WorldWideScience

Sample records for prognostic meteorological model

  1. Urbanization Effects on air Quality and Climate in the Acapulco Area Using a Prognostic Meteorological and air Quality Model

    Science.gov (United States)

    Ortinez, A. A.; Garcia, A. R.; Jazcilevich, A. D.; Caetano, E.; Moya, M. N.; Delgado, J. C.

    2007-05-01

    The effects of urbanization growth on the Acapulco coastal metropolitan area were estimated by using a prognostic meteorological and air quality model. To this end three urbanization scenarios are proposed: The current Acapulco urban area that we call "Control Scenario" and two possible urban growths that we call "Scenario 1" and "Scenario 2". We estimated the urban growth of scenarios 1 and 2, using economic factors, population distribution and historical data. The urban distribution in the "Control Scenario" was taken from the aerial photographs of Acapulco and processed by a Geographic Information System (GIS).The variables devised for the scenarios comparison was a Comfort Index based on humidity and temperature and the Potential Exposure Index for Ozone. The model used was the Penn State/NCAR MM5 mesoscale meteorological model and the Multiscale Climate and Chemistry Model (MCCM). Since there is no local information, the emissions were estimated by using data of similar socio- economic urban areas where emission data is available. The meteorology and air quality models were calibrated using data of a measuring campaign performed in December 2005. This is a preliminary effort to propose a planned urban expansion for Acapulco from the point of view of air quality and urban climate.

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

    Energy Technology Data Exchange (ETDEWEB)

    Hurley, P.J. [CSIRO Atmospheric Research, Aspendale, Vic (Australia); Blockley, A.; Rayner, K. [Department of Environmental Protection, Perth, WA (Australia)

    2001-04-01

    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 95{mu}gm{sup -3}, TAPM predicted 94{mu}gm{sup -3}, DISPMOD-O predicted 111{mu}gm{sup -3} and DISPMOD-T predicted 125{mu}gm{sup -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

  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. 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...

  5. 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...... of the working model. We further illustrate the methods by computing the concordance probability for a prognostic model of coronary heart disease (CHD) events in the presence of the competing risk of non-CHD death.......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...

  6. Model-Based Prognostics of Hybrid Systems

    Science.gov (United States)

    Daigle, Matthew; Roychoudhury, Indranil; Bregon, Anibal

    2015-01-01

    Model-based prognostics has become a popular approach to solving the prognostics problem. However, almost all work has focused on prognostics of systems with continuous dynamics. In this paper, we extend the model-based prognostics framework to hybrid systems models that combine both continuous and discrete dynamics. In general, most systems are hybrid in nature, including those that combine physical processes with software. We generalize the model-based prognostics formulation to hybrid systems, and describe the challenges involved. We present a general approach for modeling hybrid systems, and overview methods for solving estimation and prediction in hybrid systems. As a case study, we consider the problem of conflict (i.e., loss of separation) prediction in the National Airspace System, in which the aircraft models are hybrid dynamical systems.

  7. Prognostic modeling in pediatric acute liver failure.

    Science.gov (United States)

    Jain, Vandana; Dhawan, Anil

    2016-10-01

    Liver transplantation (LT) is the only proven treatment for pediatric acute liver failure (PALF). However, over a period of time, spontaneous native liver survival is increasingly reported, making us wonder if we are overtransplanting children with acute liver failure (ALF). An effective prognostic model for PALF would help direct appropriate organ allocation. Only patients who would die would undergo LT, and those who would spontaneously recover would avoid unnecessary LT. Deriving and validating such a model for PALF, however, encompasses numerous challenges. In particular, the heterogeneity of age and etiology in PALF, as well as a lack of understanding of the natural history of the disease, contributed by the availability of LT has led to difficulties in prognostic model development. Several prognostic laboratory variables have been identified, and the incorporation of these variables into scoring systems has been attempted. A reliable targeted prognostic model for ALF in Wilson's disease has been established and externally validated. The roles of physiological, immunological, and metabolomic parameters in prognosis are being investigated. This review discusses the challenges with prognostic modeling in PALF and describes predictive methods that are currently available and in development for the future. Liver Transplantation 22 1418-1430 2016 AASLD. © 2016 by the American Association for the Study of Liver Diseases.

  8. A Spatial Lattice Model Applied for Meteorological Visualization and Analysis

    Directory of Open Access Journals (Sweden)

    Mingyue Lu

    2017-03-01

    Full Text Available Meteorological information has obvious spatial-temporal characteristics. Although it is meaningful to employ a geographic information system (GIS to visualize and analyze the meteorological information for better identification and forecasting of meteorological weather so as to reduce the meteorological disaster loss, modeling meteorological information based on a GIS is still difficult because meteorological elements generally have no stable shape or clear boundary. To date, there are still few GIS models that can satisfy the requirements of both meteorological visualization and analysis. In this article, a spatial lattice model based on sampling particles is proposed to support both the representation and analysis of meteorological information. In this model, a spatial sampling particle is regarded as the basic element that contains the meteorological information, and the location where the particle is placed with the time mark. The location information is generally represented using a point. As these points can be extended to a surface in two dimensions and a voxel in three dimensions, if these surfaces and voxels can occupy a certain space, then this space can be represented using these spatial sampling particles with their point locations and meteorological information. In this case, the full meteorological space can then be represented by arranging numerous particles with their point locations in a certain structure and resolution, i.e., the spatial lattice model, and extended at a higher resolution when necessary. For practical use, the meteorological space is logically classified into three types of spaces, namely the projection surface space, curved surface space, and stereoscopic space, and application-oriented spatial lattice models with different organization forms of spatial sampling particles are designed to support the representation, inquiry, and analysis of meteorological information within the three types of surfaces. Cases

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

    DEFF Research Database (Denmark)

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

    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......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...... 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...

  10. Investigating the Propagation of Meteorological Model Uncertainty for Tracer Modeling

    Science.gov (United States)

    Lopez-Coto, I.; Ghosh, S.; Karion, A.; Martin, C.; Mueller, K. L.; Prasad, K.; Whetstone, J. R.

    2016-12-01

    The North-East Corridor project aims to use a top-down inversion method to quantify sources of Greenhouse Gas (GHG) emissions in the urban areas of Washington DC and Baltimore at approximately 1km2 resolutions. The aim of this project is to help establish reliable measurement methods for quantifying and validating GHG emissions independently of the inventory methods typically used to guide mitigation efforts. Since inversion methods depend strongly on atmospheric transport modeling, analyzing the uncertainties on the meteorological fields and their propagation through the sensitivities of observations to surface fluxes (footprints) is a fundamental step. To this end, six configurations of the Weather Research and Forecasting Model (WRF-ARW) version 3.8 were used to generate an ensemble of meteorological simulations. Specifically, we used 4 planetary boundary layer parameterizations (YSU, MYNN2, BOULAC, QNSE), 2 sources of initial and boundary conditions (NARR and HRRR) and 1 configuration including the building energy parameterization (BEP) urban canopy model. The simulations were compared with more than 150 meteorological surface stations, a wind profiler and radiosondes for a month (February) in 2016 to account for the uncertainties and the ensemble spread for wind speed, direction and mixing height. In addition, we used the Stochastic Time-Inverted Lagrangian Transport model (STILT) to derive the sensitivity of 12 hypothetical observations to surface emissions (footprints) with each WRF configuration. The footprints and integrated sensitivities were compared and the resulting uncertainties estimated.

  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. 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...

  13. Physicians' perceptions of the value of prognostic models: the benefits and risks of prognostic confidence.

    Science.gov (United States)

    Hallen, Sarah A M; Hootsmans, Norbert A M; Blaisdell, Laura; Gutheil, Caitlin M; Han, Paul K J

    2015-12-01

    The communication of prognosis in end-of-life (EOL) care is a challenging task that is limited by prognostic uncertainty and physicians' lack of confidence in their prognostic estimates. Clinical prediction models (CPMs) are increasingly common evidence-based tools that may mitigate these problems and facilitate the communication and use of prognostic information in EOL care; however, little is known about physicians' perceptions of the value of these tools. To explore physicians' perceptions of the value of CPMs in EOL care. Qualitative study using semi-structured individual interviews which were analysed using a constant comparative method. Convenience sample of 17 attending physicians representing five different medical specialties at a single large tertiary care medical centre. Physicians perceived CPMs as having three main benefits in EOL care: (i) enhancing their prognostic confidence; (ii) increasing their prognostic authority; and (iii) enabling patient persuasion in circumstances of low prognostic and therapeutic uncertainty. However, physicians also perceived CPMs as having potential risks, which include producing emotional distress in patients and promoting prognostic overconfidence in EOL care. Physicians perceive CPMs as a potentially valuable means of increasing their prognostic confidence, communication and explicit use of prognostic information in EOL care. However, physicians' perceptions of CPMs also indicate a need to establish broad and consistent implementation processes to engage patients in shared decision making in EOL care, to effectively communicate uncertainty in prognostic information and to help both patients and physicians manage uncertainty in EOL care decisions. © 2014 John Wiley & Sons Ltd.

  14. 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.

  15. Modeling conditional covariance between meteorological and hydrological drought

    Science.gov (United States)

    Modarres, R.

    2012-12-01

    This study introduces a bivariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) approach to model the time varying second order moment or conditional variance-covariance link of hydrologic and meteorological drought. The standardized streamflow and rainfall time series are selected as drought indices and the bivariate diagonal BEKK model is applied to estimate the conditional variance-covariance structure between hydrologic and meteorological drought. Results of diagonal BEKK(1,1) model indicated that the conditional variance of meteorological drought is weak and much smaller than that for hydrological drought which shows a strong volatility effect. However both drought indices show a weak memory in the conditional variance. It is also observed that the conditional covariance between two drought indices is also weak and only shows a slight short run volatility effect. This may suggest the effect of basin features such as groundwater storage and physical characteristics which attenuate and modify the effect of meteorological drought on hydrologic drought in the basin scale. conditional correlation time series between meteorological and hydrologic drought at two selected stations monthly variation of conditional correlation between meteorological and hydrologic drought at two selected stations

  16. Stochastic models for some meteorological outcomes in Niger Delta ...

    African Journals Online (AJOL)

    In this paper, stochastic models based on autoregressive integrated moving average models of various orders and its seasonalized versions are presented, with a view to identifying the optimal model for some meteorological outcomes in some cities in Niger Delta region of Nigeria, using Normalized Bayesian Information ...

  17. 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...

  18. 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...

  19. A numerical forecast model for road meteorology

    Science.gov (United States)

    Meng, Chunlei

    2017-05-01

    A fine-scale numerical model for road surface parameters prediction (BJ-ROME) is developed based on the Common Land Model. The model is validated using in situ observation data measured by the ROSA road weather stations of Vaisala Company, Finland. BJ-ROME not only takes into account road surface factors, such as imperviousness, relatively low albedo, high heat capacity, and high heat conductivity, but also considers the influence of urban anthropogenic heat, impervious surface evaporation, and urban land-use/land-cover changes. The forecast time span and the update interval of BJ-ROME in vocational operation are 24 and 3 h, respectively. The validation results indicate that BJ-ROME can successfully simulate the diurnal variation of road surface temperature both under clear-sky and rainfall conditions. BJ-ROME can simulate road water and snow depth well if the artificial removing was considered. Road surface energy balance in rainy days is quite different from that in clear-sky conditions. Road evaporation could not be neglected in road surface water cycle research. The results of sensitivity analysis show solar radiation correction coefficient, asphalt depth, and asphalt heat conductivity are important parameters in road interface temperatures simulation. The prediction results could be used as a reference of maintenance decision support system to mitigate the traffic jam and urban water logging especially in large cities.

  20. Meteorological and air pollution modeling for an urban airport

    Science.gov (United States)

    Swan, P. R.; Lee, I. Y.

    1980-01-01

    Results are presented of numerical experiments modeling meteorology, multiple pollutant sources, and nonlinear photochemical reactions for the case of an airport in a large urban area with complex terrain. A planetary boundary-layer model which predicts the mixing depth and generates wind, moisture, and temperature fields was used; it utilizes only surface and synoptic boundary conditions as input data. A version of the Hecht-Seinfeld-Dodge chemical kinetics model is integrated with a new, rapid numerical technique; both the San Francisco Bay Area Air Quality Management District source inventory and the San Jose Airport aircraft inventory are utilized. The air quality model results are presented in contour plots; the combined results illustrate that the highly nonlinear interactions which are present require that the chemistry and meteorology be considered simultaneously to make a valid assessment of the effects of individual sources on regional air quality.

  1. 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.

  2. Evaluation of meteorological drought indices for streamflow modeling

    Science.gov (United States)

    Haslinger, Klaus; Koffler, Daniel; Blöschl, Günter; Parajka, Juraj; Schöner, Wolfgang; Laaha, Gregor

    2013-04-01

    In this paper we present a comprehensive analysis which aims to link various meteorological drought indices to streamflow data in Austria and Central Europe. The motivation arises from the fact that discharge time series are usually shorter (beginning in the middle of the 20th century) than meteorological time series. In the European Greater Alpine Region we are fortunate of having a gridded dataset for temperature and solid/liquid precipitation on a monthly time scale that spans from 1801 to 2003 - the HISTALP database. If there is a link between meteorological drought indices and streamflow, a reconstruction of streamflow, with emphasis on low flows, will be possible for the last 200 years. As meteorological drought indices the self-calibrating Palmer Drought Severity Index (scPDSI), the Standardized Precipitation Index (SPI) on various time scales as well as the moisture departure value d from the soil moisture modeling procedure of the scPDSI are used. The analysis focuses on three aspects, (i) temporal co-evolution of meteorological drought and streamflow indices, (ii) their at-site correlation at gauges, and (iii) their regional correlation structure depending on different climate and catchment conditions. The whole analysis is stratified by seasons, what allows us to explore the strength of the link for the dominant low flow generating process. In order to show a connection between these indices and streamflow data the drought event of 2003 serves as a reference. We will show the temporal evolution of the drought indices parallel to streamflow indices like MQ, Q95 and MAM(7) for the period from summer 2002, which encompasses a major flood event in the northern parts of Austria, to fall 2003 when the streamflow drought was most severe. This is carried out for different regions in Austria, representing different climatic and soil-specific characteristics. To quantify the link between drought indices and streamflow indices for the whole time series from 1801

  3. An investigation of the thermal response to meteorological forcing in a hydrodynamic model of Lake Superior

    Science.gov (United States)

    Xue, Pengfei; Schwab, David J.; Hu, Song

    2015-07-01

    Lake Superior, the largest lake in the world by surface area and third largest by volume, features strong spatiotemporal thermal variability due to its immense size and complex bathymetry. The objectives of this study are to document our recent modeling experiences on the simulation of the lake thermal structure and to explore underlying dynamic explanations of the observed modeling success. In this study, we use a three-dimensional hydrodynamic model (FVCOM—Finite Volume Community Ocean Model) and an assimilative weather forecasting model (WRF—Weather Research and Forecasting Model) to study the annual heating and cooling cycle of Lake Superior. Model experiments are carried out with meteorological forcing based on interpolation of surface weather observations, on WRF and on Climate Forecast System Reanalysis (CFSR) reanalysis data, respectively. Model performance is assessed through comparison with satellite products and in situ measurements. Accurate simulations of the lake thermal structure are achieved through (1) adapting the COARE algorithm in the hydrodynamic model to derive instantaneous estimates of latent/sensible heat fluxes and upward longwave radiation based on prognostic surface water temperature simulated within the model as opposed to precomputing them with an assumed surface water temperature; (2) estimating incoming solar radiation and downward longwave radiation based on meteorological measurements as opposed to meteorological model-based estimates; (3) using the weather forecasting model to provide high-resolution dynamically constrained wind fields as opposed to wind fields interpolated from station observations. Analysis reveals that the key to the modeling success is to resolve the lake-atmosphere interactions and apply appropriate representations of different meteorological forcing fields, based on the nature of their spatiotemporal variability. The close agreement between model simulation and observations also suggests that the 3-D

  4. Distributed Damage Estimation for Prognostics based on Structural Model Decomposition

    Data.gov (United States)

    National Aeronautics and Space Administration — Model-based prognostics approaches capture system knowl- edge in the form of physics-based models of components that include how they fail. These methods consist of...

  5. Multiple Damage Progression Paths in Model-based Prognostics

    Data.gov (United States)

    National Aeronautics and Space Administration — Model-based prognostics approaches employ do- main knowledge about a system, its components, and how they fail through the use of physics-based models. Compo- nent...

  6. Overview of meteorological measurements for aerial spray modeling.

    Science.gov (United States)

    Rafferty, J E; Biltoft, C A; Bowers, J F

    1996-06-01

    The routine meteorological observations made by the National Weather Service have a spatial resolution on the order of 1,000 km, whereas the resolution needed to conduct or model aerial spray applications is on the order of 1-10 km. Routinely available observations also do not include the detailed information on the turbulence and thermal structure of the boundary layer that is needed to predict the transport, dispersion, and deposition of aerial spray releases. This paper provides an overview of the information needed to develop the meteorological inputs for an aerial spray model such as the FSCBG and discusses the different types of instruments that are available to make the necessary measurements.

  7. Modeling the Effects of Meteorological Conditions on the Neutron Flux

    Science.gov (United States)

    2017-05-22

    statistical package. New York: Springer Science Business Mediar, 2010. Print. Page 96. [21] www.nndc.bnl.gov; ENDF/B-VII.1 data. 63 Chapter 11...a statistical model that predicts environmental neutron background as a function of five meteorological variables: inverse barometric pressure...my calming voice of reason and keeping expectations realistic during the busy periods of the project. And Assistant Professor VanDerwerken for your

  8. 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...

  9. 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.

  10. 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....

  11. 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...

  12. 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...

  13. 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...

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

    Directory of Open Access Journals (Sweden)

    Matthew J. Daigle

    2011-01-01

    Full Text Available Within the area of systems health management, the task of prognostics centers on predicting when components will fail. Model-based prognostics exploits domain knowledge of the system, its components, and how they fail by casting the underlying physical phenomena in a physics-based model that is derived from first principles. Uncertainty cannot be avoided in prediction, therefore, algorithms are employed that help in managing these uncertainties. The particle filtering algorithm has become a popular choice for model-based prognostics due to its wide applicability, ease of implementation, and support for uncertainty management. We develop a general model-based prognostics methodology within a robust probabilistic framework using particle filters. As a case study, we consider a pneumatic valve from the Space Shuttle cryogenic refueling system. We develop a detailed physics-based model of the pneumatic valve, and perform comprehensive simulation experiments to illustrate our prognostics approach and evaluate its effectiveness and robustness. The approach is demonstrated using historical pneumatic valve data from the refueling system.

  15. 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.

  16. 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

  17. 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

  18. Modeling Air Pollution in Beijing: Emission Reduction vs. Meteorological Influence

    Science.gov (United States)

    Risse, Eicke-Alexander; Hao, Nan; Trautmann, Thomas

    2016-08-01

    This case study uses the Chemical Transport Model WRF-Chem to simulate and measure the efficiency of temporal large-scale emission reductions under different meteorological conditions. The Nov. 2014 Asian Pacific Economic Cooperation (APEC) summit provides a unique opportunity for this study due to the extraordinarily good and well-measured air quality which is believed to be induced by intense emission- reduction measures by the Chinese government. Four cases are simulated to inter-compare between favorable und unfavorablemeteorological conditions (in terms of air quality) as well as reduced and non-reduced emissions. Key variables of the simulation results are evaluated against AERONET measurements of Aerosol Optical Depth (AOD) and air-quality measurements by the Chinese Ministry of Environment (CME). The inter-comparison is then performed on time- and volume-averaged total concentrations of the key variables Nitrogenous Oxide (NOx) and Particulate Matter (PM2.5 and PM10).The simulation settings and some important facts about the model are shown in table 1.

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

    Science.gov (United States)

    Wozniak, Matthew C.; Steiner, Allison L.

    2017-11-01

    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 model for the

  20. 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

  1. 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.

  2. 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.

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

    Science.gov (United States)

    2016-03-01

    southern hemisphere . Approved for public release; distribution unlimited. 4 A sea surface temperature product with higher resolution than the... Meteorological Variables by J L Cogan Approved for public release; distribution unlimited. NOTICES Disclaimers The...of Model Generated Vertical Profiles of Meteorological Variables by J L Cogan Computational and Information Sciences Directorate, ARL

  4. Geoinformational prognostic model of mudflows hazard and mudflows risk for the territory of Ukrainian Carpathians

    Science.gov (United States)

    Chepurna, Tetiana B.; Kuzmenko, Eduard D.; Chepurnyj, Igor V.

    2017-06-01

    The article is devoted to the geological issue of the space-time regional prognostication of mudflow hazard. The methodology of space-time prediction of mudflows hazard by creating GIS predictive model has been developed. Using GIS technologies the relevant and representative complex of significant influence of spatial and temporal factors, adjusted to use in the regional prediction of mudflows hazard, were selected. Geological, geomorphological, technological, climatic, and landscape factors have been selected as spatial mudflow factors. Spatial analysis is based on detection of a regular connection of spatial factor characteristics with spatial distribution of the mudflow sites. The function of a standard complex spatial index (SCSI) of the probability of the mudflow sites distribution has been calculated. The temporal, long-term prediction of the mudflows activity was based on the hypothesis of the regular reiteration of natural processes. Heliophysical, seismic, meteorological, and hydrogeological factors have been selected as time mudflow factors. The function of a complex index of long standing mudflow activity (CIMA) has been calculated. The prognostic geoinformational model of mudflow hazard up to 2020 year, a year of the next peak of the mudflows activity, has been created. Mudflow risks have been counted and carogram of mudflow risk assessment within the limits of administrative-territorial units has been built for 2020 year.

  5. 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...

  6. 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.

  7. 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

  8. Modeling Current Transfer from PV Modules Based on Meteorological Data

    Energy Technology Data Exchange (ETDEWEB)

    Hacke, Peter; Smith, Ryan; Kurtz, Sarah; Jordan, Dirk; Wohlgemuth, John

    2016-11-21

    Current transferred from the active cell circuit to ground in modules undergoing potential-induced degradation (PID) stress is analyzed with respect to meteorological data. Duration and coulombs transferred as a function of whether the module is wet (from dew or rain) or the extent of uncondensed surface humidity are quantified based on meteorological indicators. With this, functions predicting the mode and rate of coulomb transfer are developed for use in estimating the relative PID stress associated with temperature, moisture, and system voltage in any climate. Current transfer in a framed crystalline silicon module is relatively high when there is no condensed water on the module, whereas current transfer in a thin-film module held by edge clips is not, and displays a greater fraction of coulombs transferred when wet compared to the framed module in the natural environment.

  9. Using Remote Sensing and Radar Meteorological Data to Support Watershed Assessments Comprising Integrated Environmental Modeling

    Science.gov (United States)

    Meteorological (MET) data required by watershed assessments comprising Integrated Environmental Modeling (IEM) traditionally have been provided by land-based weather (gauge) stations, although these data may not be the most appropriate for adequate spatial and temporal resolution...

  10. Modelling weighted mean temperature in the West African region: implications for GNSS meteorology

    National Research Council Canada - National Science Library

    Isioye, Olalekan Adekunle; Combrinck, Ludwig; Botai, Joel

    2016-01-01

    ...‐based Global Navigation Satellite System ( GNSS ) receivers. In the present study, three models of T m are developed for GNSS meteorological applications in the West African region in general and Nigeria in particular...

  11. 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

  12. Evaluation of surface and upper air fine scale WRF meteorological modeling of the May and June 2010 CalNex period in California

    Science.gov (United States)

    Baker, Kirk R.; Misenis, Chris; Obland, Michael D.; Ferrare, Richard A.; Scarino, Amy J.; Kelly, James T.

    2013-12-01

    Prognostic meteorological models such as Mesoscale Model (MM5) and Weather Research and Forecasting (WRF) are often used to supply inputs for retrospective air quality modeling done to support ozone and PM2.5 emission control demonstrations. In this study, multiple configurations of the WRF model are applied at 4 km grid resolution and compared to routine meteorological measurements and special study measurements taken in California during May-June 2010. One configuration is routinely used by US EPA to generate meteorological inputs for regulatory air quality modeling and another that is used by research scientists for evaluating meteorology and air quality. Mixing layer heights estimated from airborne High Spectral Resolution Lidar (HSRL) measurements of aerosol backscatter are compared with WRF modeled planetary boundary layer (PBL) height estimates. Both WRF configurations generally capture the variability in HSRL mixing height between days, hour-to-hour, and between surface features such as terrain and land-water interfaces. Fractional bias over all flights and both model configurations range from -38% to 32% and fractional error ranges from 22% to 58%. Surface and upper level measurements of temperature, water mixing ratio, and winds are generally well characterized by both WRF model configurations, often more closely matching surface observations than the input analysis data (12-NAM). The WRF model generally captures orographic and mesoscale meteorological features in the central Valley (bifurcation of wind flow from the San Francisco bay into the Sacramento and San Joaquin valleys) and Los Angeles air basin (ocean-land flows) during this summer period.

  13. Prognostic survival model for people diagnosed with invasive cutaneous melanoma.

    Science.gov (United States)

    Baade, Peter D; Royston, Patrick; Youl, Philipa H; Weinstock, Martin A; Geller, Alan; Aitken, Joanne F

    2015-01-31

    The ability of medical practitioners to communicate risk estimates effectively to patients diagnosed with melanoma relies on accurate information about prognostic factors and their impact on survival. This study reports the development of one of the few melanoma prognostic models, called the Melanoma Severity Index (MSI), based on population-based cancer registry data. Data from the Queensland Cancer Registry for people (20-89 years) diagnosed with a single invasive melanoma between 1995 and 2008 (n = 28,654; 1,700 melanoma deaths). Additional clinical information about metastasis, ulceration and positive lymph nodes was manually extracted from pathology forms. Flexible parametric survival models were combined with multivariable fractional polynomial for selecting variables and transformations of continuous variables. Multiple imputation was used for missing covariate values. The MSI contained the variables thickness (transformed, explained 40.6% of variation in survival), body site (additional 1.9% in variation), metastasis (1.8%), positive nodes (0.7%), ulceration (1.3%), age (1.1%). Royston and Sauerbrei's D statistic (measure of discrimination) was 1.50 (95% CI = 1.44, 1.56) and the corresponding RD2 (measure of explained variation) was 0.47 (0.45, 0.49), demonstrating strong explanatory performance. The Harrell-C statistic was 0.88 (0.88, 0.89). Lacking an external validation dataset, we applied internal-external cross validation to demonstrate the consistency of the prognostic information across geographically-defined subsets of the cohort. The MSI provides good ability to predict survival for melanoma patients. Beyond the immediate clinical use, the MSI may have important public health and research applications for evaluations of public health interventions aimed at reducing deaths from melanoma.

  14. Probabilistic streamflow predictions combining ensemble meteorological forecasts and a multi-model approach

    Science.gov (United States)

    Chen, Jie; Brissette, François; Arsenault, Richard; Gatien, Philippe; Roy, Pierre-Olivier; Li, Zhi; Turcotte, Richard

    2013-04-01

    Probabilistic streamflow prediction based on past climate records or meteorological forecasts have drawn much attention in recent years. It is usually incorporated into operational forecasting systems by government agencies and industries to deal with water resources management and regulation problems. This work presents an operational prototype for short to medium term ensemble streamflow predictions over Quebec, Canada. The system uses ensemble meteorological forecasts for short term (up to 7 days) forecasting, transitioning to a stochastic weather generator conditioned on historical data for the period exceeding 7 days. The precipitation and temperature series are then fed into a combination of 32 hydrology models to account for both the meteorological and hydrology modelling uncertainties. A novel post-processing approach was implemented to correct the biases and the under-dispersion of ensemble meteorological forecasts. This post-processing approach links the mean of the ensemble meteorological forecast to parameters of a stochastic weather generator (absolute probability of precipitation and observed precipitation mean in the case of precipitation). The stochastic weather generator is then used to generated unbiased times series with accurate spread. Results show that the post-processed meteorological forecasts displayed skill for a period up to 7 days for both precipitation and temperature. The ensemble streamflow prediction displayed more skill than when using the deterministic forecast or the stochastic weather generator not conditioned on the ensemble meteorological forecasts. To tackle the uncertainty linked to the hydrology model, 4 different models calibrated with up to 9 different efficiency metrics (for a combination of 32 models/calibrations). Nine different averaging schemes were compared to attribute weights to the 32 combinations. The best averaging method (Granger-Ramanathan) produced estimates with a much better efficiency than the best

  15. Sensitivity of Global Modeling Initiative Model Predictions of Antarctic Ozone Recovery to Input Meteorological Fields

    Science.gov (United States)

    Considine, David B.; Connell, Peter S.; Bergmann, Daniel J.; Rotman, Douglas A.; Strahan, Susan E.

    2004-01-01

    We use the Global Modeling Initiative chemistry and transport model to simulate the evolution of stratospheric ozone between 1995 and 2030, using boundary conditions consistent with the recent World Meteorological Organization ozone assessment. We compare the Antarctic ozone recovery predictions of two simulations, one driven by an annually repeated year of meteorological data from a general circulation model (GCM), the other using a year of output from a data assimilation system (DAS), to examine the sensitivity of Antarctic ozone recovery predictions to the characteristic dynamical differences between GCM- and DAS-generated meteorological data. Although the age of air in the Antarctic lower stratosphere differs by a factor of 2 between the simulations, we find little sensitivity of the 1995-2030 Antarctic ozone recovery between 350 and 650 K to the differing meteorological fields, particularly when the recovery is specified in mixing ratio units. Percent changes are smaller in the DAS-driven simulation compared to the GCM-driven simulation because of a surplus of Antarctic ozone in the DAS-driven simulation which is not consistent with observations. The peak ozone change between 1995 and 2030 in both simulations is approx.20% lower than photochemical expectations, indicating that changes in ozone transport due to changing ozone gradients at 450 K between 1995 and 2030 constitute a small negative feedback. Total winter/spring ozone loss during the base year (1995) of both simulations and the rate of ozone loss during August and September is somewhat weaker than observed. This appears to be due to underestimates of Antarctic Cl(sub y) at the 450 K potential temperature level.

  16. 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...

  17. 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...

  18. Neural-Based Pattern Matching for Selection of Biophysical Model Meteorological Forcings

    Science.gov (United States)

    Coleman, A. M.; Wigmosta, M. S.; Li, H.; Venteris, E. R.; Skaggs, R. J.

    2011-12-01

    The current interest and demand for developing renewable and sustainable bio-based energies has brought microalgae and other terrestrial feedstocks into the active research domain where different components and strategies of the lifecycle are evaluated for economic and resource efficiency. To understand the potential energy returns and resource requirements of large-scale open- and closed-pond microalgae cultivation facilities and terrestrial feedstock growth on marginal lands, a spatial modeling and biophysical modeling suite, referred to as the Biomass Assessment Tool (BAT), has been developed. BAT is in part comprised of (1) a high spatial resolution multi-criteria land suitability model; (2) a coupled, high-temporal resolution, full mass and energy balance hydrodynamic pond temperature and microalgae growth model; (3) a terrestrial water demand and biomass growth model; and (4) an spatially-based energy, land, and water use optimization routine. Depending on the criteria used, our national spatial land suitability model yields tens of thousands of potential large-scale bioenergy sites for modeling and evaluation of production and resource demand. The fundamental driver of water use and bioenergy production rates in pond-based cultivation systems and traditional terrestrial crop growth is the meteorology; however, a major obstacle in the use of high spatiotemporal resolution biophysical models is the lack of sufficient and readily available meteorological data at the appropriate scale. To address this issue, firstly, the daily meteorology data from 2,522 USDA CLIGEN stations within the conterminous United States were disaggregated to an hourly time-step using the physics-based approach of Waichler and Wigmosta (2003), yielding a high-temporal resolution 30-year meteorological record. Secondly, in order to best describe the meteorological model forcings for a given site and significantly increase modeling efficiency, we developed a novel multi-scale pattern

  19. 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...

  20. High resolution meteorological modelling of the Inntal valley atmosphere, Part II: applications to dispersion modelling

    Science.gov (United States)

    Arnold, D.; Schicker, I.; Seibert, P.

    2009-09-01

    Orography and local meteorology play a major role in Alpine valleys, as they are linked with valley and slope wind systems, stagnation and recirculation, temperature inversions and turbulence. Thus, they have a strong influence of transport and dilution of pollutants in the valley, affecting human health, and sound propagation. Shallow stable layers at the valley floor and low wind speed conditions, especially in autumn and winter, trap pollutants and thus cause unfavourable dispersion conditions , possibly leading to exceedances of air pollution limits. Moreover, under certain synoptic conditions such as persistent high-pressure systems inversion conditions prevail for days. Emissions may accumulate in the valley from day to day and thus critical levels of pollutants may be reached. With the current computer capabilities, numerical meteorological models and particle dispersion models are powerful tools to investigate such situations and their impact on emission-side measures. Output from the limited area meteorological model MM5 for selected synoptic periods of special interest regarding air quality conditions (see contribution by Schicker et al., this session) will be used as input to the Lagrangian particle dispersion model FLEXPARTv6.2. Currently two specific versions of FLEXPART exist to work in the meso and local scale, WRF-FLEXPARTv6.2 and MM5v3-7.FLEXPARTv6.2, which use WRF and MM5 fields as meteorological input, respectively. Extensive tests will be performed with the MM5-driven version and additional preliminary ones with the WRF version. Dispersion calculations for CO and NOx will be performed in a receptor-oriented approach for three stations and with different meteorological input resolutions. The target stations will be Innsbruck, located at the bottom of the Inn valley, and Nordkette, located on the northern slopes of the valley. Source-receptor sensitivities (SRS) will be obtained to see the major sources which contribute to the measurements for

  1. Prediction model for demands of the health meteorological information using a decision tree method.

    Science.gov (United States)

    Oh, Jina; Kim, Byungsoo

    2010-09-01

    Climate change affects human health and calls for health meteorological services. The purpose of this study is to find the significant predictors for the demands of the health meteorological information. This study used a descriptive design through structured self-report questionnaires. Data from 956 participants who were at least 18 years old and living in Busan, Korea, were collected from June 1 to July 31, 2009. The data was analyzed using a decision tree method, one of the data mining techniques by SAS 9.1 and Enterprise Miner 4.3 program. Two hundred and ninety participants (30.3%) demanded the information, and 505 of them (52.8%) perceived the necessity of health meteorological information. From the decision tree method, the predictors related to the demands of the health meteorological information were determined as "the perception of the necessity of health meteorological information," "the coping to the weather warnings" and "the importance of the weather forecasting in daily life." In Particular, the significant different variables in the perception of the necessity of health meteorological information were "female," "aged over 40" and "environmental diseases." Thus, the model derived in this study is considered for explaining and predicting the demands of health meteorological information. 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. Copyright © 2010 Korean Society of Nursing Science. Published by . All rights reserved.

  2. Remote sensing data assimilation for a prognostic phenology model

    Energy Technology Data Exchange (ETDEWEB)

    Thornton, Peter E [ORNL; Stockli, Reto [Colorado State University, Fort Collins

    2008-01-01

    Predicting the global carbon and water cycle requires a realistic representation of vegetation phenology in climate models. However most prognostic phenology models are not yet suited for global applications, and diagnostic satellite data can be uncertain and lack predictive power. We present a framework for data assimilation of Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) and Leaf Area Index (LAI) from the MODerate Resolution Imaging Spectroradiometer (MODIS) to constrain empirical temperature, light, moisture and structural vegetation parameters of a prognostic phenology model. We find that data assimilation better constrains structural vegetation parameters than climate control parameters. Improvements are largest for drought-deciduous ecosystems where correlation of predicted versus satellite-observed FPAR and LAI increases from negative to 0.7-0.8. Data assimilation effectively overcomes the cloud- and aerosol-related deficiencies of satellite data sets in tropical areas. Validation with a 49-year-long phenology data set reveals that the temperature-driven start of season (SOS) is light limited in warm years. The model has substantial skill (R = 0.73) to reproduce SOS inter-annual and decadal variability. Predicted SOS shows a higher inter-annual variability with a negative bias of 5-20 days compared to species-level SOS. It is however accurate to within 1-2 days compared to SOS derived from net ecosystem exchange (NEE) measurements at a FLUXNET tower. The model only has weak skill to predict end of season (EOS). Use of remote sensing data assimilation for phenology model development is encouraged but validation should be extended with phenology data sets covering mediterranean, tropical and arctic ecosystems.

  3. 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

  4. 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.

  5. 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

    2017-10-28

    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.

  6. Stage Separation Failure: Model Based Diagnostics and Prognostics

    Science.gov (United States)

    Luchinsky, Dmitry; Hafiychuk, Vasyl; Kulikov, Igor; Smelyanskiy, Vadim; Patterson-Hine, Ann; Hanson, John; Hill, Ashley

    2010-01-01

    Safety of the next-generation space flight vehicles requires development of an in-flight Failure Detection and Prognostic (FD&P) system. Development of such system is challenging task that involves analysis of many hard hitting engineering problems across the board. In this paper we report progress in the development of FD&P for the re-contact fault between upper stage nozzle and the inter-stage caused by the first stage and upper stage separation failure. A high-fidelity models and analytical estimations are applied to analyze the following sequence of events: (i) structural dynamics of the nozzle extension during the impact; (ii) structural stability of the deformed nozzle in the presence of the pressure and temperature loads induced by the hot gas flow during engine start up; and (iii) the fault induced thrust changes in the steady burning regime. The diagnostic is based on the measurements of the impact torque. The prognostic is based on the analysis of the correlation between the actuator signal and fault-induced changes in the nozzle structural stability and thrust.

  7. 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).

  8. 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

  9. 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.

  10. 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.

  11. 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.

  12. High Resolution Modelling of Aerosols-Meteorology Interactions over Northern Europe and Arctic regions

    Science.gov (United States)

    Mahura, Alexander; Nuterman, Roman; Baklanov, Alexander

    2017-04-01

    Aerosols have influence on weather, air quality and climate. Multi-scale modelling, and especially long-range atmospheric transport, dispersion, and deposition of aerosols from remote sources is especially challenging in northern latitudes. It is due to complexity of meteorological, chemical and biological processes, their interactions and especially within and above the surface layer, linking to climate change, and influence on ecosystems. The online integrated meteorology-chemistry-aerosols model Enviro-HIRLAM (Environment - High Resolution Limited Area Model) was employed for evaluating spatio-temporal variability of atmospheric aerosols and their interactions and effects on meteorology with a focus on the Northern Europe and Arctic regions. The model setup covers domain having 510 x 568 grids of latitude vs. longitude, horizontal resolution of 0.15 deg, 40 vertical hybrid levels, time step of 360 sec, 6 h meteorological surface data assimilation. The model was run for January and July-August 2010 at DMI's CRAY-XC30 supercomputer. Emissions used are anthropogenic (ECLIPSE v5), shipping (combined AU_RCP and FMI), wildfires (IS4FIRES), and interactive sea salt, dust and DMS. The boundary conditions were obtained from ECMWF: for meteorology (from IFS at 0.15 and 0.25 deg. for summer and winter, respectively) and atmospheric composition (from MACC Reanalysis at 1.125 deg. resolution). The Enviro-HIRLAM model was employed in 4 modes: the reference run (e.g. without aerosols influence on meteorology) and 3 modified runs (direct aerosol effect (DAE), indirect aerosol effect (IDAE), and both effects DAE and IDAE included). The differences between the reference run and the runs with mentioned aerosol effects were estimated on a day-by-day, monthly and diurnal cycle bases over the domain, Arctic areas, European and Nordic countries. The results of statistical analyses are summarized and presented.

  13. 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.

  14. 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...

  15. A Comparison of Filter-based Approaches for Model-based Prognostics

    Data.gov (United States)

    National Aeronautics and Space Administration — Model-based prognostics approaches use domain knowledge about a system and its failure modes through the use of physics-based models. Model-based prognosis is...

  16. Dispersion modeling of accidental releases of toxic gases - Sensitivity study and optimization of the meteorological input

    Science.gov (United States)

    Baumann-Stanzer, K.; Stenzel, S.

    2009-04-01

    Several air dispersion models are available for prediction and simulation of the hazard areas associated with accidental releases of toxic gases. The most model packages (commercial or free of charge) include a chemical database, an intuitive graphical user interface (GUI) and automated graphical output for effective presentation of results. The models are designed especially for analyzing different accidental toxic release scenarios ("worst-case scenarios"), preparing emergency response plans and optimal countermeasures as well as for real-time risk assessment and management. Uncertainties in the meteorological input together with incorrect estimates of the source play a critical role for the model results. The research project RETOMOD (reference scenarios calculations for toxic gas releases - model systems and their utility for the fire brigade) was conducted by the Central Institute for Meteorology and Geodynamics (ZAMG) in cooperation with the Vienna fire brigade, OMV Refining & Marketing GmbH and Synex Ries & Greßlehner GmbH. RETOMOD was funded by the KIRAS safety research program at the Austrian Ministry of Transport, Innovation and Technology (www.kiras.at). The main tasks of this project were 1. Sensitivity study and optimization of the meteorological input for modeling of the hazard areas (human exposure) during the accidental toxic releases. 2. Comparison of several model packages (based on reference scenarios) in order to estimate the utility for the fire brigades. This presentation gives a short introduction to the project and presents the results of task 1 (meteorological input). The results of task 2 are presented by Stenzel and Baumann-Stanzer in this session. For the aim of this project, the observation-based analysis and forecasting system INCA, developed in the Central Institute for Meteorology and Geodynamics (ZAMG) was used. INCA (Integrated Nowcasting through Comprehensive Analysis) data were calculated with 1 km horizontal resolution and

  17. 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.

  18. 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.

  19. Multidisciplinary Rehabilitation Treatment of Patients With Chronic Low Back Pain: A Prognostic Model for Its Outcome

    NARCIS (Netherlands)

    van der Hulst, Marije; Vollenbroek-Hutten, Miriam Marie Rosé; Groothuis-Oudshoorn, Catharina Gerarda Maria; Hermens, Hermanus J.

    Objectives: (1) To determine if treatment outcome in chronic low back pain can be predicted by a predefined multivariate prognostic model based on consistent predictors from the literature and (2) to explore the value of potentially prognostic factors further. Methods: Data were derived from a

  20. Systematic review of multivariable prognostic models for mild traumatic brain injury.

    Science.gov (United States)

    Silverberg, Noah D; Gardner, Andrew J; Brubacher, Jeffrey R; Panenka, William J; Li, Jun Jian; Iverson, Grant L

    2015-04-15

    Prognostic models can guide clinical management and increase statistical power in clinical trials. The availability and adequacy of prognostic models for mild traumatic brain injury (MTBI) is uncertain. The present study aimed to (1) identify and evaluate multivariable prognostic models for MTBI, and (2) determine which pre-, peri-, and early post-injury variables have independent prognostic value in the context of multivariable models. An electronic search of MEDLINE, PsycINFO, PubMed, EMBASE, and CINAHL databases for English-language MTBI cohort studies from 1970-2013 was supplemented by Web of Science citation and hand searching. This search strategy identified 7789 articles after removing duplicates. Of 182 full-text articles reviewed, 26 met eligibility criteria including (1) prospective inception cohort design, (2) prognostic information collected within 1 month post-injury, and (3) 2+variables combined to predict clinical outcome (e.g., post-concussion syndrome) at least 1 month later. Independent reviewers extracted sample characteristics, study design features, clinical outcome variables, predictor selection methods, and prognostic model discrimination, calibration, and cross-validation. These data elements were synthesized qualitatively. The present review found no multivariable prognostic model that adequately predicts individual patient outcomes from MTBI. Suboptimal methodology limits their reproducibility and clinical usefulness. The most robust prognostic factors in the context of multivariable models were pre-injury mental health and early post-injury neuropsychological functioning. Women and adults with early post-injury anxiety also have worse prognoses. Relative to these factors, the severity of MTBI had little long-term prognostic value. Future prognostic studies should consider a broad range of biopsychosocial predictors in large inception cohorts.

  1. 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.

  2. Improving Computational Efficiency of Prediction in Model-based Prognostics Using the Unscented Transform

    Data.gov (United States)

    National Aeronautics and Space Administration — 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...

  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. Modelling of snow processes in catchment hydrology by means of downscaled WRF meteorological data fields

    Science.gov (United States)

    Förster, K.; Meon, G.; Strasser, U.

    2014-04-01

    Detailed physically based snowmelt models require a complete set of meteorological forcing data at the model's scale. Besides precipitation and temperature, time series of humidity, wind speed, and radiation have to be provided. The availability of these time series is in many cases restricted to a few meteorological stations and consequently, snowmelt modelling is often highly uncertain. To overcome this dilemma, the suitability of downscaled atmospheric analysis data for physically based snowmelt simulations in hydrological modelling is studied. We used the Weather Research and Forecast model (WRF) to derive spatial and temporal fields of meteorological surface variables as boundary conditions for four different snowmelt models. The simulations were carried out at the point scale and at the catchment scale for the Sieber catchment (44.4 km2), Harz Mountains, Germany. For the latter, all snowmelt models were integrated into the hydrological modelling system PANTA RHEI. All models performed well at both scales. In conclusion, the presented approach is suitable to derive reliable estimates of snowpack and snowmelt processes as part of water balance and flood simulations for catchments exposed to snow.

  5. Modeling of High-altitude Atmospheric Dispersion Using Climate and Meteorological Forecast Data

    Energy Technology Data Exchange (ETDEWEB)

    Glascoe, L G; Chin, H S

    2005-03-30

    The overall objective of this study is to provide a demonstration of capability for importing both high altitude meteorological forecast and climatological datasets from NRL into the NARAC modeling system to simulate high altitude atmospheric droplet release and dispersion. The altitude of release for the proposed study is between 60 and 100km altitude. As either standard climatological data (over a period of 40 years) or daily meteorological forecasts can drive the particle dispersion model, we did a limited comparison of simulations with meteorological data and simulations with climatological data. The modeling tools used to address this problem are the National Atmospheric Release Advisory Center (NARAC) modeling system at LLNL which are operationally employed to assist DOE/DHS/DOD emergency response to an atmospheric release of chemical, biological, and radiological contaminants. The interrelation of the various data feeds and codes at NARAC are illustrated in Figure 1. The NARAC scientific models are all verified to both analytic solutions and other codes; the models are validated to field data such as the Prairie Grass study (Barad, 1958). NARAC has multiple real-time meteorological data feeds from the National Weather Service, from the European Center for Medium range Weather Forecasting, from the US Navy, and from the US Air Force. NARAC also keeps a historical archive of meteorological data partially for research purposes. The codes used in this effort were the Atmospheric Data Assimilation and Parameterization Techniques (ADAPT) model (Sugiyama and Chan, 1998) and a development version of the Langrangian Operational Dispersion Integrator (LODI) model (Nasstrom et al., 2000). The use of the NASA GEOS-4 dataset required the use of a development version of the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) model (Hodur, 1997; Chin and Glascoe, 2004). The specific goals of this study are the following: (1) Confirm data compatibility of NRL

  6. Influence of the meteorological spatial resolution on Radon-222 backward modelling with FLEXPART

    Science.gov (United States)

    Arnold, D.; Seibert, P.; Vargas, A.

    2009-04-01

    One of the main important origins of uncertainties in atmospheric transport modelling comes from the meteorological input fields. Currently, operational analysis from the ECMWF, one of the reference sources of meteorological inputs used for modelling, are provided with horizontal spatial resolutions from 2 degrees down to 0.2 degrees, and also with different vertical and temporal resolutions. In this work it has been studied how the increase in spatial resolution of the ECMWF fields would affect the dispersion calculations of Radon-222. Backward modelling has been done with the widely used Lagrangian particle dispersion model FLEXPARTv6.2.Simulated radon concentration time series were compared with measurements at a station located in the outskirts of Barcelona (Spain). Results show relevant differences and better agreement is achieved when using the highest resolution fields. This study shows that if good model performance is desired, it is advisable to use ECMWF with 0.2 deg resolution despite the increase in computational dema

  7. Aircraft Anomaly Prognostics Project

    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...

  8. 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....... In MUD, corresponding ensembles of atmospheric dispersion are computed from which uncertainties of predicted radionuclide concentration and deposition patterns are derived....

  9. A Networked Interactive Meteorological Modeling and Sensing System

    Science.gov (United States)

    2007-06-01

    Doppler lidar. l l ti t l it l i f t l li . 13 June 2007 8 Dual lidar winds south of OKC, July 2003 ARL Lidar ASU Lidar 13 June 2007 9... modular design allows the flexibility to handle the addition, subtraction, or replacement/upgrade of sensors, models, or other software with

  10. 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.

  11. Atmospheric dispersion modeling near a roadway under calm meteorological conditions

    OpenAIRE

    Fallah Shorshani, Masoud; Seigneur, Christian; POLO REHN, Lucie; CHANUT, Hervé; PELLAN, Yann; Jaffrezo, Jean-Luc; CHARRON, Aurélie; Andre, Michel

    2015-01-01

    Atmospheric pollutant dispersion near sources is typically simulated by Gaussian models because of their efficient compromise between reasonable accuracy and manageable com- putational time. However, the standard Gaussian dispersion formula applies downwind of a source under advective conditions with a well-defined wind direction and cannot calculate air pollutant concentrations under calm conditions with fluctuating wind direction and/or upwind of the emission source. Attempts have been made...

  12. 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.

  13. The Practicability of a Novel Prognostic Index (PI) Model and Comparison with Nottingham Prognostic Index (NPI) in Stage I-III Breast Cancer Patients Undergoing Surgical Treatment.

    Science.gov (United States)

    Wen, Jiahuai; Ye, Feng; Li, Shuaijie; Huang, Xiaojia; Yang, Lu; Xiao, Xiangsheng; Xie, Xiaoming

    2015-01-01

    Previous studies have indicated the prognostic value of various laboratory parameters in cancer patients. This study was to establish a prognostic index (PI) model for breast cancer patients based on the potential prognostic factors. A retrospective study of 1661 breast cancer patients who underwent surgical treatment between January 2002 and December 2008 at Sun Yat-sen University Cancer Center was conducted. Multivariate analysis (Cox regression model) was performed to determine the independent prognostic factors and a prognostic index (PI) model was devised based on these factors. Survival analyses were used to estimate the prognostic value of PI, and the discriminatory ability of PI was compared with Nottingham Prognostic Index (NPI) by evaluating the area under the receiver operating characteristics curves (AUC). The mean survival time of all participants was 123.6 months. The preoperative globulin >30.0g/L, triglyceride >1.10mmol/L and fibrinogen >2.83g/L were identified as risk factors for shorter cancer-specific survival. The novel prognostic index model was established and enrolled patients were classified as low- (1168 patients, 70.3%), moderate- (410 patients, 24.7%) and high-risk groups (83 patients, 5.0%), respectively. Compared with the low-risk group, higher risks of poor clinical outcome were indicated in the moderate-risk group [Hazard ratio (HR): 1.513, 95% confidence interval (CI): 1.169-1.959, p = 0.002] and high-risk group (HR: 2.481, 95%CI: 1.653-3.724, p< 0.001). The prognostic index based on three laboratory parameters was a novel and practicable prognostic tool. It may serve as complement to help predict postoperative survival in breast cancer patients.

  14. Uncertainty Representation and Interpretation in Model-based Prognostics Algorithms based on Kalman Filter Estimation

    Data.gov (United States)

    National Aeronautics and Space Administration — This article discusses several aspects of uncertainty represen- tation and management for model-based prognostics method- ologies based on our experience with Kalman...

  15. 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

    2017-01-01

    intracranial tumor volume (CITV) into the ds-GPA model for melanoma augmented its prognostic value. OBJECTIVE: To determine whether or not CITV augments the ds-GPA prognostic scale for melanoma. METHODS: We analyzed the survival pattern of 344 melanoma patients with BM treated with stereotactic radiosurgery...... (SRS) at separate institutions and validated our findings in an independent cohort of 201 patients. The prognostic value of ds-GPA for melanoma was quantitatively compared with and without the addition of CITV using the net reclassification index (NRI > 0) and integrated discrimination improvement (IDI...... validated these findings that CITV improves the prognostic utility of melanoma ds-GPA in an independent cohort of 201 melanoma cohort. CONCLUSION: The prognostic value of the ds-GPA scale for melanoma BM is enhanced by the incorporation of CITV....

  16. Development and performance evaluation of statistical models correlating air pollutants and meteorological variables at Pantnagar, India

    Science.gov (United States)

    Banerjee, T.; Singh, S. B.; Srivastava, R. K.

    2011-03-01

    Ambient air quality in respect of SO2, NO2 and total suspended particulate matter (TSPM) was monitored at Pantnagar, India from May, 2008 to April, 2009 and statistically analyzed with meteorological variables such as relative humidity (RH), wind speed (WS), precipitation (P) and mean air temperature (T). TSPM was found to be the major air pollutant causing significant deterioration of air quality with annual mean concentrations of 280 μg/m3. Further, weekly mean air pollutant concentrations were statistically analyzed through stepwise multiple linear regression analysis in respect of independent meteorological variables to develop suitable statistical models. Both NO2 and TSPM concentrations were found to have been influenced by meteorological variables with coefficient of determination (R2) of 82.21 and 92.84%, respectively. However, atmospheric SO2 revealed only 22.87% of dependencies on meteorological variables. Partial correlation coefficients revealed that wind speed has the maximum influence (77.80 and 31.50%) on proposed equations for NO2 and SO2, closely followed by weekly mean temperature (73.60 and 24.30%). However, in case of TSPM, individual contribution of ambient temperature (94.40%) was found maximum, followed by relative humidity (86.50%). Model performances were evaluated through both quantitative data analysis techniques and statistical methods. Nearly 98 and 95% of potential error has been explained by the model developed for TSPM and NO2, while in case of SO2, it is found as only 61%. Therefore, performances of models (for TSPM and NO2) to predict ambient weekly mean concentrations based on forecasted weather parameters were found to be excellent, however, performance of model developed for SO2 was found only satisfactory.

  17. Sensitivity of chemical tracers to meteorological parameters in the MOZART-3 chemical transport model

    Science.gov (United States)

    Kinnison, D. E.; Brasseur, G. P.; Walters, S.; Garcia, R. R.; Marsh, D. R.; Sassi, F.; Harvey, V. L.; Randall, C. E.; Emmons, L.; Lamarque, J. F.; Hess, P.; Orlando, J. J.; Tie, X. X.; Randel, W.; Pan, L. L.; Gettelman, A.; Granier, C.; Diehl, T.; Niemeier, U.; Simmons, A. J.

    2007-10-01

    The Model for Ozone and Related Chemical Tracers, version 3 (MOZART-3), which represents the chemical and physical processes from the troposphere through the lower mesosphere, was used to evaluate the representation of long-lived tracers and ozone using three different meteorological fields. The meteorological fields are based on (1) the Whole Atmosphere Community Climate Model, version 1b (WACCM1b), (2) the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analysis, and (3) a new reanalysis for year 2000 from ECMWF called EXP471. Model-derived tracers (methane, water vapor, and total inorganic nitrogen) and ozone are compared to data climatologies from satellites. Model mean age of air was also derived and compared to in situ CO2 and SF6 data. A detailed analysis of the chemical fields simulated by MOZART-3 shows that even though the general features characterizing the three dynamical sets are rather similar, slight differences in winds and temperature can produce substantial differences in the calculated distributions of chemical tracers. The MOZART-3 simulations that use meteorological fields from WACCM1b and ECMWF EXP471 represented best the distribution of long-lived tracers and mean age of air in the stratosphere. There was a significant improvement using the ECMWF EXP471 reanalysis data product over the ECMWF operational data product. The effect of the quasi-biennial oscillation circulation on long-lived tracers and ozone is examined.

  18. 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

  19. 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.

  20. 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.

  1. Modeling drought impact occurrence based on meteorological drought indices in Europe

    Science.gov (United States)

    Stagge, James H.; Kohn, Irene; Tallaksen, Lena M.; Stahl, Kerstin

    2015-11-01

    There is a vital need for research that links meteorological drought indices with drought impacts felt on the ground. Previously, this link has been estimated based on experience or defined based on very narrow impact measures. This study expands on earlier work by showing the feasibility of relating user-provided impact reports with meteorological drought indices, the Standardized Precipitation Index and the Standardized Precipitation-Evapotranspiration Index, through logistic regression, while controlling for seasonal and interannual effects. Analysis includes four impact types, spanning agriculture, energy and industry, public water supply, and freshwater ecosystem across five European countries. Statistically significant climate indices are retained as predictors using step-wise regression and used to compare the most relevant drought indices and accumulation periods across different impact types and regions. Agricultural impacts are explained by 2-12 month anomalies, though anomalies greater than 3 months are likely related to agricultural management practices. Energy and industrial impacts, typically related to hydropower and energy cooling water, respond slower (6-12 months). Public water supply and freshwater ecosystem impacts are explained by a more complex combination of short (1-3 month) and seasonal (6-12 month) anomalies. The resulting drought impact models have both good model fit (pseudo-R2 = 0.225-0.716) and predictive ability, highlighting the feasibility of using such models to predict drought impact likelihood based on meteorological drought indices.

  2. Climate feedbacks in a general circulation model incorporating prognostic clouds

    Energy Technology Data Exchange (ETDEWEB)

    Colman, R.; Fraser, J. [Bureau of Meteorology Research Centre, Melbourne, Vic. (Australia); Rotstayn, L. [CSIRO Atmospheric Research, Aspendale (Australia)

    2001-11-01

    This study performs a comprehensive feedback analysis on the Bureau of Meteorology Research Centre General Circulation Model, quantifying all important feedbacks operating under an increase in atmospheric CO{sub 2}. The individual feedbacks are analysed in detail, using an offline radiation perturbation method, looking at long- and shortwave components, latitudinal distributions, cloud impacts, non-linearities under 2xCO{sub 2} and 4xCO{sub 2} warmings and at interannual variability. The water vapour feedback is divided into terms due to moisture height and amount changes. The net cloud feedback is separated into terms due to cloud amount, height, water content, water phase, physical thickness and convective cloud fraction. Globally the most important feedbacks were found to be (from strongest positive to strongest negative) those due to water vapour, clouds, surface albedo, lapse rate and surface temperature. For the longwave (LW) response the most important term of the cloud 'optical property' feedbacks is due to the water content. In the shortwave (SW), both water content and water phase changes are important. Cloud amount and height terms are also important for both LW and SW. Feedbacks due to physical cloud thickness and convective cloud fraction are found to be relatively small. All cloud component feedbacks (other than height) produce conflicting LW/SW feedbacks in the model. Furthermore, the optical property and cloud fraction feedbacks are also of opposite sign. The result is that the net cloud feedback is the (relatively small) product of conflicting physical processes. Non-linearities in the feedbacks are found to be relatively small for all but the surface albedo response and some cloud component contributions. The cloud impact on non-cloud feedbacks is also discussed: greatest impact is on the surface albedo, but impact on water vapour feedback is also significant. The analysis method here proves to be a powerful tool for detailing the

  3. 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.

  4. Mesoscale atmospheric modelling technology as a tool for creating a long-term meteorological dataset

    Science.gov (United States)

    Platonov, Vladimir; Kislov, Alexander; Rivin, Gdaly; Varentsov, Mikhail; Rozinkina, Inna; Nikitin, Mikhail; Chumakov, Mikhail

    2017-11-01

    A detailed hydrodynamic simulation of major meteorological parameters for the last 30 years (1985 – 2014) has been performed for the Sea of Okhotsk and the Sakhalin Island. The regional non-hydrostatic atmospheric model COSMO-CLM was used for this long-term simulation with horizontal resolutions of ~13.2, ~6.6 and ~2.2 km. This dataset was created to help in the investigation of statistical characteristics and physical mechanisms of formation of extreme weather events (primarily wind speed extremes) on small spatio-temporal scales. The detailed meteorological information thus obtained could be used to take into account the coast configuration, mountain systems, and other important mesoscale features of the terrain. This paper describes a proposed downscaling technology for long-term simulations with three “nested domains”. The results of verification of the dataset and estimation of extreme wind velocities are presented.

  5. A meteorological potential forecast model for acid rain in Fujian Province, China.

    Science.gov (United States)

    Cai, Yi Yong; Lin, Chang Cheng; Liu, Jing Xiong; Wu, De Hui; Lian, Dong Ying; Chen, Bin Bin

    2010-05-01

    Based on the acid rain and concurrent meteorological observational data during the past 10 years in Fujian Province, China, the dependence of distribution characteristics of acid rain on season, rain rate, weather pattern and dominant airflow in four regions of Fujian Province is analyzed. On the annual average, the acid rain frequency is the highest (above 40%) in the southern and mid-eastern regions, and the lowest (16.2%) in the western region. The acid rain occurs most frequently in spring and winter, and least frequent in summer. The acid rain frequency in general increases with the increase of precipitation. It also depend on the direction of dominant airflows at 850 hPa. In the mid-eastern region, more than 40% acid rains appear when the dominant wind directions are NW, W, SW, S and SE. In the southern region, high acid rain occurrence happens when the dominant wind directions are NW, W, SW and S. In the northern region, 41.8% acid rains occur when the southwesterly is pronounced. In the western region, the southwesterly is associated with a 17% acid rain rate. The examination of meteorological sounding conditions over Fuzhou, Xiamen and Shaowu cities shows that the acid rain frequency increases with increased inversion thickness. Based on the results above, a meteorological potential forecast model for acid rain is established and tested in 2007. The result is encouraging. The model provides an objective basis for the development of acid rain forecasting operation in the province.

  6. 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.

  7. Evaluation of satellite-model proxies for hydro-meteorological services in the upper Zambezi

    Directory of Open Access Journals (Sweden)

    Mark R. Jury

    2017-10-01

    Insights: Satellite soil moisture and model run-off track the Kafue Hook gauge with correlation values of 84% and 68%, respectively. Satellite river flow estimates achieve a logarithmic fit to monthly and daily gauge of 92% and 65%, respectively. Discrepancies are related to inadequate (calibration reporting and to under-estimation of evaporation in the dry season. Statistical analyses of satellite rainfall and model evaporation are used to suggest improvements to hydro-meteorology network coverage in the upper Zambezi. An automated online network of ∼20 weather stations and river flow gauges appears sufficient, given satellite-model ability to interpolate between observations.

  8. [Prognostic factors of postoperative delayed gastric emptying after pancreaticoduodenectomy: a predictive model].

    Science.gov (United States)

    Tan, H T; Zong, Y; Zhao, Z Q; Wu, L F; Liu, J; Sun, B; Jiang, H C

    2017-05-01

    Objective: To study the prognostic factors of delayed gastric emptying(DGE) after pancreaticoduodenectomy(PD) and construct a prognostic predictive model for clinical application. Methods: Clinic data of 401 consecutive patients who underwent PD between January 2012 and July 2016 in the First Affiliated Hospital of Harbin Medical University were retrospectively collected and analyzed. The patients were randomly selected to modeling group(n=299) and validation group(n=102) at a ratio of 3∶1. The data of modeling group were subjected to univariate and multivariate analysis for prognostic factors and to construct a prognostic predictive model of DGE after PD. The data of validation group were applied to test the prognostic predictive model. Results: DGE after PD occurred in 35 of 299 patients(11.7%) in the modeling group. The multivariate analysis of the modeling group showed that upper abdominal operation history(χ(2)=6.533, P=0.011), diabetes mellitus(χ(2)=17.872, P=0.000), preoperative hemoglobin predictive model of DGE after PD was constructed based on these factors and successfully tested. The area under the receiver operating characteristic(ROC) curve was 0.761(95%CI: 0.666-0.856) of the modeling group and 0.750(95% CI: 0.577-0.923) of the validation group. Conclusions: Upper abdominal operation history, diabetes mellitus, preoperative hemoglobinmodel is a valid tool to take precautions against DGE after PD.

  9. Prognostic immune-related gene models for breast cancer: a pooled analysis.

    Science.gov (United States)

    Zhao, Jianli; Wang, Ying; Lao, Zengding; Liang, Siting; Hou, Jingyi; Yu, Yunfang; Yao, Herui; You, Na; Chen, Kai

    2017-01-01

    Breast cancer, the most common cancer among women, is a clinically and biologically heterogeneous disease. Numerous prognostic tools have been proposed, including gene signatures. Unlike proliferation-related prognostic gene signatures, many immune-related gene signatures have emerged as principal biology-driven predictors of breast cancer. Diverse statistical methods and data sets were used for building these immune-related prognostic models, making it difficult to compare or use them in clinically meaningful ways. This study evaluated successfully published immune-related prognostic gene signatures through systematic validations of publicly available data sets. Eight prognostic models that were built upon immune-related gene signatures were evaluated. The performances of these models were compared and ranked in ten publicly available data sets, comprising a total of 2,449 breast cancer cases. Predictive accuracies were measured as concordance indices (C-indices). All tests of statistical significance were two-sided. Immune-related gene models performed better in estrogen receptor-negative (ER-) and lymph node-positive (LN+) breast cancer subtypes. The three top-ranked ER- breast cancer models achieved overall C-indices of 0.62-0.63. Two models predicted better than chance for ER+ breast cancer, with C-indices of 0.53 and 0.59, respectively. For LN+ breast cancer, four models showed predictive advantage, with C-indices between 0.56 and 0.61. Predicted prognostic values were positively correlated with ER status when evaluated using univariate analyses in most of the models under investigation. Multivariate analyses indicated that prognostic values of the three models were independent of known clinical prognostic factors. Collectively, these analyses provided a comprehensive evaluation of immune-related prognostic gene signatures. By synthesizing C-indices in multiple independent data sets, immune-related gene signatures were ranked for ER+, ER-, LN+, and LN- breast

  10. Clustering of Synoptic Pattern over the Korean Peninsula from Meteorological Models

    Science.gov (United States)

    Kim, Jinah; Heo, Kiyoung; Choi, Jungwoon; Jung, Sanghoon

    2017-04-01

    Numerical modeling data on meteorological and ocean science is one of example of big geographic data sources. The properties of the data including the volume, variety, and dynamic aspects pose new challenges for geographic visualization, and visual geoanalytics using big data analysis using machine learning method. A combination of algorithmic and visual approaches that make sense of large volumes of various types of spatiotemporal data are required to gain knowledge about complex phenomena. In the East coast of Korea, it is suffering from property damages and human causalities due to abnormal high waves (swell-like high-height waves). It is known to be caused by local meteorological conditions on the East Sea of Korean Peninsula in previous research and they proposed three kinds of pressure patterns that generate abnormal high waves. However, they cannot describe all kinds of pressure patterns that generate abnormal high waves. In our study, we propose unsupervised machine learning method for pattern clustering and applied it to classify a pattern which has occurred abnormal high waves using numerical meteorological model's reanalysis data from 2000 to 2015 and past historical records of accidents by abnormal high waves. About 25,000 patterns of total spatial distribution of sea surface pressure are clustered into 30 patterns and they are classified into seasonal sea level pressure patterns based on meteorological characteristics of Korean peninsula. Moreover, in order to determine the representative patterns which occurs abnormal high waves, we classified it again using historical accidents cases among the winter season pressure patterns. In this work, we clustered synoptic pattern over the Korean Peninsula in meteorological modeling reanalysis data and we could understand a seasonal variation through identifying the occurrence of clustered synoptic pattern. For the future work, we have to identify the relationship of wave modeling data for better understanding

  11. A Framework for Model-Based Diagnostics and Prognostics of Switched-Mode Power Supplies

    Science.gov (United States)

    2014-10-02

    consumption, MOSFET voltage, diode reverse voltage, and 47K resistance consumption. ANNUAL CONFERENCE OF THE PROGNOSTICS AND HEALTH MANAGEMENT...methodology based on an equivalent circuit system simulation model developed from a commercially available switch-mode power supply, and empirical...of an integrated simulation model combining two empirical models in the application of SMPS: a circuit -based SMPS simulation model and the

  12. Predictive statistical models linking antecedent meteorological conditions and waterway bacterial contamination in urban waterways.

    Science.gov (United States)

    Farnham, David J; Lall, Upmanu

    2015-06-01

    Although the relationships between meteorological conditions and waterway bacterial contamination are being better understood, statistical models capable of fully leveraging these links have not been developed for highly urbanized settings. We present a hierarchical Bayesian regression model for predicting transient fecal indicator bacteria contamination episodes in urban waterways. Canals, creeks, and rivers of the New York City harbor system are used to examine the model. The model configuration facilitates the hierarchical structure of the underlying system with weekly observations nested within sampling sites, which in turn were nested inside of the harbor network. Models are compared using cross-validation and a variety of Bayesian and classical model fit statistics. The uncertainty of predicted enterococci concentration values is reflected by sampling from the posterior predictive distribution. Issuing predictions with the uncertainty reasonably reflected allows a water manager or a monitoring agency to issue warnings that better reflect the underlying risk of exposure. A model using only antecedent meteorological conditions is shown to correctly classify safe and unsafe levels of enterococci with good accuracy. The hierarchical Bayesian regression approach is most valuable where transient fecal indicator bacteria contamination is problematic and drainage network data are scarce. Copyright © 2015 Elsevier Ltd. All rights reserved.

  13. A prognostic model for lung adenocarcinoma patient survival with a focus on four miRNAs.

    Science.gov (United States)

    Li, Xianqiu; An, Zhaoling; Li, Peihui; Liu, Haihua

    2017-09-01

    There is currently no effective biomarker for determining the survival of patients with lung adenocarcinoma. The purpose of the present study was to construct a prognostic survival model using microRNA (miRNA) expression data from patients with lung adenocarcinoma. miRNA data were obtained from The Cancer Genome Atlas, and patients with lung adenocarcinoma were divided into either the training or validation set based on the random allocation principle. The prognostic model focusing on miRNA was constructed, and patients were divided into high-risk or low-risk groups as per the scores, to assess their survival time. The 5-year survival rate from the subgroups within the high- and low-risk groups was assessed. P-values of the prognostic model in the total population, the training set and validation set were 0.0017, 0.01986 and 0.02773, respectively, indicating that the survival time of the lung adenocarcinoma high-risk group was less than that of the low-risk group. Thus, the model had a good assessment effectiveness for the untreated group (P=0.00088) and the Caucasian patient group (P=0.00043). In addition, the model had the best prediction effect for the 5-year survival rate of the Caucasian patient group (AUC=0.629). In conclusion, the prognostic model developed in the present study can be used as an independent prognostic model for patients with lung adenocarcinoma.

  14. Urban morphological analysis for mesoscale meteorological and dispersion modeling applications : current issues

    Energy Technology Data Exchange (ETDEWEB)

    Burian, S. J. (Steven J.); Brown, M. J. (Michael J.); Ching, J. (Jason); Cheuk, M. L. (Mang Lung); Yuan, M. (May); McKinnon, A. T. (Andrew T.); Han, W. S. (Woo Suk)

    2004-01-01

    Accurate predictions of air quality and atmospheric dispersion at high spatial resolution rely on high fidelity predictions of mesoscale meteorological fields that govern transport and turbulence in urban areas. However, mesoscale meteorological models do not have the spatial resolution to directly simulate the fluid dynamics and thermodynamics in and around buildings and other urban structures that have been shown to modify micro- and mesoscale flow fields (e.g., see review by Bornstein 1987). Mesoscale models therefore have been adapted using numerous approaches to incorporate urban effects into the simulations (e.g., see reviews by Brown 2000 and Bornstein and Craig 2002). One approach is to introduce urban canopy parameterizations to approximate the drag, turbulence production, heating, and radiation attenuation induced by sub-grid scale buildings and urban surface covers (Brown 2000). Preliminary results of mesoscale meteorological and air quality simulations for Houston (Dupont et al. 2004) demonstrated the importance of introducing urban canopy parameterizations to produce results with high spatial resolution that accentuates variability, highlights important differences, and identifies critical areas. Although urban canopy parameterizations may not be applicable to all meteorological and dispersion models, they have been successfully introduced and demonstrated in many of the current operational and research mode mesoscale models, e.g., COAMPS (Holt et al. 2002), HOTMAC (Brown and Williams 1998), MM5 (e.g., Otte and Lacser 2001; Lacser and Otte 2002; Dupont et al. 2004), and RAMS (Rozoff et al. 2003). The primary consequence of implementing an urban parameterization in a mesoscale meteorological model is the need to characterize the urban terrain in greater detail. In general, urban terrain characterization for mesoscale modeling may be described as the process of collecting datasets of urban surface cover physical properties (e.g., albedo, emissivity) and

  15. An application of artificial neural network models to estimate air temperature data in areas with sparse network of meteorological stations.

    Science.gov (United States)

    Chronopoulos, Kostas I; Tsiros, Ioannis X; Dimopoulos, Ioannis F; Alvertos, Nikolaos

    2008-12-01

    In this work artificial neural network (ANN) models are developed to estimate meteorological data values in areas with sparse meteorological stations. A more traditional interpolation model (multiple regression model, MLR) is also used to compare model results and performance. The application site is a canyon in a National Forest located in southern Greece. Four meteorological stations were established in the canyon; the models were then applied to estimate air temperature values as a function of the corresponding values of one or more reference stations. The evaluation of the ANN model results showed that fair to very good air temperature estimations may be achieved depending on the number of the meteorological stations used as reference stations. In addition, the ANN model was found to have better performance than the MLR model: mean absolute error values were found to be in the range 0.82-1.72 degrees C and 0.90-1.81 degrees C, for the ANN and the MLR models, respectively. These results indicate that ANN models may provide advantages over more traditional models or methods for temperature and other data estimations in areas where meteorological stations are sparse; they may be adopted, therefore, as an important component in various environmental modeling and management studies.

  16. The search for stable prognostic models in multiple imputed data sets

    NARCIS (Netherlands)

    Vergouw, D.; Heijmans, M.W.; Peat, G.M.; Kuijpers, T.; Croft, P.R.; de Vet, H.C.W.; van der Horst, H.E.; van der Windt, D.A.W.M.

    2010-01-01

    Background: In prognostic studies model instability and missing data can be troubling factors. Proposed methods for handling these situations are bootstrapping (B) and Multiple imputation (MI). The authors examined the influence of these methods on model composition. Methods: Models were constructed

  17. An integrated system for wind energy forecast using meteorological models and statistical post-processing

    Science.gov (United States)

    Miranda, P.; Rodrigues, A.; Lopes, J.; Palma, J.; Tome, R.; Sousa, J.; Bessa, R.; Matos, J.

    2009-12-01

    With 3GW of installed wind turbines, corresponding to 23% of the total electric grid, and a 5-year plan that will grow that value above 5GW (near 40% of the grid), Portugal has been a recent success case for renewable energy development. Clearly such large share of wind energy in the national electric system implies a strong requirement for accurate wind forecasts, that can be used to forecast this highly variable energy source and allow for timely decision making in the energy markets, namely for on and off switching of alternative conventional sources. In the past 3 years, a system for 72h energy forecast in mainland Portugal was setup, using 6km resolution meteorological forecasts, forced by global GFS forecasts by NCEP. In the development phase, different boundary conditions (from NCEP and ECMWF) were tested, as well as different limited area models (namely MM5, Aladin, MesoNH and WRF) at resolutions from 12 to 2km, which were evaluated by comparison with wind observations at heights relevant for wind turbines (up to 80m) in different locations and for different synoptic conditions. The developed system, which works with a real time connection with wind farms, also includes a post-processing code that merges recent wind observations with the meteorological forecast, and converts the forecasted wind fields into forecasted energy, by incorporating empirical transfer functions of the wind farm. Wind conditions in Portugal are highly influenced by topography, as most wind farms are located in complex terrain, often in mountainous terrain, where stratification plays a significant role. Coastal effects are also highly relevant, especially during the Summer, where a strong diurnal cycle of the sea-breeze is superimposed on an equally strong boundary layer development, both with a significant impact on low level winds. These two ingredients tend to complicate wind forecasts, requiring fully developed meteorological models. In general, results from 2 full years of

  18. 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.

  19. 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

  20. Melanoma prognostic model using tissue microarrays and genetic algorithms.

    Science.gov (United States)

    Gould Rothberg, Bonnie E; Berger, Aaron J; Molinaro, Annette M; Subtil, Antonio; Krauthammer, Michael O; Camp, Robert L; Bradley, William R; Ariyan, Stephan; Kluger, Harriet M; Rimm, David L

    2009-12-01

    As a result of the questionable risk-to-benefit ratio of adjuvant therapies, stage II melanoma is currently managed by observation because available clinicopathologic parameters cannot identify the 20% to 60% of such patients likely to develop metastatic disease. Here, we propose a multimarker molecular prognostic assay that can help triage patients at increased risk of recurrence. Protein expression for 38 candidates relevant to melanoma oncogenesis was evaluated using the automated quantitative analysis (AQUA) method for immunofluorescence-based immunohistochemistry in formalin-fixed, paraffin-embedded specimens from a cohort of 192 primary melanomas collected during 1959 to 1994. The prognostic assay was built using a genetic algorithm and validated on an independent cohort of 246 serial primary melanomas collected from 1997 to 2004. Multiple iterations of the genetic algorithm yielded a consistent five-marker solution. A favorable prognosis was predicted by ATF2 ln(non-nuclear/nuclear AQUA score ratio) of more than -0.052, p21(WAF1) nuclear compartment AQUA score of more than 12.98, p16(INK4A) ln(non-nuclear/nuclear AQUA score ratio) of < or = -0.083, beta-catenin total AQUA score of more than 38.68, and fibronectin total AQUA score of < or = 57.93. Primary tumors that met at least four of these five conditions were considered a low-risk group, and those that met three or fewer conditions formed a high-risk group (log-rank P < .0001). Multivariable proportional hazards analysis adjusting for clinicopathologic parameters shows that the high-risk group has significantly reduced survival on both the discovery (hazard ratio = 2.84; 95% CI, 1.46 to 5.49; P = .002) and validation (hazard ratio = 2.72; 95% CI, 1.12 to 6.58; P = .027) cohorts. This multimarker prognostic assay, an independent determinant of melanoma survival, might be beneficial in improving the selection of stage II patients for adjuvant therapy.

  1. Sensitivity of Global Modeling Initiative chemistry and transport model simulations of radon-222 and lead-210 to input meteorological data

    Directory of Open Access Journals (Sweden)

    D. B. Considine

    2005-01-01

    Full Text Available We have used the Global Modeling Initiative chemistry and transport model to simulate the radionuclides radon-222 and lead-210 using three different sets of input meteorological information: 1. Output from the Goddard Space Flight Center Global Modeling and Assimilation Office GEOS-STRAT assimilation; 2. Output from the Goddard Institute for Space Studies GISS II' general circulation model; and 3. Output from the National Center for Atmospheric Research MACCM3 general circulation model. We intercompare these simulations with observations to determine the variability resulting from the different meteorological data used to drive the model, and to assess the agreement of the simulations with observations at the surface and in the upper troposphere/lower stratosphere region. The observational datasets we use are primarily climatologies developed from multiple years of observations. In the upper troposphere/lower stratosphere region, climatological distributions of lead-210 were constructed from ~25 years of aircraft and balloon observations compiled into the US Environmental Measurements Laboratory RANDAB database. Taken as a whole, no simulation stands out as superior to the others. However, the simulation driven by the NCAR MACCM3 meteorological data compares better with lead-210 observations in the upper troposphere/lower stratosphere region. Comparisons of simulations made with and without convection show that the role played by convective transport and scavenging in the three simulations differs substantially. These differences may have implications for evaluation of the importance of very short-lived halogen-containing species on stratospheric halogen budgets.

  2. Assessing the Relationship Between Meteorological Parameters, Air Pollution And Cardiovascular Mortality of Mashhad City Based on Time Series Model

    OpenAIRE

    Morteza Hatami; Mitra Mohammadi Mohammadi; Reza Esmaeli; Mandana Mohammadi

    2017-01-01

    Epidemiological studies conducted in the past two decades indicate that air pollution causes increase in cardiovascular, breathing and chronic bronchitis disorders and even causes cardiovascular mortality. Therefore, the aim of this study was to investigate the relationship between meteorological parameters, air pollution and cardiovascular mortality in the city of Mashhad in 2014 by a time series model. Data on mortality from cardiovascular disease, meteorological parameters and air pollutio...

  3. Artificial Neural Network Model in Prediction of Meteorological Parameters during Premonsoon Thunderstorms

    Directory of Open Access Journals (Sweden)

    A. J. Litta

    2013-01-01

    Full Text Available Forecasting thunderstorm is one of the most difficult tasks in weather prediction, due to their rather small spatial and temporal extension and the inherent nonlinearity of their dynamics and physics. Accurate forecasting of severe thunderstorms is critical for a large range of users in the community. In this paper, experiments are conducted with artificial neural network model to predict severe thunderstorms that occurred over Kolkata during May 3, 11, and 15, 2009, using thunderstorm affected meteorological parameters. The capabilities of six learning algorithms, namely, Step, Momentum, Conjugate Gradient, Quick Propagation, Levenberg-Marquardt, and Delta-Bar-Delta, in predicting thunderstorms and the usefulness for the advanced prediction were studied and their performances were evaluated by a number of statistical measures. The results indicate that Levenberg-Marquardt algorithm well predicted thunderstorm affected surface parameters and 1, 3, and 24 h advanced prediction models are able to predict hourly temperature and relative humidity adequately with sudden fall and rise during thunderstorm hour. This demonstrates its distinct capability and advantages in identifying meteorological time series comprising nonlinear characteristics. The developed model can be useful in decision making for meteorologists and others who work with real-time thunderstorm forecast.

  4. 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.

  5. Modeling analysis of secondary inorganic aerosols over China: pollution characteristics, and meteorological and dust impacts

    Science.gov (United States)

    Fu, Xiao; Wang, Shuxiao; Chang, Xing; Cai, Siyi; Xing, Jia; Hao, Jiming

    2016-01-01

    Secondary inorganic aerosols (SIA) are the predominant components of fine particulate matter (PM2.5) and have significant impacts on air quality, human health, and climate change. In this study, the Community Multiscale Air Quality modeling system (CMAQ) was modified to incorporate SO2 heterogeneous reactions on the surface of dust particles. The revised model was then used to simulate the spatiotemporal characteristics of SIA over China and analyze the impacts of meteorological factors and dust on SIA formation. Including the effects of dust improved model performance for the simulation of SIA concentrations, particularly for sulfate. The simulated annual SIA concentration in China was approximately 10.1 μg/m3 on domain average, with strong seasonal variation: highest in winter and lowest in summer. High SIA concentrations were concentrated in developed regions with high precursor emissions, such as the North China Plain, Yangtze River Delta, Sichuan Basin, and Pearl River Delta. Strong correlations between meteorological factors and SIA pollution levels suggested that heterogeneous reactions under high humidity played an important role on SIA formation, particularly during severe haze pollution periods. Acting as surfaces for heterogeneous reactions, dust particles significantly affected sulfate formation, suggesting the importance of reducing dust emissions for controlling SIA and PM2.5 pollution. PMID:27782166

  6. 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.

  7. 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)

  8. Quantification and classification of hydro-meteorological flood controls in northeast Switzerland as a basis for robust impact modelling

    Science.gov (United States)

    Keller, Luise; Rössler, Ole; Weingartner, Rolf

    2016-04-01

    Flood events are generated and shaped by different hydro-meteorological processes. Taking these drivers into account is essential for understanding flood generation and for developing robust hydrological models. We call a hydrological model robust if it is able to reproduce different flood types with different drivers at the same quality. Such models are a prerequisite for assessing climate change impact as they minimize bias associated with a potential change in frequency of projected flood types. For the same reason, identification of the key hydro-meteorological processes is crucial to enable a suitable downscaling of meteorological parameters. To gain understanding of the main hydro-meteorological processes associated with floods in a mesoscale alpine catchment (Thur River, 1700 km2), we analyse all events exceeding a 2-year flood over the past 50 years. Resulting 47 events are temporally delineated based on an adapted constant-k approach (Blume et al., 2007) using hourly runoff data. Each flood event is then characterized based on a variety of hydro-meteorological parameters and indices descriptive of catchment distributed (pre-) event conditions based on daily meteorological data. This comprehensive data set is used to classify the events based on hydro-meteorological parameters only and to derive typical flood-generating "storylines". Changes in these storylines over the past 50 years are discussed. Furthermore, the importance of each hydro-meteorological parameter is quantified which in turn might help to assess uncertainties associated with climate change impact studies. References Blume, T., Zehe, E., and Bronstert, A.: Rainfall - runoff response, event-based runoff coefficients and hydrograph separation, Hydrological Sciences Journal, 52, 843-862, doi:10.1623/hysj.52.5.843, 2007.

  9. 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.

  10. 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

  11. Modeling Hydraulic Responses to Meteorological Forcing: fromCanopy to Aquifer

    Energy Technology Data Exchange (ETDEWEB)

    Pan, Lehua; Jin, Jiming; Miller, Norman; Wu, Yu-Shu; Bodvarsson,Gudmundur

    2007-02-08

    An understanding of the hydrologic interactions amongatmosphere, land surface, and subsurface is one of the keys tounderstanding the water cycling system that supports our life system onearth. Properly modeling such interactionsis a difficult task because oftheinherent coupled processes and complex feedback structures amongsubsystems. In this paper, we present a model that simulates thelandsurface and subsurface hydrologic response to meteorological forcing.This model combines a state of the art landsurface model, the NCARCommunity Land Model version 3 (CLM3), with a variablysaturatedgroundwater model, the TOUGH2, through an internal interfacethat includes flux and state variables shared by the two submodels.Specifically, TOUGH2, in its simulation, uses infiltration, evaporation,and rootuptake rates, calculated by CLM3, as source/sink terms? CLM3, inits simulation, uses saturation and capillary pressure profiles,calculated by TOUGH2, as state variables. This new model, CLMT2,preserves the best aspects of both submodels: the state of the artmodeling capability of surface energy and hydrologic processes from CLM3and the more realistic physical process based modeling capability ofsubsurface hydrologic processes from TOUGH2. The preliminary simulationresults show that the coupled model greatly improves the predictions ofthe water table, evapotranspiration, surface temperature, and moisture inthe top 20 cm of soil at a real watershed, as evaluated from 18 years ofobserved data. The new model is also ready to be coupled with anatmospheric simulation model, representing one of the first models thatare capable to simulate hydraulic processes from top of the atmosphere todeep ground.

  12. 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...... in capturing the characteristics of probability distribution, auto-correlation and seasonal variations of wind speed compared with the traditional Markov chain Monte Carlo (MCMC) and autoregressive moving average (ARMA) model. Furthermore, the proposed model was applied to adequacy assessment of generation...... systems with wind power. The assessment results of the modified IEEE-RTS79 and IEEE-RTS96 demonstrated the effectiveness and accuracy of the proposed model....

  13. Performance of the meteorological radiation model during the solar eclipse of 29 March 2006

    Directory of Open Access Journals (Sweden)

    B. E. Psiloglou

    2007-12-01

    Full Text Available Various solar broadband models have been developed in the last half of the 20th century. The driving demand has been the estimation of available solar energy at different locations on earth for various applications. The motivation for such developments, though, has been the ample lack of solar radiation measurements at global scale. Therefore, the main goal of such codes is to generate artificial solar radiation series or calculate the availability of solar energy at a place.

    One of the broadband models to be developed in the late 80's was the Meteorological Radiation Model (MRM. The main advantage of MRM over other similar models was its simplicity in acquiring and using the necessary input data, i.e. air temperature, relative humidity, barometric pressure and sunshine duration from any of the many meteorological stations.

    The present study describes briefly the various steps (versions of MRM and in greater detail the latest version 5. To show the flexibility and great performance of the MRM, a harsh test of the code under the (almost total solar eclipse conditions of 29 March 2006 over Athens was performed and comparison of its results with real measurements was made. From this hard comparison it is shown that the MRM can simulate solar radiation during a solar eclipse event as effectively as on a typical day. Because of the main interest in solar energy applications about the total radiation component, MRM focuses on that. For this component, the RMSE and MBE statistical estimators during this study were found to be 7.64% and −1.67% on 29 March as compared to the respective 5.30% and +2.04% for 28 March. This efficiency of MRM even during an eclipse makes the model promising for easy handling of typical situations with even better results.

  14. An exploration of measures for comparing measurements with the results from meteorological models for Mexico City

    Energy Technology Data Exchange (ETDEWEB)

    Williams, M.D.; Brown, M.J.

    1995-12-31

    Los Alamos National Laboratory and Instituto Mexicano del Petroleo have completed a joint study of options for improving air quality in Mexico City. We used a three-dimensional, prognostic, higher-order turbulence model for atmospheric circulation (HOTMAC) to treat domains that include an urbanized area. We tested the model against routine measurements and those of a major field program. During the field program, measurements included: (1) lidar measurements of aerosol transport and dispersion, (2) aircraft measurements of winds, turbulence, and chemical species aloft, (3) aircraft measurements of skin temperatures, and (4) Tethersonde measurements of winds and ozone. We made both graphical and statistical comparisons and we have reported some of the comparisons to provide insight into the meaning of statistical parameters including the index of agreement.

  15. Meteorology, Macrophysics, Microphysics, Microwaves, and Mesoscale Modeling of Mediterranean Mountain Storms: The M8 Laboratory

    Science.gov (United States)

    Starr, David O. (Technical Monitor); Smith, Eric A.

    2002-01-01

    Comprehensive understanding of the microphysical nature of Mediterranean storms can be accomplished by a combination of in situ meteorological data analysis and radar-passive microwave data analysis, effectively integrated with numerical modeling studies at various scales, from synoptic scale down through the mesoscale, the cloud macrophysical scale, and ultimately the cloud microphysical scale. The microphysical properties of and their controls on severe storms are intrinsically related to meteorological processes under which storms have evolved, processes which eventually select and control the dominant microphysical properties themselves. This involves intense convective development, stratiform decay, orographic lifting, and sloped frontal lifting processes, as well as the associated vertical motions and thermodynamical instabilities governing physical processes that affect details of the size distributions and fall rates of the various types of hydrometeors found within the storm environment. Insofar as hazardous Mediterranean storms, highlighted in this study by three mountain storms producing damaging floods in northern Italy between 1992 and 2000, developing a comprehensive microphysical interpretation requires an understanding of the multiple phases of storm evolution and the heterogeneous nature of precipitation fields within a storm domain. This involves convective development, stratiform transition and decay, orographic lifting, and sloped frontal lifting processes. This also involves vertical motions and thermodynamical instabilities governing physical processes that determine details of the liquid/ice water contents, size disi:ributions, and fall rates of the various modes of hydrometeors found within hazardous storm environments.

  16. An innovative land use regression model incorporating meteorology for exposure analysis.

    Science.gov (United States)

    Su, Jason G; Brauer, Michael; Ainslie, Bruce; Steyn, Douw; Larson, Timothy; Buzzelli, Michael

    2008-02-15

    The advent of spatial analysis and geographic information systems (GIS) has led to studies of chronic exposure and health effects based on the rationale that intra-urban variations in ambient air pollution concentrations are as great as inter-urban differences. Such studies typically rely on local spatial covariates (e.g., traffic, land use type) derived from circular areas (buffers) to predict concentrations/exposures at receptor sites, as a means of averaging the annual net effect of meteorological influences (i.e., wind speed, wind direction and insolation). This is the approach taken in the now popular land use regression (LUR) method. However spatial studies of chronic exposures and temporal studies of acute exposures have not been adequately integrated. This paper presents an innovative LUR method implemented in a GIS environment that reflects both temporal and spatial variability and considers the role of meteorology. The new source area LUR integrates wind speed, wind direction and cloud cover/insolation to estimate hourly nitric oxide (NO) and nitrogen dioxide (NO(2)) concentrations from land use types (i.e., road network, commercial land use) and these concentrations are then used as covariates to regress against NO and NO(2) measurements at various receptor sites across the Vancouver region and compared directly with estimates from a regular LUR. The results show that, when variability in seasonal concentration measurements is present, the source area LUR or SA-LUR model is a better option for concentration estimation.

  17. Modeling Occurrence of Urban Mosquitos Based on Land Use Types and Meteorological Factors in Korea.

    Science.gov (United States)

    Kwon, Yong-Su; Bae, Mi-Jung; Chung, Namil; Lee, Yeo-Rang; Hwang, Suntae; Kim, Sang-Ae; Choi, Young Jean; Park, Young-Seuk

    2015-10-20

    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.

  18. Clinical prediction of 5-year survival in systemic sclerosis: validation of a simple prognostic model in EUSTAR centres

    NARCIS (Netherlands)

    Fransen, J.; Popa-Diaconu, D.A.; Hesselstrand, R.; Carreira, P.; Valentini, G.; Beretta, L.; Airo, P.; Inanc, M.; Ullman, S.; Balbir-Gurman, A.; Sierakowski, S.; Allanore, Y.; Czirjak, L.; Riccieri, V.; Giacomelli, R.; Gabrielli, A.; Riemekasten, G.; Matucci-Cerinic, M.; Farge, D.; Hunzelmann, N.; Hoogen, F.H. Van den; Vonk, M.C.

    2011-01-01

    OBJECTIVE: Systemic sclerosis (SSc) is associated with a significant reduction in life expectancy. A simple prognostic model to predict 5-year survival in SSc was developed in 1999 in 280 patients, but it has not been validated in other patients. The predictions of a prognostic model are usually

  19. Prognostic models for physical capacity at discharge and 1 year postdischarge from rehabilitation in persons with spinal cord injury

    NARCIS (Netherlands)

    Haisma, J.A.; van der Woude, L.H.V.; Stam, H.J.; Bergen, M.P.; Sluis, T.A.; de Groot, S.; Dallmeijer, A.J.; Bussmann, J.B.J.

    2007-01-01

    Haisma JA, van der Woude LH, Stam HJ, Bergen MP, Sluis TA, de Groot S, Dallmeijer AJ, Bussmann JB. Prognostic models for physical capacity at discharge and 1 year postdischarge from rehabilitation in persons with spinal cord injury. Objective: To develop prognostic models for physical capacity at

  20. 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, 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

    2016-01-01

    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

  1. 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.)

  2. 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.

  3. 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

  4. Prognostic Model for Survival in Patients With Early Stage Cervical Cancer

    NARCIS (Netherlands)

    Biewenga, Petra; van der Velden, Jacobus; Mol, Ben Willem J.; Stalpers, Lukas J. A.; Schilthuis, Marten S.; van der Steeg, Jan Willem; Burger, Matthé P. M.; Buist, Marrije R.

    2011-01-01

    BACKGROUND: In the management of early stage cervical cancer, knowledge about the prognosis is critical. Although many factors have an impact on survival, their relative importance remains controversial. This study aims to develop a prognostic model for survival in early stage cervical cancer

  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.|info:eu-repo/dai/nl/157009939; 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. Prognostic model for survival in patients with early stage cervical cancer.

    Science.gov (United States)

    Biewenga, Petra; van der Velden, Jacobus; Mol, Ben Willem J; Stalpers, Lukas J A; Schilthuis, Marten S; van der Steeg, Jan Willem; Burger, Matthé P M; Buist, Marrije R

    2011-02-15

    In the management of early stage cervical cancer, knowledge about the prognosis is critical. Although many factors have an impact on survival, their relative importance remains controversial. This study aims to develop a prognostic model for survival in early stage cervical cancer patients and to reconsider grounds for adjuvant treatment. A multivariate Cox regression model was used to identify the prognostic weight of clinical and histological factors for disease-specific survival (DSS) in 710 consecutive patients who had surgery for early stage cervical cancer (FIGO [International Federation of Gynecology and Obstetrics] stage IA2-IIA). Prognostic scores were derived by converting the regression coefficients for each prognostic marker and used in a score chart. The discriminative capacity was expressed as the area under the curve (AUC) of the receiver operating characteristic. The 5-year DSS was 92%. Tumor diameter, histological type, lymph node metastasis, depth of stromal invasion, lymph vascular space invasion, and parametrial extension were independently associated with DSS and were included in a Cox regression model. This prognostic model, corrected for the 9% overfit shown by internal validation, showed a fair discriminative capacity (AUC, 0.73). The derived score chart predicting 5-year DSS showed a good discriminative capacity (AUC, 0.85). In patients with early stage cervical cancer, DSS can be predicted with a statistical model. Models, such as that presented here, should be used in clinical trials on the effects of adjuvant treatments in high-risk early cervical cancer patients, both to stratify and to include patients. Copyright © 2010 American Cancer Society.

  8. The search for stable prognostic models in multiple imputed data sets

    Directory of Open Access Journals (Sweden)

    de Vet Henrica CW

    2010-09-01

    Full Text Available Abstract Background In prognostic studies model instability and missing data can be troubling factors. Proposed methods for handling these situations are bootstrapping (B and Multiple imputation (MI. The authors examined the influence of these methods on model composition. Methods Models were constructed using a cohort of 587 patients consulting between January 2001 and January 2003 with a shoulder problem in general practice in the Netherlands (the Dutch Shoulder Study. Outcome measures were persistent shoulder disability and persistent shoulder pain. Potential predictors included socio-demographic variables, characteristics of the pain problem, physical activity and psychosocial factors. Model composition and performance (calibration and discrimination were assessed for models using a complete case analysis, MI, bootstrapping or both MI and bootstrapping. Results Results showed that model composition varied between models as a result of how missing data was handled and that bootstrapping provided additional information on the stability of the selected prognostic model. Conclusion In prognostic modeling missing data needs to be handled by MI and bootstrap model selection is advised in order to provide information on model stability.

  9. Testing the importance of accurate meteorological input fields and parameterizations in atmospheric transport modelling using dream - validation against ETEX-1

    Science.gov (United States)

    Brandt, Jørgen; Bastrup-birk, Annemarie; Christensen, Jesper H.; Mikkelsen, Torben; Thykier-Nielsen, Søren; Zlatev, Zahari

    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 transport and dispersion of air pollutants caused by a single but strong source as, e.g. an accidental release from a nuclear power plant. The model system including the coupling of the Lagrangian model with the Eulerian model are described. Various simple and comprehensive parameterizations of the mixing height, the vertical dispersion, and different meterological input data have been implemented in the combined tracer model, and the model results have been validated against measurements from the ETEX-1 release. Several different statistical parameters have been used to estimate the differences between the parameterizations and meterological input data in order to find the best performing solution.

  10. Effect of meteorological forcing and snow model complexity on hydrological simulations in the Sieber catchment (Harz Mountains, Germany)

    Science.gov (United States)

    Förster, K.; Meon, G.; Marke, T.; Strasser, U.

    2014-11-01

    Detailed physically based snow models using energy balance approaches are spatially and temporally transferable and hence regarded as particularly suited for scenario applications including changing climate or land use. However, these snow models place high demands on meteorological input data at the model scale. Besides precipitation and temperature, time series of humidity, wind speed, and radiation have to be provided. In many catchments these time series are rarely available or provided by a few meteorological stations only. This study analyzes the effect of improved meteorological input on the results of four snow models with different complexity for the Sieber catchment (44.4 km2) in the Harz Mountains, Germany. The Weather Research and Forecast model (WRF) is applied to derive spatial and temporal fields of meteorological surface variables at hourly temporal resolution for a regular grid of 1.1 km × 1.1 km. All snow models are evaluated at the point and the catchment scale. For catchment-scale simulations, all snow models were integrated into the hydrological modeling system PANTA RHEI. The model results achieved with a simple temperature-index model using observed precipitation and temperature time series as input are compared to those achieved with WRF input. Due to a mismatch between modeled and observed precipitation, the observed melt runoff as provided by a snow lysimeter and the observed streamflow are better reproduced by application of observed meteorological input data. In total, precipitation is simulated statistically reasonably at the seasonal scale but some single precipitation events are not captured by the WRF data set. Regarding the model efficiencies achieved for all simulations using WRF data, energy balance approaches generally perform similarly compared to the temperature-index approach and partially outperform the latter.

  11. 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.

  12. Shade material evaluation using a cattle response model and meteorological instrumentation

    Science.gov (United States)

    Eigenberg, Roger A.; Brown-Brandl, Tami M.; Nienaber, John A.

    2010-11-01

    Shade structures are often considered as one method of reducing stress in feedlot cattle. Selection of a suitable shade material can be difficult without data that quantify material effectiveness for stress reduction. A summer study was conducted during 2007 using instrumented shade structures in conjunction with meteorological measurements to estimate relative effectiveness of various shade materials. Shade structures were 3.6 m by 6.0 m by 3.0 m high at the peak and 2.0 m high at the sides. Polyethylene shade cloth was used in three of the comparisons and consisted of effective coverings of 100%, 60% with a silver reflective coating, and 60% black material with no reflective coating. Additionally, one of the structures was fitted with a poly snow fence with an effective shade of about 30%. Each shade structure contained a solar radiation meter and a black globe thermometer to measure radiant energy received under the shade material. Additionally, meteorological data were collected as a non-shaded treatment and included temperature, humidity, wind speed, and solar radiation. Data analyses was conducted using a physiological model based on temperature, humidity, solar radiation and wind speed; a second model using black globe temperatures, relative humidity, and wind speed was used as well. Analyses of the data revealed that time spent in the highest stress category was reduced by all shade materials. Moreover, significant differences ( P < 0.05) existed between all shade materials (compared to no-shade) for hourly summaries during peak daylight hours and for `full sun' days.

  13. The American Meteorological Society Education Program Model for Climate Science Education

    Science.gov (United States)

    Weinbeck, R. S.; Moran, J. M.; Geer, I. W.; Hopkins, E. J.

    2007-12-01

    A guiding principle of the American Meteorological Society (AMS) Education Program is that public scientific literacy is most effectively achieved through systemic change in the classroom. The AMS, partnering with NOAA, NSF, NASA, the US Navy, and SUNY Brockport, aims for greater public scientific literacy through its successful distance learning programs that convey to pre-college teachers and undergraduates the fundamentals of meteorology, oceanography, and hydrology. The AMS DataStreme teacher-enhancement courses (Atmosphere, Water in the Earth System, and Ocean) have changed the way thousands of pre-college teachers teach and hundreds of thousands of students learn. Furthermore, teachers trained in this program are positioned to contribute to local and statewide curriculum reform. The AMS Online Weather Studies and Online Ocean Studies courses are providing tens of thousands of college undergraduates with engaging and highly motivational learning experiences. DataStreme courses are offered locally and feature mentoring of teacher participants whereas Online undergraduate courses are licensed by AMS for offering by colleges and universities. Integrated components of the AMS model are course website, investigations manual, and customized textbook. A portion of twice-weekly investigations is written to a near real-time situation and posted on the course website. Through its extensive experience with the DataStreme/Online programs, the AMS Education Program is now uniquely poised to assume a national leadership role in climate education by applying its proven teaching/learning model to climate education at the pre-college and undergraduate levels. The AMS model is ideally suited for delivering to teachers and students nationwide the basic understandings and enduring ideas of climate science and the role of the individual and society in climate variability and change. The AMS teaching/learning model incorporates an Earth system perspective, is problem focused, and

  14. 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.

  15. 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......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...... - heat flux related measurement, e.g. a temperature gradient, are used to give local values of friction velocity and Monin-Obukhov length plus an estimate of the mixing height. The METRODOS meteorological preprocessor chain is an integral part of the RODOS - Real Time On Line Decision Support - program...

  16. Mountain wave PSC dynamics and microphysics from ground-based lidar measurements and meteorological modeling

    Directory of Open Access Journals (Sweden)

    J. Reichardt

    2004-01-01

    Full Text Available The day-long observation of a polar stratospheric cloud (PSC by two co-located ground-based lidars at the Swedish research facility Esrange (67.9° N, 21.1° E on 16 January 1997 is analyzed in terms of PSC dynamics and microphysics. Mesoscale modeling is utilized to simulate the meteorological setting of the lidar measurements. Microphysical properties of the PSC particles are retrieved by comparing the measured particle depolarization ratio and the PSC-averaged lidar ratio with theoretical optical data derived for different particle shapes. In the morning, nitric acid trihydrate (NAT particles and then increasingly coexisting liquid ternary aerosol (LTA were detected as outflow from a mountain wave-induced ice PSC upwind Esrange. The NAT PSC is in good agreement with simulations for irregular-shaped particles with length-to-diameter ratios between 0.75 and 1.25, maximum dimensions from 0.7 to 0.9 µm, and a number density from 8 to 12 cm-3 and the coexisting LTA droplets had diameters from 0.7 to 0.9 µm, a refractive index of 1.39 and a number density from 7 to 11 cm-3. The total amount of condensed HNO3 was in the range of 8–12 ppbv. The data provide further observational evidence that NAT forms via deposition nucleation on ice particles as a number of recently published papers suggest. By early afternoon the mountain-wave ice PSC expanded above the lidar site. Its optical data indicate a decrease in minimum particle size from 3 to 1.9 µm with time. Later on, following the weakening of the mountain wave, wave-induced LTA was observed only. Our study demonstrates that ground-based lidar measurements of PSCs can be comprehensively interpreted if combined with mesoscale meteorological data.

  17. The Effect of Spatial Variability of Meteorological Data on Annual Average Air Concentrations Predicted by a Wind-Rose Model

    Energy Technology Data Exchange (ETDEWEB)

    Pendergast, M.M.

    2001-03-15

    This paper discusses the effect of spatial variability of meteorological data on annual average air concentrations for distance from the source ranging from 2-21 km. Annual average relative air concentrations varied by about 20 percent for the stations examined. This study also showed that annual average concentrations obtained with a simple wind-rose model, although overpredicting by a factor of 3, are not particularly sensitive to the amount of meteorological data used to represent the frequency distribution of wind and stability. These results indicate that a much smaller data recovery rate than the 90 percent required by NRC might be more appropriate.

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

    CERN Document Server

    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 addresses two problems. First, the data assimilation problem on micro-scales is investigated as a possibility to recover the turbulent fields consistent with the mean meteorological profiles. Second, the methods to incorporate of the unresolved surface structures are investigated in a priopi numerical experiments. The numerical experiments demonstrated that the simplest nudging or Newtonian relaxation technique for the data assimilation is applicable on the turbulence scales. It is also shown that the filtering property of...

  19. Artificial neural network models of relationships between Alternaria spores and meteorological factors in Szczecin (Poland)

    Science.gov (United States)

    Grinn-Gofroń, Agnieszka; Strzelczak, Agnieszka

    2008-11-01

    Alternaria is an airborne fungal spore type known to trigger respiratory allergy symptoms in sensitive patients. Aiming to reduce the risk for allergic individuals, we constructed predictive models for the fungal spore circulation in Szczecin, Poland. Monthly forecasting models were developed for the airborne spore concentrations of Alternaria, which is one of the most abundant fungal taxa in the area. Aerobiological sampling was conducted over 2004-2007, using a Lanzoni trap. Simultaneously, the following meteorological parameters were recorded: daily level of precipitation; maximum and average wind speed; relative humidity; and maximum, minimum, average, and dew point temperature. The original factors as well as with lags (up to 3 days) were used as the explaining variables. Due to non-linearity and non-normality of the data set, the modelling technique applied was the artificial neural network (ANN) method. The final model was a split model with classification (spore presence or absence) followed by regression for spore seasons and log(x+1) transformed Alternaria spore concentration. All variables except maximum wind speed and precipitation were important factors in the overall classification model. In the regression model for spore seasons, close relationships were noted between Alternaria spore concentration and average and maximum temperature (on the same day and 3 days previously), humidity (with lag 1) and maximum wind speed 2 days previously. The most important variable was humidity recorded on the same day. Our study illustrates a novel approach to modelling of time series with short spore seasons, and indicates that the ANN method provides the possibility of forecasting Alternaria spore concentration with high accuracy.

  20. Meteorological predictions for Mars 2020 Exploration Rover high-priority landing sites throug MRAMS Mesoscale Modeling

    Science.gov (United States)

    Pla-García, Jorge; Rafkin, Scot C. R.

    2015-04-01

    The Mars Regional Atmospheric Modeling System (MRAMS) is used to predict meteorological conditions that are likely to be encountered by the Mars 2020 Exploration Rover at several proposed landing sites during entry, descent, and landing (EDL). The meteorology during the EDL window at most of the sites is dynamic. The intense heating of the lower atmosphere drives intense thermals and mesoscale thermal circulations. Moderate mean winds, wind shear, turbulence, and vertical air currents associated with convection are present and potentially hazardous to EDL [1]. Nine areas with specific high-priority landing ellipses of the 2020 Rover, are investigated: NE Syrtis, Nili Fossae, Nili Fossae Carbonates, Jezero Crater Delta, Holden Crater, McLaughlin Crater, Southwest Melas Basin, Mawrth Vallis and East Margaritifer Chloride. MRAMS was applied to the landing site regions using nested grids with a spacing of 330 meters on the innermost grid that is centered over each landing site. MRAMS is ideally suited for this investigation; the model is explicitly designed to simulate Mars' atmospheric thermal circulations at the mesoscale and smaller with realistic, high-resolution surface properties [2, 3]. Horizontal wind speeds, both vertical profiles and vertical cross-sections wind speeds, are studied. For some landing sites simulations, two example configurations -including and not including Hellas basin in the mother domain- were generated, in order to study how the basin affects the innermost grids circulations. Afternoon circulations at all sites pose some risk entry, descent, and landing. Most of the atmospheric hazards are not evident in current observational data and general circulation model simulations and can only be ascertained through mesoscale modeling of the region. Decide where to go first and then design a system that can tolerate the environment would greatly minimize risk. References: [1] Rafkin, S. C. R., and T. I. Michaels (2003), J. Geophys. Res., 108(E12

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

    Science.gov (United States)

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

    2012-01-01

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

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

    Science.gov (United States)

    Biswas, Paritosh K; Islam, Md Zohorul; Debnath, Nitish C; Yamage, Mat

    2014-01-01

    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.

  3. 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.

  4. A molten salt tower model used for site selection in South Africa using SAURAN meteorological data

    Science.gov (United States)

    Poole, Ian Vincent; Dinter, Frank

    2017-06-01

    South Africa has become a hotspot for concentrating solar power (CSP) development in recent years. With an abundance of solar resource and an existing governmental framework for renewable energy development, the country has captured the attention of CSP developers worldwide. The primary limitations for CSP plants in South Africa are electrical transmission and water availability. While taking into account such infrastructure limitations, six sites were proposed. A purpose-built simulation model for a proposed 100 MWe (gross) tower plant with 12 hours of storage was developed. Using site South African Universities Radiometric Network (SAURAN) meteorological data with a resolution of up to 1 minute, each of the sites was evaluated in terms of electrical yield using the model. The investigation found that the site situated in Springbok will generate 450.8 GWhe per annum, and is the most advantageous site for the modeled plant. The most promising alternative site is situated in near Laingsburg in the Western Province. This site offered 413.7 GWhe per annum, and it is close to available transmission and surface water.

  5. Physical Modeling for Anomaly Diagnostics and Prognostics Project

    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...

  6. Crop yield monitoring based on a photosynthetic sterility model using NDVI and daily meteorological data

    Science.gov (United States)

    Kaneko, Daijiro

    2007-10-01

    This research is intended to develop a model to monitor rice yields using the photosynthetic yield index, which integrates solar radiation and air temperature effects on photosynthesis and grain-filling from heading to ripening. Monitoring crop production using remotely sensed and daily meteorological data can provide an important early warning of poor crop production to Asian countries, with their still-growing populations, and also to Japan, which produces insufficient grain for its population. The author improved a photosynthesis-and-sterility-based crop production CPI index to crop yield index CYI, which estimates rice yields, in place of the crop situation index CSI. The CSI 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, normalized difference vegetation index NDVI, and the effect of temperature on photosynthesis by grain plant leaves. The method is based on routine observation data, enabling automated monitoring of crop production at arbitrary regions without special observations. The method aims to quantity grain production at an early stage to raise the alarm in Asian countries, which are facing climate fluctuation through this century of global warming.

  7. 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.

  8. EDgE multi-model hydro-meteorological seasonal hindcast experiments over Europe

    Science.gov (United States)

    Samaniego, Luis; Thober, Stephan; Kumar, Rohini; Rakovec, Oldrich; Wood, Eric; Sheffield, Justin; Pan, Ming; Wanders, Niko; Prudhomme, Christel

    2017-04-01

    Extreme hydrometeorological events (e.g., floods, droughts and heat waves) caused serious damage to society and infrastructures over Europe during the past decades. Developing a seamless and skillful operational seasonal forecasting system of these extreme events is therefore a key tool for short-term decision making at local and regional scales. The EDgE project funded by the Copernicus programme (C3S) provides an unique opportunity to investigate the skill of a newly created large multi-model hydro-meteorological ensemble for predicting extreme events over the Pan-EU domain at a higher resolution 5×5 km2. Two state-of-the-art seasonal prediction systems were chosen for this project. Two models from the North American MultiModel ensemble (NMME) with 22 realizations, and two models provided by the ECMWF with 30 realizations. All models provide daily forcings (P, Ta, Tmin, Tmax) of the the Pan-EU at 1°. Downscaling has been carried out with the MTCLIM algorithm (Bohn et al. 2013) and external drift Kriging using elevation as drift to induce orographic effects. In this project, four high-resolution seamless hydrologic simulations with the mHM (www.ufz.de/mhm), Noah-MP, VIC and PCR-GLOBWB have been completed for the common hindcast period of 1993-2012 resulting in an ensemble size of 208 realizations. Key indicators are focussing on six terrestrial Essential Climate Variables (tECVs): river runoff, soil moisture, groundwater recharge, precipitation, potential evapotranspiration, and snow water equivalent. Impact Indicators have been co-designed with stakeholders in Norway (hydro-power), UK (water supply), and Spain (river basin authority) to provide an improved information for decision making. The Indicators encompass diverse information such as the occurrence of high and low streamflow percentiles (floods, and hydrological drought) and lower percentiles of top soil moisture (agricultural drought) among others. Preliminary results evaluated at study sites in Norway

  9. 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

  10. Model Updating and Uncertainty Management for Aircraft Prognostic Systems Project

    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...

  11. Introduction of prognostic rain in ECHAM5: design and Single Column Model simulations

    OpenAIRE

    Posselt, R.; Lohmann, U.

    2007-01-01

    International audience; Prognostic equations for the rain mass mixing ratio and the rain drop number concentration are introduced into the large-scale cloud microphysics parameterization of the ECHAM5 general circulation model (ECHAM5-PROG). To this end, a rain flux from one level to the next with the appropriate fall speed is introduced. This maintains rain water in the atmosphere to be available for the next time step. Rain formation in ECHAM5-PROG is, therefore, less dependent on the autoc...

  12. Enviro-HIRLAM online integrated meteorology-chemistry modelling system: strategy, methodology, developments and applications (v7.2)

    Science.gov (United States)

    Baklanov, Alexander; Smith Korsholm, Ulrik; Nuterman, Roman; Mahura, Alexander; Pagh Nielsen, Kristian; Hansen Sass, Bent; Rasmussen, Alix; Zakey, Ashraf; Kaas, Eigil; Kurganskiy, Alexander; Sørensen, Brian; González-Aparicio, Iratxe

    2017-08-01

    The Environment - High Resolution Limited Area Model (Enviro-HIRLAM) is developed as a fully online integrated numerical weather prediction (NWP) and atmospheric chemical transport (ACT) model for research and forecasting of joint meteorological, chemical and biological weather. The integrated modelling system is developed by the Danish Meteorological Institute (DMI) in collaboration with several European universities. It is the baseline system in the HIRLAM Chemical Branch and used in several countries and different applications. The development was initiated at DMI more than 15 years ago. The model is based on the HIRLAM NWP model with online integrated pollutant transport and dispersion, chemistry, aerosol dynamics, deposition and atmospheric composition feedbacks. To make the model suitable for chemical weather forecasting in urban areas, the meteorological part was improved by implementation of urban parameterisations. The dynamical core was improved by implementing a locally mass-conserving semi-Lagrangian numerical advection scheme, which improves forecast accuracy and model performance. The current version (7.2), in comparison with previous versions, has a more advanced and cost-efficient chemistry, aerosol multi-compound approach, aerosol feedbacks (direct and semi-direct) on radiation and (first and second indirect effects) on cloud microphysics. Since 2004, the Enviro-HIRLAM has been used for different studies, including operational pollen forecasting for Denmark since 2009 and operational forecasting atmospheric composition with downscaling for China since 2017. Following the main research and development strategy, further model developments will be extended towards the new NWP platform - HARMONIE. Different aspects of online coupling methodology, research strategy and possible applications of the modelling system, and fit-for-purpose model configurations for the meteorological and air quality communities are discussed.

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

    DEFF Research Database (Denmark)

    Asgarpour, Masoud

    2017-01-01

    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 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...

  14. 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

  15. The "APEC Blue" Phenomenon: Impacts of Regional emission control Meteorology Condition and Regional Transport from a Modeling Perspective

    Science.gov (United States)

    Gao, M.; Carmichael, G. R.; Liu, Z.; Ji, D.; Saide, P. E.; Wang, Y.; Xin, J.

    2015-12-01

    On November 5-11, China hosted the 2014 Asia-Pacific Economic Cooperation (APEC) Economic Leaders' Week in Beijing. To ensure good air quality during the APEC week, a series of strict emission control measures were taken in Beijing and surrounding provinces, which provide us with a great opportunity to examine the effectiveness of regional emission control. As important as emissions, meteorology can also significantly affect air quality in Beijing, so it's meaningful to understand the impact of meteorology conditions in the APEC week. Besides, it's important to study the impact of regional transport as its contribution to Beijing pollution levels is controversial. In this study, we investigate the impacts of emission control, meteorology and regional transport on the air quality during APEC week using a fully online coupled meteorology-chemistry model WRF-Chem. Compared to surface observations, the model has very good performance. The conclusions from this study will provide useful insights for government to control aerosol pollution in Beijing.

  16. Prognostic factors for urachal cancer: a bayesian model-averaging approach.

    Science.gov (United States)

    Kim, In Kyong; Lee, Joo Yong; Kwon, Jong Kyou; Park, Jae Joon; Cho, Kang Su; Ham, Won Sik; Hong, Sung Joon; Yang, Seung Choul; Choi, Young Deuk

    2014-09-01

    This study was conducted to evaluate prognostic factors and cancer-specific survival (CSS) in a cohort of 41 patients with urachal carcinoma by use of a Bayesian model-averaging approach. Our cohort included 41 patients with urachal carcinoma who underwent extended partial cystectomy, total cystectomy, transurethral resection, chemotherapy, or radiotherapy at a single institute. All patients were classified by both the Sheldon and the Mayo staging systems according to histopathologic reports and preoperative radiologic findings. Kaplan-Meier survival curves and Cox proportional-hazards regression models were carried out to investigate prognostic factors, and a Bayesian model-averaging approach was performed to confirm the significance of each variable by using posterior probabilities. The mean age of the patients was 49.88 ± 13.80 years and the male-to-female ratio was 24:17. The median follow-up was 5.42 years (interquartile range, 2.8-8.4 years). Five- and 10-year CSS rates were 55.9% and 43.4%, respectively. Lower Sheldon (p=0.004) and Mayo (pcancer-specific mortality in urachal carcinoma. The Mayo staging system might be more effective than the Sheldon staging system. In addition, the multivariate analyses suggested that tumor size may be a prognostic factor for urachal carcinoma.

  17. Meteorology Online.

    Science.gov (United States)

    Kahl, Jonathan D. W.

    2001-01-01

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

  18. Meteorology (OTTER)

    Data.gov (United States)

    National Aeronautics and Space Administration — ABSTRACT: Meteorology data collected on an hourly basis from stations located near the OTTER sites in 1990 and summarized to monthly data--see also: Canopy Chemistry...

  19. 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...

  20. 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

  1. Use of GIS to compute urban canopy parameters for mesoscale meteorological models

    Science.gov (United States)

    Burian, S.

    2003-04-01

    A set of GIS scripts have been written and bundled into an extension to support computation of urban canopy parameters for mesoscale meteorological models. The scripts have been applied to compute a suite of urban canopy parameters for more than 10 cities in the Western United States, including a major effort to characterize the roughness of more than 80,000 square kilometers of land surface in Texas. For each city, a three-dimensional building dataset, digital orthophotos, detailed land use/cover information, bald-earth topography, and roads were integrated and analyzed using a GIS. For some cities three-dimensional vegetation data is also incorporated into the analyses. Building height characteristics (e.g., mean height, variance of height, height histograms) were determined for each city and broken down by land use type. Other parameters describing the urban morphology that were calculated include the plan area fraction, building area density function, rooftop area density function, frontal area index, frontal area density function, complete aspect ratio, sky view factor, mean orientation of streets, and height-to-width ratio. Aerodynamic roughness length and displacement height were also calculated using morphometric equations and the computed urban morphological parameters. The presentation will include a discussion of the GIS extension development process, lessons learned working with integrated 2D and 3D digital datasets, and a summary of results compiled to date.

  2. 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...... density (power curve) and calibrated anemometers are confirmed to be prerequisites for reliable predictions; project-specific wind atlas heights are highly recommended....

  3. Implementation of a GNSS Meteorological model to the estimation of the Haines Index

    Science.gov (United States)

    Fernandez, Laura Isabel; Aragon-Paz, Juan Manuel; Mendoza, Luciano Pedro Oscar; Meza, Amalia Margarita

    2017-04-01

    Wildfire indexes evaluate the risk of forest fire occurrences and the dangerousness of its large and erratic propagation. In this context, the widely used Haines Index assesses the potential contribution of the atmosphere in forecasting and monitoring the behavior of the plume-dominated wildfires. The main goal of this study is the analysis of advantages in applying the GPT2w, an empirical model originally developed for Global Navigation Satellite System (GNSS) Meteorology, to the estimation of the Haines Index. To this aim, a statistical analysis of the differences between this estimation and the real values from radiosondes was performed. The selected area comprises a region of South America between latitudes 15° S and 35° S. This area was chosen due to the availability of the radiosonde launches required for validation during the year of study (2011). Previously, for characterizing the expected regional performance of the Haines Index, the Climatology was developed by using data from the European Centre for Medium-Range Weather Forecast (ECMWF) reanalysis model (ERA Interim) for the period 2000-2011. Afterwards a statistical analysis of the differences between the index estimation from the application of the GPT2w with respect to the real index values, that is: Haines index calculated from radiosonde measurements was performed. Moreover, the additional estimation of the Haines Index by using multi-level data from ERA Interim at the same control stations was also provided. Because the GPT2w model is freely available, the analysis of the results discusses the advantages of using this approach where radiosonde launches are scarce. Likewise, strategies for improving the deficiencies of this estimate are also presented.

  4. Introduction of prognostic rain in ECHAM5: design and single column model simulations

    Directory of Open Access Journals (Sweden)

    R. Posselt

    2008-06-01

    Full Text Available Prognostic equations for the rain mass mixing ratio and the rain drop number concentration are introduced into the large-scale cloud microphysics parameterization of the ECHAM5 general circulation model (ECHAM5-PROG. To this end, a rain flux from one level to the next with the appropriate fall speed is introduced. This maintains rain water in the atmosphere to be available for the next time step. Rain formation in ECHAM5-PROG is, therefore, less dependent on the autoconversion rate than the standard ECHAM5 but shifts the emphasis towards the accretion rates in accordance with observations. ECHAM5-PROG is tested and evaluated with Single Column Model (SCM simulations for two cases: the marine stratocumulus study EPIC (October 2001 and the continental mid-latitude ARM Cloud IOP (shallow frontal cloud case – March 2000. In case of heavy precipitation events, the prognostic equations for rain hardly affect the amount and timing of precipitation at the surface in different SCM simulations because heavy rain depends mainly on the large-scale forcing. In case of thin, drizzling clouds (i.e., stratocumulus, surface precipitation is sensitive to the number of sub-time steps used in the prognostic rain scheme. Cloud microphysical quantities, such as cloud liquid and rain water within the atmosphere, are sensitive to the number of sub-time steps in both considered cases. This results from the decreasing autoconversion rate and increasing accretion rate.

  5. A simplified prognostic model to predict mortality in patients with acute variceal bleeding.

    Science.gov (United States)

    Lee, Han Hee; Park, Jae Myung; Han, Seunghoon; Park, Sung Min; Kim, Hee Yeon; Oh, Jung Hwan; Kim, Chang Wook; Yoon, Seung Kew; Choi, Myung-Gyu

    2017-11-24

    Acute variceal bleeding (AVB) is a major cause of death in patients with liver cirrhosis. The aim of this study was to investigate mortality predictors and develop a new simple prognostic model using easily verified factors at admission in AVB patients. Between January 2009 and May 2015, 333 consecutive patients with AVB were included. A simplified prognostic model was developed using multiple logistic regression after identifying significant predictors of 6-week mortality. Mortality prediction accuracy was assessed with area under the receiver operating characteristic (AUROC) curve. We compared the new model to existing models of model for end-stage liver disease (MELD) and Child-Pugh scores. The 6-week overall mortality rate was 12.9%. Multivariate analysis showed that C-reactive protein (CRP), total bilirubin, and the international normalized ratio were independent predictors of mortality. A new logistic model using these variables was developed. This model's AUROC was 0.834, which was significantly higher than that of MELD (0.764) or Child-Pugh scores (0.699). Two external validation studies showed that the AUROC of our model was consistently higher than 0.8. Our new simplified model accurately and consistently predicted 6-week mortality in patients with AVB using objective variables measured at admission. Our system can be used to identify high risk AVB patients. Copyright © 2017 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Romin Pajouheshnia

    2017-07-01

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

  7. 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.

  8. Reduced nonlinear prognostic model construction from high-dimensional data

    Science.gov (United States)

    Gavrilov, Andrey; Mukhin, Dmitry; Loskutov, Evgeny; Feigin, Alexander

    2017-04-01

    Construction of a data-driven model of evolution operator using universal approximating functions can only be statistically justified when the dimension of its phase space is small enough, especially in the case of short time series. At the same time in many applications real-measured data is high-dimensional, e.g. it is space-distributed and multivariate in climate science. Therefore it is necessary to use efficient dimensionality reduction methods which are also able to capture key dynamical properties of the system from observed data. To address this problem we present a Bayesian approach to an evolution operator construction which incorporates two key reduction steps. First, the data is decomposed into a set of certain empirical modes, such as standard empirical orthogonal functions or recently suggested nonlinear dynamical modes (NDMs) [1], and the reduced space of corresponding principal components (PCs) is obtained. Then, the model of evolution operator for PCs is constructed which maps a number of states in the past to the current state. The second step is to reduce this time-extended space in the past using appropriate decomposition methods. Such a reduction allows us to capture only the most significant spatio-temporal couplings. The functional form of the evolution operator includes separately linear, nonlinear (based on artificial neural networks) and stochastic terms. Explicit separation of the linear term from the nonlinear one allows us to more easily interpret degree of nonlinearity as well as to deal better with smooth PCs which can naturally occur in the decompositions like NDM, as they provide a time scale separation. Results of application of the proposed method to climate data are demonstrated and discussed. The study is supported by Government of Russian Federation (agreement #14.Z50.31.0033 with the Institute of Applied Physics of RAS). 1. Mukhin, D., Gavrilov, A., Feigin, A., Loskutov, E., & Kurths, J. (2015). Principal nonlinear dynamical

  9. 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...

  10. 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

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

    Science.gov (United States)

    Picciotti, E.; Marzano, F. S.; Anagnostou, E. N.; Kalogiros, J.; Fessas, Y.; Volpi, A.; Cazac, V.; Pace, R.; Cinque, G.; Bernardini, L.; De Sanctis, K.; Di Fabio, S.; Montopoli, M.; Anagnostou, M. N.; Telleschi, A.; Dimitriou, E.; Stella, J.

    2013-05-01

    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 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 (MM5) and the Army Corps

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

    Science.gov (United States)

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

    2017-10-17

    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 different disease profiles, resource availability, and heterogeneity of the population may limit the transferability of such scores. A major shortcoming in using such models in LMICs is the unavailability of required measurements. This study proposes a simplified critical care prognostic model for use at the time of ICU admission. This was a prospective study of 3855 patients admitted to 21 ICUs from Bangladesh, India, Nepal, and Sri Lanka who were aged 16 years and over and followed to ICU discharge. Variables captured included patient age, admission characteristics, clinical assessments, laboratory investigations, and treatment measures. Multivariate logistic regression was used to develop three models for ICU mortality prediction: model 1 with clinical, laboratory, and treatment variables; model 2 with clinical and laboratory variables; and model 3, a purely clinical model. Internal validation based on bootstrapping (1000 samples) was used to calculate discrimination (area under the receiver operating characteristic curve (AUC)) and calibration (Hosmer-Lemeshow C-Statistic; higher values indicate poorer calibration). Comparison was made with the Acute Physiology and Chronic Health Evaluation (APACHE) II and Simplified Acute Physiology Score (SAPS) II models. Model 1 recorded the respiratory rate, systolic blood pressure, Glasgow Coma Scale (GCS), blood urea, haemoglobin, mechanical ventilation, and vasopressor use on ICU admission. Model 2, named TropICS (Tropical Intensive Care Score), included emergency surgery, respiratory rate, systolic blood pressure, GCS, blood urea, and haemoglobin. Model 3 included respiratory rate, emergency surgery, and GCS. AUC was 0.818 (95% confidence

  13. 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.

  14. Prognostic models in acute pulmonary embolism: a systematic review and meta-analysis

    Science.gov (United States)

    Elias, Antoine; Mallett, Susan; Daoud-Elias, Marie; Poggi, Jean-Noël; Clarke, Mike

    2016-01-01

    Objective To review the evidence for existing prognostic models in acute pulmonary embolism (PE) and determine how valid and useful they are for predicting patient outcomes. Design Systematic review and meta-analysis. Data sources OVID MEDLINE and EMBASE, and The Cochrane Library from inception to July 2014, and sources of grey literature. Eligibility criteria Studies aiming at constructing, validating, updating or studying the impact of prognostic models to predict all-cause death, PE-related death or venous thromboembolic events up to a 3-month follow-up in patients with an acute symptomatic PE. Data extraction Study characteristics and study quality using prognostic criteria. Studies were selected and data extracted by 2 reviewers. Data analysis Summary estimates (95% CI) for proportion of risk groups and event rates within risk groups, and accuracy. Results We included 71 studies (44 298 patients). Among them, 17 were model construction studies specific to PE prognosis. The most validated models were the PE Severity Index (PESI) and its simplified version (sPESI). The overall 30-day mortality rate was 2.3% (1.7% to 2.9%) in the low-risk group and 11.4% (9.9% to 13.1%) in the high-risk group for PESI (9 studies), and 1.5% (0.9% to 2.5%) in the low-risk group and 10.7% (8.8% to12.9%) in the high-risk group for sPESI (11 studies). PESI has proved clinically useful in an impact study. Shifting the cut-off or using novel and updated models specifically developed for normotensive PE improves the ability for identifying patients at lower risk for early death or adverse outcome (0.5–1%) and those at higher risk (up to 20–29% of event rate). Conclusions We provide evidence-based information about the validity and utility of the existing prognostic models in acute PE that may be helpful for identifying patients at low risk. Novel models seem attractive for the high-risk normotensive PE but need to be externally validated then be assessed in impact studies. PMID

  15. 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

  16. The meteorology of Gale Crater as determined from Rover Environmental Monitoring Station observations and numerical modeling. Part II: Interpretation

    Science.gov (United States)

    Rafkin, Scot C. R.; Pla-Garcia, Jorge; Kahre, Melinda; Gomez-Elvira, Javier; Hamilton, Victoria E.; Marín, Mercedes; Navarro, Sara; Torres, Josefina; Vasavada, Ashwin

    2016-12-01

    Numerical modeling results from the Mars Regional Atmospheric Modeling System are used to interpret the landed meteorological data from the Rover Environmental Monitoring Station onboard the Mars Science Laboratory rover Curiosity. In order to characterize seasonal changes throughout the Martian year, simulations are conducted at Ls 0, 90, 180 and 270. Two additional simulations at Ls 225 and 315 are explored to better understand the unique meteorological setting centered on Ls 270. The synergistic combination of model and observations reveals a complex meteorological environment within the crater. Seasonal planetary circulations, the thermal tide, slope flows along the topographic dichotomy, mesoscale waves, slope flows along the crater slopes and Mt. Sharp, and turbulent motions all interact in nonlinear ways to produce the observed weather. Ls 270 is shown to be an anomalous season when air within and outside the crater is well mixed by strong, flushing northerly flow and large amplitude, breaking mountain waves. At other seasons, the air in the crater is more isolated from the surrounding environment. The potential impact of the partially isolated crater air mass on the dust, water, noncondensable and methane cycles is also considered. In contrast to previous studies, the large amplitude diurnal pressure signal is attributed primarily to necessary hydrostatic adjustments associated with topography of different elevations, with contributions of less than 25% to the diurnal amplitude from the crater circulation itself. The crater circulation is shown to induce a suppressed boundary layer.

  17. Coupling meteorological and hydrological models to evaluate the uncertainty in runoff forecasting: the case study of Maggiore Lake basin

    Science.gov (United States)

    Ceppi, A.; Ravazzani, G.; Rabuffetti, D.; Mancini, M.

    2009-04-01

    In recent years, the interest in the prediction and prevention of natural hazards related to hydro-meteorological events has increased the challenge for numerical weather modelling, in particular for limited area models, to improve the Quantitative Precipitation Forecasts (QPFs) for hydrological purposes. The development and implementation of a real-time flood forecasting system with a hydro-meteorological operational alert procedure during the MAP-D-PHASE Project is described in this paper. D-PHASE stands for Demonstration of Probabilistic Hydrological and Atmospheric Simulation of flood Events in the Alpine region and is a Forecast Demonstration Project (FDP) of the WWRP (World Weather Research Programme of WMO). It aims at demonstrating some of the many achievements of the Mesoscale Alpine Programme (MAP). The MAP FDP has addressed the entire forecasting chain, ranging from limited-area ensemble forecasting, high-resolution atmospheric modelling (km-scale), hydrological modelling and nowcasting to decision making by the end users, i.e., it is foreseen to set up an end-to-end forecasting system. The D-PHASE Operations Period (DOP) was from 1 June to 30 November 2007. In this study the hydro-meteorological chain includes both probabilistic forecasting based on ensemble prediction systems with lead time of a few days and short-range forecasts based on high resolution deterministic atmospheric models. D-PHASE hydrological ensemble forecasts are based on the 16 meteorological members, provided by COSMO-LEPS model (by ARPA Emilia-Romagna) with 5 day lead-time and a horizontal resolution of 10 km. Deterministic hydrological D-PHASE forecasts are provided by MOLOCH weather model (by ISAC-CNR) with a horizontal resolution of 2.2 km, nested into BOLAM, based on GFS initial and boundary conditions with 48 h lead-time. The hydrological model used to generate the runoff simulations is the rainfall-runoff distributed FEST-WB model, developed at Politecnico di Milano. The

  18. Modelling the Effect of Black Carbon and Sulfate Aerosol on the Regional Meteorology Factors

    Science.gov (United States)

    Ma, X.; Wen, W.

    2017-07-01

    In this study, we focus on the effect of black carbon aerosol and sulfate aerosol on meteorology factors during heavy pollution period and non-heavy pollution period. The version of WRF/chem V3.4 was used in this work, Four Simulation scenarios are applied to simulate the effect of the effect of black carbon aerosol and sulfate aerosol on solar radiation, temperature, PBL high. The analysis results show that the effect of black carbon and sulfate aerosol cause decline on three meteorological factors in both heavy pollution and non-heavy pollution period in both January and July. The influence of two aerosols on meteorological factors are less significant than winter. During heavy pollution, black carbon aerosol cause the loss of solar radiation is 29.1W/m2; the warming effect of black carbon aerosol caused temperature to rise 0.05°C PBL height decreased by an average of 73.1m. Sulfate aerosols cause the loss of solar radiation is 21.5W/m2; Temperature fell an average of 0.89°C PBL height decreased by 66.6m. The change of three meteorological factors due to aerosol feedback in non-heavy pollution period in much smaller than heavy pollution period.

  19. Sensitivity of Global Modeling Initiative CTM predictions of Antarctic ozone recovery to GCM and DAS generated meteorological fields

    Energy Technology Data Exchange (ETDEWEB)

    Rotman, D; Bergmann, D

    2003-12-04

    We use the Global Modeling Initiative chemistry and transport model to simulate the evolution of stratospheric ozone between 1995 and 2030, using boundary conditions consistent with the recent World Meteorological Organization ozone assessment. We compare the Antarctic ozone recovery predictions of two simulations, one driven by meteorological data from a general circulation model (GCM), the other using the output of a data assimilation system (DAS), to examine the sensitivity of Antarctic ozone recovery predictions to the characteristic dynamical differences between GCM and DAS-generated meteorological data. Although the age of air in the Antarctic lower stratosphere differs by a factor of 2 between the simulations, we find little sensitivity of the 1995-2030 Antarctic ozone recovery between 350 K and 650 K to the differing meteorological fields, particularly when the recovery is specified in mixing ratio units. Relative changes are smaller in the DAS-driven simulation compared to the GCM-driven simulation due to a surplus of Antarctic ozone in the DAS-driven simulation which is not consistent with observations. The peak ozone change between 1995 and 2030 in both simulations is {approx}20% lower than photochemical expectations, indicating that changes in ozone transport at 450 K between 1995 and 2030 constitute a small negative feedback. Total winter/spring ozone loss during the base year (1995) of both simulations and the rate of ozone loss during August and September is somewhat weaker than observed. This appears to be due to underestimates of Antarctic Cl{sub y} at the 450 K potential temperature level.

  20. The influence of intra- and inter-annual meteorological variability on dengue transmission: a multi-level modeling analysis

    Science.gov (United States)

    Wen, Tzai-Hung; Chen, Tzu-Hsin

    2017-04-01

    Dengue fever is one of potentially life-threatening mosquito-borne diseases and IPCC Fifth Assessment Report (AR5) has confirmed that dengue incidence is sensitive to the critical weather conditions, such as effects of temperature. However, previous literature focused on the effects of monthly or weekly average temperature or accumulative precipitation on dengue incidence. The influence of intra- and inter-annual meteorological variability on dengue outbreak is under investigated. The purpose of the study focuses on measuring the effect of the intra- and inter-annual variations of temperature and precipitation on dengue outbreaks. We developed the indices of intra-annual temperature variability are maximum continuity, intermittent, and accumulation of most suitable temperature (MST) for dengue vectors; and also the indices of intra-annual precipitation variability, including the measure of continuity of wetness or dryness during a pre-epidemic period; and rainfall intensity during an epidemic period. We used multi-level modeling to investigate the intra- and inter-annual meteorological variations on dengue outbreaks in southern Taiwan from 1998-2015. Our results indicate that accumulation and maximum continuity of MST are more significant than average temperature on dengue outbreaks. The effect of continuity of wetness during the pre-epidemic period is significantly more positive on promoting dengue outbreaks than the rainfall effect during the epidemic period. Meanwhile, extremely high or low rainfall density during an epidemic period do not promote the spread of dengue epidemics. Our study differentiates the effects of intra- and inter-annual meteorological variations on dengue outbreaks and also provides policy implications for further dengue control under the threats of climate change. Keywords: dengue fever, meteorological variations, multi-level model

  1. A Hidden Semi-Markov Model with Duration-Dependent State Transition Probabilities for Prognostics

    Directory of Open Access Journals (Sweden)

    Ning Wang

    2014-01-01

    Full Text Available Realistic prognostic tools are essential for effective condition-based maintenance systems. In this paper, a Duration-Dependent Hidden Semi-Markov Model (DD-HSMM is proposed, which overcomes the shortcomings of traditional Hidden Markov Models (HMM, including the Hidden Semi-Markov Model (HSMM: (1 it allows explicit modeling of state transition probabilities between the states; (2 it relaxes observations’ independence assumption by accommodating a connection between consecutive observations; and (3 it does not follow the unrealistic Markov chain’s memoryless assumption and therefore it provides a more powerful modeling and analysis capability for real world problems. To facilitate the computation of the proposed DD-HSMM methodology, new forward-backward algorithm is developed. The demonstration and evaluation of the proposed methodology is carried out through a case study. The experimental results show that the DD-HSMM methodology is effective for equipment health monitoring and management.

  2. 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.

  3. 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

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

    Science.gov (United States)

    Wang, H.; Feingold, G.; Wood, R.; Kazil, J.

    2010-07-01

    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 stratocumulus deck that breaks up due solely to solar heating

  5. Investigating the Impact on Modeled Ozone Concentrations Using Meteorological Fields From WRF With and Updated Four-Dimensional Data Assimilation Approach”

    Science.gov (United States)

    The four-dimensional data assimilation (FDDA) technique in the Weather Research and Forecasting (WRF) meteorological model has recently undergone an important update from the original version. Previous evaluation results have demonstrated that the updated FDDA approach in WRF pr...

  6. 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.

  7. The Effects of Meteorological Forcings on Hydrologic Model Calibration and Implications for Statistical Downscaling of Climate Projections

    Science.gov (United States)

    McGuire Elsner, M.; Gangopadhyay, S.; Pruitt, T.; Brekke, L. D.

    2012-12-01

    Spatially distributed historical meteorological forcings (temperature and precipitation) are commonly used as the basis for statistical downscaling (in time and space) of general circulation model (GCM) projections. A number of such forcing datasets, gridded from station observations, have been developed over the Western U.S. and they all use different methodologies with respect to filtering stations and accounting for temporal inhomogeneities. We compare four historical meteorological datasets at 1/8th degree spatial resolution over a common historical time period (1980-1999) using basic statistical comparisons to better understand their spatial differences. We then employ the Variable Infiltration Capacity hydrologic model (VIC), which has been implemented in numerous studies to evaluate the impacts of climate variability and change on water resources, to explore the sensitivity of hydrologic model response to these forcing datasets. Specifically, we calibrate the VIC model, by means of the automated multiple objective complex evolution method, to reconstructed natural streamflows using each of the four different datasets and compare calibration parameters and water balance variables (eg. streamflow, evapotranspiration, snow water equivalent). We test the sensitivities of the calibrated models by forcing each one with the remaining three datasets. Because multiple sources of downscaled climate change scenarios are currently available for various parts of the Western US from the Coupled Model Intercomparison Project (CMIP) Phase 3 (and many of them use different historical meteorological forcing datasets as their basis) and scenarios from the next CMIP phase (Phase 5) are becoming available, we explore the implications of our findings for statistical downscaling of GCM projections.

  8. 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

  9. 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

  10. Anticipating the severity of the fire season in Northern Portugal using statistical models based on meteorological indices of fire danger

    Science.gov (United States)

    Nunes, Sílvia A.; DaCamara, Carlos C.; Turkman, Kamil F.; Ermida, Sofia L.; Calado, Teresa J.

    2017-04-01

    Like in other regions of Mediterranean Europe, climate and weather are major drivers of fire activity in Portugal. The aim of the present study is to assess the role played by meteorological factors on inter-annual variability of burned area over a region of Portugal characterized by large fire activity. Monthly cumulated values of burned area in August are obtained from the fire database of ICNF, the Portuguese authority for forests. The role of meteorological factors is characterized by means of Daily Severity Rating, DSR, an index of meteorological fire danger, which is derived from meteorological fields as obtained from ECMWF Interim Reanalysis. The study area is characterized by the predominance of forest, with high percentages of maritime pine and eucalyptus, two species with high flammability in summer. The time series of recorded burned area in August during 1980-2011 is highly correlated (correlation coefficient of 0.93) with the one for whole Portugal. First, a normal distribution model is fitted to the 32-year sample of decimal logarithms of monthly burned area. The model is improved by introducing two covariates:(1) the top-down meteorological factor (DSRtd) which consists of daily cumulated values of DSR since April 1 to July 31 and may be viewed as the cumulated stress on vegetation due to meteorological conditions during the pre-fire season; (2) the bottom-up factor (DSRbu) which consists of the square root of the mean of the squared daily deviations (restricted to days with positive departures of DSR from the corresponding long term mean) and may be viewed as the contribution of days characterized by extreme weather conditions favoring the onset and spreading of wildfires. Three different statistical models are then developed: the "climate anomaly" model, using DSRtd as covariate, the "weather anomaly", using DSRbu as covariate, and the "combined" model using both variables as covariates. These models are used to define background fire danger, fire

  11. 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

    2017-04-01

    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

  12. Volcanic ash modeling with the online NMMB/BSC-ASH-v1.0: A novel multiscale meteorological model for operational forecast

    Science.gov (United States)

    Marti, Alejandro; Folch, Arnau; Jorba, Oriol; Janjic, Zavisa

    2016-04-01

    Volcanic ash forecast became a research priority and a social concern as a consequence of the severe air-traffic disruptions caused by the eruptions of Eyjafjallajökull (Iceland, 2010) and Cordón Caulle (Chile, 2011) volcanoes. Significant progress has taken place in the aftermath of these dramatic events to improve the accuracy of Volcanic Ash Transport and Dispersal (VATD) models and lessen its associated uncertainties. Various levels of uncertainties affect both the quantification of the source term and the driving meteorological inputs. Substantial research is being performed to reduce and quantify epistemic and aleatoric uncertainties affecting the source term. However, uncertainties arising from the driving NWPMs and its coupling offline with the VATDMs have received little attention, even if the experience from other communities (e.g. air quality) highlights the importance of coupling online dispersal and meteorological modeling. Consequently, the need for integrated predictions to represent these two-way feedback effects of the volcanic pollutants on local-scale meteorology is timely. The aim of this talk is to present the NMMB/BSC-ASH, a new on-line multi-scale meteorological model to simulate the emission, transport and deposition of tephra particles released from volcanic eruptions. The model builds on the NMMB/BSC Chemical Transport Model (NMMB/BSC-CTM), which we have modified to account for the specifics of volcanic particles. The final objective in developing the NMMB/BSC-ASH model is two-fold. On one hand, at a research level, we aim at studying the differences between the online/offline approaches and quantify the two-way feedback effect of dense volcanic ash clouds on the radiative budget and regional meteorology. On the other hand, at an operational level, the low computational cost of the NMMB dynamic core suggests that NMMB/BSC-ASH could be applied in a future for more accurate online operational forecasting of volcanic ash clouds.

  13. A Prognostic Model for One-year Mortality in Patients Requiring Prolonged Mechanical Ventilation

    Science.gov (United States)

    Carson, Shannon S.; Garrett, Joanne; Hanson, Laura C.; Lanier, Joyce; Govert, Joe; Brake, Mary C.; Landucci, Dante L.; Cox, Christopher E.; Carey, Timothy S.

    2009-01-01

    Objective A measure that identifies patients who are at high risk of mortality after prolonged ventilation will help physicians communicate prognosis to patients or surrogate decision-makers. Our objective was to develop and validate a prognostic model for 1-year mortality in patients ventilated for 21 days or more. Design Prospective cohort study. Setting University-based tertiary care hospital Patients 300 consecutive medical, surgical, and trauma patients requiring mechanical ventilation for at least 21 days were prospectively enrolled. Measurements and Main Results Predictive variables were measured on day 21 of ventilation for the first 200 patients and entered into logistic regression models with 1-year and 3-month mortality as outcomes. Final models were validated using data from 100 subsequent patients. One-year mortality was 51% in the development set and 58% in the validation set. Independent predictors of mortality included requirement for vasopressors, hemodialysis, platelet count ≤150 ×109/L, and age ≥50. Areas under the ROC curve for the development model and validation model were 0.82 (se 0.03) and 0.82 (se 0.05) respectively. The model had sensitivity of 0.42 (se 0.12) and specificity of 0.99 (se 0.01) for identifying patients who had ≥90% risk of death at 1 year. Observed mortality was highly consistent with both 3- and 12-month predicted mortality. These four predictive variables can be used in a simple prognostic score that clearly identifies low risk patients (no risk factors, 15% mortality) and high risk patients (3 or 4 risk factors, 97% mortality). Conclusions Simple clinical variables measured on day 21 of mechanical ventilation can identify patients at highest and lowest risk of death from prolonged ventilation. PMID:18552692

  14. 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.

  15. 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

  16. Using an integrated hydrological model to estimate the usefulness of meteorological drought indices in a changing climate

    Directory of Open Access Journals (Sweden)

    D. von Gunten

    2016-10-01

    Full Text Available Droughts are serious natural hazards, especially in semi-arid regions. They are also difficult to characterize. Various summary metrics representing the dryness level, denoted drought indices, have been developed to quantify droughts. They typically lump meteorological variables and can thus directly be computed from the outputs of regional climate models in climate-change assessments. While it is generally accepted that drought risks in semi-arid climates will increase in the future, quantifying this increase using climate model outputs is a complex process that depends on the choice and the accuracy of the drought indices, among other factors. In this study, we compare seven meteorological drought indices that are commonly used to predict future droughts. Our goal is to assess the reliability of these indices to predict hydrological impacts of droughts under changing climatic conditions at the annual timescale. We simulate the hydrological responses of a small catchment in northern Spain to droughts in present and future climate, using an integrated hydrological model calibrated for different irrigation scenarios. We compute the correlation of meteorological drought indices with the simulated hydrological time series (discharge, groundwater levels, and water deficit and compare changes in the relationships between hydrological variables and drought indices. While correlation coefficients linked with a specific drought index are similar for all tested land uses and climates, the relationship between drought indices and hydrological variables often differs between present and future climate. Drought indices based solely on precipitation often underestimate the hydrological impacts of future droughts, while drought indices that additionally include potential evapotranspiration sometimes overestimate the drought effects. In this study, the drought indices with the smallest bias were the rainfall anomaly index, the reconnaissance drought index, and

  17. Prognostic model for survival in patients with metastatic renal cell carcinoma: results from the international kidney cancer working group.

    Science.gov (United States)

    Manola, Judith; Royston, Patrick; Elson, Paul; McCormack, Jennifer Bacik; Mazumdar, Madhu; Négrier, Sylvie; Escudier, Bernard; Eisen, Tim; Dutcher, Janice; Atkins, Michael; Heng, Daniel Y C; Choueiri, Toni K; Motzer, Robert; Bukowski, Ronald

    2011-08-15

    To develop a single validated model for survival in metastatic renal cell carcinoma (mRCC) using a comprehensive international database. A comprehensive database of 3,748 patients including previously reported clinical prognostic factors was established by pooling patient-level data from clinical trials. Following quality control and standardization, descriptive statistics were generated. Univariate analyses were conducted using proportional hazards models. Multivariable analysis using a log-logistic model stratified by center and multivariable fractional polynomials was conducted to identify independent predictors of survival. Missing data were handled using multiple imputation methods. Three risk groups were formed using the 25th and 75th percentiles of the resulting prognostic index. The model was validated using an independent data set of 645 patients treated with tyrosine kinase inhibitor (TKI) therapy. Median survival in the favorable, intermediate and poor risk groups was 26.9 months, 11.5 months, and 4.2 months, respectively. Factors contributing to the prognostic index included treatment, performance status, number of metastatic sites, time from diagnosis to treatment, and pretreatment hemoglobin, white blood count, lactate dehydrogenase, alkaline phosphatase, and serum calcium. The model showed good concordance when tested among patients treated with TKI therapy (C statistic = 0.741, 95% CI: 0.714-0.768). Nine clinical factors can be used to model survival in mRCC and form distinct prognostic groups. The model shows utility among patients treated in the TKI era. ©2011 AACR.

  18. Assessing the Relationship Between Meteorological Parameters, Air Pollution And Cardiovascular Mortality of Mashhad City Based on Time Series Model

    Directory of Open Access Journals (Sweden)

    Morteza Hatami

    2017-10-01

    Full Text Available Epidemiological studies conducted in the past two decades indicate that air pollution causes increase in cardiovascular, breathing and chronic bronchitis disorders and even causes cardiovascular mortality. Therefore, the aim of this study was to investigate the relationship between meteorological parameters, air pollution and cardiovascular mortality in the city of Mashhad in 2014 by a time series model. Data on mortality from cardiovascular disease, meteorological parameters and air pollution in 2014 were gathered from Paradises organization, meteorology organization and pollutant monitoring center, respectively. Then the relationship between these parameters was analyzed using correlation coefficient, generalized linear regression, time series models and comparison of means. The results of the study showed that the highest rate of cardiovascular mortality related to Sulfur dioxide, nitrogen dioxide and then PM2.5. So that each unit increase in SO2, NO2 and PM2.5 pollutants adds to the rate of cardiovascular mortality by 22.5, 2.9 and 0.69, respectively. Pressure, wind speed and rainfall have a significant association with mortality. So that each unit decrease in pressure and wind speed, increases the rate of cardiovascular mortality by 2.79 and 15.77, respectively. It was also found that in the case of one-unit increase in rainfall, the possibility of mortality from the mentioned disease goes up by 3.8 units. It was also found that one-year increase of the age increases the mortality caused by these diseases up to 0.57 percent. Furthermore, the highest rate of cardiovascular mortality related to cold periods of the year. Therefore, considering the growing trend of air pollution and its health effects on human health, performing actions and effective solutions is important in the field of controlling and reducing air pollution in Iranian metropolis including Mashhad.

  19. Prognostic factors for survival in adult patients with recurrent glioblastoma: a decision-tree-based model.

    Science.gov (United States)

    Audureau, Etienne; Chivet, Anaïs; Ursu, Renata; Corns, Robert; Metellus, Philippe; Noel, Georges; Zouaoui, Sonia; Guyotat, Jacques; Le Reste, Pierre-Jean; Faillot, Thierry; Litre, Fabien; Desse, Nicolas; Petit, Antoine; Emery, Evelyne; Lechapt-Zalcman, Emmanuelle; Peltier, Johann; Duntze, Julien; Dezamis, Edouard; Voirin, Jimmy; Menei, Philippe; Caire, François; Dam Hieu, Phong; Barat, Jean-Luc; Langlois, Olivier; Vignes, Jean-Rodolphe; Fabbro-Peray, Pascale; Riondel, Adeline; Sorbets, Elodie; Zanello, Marc; Roux, Alexandre; Carpentier, Antoine; Bauchet, Luc; Pallud, Johan

    2017-11-20

    We assessed prognostic factors in relation to OS from progression in recurrent glioblastomas. Retrospective multicentric study enrolling 407 (training set) and 370 (external validation set) adult patients with a recurrent supratentorial glioblastoma treated by surgical resection and standard combined chemoradiotherapy as first-line treatment. Four complementary multivariate prognostic models were evaluated: Cox proportional hazards regression modeling, single-tree recursive partitioning, random survival forest, conditional random forest. Median overall survival from progression was 7.6 months (mean, 10.1; range, 0-86) and 8.0 months (mean, 8.5; range, 0-56) in the training and validation sets, respectively (p = 0.900). Using the Cox model in the training set, independent predictors of poorer overall survival from progression included increasing age at histopathological diagnosis (aHR, 1.47; 95% CI [1.03-2.08]; p = 0.032), RTOG-RPA V-VI classes (aHR, 1.38; 95% CI [1.11-1.73]; p = 0.004), decreasing KPS at progression (aHR, 3.46; 95% CI [2.10-5.72]; p < 0.001), while independent predictors of longer overall survival from progression included surgical resection (aHR, 0.57; 95% CI [0.44-0.73]; p < 0.001) and chemotherapy (aHR, 0.41; 95% CI [0.31-0.55]; p < 0.001). Single-tree recursive partitioning identified KPS at progression, surgical resection at progression, chemotherapy at progression, and RTOG-RPA class at histopathological diagnosis, as main survival predictors in the training set, yielding four risk categories highly predictive of overall survival from progression both in training (p < 0.0001) and validation (p < 0.0001) sets. Both random forest approaches identified KPS at progression as the most important survival predictor. Age, KPS at progression, RTOG-RPA classes, surgical resection at progression and chemotherapy at progression are prognostic for survival in recurrent glioblastomas and should inform the treatment decisions.

  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. 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

  2. A prognostic model of therapy-related myelodysplastic syndrome for predicting survival and transformation to acute myeloid leukemia.

    Science.gov (United States)

    Quintás-Cardama, Alfonso; Daver, Naval; Kim, Hawk; Dinardo, Courtney; Jabbour, Elias; Kadia, Tapan; Borthakur, Gautam; Pierce, Sherry; Shan, Jianqin; Cardenas-Turanzas, Marylou; Cortes, Jorge; Ravandi, Farhad; Wierda, William; Estrov, Zeev; Faderl, Stefan; Wei, Yue; Kantarjian, Hagop; Garcia-Manero, Guillermo

    2014-10-01

    We evaluated the characteristics of a cohort of patients with myelodysplastic syndrome (MDS) related to therapy (t-MDS) to create a prognostic model. We identified 281 patients with MDS who had received previous chemotherapy and/or radiotherapy for previous malignancy. Potential prognostic factors were determined using univariate and multivariate analyses. Multivariate Cox regression analysis identified 7 factors that independently predicted short survival in t-MDS: age ≥ 65 years (hazard ratio [HR], 1.63), Eastern Cooperative Oncology Group performance status 2-4 (HR, 1.86), poor cytogenetics (-7 and/or complex; HR, 2.47), World Health Organization MDS subtype (RARs or RAEB-1/2; HR, 1.92), hemoglobin (HR, 2.24), platelets (HR, 2.01), and transfusion dependency (HR, 1.59). These risk factors were used to create a prognostic model that segregated patients into 3 groups with distinct median overall survival: good (0-2 risk factors; 34 months), intermediate (3-4 risk factors; 12 months), and poor (5-7 risk factors; 5 months) (P < .001) and 1-year leukemia-free survival (96%, 84%, and 72%, respectively, P = .003). This model also identified distinct survival groups according to t-MDS therapy. In summary, we devised a prognostic model specifically for patients with t-MDS that predicted overall survival and leukemia-free survival. This model might facilitate the development of risk-adapted therapeutic strategies. Copyright © 2014 Elsevier Inc. All rights reserved.

  3. 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.

  4. Prognostic factors in gastric cancer evaluated by using Cox regression model.

    Science.gov (United States)

    Ghiandoni, G; Rocchi, M B; Signoretti, P; Belbusti, F

    1998-06-01

    To identify the most relevant short-term predictor variables in gastric cancer removal. A retrospective survival analysis executed by using the Cox regression model; the follow-up period is included between 18 and 90 months. A district general hospital surgery unit: "Divisione di Chirurgia Generale, Ospedale Civile di Urbino" (Marche, Italy). One hundred and twenty nine consecutive patients operated for gastric cancer. Surgery (total or subtotal gastrectomy). Survival times. Lymph node involvement (N) (p extension (T) (p < 0.001) and the age of the patients (p < 0.05) have been recognized as significant prognostic factors. Results show that the short-term prognosis largely depends on both the earliness of the diagnosis and the age of the patients.

  5. Meteorological Processors and Accessory Programs

    Science.gov (United States)

    Surface and upper air data, provided by NWS, are important inputs for air quality models. Before these data are used in some of the EPA dispersion models, meteorological processors are used to manipulate the data.

  6. A prognostic model to predict the success of artificial insemination in dairy cows based on readily available data

    NARCIS (Netherlands)

    Rutten, C.J.; Steeneveld, W.; Vernooij, J.C.M.; Huijps, K.; Nielen, M.; Hogeveen, H.

    2016-01-01

    A prognosis of the likelihood of insemination success is valuable information for the decision to start inseminating a cow. This decision is important for the reproduction management of dairy farms. The aim of this study was to develop a prognostic model for the likelihood of successful first

  7. A prognostic model to predict the success of artificial insemination in dairy cows based on readily available data

    NARCIS (Netherlands)

    Rutten, C J|info:eu-repo/dai/nl/353551031; Steeneveld, W|info:eu-repo/dai/nl/304833169; Vernooij, J C M|info:eu-repo/dai/nl/340304596; Huijps, K|info:eu-repo/dai/nl/304837881; Nielen, M|info:eu-repo/dai/nl/123535298; Hogeveen, H|info:eu-repo/dai/nl/126322864

    2016-01-01

    A prognosis of the likelihood of insemination success is valuable information for the decision to start inseminating a cow. This decision is important for the reproduction management of dairy farms. The aim of this study was to develop a prognostic model for the likelihood of successful first

  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. 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

  10. 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

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

    Energy Technology Data Exchange (ETDEWEB)

    Agarwal, Vivek [Idaho National Lab. (INL), Idaho Falls, ID (United States); Lybeck, Nancy J. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Pham, Binh [Idaho National Lab. (INL), Idaho Falls, ID (United States); Rusaw, Richard [Electric Power Research Inst. (EPRI), Palo Alto, CA (United States); Bickford, Randall [Expert Microsystems, Orangevale, CA (United States)

    2015-02-01

    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.

  12. 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.

  13. Seasonal Multifactor Modelling of Weighted-Mean Temperature for Ground-Based GNSS Meteorology in Hunan, China

    Directory of Open Access Journals (Sweden)

    Li Li

    2017-01-01

    Full Text Available In this study, radiosonde observations during the period of 2012-2013 from three stations in the Hunan region, China, were used to establish regional Tm models (RTMs that are a fitting function of multiple meteorological factors (Ts, Es, and Ps. One-factor, two-factor, and three-factor RTMs were assessed by comparing their Tm against the radiosonde-derived Tm (as the truth during the period of 2013-2014. Statistical results showed that the bias and RMS of the one-factor RTM, in comparison to the BTM result, were reduced by 88% and 28%, respectively. The two-factor and three-factor RTMs showed similar accuracy and both outperformed the one-factor RTM, with an improvement of 7% in RMS. The bias and RMS of all the four seasonal two-factor RTMs were smaller than the yearly two-factor RTM, with the improvements of 3%, 10%, 2%, and 3% in RMS. The improvement of the conversion factors in mean bias and RMS resulting from the seasonal two-factor RTM is 92% and 31%. The bias and RMS of the PWV resulting from the seasonal two-factor RTM are improved by 37% and 12%, respectively. Therefore, the seasonal two-factor RTMs are recommended for the research and applications of GNSS meteorology in the Hunan region, China.

  14. 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.

  15. 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.

  16. 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.

  17. A note on prognostic accuracy evaluation of regression models applied to longitudinal autocorrelated binary data

    Directory of Open Access Journals (Sweden)

    Giulia Barbati

    2014-11-01

    Full Text Available Background: Focus of this work was on evaluating the prognostic accuracy of two approaches for modelling binary longitudinal outcomes, a Generalized Estimating Equation (GEE and a likelihood based method, Marginalized Transition Model (MTM, in which a transition model is combined with a marginal generalized linear model describing the average response as a function of measured predictors.Methods: A retrospective study on cardiovascular patients and a prospective study on sciatic pain were used to evaluate discrimination by computing the Area Under the Receiver-Operating-Characteristics curve, (AUC, the Integrated Discrimination Improvement (IDI and the Net Reclassification Improvement (NRI at different time occasions. Calibration was also evaluated. A simulation study was run in order to compare model’s performance in a context of a perfect knowledge of the data generating mechanism. Results: Similar regression coefficients estimates and comparable calibration were obtained; an higher discrimination level for MTM was observed. No significant differences in calibration and MSE (Mean Square Error emerged in the simulation study, that instead confirmed the MTM higher discrimination level. Conclusions: The choice of the regression approach should depend on the scientific question being addressed, i.e. if the overall population-average and calibration or the subject-specific patterns and discrimination are the objectives of interest, and some recently proposed discrimination indices are useful in evaluating predictive accuracy also in a context of longitudinal studies.

  18. A simple prognostic model for overall survival in metastatic renal cell carcinoma

    Science.gov (United States)

    Assi, Hazem I.; Patenaude, Francois; Toumishey, Ethan; Ross, Laura; Abdelsalam, Mahmoud; Reiman, Tony

    2016-01-01

    Introduction: The primary purpose of this study was to develop a simpler prognostic model to predict overall survival for patients treated for metastatic renal cell carcinoma (mRCC) by examining variables shown in the literature to be associated with survival. Methods: We conducted a retrospective analysis of patients treated for mRCC at two Canadian centres. All patients who started first-line treatment were included in the analysis. A multivariate Cox proportional hazards regression model was constructed using a stepwise procedure. Patients were assigned to risk groups depending on how many of the three risk factors from the final multivariate model they had. Results: There were three risk factors in the final multivariate model: hemoglobin, prior nephrectomy, and time from diagnosis to treatment. Patients in the high-risk group (two or three risk factors) had a median survival of 5.9 months, while those in the intermediate-risk group (one risk factor) had a median survival of 16.2 months, and those in the low-risk group (no risk factors) had a median survival of 50.6 months. Conclusions: In multivariate analysis, shorter survival times were associated with hemoglobin below the lower limit of normal, absence of prior nephrectomy, and initiation of treatment within one year of diagnosis. PMID:27217858

  19. Incorporating a prognostic representation of marine nitrogen fixers into the global ocean biogeochemical model HAMOCC

    Science.gov (United States)

    Paulsen, Hanna; Ilyina, Tatiana; Six, Katharina D.; Stemmler, Irene

    2017-03-01

    Nitrogen (N2) fixation is a major source of bioavailable nitrogen to the euphotic zone, thereby exerting an important control on ocean biogeochemical cycling. This paper presents the incorporation of prognostic N2 fixers into the HAMburg Ocean Carbon Cycle model (HAMOCC), a component of the Max Planck Institute Earth System Model (MPI-ESM). Growth dynamics of N2 fixers in the model are based on physiological characteristics of the cyanobacterium Trichodesmium. The applied temperature dependency confines diazotrophic growth and N2 fixation to the tropical and subtropical ocean roughly between 40°S and 40°N. Simulated large-scale spatial patterns compare well with observations, and the global N2 fixation rate of 135.6 Tg N yr-1 is within the range of current estimates. The vertical distribution of N2 fixation also matches well the observations, with a major fraction of about 85% occurring in the upper 20 m. The observed seasonal variability at the stations BATS and ALOHA is reasonably reproduced, with highest fixation rates in northern summer/fall. Iron limitation was found to be an important factor in controlling the simulated distribution of N2 fixation, especially in the Pacific Ocean. The new model component considerably improves the representation of present-day N2 fixation in HAMOCC. It provides the basis for further studies on the role of diazotrophs in global biogeochemical cycles, as well as on the response of N2 fixation to changing environmental conditions.

  20. A flexible alternative to the Cox proportional hazards model for assessing the prognostic accuracy of hospice patient survival.

    Directory of Open Access Journals (Sweden)

    Branko Miladinovic

    Full Text Available Prognostic models are often used to estimate the length of patient survival. The Cox proportional hazards model has traditionally been applied to assess the accuracy of prognostic models. However, it may be suboptimal due to the inflexibility to model the baseline survival function and when the proportional hazards assumption is violated. The aim of this study was to use internal validation to compare the predictive power of a flexible Royston-Parmar family of survival functions with the Cox proportional hazards model. We applied the Palliative Performance Scale on a dataset of 590 hospice patients at the time of hospice admission. The retrospective data were obtained from the Lifepath Hospice and Palliative Care center in Hillsborough County, Florida, USA. The criteria used to evaluate and compare the models' predictive performance were the explained variation statistic R(2, scaled Brier score, and the discrimination slope. The explained variation statistic demonstrated that overall the Royston-Parmar family of survival functions provided a better fit (R(2 =0.298; 95% CI: 0.236-0.358 than the Cox model (R(2 =0.156; 95% CI: 0.111-0.203. The scaled Brier scores and discrimination slopes were consistently higher under the Royston-Parmar model. Researchers involved in prognosticating patient survival are encouraged to consider the Royston-Parmar model as an alternative to Cox.

  1. 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.

  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

    Science.gov (United States)

    Yu, Karen; Keller, Christoph A.; Jacob, Daniel J.; Molod, Andrea M.; Eastham, Sebastian D.; Long, Michael S.

    2018-01-01

    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 online c360 simulations and underestimated by up to 40 % in the upper troposphere, while the

  3. Elevated neutrophil and monocyte counts in peripheral blood are associated with poor survival in patients with metastatic melanoma: a prognostic model

    DEFF Research Database (Denmark)

    Schmidt, H; Bastholt, L; Geertsen, P

    2005-01-01

    We aimed to create a prognostic model in metastatic melanoma based on independent prognostic factors in 321 patients receiving interleukin-2 (IL-2)-based immunotherapy with a median follow-up time for patients currently alive of 52 months (range 15-189 months). The patients were treated as part...... factors in univariate analyses. Subsequently, a multivariate Cox's regression analysis identified elevated LDH (P

  4. 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

  5. System Interdependency Modeling in the Design of Prognostic and Health Management Systems in Smart Manufacturing.

    Science.gov (United States)

    Malinowski, M L; Beling, P A; Haimes, Y Y; LaViers, A; Marvel, J A; Weiss, B A

    2015-01-01

    The fields of risk analysis and prognostics and health management (PHM) have developed in a largely independent fashion. However, both fields share a common core goal. They aspire to manage future adverse consequences associated with prospective dysfunctions of the systems under consideration due to internal or external forces. This paper describes how two prominent risk analysis theories and methodologies - Hierarchical Holographic Modeling (HHM) and Risk Filtering, Ranking, and Management (RFRM) - can be adapted to support the design of PHM systems in the context of smart manufacturing processes. Specifically, the proposed methodologies will be used to identify targets - components, subsystems, or systems - that would most benefit from a PHM system in regards to achieving the following objectives: minimizing cost, minimizing production/maintenance time, maximizing system remaining usable life (RUL), maximizing product quality, and maximizing product output. HHM is a comprehensive modeling theory and methodology that is grounded on the premise that no system can be modeled effectively from a single perspective. It can also be used as an inductive method for scenario structuring to identify emergent forced changes (EFCs) in a system. EFCs connote trends in external or internal sources of risk to a system that may adversely affect specific states of the system. An important aspect of proactive risk management includes bolstering the resilience of the system for specific EFCs by appropriately controlling the states. Risk scenarios for specific EFCs can be the basis for the design of prognostic and diagnostic systems that provide real-time predictions and recognition of scenario changes. The HHM methodology includes visual modeling techniques that can enhance stakeholders' understanding of shared states, resources, objectives and constraints among the interdependent and interconnected subsystems of smart manufacturing systems. In risk analysis, HHM is often paired

  6. Prognostic models based on patient snapshots and time windows: Predicting disease progression to assisted ventilation in Amyotrophic Lateral Sclerosis.

    Science.gov (United States)

    Carreiro, André V; Amaral, Pedro M T; Pinto, Susana; Tomás, Pedro; de Carvalho, Mamede; Madeira, Sara C

    2015-12-01

    Amyotrophic Lateral Sclerosis (ALS) is a devastating disease and the most common neurodegenerative disorder of young adults. ALS patients present a rapidly progressive motor weakness. This usually leads to death in a few years by respiratory failure. The correct prediction of respiratory insufficiency is thus key for patient management. In this context, we propose an innovative approach for prognostic prediction based on patient snapshots and time windows. We first cluster temporally-related tests to obtain snapshots of the patient's condition at a given time (patient snapshots). Then we use the snapshots to predict the probability of an ALS patient to require assisted ventilation after k days from the time of clinical evaluation (time window). This probability is based on the patient's current condition, evaluated using clinical features, including functional impairment assessments and a complete set of respiratory tests. The prognostic models include three temporal windows allowing to perform short, medium and long term prognosis regarding progression to assisted ventilation. Experimental results show an area under the receiver operating characteristics curve (AUC) in the test set of approximately 79% for time windows of 90, 180 and 365 days. Creating patient snapshots using hierarchical clustering with constraints outperforms the state of the art, and the proposed prognostic model becomes the first non population-based approach for prognostic prediction in ALS. The results are promising and should enhance the current clinical practice, largely supported by non-standardized tests and clinicians' experience. Copyright © 2015 Elsevier Inc. All rights reserved.

  7. GPU Accelerated Prognostics

    Science.gov (United States)

    Gorospe, George E., Jr.; Daigle, Matthew J.; Sankararaman, Shankar; Kulkarni, Chetan S.; Ng, Eley

    2017-01-01

    Prognostic methods enable operators and maintainers to predict the future performance for critical systems. However, these methods can be computationally expensive and may need to be performed each time new information about the system becomes available. In light of these computational requirements, we have investigated the application of graphics processing units (GPUs) as a computational platform for real-time prognostics. Recent advances in GPU technology have reduced cost and increased the computational capability of these highly parallel processing units, making them more attractive for the deployment of prognostic software. We present a survey of model-based prognostic algorithms with considerations for leveraging the parallel architecture of the GPU and a case study of GPU-accelerated battery prognostics with computational performance results.

  8. A Mesoscale Meteorological Model of Modified Land Cover to the Effect of Urban Heat Island in Jakarta, Indonesia

    Directory of Open Access Journals (Sweden)

    Yopi Ilhamsyah

    2012-08-01

    Full Text Available A mesoscale meteorological model of modified land cover to the effect of urban heat island (UHI in Jakarta was done. Although higher temperature in the city has been generally known, factors and issues that result in the increase of temperature particularly nighttime temperature over the city, however, are not well-understood. Jakarta, the capital of Indonesia, is encountering urbanization problems foremost. The increasing demand of housing as well as rapid development of sky crapper building, market places and highway diminishes the vegetation which in turn trap heat in the troposphere throughout the year, particularly during dry season on June-August. The fifth-generation mesoscale meteorological model (MM5 was employed in the study. The model involves medium range forecast planetary boundary layer (MRF PBL scheme and land surface with two following parameters: i.e. roughness length over land and thermal inertia of land. These two parameters are chosen to enhance the characteristics of land surface. The simulation was carried out for 3 days on August 5-7, 2004 during dry season. The results showed that the simulation of surface temperature done by MM5 modified land cover described a good comparison to that of weather observation data. As a result, the effect of UHI was also well-observed during day-time. In addition, MM5 modified land cover simulation also illustrated a well-development of sea-breeze and country-breeze during mid-day and nighttime, respectively. However, long-term simulation is still required. Thus, daily diurnal cycles of air temperature and their differences can be well-observed in detail.

  9. Prognostic modeling of oral cancer by gene profiles and clinicopathological co-variables.

    Science.gov (United States)

    Mes, Steven W; Te Beest, Dennis; Poli, Tito; Rossi, Silvia; Scheckenbach, Kathrin; van Wieringen, Wessel N; Brink, Arjen; Bertani, Nicoletta; Lanfranco, Davide; Silini, Enrico M; van Diest, Paul J; Bloemena, Elisabeth; Leemans, C René; van de Wiel, Mark A; Brakenhoff, Ruud H

    2017-08-29

    Accurate staging and outcome prediction is a major problem in clinical management of oral cancer patients, hampering high precision treatment and adjuvant therapy planning. Here, we have built and validated multivariable models that integrate gene signatures with clinical and pathological variables to improve staging and survival prediction of patients with oral squamous cell carcinoma (OSCC). Gene expression profiles from 249 human papillomavirus (HPV)-negative OSCCs were explored to identify a 22-gene lymph node metastasis signature (LNMsig) and a 40-gene overall survival signature (OSsig). To facilitate future clinical implementation and increase performance, these signatures were transferred to quantitative polymerase chain reaction (qPCR) assays and validated in an independent cohort of 125 HPV-negative tumors. When applied in the clinically relevant subgroup of early-stage (cT1-2N0) OSCC, the LNMsig could prevent overtreatment in two-third of the patients. Additionally, the integration of RT-qPCR gene signatures with clinical and pathological variables provided accurate prognostic models for oral cancer, strongly outperforming TNM. Finally, the OSsig gene signature identified a subpopulation of patients, currently considered at low-risk for disease-related survival, who showed an unexpected poor prognosis. These well-validated models will assist in personalizing primary treatment with respect to neck dissection and adjuvant therapies.

  10. Simulations of Tropospheric NO2 by the Global Modeling Initiative (GMI) Model Utilizing Assimilated and Forecast Meteorological Fields: Comparison to Ozone Monitoring Instrument (OMI) Measurements

    Science.gov (United States)

    Rodriquez, J. M.; Yoshida, Y.; Duncan, B. N.; Bucsela, E. J.; Gleason, J. F.; Allen, D.; Pickering, K. E.

    2007-01-01

    We present simulations of the tropospheric composition for the years 2004 and 2005, carried out by the GMI Combined Stratosphere-Troposphere (Combo) model, at a resolution of 2degx2.5deg. The model includes a new parameterization of lightning sources of NO(x) which is coupled to the cloud mass fluxes in the adopted meteorological fields. These simulations use two different sets of input meteorological fields: a)late-look assimilated fields from the Global Modeling and Assimilation Office (GMAO), GEOS-4 system and b) 12-hour forecast fields initialized with the assimilated data. Comparison of the forecast to the assimilated fields indicates that the forecast fields exhibit less vigorous convection, and yield tropical precipitation fields in better agreement with observations. Since these simulations include a complete representation of the stratosphere, they provide realistic stratosphere-tropospheric fluxes of O3 and NO(y). Furthermore, the stratospheric contribution to total columns of different troposheric species can be subtracted in a consistent fashion, and the lightning production of NO(y) will depend on the adopted meteorological field. We concentrate here on the simulated tropospheric columns of NO2, and compare them to observations by the OM1 instrument for the years 2004 and 2005. The comparison is used to address these questions: a) is there a significant difference in the agreement/disagreement between simulations for these two different meteorological fields, and if so, what causes these differences?; b) how do the simulations compare to OMI observations, and does this comparison indicate an improvement in simulations with the forecast fields? c) what are the implications of these simulations for our understanding of the NO2 emissions over continental polluted regions?

  11. Model analysis of soil dust impacts on the boundary layer meteorology and air quality over East Asia in April 2015

    Science.gov (United States)

    Chen, Lei; Zhang, Meigen; Zhu, Jia; Skorokhod, Andrei

    2017-05-01

    An online coupled meteorology-chemistry-aerosol model (WRF-Chem) is used to quantify the impact of soil dust on radiative forcing, boundary layer meteorology and air quality over East Asia. The simulation is conducted from 14 to 17 April 2015, when an intense dust storm originated in the Gobi Desert and moved through North China. An integrated comparison analysis using surface observations, satellite, and lidar measurements demonstrates the excellent performance of the WRF-Chem model for meteorological parameters, pollutant concentrations, aerosol optical characteristics, and the spatiotemporal evolution of the dust storm. The maximum aerosol optical depth induced by dust aerosols is simulated to exceed 3.0 over the dust source areas and 1.5 over the downwind regions. Dust has a cooling effect (- 1.19 W m- 2) at the surface, a warming effect (+ 0.90 W m- 2) in the atmosphere and a relatively small forcing (- 0.29 W m- 2) at the top of the atmosphere averaged over East Asia from 14 to 17 April 2015. Due to the impact of dust aerosols, the near-surface air temperature is decreased by 0.01 °C and 0.06 °C in the daytime and increased by 0.13 °C and 0.14 °C at night averaged over the dust sources and the North China Plain (NCP), respectively. The changes in relative humidity are in the range of - 0.38% to + 0.04% for dust sources and - 0.40% to + 0.27% for NCP. The maximum decrease in wind speed of 0.1 m s- 1 is found over NCP. The planetary boundary layer height during the daytime exhibits maximum decreases of 16.34 m and 41.70 m over dust sources and NCP, respectively. The pollutant concentrations are significantly influenced by dust-related heterogeneous chemical reactions, with a maximum decrease of 1.66 ppbV for SO2, 7.15 ppbV for NOy, 35.04 μg m- 3 for NO3-, and a maximum increase of 9.47 μg m- 3 for SO42 - over the downwind areas.

  12. 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...

  13. Meteorological and trace gas factors affecting the number concentration of atmospheric Aitken (Dp = 50 nm particles in the continental boundary layer: parameterization using a multivariate mixed effects model

    Directory of Open Access Journals (Sweden)

    M. C. Facchini

    2011-01-01

    Full Text Available Measurements of aerosol size distribution and different gas and meteorological parameters, made in three polluted sites in Central and Southern Europe: Po Valley, Italy, Melpitz and Hohenpeissenberg in Germany, were analysed for this study to examine which of the meteorological and trace gas variables affect the number concentration of Aitken (Dp= 50 nm particles. The aim of our study was to predict the number concentration of 50 nm particles by a combination of in-situ meteorological and gas phase parameters. The statistical model needs to describe, amongst others, the factors affecting the growth of newly formed aerosol particles (below 10 nm to 50 nm size, but also sources of direct particle emissions in that size range. As the analysis method we used multivariate nonlinear mixed effects model. Hourly averages of gas and meteorological parameters measured at the stations were used as predictor variables; the best predictive model was attained with a combination of relative humidity, new particle formation event probability, temperature, condensation sink and concentrations of SO2, NO2 and ozone. The seasonal variation was also taken into account in the mixed model structure. Model simulations with the Global Model of Aerosol Processes (GLOMAP indicate that the parameterization can be used as a part of a larger atmospheric model to predict the concentration of climatically active particles. As an additional benefit, the introduced model framework is, in theory, applicable for any kind of measured aerosol parameter.

  14. The meteorology of Gale crater as determined from rover environmental monitoring station observations and numerical modeling. Part I: Comparison of model simulations with observations

    Science.gov (United States)

    Pla-Garcia, Jorge; Rafkin, Scot C. R.; Kahre, Melinda; Gomez-Elvira, Javier; Hamilton, Victoria E.; Navarro, Sara; Torres, Josefina; Marín, Mercedes; Vasavada, Ashwin R.

    2016-12-01

    Air temperature, ground temperature, pressure, and wind speed and direction data obtained from the Rover Environmental Monitoring Station onboard the Mars Science Laboratory rover Curiosity are compared to data from the Mars Regional Atmospheric Modeling System. A full diurnal cycle at four different seasons (Ls 0, 90, 180 and 270) is investigated at the rover location within Gale crater, Mars. Model results are shown to be in good agreement with observations when considering the uncertainties in the observational data set. The good agreement provides justification for utilizing the model results to investigate the broader meteorological environment of the Gale crater region, which is described in the second, companion paper.

  15. Prognostic scoring systems for mortality in intensive care units--the APACHE model.

    Science.gov (United States)

    Niewiński, Grzegorz; Starczewska, Małgorzata; Kański, Andrzej

    2014-01-01

    The APACHE (Acute Physiology and Chronic Health Evaluation) scoring system is time consuming. The mean time for introducing a patient's data to APACHE IV is 37.3 min. Nevertheless, statisticians have known for years that the higher the number of variables the mathematical model describes, the more accurate the model. Because of the necessity of gathering data over a 24-hour period and of determining one cause for ICU admission, the system is troublesome and prone to mistakes. The evolution of the APACHE scoring system is an example of unfulfilled hopes for accurately estimating the risk of death for patients admitted to the ICU; satisfactory prognostic effects resulting from the use of APACHE II and III have been recently studied in patients undergoing liver transplantations. Because no increase in the predictive properties of successive versions has been observed, the search for other solutions continues. The APACHE IV scoring system is helpful; however, its use without prepared spreadsheets is almost impractical. Therefore, although many years have passed since its original publication, APACHE II or its extension APACHE III is currently used in clinical practice.

  16. LANL Meteorology Program

    Energy Technology Data Exchange (ETDEWEB)

    Dewart, Jean Marie [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2015-02-09

    The goal of the Meteorology Program is to provide all routine meteorology measurements for LANL operational requirements. This report discusses the program, its routine operations, and other services.

  17. 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

  18. Advanced Methods for Determining Prediction Uncertainty in Model-Based Prognostics with Application to Planetary Rovers

    Science.gov (United States)

    Daigle, Matthew J.; Sankararaman, Shankar

    2013-01-01

    Prognostics is centered on predicting the time of and time until adverse events in components, subsystems, and systems. It typically involves both a state estimation phase, in which the current health state of a system is identified, and a prediction phase, in which the state is projected forward in time. Since prognostics is mainly a prediction problem, prognostic approaches cannot avoid uncertainty, which arises due to several sources. Prognostics algorithms must both characterize this uncertainty and incorporate it into the predictions so that informed decisions can be made about the system. In this paper, we describe three methods to solve these problems, including Monte Carlo-, unscented transform-, and first-order reliability-based methods. Using a planetary rover as a case study, we demonstrate and compare the different methods in simulation for battery end-of-discharge prediction.

  19. 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...

  20. 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.

  1. Clinical Risk Factors and Prognostic Model for Primary Graft Dysfunction after Lung Transplantation in Patients with Pulmonary Hypertension.

    Science.gov (United States)

    Porteous, Mary K; Lee, James C; Lederer, David J; Palmer, Scott M; Cantu, Edward; Shah, Rupal J; Bellamy, Scarlett L; Lama, Vibha N; Bhorade, Sangeeta M; Crespo, Maria M; McDyer, John F; Wille, Keith M; Localio, A Russell; Orens, Jonathan B; Shah, Pali D; Weinacker, Ann B; Arcasoy, Selim; Wilkes, David S; Ware, Lorraine B; Christie, Jason D; Kawut, Steven M; Diamond, Joshua M

    2017-10-01

    Pulmonary hypertension from pulmonary arterial hypertension or parenchymal lung disease is associated with an increased risk for primary graft dysfunction after lung transplantation. We evaluated the clinical determinants of severe primary graft dysfunction in pulmonary hypertension and developed and validated a prognostic model. We conducted a retrospective cohort study of patients in the multicenter Lung Transplant Outcomes Group with pulmonary hypertension at transplant listing. Severe primary graft dysfunction was defined as PaO2/FiO2 ≤200 with allograft infiltrates at 48 or 72 hours after transplantation. Donor, recipient, and operative characteristics were evaluated in a multivariable explanatory model. A prognostic model derived using donor and recipient characteristics was then validated in a separate cohort. In the explanatory model of 826 patients with pulmonary hypertension, donor tobacco smoke exposure, higher recipient body mass index, female sex, listing mean pulmonary artery pressure, right atrial pressure and creatinine at transplant, cardiopulmonary bypass use, transfusion volume, and reperfusion fraction of inspired oxygen were associated with primary graft dysfunction. Donor obesity was associated with a lower risk for primary graft dysfunction. Using a 20% threshold for elevated risk, the prognostic model had good negative predictive value in both derivation and validation cohorts (89.1% [95% confidence interval, 85.3-92.8] and 83.3% [95% confidence interval, 78.5-88.2], respectively), but low positive predictive value. Several recipient, donor, and operative characteristics were associated with severe primary graft dysfunction in patients with pulmonary hypertension, including several risk factors not identified in the overall transplant population. A prognostic model with donor and recipient clinical risk factors alone had low positive predictive value, but high negative predictive value, to rule out high risk for primary graft dysfunction.

  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. 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

  4. 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.

  5. [Molecular biological evaluation of prognostic parameters in GIST. Development of an integrative model of tumor progression].

    Science.gov (United States)

    Haller, F

    2010-10-01

    Prognosis evaluation in gastrointestinal stromal tumors (GIST) is currently based on tumor diameter, mitotic counts and anatomic localisation. There are two risk classifications as well as the first ever TNM classification for GISTs, whereby the risk classification according to the Armed Forces Institute of Pathology (AFIP) has the best correlation with clinical follow-up according to own experiences. "Very low/low risk" GISTs are almost benign, while the majority of "high risk" GISTs metastasize and benefit from adjuvant therapy. Careful evaluation of mitotic counts in 50 high-power fields is of particular relevance for correct risk classification. Apart from these classical prognostic factors, many molecular genetic parameters with correlation to follow-up have been evaluated and may help to improve prognosis evaluation of GISTs in the future. Since most of the molecular genetic parameters are associated or even determined by the clinico-pathological parameters, an integrated model for tumor progression of GISTs is helpful to interpret the different factors in correlation to one another. In particular for "intermediate risk" GISTs, additional parameters are needed for improved prognosis evaluation.

  6. 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.

  7. Development and testing of a high-resolution model for tropospheric sulfate driven by observation-derived meteorology

    Energy Technology Data Exchange (ETDEWEB)

    Benkovitz, C.M. [Brookhaven National Lab., Upton, NY (United States). Environmental Chemistry Div.

    1994-05-01

    A high-resolution three-dimensional Eulerian transport and transformation model has been developed to simulate concentrations of tropospheric sulfate for specific times and locations; it was applied over the North Atlantic and adjacent continental regions during October and November, 1986. The model represents emissions of anthropogenic SO{sub 2} and sulfate and of biogenic sulfur species, horizontal and vertical transport, gas-phase oxidation of SO{sub 2} and dimethylsulfide, aqueous-phase oxidation of SO{sub 2}, and wet and dry deposition of SO{sub 2}, sulfate, and methanesulfonic acid (MSA). The meteorological driver is the 6-hour output from the forecast model of the European Centre for Medium-Range Weather Forecasts. Calculated sulfate concentrations and column burdens, examined in detail for October 15 and October 22 at 6Z, are related to existing weather patterns. These results exhibit rich temporal and spatial structure; the characteristic (1/e) temporal autocorrelation time for the sulfate column burdens over the central North Atlantic averages 20 hours; 95% of the values were 25 hours or less. The characteristic distance of spatial autocorrelation over this region depends on direction and averages 1,600 km; with 10{sup th} percentile value of 400 km and 90{sup th} percentile value of 1,700 km. Daily average model sulfate concentrations at the lowest vertical accurately represent the spatial variability, temporal episodicity, and absolute magnitudes of surface concentrations measured by monitoring stations in Europe, Canada and Barbados.

  8. Atmospheric dispersion models and pre-processing of meteorological data for real-time application

    DEFF Research Database (Denmark)

    Mikkelsen, T.; Desiato, F.

    1993-01-01

    considerations, model performance and evaluation records, computational needs, user expertise, and type of sources to be modelled. Models suitable for a given accident scenario are chosen from this hierarchy in order to provide the dose assessments via the dispersion module. A forecasting feasibility...

  9. Impacts of weighting climate models for hydro-meteorological climate change studies

    Science.gov (United States)

    Chen, Jie; Brissette, François P.; Lucas-Picher, Philippe; Caya, Daniel

    2017-06-01

    Weighting climate models is controversial in climate change impact studies using an ensemble of climate simulations from different climate models. In climate science, there is a general consensus that all climate models should be considered as having equal performance or in other words that all projections are equiprobable. On the other hand, in the impacts and adaptation community, many believe that climate models should be weighted based on their ability to better represent various metrics over a reference period. The debate appears to be partly philosophical in nature as few studies have investigated the impact of using weights in projecting future climate changes. The present study focuses on the impact of assigning weights to climate models for hydrological climate change studies. Five methods are used to determine weights on an ensemble of 28 global climate models (GCMs) adapted from the Coupled Model Intercomparison Project Phase 5 (CMIP5) database. Using a hydrological model, streamflows are computed over a reference (1961-1990) and future (2061-2090) periods, with and without post-processing climate model outputs. The impacts of using different weighting schemes for GCM simulations are then analyzed in terms of ensemble mean and uncertainty. The results show that weighting GCMs has a limited impact on both projected future climate in term of precipitation and temperature changes and hydrology in terms of nine different streamflow criteria. These results apply to both raw and post-processed GCM model outputs, thus supporting the view that climate models should be considered equiprobable.

  10. Modeling SF{sub 6} plume dispersion in complex terrain and meteorology with a limited data set

    Energy Technology Data Exchange (ETDEWEB)

    Schalk, W.W. III

    1996-10-01

    Early actions of emergency responders during hazardous material releases are intended to assess contamination and potential public exposure. As measurements are collected, an integration of model calculations and measurements can assist to better understand the situation. This study applied a high resolution version of the operational 3-D numerical models used by Lawrence Livermore National Laboratory to a limited meteorological and tracer data set to assist in the interpretation of the dispersion pattern on a 140 km scale. The data set was collected from a tracer release during the morning surface inversion and transition period in the complex terrain of the Snake River Plain near Idaho Falls, Idaho in November 1993 by the United States Air Force. Sensitivity studies were conducted to determine model input parameters that best represented the study environment. These studies showed that mixing and boundary layer heights, atmospheric stability, and rawinsonde data are the most important model input parameters affecting wind field generation and tracer dispersion. Numerical models and limited measurement data were used to interpret dispersion patterns through the use of data analysis, model input determination, and sensitivity studies. Comparison of the best-estimate calculation to measurement data showed that model results compared well with the aircraft data, but had moderate success with the few surface measurements taken. The moderate success of the surface measurement comparison, may be due to limited downward mixing of the tracer as a result of the model resolution determined by the domain size selected to study the overall plume dispersion. 8 refs., 40 figs., 7 tabs.

  11. 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...

  12. Utilization of remote sensing data on meteorological and vegetation characteristics for modeling water and heat regimes of large agricultural region

    Science.gov (United States)

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

    2016-04-01

    Presently, physical-mathematical models such as SVAT (Soil-Vegetation-Atmosphere-Transfer) developed with varying degrees of detail are one of the most effective tools to evaluate the characteristics of the water and heat regimes of vegetation covered territories. The produced SVAT model is designed to calculate the soil water content, evapotranspiration (evaporation from bare soil and transpiration), infiltration of water into the soil, vertical latent and sensible heat fluxes and other water and heat regime characteristics as well as vegetation and soil surface temperatures and the temperature and soil moisture distributions in depth. The model is adapted to satellite-derived estimates of precipitation, land surface temperatures and vegetation cover characteristics. The case study has been carried out for the located in the forest-steppe zone territory of part of the agricultural Central Black Earth Region of Russia with coordinates 49° 30'-54° N and 31° -43° E and area of 227 300 km2 for years 2011-2014 vegetation seasons. The soil and vegetation characteristics are used as the model parameters and the meteorological characteristics are considered to be input variables. These values have been obtained from ground-based observations and satellite-based measurements by radiometers AVHRR/NOAA, MODIS/EOS Terra and Aqua, SEVIRI/MSG-2,-3 (Meteosat-9, -10). To provide the retrieval of meteorological and vegetation cover characteristics the new and pre-existing methods and technologies of above radiometer thematic processing data have been developed or refined. From AVHRR data there have been derived estimates of precipitation P, efficient land surface temperature (LST) Ts.eff and emissivity E, surface-air temperature at a level of vegetation cover Ta, normalized difference vegetation index NDVI, leaf area index LAI and vegetation cover fraction B. The remote sensing products LST Tls, E, NDVI, LAI derived from MODIS data and covering the study area have been

  13. 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.

  14. 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.

  15. Improved sub-seasonal meteorological forecast skill using weighted multi-model ensemble simulations

    NARCIS (Netherlands)

    Wanders, Niko; Wood, Eric F.

    2016-01-01

    Sub-seasonal to seasonal weather and hydrological forecasts have the potential to provide vital information for a variety of water-related decision makers. Here, we investigate the skill of four sub-seasonal forecast models from phase-2 of the North American Multi-Model Ensemble using reforecasts

  16. The effect of model resolution in predicting meteorological parameters used in fire danger rating.

    Science.gov (United States)

    Jeanne L. Hoadley; Ken Westrick; Sue A. Ferguson; Scott L. Goodrick; Larry Bradshaw; Paul. Werth

    2004-01-01

    Previous studies of model performance at varying resolutions have focused on winter storms or isolated convective events. Little attention has been given to the static high pressure situations that may lead to severe wildfire outbreaks. This study focuses on such an event so as to evaluate the value of increased model resolution for prediction of fire danger. The...

  17. Simulation of Urban Heat Island Mitigation Strategies in Atlanta, GA Using High-Resolution Land Use/Land Cover Data Set to Enhance Meteorological Modeling

    Science.gov (United States)

    Crosson, William L.; Dembek, Scott; Estes, Maurice G., Jr.; Limaye, Ashutosh S.; Lapenta, William; Quattrochi, Dale A.; Johnson, Hoyt; Khan, Maudood

    2006-01-01

    The specification of land use/land cover (LULC) and associated land surface parameters in meteorological models at all scales has a major influence on modeled surface energy fluxes and boundary layer states. In urban areas, accurate representation of the land surface may be even more important than in undeveloped regions due to the large heterogeneity within the urban area. Deficiencies in the characterization of the land surface related to the spatial or temporal resolution of the data, the number of LULC classes defined, the accuracy with which they are defined, or the degree of heterogeneity of the land surface properties within each class may degrade the performance of the models. In this study, an experiment was conducted to test a new high-resolution LULC data set for meteorological simulations for the Atlanta, Georgia metropolitan area using a mesoscale meteorological model and to evaluate the effects of urban heat island (UHI) mitigation strategies on modeled meteorology for 2030. Simulation results showed that use of the new LULC data set reduced a major deficiency of the land use data used previously, specifically the poor representation of urban and suburban land use. Performance of the meteorological model improved substantially, with the overall daytime cold bias reduced by over 30%. UHI mitigation strategies were projected to offset much of a predicted urban warming between 2000 and 2030. In fact, for the urban core, the cooling due to UHI mitigation strategies was slightly greater than the warming associated with urbanization over this period. For the larger metropolitan area, cooling only partially offset the projected warming trend.

  18. A linearized Eulerian sound propagation model for studies of complex meteorological effects

    Science.gov (United States)

    Blumrich, Reinhard; Heimann, Dietrich

    2002-08-01

    Outdoor sound propagation is significantly affected by the topography (including ground characteristics) and the state of the atmosphere. The atmosphere on its part is also influenced by the topography. A sound propagation model and a flow model based on a numerical integration of the linearized Euler equations have been developed to take these interactions into account. The output of the flow model enables the calculation of the sound propagation in a three-dimensionally inhomogeneous atmosphere. Rigid, partly reflective, or fully absorptive ground can be considered. The linearized Eulerian (LE) sound propagation model has been validated by means of four different scenarios. Calculations of sound fields above rigid and grass-covered ground including a homogeneous atmosphere deviate from analytic solutions by less-than-or-equal1 dB in most parts of the computed domain. Calculations of sound propagation including wind and temperature gradients above rigid ground agree well with measured scale model data. Calculations of sound propagation over a screen including ground of finite impedance show little deviations to measured scale model data which are probably caused by an insufficient representation of the complex ground impedance. Further calculations included the effect of wind on shading by a screen. The results agree well with the measured scale model data. copyright 2002 Acoustical Society of America.

  19. Dependence of simulations of long range transport on meteorology, model and dust size

    Science.gov (United States)

    Mahowald, N. M.; Albani, S.; Smith, M.; Losno, R.; Marticorena, B.; Ridley, D. A.; Heald, C. L.; Qu, Z.

    2015-12-01

    Mineral aerosols interact with radiation directly, as well as modifying climate, and provide important micronutrients to ocean and land ecosystems. Mineral aerosols are transported long distances from the source regions to remote regions, but the rates at which this occurs can be difficult to deduce from either observations or models. Here we consider interactions between the details of the simulation of dust size and long-range transport. In addition, we compare simulations of dust using multiple reanalysis datasets, as well as different model basis to understand how robust the mean, seasonality and interannual variability are in models. Models can provide insight into how long observations are required in order to characterize the atmospheric concentration and deposition to remote regions.

  20. Study of drought processes in Spain by means of offline Land-Surface Model simulations. Evaluation of model sensitivity to the meteorological forcing dataset.

    Science.gov (United States)

    Quintana-Seguí, Pere; Míguez-Macho, Gonzalo; Barella-Ortiz, Anaïs

    2017-04-01

    Drought affects different aspects of the continental water cycle, from precipitation (meteorological drought), to soil moisture (agricultural drought), streamflow, lake volume and piezometric levels (hydrological drought). The spatial and temporal scales of drought, together with its propagation through the system must be well understood. Drought is a hazard impacting all climates and regions of the world; but in some areas, such as Spain, its societal impacts may be especially severe, creating water resources related tensions between regions and sectors. Indices are often used to characterize different aspects of drought. Similar indices can be built for precipitation (SPI), soil moisture (SSMI), and streamflow (SSI), allowing to analyse the temporal scales of drought and its spatial patterns. Precipitation and streamflow data are abundant in Spain; however soil moisture data is scarce. Land-Surface Models (LSM) physically simulate the continental water cycle and, thus, are appropriate tools to quantify soil moisture and other relevant variables and processes. These models can be run offline, forced by a gridded dataset of meteorological variables, usually a re-analysis. The quality of the forcing dataset affects the quality of the subsequent modeling results and is, thus, crucial. The objective of this study is to investigate how sensitive LSM simulations are to the forcing dataset, with a focus on drought. A global and a local dataset are used at different resolutions. The global dataset is the eartH2Observe dataset, which is based on ERA-Interim. The local dataset is the SAFRAN meteorological analysis system. The LSMs used are SURFEX and LEAFHYDRO. Standardized indices of the relevant variables are produced for all the simulations performed. Then, we analyze how differently drought propagates through the system in the different simulations and how similar are spatial and temporal scales of drought. The results of this study will be useful to understand the

  1. 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.

  2. A comparative analysis of three vector-borne diseases across Australia using seasonal and meteorological models

    Science.gov (United States)

    Stratton, Margaret D.; Ehrlich, Hanna Y.; Mor, Siobhan M.; Naumova, Elena N.

    2017-01-01

    Ross River virus (RRV), Barmah Forest virus (BFV), and dengue are three common mosquito-borne diseases in Australia that display notable seasonal patterns. Although all three diseases have been modeled on localized scales, no previous study has used harmonic models to compare seasonality of mosquito-borne diseases on a continent-wide scale. We fit Poisson harmonic regression models to surveillance data on RRV, BFV, and dengue (from 1993, 1995 and 1991, respectively, through 2015) incorporating seasonal, trend, and climate (temperature and rainfall) parameters. The models captured an average of 50-65% variability of the data. Disease incidence for all three diseases generally peaked in January or February, but peak timing was most variable for dengue. The most significant predictor parameters were trend and inter-annual periodicity for BFV, intra-annual periodicity for RRV, and trend for dengue. We found that a Temperature Suitability Index (TSI), designed to reclassify climate data relative to optimal conditions for vector establishment, could be applied to this context. Finally, we extrapolated our models to estimate the impact of a false-positive BFV epidemic in 2013. Creating these models and comparing variations in periodicities may provide insight into historical outbreaks as well as future patterns of mosquito-borne diseases.

  3. 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

    OpenAIRE

    Guinney, Justin; Tao WANG; 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

    2017-01-01

    Background: 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 ...

  4. Prognostic value of a systemic inflammatory response index in metastatic renal cell carcinoma and construction of a predictive model

    Science.gov (United States)

    Li, Hongzhao; Chen, Luyao; Li, Xintao; Zhang, Yu; Xie, Yongpeng; Zhang, Xu

    2017-01-01

    Inflammation act as a crucial role in carcinogenesis and tumor progression. In this study, we aim to investigate the prognostic significance of systemic inflammatory biomarkers in metastatic renal cell carcinoma (mRCC) and develop a survival predictive model. One hundred and sixty-one mRCC patients who had undergone cytoreductive nephrectomy were enrolled from January 2006 to December 2013. We created a systemic inflammation response index (SIRI) basing on pretreatment hemoglobin and lymphocyte to monocyte ratio (LMR), and evaluated its associations with overall survival (OS) and clinicopathological features. Pretreatment hemoglobin and LMR both remained as independent factors adjusted for other markers of systemic inflammation responses and conventional clinicopathological parameters. A high SIRI seems to be an independent prognosis predictor of worse OS and was significantly correlated with aggressive tumor behaviors. Inclusion of the SIRI into a prognostic model including Fuhrman grade, histology, tumor necrosis and targeted therapy established a nomogram, which accurately predicted 1-year survival for mRCC patients. The SIRI seems to be a prognostic biomarker in mRCC patients. The proposed nomogram can be applied to predict OS of patients with mRCC after nephrectomy. PMID:28881716

  5. An Idealized Meteorological-Hydrodynamic Model for Exploring Extreme Storm Surge Statistics in the North Sea

    NARCIS (Netherlands)

    Van Ledden, M.; Van den Berg, N.J.F.; De Jong, M.S.; Van Gelder, P.H.A.J.M.; Den Heijer, C.; Vrijling, J.K.; Jonkman, S.N.; Roos, P.C.; Hulscher, S.J.M.H.; Lansen, A.J.

    2014-01-01

    This paper explores an alternative method to determine extreme surge levels at the Dutch Coast. For this exploration, specific focus is on the extreme water level at Hoek van Holland, The Netherlands. The alternative method has been based on a joint probability model of the storm characteristics at

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

    Science.gov (United States)

    El-Samra, R.; Bou-Zeid, E.; Bangalath, H. K.; Stenchikov, G.; El-Fadel, M.

    2017-12-01

    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.

  7. Modeling the meteorological and chemical effects of secondary organic aerosols during an EUCAARI campaign

    Directory of Open Access Journals (Sweden)

    E. Athanasopoulou

    2013-01-01

    Full Text Available A volatility basis set (VBS approach for the simulation of secondary organic aerosol (SOA formation is incorporated in the online coupled atmospheric model system COSMO-ART and applied over Europe during the EUCAARI May 2008 campaign. Organic aerosol performance is improved when compared to the default SOA module of COSMO-ART (SORGAM against high temporal resolution aerosol mass spectrometer ground measurements. The impact of SOA on the overall radiative budget was investigated. The mean direct surface radiative cooling averaged over Europe is −1.2 W m−2, representing approximately 20% of the total effect of aerosols on the radiative budget. However, responses are not spatially correlated with the radiative forcing, due to the nonlinear interactions among changes in particle chemical composition, water content, size distribution and cloud cover. These interactions initiated~by the effect of SOA on radiation are found to result even in a positive forcing in specific areas. Further model experiments showed that the availability of nitrogen oxides slightly affects SOA production, but that the aging rate constant used in the VBS approximation and boundary concentrations assumed in the model should be carefully selected. The aging of SOA is found to reduce hourly nitrate levels by up to 30%, while the condensation of inorganic species upon pre-existing, SOA-rich particles results in a monthly average increase of 5% in sulfate and ammonium formation in the accumulation mode.

  8. 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.

  9. Snowpack modelling in the Pyrenees driven by kilometric-resolution meteorological forecasts

    Science.gov (United States)

    Quéno, Louis; Vionnet, Vincent; Dombrowski-Etchevers, Ingrid; Lafaysse, Matthieu; Dumont, Marie; Karbou, Fatima

    2016-07-01

    Distributed snowpack simulations in the French and Spanish Pyrenees are carried out using the detailed snowpack model Crocus driven by the numerical weather prediction system AROME at 2.5 km grid spacing, during four consecutive winters from 2010 to 2014. The aim of this study is to assess the benefits of a kilometric-resolution atmospheric forcing to a snowpack model for describing the spatial variability of the seasonal snow cover over a mountain range. The evaluation is performed by comparisons to ground-based measurements of the snow depth, the snow water equivalent and precipitations, to satellite snow cover images and to snowpack simulations driven by the SAFRAN analysis system. Snow depths simulated by AROME-Crocus exhibit an overall positive bias, particularly marked over the first summits near the Atlantic Ocean. The simulation of mesoscale orographic effects by AROME gives a realistic regional snowpack variability, unlike SAFRAN-Crocus. The categorical study of daily snow depth variations gives a differentiated perspective of accumulation and ablation processes. Both models underestimate strong snow accumulations and strong snow depth decreases, which is mainly due to the non-simulated wind-induced erosion, the underestimation of strong melting and an insufficient settling after snowfalls. The problematic assimilation of precipitation gauge measurements is also emphasized, which raises the issue of a need for a dedicated analysis to complement the benefits of AROME kilometric resolution and dynamical behaviour in mountainous terrain.

  10. Influence of low-altitude meteorological conditions on local infrasound propagation investigated by 3-D full-waveform modeling

    Science.gov (United States)

    Kim, Keehoon; Rodgers, Arthur

    2017-08-01

    Vertical stratification in the low atmosphere impacts near-ground sound propagation. On clear days, for example, negative gradients of low-atmospheric temperature can lead to upward refraction of acoustic waves and create a zone of silence near the ground, where no acoustic rays can arrive. We investigate impacts of lower tropospheric temperature and wind-velocity gradient on acoustic wave propagation using numerical simulations. Sound refraction in the atmosphere is a frequency-dependent wave phenomenon, and therefore classical ray methods based on infinite-frequency approximation may not be suitable for modeling acoustic wave amplitudes. In this study, a full-waveform acoustic solver was used to predict amplitudes of acoustic waves taking into account meteorological conditions (temperature, pressure and wind). Local radiosonde sounding data were input into acoustic simulations to characterize the background conditions of the local atmosphere. The results of numerical modeling indicate that acoustic overpressure amplitudes were significantly affected by local atmospheric wind speed and direction near the ground. Local wind changes the effective sound speed profile in the atmosphere and influences overpressure amplitude decay governed by upward refraction. We compared 3-D finite-difference modeling results with acoustic overpressure measurements from the Humming Roadrunner explosion experiments conducted in New Mexico in 2012. The modeling results showed good agreement with the observations in peak amplitudes when a background wind was weak and well characterized by local atmospheric data. However, when a strong wind was present at an explosion and its variability was poorly characterized by local radiosonde sounding, the numerical prediction of local acoustic amplitude agreed poorly with the observations. Additional numerical simulations with the inclusion of surface wind data indicate that local acoustic amplitudes could be significantly variable depending on

  11. Joint meteorological and hydrological drought model: a management tool for proactive water resources planning of semi-arid regions

    Science.gov (United States)

    Modaresi Rad, Arash; Ahmadi Ardakani, Samira; Ghahremani, Zahra; Ghahreman, Bijan; Khalili, Davar

    2016-04-01

    Conventionally drought analysis has been limited to single drought category. Utilization of models incorporating multiple drought categories, can relax this limitation. A copula-based model is proposed, which uses meteorological and hydrological drought indices to assess drought events for ultimate management of water resources, at small scales, i.e., sub-watersheds. The study area is a sub basin located at Karkheh watershed (western Iran), utilizing 41-year data of 4 raingauge stations and one hydrometric station located upstream and at the outlet respectively. Prior to drought analysis, time series of precipitation and streamflow records are investigated for possible dependency/significant trend. Considering the semi-arid nature of the study area, boxplots are utilized to graphically capture the rainy months, which used to evaluate the degree of correlation between streamflow and precipitation records via nonparametric correlations and bivariate tail dependence. Time scales of 3- and 12-month are considered, which are used to study vulnerability of early vegetation establishment and long-term ecosystem resilience, respectively. Among four common goodness of fit tests, the Cramér-von-Mises is found preferable for defining copula distribution functions through Akaike & Bayesian information criteria and coefficient of determination. Furthermore the uncertainty associated with different copula models is measured using the concept of entropy. A new bivariate drought modeling approach is proposed through copulas. The proposed index, named standardized precipitation-streamflow index (SPSI) is compared with two separate indices of streamflow drought index (SDI) and standardized precipitation index (SPI). According to results, the SPSI could detect onset of droughts dominated by precipitation as is similarly indicated by SPI index. It also captures discordant case of normal period precipitation with dry period streamflow and vice versa. Finally, combination of severity

  12. 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.

  13. Baseline elevated leukocyte count in peripheral blood is associated with poor survival in patients with advanced non-small cell lung cancer: a prognostic model.

    Science.gov (United States)

    Tibaldi, C; Vasile, E; Bernardini, I; Orlandini, C; Andreuccetti, M; Falcone, A

    2008-10-01

    We aimed to investigate the prognostic significance of several baseline variables in stage IIIB-IV non-small cell lung cancer to create a model based on independent prognostic factors. A total of 320 patients were treated with last generation chemotherapy regimens. The majority of patients received treatment with cisplatin + gemcitabine or gemcitabine alone if older than 70 years or with an ECOG performance status (PS) = 2. Performance status of 2, squamous histology, number of metastatic sites >2, presence of bone, brain, liver and contralateral lung metastases and elevated leukocyte count in peripheral blood were all statistically significant prognostic factors in univariate analyses whereas the other tested variables (sex, stage, age, presence of adrenal gland and skin metastases) were not. Subsequently, a multivariate Cox's regression analysis identified PS 2 (P leukocyte count (P Leukocyte count resulted the stronger factor after performance status. If prospectly validated, the proposed prognostic model could be useful to stratify performance status 2 patients in specific future trials.

  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. Aerosol-radiation interaction modelling using online coupling between the WRF 3.7.1 meteorological model and the CHIMERE 2016 chemistry-transport model, through the OASIS3-MCT coupler

    Science.gov (United States)

    Briant, Régis; Tuccella, Paolo; Deroubaix, Adrien; Khvorostyanov, Dmitry; Menut, Laurent; Mailler, Sylvain; Turquety, Solène

    2017-02-01

    The presence of airborne aerosols affects the meteorology as it induces a perturbation in the radiation budget, the number of cloud condensation nuclei and the cloud micro-physics. Those effects are difficult to model at regional scale as regional chemistry-transport models are usually driven by a distinct meteorological model or data. In this paper, the coupling of the CHIMERE chemistry-transport model with the WRF meteorological model using the OASIS3-MCT coupler is presented. WRF meteorological fields along with CHIMERE aerosol optical properties are exchanged through the coupler at a high frequency in order to model the aerosol-radiation interactions. The WRF-CHIMERE online model has a higher computational burden than both models run separately in offline mode (up to 42 % higher). This is mainly due to some additional computations made within the models such as more frequent calls to meteorology treatment routines or calls to optical properties computation routines. On the other hand, the overall time required to perform the OASIS3-MCT exchanges is not significant compared to the total duration of the simulations. The impact of the coupling is evaluated on a case study over Europe, northern Africa, the Middle East and western Asia during the summer of 2012, through comparisons of the offline and two online simulations (with and without the aerosol optical properties feedback) to observations of temperature, aerosol optical depth (AOD) and surface PM10 (particulate matter with diameters lower than 10 µm) concentrations. The result shows that using the optical properties feedback induces a radiative forcing (average forcing of -4.8 W m-2) which creates a perturbation in the average surface temperatures over desert areas (up to 2.6° locally) along with an increase in both AOD and PM10 concentrations.

  16. 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 < 0.001), presenting adjusted R 2 between 0.69 and 0.90. Center-Southern Brazil is mainly hit by frosts from mid-fall (April) to mid-spring (October). The period from November to March is considered as frost-free, being very rare a frost day within that period. Monthly F MET and F AGR presented significant sigmoidal relationships with T MN (p < 0.0001), with adjusted R 2 above of 0.82. The residuals of the frost day models were random, which means that the sigmoidal models performed quite well for interpreting the frost day variability throughout the study area. The highlands of Santa Catarina, Rio Grande do Sul, São Paulo, and Minas Gerais had in average more than 25 and 13 frosts per year, respectively, for F MET and F AGR. The F MET and F AGR maps developed in this study for Center-Southern Brazil is a useful tool for farmers, foresters, and researchers, since they contribute to reduce frost spatial and temporal uncertainty, helping in planning project for strategic purposes. Furthermore, the monthly F MET and F AGR maps

  17. Integrated meteorological and hydrological drought model: A management tool for proactive water resources planning of semi-arid regions

    Science.gov (United States)

    Rad, Arash Modaresi; Ghahraman, Bijan; Khalili, Davar; Ghahremani, Zahra; Ardakani, Samira Ahmadi

    2017-09-01

    Conventionally, drought analysis has been limited to single drought category. Utilization of models incorporating multiple drought categories, can relax this limitation. A copula-based model is proposed, which uses meteorological and hydrological drought characteristics to assess drought events for ultimate management of water resources, at small scales, i.e., sub-watersheds. The chosen study area is a sub-basin located at Karkheh watershed (western Iran), with five raingauge stations and one hydrometric station, located upstream and at the outlet, respectively, which represent 41-year of data. Prior to drought analysis, time series of precipitation and streamflow records are investigated for possible dependency/significant trend. Considering semi-arid nature of the study area, boxplots are utilized to graphically capture the rainy months, which are used to evaluate the degree of correlation between streamflow and precipitation records via nonparametric correlations. Time scales of 3- and 12-month are considered, which are used to study vulnerability of early vegetation establishment and long-term ecosystem resilience, respectively. Among four common goodness of fit (GOF) tests, Anderson-Darling is found preferable for defining copula distribution functions through GOF measures, i.e., Akaike and Bayesian information criteria and normalized root mean square error. Furthermore, a GOF method is proposed to evaluate the uncertainty associated with different copula models using the concept of entropy. A new bivariate drought modeling approach is proposed through copulas. The proposed index named standardized precipitation-streamflow index (SPSI) unlike common indices which are used in conjunction with station data, can be applied on a regional basis. SPDI is compared with widely applied streamflow drought index (SDI) and standardized precipitation index (SPI). To assess the homogeneity of the dependence structure of SPSI regionally, Kendall-τ and upper tail coefficient

  18. 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

  19. Simulation of the initial stage of the Mt. Pinatubo eruption using the coupled meteorology-chemistry WRF-Chem model

    Science.gov (United States)

    Stenchikov, Georgiy; Ukhov, Alexander; Ahmadov, Ravan

    2017-04-01

    Big explosive volcanic eruptions emit in the atmosphere, among other species, millions of tons of SO2, water vapor, and solid particles, volcanic ash. SO2 is oxidized to produce sulfate aerosols that are transported globally and cause widespread long-term climate effects. Ash particles deposit within a few months, as they are relatively large, and, it is believed, do not produce long-term climate effects. However, at the initial stage of the evolution of a volcanic cloud SO2, volcanic water, sulfate, and ash coexist and their chemical, microphysical, and radiation interaction might be important to precondition the long-term formation and transport of a volcanic aerosol cloud. To better understand this initial stage of a volcanic impact we simulate the aerosol plume from the largest 20th-century eruption of Mt. Pinatubo in the Philippines in June 1991 using the specifically modified Weather Research and Forecasting model coupled with chemistry (WRF-Chem). Ash, SO2, and sulfate emission, transport, dispersion, chemical transformation and deposition are calculated using the GOCART aerosol and chemistry scheme. Effect of volcanic aerosol interaction with radiation (short and long wave) is assessed using RRTMG radiative transfer model. The simulations are conducted for two months in the equatorial belt (45S, 45N) with the periodic boundary conditions in longitude and imposing aerosols and chemicals from the MERRA2, and meteorology from the ERA-Interim along the belt's borders in latitude. The simulations reveal the vertical separation of the aerosol plume due to aerosol (both ash and sulfate) gravitational settling and a complex dynamic evolution of the multi-layer cloud with sharp gradients of radiative heating within the plume that affects the cloud dispersion and the equilibrium altitude that are crucially important for the further large-scale plume evolution.

  20. 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.

  1. 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

  2. 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.

  3. Diagnostic and prognostic simulations with a full Stokes model accounting for superimposed ice of Midtre Lovénbreen, Svalbard

    Directory of Open Access Journals (Sweden)

    T. Zwinger

    2009-11-01

    Full Text Available We present steady state (diagnostic and transient (prognostic simulations of Midtre Lovénbreen, Svalbard performed with the thermo-mechanically coupled full-Stokes code Elmer. This glacier has an extensive data set of geophysical measurements available spanning several decades, that allow for constraints on model descriptions. Consistent with this data set, we included a simple model accounting for the formation of superimposed ice. Diagnostic results indicated that a dynamic adaptation of the free surface is necessary, to prevent non-physically high velocities in a region of under determined bedrock depths. Observations from ground penetrating radar of the basal thermal state agree very well with model predictions, while the dip angles of isochrones in radar data also match reasonably well with modelled isochrones, despite the numerical deficiencies of estimating ages with a steady state model.

    Prognostic runs for 53 years, using a constant accumulation/ablation pattern starting from the steady state solution obtained from the configuration of the 1977 DEM show that: 1 the unrealistic velocities in the under determined parts of the DEM quickly damp out; 2 the free surface evolution matches well measured elevation changes; 3 the retreat of the glacier under this scenario continues with the glacier tongue in a projection to 2030 being situated ≈500 m behind the position in 1977.

  4. 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

  5. Evolution of ozone, particulates, and aerosol direct radiative forcing in the vicinity of Houston using a fully coupled meteorology-chemistry-aerosol model

    Science.gov (United States)

    Fast, Jerome D.; Gustafson, William I.; Easter, Richard C.; Zaveri, Rahul A.; Barnard, James C.; Chapman, Elaine G.; Grell, Georg A.; Peckham, Steven E.

    2006-11-01

    A new fully coupled meteorology-chemistry-aerosol model is used to simulate the urban- to regional-scale variations in trace gases, particulates, and aerosol direct radiative forcing in the vicinity of Houston over a 5 day summer period. Model performance is evaluated using a wide range of meteorological, chemistry, and particulate measurements obtained during the 2000 Texas Air Quality Study. The predicted trace gas and particulate distributions were qualitatively similar to the surface and aircraft measurements with considerable spatial variations resulting from urban, power plant, and industrial sources of primary pollutants. Sulfate, organic carbon, and other inorganics were the largest constituents of the predicted particulates. The predicted shortwave radiation was 30 to 40 W m-2 closer to the observations when the aerosol optical properties were incorporated into the shortwave radiation scheme; however, the predicted hourly aerosol radiative forcing was still underestimated by 10 to 50 W m-2. The predicted aerosol radiative forcing was larger over Houston and the industrial ship channel than over the rural areas, consistent with surface measurements. The differences between the observed and simulated aerosol radiative forcing resulted from transport errors, relative humidity errors in the upper convective boundary layer that affect aerosol water content, secondary organic aerosols that were not yet included in the model, and uncertainties in the primary particulate emission rates. The current model was run in a predictive mode and demonstrates the challenges of accurately simulating all of the meteorological, chemical, and aerosol parameters over urban to regional scales that can affect aerosol radiative forcing.

  6. Electrohydraulic Servomechanisms Affected by Multiple Failures: A Model-Based Prognostic Method Using Genetic Algorithms

    OpenAIRE

    Dalla Vedova, Matteo Davide Lorenzo; Maggiore, Paolo

    2016-01-01

    In order to detect incipient failures due to a progressive wear of a primary flight command electro hydraulic actuator (EHA), prognostics could employ several approaches; the choice of the best ones is driven by the efficacy shown in failure detection, since not all the algorithms might be useful for the proposed purpose. In other words, some of them could be suitable only for certain applications while they could not give useful results for others. Developing a fault detection algorithm able...

  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. Interpretation of the Meteorological Gale Environment through Mars Science Laboratory (MSL) Rover Environmental Monitoring Station (REMS) Observations and Mesoscale Modeling (MRAMS)

    Science.gov (United States)

    Pla-García, J.; Rafkin, S. C.; Gómez-Elvira, J.; Martín-Torres, J.; Zorzano, M. P.

    2014-12-01

    Gale Crater, in which the Mars Science Laboratory (MSL) landed in August 2012, is the most topographically complex area visited to date on Mars. The meteorology within the crater may also be one of the most dynamically complex meteorological environments, because topography is thought to strongly drive the near-surface atmospheric circulations. The Rover Environmental Monitoring Station (REMS) on the Curiosity rover consists of a suite of meteorological instruments that measure pressure, temperature (air and ground), wind (speed and direction), relative humidity, and the UV flux. REMS has provided some clues on the nature of the local meteorology strongly influenced by the complex topography, as predicted by numerous previous studies. As with all single station measurements, the meteorological interpretation is typically hindered by a lack of spatial context in which to place the observations. Numerical modeling results, when properly validated against observations, can provide interpretive context. In an effort to better understand the atmospheric circulations of the Gale Crater, the Mars Regional Atmospheric Modeling System (MRAMS) was applied to the landing site region using nested grids with a spacing of 330 meters on the innermost grid that is centered over the landing site. MRAMS is ideally suited for this investigation; the model is explicitly designed to simulate Mars' atmospheric circulations at the mesoscale and smaller with realistic, high resolution surface properties. Simulations with MRAMS indicate thermal and wind thermal signatures associated with slope flows, katabatic winds, and nocturnal mixing events that are consistent with the rover environment monitored by REMS. Of particular note is evidence for two distinct air masses—one in the bottom of the crater (a relatively cold potential temperature air mass) and one on the plateau—that have minimal interaction with one another. If there are indeed two distinct air masses, there are strong

  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. Prognostic factors for metachronous contralateral breast cancer: a comparison of the linear Cox regression model and its artificial neural network extension.

    Science.gov (United States)

    Mariani, L; Coradini, D; Biganzoli, E; Boracchi, P; Marubini, E; Pilotti, S; Salvadori, B; Silvestrini, R; Veronesi, U; Zucali, R; Rilke, F

    1997-06-01

    The purpose of the present study was to assess prognostic factor for metachronous contralateral recurrence of breast cancer (CBC). Two factors were of particular interest, namely estrogen (ER) and progesterone (PgR) receptors assayed with the biochemical method in primary tumor tissue. Information was obtained from a prospective clinical database for 1763 axillary node-negative women who had received curative surgery, mostly of the conservative type, and followed-up for a median of 82 months. The analysis was performed based on both a standard (linear) Cox model and an artificial neural network (ANN) extension of this model proposed by Faraggi and Simon. Furthermore, to assess the prognostic importance of the factors considered, model predictive ability was computed. In agreement with already published studies, the results of our analysis confirmed the prognostic role of age at surgery, histology, and primary tumor site, in that young patients (extensive intraductal component, and a lower hazard in infiltrating lobular carcinoma or other histotypes. In spite of the above findings, the predictive value of both the standard and ANN Cox models was relatively low, thus suggesting an intrinsic limitation of the prognostic variables considered, rather than their suboptimal modeling. Research for better prognostic variables should therefore continue.

  11. 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

  12. JMA's regional atmospheric transport model calculations for the WMO technical task team on meteorological analyses for Fukushima Daiichi Nuclear Power Plant accident.

    Science.gov (United States)

    Saito, Kazuo; Shimbori, Toshiki; Draxler, Roland

    2015-01-01

    The World Meteorological Organization (WMO) convened a small technical task team of experts to produce a set of meteorological analyses to drive atmospheric transport, dispersion and deposition models (ATDMs) for the United Nations Scientific Committee on the Effects of Atomic Radiation's assessment of the Fukushima Daiichi Nuclear Power Plant (DNPP) accident. The Japan Meteorological Agency (JMA) collaborated with the WMO task team as the regional specialized meteorological center of the country where the accident occurred, and provided its operational 5-km resolution mesoscale (MESO) analysis and its 1-km resolution radar/rain gauge-analyzed precipitation (RAP) data. The JMA's mesoscale tracer transport model was modified to a regional ATDM for radionuclides (RATM), which included newly implemented algorithms for dry deposition, wet scavenging, and gravitational settling of radionuclide aerosol particles. Preliminary and revised calculations of the JMA-RATM were conducted according to the task team's protocol. Verification against Cesium 137 ((137)Cs) deposition measurements and observed air concentration time series showed that the performance of RATM with MESO data was significantly improved by the revisions to the model. The use of RAP data improved the (137)Cs deposition pattern but not the time series of air concentrations at Tokai-mura compared with calculations just using the MESO data. Sensitivity tests of some of the more uncertain parameters were conducted to determine their impacts on ATDM calculations, and the dispersion and deposition of radionuclides on 15 March 2011, the period of some of the largest emissions and deposition to the land areas of Japan. The area with high deposition in the northwest of Fukushima DNPP and the hotspot in the central part of Fukushima prefecture were primarily formed by wet scavenging influenced by the orographic effect of the mountainous area in the west of the Fukushima prefecture. Copyright © 2014 The Authors

  13. A laboratory prognostic index model for patients with advanced non-small cell lung cancer.

    Directory of Open Access Journals (Sweden)

    Arife Ulas

    Full Text Available We aimed to establish a laboratory prognostic index (LPI in advanced non-small cell lung cancer (NSCLC patients based on hematologic and biochemical parameters and to analyze the predictive value of LPI on NSCLC survival.The study retrospectively reviewed 462 patients with advanced NSCLC diagnosed between 2000 and 2010 in a single institution. We developed an LPI that included serum levels of white blood cells (WBC, lactate dehydrogenase (LDH, albumin, calcium, and alkaline phosphatase (ALP, based on the results of a Cox regression analysis. The patients were classified into 3 LPI groups as follows: LPI 0: normal; LPI 1: one abnormal laboratory finding; and LPI 2: at least 2 abnormal laboratory findings.The median follow up period was 44 months; the median overall survival (OS and median progression-free survival (PFS were 11 and 6 months, respectively. A multivariate analysis revealed that the following could be used as independent prognostic factors: an Eastern Cooperative Oncology Group performance status score (ECOG PS ≥2, a high LDH level, serum albumin 10.5 g/dL, number of metastases>2, presence of liver metastases, malignant pleural effusion, or receiving chemotherapy ≥4 cycles. The 1-year OS rates according to LPI 0, LPI 1, and LPI 2 were 54%, 34%, and 17% (p<0.001, respectively and 6-month PFS rates were 44%, 27%, and 15% (p<0.001, respectively. The LPI was a significant predictor for OS (Hazard Ratio (HR: 1.41; 1.05-1.88, p<0.001 and PFS (HR: 1.48; 1.14-1.93, p<0.001.An LPI is an inexpensive, easily accessible and independent prognostic index for advanced NSCLC and may be helpful in making individualized treatment plans and predicting survival rates when combined with clinical parameters.

  14. Significance of stromal-1 and stromal-2 signatures and biologic prognostic model in diffuse large B-cell lymphoma

    Science.gov (United States)

    Abdou, Asmaa Gaber; Asaad, Nancy; Kandil, Mona; Shabaan, Mohammed; Shams, Asmaa

    2017-01-01

    Objective : Diffuse Large B Cell Lymphoma (DLBCL) is a heterogeneous group of tumors with different biological and clinical characteristics that have diverse clinical outcomes and response to therapy. Stromal-1 signature of tumor microenvironment of DLBCL represents extracellular matrix deposition and histiocytic infiltrate, whereas stromal-2 represents angiogenesis that could affect tumor progression. Methods : The aim of the present study is to assess the significance of stromal-1 signature using SPARC-1 and stromal-2 signature using CD31 expression and then finally to construct biologic prognostic model (BPM) in 60 cases of DLBCL via immunohistochemistry. Results : Microvessel density (PBPM showed that 42 cases (70%) were of low biologic score (0–1) and 18 cases (30%) were of high biologic score (2–3). Low BPM cases showed less probability for splenic involvement (P=0.04) and a higher rate of complete response to therapy compared with high score cases (P=0.08). Conclusions : The DLBCL microenvironment could modulate tumor progression behavior since angiogenesis and SPARC positive stromal cells promote dissemination by association with spleen involvement and capsular invasion. Biologic prognostic models, including modified BPM, which considered cell origin of DLBCL and stromal signature pathways, could determine DLBCL progression and response to therapy. PMID:28607806

  15. Composite prognostic models across the non-alcoholic fatty liver disease spectrum: Clinical application in developing countries

    Science.gov (United States)

    Lückhoff, Hilmar K; Kruger, Frederik C; Kotze, Maritha J

    2015-01-01

    Heterogeneity in clinical presentation, histological severity, prognosis and therapeutic outcomes characteristic of non-alcoholic fatty liver disease (NAFLD) necessitates the development of scientifically sound classification schemes to assist clinicians in stratifying patients into meaningful prognostic subgroups. The need for replacement of invasive liver biopsies as the standard method whereby NAFLD is diagnosed, graded and staged with biomarkers of histological severity injury led to the development of composite prognostic models as potentially viable surrogate alternatives. In the present article, we review existing scoring systems used to (1) confirm the presence of undiagnosed hepatosteatosis; (2) distinguish between simple steatosis and NASH; and (3) predict advanced hepatic fibrosis, with particular emphasis on the role of NAFLD as an independent cardio-metabolic risk factor. In addition, the incorporation of functional genomic markers and application of emerging imaging technologies are discussed as a means to improve the diagnostic accuracy and predictive performance of promising composite models found to be most appropriate for widespread clinical adoption. PMID:26019735

  16. 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.)

  17. 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

  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. 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.

  20. Development of tools for evaluating rainfall estimation models in real- time using the Integrated Meteorological Observation Network in Castilla y León (Spain)

    Science.gov (United States)

    Merino, Andres; Guerrero-Higueras, Angel Manuel; López, Laura; Gascón, Estibaliz; Sánchez, José Luis; Lorente, José Manuel; Marcos, José Luis; Matía, Pedro; Ortiz de Galisteo, José Pablo; Nafría, David; Fernández-González, Sergio; Weigand, Roberto; Hermida, Lucía; García-Ortega, Eduardo

    2014-05-01

    The integration of various public and private observation networks into the Observation Network of Castile-León (ONet_CyL), Spain, allows us to monitor the risks in real-time. One of the most frequent risks in this region is severe precipitation. Thus, the data from the network allows us to determine the area where precipitation was registered and also to know the areas with precipitation in real-time. The observation network is managed with a LINUX system. The observation platform makes it possible to consult the observation data in a specific point in the region, or otherwise to see the spatial distribution of the precipitation in a user-defined area and time interval. In this study, we compared several rainfall estimation models, based on satellite data for Castile-León, with precipitation data from the meteorological observation network. The rainfall estimation models obtained from the meteorological satellite data provide us with a precipitation field covering a wide area, although its operational use requires a prior evaluation using ground truth data. The aim is to develop a real-time evaluation tool for rainfall estimation models that allows us to monitor the accuracy of its forecasting. This tool makes it possible to visualise different Skill Scores (Probability of Detection, False Alarm Ratio and others) of each rainfall estimation model in real time, thereby not only allowing us to know the areas where the rainfall models indicate precipitation, but also the validation of the model in real-time for each specific meteorological situation. Acknowledgements The authors would like to thank the Regional Government of Castile-León for its financial support through the project LE220A11-2. This study was supported by the following grants: GRANIMETRO (CGL2010-15930); MICROMETEO (IPT-310000-2010-22).

  1. Simulations over South Asia using the Weather Research and Forecasting model with Chemistry (WRF-Chem: set-up and meteorological evaluation

    Directory of Open Access Journals (Sweden)

    R. Kumar

    2012-03-01

    Full Text Available The configuration and evaluation of the meteorology is presented for simulations over the South Asian region using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem. Temperature, water vapor, dew point temperature, zonal and meridional wind components, precipitation and tropopause pressure are evaluated against radiosonde and satellite-borne (AIRS and TRMM observations along with NCEP/NCAR reanalysis fields for the year 2008. Chemical fields, with focus on tropospheric ozone, are evaluated in a companion paper. The spatial and temporal variability in meteorological variables is well simulated by the model with temperature, dew point temperature and precipitation showing higher values during summer/monsoon and lower during winter. The index of agreement for all the parameters is estimated to be greater than 0.6 indicating that WRF-Chem is capable of simulating the variations around the observed mean. The mean bias (MB and root mean square error (RMSE in modeled temperature, water vapor and wind components show an increasing tendency with altitude. MB and RMSE values are within ±2 K and 1–4 K for temperature, 30% and 20–65% for water vapor and 1.6 m s−1 and 5.1 m s−1 for wind components. The spatio-temporal variability of precipitation is also reproduced reasonably well by the model but the model overestimates precipitation in summer and underestimates precipitation during other seasons. Such a behavior of modeled precipitation is in agreement with previous studies on South Asian monsoon. The comparison with radiosonde observations indicates a relatively better model performance for inland sites as compared to coastal and island sites. The MB and RMSE in tropopause pressure are estimated to be less than 25 hPa. Sensitivity simulations show that biases in meteorological simulations can introduce errors of ±(10–25% in simulations of tropospheric ozone, CO and NO

  2. 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.

  3. A Generic Software Architecture For Prognostics

    Science.gov (United States)

    Teubert, Christopher; Daigle, Matthew J.; Sankararaman, Shankar; Goebel, Kai; Watkins, Jason

    2017-01-01

    Prognostics is a systems engineering discipline focused on predicting end-of-life of components and systems. As a relatively new and emerging technology, there are few fielded implementations of prognostics, due in part to practitioners perceiving a large hurdle in developing the models, algorithms, architecture, and integration pieces. As a result, no open software frameworks for applying prognostics currently exist. This paper introduces the Generic Software Architecture for Prognostics (GSAP), an open-source, cross-platform, object-oriented software framework and support library for creating prognostics applications. GSAP was designed to make prognostics more accessible and enable faster adoption and implementation by industry, by reducing the effort and investment required to develop, test, and deploy prognostics. This paper describes the requirements, design, and testing of GSAP. Additionally, a detailed case study involving battery prognostics demonstrates its use.

  4. 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...

  5. Noncutaneous malignant melanoma: a prognostic model from a retrospective multicenter study

    Directory of Open Access Journals (Sweden)

    Kim Jung Han

    2010-04-01

    Full Text Available Abstract Background We performed multicenter study to define clinical characteristics of noncutaneous melanomas and to establish prognostic factors patients who received curative resection. Methods Of the 141 patients who were diagnosed of non-cutaneous melanoma at 4 institutions in Korea between June 1992 and May 2005, 129 (91.5% satisfied the selection criteria. Results Of the 129 noncutaneous melanoma patients, 14 patients had ocular melanoma and 115 patients had mucosal melanoma. For mucosal melanoma, anorectum was the most common anatomic site (n = 39, 30.2% which was followed by nasal cavity (n = 30, 23.3%, genitourinary (n = 21, 16.3%, oral cavity (n = 14, 10.9%, upper gastrointestinal tract (n = 6, 4.7% and maxillary sinus (n = 5, 3.9% in the order of frequency. With the median 64.5 (range 4.3-213.0 months follow-up, the median overall survival were 24.4 months (95% CI 13.2-35.5 for all patients, and 34.6 (95% CI 24.5-44.7 months for curatively resected mucosal melanoma patients. Adverse prognostic factors of survival for 87 curatively resected mucosal melanoma patients were complete resection (R1 resection margin, and age > 50 years. For 14 ocular melanoma, Survival outcome was much better than mucosal melanoma with 73.3% of 2 year OS and 51.2 months of median OS (P = .04. Conclusion Prognosis differed according to primary sites of noncutaneous melanoma. Based on our study, noncutaneous melanoma patients should be treated differently to improve survival outcome.

  6. The DACCIWA model evaluation project: representation of the meteorology of southern West Africa in state-of-the-art weather, seasonal and climate prediction models

    Science.gov (United States)

    Kniffka, Anke; Benedetti, Angela; Knippertz, Peter; Stanelle, Tanja; Brooks, Malcolm; Deetz, Konrad; Maranan, Marlon; Rosenberg, Philip; Pante, Gregor; Allan, Richard; Hill, Peter; Adler, Bianca; Fink, Andreas; Kalthoff, Norbert; Chiu, Christine; Vogel, Bernhard; Field, Paul; Marsham, John

    2017-04-01

    DACCIWA (Dynamics-Aerosol-Chemistry-Cloud Interactions in West Africa) is an EU-funded project that aims to determine the influence of anthropogenic and natural emissions on the atmospheric composition, air quality, weather and climate over southern West Africa. DACCIWA organised a major international field campaign in June-July 2016 and involves a wide range of modelling activities. Here we report about the coordinated model evaluation performed in the framework of DACCIWA focusing on meteorological fields. This activity consists of two elements: (a) the quality of numerical weather prediction during the field campaign, (b) the ability of seasonal and climate models to represent the mean state and its variability. For the first element, the extensive observations from the main field campaign in West Africa in June-July 2016 (ground supersites, radiosondes, aircraft measurements) will be combined with conventional data (synoptic stations, satellites data from various sensors) to evaluate models against. The forecasts include operational products from centres such as the ECMWF, UK MetOffice and the German Weather Service and runs specifically conducted for the planning and the post-analysis of the field campaign using higher resolutions (e.g., WRF, COSMO). The forecast and the observations are analysed in a concerted way to assess the ability of the models to represent the southern West African weather systems and secondly to provide a comprehensive synoptic overview of the state of the atmosphere. In a second step the process will be extended to long-term modelling periods. This includes both seasonal and climate models, respectively. In this case, the observational dataset contains long-term satellite observations and station data, some of which were digitised from written records in the framework of DACCIWA. Parameter choice and spatial averaging will build directly on the weather forecasting evaluation to allow an assessment of the impact of short-term errors on

  7. 77 FR 4808 - Conference on Air Quality Modeling

    Science.gov (United States)

    2012-01-31

    ... the incorporation of the PRIME downwash algorithm in the AERMOD dispersion model in response to... state-of-the-science prognostic meteorological data for informing the dispersion models. The most recent... an overview presentation and review of Appendix W and plans to reinstitute the Model Clearinghouse...

  8. Population-specific prognostic models are needed to stratify outcomes for African-Americans with diffuse large B-cell lymphoma.

    Science.gov (United States)

    Chen, Qiushi; Ayer, Turgay; Nastoupil, Loretta J; Koff, Jean L; Staton, Ashley D; Chhatwal, Jagpreet; Flowers, Christopher R

    2016-01-01

    Diffuse large B-cell lymphoma (DLBCL) demonstrates significant racial differences in age of onset, stage, and survival. To examine whether population-specific models improve prediction of outcomes for African-American (AA) patients with DLBCL, we utilized Surveillance, Epidemiology, and End Results data and compared stratification by the international prognostic index (IPI) in general and AA populations. We also constructed and compared prognostic models for general and AA populations using multivariable logistic regression (LR) and artificial neural network approaches. While the IPI adequately stratified outcomes for the general population, it failed to separate AA DLBCL patients into distinct risk groups. Our AA LR model identified age ≥ 55 (odds ratio 0.45, [95% CI: 0.36, 0.56], male sex (0.75, [0.60, 0.93]), and stage III/IV disease (0.43, [0.34, 0.54]) as adverse predictors of 5-year survival for AA patients. In addition, general-population prognostic models were poorly calibrated for AAs with DLBCL, indicating a need for validated AA-specific prognostic models.

  9. Modeling study on the air quality impacts from emission reductions and atypical meteorological conditions during the 2008 Beijing Olympics

    Science.gov (United States)

    Xing, Jia; Zhang, Yang; Wang, Shuxiao; Liu, Xiaohuan; Cheng, Shuhui; Zhang, Qiang; Chen, Yaosheng; Streets, David G.; Jang, Carey; Hao, Jiming; Wang, Wenxing

    2011-04-01

    Understanding of the relative impacts of emission reductions and meteorological variations on air quality during the 2008 Beijing Olympics has an important policy implication. In this work, detailed process analyses and sensitivity simulations under different emission and meteorology scenarios were conducted using CMAQ and the Process Analysis tool to quantify the air quality benefits from emission reductions and meteorological variations in August 2008. The results indicate that emission-driven changes dominate surface concentration reductions of SO 2, NO 2, VOCs, daily maxima O 3 and PM 2.5 by -11% to -83%. The effect of meteorology-driven changes on species concentrations can be either ways (by -46% to 105%) at different locations. The dominant processes contributing to O 3, PM 2.5, SO 42-, NO 3-, and secondary organic aerosol (SOA) are identified. Gas-phase chemistry is a major process for O 3 production, and PM processes are dominant sources for PM 2.5 in the planetary boundary layer (PBL). The reduced emissions weaken the source contributions of gas-phase chemistry to O 3 and those of PM processes to PM 2.5, with weaker vertical mixing processes and horizontal transport in the PBL. Compared with 2007, 2008 has a higher humidity, lower temperature and more precipitation that benefit O 3 reduction within the PBL, and a weaker vertical mixing that disbenefits reductions of all pollutants concentrations. Stronger process contributions of cloud processes (e.g., below- and in-cloud scavenging, and wet deposition) in 2008 help reduce concentrations of PM 2.5, NO 3-, and SOA, but they (e.g., aqueous-phase chemistry) enhance surface SO 42- concentrations. Smaller process contributions of aerosol processes help reduce the concentrations of SOA and SO 42- but enhance NO 3- and PM 2.5 in lower layers (1-6) due to the evaporation of NO 3-. The ratios of P O /P increase under the controlled simulation, indicating that the emission control actions enforced during the 2008

  10. 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...

  11. A clinical prognostic model for the identification of low-risk patients with acute symptomatic pulmonary embolism and active cancer.

    Science.gov (United States)

    den Exter, Paul L; Gómez, Vicente; Jiménez, David; Trujillo-Santos, Javier; Muriel, Alfonso; Huisman, Menno V; Monreal, Manuel

    2013-01-01

    Physicians need a specific risk-stratification tool to facilitate safe and cost-effective approaches to the management of patients with cancer and acute pulmonary embolism (PE). The objective of this study was to develop a simple risk score for predicting 30-day mortality in patients with PE and cancer by using measures readily obtained at the time of PE diagnosis. Investigators randomly allocated 1,556 consecutive patients with cancer and acute PE from the international multicenter Registro Informatizado de la Enfermedad TromboEmbólica to derivation (67%) and internal validation (33%) samples. The external validation cohort for this study consisted of 261 patients with cancer and acute PE. Investigators compared 30-day all-cause mortality and nonfatal adverse medical outcomes across the derivation and two validation samples. In the derivation sample, multivariable analyses produced the risk score, which contained six variables: age > 80 years, heart rate ≥ 110/min, systolic BP < 100 mm Hg, body weight < 60 kg, recent immobility, and presence of metastases. In the internal validation cohort (n = 508), the 22.2% of patients (113 of 508) classified as low risk by the prognostic model had a 30-day mortality of 4.4% (95% CI, 0.6%-8.2%) compared with 29.9% (95% CI, 25.4%-34.4%) in the high-risk group. In the external validation cohort, the 18% of patients (47 of 261) classified as low risk by the prognostic model had a 30-day mortality of 0%, compared with 19.6% (95% CI, 14.3%-25.0%) in the high-risk group. The developed clinical prediction rule accurately identifies low-risk patients with cancer and acute PE.

  12. 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...

  13. 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.

  14. The added value that increasing levels of diagnostic information provide in prognostic models to estimate hospital mortality for adult intensive care patients

    NARCIS (Netherlands)

    de Keizer, N. F.; Bonsel, G. J.; Goldfad, C.; Rowan, K. M.

    2000-01-01

    To investigate in a systematic, reproducible way the potential of adding increasing levels of diagnostic information to prognostic models for estimating hospital mortality. Prospective cohort study. Thirty UK intensive care units (ICUs) participating in the ICNARC Case Mix Programme. Eight thousand

  15. Statistical modelling of wildfire size and intensity: a step toward meteorological forecasting of summer extreme fire risk

    Science.gov (United States)

    Hernandez, C.; Keribin, C.; Drobinski, P.; Turquety, S.

    2015-12-01

    In this article we investigate the use of statistical methods for wildfire risk assessment in the Mediterranean Basin using three meteorological covariates, the 2 m temperature anomaly, the 10 m wind speed and the January-June rainfall occurrence anomaly. We focus on two remotely sensed characteristic fire variables, the burnt area (BA) and the fire radiative power (FRP), which are good proxies for fire size and intensity respectively. Using the fire data we determine an adequate parametric distribution function which fits best the logarithm of BA and FRP. We reconstruct the conditional density function of both variables with respect to the chosen meteorological covariates. These conditional density functions for the size and intensity of a single event give information on fire risk and can be used for the estimation of conditional probabilities of exceeding certain thresholds. By analysing these probabilities we find two fire risk regimes different from each other at the 90 % confidence level: a "background" summer fire risk regime and an "extreme" additional fire risk regime, which corresponds to higher probability of occurrence of larger fire size or intensity associated with specific weather conditions. Such a statistical approach may be the ground for a future fire risk alert system.

  16. Application of high resolution land use and land cover data for atmospheric modeling in the Houston-Galveston metropolitan area, Part I: Meteorological simulation results

    Science.gov (United States)

    Cheng, Fang-Yi; Byun, Daewon W.

    To predict atmospheric conditions in an urban environment, the land surface processes must be accurately described through the use of detailed land use (LU) and land cover (LC) data. Use of the U.S. Geological Survey (USGS) 25-category data, currently in the Fifth-generation Mesoscale Model (MM5), with the Noah land surface model (LSM) and MRF (medium-range forecast) planetary boundary layer (PBL) schemes resulted in the over-prediction of daytime temperatures in the Houston downtown area due to the inaccurate representation as a completely impervious surface. This bias could be corrected with the addition of canopy water in the urban areas from the evapotranspiration effects of urban vegetation. A more fundamental approach would be to utilize an LULC dataset that represents land surface features accurately. The Texas Forest Service (TFS) LULC dataset established with the LANDSAT satellite imagery correctly represents the Houston-Galveston-Brazoria (HGB) area as mixtures of urban, residential, grass, and forest LULC types. This paper describes how the Noah LSM and PBL schemes in the MM5 were modified to accommodate the TFS-LULC data. Comparisons with various meteorological measurements show that the MM5 simulation made with the high resolution LULC data improves the boundary layer mixing conditions and local wind patterns in the Houston Ship Channel, which is a critically important anthropogenic emission area affecting the HGB air pollution problems. In particular, when the synoptic flows are weak, the improved LULC data simulates the asymmetrically elongated Houston heat island convergence zone influencing the location of the afternoon Gulf of Mexico sea-breeze front and the Galveston Bay breeze flows. This paper is part I of a two-part study and focuses on the meteorological simulation. In part II, effects of using the different meteorological inputs on air quality simulations are discussed.

  17. Modeling of long range transport pathways for radionuclides to Korea during the Fukushima Dai-ichi nuclear accident and their association with meteorological circulations.

    Science.gov (United States)

    Lee, Kwan-Hee; Kim, Ki-Hyun; Lee, Jin-Hong; Yun, Ju-Yong; Kim, Cheol-Hee

    2015-10-01

    The Lagrangian FLEXible PARTicle (FLEXPART) dispersion model and National Centers for Environmental Prediction/Global Forecast System (NCEP/GFS) meteorological data were used to simulate the long range transport pathways of three artificial radionuclides: (131)I, (137)Cs, and (133)Xe, coming into Korean Peninsula during the Fukushima Dai-ichi nuclear accident. Using emission rates of these radionuclides estimated from previous studies, three distinctive transport routes of these radionuclides toward the Korean Peninsula for a period from 10 March to 20 April 2011 were exploited by three spatial scales: 1) intercontinental scale - plume released since mid-March 2011 and transported to the North to arrive Korea on 23 March 2011, 2) global (hemispherical) scale - plume traveling over the whole northern hemisphere passing through the Pacific Ocean/Europe to reach the Korean Peninsula with relatively low concentrations in late March 2011 and, 3) regional scale - plume released on early April 2011 arrived at the Korean Peninsula via southwest sea of Japan influenced directly by veering mesoscale wind circulations. Our identification of these transport routes at three different scales of meteorological circulations suggests the feasibility of a multi-scale approach for more accurate prediction of radionuclide transport in the study area. In light of the fact that the observed arrival/duration time of peaks were explained well by the FLEXPART model coupled with NCEP/GFS input data, our approach can be used meaningfully as a decision support model for radiation emergency situations. Copyright © 2015 Elsevier Ltd. All rights reserved.

  18. Comparison of modelled and measured ozone concentrations and meteorology for a site in south-west Sweden: implications for ozone uptake calculations.

    Science.gov (United States)

    Klingberg, Jenny; Danielsson, Helena; Simpson, David; Pleijel, Håkan

    2008-09-01

    Measurements of ground-level ozone concentrations and meteorology (temperature, vapour pressure deficit (VPD), solar radiation) at the monitoring site Ostad (south-west Sweden) were compared to data from the corresponding grid in the EMEP photo-oxidant model for 1997, 1999 and 2000. The influence of synoptic weather on the agreement between model and measurements was studied. Implications of differences between modelled and observed inputs for ozone flux calculations for wheat and potato were investigated. The EMEP model output of ozone, temperature and VPD correlated well with measurements during daytime. Deviations were larger during the night, especially in calm conditions, attributed to local climatological conditions at the monitoring site deviating from average conditions of the grid. These differences did not lead to significant differences in calculated ozone uptake, which was reproduced remarkably well. The uptake calculations were sensitive to errors in the ozone and temperature input data, especially when including a flux threshold.

  19. FleaTickRisk: a meteorological model developed to monitor and predict the activity and density of three tick species and the cat flea in Europe

    Directory of Open Access Journals (Sweden)

    Frédéric Beugnet

    2009-11-01

    Full Text Available Mathematical modelling is quite a recent tool in epidemiology. Geographical information system (GIS combined with remote sensing (data collection and analysis provide valuable models, but the integration of climatologic models in parasitology and epidemiology is less common. The aim of our model, called “FleaTickRisk”, was to use meteorological data and forecasts to monitor the activity and density of some arthropods. Our parasitological model uses the Weather Research and Forecasting (WRF meteorological model integrating biological parameters. The WRF model provides a temperature and humidity picture four times a day (at 6:00, 12:00, 18:00 and 24:00 hours. Its geographical resolution is 27 x 27 km over Europe (area between longitudes 10.5° W and 30° E and latitudes 37.75° N and 62° N. The model also provides weekly forecasts. Past data were compared and revalidated using current meteorological data generated by ground stations and weather satellites. The WRF model also includes geographical information stemming from United States Geophysical Survey biotope maps with a 30’’ spatial resolution (approximately 900 x 900 m. WRF takes into account specific climatic conditions due to valleys, altitudes, lakes and wind specificities. The biological parameters of Ixodes ricinus, Dermacentor reticulatus, Rhipicephalus sanguineus and Ctenocephalides felis felis were transformed into a matrix of activity. This activity matrix is expressed as a percentage, ranging from 0 to 100, for each interval of temperature x humidity. The activity of these arthropods is defined by their ability to infest hosts, take blood meals and reproduce. For each arthropod, the matrix was calculated using existing data collected under optimal temperature and humidity conditions, as well as the timing of the life cycle. The mathematical model integrating both the WRF model (meteorological data + geographical data and the biological matrix provides two indexes: an

  20. The 1-way on-line coupled atmospheric chemistry model system MECO(n) - Part 3: Meteorological evaluation of the on-line coupled system

    Science.gov (United States)

    Hofmann, C.; Kerkweg, A.; Wernli, H.; Jöckel, P.

    2012-01-01

    Three detailed meteorological case studies are conducted with the global and regional atmospheric chemistry model system ECHAM5/MESSy(→COSMO/MESSy)n, shortly named MECO(n). The aim of this article is to assess the general performance of the on-line coupling of the regional model COSMO to the global model ECHAM5. The cases are characterised by intense weather systems in Central Europe: a cold front passage in March 2010, a convective frontal event in July 2007, and the high impact winter storm "Kyrill" in January 2007. Simulations are performed with the new on-line-coupled model system and compared to classical, off-line COSMO hindcast simulations driven by ECMWF analyses. Precipitation observations from rain gauges and ECMWF analysis fields are used as reference, and both qualitative and quantitative measures are used to characterise the quality of the various simulations. It is shown that, not surprisingly, simulations with a shorter lead time generally produce more accurate simulations. Irrespective of lead time, the accuracy of the on-line and off-line COSMO simulations are comparable for the three cases. This result indicates that the new global and regional model system MECO(n) is able to simulate key mid-latitude weather systems, including cyclones, fronts, and convective precipitation, as accurately as present-day state-of-the-art regional weather prediction models in standard off-line configuration. Therefore, MECO(n) will be applied to simulate atmospheric chemistry exploring the model's full capabilities during meteorologically challenging conditions.

  1. The 1-way on-line coupled atmospheric chemistry model system MECO(n – Part 3: Meteorological evaluation of the on-line coupled system

    Directory of Open Access Journals (Sweden)

    C. Hofmann

    2012-01-01

    Full Text Available Three detailed meteorological case studies are conducted with the global and regional atmospheric chemistry model system ECHAM5/MESSy(→COSMO/MESSyn, shortly named MECO(n. The aim of this article is to assess the general performance of the on-line coupling of the regional model COSMO to the global model ECHAM5. The cases are characterised by intense weather systems in Central Europe: a cold front passage in March 2010, a convective frontal event in July 2007, and the high impact winter storm "Kyrill" in January 2007. Simulations are performed with the new on-line-coupled model system and compared to classical, off-line COSMO hindcast simulations driven by ECMWF analyses. Precipitation observations from rain gauges and ECMWF analysis fields are used as reference, and both qualitative and quantitative measures are used to characterise the quality of the various simulations. It is shown that, not surprisingly, simulations with a shorter lead time generally produce more accurate simulations. Irrespective of lead time, the accuracy of the on-line and off-line COSMO simulations are comparable for the three cases. This result indicates that the new global and regional model system MECO(n is able to simulate key mid-latitude weather systems, including cyclones, fronts, and convective precipitation, as accurately as present-day state-of-the-art regional weather prediction models in standard off-line configuration. Therefore, MECO(n will be applied to simulate atmospheric chemistry exploring the model's full capabilities during meteorologically challenging conditions.

  2. 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.

  3. Development of prognostic model for predicting survival after retrograde placement of ureteral stent in advanced gastrointestinal cancer patients and its evaluation by decision curve analysis.

    Science.gov (United States)

    Kawano, Shingo; Komai, Yoshinobu; Ishioka, Junichiro; Sakai, Yasuyuki; Fuse, Nozomu; Ito, Masaaki; Kihara, Kazunori; Saito, Norio

    2016-10-01

    The aim of this study was to determine risk factors for survival after retrograde placement of ureteral stents and develop a prognostic model for advanced gastrointestinal tract (GIT: esophagus, stomach, colon and rectum) cancer patients. We examined the clinical records of 122 patients who underwent retrograde placement of a ureteral stent against malignant extrinsic ureteral obstruction. A prediction model for survival after stenting was developed. We compared its clinical usefulness with our previous model based on the results from nephrostomy cases by decision curve analysis. Median follow-up period was 201 days (8-1490) and 97 deaths occurred. The 1-year survival rate in this cohort was 29%. Based on multivariate analysis, primary site of colon origin, absence of retroperitoneal lymph node metastasis and serum albumin >3g/dL were significantly associated with a prolonged survival time. To develop a prognostic model, we divided the patients into 3 risk groups of favorable: 0-1 factors (N.=53), intermediate: 2 risk factors (N.=54), and poor: 3 risk factors (N.=15). There were significant differences in the survival profiles of these 3 risk groups (P<0.0001). Decision curve analyses revealed that the current model has a superior net benefit than our previous model for most of the examined probabilities. We have developed a novel prognostic model for GIT cancer patients who were treated with retrograde placement of a ureteral stent. The current model should help urologists and medical oncologists to predict survival in cases of malignant extrinsic ureteral obstruction.

  4. Prognostic value of the seventh AJCC/UICC TNM classification of intestinal-type ethmoid adenocarcinoma: Systematic review and risk prediction model.

    Science.gov (United States)

    Fiaux-Camous, Domitille; Chevret, Sylvie; Oker, Natalie; Turri-Zanoni, Mario; Lombardi, Davide; Choussy, Olivier; Duprez, Frederic; Jorissen, Marc; de Gabory, Ludovic; Malard, Olivier; Herman, Philippe; Nicolai, Piero; Castelnuovo, Paolo; Verillaud, Benjamin

    2017-04-01

    The purpose of this study was to propose a prognostic classification of intestinal-type adenocarcinoma (ITAC) based on literature search and prognostic modeling of cohort data. We first conducted a literature search to assess the homogeneity of the reported estimates of 5-year survival and to identify the influence of T classification. We then pooled prospective data from 3 large French and Italian series to predict time to all-cause mortality. The sample was randomly split to derive and then to validate the proposed prognostic model. Literature analysis confirmed the heterogeneity in 5-year survival rates, partly explained in subsets of homogeneous T-values. The sample included 223 patients, randomly separated into a derivation (n = 141) and a validation set (n = 82). Invasion of the sphenoid lateral and/or posterior walls and dura/cerebral invasion were systematically associated with a poor survival. The incorporation of the invasion of the sphenoid lateral or posterior walls should be considered for ITAC management and prognostication. © 2017 Wiley Periodicals, Inc. Head Neck 39: 668-678, 2017. © 2017 Wiley Periodicals, Inc.

  5. Establishment and evaluation of a prognostic model for surgical outcomes of patients with atlanto-axial dislocations.

    Science.gov (United States)

    Guo, Shuai; Chen, Jie; Yang, Baohui; Li, Haopeng

    2016-12-01

    Objective Atlanto-axial dislocations (AADs) are potentially fatal disturbances with high spinal cord compression syndrome. As surgeons are still uncertain who is likely to benefit the most from surgery, a prediction tool is needed to provide decision-making support. Methods The model was established based on 108 patients with AADs using multiple binary logistic regression analysis and evaluated by calibration plot and the area under the receiver operating curve (AUC). Bootstrapping was used for internal validation. Results The prognostic model can be expressed as: logit(P) = -2.2428 + 0.3168SCOPE - 2.0375SIGNAL, in which two covariates were accepted (SCORE represents the preoperative modified Japanese Orthopedic Association (mJOA) score and SIGNAL represents the intramedullary hyperintense T2-weighted imaging (T2WI) with AUC = 0.8081). Conclusions The model was internally valid, and the preoperative mJOA score and hyperintense T2WI were important predictors of outcomes. The threshold was defined as logit(P) = -0.7282 according to the receiver operating curve (ROC).

  6. 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.

  7. 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...

  8. 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.

  9. NEMEFO: NEural MEteorological FOrecast

    Energy Technology Data Exchange (ETDEWEB)

    Pasero, E.; Moniaci, W.; Meindl, T.; Montuori, A. [Polytechnic of Turin (Italy). Dept. of Electronics

    2004-07-01

    Artificial Neural Systems are a well-known technique used to classify and recognize objects. Introducing the time dimension they can be used to forecast numerical series. NEMEFO is a ''nowcasting'' tool, which uses both statistical and neural systems to forecast meteorological data in a restricted area close to a meteorological weather station in a short time range (3 hours). Ice, fog, rain are typical events which can be anticipated by NEMEFO. (orig.)

  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. 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

  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. 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

  14. Understanding meteorology for pollution transport over Bhutan

    Science.gov (United States)

    Ghimire, Shreta; Adhikary, Bhupesh; Praveen, Ps; Panday, Arnico

    2016-04-01

    The country of Bhutan spans over complex terrain in the Eastern Himalayan region. Several studies in the past have reported about transport of air pollution into the Himalayas from Indo-Gangetic plains. However, there is a lack of studies focusing over eastern Himalaya and particularly over Bhutan. Understanding air pollutant flows over this region requires good understanding of weather and atmospheric circulation pattern. We have used decadal data from ground based meteorological stations made available from the Department of Hydro-Meteorological Service (DHMS), Government of Bhutan to study rainfall and temperature patterns over different elevation. We also present preliminary results from few automatic weather stations that are analyzed for diurnal and seasonal variability. Weather Research and Forecast (WRF) model was run to understand meteorological flows over the region. Preliminary results from WRF model will also be presented. Keywords: Bhutan, Meteorology, Air Pollution, Eastern Himalayas.

  15. Improving temporal profiles of anthropogenic emissions influenced by human activities and meteorological drivers for atmospheric dispersion modelling - a case study for the UK

    Science.gov (United States)

    Vieno, M.; Reis, S.; Ambelas Skjøth, C.; Lang, M.; Geels, C.

    2010-12-01

    Emissions of trace gases originating from anthropogenic activities are vital input data for chemical transport models (CTMs). Other key input datasets such as meteorological drivers, and biogeochemical and physical processes have been subject to detailed investigation and research in the recent past, while the representation of emission data in CTMs has been somewhat neglected. Arguably, this has less impact on the regional to hemispheric or global scale, where the grid sizes of currently applied CTMs represent well mixed average concentrations or deposition values. Evaluating model output against ground-based observations or remote sensing results on these spatial levels appears not to be overly sensitive to the temporal (and spatial) profiles of emission input data. With increasing level of detail and spatiotemporal resolution, CTMs applied to determine national or local scale air quality are likely prone to be more sensitive to the spatial and temporal patterns of anthropogenic emissions. This paper discusses results of the application of the EMEP4UK CTM on a 5 km x 5 km resolution for the whole of the United Kingdom. In a first instance, detailed temporal profiles for emissions from road transport, power generation and other stationary sources have been developed. Depending on typical sectoral activities, hourly to seasonal variations have been quantified and (sub-)sectoral time curves generated. Here, we will discuss how these improved time curves influence modelled concentrations of NO2, Ozone and PM10 for selected episodes in the year 2007. In a second application, both anthropogenic activities (e.g. manure spreading and fertilizer application) and meteorological factors (e.g. temperature and seasonality) have been investigated regarding their influence on the spatiotemporal distribution of NH3 emissions (Skjøth et al., 2004; see as well Pinder et al., 2004; Gilliland et al., 2006). The discussion of results in this case will focus on the impact on the

  16. A 60-year reconstructed high-resolution local meteorological data set in Central Sahel (1950-2009): evaluation, analysis and application to land surface modelling

    Science.gov (United States)

    Leauthaud, Crystele; Cappelaere, Bernard; Demarty, Jérôme; Guichard, Françoise; Velluet, Cécile; Kergoat, Laurent; Vischel, Théo; Grippa, Manuela; Mouhaimouni, Mohammed; Bouzou Moussa, Ibrahim; Mainassara, Ibrahim; Sultan, Benjamin

    2017-04-01

    The Sahel has experienced strong climate variability in the past decades. Understanding its implications for natural and cultivated ecosystems is pivotal in a context of high population growth and mainly agriculture-based livelihoods. However, efforts to model processes at the land-atmosphere interface are hindered, particularly when the multi-decadal timescale is targeted, as climatic data are scarce, largely incomplete and often unreliable. This study presents the generation of a long-term, high-temporal resolution, multivariate local climatic data set for Niamey, Central Sahel. The continuous series spans the period 1950-2009 at a 30-min timescale and includes ground station-based meteorological variables (precipitation, air temperature, relative and specific humidity, air pressure, wind speed, downwelling long- and short-wave radiation) as well as process-modelled surface fluxes (upwelling long- and short-wave radiation,latent, sensible and soil heat fluxes and surface temperature). A combination of complementary techniques (linear/spline regressions, a multivariate analogue method, artificial neural networks and recursive gap filling) was used to reconstruct missing meteorological data. The complete surface energy budget was then obtained for two dominant land cover types, fallow bush and millet, by applying the meteorological forcing data set to a finely field-calibrated land surface model. Uncertainty in reconstructed data was expressed by means of a stochastic ensemble of plausible historical time series. Climatological statistics were computed at sub-daily to decadal timescales and compared with local, regional and global data sets such as CRU and ERA-Interim. The reconstructed precipitation statistics, ˜1°C increase in mean annual temperature from 1950 to 2009, and mean diurnal and annual cycles for all variables were in good agreement with previous studies. The new data set, denoted NAD (Niamey Airport-derived set) and publicly available, can be used

  17. There’s Risk, and Then There’s RISK: The Latest Clinical Prognostic Risk Stratification Models in Myelodysplastic Syndromes

    OpenAIRE

    Zeidan, Amer M.; Komrokji, Rami S.

    2013-01-01

    Myelodysplastic syndromes (MDS) include a diverse group of clonal hematopoietic disorders characterized by progressive cytopenias and propensity for leukemic progression. The biologic heterogeneity that underlies MDS translates clinically in wide variations of clinical outcomes. Several prognostic schemes were developed to predict the natural course of MDS, counsel patients, and allow evidence-based, risk-adaptive implementation of therapeutic strategies. The prognostic schemes divide patient...

  18. Utilizing patch and site level greenhouse-gas concentration measurements in tandem with the prognostic model, ecosys

    Science.gov (United States)

    Morin, T. H.; Rey Sanchez, C.; Bohrer, G.; Riley, W. J.; Angle, J.; Mekonnen, Z. A.; Stefanik, K. C.; Wrighton, K. C.

    2016-12-01

    Estimates of wetland greenhouse gas (GHG) budgets currently have large uncertainties. While wetlands are the largest source of natural methane (CH4) emissions worldwide, they are also important carbon dioxide (CO2) sinks. Determining the GHG budget of a wetland is challenging, particularly because wetlands have intrinsically temporally and spatially heterogeneous land cover patterns and complex dynamics of CH4 production and emissions. These issues pose challenges to both measuring and modeling GHG budgets from wetlands. To improve wetland GHG flux predictability, we utilized the ecosys model to predict CH4 fluxes from a natural temperate estuarine wetland in northern Ohio. Multiple patches of terrain (that included Typha spp. and Nelumbo lutea) were represented as separate grid cells in the model. Cells were initialized with measured values but were allowed to dynamically evolve in response to meteorological, hydrological, and thermodynamic conditions. Trace gas surface emissions were predicted as the end result of microbial activity, physical transport, and plant processes. Corresponding to each model gridcell, measurements of dissolved gas concentrations were conducted with pore-water dialysis samplers (peepers). The peeper measurements were taken via a series of tubes, providing an undisturbed observation of the pore water concentrations of in situ dissolved gases along a vertical gradient. Non-steady state chambers and a flux tower provided both patch level and integrated site-level fluxes of CO2 and CH4. New Typha chambers were also developed to enclose entire plants and segregate the plant fluxes from soil/water fluxes. We expect ecosys to predict the seasonal and diurnal fluxes of CH4 from within each land cover type and to resolve where CH4 is generated within the soil column and its transmission mechanisms. We demonstrate the need for detailed information at both the patch and site level when using models to predict whole wetland ecosystem-scale GHG

  19. 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...

  20. Two novel mixed effects models for prognostics of rolling element bearings

    Science.gov (United States)

    Wang, Dong; Tsui, Kwok-Leung

    2018-01-01

    Rolling element bearings are widely used in various machines to support rotating shafts. Due to harsh working environments, the health condition of a bearing degrades over time. A typical bearing degradation process includes two phases. In Phase I, the health condition of the bearing is in normal and it exhibits a stable trend. In Phase II, the health condition of the bearing degrades exponentially. To analytically model the bearing degradation process, two novel mixed effects models are proposed in this paper. Each of the two mixed effects models is able to simultaneously model Phases I and II of the bearing degradation process. The main difference between the two mixed effects models is that different error assumptions including multiplicative normal random error and multiplicative Brownian motion error are respectively considered in the two mixed effects models. Consequently, two different closed-form distributions of bearing remaining useful life are derived from the two mixed effects models via Bayes' theorem once real-time bearing condition monitoring data are available. 25 sets of bearing degradation data collected from an experimental machine are used to illustrate how the two mixed effects models work. Comparisons are conducted to show that the mixed effects model with multiplicative Brownian motion error results in lower prediction errors than the mixed effects model with multiplicative normal random error for bearing remaining useful life prediction.

  1. External validation of a prognostic model for predicting survival of cirrhotic patients with refractory ascites.

    Science.gov (United States)

    Guardiola, Jordi; Baliellas, Carme; Xiol, Xavier; Fernandez Esparrach, Glòria; Ginès, Pere; Ventura, Pere; Vazquez, Santiago

    2002-09-01

    Cirrhotic patients with refractory ascites (RA) have a poor prognosis, although individual survival varies greatly. A model that could predict survival for patients with RA would be helpful in planning treatment. Moreover, in cases of potential liver transplantation, a model of these characteristics would provide the bases for establishing priorities of organ allocation and the selection of patients for a living donor graft. Recently, we developed a model to predict survival of patients with RA. The aim of this study was to establish its generalizability for predicting the survival of patients with RA. The model was validated by assessing its performance in an external cohort of patients with RA included in a multicenter, randomized, controlled trial that compared large-volume paracentesis and peritoneovenous shunt. The values for actual and model-predicted survival of three risk groups of patients, established according to the model, were compared graphically and by means of the one-sample log-rank test. The model provided a very good fit to the survival data of the three risk groups in the validation cohort. We also found good agreement between the survival predicted from the model and the observed survival when patients treated with peritoneovenous shunt and with paracentesis were considered separately. Our survival model can be used to predict the survival of patients with RA and may be a useful tool in clinical decision making, especially in deciding priority for liver transplantation.

  2. Prediction of complications in early-onset pre-eclampsia (PREP): development and external multinational validation of prognostic models.

    Science.gov (United States)

    Thangaratinam, Shakila; Allotey, John; Marlin, Nadine; Dodds, Julie; Cheong-See, Fiona; von Dadelszen, Peter; Ganzevoort, Wessel; Akkermans, Joost; Kerry, Sally; Mol, Ben W; Moons, Karl G M; Riley, Richard D; Khan, Khalid S

    2017-03-30

    Unexpected clinical deterioration before 34 weeks gestation is an undesired course in early-onset pre-eclampsia. To safely prolong preterm gestation, accurate and timely prediction of complications is required. Women with confirmed early onset pre-eclampsia were recruited from 53 maternity units in the UK to a large prospective cohort study (PREP-946) for development of prognostic models for the overall risk of experiencing a complication using logistic regression (PREP-L), and for predicting the time to adverse maternal outcome using a survival model (PREP-S). External validation of the models were carried out in a multinational cohort (PIERS-634) and another cohort from the Netherlands (PETRA-216). Main outcome measures were C-statistics to summarise discrimination of the models and calibration plots and calibration slopes. A total of 169 mothers (18%) in the PREP dataset had adverse outcomes by 48 hours, and 633 (67%) by discharge. The C-statistics of the models for predicting complications by 48 hours and by discharge were 0.84 (95% CI, 0.81-0.87; PREP-S) and 0.82 (0.80-0.84; PREP-L), respectively. The PREP-S model included maternal age, gestation, medical history, systolic blood pressure, deep tendon reflexes, urine protein creatinine ratio, platelets, serum alanine amino transaminase, urea, creatinine, oxygen saturation and treatment with antihypertensives or magnesium sulfate. The PREP-L model included the above except deep tendon reflexes, serum alanine amino transaminase and creatinine. On validation in the external PIERS dataset, the reduced PREP-S model showed reasonable calibration (slope 0.80) and discrimination (C-statistic 0.75) for predicting adverse outcome by 48 hours. Reduced PREP-L model showed excellent calibration (slope: 0.93 PIERS, 0.90 PETRA) and discrimination (0.81 PIERS, 0.75 PETRA) for predicting risk by discharge in the two external datasets. PREP models can be used to obtain predictions of adverse maternal outcome risk, including

  3. Comparison of the predictive qualities of three prognostic models of colorectal cancer

    Science.gov (United States)

    Anderson, Billie; Hardin, J. Michael; Alexander, Dominik D.; Meleth, Sreelatha; Grizzle, William E.; Manne, Upender

    2013-01-01

    Most discoveries of cancer biomarkers involve construction of a single model to determine predictions of survival. ‘Data-mining’ techniques, such as artificial neural networks (ANNs), perform better than traditional methods, such as logistic regression. In this study, the quality of multiple predictive models built on a molecular data set for colorectal cancer (CRC) was evaluated. Predictive models (logistic regressions, ANNs, and decision trees) were compared, and the effect of techniques for variable selection on the predictive quality of these models was investigated. The Kolmogorov-Smirnoff (KS) statistic was used to compare the models. Overall, the logistic regression and ANN methods outperformed use of a decision tree. In some instances (e.g., for a model that included ‘all variables without tumor stage’ and use of a decision tree for variable selection), the ANN marginally outperformed logistic regression, although the difference between the accuracy of the KS statistic was minimal (0.80 versus 0.82). Regardless of the variable(s) and the methods for variable selection, all three predictive models identified survivors and non-survivors with the same level of statistical accuracy. PMID:20515758

  4. Model-based prognostics for batteries which estimates useful life and uses a probability density function

    Data.gov (United States)

    National Aeronautics and Space Administration — 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...

  5. A mathematical model describes the malignant transformation of low grade gliomas: Prognostic implications.

    Directory of Open Access Journals (Sweden)

    Magdalena U Bogdańska

    Full Text Available Gliomas are the most frequent type of primary brain tumours. Low grade gliomas (LGGs, WHO grade II gliomas may grow very slowly for the long periods of time, however they inevitably cause death due to the phenomenon known as the malignant transformation. This refers to the transition of LGGs to more aggressive forms of high grade gliomas (HGGs, WHO grade III and IV gliomas. In this paper we propose a mathematical model describing the spatio-temporal transition of LGGs into HGGs. Our modelling approach is based on two cellular populations with transitions between them being driven by the tumour microenvironment transformation occurring when the tumour cell density grows beyond a critical level. We show that the proposed model describes real patient data well. We discuss the relationship between patient prognosis and model parameters. We approximate tumour radius and velocity before malignant transformation as well as estimate the onset of this process.

  6. A radiobiological model of radiotherapy response and its correlation with prognostic imaging variables

    Science.gov (United States)

    Crispin-Ortuzar, Mireia; Jeong, Jeho; Fontanella, Andrew N.; Deasy, Joseph O.

    2017-04-01

    Radiobiological models of tumour control probability (TCP) can be personalized using imaging data. We propose an extension to a voxel-level radiobiological TCP model in order to describe patient-specific differences and intra-tumour heterogeneity. In the proposed model, tumour shrinkage is described by means of a novel kinetic Monte Carlo method for inter-voxel cell migration and tumour deformation. The model captures the spatiotemporal evolution of the tumour at the voxel level, and is designed to take imaging data as input. To test the performance of the model, three image-derived variables found to be predictive of outcome in the literature have been identified and calculated using the model’s own parameters. Simulating multiple tumours with different initial conditions makes it possible to perform an in silico study of the correlation of these variables with the dose for 50% tumour control (\\text{TC}{{\\text{D}}50} ) calculated by the model. We find that the three simulated variables correlate with the calculated \\text{TC}{{\\text{D}}50} . In addition, we find that different variables have different levels of sensitivity to the spatial distribution of hypoxia within the tumour, as well as to the dynamics of the migration mechanism. Finally, based on our results, we observe that an adequate combination of the variables may potentially result in higher predictive power.

  7. Interim report on the meteorological database

    Energy Technology Data Exchange (ETDEWEB)

    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.

  8. The effect of plant water stress approach on the modelled energy-, water and carbon balance for Mediterranean vegetation; implications for (agro)meteorological applications.

    Science.gov (United States)

    Verhoef, Anne; Egea, Gregorio; Garrigues, Sebastien; Vidale, Pier Luigi; Balan Sarojini, Beena

    2017-04-01

    Current land surface schemes in many crop, weather and climate models make use of the coupled photosynthesis-stomatal conductance (A-gs) models of plant function to determine the transpiration flux and gross primary productivity. Vegetation exchange is controlled by many environmental factors, and soil moisture control on root water uptake and stomatal function is a primary pathway for feedbacks in sub-tropical to temperate ecosystems. Representations of the above process of soil moisture control on plant function (often referred to as a 'beta' factor) vary among models. This matters because the simulated energy, water and carbon balances are very sensitive to the representation of water stress in these models. Building on Egea et al. (2011) and Verhoef and Egea (2014), we tested a range of 'beta' approaches in a leaf-level A-gs model (compatible with models such as JULES, CHTESSEL, ISBA, CLM), as well as some beta-approaches borrowed from the agronomic, and plant physiological communities (a combined soil-plant hydraulic approach, see Verhoef and Egea, 2014). Root zone soil moisture was allowed to limit plant function via individual routes (via CO2 assimilation, stomatal conductance, or mesophyll conductance) as well as combinations of that. The simulations were conducted for a typical Mediterranean field site (Avignon, France; Garrigues et al., 2015) which provides 14 years of near-continuous measurements of soil moisture and atmospheric driving data. Daytime (8-16 hrs local time) data between April-September were used. This allowed a broad range of atmospheric and soil moisture/vegetation states to be explored. A number of crops and tree types were investigated in this way. We evaluated the effect of choice of beta-function for Mediterranean climates in relation to stomatal conductance, transpiration, photosynthesis, and leaf surface temperature. We also studied the implications for a range of widely used agro-/micro-meteorological indicators such as Bowen ratio

  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. 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.

  11. 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.

  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. Assessing the utility of a prognostication model to predict 1-year mortality in patients receiving radiation therapy for spinal metastases.

    Science.gov (United States)

    Shi, Diana D; Chen, Yu-Hui; Lam, Tai Chung; Leonard, Dana; Balboni, Tracy Anne; Schoenfeld, Andrew; Skamene, Sonia; Cagney, Daniel N; Chi, John H; Cho, Charles H; Harris, Mitchel; Ferrone, Marco L; Hertan, Lauren M

    2017-10-12

    Predicting survival outcomes after radiation therapy alone for metastatic disease of the spine is a challenging task that is important to guiding treatment decisions (e.g., determining dose fractionation and intensity). The New England Spinal Metastasis Score (NESMS) was recently introduced and validated in independent cohorts as a tool to predict 1-year survival following surgery for spinal metastases. This metric is composed of 3 factors: pre-operative albumin, ambulatory status, and modified Bauer score, with the total score ranging from 0 to 3. The purpose of this study is to assess the applicability of the NESMS model to predict 1-year survival among patients treated with radiation therapy alone for spinal metastases. This study is a retrospective analysis. This sample included 290 patients who underwent conventional radiation therapy alone for spinal metastases. Patients' NESMS scores (comprised of ambulatory status, pre-treatment serum albumin, and modified Bauer score) were assessed as well as their 1-year overall survival rates following radiation for metastatic disease of the spine. This study is a single-institution retrospective analysis of 290 patients treated with conventional radiation alone for spinal metastases from 2008 to 2013. The predictive value of the NESMS was assessed using multivariable logistic regression modeling, adjusted for potential confounding variables. This analysis indicated that patients with lower NESMS scores had higher rates of 1-year mortality. Multivariable analysis demonstrated a strong association between lower NESMS scores and lower rates of survival. The NESMS score is a simple prognostic scheme that requires clinical data that is often readily available and has been validated in independent cohorts of surgical patients. This study serves to validate the utility of the NESMS composite score to predict 1-year mortality in patients treated with radiation alone for spinal metastases. Copyright © 2017 Elsevier Inc. All

  14. Generating emissions and meteorology to model the impacts of biomass burning emissions on regional air quality in South Africa

    CSIR Research Space (South Africa)

    Carter, WS

    2008-10-01

    Full Text Available Biomass burning is a source category that is often ignored in pollution dispersion modelling simulations. This study presents a unique contribution to the quantification of emissions from biomass burning for high resolution modelling. Previous work...

  15. 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...

  16. The Meteorology-Chemistry Interface Processor (MCIP) for the CMAQ Modeling System: Updates through MCIPv3.4.1

    Science.gov (United States)

    The Community Multiscale Air Quality (CMAQ) modeling system is a state-of-the science regional air quality modeling system. The CMAQ modeling system has been primarily developed by the U.S. Environmental Protection Agency, and it has been publically and freely available for more...

  17. 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…

  18. 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

    2017-01-01

    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

  19. γ-H2AX: A Novel Prognostic Marker in a Prognosis Prediction Model of Patients with Early Operable Non-Small Cell Lung Cancer

    Directory of Open Access Journals (Sweden)

    E. Chatzimichail

    2014-01-01

    Full Text Available Cancer is a leading cause of death worldwide and the prognostic evaluation of cancer patients is of great importance in medical care. The use of artificial neural networks in prediction problems is well established in human medical literature. The aim of the current study was to assess the prognostic value of a series of clinical and molecular variables with the addition of γ-H2AX—a new DNA damage response marker—for the prediction of prognosis in patients with early operable non-small cell lung cancer by comparing the γ-H2AX-based artificial network prediction model with the corresponding LR one. Two prognostic models of 96 patients with 27 input variables were constructed by using the parameter-increasing method in order to compare the predictive accuracy of neural network and logistic regression models. The quality of the models was evaluated by an independent validation data set of 11 patients. Neural networks outperformed logistic regression in predicting the patient’s outcome according to the experimental results. To assess the importance of the two factors p53 and γ-H2AX, models without these two variables were also constructed. JR and accuracy of these models were lower than those of the models using all input variables, suggesting that these biological markers are very important for optimal performance of the models. This study indicates that neural networks may represent a potentially more useful decision support tool than conventional statistical methods for predicting the outcome of patients with non-small cell lung cancer and that some molecular markers, such as γ-H2AX, enhance their predictive ability.

  20. Chronic lymphocytic leukemia: A prognostic model comprising only two biomarkers (IGHV mutational status and FISH cytogenetics) separates patients with different outcome and simplifies the CLL-IPI.

    Science.gov (United States)

    Delgado, Julio; Doubek, Michael; Baumann, Tycho; Kotaskova, Jana; Molica, Stefano; Mozas, Pablo; Rivas-Delgado, Alfredo; Morabito, Fortunato; Pospisilova, Sarka; Montserrat, Emili

    2017-04-01

    Rai and Binet staging systems are important to predict the outcome of patients with chronic lymphocytic leukemia (CLL) but do not reflect the biologic diversity of the disease nor predict response to therapy, which ultimately shape patients' outcome. We devised a biomarkers-only CLL prognostic system based on the two most important prognostic parameters in CLL (i.e., IGHV mutational status and fluorescence in situ hybridization [FISH] cytogenetics), separating three different risk groups: (1) low-risk (mutated IGHV + no adverse FISH cytogenetics [del(17p), del(11q)]); (2) intermediate-risk (either unmutated IGHV or adverse FISH cytogenetics) and (3) high-risk (unmutated IGHV + adverse FISH cytogenetics). In 524 unselected subjects with CLL, the 10-year overall survival was 82% (95% CI 76%-88%), 52% (45%-62%), and 27% (17%-42%) for the low-, intermediate-, and high-risk groups, respectively. Patients with low-risk comprised around 50% of the series and had a life expectancy comparable to the general population. The prognostic model was fully validated in two independent cohorts, including 417 patients representative of general CLL population and 337 patients with Binet stage A CLL. The model had a similar discriminatory value as the CLL-IPI. Moreover, it applied to all patients with CLL independently of age, and separated patients with different risk within Rai or Binet clinical stages. The biomarkers-only CLL prognostic system presented here simplifies the CLL-IPI and could be useful in daily practice and to stratify patients in clinical trials. © 2017 Wiley Periodicals, Inc.

  1. A two-dimensional volatility basis set – Part 3: Prognostic modeling and NOx dependence

    Directory of Open Access Journals (Sweden)

    W. K. Chuang

    2016-01-01

    Full Text Available When NOx is introduced to organic emissions, aerosol production is sometimes, but not always, reduced. Under certain conditions, these interactions will instead increase aerosol concentrations. We expanded the two-dimensional volatility basis set (2D-VBS to include the effects of NOx on aerosol formation. This includes the formation of organonitrates, where the addition of a nitrate group contributes to a decrease of 2.5 orders of magnitude in volatility. With this refinement, we model outputs from experimental results, such as the atomic N : C ratio, organonitrate mass, and nitrate fragments in Aerosol Mass Spectrometer (AMS measurements. We also discuss the mathematical methods underlying the implementation of the 2D-VBS and provide the complete code in the Supplement. A developer version is available on Bitbucket, an online community repository.

  2. A two-dimensional volatility basis set - Part 3: Prognostic modeling and NOx dependence

    Science.gov (United States)

    Chuang, W. K.; Donahue, N. M.

    2016-01-01

    When NOx is introduced to organic emissions, aerosol production is sometimes, but not always, reduced. Under certain conditions, these interactions will instead increase aerosol concentrations. We expanded the two-dimensional volatility basis set (2D-VBS) to include the effects of NOx on aerosol formation. This includes the formation of organonitrates, where the addition of a nitrate group contributes to a decrease of 2.5 orders of magnitude in volatility. With this refinement, we model outputs from experimental results, such as the atomic N : C ratio, organonitrate mass, and nitrate fragments in Aerosol Mass Spectrometer (AMS) measurements. We also discuss the mathematical methods underlying the implementation of the 2D-VBS and provide the complete code in the Supplement. A developer version is available on Bitbucket, an online community repository.

  3. Basic critical care echocardiography by pulmonary fellows: learning trajectory and prognostic impact using a minimally resourced training model*.

    Science.gov (United States)

    See, Kay Choong; Ong, Venetia; Ng, Jeffrey; Tan, Rou An; Phua, Jason

    2014-10-01

    The spread of basic critical care echocardiography may be limited by training resources. Another barrier is the lack of information about the learning trajectory and prognostic impact of individual basic critical care echocardiography domains like acute cor pulmonale determination and left ventricular function estimation. We thus developed a minimally resourced training model and studied the latter outcomes. Prospective observational study. Twenty-bed medical ICU. Echocardiography-naive trainees enrolled in the first year of our Pulmonary Medicine Fellowship Program from September 2012 to September 2013. We described the learning trajectory in six basic critical care echocardiography domains (adequate views, pericardial effusion, acute cor pulmonale, left ventricular ejection fraction, mitral regurgitation, and inferior vena cava variability) and correlated abnormalities in selected basic critical care echocardiography domains with clinical outcomes (mortality and length of stay). Three-hundred forty-three basic critical care echocardiography scans were done for 318 patients by seven fellows (median of 40 scans per fellow; range, 34-105). Only one-third patients had normal basic critical care echocardiography studies. Accuracy in various basic critical care echocardiography domains was high (> 90%), especially beyond the first 30 examinations. Acute cor pulmonale was associated with ICU mortality when adjusted for Acute Physiology and Chronic Health Evaluation II score and presence of sepsis, whereas mitral regurgitation was associated with longer hospitalization only on univariate analysis. Basic critical care echocardiography training using minimal resources is feasible. New trainees can achieve reasonable competency in most basic critical care echocardiography domains after performing about 30 examinations within the first year. The relatively high prevalence of abnormalities and the significant association of acute cor pulmonale with ICU mortality support the

  4. Prognostic and symptomatic aspects of rapid eye movement sleep in a mouse model of posttraumatic stress disorder

    Directory of Open Access Journals (Sweden)

    Stephanie A. Polta

    2013-05-01

    Full Text Available Not every individual develops Posttraumatic Stress Disorder (PTSD after the exposure to a potentially traumatic event. Therefore, the identification of pre-existing risk factors and early diagnostic biomarkers is of high medical relevance. However, no objective biomarker has yet progressed into clinical practice. Sleep disturbances represent commonly reported complaints in PTSD patients. In particular, changes in rapid eye movement sleep (REMS properties are frequently observed in PTSD patients. Here, we examined in a mouse model of PTSD whether (1 mice developed REMS alterations after trauma and (2 whether REMS architecture before and/or shortly after trauma predicted the development of PTSD-like symptoms. We monitored sleep-wake behavior via combined EEG/EMG recordings immediately before (24 h pre, immediately after (0-48 h post and two months after exposure to an electric foot shock in male C57BL/6N mice (n=15. PTSD-like symptoms, including hyperarousal, contextual and generalized fear, were assessed one month post-trauma.Shocked mice showed early-onset and sustained elevation of REMS compared to non-shocked controls. In addition, REMS architecture before trauma was correlated with the intensity of acoustic startle responses, but not contextual fear, one month after trauma.Our data suggest REMS as prognostic (pre-trauma and symptomatic (post-trauma marker of PTSD-like symptoms in mice. Translated to the situation in humans, REMS may constitute a viable, objective and non-invasive biomarker in PTSD and other trauma-related psychiatric disorders, which could guide pharmacological interventions in humans at high risk.

  5. Model simulation of meteorology and air quality during the summer PUMA intensive measurement campaign in the UK West Midlands conurbation.

    Science.gov (United States)

    Baggott, Sarah; Cai, Xiaoming; McGregor, Glenn; Harrison, Roy M

    2006-05-01

    The Regional Atmospheric Modeling System (RAMS) and Urban Airshed Model (UAM IV) have been implemented for prediction of air pollutant concentrations within the West Midlands conurbation of the United Kingdom. The modelling results for wind speed, direction and temperature are in reasonable agreement with observations for two stations, one in a rural area and the other in an urban area. Predictions of surface temperature are generally good for both stations, but the results suggest that the quality of temperature prediction is sensitive to whether cloud cover is reproduced reliably by the model. Wind direction is captured very well by the model, while wind speed is generally overestimated. The air pollution climate of the UK West Midlands is very different to those for which the UAM model was primarily developed, and the methods used to overcome these limitations are described. The model shows a tendency towards under-prediction of primary pollutant (NOx and CO) concentrations, but with suitable attention to boundary conditions and vertical profiles gives fairly good predictions of ozone concentrations. Hourly updating of chemical concentration boundary conditions yields the best results, with input of vertical profiles desirable. The model seriously underpredicts NO2/NO ratios within the urban area and this appears to relate to inadequate production of peroxy radicals. Overall, the chemical reactivity predicted by the model appears to fall well below that occurring in the atmosphere.

  6. A multi-variate statistical model integrating passive sampler and meteorology data to predict the frequency distributions of hourly ambient ozone (O3) concentrations.

    Science.gov (United States)

    Krupa, S; Nosal, M; Ferdinand, J A; Stevenson, R E; Skelly, J M

    2003-01-01

    A multi-variate, non-linear statistical model is described to simulate passive O3 sampler data to mimic the hourly frequency distributions of continuous measurements using climatologic O3 indicators and passive sampler measurements. The main meteorological parameters identified by the model were, air temperature, relative humidity, solar radiation and wind speed, although other parameters were also considered. Together, air temperature, relative humidity and passive sampler data by themselves could explain 62.5-67.5% (R(2)) of the corresponding variability of the continuously measured O3 data. The final correlation coefficients (r) between the predicted hourly O3 concentrations from the passive sampler data and the true, continuous measurements were 0.819-0.854, with an accuracy of 92-94% for the predictive capability. With the addition of soil moisture data, the model can lead to the first order approximation of atmospheric O3 flux and plant stomatal uptake. Additionally, if such data are coupled to multi-point plant response measurements, meaningful cause-effect relationships can be derived in the future.

  7. A Prognostic Model of Surgical Site Infection Using Daily Clinical Wound Assessment.

    Science.gov (United States)

    Sanger, Patrick C; van Ramshorst, Gabrielle H; Mercan, Ezgi; Huang, Shuai; Hartzler, Andrea L; Armstrong, Cheryl A L; Lordon, Ross J; Lober, William B; Evans, Heather L

    2016-08-01

    Surgical site infection (SSI) remains a common, costly, and morbid health care-associated infection. Early detection can improve outcomes, yet previous risk models consider only baseline risk factors (BF) not incorporating a proximate and timely data source-the wound itself. We hypothesize that incorporation of daily wound assessment improves the accuracy of SSI identification compared with traditional BF alone. A prospective cohort of 1,000 post open abdominal surgery patients at an academic teaching hospital were examined daily for serial features (SF), for example, wound characteristics and vital signs, in addition to standard BF, for example, wound class. Using supervised machine learning, we trained 3 Naïve Bayes classifiers (BF, SF, and BF+SF) using patient data from 1 to 5 days before diagnosis to classify SSI on the following day. For comparison, we also created a simplified SF model that used logistic regression. Control patients without SSI were matched on 5 similar consecutive postoperative days to avoid confounding by length of stay. Accuracy, sensitivity/specificity, and area under the receiver operating characteristic curve were calculated on a training and hold-out testing set. Of 851 patients, 19.4% had inpatient SSIs. Univariate analysis showed differences in C-reactive protein, surgery duration, and contamination, but no differences in American Society of Anesthesiologists scores, diabetes, or emergency surgery. The BF, SF, and BF+SF classifiers had area under the receiver operating characteristic curves of 0.67, 0.76, and 0.76, respectively. The best-performing classifier (SF) had optimal sensitivity of 0.80, specificity of 0.64, positive predictive value of 0.35, and negative predictive value of 0.93. Features most associated with subsequent SSI diagnosis were granulation degree, exudate amount, nasogastric tube presence, and heart rate. Serial features provided moderate positive predictive value and high negative predictive value for early

  8. 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...

  9. Forecasting of meteorological drought using Hidden Markov Model (case study: The upper Blue Nile river basin, Ethiopia

    Directory of Open Access Journals (Sweden)

    Mosaad Khadr

    2016-03-01

    Full Text Available An improved drought management must rely on an accurate monitoring and forecasting of the phenomenon in order to activate appropriate mitigation measures. In this study, several homogenous Hidden Markov Models (HMMs were developed to forecast droughts using the Standardized Precipitation Index, SPI, at short-medium term. Validation of the developed models was carried out with reference to precipitation series observed in 22 stations located in the upper Blue Nile river basin. The performance of the HMM was measured using various forecast skill criteria. Results indicate that Hidden Markov Model provides a fairly good agreement between observed and forecasted values in terms of the SPI time series on various lead time. Results seem to confirm the reliability of the proposed models to discriminate between events and non-events relatively well, thus suggesting the suitability of the proposed procedure as a tool for drought management and drought early warning.

  10. The sequential organ failure assessment (SOFA) score is prognostically superior to the model for end-stage liver disease (MELD) and MELD variants following paracetamol (acetaminophen) overdose.

    Science.gov (United States)

    Craig, D G N; Reid, T W D J; Wright, E C; Martin, K G; Davidson, J S; Hayes, P C; Simpson, K J

    2012-03-01

    The prognostic value of the model for end-stage liver disease (MELD) and sodium-based MELD variants in predicting survival following paracetamol overdose remains unclear. To examine the prognostic accuracy of sodium-based MELD variants in paracetamol-induced acute liver injury compared with the sequential organ failure assessment (SOFA) score. Retrospective analysis of 138 single time point paracetamol overdoses admitted to a tertiary liver centre. Individual laboratory samples were correlated with the corresponding clinical parameters in relation to time post-overdose, and the daily MELD, MELD-Na, MELDNa, MESO, iMELD, UKELD, updated MELD and SOFA scores were calculated. Sixty-six (47.8%) patients developed hepatic encephalopathy, of whom 7 were transplanted and 21 died without liver transplantation. SOFA had a significantly greater area under the receiver operator characteristic for the prediction of spontaneous survival compared with MELD at both 72 (P = 0.024) and 96 (P = 0.017) h post-overdose. None of the sodium-based MELD variants improved the prognostic accuracy of MELD. A SOFA score >6 by 72 h or >7 by 96 h, post-overdose predicted death/transplantation with a negative predictive value of 96.9 (95% CI 90.2-99.4) and 98.8 (95% CI 93.6-99.9) respectively. SOFA and MELD had similar accuracy for predicting the development of hepatic encephalopathy (P = 0.493). The SOFA score is superior to MELD in predicting spontaneous survival following paracetamol-induced acute liver injury. Modification of the MELD score to include serum sodium does not improve prognostic accuracy in this setting. SOFA may have potential as a quantitative triage marker following paracetamol overdose. © 2012 Blackwell Publishing Ltd.

  11. Meteorological Support in Scientific Ballooning

    Science.gov (United States)

    Schwantes, Chris; Mullenax, Robert

    2017-01-01

    The weather affects every portion of a scientific balloon mission, from payload integration to launch, float, and impact and recovery. Forecasting for these missions is very specialized and unique in many aspects. CSBF Meteorology incorporates data from NWSNCEP, as well as several international meteorological organizations, and NCAR. This presentation will detail the tools used and specifics on how CSBF Meteorology produces its forecasts.

  12. From meteorological to hydrological drought in the Upper Niger Basin: trend and uncertainty analysis in the monitoring and the modeling of rainfall deficits and low flow responses

    Science.gov (United States)

    Fournet, S.; Aich, V.; Liersch, S.; Hattermann, F. F.

    2012-04-01

    From 1970 to 2002, the Sahel experienced a fairly abrupt, severe and continuous dry episode. The main reason is the oceanic forcing ruling the West African monsoon dynamic. Also, a combinative effect of climate and anthropogenic changes (demographic pressure on land associated to inappropriate land-use practices) initiates and supports the interactive processes of drying and land cover degradation forming a complex land atmosphere feedback convection. The Great Drought in Mali largely affected the regional food security, the human societies and economic development and the conservation of wet and semi-arid ecosystems. It results in an increasing competition and conflicts for water access between vulnerable local stakeholders (rainfed and controlled irrigation farming, nomad pastoralism, traditional fishing) and steers national investments with the construction of dams and diversion channels for development of hydropower energy and fully governed irrigated agriculture. To support drought adaptations in regional development strategies, climate and hydrological forecasting are thus of paramount importance. Whilst climate change is typically associated with an increase in mean global surface temperature, what matters regionally and still remains uncertain is the change in rainfall, discharge and drought patterns from daily intensity to large inter-annual and multi-decadal variability. Different climate data sources exist for investigation of climate variability and change: daily measurements, reanalysis data and climate scenarios using Global and Regional Circulation Models (GCMs and RCMs). This study aims at analyzing the suitability of the different data sources for drought investigation in the target area, the Upper Niger Basin. First, the performance of meteorological data sets based on climate reanalysis is assessed in comparison of data of synoptic stations. Second, one statistical (STAR) and two dynamical regional RCMs (CCLM, REMO) are compared to IPCC-GCM data

  13. 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.

  14. Predicting Overall Survival After Stereotactic Ablative Radiation Therapy in Early-Stage Lung Cancer: Development and External Validation of the Amsterdam Prognostic Model.

    Science.gov (United States)

    Louie, Alexander V; Haasbeek, Cornelis J A; Mokhles, Sahar; Rodrigues, George B; Stephans, Kevin L; Lagerwaard, Frank J; Palma, David A; Videtic, Gregory M M; Warner, Andrew; Takkenberg, Johanna J M; Reddy, Chandana A; Maat, Alex P W M; Woody, Neil M; Slotman, Ben J; Senan, Suresh

    2015-09-01

    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). 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. 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(2) = 0.97) and external SABR (r(2) = 0.79) and surgical cohorts (r(2) = 0.91). 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. Copyright © 2015 Elsevier Inc. All rights reserved.

  15. Improving SWAT model performance in the upper Blue Nile Basin using meteorological data integration and subcatchment discretization

    Science.gov (United States)

    Polanco, Erwin Isaac; Fleifle, Amr; Ludwig, Ralf; Disse, Markus

    2017-09-01

    The Blue Nile Basin is confronted by land degradation problems, insufficient agricultural production, and a limited number of developed energy sources. Hydrological models provide useful tools to better understand such complex systems and improve water resources and land management practices. In this study, SWAT was used to model the hydrological processes in the upper Blue Nile Basin. Comparisons between a Climate Forecast System Reanalysis (CFSR) and a conventional ground weather dataset were done under two sub-basin discretization levels (30 and 87 sub-basins) to create an integrated dataset to improve the spatial and temporal limitations of both datasets. A SWAT error index (SEI) was also proposed to compare the reliability of the models under different discretization levels and weather datasets. This index offers an assessment of the model quality based on precipitation and evapotranspiration. SEI demonstrates to be a reliable additional and useful method to measure the level of error of SWAT. The results showed the discrepancies of using different weather datasets with different sub-basin discretization levels. Datasets under 30 sub-basins achieved Nash-Sutcliffe coefficient (NS) values of -0.51, 0.74, and 0.84; p factors of 0.53, 0.66, and 0.70; and r factors of 1.11, 0.83, and 0.67 for the CFSR, ground, and integrated datasets, respectively. Meanwhile, models under 87 sub-basins achieved NS values of -1.54, 0.43, and 0.80; p factors of 0.36, 0.67, and 0.77; r factors of 0.93, 0.68, and 0.54 for the CFSR, ground, and integrated datasets, respectively. Based on the obtained statistical results, the integrated dataset provides a better model of the upper Blue Nile Basin.

  16. Simulating the meteorology and PM10concentrations in Arizona dust storms with the Weather Research and Forecasting model with Chemistry (Wrf-Chem).

    Science.gov (United States)

    Hyde, Peter; Mahalov, Alex; Li, Jialun

    2017-07-24

    Nine dust storms in south-central Arizona, USA 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 microns and smaller ([PM 10 ]).Furthermore, the model under-estimated [PM 10 ] in highly agricultural Pinal County because it under-estimated 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 over-estimated [PM 10 ] in western Arizona along the Colorado River because it generated daytime sea breezes (from the nearby Gulf of California) whose surface-layer speeds were too strong. In Phoenix the model's performance depended on the event, with both under- and over-estimations partly due to incorrect representation of urban features. Sensitivity tests indicate that [PM 10 ] highly rely 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 tenfold and the latter exceeding health-based guidelines by as much as seventy-fold. Monsoonal thunderstorms not only produce elevated [PM 10 ], but also cause 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

  17. 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....... In this study, a MOS based on multiple linear regression is proposed: the model screens the most relevant NWP forecast variables and selects the best predictors to fit a regression equation that minimizes the forecast errors, utilizing wind farm power output measurements as input. The performance of the method...... is evaluated in two wind farms, located in different topographical areas and with different NWP grid spacing. Because of the high seasonal variability of NWP forecasts, it was considered appropriate to implement monthly stratified MOS. In both wind farms, the first predictors were always wind speeds (at...

  18. 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

  19. Suitability of a Coupled Hydrodynamic Water Quality Model to Predict Changes in Water Quality from Altered Meteorological Boundary Conditions

    Directory of Open Access Journals (Sweden)

    Leon van der Linden

    2015-01-01

    Full Text Available Downscaled climate scenarios can be used to inform management decisions on investment in infrastructure or alternative water sources within water supply systems. Appropriate models of the system components, such as catchments, rivers, lakes and reservoirs, are required. The climatic sensitivity of the coupled hydrodynamic water quality model ELCOM-CAEDYM was investigated, by incrementally altering boundary conditions, to determine its suitability for evaluating climate change impacts. A series of simulations were run with altered boundary condition inputs for the reservoir. Air and inflowing water temperature (TEMP, wind speed (WIND and reservoir inflow and outflow volumes (FLOW were altered to investigate the sensitivity of these key drivers over relevant domains. The simulated water quality variables responded in broadly plausible ways to the altered boundary conditions; sensitivity of the simulated cyanobacteria population to increases in temperature was similar to published values. However the negative response of total chlorophyll-a suggested by the model was not supported by an empirical analysis of climatic sensitivity. This study demonstrated that ELCOM-CAEDYM is sensitive to climate drivers and may be suitable for use in climate impact studies. It is recommended that the influence of structural and parameter derived uncertainty on the results be evaluated. Important factors in determining phytoplankton growth were identified and the importance of inflowing water quality was emphasized.

  20. 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

  1. A multi-factorial genetic model for prognostic assessment of high risk melanoma patients receiving adjuvant interferon.

    Directory of Open Access Journals (Sweden)

    Ena Wang

    Full Text Available PURPOSE: IFNa was the first cytokine to demonstrate anti-tumor activity in advanced melanoma. Despite the ability of high-dose IFNa reducing relapse and mortality by up to 33%, large majority of patients experience side effects and toxicity which outweigh the benefits. The current study attempts to identify genetic markers likely to be associated with benefit from IFN-a2b treatment and predictive for survival. EXPERIMENTAL DESIGN: We tested the association of variants in FOXP3 microsatellites, CTLA4 SNPs and HLA genotype in 284 melanoma patients and their association with prognosis and survival of melanoma patients who received IFNa adjuvant therapy. RESULTS: Univariate survival analysis suggested that patients bearing either the DRB1*15 or HLA-Cw7 allele suffered worse OS while patients bearing either HLA-Cw6 or HLA-B44 enjoyed better OS. DRB1*15 positive patients suffered also worse RFS and conversely HLA-Cw6 positive patients had better RFS. Multivariate analysis revealed that a five-marker genotyping signature was prognostic of OS independent of disease stage. In the multivariate Cox regression model, HLA-B38 (p = 0.021, HLA-C15 (p = 0.025, HLA-C3 (p = 0.014, DRB1*15 (p = 0.005 and CT60*G/G (0.081 were significantly associated with OS with risk ratio of 0.097 (95% CI, 0.013-0.709, 0.387 (95% CI, 0.169-0.889, 0.449 (95% CI, 0.237-0.851, 1.948 (95% CI, 1.221-3.109 and 1.484 (95% IC, 0.953-2.312 respectively. CONCLUSION: These results suggest that gene polymorphisms relevant to a biological occurrence are more likely to be informative when studied in concert to address potential redundant or conflicting functions that may limit each gene individual contribution. The five markers identified here exemplify this concept though prospective validation in independent cohorts is needed.

  2. External validation of a proposed prognostic model for the prediction of 1-year postoperative eGFR after living donor nephrectomy.

    Science.gov (United States)

    Kulik, Ulf; Gwiasda, Jill; Oldhafer, Felix; Kaltenborn, Alexander; Arelin, Viktor; Gueler, Faikah; Richter, Nicolas; Klempnauer, Juergen; Schrem, Harald

    2017-11-01

    The goal of this study was to externally validate the recently proposed prognostic model for the prediction of estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73 m(2) 1 year after living donor nephrectomy. 130 living kidney donors (median age at donation 52.3 years, range 24.7-75.6 years) were investigated before and after donation between March 2000 and April 2016. Preoperative eGFR values varied between 61.7 and 148.4 ml/min (mean: 89, median: 88). Observed eGFR 1 year after transplantation (±45 days) ranged between 36.3 and 97.1 ml/min (mean: 55, median: 53). 70.8% of donors displayed eGFR values < 60 ml/min 1 year after donation. Predicted eGFR 1 year after donation was determined using the prognostic model proposed by Benoit et al. (Int Urol Nephrol 49(5):793-801. doi: 10.1007/s11255-017-1559-1 , 2017): postoperative eGFR ml/min/1.73 m(2) = 31.71 + (0.521 × eGFR in ml/min prior to donation -0.314 × Age in years at donation). Pearson correlation and receiver operating characteristics curve (ROC-curve) were used to assess external validity of the proposed prognostic model to predict postoperative eGFR in ml/min and eGFR < 60 ml/min. The correlation between predicted and observed eGFR 1 year after donation was significant (p < 0.001; R (2) = 0.594). The area under the ROC-curve (AUROC) demonstrated a high sensitivity and specificity for predicted eGFR values < 60 ml/min (AUROC = 0.866). The proposed prognostic model for the prediction of postoperative eGFR was successfully validated in our cohort. We therefore consider the model as generally applicable.

  3. Feasibility of transitioning from APACHE II to SAPS III as prognostic model in a Brazilian general intensive care unit. A retrospective study.

    Science.gov (United States)

    Serpa Neto, Ary; Assunção, Murillo Santucci Cesar de; Pardini, Andréia; Silva, Eliézer

    2015-01-01

    Prognostic models reflect the population characteristics of the countries from which they originate. Predictive models should be customized to fit the general population where they will be used. The aim here was to perform external validation on two predictive models and compare their performance in a mixed population of critically ill patients in Brazil. Retrospective study in a Brazilian general intensive care unit (ICU). This was a retrospective review of all patients admitted to a 41-bed mixed ICU from August 2011 to September 2012. Calibration (assessed using the Hosmer-Lemeshow goodness-of-fit test) and discrimination (assessed using area under the curve) of APACHE II and SAPS III were compared. The standardized mortality ratio (SMR) was calculated by dividing the number of observed deaths by the number of expected deaths. A total of 3,333 ICU patients were enrolled. The Hosmer-Lemeshow goodness-of-fit test showed good calibration for all models in relation to hospital mortality. For in-hospital mortality there was a worse fit for APACHE II in clinical patients. Discrimination was better for SAPS III for in-ICU and in-hospital mortality (P = 0.042). The SMRs for the whole population were 0.27 (confidence interval [CI]: 0.23 - 0.33) for APACHE II and 0.28 (CI: 0.22 - 0.36) for SAPS III. In this group of critically ill patients, SAPS III was a better prognostic score, with higher discrimination and calibration power.

  4. Predicting intracranial hemorrhage after traumatic brain injury in low and middle-income countries: A prognostic model based on a large, multi-center, international cohort

    Directory of Open Access Journals (Sweden)

    Subaiya Saleena

    2012-11-01

    Full Text Available Abstract Background Traumatic brain injury (TBI affects approximately 10 million people annually, of which intracranial hemorrhage is a devastating sequelae, occurring in one-third to half of cases. Patients in low and middle-income countries (LMIC are twice as likely to die following TBI as compared to those in high-income countries. Diagnostic capabilities and treatment options for intracranial hemorrhage are limited in LMIC as there are fewer computed tomography (CT scanners and neurosurgeons per patient as in high-income countries. Methods The Medical Research Council CRASH-1 trial was utilized to build this model. The study cohort included all patients from LMIC who received a CT scan of the brain (n = 5669. Prognostic variables investigated included age, sex, time from injury to randomization, pupil reactivity, cause of injury, seizure and the presence of major extracranial injury. Results There were five predictors that were included in the final model; age, Glasgow Coma Scale, pupil reactivity, the presence of a major extracranial injury and time from injury to presentation. The model demonstrated good discrimination and excellent calibration (c-statistic 0.71. A simplified risk score was created for clinical settings to estimate the percentage risk of intracranial hemorrhage among TBI patients. Conclusion Simple prognostic models can be used in LMIC to estimate the risk of intracranial hemorrhage among TBI patients. Combined with clinical judgment this may facilitate risk stratification, rapid transfer to higher levels of care and treatment in resource-poor settings.

  5. A dynamic prognostic model to predict survival in primary myelofibrosis: a study by the IWG-MRT (International Working Group for Myeloproliferative Neoplasms Research and Treatment).

    Science.gov (United States)

    Passamonti, Francesco; Cervantes, Francisco; Vannucchi, Alessandro Maria; Morra, Enrica; Rumi, Elisa; Pereira, Arturo; Guglielmelli, Paola; Pungolino, Ester; Caramella, Marianna; Maffioli, Margherita; Pascutto, Cristiana; Lazzarino, Mario; Cazzola, Mario; Tefferi, Ayalew

    2010-03-04

    Age older than 65 years, hemoglobin level lower than 100 g/L (10 g/dL), white blood cell count greater than 25 x 10(9)/L, peripheral blood blasts 1% or higher, and constitutional symptoms have been shown to predict poor survival in primary myelofibrosis (PMF) at diagnosis. To investigate whether the acquisition of these factors during follow-up predicts survival, we studied 525 PMF patients regularly followed. All 5 variables had a significant impact on survival when analyzed as time-dependent covariates in a multivariate Cox proportional hazard model and were included in 2 separate models, 1 for all patients (Dynamic International Prognostic Scoring System [DIPSS]) and 1 for patients younger than 65 years (age-adjusted DIPSS). Risk factors were assigned score values based on hazard ratios (HRs). Risk categories were low, intermediate-1, intermediate-2, and high in both models. Survival was estimated by the HR. When shifting to the next risk category, the HR was 4.13 for low risk, 4.61 for intermediate-1, and 2.54 for intermediate-2 according to DIPSS; 3.97 for low risk, 2.84 for intermediate-1, and 1.81 for intermediate-2 according to the age-adjusted DIPSS. The novelty of these models is the prognostic assessment of patients with PMF anytime during their clinical course, which may be useful for treatment decision-making.

  6. The DAURE field campaign: meteorological overview

    Science.gov (United States)

    Jorba, O.; Pandolfi, M.; Spada, M.; Baldasano, J. M.; Pey, J.; Alastuey, A.; Arnold, D.; Sicard, M.; Artiñano, B.; Revuelta, M. A.; Querol, X.

    2011-02-01

    From end of February until March 2009 and July 2009 the experimental campaign named DAURE took place in northeastern Spain in both an urban and rural sites (Barcelona city and Montseny Natural Park) with the main objective of studying the formation and transport processes of particulate matter in the region. Several groups collaborated in an extensive measurement campaign with aerosol monitoring, meteorological measurements, atmospheric vertical structure retrievals from LIDAR and supported by numerical simulations of the meteorological and air quality conditions over the region. In this article, we present a description of the main meteorological conditions that affected the Barcelona geographical area during the campaign. The main synoptic conditions are identified and discussed by means of meteorological observations and numerical weather prediction models. Furthermore, a detailed analysis of the local meteorological conditions during the campaign is also presented. The characteristic surface wind field and the vertical structure of the main flows affecting Barcelona and the Montseny rural site are discussed using high-resolution mesoscale meteorological simulations, vertical profiles of LIDAR measurements, radiosoundings, and analysis of backward dispersion simulations with a Lagrangian model. The analysis permits the identification of three main meteorological regimes for the winter campaign (February and March 2009): a first regime dominated by high-pressure conditions over Barcelona and western Mediterranean Basin, high insolation, and the development of thermally-driven wind flows. A second regime is characterized by a strong northwestern advection that produced a cleansing action over the atmosphere. And a third identified regime is dominated by strong stagnant conditions produced by thermal inversions that decouple the low troposphere of plain and coastal areas from mountainous terrains. On the other hand, the main meteorological regimes identified for

  7. 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

  8. 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.

  9. 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

  10. Investigation of Multi-decadal Trends in Aerosol Direct Radiative Effects over North America using a Coupled Meteorology-Chemistry Model

    Science.gov (United States)

    Mathur, R.; Pleim, J.; Wong, D.; Wei, C.; Xing, J.; Gan, M.; Yu, S.; Binkowski, F.

    2012-12-01

    While aerosol radiative effects have been recognized as some of the largest sources of uncertainty among the forcers of climate change, there has been little effort devoted to verification of the spatial and temporal variability of the magnitude and directionality of aerosol radiative forcing. A comprehensive investigation of the processes regulating aerosol distributions, their optical properties, and their radiative effects and verification of their simulated effects for past conditions relative to measurements is needed in order to build confidence in the estimates of the projected impacts arising from changes in both anthropogenic forcing and climate change. This study aims at addressing this issue through a systematic investigation of changes in anthropogenic emissions of SO2 and NOx over the past two decades in the United States, their impacts on anthropogenic aerosol loading in the North American troposphere, and subsequent impacts on regional radiation budgets. A newly developed 2-way coupled meteorology and air pollution model composed of the Weather Research and Forecasting (WRF) model and the Community Multiscale Air Quality (CMAQ) model is being run for 20 years (1990 - 2010) on a 12 km resolution grid that covers most of North America including the entire conterminous US. During this period US emissions of SO2 and NOx have been reduced by about 66% and 50%, respectively, mainly due to Title IV of the U.S. Clean Air Act Amendments (CAA) that aimed to reduce emissions that contribute to acid deposition. A methodology is developed to consistently estimate emission inventories for the 20-year period accounting for air quality regulations as well as population trends, economic conditions, and technology changes in motor vehicles and electric power generation. The coupled WRF-CMAQ model includes detailed treatment of direct effects of aerosols on photolysis rates as well as on shortwave radiation and the direct effects of tropospheric ozone on the long

  11. 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

  12. 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....... 82% for low risk group (P new drug trials....

  13. Prognostic model for advanced breast carcinoma with luminal subtype and impact of hormonal maintenance: Implications for post-progression and conditional survival.

    Science.gov (United States)

    Carbognin, Luisa; Sperduti, Isabella; Ciccarese, Mariangela; Fabi, Alessandra; Petrucelli, Luciana; Vari, Sabrina; Forcignanò, Rosa Chiara; Nortilli, Rolando; Vicentini, Cecilia; Pilotto, Sara; Merler, Sara; Zampiva, Ilaria; Brunelli, Matteo; Manfrin, Erminia; Giannarelli, Diana; Tortora, Giampaolo; Bria, Emilio

    2016-10-01

    The aim of this analysis was to develop and validate a prognostic model for advanced breast cancer (ABC) with luminal subtype based on the combination of clinical, pathological and therapeutic predictors to provide a practical tool to evaluate patients' prognosis. Clinical and pathological data were retrospectively correlated to progression-free and overall survival (PFS/OS) using a Cox model. Significant treatment variables were adjusted with the propensity score analysis. A continuous score to identify risk classes was derived according to model ratios. The performance of the risk-class model was tested for post-progression survival (PPS) and conditional survival (CS) as well. Data from 335 patients (3 institutions) were gathered (median follow-up 58 months). At multivariate analysis Ki67, Performance Status (PS) and number of metastatic sites were significant predictors for PFS, whereas Ki67, PS, brain metastases, PFS after 1st-line therapy, number of chemotherapy lines, hormonal therapy and maintenance were significant predictors for OS. The hormonal maintenance resulted to be prognostic after adjustment with propensity score analysis. A two-class model significantly differentiated low-risk and high-risk patients for 2-year PFS (31.5% and 11.0%, p model separated low risk, intermediate-risk, and high-risk patients for 2-year PFS (40.8%, 24.4%, and 11.0%, p models equally discriminate the luminal ABC prognosis in terms of PPS and CS. A risk stratification model including 'easy-to-obtain' clinical, pathological and therapeutic parameters accurately separates luminal ABC patients into different risk classes. Copyright © 2016 Elsevier Ltd. All rights reserved.

  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.

    2014-01-01

    Purpose 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. Patients and Methods 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. Results 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. Conclusion 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. PMID

  15. Preoperative multivariable prognostic models for prediction of survival and major complications following surgical resection of renal cell carcinoma with suprahepatic caval tumor thrombus.

    Science.gov (United States)

    Haddad, Ahmed Q; Leibovich, Bradley C; Abel, Edwin Jason; Luo, Jun-Hang; Krabbe, Laura-Maria; Thompson, Robert Houston; Heckman, Jennifer E; Merrill, Megan M; Gayed, Bishoy A; Sagalowsky, Arthur I; Boorjian, Stephen A; Wood, Christopher G; Margulis, Vitaly

    2015-09-01

    Surgical resection for renal cell carcinoma (RCC) with suprahepatic inferior vena cava tumor thrombus is associated with significant morbidity, yet there are currently no tools for preoperative prognostic evaluation. Our goal was to develop a preoperative multivariable model for prediction of survival and risk of major complications in patients with suprahepatic thrombi. We identified patients who underwent surgery for RCC with suprahepatic tumor thrombus extension from 2000 to 2013 at 4 tertiary centers. A Cox proportional hazard model was used for analysis of overall survival (OS) and logistic regression was used for major complications within 90 days of surgery (Clavien ≥ 3A). Nomograms were internally calibrated by bootstrap resampling method. A total of 49 patients with level III thrombus and 83 patients with level IV thrombus were identified. During median follow-up of 24.5 months, 80 patients (60.6%) died and 46 patients (34.8%) experienced major complication. Independent prognostic factors for OS included distant metastases at presentation (hazard ratio = 2.52, P = 0.002) and Eastern Cooperative Oncology Group (ECOG) performance status (hazard ratio = 1.84, Pmodels for the prediction of survival and major complications in patients with RCC who have a suprahepatic inferior vena cava thrombus. If externally validated, these tools may aid in patient selection for surgical intervention. Copyright © 2015 Elsevier Inc. All rights reserved.

  16. 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 ...

  17. 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...

  18. 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.

  19. The Dynamic Meteorology of Gale Crater (Invited)

    Science.gov (United States)

    Rafkin, S. C.; Pla-Garcia, J.

    2013-12-01

    Gale Crater, in which the Mars Science Laboratory (MSL) landed in August 2012, is the most topographically complex area visited to date on Mars. The meteorology within the crater may also be one of the most dynamically complex meteorological environments, because topography is thought to very strongly drive the near-surface atmospheric circulations. The Rover Environmental Monitoring Station (REMS) has provided some clues on the nature of the local meteorology. As with all single station measurements, the meteorological interpretation is typically limited by a lack of spatial context in which to place the observations. Ideally, numerical models properly validated against the observations can be used to provide this context. In the case of REMS, it is a challenge to validate the model directly against the observations. However, there are notable thermal and wind thermal signatures in mesoscale model simulations that are associated with slope flows, katabatic winds, and nocturnal mixing events. These signatures appear to be at least consistent with the rover environment monitored by REMS. Of particular note is evidence for two distinct air masses--one in the bottom of the crater and one on the plateau--that have minimal interaction with one another. If there are indeed two distinct air masses, the implications for dust and water vapor cycling between Gale Crater and the greater atmosphere of Mars are presented.

  20. Validating Basic Surface Variables in the Australian Bureau of Meteorology Model with CEOP EOP3 In-situ Data(Coordinated Enhanced Observing Period(CEOP))

    OpenAIRE

    Lawrie, RIKUS; Bureau of Meteorology Research Centre

    2007-01-01

    The Coordinated Enhanced Observing Period (CEOP) Project has initiated the collection of model output location time series (MOLTS) data from numerical weather prediction and assimilation centers, including the Australian Bureau of Meteorology. These were designed to complement the collection of in-situ observational data sets at the same 41 locations. This study is a preliminary attempt to assess the differences and similarities between the MOLTS and time series of the in-situ data. The MOLTS...

  1. 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...

  2. Prognostic model to identify patients with myelofibrosis at the highest risk of transformation to acute myeloid leukemia.

    Science.gov (United States)

    Quintás-Cardama, Alfonso; Kantarjian, Hagop; Pierce, Sherry; Cortes, Jorge; Verstovsek, Srdan

    2013-06-01

    Some patients with myelofibrosis (MF) progress to acute myeloid leukemia (AML). Current prognostic tools were not devised to assess risk of AML transformation. Multivariate analysis in 649 patients followed for a median of 19 months (range, 1-180 months). We identified age > 60 (P = .004; hazard ratio [HR], 1.63), platelets HR, 1.62), bone marrow blast > 10% (P = .002; HR, 2.18), high-risk karyotype (P HR, 2.44), transfusion dependency (P HR, 2.64), performance status > 1 (P = .003; HR, 1.47), lactate dehydrogenase > 2000 U/L (P HR, 1.62), previous hydroxyurea (P HR, 1.69), and male sex (P = .005; HR, 1.41) as independent poor prognostic factors for survival. Using the same baseline variables we identified bone marrow blasts >10% and worst karyotype as independent risk factors for AML transformation. Patients with 1 or both of these risk factors (n = 80; 12%) had a median survival of 10 months and a 1-year AML transformation rate of 13% (2% if none of those factors, P = .001). We have identified risk factors that predict high risk of transformation from MF to AML. Copyright © 2013 Elsevier Inc. All rights reserved.

  3. 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

  4. Grid-based Meteorological and Crisis Applications

    Science.gov (United States)

    Hluchy, Ladislav; Bartok, Juraj; Tran, Viet; Lucny, Andrej; Gazak, Martin

    2010-05-01

    We present several applications from domain of meteorology and crisis management we developed and/or plan to develop. Particularly, we present IMS Model Suite - a complex software system designed to address the needs of accurate forecast of weather and hazardous weather phenomena, environmental pollution assessment, prediction of consequences of nuclear accident and radiological emergency. We discuss requirements on computational means and our experiences how to meet them by grid computing. The process of a pollution assessment and prediction of the consequences in case of radiological emergence results in complex data-flows and work-flows among databases, models and simulation tools (geographical databases, meteorological and dispersion models, etc.). A pollution assessment and prediction requires running of 3D meteorological model (4 nests with resolution from 50 km to 1.8 km centered on nuclear power plant site, 38 vertical levels) as well as running of the dispersion model performing the simulation of the release transport and deposition of the pollutant with respect to the numeric weather prediction data, released material description, topography, land use description and user defined simulation scenario. Several post-processing options can be selected according to particular situation (e.g. doses calculation). Another example is a forecasting of fog as one of the meteorological phenomena hazardous to the aviation as well as road traffic. It requires complicated physical model and high resolution meteorological modeling due to its dependence on local conditions (precise topography, shorelines and land use classes). An installed fog modeling system requires a 4 time nested parallelized 3D meteorological model with 1.8 km horizontal resolution and 42 levels vertically (approx. 1 million points in 3D space) to be run four times daily. The 3D model outputs and multitude of local measurements are utilized by SPMD-parallelized 1D fog model run every hour. The fog

  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. Prognostic Factors and a New Prognostic Index Model for Children and Adolescents with Hodgkin’s Lymphoma Who Underwent Autologous Hematopoietic Stem Cell Transplantation: A Multicenter Study of the Turkish Pediatric Bone Marrow Transplantation Study

    Directory of Open Access Journals (Sweden)

    Vural Kesik

    2016-12-01

    Full Text Available Objective: The prognostic factors and a new childhood prognostic index after autologous hematopoietic stem cell transplantation (AHSCT in patients with relapsed/refractory Hodgkin’s lymphoma (HL were evaluated. Materials and Methods: The prognostic factors of 61 patients who underwent AHSCT between January 1990 and December 2014 were evaluated. In addition, the Age-Adjusted International Prognostic Index and the Childhood International Prognostic Index (CIPI were evaluated for their impact on prognosis. Results: The median age of the 61 patients was 14.8 years (minimummaximum: 5-20 years at the time of AHSCT. There were single relapses in 28 patients, ≥2 relapses in eight patients, and refractory disease in 25 patients. The chemosensitivity/chemorefractory ratio was 36/25. No pretransplant radiotherapy, no remission at the time of transplantation, posttransplant white blood cell count over 10x103/ μL, posttransplant positron emission tomography positivity at day 100, and serum albumin of <2.5 g/dL at diagnosis were correlated with progression-free survival. No remission at the time of transplantation, bone marrow positivity at diagnosis, and relapse after AHSCT were significant parameters for overall survival. Conclusion: The major factors affecting the progression-free and overall survival were clearly demonstrated. A CIPI that uses a lactate dehydrogenase level of 500 IU/L worked well for estimating the prognosis. We recommend AHSCT at first complete remission for relapsed cases, and it should also be taken into consideration for patients with high prognostic scores at diagnosis.

  7. The Dynamic Mesoscale Meteorology of Gale Crater

    Science.gov (United States)

    Rafkin, Scot; Pla-Garcia, J.

    Gale Crater, in which the Mars Science Laboratory (MSL) landed in August 2012, is the most topographically complex area visited to date on Mars. The meteorology within the crater may also be one of the most dynamically complex meteorological environments, because topography is thought to strongly drive the near-surface atmospheric circulations. The Rover Environmental Monitoring Station (REMS) has provided some clues on the nature of the local meteorology. As with all single station measurements, the meteorological interpretation is typically hindered by a lack of spatial context in which to place the observations. Numerical modeling results, when properly validated against observations, can provide interpretive context. Simulations with the Mars Regional Atmospheric Modeling System indicate thermal and wind thermal signatures associated with slope flows, katabatic winds, and nocturnal mixing events that are consistent with the rover environment monitored by REMS. Of particular note is evidence for two distinct air masses—one in the bottom of the crater and one on the plateau—that have minimal interaction with one another. If there are indeed two distinct air masses, there are strong implications for dust and water vapor cycling within Gale Crater.

  8. 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

  9. 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...

  10. 心力衰竭预后评估模型与评价%Different prognostic models of heart failure and their evaluation

    Institute of Scientific and Technical Information of China (English)

    黄樱硕; 孙颖

    2013-01-01

    Heart failure is the end-stage of various cardiomyopathy, and is of characteristics with difficult therapeutic options, poor prognosis and very high mortality. Many studies reported a wide variety of risk factors associated to heart failure. So, clinical physicians have to face some important decisions, such as, optimal therapeutic option, left ventricular assisted devices, and priority groups for heart transplantation, and prognosis and death risk. Though prognostic models of heart failure are helpful to assess prognosis, different models have their own characteristics, and are suitable for different population. This article introduced several prognostic models of heart failure.%心力衰竭是各种器质性心脏病的终末阶段,其治疗难度大、预后差、死亡率高。已有很多研究分析了心力衰竭的危险因素。选择正确的治疗措施,识别左室辅助装置及心脏移植的优先人群,正确判断预后及死亡风险,是临床医师面临的重要问题。心力衰竭的风险预后模型有助于评估预后,但现有模型各有特点,适用于不同人群。本文分别介绍心力衰竭的几种预后评估模型。

  11. Reformatting Meteorological Data for use in the Hazard Prediction and Assessment Capability

    Science.gov (United States)

    2004-11-01

    forecast data from mesoscale model runs. In Australia, this meteorological data is produced by the Bureau of Meteorology (BoM). HPAC was developed in the...be interpreted by HPAC. The Bureau of Meteorology (BoM) collects large amounts of observational data from across the country and uses Numerical Weather...supplied by the Bureau of Meteorology are stored in a binary form and contain the variables shown below in Table 3. Table 3: Current NetCDF format

  12. Prediction of outcome after moderate and severe traumatic brain injury: External validation of the International Mission on Prognosis and Analysis of Clinical Trials (IMPACT) and Corticoid Randomisation after Significant Head injury (CRASH) prognostic models

    NARCIS (Netherlands)

    B. Roozenbeek (Bob); H.F. Lingsma (Hester); F.E. Lecky (Fiona); J. Lu (Juan); J. Weir (James); I. Butcher (Isabella); G.S. McHugh (Gillian); G.D. Murray (Gordon); P. Perel (Pablo); A.I.R. Maas (Andrew); E.W. Steyerberg (Ewout)

    2012-01-01

    textabstractObjective: The International Mission on Prognosis and Analysis of Clinical Trials and Corticoid Randomisation After Significant Head injury prognostic models predict outcome after traumatic brain injury but have not been compared in large datasets. The objective of this is study is to

  13. 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.

  14. Assessing the cost effectiveness of using prognostic biomarkers with decision models: case study in prioritising patients waiting for coronary artery surgery.

    Science.gov (United States)

    Henriksson, Martin; Palmer, Stephen; Chen, Ruoling; Damant, Jacqueline; Fitzpatrick, Natalie K; Abrams, Keith; Hingorani, Aroon D; Stenestrand, Ulf; Janzon, Magnus; Feder, Gene; Keogh, Bruce; Shipley, Martin J; Kaski, Juan-Carlos; Timmis, Adam; Sculpher, Mark; Hemingway, Harry

    2010-01-19

    To determine the effectiveness and cost effectiveness of using information from circulating biomarkers to inform the prioritisation process of patients with stable angina awaiting coronary artery bypass graft surgery. Decision analytical model comparing four prioritisation strategies without biomarkers (no formal prioritisation, two urgency scores, and a risk score) and three strategies based on a risk score using biomarkers: a routinely assessed biomarker (estimated glomerular filtration rate), a novel biomarker (C reactive protein), or both. The order in which to perform coronary artery bypass grafting in a cohort of patients was determined by each prioritisation strategy, and mean lifetime costs and quality adjusted life years (QALYs) were compared. Swedish Coronary Angiography and Angioplasty Registry (9935 patients with stable angina awaiting coronary artery bypass grafting and then followed up for cardiovascular events after the procedure for 3.8 years), and meta-analyses of prognostic effects (relative risks) of biomarkers. The observed risk of cardiovascular events while on the waiting list for coronary artery bypass grafting was 3 per 10,000 patients per day within the first 90 days (184 events in 9935 patients). Using a cost effectiveness threshold of pound20,000- pound30,000 (euro22,000-euro33,000; $32,000-$48,000) per additional QALY, a prioritisation strategy using a risk score with estimated glomerular filtration rate was the most cost effective strategy (cost per additional QALY was < pound410 compared with the Ontario urgency score). The impact on population health of implementing this strategy was 800 QALYs per 100,000 patients at an additional cost of pound 245,000 to the National Health Service. The prioritisation strategy using a risk score with C reactive protein was associated with lower QALYs and higher costs compared with a risk score using estimated glomerular filtration rate. Evaluating the cost effectiveness of prognostic biomarkers is

  15. Validation and Extension of the Prolonged Mechanical Ventilation Prognostic Model (ProVent) Score for Predicting 1-Year Mortality after Prolonged Mechanical Ventilation.

    Science.gov (United States)

    Udeh, Chiedozie I; Hadder, Brent; Udeh, Belinda L

    2015-12-01

    Prognostic models can inform management decisions for patients requiring prolonged mechanical ventilation. The Prolonged Mechanical Ventilation Prognostic model (ProVent) score was developed to predict 1-year mortality in these patients. External evaluation of such models is needed before they are adopted for routine use. The goal was to perform an independent external validation of the modified ProVent score and assess for spectrum extension at 14 days of mechanical ventilation. This was a retrospective cohort analysis of patients who received prolonged mechanical ventilation at the University of Iowa Hospitals. Patients who received 14 or more days of mechanical ventilation were identified from a database. Manual review of their medical records was performed to abstract relevant data including the four model variables at Days 14 and 21 of mechanical ventilation. Vital status at 1 year was checked in the medical records or the social security death index. Logistic regressions examined the associations between the different variables and mortality. Model performance at 14 to 20 days and 21+ days was assessed for discrimination by calculating the area under the receiver operating characteristic curve, and calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test. A total of 180 patients (21+ d) and 218 patients (14-20 d) were included. Overall, 75% were surgical patients. One-year mortality was 51% for 21+ days and 32% for 14 to 20 days of mechanical ventilation. Age greater than 65 years was the strongest predictor of mortality at 1 year in all cohorts. There was no significant difference between predicted and observed mortality rates for patients stratified by ProVent score. There was near-perfect specificity for mortality in the groups with higher ProVent scores. Areas under the curve were 0.69 and 0.75 for the 21+ days and the 14 to 20 days cohorts respectively. P values for the Hosmer-Lemeshow statistics were 0.24 for 21+ days and 0.22 for 14 to

  16. 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,...

  17. Particle Reduction Strategies - PAREST. A meteorology comparision for better understanding of different pollutant concentration simulations with different chemistry transport models. Sub-report; Strategien zur Verminderung der Feinstaubbelastung - PAREST. Ein Meteorologievergleich zum besseren Verstaendnis der unterschiedlichen Schadstoffkonzentrationssimulationen mit verschiedenen Chemie-Transport-Modellen. Teilbericht

    Energy Technology Data Exchange (ETDEWEB)

    Kerschbaumer, Andreas [Freie Univ. Berlin (Germany). Inst. fuer Meteorologie, Troposphaerische Umweltforschung

    2013-06-15

    In this study, input variables for chemical transport models were compared from different meteorological drivers each with other and with the measured friction velocities in one place. Friction velocities are used as a measure of the mechanical turbulence, but are made available neither by numerical weather models nor routinely available observations: so they must either recalculate or estimated. In this study, friction velocities (u{sup *}), which is measured for years by the DWD in Lindenberg near Berlin, averaged over 30 minutes and have been made available to FU Berlin, compared with modeled values. The considered models are firstly the diagnostic interpolation model TRAMPER, on the other the prognostic models of the DWD COSMO-EU and the EZMWF. The boundary layer relevant parameters from the NWM were recalculated from the involved groups, which has been made for the COSMO-EU the one from the FU Berlin, the other from IfT in Leipzig. The boundary layer variables in the EZMWF model, however, were determined by TNO Netherlands. The measurements have shown that u{sup *} in the forest twice as high as above the meadow. [German] In dieser Studie wurden Eingangsvariablen fuer Chemie-Transport-Modelle aus verschiedenen meteorologischen Treibern miteinander und mit an einem Ort gemessenen Schubspannungsgeschwindigkeiten verglichen. Schubspannungsgeschwindigkeiten werden als Mass fuer die mechanische Turbulenz benutzt, werden aber weder von numerischen Wettermodellen noch routinemaessig von Beobachtungen zur Verfuegung gestellt: sie muessen also entweder nachgerechnet oder abgeschaetzt werden. In dieser Arbeit wurden Schubspannungsgeschwindigkeiten (u{sup *}), die vom DWD in Lindenberg bei Berlin ueber Jahre gemessen, gemittelt ueber 30 Minuten, und der FUBerlin zur Verfuegung gestellt worden sind, mit modellierten Werten verglichen. Die beruecksichtigten Modelle sind zum einen das diagnostische Interpolationsmodell TRAMPER, zum anderen die prognostischen Modelle des DWD

  18. 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...

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

    Directory of Open Access Journals (Sweden)

    Z. Lin

    2016-06-01

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

  20. A prognostic model for platinum-doublet as second-line chemotherapy in advanced non-small-cell lung cancer patients.

    Science.gov (United States)

    Mo, Hongnan; Hao, Xuezhi; Liu, Yutao; Wang, Lin; Hu, Xingsheng; Xu, Jianping; Yang, Sheng; Xing, Puyuan; Shi, Youwu; Jia, Bo; Wang, Yan; Li, Junling; Wang, Hongyu; Wang, Ziping; Sun, Yan; Shi, Yuankai

    2016-06-01

    Poor prognosis of advanced non-small-cell lung cancer (NSCLC) patients and the promising therapeutic effect of platinum urge the oncologists to evaluate the role of platinum doublet as second-line chemotherapy and establish the definition of platinum sensitivity in NSCLC. We retrospectively analyzed 364 advanced NSCLC patients who received platinum-doublet regimens as second-line chemotherapy after platinum-based first-line treatment. Patients were divided into four groups by their time-to-progression (TTP) after first-line chemotherapy: 0-3, 4-6, 7-12, and >12-month group, respectively. Treatment efficacy of patients' overall survival (OS), progression-free survival (PFS), and response rate (RR), as well as treatment-related toxicity, were compared among the four groups. A prognosis score system and a nomogram were established by Cox proportional hazard model, and validated by concordance index (c-index). Median OS was 14.0, 16.0, 20.0, 25.0 months for patients in the 0-3, 4-6, 7-12, >12-month group, respectively. Age ≤60 years (P = 0.002), female (P = 0.019), and TTP>12 months (P = 0.003) were independent prognostic factors. Prognostic score was calculated by adding 1 point each for any of the above three indicators, with a c-index of 0.590 (95% confidential interval [CI], 0.552-0.627). Median OS were equal to 25.0, 16.0, and 11.0 months for best (2-3 points), intermediate (1 point) and worst (0 point) category, respectively (P chemotherapy in advanced NSCLCpatients. © 2016 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

  1. Palliative medicine review: prognostication.

    Science.gov (United States)

    Glare, Paul A; Sinclair, Christian T

    2008-01-01

    Prognostication, along with diagnosis and treatment, is a traditional core clinical skill of the physician. Many patients and families receiving palliative care want information about life expectancy to help plan realistically for their futures. Although underappreciated, prognosis is, or at least should be, part of every clinical decision. Despite this crucial role, expertise in the art and science of prognostication diminished during the twentieth century, due largely to the ascendancy of accurate diagnostic tests and effective therapies. Consequently, "Doctor, how long do I have?" is a question most physicians find unprepared to answer effectively. As we focus on palliative care in the twenty-first century, prognostication will need to be restored as a core clinical proficiency. The discipline of palliative medicine can provide leadership in this direction. This paper begins by discussing a framework for understanding prognosis and how its different domains might be applied to all patients with life limiting illness, although the main focus of the paper is predicting survival in patients with cancer. Examples of prognostic tools are provided, although the subjective assessment of prognosis remains important in the terminally ill. Other issues addressed include: the importance of prognostication in terms of clinical decision-making, discharge planning, and care planning; the impact of prognosis on hospice referrals and patient/family satisfaction; and physicians' willingness to prognosticate.

  2. Prognostic Model for Resected Squamous-Cell Lung Cancer: External Multicenter Validation and Propensity Score Analysis exploring the Impact of Adjuvant and Neoadjuvant Treatment.

    Science.gov (United States)

    Pilotto, Sara; Sperduti, Isabella; Leuzzi, Giovanni; Chiappetta, Marco; Mucilli, Felice; Ratto, Giovanni Battista; Lococo, Filippo; Filosso, Pierluigi; Spaggiari, Lorenzo; Novello, Silvia; Milella, Michele; Santo, Antonio; Scarpa, Aldo; Infante, Maurizio; Tortora, Giampaolo; Facciolo, Francesco; Bria, Emilio

    2017-12-18

    We developed one of the first clinicopathological prognostic nomograms for resected squamous cell lung cancer (SQLC). Herein, we validate the model in a larger multicenter cohort and we explore the impact of adjuvant/neoadjuvant treatment (ANT). Resected SQLC patients from January 2002 to December 2012 in six institutions were eligible. To each patient was assigned a prognostic score based on those clinicopathological factors included in the model (age, T-descriptor according to TNM 7th edition, lymph nodes, grading). Kaplan-Meier analysis for disease-free/cancer-specific/overall survival (DFS/CSS/OS) was performed according to three-class risk model. Harrell's C-statistics were adopted for model validation. The effect of ANT was adjusted with propensity score (PS). Data from 1,375 patients was gathered (median age: 68 years; male: 86.8%; T-descriptor 1-2/3-4: 71.7%/24.9%; nodes negative/positive: 53.4%/46.6%; grading 1-2/3: 35.0%/41.1%). Data for survival analysis was available for 1,097 patients. With a median follow-up of 55 months, patients at low risk had a significantly longer DFS versus intermediate (HR 1.67, 95% CI 1.40-2.01) and high risk (HR 2.46, 95% CI 1.90-3.19), as well as for CSS (HR 2.46, 95% CI 1.80-3.36; HR 4.30, 95% CI 2.92-6.33) and OS (HR 1.79, 95% CI 1.48-2.17; HR 2.33, 95% CI 1.76-3.07). A trend in favor of ANT was observed for intermediate/high risk patients, particularly for CSS (p=0.06; 5-year CSS 72.7% versus 60.8%). A model based on a combination of easily available clinicopathological factors effectively stratifies resected SQLC patients in three-risk classes. Copyright © 2017 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

  3. Vehicle Integrated Prognostic Reasoner (VIPR) Metric Report

    Science.gov (United States)

    Cornhill, Dennis; Bharadwaj, Raj; Mylaraswamy, Dinkar

    2013-01-01

    This document outlines a set of metrics for evaluating the diagnostic and prognostic schemes developed for the Vehicle Integrated Prognostic Reasoner (VIPR), a system-level reasoner that encompasses the multiple levels of large, complex systems such as those for aircraft and spacecraft. VIPR health managers are organized hierarchically and operate together to derive diagnostic and prognostic inferences from symptoms and conditions reported by a set of diagnostic and prognostic monitors. For layered reasoners such as VIPR, the overall performance cannot be evaluated by metrics solely directed toward timely detection and accuracy of estimation of the faults in individual components. Among other factors, overall vehicle reasoner performance is governed by the effectiveness of the communication schemes between monitors and reasoners in the architecture, and the ability to propagate and fuse relevant information to make accurate, consistent, and timely predictions at different levels of the reasoner hierarchy. We outline an extended set of diagnostic and prognostics metrics that can be broadly categorized as evaluation measures for diagnostic coverage, prognostic coverage, accuracy of inferences, latency in making inferences, computational cost, and sensitivity to different fault and degradation conditions. We report metrics from Monte Carlo experiments using two variations of an aircraft reference model that supported both flat and hierarchical reasoning.

  4. Validating HYLARSMET: a Hydrologically Consistent Land Surface Model for Soil Moisture and Evapotranspiration Modelling over Southern Africa using Remote Sensing and Meteorological Data

    Science.gov (United States)

    Sinclair, Scott; Pegram, Geoff; Mengitsu, Michael; Everson, Colin

    2015-04-01

    Timeous knowledge of the spatial distribution of soil moisture and evapotranspiration over a large region in fine detail has great value for coping with two weather extremes: flash floods and droughts, since the state of the wetness of the land surface has a major impact on runoff response. Also, the ability to monitor the wetness of the soil and the actual evapotranspiration over large regions, without having to laboriously take expensive samples, is a bonus for agricultural managers who need to predict crop yields. We present samples of the daily national Soil Moisture and Evapotranspiration estimates on a grid of 7300 locations centred in 12 km squares, then move on to the results of a validation study for soil moisture and evapotranspiration estimated using the PyTOPKAPI hydrological model in Land Surface Modelling mode, a system called HYLARSMET. The HYLARSMET estimates are compared with detailed evapotranspiration and soil moisture measurements made at the Baynesfield experimental farm in the KwaZulu-Natal province of South Africa, run by the University of KZN. The HYLARSMET evapotranspiration estimates compared very well with the measured estimates for the two chosen crop types, in spite of the fact that the HYLARSMET estimates were not designed to explicitly account for the crop types at each site. The same seasonality effects were evident in all 3 estimates, and there was a stronger ET relationship between HYLARSMET and the Soybean site (Pearson r = 0.81) than for Maize, (r = 0.59). The soil moisture relationship was stronger between the two in situ measured estimates (r = 0.98 at 0.5 m depth) than it was between HYLARSMET and the field estimates (r about 0.52 in both cases). Overall there was a reasonably good relationship between HYLARSMET and the in situ measurements of ET and SM at each site, indicating the value of the modelling procedure.

  5. Implementing targeted expectant management in fertility care using prognostic modelling: a cluster randomized trial with a multifaceted strategy.

    Science.gov (United States)

    Kersten, F A M; Nelen, W L D M; van den Boogaard, N M; van Rumste, M M; Koks, C A; IntHout, J; Verhoeve, H R; Pelinck, M J; Boks, D E S; Gianotten, J; Broekmans, F J M; Goddijn, M; Braat, D D M; Mol, B W J; Hermens, R P G M

    2017-08-01

    What is the effectiveness of a multifaceted implementation strategy compared to usual care on improving the adherence to guideline recommendations on expectant management for couples with unexplained infertility? The multifaceted implementation strategy did not significantly increase adherence to guideline recommendations on expectant management compared to care as usual. Intrauterine insemination (IUI) with or without ovarian hyperstimulation has no beneficial effect compared to no treatment for 6 months after the fertility work-up for couples with unexplained infertility and a good prognosis of natural conception. Therefore, various professionals and policy makers have advocated the use of prognostic profiles and expectant management in guideline recommendations. A cluster randomized controlled trial in 25 clinics in the Netherlands was conducted between March 2013 and May 2014. Clinics were randomized between the implementation strategy (intervention, n = 13) and care as usual (control, n = 12). The effect of the implementation strategy was evaluated by comparing baseline and effect measurement data. Data collection was retrospective and obtained from medical record research and a patient questionnaire. A total of 544 couples were included at baseline and 485 at the effect measurement (247 intervention group/238 control group). Guideline adherence increased from 49 to 69% (OR 2.66; 95% CI 1.45-4.89) in the intervention group, and from 49 to 61% (OR 2.03; 95% CI 1.38-3.00) in the control group. Multilevel analysis with case-mix adjustment showed that the difference of 8% was not statistically significant (OR 1.31; 95% CI 0.67-2.59). The ongoing pregnancy rate within six months after fertility work-up did not significantly differ between intervention and control group (25% versus 27%: OR 0.72; 95% CI 0.40-1.27). There is a possible selection bias, couples included in the study had a higher socio-economic status than non-responders. How this affects guideline

  6. Prognostic value of blood-biomarkers related to hypoxia, inflammation, immune response and tumour load in non-small cell lung cancer - A survival model with external validation.

    Science.gov (United States)

    Carvalho, Sara; Troost, Esther G C; Bons, Judith; Menheere, Paul; Lambin, Philippe; Oberije, Cary

    2016-06-01

    Improve the prognostic prediction of clinical variables for non-small cell lung cancer (NSCLC), by selecting from blood-biomarkers, non-invasively describing hypoxia, inflammation and tumour load. Model development and validation included 182 and 181 inoperable stage I-IIIB NSCLC patients treated radically with radiotherapy (55.2%) or chemo-radiotherapy (44.8%). Least absolute shrinkage and selection operator (LASSO), selected from blood-biomarkers related to hypoxia [osteopontin (OPN) and carbonic anhydrase IX (CA-IX)], inflammation [interleukin-6 (IL-6), IL-8, and C-reactive protein (CRP)], and tumour load [carcinoembryonic antigen (CEA), and cytokeratin fragment 21-1 (Cyfra 21-1)]. Sequent model extension selected from alpha-2-macroglobulin (α2M), serum interleukin-2 receptor (sIL2r), toll-like receptor 4 (TLR4), and vascular endothelial growth factor (VEGF). Discrimination was reported by concordance-index. OPN and Cyfra 21-1 (hazard ratios of 3.3 and 1.7) significantly improved a clinical model comprising gender, World Health Organization performance-status, forced expiratory volume in 1s, number of positive lymph node stations, and gross tumour volume, from a concordance-index of 0.66 to 0.70 (validation=0.62 and 0.66). Extension of the validated model yielded a concordance-index of 0.67, including α2M, sIL2r and VEGF (hazard ratios of 4.6, 3.1, and 1.4). Improvement of a clinical model including hypoxia and tumour load blood-biomarkers was validated. New immunological markers were associated with overall survival. Data and models can be found at www.cancerdata.org (http://dx.doi.org/10.17195/candat.2016.04.1) and www.predictcancer.org. Copyright © 2016 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

  7. Integrated Diagnostics and Prognostics of Rotating Machinery

    Directory of Open Access Journals (Sweden)

    Kam W. Ng

    1999-01-01

    Full Text Available This paper provides an overview of current research efforts in integrated diagnostics and prognostics of rotating machinery. Specifically, the following topics are discussed: sensing techniques and sensors; signal detection, identification and extraction; classification of faults; predictive failure models; data/model fusion; information management; and man–machine interface. Technical issues, recommendations, and future research directions are also addressed.

  8. Inversion of GPS meteorology data

    Directory of Open Access Journals (Sweden)

    K. Hocke

    Full Text Available The GPS meteorology (GPS/MET experiment, led by the Universities Corporation for Atmospheric Research (UCAR, consists of a GPS receiver aboard a low earth orbit (LEO satellite which was launched on 3 April 1995. During a radio occultation the LEO satellite rises or sets relative to one of the 24 GPS satellites at the Earth's horizon. Thereby the atmospheric layers are successively sounded by radio waves which propagate from the GPS satellite to the LEO satellite. From the observed phase path increases, which are due to refraction of the radio waves by the ionosphere and the neutral atmosphere, the atmospheric parameter refractivity, density, pressure and temperature are calculated with high accuracy and resolution (0.5–1.5 km. In the present study, practical aspects of the GPS/MET data analysis are discussed. The retrieval is based on the Abelian integral inversion of the atmospheric bending angle profile into the refractivity index profile. The problem of the upper boundary condition of the Abelian integral is described by examples. The statistical optimization approach which is applied to the data above 40 km and the use of topside bending angle profiles from model atmospheres stabilize the inversion. The retrieved temperature profiles are compared with corresponding profiles which have already been calculated by scientists of UCAR and Jet Propulsion Laboratory (JPL, using Abelian integral inversion too. The comparison shows that in some cases large differences occur (5 K and more. This is probably due to different treatment of the upper boundary condition, data runaways and noise. Several temperature profiles with wavelike structures at tropospheric and stratospheric heights are shown. While the periodic structures at upper stratospheric heights could be caused by residual errors of the ionospheric correction method, the periodic temperature fluctuations at heights below 30 km are most likely caused by atmospheric waves (vertically

  9. Spatial data standards meet meteorological data - pushing the boundaries

    Science.gov (United States)

    Wagemann, Julia; Siemen, Stephan; Lamy-Thepaut, Sylvie

    2017-04-01

    The data archive of the European Centre for Medium-Range Weather Forecasts (ECMWF) holds around 120 PB of data and is world's largest archive of meteorological data. This information is of great value for many Earth Science disciplines, but the complexity of the data (up to five dimensions and different time axis domains) and its native data format GRIB, while being an efficient archive format, limits the overall data uptake especially from users outside the MetOcean domain. ECMWF's MARS WebAPI is a very efficient and flexible system for expert users to access and retrieve meteorological data, though challenging for users outside the MetOcean domain. With the help of web-based standards for data access and processing, ECMWF wants to make more than 1 PB of meteorological and climate data easier accessible to users across different Earth Science disciplines. As climate data provider for the H2020 project EarthServer-2, ECMWF explores the feasibility to give on-demand access to it's MARS archive via the OGC standard interface Web Coverage Service (WCS). Despite the potential a WCS for climate and meteorological data offers, the standards-based modelling of meteorological and climate data entails many challenges and reveals the boundaries of the current Web Coverage Service 2.0 standard. Challenges range from valid semantic data models for meteorological data to optimal and efficient data structures for a scalable web service. The presentation reviews the applicability of the current Web Coverage Service 2.0 standard to meteorological and climate data and discusses challenges that are necessary to overcome in order to achieve real interoperability and to ensure the conformant sharing and exchange of meteorological data.

  10. Prognostic Disclosures to Children: A Historical Perspective.

    Science.gov (United States)

    Sisk, Bryan A; Bluebond-Langner, Myra; Wiener, Lori; Mack, Jennifer; Wolfe, Joanne

    2016-09-01

    Prognostic disclosure to children has perpetually challenged clinicians and parents. In this article, we review the historical literature on prognostic disclosure to children in the United States using cancer as an illness model. Before 1948, there was virtually no literature focused on prognostic disclosure to children. As articles began to be published in the 1950s and 1960s, many clinicians and researchers initially recommended a "protective" approach to disclosure, where children were shielded from the harms of bad news. We identified 4 main arguments in the literature at this time supporting this "protective" approach. By the late 1960s, however, a growing number of clinicians and researchers were recommending a more "open" approach, where children were included in discussions of diagnosis, which at the time was often synonymous with a terminal prognosis. Four different arguments in the literature were used at this time supporting this "open" approach. Then, by the late 1980s, the recommended approach to prognostic disclosure in pediatrics shifted largely from "never tell" to "always tell." In recent years, however, there has been a growing appreciation for the complexity of prognostic disclosure in pediatrics. Current understanding of pediatric disclosure does not lead to simple "black-and-white" recommendations for disclosure practices. As with most difficult questions, we are left to balance competing factors on a case-by-case basis. We highlight 4 categories of current considerations related to prognostic disclosure in pediatrics, and we offer several approaches to prognostic disclosure for clinicians who care for these young patients and their families. Copyright © 2016 by the American Academy of Pediatrics.

  11. Meteorological measurements. Chapter 3

    Science.gov (United States)

    David Y. Hollinger

    2008-01-01

    Environmental measurements are useful for detecting climatic trends, understanding how the environment influences biological processes, and as input to ecosystem models. Landscape-scale monitoring requires a suite of environmental measures for all of these purposes, including air and soil temperature, humidity, wind speed, precipitation and soil moisture, and different...

  12. Delta model for end-stage liver disease and delta clinical prognostic indicator as predictors of mortality in patients with viral acute liver failure.

    Science.gov (United States)

    Pannu, Ashok Kumar; Bhalla, Ashish; Rao, Chelapati; Singh, Charanpreet

    2017-01-01

    The objective of the study is to compare the model for end-stage liver disease (MELD) with clinical prognostic indicators (CPI) specifically the change in these parameters after 48 h of admission in predicting the mortality in patients with acute liver failure (ALF) due to acute viral hepatitis. An open label, investigator-initiated prospective study was conducted that included 41 patients with acute viral hepatitis with ALF. The cases were followed prospectively till death or discharge. The MELD and CPI were calculated at admission and 48 h of admission. Patients having no change or worsening in CPI score, i.e., delta CPI more negative had a higher mortality over the next 48 h compared to patients having an improvement in their respective CPI score. Delta CPI predicted adverse outcome better than the presence of any three CPI on admission (P = 0.019). Patients having no change or a worsening in MELD score, i.e., delta MELD more negative, had a higher mortality in the next 48 h compa