear-CR, CRAE, and Katerji-Perrier models are all capable of efficiently simulating daily ETa, provided their parameter values were appropriately calibrated. The Katerji-Perrier model performed best since its site-specific parameters take the soil water status into account. The Nonlinear-CR model also performed well with the advantage of not requiring the user to choose between a symmetric and asymmetric CR. The CRAE model, even with a relatively low Nash-Sutcliffe efficiency (NSE) value, is also an acceptable approach in this data-scarce region as it does not need information of wind speed and ground surface conditions. In contrast, application of the Todorovic model was found to be inappropriate in the dry regions of the TP due to its significant overestimation of ETa as it neglects the effect of water stress on the bulk surface resistance. Sensitivity analysis of the parameter values demonstrated the relative importance of each parameter in the

  7. 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.

  8. 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.

  9. Sensitivity analysis of the meteorological model applied in the German risk study (DRS)

    International Nuclear Information System (INIS)

    Vogt, S.

    1982-01-01

    In the first part of this paper it will be shown how the influence of uncertainties in estimation on risk statements is determined using methods of the probability theory. In particular the parameters contained in the dispersion model are studied more thoroughly. In the second part, based on the knowledge gathered in the previous investigations, new and more realistic best estimate values will be proposed for four selected parameters to be used in future work. The modifications in the risk statements by these new parameter values will be commented upon

  10. 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)

  11. VTE Risk assessment - a prognostic Model: BATER Cohort Study of young women.

    Science.gov (United States)

    Heinemann, Lothar Aj; Dominh, Thai; Assmann, Anita; Schramm, Wolfgang; Schürmann, Rolf; Hilpert, Jan; Spannagl, Michael

    2005-04-18

    BACKGROUND: Community-based cohort studies are not available that evaluated the predictive power of both clinical and genetic risk factors for venous thromboembolism (VTE). There is, however, clinical need to forecast the likelihood of future occurrence of VTE, at least qualitatively, to support decisions about intensity of diagnostic or preventive measures. MATERIALS AND METHODS: A 10-year observation period of the Bavarian Thromboembolic Risk (BATER) study, a cohort study of 4337 women (18-55 years), was used to develop a predictive model of VTE based on clinical and genetic variables at baseline (1993). The objective was to prepare a probabilistic scheme that discriminates women with virtually no VTE risk from those at higher levels of absolute VTE risk in the foreseeable future. A multivariate analysis determined which variables at baseline were the best predictors of a future VTE event, provided a ranking according to the predictive power, and permitted to design a simple graphic scheme to assess the individual VTE risk using five predictor variables. RESULTS: Thirty-four new confirmed VTEs occurred during the observation period of over 32,000 women-years (WYs). A model was developed mainly based on clinical information (personal history of previous VTE and family history of VTE, age, BMI) and one composite genetic risk markers (combining Factor V Leiden and Prothrombin G20210A Mutation). Four levels of increasing VTE risk were arbitrarily defined to map the prevalence in the study population: No/low risk of VTE (61.3%), moderate risk (21.1%), high risk (6.0%), very high risk of future VTE (0.9%). In 10.6% of the population the risk assessment was not possible due to lacking VTE cases. The average incidence rates for VTE in these four levels were: 4.1, 12.3, 47.2, and 170.5 per 104 WYs for no, moderate, high, and very high risk, respectively. CONCLUSION: Our prognostic tool - containing clinical information (and if available also genetic data) - seems to be

  12. VTE Risk assessment – a prognostic Model: BATER Cohort Study of young women

    Directory of Open Access Journals (Sweden)

    Schürmann Rolf

    2005-04-01

    Full Text Available Abstract Background Community-based cohort studies are not available that evaluated the predictive power of both clinical and genetic risk factors for venous thromboembolism (VTE. There is, however, clinical need to forecast the likelihood of future occurrence of VTE, at least qualitatively, to support decisions about intensity of diagnostic or preventive measures. Materials and methods A 10-year observation period of the Bavarian Thromboembolic Risk (BATER study, a cohort study of 4337 women (18–55 years, was used to develop a predictive model of VTE based on clinical and genetic variables at baseline (1993. The objective was to prepare a probabilistic scheme that discriminates women with virtually no VTE risk from those at higher levels of absolute VTE risk in the foreseeable future. A multivariate analysis determined which variables at baseline were the best predictors of a future VTE event, provided a ranking according to the predictive power, and permitted to design a simple graphic scheme to assess the individual VTE risk using five predictor variables. Results Thirty-four new confirmed VTEs occurred during the observation period of over 32,000 women-years (WYs. A model was developed mainly based on clinical information (personal history of previous VTE and family history of VTE, age, BMI and one composite genetic risk markers (combining Factor V Leiden and Prothrombin G20210A Mutation. Four levels of increasing VTE risk were arbitrarily defined to map the prevalence in the study population: No/low risk of VTE (61.3%, moderate risk (21.1%, high risk (6.0%, very high risk of future VTE (0.9%. In 10.6% of the population the risk assessment was not possible due to lacking VTE cases. The average incidence rates for VTE in these four levels were: 4.1, 12.3, 47.2, and 170.5 per 104 WYs for no, moderate, high, and very high risk, respectively. Conclusion Our prognostic tool – containing clinical information (and if available also genetic data

  13. 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.

  14. 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.

  15. 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

  16. Model analysis of urbanization impacts on boundary layer meteorology under hot weather conditions: a case study of Nanjing, China

    Science.gov (United States)

    Chen, Lei; Zhang, Meigen; Wang, Yongwei

    2016-08-01

    The Weather Research and Forecasting (WRF) model, configured with a single-layer urban canopy model, was employed to investigate the influence of urbanization on boundary layer meteorological parameters during a long-lasting heat wave. This study was conducted over Nanjing city, East China, from 26 July to 4 August 2010. The impacts of urban expansion and anthropogenic heat (AH) release were simulated to quantify their effects on 2-m temperature, 2-m water vapor mixing ratio, and 10-m wind speed and heat stress index. Urban sprawl increased the daily 2-m temperature in urbanized areas by around 1.6 °C and decreased the urban diurnal temperature range (DTR) by 1.24 °C. The contribution of AH release to the atmospheric warming was nearly 22 %, but AH had little influence on the DTR. The urban regional mean surface wind speed decreased by about 0.4 m s-1, and this decrease was successfully simulated from the surface to 300 m. The influence of urbanization on 2-m water vapor mixing ratio was significant over highly urbanized areas with a decrease of 1.1-1.8 g kg-1. With increased urbanization ratio, the duration of the inversion layer was about 4 h shorter, and the lower atmospheric layer was less stable. Urban heat island (UHI) intensity was significantly enhanced when synthesizing both urban sprawl and AH release and the daily mean UHI intensity increased by 0.74 °C. Urbanization increased the time under extreme heat stress (about 40 %) and worsened the living environment in urban areas.

  17. A Bayesian analysis of sensible heat flux estimation: Quantifying uncertainty in meteorological forcing to improve model prediction

    KAUST Repository

    Ershadi, Ali

    2013-05-01

    The influence of uncertainty in land surface temperature, air temperature, and wind speed on the estimation of sensible heat flux is analyzed using a Bayesian inference technique applied to the Surface Energy Balance System (SEBS) model. The Bayesian approach allows for an explicit quantification of the uncertainties in input variables: a source of error generally ignored in surface heat flux estimation. An application using field measurements from the Soil Moisture Experiment 2002 is presented. The spatial variability of selected input meteorological variables in a multitower site is used to formulate the prior estimates for the sampling uncertainties, and the likelihood function is formulated assuming Gaussian errors in the SEBS model. Land surface temperature, air temperature, and wind speed were estimated by sampling their posterior distribution using a Markov chain Monte Carlo algorithm. Results verify that Bayesian-inferred air temperature and wind speed were generally consistent with those observed at the towers, suggesting that local observations of these variables were spatially representative. Uncertainties in the land surface temperature appear to have the strongest effect on the estimated sensible heat flux, with Bayesian-inferred values differing by up to ±5°C from the observed data. These differences suggest that the footprint of the in situ measured land surface temperature is not representative of the larger-scale variability. As such, these measurements should be used with caution in the calculation of surface heat fluxes and highlight the importance of capturing the spatial variability in the land surface temperature: particularly, for remote sensing retrieval algorithms that use this variable for flux estimation.

  18. 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.

  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. 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.

  1. 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

  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. Evaluating the improvements of the BOLAM meteorological model operational at ISPRA: A case study approach - preliminary results

    Science.gov (United States)

    Mariani, S.; Casaioli, M.; Lastoria, B.; Accadia, C.; Flavoni, S.

    2009-04-01

    The Institute for Environmental Protection and Research - ISPRA (former Agency for Environmental Protection and Technical Services - APAT) runs operationally since 2000 an integrated meteo-marine forecasting chain, named the Hydro-Meteo-Marine Forecasting System (Sistema Idro-Meteo-Mare - SIMM), formed by a cascade of four numerical models, telescoping from the Mediterranean basin to the Venice Lagoon, and initialized by means of analyses and forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF). The operational integrated system consists of a meteorological model, the parallel verision of BOlogna Limited Area Model (BOLAM), coupled over the Mediterranean sea with a WAve Model (WAM), a high-resolution shallow-water model of the Adriatic and Ionian Sea, namely the Princeton Ocean Model (POM), and a finite-element version of the same model (VL-FEM) on the Venice Lagoon, aimed to forecast the acqua alta events. Recently, the physically based, fully distributed, rainfall-runoff TOPographic Kinematic APproximation and Integration (TOPKAPI) model has been integrated into the system, coupled to BOLAM, over two river basins, located in the central and northeastern part of Italy, respectively. However, at the present time, this latter part of the forecasting chain is not operational and it is used in a research configuration. BOLAM was originally implemented in 2000 onto the Quadrics parallel supercomputer (and for this reason referred to as QBOLAM, as well) and only at the end of 2006 it was ported (together with the other operational marine models of the forecasting chain) onto the Silicon Graphics Inc. (SGI) Altix 8-processor machine. In particular, due to the Quadrics implementation, the Kuo scheme was formerly implemented into QBOLAM for the cumulus convection parameterization. On the contrary, when porting SIMM onto the Altix Linux cluster, it was achievable to implement into QBOLAM the more advanced convection parameterization by Kain and

  4. 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.

  5. A first appraisal of prognostic ocean DMS models and prospects for their use in climate models

    NARCIS (Netherlands)

    Le Clainche, Yvonnick; Vezina, Alain; Levasseur, Maurice; Cropp, Roger A.; Gunson, Jim R.; Vallina, Sergio M.; Vogt, Meike; Lancelot, Christiane; Allen, J. Icarus; Archer, Stephen D.; Bopp, Laurent; Deal, Clara; Elliott, Scott; Jin, Meibing; Malin, Gill; Schoemann, Veronique; Simo, Rafel; Six, Katharina D.; Stefels, Jacqueline

    2010-01-01

    Ocean dimethylsulfide (DMS) produced by marine biota is the largest natural source of atmospheric sulfur, playing a major role in the formation and evolution of aerosols, and consequently affecting climate. Several dynamic process-based DMS models have been developed over the last decade, and work

  6. 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

  7. 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.

  8. 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

  9. 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.

  10. 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

  11. A clinical-molecular prognostic model to predict survival in patients with post polycythemia vera and post essential thrombocythemia myelofibrosis.

    Science.gov (United States)

    Passamonti, F; Giorgino, T; Mora, B; Guglielmelli, P; Rumi, E; Maffioli, M; Rambaldi, A; Caramella, M; Komrokji, R; Gotlib, J; Kiladjian, J J; Cervantes, F; Devos, T; Palandri, F; De Stefano, V; Ruggeri, M; Silver, R T; Benevolo, G; Albano, F; Caramazza, D; Merli, M; Pietra, D; Casalone, R; Rotunno, G; Barbui, T; Cazzola, M; Vannucchi, A M

    2017-12-01

    Polycythemia vera (PV) and essential thrombocythemia (ET) are myeloproliferative neoplasms with variable risk of evolution into post-PV and post-ET myelofibrosis, from now on referred to as secondary myelofibrosis (SMF). No specific tools have been defined for risk stratification in SMF. To develop a prognostic model for predicting survival, we studied 685 JAK2, CALR, and MPL annotated patients with SMF. Median survival of the whole cohort was 9.3 years (95% CI: 8-not reached-NR-). Through penalized Cox regressions we identified negative predictors of survival and according to beta risk coefficients we assigned 2 points to hemoglobin level <11 g/dl, to circulating blasts ⩾3%, and to CALR-unmutated genotype, 1 point to platelet count <150 × 10 9 /l and to constitutional symptoms, and 0.15 points to any year of age. Myelofibrosis Secondary to PV and ET-Prognostic Model (MYSEC-PM) allocated SMF patients into four risk categories with different survival (P<0.0001): low (median survival NR; 133 patients), intermediate-1 (9.3 years, 95% CI: 8.1-NR; 245 patients), intermediate-2 (4.4 years, 95% CI: 3.2-7.9; 126 patients), and high risk (2 years, 95% CI: 1.7-3.9; 75 patients). Finally, we found that the MYSEC-PM represents the most appropriate tool for SMF decision-making to be used in clinical and trial settings.

  12. 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.

  13. Four-dimensional data assimilation as a method for coupling two meteorological model systems of different scales; Vierdimensionale Datenassimilation als Methode zur Kopplung zweier verschiedenskaliger meteorologischer Modellsysteme

    Energy Technology Data Exchange (ETDEWEB)

    Wilms-Grabe, W.

    2001-10-01

    A method of coupling two meteorological models of different scales must meet certain requirements: First, even a big difference in resolution between the two models must not produce disturbances in the smaller-scale model. This means that most nesting approaches are unfit for the purpose. The method presented here is not limited to meteorological models. It can be enhanced without much difficulty to dispersion models taking account of chemical atmospheric reactions, as e.g. in the CTM of the EURAD model or in DRAIS as an enhancement of the KAMM version. [German] Das Ziel dieser Arbeit bestand in der Entwicklung eines geeigneten Kopplungsverfahrens zweier verschiedenskaliger meteorologischer Modelle, das in einem Ein-Weg-Nesting dem kleinerskaligen Modell ermoeglicht, sowohl instationaere superskalige Verhaeltnisse als auch modellinterne kleinraeumige Prozesse miteinander zu verbinden. Im Rahmen des Projektes TFS mussten hierbei entscheidende Bedingungen an das Kopplungsverfahren gestellt werden. Allen voran darf vor allem ein auch grosser Unterschied in der Aufloesung der beiden verschiedenskaligen Modelle keine kuenstlichen Stoerungen im kleinerskaligen Modell produzieren. Somit scheiden die sonst ueblichen Nestingansaetze fuer diesen Fall aus. Das Verfahren ist derart konzipiert, dass es sich nicht auf rein meteorologische Modelle beschraenkt. Eine Ausweitung auf Ausbreitungsmodelle unter Beruecksichtigung chemischer Reaktionen in der Atmosphaere, wie sie etwa im CTM des EURAD-Modells oder im DRAIS als Erweiterung der KAMM-Version zur Verfuegung stehen, stellt keinen allzu grossen Schritt mehr dar. (orig.)

  14. Performance and customization of 4 prognostic models for postoperative onset of nausea and vomiting in ear, nose, and throat surgery.

    Science.gov (United States)

    Engel, Jörg M; Junger, Axel; Hartmann, Bernd; Little, Simon; Schnöbel, Rose; Mann, Valesco; Jost, Andreas; Welters, Ingeborg D; Hempelmann, Gunter

    2006-06-01

    To evaluate the performance of 4 published prognostic models for postoperative onset of nausea and vomiting (PONV) by means of discrimination and calibration and the possible impact of customization on these models. Prospective, observational study. Tertiary care university hospital. 748 adult patients (>18 years old) enrolled in this study. Severe obesity (weight > 150 kg or body mass index > 40 kg/m) was an exclusion criterion. All perioperative data were recorded with an anesthesia information management system. A standardized patient interview was performed on the postoperative morning and afternoon. Individual PONV risk was calculated using 4 original regression equations by Koivuranta et al, Apfel et al, Sinclair et al, and Junger et al Discrimination was assessed using receiver operating characteristic (ROC) curves. Calibration was tested using Hosmer-Lemeshow goodness-of-fit statistics. New predictive equations for the 4 models were derived by means of logistic regression (customization). The prognostic performance of the customized models was validated using the "leaving-one-out" technique. Postoperative onset of nausea and vomiting was observed in 11.2% of the specialized patient population. Discrimination could be demonstrated as shown by areas under the receiver operating characteristic curve of 0.62 for the Koivuranta et al model, 0.63 for the Apfel et al model, 0.70 for the Sinclair et al model, and 0.70 for the Junger et al model. Calibration was poor for all 4 original models, indicated by a P value lower than 0.01 in the C and H statistics. Customization improved the accuracy of the prediction for all 4 models. However, the simplified risk scores of the Koivuranta et al model and the Apfel et al model did not show the same efficiency as those of the Sinclair et al model and the Junger et al model. This is possibly a result of having relatively few patients at high risk for PONV in combination with an information loss caused by too few dichotomous

  15. 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

  16. A proof-of-concept for linking the global meteorological model, MPAS-A with the air quality model, CMAQ

    Science.gov (United States)

    Researchers who perform air quality modeling studies usually do so on a regional scale. Typically, the boundary conditions are generated by another model which might have a different chemical mechanism, spatial resolution, and/or map projection. Hence, a necessary conversion/inte...

  17. 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.

  18. 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

  19. 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

  20. 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.

  1. 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...

  2. 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

  3. High resolution modelling results of the wind flow over Canary Islands during the meteorological situation of the extratropical storm Delta (28–30 November 2005

    Directory of Open Access Journals (Sweden)

    J. M. Baldasano

    2008-05-01

    Full Text Available On 28–29 November 2005 an extratropical storm affected the Canary Islands causing significant damage related to high average wind speeds and intense gusts over some islands of the archipelago. Delta was the twenty-sixth tropical or subtropical storm of the 2005 Atlantic hurricane season. It represents an unusual meteorological phenomenon for that region, and its impacts were underestimated by the different operational meteorological forecasts during the previous days of the arrival of the low near Canary Islands. The aim of this study is to reproduce the local effects of the flow that were observed over the Canary Islands during the travel of the Delta storm near the region using high-resolution mesoscale meteorological simulations. The Advanced Research Weather Research & Forecasting Model (WRF-ARW is applied at 9, 3 and 1 km horizontal resolution using ECMWF forecasts as initial and boundary conditions. The high-resolution simulation will outline the main features that contributed to the high wind speeds observed in the archipelago. Variations in vertical static stability, vertical windshear and the intense synoptic winds of the southwestern part of Delta with a warm core at 850 hPa were the main characteristics that contributed to the development and amplification of intense gravity waves while the large-scale flow interacted with the complex topography of the islands.

  4. Studying the effect of meteorological factors on the SO2 and PM10 pollution levels with refined versions of the SARIMA model

    Energy Technology Data Exchange (ETDEWEB)

    Voynikova, D. S., E-mail: desi-sl2000@yahoo.com; Gocheva-Ilieva, S. G., E-mail: snegocheva@yahoo.com; Ivanov, A. V., E-mail: aivanov-99@yahoo.com [Department of Applied Mathematics and Modeling, Faculty of Mathematics and Informatics, Paisii Hilendarski University of Plovdiv, 24 Tzar Assen str., 4000 Plovdiv (Bulgaria); Iliev, I. P., E-mail: iliev55@abv.bg [Department of Physics, Technical University – Plovdiv, 25 Tzanko Djusstabanov str., 4000 Plovdiv (Bulgaria)

    2015-10-28

    Numerous time series methods are used in environmental sciences allowing the detailed investigation of air pollution processes. The goal of this study is to present the empirical analysis of various aspects of stochastic modeling and in particular the ARIMA/SARIMA methods. The subject of investigation is air pollution in the town of Kardzhali, Bulgaria with 2 problematic pollutants – sulfur dioxide (SO2) and particulate matter (PM10). Various SARIMA Transfer Function models are built taking into account meteorological factors, data transformations and the use of different horizons selected to predict future levels of concentrations of the pollutants.

  5. A prognostic model for temporal courses that combines temporal abstraction and case-based reasoning.

    Science.gov (United States)

    Schmidt, Rainer; Gierl, Lothar

    2005-03-01

    Since clinical management of patients and clinical research are essentially time-oriented endeavours, reasoning about time has become a hot topic in medical informatics. Here we present a method for prognosis of temporal courses, which combines temporal abstractions with case-based reasoning. It is useful for application domains where neither well-known standards, nor known periodicity, nor a complete domain theory exist. We have used our method in two prognostic applications. The first one deals with prognosis of the kidney function for intensive care patients. The idea is to elicit impairments on time, especially to warn against threatening kidney failures. Our second application deals with a completely different domain, namely geographical medicine. Its intention is to compute early warnings against approaching infectious diseases, which are characterised by irregular cyclic occurrences. So far, we have applied our program on influenza and bronchitis. In this paper, we focus on influenza forecast and show first experimental results.

  6. Integration of prognostic aerosol-cloud interactions in a chemistry transport model coupled offline to a regional climate model

    Science.gov (United States)

    Thomas, M. A.; Kahnert, M.; Andersson, C.; Kokkola, H.; Hansson, U.; Jones, C.; Langner, J.; Devasthale, A.

    2015-06-01

    To reduce uncertainties and hence to obtain a better estimate of aerosol (direct and indirect) radiative forcing, next generation climate models aim for a tighter coupling between chemistry transport models and regional climate models and a better representation of aerosol-cloud interactions. In this study, this coupling is done by first forcing the Rossby Center regional climate model (RCA4) with ERA-Interim lateral boundaries and sea surface temperature (SST) using the standard cloud droplet number concentration (CDNC) formulation (hereafter, referred to as the "stand-alone RCA4 version" or "CTRL" simulation). In the stand-alone RCA4 version, CDNCs are constants distinguishing only between land and ocean surface. The meteorology from this simulation is then used to drive the chemistry transport model, Multiple-scale Atmospheric Transport and Chemistry (MATCH), which is coupled online with the aerosol dynamics model, Sectional Aerosol module for Large Scale Applications (SALSA). CDNC fields obtained from MATCH-SALSA are then fed back into a new RCA4 simulation. In this new simulation (referred to as "MOD" simulation), all parameters remain the same as in the first run except for the CDNCs provided by MATCH-SALSA. Simulations are carried out with this model setup for the period 2005-2012 over Europe, and the differences in cloud microphysical properties and radiative fluxes as a result of local CDNC changes and possible model responses are analysed. Our study shows substantial improvements in cloud microphysical properties with the input of the MATCH-SALSA derived 3-D CDNCs compared to the stand-alone RCA4 version. This model setup improves the spatial, seasonal and vertical distribution of CDNCs with a higher concentration observed over central Europe during boreal summer (JJA) and over eastern Europe and Russia during winter (DJF). Realistic cloud droplet radii (CD radii) values have been simulated with the maxima reaching 13 μm, whereas in the stand

  7. 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...

  8. 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.

  9. 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...

  10. A probabilistic physics-of-failure model for prognostic health management of structures subject to pitting and corrosion-fatigue

    International Nuclear Information System (INIS)

    Chookah, M.; Nuhi, M.; Modarres, M.

    2011-01-01

    A combined probabilistic physics-of-failure-based model for pitting and corrosion-fatigue degradation mechanisms is proposed to estimate the reliability of structures and to perform prognosis and health management. A mechanistic superposition model for corrosion-fatigue mechanism was used as a benchmark model to propose the simple model. The proposed model describes the degradation of the structures as a function of physical and critical environmental stresses, such as amplitude and frequency of mechanical loads (for example caused by the internal piping pressure) and the concentration of corrosive chemical agents. The parameters of the proposed model are represented by the probability density functions and estimated through a Bayesian approach based on the data taken from the experiments performed as part of this research. For demonstrating applications, the proposed model provides prognostic information about the reliability of aging of structures and is helpful in developing inspection and replacement strategies. - Highlights: ► We model an inventory system under static–dynamic uncertainty strategy. ► The demand is stochastic and non-stationary. ► The optimal ordering policy is proven to be a base stock policy. ► A solution algorithm for finding an optimal solution is provided. ► Two heuristics developed produce high quality solutions and scale-up efficiently.

  11. A possible model of growth and production of durum wheat (Triticum durum Desf.) in relation to meteorological parameters in Mediterranean environment

    International Nuclear Information System (INIS)

    Tuttobene, R.; Cosentino, S.; Cavallaro, V.

    1993-01-01

    This research aimed at studying a simulation model to describe the growth and yield of durum wheat in relation to meterological parameters in Mediterranean environment, using data of trials carried out by the Institute of Agronomy and filed crops of the University of Catania in two years and three localities of the Eastern Sicily. The model is divided into four modules which determine separately main phenological phases, leaf area index, total biomass and yield and soil water balance. The model uses air maximum and minimum temperature, photoperiod, solar radiation, rainfall and evaporation from a class A pan. Simulation carried out on the data of the trials showed a good interpretation of the variability of the actual data in relation to meteorological parameters. The model could simulate the variation due to plant density and sowing date. The model should be verified using independent data [it

  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. 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.

  14. 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.

  15. Legionnaires' disease from a cooling tower in a community outbreak in Lidköping, Sweden- epidemiological, environmental and microbiological investigation supported by meteorological modelling.

    Science.gov (United States)

    Ulleryd, Peter; Hugosson, Anna; Allestam, Görel; Bernander, Sverker; Claesson, Berndt E B; Eilertz, Ingrid; Hagaeus, Anne-Christine; Hjorth, Martin; Johansson, Agneta; de Jong, Birgitta; Lindqvist, Anna; Nolskog, Peter; Svensson, Nils

    2012-11-21

    An outbreak of Legionnaires' Disease took place in the Swedish town Lidköping on Lake Vänern in August 2004 and the number of pneumonia cases at the local hospital increased markedly. As soon as the first patients were diagnosed, health care providers were informed and an outbreak investigation was launched. Classical epidemiological investigation, diagnostic tests, environmental analyses, epidemiological typing and meteorological methods. Thirty-two cases were found. The median age was 62 years (range 36 - 88) and 22 (69%) were males. No common indoor exposure was found. Legionella pneumophila serogroup 1 was found at two industries, each with two cooling towers. In one cooling tower exceptionally high concentrations, 1.2 × 109 cfu/L, were found. Smaller amounts were also found in the other tower of the first industry and in one tower of the second plant. Sero- and genotyping of isolated L. pneumophila serogroup 1 from three patients and epidemiologically suspected environmental strains supported the cooling tower with the high concentration as the source. In all, two L. pneumophila strains were isolated from three culture confirmed cases and both these strains were detected in the cooling tower, but one strain in another cooling tower as well. Meteorological modelling demonstrated probable spread from the most suspected cooling tower towards the town centre and the precise location of four cases that were stray visitors to Lidköping. Classical epidemiological, environmental and microbiological investigation of an LD outbreak can be supported by meteorological modelling methods.The broad competence and cooperation capabilities in the investigation team from different authorities were of paramount importance in stopping this outbreak.

  16. Legionnaires’ disease from a cooling tower in a community outbreak in Lidköping, Sweden- epidemiological, environmental and microbiological investigation supported by meteorological modelling

    Science.gov (United States)

    2012-01-01

    Background An outbreak of Legionnaires’ Disease took place in the Swedish town Lidköping on Lake Vänern in August 2004 and the number of pneumonia cases at the local hospital increased markedly. As soon as the first patients were diagnosed, health care providers were informed and an outbreak investigation was launched. Methods Classical epidemiological investigation, diagnostic tests, environmental analyses, epidemiological typing and meteorological methods. Results Thirty-two cases were found. The median age was 62 years (range 36 – 88) and 22 (69%) were males. No common indoor exposure was found. Legionella pneumophila serogroup 1 was found at two industries, each with two cooling towers. In one cooling tower exceptionally high concentrations, 1.2 × 109 cfu/L, were found. Smaller amounts were also found in the other tower of the first industry and in one tower of the second plant. Sero- and genotyping of isolated L. pneumophila serogroup 1 from three patients and epidemiologically suspected environmental strains supported the cooling tower with the high concentration as the source. In all, two L. pneumophila strains were isolated from three culture confirmed cases and both these strains were detected in the cooling tower, but one strain in another cooling tower as well. Meteorological modelling demonstrated probable spread from the most suspected cooling tower towards the town centre and the precise location of four cases that were stray visitors to Lidköping. Conclusions Classical epidemiological, environmental and microbiological investigation of an LD outbreak can be supported by meteorological modelling methods. The broad competence and cooperation capabilities in the investigation team from different authorities were of paramount importance in stopping this outbreak. PMID:23171054

  17. Legionnaires’ disease from a cooling tower in a community outbreak in Lidköping, Sweden- epidemiological, environmental and microbiological investigation supported by meteorological modelling

    Directory of Open Access Journals (Sweden)

    Ulleryd Peter

    2012-11-01

    Full Text Available Abstract Background An outbreak of Legionnaires’ Disease took place in the Swedish town Lidköping on Lake Vänern in August 2004 and the number of pneumonia cases at the local hospital increased markedly. As soon as the first patients were diagnosed, health care providers were informed and an outbreak investigation was launched. Methods Classical epidemiological investigation, diagnostic tests, environmental analyses, epidemiological typing and meteorological methods. Results Thirty-two cases were found. The median age was 62 years (range 36 – 88 and 22 (69% were males. No common indoor exposure was found. Legionella pneumophila serogroup 1 was found at two industries, each with two cooling towers. In one cooling tower exceptionally high concentrations, 1.2 × 109 cfu/L, were found. Smaller amounts were also found in the other tower of the first industry and in one tower of the second plant. Sero- and genotyping of isolated L. pneumophila serogroup 1 from three patients and epidemiologically suspected environmental strains supported the cooling tower with the high concentration as the source. In all, two L. pneumophila strains were isolated from three culture confirmed cases and both these strains were detected in the cooling tower, but one strain in another cooling tower as well. Meteorological modelling demonstrated probable spread from the most suspected cooling tower towards the town centre and the precise location of four cases that were stray visitors to Lidköping. Conclusions Classical epidemiological, environmental and microbiological investigation of an LD outbreak can be supported by meteorological modelling methods. The broad competence and cooperation capabilities in the investigation team from different authorities were of paramount importance in stopping this outbreak.

  18. Sensitivity of the Community Multiscale Air Quality (CMAQ model v4.7 results for the eastern United States to MM5 and WRF meteorological drivers

    Directory of Open Access Journals (Sweden)

    K. W. Appel

    2010-02-01

    Full Text Available This paper presents a comparison of the operational performances of two Community Multiscale Air Quality (CMAQ model v4.7 simulations that utilize input data from the 5th-generation Mesoscale Model (MM5 and the Weather Research and Forecasting (WRF meteorological models. Two sets of CMAQ model simulations were performed for January and August 2006. One set utilized MM5 meteorology (MM5-CMAQ and the other utilized WRF meteorology (WRF-CMAQ, while all other model inputs and options were kept the same. For January, predicted ozone (O3 mixing ratios were higher in the Southeast and lower Mid-west regions in the WRF-CMAQ simulation, resulting in slightly higher bias and error as compared to the MM5-CMAQ simulations. The higher predicted O3 mixing ratios are attributed to less dry deposition of O3 in the WRF-CMAQ simulation due to differences in the calculation of the vegetation fraction between the MM5 and WRF models. The WRF-CMAQ results showed better performance for particulate sulfate (SO42−, similar performance for nitrate (NO3, and slightly worse performance for nitric acid (HNO3, total carbon (TC and total fine particulate (PM2.5 mass than the corresponding MM5-CMAQ results. For August, predictions of O3 were notably higher in the WRF-CMAQ simulation, particularly in the southern United States, resulting in increased model bias. Concentrations of predicted particulate SO42− were lower in the region surrounding the Ohio Valley and higher along the Gulf of Mexico in the WRF-CMAQ simulation, contributing to poorer model performance. The primary causes of the differences in the MM5-CMAQ and WRF-CMAQ simulations appear to be due to differences in the calculation of wind speed, planetary boundary layer height, cloud cover and the friction velocity (u in the MM5 and WRF model simulations, while

  19. Can the combined use of an ensemble based modelling approach and the analysis of measured meteorological trends lead to increased confidence in climate change impact assessments?

    Science.gov (United States)

    Gädeke, Anne; Koch, Hagen; Pohle, Ina; Grünewald, Uwe

    2014-05-01

    In anthropogenically heavily impacted river catchments, such as the Lusatian river catchments of Spree and Schwarze Elster (Germany), the robust assessment of possible impacts of climate change on the regional water resources is of high relevance for the development and implementation of suitable climate change adaptation strategies. Large uncertainties inherent in future climate projections may, however, reduce the willingness of regional stakeholder to develop and implement suitable adaptation strategies to climate change. This study provides an overview of different possibilities to consider uncertainties in climate change impact assessments by means of (1) an ensemble based modelling approach and (2) the incorporation of measured and simulated meteorological trends. The ensemble based modelling approach consists of the meteorological output of four climate downscaling approaches (DAs) (two dynamical and two statistical DAs (113 realisations in total)), which drive different model configurations of two conceptually different hydrological models (HBV-light and WaSiM-ETH). As study area serve three near natural subcatchments of the Spree and Schwarze Elster river catchments. The objective of incorporating measured meteorological trends into the analysis was twofold: measured trends can (i) serve as a mean to validate the results of the DAs and (ii) be regarded as harbinger for the future direction of change. Moreover, regional stakeholders seem to have more trust in measurements than in modelling results. In order to evaluate the nature of the trends, both gradual (Mann-Kendall test) and step changes (Pettitt test) are considered as well as both temporal and spatial correlations in the data. The results of the ensemble based modelling chain show that depending on the type (dynamical or statistical) of DA used, opposing trends in precipitation, actual evapotranspiration and discharge are simulated in the scenario period (2031-2060). While the statistical DAs

  20. Modelling the water and heat balances of the Mediterranean Sea using a two-basin model and available meteorological, hydrological, and ocean data

    Directory of Open Access Journals (Sweden)

    Mohamed Shaltout

    2015-04-01

    Full Text Available This paper presents a two-basin model of the water and heat balances of the Western and Eastern Mediterranean sub-basins (WMB and EMB, respectively over the 1958–2010 period using available meteorological and hydrological data. The results indicate that the simulated temperature and salinity in both studied Mediterranean sub-basins closely follow the reanalysed data. In addition, simulated surface water in the EMB had a higher mean temperature (by approximately 1.6°C and was more saline (by approximately 0.87 g kg−1 than in the WMB over the studied period. The net evaporation over the EMB (1.52 mm day−1 was approximately 1.7 times greater than over the WMB (0.88 mm day−1. The water balance of the Mediterranean Sea was controlled by net inflow through the Gibraltar Strait and Sicily Channel, the net evaporation rate and freshwater input. The heat balance simulations indicated that the heat loss from the water body was nearly balanced by the solar radiation to the water body, resulting in a net export (import of approximately 13 (11 W m−2 of heat from the WMB (to the EMB.

  1. 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...

  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. 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.

  4. Model for breast cancer survival: relative prognostic roles of axillary nodal status, TNM stage, estrogen receptor concentration, and tumor necrosis.

    Science.gov (United States)

    Shek, L L; Godolphin, W

    1988-10-01

    The independent prognostic effects of certain clinical and pathological variables measured at the time of primary diagnosis were assessed with Cox multivariate regression analysis. The 859 patients with primary breast cancer, on which the proportional hazards model was based, had a median follow-up of 60 months. Axillary nodal status (categorized as N0, N1-3 or N4+) was the most significant and independent factor in overall survival, but inclusion of TNM stage, estrogen receptor (ER) concentration and tumor necrosis significantly improved survival predictions. Predictions made with the model showed striking subset survival differences within stage: 5-year survival from 36% (N4+, loge[ER] = 0, marked necrosis) to 96% (N0, loge[ER] = 6, no necrosis) in TNM I, and from 0 to 70% for the same categories in TNM IV. Results of the model were used to classify patients into four distinct risk groups according to a derived hazard index. An 8-fold variation in survival was seen with the highest (greater than 3) to lowest index values (less than 1). Each hazard index level included patients with varied combinations of the above factors, but could be considered to denote the same degree of risk of breast cancer mortality. A model with ER concentration, nodal status, and tumor necrosis was found to best predict survival after disease recurrence in 369 patients, thus confirming the enduring biological significance of these factors.

  5. 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

  6. 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.

  7. Mucins as diagnostic and prognostic biomarkers in a fish-parasite model: transcriptional and functional analysis.

    Directory of Open Access Journals (Sweden)

    Jaume Pérez-Sánchez

    Full Text Available Mucins are O-glycosylated glycoproteins present on the apex of all wet-surfaced epithelia with a well-defined expression pattern, which is disrupted in response to a wide range of injuries or challenges. The aim of this study was to identify mucin gene sequences of gilthead sea bream (GSB, to determine its pattern of distribution in fish tissues and to analyse their transcriptional regulation by dietary and pathogenic factors. Exhaustive search of fish mucins was done in GSB after de novo assembly of next-generation sequencing data hosted in the IATS transcriptome database (www.nutrigroup-iats.org/seabreamdb. Six sequences, three categorized as putative membrane-bound mucins and three putative secreted-gel forming mucins, were identified. The transcriptional tissue screening revealed that Muc18 was the predominant mucin in skin, gills and stomach of GSB. In contrast, Muc19 was mostly found in the oesophagus and Muc13 was along the entire intestinal tract, although the posterior intestine exhibited a differential pattern with a high expression of an isoform that does not share a clear orthologous in mammals. This mucin was annotated as intestinal mucin (I-Muc. Its RNA expression was highly regulated by the nutritional background, whereas the other mucins, including Muc2 and Muc2-like, were expressed more constitutively and did not respond to high replacement of fish oil (FO by vegetable oils (VO in plant protein-based diets. After challenge with the intestinal parasite Enteromyxum leei, the expression of a number of mucins was decreased mainly in the posterior intestine of infected fish. But, interestingly, the highest down-regulation was observed for the I-Muc. Overall, the magnitude of the changes reflected the intensity and progression of the infection, making mucins and I-Muc, in particular, reliable markers of prognostic and diagnostic value of fish intestinal health.

  8. 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...

  9. A low-order coupled chemistry meteorology model for testing online and offline data assimilation schemes: L95-GRS (v1.0)

    Science.gov (United States)

    Haussaire, J.-M.; Bocquet, M.

    2016-01-01

    Bocquet and Sakov (2013) introduced a low-order model based on the coupling of the chaotic Lorenz-95 (L95) 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 (generic reaction set) 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 into 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 estimate can be quantitatively assessed. 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.

  10. Assessing Wheat Frost Risk with the Support of GIS: An Approach Coupling a Growing Season Meteorological Index and a Hybrid Fuzzy Neural Network Model

    Directory of Open Access Journals (Sweden)

    Yaojie Yue

    2016-12-01

    Full Text Available Crop frost, one kind of agro-meteorological disaster, often causes significant loss to agriculture. Thus, evaluating the risk of wheat frost aids scientific response to such disasters, which will ultimately promote food security. Therefore, this paper aims to propose an integrated risk assessment model of wheat frost, based on meteorological data and a hybrid fuzzy neural network model, taking China as an example. With the support of a geographic information system (GIS, a comprehensive method was put forward. Firstly, threshold temperatures of wheat frost at three growth stages were proposed, referring to phenology in different wheat growing areas and the meteorological standard of Degree of Crop Frost Damage (QX/T 88-2008. Secondly, a vulnerability curve illustrating the relationship between frost hazard intensity and wheat yield loss was worked out using hybrid fuzzy neural network model. Finally, the wheat frost risk was assessed in China. Results show that our proposed threshold temperatures are more suitable than using 0 °C in revealing the spatial pattern of frost occurrence, and hybrid fuzzy neural network model can further improve the accuracy of the vulnerability curve of wheat subject to frost with limited historical hazard records. Both these advantages ensure the precision of wheat frost risk assessment. In China, frost widely distributes in 85.00% of the total winter wheat planting area, but mainly to the north of 35°N; the southern boundary of wheat frost has moved northward, potentially because of the warming climate. There is a significant trend that suggests high risk areas will enlarge and gradually expand to the south, with the risk levels increasing from a return period of 2 years to 20 years. Among all wheat frost risk levels, the regions with loss rate ranges from 35.00% to 45.00% account for the largest area proportion, ranging from 58.60% to 63.27%. We argue that for wheat and other frost-affected crops, it is

  11. 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

  12. 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

  13. Presentation of the EURODELTA III intercomparison exercise – evaluation of the chemistry transport models' performance on criteria pollutants and joint analysis with meteorology

    Directory of Open Access Journals (Sweden)

    B. Bessagnet

    2016-10-01

    Full Text Available 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

  14. 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

  15. Planned Burn-Piedmont. A local operational numerical meteorological model for tracking smoke on the ground at night: Model development and sensitivity tests

    Science.gov (United States)

    Gary L. Achtemeier

    2005-01-01

    Smoke from both prescribed fires and wildfires can, under certain meteorological conditions, become entrapped within shallow layers of air near the ground at night and get carried to unexpected destinations as a combination of weather systems push air through interlocking ridge-valley terrain typical of the Piedmont of the Soutthern United States. Entrapped smoke...

  16. A state-space-based prognostics model for lithium-ion battery degradation

    International Nuclear Information System (INIS)

    Xu, Xin; Chen, Nan

    2017-01-01

    This paper proposes to analyze the degradation of lithium-ion batteries with the sequentially observed discharging profiles. A general state-space model is developed in which the observation model is used to approximate the discharging profile of each cycle, the corresponding parameter vector is treated as the hidden state, and the state-transition model is used to track the evolution of the parameter vector as the battery ages. The EM and EKF algorithms are adopted to estimate and update the model parameters and states jointly. Based on this model, we construct prediction on the end of discharge times for unobserved cycles and the remaining useful cycles before the battery failure. The effectiveness of the proposed model is demonstrated using a real lithium-ion battery degradation data set. - Highlights: • Unifying model for Li-Ion battery SOC and SOH estimation. • Extended Kalman filter based efficient inference algorithm. • Using voltage curves in discharging to have wide validity.

  17. 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

  18. Using Enthalpy as a Prognostic Variable in Atmospheric Modelling with Variable Composition

    Science.gov (United States)

    2016-04-14

    Sela, personal communication, 2005). These terms are also routinely neglected in models. In models with a limited number of gaseous tracers, such as...so-called energy- exchange term (second term on the left- hand side) in Equation (5). The finite-difference schemes in existing atmospheric models have...equation for the sum of enthalpy and kinetic energy of horizontal motion is solved. This eliminates the energy- exchange term and automatically

  19. 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.

  20. 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

  1. Implementation of a micro-physical scheme for warm clouds in the meteorological model 'MERCURE': Application to cooling tower plumes and to orographic precipitation

    International Nuclear Information System (INIS)

    Bouzereau, Emmanuel

    2004-01-01

    A two-moment semi-spectral warm micro-physical scheme has been implemented inside the meteorological model 'MERCURE'. A new formulation of the buoyancy flux () is proposed, which is coherent with the corrigendum of Mellor (1977) but differs from Bougeault (1981). The non-precipitating cloud microphysics is validated by comparing the numerical simulations of fifteen cases of cooling tower plumes with data from a measurement campaign in Bugey in 1980. Satisfactory results are obtained on the plumes shape, on the temperature and vertical velocity fields and on the droplets spectrums, although the liquid water contents tend to be overestimated. The precipitating cloud microphysics is tested by reproducing the academical cases of orographic precipitation of Chaumerliac et al. (1987) and Richard and Chaumerliac (1989). The simulations allow a check of the action of different micro-physical terms. (author) [fr

  2. 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.)

  3. Suspended Sediment Dynamics in the Macrotidal Seine Estuary (France): 2. Numerical Modeling of Sediment Fluxes and Budgets Under Typical Hydrological and Meteorological Conditions

    Science.gov (United States)

    Schulz, E.; Grasso, F.; Le Hir, P.; Verney, R.; Thouvenin, B.

    2018-01-01

    Understanding the sediment dynamics in an estuary is important for its morphodynamic and ecological assessment as well as, in case of an anthropogenically controlled system, for its maintenance. However, the quantification of sediment fluxes and budgets is extremely difficult from in-situ data and requires thoroughly validated numerical models. In the study presented here, sediment fluxes and budgets in the lower Seine Estuary were quantified and investigated from seasonal to annual time scales with respect to realistic hydro- and meteorological conditions. A realistic three-dimensional process-based hydro- and sediment-dynamic model was used to quantify mud and sand fluxes through characteristic estuarine cross-sections. In addition to a reference experiment with typical forcing, three experiments were carried out and analyzed, each differing from the reference experiment in either river discharge or wind and waves so that the effects of these forcings could be separated. Hydro- and meteorological conditions affect the sediment fluxes and budgets in different ways and at different locations. Single storm events induce strong erosion in the lower estuary and can have a significant effect on the sediment fluxes offshore of the Seine Estuary mouth, with the flux direction depending on the wind direction. Spring tides cause significant up-estuary fluxes at the mouth. A high river discharge drives barotropic down-estuary fluxes at the upper cross-sections, but baroclinic up-estuary fluxes at the mouth and offshore so that the lower estuary gains sediment during wet years. This behavior is likely to be observed worldwide in estuaries affected by density gradients and turbidity maximum dynamics.

  4. 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

  5. Model Updating and Uncertainty Management for Aircraft Prognostic Systems, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — This proposal addresses the integration of physics-based damage propagation models with diagnostic measures of current state of health in a mathematically rigorous...

  6. Comparison of prognostic models to predict the occurrence of colorectal cancer in asymptomatic individuals

    DEFF Research Database (Denmark)

    Smith, Todd; Muller, David C; Moons, Karel G M

    2018-01-01

    in the European Prospective Investigation into Cancer and Nutrition (EPIC) and the UK Biobank. The performance of the models to predict the occurrence of colorectal cancer within 5 or 10 years after study enrolment was assessed by discrimination (C-statistic) and calibration (plots of observed vs predicted......-based colorectal screening programmes. Future work should both evaluate this potential, through modelling and impact studies, and ascertain if further enhancement in their performance can be obtained....

  7. Verification of two simple meteorological models at large travel distances using ten-hour 85Kr measurements

    International Nuclear Information System (INIS)

    Pendergast, M.M.

    1978-01-01

    This report evaluates the wind-rose model and a trajectory model for calculating air concentrations averaged over 10 hours at distances greater than 100 km. Ten-hour averaged 85 Kr air concentrations from 13 sites located withing 143 km of the Savannah River Plant (SRP) were used to evaluate two simple models: a wind-rose model, and a trajectory model. The wind-rose model has previously been shown to estimate annual average concentrations to within a factor of four for travel distances of about 100 km. When 85 Kr concentrations were observed (80 cases), the wind-rose model predicted above background for only 36 percent of the cases, while the trajectory model predicted above background for 56 percent of the cases. The wind-rose model tended to overpredict air concentration by a factor of 4 while the trajectory model tended to underpredict by a factor of 3 for those occasions where observations and calculations were above background

  8. Impact of 2000–2050 climate change on fine particulate matter (PM2.5 air quality inferred from a multi-model analysis of meteorological modes

    Directory of Open Access Journals (Sweden)

    D. J. Jacob

    2012-12-01

    Full Text Available Studies of the effect of climate change on fine particulate matter (PM2.5 air quality using general circulation models (GCMs show inconsistent results including in the sign of the effect. This reflects uncertainty in the GCM simulations of the regional meteorological variables affecting PM2.5. Here we use the CMIP3 archive of data from fifteen different IPCC AR4 GCMs to obtain improved statistics of 21st-century trends in the meteorological modes driving PM2.5 variability over the contiguous US. We analyze 1999–2010 observations to identify the dominant meteorological modes driving interannual PM2.5 variability and their synoptic periods T. We find robust correlations (r > 0.5 of annual mean PM2.5 with T, especially in the eastern US where the dominant modes represent frontal passages. The GCMs all have significant skill in reproducing present-day statistics for T and we show that this reflects their ability to simulate atmospheric baroclinicity. We then use the local PM2.5-to-period sensitivity (dPM2.5/dT from the 1999–2010 observations to project PM2.5 changes from the 2000–2050 changes in T simulated by the 15 GCMs following the SRES A1B greenhouse warming scenario. By weighted-average statistics of GCM results we project a likely 2000–2050 increase of ~ 0.1 μg m−3 in annual mean PM2.5 in the eastern US arising from less frequent frontal ventilation, and a likely decrease albeit with greater inter-GCM variability in the Pacific Northwest due to more frequent maritime inflows. Potentially larger regional effects of 2000–2050 climate change on PM2.5 may arise from changes in temperature, biogenic emissions, wildfires, and vegetation, but are still unlikely to affect annual PM2.5 by more than 0.5 μg m−3.

  9. Global O3-CO correlations in a chemistry and transport model during July-August: evaluation with TES satellite observations and sensitivity to input meteorological data and emissions

    Science.gov (United States)

    Choi, Hyun-Deok; Liu, Hongyu; Crawford, James H.; Considine, David B.; Allen, Dale J.; Duncan, Bryan N.; Horowitz, Larry W.; Rodriguez, Jose M.; Strahan, Susan E.; Zhang, Lin; Liu, Xiong; Damon, Megan R.; Steenrod, Stephen D.

    2017-07-01

    We examine the capability of the Global Modeling Initiative (GMI) chemistry and transport model to reproduce global mid-tropospheric (618 hPa) ozone-carbon monoxide (O3-CO) correlations determined by the measurements from the Tropospheric Emission Spectrometer (TES) aboard NASA's Aura satellite during boreal summer (July-August). The model is driven by three meteorological data sets (finite-volume General Circulation Model (fvGCM) with sea surface temperature for 1995, Goddard Earth Observing System Data Assimilation System Version 4 (GEOS-4 DAS) for 2005, and Modern-Era Retrospective Analysis for Research and Applications (MERRA) for 2005), allowing us to examine the sensitivity of model O3-CO correlations to input meteorological data. Model simulations of radionuclide tracers (222Rn, 210Pb, and 7Be) are used to illustrate the differences in transport-related processes among the meteorological data sets. Simulated O3 values are evaluated with climatological profiles from ozonesonde measurements and satellite tropospheric O3 columns. Despite the fact that the three simulations show significantly different global and regional distributions of O3 and CO concentrations, they show similar patterns of O3-CO correlations on a global scale. All model simulations sampled along the TES orbit track capture the observed positive O3-CO correlations in the Northern Hemisphere midlatitude continental outflow and the Southern Hemisphere subtropics. While all simulations show strong negative correlations over the Tibetan Plateau, northern Africa, the subtropical eastern North Pacific, and the Caribbean, TES O3 and CO concentrations at 618 hPa only show weak negative correlations over much narrower areas (i.e., the Tibetan Plateau and northern Africa). Discrepancies in regional O3-CO correlation patterns in the three simulations may be attributed to differences in convective transport, stratospheric influence, and subsidence, among other processes. To understand how various

  10. Computer Exercises in Meteorology.

    Science.gov (United States)

    Trapasso, L. Michael; Conner, Glen; Stallins, Keith

    Beginning with Western Kentucky University's (Bowling Green) fall 1999 semester, exercises required for the geography and meteorology course used computers for learning. This course enrolls about 250 students per year, most of whom choose it to fulfill a general education requirement. Of the 185 geography majors, it is required for those who…

  11. 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

  12. Implicit coupling of turbulent diffusion with chemical reaction mechanisms for prognostic atmospheric dispersion models

    Energy Technology Data Exchange (ETDEWEB)

    Berlowitz, D.R.

    1996-11-01

    In the last few decades the negative impact by humans on the thin atmospheric layer enveloping the earth, the basis for life on this planet, has increased steadily. In order to halt, or at least slow down this development, the knowledge and study of these anthropogenic influence has to be increased and possible remedies have to be suggested. An important tool for these studies are computer models. With their help the atmospheric system can be approximated and the various processes, which have led to the current situation can be quantified. They also serve as an instrument to assess short or medium term strategies to reduce this human impact. However, to assure efficiency as well as accuracy, a careful analysis of the numerous processes involved in the dispersion of pollutants in the atmosphere is called for. This should help to concentrate on the essentials and also prevent excessive usage of sometimes scarce computing resources. The basis of the presented work is the EUMAC Zooming Model (ETM), and particularly the component calculating the dispersion of pollutants in the atmosphere, the model MARS. The model has two main parts: an explicit solver, where the advection and the horizontal diffusion of pollutants are calculated, and an implicit solution mechanism, allowing the joint computation of the change of concentration due to chemical reactions, coupled with the respective influence of the vertical diffusion of the species. The aim of this thesis is to determine particularly the influence of the horizontal components of the turbulent diffusion on the existing implicit solver of the model. Suggestions for a more comprehensive inclusion of the full three dimensional diffusion operator in the implicit solver are made. This is achieved by an appropriate operator splitting. A selection of numerical approaches to tighten the coupling of the diffusion processes with the calculation of the applied chemical reaction mechanisms are examined. (author) figs., tabs., refs.

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

    Data.gov (United States)

    National Aeronautics and Space Administration — This article presented a discussion on uncertainty representation and management for model-based prog- nostics methodologies based on the Bayesian tracking framework...

  14. Establishment of a Site-Specific Tropospheric Model Based on Ground Meteorological Parameters over the China Region.

    Science.gov (United States)

    Zhou, Chongchong; Peng, Bibo; Li, Wei; Zhong, Shiming; Ou, Jikun; Chen, Runjing; Zhao, Xinglong

    2017-07-27

    China is a country of vast territory with complicated geographical environment and climate conditions. With the rapid progress of the Chinese BeiDou satellite navigation system (BDS); more accurate tropospheric models must be applied to improve the accuracy of navigation and positioning. Based on the formula of the Saastamoinen and Callahan models; this study develops two single-site tropospheric models (named SAAS_S and CH_S models) for the Chinese region using radiosonde data from 2005 to 2012. We assess the two single-site tropospheric models with radiosonde data for 2013 and zenith tropospheric delay (ZTD) data from four International GNSS Service (IGS) stations and compare them to the results of the Saastamoinen and Callahan models. The experimental results show that: the mean accuracy of the SAAS_S model (bias: 0.19 cm; RMS: 3.19 cm) at all radiosonde stations is superior to those of the Saastamoinen (bias: 0.62 cm; RMS: 3.62 cm) and CH_S (bias: -0.05 cm; RMS: 3.38 cm) models. In most Chinese regions; the RMS values of the SAAS_S and CH_S models are about 0.51~2.12 cm smaller than those of their corresponding source models. The SAAS_S model exhibits a clear improvement in the accuracy over the Saastamoinen model in low latitude regions. When the SAAS_S model is replaced by the SAAS model in the positioning of GNSS; the mean accuracy of vertical direction in the China region can be improved by 1.12~1.55 cm and the accuracy of vertical direction in low latitude areas can be improved by 1.33~7.63 cm. The residuals of the SAAS_S model are closer to a normal distribution compared to those of the Saastamoinen model. Single-site tropospheric models based on the short period of the most recent data (for example 2 years) can also achieve a satisfactory accuracy. The average performance of the SAAS_S model (bias: 0.83 cm; RMS: 3.24 cm) at four IGS stations is superior to that of the Saastamoinen (bias: -0.86 cm; RMS: 3.59 cm) and CH_S (bias: 0.45 cm; RMS: 3.38 cm

  15. 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

  16. 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...

  17. Direct Radiative Impacts of Central American Biomass Burning Smoke Aerosols: Analysis from a Coupled Aerosol-Radiation-Meteorology Model RAMS-AROMA

    Science.gov (United States)

    Wang, J.; Christopher, S. A.; Nair, U. S.; Reid, J. S.; Prins, E. M.

    2005-12-01

    Considerable efforts including various field experiments have been carried out in the last decade for studying the regional climatic impact of smoke aerosols produced by biomass burning activities in Africa and South America. In contrast, only few investigations have been conducted for Central American Biomass Burning (CABB) region. Using a coupled aerosol-radiation-meteorology model called RAMS-AROMA together with various ground-based observations, we present a comprehensive analysis of the smoke direct radiative impacts on the surface energy budget, boundary layer evolution, and e precipitation process during the CABB events in Spring 2003. Quantitative estimates are also made regarding the transboundary carbon mass to the U.S. in the form of smoke particles. Buult upon the Regional Atmospheric Modeling System (RAMS) mesoscale model, the RAMS AROMA has several features including Assimilation and Radiation Online Modeling of Aerosols (AROMA) algorithms. The model simulates smoke transport by using hourly smoke emission inventory from the Fire Locating and Modeling of Burning Emissions (FLAMBE) geostationary satellite database. It explicitly considers the smoke effects on the radiative transfer at each model time step and model grid, thereby coupling the dynamical processes and aerosol transport. Comparison with ground-based observation show that the simulation realistically captured the smoke transport timeline and distribution from daily to hourly scales. The effects of smoke radiative extinction on the decrease of 2m air temperature (2mT), diurnal temperature range (DTR), and boundary layer height over the land surface are also quantified. Warming due to smoke absorption of solar radiation can be found in the lower troposphere over the ocean, but not near the underlying land surface. The increase of boundary layer stability produces a positive feedback where more smoke particles are trapped in the lower boundary layer. These changes in temperature, surface

  18. A set of numerical meteorological models for solving basic and some special problems in the Boris Kidric Institute

    International Nuclear Information System (INIS)

    Grscic, Z.

    1989-01-01

    Models for solving transport and dispersion problems of radioactive pollutants through atmosphere are briefly shown. These models are the base for solving and some special problems such as: estimating effective and physical heights of radioactive sources, computation of radioactive concentration distribution from multiple sources etc (author)

  19. Coupling of Large Eddy Simulations with Meteorological Models to simulate Methane Leaks from Natural Gas Storage Facilities

    Science.gov (United States)

    Prasad, K.

    2017-12-01

    Atmospheric transport is usually performed with weather models, e.g., the Weather Research and Forecasting (WRF) model that employs a parameterized turbulence model and does not resolve the fine scale dynamics generated by the flow around buildings and features comprising a large city. The NIST Fire Dynamics Simulator (FDS) is a computational fluid dynamics model that utilizes large eddy simulation methods to model flow around buildings at length scales much smaller than is practical with models like WRF. FDS has the potential to evaluate the impact of complex topography on near-field dispersion and mixing that is difficult to simulate with a mesoscale atmospheric model. A methodology has been developed to couple the FDS model with WRF mesoscale transport models. The coupling is based on nudging the FDS flow field towards that computed by WRF, and is currently limited to one way coupling performed in an off-line mode. This approach allows the FDS model to operate as a sub-grid scale model with in a WRF simulation. To test and validate the coupled FDS - WRF model, the methane leak from the Aliso Canyon underground storage facility was simulated. Large eddy simulations were performed over the complex topography of various natural gas storage facilities including Aliso Canyon, Honor Rancho and MacDonald Island at 10 m horizontal and vertical resolution. The goal of these simulations included improving and validating transport models as well as testing leak hypotheses. Forward simulation results were compared with aircraft and tower based in-situ measurements as well as methane plumes observed using the NASA Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) and the next generation instrument AVIRIS-NG. Comparison of simulation results with measurement data demonstrate the capability of the coupled FDS-WRF models to accurately simulate the transport and dispersion of methane plumes over urban domains. Simulated integrated methane enhancements will be presented and

  20. 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...

  1. 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.

  2. 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.

  3. 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

  4. 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

  5. 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

  6. A Bayesian analysis of sensible heat flux estimation: Quantifying uncertainty in meteorological forcing to improve model prediction

    KAUST Repository

    Ershadi, Ali; McCabe, Matthew; Evans, Jason P.; Mariethoz, Gregoire; Kavetski, Dmitri

    2013-01-01

    The influence of uncertainty in land surface temperature, air temperature, and wind speed on the estimation of sensible heat flux is analyzed using a Bayesian inference technique applied to the Surface Energy Balance System (SEBS) model

  7. 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

  8. Urban Flow and Pollutant Dispersion Simulation with Multi-scale coupling of Meteorological Model with Computational Fluid Dynamic Analysis

    Science.gov (United States)

    Liu, Yushi; Poh, Hee Joo

    2014-11-01

    The Computational Fluid Dynamics analysis has become increasingly important in modern urban planning in order to create highly livable city. This paper presents a multi-scale modeling methodology which couples Weather Research and Forecasting (WRF) Model with open source CFD simulation tool, OpenFOAM. This coupling enables the simulation of the wind flow and pollutant dispersion in urban built-up area with high resolution mesh. In this methodology meso-scale model WRF provides the boundary condition for the micro-scale CFD model OpenFOAM. The advantage is that the realistic weather condition is taken into account in the CFD simulation and complexity of building layout can be handled with ease by meshing utility of OpenFOAM. The result is validated against the Joint Urban 2003 Tracer Field Tests in Oklahoma City and there is reasonably good agreement between the CFD simulation and field observation. The coupling of WRF- OpenFOAM provide urban planners with reliable environmental modeling tool in actual urban built-up area; and it can be further extended with consideration of future weather conditions for the scenario studies on climate change impact.

  9. 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

  10. Implementation of a Model Output Statistics based on meteorological variable screening for short‐term wind power forecast

    DEFF Research Database (Denmark)

    Ranaboldo, Matteo; Giebel, Gregor; Codina, Bernat

    2013-01-01

    A combination of physical and statistical treatments to post‐process numerical weather predictions (NWP) outputs is needed for successful short‐term wind power forecasts. One of the most promising and effective approaches for statistical treatment is the Model Output Statistics (MOS) technique....... The proposed MOS performed well in both wind farms, and its forecasts compare positively with an actual operative model in use at Risø DTU and other MOS types, showing minimum BIAS and improving NWP power forecast of around 15% in terms of root mean square error. Further improvements could be obtained...

  11. 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

  12. Impact of Highly Reflective Materials on Meteorology, PM10 and Ozone in Urban Areas: A Modeling Study with WRF-CHIMERE at High Resolution over Milan (Italy

    Directory of Open Access Journals (Sweden)

    Serena Falasca

    2018-02-01

    Full Text Available The Urban Heat Island (UHI is a well-known phenomenon concerning an increasing percentage of the world’s population due to the growth rates of metropolitan areas. Given the health and economic implications of UHIs, several mitigation techniques are being evaluated and tested. In this study, we consider the use of highly reflective materials for urban surfaces, and we carried out numerical experiments using the Weather Research and Forecasting model coupled with the CHIMERE model in order to investigate the effects of these materials on the meteorology and air quality in the urban area of Milan (Italy. Results show that an increase in albedo from 0.2 to 0.7 for urban roofs, walls and streets leads to a decrease in UHI intensity by up to 2–3 °C and of the planetary boundary layer (PBL height of about 500 m. However, the difference of PM10 and ozone between urban and surrounding areas increases by a factor of about 2, attributable to the reduction of PBL height and wind speed and to the increased reflected solar radiation that may enhance photochemical production during the daytime. Therefore, if anthropogenic emissions are held at the same levels, the potential benefit to the UHI in terms of thermal discomfort may have negative repercussions on air quality.

  13. Evaluation of meteorological parameters over a coniferous forest in a single-column chemistry-climate model

    NARCIS (Netherlands)

    Ganzeveld, L.N.; Klemm, O.; Rappenglück, B.; Valverde-Canossa, J.

    2006-01-01

    The simulated micrometerology by a single-column chemistry-climate model (SCM) has been evaluated by comparison with BEWA2000 field campaign measurements over a coniferous forest, July-August 2001. This comparison indicates the limitations in the representation of the SCM's micrometeorological

  14. Meteorological modeling of arrival and deposition of fallout at intermediate distances downwind of the Nevada Test Site

    International Nuclear Information System (INIS)

    Cederwall, R.T.; Peterson, K.R.

    1990-01-01

    A three-dimensional atmospheric transport and diffusion model is used to calculate the arrival and deposition of fallout from 13 selected nuclear tests at the Nevada Test Site (NTS) in the 1950s. Results are used to extend NTS fallout patterns to intermediate downwind distances (300 to 1200 km). The radioactive cloud is represented in the model by a population of Lagrangian marker particles, with concentrations calculated on an Eulerian grid. Use of marker particles, with fall velocities dependent on particle size, provides a realistic simulation of fallout as the debris cloud travels downwind. The three-dimensional wind field is derived from observed data, adjusted for mass consistency. Terrain is represented in the grid, which extends up to 1200 km downwind of NTS and has 32-km horizontal resolution and 1-km vertical resolution. Ground deposition is calculated by a deposition-velocity approach. Source terms and relationships between deposition and exposure rate are based on work by Hicks. Uncertainty in particle size and vertical distributions within the debris cloud (and stem) allow for some model tuning to better match measured ground-deposition values. Particle trajectories representing different sizes and starting heights above ground zero are used to guide source specification. An hourly time history of the modeled fallout pattern as the debris cloud moves downwind provides estimates of fallout arrival times. Results for event HARRY illustrate the methodology. The composite deposition pattern for all 13 tests is characterized by two lobes extending out to the north-northeast and east-northeast, respectively, at intermediate distances from NTS. Arrival estimates, along with modeled deposition values, augment measured deposition data in the development of data bases at the county level

  15. A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset

    International Nuclear Information System (INIS)

    Deo, Ravinesh C.; Wen, Xiaohu; Qi, Feng

    2016-01-01

    Highlights: • A forecasting model for short- and long-term global incident solar radiation (R_n) has been developed. • The support vector machine and discrete wavelet transformation algorithm has been integrated. • The precision of the wavelet-coupled hybrid model is assessed using several prediction score metrics. • The proposed model is an appealing tool for forecasting R_n in the present study region. - Abstract: A solar radiation forecasting model can be utilized is a scientific contrivance for investigating future viability of solar energy potentials. In this paper, a wavelet-coupled support vector machine (W-SVM) model was adopted to forecast global incident solar radiation based on the sunshine hours (S_t), minimum temperature (T_m_a_x), maximum temperature (T_m_a_x), windspeed (U), evaporation (E) and precipitation (P) as the predictor variables. To ascertain conclusive results, the merit of the W-SVM was benchmarked with the classical SVM model. For daily forecasting, sixteen months of data (01-March-2014 to 30-June-2015) partitioned into the train (65%) and test (35%) set for the three metropolitan stations (Brisbane City, Cairns Aero and Townsville Aero) were utilized. Data were decomposed into their wavelet sub-series by discrete wavelet transformation algorithm and summed up to create new series with one approximation and four levels of detail using Daubechies-2 mother wavelet. For daily forecasting, six model scenarios were formulated where the number of input was increased and the forecast was assessed by statistical metrics (correlation coefficient r; Willmott’s index d; Nash-Sutcliffe coefficient E_N_S; peak deviation P_d_v), distribution statistics and prediction errors (mean absolute error MAE; root mean square error RMSE; mean absolute percentage error MAPE; relative root mean square error RMSE). Results for daily forecasts showed that the W-SVM model outperformed the classical SVM model for optimum input combinations. A sensitivity

  16. 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.

  17. 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

  18. Modelling deposition and air concentration of reduced nitrogen in Poland and sensitivity to variability in annual meteorology.

    Science.gov (United States)

    Kryza, Maciej; Dore, Anthony J; Błaś, Marek; Sobik, Mieczysław

    2011-04-01

    The relative contribution of reduced nitrogen to acid and eutrophic deposition in Europe has increased recently as a result of European policies which have been successful in reducing SO(2) and NO(x) emissions but have had smaller impacts on ammonia (NH(3)) emissions. In this paper the Fine Resolution Atmospheric Multi-pollutant Exchange (FRAME) model was used to calculate the spatial patterns of annual average ammonia and ammonium (NH(4)(+)) air concentrations and reduced nitrogen (NH(x)) dry and wet deposition with a 5 km × 5 km grid for years 2002-2005. The modelled air concentrations of NH(3) and dry deposition of NH(x) show similar spatial patterns for all years considered. The largest year to year changes were found for wet deposition, which vary considerably with precipitation amount. The FRAME modelled air concentrations and wet deposition are in reasonable agreement with available measurements (Pearson's correlation coefficients above 0.6 for years 2002-2005), and with spatial patterns of concentrations and deposition of NH(x) reported with the EMEP results, but show larger spatial gradients. The error statistics show that the FRAME model results are in better agreement with measurements if compared with EMEP estimates. The differences in deposition budgets calculated with FRAME and EMEP do not exceed 17% for wet and 6% for dry deposition, with FRAME estimates higher than for EMEP wet deposition for modelled period and lower or equal for dry deposition. The FRAME estimates of wet deposition budget are lower than the measurement-based values reported by the Chief Inspectorate of Environmental Protection of Poland, with the differences by approximately 3%. Up to 93% of dry and 53% of wet deposition of NH(x) in Poland originates from national sources. Over the western part of Poland and mountainous areas in the south, transboundary transport can contribute over 80% of total (dry + wet) NH(x) deposition. The spatial pattern of the relative contribution of

  19. Simulating the meteorology and PM10 concentrations in Arizona dust storms using the Weather Research and Forecasting model with Chemistry (Wrf-Chem).

    Science.gov (United States)

    Hyde, Peter; Mahalov, Alex; Li, Jialun

    2018-03-01

    Nine dust storms in south-central Arizona were simulated with the Weather Research and Forecasting with Chemistry model (WRF-Chem) at 2 km resolution. The windblown dust emission algorithm was the Air Force Weather Agency model. In comparison with ground-based PM 10 observations, the model unevenly reproduces the dust-storm events. The model adequately estimates the location and timing of the events, but it is unable to precisely replicate the magnitude and timing of the elevated hourly concentrations of particles 10 µm and smaller ([PM 10 ]).Furthermore, the model underestimated [PM 10 ] in highly agricultural Pinal County because it underestimated surface wind speeds and because the model's erodible fractions of the land surface data were too coarse to effectively resolve the active and abandoned agricultural lands. In contrast, the model overestimated [PM 10 ] in western Arizona along the Colorado River because it generated daytime sea breezes (from the nearby Gulf of California) for which the surface-layer speeds were too strong. In Phoenix, AZ, the model's performance depended on the event, with both under- and overestimations partly due to incorrect representation of urban features. Sensitivity tests indicate that [PM 10 ] highly relies on meteorological forcing. Increasing the fraction of erodible surfaces in the Pinal County agricultural areas improved the simulation of [PM 10 ] in that region. Both 24-hr and 1-hr measured [PM 10 ] were, for the most part, and especially in Pinal County, extremely elevated, with the former exceeding the health standard by as much as 10-fold and the latter exceeding health-based guidelines by as much as 70-fold. Monsoonal thunderstorms not only produce elevated [PM 10 ], but also cause urban flash floods and disrupt water resource deliveries. Given the severity and frequency of these dust storms, and conceding that the modeling system applied in this work did not produce the desired agreement between simulations and

  20. Producing physically consistent and bias free extreme precipitation events over the Switzerland: Bridging gaps between meteorology and impact models

    Science.gov (United States)

    José Gómez-Navarro, Juan; Raible, Christoph C.; Blumer, Sandro; Martius, Olivia; Felder, Guido

    2016-04-01

    Extreme precipitation episodes, although rare, are natural phenomena that can threat human activities, especially in areas densely populated such as Switzerland. Their relevance demands the design of public policies that protect public assets and private property. Therefore, increasing the current understanding of such exceptional situations is required, i.e. the climatic characterisation of their triggering circumstances, severity, frequency, and spatial distribution. Such increased knowledge shall eventually lead us to produce more reliable projections about the behaviour of these events under ongoing climate change. Unfortunately, the study of extreme situations is hampered by the short instrumental record, which precludes a proper characterization of events with return period exceeding few decades. This study proposes a new approach that allows studying storms based on a synthetic, but physically consistent database of weather situations obtained from a long climate simulation. Our starting point is a 500-yr control simulation carried out with the Community Earth System Model (CESM). In a second step, this dataset is dynamically downscaled with the Weather Research and Forecasting model (WRF) to a final resolution of 2 km over the Alpine area. However, downscaling the full CESM simulation at such high resolution is infeasible nowadays. Hence, a number of case studies are previously selected. This selection is carried out examining the precipitation averaged in an area encompassing Switzerland in the ESM. Using a hydrological criterion, precipitation is accumulated in several temporal windows: 1 day, 2 days, 3 days, 5 days and 10 days. The 4 most extreme events in each category and season are selected, leading to a total of 336 days to be simulated. The simulated events are affected by systematic biases that have to be accounted before this data set can be used as input in hydrological models. Thus, quantile mapping is used to remove such biases. For this task

  1. Tsengwen Reservoir Watershed Hydrological Flood Simulation Under Global Climate Change Using the 20 km Mesh Meteorological Research Institute Atmospheric General Circulation Model (MRI-AGCM

    Directory of Open Access Journals (Sweden)

    Nobuaki Kimura

    2014-01-01

    Full Text Available Severe rainstorms have occurred more frequently in Taiwan over the last decade. To understand the flood characteristics of a local region under climate change, a hydrological model simulation was conducted for the Tsengwen Reservoir watershed. The model employed was the Integrated Flood Analysis System (IFAS, which has a conceptual, distributed rainfall-runoff analysis module and a GIS data-input function. The high-resolution rainfall data for flood simulation was categorized into three terms: 1979 - 2003 (Present, 2015 - 2039 (Near-future, and 2075 - 2099 (Future, provided by the Meteorological Research Institute atmospheric general circulation model (MRI-AGCM. Ten extreme rainfall (top ten events were selected for each term in descending order of total precipitation volume. Due to the small watershed area the MRI-AGCM3.2S data was downsized into higher resolution data using the Weather Research and Forecasting Model. The simulated discharges revealed that most of the Near-future and Future peaks caused by extreme rainfall increased compared to the Present peak. These ratios were 0.8 - 1.6 (Near-future/Present and 0.9 - 2.2 (Future/Present, respectively. Additionally, we evaluated how these future discharges would affect the reservoir¡¦s flood control capacity, specifically the excess water volume required to be stored while maintaining dam releases up to the dam¡¦s spillway capacity or the discharge peak design for flood prevention. The results for the top ten events show that the excess water for the Future term exceeded the reservoir¡¦s flood control capacity and was approximately 79.6 - 87.5% of the total reservoir maximum capacity for the discharge peak design scenario.

  2. The Mexican hydro-meteorological disasters and climate network (redesclim) as model on outreach decision makers on disaster public policy in Mexico.

    Science.gov (United States)

    Welsh-Rodriguez, C. M.; Rodriguez-Estevez, J. M., Sr.; Romo-Aguilar, M. D. L.; Brito-Castillo, L.; Salinas-Prieto, A.; Gonzalez-Sosa, E.; Pérez-Campuzano, E.

    2017-12-01

    REDESCLIM was designed and develop in 2011 due to a public call from The Science and Technology Mexican Council (CONACYT); CONACYT lead the activities for its organization and development among the academic community. REDESCLIM was created to enhance the capacity of response to hydro-meteorological disasters and climate events through an integrative effort of researchers, technologists, entrepreneurs, politicians and society. Brief summary of our objectives: 1) Understand the causes of disasters, to reduce risks to society and ecosystems 2) Support research and interdisciplinary assessment of the physical processes in natural and social phenomena to improve understanding of causes and impacts 3) Strengths collaboration with academic, government, private and other interdisciplinary networks from Mexico and other countries 4) Build human capacity and promote the development of skills 5) Recommend strategies for climate hazard prevention, mitigation and response, especially for hazard with the greatest impacts in Mexico, such as hurricanes, floods, drought, wild fires and other extremes events. We provide a continues communication channel on members research results to provide scientific information that could be used for different proposes, specificaly for decision makers who are dealing with ecological and hydro meteorological problems that can result in disasters, and provide a services menu based on the members scientific projects, publications, teaching courses, in order to impact public policy as final result. http://www.redesclim.org.mx. So far we have some basic results: Fiver national meetings (participants from 35 countries around the world), 7 Workshops and seminars (virtual and in-person), Climatic data platforms ( http://clicom.mex.cicese.mx, http://clicom-mex.cicese.mx/malla, http://atlasclimatico.unam.mx/REDESCLIM2/ ), climate change scenarios for the general public at http://escenarios.inecc.gob.mx, 14 seed projects, one model to hurricane simulation

  3. Evaluating meteorological data from weather stations, and from satellites and global models for a multi-site epidemiological study.

    Science.gov (United States)

    Colston, Josh M; Ahmed, Tahmeed; Mahopo, Cloupas; Kang, Gagandeep; Kosek, Margaret; de Sousa Junior, Francisco; Shrestha, Prakash Sunder; Svensen, Erling; Turab, Ali; Zaitchik, Benjamin

    2018-04-21

    Longitudinal and time series analyses are needed to characterize the associations between hydrometeorological parameters and health outcomes. Earth Observation (EO) climate data products derived from satellites and global model-based reanalysis have the potential to be used as surrogates in situations and locations where weather-station based observations are inadequate or incomplete. However, these products often lack direct evaluation at specific sites of epidemiological interest. Standard evaluation metrics of correlation, agreement, bias and error were applied to a set of ten hydrometeorological variables extracted from two quasi-global, commonly used climate data products - the Global Land Data Assimilation System (GLDAS) and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) - to evaluate their performance relative to weather-station derived estimates at the specific geographic locations of the eight sites in a multi-site cohort study. These metrics were calculated for both daily estimates and 7-day averages and for a rotavirus-peak-season subset. Then the variables from the two sources were each used as predictors in longitudinal regression models to test their association with rotavirus infection in the cohort after adjusting for covariates. The availability and completeness of station-based validation data varied depending on the variable and study site. The performance of the two gridded climate models varied considerably within the same location and for the same variable across locations, according to different evaluation criteria and for the peak-season compared to the full dataset in ways that showed no obvious pattern. They also differed in the statistical significance of their association with the rotavirus outcome. For some variables, the station-based records showed a strong association while the EO-derived estimates showed none, while for others, the opposite was true. Researchers wishing to utilize publicly available climate data

  4. Study on the cloud detection of GOCI by using the simulated surface reflectance from BRDF-model for the land application and meteorological utilization

    Science.gov (United States)

    Kim, Hye-Won; Yeom, Jong-Min; Woo, Sun-Hee; Chae, Tae-Byeong

    2016-04-01

    COMS (Communication, Ocean, and Meteorological Satellite) was launched at French Guiana Kourou space center on 27 June 2010. Geostationary Ocean Color Imager (GOCI), which is the first ocean color geostationary satellite in the world for observing the ocean phenomena, is able to obtain the scientific data per an hour from 00UTC to 07UTC. Moreover, the spectral channels of GOCI would enable not only monitoring for the ocean, but for extracting the information of the land surface over the Korean Peninsula, Japan, and Eastern China. Since it is extremely important to utilize GOCI data accurately for the land application, cloud pixels over the surface have to be removed. Unfortunately, infra-red (IR) channels that can easily detect the water vapor with the cloud top temperature, are not included in the GOCI sensor. In this paper, the advanced cloud masking algorithm will be proposed with visible and near-IR (NIR) bands that are within GOCI bands. The main obstacle of cloud masking with GOCI is how to handle the high variable surface reflectance, which is mainly depending on the solar zenith angle. In this study, we use semi-empirical BRDF model to simulate the surface reflectance by using 16 day composite cloudy free image. When estimating the simulated surface reflectance, same geometry for GOCI observation was applied. The simulated surface reflectance is used to discriminate cloud areas especially for the thin cloud and shows more reasonable result than original threshold methods.

  5. Development of a model for predicting the dry matter production of mulberry [Morus alba] based on meteorological factors

    International Nuclear Information System (INIS)

    Fukui, K.; Ito, D.

    1999-01-01

    It is necessary to predict mulberry growth and yield precisely at any time during the growing period, since mulberry trees are cut anytime along with the increase of the frequency of silkworm rearing per year. Therefore, in this study, attempts were made to develop a model to predict the dry matter production in mulberry fields with standard density with the cooperation of the prefectural experimental stations of Ibaraki, Tochigi, Gunma, Saitama, Tokyo and Gifu. To construct the model, we conducted three experiments. In the first year, we estimated the dry weight of mulberry new shoots based on the length and base width. Logarithm of leaf dry weight of a new shoot was regressed linearly on the logarithm of the product of length and base width. Stem dry weight was estimated with a linear regression of the logarithm on the logarithm of the product of length and base square width. In the next year, we evaluated the maximal effective radiation (Smax) of mulberry, over which mulberry cannot use radiation to produce dry matter. This experiment included shaded and control (non-shaded) plots, and the difference between these plots was analyzed. Shading treatment decreased the dry matter production, but did not affect the radiation conversion efficiency. Shoot dry matter production increased almost proportionally with intercepted radiation except for the later growth periods. Therefore, no Smax was revealed in mulberry fields with standard density. The effect of temperature and growth stage on the radiation conversion efficiency was investigated last year. Relation of temperature and radiation conversion efficiency was not clear for shoot dry matter production. However, there was a positive relation for stem dry mater production. Although the efficiency decreased with mulberry growth for leaf dry matter production, it increased at the early growth stage and decreased at the late stage for stem dry matter production

  6. A hybrid land-water-environment model for identification of ecological effect and risk under uncertain meteorological precipitation in an agroforestry ecosystem.

    Science.gov (United States)

    Zeng, Xueting; Li, Tienan; Chen, Cong; Si, Zhenjiang; Huang, Guohe; Guo, Ping; Zhuang, Xiaowen

    2018-08-15

    In this study, a hybrid land-water-environment (LWE) model is developed for identifying ecological effect and risk under uncertain precipitation in an agroforestry ecosystem. A simulation-based fuzzy-stochastic programming with risk analysis (SFSR) method is used into LWE model to reflect the meteorological impacts; meanwhile, it also can quantify artificial fuzziness (e.g., risk attitude of policymaker) and natural vagueness (e.g., ecological function) in decision-making. The developed LWE model with SFSR method is applied to a practical agroforestry ecosystem in China. Results of optimized planting scale, irrigative water schedule, pollution mitigation scheme, and system benefit under changed rainfall, precise risk-adoption and vague ecological function are obtained; meanwhile their corresponding ecological effects and risks are analyzed. It found that current LWE plans could generate massive water deficits (e.g., 23.22×10 6 m 3 in crop irrigation and 26.32×10 6 m 3 in forest protection at highest) due to over-cultivation and excessive pollution discharges (e.g., the highest excessive TP and TN discharges would reach 460.64 and 15.30×10 3 ton) due to irrational fertilization, which would increase regional ecological risks. In addition, fifteen scenarios associated with withdrawing cultivation and recovering forest based on regional environment heterogeneity (such as soil types) have been discussed to adjust current agriculture-environment policies. It found that, the excessive pollution discharges (TN and TP) could be reduced 12.95% and 18.32% at highest through ecological expansions, which would generate higher system benefits than that without withdrawing farmland and recovering forest. All above can facilitate local policymakers to modulate a comprehensive LWE with more sustainable and robust manners, achieving regional harmony between socio-economy and eco-environment. Copyright © 2018 Elsevier B.V. All rights reserved.

  7. 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.

  8. Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data.

    Science.gov (United States)

    Guinney, Justin; Wang, Tao; Laajala, Teemu D; Winner, Kimberly Kanigel; Bare, J Christopher; Neto, Elias Chaibub; Khan, Suleiman A; Peddinti, Gopal; Airola, Antti; Pahikkala, Tapio; Mirtti, Tuomas; Yu, Thomas; Bot, Brian M; Shen, Liji; Abdallah, Kald; Norman, Thea; Friend, Stephen; Stolovitzky, Gustavo; Soule, Howard; Sweeney, Christopher J; Ryan, Charles J; Scher, Howard I; Sartor, Oliver; Xie, Yang; Aittokallio, Tero; Zhou, Fang Liz; Costello, James C

    2017-01-01

    Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a

  9. 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.

  10. Using satellite data on meteorological and vegetation characteristics and soil surface humidity in the Land Surface Model for the vast territory of agricultural destination

    Science.gov (United States)

    Muzylev, Eugene; Startseva, Zoya; Uspensky, Alexander; Vasilenko, Eugene; Volkova, Elena; Kukharsky, Alexander

    2017-04-01

    The model of water and heat exchange between vegetation covered territory and atmosphere (LSM, Land Surface Model) for vegetation season has been developed to calculate soil water content, evapotranspiration, infiltration of water into the soil, vertical latent and sensible heat fluxes and other water and heat balances components as well as soil surface and vegetation cover temperatures and depth distributions of moisture and temperature. The LSM is suited for utilizing satellite-derived estimates of precipitation, land surface temperature and vegetation characteristics and soil surface humidity for each pixel. Vegetation and meteorological characteristics being the model parameters and input variables, correspondingly, have been estimated by ground observations and thematic processing measurement data of scanning radiometers AVHRR/NOAA, SEVIRI/Meteosat-9, -10 (MSG-2, -3) and MSU-MR/Meteor-M № 2. Values of soil surface humidity has been calculated from remote sensing data of scatterometers ASCAT/MetOp-A, -B. The case study has been carried out for the territory of part of the agricultural Central Black Earth Region of European Russia with area of 227300 km2 located in the forest-steppe zone for years 2012-2015 vegetation seasons. The main objectives of the study have been: - to built estimates of precipitation, land surface temperatures (LST) and vegetation characteristics from MSU-MR measurement data using the refined technologies (including algorithms and programs) of thematic processing satellite information matured on AVHRR and SEVIRI data. All technologies have been adapted to the area of interest; - to investigate the possibility of utilizing satellite-derived estimates of values above in the LSM including verification of obtained estimates and development of procedure of their inputting into the model. From the AVHRR data there have been built the estimates of precipitation, three types of LST: land skin temperature Tsg, air temperature at a level of

  11. Analysis of CO in the tropical troposphere using Aura satellite data and the GEOS-Chem model: insights into transport characteristics of the GEOS meteorological products

    Directory of Open Access Journals (Sweden)

    Junhua Liu

    2010-12-01

    Full Text Available We use the GEOS-Chem chemistry-transport model (CTM to interpret the spatial and temporal variations of tropical tropospheric CO observed by the Microwave Limb Sounder (MLS and the Tropospheric Emission Spectrometer (TES. In so doing, we diagnose and evaluate transport in the GEOS-4 and GEOS-5 assimilated meteorological fields that drive the model, with a particular focus on vertical mixing at the end of the dry season when convection moves over the source regions. The results indicate that over South America, deep convection in both GEOS-4 and GEOS-5 decays at too low an altitude early in the wet season, and the source of CO from isoprene in the model (MEGAN v2.1 is too large, causing a lag in the model's seasonal maximum of CO compared to MLS CO in the upper troposphere (UT. TES and MLS data reveal problems with excessive transport of CO to the eastern equatorial Pacific and lofting in the ITCZ in August and September, particularly in GEOS-4. Over southern Africa, GEOS-4 and GEOS-5 simulations match the phase of the observed CO variation from the lower troposphere (LT to the UT fairly well, although the magnitude of the seasonal maximum is underestimated considerably due to low emissions in the model. A sensitivity run with increased emissions leads to improved agreement with observed CO in the LT and middle troposphere (MT, but the amplitude of the seasonal variation is too high in the UT in GEOS-4. Difficulty in matching CO in the LT and UT implies there may be overly vigorous vertical mixing in GEOS-4 early in the wet season. Both simulations and observations show a time lag between the peak in fire emissions (July and August and in CO (September and October. We argue that it is caused by the prevailing subsidence in the LT until convection moves south in September, as well as the low sensitivity of TES data in the LT over the African Plateau. The MLS data suggest that too much CO has been transported from fires in northern Africa to the UT

  12. Modeling water and heat balance components of large territory for vegetation season using information from polar-orbital and geostationary meteorological satellites

    Science.gov (United States)

    Muzylev, Eugene; Startseva, Zoya; Uspensky, Alexander; Volkova, Elena; Kukharsky, Alexander; Uspensky, Sergey

    2015-04-01

    To date, physical-mathematical modeling processes of land surface-atmosphere interaction is considered to be the most appropriate tool for obtaining reliable estimates of water and heat balance components of large territories. The model of these processes (Land Surface Model, LSM) developed for vegetation period is destined for simulating soil water content W, evapotranspiration Ev, vertical latent LE and heat fluxes from land surface as well as vertically distributed soil temperature and moisture, soil surface Tg and foliage Tf temperatures, and land surface skin temperature (LST) Ts. The model is suitable for utilizing remote sensing data on land surface and meteorological conditions. In the study these data have been obtained from measurements by scanning radiometers AVHRR/NOAA, MODIS/EOS Terra and Aqua, SEVIRI/geostationary satellites Meteosat-9, -10 (MSG-2, -3). The heterogeneity of the land surface and meteorological conditions has been taken into account in the model by using soil and vegetation characteristics as parameters and meteorological characteristics as input variables. Values of these characteristics have been determined from ground observations and remote sensing information. So, AVHRR data have been used to build the estimates of effective land surface temperature (LST) Ts.eff and emissivity E, vegetation-air temperature (temperature at the vegetation level) Ta, normalized vegetation index NDVI, vegetation cover fraction B, the leaf area index LAI, and precipitation. From MODIS data the values of LST Tls, Å, NDVI, LAI have been derived. From SEVIRI data there have been retrieved Tls, E, Ta, NDVI, LAI and precipitation. All named retrievals covered the vast territory of the part of the agricultural Central Black Earth Region located in the steppe-forest zone of European Russia. This territory with coordinates 49°30'-54°N, 31°-43°E and a total area of 227,300 km2 has been chosen for investigation. It has been carried out for years 2009

  13. Atlantic Oceanographic and Meteorological Laboratory

    Data.gov (United States)

    Federal Laboratory Consortium — The Atlantic Oceanographic and Meteorological Laboratory conducts research to understand the physical, chemical, and biological characteristics and processes of the...

  14. 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.

  15. 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

  16. 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...

  17. Meteorology ans solar physics

    Science.gov (United States)

    Schwarz, Oliver

    When in the second half of the 19th century both solar physics and astrophysics came into existence, various solar phenomena were described by analogies encountered in the terrestrial atmosphere. For a certain time, meteorology played a central role in research on solar processes. At first glance, this may appear as a curious and old-fashioned specialty. However, solar physics owes its first insights into solar structure to various analogies in terrestrial atmospheric studies. The present investigation intends to elucidate this fact, to present details of the historical development, and to demonstrate how our present knowledge in certain fields is based on considerations which were originally taken from the description of the terrestrial atmosphere.

  18. Meteorology as an infratechnology

    Science.gov (United States)

    Williams, G. A.; Smith, L. A.

    2003-04-01

    From an economists perspective, meteorology is an underpinning or infratechnology in the sense that in general it does not of its own accord lead to actual products. Its value added comes from the application of its results to the activities of other forms of economic and technological activity. This contribution discusses both the potential applications of meteorology as an ininfratechnology, and quantifying its socio-economic impact. Large economic and social benefits are both likely in theory and can be identified in practice. Case studies of particular weather dependent industries or particular episodes are suggested, based on the methodology developed by NIST to analyze the social impact of technological innovation in US industries (see www.nist.gov/director/planning/strategicplanning.htm ). Infratechnologies can provide economic benefits in the support of markets. Incomplete information is a major cause of market failure because it inhibits the proper design of contracts. The performance of markets in general can be influenced by strategies adopted by different firms within a market to regulate the performance of others especially suppliers or purchasers. This contribution will focus on benefits to society from mechanisms which enhance and enforce mitigating actions. When the market mechanism fails, who might social benefits be gained, for example, by widening the scope of authorities to ensure that those who could have taken mitigating action, given prior warning, cover the costs. This goes beyond the design and implementation of civil responses to severe weather warnings to include the design of legislative recourse in the event of negligence given prior knowledge, or the modification of insurance contracts. The aim here, for example, would be to avoid the loss of an oil tanker in heavy seas at a location where a high probability of heavy seas had been forecast for some time.

  19. The impact of urbanization during half a century on surface meteorology based on WRF model simulations over National Capital Region, India

    Science.gov (United States)

    Sati, Ankur Prabhat; Mohan, Manju

    2017-10-01

    An estimated 50% of the global population lives in the urban areas, and this percentage is projected to reach around 69% by the year 2050 (World Urbanization Prospects 2009). There is a considerable growth of urban and built-up area during the recent decades over National Capital Region (NCR) of India (17-fold increase in the urban extent). The proposed study estimates the land use land cover changes particularly changes to urban class from other land use types such as croplands, shrubland, open areas, and water bodies and quantify these changes for a span of about five decades. Further, the impact of these land use/land cover changes is examined on spatial and temporal variations of meteorological parameters using the Weather Research and Forecast (WRF) Model. The urbanized areas appear to be one of the regions with highest changes in the values of the fluxes and temperatures where during daytime, the surface sensible heat flux values show a noticeable increase of 60-70 W m-2 which commensurate with increase in urbanization. Similarly, the nighttime LST and T2m show an increase of 3-5 and 2-3 K, respectively. The diurnal temperature range (DTR) of LST and surface temperature also shows a decrease of about 5 and 2-3 K, respectively, with increasing urbanization. Significant decrease in the magnitude of surface winds and relative humidity is also observed over the areas converted to urban form over a period of half a century. The impacts shown here have serious implications on human health, energy consumption, ventilation, and atmospheric pollution.

  20. 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.

  1. 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 ...

  2. 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.

  3. Modeling of the anthropogenic heat flux and its effect on regional meteorology and air quality over the Yangtze River Delta region, China

    Directory of Open Access Journals (Sweden)

    M. Xie

    2016-05-01

    Full Text Available Anthropogenic heat (AH emissions from human activities caused by urbanization can affect the city environment. Based on the energy consumption and the gridded demographic data, the spatial distribution of AH emission over the Yangtze River Delta (YRD region is estimated. Meanwhile, a new method for the AH parameterization is developed in the WRF/Chem model, which incorporates the gridded AH emission data with the seasonal and diurnal variations into the simulations. By running this upgraded WRF/Chem for 2 typical months in 2010, the impacts of AH on the meteorology and air quality over the YRD region are studied. The results show that the AH fluxes over the YRD have been growing in recent decades. In 2010, the annual-mean values of AH over Shanghai, Jiangsu and Zhejiang are 14.46, 2.61 and 1.63 W m−2, respectively, with the high value of 113.5 W m−2 occurring in the urban areas of Shanghai. These AH emissions can significantly change the urban heat island and urban-breeze circulations in the cities of the YRD region. In Shanghai, 2 m air temperature increases by 1.6 °C in January and 1.4 °C in July, the PBLH (planetary boundary layer height rises up by 140 m in January and 160 m in July, and 10 m wind speed is enhanced by 0.7 m s−1 in January and 0.5 m s−1 in July, with a higher increment at night. The enhanced vertical movement can transport more moisture to higher levels, which causes the decrease in water vapor at ground level and the increase in the upper PBL (planetary boundary layer, and thereby induces the accumulative precipitation to increase by 15–30 % over the megacities in July. The adding of AH can impact the spatial and vertical distributions of the simulated pollutants as well. The concentrations of primary air pollutants decrease near the surface and increase at the upper levels, due mainly to the increases in PBLH, surface wind speed and upward air vertical movement. But surface O3

  4. Modeling of the anthropogenic heat flux and its effect on regional meteorology and air quality over the Yangtze River Delta region, China

    Science.gov (United States)

    Xie, Min; Liao, Jingbiao; Wang, Tijian; Zhu, Kuanguang; Zhuang, Bingliang; Han, Yong; Li, Mengmeng; Li, Shu

    2016-05-01

    Anthropogenic heat (AH) emissions from human activities caused by urbanization can affect the city environment. Based on the energy consumption and the gridded demographic data, the spatial distribution of AH emission over the Yangtze River Delta (YRD) region is estimated. Meanwhile, a new method for the AH parameterization is developed in the WRF/Chem model, which incorporates the gridded AH emission data with the seasonal and diurnal variations into the simulations. By running this upgraded WRF/Chem for 2 typical months in 2010, the impacts of AH on the meteorology and air quality over the YRD region are studied. The results show that the AH fluxes over the YRD have been growing in recent decades. In 2010, the annual-mean values of AH over Shanghai, Jiangsu and Zhejiang are 14.46, 2.61 and 1.63 W m-2, respectively, with the high value of 113.5 W m-2 occurring in the urban areas of Shanghai. These AH emissions can significantly change the urban heat island and urban-breeze circulations in the cities of the YRD region. In Shanghai, 2 m air temperature increases by 1.6 °C in January and 1.4 °C in July, the PBLH (planetary boundary layer height) rises up by 140 m in January and 160 m in July, and 10 m wind speed is enhanced by 0.7 m s-1 in January and 0.5 m s-1 in July, with a higher increment at night. The enhanced vertical movement can transport more moisture to higher levels, which causes the decrease in water vapor at ground level and the increase in the upper PBL (planetary boundary layer), and thereby induces the accumulative precipitation to increase by 15-30 % over the megacities in July. The adding of AH can impact the spatial and vertical distributions of the simulated pollutants as well. The concentrations of primary air pollutants decrease near the surface and increase at the upper levels, due mainly to the increases in PBLH, surface wind speed and upward air vertical movement. But surface O3 concentrations increase in the urban areas, with maximum

  5. Forecasting of meteorological drought using Wavelet-ANFIS hybrid model for different time steps (case study: Southeastern part of east Azerbaijan province, Iran)

    NARCIS (Netherlands)

    Shirmohammadi Chelan, Bagher; Moradi, Hamidreza; Moosavi, Vahid; Semiromi, Majid Taie; Zeinali, Ali

    2013-01-01

    Drought is accounted as one of the most natural hazards. Studying on drought is important for designing and managing of water resources systems. This research is carried out to evaluate the ability of Wavelet-ANN and adaptive neuro-fuzzy inference system (ANFIS) techniques for meteorological drought

  6. An 18-yr long (1993–2011 snow and meteorological dataset from a mid-altitude mountain site (Col de Porte, France, 1325 m alt. for driving and evaluating snowpack models

    Directory of Open Access Journals (Sweden)

    S. Morin

    2012-07-01

    Full Text Available A quality-controlled snow and meteorological dataset spanning the period 1 August 1993–31 July 2011 is presented, originating from the experimental station Col de Porte (1325 m altitude, Chartreuse range, France. Emphasis is placed on meteorological data relevant to the observation and modelling of the seasonal snowpack. In-situ driving data, at the hourly resolution, consist of measurements of air temperature, relative humidity, windspeed, incoming short-wave and long-wave radiation, precipitation rate partitioned between snow- and rainfall, with a focus on the snow-dominated season. Meteorological data for the three summer months (generally from 10 June to 20 September, when the continuity of the field record is not warranted, are taken from a local meteorological reanalysis (SAFRAN, in order to provide a continuous and consistent gap-free record. Data relevant to snowpack properties are provided at the daily (snow depth, snow water equivalent, runoff and albedo and hourly (snow depth, albedo, runoff, surface temperature, soil temperature time resolution. Internal snowpack information is provided from weekly manual snowpit observations (mostly consisting in penetration resistance, snow type, snow temperature and density profiles and from a hourly record of temperature and height of vertically free ''settling'' disks. This dataset has been partially used in the past to assist in developing snowpack models and is presented here comprehensively for the purpose of multi-year model performance assessment. The data is placed on the PANGAEA repository (http://dx.doi.org/10.1594/PANGAEA.774249 as well as on the public ftp server ftp://ftp-cnrm.meteo.fr/pub-cencdp/.

  7. Long-term variations and trends in the simulation of the middle atmosphere 1980–2004 by the chemistry-climate model of the Meteorological Research Institute

    Directory of Open Access Journals (Sweden)

    M. Deushi

    2008-05-01

    Full Text Available A middle-atmosphere simulation of the past 25 years (from 1980 to 2004 has been performed with a chemistry-climate model (CCM of the Meteorological Research Institute (MRI under observed forcings of sea-surface temperature, greenhouse gases, halogens, volcanic aerosols, and solar irradiance variations. The dynamics module of MRI-CCM is a spectral global model truncated triangularly at a maximum wavenumber of 42 with 68 layers extending from the surface to 0.01 hPa (about 80 km, wherein the vertical spacing is 500 m from 100 to 10 hPa. The chemistry-transport module treats 51 species with 124 reactions including heterogeneous reactions. Transport of chemical species is based on a hybrid semi-Lagrangian scheme, which is a flux form in the vertical direction and an ordinary semi-Lagrangian form in the horizontal direction. The MRI-CCM used in this study reproduced a quasi-biennial oscillation (QBO of about a 20-month period for wind and ozone in the equatorial stratosphere. Multiple linear regression analysis with time lags for volcanic aerosols was performed on the zonal-mean quantities of the simulated result to separate the trend, the QBO, the El Chichón and Mount Pinatubo, the 11-year solar cycle, and the El Niño/Southern Oscillation (ENSO signals. It is found that MRI-CCM can more or less realistically reproduce observed trends of annual mean temperature and ozone, and those of total ozone in each month. MRI-CCM also reproduced the vertical multi-cell structures of tropical temperature, zonal-wind, and ozone associated with the QBO, and the mid-latitude total ozone QBO in each winter hemisphere. Solar irradiance variations of the 11-year cycle were found to affect radiation alone (not photodissociation because of an error in making the photolysis lookup table. Nevertheless, though the heights of the maximum temperature (ozone in the tropics are much higher (lower than observations, MRI-CCM could reproduce the second maxima of temperature and

  8. Instruments for meteorological measurement

    International Nuclear Information System (INIS)

    1983-08-01

    The Fundamental Safety Rules applicable to certain types of nuclear installation are intended to clarify the conditions of which observance, for the type of installation concerned and for the subject that they deal with, is considered as equivalent to compliance with regulatory French technical practice. These Rules should facilitate safety analysises and the clear understanding between persons interested in matters related to nuclear safety. They in no way reduce the operator's liability and pose no obstacle to statutory provisions in force. For any installation to which a Fundamental Safety Rule applies according to the foregoing paragraph, the operator may be relieved from application of the Rule if he shows proof that the safety objectives set by the Rule are attained by other means that he proposes within the framework of statutory procedures. Furthermore, the Central Service for the Safety of Nuclear Installations reserves the right at all times to alter any Fundamental Safety Rule, as required, should it deem this necessary, while specifying the applicability conditions. This present rule has for objective to determine the means for meteorological measurement near a site of nuclear facility in which there is not a PWR power plant [fr

  9. 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

  10. Prognostic parameterization of cloud ice with a single category in the aerosol-climate model ECHAM(v6.3.0)-HAM(v2.3)

    Science.gov (United States)

    Dietlicher, Remo; Neubauer, David; Lohmann, Ulrike

    2018-04-01

    A new scheme for stratiform cloud microphysics has been implemented in the ECHAM6-HAM2 general circulation model. It features a widely used description of cloud water with two categories for cloud droplets and raindrops. The unique aspect of the new scheme is the break with the traditional approach to describe cloud ice analogously. Here we parameterize cloud ice by a single category that predicts bulk particle properties (P3). This method has already been applied in a regional model and most recently also in the Community Atmosphere Model 5 (CAM5). A single cloud ice category does not rely on heuristic conversion rates from one category to another. Therefore, it is conceptually easier and closer to first principles. This work shows that a single category is a viable approach to describe cloud ice in climate models. Prognostic representation of sedimentation is achieved by a nested approach for sub-stepping the cloud microphysics scheme. This yields good results in terms of accuracy and performance as compared to simulations with high temporal resolution. Furthermore, the new scheme allows for a competition between various cloud processes and is thus able to unbiasedly represent the ice formation pathway from nucleation to growth by vapor deposition and collisions to sedimentation. Specific aspects of the P3 method are evaluated. We could not produce a purely stratiform cloud where rime growth dominates growth by vapor deposition and conclude that the lack of appropriate conditions renders the prognostic parameters associated with the rime properties unnecessary. Limitations inherent in a single category are examined.

  11. A photosynthesis-based two-leaf canopy stomatal conductance model for meteorology and air quality modeling with WRF/CMAQ PX LSM

    Science.gov (United States)

    A coupled photosynthesis-stomatal conductance model with single-layer sunlit and shaded leaf canopy scaling is implemented and evaluated in a diagnostic box model with the Pleim-Xiu land surface model (PX LSM) and ozone deposition model components taken directly from the meteorol...

  12. Impacts of Detailed Land-Use Types and Urban Heat in an Urban Canopy Model on Local Meteorology and Ozone Levels for Air Quality Modeling in a Coastal City, Korea

    Directory of Open Access Journals (Sweden)

    Yoon-Hee Kang

    2016-01-01

    Full Text Available An urban canopy model (UCM, with detailed urban land-use and anthropogenic heat information, is required to reproduce and understand the urbanization process and its impact on regional climate and urban air quality. This study investigates the UCM impact on simulated meteorology and surface ozone in the coastal city of Busan using the WRF-SMOKE-CMAQ model coupled with (UCM case, and without the UCM (NOUCM case. The UCM and NOUCM case results suggest that UCM case generally produces warmer temperatures and deeper planetary boundary layer (PBL heights, especially in the early morning and night time, than the NOUCM case. Owing to urban heating and enhanced turbulent mixing incorporation in the center of the city, the sea breeze in the UCM case tends to penetrate faster and more strongly than in the NOUCM case. After sea breeze arrival at the urban center, the urban heat island effect prevents its penetration further inland. In the UCM case in the late afternoon, local meteorological changes induce remarkable increases in simulated O3 concentrations over the downwind (up to 17.1 ppb and downtown (up to 10.6 ppb areas. This is probably due to an increase in temperature in the urban areas and the wind convergence zone movement due to the sea breeze interaction and offshore flows. The increase in O3 concentration in the late afternoon results in the model bias reduction under previously underestimated O3 conditions due to high NOx emissions. The simulated O3 concentrations in the UCM case are more similar to the observed O3 concentrations compared to those of the NOUCM case.

  13. 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

  14. 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

  15. Addressing the challenges of obtaining functional outcomes in traumatic brain injury research: missing data patterns, timing of follow-up, and three prognostic models.

    Science.gov (United States)

    Zelnick, Leila R; Morrison, Laurie J; Devlin, Sean M; Bulger, Eileen M; Brasel, Karen J; Sheehan, Kellie; Minei, Joseph P; Kerby, Jeffrey D; Tisherman, Samuel A; Rizoli, Sandro; Karmy-Jones, Riyad; van Heest, Rardi; Newgard, Craig D

    2014-06-01

    Traumatic brain injury (TBI) is common and debilitating. Randomized trials of interventions for TBI ideally assess effectiveness by using long-term functional neurological outcomes, but such outcomes are difficult to obtain and costly. If there is little change between functional status at hospital discharge versus 6 months, then shorter-term outcomes may be adequate for use in future clinical trials. Using data from a previously published multi-center, randomized, placebo-controlled TBI clinical trial, we evaluated patterns of missing outcome data, changes in functional status between hospital discharge and 6 months, and three prognostic models to predict long-term functional outcome from covariates available at hospital discharge (functional measures, demographics, and injury characteristics). The Resuscitation Outcomes Consortium Hypertonic Saline trial enrolled 1282 TBI patients, obtaining the primary outcome of 6-month Glasgow Outcome Score Extended (GOSE) for 85% of patients, but missing the primary outcome for the remaining 15%. Patients with missing outcomes had less-severe injuries, higher neurological function at discharge (GOSE), and shorter hospital stays than patients whose GOSE was obtained. Of 1066 (83%) patients whose GOSE was obtained both at hospital discharge and at 6-months, 71% of patients had the same dichotomized functional status (severe disability/death vs. moderate/no disability) after 6 months as at discharge, 28% had an improved functional status, and 1% had worsened. Performance was excellent (C-statistic between 0.88 and 0.91) for all three prognostic models and calibration adequate for two models (p values, 0.22 and 0.85). Our results suggest that multiple imputation of the standard 6-month GOSE may be reasonable in TBI research when the primary outcome cannot be obtained through other means.

  16. Discovery of a Novel Immune Gene Signature with Profound Prognostic Value in Colorectal Cancer: A Model of Cooperativity Disorientation Created in the Process from Development to Cancer.

    Directory of Open Access Journals (Sweden)

    Ning An

    Full Text Available Immune response-related genes play a major role in colorectal carcinogenesis by mediating inflammation or immune-surveillance evasion. Although remarkable progress has been made to investigate the underlying mechanism, the understanding of the complicated carcinogenesis process was enormously hindered by large-scale tumor heterogeneity. Development and carcinogenesis share striking similarities in their cellular behavior and underlying molecular mechanisms. The association between embryonic development and carcinogenesis makes embryonic development a viable reference model for studying cancer thereby circumventing the potentially misleading complexity of tumor heterogeneity. Here we proposed that the immune genes, responsible for intra-immune cooperativity disorientation (defined in this study as disruption of developmental expression correlation patterns during carcinogenesis, probably contain untapped prognostic resource of colorectal cancer. In this study, we determined the mRNA expression profile of 137 human biopsy samples, including samples from different stages of human colonic development, colorectal precancerous progression and colorectal cancer samples, among which 60 were also used to generate miRNA expression profile. We originally established Spearman correlation transition model to quantify the cooperativity disorientation associated with the transition from normal to precancerous to cancer tissue, in conjunction with miRNA-mRNA regulatory network and machine learning algorithm to identify genes with prognostic value. Finally, a 12-gene signature was extracted, whose prognostic value was evaluated using Kaplan-Meier survival analysis in five independent datasets. Using the log-rank test, the 12-gene signature was closely related to overall survival in four datasets (GSE17536, n = 177, p = 0.0054; GSE17537, n = 55, p = 0.0039; GSE39582, n = 562, p = 0.13; GSE39084, n = 70, p = 0.11, and significantly associated with disease

  17. Defense Meteorological Satellite Program (DMSP)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Defense Meteorological Satellite Program (DMSP) satellites collect visible and infrared cloud imagery as well as monitoring the atmospheric, oceanographic,...

  18. 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.

  19. Implementation and evaluation of prognostic representations of the optical diameter of snow in the SURFEX/ISBA-Crocus detailed snowpack model

    Science.gov (United States)

    Carmagnola, C. M.; Morin, S.; Lafaysse, M.; Domine, F.; Lesaffre, B.; Lejeune, Y.; Picard, G.; Arnaud, L.

    2014-03-01

    In the SURFEX/ISBA-Crocus multi-layer snowpack model, the snow microstructure has up to now been characterised by the grain size and by semi-empirical shape variables which cannot be measured easily in the field or linked to other relevant snow properties. In this work we introduce a new formulation of snow metamorphism directly based on equations describing the rate of change of the optical diameter (dopt). This variable is considered here to be equal to the equivalent sphere optical diameter, which is inversely proportional to the specific surface area (SSA). dopt thus represents quantitatively some of the geometric characteristics of a porous medium. Different prognostic rate equations of dopt, including a re-formulation of the original Crocus scheme and the parameterisations from Taillandier et al. (2007) and Flanner and Zender (2006), were evaluated by comparing their predictions to field measurements carried out at Summit Camp (Greenland) in May and June 2011 and at Col de Porte (French Alps) during the 2009/10 and 2011/12 winter seasons. We focused especially on results in terms of SSA. In addition, we tested the impact of the different formulations on the simulated density profile, the total snow height, the snow water equivalent (SWE) and the surface albedo. Results indicate that all formulations perform well, with median values of the RMSD between measured and simulated SSA lower than 10 m2 kg-1. Incorporating the optical diameter as a fully fledged prognostic variable is an important step forward in the quantitative description of the snow microstructure within snowpack models, because it opens the way to data assimilation of various electromagnetic observations.

  20. Implementation and evaluation of prognostic representations of the optical diameter of snow in the detailed snowpack model SURFEX/ISBA-Crocus

    Science.gov (United States)

    Carmagnola, C. M.; Morin, S.; Lafaysse, M.; Domine, F.; Lesaffre, B.; Lejeune, Y.; Picard, G.; Arnaud, L.

    2013-09-01

    In the SURFEX/ISBA-Crocus multi-layer snowpack model, the snow microstructure was up to now characterized by the grain size and by semi-empirical shape variables which cannot be measured easily in the field or linked to other relevant snow properties. In this work we introduce a new formulation of snow metamorphism directly based on equations describing the rate of change of the optical diameter (dopt). This variable is considered here to be equal to the equivalent sphere optical diameter, which is inversely proportional to the specific surface area (SSA). dopt thus represents quantitatively some of the geometric characteristics of a porous medium. Different prognostic rate equations of dopt, including a re-formulation of the original Crocus scheme and the parametrizations from Taillandier et al. (2007) and Flanner and Zender (2006), were evaluated by comparing their predictions to field measurements carried out at Summit Camp (Greenland) in May and June 2011 and at Col de Porte (French Alps) during the 2009/10 and 2011/12 winter seasons. We focused especially on results in terms of SSA. In addition, we tested the impact of the different formulations on the simulated density profile, the total snow height, the snow water equivalent (SWE) and the surface albedo. Results indicate that all formulations perform well, with median values of the RMSD between measured and simulated SSA lower than 10 m2 kg-1. Incorporating the optical diameter as a fully-fledged prognostic variable is an important step forward in the quantitative description of the snow microstructure within snowpack models, because it opens the way to data assimilation of various electromagnetic observations.

  1. Multiplex polymerase chain reaction-based prognostic models in diffuse large B-cell lymphoma patients treated with R-CHOP

    DEFF Research Database (Denmark)

    Green, Tina M; Jensen, Andreas K; Holst, René

    2016-01-01

    We present a multiplex analysis for genes known to have prognostic value in an attempt to design a clinically useful classification model in patients with diffuse large B-cell lymphoma (DLBCL). Real-time polymerase chain reaction was used to measure transcript levels of 28 relevant genes in 194 de...... models. The best model was validated in data from an online available R-CHOP treated cohort. With progression-free survival (PFS) as primary endpoint, the best performing IPI independent model incorporated the LMO2 and HLADQA1 as well as gene interactions for GCSAMxMIB1, GCSAMxCTGF and FOXP1xPDE4B....... This model assigned 33% of patients (n = 60) to poor outcome with an estimated 3-year PFS of 40% vs. 87% for low risk (n = 61) and intermediate (n = 60) risk groups (P model incorporated LMO2 and BCL2 and assigned 33% of the patients with a 3-year PFS of 35% vs...

  2. 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 ...

  3. Prognostics of Power MOSFET

    Data.gov (United States)

    National Aeronautics and Space Administration — This paper demonstrates how to apply prognostics to power MOSFETs (metal oxide field effect transistor). The methodology uses thermal cycling to age devices and...

  4. Modeled and measured glacier change and related glaciological, hydrological, and meteorological conditions at South Cascade Glacier, Washington, balance and water years 2006 and 2007

    Science.gov (United States)

    Bidlake, William R.; Josberger, Edward G.; Savoca, Mark E.

    2010-01-01

    Winter snow accumulation and summer snow and ice ablation were measured at South Cascade Glacier, Washington, to estimate glacier mass balance quantities for balance years 2006 and 2007. Mass balances were computed with assistance from a new model that was based on the works of other glacier researchers. The model, which was developed for mass balance practitioners, coupled selected meteorological and glaciological data to systematically estimate daily mass balance at selected glacier sites. The North Cascade Range in the vicinity of South Cascade Glacier accumulated approximately average to above average winter snow packs during 2006 and 2007. Correspondingly, the balance years 2006 and 2007 maximum winter snow mass balances of South Cascade Glacier, 2.61 and 3.41 meters water equivalent, respectively, were approximately equal to or more positive (larger) than the average of such balances since 1959. The 2006 glacier summer balance, -4.20 meters water equivalent, was among the four most negative since 1959. The 2007 glacier summer balance, -3.63 meters water equivalent, was among the 14 most negative since 1959. The glacier continued to lose mass during 2006 and 2007, as it commonly has since 1953, but the loss was much smaller during 2007 than during 2006. The 2006 glacier net balance, -1.59 meters water equivalent, was 1.02 meters water equivalent more negative (smaller) than the average during 1953-2005. The 2007 glacier net balance, -0.22 meters water equivalent, was 0.37 meters water equivalent less negative (larger) than the average during 1953-2006. The 2006 accumulation area ratio was less than 0.10, owing to isolated patches of accumulated snow that endured the 2006 summer season. The 2006 equilibrium line altitude was higher than the glacier. The 2007