WorldWideScience

Sample records for model meam weatherization

  1. Hierarchical Bridging Between Ab Initio and Atomistic Level Computations: Sensitivity and Uncertainty Analysis for the Modified Embedded-Atom Method (MEAM) Potential (Part B)

    Science.gov (United States)

    Hughes, J. M.; Horstemeyer, M. F.; Carino, R.; Sukhija, N.; Lawrimore, W. B.; Kim, S.; Baskes, M. I.

    2015-01-01

    In this paper, a sensitivity and general uncertainty analysis is performed related to the modified embedded-atom method (MEAM) potential calibration of pure aluminum for data garnered from lower length scale (ab initio) simulations. Input uncertainties were quantified from 95% normal distribution confidence intervals of the various calibrated MEAM potential parameters from Part A of this study. A perturbation method was used to quantify the MEAM sensitivities to input parameters. The input uncertainties and sensitivities were then combined in a general uncertainty propagation analysis method. The results of the sensitivity analysis show that all the MEAM parameters interdependently influence all MEAM model outputs to varying degrees, allowing for the definition of an ordered calibration procedure to target specific MEAM outputs. In relation to the generalized stacking fault energy (GSFE) curve, the coefficient of the embedding function related to the background electron density, asub, was the most influential parameter related to the first peak. The first peak of the GSFE curve is related to unstable dislocations, in effect dislocation nucleation, and the first trough is related to stable dislocations. This connection of tying asub to the dislocation nucleation and motion was not obvious before this study indicating the power of the sensitivity and uncertainty method that was employed.

  2. Oogenesis in the Bemisia tabaci MEAM1 species complex.

    Science.gov (United States)

    Guo, Jian-Yang; Wan, Fang-Hao; Ye, Gong-Yin

    2016-04-01

    The whitefly Bemisia tabaci MEAM1 species complex has invaded several parts of the world in the past 30 years and replaced native whitefly populations in the invaded regions, including certain areas of China. One of the possible reasons for the invasion is that MEAM1 whiteflies are more fecund than native species. However, the factors that affect the reproduction of the B. tabaci cryptic species are not clearly known. The regulation of oogenesis is thought to be one of the essential processes for egg formation and ovary development and could affect its population dynamics. In this study, the ovariole structure and oogenesis of the MEAM1 species complex was examined using light and transmission electron microscopy. Telotrophic ovarioles were observed in the MEAM1 species complex. Each ovariole had two well defined regions: the tropharium and the vitellarium. The tropharium always had more than ten trophocytes. The development of a single oocyte in the vitellarium has four phases: oocyte formation, previtellogenesis, vitellogenesis and choriogenesis. Two arrested oocytes, follicular cells and uncompleted oocytes were separated from the tropharium by microtubule and microfilaments. Early previtellogenesis oocytes absorbed nutrients and endosymbiont bacteria through a nutritive cord. However, the vitellogenesis of oocytes transmitted Vg through both the nutritive cord and the space between follicular cells. Each mature oocyte with deposited yolk proteins had only one bacteriocyte and was surrounded by a single layer of follicular cells. The oogenesis in the B. tabaci MEAM1 species complex concluded with the differentiation of oocytes, the transport of yolk and endosymbionts as well as the development and maturation of oocytes. This result provides important information that further defines the regulation of oogenesis in the B. tabaci complex. Copyright © 2016 Elsevier Ltd. All rights reserved.

  3. Models of Weather Impact on Air Traffic

    Science.gov (United States)

    Kulkarni, Deepak; Wang, Yao

    2017-01-01

    Flight delays have been a serious problem in the national airspace system costing about $30B per year. About 70 of the delays are attributed to weather and upto two thirds of these are avoidable. Better decision support tools would reduce these delays and improve air traffic management tools. Such tools would benefit from models of weather impacts on the airspace operations. This presentation discusses use of machine learning methods to mine various types of weather and traffic data to develop such models.

  4. Modelling Environment Changes for Pricing Weather Derivatives

    Directory of Open Access Journals (Sweden)

    Kabaivanov Stanimir

    2017-12-01

    Full Text Available This paper focuses on modelling environment changes in a way that allows to price weather derivatives in a flexible and efficient way. Applications and importance of climate and weather contracts extends beyond financial markets and hedging as they can be used as complementary tools for risk assessment. In addition, option-based approach toward resource management can offer very special insights on rare-events and allow to reuse derivative pricing methods to improve natural resources management. To demonstrate this general concept, we use Monte Carlo and stochastic modelling of temperatures to evaluate weather options. Research results are accompanied by R and Python code.

  5. Modeling Weather Impact on Ground Delay Programs

    Science.gov (United States)

    Wang, Yao; Kulkarni, Deepak

    2011-01-01

    Scheduled arriving aircraft demand may exceed airport arrival capacity when there is abnormal weather at an airport. In such situations, Federal Aviation Administration (FAA) institutes ground-delay programs (GDP) to delay flights before they depart from their originating airports. Efficient GDP planning depends on the accuracy of prediction of airport capacity and demand in the presence of uncertainties in weather forecast. This paper presents a study of the impact of dynamic airport surface weather on GDPs. Using the National Traffic Management Log, effect of weather conditions on the characteristics of GDP events at selected busy airports is investigated. Two machine learning methods are used to generate models that map the airport operational conditions and weather information to issued GDP parameters and results of validation tests are described.

  6. Investigation and Modeling of Cranberry Weather Stress.

    Science.gov (United States)

    Croft, Paul Joseph

    Cranberry bog weather conditions and weather-related stress were investigated for development of crop yield prediction models and models to predict daily weather conditions in the bog. Field investigations and data gathering were completed at the Rutgers University Blueberry/Cranberry Research Center experimental bogs in Chatsworth, New Jersey. Study indicated that although cranberries generally exhibit little or no stomatal response to changing atmospheric conditions, the evaluation of weather-related stress could be accomplished via use of micrometeorological data. Definition of weather -related stress was made by establishing critical thresholds of the frequencies of occurrence, and magnitudes of, temperature and precipitation in the bog based on values determined by a review of the literature and a grower questionnaire. Stress frequencies were correlated with cranberry yield to develop predictive models based on the previous season's yield, prior season data, prior and current season data, current season data; and prior and current season data through July 31 of the current season. The predictive ability of the prior season models was best and could be used in crop planning and production. Further examination of bog micrometeorological data permitted the isolation of those weather conditions conducive to cranberry scald and allowed for the institution of a pilot scald advisory program during the 1991 season. The micrometeorological data from the bog was also used to develop models to predict daily canopy temperature and precipitation, based on upper air data, for grower use. Models were developed for each month for maximum and minimum temperatures and for precipitation and generally performed well. The modeling of bog weather conditions is an important first step toward daily prediction of cranberry weather-related stress.

  7. WRF-Fire: coupled weather-wildland fire modeling with the weather research and forecasting model

    Science.gov (United States)

    Janice L. Coen; Marques Cameron; John Michalakes; Edward G. Patton; Philip J. Riggan; Kara M. Yedinak

    2012-01-01

    A wildland fire behavior module (WRF-Fire) was integrated into the Weather Research and Forecasting (WRF) public domain numerical weather prediction model. The fire module is a surface fire behavior model that is two-way coupled with the atmospheric model. Near-surface winds from the atmospheric model are interpolated to a finer fire grid and used, with fuel properties...

  8. Temperature stochastic modeling and weather derivatives pricing ...

    African Journals Online (AJOL)

    ... over a sufficient period to apply a stochastic process that describes the evolution of the temperature. A numerical example of a swap contract pricing is presented, using an approximation formula as well as Monte Carlo simulations. Keywords: Weather derivatives, temperature stochastic model, Monte Carlo simulation.

  9. Adaptive numerical algorithms in space weather modeling

    Science.gov (United States)

    Tóth, Gábor; van der Holst, Bart; Sokolov, Igor V.; De Zeeuw, Darren L.; Gombosi, Tamas I.; Fang, Fang; Manchester, Ward B.; Meng, Xing; Najib, Dalal; Powell, Kenneth G.; Stout, Quentin F.; Glocer, Alex; Ma, Ying-Juan; Opher, Merav

    2012-02-01

    Space weather describes the various processes in the Sun-Earth system that present danger to human health and technology. The goal of space weather forecasting is to provide an opportunity to mitigate these negative effects. Physics-based space weather modeling is characterized by disparate temporal and spatial scales as well as by different relevant physics in different domains. A multi-physics system can be modeled by a software framework comprising several components. Each component corresponds to a physics domain, and each component is represented by one or more numerical models. The publicly available Space Weather Modeling Framework (SWMF) can execute and couple together several components distributed over a parallel machine in a flexible and efficient manner. The framework also allows resolving disparate spatial and temporal scales with independent spatial and temporal discretizations in the various models. Several of the computationally most expensive domains of the framework are modeled by the Block-Adaptive Tree Solarwind Roe-type Upwind Scheme (BATS-R-US) code that can solve various forms of the magnetohydrodynamic (MHD) equations, including Hall, semi-relativistic, multi-species and multi-fluid MHD, anisotropic pressure, radiative transport and heat conduction. Modeling disparate scales within BATS-R-US is achieved by a block-adaptive mesh both in Cartesian and generalized coordinates. Most recently we have created a new core for BATS-R-US: the Block-Adaptive Tree Library (BATL) that provides a general toolkit for creating, load balancing and message passing in a 1, 2 or 3 dimensional block-adaptive grid. We describe the algorithms of BATL and demonstrate its efficiency and scaling properties for various problems. BATS-R-US uses several time-integration schemes to address multiple time-scales: explicit time stepping with fixed or local time steps, partially steady-state evolution, point-implicit, semi-implicit, explicit/implicit, and fully implicit

  10. Adaptive numerical algorithms in space weather modeling

    International Nuclear Information System (INIS)

    Tóth, Gábor; Holst, Bart van der; Sokolov, Igor V.; De Zeeuw, Darren L.; Gombosi, Tamas I.; Fang, Fang; Manchester, Ward B.; Meng Xing; Najib, Dalal; Powell, Kenneth G.; Stout, Quentin F.; Glocer, Alex; Ma, Ying-Juan; Opher, Merav

    2012-01-01

    Space weather describes the various processes in the Sun–Earth system that present danger to human health and technology. The goal of space weather forecasting is to provide an opportunity to mitigate these negative effects. Physics-based space weather modeling is characterized by disparate temporal and spatial scales as well as by different relevant physics in different domains. A multi-physics system can be modeled by a software framework comprising several components. Each component corresponds to a physics domain, and each component is represented by one or more numerical models. The publicly available Space Weather Modeling Framework (SWMF) can execute and couple together several components distributed over a parallel machine in a flexible and efficient manner. The framework also allows resolving disparate spatial and temporal scales with independent spatial and temporal discretizations in the various models. Several of the computationally most expensive domains of the framework are modeled by the Block-Adaptive Tree Solarwind Roe-type Upwind Scheme (BATS-R-US) code that can solve various forms of the magnetohydrodynamic (MHD) equations, including Hall, semi-relativistic, multi-species and multi-fluid MHD, anisotropic pressure, radiative transport and heat conduction. Modeling disparate scales within BATS-R-US is achieved by a block-adaptive mesh both in Cartesian and generalized coordinates. Most recently we have created a new core for BATS-R-US: the Block-Adaptive Tree Library (BATL) that provides a general toolkit for creating, load balancing and message passing in a 1, 2 or 3 dimensional block-adaptive grid. We describe the algorithms of BATL and demonstrate its efficiency and scaling properties for various problems. BATS-R-US uses several time-integration schemes to address multiple time-scales: explicit time stepping with fixed or local time steps, partially steady-state evolution, point-implicit, semi-implicit, explicit/implicit, and fully implicit

  11. Adaptive Numerical Algorithms in Space Weather Modeling

    Science.gov (United States)

    Toth, Gabor; vanderHolst, Bart; Sokolov, Igor V.; DeZeeuw, Darren; Gombosi, Tamas I.; Fang, Fang; Manchester, Ward B.; Meng, Xing; Nakib, Dalal; Powell, Kenneth G.; hide

    2010-01-01

    Space weather describes the various processes in the Sun-Earth system that present danger to human health and technology. The goal of space weather forecasting is to provide an opportunity to mitigate these negative effects. Physics-based space weather modeling is characterized by disparate temporal and spatial scales as well as by different physics in different domains. A multi-physics system can be modeled by a software framework comprising of several components. Each component corresponds to a physics domain, and each component is represented by one or more numerical models. The publicly available Space Weather Modeling Framework (SWMF) can execute and couple together several components distributed over a parallel machine in a flexible and efficient manner. The framework also allows resolving disparate spatial and temporal scales with independent spatial and temporal discretizations in the various models. Several of the computationally most expensive domains of the framework are modeled by the Block-Adaptive Tree Solar wind Roe Upwind Scheme (BATS-R-US) code that can solve various forms of the magnetohydrodynamics (MHD) equations, including Hall, semi-relativistic, multi-species and multi-fluid MHD, anisotropic pressure, radiative transport and heat conduction. Modeling disparate scales within BATS-R-US is achieved by a block-adaptive mesh both in Cartesian and generalized coordinates. Most recently we have created a new core for BATS-R-US: the Block-Adaptive Tree Library (BATL) that provides a general toolkit for creating, load balancing and message passing in a 1, 2 or 3 dimensional block-adaptive grid. We describe the algorithms of BATL and demonstrate its efficiency and scaling properties for various problems. BATS-R-US uses several time-integration schemes to address multiple time-scales: explicit time stepping with fixed or local time steps, partially steady-state evolution, point-implicit, semi-implicit, explicit/implicit, and fully implicit numerical

  12. Weather forecasting based on hybrid neural model

    Science.gov (United States)

    Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.

    2017-11-01

    Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.

  13. Weather Research and Forecasting (WRF) Regional Atmospheric Model: Oahu

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 3.5-day hourly forecast for the region surrounding the Hawaiian island of Oahu at...

  14. Weather Research and Forecasting (WRF) Regional Atmospheric Model: CNMI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the Commonwealth of the Northern...

  15. Weather Research and Forecasting (WRF) Regional Atmospheric Model: Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the island of Guam at...

  16. Weather Research and Forecasting (WRF) Regional Atmospheric Model: Samoa

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the islands of Samoa at...

  17. Sensitivity of weather besed irrigation scheduling model

    International Nuclear Information System (INIS)

    Laghari, K.Q.; Lashari, B.K.; Laghari, N.U.Z.

    2009-01-01

    This study describes the sensitivity of irrigation scheduling model (Mehran) carried out by changing input weather parameters (Temperatures, Wind velocity, Rainfall, and Sunshine hours) to see model sensitivity in computation/estimations (output) for Transpiration (T), Evaporation (E), and allocation of irrigation (I) water. Sensitivity analysis depends on the site and environmental conditions and is therefore an essential step in model validation and application. Mehran Model is weather based crop growth simulation model, which uses daily input data of max and min temperatures (temp), dew point temp (humidity), wind speed, daily sunshine hours (radiation) and computes T/sub c/E/sub s/, and allocates Irrigation accordingly. The input and output base values are taken as an average of three years actual field data used during the Mehran Model testing and calibration on wheat and cotton crops. The model sensitivity of specific input parameter was obtained by varying its value and keeping other input parameters at their base values. The input base values varied by+-10 and +-25%. The model was run for each modified input parameter, and output was compared statistically with base outputs. The ME% (Mean Percent Error) was used to obtain variations in output values. The results reveal that the model is most sensitive with variations in temperature. The 10 and 25% increase in temperature resulted increase in Cotton crop's Tc by 12.18 and 28.54%, corresponding Es by 22.32 and 37.88% and irrigation water allocation by 18.41 and 47.83 % respectively increased from average base values. (author)

  18. Hydrological modeling using a multi-site stochastic weather generator

    Science.gov (United States)

    Weather data is usually required at several locations over a large watershed, especially when using distributed models for hydrological simulations. In many applications, spatially correlated weather data can be provided by a multi-site stochastic weather generator which considers the spatial correl...

  19. A new prediction model of daily weather elements in Hainan province under the typhoon weather

    Science.gov (United States)

    Zhou, Ruixu; Gao, Wensheng; Zhang, Bowen; Chen, Qinzhu; Liang, Yafeng; Yao, Dong; Han, Laijun; Liao, Xinzheng; Li, Ruihai

    2017-11-01

    This paper proposes a new prediction model for severe natural disasters, especially typhoon using daily weather analysis. Hainan province in China is selected to be a typical application region, where natural disasters, especially typhoons take place frequently. These disasters have great impacts on the life and property safety of the residents, and therefore are in specific need of accurate prediction. A new prediction model of daily weather in Hainan province under the typhoon weather is proposed in this paper based on the best track datasets of typhoons and the corresponding daily weather data. This model utilizes the statistical methods and data mining technology in combination with the dynamic migration information of tropical cyclones and can provide the dynamic prediction of daily weather elements in any designated location. Three surface meteorological observation stations of Hainan province during the years 1951-1920 are used to test the model. Test results show that the prediction equations established for the vast majority of daily weather elements have passed the significant test. Besides, Typhoon Damrey is used as a case to illustrate the whole daily weather prediction model in detail and comparisons between the model and other official forecast (such as JTWC, UKMO and CMA) are performed thoroughly. It is worth noting that the model proposed in this paper is not limited to Hainan province and can be generalized to other areas in the world.

  20. Detecting Weather Radar Clutter by Information Fusion With Satellite Images and Numerical Weather Prediction Model Output

    DEFF Research Database (Denmark)

    Bøvith, Thomas; Nielsen, Allan Aasbjerg; Hansen, Lars Kai

    2006-01-01

    A method for detecting clutter in weather radar images by information fusion is presented. Radar data, satellite images, and output from a numerical weather prediction model are combined and the radar echoes are classified using supervised classification. The presented method uses indirect...... information on precipitation in the atmosphere from Meteosat-8 multispectral images and near-surface temperature estimates from the DMI-HIRLAM-S05 numerical weather prediction model. Alternatively, an operational nowcasting product called 'Precipitating Clouds' based on Meteosat-8 input is used. A scale...

  1. Modeling rock weathering in small watersheds

    NARCIS (Netherlands)

    Pacheco, F.A.L.; van der Weijden, C.H.

    2014-01-01

    Many mountainous watersheds are conceived as aquifer media where multiple groundwater flow systems have developed (Tóth, 1963), and as bimodal landscapes where differential weathering of bare and soil-mantled rock has occurred (Wahrhaftig, 1965). The results of a weathering algorithm (Pacheco and

  2. Space weather: Modeling and forecasting ionospheric

    International Nuclear Information System (INIS)

    Calzadilla Mendez, A.

    2008-01-01

    Full text: Space weather is the set of phenomena and interactions that take place in the interplanetary medium. It is regulated primarily by the activity originating in the Sun and affects both the artificial satellites that are outside of the protective cover of the Earth's atmosphere as the rest of the planets in the solar system. Among the phenomena that are of great relevance and impact on Earth are the auroras and geomagnetic storms , these are a direct result of irregularities in the flow of the solar wind and the interplanetary magnetic field . Given the high complexity of the physical phenomena involved (magnetic reconnection , particle inlet and ionizing radiation to the atmosphere) one of the great scientific challenges today is to forecast the state of plasmatic means either the interplanetary medium , the magnetosphere and ionosphere , for their importance to the development of various human activities such as radio , global positioning , navigation, etc. . It briefly address some of the international ionospheric modeling methods and contributions and participation that currently has the space group of the Institute of Geophysics Geophysics and Astronomy (IGA) in these activities of modeling and forecasting ionospheric. (author)

  3. Simple model for crop photosynthesis in terms of weather variables ...

    African Journals Online (AJOL)

    A theoretical mathematical model for describing crop photosynthetic rate in terms of the weather variables and crop characteristics is proposed. The model utilizes a series of efficiency parameters, each of which reflect the fraction of possible photosynthetic rate permitted by the different weather elements or crop architecture.

  4. Quality assurance of weather data for agricultural system model input

    Science.gov (United States)

    It is well known that crop production and hydrologic variation on watersheds is weather related. Rarely, however, is meteorological data quality checks reported for agricultural systems model research. We present quality assurance procedures for agricultural system model weather data input. Problems...

  5. Mountain range specific analog weather forecast model for ...

    Indian Academy of Sciences (India)

    Home; Journals; Journal of Earth System Science; Volume 117; Issue 5. Mountain range specific ... Mountain range specific analog weather forecast model is developed utilizing surface weather observations of reference stations in each mountain range in northwest Himalaya (NW-Himalaya).The model searches past ...

  6. Supercomputing for weather and climate modelling: convenience or necessity

    CSIR Research Space (South Africa)

    Landman, WA

    2009-12-01

    Full Text Available Weather and climate modelling require dedicated computer infrastructure in order to generate high-resolution, large ensemble, various models with different configurations, etc. in order to optimise operational forecasts and climate projections. High...

  7. A simple stochastic weather generator for ecological modeling

    Science.gov (United States)

    A.G. Birt; M.R. Valdez-Vivas; R.M. Feldman; C.W. Lafon; D. Cairns; R.N. Coulson; M. Tchakerian; W. Xi; Jim Guldin

    2010-01-01

    Stochastic weather generators are useful tools for exploring the relationship between organisms and their environment. This paper describes a simple weather generator that can be used in ecological modeling projects. We provide a detailed description of methodology, and links to full C++ source code (http://weathergen.sourceforge.net) required to implement or modify...

  8. A Data Model for Determining Weather's Impact on Travel Time

    DEFF Research Database (Denmark)

    Andersen, Ove; Torp, Kristian

    2016-01-01

    Accurate estimating travel times in road networks is a complex task because travel times depends on factors such as the weather. In this paper, we present a generic model for integrating weather data with GPS data to improve the accuracy of the estimated travel times. First, we present a data mod...

  9. Economics of extreme weather events: Terminology and regional impact models

    Directory of Open Access Journals (Sweden)

    Malte Jahn

    2015-12-01

    Full Text Available Impacts of extreme weather events are relevant for regional (in the sense of subnational economies and in particular cities in many aspects. Cities are the cores of economic activity and the amount of people and assets endangered by extreme weather events is large, even under the current climate. A changing climate with changing extreme weather patterns and the process of urbanization will make the whole issue even more relevant in the future. In this paper, definitions and terminology in the field of extreme weather events are discussed. Possible regional impacts of extreme weather events are collected, focusing on European cities. The human contributions to those impacts are emphasized. Furthermore, methodological aspects of economic impact assessment are discussed along a temporal and a sectoral dimension. Finally, common economic impact models are compared, analyzing their strengths and weaknesses.

  10. Implementation of bayesian model averaging on the weather data forecasting applications utilizing open weather map

    Science.gov (United States)

    Rahmat, R. F.; Nasution, F. R.; Seniman; Syahputra, M. F.; Sitompul, O. S.

    2018-02-01

    Weather is condition of air in a certain region at a relatively short period of time, measured with various parameters such as; temperature, air preasure, wind velocity, humidity and another phenomenons in the atmosphere. In fact, extreme weather due to global warming would lead to drought, flood, hurricane and other forms of weather occasion, which directly affects social andeconomic activities. Hence, a forecasting technique is to predict weather with distinctive output, particullary mapping process based on GIS with information about current weather status in certain cordinates of each region with capability to forecast for seven days afterward. Data used in this research are retrieved in real time from the server openweathermap and BMKG. In order to obtain a low error rate and high accuracy of forecasting, the authors use Bayesian Model Averaging (BMA) method. The result shows that the BMA method has good accuracy. Forecasting error value is calculated by mean square error shows (MSE). The error value emerges at minumum temperature rated at 0.28 and maximum temperature rated at 0.15. Meanwhile, the error value of minimum humidity rates at 0.38 and the error value of maximum humidity rates at 0.04. Afterall, the forecasting error rate of wind speed is at 0.076. The lower the forecasting error rate, the more optimized the accuracy is.

  11. On the Influence of Weather Forecast Errors in Short-Term Load Forecasting Models

    OpenAIRE

    Fay, D.; Ringwood, John; Condon, M.

    2004-01-01

    Weather information is an important factor in load forecasting models. This weather information usually takes the form of actual weather readings. However, online operation of load forecasting models requires the use of weather forecasts, with associated weather forecast errors. A technique is proposed to model weather forecast errors to reflect current accuracy. A load forecasting model is then proposed which combines the forecasts of several load forecasting models. This approach allows the...

  12. Integrated modelling of physical, chemical and biological weather

    DEFF Research Database (Denmark)

    Kurganskiy, Alexander

    Integrated modelling of physical, chemical and biological weather has been widely considered during the recent decades. Such modelling includes interactions of atmospheric physics and chemical/biological aerosol concentrations. Emitted aerosols are subject to atmospheric transport, dispersion...... and deposition, but in turn they impact the radiation as well as cloud and precipitation formation. The present study focuses on birch pollen modelling as well as on physical and chemical weather with emphasis on black carbon (BC) aerosol modelling. The Enviro-HIRLAM model has been used for the study...

  13. Validation of crop weather models for crop assessment arid yield ...

    African Journals Online (AJOL)

    IRSIS and CRPSM models were used in this study to see how closely they could predict grain yields for selected stations in Tanzania. Input for the models comprised of weather, crop and soil data collected from five selected stations. Simulation results show that IRSIS model tends to over predict grain yields of maize, ...

  14. Evaluating Climate Models: Should We Use Weather or Climate Observations?

    Science.gov (United States)

    Oglesby, R. J.; Rowe, C. M.; Maasch, K. A.; Erickson, D. J.; Hays, C.

    2009-12-01

    Calling the numerical models that we use for simulations of climate change 'climate models' is a bit of a misnomer. These 'general circulation models' (GCMs, AKA global climate models) and their cousins the 'regional climate models' (RCMs) are actually physically-based weather simulators. That is, these models simulate, either globally or locally, daily weather patterns in response to some change in forcing or boundary condition. These simulated weather patterns are then aggregated into climate statistics, very much as we aggregate observations into 'real climate statistics'. Traditionally, the output of GCMs has been evaluated using climate statistics, as opposed to their ability to simulate realistic daily weather observations. At the coarse global scale this may be a reasonable approach, however, as RCM's downscale to increasingly higher resolutions, the conjunction between weather and climate becomes more problematic. We present results from a series of present-day climate simulations using the WRF ARW for domains that cover North America, much of Latin America, and South Asia. The basic domains are at a 12 km resolution, but several inner domains at 4 km have also been simulated. These include regions of complex topography in Mexico, Colombia, Peru, and Sri Lanka, as well as a region of low topography and fairly homogeneous land surface type (the U.S. Great Plains). Model evaluations are performed using standard climate analyses (e.g., reanalyses; NCDC data) but also using time series of daily station observations. Preliminary results suggest little difference in the assessment of long-term mean quantities, but the variability on seasonal and interannual timescales is better described. Furthermore, the value-added by using daily weather observations as an evaluation tool increases with the model resolution.

  15. Assessing normative cut points through differential item functioning analysis: An example from the adaptation of the Middlesex Elderly Assessment of Mental State (MEAMS for use as a cognitive screening test in Turkey

    Directory of Open Access Journals (Sweden)

    Kutlay Sehim

    2006-03-01

    Full Text Available Abstract Background The Middlesex Elderly Assessment of Mental State (MEAMS was developed as a screening test to detect cognitive impairment in the elderly. It includes 12 subtests, each having a 'pass score'. A series of tasks were undertaken to adapt the measure for use in the adult population in Turkey and to determine the validity of existing cut points for passing subtests, given the wide range of educational level in the Turkish population. This study focuses on identifying and validating the scoring system of the MEAMS for Turkish adult population. Methods After the translation procedure, 350 normal subjects and 158 acquired brain injury patients were assessed by the Turkish version of MEAMS. Initially, appropriate pass scores for the normal population were determined through ANOVA post-hoc tests according to age, gender and education. Rasch analysis was then used to test the internal construct validity of the scale and the validity of the cut points for pass scores on the pooled data by using Differential Item Functioning (DIF analysis within the framework of the Rasch model. Results Data with the initially modified pass scores were analyzed. DIF was found for certain subtests by age and education, but not for gender. Following this, pass scores were further adjusted and data re-fitted to the model. All subtests were found to fit the Rasch model (mean item fit 0.184, SD 0.319; person fit -0.224, SD 0.557 and DIF was then found to be absent. Thus the final pass scores for all subtests were determined. Conclusion The MEAMS offers a valid assessment of cognitive state for the adult Turkish population, and the revised cut points accommodate for age and education. Further studies are required to ascertain the validity in different diagnostic groups.

  16. Managing wildland fires: integrating weather models into fire projections

    Science.gov (United States)

    Anne M. Rosenthal; Francis Fujioka

    2004-01-01

    Flames from the Old Fire sweep through lands north of San Bernardino during late fall of 2003. Like many Southern California fires, the Old Fire consumed susceptible forests at the urban-wildland interface and spread to nearby city neighborhoods. By incorporating weather models into fire perimeter projections, scientist Francis Fujioka is improving fire modeling as a...

  17. Model for expressing leaf photosynthesis in terms of weather variables

    African Journals Online (AJOL)

    A theoretical mathematical model for describing photosynthesis in individual leaves in terms of weather variables is proposed. The model utilizes a series of efficiency parameters, each of which reflect the fraction of potential photosynthetic rate permitted by the different environmental elements. These parameters are useful ...

  18. Modeling and Forecasting Average Temperature for Weather Derivative Pricing

    Directory of Open Access Journals (Sweden)

    Zhiliang Wang

    2015-01-01

    Full Text Available The main purpose of this paper is to present a feasible model for the daily average temperature on the area of Zhengzhou and apply it to weather derivatives pricing. We start by exploring the background of weather derivatives market and then use the 62 years of daily historical data to apply the mean-reverting Ornstein-Uhlenbeck process to describe the evolution of the temperature. Finally, Monte Carlo simulations are used to price heating degree day (HDD call option for this city, and the slow convergence of the price of the HDD call can be found through taking 100,000 simulations. The methods of the research will provide a frame work for modeling temperature and pricing weather derivatives in other similar places in China.

  19. Aerosol Radiative Forcing and Weather Forecasts in the ECMWF Model

    Science.gov (United States)

    Bozzo, A.; Benedetti, A.; Rodwell, M. J.; Bechtold, P.; Remy, S.

    2015-12-01

    Aerosols play an important role in the energy balance of the Earth system via direct scattering and absorpiton of short-wave and long-wave radiation and indirect interaction with clouds. Diabatic heating or cooling by aerosols can also modify the vertical stability of the atmosphere and influence weather pattern with potential impact on the skill of global weather prediction models. The Copernicus Atmosphere Monitoring Service (CAMS) provides operational daily analysis and forecast of aerosol optical depth (AOD) for five aerosol species using a prognostic model which is part of the Integrated Forecasting System of the European Centre for Medium-Range Weather Forecasts (ECMWF-IFS). The aerosol component was developed during the research project Monitoring Atmospheric Composition and Climate (MACC). Aerosols can have a large impact on the weather forecasts in case of large aerosol concentrations as found during dust storms or strong pollution events. However, due to its computational burden, prognostic aerosols are not yet feasible in the ECMWF operational weather forecasts, and monthly-mean climatological fields are used instead. We revised the aerosol climatology used in the operational ECMWF IFS with one derived from the MACC reanalysis. We analyse the impact of changes in the aerosol radiative effect on the mean model climate and in medium-range weather forecasts, also in comparison with prognostic aerosol fields. The new climatology differs from the previous one by Tegen et al 1997, both in the spatial distribution of the total AOD and the optical properties of each aerosol species. The radiative impact of these changes affects the model mean bias at various spatial and temporal scales. On one hand we report small impacts on measures of large-scale forecast skill but on the other hand details of the regional distribution of aerosol concentration have a large local impact. This is the case for the northern Indian Ocean where the radiative impact of the mineral

  20. Integrated modelling of physical, chemical and biological weather

    DEFF Research Database (Denmark)

    Kurganskiy, Alexander

    forecasts. The BC modelling study was performed for a modelling domain covering most of the Northern Hemisphere with focus on the EU and Arctic regions. Verification of BC concentrations against observations showed a good agreement for the EU air quality measurement sites. However, the Arctic region turned......Integrated modelling of physical, chemical and biological weather has been widely considered during the recent decades. Such modelling includes interactions of atmospheric physics and chemical/biological aerosol concentrations. Emitted aerosols are subject to atmospheric transport, dispersion...... and deposition, but in turn they impact the radiation as well as cloud and precipitation formation. The present study focuses on birch pollen modelling as well as on physical and chemical weather with emphasis on black carbon (BC) aerosol modelling. The Enviro-HIRLAM model has been used for the study...

  1. Earth Radii Used in Numerical Weather Models

    Science.gov (United States)

    2005-09-26

    In the development of numerical atmospheric models , many simplifying assumptions are made. One of the simplifying assumptions is that the Earth can...geometric properties within or among spatial reference frames. This paper serves to document the values used for the Earth’s radius by several operational numerical atmospheric models for use in the SRM.

  2. Weather Derivatives and Stochastic Modelling of Temperature

    Directory of Open Access Journals (Sweden)

    Fred Espen Benth

    2011-01-01

    Full Text Available We propose a continuous-time autoregressive model for the temperature dynamics with volatility being the product of a seasonal function and a stochastic process. We use the Barndorff-Nielsen and Shephard model for the stochastic volatility. The proposed temperature dynamics is flexible enough to model temperature data accurately, and at the same time being analytically tractable. Futures prices for commonly traded contracts at the Chicago Mercantile Exchange on indices like cooling- and heating-degree days and cumulative average temperatures are computed, as well as option prices on them.

  3. Ambient Weather Model Research and Development: Final Report.

    Energy Technology Data Exchange (ETDEWEB)

    Walker, Stel Nathan; Wade, John Edward

    1990-08-31

    Ratings for Bonneville Power Administration (BPA) transmission lines are based upon the IEEE Standard for Calculation of Bare Overhead Conductor Temperatures and Ampacity under Steady-State Conditions (1985). This steady-state model is very sensitive to the ambient weather conditions of temperature and wind speed. The model does not account for wind yaw, turbulence, or conductor roughness as proposed by Davis (1976) for a real time rating system. The objective of this research has been to determine (1) how conservative the present rating system is for typical ambient weather conditions, (2) develop a probability-based methodology, (3) compile available weather data into a compatible format, and (4) apply the rating methodology to a hypothetical line. The potential benefit from this research is to rate transmission lines statistically which will allow BPA to take advantage of any unknown thermal capacity. The present deterministic weather model is conservative overall and studies suggest a refined model will uncover additional unknown capacity. 14 refs., 40 figs., 7 tabs.

  4. Impact of bacterial ice nucleating particles on weather predicted by a numerical weather prediction model

    Science.gov (United States)

    Sahyoun, Maher; Korsholm, Ulrik S.; Sørensen, Jens H.; Šantl-Temkiv, Tina; Finster, Kai; Gosewinkel, Ulrich; Nielsen, Niels W.

    2017-12-01

    Bacterial ice-nucleating particles (INP) have the ability to facilitate ice nucleation from super-cooled cloud droplets at temperatures just below the melting point. Bacterial INP have been detected in cloud water, precipitation, and dry air, hence they may have an impact on weather and climate. In modeling studies, the potential impact of bacteria on ice nucleation and precipitation formation on global scale is still uncertain due to their small concentration compared to other types of INP, i.e. dust. Those earlier studies did not account for the yet undetected high concentration of nanoscale fragments of bacterial INP, which may be found free or attached to soil dust in the atmosphere. In this study, we investigate the sensitivity of modeled cloud ice, precipitation and global solar radiation in different weather scenarios to changes in the fraction of cloud droplets containing bacterial INP, regardless of their size. For this purpose, a module that calculates the probability of ice nucleation as a function of ice nucleation rate and bacterial INP fraction was developed and implemented in a numerical weather prediction model. The threshold value for the fraction of cloud droplets containing bacterial INP needed to produce a 1% increase in cloud ice was determined at 10-5 to 10-4. We also found that increasing this fraction causes a perturbation in the forecast, leading to significant differences in cloud ice and smaller differences in convective and total precipitation and in net solar radiation reaching the surface. These effects were most pronounced in local convective events. Our results show that bacterial INP can be considered as a trigger factor for precipitation, but not an enhancement factor.

  5. Geodetic Space Weather Monitoring by means of Ionosphere Modelling

    Science.gov (United States)

    Schmidt, Michael

    2017-04-01

    The term space weather indicates physical processes and phenomena in space caused by radiation of energy mainly from the Sun. Manifestations of space weather are (1) variations of the Earth's magnetic field, (2) the polar lights in the northern and southern hemisphere, (3) variations within the ionosphere as part of the upper atmosphere characterized by the existence of free electrons and ions, (4) the solar wind, i.e. the permanent emission of electrons and photons, (5) the interplanetary magnetic field, and (6) electric currents, e.g. the van Allen radiation belt. It can be stated that ionosphere disturbances are often caused by so-called solar storms. A solar storm comprises solar events such as solar flares and coronal mass ejections (CMEs) which have different effects on the Earth. Solar flares may cause disturbances in positioning, navigation and communication. CMEs can effect severe disturbances and in extreme cases damages or even destructions of modern infrastructure. Examples are interruptions to satellite services including the global navigation satellite systems (GNSS), communication systems, Earth observation and imaging systems or a potential failure of power networks. Currently the measurements of solar satellite missions such as STEREO and SOHO are used to forecast solar events. Besides these measurements the Earth's ionosphere plays another key role in monitoring the space weather, because it responses to solar storms with an increase of the electron density. Space-geodetic observation techniques, such as terrestrial GNSS, satellite altimetry, space-borne GPS (radio occultation), DORIS and VLBI provide valuable global information about the state of the ionosphere. Additionally geodesy has a long history and large experience in developing and using sophisticated analysis and combination techniques as well as empirical and physical modelling approaches. Consequently, geodesy is predestinated for strongly supporting space weather monitoring via

  6. Modeling the influence of organic acids on soil weathering

    Science.gov (United States)

    Lawrence, Corey R.; Harden, Jennifer W.; Maher, Kate

    2014-01-01

    Biological inputs and organic matter cycling have long been regarded as important factors in the physical and chemical development of soils. In particular, the extent to which low molecular weight organic acids, such as oxalate, influence geochemical reactions has been widely studied. Although the effects of organic acids are diverse, there is strong evidence that organic acids accelerate the dissolution of some minerals. However, the influence of organic acids at the field-scale and over the timescales of soil development has not been evaluated in detail. In this study, a reactive-transport model of soil chemical weathering and pedogenic development was used to quantify the extent to which organic acid cycling controls mineral dissolution rates and long-term patterns of chemical weathering. Specifically, oxalic acid was added to simulations of soil development to investigate a well-studied chronosequence of soils near Santa Cruz, CA. The model formulation includes organic acid input, transport, decomposition, organic-metal aqueous complexation and mineral surface complexation in various combinations. Results suggest that although organic acid reactions accelerate mineral dissolution rates near the soil surface, the net response is an overall decrease in chemical weathering. Model results demonstrate the importance of organic acid input concentrations, fluid flow, decomposition and secondary mineral precipitation rates on the evolution of mineral weathering fronts. In particular, model soil profile evolution is sensitive to kaolinite precipitation and oxalate decomposition rates. The soil profile-scale modeling presented here provides insights into the influence of organic carbon cycling on soil weathering and pedogenesis and supports the need for further field-scale measurements of the flux and speciation of reactive organic compounds.

  7. A Reactive Transport Model for Marcellus Shale Weathering

    Science.gov (United States)

    Li, L.; Heidari, P.; Jin, L.; Williams, J.; Brantley, S.

    2017-12-01

    Shale formations account for 25% of the land surface globally. One of the most productive shale-gas formations is the Marcellus, a black shale that is rich in organic matter and pyrite. As a first step toward understanding how Marcellus shale interacts with water, we developed a reactive transport model to simulate shale weathering under ambient temperature and pressure conditions, constrained by soil chemistry and water data. The simulation was carried out for 10,000 years, assuming bedrock weathering and soil genesis began right after the last glacial maximum. Results indicate weathering was initiated by pyrite dissolution for the first 1,000 years, leading to low pH and enhanced dissolution of chlorite and precipitation of iron hydroxides. After pyrite depletion, chlorite dissolved slowly, primarily facilitated by the presence of CO2 and organic acids, forming vermiculite as a secondary mineral. A sensitivity analysis indicated that the most important controls on weathering include the presence of reactive gases (CO2 and O2), specific surface area, and flow velocity of infiltrating meteoric water. The soil chemistry and mineralogy data could not be reproduced without including the reactive gases. For example, pyrite remained in the soil even after 10,000 years if O2 was not continuously present in the soil column; likewise, chlorite remained abundant and porosity remained small with the presence of soil CO2. The field observations were only simulated successfully when the specific surface areas of the reactive minerals were 1-3 orders of magnitude smaller than surface area values measured for powdered minerals, reflecting the lack of accessibility of fluids to mineral surfaces and potential surface coating. An increase in the water infiltration rate enhanced weathering by removing dissolution products and maintaining far-from-equilibrium conditions. We conclude that availability of reactive surface area and transport of H2O and gases are the most important

  8. Atlas : A library for numerical weather prediction and climate modelling

    Science.gov (United States)

    Deconinck, Willem; Bauer, Peter; Diamantakis, Michail; Hamrud, Mats; Kühnlein, Christian; Maciel, Pedro; Mengaldo, Gianmarco; Quintino, Tiago; Raoult, Baudouin; Smolarkiewicz, Piotr K.; Wedi, Nils P.

    2017-11-01

    The algorithms underlying numerical weather prediction (NWP) and climate models that have been developed in the past few decades face an increasing challenge caused by the paradigm shift imposed by hardware vendors towards more energy-efficient devices. In order to provide a sustainable path to exascale High Performance Computing (HPC), applications become increasingly restricted by energy consumption. As a result, the emerging diverse and complex hardware solutions have a large impact on the programming models traditionally used in NWP software, triggering a rethink of design choices for future massively parallel software frameworks. In this paper, we present Atlas, a new software library that is currently being developed at the European Centre for Medium-Range Weather Forecasts (ECMWF), with the scope of handling data structures required for NWP applications in a flexible and massively parallel way. Atlas provides a versatile framework for the future development of efficient NWP and climate applications on emerging HPC architectures. The applications range from full Earth system models, to specific tools required for post-processing weather forecast products. The Atlas library thus constitutes a step towards affordable exascale high-performance simulations by providing the necessary abstractions that facilitate the application in heterogeneous HPC environments by promoting the co-design of NWP algorithms with the underlying hardware.

  9. A Product Development Decision Model for Cockpit Weather Information System

    Science.gov (United States)

    Sireli, Yesim; Kauffmann, Paul; Gupta, Surabhi; Kachroo, Pushkin; Johnson, Edward J., Jr. (Technical Monitor)

    2003-01-01

    There is a significant market demand for advanced cockpit weather information products. However, it is unclear how to identify the most promising technological options that provide the desired mix of consumer requirements by employing feasible technical systems at a price that achieves market success. This study develops a unique product development decision model that employs Quality Function Deployment (QFD) and Kano's model of consumer choice. This model is specifically designed for exploration and resolution of this and similar information technology related product development problems.

  10. A Product Development Decision Model for Cockpit Weather Information Systems

    Science.gov (United States)

    Sireli, Yesim; Kauffmann, Paul; Gupta, Surabhi; Kachroo, Pushkin

    2003-01-01

    There is a significant market demand for advanced cockpit weather information products. However, it is unclear how to identify the most promising technological options that provide the desired mix of consumer requirements by employing feasible technical systems at a price that achieves market success. This study develops a unique product development decision model that employs Quality Function Deployment (QFD) and Kano's model of consumer choice. This model is specifically designed for exploration and resolution of this and similar information technology related product development problems.

  11. Modeling Apple Surface Temperature Dynamics Based on Weather Data

    Directory of Open Access Journals (Sweden)

    Lei Li

    2014-10-01

    Full Text Available The exposure of fruit surfaces to direct sunlight during the summer months can result in sunburn damage. Losses due to sunburn damage are a major economic problem when marketing fresh apples. The objective of this study was to develop and validate a model for simulating fruit surface temperature (FST dynamics based on energy balance and measured weather data. A series of weather data (air temperature, humidity, solar radiation, and wind speed was recorded for seven hours between 11:00–18:00 for two months at fifteen minute intervals. To validate the model, the FSTs of “Fuji” apples were monitored using an infrared camera in a natural orchard environment. The FST dynamics were measured using a series of thermal images. For the apples that were completely exposed to the sun, the RMSE of the model for estimating FST was less than 2.0 °C. A sensitivity analysis of the emissivity of the apple surface and the conductance of the fruit surface to water vapour showed that accurate estimations of the apple surface emissivity were important for the model. The validation results showed that the model was capable of accurately describing the thermal performances of apples under different solar radiation intensities. Thus, this model could be used to more accurately estimate the FST relative to estimates that only consider the air temperature. In addition, this model provides useful information for sunburn protection management.

  12. Linking the M&Rfi Weather Generator with Agrometeorological Models

    Science.gov (United States)

    Dubrovsky, Martin; Trnka, Miroslav

    2015-04-01

    Realistic meteorological inputs (representing the present and/or future climates) for the agrometeorological model simulations are often produced by stochastic weather generators (WGs). This contribution presents some methodological issues and results obtained in our recent experiments. We also address selected questions raised in the synopsis of this session. The input meteorological time series for our experiments are produced by the parametric single site weather generator (WG) Marfi, which is calibrated from the available observational data (or interpolated from surrounding stations). To produce meteorological series representing the future climate, the WG parameters are modified by climate change scenarios, which are prepared by the pattern scaling method: the standardised scenarios derived from Global or Regional Climate Models are multiplied by the change in global mean temperature (ΔTG) determined by the simple climate model MAGICC. The presentation will address following questions: (i) The dependence of the quality of the synthetic weather series and impact results on the WG settings. An emphasis will be put on an effect of conditioning the daily WG on monthly WG (presently being one of our hot topics), which aims at improvement of the reproduction of the low-frequency weather variability. Comparison of results obtained with various WG settings is made in terms of climatic and agroclimatic indices (including extreme temperature and precipitation characteristics and drought indices). (ii) Our methodology accounts for the uncertainties coming from various sources. We will show how the climate change impact results are affected by 1. uncertainty in climate modelling, 2. uncertainty in ΔTG, and 3. uncertainty related to the complexity of the climate change scenario (focusing on an effect of inclusion of changes in variability into the climate change scenarios). Acknowledgements: This study was funded by project "Building up a multidisciplinary scientific

  13. Time series regression model for infectious disease and weather.

    Science.gov (United States)

    Imai, Chisato; Armstrong, Ben; Chalabi, Zaid; Mangtani, Punam; Hashizume, Masahiro

    2015-10-01

    Time series regression has been developed and long used to evaluate the short-term associations of air pollution and weather with mortality or morbidity of non-infectious diseases. The application of the regression approaches from this tradition to infectious diseases, however, is less well explored and raises some new issues. We discuss and present potential solutions for five issues often arising in such analyses: changes in immune population, strong autocorrelations, a wide range of plausible lag structures and association patterns, seasonality adjustments, and large overdispersion. The potential approaches are illustrated with datasets of cholera cases and rainfall from Bangladesh and influenza and temperature in Tokyo. Though this article focuses on the application of the traditional time series regression to infectious diseases and weather factors, we also briefly introduce alternative approaches, including mathematical modeling, wavelet analysis, and autoregressive integrated moving average (ARIMA) models. Modifications proposed to standard time series regression practice include using sums of past cases as proxies for the immune population, and using the logarithm of lagged disease counts to control autocorrelation due to true contagion, both of which are motivated from "susceptible-infectious-recovered" (SIR) models. The complexity of lag structures and association patterns can often be informed by biological mechanisms and explored by using distributed lag non-linear models. For overdispersed models, alternative distribution models such as quasi-Poisson and negative binomial should be considered. Time series regression can be used to investigate dependence of infectious diseases on weather, but may need modifying to allow for features specific to this context. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

  14. Using fuzzy cognitive maps in modelling and representing weather ...

    African Journals Online (AJOL)

    ... and characterization of visual sky objects (such as moon, clouds, stars, rainbow, etc) in forecasting weather is a significant subject of research. In order to realize the integration of visual weather lore knowledge in modern weather forecasting systems, there is a need to represent and scientifically substantiate weather lore.

  15. Parameterisation of sea and lake ice in numerical weather prediction models of the German Weather Service

    Directory of Open Access Journals (Sweden)

    Dmitrii Mironov

    2012-04-01

    Full Text Available A bulk thermodynamic (no rheology sea-ice parameterisation scheme for use in numerical weather prediction (NWP is presented. The scheme is based on a self-similar parametric representation (assumed shape of the evolving temperature profile within the ice and on the integral heat budget of the ice slab. The scheme carries ordinary differential equations (in time for the ice surface temperature and the ice thickness. The proposed sea-ice scheme is implemented into the NWP models GME (global and COSMO (limited-area of the German Weather Service. In the present operational configuration, the horizontal distribution of the sea ice is governed by the data assimilation scheme, no fractional ice cover within the GME/COSMO grid box is considered, and the effect of snow above the ice is accounted for through an empirical temperature dependence of the ice surface albedo with respect to solar radiation. The lake ice is treated similarly to the sea ice, except that freeze-up and break-up of lakes occurs freely, independent of the data assimilation. The sea and lake ice schemes (the latter is a part of the fresh-water lake parameterisation scheme FLake show a satisfactory performance in GME and COSMO. The ice characteristics are not overly sensitive to the details of the treatment of heat transfer through the ice layer. This justifies the use of a simplified but computationally efficient bulk approach to model the ice thermodynamics in NWP, where the ice surface temperature is a major concern whereas details of the temperature distribution within the ice are of secondary importance. In contrast to the details of the heat transfer through the ice, the cloud cover is of decisive importance for the ice temperature as it controls the radiation energy budget at the ice surface. This is particularly true for winter, when the long-wave radiation dominates the surface energy budget. During summer, the surface energy budget is also sensitive to the grid-box mean ice

  16. Modelling Wind Turbine Failures based on Weather Conditions

    Science.gov (United States)

    Reder, Maik; Melero, Julio J.

    2017-11-01

    A large proportion of the overall costs of a wind farm is directly related to operation and maintenance (O&M) tasks. By applying predictive O&M strategies rather than corrective approaches these costs can be decreased significantly. Here, especially wind turbine (WT) failure models can help to understand the components’ degradation processes and enable the operators to anticipate upcoming failures. Usually, these models are based on the age of the systems or components. However, latest research shows that the on-site weather conditions also affect the turbine failure behaviour significantly. This study presents a novel approach to model WT failures based on the environmental conditions to which they are exposed to. The results focus on general WT failures, as well as on four main components: gearbox, generator, pitch and yaw system. A penalised likelihood estimation is used in order to avoid problems due to for example highly correlated input covariates. The relative importance of the model covariates is assessed in order to analyse the effect of each weather parameter on the model output.

  17. Weather Research and Forecasting (WRF) Regional Atmospheric Model: Maui-Oahu

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the Hawaiian islands of Oahu,...

  18. Traffic analysis toolbox volume XI : weather and traffic analysis, modeling and simulation.

    Science.gov (United States)

    2010-12-01

    This document presents a weather module for the traffic analysis tools program. It provides traffic engineers, transportation modelers and decisions makers with a guide that can incorporate weather impacts into transportation system analysis and mode...

  19. Weather Research and Forecasting (WRF) Regional Atmospheric Model: Main Hawaiian Islands

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the Main Hawaiian Islands (MHI)...

  20. The origins of computer weather prediction and climate modeling

    Science.gov (United States)

    Lynch, Peter

    2008-03-01

    Numerical simulation of an ever-increasing range of geophysical phenomena is adding enormously to our understanding of complex processes in the Earth system. The consequences for mankind of ongoing climate change will be far-reaching. Earth System Models are capable of replicating climate regimes of past millennia and are the best means we have of predicting the future of our climate. The basic ideas of numerical forecasting and climate modeling were developed about a century ago, long before the first electronic computer was constructed. There were several major practical obstacles to be overcome before numerical prediction could be put into practice. A fuller understanding of atmospheric dynamics allowed the development of simplified systems of equations; regular radiosonde observations of the free atmosphere and, later, satellite data, provided the initial conditions; stable finite difference schemes were developed; and powerful electronic computers provided a practical means of carrying out the prodigious calculations required to predict the changes in the weather. Progress in weather forecasting and in climate modeling over the past 50 years has been dramatic. In this presentation, we will trace the history of computer forecasting through the ENIAC integrations to the present day. The useful range of deterministic prediction is increasing by about one day each decade, and our understanding of climate change is growing rapidly as Earth System Models of ever-increasing sophistication are developed.

  1. Nowcasting Ground Magnetic Perturbations with the Space Weather Modeling Framework

    Science.gov (United States)

    Welling, D. T.; Toth, G.; Singer, H. J.; Millward, G. H.; Gombosi, T. I.

    2015-12-01

    Predicting ground-based magnetic perturbations is a critical step towards specifying and predicting geomagnetically induced currents (GICs) in high voltage transmission lines. Currently, the Space Weather Modeling Framework (SWMF), a flexible modeling framework for simulating the multi-scale space environment, is being transitioned from research to operational use (R2O) by NOAA's Space Weather Prediction Center. Upon completion of this transition, the SWMF will provide localized B/t predictions using real-time solar wind observations from L1 and the F10.7 proxy for EUV as model input. This presentation describes the operational SWMF setup and summarizes the changes made to the code to enable R2O progress. The framework's algorithm for calculating ground-based magnetometer observations will be reviewed. Metrics from data-model comparisons will be reviewed to illustrate predictive capabilities. Early data products, such as regional-K index and grids of virtual magnetometer stations, will be presented. Finally, early successes will be shared, including the code's ability to reproduce the recent March 2015 St. Patrick's Day Storm.

  2. The origins of computer weather prediction and climate modeling

    International Nuclear Information System (INIS)

    Lynch, Peter

    2008-01-01

    Numerical simulation of an ever-increasing range of geophysical phenomena is adding enormously to our understanding of complex processes in the Earth system. The consequences for mankind of ongoing climate change will be far-reaching. Earth System Models are capable of replicating climate regimes of past millennia and are the best means we have of predicting the future of our climate. The basic ideas of numerical forecasting and climate modeling were developed about a century ago, long before the first electronic computer was constructed. There were several major practical obstacles to be overcome before numerical prediction could be put into practice. A fuller understanding of atmospheric dynamics allowed the development of simplified systems of equations; regular radiosonde observations of the free atmosphere and, later, satellite data, provided the initial conditions; stable finite difference schemes were developed; and powerful electronic computers provided a practical means of carrying out the prodigious calculations required to predict the changes in the weather. Progress in weather forecasting and in climate modeling over the past 50 years has been dramatic. In this presentation, we will trace the history of computer forecasting through the ENIAC integrations to the present day. The useful range of deterministic prediction is increasing by about one day each decade, and our understanding of climate change is growing rapidly as Earth System Models of ever-increasing sophistication are developed

  3. Modeling Inclement Weather Impacts on Traffic Stream Behavior

    Directory of Open Access Journals (Sweden)

    Hesham Rakha, PhD., P.Eng.

    2012-03-01

    Full Text Available The research identifies the steady-state car-following model parameters within state-of-the-practice traffic simulation software that require calibration to reflect inclement weather and roadway conditions. The research then develops procedures for calibrating non-steady state car-following models to capture inclement weather impacts and applies the procedures to the INTEGRATION software on a sample network. The results demonstrate that the introduction of rain precipitation results in a 5% reduction in light-duty vehicle speeds and a 3% reduction in heavy-duty vehicle speeds. An increase in the rain intensity further reduces light-duty vehicle and heavy-duty truck speeds resulting in a maximum reduction of 9.5% and 5.5% at the maximum rain intensity of 1.5 cm/h, respectively. The results also demonstrate that the impact of rain on traffic stream speed increases with the level of congestion and is more significant than speed differences attributed to various traffic operational improvements and thus should be accounted for in the analysis of alternatives. In the case of snow precipitation, the speed reductions are much more significant (in the range of 55%. Furthermore, the speed reductions are minimally impacted by the snow precipitation intensity. The study further demonstrates that precipitation intensity has no impact on the relative merit of various scenarios (i.e. the ranking of the scenario results are consistent across the various rain intensity levels. This finding is important given that it demonstrates that a recommendation on the optimal scenario is not impacted by the weather conditions that are considered in the analysis.

  4. Effects of high-gossypol cotton on the development and reproduction of Bemisia tabaci (Hemiptera: Aleyrodidae) MEAM1 cryptic species.

    Science.gov (United States)

    Guo, Jian-Ying; Wu, Gang; Wan, Fang-Hao

    2013-06-01

    Use of plant secondary metabolic compounds is an important method for insect pest control. In this study, the survival, development, and reproduction of Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) MEAM1 cryptic species were compared over two consecutive generations on three cotton cultivars of different gossypol levels. Both cotton cultivar and generation significantly affected the fitness of the whitefly. In both generations, the immature development times on the low-gossypol cultivar ZMS13 were significantly longer than those on the high-gossypol cultivar M9101 or medium-gossypol cultivar HZ401. The female fecundity and rate of population increase of the whitefly ranked in the following order: ZMS13 > HZ401 > M9101. On each cultivar, the immature development time was shorter and the immature survival rate was higher in the second generation than those in the first generation. Rate of increase was also higher in the second generation. These results demonstrated that the fitness of B. tabaci MEAM1 cryptic species on the low-gossypol cotton cultivar ZMS13 was higher than that on the medium- or high-gossypol cultivar. The comparison of the life histories of B. tabaci MEAM1 cryptic species on different cotton varieties is important for the development of an integrated pest management program of the whitefly by using plant secondary metabolic compounds.

  5. Weather Driven Renewable Energy Analysis, Modeling New Technologies

    Science.gov (United States)

    Paine, J.; Clack, C.; Picciano, P.; Terry, L.

    2015-12-01

    Carbon emission reduction is essential to hampering anthropogenic climate change. While there are several methods to broach carbon reductions, the National Energy with Weather System (NEWS) model focuses on limiting electrical generation emissions by way of a national high-voltage direct-current transmission that takes advantage of the strengths of different regions in terms of variable sources of energy. Specifically, we focus upon modeling concentrating solar power (CSP) as another source to contribute to the electric grid. Power tower solar fields are optimized taking into account high spatial and temporal resolution, 13km and hourly, numerical weather prediction model data gathered by NOAA from the years of 2006-2008. Importantly, the optimization of these CSP power plants takes into consideration factors that decrease the optical efficiency of the heliostats reflecting solar irradiance. For example, cosine efficiency, atmospheric attenuation, and shadowing are shown here; however, it should be noted that they are not the only limiting factors. While solar photovoltaic plants can be combined for similar efficiency to the power tower and currently at a lower cost, they do not have a cost-effective capability to provide electricity when there are interruptions in solar irradiance. Power towers rely on a heat transfer fluid, which can be used for thermal storage changing the cost efficiency of this energy source. Thermal storage increases the electric stability that many other renewable energy sources lack, and thus, the ability to choose between direct electric conversion and thermal storage is discussed. The figure shown is a test model of a CSP plant made up of heliostats. The colors show the optical efficiency of each heliostat at a single time of the day.

  6. Sol-Terra - AN Operational Space Weather Forecasting Model Framework

    Science.gov (United States)

    Bisi, M. M.; Lawrence, G.; Pidgeon, A.; Reid, S.; Hapgood, M. A.; Bogdanova, Y.; Byrne, J.; Marsh, M. S.; Jackson, D.; Gibbs, M.

    2015-12-01

    The SOL-TERRA project is a collaboration between RHEA Tech, the Met Office, and RAL Space funded by the UK Space Agency. The goal of the SOL-TERRA project is to produce a Roadmap for a future coupled Sun-to-Earth operational space weather forecasting system covering domains from the Sun down to the magnetosphere-ionosphere-thermosphere and neutral atmosphere. The first stage of SOL-TERRA is underway and involves reviewing current models that could potentially contribute to such a system. Within a given domain, the various space weather models will be assessed how they could contribute to such a coupled system. This will be done both by reviewing peer reviewed papers, and via direct input from the model developers to provide further insight. Once the models have been reviewed then the optimal set of models for use in support of forecast-based SWE modelling will be selected, and a Roadmap for the implementation of an operational forecast-based SWE modelling framework will be prepared. The Roadmap will address the current modelling capability, knowledge gaps and further work required, and also the implementation and maintenance of the overall architecture and environment that the models will operate within. The SOL-TERRA project will engage with external stakeholders in order to ensure independently that the project remains on track to meet its original objectives. A group of key external stakeholders have been invited to provide their domain-specific expertise in reviewing the SOL-TERRA project at critical stages of Roadmap preparation; namely at the Mid-Term Review, and prior to submission of the Final Report. This stakeholder input will ensure that the SOL-TERRA Roadmap will be enhanced directly through the input of modellers and end-users. The overall goal of the SOL-TERRA project is to develop a Roadmap for an operational forecast-based SWE modelling framework with can be implemented within a larger subsequent activity. The SOL-TERRA project is supported within

  7. Lightning Forecasts and Data Assimilation into Numerical Weather Prediction Models

    Science.gov (United States)

    MacGorman, D. R.; Mansell, E. R.; Fierro, A.; Ziegler, C.

    2012-12-01

    This presentation reviews two aspects of lightning in numerical weather prediction (NWP) models: forecasting lightning and assimilating lightning data into NWP models to improve weather forecasts. One of the earliest routine forecasts of lightning was developed for fire weather operations. This approach used a multi-parameter regression analysis of archived cloud-to-ground (CG) lightning data and archived NWP data to optimize the combination of model state variables to use in forecast equations for various CG rates. Since then, understanding of how storms produce lightning has improved greatly. As the treatment of ice in microphysics packages used by NWP models has improved and the horizontal resolution of models has begun approaching convection-permitting scales (with convection-resolving scales on the horizon), it is becoming possible to use this improved understanding in NWP models to predict lightning more directly. An important role for data assimilation in NWP models is to depict the location, timing, and spatial extent of thunderstorms during model spin-up so that the effects of prior convection that can strongly influence future thunderstorm activity, such as updrafts and outflow boundaries, can be included in the initial state of a NWP model run. Radar data have traditionally been used, but systems that map lightning activity with varying degrees of coverage, detail, and detection efficiency are now available routinely over large regions and reveal information about storms that is complementary to the information provided by radar. Because data from lightning mapping systems are compact, easily handled, and reliably indicate the location and timing of thunderstorms, even in regions with little or no radar coverage, several groups have investigated techniques for assimilating these data into NWP models. This application will become even more valuable with the launch of the Geostationary Lightning Mapper on the GOES-R satellite, which will extend routine

  8. Space Weather Forecasting and Research at the Community Coordinated Modeling Center

    Science.gov (United States)

    Aronne, M.

    2015-12-01

    The Space Weather Research Center (SWRC), within the Community Coordinated Modeling Center (CCMC), provides experimental research forecasts and analysis for NASA's robotic mission operators. Space weather conditions are monitored to provide advance warning and forecasts based on observations and modeling using the integrated Space Weather Analysis Network (iSWA). Space weather forecasters come from a variety of backgrounds, ranging from modelers to astrophysicists to undergraduate students. This presentation will discuss space weather operations and research from an undergraduate perspective. The Space Weather Research, Education, and Development Initiative (SW REDI) is the starting point for many undergraduate opportunities in space weather forecasting and research. Space weather analyst interns play an active role year-round as entry-level space weather analysts. Students develop the technical and professional skills to forecast space weather through a summer internship that includes a two week long space weather boot camp, mentorship, poster session, and research opportunities. My unique development of research projects includes studying high speed stream events as well as a study of 20 historic, high-impact solar energetic particle events. This unique opportunity to combine daily real-time analysis with related research prepares students for future careers in Heliophysics.

  9. Fate, weathering, and modelling research: A Canadian perspective

    International Nuclear Information System (INIS)

    Fingas, M.

    1992-01-01

    Fate and behavior studies are fundamental to oil spill research and their results are important for operational response. Knowledge of the ultimate fate and behavior of oil should drive countermeasures decisions. Research has been conducted around the world on oil fate and behavior. The effort has not been, in this author's opinion, focussed and long-term as it should have been. Unfortunately, research funding for oil spills is very oscillatory. Fate and behavior studies require a long, concerted effort to yield valuable results. Because of this, fate and behavior studies have suffered much more than others from funding spurts. Little research has been maintained at universities because of the lack of sustained funding. Few other research organizations have facilities, equipment and expertise to carry out fundamental studies. A second difficulty in the field has been the tendency to fund one-year studies. In many cases little can be answered in a year. Specialized apparatus take 6-12 months to build or to acquire. Little time is left to operate these. The learning curve is also a factor. It is generally accepted in a specialized field that it takes a new scientist 6 months to produce any useful work, 2 years to become productive, and 5 years to be fully productive. Hopefully, future efforts will allow for longer-term studies on fate and behavior. The state-of-the-art in the field of fate, behavior, weathering and modelling could be summarized as variable. There are many deficiencies in our knowledge about the fate, weathering and modelling of oil spills. The fate, behavior and transformation of oil is dominated by the reality that oil is a varying mixture of hundreds of compounds

  10. Two adaptive radiative transfer schemes for numerical weather prediction models

    Directory of Open Access Journals (Sweden)

    V. Venema

    2007-11-01

    Full Text Available Radiative transfer calculations in atmospheric models are computationally expensive, even if based on simplifications such as the δ-two-stream approximation. In most weather prediction models these parameterisation schemes are therefore called infrequently, accepting additional model error due to the persistence assumption between calls. This paper presents two so-called adaptive parameterisation schemes for radiative transfer in a limited area model: A perturbation scheme that exploits temporal correlations and a local-search scheme that mainly takes advantage of spatial correlations. Utilising these correlations and with similar computational resources, the schemes are able to predict the surface net radiative fluxes more accurately than a scheme based on the persistence assumption. An important property of these adaptive schemes is that their accuracy does not decrease much in case of strong reductions in the number of calls to the δ-two-stream scheme. It is hypothesised that the core idea can also be employed in parameterisation schemes for other processes and in other dynamical models.

  11. Development and Implementation of Dynamic Scripts to Support Local Model Verification at National Weather Service Weather Forecast Offices

    Science.gov (United States)

    Zavodsky, Bradley; Case, Jonathan L.; Gotway, John H.; White, Kristopher; Medlin, Jeffrey; Wood, Lance; Radell, Dave

    2014-01-01

    Local modeling with a customized configuration is conducted at National Weather Service (NWS) Weather Forecast Offices (WFOs) to produce high-resolution numerical forecasts that can better simulate local weather phenomena and complement larger scale global and regional models. The advent of the Environmental Modeling System (EMS), which provides a pre-compiled version of the Weather Research and Forecasting (WRF) model and wrapper Perl scripts, has enabled forecasters to easily configure and execute the WRF model on local workstations. NWS WFOs often use EMS output to help in forecasting highly localized, mesoscale features such as convective initiation, the timing and inland extent of lake effect snow bands, lake and sea breezes, and topographically-modified winds. However, quantitatively evaluating model performance to determine errors and biases still proves to be one of the challenges in running a local model. Developed at the National Center for Atmospheric Research (NCAR), the Model Evaluation Tools (MET) verification software makes performing these types of quantitative analyses easier, but operational forecasters do not generally have time to familiarize themselves with navigating the sometimes complex configurations associated with the MET tools. To assist forecasters in running a subset of MET programs and capabilities, the Short-term Prediction Research and Transition (SPoRT) Center has developed and transitioned a set of dynamic, easily configurable Perl scripts to collaborating NWS WFOs. The objective of these scripts is to provide SPoRT collaborating partners in the NWS with the ability to evaluate the skill of their local EMS model runs in near real time with little prior knowledge of the MET package. The ultimate goal is to make these verification scripts available to the broader NWS community in a future version of the EMS software. This paper provides an overview of the SPoRT MET scripts, instructions for how the scripts are run, and example use

  12. Weather Radar Estimations Feeding an Artificial Neural Network Model Weather Radar Estimations Feeding an Artificial Neural Network Model

    Directory of Open Access Journals (Sweden)

    Dawei Han

    2012-02-01

    Full Text Available The application of ANNs (Artifi cial Neural Networks has been studied by many researchers in modelling rainfall runoff processes. However, the work so far has been focused on the rainfall data from traditional raingauges. Weather radar is a modern technology which could provide high resolution rainfall in time and space. In this study, a comparison in rainfall runoff modelling between the raingauge and weather radar has been carried out. The data were collected from Brue catchment in Southwest of England, with 49 raingauges covering 136 km2 and two C-band weather radars. This raingauge network is extremely dense (for research purposes and does not represent the usual raingauge density in operational flood forecasting systems. The ANN models were set up with both lumped and spatial rainfall input. The results showed that raingauge data outperformed radar data in all the events tested, regardless of the lumped and spatial input. La aplicación de Redes Neuronales Artificiales (RNA en el modelado de lluvia-flujo ha sido estudiada ampliamente. Sin embargo, hasta ahora se han utilizado datos provenientes de pluviómetros tradicionales. Los radares meteorológicos son una tecnología moderna que puede proveer datos de lluvia de alta resolución en tiempo y espacio. Este es un trabajo de comparación en el modelado lluvia-flujo entre pluviómetros y radares meteorológicos. Los datos provienen de la cuenca del río Brue en el suroeste de Inglaterra, con 49 pluviómetros cubriendo 136 km2 y dos radares meteorológicos en la banda C. Esta red de pluviómetros es extremadamente densa (para investigación y no representa la densidad usual en sistemas de predicción de inundaciones. Los modelos de RNA fueron implementados con datos de entrada de lluvia tanto espaciados como no distribuidos. Los resultados muestran que los datos de los pluviómetros fueron mejores que los datos de los radares en todos los eventos probados.

  13. User's guide to the weather model: a component of the western spruce budworm modeling system.

    Science.gov (United States)

    W. P. Kemp; N. L. Crookston; P. W. Thomas

    1989-01-01

    A stochastic model useful in simulating daily maximum and minimum temperature and precipitation developed by Bruhn and others has been adapted for use in the western spruce budworm modeling system. This document describes how to use the weather model and illustrates some aspects of its behavior.

  14. Space Weather Forecasts Driven by the ADAPT Model

    Science.gov (United States)

    Henney, C. J.; Arge, C. N.; Shurkin, K.; Schooley, A. K.; Hock, R. A.; White, S.

    2015-12-01

    In this presentation, we highlight recent progress to forecast key space weather parameters with the ADAPT (Air Force Data Assimilative Photospheric flux Transport) model. Driven by a magnetic flux transport model, ADAPT evolves global solar magnetic maps forward 1 to 7 days in the future to provide realistic estimates of the solar near-side field distribution used to forecast the solar wind, F10.7 (i.e., the solar 10.7 cm radio flux), extreme ultraviolet (EUV) and far ultraviolet (FUV) irradiance. Input to the ADAPT model includes solar near-side estimates of the inferred photospheric magnetic field from space-based (i.e., HMI) and ground-based (e.g., GONG & VSM) instruments. We summarize the recent findings that: 1) the sum of the absolute value of strong magnetic fields, associated with sunspots, is shown to correlate well with the observed daily F10.7 variability (Henney et al. 2012); and 2) the sum of the absolute value of weak magnetic fields, associated with plage regions, is shown to correlate well with EUV and FUV irradiance variability (Henney et al. 2015). In addition, recent progress to utilize the ADAPT global maps as input to the Wang-Sheeley-Arge (WSA) coronal and solar wind model is presented. We also discuss the challenges of observing less than half of the solar surface at any given time and the need for future magnetograph instruments near L1 and L5.

  15. An Overview of the National Weather Service National Water Model

    Science.gov (United States)

    Cosgrove, B.; Gochis, D.; Clark, E. P.; Cui, Z.; Dugger, A. L.; Feng, X.; Karsten, L. R.; Khan, S.; Kitzmiller, D.; Lee, H. S.; Liu, Y.; McCreight, J. L.; Newman, A. J.; Oubeidillah, A.; Pan, L.; Pham, C.; Salas, F.; Sampson, K. M.; Sood, G.; Wood, A.; Yates, D. N.; Yu, W.

    2016-12-01

    The National Weather Service (NWS) Office of Water Prediction (OWP), in conjunction with the National Center for Atmospheric Research (NCAR) and the NWS National Centers for Environmental Prediction (NCEP) recently implemented version 1.0 of the National Water Model (NWM) into operations. This model is an hourly cycling uncoupled analysis and forecast system that provides streamflow for 2.7 million river reaches and other hydrologic information on 1km and 250m grids. It will provide complementary hydrologic guidance at current NWS river forecast locations and significantly expand guidance coverage and type in underserved locations. The core of this system is the NCAR-supported community Weather Research and Forecasting (WRF)-Hydro hydrologic model. It ingests forcing from a variety of sources including Multi-Sensor Multi-Radar (MRMS) radar-gauge observed precipitation data and High Resolution Rapid Refresh (HRRR), Rapid Refresh (RAP), Global Forecast System (GFS) and Climate Forecast System (CFS) forecast data. WRF-Hydro is configured to use the Noah-Multi Parameterization (Noah-MP) Land Surface Model (LSM) to simulate land surface processes. Separate water routing modules perform diffusive wave surface routing and saturated subsurface flow routing on a 250m grid, and Muskingum-Cunge channel routing down National Hydrogaphy Dataset Plus V2 (NHDPlusV2) stream reaches. River analyses and forecasts are provided across a domain encompassing the Continental United States (CONUS) and hydrologically contributing areas, while land surface output is available on a larger domain that extends beyond the CONUS into Canada and Mexico (roughly from latitude 19N to 58N). The system includes an analysis and assimilation configuration along with three forecast configurations. These include a short-range 15 hour deterministic forecast, a medium-Range 10 day deterministic forecast and a long-range 30 day 16-member ensemble forecast. United Sates Geologic Survey (USGS) streamflow

  16. The Trouble with MEAM2: Implications of Pseudogenes on Species Delimitation in the Globally Invasive Bemisia tabaci (Hemiptera: Aleyrodidae) Cryptic Species Complex.

    Science.gov (United States)

    Tay, Wee Tek; Elfekih, Samia; Court, Leon N; Gordon, Karl H J; Delatte, Hélène; De Barro, Paul J

    2017-10-01

    Molecular species identification using suboptimal PCR primers can over-estimate species diversity due to coamplification of nuclear mitochondrial (NUMT) DNA/pseudogenes. For the agriculturally important whitefly Bemisia tabaci cryptic pest species complex, species identification depends primarily on characterization of the mitochondrial DNA cytochrome oxidase I (mtDNA COI) gene. The lack of robust PCR primers for the mtDNA COI gene can undermine correct species identification which in turn compromises management strategies. This problem is identified in the B. tabaci Africa/Middle East/Asia Minor clade which comprises the globally invasive Mediterranean (MED) and Middle East Asia Minor I (MEAM1) species, Middle East Asia Minor 2 (MEAM2), and the Indian Ocean (IO) species. Initially identified from the Indian Ocean island of Réunion, MEAM2 has since been reported from Japan, Peru, Turkey and Iraq. We identified MEAM2 individuals from a Peruvian population via Sanger sequencing of the mtDNA COI gene. In attempting to characterize the MEAM2 mitogenome, we instead characterized mitogenomes of MEAM1. We also report on the mitogenomes of MED, AUS, and IO thereby increasing genomic resources for members of this complex. Gene synteny (i.e., same gene composition and orientation) was observed with published B. tabaci cryptic species mitogenomes. Pseudogene fragments matching MEAM2 partial mtDNA COI gene exhibited low frequency single nucleotide polymorphisms that matched low copy number DNA fragments (species. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

  17. Spatially explicit modelling of extreme weather and climate events ...

    African Journals Online (AJOL)

    The reality of climate change continues to influence the intensity and frequency of extreme weather events such as heat waves, droughts, floods, and landslides. The impacts of the cumulative interplay of these extreme weather and climate events variation continue to perturb governments causing a scramble into formation ...

  18. Weather forecasting for Eastern Amazon with OLAM model

    Directory of Open Access Journals (Sweden)

    Renato Ramos da Silva

    2014-12-01

    Full Text Available The OLAM model has as its characteristics the advantage to represent simultaneously the global and regional meteorological phenomena using the application of a grid refinement scheme. During the REMAM project the model was applied for a few case studies to evaluate its performance on numerical weather prediction for the eastern Amazon region. Case studies were performed for the twelve months of the year of 2009. The model results for those numerical experiments were compared with the observed data for the region of study. Precipitation data analysis showed that OLAM is able to represent the average mean accumulated precipitation and the seasonal features of the events occurrence, but can't predict the local total amount of precipitation. However, individual evaluation for a few cases had shown that OLAM was able to represent the dynamics and forecast a few days in advance the development of coastal meteorological systems such as the squall lines that are one of the most important precipitating systems of the Amazon.

  19. Improved wet weather wastewater influent modelling at Viikinmäki WWTP by on-line weather radar information.

    Science.gov (United States)

    Heinonen, M; Jokelainen, M; Fred, T; Koistinen, J; Hohti, H

    2013-01-01

    Municipal wastewater treatment plant (WWTP) influent is typically dependent on diurnal variation of urban production of liquid waste, infiltration of stormwater runoff and groundwater infiltration. During wet weather conditions the infiltration phenomenon typically increases the risk of overflows in the sewer system as well as the risk of having to bypass the WWTP. Combined sewer infrastructure multiplies the role of rainwater runoff in the total influent. Due to climate change, rain intensity and magnitude is tending to rise as well, which can already be observed in the normal operation of WWTPs. Bypass control can be improved if the WWTP is prepared for the increase of influent, especially if there is some storage capacity prior to the treatment plant. One option for this bypass control is utilisation of on-line weather-radar-based forecast data of rainfall as an input for the on-line influent model. This paper reports the Viikinmäki WWTP wet weather influent modelling project results where gridded exceedance probabilities of hourly rainfall accumulations for the next 3 h from the Finnish Meteorological Institute are utilised as on-line input data for the influent model.

  20. Improving High-resolution Weather Forecasts using the Weather Research and Forecasting (WRF) Model with Upgraded Kain-Fritsch Cumulus Scheme

    Science.gov (United States)

    High-resolution weather forecasting is affected by many aspects, i.e. model initial conditions, subgrid-scale cumulus convection and cloud microphysics schemes. Recent 12km grid studies using the Weather Research and Forecasting (WRF) model have identified the importance of inco...

  1. The effect of different weather data sets and their resolution on climate-based daylight modelling

    DEFF Research Database (Denmark)

    Iversen, A; Svendsen, Svend; Nielsen, Toke Rammer

    2013-01-01

    Climate-based daylight modelling is based on the available weather data, which means that the weather data used as input to the daylight simulations are of great importance. In this paper, the effect on the outcome of the daylight simulations of using one weather data file rather than another...... for the same location was investigated. Furthermore, the effect of using weather data sets with an hourly resolution compared to a one-minute resolution was investigated. The results showed that the lighting dependencies varied by up to 2% depending on the chosen weather data file and indoor illuminance...... threshold. The energy consumption for artificial lighting was underestimated when simulating with time steps of hourly means compared to one-minute resolution. The findings from this comparison show that the dynamic, short-term effects of the weather have a surprisingly small impact on the simulation...

  2. Modeling extreme (Carrington-type) space weather events using three-dimensional MHD code simulations

    Science.gov (United States)

    Ngwira, C. M.; Pulkkinen, A. A.; Kuznetsova, M. M.; Glocer, A.

    2013-12-01

    There is growing concern over possible severe societal consequences related to adverse space weather impacts on man-made technological infrastructure and systems. In the last two decades, significant progress has been made towards the modeling of space weather events. Three-dimensional (3-D) global magnetohydrodynamics (MHD) models have been at the forefront of this transition, and have played a critical role in advancing our understanding of space weather. However, the modeling of extreme space weather events is still a major challenge even for existing global MHD models. In this study, we introduce a specially adapted University of Michigan 3-D global MHD model for simulating extreme space weather events that have a ground footprint comparable (or larger) to the Carrington superstorm. Results are presented for an initial simulation run with ``very extreme'' constructed/idealized solar wind boundary conditions driving the magnetosphere. In particular, we describe the reaction of the magnetosphere-ionosphere system and the associated ground induced geoelectric field to such extreme driving conditions. We also discuss the results and what they might mean for the accuracy of the simulations. The model is further tested using input data for an observed space weather event to verify the MHD model consistence and to draw guidance for future work. This extreme space weather MHD model is designed specifically for practical application to the modeling of extreme geomagnetically induced electric fields, which can drive large currents in earth conductors such as power transmission grids.

  3. Climate and weather risk in natural resource models

    Science.gov (United States)

    Merrill, Nathaniel Henry

    This work, consisting of three manuscripts, addresses natural resource management under risk due to variation in climate and weather. In three distinct but theoretically related applications, I quantify the role of natural resources in stabilizing economic outcomes. In Manuscript 1, we address policy designed to effect the risk of cyanobacteria blooms in a drinking water reservoir through watershed wide policy. Combining a hydrologic and economic model for a watershed in Rhode Island, we solve for the efficient allocation of best management practices (BMPs) on livestock pastures to meet a monthly risk-based as well as mean-based water quality objective. In order to solve for the efficient allocations of nutrient control effort, we optimize a probabilistically constrained integer-programming problem representing the choices made on each farm and the resultant conditions that support cyanobacteria blooms. In doing so, we employ a genetic algorithm (GA). We hypothesize that management based on controlling the upper tail of the probability distribution of phosphorus loading implies different efficient management actions as compared to controlling mean loading. We find a shift to more intense effort on fewer acres when a probabilistic objective is specified with cost savings of meeting risk levels of up to 25% over mean loading based policies. Additionally, we illustrate the relative cost effectiveness of various policies designed to meet this risk-based objective. Rainfall and the subsequent overland runoff is the source of transportation of nutrients to a receiving water body, with larger amounts of phosphorus moving in more intense rainfall events. We highlight the importance of this transportation mechanism by comparing policies under climate change scenarios, where the intensity of rainfall is projected to increase and the time series process of rainfall to change. In Manuscript 2, we introduce a new economic groundwater model that incorporates the gradual shift

  4. Comparison of three weather generators for crop modeling: a case study for subtropical environments

    NARCIS (Netherlands)

    Hartkamp, A.D.; White, J.W.; Hoogenboom, G.

    2003-01-01

    The use and application of decision support systems (DDS) that consider variation in climate and soil conditions has expanded in recent years. Most of these DSS are based on crop simulation models that require daily weather data, so access to weather data, at single sites as well as large amount of

  5. A Physically Based Coupled Chemical and Physical Weathering Model for Simulating Soilscape Evolution

    Science.gov (United States)

    Willgoose, G. R.; Welivitiya, D.; Hancock, G. R.

    2015-12-01

    A critical missing link in existing landscape evolution models is a dynamic soil evolution models where soils co-evolve with the landform. Work by the authors over the last decade has demonstrated a computationally manageable model for soil profile evolution (soilscape evolution) based on physical weathering. For chemical weathering it is clear that full geochemistry models such as CrunchFlow and PHREEQC are too computationally intensive to be couplable to existing soilscape and landscape evolution models. This paper presents a simplification of CrunchFlow chemistry and physics that makes the task feasible, and generalises it for hillslope geomorphology applications. Results from this simplified model will be compared with field data for soil pedogenesis. Other researchers have previously proposed a number of very simple weathering functions (e.g. exponential, humped, reverse exponential) as conceptual models of the in-profile weathering process. The paper will show that all of these functions are possible for specific combinations of in-soil environmental, geochemical and geologic conditions, and the presentation will outline the key variables controlling which of these conceptual models can be realistic models of in-profile processes and under what conditions. The presentation will finish by discussing the coupling of this model with a physical weathering model, and will show sample results from our SSSPAM soilscape evolution model to illustrate the implications of including chemical weathering in the soilscape evolution model.

  6. Interactions between tectonics, silicate weathering, and climate explored with carbon cycle modeling

    Science.gov (United States)

    Penman, D. E.; Caves Rugenstein, J. K.; Ibarra, D. E.; Winnick, M.

    2017-12-01

    Earth's long-term carbon cycle is thought to benefit from a stabilizing negative feedback in the form of CO2 consumption by the chemical weathering of silicate minerals: during periods of elevated atmospheric pCO2, chemical weathering rates increase, thus consuming more atmospheric CO2 and cooling global climate, whereas during periods of low pCO2, weathering rates decrease, allowing buildup of CO2 in the atmosphere and warming. At equilibrium, CO2 consumption by silicate weathering balances volcanic CO2 degassing at a specific atmospheric pCO2 dictated by the relationship between total silicate weathering rate and pCO2: Earth's "weathering curve." We use numerical carbon cycle modeling to demonstrate that the shape and slope of the weathering curve is crucial to understanding proposed tectonic controls on pCO2 and climate. First, the shape of the weathering curve dictates the equilibrium response of the carbon cycle to changes in the rate of background volcanic/solid Earth CO2 degassing, which has been suggested to vary significantly with plate tectonic reorganizations over geologic timescales. Second, we demonstrate that if tectonic events can significantly change the weathering curve, this can act as an effective driver of pCO2 and climate on tectonic timescales by changing the atmospheric pCO2 at which silicate weathering balances a constant volcanic/solid Earth degassing rate. Finally, we review the complex interplay of environmental factors that affect modern weathering rates in the field and highlight how the resulting uncertainty surrounding the shape of Earth's weathering curve significantly hampers our ability to quantitatively predict the response of pCO2 and climate to tectonic forcing, and thus represents a substantial knowledge gap in Earth science. We conclude with strategies for closing this knowledge gap by using precise paleoclimatic reconstructions of intervals with known tectonic forcings.

  7. Mountain range specific analog weather forecast model for ...

    Indian Academy of Sciences (India)

    various road management activities and for better assessment of avalanche danger situation during the winter period (November to April). Weather forecasting in the context of avalanche forecasting can be viewed as an initial essential process to begin assessment of an avalanche danger situation for a given area and ...

  8. Combining weather radar nowcasts and numerical weather prediction models to estimate short-term quantitative precipitation and uncertainty

    DEFF Research Database (Denmark)

    Jensen, David Getreuer

    The topic of this Ph.D. thesis is short term forecasting of precipitation for up to 6 hours called nowcasts. The focus is on improving the precision of deterministic nowcasts, assimilation of radar extrapolation model (REM) data into Danish Meteorological Institutes (DMI) HIRLAM numerical weather...... prediction (NWP) model and produce quantitative estimations of nowcast uncertainty. In real time control of urban drainage systems, nowcasting is used to increase the margin for decision-making. The spatial extent of urban drainag e catchments is very small in a meteorological context. This is a problem...... by the relative standard deviation. A significant result of this Ph.D. study is major improvements in predictability of DMI HIRLAM NWP model by assimilation of REM data. A new nudging assimilation method developed at DMI was used to assimilate the REM data. The assimilation technique enhances convection in case...

  9. Using Weather Data and Climate Model Output in Economic Analyses of Climate Change

    Energy Technology Data Exchange (ETDEWEB)

    Auffhammer, M.; Hsiang, S. M.; Schlenker, W.; Sobel, A.

    2013-06-28

    Economists are increasingly using weather data and climate model output in analyses of the economic impacts of climate change. This article introduces a set of weather data sets and climate models that are frequently used, discusses the most common mistakes economists make in using these products, and identifies ways to avoid these pitfalls. We first provide an introduction to weather data, including a summary of the types of datasets available, and then discuss five common pitfalls that empirical researchers should be aware of when using historical weather data as explanatory variables in econometric applications. We then provide a brief overview of climate models and discuss two common and significant errors often made by economists when climate model output is used to simulate the future impacts of climate change on an economic outcome of interest.

  10. Integrating topography, hydrology and rock structure in weathering rate models of spring watersheds

    NARCIS (Netherlands)

    Pacheco, F.A.L.; Weijden, C.H. van der

    2012-01-01

    Weathering rate models designed for watersheds combine chemical data of discharging waters with morphologic and hydrologic parameters of the catchments. At the spring watershed scale, evaluation of morphologic parameters is subjective due to difficulties in conceiving the catchment geometry.

  11. Toward Seamless Weather-Climate Prediction with a Global Cloud Resolving Model

    Science.gov (United States)

    2016-01-14

    distribution is unlimited. TOWARD SEAMLESS WEATHER- CLIMATE PREDICTION WITH A GLOBAL CLOUD RESOLVING MODEL PI: Tim Li IPRC/SOEST, University of Hawaii at...Project Final Report 3. DATES COVERED (From - To) 1 May 2012 - 30 September 2015 4. TITLE AND SUBTITLE TOWARD SEAMLESS WEATHER- CLIMATE PREDICTION WITH...A GLOBAL CLOUD RESOLVING MODEL 5a. CONTRACT NUMBER 5b. GRANT NUMBER N000141210450 5c. PROGRAM ELEMENT NUMBER ONR Marine Meteorology Program 6

  12. An equilibrium pricing model for weather derivatives in a multi-commodity setting

    International Nuclear Information System (INIS)

    Lee, Yongheon; Oren, Shmuel S.

    2009-01-01

    Many industries are exposed to weather risk. Weather derivatives can play a key role in hedging and diversifying such risk because the uncertainty in a company's profit function can be correlated to weather condition which affects diverse industry sectors differently. Unfortunately the weather derivatives market is a classical example of an incomplete market that is not amenable to standard methodologies used for derivative pricing in complete markets. In this paper, we develop an equilibrium pricing model for weather derivatives in a multi-commodity setting. The model is constructed in the context of a stylized economy where agents optimize their hedging portfolios which include weather derivatives that are issued in a fixed quantity by a financial underwriter. The supply and demand resulting from hedging activities and the supply by the underwriter are combined in an equilibrium pricing model under the assumption that all agents maximize some risk averse utility function. We analyze the gains due to the inclusion of weather derivatives in hedging portfolios and examine the components of that gain attributable to hedging and to risk sharing. (author)

  13. Transcriptome changes occurred in the whitefly, B. tabaci MEAM1 in response to feeding on melon infected with the crinivirus, CYSDV

    Science.gov (United States)

    CYSDV, a crinivirus transmitted by the whitefly, B. tabaci, causes widespread losses in melon and other cucurbits. The virus emerged in the southwestern United States in 2006. It established in crops and weeds, and is transmitted to cucurbits by the resident population of B. tabaci MEAM1 each year. ...

  14. Asymmetric consequences of host plant occupation on the competition between the whiteflies Bemisia tabaci cryptic species MEAM1 and Trialeurodes vaporariorum (Hemiptera: Aleyrodidae)

    DEFF Research Database (Denmark)

    Zhang, Gui-Fen; Lövei, Gabor L; Hu, Man

    2014-01-01

    BACKGROUND: The two common whitefly species, Bemisia tabaci (Gennadius) MEAM1 and Trialeurodes vaporariorum (Westwood), often co-occur on their host plants. The effect of host plant occupation by one species on later-arriving conspecific individuals or on the other competing species was examined...

  15. Space Weather Data Dissemination Tools from the Community Coordinated Modeling Center

    Science.gov (United States)

    Donti, N.; Berrios, D.; Boblitt, J.; LaSota, J.; Maddox, M. M.; Mullinix, R.; Hesse, M.

    2011-12-01

    The Community Coordinated Modeling Center (CCMC) at NASA Goddard Space Flight Center has developed new space weather data dissemination products. These include a Java-based conversion software for space weather simulation data, an interactive and customizable timeline tool for time series data, and Android phone and tablet versions of the NASA Space Weather App for mobile devices. We highlight the new features of all the updated services, discuss the back-end capabilities required to realize these services, and talk about future services in development.

  16. Modeling extreme "Carrington-type" space weather events using three-dimensional global MHD simulations

    Science.gov (United States)

    Ngwira, Chigomezyo M.; Pulkkinen, Antti; Kuznetsova, Maria M.; Glocer, Alex

    2014-06-01

    There is a growing concern over possible severe societal consequences related to adverse space weather impacts on man-made technological infrastructure. In the last two decades, significant progress has been made toward the first-principles modeling of space weather events, and three-dimensional (3-D) global magnetohydrodynamics (MHD) models have been at the forefront of this transition, thereby playing a critical role in advancing our understanding of space weather. However, the modeling of extreme space weather events is still a major challenge even for the modern global MHD models. In this study, we introduce a specially adapted University of Michigan 3-D global MHD model for simulating extreme space weather events with a Dst footprint comparable to the Carrington superstorm of September 1859 based on the estimate by Tsurutani et. al. (2003). Results are presented for a simulation run with "very extreme" constructed/idealized solar wind boundary conditions driving the magnetosphere. In particular, we describe the reaction of the magnetosphere-ionosphere system and the associated induced geoelectric field on the ground to such extreme driving conditions. The model setup is further tested using input data for an observed space weather event of Halloween storm October 2003 to verify the MHD model consistence and to draw additional guidance for future work. This extreme space weather MHD model setup is designed specifically for practical application to the modeling of extreme geomagnetically induced electric fields, which can drive large currents in ground-based conductor systems such as power transmission grids. Therefore, our ultimate goal is to explore the level of geoelectric fields that can be induced from an assumed storm of the reported magnitude, i.e., Dst˜=-1600 nT.

  17. Psychological mechanisms in outdoor place and weather assessment: towards a conceptual model

    Science.gov (United States)

    Knez, Igor; Thorsson, Sofia; Eliasson, Ingegärd; Lindberg, Fredrik

    2009-01-01

    The general aim has been to illuminate the psychological mechanisms involved in outdoor place and weather assessment. This reasoning was conceptualized in a model, tentatively proposing direct and indirect links of influence in an outdoor place-human relationship. The model was subsequently tested by an empirical study, performed in a Nordic city, on the impact of weather and personal factors on participants’ perceptual and emotional estimations of outdoor urban places. In line with our predictions, we report significant influences of weather parameters (air temperature, wind, and cloudlessness) and personal factors (environmental attitude and age) on participants’ perceptual and emotional estimations of outdoor urban places. All this is a modest, yet significant, step towards an understanding of the psychology of outdoor place and weather assessment.

  18. Analysis and Modeling of Influenza Outbreaks as Driven by Weather

    Science.gov (United States)

    Thrastarson, H. T.; Teixeira, J.; Serman, E. A.; Parekh, A.; Yeo, E.

    2017-12-01

    Seasonal influenza outbreaks are a major source of illness, mortality and economic burden worldwide. Attributing what drives the seasonality of the outbreaks is still an unsettled problem. But in temperate regions absolute humidity conditions are a strong candidate (Shaman et al., 2010) and some studies have associated temperature conditions with influenza outbreaks. We use humidity and temperature data from NASA's AIRS (Atmospheric Infra-Red Sounder) instrument as well as data for influenza incidence in the US and South Africa to explore the connection between weather and influenza seasonality at different spatial scales. We also incorporate influenza surveillance data, satellite data and humidity forecasts into a numerical epidemiological prediction system. Our results give support for the role of local weather conditions as drivers of the seasonality of influenza in temperate regions. This can have implications for public health efforts where forecasting of the timing and intensity of influenza outbreaks has a great potential role (e.g., aiding management and organization of vaccines, drugs and other resources).

  19. Sensitivity of mineral dissolution rates to physical weathering : A modeling approach

    Science.gov (United States)

    Opolot, Emmanuel; Finke, Peter

    2015-04-01

    There is continued interest on accurate estimation of natural weathering rates owing to their importance in soil formation, nutrient cycling, estimation of acidification in soils, rivers and lakes, and in understanding the role of silicate weathering in carbon sequestration. At the same time a challenge does exist to reconcile discrepancies between laboratory-determined weathering rates and natural weathering rates. Studies have consistently reported laboratory rates to be in orders of magnitude faster than the natural weathering rates (White, 2009). These discrepancies have mainly been attributed to (i) changes in fluid composition (ii) changes in primary mineral surfaces (reactive sites) and (iii) the formation of secondary phases; that could slow natural weathering rates. It is indeed difficult to measure the interactive effect of the intrinsic factors (e.g. mineral composition, surface area) and extrinsic factors (e.g. solution composition, climate, bioturbation) occurring at the natural setting, in the laboratory experiments. A modeling approach could be useful in this case. A number of geochemical models (e.g. PHREEQC, EQ3/EQ6) already exist and are capable of estimating mineral dissolution / precipitation rates as a function of time and mineral mass. However most of these approaches assume a constant surface area in a given volume of water (White, 2009). This assumption may become invalid especially at long time scales. One of the widely used weathering models is the PROFILE model (Sverdrup and Warfvinge, 1993). The PROFILE model takes into account the mineral composition, solution composition and surface area in determining dissolution / precipitation rates. However there is less coupling with other processes (e.g. physical weathering, clay migration, bioturbation) which could directly or indirectly influence dissolution / precipitation rates. We propose in this study a coupling between chemical weathering mechanism (defined as a function of reactive area

  20. DEM investigation of weathered rocks using a novel bond contact model

    Directory of Open Access Journals (Sweden)

    Zhenming Shi

    2015-06-01

    Full Text Available The distinct element method (DEM incorporated with a novel bond contact model was applied in this paper to shed light on the microscopic physical origin of macroscopic behaviors of weathered rock, and to achieve the changing laws of microscopic parameters from observed decaying properties of rocks during weathering. The changing laws of macroscopic mechanical properties of typical rocks were summarized based on the existing research achievements. Parametric simulations were then conducted to analyze the relationships between macroscopic and microscopic parameters, and to derive the changing laws of microscopic parameters for the DEM model. Equipped with the microscopic weathering laws, a series of DEM simulations of basic laboratory tests on weathered rock samples was performed in comparison with analytical solutions. The results reveal that the relationships between macroscopic and microscopic parameters of rocks against the weathering period can be successfully attained by parametric simulations. In addition, weathering has a significant impact on both stress–strain relationship and failure pattern of rocks.

  1. NOAA SWPC / NASA CCMC Space Weather Modeling Assessment Project: Toward the Validation of Advancements in Heliospheric Space Weather Prediction Within WSA-Enlil

    Science.gov (United States)

    Adamson, E. T.; Pizzo, V. J.; Biesecker, D. A.; Mays, M. L.; MacNeice, P. J.; Taktakishvili, A.; Viereck, R. A.

    2017-12-01

    In 2011, NOAA's Space Weather Prediction Center (SWPC) transitioned the world's first operational space weather model into use at the National Weather Service's Weather and Climate Operational Supercomputing System (WCOSS). This operational forecasting tool is comprised of the Wang-Sheeley-Arge (WSA) solar wind model coupled with the Enlil heliospheric MHD model. Relying on daily-updated photospheric magnetograms produced by the National Solar Observatory's Global Oscillation Network Group (GONG), this tool provides critical predictive knowledge of heliospheric dynamics such as high speed streams and coronal mass ejections. With the goal of advancing this predictive model and quantifying progress, SWPC and NASA's Community Coordinated Modeling Center (CCMC) have initiated a collaborative effort to assess improvements in space weather forecasts at Earth by moving from a single daily-updated magnetogram to a sequence of time-dependent magnetograms to drive the ambient inputs for the WSA-Enlil model as well as incorporating the newly developed Air Force Data Assimilative Photospheric Flux Transport (ADAPT) model. We will provide a detailed overview of the scope of this effort and discuss preliminary results from the first phase focusing on the impact of time-dependent magnetogram inputs to the WSA-Enlil model.

  2. Modeled Forecasts of Dengue Fever in San Juan, Puerto Rico Using NASA Satellite Enhanced Weather Forecasts

    Science.gov (United States)

    Morin, C.; Quattrochi, D. A.; Zavodsky, B.; Case, J.

    2015-12-01

    Dengue fever (DF) is an important mosquito transmitted disease that is strongly influenced by meteorological and environmental conditions. Recent research has focused on forecasting DF case numbers based on meteorological data. However, these forecasting tools have generally relied on empirical models that require long DF time series to train. Additionally, their accuracy has been tested retrospectively, using past meteorological data. Consequently, the operational utility of the forecasts are still in question because the error associated with weather and climate forecasts are not reflected in the results. Using up-to-date weekly dengue case numbers for model parameterization and weather forecast data as meteorological input, we produced weekly forecasts of DF cases in San Juan, Puerto Rico. Each week, the past weeks' case counts were used to re-parameterize a process-based DF model driven with updated weather forecast data to generate forecasts of DF case numbers. Real-time weather forecast data was produced using the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) system enhanced using additional high-resolution NASA satellite data. This methodology was conducted in a weekly iterative process with each DF forecast being evaluated using county-level DF cases reported by the Puerto Rico Department of Health. The one week DF forecasts were accurate especially considering the two sources of model error. First, weather forecasts were sometimes inaccurate and generally produced lower than observed temperatures. Second, the DF model was often overly influenced by the previous weeks DF case numbers, though this phenomenon could be lessened by increasing the number of simulations included in the forecast. Although these results are promising, we would like to develop a methodology to produce longer range forecasts so that public health workers can better prepare for dengue epidemics.

  3. Assessing models for ionospheric weather specifications over Australia during the 2004 Climate and Weather of the Sun-Earth-System (CAWSES) campaign

    Science.gov (United States)

    Sojka, J. J.; Thompson, D. C.; Scherliess, L.; Schunk, R. W.; Harris, T. J.

    2007-09-01

    The Utah State University (USU) Global Assimilation of Ionospheric Measurements (GAIM) program is developing assimilation models to specify ionospheric weather. In this study the Gauss Markov Kalman Filter (GMKF) GAIM model was used. The period 20 March through 19 April 2004, which spanned the Climate and Weather of the Sun-Earth-System (CAWSES) first study period, has been extensively studied to validate the performance of the GAIM model. Although the USU-GAIM model has both regional and global capabilities and can assimilate data from a wide variety of ionospheric observations, for this study the GMKF model was run in a global mode using data only from 162 ground-based GPS slant total electron content (TEC) stations and in situ measurements from three satellites. Using measurements from the 11 ionosonde stations of the Australian Department of Defence sounder network as an independent bottomside ground-truth, the International Reference Ionosphere (IRI), Ionospheric Forecast Model (IFM), and GMKF were compared for (1) monthly mean climatology and (2) the day-to-day weather during the 31 day period. A skill score was developed for the day-to-day weather by defining the IRI as the reference model. IFM is found to be a 10% improvement, while the GMKF is 39% more capable to capture weather variability. However, the study also identifies that this global version of GMKF has difficulty around sunrise, during which time the GMKF performance can be poorer than IRI. Excluding this interval from the skill score analysis increases the GMKF ability to track weather to 48%. The use of more data and different data types should further increase the GMKF's ability to capture weather variations.

  4. Assessing Individual Weather Risk-Taking and Its Role in Modeling Likelihood of Hurricane Evacuation

    Science.gov (United States)

    Stewart, A. E.

    2017-12-01

    This research focuses upon measuring an individual's level of perceived risk of different severe and extreme weather conditions using a new self-report measure, the Weather Risk-Taking Scale (WRTS). For 32 severe and extreme situations in which people could perform an unsafe behavior (e. g., remaining outside with lightning striking close by, driving over roadways covered with water, not evacuating ahead of an approaching hurricane, etc.), people rated: 1.their likelihood of performing the behavior, 2. The perceived risk of performing the behavior, 3. the expected benefits of performing the behavior, and 4. whether the behavior has actually been performed in the past. Initial development research with the measure using 246 undergraduate students examined its psychometric properties and found that it was internally consistent (Cronbach's a ranged from .87 to .93 for the four scales) and that the scales possessed good temporal (test-retest) reliability (r's ranged from .84 to .91). A second regression study involving 86 undergraduate students found that taking weather risks was associated with having taken similar risks in one's past and with the personality trait of sensation-seeking. Being more attentive to the weather and perceiving its risks when it became extreme was associated with lower likelihoods of taking weather risks (overall regression model, R2adj = 0.60). A third study involving 334 people examined the contributions of weather risk perceptions and risk-taking in modeling the self-reported likelihood of complying with a recommended evacuation ahead of a hurricane. Here, higher perceptions of hurricane risks and lower perceived benefits of risk-taking along with fear of severe weather and hurricane personal self-efficacy ratings were all statistically significant contributors to the likelihood of evacuating ahead of a hurricane. Psychological rootedness and attachment to one's home also tend to predict lack of evacuation. This research highlights the

  5. GLUE Based Uncertainty Estimation of Urban Drainage Modeling Using Weather Radar Precipitation Estimates

    DEFF Research Database (Denmark)

    Nielsen, Jesper Ellerbæk; Thorndahl, Søren Liedtke; Rasmussen, Michael R.

    2011-01-01

    the uncertainty of the weather radar rainfall input. The main findings of this work, is that the input uncertainty propagate through the urban drainage model with significant effects on the model result. The GLUE methodology is in general a usable way to explore this uncertainty although; the exact width......Distributed weather radar precipitation measurements are used as rainfall input for an urban drainage model, to simulate the runoff from a small catchment of Denmark. It is demonstrated how the Generalized Likelihood Uncertainty Estimation (GLUE) methodology can be implemented and used to estimate...

  6. Don Quixote Pond: A Small Scale Model of Weathering and Salt Accumulation

    Science.gov (United States)

    Englert, P.; Bishop, J. L.; Patel, S. N.; Gibson, E. K.; Koeberl, C.

    2015-01-01

    The formation of Don Quixote Pond in the North Fork of Wright Valley, Antarctica, is a model for unique terrestrial calcium, chlorine, and sulfate weathering, accumulation, and distribution processes. The formation of Don Quixote Pond by simple shallow and deep groundwater contrasts more complex models for Don Juan Pond in the South Fork of Wright Valley. Our study intends to understand the formation of Don Quixote Pond as unique terrestrial processes and as a model for Ca, C1, and S weathering and distribution on Mars.

  7. A critical view on temperature modelling for application in weather derivatives markets

    International Nuclear Information System (INIS)

    Šaltytė Benth, Jūratė; Benth, Fred Espen

    2012-01-01

    In this paper we present a stochastic model for daily average temperature. The model contains seasonality, a low-order autoregressive component and a variance describing the heteroskedastic residuals. The model is estimated on daily average temperature records from Stockholm (Sweden). By comparing the proposed model with the popular model of Campbell and Diebold (2005), we point out some important issues to be addressed when modelling the temperature for application in weather derivatives market. - Highlights: ► We present a stochastic model for daily average temperature, containing seasonality, a low-order autoregressive component and a variance describing the heteroskedastic residuals. ► We compare the proposed model with the popular model of Campbell and Diebold (2005). ► Some important issues to be addressed when modelling the temperature for application in weather derivatives market are pointed out.

  8. Applying Research Models and Data to Space Weather Products and Services

    Science.gov (United States)

    Viereck, R.; Onsager, T.; Pizzo, V.; Biesecker, D.

    2008-12-01

    The Space Weather Prediction Center (SWPC) has evaluated its customers and their requirements and identified a number of key areas where improvements in service can be achieved. A key element of these improvements is to use research models and data in the operational environment of SWPC. Currently, this process is initiated by multi-agency activities and completed at SWPC. The future holds great promise with the development of a Space Weather Prediction Testbed. In this presentation, we will identify the areas where critical advances in research are needed. This will be followed by a description of the models that will be made operational both in the near term as well as those that are further down the road. Then we will present the concept of the Space Weather Prediction Testbed and the plans for accepting models and implementing the transition process through this new Testbed.

  9. A short-range multi-model ensemble weather prediction system for South Africa

    CSIR Research Space (South Africa)

    Landman, S

    2010-09-01

    Full Text Available prediction system (EPS) at the South African Weather Service (SAWS) are examined. The ensemble consists of different forecasts from the 12-km LAM of the UK Met Office Unified Model (UM) and the Conformal-Cubic Atmospheric Model (CCAM) covering the South...

  10. Improving the Performance of Water Demand Forecasting Models by Using Weather Input

    NARCIS (Netherlands)

    Bakker, M.; Van Duist, H.; Van Schagen, K.; Vreeburg, J.; Rietveld, L.

    2014-01-01

    Literature shows that water demand forecasting models which use water demand as single input, are capable of generating a fairly accurate forecast. However, at changing weather conditions the forecasting errors are quite large. In this paper three different forecasting models are studied: an

  11. Development of a High Resolution Weather Forecast Model for Mesoamerica Using the NASA Ames Code I Private Cloud Computing Environment

    Science.gov (United States)

    Molthan, Andrew; Case, Jonathan; Venner, Jason; Moreno-Madrinan, Max J.; Delgado, Francisco

    2012-01-01

    Two projects at NASA Marshall Space Flight Center have collaborated to develop a high resolution weather forecast model for Mesoamerica: The NASA Short-term Prediction Research and Transition (SPoRT) Center, which integrates unique NASA satellite and weather forecast modeling capabilities into the operational weather forecasting community. NASA's SERVIR Program, which integrates satellite observations, ground-based data, and forecast models to improve disaster response in Central America, the Caribbean, Africa, and the Himalayas.

  12. Towards assimilation of InSAR data in operational weather models

    Science.gov (United States)

    Mulder, Gert; van Leijen, Freek; Barkmeijer, Jan; de Haan, Siebren; Hanssen, Ramon

    2017-04-01

    InSAR signal delays due to the varying atmospheric refractivity are a potential data source to improve weather models [1]. Especially with the launch of the new Sentinel-1 satellites, which increases data coverage, latency and accessibility, it may become possible to operationalize the assimilation of differential integrated refractivity (DIR) values in numerical weather models. Although studies exist on comparison between InSAR data and weather models [2], the impact of assimilation of DIR values in an operational weather model has never been assessed. In this study we present different ways to assimilate DIR values in an operational weather model and show the first forecast results. There are different possibilities to assimilate InSAR-data in a weather model. For example, (i) absolute DIR values can be derived using additional GNSS zenith or slant delay values, (ii) DIR values can be converted to water vapor pressures, or (iii) water vapor pressures can be derived for different heights by combining GNSS and InSAR data. However, an increasing number of assumptions in these processing steps will increase the uncertainty in the final results. Therefore, we chose to insert the InSAR derived DIR values after minimal additional processing. In this study we use the HARMONIE model [3], which is a spectral, non-hydrostatic model with a resolution of about 2.5 km. Currently, this is the operational model in 11 European countries and based on the AROME model [4]. To assimilate the DIR values in the weather model we use a simple adjustment of the weather parameters over the full slant column to match the DIR values. This is a first step towards a more sophisticated approach based on the 3D-VAR or 4D-VAR schemes [5]. Where both assimilation schemes can correct for different weather parameters simultaneously, and 4D-VAR allow us to assimilate DIR values at the exact moment of satellite overpass instead of the start of the forecast window. The approach will be demonstrated

  13. Predictive Models for Photovoltaic Electricity Production in Hot Weather Conditions

    Directory of Open Access Journals (Sweden)

    Jabar H. Yousif

    2017-07-01

    Full Text Available The process of finding a correct forecast equation for photovoltaic electricity production from renewable sources is an important matter, since knowing the factors affecting the increase in the proportion of renewable energy production and reducing the cost of the product has economic and scientific benefits. This paper proposes a mathematical model for forecasting energy production in photovoltaic (PV panels based on a self-organizing feature map (SOFM model. The proposed model is compared with other models, including the multi-layer perceptron (MLP and support vector machine (SVM models. Moreover, a mathematical model based on a polynomial function for fitting the desired output is proposed. Different practical measurement methods are used to validate the findings of the proposed neural and mathematical models such as mean square error (MSE, mean absolute error (MAE, correlation (R, and coefficient of determination (R2. The proposed SOFM model achieved a final MSE of 0.0007 in the training phase and 0.0005 in the cross-validation phase. In contrast, the SVM model resulted in a small MSE value equal to 0.0058, while the MLP model achieved a final MSE of 0.026 with a correlation coefficient of 0.9989, which indicates a strong relationship between input and output variables. The proposed SOFM model closely fits the desired results based on the R2 value, which is equal to 0.9555. Finally, the comparison results of MAE for the three models show that the SOFM model achieved a best result of 0.36156, whereas the SVM and MLP models yielded 4.53761 and 3.63927, respectively. A small MAE value indicates that the output of the SOFM model closely fits the actual results and predicts the desired output.

  14. Report 3: Guidance document on practices to model and implement Extreme Weather hazards in extended PSA

    International Nuclear Information System (INIS)

    Alzbutas, R.; Ostapchuk, S.; Borysiewicz, M.; Decker, K.; Kumar, Manorma; Haeggstroem, A.; Nitoi, M.; Groudev, P.; Parey, S.; Potempski, S.; Raimond, E.; Siklossy, T.

    2016-01-01

    The goal of this report is to provide guidance on practices to model Extreme Weather hazards and implement them in extended level 1 PSA. This report is a joint deliverable of work package 21 (WP21) and work package 22 (WP22). The general objective of WP21 is to provide guidance on all of the individual hazards selected at the End Users Workshop. This guidance is focusing on extreme weather hazards, namely: extreme wind, extreme temperature and snow pack. Other hazards, however, are considered in cases where they are correlated/ associated with the hazard under discussion. Guidance developed refers to existing guidance whenever possible. As it was recommended by end users this guidance covers questions of developing integrated and/or separated extreme weathers PSA models. (authors)

  15. CrowdSourced weather reports: An implementation of the µ model for spotting weather information in Twitter

    CSIR Research Space (South Africa)

    Butgereit, L

    2014-05-01

    Full Text Available the current weather by analysing those tweets. Keywords: Twitter, Weather, Micro-blogging 1. Introduction Twitter is a microblogging facility that allows people to freely publish 140 character status updates or tweets [1, 2]. When events are changing... secrets, etc. Being able to extract information from Twitter to create helpful intelligence is important. 3. Research Methodology Information Systems research can be divided into two paradigms: behavioural science and design science. The behavioural...

  16. Plausible Effect of Weather on Atlantic Meridional Overturning Circulation with a Coupled General Circulation Model

    Science.gov (United States)

    Liu, Zedong; Wan, Xiuquan

    2018-04-01

    The Atlantic meridional overturning circulation (AMOC) is a vital component of the global ocean circulation and the heat engine of the climate system. Through the use of a coupled general circulation model, this study examines the role of synoptic systems on the AMOC and presents evidence that internally generated high-frequency, synoptic-scale weather variability in the atmosphere could play a significant role in maintaining the overall strength and variability of the AMOC, thereby affecting climate variability and change. Results of a novel coupling technique show that the strength and variability of the AMOC are greatly reduced once the synoptic weather variability is suppressed in the coupled model. The strength and variability of the AMOC are closely linked to deep convection events at high latitudes, which could be strongly affected by the weather variability. Our results imply that synoptic weather systems are important in driving the AMOC and its variability. Thus, interactions between atmospheric weather variability and AMOC may be an important feedback mechanism of the global climate system and need to be taken into consideration in future climate change studies.

  17. Investigation of time and weather effects on crash types using full Bayesian multivariate Poisson lognormal models.

    Science.gov (United States)

    El-Basyouny, Karim; Barua, Sudip; Islam, Md Tazul

    2014-12-01

    Previous research shows that various weather elements have significant effects on crash occurrence and risk; however, little is known about how these elements affect different crash types. Consequently, this study investigates the impact of weather elements and sudden extreme snow or rain weather changes on crash type. Multivariate models were used for seven crash types using five years of daily weather and crash data collected for the entire City of Edmonton. In addition, the yearly trend and random variation of parameters across the years were analyzed by using four different modeling formulations. The proposed models were estimated in a full Bayesian context via Markov Chain Monte Carlo simulation. The multivariate Poisson lognormal model with yearly varying coefficients provided the best fit for the data according to Deviance Information Criteria. Overall, results showed that temperature and snowfall were statistically significant with intuitive signs (crashes decrease with increasing temperature; crashes increase as snowfall intensity increases) for all crash types, while rainfall was mostly insignificant. Previous snow showed mixed results, being statistically significant and positively related to certain crash types, while negatively related or insignificant in other cases. Maximum wind gust speed was found mostly insignificant with a few exceptions that were positively related to crash type. Major snow or rain events following a dry weather condition were highly significant and positively related to three crash types: Follow-Too-Close, Stop-Sign-Violation, and Ran-Off-Road crashes. The day-of-the-week dummy variables were statistically significant, indicating a possible weekly variation in exposure. Transportation authorities might use the above results to improve road safety by providing drivers with information regarding the risk of certain crash types for a particular weather condition. Copyright © 2014 Elsevier Ltd. All rights reserved.

  18. An approach to secure weather and climate models against hardware faults

    Science.gov (United States)

    Düben, Peter; Dawson, Andrew

    2017-04-01

    Enabling Earth System models to run efficiently on future supercomputers is a serious challenge for model development. Many publications study efficient parallelisation to allow better scaling of performance on an increasing number of computing cores. However, one of the most alarming threats for weather and climate predictions on future high performance computing architectures is widely ignored: the presence of hardware faults that will frequently hit large applications as we approach exascale supercomputing. Changes in the structure of weather and climate models that would allow them to be resilient against hardware faults are hardly discussed in the model development community. We present an approach to secure the dynamical core of weather and climate models against hardware faults using a backup system that stores coarse resolution copies of prognostic variables. Frequent checks of the model fields on the backup grid allow the detection of severe hardware faults, and prognostic variables that are changed by hardware faults on the model grid can be restored from the backup grid to continue model simulations with no significant delay. To justify the approach, we perform simulations with a C-grid shallow water model in the presence of frequent hardware faults. As long as the backup system is used, simulations do not crash and a high level of model quality can be maintained. The overhead due to the backup system is reasonable and additional storage requirements are small. Runtime is increased by only 13% for the shallow water model.

  19. Testing the SWAT Model with Gridded Weather Data of Different Spatial Resolutions

    Directory of Open Access Journals (Sweden)

    Youen Grusson

    2017-01-01

    Full Text Available This study explored the influence of the spatial resolution of a gridded weather dataset when inputted in the soil and water assessment tool (SWAT over the Garonne River watershed. Several datasets are compared: ground-based weather stations, the 8-km SAFRAN product (Système d’Analyse Fournissant des Renseignements Adaptés à la Nivologie, the 0.5° CFSR product (Climate Forecasting System Reanalysis and several derived SAFRAN grids upscaled to 16, 32, 64 and 128 km. The SWAT model, calibrated on weather stations, was successively run with each gridded weather dataset. Performances with SAFRAN up to 64 or 128 km were poor, due to a contraction of the spatial variance of daily precipitation. Performances with 8-km SAFRAN are similar to that of the aggregated 16- and 32-km SAFRAN grids. The ~30-km CFSR product was found to perform well at some sites, while in others, its performance was considerably inferior because of grid points where precipitation was overestimated. The same problem was found in the calibration, where data at some weather stations did not appear to be representative of the subwatershed in which they are used to compute hydrology. These results suggest that the difference in the representation of the climate was more influential than its spatial resolution, an analysis that was confirmed by similar performances obtained with the SWAT model calibrated on the 16- and 32-km SAFRAN grids. However, the better performances obtained from these two weather datasets than from the ground-based stations’ dataset confirmed the advantage of using the SAFRAN product in SWAT modelling.

  20. Modeling and projection of dengue fever cases in Guangzhou based on variation of weather factors.

    Science.gov (United States)

    Li, Chenlu; Wang, Xiaofeng; Wu, Xiaoxu; Liu, Jianing; Ji, Duoying; Du, Juan

    2017-12-15

    Dengue fever is one of the most serious vector-borne infectious diseases, especially in Guangzhou, China. Dengue viruses and their vectors Aedes albopictus are sensitive to climate change primarily in relation to weather factors. Previous research has mainly focused on identifying the relationship between climate factors and dengue cases, or developing dengue case models with some non-climate factors. However, there has been little research addressing the modeling and projection of dengue cases only from the perspective of climate change. This study considered this topic using long time series data (1998-2014). First, sensitive weather factors were identified through meta-analysis that included literature review screening, lagged analysis, and collinear analysis. Then, key factors that included monthly average temperature at a lag of two months, and monthly average relative humidity and monthly average precipitation at lags of three months were determined. Second, time series Poisson analysis was used with the generalized additive model approach to develop a dengue model based on key weather factors for January 1998 to December 2012. Data from January 2013 to July 2014 were used to validate that the model was reliable and reasonable. Finally, future weather data (January 2020 to December 2070) were input into the model to project the occurrence of dengue cases under different climate scenarios (RCP 2.6 and RCP 8.5). Longer time series analysis and scientifically selected weather variables were used to develop a dengue model to ensure reliability. The projections suggested that seasonal disease control (especially in summer and fall) and mitigation of greenhouse gas emissions could help reduce the incidence of dengue fever. The results of this study hope to provide a scientifically theoretical basis for the prevention and control of dengue fever in Guangzhou. Copyright © 2017 Elsevier B.V. All rights reserved.

  1. Evaluation and Application of the Weather Research and Forecast Model

    National Research Council Canada - National Science Library

    Passner, Jeffrey E

    2007-01-01

    ... by the U.S. Army Research Laboratory (ARL) to determine how accurate and robust the model is under a variety of meteorological conditions, with an emphasis on fine resolution, short-range forecasts in complex terrain...

  2. A review of operational, regional-scale, chemical weather forecasting models in Europe

    Czech Academy of Sciences Publication Activity Database

    Kukkonen, J.; Olsson, T.; Schultz, D.M.; Baklanov, A.; Klein, T.; Miranda, A.I.; Monteiro, A.; Hirtl, M.; Tarvainen, V.; Boy, M.; Peuch, V.H.; PoupKou, A.; Kioutsioukis, I.; Finardi, S.; Sofiev, M.; Sokhi, R.; Lehtinen, K.E.J.; Karatzas, K.; San José, R.; Astitha, M.; Kallos, G.; Schaap, M.; Reimer, E.; Jakobs, H.; Eben, Kryštof

    2012-01-01

    Roč. 12, - (2012), s. 1-87 ISSN 1680-7316 Institutional research plan: CEZ:AV0Z10300504 Keywords : chemical weather * numerical models * operational forecasting * air Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 5.510, year: 2012

  3. A Model for Community Colleges: Engaging Students in Space Weather Research

    Science.gov (United States)

    Damas, M. C.; Cheung, T. D.; Ngwira, C.; Mohamed, A.; Knipp, D. J.; Zheng, Y.; Johnson, L. P.; Mays, M. L.; Kuznetsova, M. M.; Dorsinville, R.

    2016-12-01

    Through a 2-year NSF EAGER (Early Concept Grants for Exploratory Research) award, the Queensborough Community College (QCC) of the City University of New York (CUNY), a Hispanic and minority-serving institution has implemented a high-impact integrated research and education program in solar and atmospheric physics under the umbrella discipline of space weather. A year-long space weather curriculum was developed which consists of two parts: 1) during the academic year, students are enrolled in two course-based introductory research (CURE) courses where they are introduced to space weather research; and 2) summer internship program where students are placed at partner institutions for 10-weeks. Project partners include the NASA Goddard Space Flight Center's Community Coordinated Modeling Center, CUNY/City College of New York and the University of Colorado at Boulder. We will present the results of this 2-year NSF EAGER project, including successes and challenges.

  4. Radar Scan Strategies for the Patrick Air Force Base Weather Surveillance Radar, Model-74C, Replacement

    Science.gov (United States)

    Short, David

    2008-01-01

    The 45th Weather Squadron (45 WS) is replacing the Weather Surveillance Radar, Model 74C (WSR-74C) at Patrick Air Force Base (PAFB), with a Doppler, dual polarization radar, the Radtec 43/250. A new scan strategy is needed for the Radtec 43/250, to provide high vertical resolution data over the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) launch pads, while taking advantage of the new radar's advanced capabilities for detecting severe weather phenomena associated with convection within the 45 WS area of responsibility. The Applied Meteorology Unit (AMU) developed several scan strategies customized for the operational needs of the 45 WS. The AMU also developed a plan for evaluating the scan strategies in the period prior to operational acceptance, currently scheduled for November 2008.

  5. Susceptibility of Bemisia tabaci MEAM1 (Hemiptera: Aleyrodidae to Imidacloprid, Thiamethoxam, Dinotefuran and Flupyradifurone in South Florida

    Directory of Open Access Journals (Sweden)

    Hugh A. Smith

    2016-10-01

    Full Text Available Populations of Bemisa tabaci MEAM1 were established from nineteen locations in south Florida, primarily from commercial tomato fields, and were tested using a cotton leaf petiole systemic uptake method for susceptibility to the nicotinic acetylcholine agonist insecticides imidacloprid, thiamethoxam, dinotefuran and flupyradifurone. Eleven populations produced LC50s for one or more chemicals that were not significantly different from the susceptible laboratory colony based on overlapping fiducial limits, indicating some degree of susceptibility. LC50s more than a 100-fold the laboratory colony were measured in at least one population for each material tested, indicating tolerance. LC50s (ppm from field populations ranged from 0.901–24.952 for imidacloprid, 0.965–24.430 for thiamethoxam, 0.043–3.350 for dinotefuran and 0.011–1.471 for flupyradifurone. Based on overlapping fiducial limits, there were no significant differences in relative mean potency estimates for flupyradifurone and dinotefuran in relation to imidacloprid and thiamethoxam.

  6. Modeling Temperature and Pricing Weather Derivatives Based on Temperature

    Directory of Open Access Journals (Sweden)

    Birhan Taştan

    2017-01-01

    Full Text Available This study first proposes a temperature model to calculate the temperature indices upon which temperature-based derivatives are written. The model is designed as a mean-reverting process driven by a Levy process to represent jumps and other features of temperature. Temperature indices are mainly measured as deviations from a base temperature, and, hence, the proposed model includes jumps because they may constitute an important part of this deviation for some locations. The estimated value of a temperature index and its distribution in this model apply an inversion formula to the temperature model. Second, this study develops a pricing process over calculated index values, which returns a customized price for temperature-based derivatives considering that temperature has unique effects on every economic entity. This personalized price is also used to reveal the trading behavior of a hypothesized entity in a temperature-based derivative trade with profit maximization as the objective. Thus, this study presents a new method that does not need to evaluate the risk-aversion behavior of any economic entity.

  7. Preparing Middle School Teachers to Use Science Models Effectively when Teaching about Weather and Climate Topics

    Science.gov (United States)

    Yarker, M. B.; Stanier, C. O.; Forbes, C.; Park, S.

    2012-12-01

    According to the National Science Education Standards (NSES), teachers are encouraged to use science models in the classroom as a way to aid in the understanding of the nature of the scientific process. This is of particular importance to the atmospheric science community because climate and weather models are very important when it comes to understanding current and future behaviors of our atmosphere. Although familiar with weather forecasts on television and the Internet, most people do not understand the process of using computer models to generate weather and climate forecasts. As a result, the public often misunderstands claims scientists make about their daily weather as well as the state of climate change. Therefore, it makes sense that recent research in science education indicates that scientific models and modeling should be a topic covered in K-12 classrooms as part of a comprehensive science curriculum. The purpose of this research study is to describe how three middle school teachers use science models to teach about topics in climate and weather, as well as the challenges they face incorporating models effectively into the classroom. Participants in this study took part in a week long professional development designed to orient them towards appropriate use of science models for a unit on weather, climate, and energy concepts. The course design was based on empirically tested features of effective professional development for science teachers and was aimed at teaching content to the teachers while simultaneously orienting them towards effective use of science models in the classroom in a way that both aids in learning about the content knowledge as well as how models are used in scientific inquiry. Results indicate that teachers perceive models to be physical representations that can be used as evidence to convince students that the teacher's conception of the concept is correct. Additionally, teachers tended to use them as ways to explain an idea to

  8. Waterspout Forecasting Method Over the Eastern Adriatic Using a High-Resolution Numerical Weather Model

    Science.gov (United States)

    Renko, Tanja; Ivušić, Sarah; Telišman Prtenjak, Maja; Šoljan, Vinko; Horvat, Igor

    2018-03-01

    In this study, a synoptic and mesoscale analysis was performed and Szilagyi's waterspout forecasting method was tested on ten waterspout events in the period of 2013-2016. Data regarding waterspout occurrences were collected from weather stations, an online survey at the official website of the National Meteorological and Hydrological Service of Croatia and eyewitness reports from newspapers and the internet. Synoptic weather conditions were analyzed using surface pressure fields, 500 hPa level synoptic charts, SYNOP reports and atmospheric soundings. For all observed waterspout events, a synoptic type was determined using the 500 hPa geopotential height chart. The occurrence of lightning activity was determined from the LINET lightning database, and waterspouts were divided into thunderstorm-related and "fair weather" ones. Mesoscale characteristics (with a focus on thermodynamic instability indices) were determined using the high-resolution (500 m grid length) mesoscale numerical weather model and model results were compared with the available observations. Because thermodynamic instability indices are usually insufficient for forecasting waterspout activity, the performance of the Szilagyi Waterspout Index (SWI) was tested using vertical atmospheric profiles provided by the mesoscale numerical model. The SWI successfully forecasted all waterspout events, even the winter events. This indicates that the Szilagyi's waterspout prognostic method could be used as a valid prognostic tool for the eastern Adriatic.

  9. Deterioration modeling for condition assessment of flexible pavements considering extreme weather events

    Science.gov (United States)

    Hashemi Tari, Yasamin; Shahini Shamsabadi, Salar; Birken, Ralf; Wang, Ming

    2015-04-01

    Accurate pavement management systems are essential for states' Department Of Transportation and roadway agencies to plan for cost-effective maintenance and repair (M and R) strategies. Pavement deterioration model is an imperative component of any pavement management system since the future budget and M and R plans would be developed based on the predicted pavement performance measures. It is crucial for the pavement deterioration models to consider the factors that significantly aggravate the pavement condition. While many studies have highlighted the impact of different environmental, load, and pavement's structure on the life cycle of the pavement, effect of extreme weather events such as Floods and Snow Storms have often been overlooked. In this study, a pavement deterioration model is proposed which would consider the effect of traffic loads, climate conditions, and extreme weather events. Climate, load and performance data has been compiled for over twenty years and for eight states using the Long Term Pavement Performance (LTPP) and National Oceanic and Atmospheric Administration (NOAA) databases. A stepwise regression approach is undertaken to quantify the effect of the extreme weather events, along with other influential factors on pavement performance in terms of International Roughness Index (IRI). Final results rendered more than 90% correlation with the quantified impact values of extreme weather events.

  10. Weathering model for the quantification of atmospheric oxygen evolution during the Paleoproterozoic

    Science.gov (United States)

    Yokota, Kohei; Kanzaki, Yoshiki; Murakami, Takashi

    2013-09-01

    A weathering model has been developed to quantify atmospheric oxygen evolution during the Paleoproterozoic. The weathering model calculates the concentrations of Fe2+ dissolved from Fe2+-bearing primary minerals and oxidized Fe3+ out of the dissolved Fe2+ at a given partial pressure of atmospheric oxygen (PO2) during weathering and establishes the relationships between PO2 and ϕ, where ϕ is the ratio of oxidized and then precipitated Fe3+ out of the Fe2+ dissolved from primary minerals to the dissolved Fe2+ in a whole weathering profile. The weathering model considers controlling factors of the redistribution of Fe during weathering, that is, the dissolution rate of Fe2+-bearing primary minerals, the oxidation rate of Fe2+, and the groundwater flow rate. The validity of the model was confirmed by applying the model to the experimental data of olivine dissolution carried out under low O2 conditions. The sensitivity analysis of the model has revealed that the formation time of weathering, the mineral dissolution rate and the diffusion of O2 into a weathering profile have no or slight influence on ϕ, resulting in ˜0, 0 and 0.3 changes in log(PO2) caused by four orders of magnitude change of the formation time, more than 10 orders change of the mineral dissolution rate, and assumed change of the O2 diffusion, respectively. On the other hand, the temperature, the pH and the groundwater flow rate have moderate to large effects on ϕ: 0.6, 1.4 and 1.5 changes in log(PO2) for changes of 5 °C in temperature, 0.5 in pH, and one order of magnitude in groundwater flow rate, respectively. Using possible surface temperature, pH and groundwater flow rate estimated from the literature, we calculated the ϕ-PO2 relationships which were then applied to the ϕ values of paleosols (fossil weathering profiles) formed between 2.5 and 1.8 Ga. Taking account of the constraints given by the records of mass independent fractionation in sulfur isotopes and other geological proxies (i

  11. Review and Extension of Suitability Assessment Indicators of Weather Model Output for Analyzing Decentralized Energy Systems

    Directory of Open Access Journals (Sweden)

    Hans Schermeyer

    2015-12-01

    Full Text Available Electricity from renewable energy sources (RES-E is gaining more and more influence in traditional energy and electricity markets in Europe and around the world. When modeling RES-E feed-in on a high temporal and spatial resolution, energy systems analysts frequently use data generated by numerical weather models as input since there is no spatial inclusive and comprehensive measurement data available. However, the suitability of such model data depends on the research questions at hand and should be inspected individually. This paper focuses on new methodologies to carry out a performance evaluation of solar irradiation data provided by a numerical weather model when investigating photovoltaic feed-in and effects on the electricity grid. Suitable approaches of time series analysis are researched from literature and applied to both model and measurement data. The findings and limits of these approaches are illustrated and a new set of validation indicators is presented. These novel indicators complement the assessment by measuring relevant key figures in energy systems analysis: e.g., gradients in energy supply, maximum values and volatility. Thus, the results of this paper contribute to the scientific community of energy systems analysts and researchers who aim at modeling RES-E feed-in on a high temporal and spatial resolution using weather model data.

  12. Validation of doppler lidar wind measurements with the local model of the german weather service

    Science.gov (United States)

    Werner, Christian; Leike, Ines; Streicher, Juergen; Reitebuch, Oliver; Cress, Alexander; Wergen, Werner

    2017-11-01

    Doppler lidars measure the range resolved Line-of-Sight (LOS) wind component by extracting the Doppler shift of radiation backscattered from atmospheric aerosols and molecules.The virtual instrument was tested with an existing airborne Doppler lidar to get confidence on the simulation.The local model (LM) of the German Weather Service produced data for all the levels in the atmosphere with the same information as for the global model within a finer grid (7km).

  13. Domain-Level Assessment of the Weather Running Estimate-Nowcast (WREN) Model

    Science.gov (United States)

    2016-11-01

    contaminant concentration fields resulting from atmospheric boundary layer depth uncertainty. J App Meteo Clim. 2014;53:2610–2626. Skamarock WC...Center for Atmospheric Research, which has developed a suite of Model Evaluation Tools (MET) to evaluate the accuracy of WRF forecasts. In this...Weather Impacts Decision Aid. WRF is maintained by the National Center for Atmospheric Research, which has developed a suite of Model Evaluation Tools

  14. Noodles: a tool for visualization of numerical weather model ensemble uncertainty.

    Science.gov (United States)

    Sanyal, Jibonananda; Zhang, Song; Dyer, Jamie; Mercer, Andrew; Amburn, Philip; Moorhead, Robert J

    2010-01-01

    Numerical weather prediction ensembles are routinely used for operational weather forecasting. The members of these ensembles are individual simulations with either slightly perturbed initial conditions or different model parameterizations, or occasionally both. Multi-member ensemble output is usually large, multivariate, and challenging to interpret interactively. Forecast meteorologists are interested in understanding the uncertainties associated with numerical weather prediction; specifically variability between the ensemble members. Currently, visualization of ensemble members is mostly accomplished through spaghetti plots of a single mid-troposphere pressure surface height contour. In order to explore new uncertainty visualization methods, the Weather Research and Forecasting (WRF) model was used to create a 48-hour, 18 member parameterization ensemble of the 13 March 1993 "Superstorm". A tool was designed to interactively explore the ensemble uncertainty of three important weather variables: water-vapor mixing ratio, perturbation potential temperature, and perturbation pressure. Uncertainty was quantified using individual ensemble member standard deviation, inter-quartile range, and the width of the 95% confidence interval. Bootstrapping was employed to overcome the dependence on normality in the uncertainty metrics. A coordinated view of ribbon and glyph-based uncertainty visualization, spaghetti plots, iso-pressure colormaps, and data transect plots was provided to two meteorologists for expert evaluation. They found it useful in assessing uncertainty in the data, especially in finding outliers in the ensemble run and therefore avoiding the WRF parameterizations that lead to these outliers. Additionally, the meteorologists could identify spatial regions where the uncertainty was significantly high, allowing for identification of poorly simulated storm environments and physical interpretation of these model issues.

  15. The NASA Community Coordinated Modeling Center (CCMC) Next Generation Space Weather Data Warehouse

    Science.gov (United States)

    Maddox, M. M.; Kuznetsova, M. M.; Pulkkinen, A. A.; Zheng, Y.; Rastaetter, L.; Chulaki, A.; Pembroke, A. D.; Wiegand, C.; Mullinix, R.; Boblitt, J.; Mendoza, A. M. M.; Swindell, M. J., IV; Bakshi, S. S.; Mays, M. L.; Shim, J. S.; Hesse, M.; Collado-Vega, Y. M.; Taktakishvili, A.; MacNeice, P. J.

    2014-12-01

    The Community Coordinated Modeling Center (CCMC) at NASA Goddard Space Flight Center enables, supports, and performs research and development for next generation space science and space weather models. The CCMC currently hosts a large and expanding collection of state-or-the-art, physics-based space weather models that have been developed by the international research community. There are many tools and services provided by the CCMC that are currently available world-wide, along with the ongoing development of new innovative systems and software for research, discovery, validation, visualization, and forecasting. Over the history of the CCMC's existence, there has been one constant engineering challenge - describing, managing, and disseminating data. To address the challenges that accompany an ever-expanding number of models to support, along with a growing catalog of simulation output - the CCMC is currently developing a flexible and extensible space weather data warehouse to support both internal and external systems and applications. This paper intends to chronicle the evolution and future of the CCMC's data infrastructure, and the current infrastructure re-engineering activities that seek to leverage existing community data model standards like SPASE and the IMPEx Simulation Data Model.

  16. Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2014-11-01

    Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.

  17. TS07D Empirical Geomagnetic Field Model as a Space Weather Tool

    Science.gov (United States)

    Sharp, N. M.; Stephens, G. K.; Sitnov, M. I.

    2011-12-01

    Empirical modeling and forecasting of the geomagnetic field is a key element of the space weather research. A dramatic increase in the number of data available for the terrestrial magnetosphere required a new generation of empirical models with large numbers of degrees of freedom and sophisticated data-mining techniques. A set of the corresponding data binning, fitting and visualization procedures known as the TS07D model is now available at \\url{http://geomag_field.jhuapl.edu/model/} and it is used for detailed investigation of storm-scale phenomena in the magnetosphere. However, the transformation of this research model into a practical space weather application, which implies its extensive running for validation and interaction with other space weather codes, requires its presentation in the form of a single state-of-the-art code, well documented and optimized for the highest performance. To this end, the model is implemented in the Java programming language with extensive self-sufficient library and a set of optimization tools, including multi-thread operations that assume the use of the code in multi-core computers and clusters. The results of the new code validation and optimization of its binning, fitting and visualization parts are presented as well as some examples of the processed storms are discussed.

  18. Do regional weather models contribute to better wind power forecasts? A few Norwegian case studies

    DEFF Research Database (Denmark)

    Bremnes, John Bjørnar; Giebel, Gregor

    2017-01-01

    In most operational wind power forecasting systems statistical methods are applied to map wind forecasts from numerical weather prediction (NWP) models into wind power forecasts. NWP models are complex mathematical models of the atmosphere that divide the earth’s surface into a grid. The spatial...... resolution of this grid determines how accurate meteorological processes can be modeled and thereby also limits forecast quality. In this study, two global and four regional operational NWP models with spatial horizontal resolutions ranging from 1 to 32 km were applied to make wind power forecasts up to 66...

  19. Medium-term hydrologic forecasting in mountain basins using forecasting of a mesoscale numerical weather model

    Science.gov (United States)

    Castro Heredia, L. M.; Suarez, F. I.; Fernandez, B.; Maass, T.

    2016-12-01

    For forecasting of water resources, weather outputs provide a valuable source of information which is available online. Compared to traditional ground-based meteorological gauges, weather forecasts data offer spatially and temporally continuous data not yet evaluated and used in the forecasting of water resources in mountainous regions in Chile. Nevertheless, the use of this non-conventional data has been limited or null in developing countries, basically because of the spatial resolution, despite the high potential in the management of water resources. The adequate incorporation of these data in hydrological models requires its evaluation while taking into account the features of river basins in mountainous regions. This work presents an integrated forecasting system which represents a radical change in the way of making the streamflow forecasts in Chile, where the snowmelt forecast is the principal component of water resources management. The integrated system is composed of a physically based hydrological model, which is the prediction tool itself, together with a methodology for remote sensing data gathering that allows feed the hydrological model in real time, and meteorological forecasts from NCEP-CFSv2. Previous to incorporation of meteorological forecasts into the hydrological model, the weather outputs were evaluated and downscaling using statistical downscaling methods. The hydrological forecasts were evaluated in two mountain basins in Chile for a term of six months for the snowmelt period. In every month an assimilation process was performed, and the hydrological forecast was improved. Each month, the snow cover area (from remote sensing) and the streamflow observed were used to assimilate the model parameters in order to improve the next hydrological forecast using meteorological forecasts. The operation of the system in real time shows a good agreement between the streamflow and the snow cover area observed. The hydrological model and the weather

  20. Initial Analysis of and Predictive Model Development for Weather Reroute Advisory Use

    Science.gov (United States)

    Arneson, Heather M.

    2016-01-01

    In response to severe weather conditions, traffic management coordinators specify reroutes to route air traffic around affected regions of airspace. Providing analysis and recommendations of available reroute options would assist the traffic management coordinators in making more efficient rerouting decisions. These recommendations can be developed by examining historical data to determine which previous reroute options were used in similar weather and traffic conditions. Essentially, using previous information to inform future decisions. This paper describes the initial steps and methodology used towards this goal. A method to extract relevant features from the large volume of weather data to quantify the convective weather scenario during a particular time range is presented. Similar routes are clustered. A description of the algorithm to identify which cluster of reroute advisories were actually followed by pilots is described. Models built for fifteen of the top twenty most frequently used reroute clusters correctly predict the use of the cluster for over 60 of the test examples. Results are preliminary but indicate that the methodology is worth pursuing with modifications based on insight gained from this analysis.

  1. Stochastic Hourly Weather Generator HOWGH: Validation and its Use in Pest Modelling under Present and Future Climates

    Science.gov (United States)

    Dubrovsky, M.; Hirschi, M.; Spirig, C.

    2014-12-01

    To quantify impact of the climate change on a specific pest (or any weather-dependent process) in a specific site, we may use a site-calibrated pest (or other) model and compare its outputs obtained with site-specific weather data representing present vs. perturbed climates. The input weather data may be produced by the stochastic weather generator. Apart from the quality of the pest model, the reliability of the results obtained in such experiment depend on an ability of the generator to represent the statistical structure of the real world weather series, and on the sensitivity of the pest model to possible imperfections of the generator. This contribution deals with the multivariate HOWGH weather generator, which is based on a combination of parametric and non-parametric statistical methods. Here, HOWGH is used to generate synthetic hourly series of three weather variables (solar radiation, temperature and precipitation) required by a dynamic pest model SOPRA to simulate the development of codling moth. The contribution presents results of the direct and indirect validation of HOWGH. In the direct validation, the synthetic series generated by HOWGH (various settings of its underlying model are assumed) are validated in terms of multiple climatic characteristics, focusing on the subdaily wet/dry and hot/cold spells. In the indirect validation, we assess the generator in terms of characteristics derived from the outputs of SOPRA model fed by the observed vs. synthetic series. The weather generator may be used to produce weather series representing present and future climates. In the latter case, the parameters of the generator may be modified by the climate change scenarios based on Global or Regional Climate Models. To demonstrate this feature, the results of codling moth simulations for future climate will be shown. Acknowledgements: The weather generator is developed and validated within the frame of projects WG4VALUE (project LD12029 sponsored by the Ministry

  2. Stochastic Modeling of Rainfall Series in Kelantan Using an Advanced Weather Generator

    Directory of Open Access Journals (Sweden)

    A. H. Syafrina

    2018-02-01

    Full Text Available Weather generator is a numerical tool that uses existing meteorological records to generate series of synthetic weather data. The AWE-GEN (Advanced Weather Generator model has been successful in producing a broad range of temporal scale weather variables, ranging from the high-frequency hourly values to the low-frequency inter-annual variability. In Malaysia, AWE-GEN has produced reliable projections of extreme rainfall events for some parts of Peninsular Malaysia. This study focuses on the use of AWE-GEN model to assess rainfall distribution in Kelantan. Kelantan is situated on the north east of the Peninsular, a region which is highly susceptible to flood. Embedded within the AWE-GEN model is the Neyman Scott process which employs parameters to represent physical rainfall characteristics. The use of correct probability distributions to represent the parameters is imperative to allow reliable results to be produced. This study compares the performance of two probability distributions, Weibull and Gamma to represent rainfall intensity and the better distribution found was used subsequently to simulate hourly scaled rainfall series. Thirty years of hourly scaled meteorological data from two stations in Kelantan were used in model construction. Results indicate that both probability distributions are capable of replicating the rainfall series at both stations very well, however numerical evaluations suggested that Gamma performs better. Despite Gamma not being a heavy tailed distribution, it is able to replicate the key characteristics of rainfall series and particularly extreme values. The overall simulation results showed that the AWE-GEN model is capable of generating tropical rainfall series which could be beneficial in flood preparedness studies in areas vulnerable to flood.

  3. Heavy Rainfall Simulation over Sinai Peninsula Using the Weather Research and Forecasting Model

    Directory of Open Access Journals (Sweden)

    Gamal El Afandi

    2013-01-01

    Full Text Available Heavy rainfall is one of major severe weather over Sinai Peninsula and causes many flash floods over the region. The good forecasting of rainfall is very much necessary for providing early warning before the flash flood events to avoid or minimize disasters. In the present study using the Weather Research and Forecasting (WRF Model, heavy rainfall events that occurred over Sinai Peninsula and caused flash flood have been investigated. The flash flood that occurred on January 18, 2010, over different parts of Sinai Peninsula has been predicted and analyzed using the Advanced Weather Research and Forecast (WRF-ARW Model. The predicted rainfall in four dimensions (space and time has been calibrated with the measurements recorded at rain gauge stations. The results show that the WRF model was able to capture the heavy rainfall events over different regions of Sinai. It is also observed that WRF model was able to predict rainfall in a significant consistency with real measurements. In this study, several synoptic characteristics of the depressions that developed during the course of study have been investigated. Also, several dynamic characteristics during the evolution of the depressions were studied: relative vorticity, thermal advection, and geopotential height.

  4. Processing of 3D Weather Radar Data with Application for Assimilation in the NWP Model

    Directory of Open Access Journals (Sweden)

    Ośródka Katarzyna

    2014-09-01

    Full Text Available The paper is focused on the processing of 3D weather radar data to minimize the impact of a number of errors from different sources, both meteorological and non-meteorological. The data is also quantitatively characterized in terms of its quality. A set of dedicated algorithms based on analysis of the reflectivity field pattern is described. All the developed algorithms were tested on data from the Polish radar network POLRAD. Quality control plays a key role in avoiding the introduction of incorrect information into applications using radar data. One of the quality control methods is radar data assimilation in numerical weather prediction models to estimate initial conditions of the atmosphere. The study shows an experiment with quality controlled radar data assimilation in the COAMPS model using the ensemble Kalman filter technique. The analysis proved the potential of radar data for such applications; however, further investigations will be indispensable.

  5. Coding a Weather Model: DOE-FIU Science & Technology Workforce Development Program.

    Energy Technology Data Exchange (ETDEWEB)

    Bradley, Jon David [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-12-01

    DOE Fellow, Andres Cremisini, completed a 10-week internship with Sandia National Laboratories (SNL) in Albuquerque, New Mexico. Under the management of Kristopher Klingler and the mentorship of Jon Bradley, he was tasked with conceiving and coding a realistic weather model for use in physical security applications. The objective was to make a weather model that could use real data to accurately predict wind and precipitation conditions at any location of interest on the globe at any user-determined time. The intern received guidance on software design, the C++ programming language and clear communication of project goals and ongoing progress. In addition, Mr. Cremisini was given license to structure the program however he best saw fit, an experience that will benefit ongoing research endeavors.

  6. Weather forecast

    CERN Document Server

    Courtier, P

    1994-02-07

    Weather prediction is performed using the numerical model of the atmosphere evolution.The evolution equations are derived from the Navier Stokes equation for the adiabatic part but the are very much complicated by the change of phase of water, the radiation porocess and the boundary layer.The technique used operationally is described. Weather prediction is an initial value problem and accurate initial conditions need to be specified. Due to the small number of observations available (105 ) as compared to the dimension of the model state variable (107),the problem is largely underdetermined. Techniques of optimal control and inverse problems are used and have been adapted to the large dimension of our problem. our problem.The at mosphere is a chaotic system; the implication for weather prediction is discussed. Ensemble prediction is used operationally and the technique for generating initial conditions which lead to a numerical divergence of the subsequent forecasts is described.

  7. Using the Advanced Research Version of the Weather Research and Forecasting Model (WRF-ARW) to Forecast Turbulence at Small Scales

    National Research Council Canada - National Science Library

    Passner, Jeffrey E

    2008-01-01

    ...) as well as for longer-range forecasting support. The model utilized to investigate fine-scale weather processes, the Advanced Research version of the Weather Research and Forecasting model (WRF-ARW...

  8. Characteristic 'fingerprints' of crop model responses data at different spatial resolutions to weather input

    Czech Academy of Sciences Publication Activity Database

    Angulo, C.; Rotter, R.; Trnka, Miroslav; Pirttioja, N. K.; Gaiser, T.; Hlavinka, Petr; Ewert, F.

    2013-01-01

    Roč. 49, AUG 2013 (2013), s. 104-114 ISSN 1161-0301 R&D Projects: GA MŠk(CZ) EE2.3.20.0248; GA MŠk(CZ) EE2.4.31.0056 Institutional support: RVO:67179843 Keywords : Crop model * Weather data resolution * Aggregation * Yield distribution Subject RIV: EH - Ecology, Behaviour Impact factor: 2.918, year: 2013

  9. Blizzards to hurricanes: computer modeling of hydrology, weathering, and isotopic fractionation across hydroclimatic regions

    Science.gov (United States)

    Richard MT Webb; David L. Parkhurst

    2016-01-01

    The U.S. Geological Survey’s (USGS) Water, Energy, and Biogeochemical Model (WEBMOD) was used to simulate hydrology, weathering, and isotopic fractionation in the Andrews Creek watershed in Rocky Mountain National Park, Colorado and the Icacos River watershed in the Luquillo Experimental Forest, Puerto Rico. WEBMOD includes hydrologic modules derived from the USGS...

  10. Introducing a rainfall compound distribution model based on weather patterns sub-sampling

    Directory of Open Access Journals (Sweden)

    F. Garavaglia

    2010-06-01

    Full Text Available This paper presents a probabilistic model for daily rainfall, using sub-sampling based on meteorological circulation. We classified eight typical but contrasted synoptic situations (weather patterns for France and surrounding areas, using a "bottom-up" approach, i.e. from the shape of the rain field to the synoptic situations described by geopotential fields. These weather patterns (WP provide a discriminating variable that is consistent with French climatology, and allows seasonal rainfall records to be split into more homogeneous sub-samples, in term of meteorological genesis.

    First results show how the combination of seasonal and WP sub-sampling strongly influences the identification of the asymptotic behaviour of rainfall probabilistic models. Furthermore, with this level of stratification, an asymptotic exponential behaviour of each sub-sample appears as a reasonable hypothesis. This first part is illustrated with two daily rainfall records from SE of France.

    The distribution of the multi-exponential weather patterns (MEWP is then defined as the composition, for a given season, of all WP sub-sample marginal distributions, weighted by the relative frequency of occurrence of each WP. This model is finally compared to Exponential and Generalized Pareto distributions, showing good features in terms of robustness and accuracy. These final statistical results are computed from a wide dataset of 478 rainfall chronicles spread on the southern half of France. All these data cover the 1953–2005 period.

  11. Predicting motorcycle crash injury severity using weather data and alternative Bayesian multivariate crash frequency models.

    Science.gov (United States)

    Cheng, Wen; Gill, Gurdiljot Singh; Sakrani, Taha; Dasu, Mohan; Zhou, Jiao

    2017-11-01

    Motorcycle crashes constitute a very high proportion of the overall motor vehicle fatalities in the United States, and many studies have examined the influential factors under various conditions. However, research on the impact of weather conditions on the motorcycle crash severity is not well documented. In this study, we examined the impact of weather conditions on motorcycle crash injuries at four different severity levels using San Francisco motorcycle crash injury data. Five models were developed using Full Bayesian formulation accounting for different correlations commonly seen in crash data and then compared for fitness and performance. Results indicate that the models with serial and severity variations of parameters had superior fit, and the capability of accurate crash prediction. The inferences from the parameter estimates from the five models were: an increase in the air temperature reduced the possibility of a fatal crash but had a reverse impact on crashes of other severity levels; humidity in air was not observed to have a predictable or strong impact on crashes; the occurrence of rainfall decreased the possibility of crashes for all severity levels. Transportation agencies might benefit from the research results to improve road safety by providing motorcyclists with information regarding the risk of certain crash severity levels for special weather conditions. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. Estimating Rice Yield under Changing Weather Conditions in Kenya Using CERES Rice Model

    Directory of Open Access Journals (Sweden)

    W. O. Nyang’au

    2014-01-01

    Full Text Available Effects of change in weather conditions on the yields of Basmati 370 and IR 2793-80-1 cultivated under System of Rice Intensification (SRI in Mwea and Western Kenya irrigation schemes were assessed through sensitivity analysis using the Ceres rice model v 4.5 of the DSSAT modeling system. Genetic coefficients were determined using 2010 experimental data. The model was validated using rice growth and development data during the 2011 cropping season. Two SRI farmers were selected randomly from each irrigation scheme and their farms were used as research fields. Daily maximum and minimum temperatures and precipitation were collected from the weather station in each of the irrigation schemes while daily solar radiation was generated using weatherman in the DSSAT shell. The study revealed that increase in both maximum and minimum temperatures affects Basmati 370 and IR 2793-80-1 grain yield under SRI. Increase in atmospheric CO2 concentration led to an increase in grain yield for both Basmati and IR 2793-80-1 under SRI and increase in solar radiation also had an increasing impact on both Basmati 370 and IR 2793-80-1 grain yield. The results of the study therefore show that weather conditions in Kenya affect rice yield under SRI and should be taken into consideration to improve food security.

  13. Improving Estimates of Regional Infrasound Propagation by Incorporating Three-Dimensional Weather Modeling

    Science.gov (United States)

    McKenna, M. H.; Alter, R. E.; Swearingen, M. E.; Wilson, D. K.

    2017-12-01

    Many larger sources, such as volcanic eruptions and nuclear detonations, produce infrasound (acoustic waves with a frequency lower than humans can hear, namely 0.1-20 Hz) that can propagate over global scales. But many smaller infrastructure sources, such as bridges, dams, and buildings, also produce infrasound, though with a lower amplitude that tends to propagate only over regional scales (up to 150 km). In order to accurately calculate regional-scale infrasound propagation, we have incorporated high-resolution, three-dimensional forecasts from the Weather Research and Forecasting (WRF) meteorological model into a signal propagation modeling system called Environmental Awareness for Sensor and Emitter Employment (EASEE), developed at the US Army Engineer Research and Development Center. To quantify the improvement of infrasound propagation predictions with more realistic weather data, we conducted sensitivity studies with different propagation ranges and horizontal resolutions and compared them to default predictions with no weather model data. We describe the process of incorporating WRF output into EASEE for conducting these acoustic propagation simulations and present the results of the aforementioned sensitivity studies.

  14. Significance of settling model structures and parameter subsets in modelling WWTPs under wet-weather flow and filamentous bulking conditions

    DEFF Research Database (Denmark)

    Ramin, Elham; Sin, Gürkan; Mikkelsen, Peter Steen

    2014-01-01

    Current research focuses on predicting and mitigating the impacts of high hydraulic loadings on centralized wastewater treatment plants (WWTPs) under wet-weather conditions. The maximum permissible inflow to WWTPs depends not only on the settleability of activated sludge in secondary settling tanks...... (SSTs) but also on the hydraulic behaviour of SSTs. The present study investigates the impacts of ideal and non-ideal flow (dry and wet weather) and settling (good settling and bulking) boundary conditions on the sensitivity of WWTP model outputs to uncertainties intrinsic to the one-dimensional (1-D......) SST model structures and parameters. We identify the critical sources of uncertainty in WWTP models through global sensitivity analysis (GSA) using the Benchmark simulation model No. 1 in combination with first- and second-order 1-D SST models. The results obtained illustrate that the contribution...

  15. Numerical weather prediction (NWP) and hybrid ARMA/ANN model to predict global radiation

    International Nuclear Information System (INIS)

    Voyant, Cyril; Muselli, Marc; Paoli, Christophe; Nivet, Marie-Laure

    2012-01-01

    We propose in this paper an original technique to predict global radiation using a hybrid ARMA/ANN model and data issued from a numerical weather prediction model (NWP). We particularly look at the multi-layer perceptron (MLP). After optimizing our architecture with NWP and endogenous data previously made stationary and using an innovative pre-input layer selection method, we combined it to an ARMA model from a rule based on the analysis of hourly data series. This model has been used to forecast the hourly global radiation for five places in Mediterranean area. Our technique outperforms classical models for all the places. The nRMSE for our hybrid model MLP/ARMA is 14.9% compared to 26.2% for the naïve persistence predictor. Note that in the standalone ANN case the nRMSE is 18.4%. Finally, in order to discuss the reliability of the forecaster outputs, a complementary study concerning the confidence interval of each prediction is proposed. -- Highlights: ► Time series forecasting with hybrid method based on the use of ALADIN numerical weather model, ANN and ARMA. ► Innovative pre-input layer selection method. ► Combination of optimized MLP and ARMA model obtained from a rule based on the analysis of hourly data series. ► Stationarity process (method and control) for the global radiation time series.

  16. Contributions of the ARM Program to Radiative Transfer Modeling for Climate and Weather Applications

    Science.gov (United States)

    Mlawer, Eli J.; Iacono, Michael J.; Pincus, Robert; Barker, Howard W.; Oreopoulos, Lazaros; Mitchell, David L.

    2016-01-01

    Accurate climate and weather simulations must account for all relevant physical processes and their complex interactions. Each of these atmospheric, ocean, and land processes must be considered on an appropriate spatial and temporal scale, which leads these simulations to require a substantial computational burden. One especially critical physical process is the flow of solar and thermal radiant energy through the atmosphere, which controls planetary heating and cooling and drives the large-scale dynamics that moves energy from the tropics toward the poles. Radiation calculations are therefore essential for climate and weather simulations, but are themselves quite complex even without considering the effects of variable and inhomogeneous clouds. Clear-sky radiative transfer calculations have to account for thousands of absorption lines due to water vapor, carbon dioxide, and other gases, which are irregularly distributed across the spectrum and have shapes dependent on pressure and temperature. The line-by-line (LBL) codes that treat these details have a far greater computational cost than can be afforded by global models. Therefore, the crucial requirement for accurate radiation calculations in climate and weather prediction models must be satisfied by fast solar and thermal radiation parameterizations with a high level of accuracy that has been demonstrated through extensive comparisons with LBL codes. See attachment for continuation.

  17. Understanding, modeling and predicting weather and climate extremes: Challenges and opportunities

    Directory of Open Access Journals (Sweden)

    Jana Sillmann

    2017-12-01

    Full Text Available Weather and climate extremes are identified as major areas necessitating further progress in climate research and have thus been selected as one of the World Climate Research Programme (WCRP Grand Challenges. Here, we provide an overview of current challenges and opportunities for scientific progress and cross-community collaboration on the topic of understanding, modeling and predicting extreme events based on an expert workshop organized as part of the implementation of the WCRP Grand Challenge on Weather and Climate Extremes. In general, the development of an extreme event depends on a favorable initial state, the presence of large-scale drivers, and positive local feedbacks, as well as stochastic processes. We, therefore, elaborate on the scientific challenges related to large-scale drivers and local-to-regional feedback processes leading to extreme events. A better understanding of the drivers and processes will improve the prediction of extremes and will support process-based evaluation of the representation of weather and climate extremes in climate model simulations. Further, we discuss how to address these challenges by focusing on short-duration (less than three days and long-duration (weeks to months extreme events, their underlying mechanisms and approaches for their evaluation and prediction.

  18. Comparison of Microclimate Simulated weather data to ASHRAE Clear Sky Model and Measured Data

    Energy Technology Data Exchange (ETDEWEB)

    Bhandari, Mahabir S. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

    2017-06-01

    In anticipation of emerging global urbanization and its impact on microclimate, a need exists to better understand and quantify microclimate effects on building energy use. Satisfaction of this need will require coordinated research of microclimate impacts on and from “human systems.” The Urban Microclimate and Energy Tool (Urban-MET) project seeks to address this need by quantifying and analyzing the relationships among climatic conditions, urban morphology, land cover, and energy use; and using these relationships to inform energy-efficient urban development and planning. Initial research will focus on analysis of measured and modeled energy efficiency of various building types in selected urban areas and temporal variations in energy use for different urban morphologies under different microclimatic conditions. In this report, we analyze the differences between microclimate weather data sets for the Oak Ridge National Laboratory campus produced by ENVI-met and Weather Research Forecast (WRF) models, the ASHRAE clear sky which defines the maximum amounts of solar radiation that can be expected, and measured data from a weather station on campus. Errors with climate variables and their impact on building energy consumption will be shown for the microclimate simulations to help prioritize future improvement for use in microclimate simulation impacts to energy use of buildings.

  19. Influence of forest cover changes on regional weather conditions: estimations using the mesoscale model COSMO

    Science.gov (United States)

    Olchev, A. V.; Rozinkina, I. A.; Kuzmina, E. V.; Nikitin, M. A.; Rivin, G. S.

    2018-01-01

    This modeling study intends to estimate the possible influence of forest cover change on regional weather conditions using the non-hydrostatic model COSMO. The central part of the East European Plain was selected as the ‘model region’ for the study. The results of numerical experiments conducted for the warm period of 2010 for the modeling domain covering almost the whole East European Plain showed that deforestation and afforestation processes within the selected model region of the area about 105 km2 can lead to significant changes in regional weather conditions. The deforestation processes have resulted in an increase of the air temperature and a reduction in the amount of precipitation. The afforestation processes can produce the opposite effects, as manifested in decreased air temperature and increased precipitation. Whereas a change of the air temperature is observed mainly inside of the model region, the changes of the precipitation are evident within the entire East European Plain, even in regions situated far away from the external boundaries of the model region.

  20. Relative performance of different numerical weather prediction models for short term predition of wind wnergy

    Energy Technology Data Exchange (ETDEWEB)

    Giebel, G.; Landberg, L. [Risoe National Lab., Wind Energy and Atmospheric Physics Dept., Roskilde (Denmark); Moennich, K.; Waldl, H.P. [Carl con Ossietzky Univ., Faculty of Physics, Dept. of Energy and Semiconductor, Oldenburg (Germany)

    1999-03-01

    In several approaches presented in other papers in this conference, short term forecasting of wind power for a time horizon covering the next two days is done on the basis of Numerical Weather Prediction (NWP) models. This paper explores the relative merits of HIRLAM, which is the model used by the Danish Meteorological Institute, the Deutschlandmodell from the German Weather Service and the Nested Grid Model used in the US. The performance comparison will be mainly done for a site in Germany which is in the forecasting area of both the Deutschlandmodell and HIRLAM. In addition, a comparison of measured data with the forecasts made for one site in Iowa will be included, which allows conclusions on the merits of all three models. Differences in the relative performances could be due to a better tailoring of one model to its country, or to a tighter grid, or could be a function of the distance between the grid points and the measuring site. Also the amount, in which the performance can be enhanced by the use of model output statistics (topic of other papers in this conference) could give insights into the performance of the models. (au)

  1. Weathering model in paleomagnetic field intensity measurements on ancient fired clays

    International Nuclear Information System (INIS)

    Singalas, I.; Gangas, N-H.J.; Danon, J.

    1978-03-01

    Nonlinearities observed in Thellier's plots are explained in terms of a weathering model. This model is based on the reduction in size of the originaly present iron oxide particles, due to leaching. In the general case, the slope of the Thellier's plot is a function of the particle size destributions of the magnetic particles, both newly formed and leached ones. In the special case in which the newly formed magnetic particles are superparamagnetic, the limiting value of the slope of th Thellier's plot towards the magnetic ordering temperature is equal to the ratio of the ancient field intensity to the modern one

  2. 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.

  3. Evaluating Weather Research and Forecasting Model Sensitivity to Land and Soil Conditions Representative of Karst Landscapes

    Science.gov (United States)

    Johnson, Christopher M.; Fan, Xingang; Mahmood, Rezaul; Groves, Chris; Polk, Jason S.; Yan, Jun

    2017-10-01

    Due to their particular physiographic, geomorphic, soil cover, and complex surface-subsurface hydrologic conditions, karst regions produce distinct land-atmosphere interactions. It has been found that floods and droughts over karst regions can be more pronounced than those in non-karst regions following a given rainfall event. Five convective weather events are simulated using the Weather Research and Forecasting model to explore the potential impacts of land-surface conditions on weather simulations over karst regions. Since no existing weather or climate model has the ability to represent karst landscapes, simulation experiments in this exploratory study consist of a control (default land-cover/soil types) and three land-surface conditions, including barren ground, forest, and sandy soils over the karst areas, which mimic certain karst characteristics. Results from sensitivity experiments are compared with the control simulation, as well as with the National Centers for Environmental Prediction multi-sensor precipitation analysis Stage-IV data, and near-surface atmospheric observations. Mesoscale features of surface energy partition, surface water and energy exchange, the resulting surface-air temperature and humidity, and low-level instability and convective energy are analyzed to investigate the potential land-surface impact on weather over karst regions. We conclude that: (1) barren ground used over karst regions has a pronounced effect on the overall simulation of precipitation. Barren ground provides the overall lowest root-mean-square errors and bias scores in precipitation over the peak-rain periods. Contingency table-based equitable threat and frequency bias scores suggest that the barren and forest experiments are more successful in simulating light to moderate rainfall. Variables dependent on local surface conditions show stronger contrasts between karst and non-karst regions than variables dominated by large-scale synoptic systems; (2) significant

  4. Evaluating Weather Research and Forecasting Model Sensitivity to Land and Soil Conditions Representative of Karst Landscapes

    Science.gov (United States)

    Johnson, Christopher M.; Fan, Xingang; Mahmood, Rezaul; Groves, Chris; Polk, Jason S.; Yan, Jun

    2018-03-01

    Due to their particular physiographic, geomorphic, soil cover, and complex surface-subsurface hydrologic conditions, karst regions produce distinct land-atmosphere interactions. It has been found that floods and droughts over karst regions can be more pronounced than those in non-karst regions following a given rainfall event. Five convective weather events are simulated using the Weather Research and Forecasting model to explore the potential impacts of land-surface conditions on weather simulations over karst regions. Since no existing weather or climate model has the ability to represent karst landscapes, simulation experiments in this exploratory study consist of a control (default land-cover/soil types) and three land-surface conditions, including barren ground, forest, and sandy soils over the karst areas, which mimic certain karst characteristics. Results from sensitivity experiments are compared with the control simulation, as well as with the National Centers for Environmental Prediction multi-sensor precipitation analysis Stage-IV data, and near-surface atmospheric observations. Mesoscale features of surface energy partition, surface water and energy exchange, the resulting surface-air temperature and humidity, and low-level instability and convective energy are analyzed to investigate the potential land-surface impact on weather over karst regions. We conclude that: (1) barren ground used over karst regions has a pronounced effect on the overall simulation of precipitation. Barren ground provides the overall lowest root-mean-square errors and bias scores in precipitation over the peak-rain periods. Contingency table-based equitable threat and frequency bias scores suggest that the barren and forest experiments are more successful in simulating light to moderate rainfall. Variables dependent on local surface conditions show stronger contrasts between karst and non-karst regions than variables dominated by large-scale synoptic systems; (2) significant

  5. Image-based optimization of coronal magnetic field models for improved space weather forecasting

    Science.gov (United States)

    Uritsky, V. M.; Davila, J. M.; Jones, S. I.; MacNeice, P. J.

    2017-12-01

    The existing space weather forecasting frameworks show a significant dependence on the accuracy of the photospheric magnetograms and the extrapolation models used to reconstruct the magnetic filed in the solar corona. Minor uncertainties in the magnetic field magnitude and direction near the Sun, when propagated through the heliosphere, can lead to unacceptible prediction errors at 1 AU. We argue that ground based and satellite coronagraph images can provide valid geometric constraints that could be used for improving coronal magnetic field extrapolation results, enabling more reliable forecasts of extreme space weather events such as major CMEs. In contrast to the previously developed loop segmentation codes designed for detecting compact closed-field structures above solar active regions, we focus on the large-scale geometry of the open-field coronal regions up to 1-2 solar radii above the photosphere. By applying the developed image processing techniques to high-resolution Mauna Loa Solar Observatory images, we perform an optimized 3D B-line tracing for a full Carrington rotation using the magnetic field extrapolation code developed S. Jones at al. (ApJ 2016, 2017). Our tracing results are shown to be in a good qualitative agreement with the large-scale configuration of the optical corona, and lead to a more consistent reconstruction of the large-scale coronal magnetic field geometry, and potentially more accurate global heliospheric simulation results. Several upcoming data products for the space weather forecasting community will be also discussed.

  6. Precipitation forecasting by a mesoscale numerical weather prediction (NWP model: eight years of experience

    Directory of Open Access Journals (Sweden)

    P. Kaufmann

    2003-01-01

    Full Text Available The Swiss Model, a hydrostatic numerical weather prediction model, has been used at MeteoSwiss for operational forecasting at the meso-beta scale (mesh-size 14 km from 1994 until 2001. The quality of the quantitative precipitation forecasts is evaluated for the eight years of operation. The seasonal precipitation over Switzerland and its dependence on altitude is examined for both model forecasts and observations using the Swiss rain gauge network sampling daily precipitation at over 400 stations for verification. The mean diurnal cycle of precipitation is verified against the automatic surface observation network on the basis of hourly recordings. In winter, there is no diurnal forcing of precipitation and the modelled precipitation agrees with the observed values. In summer, the convection in the model starts too early, overestimates the amount of precipitation and is too short-lived. Skill scores calculated for six-hourly precipitation sums show a constant level of performance over the model life cycle. Dry and wet seasons influence the model performance more than the model changes during its operational period. The comprehensive verification of the model precipitation is complemented by the discussion of a number of heavy rain events investigated during the RAPHAEL project. The sensitivities to a number of model components are illustrated, namely the driving boundary fields, the internal partitioning of parameterised and grid-scale precipitation, the advection scheme and the vertical resolution. While a small impact of the advection scheme had to be expected, the increasing overprediction of rain with increasing vertical resolution in the RAPHAEL case studies was larger than previously thought. The frequent update of the boundary conditions enhances the positioning of the rain in the model. Keywords: numerical weather prediction, quantitative precipitation forecast, model verification

  7. Numerical Modeling of the Severe Cold Weather Event over Central Europe (January 2006

    Directory of Open Access Journals (Sweden)

    D. Hari Prasad

    2010-01-01

    Full Text Available Cold waves commonly occur in higher latitudes under prevailing high pressure systems especially during winter season which cause serious economical loss and cold related death. Accurate prediction of such severe weather events is important for decision making by administrators and for mitigation planning. An Advanced high resolution Weather Research and Forecasting mesoscale model is used to simulate a severe cold wave event occurred during January 2006 over Europe. The model is integrated for 31 days starting from 00UTC of 1 January 2006 with 30 km horizontal resolution. Comparison of the model derived area averaged daily mean temperatures at 2m height from different zones over the central Europe with observations indicates that the model is able to simulate the occurrence of the cold wave with the observed time lag of 1 to 3days but with lesser intensity. The temperature, winds, surface pressure and the geopential heights at 500 hPa reveal that the cold wave development associates with the southward progression of a high pressure system and cold air advection. The results have good agreement with the analysis fields indicates that the model has the ability to reproduce the time evolution of the cold wave event.

  8. Biologically enhanced mineral weathering: what does it look like, can we model it?

    Science.gov (United States)

    Schulz, M. S.; Lawrence, C. R.; Harden, J. W.; White, A. F.

    2011-12-01

    The interaction between plants and minerals in soils is hugely important and poorly understood as it relates to the fate of soil carbon. Plant roots, fungi and bacteria inhabit the mineral soil and work symbiotically to extract nutrients, generally through low molecular weight exudates (organic acids, extracelluar polysachrides (EPS), siderophores, etc.). Up to 60% of photosynthetic carbon is allocated below ground as roots and exudates, both being important carbon sources in soils. Some exudates accelerate mineral weathering. To test whether plant exudates are incorporated into poorly crystalline secondary mineral phases during precipitation, we are investigating the biologic-mineral interface. We sampled 5 marine terraces along a soil chronosequence (60 to 225 ka), near Santa Cruz, CA. The effects of the biologic interactions with mineral surfaces were characterized through the use of Scanning Electron Microscopy (SEM). Morphologically, mycorrhizal fungi were observed fully surrounding minerals, fungal hyphae were shown to tunnel into primary silicate minerals and we have observed direct hyphal attachment to mineral surfaces. Fungal tunneling was seen in all 5 soils by SEM. Additionally, specific surface area (using a nitrogen BET method) of primary minerals was measured to determine if the effects of mineral tunneling are quantifiable in older soils. Results suggest that fungal tunneling is more extensive in the primary minerals of older soils. We have also examined the influence of organic acids on primary mineral weathering during soil development using a geochemical reactive transport model (CrunchFlow). Addition of organic acids in our models of soil development at Santa Cruz result in decreased activity of Fe and Al in soil pore water, which subsequently alters the spatial extent of primary mineral weathering and kaolinite precipitation. Overall, our preliminary modeling results suggest biological processes may be an important but underrepresented aspect of

  9. Weather forecast in north-western Greece: RISKMED warnings and verification of MM5 model

    Directory of Open Access Journals (Sweden)

    A. Bartzokas

    2010-02-01

    Full Text Available The meteorological model MM5 is applied operationally for the area of north-western Greece for one-year period (1 June 2007–31 May 2008. The model output is used for daily weather forecasting over the area. An early warning system is developed, by dividing the study area in 16 sub-regions and defining specific thresholds for issuing alerts for adverse weather phenomena. The verification of the model is carried out by comparing the model results with observations from three automatic meteorological stations. For air temperature and wind speed, correlation coefficients and biases are calculated, revealing that there is a significant overestimation of the early morning air temperature. For precipitation amount, yes/no contingency tables are constructed for 4 specific thresholds and some categorical statistics are applied, showing that the prediction of precipitation in the area under study is generally satisfactory. Finally, the thunderstorm warnings issued by the system are verified against the observed lightning activity.

  10. Latest Community Coordinated Modeling Center (CCMC) services and innovative tools supporting the space weather research and operational communities.

    Science.gov (United States)

    Mendoza, A. M. M.; Rastaetter, L.; Kuznetsova, M. M.; Mays, M. L.; Chulaki, A.; Shim, J. S.; MacNeice, P. J.; Taktakishvili, A.; Collado-Vega, Y. M.; Weigand, C.; Zheng, Y.; Mullinix, R.; Patel, K.; Pembroke, A. D.; Pulkkinen, A. A.; Boblitt, J. M.; Bakshi, S. S.; Tsui, T.

    2017-12-01

    The Community Coordinated Modeling Center (CCMC), with the fundamental goal of aiding the transition of modern space science models into space weather forecasting while supporting space science research, has been serving as an integral hub for over 15 years, providing invaluable resources to both space weather scientific and operational communities. CCMC has developed and provided innovative web-based point of access tools varying from: Runs-On-Request System - providing unprecedented global access to the largest collection of state-of-the-art solar and space physics models, Integrated Space Weather Analysis (iSWA) - a powerful dissemination system for space weather information, Advanced Online Visualization and Analysis tools for more accurate interpretation of model results, Standard Data formats for Simulation Data downloads, and Mobile apps to view space weather data anywhere to the scientific community. In addition to supporting research and performing model evaluations, CCMC also supports space science education by hosting summer students through local universities. In this poster, we will showcase CCMC's latest innovative tools and services, and CCMC's tools that revolutionized the way we do research and improve our operational space weather capabilities. CCMC's free tools and resources are all publicly available online (http://ccmc.gsfc.nasa.gov).

  11. Application of new methods based on ECMWF ensemble model for predicting severe convective weather situations

    Science.gov (United States)

    Lazar, Dora; Ihasz, Istvan

    2013-04-01

    The short and medium range operational forecasts, warning and alarm of the severe weather are one of the most important activities of the Hungarian Meteorological Service. Our study provides comprehensive summary of newly developed methods based on ECMWF ensemble forecasts to assist successful prediction of the convective weather situations. . In the first part of the study a brief overview is given about the components of atmospheric convection, which are the atmospheric lifting force, convergence and vertical wind shear. The atmospheric instability is often used to characterize the so-called instability index; one of the most popular and often used indexes is the convective available potential energy. Heavy convective events, like intensive storms, supercells and tornadoes are needed the vertical instability, adequate moisture and vertical wind shear. As a first step statistical studies of these three parameters are based on nine years time series of 51-member ensemble forecasting model based on convective summer time period, various statistical analyses were performed. Relationship of the rate of the convective and total precipitation and above three parameters was studied by different statistical methods. Four new visualization methods were applied for supporting successful forecasts of severe weathers. Two of the four visualization methods the ensemble meteogram and the ensemble vertical profiles had been available at the beginning of our work. Both methods show probability of the meteorological parameters for the selected location. Additionally two new methods have been developed. First method provides probability map of the event exceeding predefined values, so the incident of the spatial uncertainty is well-defined. The convective weather events are characterized by the incident of space often rhapsodic occurs rather have expected the event area can be selected so that the ensemble forecasts give very good support. Another new visualization tool shows time

  12. The Air Weather Service Primitive Equation Models. Part 1. The 6-Layer AWSPE Model. Part 2. The 7-Layer AWSPE Model.

    Science.gov (United States)

    1982-05-01

    publication could not have been complted without the assistance of many people: -Mr. Ken Campana, VSC Development Division, for very valuable discussions...Cams g.eso Miss Nary Ann Koemicki (DAN) L S A I Mrs. Mllisa Bookman (DANI) A TAB T 0. Major Terry C. Tarbell it~Ot Captain Fred P. Lewis First...forecaster portion of the 7U P. model ................ 49 -.. , LIST OF ABBREVIAT IONS AFGNC Air Forc* Global Weather Central ANN Automated Weather Network

  13. An initialization procedure for assimilating geostationary satellite data into numerical weather prediction models

    Science.gov (United States)

    Gal-Chen, T.; Schmidt, B.; Uccellini, L. W.

    1985-01-01

    An attempt was made to offset the limitations of GEO satellites for supplying timely initialization data for numerical weather prediction models (NWP). The NWP considered combined an isentropic representation of the free atmosphere with a sigma-coordinate model for the lower 200 mb. A flux form of the predictive equations described vertical transport interactions at the boundary of the two model domains, thereby accounting for the poor vertical temperature and wind field resolution of GEO satellite data. A variational analysis approach was employed to insert low resolution satellite-sensed temperature data at varying rates. The model vertical resolution was limited to that available from the satellite. Test simulations demonstrated that accuracy increases with the frequency of data updates, e.g., every 0.5-1 hr. The tests also showed that extensive cloud cover negates the capabilities of IR sensors and that microwave sensors will be needed for temperature estimations for 500-1000 mb levels.

  14. Prognostic diffusion models of the German Weather Service (DWD) under IMIS

    International Nuclear Information System (INIS)

    Jacobsen, I.

    1993-01-01

    At present the DWD can perform trajectory calculations for any place on the globe and quantitative diffusion calculations by means of a Lagrangian particle dispersion model throughout Europe. Since the smallest horizontal mesh size of the operational numerical weather forecast models amounts to 55 km, the fine structures of the low mountain ranges and ther airflows induced by them cannot be detected at present. Experiments with higher-resolution hydrostatic and not hydrostatic models show that forecasts of a resolution quality required for emergency planning are possible. Available computer technology is sufficient for operationally making such forecasts already now. The high-resolution models, however, must be carefully tested by using all available data. Among them are measuring data from nuclear power plant sites which in case of emergency are needed in order to determine the initial values. (orig.) [de

  15. Model Development for Risk Assessment of Driving on Freeway under Rainy Weather Conditions.

    Directory of Open Access Journals (Sweden)

    Xiaonan Cai

    Full Text Available Rainy weather conditions could result in significantly negative impacts on driving on freeways. However, due to lack of enough historical data and monitoring facilities, many regions are not able to establish reliable risk assessment models to identify such impacts. Given the situation, this paper provides an alternative solution where the procedure of risk assessment is developed based on drivers' subjective questionnaire and its performance is validated by using actual crash data. First, an ordered logit model was developed, based on questionnaire data collected from Freeway G15 in China, to estimate the relationship between drivers' perceived risk and factors, including vehicle type, rain intensity, traffic volume, and location. Then, weighted driving risk for different conditions was obtained by the model, and further divided into four levels of early warning (specified by colors using a rank order cluster analysis. After that, a risk matrix was established to determine which warning color should be disseminated to drivers, given a specific condition. Finally, to validate the proposed procedure, actual crash data from Freeway G15 were compared with the safety prediction based on the risk matrix. The results show that the risk matrix obtained in the study is able to predict driving risk consistent with actual safety implications, under rainy weather conditions.

  16. 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.

  17. Forcing the snow-cover model SNOWPACK with forecasted weather data

    Directory of Open Access Journals (Sweden)

    S. Bellaire

    2011-12-01

    Full Text Available Avalanche danger is often estimated based on snow cover stratigraphy and snow stability data. In Canada, single forecasting regions are very large (>50 000 km2 and snow cover data are often not available. To provide additional information on the snow cover and its seasonal evolution the Swiss snow cover model SNOWPACK was therefore coupled with a regional weather forecasting model GEM15. The output of GEM15 was compared to meteorological as well as snow cover data from Mt. Fidelity, British Columbia, Canada, for five winters between 2005 and 2010. Precipitation amounts are most difficult to predict for weather forecasting models. Therefore, we first assess the capability of the model chain to forecast new snow amounts and consequently snow depth. Forecasted precipitation amounts were generally over-estimated. The forecasted data were therefore filtered and used as input for the snow cover model. Comparison between the model output and manual observations showed that after pre-processing the input data the snow depth and new snow events were well modelled. In a case study two key factors of snow cover instability, i.e. surface hoar formation and crust formation were investigated at a single point. Over half of the relevant critical layers were reproduced. Overall, the model chain shows promising potential as a future forecasting tool for avalanche warning services in Canadian data sparse areas and could thus well be applied to similarly large regions elsewhere. However, a more detailed analysis of the simulated snow cover structure is still required.

  18. Prediction skill of rainstorm events over India in the TIGGE weather prediction models

    Science.gov (United States)

    Karuna Sagar, S.; Rajeevan, M.; Vijaya Bhaskara Rao, S.; Mitra, A. K.

    2017-12-01

    Extreme rainfall events pose a serious threat of leading to severe floods in many countries worldwide. Therefore, advance prediction of its occurrence and spatial distribution is very essential. In this paper, an analysis has been made to assess the skill of numerical weather prediction models in predicting rainstorms over India. Using gridded daily rainfall data set and objective criteria, 15 rainstorms were identified during the monsoon season (June to September). The analysis was made using three TIGGE (THe Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble) models. The models considered are the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centre for Environmental Prediction (NCEP) and the UK Met Office (UKMO). Verification of the TIGGE models for 43 observed rainstorm days from 15 rainstorm events has been made for the period 2007-2015. The comparison reveals that rainstorm events are predictable up to 5 days in advance, however with a bias in spatial distribution and intensity. The statistical parameters like mean error (ME) or Bias, root mean square error (RMSE) and correlation coefficient (CC) have been computed over the rainstorm region using the multi-model ensemble (MME) mean. The study reveals that the spread is large in ECMWF and UKMO followed by the NCEP model. Though the ensemble spread is quite small in NCEP, the ensemble member averages are not well predicted. The rank histograms suggest that the forecasts are under prediction. The modified Contiguous Rain Area (CRA) technique was used to verify the spatial as well as the quantitative skill of the TIGGE models. Overall, the contribution from the displacement and pattern errors to the total RMSE is found to be more in magnitude. The volume error increases from 24 hr forecast to 48 hr forecast in all the three models.

  19. Improving weather predictability by including land-surface model parameter uncertainty

    Science.gov (United States)

    Orth, Rene; Dutra, Emanuel; Pappenberger, Florian

    2016-04-01

    The land surface forms an important component of Earth system models and interacts nonlinearly with other parts such as ocean and atmosphere. To capture the complex and heterogenous hydrology of the land surface, land surface models include a large number of parameters impacting the coupling to other components of the Earth system model. Focusing on ECMWF's land-surface model HTESSEL we present in this study a comprehensive parameter sensitivity evaluation using multiple observational datasets in Europe. We select 6 poorly constrained effective parameters (surface runoff effective depth, skin conductivity, minimum stomatal resistance, maximum interception, soil moisture stress function shape, total soil depth) and explore their sensitivity to model outputs such as soil moisture, evapotranspiration and runoff using uncoupled simulations and coupled seasonal forecasts. Additionally we investigate the possibility to construct ensembles from the multiple land surface parameters. In the uncoupled runs we find that minimum stomatal resistance and total soil depth have the most influence on model performance. Forecast skill scores are moreover sensitive to the same parameters as HTESSEL performance in the uncoupled analysis. We demonstrate the robustness of our findings by comparing multiple best performing parameter sets and multiple randomly chosen parameter sets. We find better temperature and precipitation forecast skill with the best-performing parameter perturbations demonstrating representativeness of model performance across uncoupled (and hence less computationally demanding) and coupled settings. Finally, we construct ensemble forecasts from ensemble members derived with different best-performing parameterizations of HTESSEL. This incorporation of parameter uncertainty in the ensemble generation yields an increase in forecast skill, even beyond the skill of the default system. Orth, R., E. Dutra, and F. Pappenberger, 2016: Improving weather predictability by

  20. Crop modelling as a tool to separate the influence of the soil and weather on crop yields

    Science.gov (United States)

    Mathe-Gaspar, Gabriella; Fodor, Nandor; Pokovai, Klara; Kovacs, Geza Janos

    The yield of traditional food and feed crops in a given habitat is controlled by the soil and weather conditions as the main environmental factors. In real world it is not possible to segregate the influences of the soil and the weather on the crop production. Using simulation models there are ways to analyse the effects of the changes of soil characteristics or weather elements separately. The role of different soil characteristics can be studied in a way that the first run is considered as a control, then one of the soil characteristics is changed within a realistic range while all the other soil factors and weather inputs are left original. This way all the soil characteristic and weather elements can be changed one by one or different combinations of them can be used as input series. A more practical approach is when the role of local soils and weather are compared by a series of runs applying observed weather data from different years and real soil profiles from different fields of the selected farm. The results of the simulation can be evaluated from many different aspects: biomass or yield production, vulnerability to nitrate leaching or denitrification and profitability. In this study real Hungarian soil and weather scenarios were used that are significantly different from one another. The two main crops of Hungary were used: maize and wheat plus field pea as an addition. Pea is known as a sensitive crop to weather. 4M-simulation package was used as a modelling tool. Our group at RISSAC based on CERES and CROPGRO models has developed it. The results showed that the weather differences caused more significant changes in yields then soil differences though soils could moderate the effects of the extreme weather scenarios. The measure of reactions is meaningfully different depending on the species and cultivars. Analysis of separated effects of soil and weather factors has not only theoretical and methodological importance, but useful for the practice, too

  1. Crop modelling as a tool to separate the influences of the soil and weather on crop yields

    Science.gov (United States)

    Mathe-Gaspar, G.; Fodor, N.; Pokovai, K.; Kovacs, G. J.

    2003-04-01

    The yield of traditional food and feed crops in a given habitat is controlled by the soil and weather conditions as the main environmental factors. In real world it is not possible to segregate the influences of the soil and the weather on the crop production. Using simulation models there are ways to analyse the effects of the changes of soil characteristics or weather elements separately. The role of different soil characteristics can be studied in a way that the first run is considered as a control, then one of the soil characteristics is changed within a realistic range while all the other soil factors and weather inputs are left original. This way all the soil characteristic and weather elements can be changed one by one or different combinations of them can be used as input series. A more practical approach is when the role of local soils and weather are compared by a series of runs applying observed weather data from different years and real soil profiles from different fields of the selected farm. The results of the simulation can be evaluated from many different aspects: biomass or yield production, vulnerability to nitrate leaching or denitrification and profitability. In this study real Hungarian soil and weather scenarios were used that are significantly different from one another. The two main crops of Hungary were used: maize and wheat plus field pea as an addition. Pea is known as a sensitive crop to weather. 4M-simulation package was used as a modelling tool. Our group at RISSAC based on CERES and CROPGRO models has developed it. The results showed that the weather differences caused more significant changes in yields then soil differences though soils could moderate the effects of the extreme weather scenarios. The measure of reactions is meaningfully different depending on the species and cultivars. Analysis of separated effects of soil and weather factors has not only theoretical and methodological importance, but useful for the practice, too

  2. Generating daily weather data for ecosystem modelling in the Congo River Basin

    Science.gov (United States)

    Petritsch, Richard; Pietsch, Stephan A.

    2010-05-01

    Daily weather data are an important constraint for diverse applications in ecosystem research. In particular, temperature and precipitation are the main drivers for forest ecosystem productivity. Mechanistic modelling theory heavily relies on daily values for minimum and maximum temperatures, precipitation, incident solar radiation and vapour pressure deficit. Although the number of climate measurement stations increased during the last centuries, there are still regions with limited climate data. For example, in the WMO database there are only 16 stations located in Gabon with daily weather measurements. Additionally, the available time series are heavily affected by measurement errors or missing values. In the WMO record for Gabon, on average every second day is missing. Monthly means are more robust and may be estimated over larger areas. Therefore, a good alternative is to interpolate monthly mean values using a sparse network of measurement stations, and based on these monthly data generate daily weather data with defined characteristics. The weather generator MarkSim was developed to produce climatological time series for crop modelling in the tropics. It provides daily values for maximum and minimum temperature, precipitation and solar radiation. The monthly means can either be derived from the internal climate surfaces or prescribed as additional inputs. We compared the generated outputs observations from three climate stations in Gabon (Lastourville, Moanda and Mouilla) and found that maximum temperature and solar radiation were heavily overestimated during the long dry season. This is due to the internal dependency of the solar radiation estimates to precipitation. With no precipitation a cloudless sky is assumed and thus high incident solar radiation and a large diurnal temperature range. However, in reality it is cloudy in the Congo River Basin during the long dry season. Therefore, we applied a correction factor to solar radiation and temperature range

  3. Detection of mesoscale zones of atmospheric instabilities using remote sensing and weather forecasting model data

    Science.gov (United States)

    Winnicki, I.; Jasinski, J.; Kroszczynski, K.; Pietrek, S.

    2009-04-01

    The paper presents elements of research conducted in the Faculty of Civil Engineering and Geodesy of the Military University of Technology, Warsaw, Poland, concerning application of mesoscale models and remote sensing data to determining meteorological conditions of aircraft flight directly related with atmospheric instabilities. The quality of meteorological support of aviation depends on prompt and effective forecasting of weather conditions changes. The paper presents a computer module for detecting and monitoring zones of cloud cover, precipitation and turbulence along the aircraft flight route. It consists of programs and scripts for managing, processing and visualizing meteorological and remote sensing databases. The application was developed in Matlab® for Windows®. The module uses products of COAMPS (Coupled Ocean/Atmosphere Mesoscale Prediction System) mesoscale non-hydrostatic model of the atmosphere developed by the US Naval Research Laboratory, satellite images acquisition system from the MSG-2 (Meteosat Second Generation) of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) and meteorological radars data acquired from the Institute of Meteorology and Water Management (IMGW), Warsaw, Poland. The satellite images acquisition system and the COAMPS model are run operationally in the Faculty of Civil Engineering and Geodesy. The mesoscale model is run on an IA64 Feniks multiprocessor 64-bit computer cluster. The basic task of the module is to enable a complex analysis of data sets of miscellaneous information structure and to verify COAMPS results using satellite and radar data. The research is conducted using uniform cartographic projection of all elements of the database. Satellite and radar images are transformed into the Lambert Conformal projection of COAMPS. This facilitates simultaneous interpretation and supports decision making process for safe execution of flights. Forecasts are based on horizontal

  4. Advanced Corrections for InSAR Using GPS and Numerical Weather Models

    Science.gov (United States)

    Cossu, F.; Foster, J. H.; Amelung, F.; Varugu, B. K.; Businger, S.; Cherubini, T.

    2017-12-01

    We present results from an investigation into the application of numerical weather models for generating tropospheric correction fields for Interferometric Synthetic Aperture Radar (InSAR). We apply the technique to data acquired from a UAVSAR campaign as well as from the CosmoSkyMed satellites. The complex spatial and temporal changes in the atmospheric propagation delay of the radar signal remain the single biggest factor limiting InSAR's potential for hazard monitoring and mitigation. A new generation of InSAR systems is being built and launched, and optimizing the science and hazard applications of these systems requires advanced methodologies to mitigate tropospheric noise. We use the Weather Research and Forecasting (WRF) model to generate a 900 m spatial resolution atmospheric models covering the Big Island of Hawaii and an even higher, 300 m resolution grid over the Mauna Loa and Kilauea volcanoes. By comparing a range of approaches, from the simplest, using reanalyses based on typically available meteorological observations, through to the "kitchen-sink" approach of assimilating all relevant data sets into our custom analyses, we examine the impact of the additional data sets on the atmospheric models and their effectiveness in correcting InSAR data. We focus particularly on the assimilation of information from the more than 60 GPS sites in the island. We ingest zenith tropospheric delay estimates from these sites directly into the WRF analyses, and also perform double-difference tomography using the phase residuals from the GPS processing to robustly incorporate heterogeneous information from the GPS data into the atmospheric models. We assess our performance through comparisons of our atmospheric models with external observations not ingested into the model, and through the effectiveness of the derived phase screens in reducing InSAR variance. Comparison of the InSAR data, our atmospheric analyses, and assessments of the active local and mesoscale

  5. Urban weather data and building models for the inclusion of the urban heat island effect in building performance simulation.

    Science.gov (United States)

    Palme, M; Inostroza, L; Villacreses, G; Lobato, A; Carrasco, C

    2017-10-01

    This data article presents files supporting calculation for urban heat island (UHI) inclusion in building performance simulation (BPS). Methodology is used in the research article "From urban climate to energy consumption. Enhancing building performance simulation by including the urban heat island effect" (Palme et al., 2017) [1]. In this research, a Geographical Information System (GIS) study is done in order to statistically represent the most important urban scenarios of four South-American cities (Guayaquil, Lima, Antofagasta and Valparaíso). Then, a Principal Component Analysis (PCA) is done to obtain reference Urban Tissues Categories (UTC) to be used in urban weather simulation. The urban weather files are generated by using the Urban Weather Generator (UWG) software (version 4.1 beta). Finally, BPS is run out with the Transient System Simulation (TRNSYS) software (version 17). In this data paper, four sets of data are presented: 1) PCA data (excel) to explain how to group different urban samples in representative UTC; 2) UWG data (text) to reproduce the Urban Weather Generation for the UTC used in the four cities (4 UTC in Lima, Guayaquil, Antofagasta and 5 UTC in Valparaíso); 3) weather data (text) with the resulting rural and urban weather; 4) BPS models (text) data containing the TRNSYS models (four building models).

  6. Urban weather data and building models for the inclusion of the urban heat island effect in building performance simulation

    Directory of Open Access Journals (Sweden)

    M. Palme

    2017-10-01

    Full Text Available This data article presents files supporting calculation for urban heat island (UHI inclusion in building performance simulation (BPS. Methodology is used in the research article “From urban climate to energy consumption. Enhancing building performance simulation by including the urban heat island effect” (Palme et al., 2017 [1]. In this research, a Geographical Information System (GIS study is done in order to statistically represent the most important urban scenarios of four South-American cities (Guayaquil, Lima, Antofagasta and Valparaíso. Then, a Principal Component Analysis (PCA is done to obtain reference Urban Tissues Categories (UTC to be used in urban weather simulation. The urban weather files are generated by using the Urban Weather Generator (UWG software (version 4.1 beta. Finally, BPS is run out with the Transient System Simulation (TRNSYS software (version 17. In this data paper, four sets of data are presented: 1 PCA data (excel to explain how to group different urban samples in representative UTC; 2 UWG data (text to reproduce the Urban Weather Generation for the UTC used in the four cities (4 UTC in Lima, Guayaquil, Antofagasta and 5 UTC in Valparaíso; 3 weather data (text with the resulting rural and urban weather; 4 BPS models (text data containing the TRNSYS models (four building models.

  7. Operational forecasting based on a modified Weather Research and Forecasting model

    Energy Technology Data Exchange (ETDEWEB)

    Lundquist, J; Glascoe, L; Obrecht, J

    2010-03-18

    Accurate short-term forecasts of wind resources are required for efficient wind farm operation and ultimately for the integration of large amounts of wind-generated power into electrical grids. Siemens Energy Inc. and Lawrence Livermore National Laboratory, with the University of Colorado at Boulder, are collaborating on the design of an operational forecasting system for large wind farms. The basis of the system is the numerical weather prediction tool, the Weather Research and Forecasting (WRF) model; large-eddy simulations and data assimilation approaches are used to refine and tailor the forecasting system. Representation of the atmospheric boundary layer is modified, based on high-resolution large-eddy simulations of the atmospheric boundary. These large-eddy simulations incorporate wake effects from upwind turbines on downwind turbines as well as represent complex atmospheric variability due to complex terrain and surface features as well as atmospheric stability. Real-time hub-height wind speed and other meteorological data streams from existing wind farms are incorporated into the modeling system to enable uncertainty quantification through probabilistic forecasts. A companion investigation has identified optimal boundary-layer physics options for low-level forecasts in complex terrain, toward employing decadal WRF simulations to anticipate large-scale changes in wind resource availability due to global climate change.

  8. The Impact of Incorporating Chemistry to Numerical Weather Prediction Models: An Ensemble-Based Sensitivity Analysis

    Science.gov (United States)

    Barnard, P. A.; Arellano, A. F.

    2011-12-01

    Data assimilation has emerged as an integral part of numerical weather prediction (NWP). More recently, atmospheric chemistry processes have been incorporated into NWP models to provide forecasts and guidance on air quality. There is, however, a unique opportunity within this coupled system to investigate the additional benefit of constraining model dynamics and physics due to chemistry. Several studies have reported the strong interaction between chemistry and meteorology through radiation, transport, emission, and cloud processes. To examine its importance to NWP, we conduct an ensemble-based sensitivity analysis of meteorological fields to the chemical and aerosol fields within the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and the Data Assimilation Research Testbed (DART) framework. In particular, we examine the sensitivity of the forecasts of surface temperature and related dynamical fields to the initial conditions of dust and aerosol concentrations in the model over the continental United States within the summer 2008 time period. We use an ensemble of meteorological and chemical/aerosol predictions within WRF-Chem/DART to calculate the sensitivities. This approach is similar to recent ensemble-based sensitivity studies in NWP. The use of an ensemble prediction is appealing because the analysis does not require the adjoint of the model, which to a certain extent becomes a limitation due to the rapidly evolving models and the increasing number of different observations. Here, we introduce this approach as applied to atmospheric chemistry. We also show our initial results of the calculated sensitivities from joint assimilation experiments using a combination of conventional meteorological observations from the National Centers for Environmental Prediction, retrievals of aerosol optical depth from NASA's Moderate Resolution Imaging Spectroradiometer, and retrievals of carbon monoxide from NASA's Measurements of Pollution in the

  9. Predictive models of poly(ethylene-terephthalate) film degradation under multi-factor accelerated weathering exposures.

    Science.gov (United States)

    Gok, Abdulkerim; Ngendahimana, David K; Fagerholm, Cara L; French, Roger H; Sun, Jiayang; Bruckman, Laura S

    2017-01-01

    Accelerated weathering exposures were performed on poly(ethylene-terephthalate) (PET) films. Longitudinal multi-level predictive models as a function of PET grades and exposure types were developed for the change in yellowness index (YI) and haze (%). Exposures with similar change in YI were modeled using a linear fixed-effects modeling approach. Due to the complex nature of haze formation, measurement uncertainty, and the differences in the samples' responses, the change in haze (%) depended on individual samples' responses and a linear mixed-effects modeling approach was used. When compared to fixed-effects models, the addition of random effects in the haze formation models significantly increased the variance explained. For both modeling approaches, diagnostic plots confirmed independence and homogeneity with normally distributed residual errors. Predictive R2 values for true prediction error and predictive power of the models demonstrated that the models were not subject to over-fitting. These models enable prediction under pre-defined exposure conditions for a given exposure time (or photo-dosage in case of UV light exposure). PET degradation under cyclic exposures combining UV light and condensing humidity is caused by photolytic and hydrolytic mechanisms causing yellowing and haze formation. Quantitative knowledge of these degradation pathways enable cross-correlation of these lab-based exposures with real-world conditions for service life prediction.

  10. Effects of Real-Time NASA Vegetation Data on Model Forecasts of Severe Weather

    Science.gov (United States)

    Case, Jonathan L.; Bell, Jordan R.; LaFontaine, Frank J.; Peters-Lidard, Christa D.

    2012-01-01

    The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed a Greenness Vegetation Fraction (GVF) dataset, which is updated daily using swaths of Normalized Difference Vegetation Index data from the Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the NASA-EOS Aqua and Terra satellites. NASA SPoRT started generating daily real-time GVF composites at 1-km resolution over the Continental United States beginning 1 June 2010. A companion poster presentation (Bell et al.) primarily focuses on impact results in an offline configuration of the Noah land surface model (LSM) for the 2010 warm season, comparing the SPoRT/MODIS GVF dataset to the current operational monthly climatology GVF available within the National Centers for Environmental Prediction (NCEP) and Weather Research and Forecasting (WRF) models. This paper/presentation primarily focuses on individual case studies of severe weather events to determine the impacts and possible improvements by using the real-time, high-resolution SPoRT-MODIS GVFs in place of the coarser-resolution NCEP climatological GVFs in model simulations. The NASA-Unified WRF (NU-WRF) modeling system is employed to conduct the sensitivity simulations of individual events. The NU-WRF is an integrated modeling system based on the Advanced Research WRF dynamical core that is designed to represents aerosol, cloud, precipitation, and land processes at satellite-resolved scales in a coupled simulation environment. For this experiment, the coupling between the NASA Land Information System (LIS) and the WRF model is utilized to measure the impacts of the daily SPoRT/MODIS versus the monthly NCEP climatology GVFs. First, a spin-up run of the LIS is integrated for two years using the Noah LSM to ensure that the land surface fields reach an equilibrium state on the 4-km grid mesh used. Next, the spin-up LIS is run in two separate modes beginning on 1 June 2010, one continuing with the climatology GVFs while the

  11. Impacts of weather versus climate and driver uncertainty on multi-centennial ecosystem model simulations

    Science.gov (United States)

    Rollinson, C.; Simkins, J.; Fer, I.; Desai, A. R.; Dietze, M.

    2017-12-01

    Simulations of ecosystem dynamics and comparisons with empirical data require accurate, continuous, and often sub-daily meteorology records that are spatially aligned to the scale of the empirical data. A wealth of meteorology data for the past, present, and future is available through site-specific observations, modern reanalysis products, and gridded GCM simulations. However, these products are mismatched in spatial and temporal resolution, often with both different means and seasonal patterns. We have designed and implemented a two-step meteorological downscaling and ensemble generation method that combines multiple meteorology data products through debiasing and temporal downscaling protocols. Our methodology is designed to preserve the covariance among seven meteorological variables for use as drivers in ecosystem model simulations: temperature, precipitation, short- and longwave radiation, surface pressure, humidity, and wind. Furthermore, our method propagates uncertainty through the downscaling process and results in ensembles of meteorology that can be compared to paleoclimate reconstructions and used to analyze the effects of both high- and low-frequency climate anomalies on ecosystem dynamics. Using a multiple linear regression approach, we have combined hourly, 0.125-degree gridded data from the NLDAS (1980-present) with CRUNCEP (1901-2010) and CMIP5 historical (1850-2005), past millennium (850-1849), and future (1950-2100) GCM simulations. This has resulted in an ensemble of continuous, hourly-resolved meteorology from from the paleo era into the future with variability in weather events as well as low-frequency climatic changes. We investigate the influence of extreme sub-daily weather phenomena versus long-term climatic changes in an ensemble of ecosystem models that range in atmospheric and biological complexity. Through data assimilation with paleoclimate reconstructions of past climate, we can improve data-model comparisons using observations of

  12. Integration of Weather Research Forecast (WRF) Hurricane model with socio-economic data in an interactive web mapping service

    Science.gov (United States)

    Boehnert, J.; Wilhelmi, O.; Sampson, K. M.

    2009-12-01

    The integration of weather forecast models and socio-economic data is key to better understanding of the weather forecast and its impact upon society. Whether the forecast is looking at a hurricane approaching land or a snow storm over an urban corridor; the public is most interested in how this weather will affect day-to-day activities, and in extreme events how it will impact human lives, property and livelihoods. The GIS program at NCAR is developing an interactive web mapping portal which will integrate weather forecasts with socio-economic and infrastructure data. This integration of data is essential to better communication of the weather models and their impact on society. As a pilot project, we are conducting a case study on hurricane Ike, which made landfall at Galveston, Texas on 13 September, 2008, with winds greater than 70 mph. There was heavy flooding and loss of electricity due to high winds. This case study is an extreme event, which we are using to demonstrate how the Weather Research Forecasts (WRF) model runs at NCAR can be used to answer questions about how storms impact society. We are integrating WRF model output with the U.S. Census and infrastructure data in a Geographic Information System (GIS) web mapping framework. In this case study, we have identified a series of questions and custom queries which can be viewed through the interactive web portal; such as who will be affected by rain greater than 5 mm/h, or which schools will be affected by winds greater than 90 mph. These types of queries demonstrate the power of GIS and the necessity of integrating weather models with other spatial data in order to improve its effectiveness and understanding for society.

  13. Cloud detection using Meteosat imagery and numerical weather prediction model data

    CERN Document Server

    Feijt, A; Van der Veen, S

    2000-01-01

    The cloud detection algorithm of the Royal Netherlands Meteorological Institute (KNMI) Meteosat Cloud Detection and Characterization KNMI (Metclock) scheme is introduced. The algorithm analyzes the Meteosat infrared and visual channel measurements over an area from about 25 degrees W to 25 degrees E and from 35 degrees to 70 degrees N, encompassing Europe and a small part of northern Africa. The scheme utilizes surface temperatures from a numerical weather prediction model. Synoptic observations are used to adjust the model surface temperatures to represent satellite brightness temperatures for cloud-free conditions. The measured reflected sunlight is analyzed using a minimum reflectivity atlas. Comparison of cloud detection results with synoptic observations of cloud cover at about 800 synoptic stations over land and 50 over sea were made on a 3-h basis for 1997. In total, two million synoptic observations were used to evaluate the detection method. Of the reported cloud cover, Metclock detected 89% during d...

  14. High resolution weather data for urban hydrological modelling and impact assessment, ICT requirements and future challenges

    Science.gov (United States)

    ten Veldhuis, Marie-claire; van Riemsdijk, Birna

    2013-04-01

    Hydrological analysis of urban catchments requires high resolution rainfall and catchment information because of the small size of these catchments, high spatial variability of the urban fabric, fast runoff processes and related short response times. Rainfall information available from traditional radar and rain gauge networks does no not meet the relevant scales of urban hydrology. A new type of weather radars, based on X-band frequency and equipped with Doppler and dual polarimetry capabilities, promises to provide more accurate rainfall estimates at the spatial and temporal scales that are required for urban hydrological analysis. Recently, the RAINGAIN project was started to analyse the applicability of this new type of radars in the context of urban hydrological modelling. In this project, meteorologists and hydrologists work closely together in several stages of urban hydrological analysis: from the acquisition procedure of novel and high-end radar products to data acquisition and processing, rainfall data retrieval, hydrological event analysis and forecasting. The project comprises of four pilot locations with various characteristics of weather radar equipment, ground stations, urban hydrological systems, modelling approaches and requirements. Access to data processing and modelling software is handled in different ways in the pilots, depending on ownership and user context. Sharing of data and software among pilots and with the outside world is an ongoing topic of discussion. The availability of high resolution weather data augments requirements with respect to the resolution of hydrological models and input data. This has led to the development of fully distributed hydrological models, the implementation of which remains limited by the unavailability of hydrological input data. On the other hand, if models are to be used in flood forecasting, hydrological models need to be computationally efficient to enable fast responses to extreme event conditions. This

  15. A new model of tropospheric directional gradients and its application to investigate specific extreme weather events

    Science.gov (United States)

    Masoumi, Salim; McClusky, Simon; Koulali, Achraf; Tregoning, Paul

    2016-04-01

    Troposphere is usually considered azimuthally symmetric in mapping functions used in Global Positioning System (GPS) modelling. To compensate for this assumption, horizontal gradients are generally estimated together with other parameters. However, the gradient model currently in use by most analysts is a simple planar model. While such a planar model is sufficient in most cases, there are specific cases where a plane is not truly representative of the troposphere. A new model of gradients is developed and implemented in which the gradients are estimated at different directions around the site with a piecewise linear function between the nodes. The new model has the capability to detect isolated rapid spatial changes in specific azimuth angles. Simulations are performed to validate this capability of the model and to evaluate the effect of the new model on other parameters. A real case study of the 9 September 2002 extreme precipitation in Southern France is used as an example of how the directional gradients can provide information about the local variability of the troposphere around a GPS site, and how they might potentially be used for investigating specific extreme weather events.

  16. Improving aerosol interaction with clouds and precipitation in a regional chemical weather modeling system

    Directory of Open Access Journals (Sweden)

    C. Zhou

    2016-01-01

    Full Text Available A comprehensive aerosol–cloud–precipitation interaction (ACI scheme has been developed under a China Meteorological Administration (CMA chemical weather modeling system, GRAPES/CUACE (Global/Regional Assimilation and PrEdiction System, CMA Unified Atmospheric Chemistry Environment. Calculated by a sectional aerosol activation scheme based on the information of size and mass from CUACE and the thermal-dynamic and humid states from the weather model GRAPES at each time step, the cloud condensation nuclei (CCN are interactively fed online into a two-moment cloud scheme (WRF Double-Moment 6-class scheme – WDM6 and a convective parameterization to drive cloud physics and precipitation formation processes. The modeling system has been applied to study the ACI for January 2013 when several persistent haze-fog events and eight precipitation events occurred.The results show that aerosols that interact with the WDM6 in GRAPES/CUACE obviously increase the total cloud water, liquid water content, and cloud droplet number concentrations, while decreasing the mean diameters of cloud droplets with varying magnitudes of the changes in each case and region. These interactive microphysical properties of clouds improve the calculation of their collection growth rates in some regions and hence the precipitation rate and distributions in the model, showing 24 to 48 % enhancements of threat score for 6 h precipitation in almost all regions. The aerosols that interact with the WDM6 also reduce the regional mean bias of temperature by 3 °C during certain precipitation events, but the monthly means bias is only reduced by about 0.3 °C.

  17. A PBL-radiation model for application to regional numerical weather prediction

    Science.gov (United States)

    Chang, Chia-Bo

    1989-01-01

    Often in the short-range limited-area numerical weather prediction (NWP) of extratropical weather systems the effects of planetary boundary layer (PBL) processes are considered secondarily important. However, it may not be the case for the regional NWP of mesoscale convective systems over the arid and semi-arid highlands of the southwestern and south-central United States in late spring and summer. Over these dry regions, the PBL can grow quite high up into the lower middle troposphere (600 mb) due to very effective solar heating and hence a vigorous air-land thermal interaction can occur. The interaction representing a major heat source for regional dynamical systems can not be ignored. A one-dimensional PBL-radiation model was developed. The model PBL consists of a constant-flux surface layer superposed with a well-mixed (Ekman) layer. The vertical eddy mixing coefficients for heat and momentum in the surface layer are determined according to the surface similarity theory, while their vertical profiles in the Ekman layer are specified with a cubic polynomial. Prognostic equations are used for predicting the height of the nonneutral PBL. The atmospheric radiation is parameterized to define the surface heat source/sink for the growth and decay of the PBL. A series of real-data numerical experiments has been carried out to obtain a physical understanding how the model performs under various atmospheric and surface conditions. This one-dimensional model will eventually be incorporated into a mesoscale prediction system. The ultimate goal of this research is to improve the NWP of mesoscale convective storms over land.

  18. Adjusting weather radar data to rain gauge measurements with data-driven models

    Science.gov (United States)

    Teschl, Reinhard; Randeu, Walter; Teschl, Franz

    2010-05-01

    Weather radar networks provide data with good spatial coverage and temporal resolution. Hence they are able to describe the variability of precipitation. Typical radar stations determine the rain rate for every square kilometre and make a full volume scan within about 5 minutes. A weakness however, is their often poor metering precision limiting the applicability of the radar for hydrological purposes. In contrast to rain gauges, which measure precipitation directly on the ground, the radar determines the reflectivity aloft and remote. Due to this principle, several sources of possible errors occur. Therefore improving the radar estimates of rainfall is still a vital topic in radar meteorology and hydrology. This paper presents data-driven approaches to improve radar estimates of rainfall by mapping radar reflectivity measurements Z to rain gauge data R. The analysis encompasses several input configurations and data-driven models. Reflectivity measurements at a constant altitude and the vertical profiles of reflectivity above a rain gauge are used as input parameters. The applied models are Artificial Neural Network (ANN), Model Tree (MT), and IBk a k-nearest-neighbour classifier. The relationship found between the data of a rain gauge and the reflectivity measurements is subsequently applied to another site with comparable terrain. Based on this independent dataset the performance of the data-driven models in the various input configurations is evaluated. For this study, rain gauge and radar data from the province of Styria, Austria, were available. The data sets extend over a two-year period (2001 and 2002). The available rain gauges use the tipping bucket principle with a resolution of 0.1 mm. Reflectivity measurements are obtained from the Doppler weather radar station on Mt. Zirbitzkogel (by courtesy of AustroControl GmbH). The designated radar is a high-resolution C-band weather-radar situated at an altitude of 2372 m above mean sea level. The data

  19. Coupling a Mesoscale Numerical Weather Prediction Model with Large-Eddy Simulation for Realistic Wind Plant Aerodynamics Simulations (Poster)

    Energy Technology Data Exchange (ETDEWEB)

    Draxl, C.; Churchfield, M.; Mirocha, J.; Lee, S.; Lundquist, J.; Michalakes, J.; Moriarty, P.; Purkayastha, A.; Sprague, M.; Vanderwende, B.

    2014-06-01

    Wind plant aerodynamics are influenced by a combination of microscale and mesoscale phenomena. Incorporating mesoscale atmospheric forcing (e.g., diurnal cycles and frontal passages) into wind plant simulations can lead to a more accurate representation of microscale flows, aerodynamics, and wind turbine/plant performance. Our goal is to couple a numerical weather prediction model that can represent mesoscale flow [specifically the Weather Research and Forecasting model] with a microscale LES model (OpenFOAM) that can predict microscale turbulence and wake losses.

  20. Space Weather Analysis

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Space Weather Analysis archives are model output of ionospheric, thermospheric and magnetospheric particle populations, energies and electrodynamics

  1. Space Weather Laboratory

    Data.gov (United States)

    Federal Laboratory Consortium — The Space Weather Computational Laboratory is a Unix and PC based modeling and simulation facility devoted to research analysis of naturally occurring electrically...

  2. Integrating K-means Clustering with Kernel Density Estimation for the Development of a Conditional Weather Generation Downscaling Model

    Science.gov (United States)

    Chen, Y.; Ho, C.; Chang, L.

    2011-12-01

    In previous decades, the climate change caused by global warming increases the occurrence frequency of extreme hydrological events. Water supply shortages caused by extreme events create great challenges for water resource management. To evaluate future climate variations, general circulation models (GCMs) are the most wildly known tools which shows possible weather conditions under pre-defined CO2 emission scenarios announced by IPCC. Because the study area of GCMs is the entire earth, the grid sizes of GCMs are much larger than the basin scale. To overcome the gap, a statistic downscaling technique can transform the regional scale weather factors into basin scale precipitations. The statistic downscaling technique can be divided into three categories include transfer function, weather generator and weather type. The first two categories describe the relationships between the weather factors and precipitations respectively based on deterministic algorithms, such as linear or nonlinear regression and ANN, and stochastic approaches, such as Markov chain theory and statistical distributions. In the weather type, the method has ability to cluster weather factors, which are high dimensional and continuous variables, into weather types, which are limited number of discrete states. In this study, the proposed downscaling model integrates the weather type, using the K-means clustering algorithm, and the weather generator, using the kernel density estimation. The study area is Shihmen basin in northern of Taiwan. In this study, the research process contains two steps, a calibration step and a synthesis step. Three sub-steps were used in the calibration step. First, weather factors, such as pressures, humidities and wind speeds, obtained from NCEP and the precipitations observed from rainfall stations were collected for downscaling. Second, the K-means clustering grouped the weather factors into four weather types. Third, the Markov chain transition matrixes and the

  3. Influence of weathering and pre-existing large scale fractures on gravitational slope failure: insights from 3-D physical modelling

    Directory of Open Access Journals (Sweden)

    D. Bachmann

    2004-01-01

    Full Text Available Using a new 3-D physical modelling technique we investigated the initiation and evolution of large scale landslides in presence of pre-existing large scale fractures and taking into account the slope material weakening due to the alteration/weathering. The modelling technique is based on the specially developed properly scaled analogue materials, as well as on the original vertical accelerator device enabling increases in the 'gravity acceleration' up to a factor 50. The weathering primarily affects the uppermost layers through the water circulation. We simulated the effect of this process by making models of two parts. The shallower one represents the zone subject to homogeneous weathering and is made of low strength material of compressive strength σl. The deeper (core part of the model is stronger and simulates intact rocks. Deformation of such a model subjected to the gravity force occurred only in its upper (low strength layer. In another set of experiments, low strength (σw narrow planar zones sub-parallel to the slope surface (σwl were introduced into the model's superficial low strength layer to simulate localized highly weathered zones. In this configuration landslides were initiated much easier (at lower 'gravity force', were shallower and had smaller horizontal size largely defined by the weak zone size. Pre-existing fractures were introduced into the model by cutting it along a given plan. They have proved to be of small influence on the slope stability, except when they were associated to highly weathered zones. In this latter case the fractures laterally limited the slides. Deep seated rockslides initiation is thus directly defined by the mechanical structure of the hillslope's uppermost levels and especially by the presence of the weak zones due to the weathering. The large scale fractures play a more passive role and can only influence the shape and the volume of the sliding units.

  4. Trends of air pollution in Denmark - Normalised by a simple weather index model

    International Nuclear Information System (INIS)

    Kiilsholm, S.; Rasmussen, A.

    2000-01-01

    This report is a part of the Traffic Pool projects on 'Traffic and Environments', 1995-99, financed by the Danish Ministry of Transport. The Traffic Pool projects included five different projects on 'Surveillance of the Air Quality', 'Atmospheric Modelling', 'Atmospheric Chemistry Modelling', 'Smog and ozone' and 'Greenhouse effects and Climate', [Rasmussen, 2000]. This work is a part of the project on 'Surveillance of the Air Quality' with the main objectives to make trend analysis of levels of air pollution from traffic in Denmark. Other participants were from the Road Directory mainly focusing on measurement of traffic and trend analysis of the air quality utilising a nordic model for the air pollution in street canyons called BLB (Beregningsmodel for Luftkvalitet i Byluftgader) [Vejdirektoratet 2000], National Environmental Research Institute (HERI) mainly focusing on. measurements of air pollution and trend analysis with the Operational Street Pollution Model (OSPM) [DMU 2000], and the Copenhagen Environmental Protection Agency mainly focusing on measurements. In this study a more simple statistical model has been developed for trend analysis of the air quality. The model is filtering out the influence of the variations from year to year in the meteorological conditions on the air pollution levels. The weather factors found most important are wind speed, wind direction and mixing height. Measurements of CO, NO and NO 2 from three streets in Copenhagen have been used, these streets are Jagtvej, Bredgade and H. C. Andersen's Boulevard (HCAB). The years 1994-1996 were used for evaluation of the method and annual indexes of air pollution index dependent only on meteorological parameters, called WEATHIX, were calculated for the years 1990-1997 and used for normalisation of the observed air pollution trends. Meteorological data were taken from either the background stations at the H.C. Oersted - building situated close to one of the street stations or the synoptic

  5. A Two-Dimensional Gridded Solar Forecasting System using Situation-Dependent Blending of Multiple Weather Models

    Science.gov (United States)

    Lu, S.; Hwang, Y.; Shao, X.; Hamann, H.

    2015-12-01

    Previously, we reported the application of a "weather situation" dependent multi-model blending approach to improve the forecast accuracy of solar irradiance and other atmospheric parameters. The approach uses machine-learning techniques to classify "weather situations" by a set of atmospheric parameters. The "weather situation" classification is location-dependent and each "weather situation" has characteristic forecast errors from a set of individual input numerical weather prediction (NWP) models. The input models are thus corrected or combined differently for different "weather situations" to minimize the overall forecast error. While the original implementation of the model-blending is applicable to only point-like locations having historical data of both measurements and forecasts, here we extend the approach to provide two-dimensional (2D) gridded forecasts. An experimental 2D forecasting system has been set up to provide gridded forecasts of solar irradiance (global horizontal irradiance), temperature, wind speed, and humidity for the contiguous United States (CONUS). Validation results show around 30% enhancement of 0 to 48 hour ahead solar irradiance forecast accuracy compared to the best input NWP model. The forecasting system may be leveraged by other site- or region-specific solar energy forecast products. To enable the 2D forecasting system, historical solar irradiance measurements from around 1,600 selected sites of the remote automated weather stations (RAWS) network have been employed. The CONUS was divided into smaller sub-regions, each containing a group of 10 to 20 RAWS sites. A group of sites, as classified by statistical analysis, have similar "weather patterns", i.e. the NWPs have similar "weather situation" dependent forecast errors for all sites in a group. The model-blending trained by the historical data from a group of sites is then applied for all locations in the corresponding sub-region. We discuss some key techniques developed for

  6. Ensemble downscaling in coupled solar wind-magnetosphere modeling for space weather forecasting.

    Science.gov (United States)

    Owens, M J; Horbury, T S; Wicks, R T; McGregor, S L; Savani, N P; Xiong, M

    2014-06-01

    Advanced forecasting of space weather requires simulation of the whole Sun-to-Earth system, which necessitates driving magnetospheric models with the outputs from solar wind models. This presents a fundamental difficulty, as the magnetosphere is sensitive to both large-scale solar wind structures, which can be captured by solar wind models, and small-scale solar wind "noise," which is far below typical solar wind model resolution and results primarily from stochastic processes. Following similar approaches in terrestrial climate modeling, we propose statistical "downscaling" of solar wind model results prior to their use as input to a magnetospheric model. As magnetospheric response can be highly nonlinear, this is preferable to downscaling the results of magnetospheric modeling. To demonstrate the benefit of this approach, we first approximate solar wind model output by smoothing solar wind observations with an 8 h filter, then add small-scale structure back in through the addition of random noise with the observed spectral characteristics. Here we use a very simple parameterization of noise based upon the observed probability distribution functions of solar wind parameters, but more sophisticated methods will be developed in the future. An ensemble of results from the simple downscaling scheme are tested using a model-independent method and shown to add value to the magnetospheric forecast, both improving the best estimate and quantifying the uncertainty. We suggest a number of features desirable in an operational solar wind downscaling scheme. Solar wind models must be downscaled in order to drive magnetospheric models Ensemble downscaling is more effective than deterministic downscaling The magnetosphere responds nonlinearly to small-scale solar wind fluctuations.

  7. Weather Correlations to Calculate Infiltration Rates for U. S. Commercial Building Energy Models.

    Science.gov (United States)

    Ng, Lisa C; Quiles, Nelson Ojeda; Dols, W Stuart; Emmerich, Steven J

    2018-01-01

    As building envelope performance improves, a greater percentage of building energy loss will occur through envelope leakage. Although the energy impacts of infiltration on building energy use can be significant, current energy simulation software have limited ability to accurately account for envelope infiltration and the impacts of improved airtightness. This paper extends previous work by the National Institute of Standards and Technology that developed a set of EnergyPlus inputs for modeling infiltration in several commercial reference buildings using Chicago weather. The current work includes cities in seven additional climate zones and uses the updated versions of the prototype commercial building types developed by the Pacific Northwest National Laboratory for the U. S. Department of Energy. Comparisons were made between the predicted infiltration rates using three representations of the commercial building types: PNNL EnergyPlus models, CONTAM models, and EnergyPlus models using the infiltration inputs developed in this paper. The newly developed infiltration inputs in EnergyPlus yielded average annual increases of 3 % and 8 % in the HVAC electrical and gas use, respectively, over the original infiltration inputs in the PNNL EnergyPlus models. When analyzing the benefits of building envelope airtightening, greater HVAC energy savings were predicted using the newly developed infiltration inputs in EnergyPlus compared with using the original infiltration inputs. These results indicate that the effects of infiltration on HVAC energy use can be significant and that infiltration can and should be better accounted for in whole-building energy models.

  8. Predicting the weathering of fuel and oil spills: A diffusion-limited evaporation model.

    Science.gov (United States)

    Kotzakoulakis, Konstantinos; George, Simon C

    2018-01-01

    The majority of the evaporation models currently available in the literature for the prediction of oil spill weathering do not take into account diffusion-limited mass transport and the formation of a concentration gradient in the oil phase. The altered surface concentration of the spill caused by diffusion-limited transport leads to a slower evaporation rate compared to the predictions of diffusion-agnostic evaporation models. The model presented in this study incorporates a diffusive layer in the oil phase and predicts the diffusion-limited evaporation rate. The information required is the composition of the fluid from gas chromatography or alternatively the distillation data. If the density or a single viscosity measurement is available the accuracy of the predictions is higher. Environmental conditions such as water temperature, air pressure and wind velocity are taken into account. The model was tested with synthetic mixtures, petroleum fuels and crude oils with initial viscosities ranging from 2 to 13,000 cSt. The tested temperatures varied from 0 °C to 23.4 °C and wind velocities from 0.3 to 3.8 m/s. The average absolute deviation (AAD) of the diffusion-limited model ranged between 1.62% and 24.87%. In comparison, the AAD of a diffusion-agnostic model ranged between 2.34% and 136.62% against the same tested fluids. Copyright © 2017 Elsevier Ltd. All rights reserved.

  9. Relative importance of fuel management, ignition management and weather for area burned: Evidence from five landscape-fire-succession models

    Science.gov (United States)

    Geoffrey J. Cary; Mike D. Flannigan; Robert E. Keane; Ross A. Bradstock; Ian D. Davies; James M. Lenihan; Chao Li; Kimberley A. Logan; Russell A. Parsons

    2009-01-01

    The behaviour of five landscape fire models (CAFE, FIRESCAPE, LAMOS(HS), LANDSUM and SEMLAND) was compared in a standardised modelling experiment. The importance of fuel management approach, fuel management effort, ignition management effort and weather in determining variation in area burned and number of edge pixels burned (a measure of potential impact on assets...

  10. Reference Evapotranspiration Retrievals from a Mesoscale Model Based Weather Variables for Soil Moisture Deficit Estimation

    Directory of Open Access Journals (Sweden)

    Prashant K. Srivastava

    2017-10-01

    Full Text Available Reference Evapotranspiration (ETo and soil moisture deficit (SMD are vital for understanding the hydrological processes, particularly in the context of sustainable water use efficiency in the globe. Precise estimation of ETo and SMD are required for developing appropriate forecasting systems, in hydrological modeling and also in precision agriculture. In this study, the surface temperature downscaled from Weather Research and Forecasting (WRF model is used to estimate ETo using the boundary conditions that are provided by the European Center for Medium Range Weather Forecast (ECMWF. In order to understand the performance, the Hamon’s method is employed to estimate the ETo using the temperature from meteorological station and WRF derived variables. After estimating the ETo, a range of linear and non-linear models is utilized to retrieve SMD. The performance statistics such as RMSE, %Bias, and Nash Sutcliffe Efficiency (NSE indicates that the exponential model (RMSE = 0.226; %Bias = −0.077; NSE = 0.616 is efficient for SMD estimation by using the Observed ETo in comparison to the other linear and non-linear models (RMSE range = 0.019–0.667; %Bias range = 2.821–6.894; NSE = 0.013–0.419 used in this study. On the other hand, in the scenario where SMD is estimated using WRF downscaled meteorological variables based ETo, the linear model is found promising (RMSE = 0.017; %Bias = 5.280; NSE = 0.448 as compared to the non-linear models (RMSE range = 0.022–0.707; %Bias range = −0.207–−6.088; NSE range = 0.013–0.149. Our findings also suggest that all the models are performing better during the growing season (RMSE range = 0.024–0.025; %Bias range = −4.982–−3.431; r = 0.245–0.281 than the non−growing season (RMSE range = 0.011–0.12; %Bias range = 33.073–32.701; r = 0.161–0.244 for SMD estimation.

  11. Development of a High Resolution Weather Forecast Model for Mesoamerica Using the NASA Nebula Cloud Computing Environment

    Science.gov (United States)

    Molthan, Andrew L.; Case, Jonathan L.; Venner, Jason; Moreno-Madrinan, Max. J.; Delgado, Francisco

    2012-01-01

    Over the past two years, scientists in the Earth Science Office at NASA fs Marshall Space Flight Center (MSFC) have explored opportunities to apply cloud computing concepts to support near real ]time weather forecast modeling via the Weather Research and Forecasting (WRF) model. Collaborators at NASA fs Short ]term Prediction Research and Transition (SPoRT) Center and the SERVIR project at Marshall Space Flight Center have established a framework that provides high resolution, daily weather forecasts over Mesoamerica through use of the NASA Nebula Cloud Computing Platform at Ames Research Center. Supported by experts at Ames, staff at SPoRT and SERVIR have established daily forecasts complete with web graphics and a user interface that allows SERVIR partners access to high resolution depictions of weather in the next 48 hours, useful for monitoring and mitigating meteorological hazards such as thunderstorms, heavy precipitation, and tropical weather that can lead to other disasters such as flooding and landslides. This presentation will describe the framework for establishing and providing WRF forecasts, example applications of output provided via the SERVIR web portal, and early results of forecast model verification against available surface ] and satellite ]based observations.

  12. New efficient optimizing techniques for Kalman filters and numerical weather prediction models

    Science.gov (United States)

    Famelis, Ioannis; Galanis, George; Liakatas, Aristotelis

    2016-06-01

    The need for accurate local environmental predictions and simulations beyond the classical meteorological forecasts are increasing the last years due to the great number of applications that are directly or not affected: renewable energy resource assessment, natural hazards early warning systems, global warming and questions on the climate change can be listed among them. Within this framework the utilization of numerical weather and wave prediction systems in conjunction with advanced statistical techniques that support the elimination of the model bias and the reduction of the error variability may successfully address the above issues. In the present work, new optimization methods are studied and tested in selected areas of Greece where the use of renewable energy sources is of critical. The added value of the proposed work is due to the solid mathematical background adopted making use of Information Geometry and Statistical techniques, new versions of Kalman filters and state of the art numerical analysis tools.

  13. Assimilation of Sentinel-1 estimates of Precipitable Water Vapor (PWV) into a Numerical Weather Model for a more accurate forecast of extreme weather events

    Science.gov (United States)

    Mateus, Pedro; Nico, Giovanni; Catalao, Joao

    2017-04-01

    In the last two decades, SAR interferometry has been used to obtain maps of Precipitable Water Vapor (PWV).This maps are characterized by their high spatial resolution when compared to the currently available PWV measurements (e.g. GNSS, radiometers or radiosondes). Several previous works have shown that assimilating PWV values, mainly derived from GNSS observations, into Numerical Weather Models (NWMs) can significantly improve rainfall predictions.It is noteworthy that the PWV-derived from GNSS observations have a high temporal resolution but a low spatialone. In addition, there are many regions without any GNSS stations, where temporal and spatial distribution of PWV areonly available through satellite measurements. The first attempt to assimilate InSAR-derived maps of PWV (InSAR-PWV) into a NWM was made by Pichelli et al. [1].They used InSAR-PWV maps obtained from ENVISAT-ASAR images and the mesoscale weather prediction model MM5 over the city of Rome, Italy. The statistical indices show that the InSAR-PWVdata assimilation improves the forecast of weak to moderateprecipitation (BPD/96069/2013. References: [1] E. Pichelli et al., "InSAR water vapor data assimilation into mesoscale model MM5: Technique and pilot study," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 8, no. 8, pp. 3859-3875, Aug. 2015. [2] P. Mateus, R. Tomé, G. Nico, and J. Catalão, "Three-Dimensional Variational Assimilation of InSAR PWV Using the WRFDA Model," IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 12, pp. 7323-7330, 2016.

  14. A hybrid convection scheme for use in non-hydrostatic numerical weather prediction models

    Directory of Open Access Journals (Sweden)

    Volker Kuell

    2008-12-01

    Full Text Available The correct representation of convection in numerical weather prediction (NWP models is essential for quantitative precipitation forecasts. Due to its small horizontal scale convection usually has to be parameterized, e.g. by mass flux convection schemes. Classical schemes originally developed for use in coarse grid NWP models assume zero net convective mass flux, because the whole circulation of a convective cell is confined to the local grid column and all convective mass fluxes cancel out. However, in contemporary NWP models with grid sizes of a few kilometers this assumption becomes questionable, because here convection is partially resolved on the grid. To overcome this conceptual problem we propose a hybrid mass flux convection scheme (HYMACS in which only the convective updrafts and downdrafts are parameterized. The generation of the larger scale environmental subsidence, which may cover several grid columns, is transferred to the grid scale equations. This means that the convection scheme now has to generate a net convective mass flux exerting a direct dynamical forcing to the grid scale model via pressure gradient forces. The hybrid convection scheme implemented into the COSMO model of Deutscher Wetterdienst (DWD is tested in an idealized simulation of a sea breeze circulation initiating convection in a realistic manner. The results are compared with analogous simulations with the classical Tiedtke and Kain-Fritsch convection schemes.

  15. Simulating carbon capture by enhanced weathering with croplands: an overview of key processes highlighting areas of future model development.

    Science.gov (United States)

    Taylor, Lyla L; Beerling, David J; Quegan, Shaun; Banwart, Steven A

    2017-04-01

    Enhanced weathering (EW) aims to amplify a natural sink for CO 2 by incorporating powdered silicate rock with high reactive surface area into agricultural soils. The goal is to achieve rapid dissolution of minerals and release of alkalinity with accompanying dissolution of CO 2 into soils and drainage waters. EW could counteract phosphorus limitation and greenhouse gas (GHG) emissions in tropical soils, and soil acidification, a common agricultural problem studied with numerical process models over several decades. Here, we review the processes leading to soil acidification in croplands and how the soil weathering CO 2 sink is represented in models. Mathematical models capturing the dominant processes and human interventions governing cropland soil chemistry and GHG emissions neglect weathering, while most weathering models neglect agricultural processes. We discuss current approaches to modelling EW and highlight several classes of model having the potential to simulate EW in croplands. Finally, we argue for further integration of process knowledge in mathematical models to capture feedbacks affecting both longer-term CO 2 consumption and crop growth and yields. © 2017 The Authors.

  16. Modeling very large-fire occurrences over the continental United States from weather and climate forcing

    International Nuclear Information System (INIS)

    Barbero, R; Abatzoglou, J T; Steel, E A; K Larkin, Narasimhan

    2014-01-01

    Very large-fires (VLFs) have widespread impacts on ecosystems, air quality, fire suppression resources, and in many regions account for a majority of total area burned. Empirical generalized linear models of the largest fires (>5000 ha) across the contiguous United States (US) were developed at ∼60 km spatial and weekly temporal resolutions using solely atmospheric predictors. Climate−fire relationships on interannual timescales were evident, with wetter conditions than normal in the previous growing season enhancing VLFs probability in rangeland systems and with concurrent long-term drought enhancing VLFs probability in forested systems. Information at sub-seasonal timescales further refined these relationships, with short-term fire weather being a significant predictor in rangelands and fire danger indices linked to dead fuel moisture being a significant predictor in forested lands. Models demonstrated agreement in capturing the observed spatial and temporal variability including the interannual variability of VLF occurrences within most ecoregions. Furthermore the model captured the observed increase in VLF occurrences across parts of the southwestern and southeastern US from 1984 to 2010 suggesting that, irrespective of changes in fuels and land management, climatic factors have become more favorable for VLF occurrence over the past three decades in some regions. Our modeling framework provides a basis for simulations of future VLF occurrences from climate projections. (letter)

  17. Seepage weathering impacts on erosivity of arid stream banks: A new conceptual model

    Science.gov (United States)

    Nachshon, Uri

    2016-05-01

    Field observations have indicated the formation of horizontal, pipe shape cavities, along gully and dry stream channel banks in the semi-arid region of the northern Negev Desert, Israel. Piping is a well-known phenomenon in humid regions due to subsurface water flow and seepage weathering. However, in dry environments where rain events are scarce and subsurface water flow is rare, it is proposed here that capillary flow of saline water in the vadose zone leads to similar processes. It is suggested that where saline and shallow ground water persists, capillary flow may result in salt accumulation and precipitation at the top of the capillary fringe, consequently rendering this zone to be more susceptible to erosion. A conceptual model is presented and field observations, laboratory experiments, and a physically-based model are used to prove the feasibility of the proposed conceptual model and to explain why salts accumulate at the top of the capillary fringe, even though evaporation acts all along the vertical stream channel or gully banks. It is suggested that the low evaporative flux, in comparison to the liquid water flux, disables salt accumulation along the profile to the top of the capillary fringe where the liquid water flux is minimal. The presented findings strengthen the conceptual model, but thorough field studies are needed to estimate the impact of the proposed mechanism on erosion processes on a field scale.

  18. Weather Research and Forecasting Model Wind Sensitivity Study at Edwards Air Force Base, CA

    Science.gov (United States)

    Watson, Leela R.; Bauman, William H., III; Hoeth, Brian

    2009-01-01

    This abstract describes work that will be done by the Applied Meteorology Unit (AMU) in assessing the success of different model configurations in predicting "wind cycling" cases at Edwards Air Force Base, CA (EAFB), in which the wind speeds and directions oscillate among towers near the EAFB runway. The Weather Research and Forecasting (WRF) model allows users to choose among two dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM). There are also data assimilation analysis packages available for the initialization of the WRF model - the Local Analysis and Prediction System (LAPS) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS). Having a series of initialization options and WRF cores, as well as many options within each core, creates challenges for local forecasters, such as determining which configuration options are best to address specific forecast concerns. The goal of this project is to assess the different configurations available and determine which configuration will best predict surface wind speed and direction at EAFB.

  19. Validation of mixing heights derived from the operational NWP models at the German weather service

    Energy Technology Data Exchange (ETDEWEB)

    Fay, B.; Schrodin, R.; Jacobsen, I. [Deutscher Wetterdienst, Offenbach (Germany); Engelbart, D. [Deutscher Wetterdienst, Meteorol. Observ. Lindenberg (Germany)

    1997-10-01

    NWP models incorporate an ever-increasing number of observations via four-dimensional data assimilation and are capable of providing comprehensive information about the atmosphere both in space and time. They describe not only near surface parameters but also the vertical structure of the atmosphere. They operate daily, are well verified and successfully used as meteorological pre-processors in large-scale dispersion modelling. Applications like ozone forecasts, emission or power plant control calculations require highly resolved, reliable, and routine values of the temporal evolution of the mixing height (MH) which is a critical parameter in determining the mixing and transformation of substances and the resulting pollution levels near the ground. The purpose of development at the German Weather Service is a straightforward mixing height scheme that uses only parameters derived from NWP model variables and thus automatically provides spatial and temporal fields of mixing heights on an operational basis. An universal parameter to describe stability is the Richardson number Ri. Compared to the usual diagnostic or rate equations, the Ri number concept of determining mixing heights has the advantage of using not only surface layer parameters but also regarding the vertical structure of the boundary layer resolved in the NWP models. (au)

  20. 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.

  1. Strategies for Effective Implementation of Science Models into 6-9 Grade Classrooms on Climate, Weather, and Energy Topics

    Science.gov (United States)

    Yarker, M. B.; Stanier, C. O.; Forbes, C.; Park, S.

    2011-12-01

    As atmospheric scientists, we depend on Numerical Weather Prediction (NWP) models. We use them to predict weather patterns, to understand external forcing on the atmosphere, and as evidence to make claims about atmospheric phenomenon. Therefore, it is important that we adequately prepare atmospheric science students to use computer models. However, the public should also be aware of what models are in order to understand scientific claims about atmospheric issues, such as climate change. Although familiar with weather forecasts on television and the Internet, the general public does not understand the process of using computer models to generate a weather and climate forecasts. As a result, the public often misunderstands claims scientists make about their daily weather as well as the state of climate change. Since computer models are the best method we have to forecast the future of our climate, scientific models and modeling should be a topic covered in K-12 classrooms as part of a comprehensive science curriculum. According to the National Science Education Standards, teachers are encouraged to science models into the classroom as a way to aid in the understanding of the nature of science. However, there is very little description of what constitutes a science model, so the term is often associated with scale models. Therefore, teachers often use drawings or scale representations of physical entities, such as DNA, the solar system, or bacteria. In other words, models used in classrooms are often used as visual representations, but the purpose of science models is often overlooked. The implementation of a model-based curriculum in the science classroom can be an effective way to prepare students to think critically, problem solve, and make informed decisions as a contributing member of society. However, there are few resources available to help teachers implement science models into the science curriculum effectively. Therefore, this research project looks at

  2. Global distribution of urban parameters derived from high-resolution global datasets for weather modelling

    Science.gov (United States)

    Kawano, N.; Varquez, A. C. G.; Dong, Y.; Kanda, M.

    2016-12-01

    Numerical model such as Weather Research and Forecasting model coupled with single-layer Urban Canopy Model (WRF-UCM) is one of the powerful tools to investigate urban heat island. Urban parameters such as average building height (Have), plain area index (λp) and frontal area index (λf), are necessary inputs for the model. In general, these parameters are uniformly assumed in WRF-UCM but this leads to unrealistic urban representation. Distributed urban parameters can also be incorporated into WRF-UCM to consider a detail urban effect. The problem is that distributed building information is not readily available for most megacities especially in developing countries. Furthermore, acquiring real building parameters often require huge amount of time and money. In this study, we investigated the potential of using globally available satellite-captured datasets for the estimation of the parameters, Have, λp, and λf. Global datasets comprised of high spatial resolution population dataset (LandScan by Oak Ridge National Laboratory), nighttime lights (NOAA), and vegetation fraction (NASA). True samples of Have, λp, and λf were acquired from actual building footprints from satellite images and 3D building database of Tokyo, New York, Paris, Melbourne, Istanbul, Jakarta and so on. Regression equations were then derived from the block-averaging of spatial pairs of real parameters and global datasets. Results show that two regression curves to estimate Have and λf from the combination of population and nightlight are necessary depending on the city's level of development. An index which can be used to decide which equation to use for a city is the Gross Domestic Product (GDP). On the other hand, λphas less dependence on GDP but indicated a negative relationship to vegetation fraction. Finally, a simplified but precise approximation of urban parameters through readily-available, high-resolution global datasets and our derived regressions can be utilized to estimate a

  3. Machine learning and linear regression models to predict catchment-level base cation weathering rates across the southern Appalachian Mountain region, USA

    Science.gov (United States)

    Nicholas A. Povak; Paul F. Hessburg; Todd C. McDonnell; Keith M. Reynolds; Timothy J. Sullivan; R. Brion Salter; Bernard J. Crosby

    2014-01-01

    Accurate estimates of soil mineral weathering are required for regional critical load (CL) modeling to identify ecosystems at risk of the deleterious effects from acidification. Within a correlative modeling framework, we used modeled catchment-level base cation weathering (BCw) as the response variable to identify key environmental correlates and predict a continuous...

  4. Creating long-term weather data from thin air for crop simulation modeling

    NARCIS (Netherlands)

    Wart, Van Justin; Grassini, Patricio; Yang, Haishun; Claessens, Lieven; Jarvis, Andrew; Cassman, Kenneth G.

    2015-01-01

    Simulating crop yield and yield variability requires long-term, high-quality daily weather data, including solar radiation, maximum (Tmax) and minimum temperature (Tmin), and precipitation. In many regions, however, daily weather data of sufficient quality and duration are not available. To overcome

  5. Ensemble Data Assimilation to Characterize Surface-Layer Errors In Numerical Weather Prediction Models

    Science.gov (United States)

    Hacker, Joshua; Angevine, Wayne

    2013-04-01

    Experiments with the single-column implementation of the Weather Research and Forecasting mesoscale model provide a basis for deducing land-atmosphere coupling errors in the model. Coupling occurs both through heat and moisture fluxes through the land-atmosphere interface and roughness sub-layer, and turbulent heat, moisture, and momentum fluxes through the atmospheric surface layer. This work primarily addresses the turbulent fluxes, which are parameterized following Monin-Obukhov similarity theory applied to the atmospheric surface layer. By combining ensemble data assimilation and parameter estimation, the model error can be characterized. Ensemble data assimilation of 2-m temperature and water vapor mixing ratio, and 10-m wind components, forces the model to follow observations during a month-long simulation for a column over the well-instrumented ARM Central Facility near Lamont, OK. One-hour errors in predicted observations are systematically small but non-zero, and the systematic errors measure bias as a function of local time of day. Analysis increments for state elements nearby (15-m AGL) can be too small or have the wrong sign, indicating systematically biased covariances and model error. Experiments using the ensemble filter to objectively estimate a parameter controlling the thermal land-atmosphere coupling show that the parameter adapts to offset the model errors, but that the errors cannot be eliminated. Results suggest either structural error or further parametric error that may be difficult to estimate. Experiments omitting atypical observations such as soil and flux measurements lead to qualitatively similar deductions, showing potential for assimilating common in-situ observations as an inexpensive framework for deducing and isolating model errors. We finish by presenting recent results from a deeper examination of the second-moment ensemble statistics, which demonstrate the effect of assimilation on the coupling through the stability function in

  6. Numerical simulation of rainfall and temperature over Kenya using weather research and forecasting-environmental modelling system (WRF-EMS

    Directory of Open Access Journals (Sweden)

    Sagero Obaigwa Philip

    2016-01-01

    Full Text Available This paper focuses on one of the high resolution models used for weather forecasting at Kenya Meteorological Department (KMD. It reviews the skill and accuracy of the Weather Research and Forecasting (WRF - Environmental Modeling System (EMS model, in simulating weather over Kenya. The study period was March to May 2011, during the rainy season over Kenya. The model output was compared with the observed data from 27 synoptic stations spread over the study area, to determine the performance of the model in terms of its skill and accuracy in forecasting. The spatial distribution of rainfall and temperature showed that the WRF model was capable of reproducing the observed general pattern especially for temperature. The model has skill in forecasting both rainfall and temperature over the study area. However, the model may underestimate rainfall of more than 10 mm/day and displace its location and overestimate rainfall of less than 1 mm/day. Therefore, during the period of enhanced rainfall especially in the month of April and part of May the model forecast needs to be complemented by other models or forecasting methods before giving a forecast. There is need to improve its performance over the domain through review of the parameterization of small scale physical processes and more observed data need to be simulated into the model.

  7. Predicting the Storm Surge Threat of Hurricane Sandy with the National Weather Service SLOSH Model

    Directory of Open Access Journals (Sweden)

    Cristina Forbes

    2014-05-01

    Full Text Available Numerical simulations of the storm tide that flooded the US Atlantic coastline during Hurricane Sandy (2012 are carried out using the National Weather Service (NWS Sea Lakes and Overland Surges from Hurricanes (SLOSH storm surge prediction model to quantify its ability to replicate the height, timing, evolution and extent of the water that was driven ashore by this large, destructive storm. Recent upgrades to the numerical model, including the incorporation of astronomical tides, are described and simulations with and without these upgrades are contrasted to assess their contributions to the increase in forecast accuracy. It is shown, through comprehensive verifications of SLOSH simulation results against peak water surface elevations measured at the National Oceanic and Atmospheric Administration (NOAA tide gauge stations, by storm surge sensors deployed and hundreds of high water marks collected by the U.S. Geological Survey (USGS, that the SLOSH-simulated water levels at 71% (89% of the data measurement locations have less than 20% (30% relative error. The RMS error between observed and modeled peak water levels is 0.47 m. In addition, the model’s extreme computational efficiency enables it to run large, automated ensembles of predictions in real-time to account for the high variability that can occur in tropical cyclone forecasts, thus furnishing a range of values for the predicted storm surge and inundation threat.

  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. Can Agrometeorological Indices of Adverse Weather Conditions Help to Improve Yield Prediction by Crop Models?

    Directory of Open Access Journals (Sweden)

    Branislava Lalić

    2014-12-01

    Full Text Available The impact of adverse weather conditions (AWCs on crop production is random in both time and space and depends on factors such as severity, previous agrometeorological conditions, and plant vulnerability at a specific crop development stage. Any exclusion or improper treatment of any of these factors can cause crop models to produce significant under- or overestimates of yield. The analysis presented in this paper focuses on a range of agrometeorological indices (AMI related to AWCs that might affect real yield as well as simulated yield. For this purpose, the analysis addressed four indicators of extreme temperatures and three indicators of dry conditions during the growth period of maize and winter wheat in Austria, Croatia, Serbia, Slovakia, and Sweden. It is shown that increases in the number and intensity of AWCs cannot be unambiguously associated with increased deviations in simulated yields. The identified correlations indicate an increase in modeling uncertainty. This finding represents important information for the crop modeling community. Additionally, it opens a window of opportunity for a statistical (“event scenario” approach based on correlations between agrometeorological indices of AWCs and crop yield data series. This approach can provide scenarios for certain locations, crop types, and AWC patterns and, therefore, improve yield forecasting in the presence of AWCs.

  10. Tile Low Rank Cholesky Factorization for Climate/Weather Modeling Applications on Manycore Architectures

    KAUST Repository

    Akbudak, Kadir

    2017-05-11

    Covariance matrices are ubiquitous in computational science and engineering. In particular, large covariance matrices arise from multivariate spatial data sets, for instance, in climate/weather modeling applications to improve prediction using statistical methods and spatial data. One of the most time-consuming computational steps consists in calculating the Cholesky factorization of the symmetric, positive-definite covariance matrix problem. The structure of such covariance matrices is also often data-sparse, in other words, effectively of low rank, though formally dense. While not typically globally of low rank, covariance matrices in which correlation decays with distance are nearly always hierarchically of low rank. While symmetry and positive definiteness should be, and nearly always are, exploited for performance purposes, exploiting low rank character in this context is very recent, and will be a key to solving these challenging problems at large-scale dimensions. The authors design a new and flexible tile row rank Cholesky factorization and propose a high performance implementation using OpenMP task-based programming model on various leading-edge manycore architectures. Performance comparisons and memory footprint saving on up to 200K×200K covariance matrix size show a gain of more than an order of magnitude for both metrics, against state-of-the-art open-source and vendor optimized numerical libraries, while preserving the numerical accuracy fidelity of the original model. This research represents an important milestone in enabling large-scale simulations for covariance-based scientific applications.

  11. Cloud-Enabled Space Weather Modeling and Data Assimilation Platform (CESWP)

    Science.gov (United States)

    Satchwill, B.; Rankin, R.; Shillington, J.; Toews, E.

    2010-12-01

    Multi-space-agency partnerships in the development and flight of space science payloads, and in sharing of complex models and data sets (including ground-based data sets), make a compelling case for providing standardized interfaces and platforms to access data and models. However, developing and executing simulations requires space physicists to either develop knowledge of specialized high-performance computing environments and environment-specific simulations, or run simulations multiple times serially in order to examine the results of different parametric inputs. Barriers also exist where data and models reside in different geographic locations, which is typically the case. The emergence of cloud computing, and its Infrastructure-as-a-Service (IaaS) variant, provides an opportunity to develop software architectures that reduce barriers to simulation development and use, while simultaneously reducing the proliferation of hardware in the research community, and all its inherent high cost. The Cloud-Enabled Space Weather Modeling and Data Assimilation Platform (CESWP) utilizes cloud technologies to dramatically improve the sustainability, flexibility and performance of research tools and services, enabling an attendant improvement in researcher productivity and research funding efficacy. CESWP integrates complex modeling and simulation functionality into the federated data capabilities of the Canadian Space Sciences Data Portal (http://cssdp.ca). The CESWP cloud is innovative in its use of a versatile IaaS approach to provision a space sciences cloud. The platform helps researchers deal with the explosion of new data sets that require international cooperation and complex modeling as part of their analysis. This paper will describe the current implementation of the CESWP private cloud, which is based on Eucalyptus, KVM, CentOS, and Amazon Web Services compatible API’s.

  12. Space Weather Forecasting at NOAA with Michigan's Geospace Model: Results from the First Year in Real-Time Operations

    Science.gov (United States)

    Cash, M. D.; Singer, H. J.; Millward, G. H.; Balch, C. C.; Toth, G.; Welling, D. T.

    2017-12-01

    In October 2016, the first version of the Geospace model was transitioned into real-time operations at NOAA Space Weather Prediction Center (SWPC). The Geospace model is a part of the Space Weather Modeling Framework (SWMF) developed at the University of Michigan, and the model simulates the full time-dependent 3D Geospace environment (Earth's magnetosphere, ring current and ionosphere) and predicts global space weather parameters such as induced magnetic perturbations in space and on Earth's surface. The current version of the Geospace model uses three coupled components of SWMF: the BATS-R-US global magnetosphere model, the Rice Convection Model (RCM) of the inner magnetosphere, and the Ridley Ionosphere electrodynamics Model (RIM). In the operational mode, SWMF/Geospace runs continually in real-time as long as there is new solar wind data arriving from a satellite at L1, either DSCOVR or ACE. We present an analysis of the overall performance of the Geospace model during the first year of real-time operations. Evaluation metrics include Kp, Dst, as well as regional magnetometer stations. We will also present initial results from new products, such as the AE index, available with the recent upgrade to the Geospace model.

  13. Preliminary results of an attempt to provide soil moisture datasets in order to verify numerical weather prediction models

    Energy Technology Data Exchange (ETDEWEB)

    Cassardo, C. [Torino Univ., Torino (Italy). Dipartimento di fisica generale Amedeo Avogadro; Loglisci, N. [ARPA, Torino (Italy). Servizio meteorologico regionale

    2005-03-15

    In the recent years, there has been a significant growth in the recognition of the soil moisture importance in large-scale hydrology and climate modelling. Soil moisture is a lower boundary condition, which rules the partitioning of energy in terms of sensible and latent heat flux. Wrong estimations of soil moisture lead to wrong simulation of the surface layer evolution and hence precipitations and cloud cover forecasts could be consequently affected. This is true for large scale medium-range weather forecasts as well as for local-scale short range weather forecasts, particularly in those situations in which local convection is well developed. Unfortunately; despite the importance of this physical parameter there are only few soil moisture data sets sparse in time and in space around in the world. Due to this scarcity of soil moisture observations, we developed an alternative method to provide soil moisture datasets in order to verify numerical weather prediction models. In this paper are presented the preliminary results of an attempt to verify soil moisture fields predicted by a mesoscale model. The data for the comparison were provided by the simulations of the diagnostic land surface scheme LSPM (Land Surface Process Model), widely used at the Piedmont Regional Weather Service for agro-meteorological purposes. To this end, LSPM was initialized and driven by Synop observations, while the surface (vegetation and soil) parameter values were initialized by ECOCLIMAP global dataset at 1km{sup 2} resolution.

  14. Sensitivities of crop models to extreme weather conditions during flowering period demonstrated for maize and winter wheat in Austria

    Czech Academy of Sciences Publication Activity Database

    Eitzinger, Josef; Thaler, S.; Schmid, E.; Strauss, F.; Ferrise, R.; Moriondo, M.; Bindi, M.; Palosuo, T.; Rötter, R.; Kersebaum, K. C.; Olesen, J. E.; Patil, R. H.; Saylan, L.; Çaldag, B.; Caylak, O.

    2013-01-01

    Roč. 151, č. 6 (2013), s. 813-835 ISSN 0021-8596 R&D Projects: GA MŠk(CZ) ED1.1.00/02.0073 Institutional support: RVO:67179843 Keywords : crop models * weather conditions * winter wheat * Austria Subject RIV: EH - Ecology, Behaviour Impact factor: 2.891, year: 2013

  15. Preliminary results of an attempt to provide soil moisture datasets in order to verify numerical weather prediction models

    International Nuclear Information System (INIS)

    Cassardo, C.; Loglisci, N.

    2005-01-01

    In the recent years, there has been a significant growth in the recognition of the soil moisture importance in large-scale hydrology and climate modelling. Soil moisture is a lower boundary condition, which rules the partitioning of energy in terms of sensible and latent heat flux. Wrong estimations of soil moisture lead to wrong simulation of the surface layer evolution and hence precipitations and cloud cover forecasts could be consequently affected. This is true for large scale medium-range weather forecasts as well as for local-scale short range weather forecasts, particularly in those situations in which local convection is well developed. Unfortunately; despite the importance of this physical parameter there are only few soil moisture data sets sparse in time and in space around in the world. Due to this scarcity of soil moisture observations, we developed an alternative method to provide soil moisture datasets in order to verify numerical weather prediction models. In this paper are presented the preliminary results of an attempt to verify soil moisture fields predicted by a mesoscale model. The data for the comparison were provided by the simulations of the diagnostic land surface scheme LSPM (Land Surface Process Model), widely used at the Piedmont Regional Weather Service for agro-meteorological purposes. To this end, LSPM was initialized and driven by Synop observations, while the surface (vegetation and soil) parameter values were initialized by ECOCLIMAP global dataset at 1km 2 resolution

  16. Parameterizing road construction in route-based road weather models: can ground-penetrating radar provide any answers?

    Science.gov (United States)

    Hammond, D. S.; Chapman, L.; Thornes, J. E.

    2011-05-01

    A ground-penetrating radar (GPR) survey of a 32 km mixed urban and rural study route is undertaken to assess the usefulness of GPR as a tool for parameterizing road construction in a route-based road weather forecast model. It is shown that GPR can easily identify even the smallest of bridges along the route, which previous thermal mapping surveys have identified as thermal singularities with implications for winter road maintenance. Using individual GPR traces measured at each forecast point along the route, an inflexion point detection algorithm attempts to identify the depth of the uppermost subsurface layers at each forecast point for use in a road weather model instead of existing ordinal road-type classifications. This approach has the potential to allow high resolution modelling of road construction and bridge decks on a scale previously not possible within a road weather model, but initial results reveal that significant future research will be required to unlock the full potential that this technology can bring to the road weather industry.

  17. Parameterizing road construction in route-based road weather models: can ground-penetrating radar provide any answers?

    International Nuclear Information System (INIS)

    Hammond, D S; Chapman, L; Thornes, J E

    2011-01-01

    A ground-penetrating radar (GPR) survey of a 32 km mixed urban and rural study route is undertaken to assess the usefulness of GPR as a tool for parameterizing road construction in a route-based road weather forecast model. It is shown that GPR can easily identify even the smallest of bridges along the route, which previous thermal mapping surveys have identified as thermal singularities with implications for winter road maintenance. Using individual GPR traces measured at each forecast point along the route, an inflexion point detection algorithm attempts to identify the depth of the uppermost subsurface layers at each forecast point for use in a road weather model instead of existing ordinal road-type classifications. This approach has the potential to allow high resolution modelling of road construction and bridge decks on a scale previously not possible within a road weather model, but initial results reveal that significant future research will be required to unlock the full potential that this technology can bring to the road weather industry. (technical design note)

  18. Spatial analysis and modeling to assess and map current vulnerability to extreme weather events in the Grijalva - Usumacinta watershed, Mexico

    Energy Technology Data Exchange (ETDEWEB)

    Lopez L, D, E-mail: dlopez@centrogeo.org.m [Centro de Investigacion en GeografIa y Geomatica, Ing. Jorge L. Tamayo A.C., Contoy 137, col. Lomas de Padierna, del Tlalpan, Maxico D.F (Mexico)

    2009-11-01

    One of the major concerns over a potential change in climate is that it will cause an increase in extreme weather events. In Mexico, the exposure factors as well as the vulnerability to the extreme weather events have increased during the last three or four decades. In this study spatial analysis and modeling were used to assess and map settlement and crop systems vulnerability to extreme weather events in the Grijalva - Usumacinta watershed. Sensitivity and coping adaptive capacity maps were constructed using decision models; these maps were then combined to produce vulnerability maps. The most vulnerable area in terms of both settlement and crop systems is the highlands, where the sensitivity is high and the adaptive capacity is low. In lowlands, despite the very high sensitivity, the higher adaptive capacity produces only moderate vulnerability. I conclude that spatial analysis and modeling are powerful tools to assess and map vulnerability. These preliminary results can guide the formulation of adaptation policies to an increasing risk of extreme weather events.

  19. Wind gust estimation by combining numerical weather prediction model and statistical post-processing

    Science.gov (United States)

    Patlakas, Platon; Drakaki, Eleni; Galanis, George; Spyrou, Christos; Kallos, George

    2017-04-01

    The continuous rise of off-shore and near-shore activities as well as the development of structures, such as wind farms and various offshore platforms, requires the employment of state-of-the-art risk assessment techniques. Such analysis is used to set the safety standards and can be characterized as a climatologically oriented approach. Nevertheless, a reliable operational support is also needed in order to minimize cost drawbacks and human danger during the construction and the functioning stage as well as during maintenance activities. One of the most important parameters for this kind of analysis is the wind speed intensity and variability. A critical measure associated with this variability is the presence and magnitude of wind gusts as estimated in the reference level of 10m. The latter can be attributed to different processes that vary among boundary-layer turbulence, convection activities, mountain waves and wake phenomena. The purpose of this work is the development of a wind gust forecasting methodology combining a Numerical Weather Prediction model and a dynamical statistical tool based on Kalman filtering. To this end, the parameterization of Wind Gust Estimate method was implemented to function within the framework of the atmospheric model SKIRON/Dust. The new modeling tool combines the atmospheric model with a statistical local adaptation methodology based on Kalman filters. This has been tested over the offshore west coastline of the United States. The main purpose is to provide a useful tool for wind analysis and prediction and applications related to offshore wind energy (power prediction, operation and maintenance). The results have been evaluated by using observational data from the NOAA's buoy network. As it was found, the predicted output shows a good behavior that is further improved after the local adjustment post-process.

  20. Evaluating accuracy of DSSAT model for soybean yield estimation using satellite weather data

    Science.gov (United States)

    Ovando, Gustavo; Sayago, Silvina; Bocco, Mónica

    2018-04-01

    Crop models allow simulating the development and yield of the crops, to represent and to evaluate the influence of multiple factors. The DSSAT cropping system model is one of the most widely used and contains CROPGRO module for soybean. This crop has a great importance for many southern countries of Latin America and for Argentina. Solar radiation and rainfall are necessary variables as inputs for crop models; however these data are not as readily available. The satellital products from Clouds and Earth's Radiant Energy System (CERES) and Tropic Rainfall Measurement Mission (TRMM) provide continuous spatial and temporal information of solar radiation and precipitation, respectively. This study evaluates and quantifies the uncertainty in estimating soybean yield using a DSSAT model, when recorded weather data are replaced with CERES and TRMM ones. Different percentages of data replacements, soybean maturity groups and planting dates are considered, for 2006-2016 period in Oliveros (Argentina). Results show that CERES and TRMM products can be used for soybean yield estimation with DSSAT considering that: percentage of data replacement, campaign, planting date and maturity group, determine the amounts and trends of yield errors. Replacements with CERES data up to 30% result in %RMSE lower than 10% in 87% of the cases; while the replacement with TRMM data presents the best statisticals in campaigns with high yields. Simulations based entirely on CERES solar radiation give better results than those with TRMM. In general, similar percentages of replacement show better performance in the estimation of soybean yield for solar radiation than the replacement of precipitation values.

  1. Computing tomorrow's weather

    Science.gov (United States)

    Lynch, Peter

    2009-06-01

    The development of computer models that simulate the Earth's atmosphere, allowing us to predict weather and anticipate climate change, is one of the triumphs of 20th-century science. Weather forecasting used to be very hit-and-miss, based on rough rules of thumb and the assumption that similar weather patterns would evolve in a similar manner. But from 1950 onwards, digital computers revolutionized the field, transforming it from a woolly empirical activity to a precise, quantitative, science-based procedure. Weather forecasting was among the first computational sciences and is still a major application for high-end computers today. In Weather by the Numbers, the historian Kristine Harper tells the fascinating story of how numerical weather prediction became possible.

  2. Weather it's Climate Change?

    Science.gov (United States)

    Bostrom, A.; Lashof, D.

    2004-12-01

    For almost two decades both national polls and in-depth studies of global warming perceptions have shown that people commonly conflate weather and global climate change. Not only are current weather events such as anecdotal heat waves, droughts or cold spells treated as evidence for or against global warming, but weather changes such as warmer weather and increased storm intensity and frequency are the consequences most likely to come to mind. Distinguishing weather from climate remains a challenge for many. This weather 'framing' of global warming may inhibit behavioral and policy change in several ways. Weather is understood as natural, on an immense scale that makes controlling it difficult to conceive. Further, these attributes contribute to perceptions that global warming, like weather, is uncontrollable. This talk presents an analysis of data from public opinion polls, focus groups, and cognitive studies regarding people's mental models of and 'frames' for global warming and climate change, and the role weather plays in these. This research suggests that priming people with a model of global warming as being caused by a "thickening blanket of carbon dioxide" that "traps heat" in the atmosphere solves some of these communications problems and makes it more likely that people will support policies to address global warming.

  3. Using NLDAS weather forcing data on remote sensing of evapotranspiration using a two source energy balance model

    Science.gov (United States)

    Geli, H. M.; Neale, C. M.; Verdin, J. P.; Senay, G. B.

    2012-12-01

    Using NLDAS weather forcing data on remote sensing of evapotranspiration using a two source model Hatim M. E. Geli, Christopher M. U. Neale, James P. Verdin, and Gabriel Senay Estimates of evapotranspiration (ET) on regional scale over agricultural areas provide a means for efficient water resource management and allocation. Different thermal infrared remote sensing-based energy balance models can be used to obtain such estimates with a reasonable accuracy. Ground-based point measurements of weather variable are generally used in the application of most of these models. However at large scales, using point weather data might not be representative of local variability. The North American Land Data Assimilation System (NLDAS) provides spatial weather forcing data on a gridded basis that can account for local and regional weather variability. The NLDAS forcing data is available at 1/8th degree grid (~12 km × 12 km) which is considered relatively coarse resolution to represent field to field variability in agricultural areas. In this study we will be investigating the associated uncertainty and bias of using NLDAS-2 forcing data with respect to ground-based measurements from automated weather stations. The two source energy balance (TSEB) model of Norman et al. (1995) is used to obtain estimates of surface energy balance fluxes (SEBF) and ET over the Palo Verde Irrigation District (PVID), CA. The PVID is about 500 km2 in area covered mostly with alfalfa and cotton crops as well as fallow fields. Estimates of SEBF/ET obtained using both type of weather data (NLDAS-2 and ground-based) are compared to ground-based eddy covariance and Bowen ratio flux measurements. The analysis is conducted during the 2008 growing season using thermal IR and multispectral images from the Landsat Thematic Mapper sensor. This study is conducted in an effort to, standardize and provide a framework for spatial ET maps that can be used by the USGS in the implementation of the Water

  4. Understanding land use change impacts on microclimate using Weather Research and Forecasting (WRF) model

    Science.gov (United States)

    Li, Xia; Mitra, Chandana; Dong, Li; Yang, Qichun

    2018-02-01

    To explore potential climatic consequences of land cover change in the Kolkata Metropolitan Development area, we projected microclimate conditions in this area using the Weather Research and Forecasting (WRF) model driven by future land use scenarios. Specifically, we considered two land conversion scenarios including an urbanization scenario that all the wetlands and croplands would be converted to built-up areas, and an irrigation expansion scenario in which all wetlands and dry croplands would be replaced by irrigated croplands. Results indicated that land use and land cover (LULC) change would dramatically increase regional temperature in this area under the urbanization scenario, but expanded irrigation tended to have a cooling effect. In the urbanization scenario, precipitation center tended to move eastward and lead to increased rainfall in eastern parts of this region. Increased irrigation stimulated rainfall in central and eastern areas but reduced rainfall in southwestern and northwestern parts of the study area. This study also demonstrated that urbanization significantly reduced latent heat fluxes and albedo of land surface; while increased sensible heat flux changes following urbanization suggested that developed land surfaces mainly acted as heat sources. In this study, climate change projection not only predicts future spatiotemporal patterns of multiple climate factors, but also provides valuable insights into policy making related to land use management, water resource management, and agriculture management to adapt and mitigate future climate changes in this populous region.

  5. Space Weathering of Super-Earths: Model Simulations of Exospheric Sodium Escape from 61 Virgo b

    Energy Technology Data Exchange (ETDEWEB)

    Yoneda, M.; Berdyugina, S.; Kuhn, J. [Kiepenheuer Institute for Solar Physics, Schöneckstraße 6, 79104 Freiburg im Breisgau (Germany)

    2017-10-01

    Rocky exoplanets are expected to be eroded by space weather in a similar way as in the solar system. In particular, Mercury is one of the dramatically eroded planets whose material continuously escapes into its exosphere and further into space. This escape is well traced by sodium atoms scattering sunlight. Due to solar wind impact, micrometeorite impacts, photo-stimulated desorption and thermal desorption, sodium atoms are released from surface regolith. Some of these released sodium atoms are escaping from Mercury’s gravitational-sphere. They are dragged anti-Sun-ward and form a tail structure. We expect similar phenomena on exoplanets. The hot super-Earth 61 Vir b orbiting a G3V star at only 0.05 au may show a similar structure. Because of its small separation from the star, the sodium release mechanisms may be working more efficiently on hot super-Earths than on Mercury, although the strong gravitational force of Earth-sized or even more massive planets may be keeping sodium atoms from escaping from the planet. Here, we performed model simulations for Mercury (to verify our model) and 61 Vir b as a representative super-Earth. We have found that sodium atoms can escape from this exoplanet due to stellar wind sputtering and micrometeorite impacts, to form a sodium tail. However, in contrast to Mercury, the tail on this hot super-Earth is strongly aligned with the anti-starward direction because of higher light pressure. Our model suggests that 61 Vir b seems to have an exo-base atmosphere like that of Mercury.

  6. An Integrated Decision-Making Model for Categorizing Weather Products and Decision Aids

    Science.gov (United States)

    Elgin, Peter D.; Thomas, Rickey P.

    2004-01-01

    The National Airspace System s capacity will experience considerable growth in the next few decades. Weather adversely affects safe air travel. The FAA and NASA are working to develop new technologies that display weather information to support situation awareness and optimize pilot decision-making in avoiding hazardous weather. Understanding situation awareness and naturalistic decision-making is an important step in achieving this goal. Information representation and situation time stress greatly influence attentional resource allocation and working memory capacity, potentially obstructing accurate situation awareness assessments. Three naturalistic decision-making theories were integrated to provide an understanding of the levels of decision making incorporated in three operational situations and two conditions. The task characteristics associated with each phase of flight govern the level of situation awareness attained and the decision making processes utilized. Weather product s attributes and situation task characteristics combine to classify weather products according to the decision-making processes best supported. In addition, a graphical interface is described that affords intuitive selection of the appropriate weather product relative to the pilot s current flight situation.

  7. Predicting favorable conditions for early leaf spot of peanut using output from the Weather Research and Forecasting (WRF) model.

    Science.gov (United States)

    Olatinwo, Rabiu O; Prabha, Thara V; Paz, Joel O; Hoogenboom, Gerrit

    2012-03-01

    Early leaf spot of peanut (Arachis hypogaea L.), a disease caused by Cercospora arachidicola S. Hori, is responsible for an annual crop loss of several million dollars in the southeastern United States alone. The development of early leaf spot on peanut and subsequent spread of the spores of C. arachidicola relies on favorable weather conditions. Accurate spatio-temporal weather information is crucial for monitoring the progression of favorable conditions and determining the potential threat of the disease. Therefore, the development of a prediction model for mitigating the risk of early leaf spot in peanut production is important. The specific objective of this study was to demonstrate the application of the high-resolution Weather Research and Forecasting (WRF) model for management of early leaf spot in peanut. We coupled high-resolution weather output of the WRF, i.e. relative humidity and temperature, with the Oklahoma peanut leaf spot advisory model in predicting favorable conditions for early leaf spot infection over Georgia in 2007. Results showed a more favorable infection condition in the southeastern coastline of Georgia where the infection threshold were met sooner compared to the southwestern and central part of Georgia where the disease risk was lower. A newly introduced infection threat index indicates that the leaf spot threat threshold was met sooner at Alma, GA, compared to Tifton and Cordele, GA. The short-term prediction of weather parameters and their use in the management of peanut diseases is a viable and promising technique, which could help growers make accurate management decisions, and lower disease impact through optimum timing of fungicide applications.

  8. Storm time dynamics of auroral electrojets: CHAMP observation and the Space Weather Modeling Framework comparison

    Directory of Open Access Journals (Sweden)

    H. Wang

    2008-03-01

    Full Text Available We investigate variations of the location and intensity of auroral currents during two magnetic storm periods based on magnetic field measurements from CHAMP separately for both hemispheres, as well as for the dayside and nightside. The corresponding auroral electrojet current densities are on average enhanced by about a factor of 7 compared to the quiet time current strengths. The nightside westward current densities are on average 1.8 (2.2 times larger than the dayside eastward current densities in the Northern (Southern Hemisphere. Both eastward and westward currents are present during the storm periods with the most intense electrojets appearing during the main phase of the storm, before the ring current maximizes in strength. The eastward and westward electrojet centers can expand to 55° MLat during intense storms, as is observed on 31 March 2001 with Dst=−387 nT. The equatorward shift of auroral currents on the dayside is closely controlled by the southward IMF, while the latitudinal variations on the nightside are better described by the variations of the Dst index. However, the equatorward and poleward motion of the nightside auroral currents occur earlier than the Dst variations. The Space Weather Modeling Framework (SWMF can capture the general dynamics of the storm time current variations. Both the model and the actual data show that the currents tend to saturate when the merging electric field is larger than 10 mV/m. However, the exact prediction of the temporal development of the currents is still not satisfactory.

  9. Storm time dynamics of auroral electrojets: CHAMP observation and the Space Weather Modeling Framework comparison

    Directory of Open Access Journals (Sweden)

    H. Wang

    2008-03-01

    Full Text Available We investigate variations of the location and intensity of auroral currents during two magnetic storm periods based on magnetic field measurements from CHAMP separately for both hemispheres, as well as for the dayside and nightside. The corresponding auroral electrojet current densities are on average enhanced by about a factor of 7 compared to the quiet time current strengths. The nightside westward current densities are on average 1.8 (2.2 times larger than the dayside eastward current densities in the Northern (Southern Hemisphere. Both eastward and westward currents are present during the storm periods with the most intense electrojets appearing during the main phase of the storm, before the ring current maximizes in strength. The eastward and westward electrojet centers can expand to 55° MLat during intense storms, as is observed on 31 March 2001 with Dst=−387 nT. The equatorward shift of auroral currents on the dayside is closely controlled by the southward IMF, while the latitudinal variations on the nightside are better described by the variations of the Dst index. However, the equatorward and poleward motion of the nightside auroral currents occur earlier than the Dst variations. The Space Weather Modeling Framework (SWMF can capture the general dynamics of the storm time current variations. Both the model and the actual data show that the currents tend to saturate when the merging electric field is larger than 10 mV/m. However, the exact prediction of the temporal development of the currents is still not satisfactory.

  10. Modelling natural electromagnetic interference in man-made conductors for space weather applications

    Directory of Open Access Journals (Sweden)

    L. Trichtchenko

    2016-04-01

    Full Text Available Power transmission lines above the ground, cables and pipelines in the ground and under the sea, and in general all man-made long grounded conductors are exposed to the variations of the natural electromagnetic field. The resulting currents in the networks (commonly named geomagnetically induced currents, GIC, are produced by the conductive and/or inductive coupling and can compromise or even disrupt system operations and, in extreme cases, cause power blackouts, railway signalling mis-operation, or interfere with pipeline corrosion protection systems. To properly model the GIC in order to mitigate their impacts it is necessary to know the frequency dependence of the response of these systems to the geomagnetic variations which naturally span a wide frequency range. For that, the general equations of the electromagnetic induction in a multi-layered infinitely long cylinder (representing cable, power line wire, rail or pipeline embedded in uniform media have been solved utilising methods widely used in geophysics. The derived electromagnetic fields and currents include the effects of the electromagnetic properties of each layer and of the different types of the surrounding media. This exact solution then has been used to examine the electromagnetic response of particular samples of long conducting structures to the external electromagnetic wave for a wide range of frequencies. Because the exact solution has a rather complicated structure, simple approximate analytical formulas have been proposed, analysed and compared with the results from the exact model. These approximate formulas show good coincidence in the frequency range spanning from geomagnetic storms (less than mHz to pulsations (mHz to Hz to atmospherics (kHz and above, and can be recommended for use in space weather applications.

  11. Application of the NASA A-Train to Evaluate Clouds Simulated by the Weather Research and Forecast Model

    Science.gov (United States)

    Molthan, Andrew L.; Jedlovec, Gary J.; Lapenta, William M.

    2008-01-01

    The CloudSat Mission, part of the NASA A-Train, is providing the first global survey of cloud profiles and cloud physical properties, observing seasonal and geographical variations that are pertinent to evaluating the way clouds are parameterized in weather and climate forecast models. CloudSat measures the vertical structure of clouds and precipitation from space through the Cloud Profiling Radar (CPR), a 94 GHz nadir-looking radar measuring the power backscattered by clouds as a function of distance from the radar. One of the goals of the CloudSat mission is to evaluate the representation of clouds in forecast models, thereby contributing to improved predictions of weather, climate and the cloud-climate feedback problem. This paper highlights potential limitations in cloud microphysical schemes currently employed in the Weather Research and Forecast (WRF) modeling system. The horizontal and vertical structure of explicitly simulated cloud fields produced by the WRF model at 4-km resolution are being evaluated using CloudSat observations in concert with products derived from MODIS and AIRS. A radiative transfer model is used to produce simulated profiles of radar reflectivity given WRF input profiles of hydrometeor mixing ratios and ambient atmospheric conditions. The preliminary results presented in the paper will compare simulated and observed reflectivity fields corresponding to horizontal and vertical cloud structures associated with midlatitude cyclone events.

  12. Climate Variability and Weather Extremes: Model-Simulated and Historical Data. Chapter 9

    Science.gov (United States)

    Schubert, Siegfried D.; Lim, Young-Kwon

    2012-01-01

    basic mechanisms by which extremes vary is incomplete. As noted in IPCC (2007), Incomplete global data sets and remaining model uncertainties still restrict understanding of changes in extremes and attribution of changes to causes, although understanding of changes in the intensity, frequency and risk of extremes has improved. Separating decadal and other shorter-term variability from climate change impacts on extremes requires a better understanding of the processes responsible for the changes. In particular, the physical processes linking sea surface temperature changes to regional climate changes, and a basic understanding of the inherent variability in weather extremes and how that is impacted by atmospheric circulation changes at subseasonal to decadal and longer time scales, are still inadequately understood. Given the fundamental limitations in the time span and quality of global observations, substantial progress on these issues will rely increasingly on improvements in models, with observations continuing to play a critical role, though less as a detection tool, and more as a tool for addressing physical processes, and to insure the quality of the climate models and the verisimilitude of the simulations (CCSP SAP 1.3, 2008).

  13. Tropospheric Signal Delay Estimates Derived from Numerical Weather Prediction Models and Their Impact on Real-Time GNSS Positioning Accuracy

    Science.gov (United States)

    Gutman, S. I.; Bock, Y.

    2007-12-01

    The accurate characterization of atmospheric moisture fields (including water vapor and clouds) is essential for improved weather forecasting and climate monitoring. Despite its importance, the ability to do so under all weather conditions has been a continuing problem for atmospheric scientists. The principle reason why this problem has been so difficult to solve is related to the high temporal and spatial variability of water in the free atmosphere. Under certain circumstances the distribution of moisture in the atmosphere can change abruptly over short distances, and this causes it to be under-observed using conventional weather observing systems. As water vapor, temperature and pressure change in the atmosphere, the refractivity of the troposphere changes accordingly and GNSS accuracy can suffer if the hydrostatic and wet signal delays are mismodeled. Recognizing this, the geodetic community developed techniques to treat the signal delays caused by the neutral atmosphere as nuisance parameters and remove them for high accuracy positioning applications. In ground-based GNSS/GPS Meteorology at NOAA, the tropospheric signal delay is estimated in near real-time from a network of about 400 continuously operating reference stations distributed across the U.S. using an 8-hr sliding window technique. Estimates of tropospheric refractivity (and/or integrated precipitable water vapor retrieved from these delays) have been assimilated into numerical weather prediction models in the U.S., Canada, Europe and Japan with exceptionally good results. Based on these and other findings, GNSS/GPS-Met is scheduled to transition from NOAA Research into operational use in NOAA's National Weather Service starting in 2009. Recognizing the need for improved ways to mitigate tropospheric effects on GNSS accuracy, especially for applications requiring low-latency measurements of height, scientists at NOAA's Earth System Research Laboratory began to investigate the feasibility of using

  14. Developing a Time Series Predictive Model for Dengue in Zhongshan, China Based on Weather and Guangzhou Dengue Surveillance Data.

    Directory of Open Access Journals (Sweden)

    Yingtao Zhang

    2016-02-01

    Full Text Available Dengue is a re-emerging infectious disease of humans, rapidly growing from endemic areas to dengue-free regions due to favorable conditions. In recent decades, Guangzhou has again suffered from several big outbreaks of dengue; as have its neighboring cities. This study aims to examine the impact of dengue epidemics in Guangzhou, China, and to develop a predictive model for Zhongshan based on local weather conditions and Guangzhou dengue surveillance information.We obtained weekly dengue case data from 1st January, 2005 to 31st December, 2014 for Guangzhou and Zhongshan city from the Chinese National Disease Surveillance Reporting System. Meteorological data was collected from the Zhongshan Weather Bureau and demographic data was collected from the Zhongshan Statistical Bureau. A negative binomial regression model with a log link function was used to analyze the relationship between weekly dengue cases in Guangzhou and Zhongshan, controlling for meteorological factors. Cross-correlation functions were applied to identify the time lags of the effect of each weather factor on weekly dengue cases. Models were validated using receiver operating characteristic (ROC curves and k-fold cross-validation.Our results showed that weekly dengue cases in Zhongshan were significantly associated with dengue cases in Guangzhou after the treatment of a 5 weeks prior moving average (Relative Risk (RR = 2.016, 95% Confidence Interval (CI: 1.845-2.203, controlling for weather factors including minimum temperature, relative humidity, and rainfall. ROC curve analysis indicated our forecasting model performed well at different prediction thresholds, with 0.969 area under the receiver operating characteristic curve (AUC for a threshold of 3 cases per week, 0.957 AUC for a threshold of 2 cases per week, and 0.938 AUC for a threshold of 1 case per week. Models established during k-fold cross-validation also had considerable AUC (average 0.938-0.967. The sensitivity and

  15. Fair weather atmospheric electricity

    International Nuclear Information System (INIS)

    Harrison, R G

    2011-01-01

    Not long after Franklin's iconic studies, an atmospheric electric field was discovered in 'fair weather' regions, well away from thunderstorms. The origin of the fair weather field was sought by Lord Kelvin, through development of electrostatic instrumentation and early data logging techniques, but was ultimately explained through the global circuit model of C.T.R. Wilson. In Wilson's model, charge exchanged by disturbed weather electrifies the ionosphere, and returns via a small vertical current density in fair weather regions. New insights into the relevance of fair weather atmospheric electricity to terrestrial and planetary atmospheres are now emerging. For example, there is a possible role of the global circuit current density in atmospheric processes, such as cloud formation. Beyond natural atmospheric processes, a novel practical application is the use of early atmospheric electrostatic investigations to provide quantitative information on past urban air pollution.

  16. Applying volumetric weather radar data for rainfall runoff modeling: The importance of error correction.

    Science.gov (United States)

    Hazenberg, P.; Leijnse, H.; Uijlenhoet, R.; Delobbe, L.; Weerts, A.; Reggiani, P.

    2009-04-01

    data. It is expected that these difference are even larger when a distributed hydrological model is used. Therefore, we apply the representative elementary watershed (REW) model which has already been calibrated using raingauge data and shows the ability of correctly estimating discharge values both at the outlet and upstream points. The overall goal of this study is to make use of the benefits of the high spatial and temporal resolution of weather radar data compared to a conventional raingauge network in order to gain a better understanding of the hydrological behavior of the Ourthe catchment.

  17. Evaluation of weather forecast systems for storm surge modeling in the Chesapeake Bay

    Science.gov (United States)

    Garzon, Juan L.; Ferreira, Celso M.; Padilla-Hernandez, Roberto

    2018-01-01

    Accurate forecast of sea-level heights in coastal areas depends, among other factors, upon a reliable coupling of a meteorological forecast system to a hydrodynamic and wave system. This study evaluates the predictive skills of the coupled circulation and wind-wave model system (ADCIRC+SWAN) for simulating storm tides in the Chesapeake Bay, forced by six different products: (1) Global Forecast System (GFS), (2) Climate Forecast System (CFS) version 2, (3) North American Mesoscale Forecast System (NAM), (4) Rapid Refresh (RAP), (5) European Center for Medium-Range Weather Forecasts (ECMWF), and (6) the Atlantic hurricane database (HURDAT2). This evaluation is based on the hindcasting of four events: Irene (2011), Sandy (2012), Joaquin (2015), and Jonas (2016). By comparing the simulated water levels to observations at 13 monitoring stations, we have found that the ADCIR+SWAN System forced by the following: (1) the HURDAT2-based system exhibited the weakest statistical skills owing to a noteworthy overprediction of the simulated wind speed; (2) the ECMWF, RAP, and NAM products captured the moment of the peak and moderately its magnitude during all storms, with a correlation coefficient ranging between 0.98 and 0.77; (3) the CFS system exhibited the worst averaged root-mean-square difference (excepting HURDAT2); (4) the GFS system (the lowest horizontal resolution product tested) resulted in a clear underprediction of the maximum water elevation. Overall, the simulations forced by NAM and ECMWF systems induced the most accurate results best accuracy to support water level forecasting in the Chesapeake Bay during both tropical and extra-tropical storms.

  18. Evaluating the Impacts of NASA/SPoRT Daily Greenness Vegetation Fraction on Land Surface Model and Numerical Weather Forecasts

    Science.gov (United States)

    Bell, Jordan R.; Case, Jonathan L.; Molthan, Andrew L.

    2011-01-01

    The NASA Short-term Prediction Research and Transition (SPoRT) Center develops new products and techniques that can be used in operational meteorology. The majority of these products are derived from NASA polar-orbiting satellite imagery from the Earth Observing System (EOS) platforms. One such product is a Greenness Vegetation Fraction (GVF) dataset, which is produced from Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the NASA EOS Aqua and Terra satellites. NASA SPoRT began generating daily real-time GVF composites at 1-km resolution over the Continental United States (CONUS) on 1 June 2010. The purpose of this study is to compare the National Centers for Environmental Prediction (NCEP) climatology GVF product (currently used in operational weather models) to the SPoRT-MODIS GVF during June to October 2010. The NASA Land Information System (LIS) was employed to study the impacts of the new SPoRT-MODIS GVF dataset on land surface models apart from a full numerical weather prediction (NWP) model. For the 2010 warm season, the SPoRT GVF in the western portion of the CONUS was generally higher than the NCEP climatology. The eastern CONUS GVF had variations both above and below the climatology during the period of study. These variations in GVF led to direct impacts on the rates of heating and evaporation from the land surface. The second phase of the project is to examine the impacts of the SPoRT GVF dataset on NWP using the Weather Research and Forecasting (WRF) model. Two separate WRF model simulations were made for individual severe weather case days using the NCEP GVF (control) and SPoRT GVF (experimental), with all other model parameters remaining the same. Based on the sensitivity results in these case studies, regions with higher GVF in the SPoRT model runs had higher evapotranspiration and lower direct surface heating, which typically resulted in lower (higher) predicted 2-m temperatures (2-m dewpoint temperatures). The opposite was true

  19. Modeling spatial patterns of wildfire susceptibility in southern California: Applications of MODIS remote sensing data and mesoscale numerical weather models

    Science.gov (United States)

    Schneider, Philipp

    This dissertation investigates the potential of Moderate Resolution Imaging Spectroradiometer (MODIS) imagery and mesoscale numerical weather models for mapping wildfire susceptibility in general and for improving the Fire Potential Index (FPI) in southern California in particular. The dissertation explores the use of the Visible Atmospherically Resistant Index (VARI) from MODIS data for mapping relative greenness (RG) of vegetation and subsequently for computing the FPI. VARI-based RG was validated against in situ observations of live fuel moisture. The results indicate that VARI is superior to the previously used Normalized Difference Vegetation Index (NDVI) for computing RG. FPI computed using VARI-based RG was found to outperform the traditional FPI when validated against historical fire detections using logistic regression. The study further investigates the potential of using Multiple Endmember Spectral Mixture Analysis (MESMA) on MODIS data for estimating live and dead fractions of vegetation. MESMA fractions were compared against in situ measurements and fractions derived from data of a high-resolution, hyperspectral sensor. The results show that live and dead fractions obtained from MODIS using MESMA are well correlated with the reference data. Further, FPI computed using MESMA-based green vegetation fraction in lieu of RG was validated against historical fire occurrence data. MESMA-based FPI performs at a comparable level to the traditional NDVI-based FPI, but can do so using a single MODIS image rather than an extensive remote sensing time series as required for the RG approach. Finally this dissertation explores the potential of integrating gridded wind speed data obtained from the MM5 mesoscale numerical weather model in the FPI. A new fire susceptibility index, the Wind-Adjusted Fire Potential Index (WAFPI), was introduced. It modifies the FPI algorithm by integrating normalized wind speed. Validating WAFPI against historical wildfire events using

  20. Numerical simulation for regional ozone concentrations: A case study by weather research and forecasting/chemistry (WRF/Chem) model

    OpenAIRE

    Khandakar Md Habib Al Razi, Moritomi Hiroshi

    2013-01-01

    The objective of this research is to better understand and predict the atmospheric concentration distribution of ozone and its precursor (in particular, within the Planetary Boundary Layer (Within 110 km to 12 km) over Kasaki City and the Greater Tokyo Area using fully coupled online WRF/Chem (Weather Research and Forecasting/Chemistry) model. In this research, a serious and continuous high ozone episode in the Greater Tokyo Area (GTA) during the summer of 14–18 August 2010 was investigated u...

  1. Basic concepts for convection parameterization in weather forecast and climate models: COST Action ES0905 final report

    OpenAIRE

    Yano, J.-I.; Geleyn, J.-F.; Koller, M.; Mironov, D.; Quass, J.; Soares, P. M. M.; Phillips, V. J. T. P.; Plant, R S; Deluca, A.; Marquet, P.; Stulic, L.; Fuchs, Z.

    2015-01-01

    The research network “Basic Concepts for Convection Parameterization in Weather Forecast and Climate Models” was organized with European funding (COST Action ES0905) for the period of 2010–2014. Its extensive brainstorming suggests how the subgrid-scale parameterization problem in atmospheric modeling, especially for convection, can be examined and developed from the point of view of a robust theoretical basis. Our main cautions are current emphasis on massive observational data analyses and ...

  2. Operational Exploitation of Satellite-Based Sounding Data and Numerical Weather Prediction Models for Directed Energy Applications

    Science.gov (United States)

    2015-12-01

    Environmental Satellites HEL High Energy Laser HRRR High Resolution Rapid Refresh H-V 5/7 Hufnagel-Valley 5/7 HSB Humidity Sounder for Brazil ...FOR DIRECTED ENERGY APPLICATIONS DISSERTATION David C. Meier, Lieutenant Colonel, USAF AFIT-ENP-DS-15-D-009 DEPARTMENT OF THE AIR FORCE...DATA AND NUMERICAL WEATHER PREDICTION MODELS FOR DIRECTED ENERGY APPLICATIONS DISSERTATION Presented to the Faculty Department of

  3. Evaluating the Impacts of NASA/SPoRT Daily Greenness Vegetation Fraction on Land Surface Model and Numerical Weather Forecasts

    Science.gov (United States)

    Bell, Jordan R.; Case, Jonathan L.; LaFontaine, Frank J.; Kumar, Sujay V.

    2012-01-01

    The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed a Greenness Vegetation Fraction (GVF) dataset, which is updated daily using swaths of Normalized Difference Vegetation Index data from the Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the NASA EOS Aqua and Terra satellites. NASA SPoRT began generating daily real-time GVF composites at 1-km resolution over the Continental United States (CONUS) on 1 June 2010. The purpose of this study is to compare the National Centers for Environmental Prediction (NCEP) climatology GVF product (currently used in operational weather models) to the SPoRT-MODIS GVF during June to October 2010. The NASA Land Information System (LIS) was employed to study the impacts of the SPoRT-MODIS GVF dataset on a land surface model (LSM) apart from a full numerical weather prediction (NWP) model. For the 2010 warm season, the SPoRT GVF in the western portion of the CONUS was generally higher than the NCEP climatology. The eastern CONUS GVF had variations both above and below the climatology during the period of study. These variations in GVF led to direct impacts on the rates of heating and evaporation from the land surface. In the West, higher latent heat fluxes prevailed, which enhanced the rates of evapotranspiration and soil moisture depletion in the LSM. By late Summer and Autumn, both the average sensible and latent heat fluxes increased in the West as a result of the more rapid soil drying and higher coverage of GVF. The impacts of the SPoRT GVF dataset on NWP was also examined for a single severe weather case study using the Weather Research and Forecasting (WRF) model. Two separate coupled LIS/WRF model simulations were made for the 17 July 2010 severe weather event in the Upper Midwest using the NCEP and SPoRT GVFs, with all other model parameters remaining the same. Based on the sensitivity results, regions with higher GVF in the SPoRT model runs had higher evapotranspiration and

  4. Improving Air Quality (and Weather) Predictions using Advanced Data Assimilation Techniques Applied to Coupled Models during KORUS-AQ

    Science.gov (United States)

    Carmichael, G. R.; Saide, P. E.; Gao, M.; Streets, D. G.; Kim, J.; Woo, J. H.

    2017-12-01

    Ambient aerosols are important air pollutants with direct impacts on human health and on the Earth's weather and climate systems through their interactions with radiation and clouds. Their role is dependent on their distributions of size, number, phase and composition, which vary significantly in space and time. There remain large uncertainties in simulated aerosol distributions due to uncertainties in emission estimates and in chemical and physical processes associated with their formation and removal. These uncertainties lead to large uncertainties in weather and air quality predictions and in estimates of health and climate change impacts. Despite these uncertainties and challenges, regional-scale coupled chemistry-meteorological models such as WRF-Chem have significant capabilities in predicting aerosol distributions and explaining aerosol-weather interactions. We explore the hypothesis that new advances in on-line, coupled atmospheric chemistry/meteorological models, and new emission inversion and data assimilation techniques applicable to such coupled models, can be applied in innovative ways using current and evolving observation systems to improve predictions of aerosol distributions at regional scales. We investigate the impacts of assimilating AOD from geostationary satellite (GOCI) and surface PM2.5 measurements on predictions of AOD and PM in Korea during KORUS-AQ through a series of experiments. The results suggest assimilating datasets from multiple platforms can improve the predictions of aerosol temporal and spatial distributions.

  5. Application of Volumetric Weather Radar Data and the Distributed Rainfall Runoff Model REW in the Ourthe Catchment

    Science.gov (United States)

    Hazenberg, P.; Leijnse, H.; Torfs, P.; Uijlenhoet, R.; Weerts, A.; Reggiani, P.; Delobbe, L.

    2008-12-01

    In the southern Ardennes region of Belgium near the border with Luxembourg, the Royal Meteorological Institute of Belgium (RMI) installed a C-band Doppler weather radar at an elevation of 600 m in the year 2001. This volumetric weather radar scans over multiple elevations at a temporal resolution of 5 minutes. The current study explores the possibility of using the volumetric information of the precipitation field to correct for the effects of the Vertical Profile of Reflectivity (VPR) over the period October 1, 2002 until March 31, 2003. During this winter half year storm events are mainly stratiform, giving rise to bright band effects which can decrease the performance of the radar. Previous studies have shown multiple drawbacks in applying a single estimated VPR profile to correct such reflectivity data. Therefore, the focus here is on the temporal variability of the VPR as measured by the radar and its variability over different spatial scales. This information is applied to generate a number of possible rainfall fields. These realizations are employed to try to quantify some of the discrepancies in precipitation intensities as estimated by the weather radar and those measured by a raingauge network. The final step then is to assess their potential within a distributed rainfall runoff model. The 1597 km2 Ourthe catchment lies within 60 km of the radar. Over this medium sized watershed ten raingauges measuring at an hourly interval are more or less equally distributed. Near the outlet discharge data are collected at the same time step. The distributed hydrological Representative Elementary Watershed (REW) model is applied to model the hydrological behavior of the Ourthe over the six month period. The benefits of the high spatial and temporal resolution of weather radar data compared to a conventional raingauge network plus the possibility of generating multiple realizations of the precipitation field are expected to yield more information about the hydrological

  6. Analysis of Highly Wind Power Integrated Power System model performance during Critical Weather conditions

    DEFF Research Database (Denmark)

    Basit, Abdul; Hansen, Anca Daniela; Sørensen, Poul Ejnar

    2014-01-01

    Secure power system operation of a highly wind power integrated power system is always at risk during critical weather conditions, e.g. in extreme high winds. The risk is even higher when 50% of the total electricity consumption has to be supplied by wind power, as the case for the future Danish...

  7. Assesment of a soil moisture retrieval with numerical weather prediction model temperature

    Science.gov (United States)

    The effect of using a Numerical Weather Prediction (NWP) soil temperature product instead of estimates provided by concurrent 37 GHz data on satellite-based passive microwave retrieval of soil moisture retrieval was evaluated. This was prompted by the change in system configuration of preceding mult...

  8. Modeling fire behavior on tropical islands with high-resolution weather data

    Science.gov (United States)

    John W. Benoit; Francis M. Fujioka; David R. Weise

    2009-01-01

    In this study, we consider fire behavior simulation in tropical island scenarios such as Hawaii and Puerto Rico. The development of a system to provide real-time fire behavior prediction in Hawaii is discussed. This involves obtaining fuels and topography information at a fine scale, as well as supplying daily high-resolution weather forecast data for the area of...

  9. A model for the identification of tropical weather systems over South ...

    African Journals Online (AJOL)

    drinie

    2002-07-03

    Jul 3, 2002 ... Weather forecasters in South Africa are trained on and experienced in forecasting rainfall from these systems. During late summer .... T = temperature. If the horizontal gradient vector of the average column temperature is zero then the thermal wind will be zero. The thermal wind is defined as the vertical ...

  10. Comparison of radar and numerical weather model rainfall forecasts in the perspective of urban flood prediction

    DEFF Research Database (Denmark)

    Lovring, Maite Monica; Löwe, Roland; Courdent, Vianney Augustin Thomas

    An early flood warning system has been developed for urban catchments and is currently running in online operation in Copenhagen. The system is highly dependent on the quality of rainfall forecast inputs. An investigation of precipitation inputs from Radar Nowcast (RN), Numerical Weather Prediction...... of the three forecast products is expected to yield the optimal input for flood warning....

  11. A model for the identification of tropical weather systems over South ...

    African Journals Online (AJOL)

    South Africa forms the southern end of Africa with its northern boundary at approximately 22°S and the southern-most point, Cape Agulhas, at approximately 35°S. During most of the year atmospheric circulation over South Africa, especially the central and southern regions, is dominated by extra tropical weather systems ...

  12. Winter weather demand considerations.

    Science.gov (United States)

    2015-04-01

    Winter weather has varied effects on travel behavior. Using 418 survey responses from the Northern Virginia : commuting area of Washington, D.C. and binary logit models, this study examines travel related changes under : different types of winter wea...

  13. National Weather Service

    Science.gov (United States)

    ... Daily Briefing Damage/Fatality/Injury Statistics Forecast Models GIS Data Portal NOAA Weather Radio Publications SKYWARN Storm Spotters StormReady TsunamiReady EDUCATION Be A Force of Nature NWS Education Home ...

  14. Progress in Space Weather Modeling and Observations Needed to Improve the Operational NAIRAS Model Aircraft Radiation Exposure Predictions

    Science.gov (United States)

    Mertens, C. J.; Kress, B. T.; Wiltberger, M. J.; Tobiska, W.; Xu, X.

    2011-12-01

    The Nowcast of Atmospheric Ionizing Radiation for Aviation Safety (NAIRAS) is a prototype operational model for predicting commercial aircraft radiation exposure from galactic and solar cosmic rays. NAIRAS predictions are currently streaming live from the project's public website, and the exposure rate nowcast is also available on the SpaceWx smartphone app for iPhone, IPad, and Android. Cosmic rays are the primary source of human exposure to high linear energy transfer radiation at aircraft altitudes, which increases the risk of cancer and other adverse health effects. Thus, the NAIRAS model addresses an important national need with broad societal, public health and economic benefits. The processes responsible for the variability in the solar wind, interplanetary magnetic field, solar energetic particle spectrum, and the dynamical response of the magnetosphere to these space environment inputs, strongly influence the composition and energy distribution of the atmospheric ionizing radiation field. During the development of the NAIRAS model, new science questions were identified that must be addressed in order to obtain a more reliable and robust operational model of atmospheric radiation exposure. Addressing these science questions require improvements in both space weather modeling and observations. The focus of this talk is to present these science questions, the proposed methodologies for addressing these science questions, and the anticipated improvements to the operational predictions of atmospheric radiation exposure. The overarching goal of this work is to provide a decision support tool for the aviation industry that will enable an optimal balance to be achieved between minimizing health risks to passengers and aircrew while simultaneously minimizing costs to the airline companies.

  15. Dynamical Downscaling of Global Circulation Models With the Weather Research and Forecast Model in the Northern Great Plains

    Science.gov (United States)

    Burtch, D.; Mullendore, G. L.; Kennedy, A. D.; Simms, M.; Kirilenko, A.; Coburn, J.

    2015-12-01

    Understanding the impacts of global climate change on regional scales is crucial for accurate decision-making by state and local governments. This is especially true in North Dakota, where climate change can have significant consequences on agriculture, its traditionally strongest economic sector. This region of the country shows a high variability in precipitation, especially in the summer months and so the focus of this study is on warm season processes over decadal time scales. The Weather Research and Forecast (WRF) model is used to dynamically downscale two Global Circulation Models (GCMs) from the CMIP5 ensemble in order to determine the microphysical parameterization and nudging techniques (spectral or analysis) best suited for this region. The downscaled domain includes the entirety of North Dakota at a horizontal resolution of 5 km. In addition, smaller domains of 1 km horizontal resolution are centered over regions of focused hydrological importance. The dynamically downscaled simulations are compared with both gridded observational data and statistically downscaled data to evaluate the performance of the simulations. Preliminary results have shown a marked difference between the two downscaled GCMs in terms of temperature and precipitation bias. Choice of microphysical parameterization has not shown to create any significant differences in the temperature fields. However, the precipitation fields do appear to be most affected by the microphysical parameterization, regardless of the choice of GCM. Implications on the unique water resource challenges faced in this region will also be discussed.

  16. Urban pluvial flood prediction: a case study evaluating radar rainfall nowcasts and numerical weather prediction models as model inputs.

    Science.gov (United States)

    Thorndahl, Søren; Nielsen, Jesper Ellerbæk; Jensen, David Getreuer

    2016-12-01

    Flooding produced by high-intensive local rainfall and drainage system capacity exceedance can have severe impacts in cities. In order to prepare cities for these types of flood events - especially in the future climate - it is valuable to be able to simulate these events numerically, both historically and in real-time. There is a rather untested potential in real-time prediction of urban floods. In this paper, radar data observations with different spatial and temporal resolution, radar nowcasts of 0-2 h leadtime, and numerical weather models with leadtimes up to 24 h are used as inputs to an integrated flood and drainage systems model in order to investigate the relative difference between different inputs in predicting future floods. The system is tested on the small town of Lystrup in Denmark, which was flooded in 2012 and 2014. Results show it is possible to generate detailed flood maps in real-time with high resolution radar rainfall data, but rather limited forecast performance in predicting floods with leadtimes more than half an hour.

  17. Frost Monitoring and Forecasting Using MODIS Land Surface Temperature Data and a Numerical Weather Prediction Model Forecasts for Eastern Africa

    Science.gov (United States)

    Kabuchanga, Eric; Flores, Africa; Malaso, Susan; Mungai, John; Sakwa, Vincent; Shaka, Ayub; Limaye, Ashutosh

    2014-01-01

    Frost is a major challenge across Eastern Africa, severely impacting agricultural farms. Frost damages have wide ranging economic implications on tea and coffee farms, which represent a major economic sector. Early monitoring and forecasting will enable farmers to take preventive actions to minimize the losses. Although clearly important, timely information on when to protect crops from freezing is relatively limited. MODIS Land Surface Temperature (LST) data, derived from NASA's Terra and Aqua satellites, and 72-hr weather forecasts from the Kenya Meteorological Service's operational Weather Research Forecast model are enabling the Regional Center for Mapping of Resources for Development (RCMRD) and the Tea Research Foundation of Kenya to provide timely information to farmers in the region. This presentation will highlight an ongoing collaboration among the Kenya Meteorological Service, RCMRD, and the Tea Research Foundation of Kenya to identify frost events and provide farmers with potential frost forecasts in Eastern Africa.

  18. Application of fuzzy – Neuro to model weather parameter variability impacts on electrical load based on long-term forecasting

    Directory of Open Access Journals (Sweden)

    Danladi Ali

    2018-03-01

    Full Text Available Long-term load forecasting provides vital information about future load and it helps the power industries to make decision regarding electrical energy generation and delivery. In this work, fuzzy – neuro model is developed to forecast a year ahead load in relation to weather parameter (temperature and humidity in Mubi, Adamawa State. It is observed that: electrical load increased with increase in temperature and relative humidity does not show notable effect on electrical load. The accuracy of the prediction is obtained at 98.78% with the corresponding mean absolute percentage error (MAPE of 1.22%. This confirms that fuzzy – neuro is a good tool for load forecasting. Keywords: Electrical load, Load forecasting, Fuzzy logic, Back propagation, Neuro-fuzzy, Weather parameter

  19. The representation of low-level clouds during the West African monsoon in weather and climate models

    Science.gov (United States)

    Kniffka, Anke; Hannak, Lisa; Knippertz, Peter; Fink, Andreas

    2016-04-01

    The West African monsoon is one of the most important large-scale circulation features in the tropics and the associated seasonal rainfalls are crucial to rain-fed agriculture and water resources for hundreds of millions of people. However, numerical weather and climate models still struggle to realistically represent salient features of the monsoon across a wide range of scales. Recently it has been shown that substantial errors in radiation and clouds exist in the southern parts of West Africa (8°W-8°E, 5-10°N) during summer. This area is characterised by strong low-level jets associated with the formation of extensive ultra-low stratus clouds. Often persisting long after sunrise, these clouds have a substantial impact on the radiation budget at the surface and thus the diurnal evolution of the planetary boundary layer (PBL). Here we present some first results from a detailed analysis of the representation of these clouds and the associated PBL features across a range of weather and climate models. Recent climate model simulations for the period 1991-2010 run in the framework of the Year of Tropical Convection (YOTC) offer a great opportunity for this analysis. The models are those used for the latest Assessment Report of the Intergovernmental Panel on Climate Change, but for YOTC the model output has a much better temporal resolution, allowing to resolve the diurnal cycle, and includes diabatic terms, allowing to much better assess physical reasons for errors in low-level temperature, moisture and thus cloudiness. These more statistical climate model analyses are complemented by experiments using ICON (Icosahedral non-hydrostatic general circulation model), the new numerical weather prediction model of the German Weather Service and the Max Planck Institute for Meteorology. ICON allows testing sensitivities to model resolution and numerical schemes. These model simulations are validated against (re-)analysis data, satellite observations (e.g. CM SAF cloud and

  20. 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

  1. Aviation Model: A Fine-Scale Numerical Weather Prediction System for Aviation Applications at the Hong Kong International Airport

    Directory of Open Access Journals (Sweden)

    Wai-Kin Wong

    2013-01-01

    Full Text Available The Hong Kong Observatory (HKO is planning to implement a fine-resolution Numerical Weather Prediction (NWP model for supporting the aviation weather applications at the Hong Kong International Airport (HKIA. This new NWP model system, called Aviation Model (AVM, is configured at a horizontal grid spacing of 600 m and 200 m. It is based on the WRF-ARW (Advance Research WRF model that can have sufficient computation efficiency in order to produce hourly updated forecasts up to 9 hours ahead on a future high performance computer system with theoretical peak performance of around 10 TFLOPS. AVM will be nested inside the operational mesoscale NWP model of HKO with horizontal resolution of 2 km. In this paper, initial numerical experiment results in forecast of windshear events due to seabreeze and terrain effect are discussed. The simulation of sea-breeze-related windshear is quite successful, and the headwind change observed from flight data could be reproduced in the model forecast. Some impacts of physical processes on generating the fine-scale wind circulation and development of significant convection are illustrated. The paper also discusses the limitations in the current model setup and proposes methods for the future development of AVM.

  2. Tracking sensitive source areas of different weather pollution types using GRAPES-CUACE adjoint model

    Science.gov (United States)

    Wang, Chao; An, Xingqin; Zhai, Shixian; Hou, Qing; Sun, Zhaobin

    2018-02-01

    In this study, the sustained pollution processes were selected during which daily PM2.5 concentration exceeded 75 μg/m3 for three days continuously based on the hourly data of Beijing observation sites from July 2012 to December 2015. Using the China Meteorological Administration (CMA) MICAPS meteorological processing system, synoptic situation during PM2.5 pollution processes was classified into five weather types: low pressure and weak high pressure alternating control, weak high pressure, low pressure control, high rear, and uniform pressure field. Then, we chose the representative pollution cases corresponding to each type, adopted the GRAPES-CUACE adjoint model tracking the sensitive source areas of the five types, and analyzed the critical discharge periods of Beijing and neighboring provinces as well as their contribution to the PM2.5 peak concentration in Beijing. The results showed that the local source plays the main theme in the 30 h before the objective time, and prior to 72 h before the objective time contribution of local sources for the five pollution types are 37.5%, 25.0%, 39.4%, 31.2%, and 42.4%, respectively; the Hebei source contributes constantly in the 57 h ahead of the objective time with the contribution proportion ranging from 37% to 64%; the contribution period and rate of Tianjin and Shanxi sources are shorter and smaller. Based on the adjoint sensitivity analysis, we further discussed the effect of emission reduction control measures in different types, finding that the effect of local source reduction in the first 20 h of the objective time is better, and if the local source is reduced 50% within 72 h before the objective time, the decline rates of PM2.5 in the five types are 11.6%, 9.4%, 13.8%, 9.9% and 15.2% respectively. And the reduction effect of the neighboring sources is better within the 3-57 h before the objective time.

  3. An investigation of the Midsummer Drought over Mesoamerica with the Weather Research and Forecast regional model

    Science.gov (United States)

    Berthet, S.; Sheinbaum, J.; Jouanno, J.

    2011-12-01

    The relative drought observed over the intra-americas sea (IAS) region in the middle of the summer rainy season (observed from May to October) is called ''midsummer drought'' (MSD). Although the magnitude of this MSD event varies from year to year, it clearly forces the climatological rainfall structure to be bimodal at summertime, with maxima in the early and later rainy season. Regional numerical experiments of a 30-km horizontal grid spacing domain have been performed with version 3.2 of the ''Weather Research and Forecast model''. Simulations have been run from 1999 to 2008 using interannual meteorological boundary conditions from NCEP2 reanalysis. In order to investigate the roles of the surrounding warm pools and the air-sea interactions responsible for the occurrence of the MSD, runs have been forced with time-varying Sea Surface Temperatures (SST) from the NCEP Real-Time SST archives. Different simulations have been carried out to get representative conditions of the annual climate within the IAS area. The selected configuration reproduces properly the large-scale features observed during the MSD. The seasonal cycle of the Pacific intertropical convergence zone is succesfully captured. Its northward migration up to the 10N latitude following warm SST is seen at summertime. Moreover the numerical experiment accounts for the westward intrusion of the north Atlantic subtropical high allowing us to simulate the semi-annual strengthening of the Caribbean Low-Level Jet during the MSD period. This jet is known to be determinant for moisture transport in the region. Consequently, analysis of the regional simulation are performed to discuss first the impact of the variability of the large-scale features on the interannual variability of the MSD. Secondly, new simulations with different SST conditions over the eastern tropical Pacific and northern Atlantic Warm Pools are compared in order to characterize the role of the oceanic conditions east and west of Central

  4. Evaluation of Thompson-type trend and monthly weather data models for corn yields in Iowa, Illinois, and Indiana

    Science.gov (United States)

    French, V. (Principal Investigator)

    1982-01-01

    An evaluation was made of Thompson-Type models which use trend terms (as a surrogate for technology), meteorological variables based on monthly average temperature, and total precipitation to forecast and estimate corn yields in Iowa, Illinois, and Indiana. Pooled and unpooled Thompson-type models were compared. Neither was found to be consistently superior to the other. Yield reliability indicators show that the models are of limited use for large area yield estimation. The models are objective and consistent with scientific knowledge. Timely yield forecasts and estimates can be made during the growing season by using normals or long range weather forecasts. The models are not costly to operate and are easy to use and understand. The model standard errors of prediction do not provide a useful current measure of modeled yield reliability.

  5. Evaluating winds and vertical wind shear from Weather Research and Forecasting model forecasts using seven planetary boundary layer schemes

    DEFF Research Database (Denmark)

    Draxl, Caroline; Hahmann, Andrea N.; Pena Diaz, Alfredo

    2014-01-01

    with different PBL parameterizations at one coastal site over western Denmark. The evaluation focuses on determining which PBL parameterization performs best for wind energy forecasting, and presenting a validation methodology that takes into account wind speed at different heights. Winds speeds at heights...... regarding wind energy at these levels partly depends on the formulation and implementation of planetary boundary layer (PBL) parameterizations in these models. This study evaluates wind speeds and vertical wind shears simulated by theWeather Research and Forecasting model using seven sets of simulations...

  6. Approach to Integrate Global-Sun Models of Magnetic Flux Emergence and Transport for Space Weather Studies

    Science.gov (United States)

    Mansour, Nagi N.; Wray, Alan A.; Mehrotra, Piyush; Henney, Carl; Arge, Nick; Godinez, H.; Manchester, Ward; Koller, J.; Kosovichev, A.; Scherrer, P.; hide

    2013-01-01

    The Sun lies at the center of space weather and is the source of its variability. The primary input to coronal and solar wind models is the activity of the magnetic field in the solar photosphere. Recent advancements in solar observations and numerical simulations provide a basis for developing physics-based models for the dynamics of the magnetic field from the deep convection zone of the Sun to the corona with the goal of providing robust near real-time boundary conditions at the base of space weather forecast models. The goal is to develop new strategic capabilities that enable characterization and prediction of the magnetic field structure and flow dynamics of the Sun by assimilating data from helioseismology and magnetic field observations into physics-based realistic magnetohydrodynamics (MHD) simulations. The integration of first-principle modeling of solar magnetism and flow dynamics with real-time observational data via advanced data assimilation methods is a new, transformative step in space weather research and prediction. This approach will substantially enhance an existing model of magnetic flux distribution and transport developed by the Air Force Research Lab. The development plan is to use the Space Weather Modeling Framework (SWMF) to develop Coupled Models for Emerging flux Simulations (CMES) that couples three existing models: (1) an MHD formulation with the anelastic approximation to simulate the deep convection zone (FSAM code), (2) an MHD formulation with full compressible Navier-Stokes equations and a detailed description of radiative transfer and thermodynamics to simulate near-surface convection and the photosphere (Stagger code), and (3) an MHD formulation with full, compressible Navier-Stokes equations and an approximate description of radiative transfer and heating to simulate the corona (Module in BATS-R-US). CMES will enable simulations of the emergence of magnetic structures from the deep convection zone to the corona. Finally, a plan

  7. Implementation of a Generalized Actuator Line Model for Wind Turbine Parameterization in the Weather Research and Forecasting Model

    Energy Technology Data Exchange (ETDEWEB)

    Lundquist, Julie [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Marjanovic, Nikola [University of California, Berkeley; Lawrence Livermore National Laboratory; Mirocha, Jeffrey D. [Lawrence Livermore National Laboratory; Kosovic, Branko [University Corporation for Atmospheric Research; Chow, Fotini Katopodes [University of California, Berkeley

    2017-12-22

    A generalized actuator line (GAL) wind turbine parameterization is implemented within the Weather Research and Forecasting model to enable high-fidelity large-eddy simulations of wind turbine interactions with boundary layer flows under realistic atmospheric forcing conditions. Numerical simulations using the GAL parameterization are evaluated against both an already implemented generalized actuator disk (GAD) wind turbine parameterization and two field campaigns that measured the inflow and near-wake regions of a single turbine. The representation of wake wind speed, variance, and vorticity distributions is examined by comparing fine-resolution GAL and GAD simulations and GAD simulations at both fine and coarse-resolutions. The higher-resolution simulations show slightly larger and more persistent velocity deficits in the wake and substantially increased variance and vorticity when compared to the coarse-resolution GAD. The GAL generates distinct tip and root vortices that maintain coherence as helical tubes for approximately one rotor diameter downstream. Coarse-resolution simulations using the GAD produce similar aggregated wake characteristics to both fine-scale GAD and GAL simulations at a fraction of the computational cost. The GAL parameterization provides the capability to resolve near wake physics, including vorticity shedding and wake expansion.

  8. EMD-regression for modelling multi-scale relationships, and application to weather-related cardiovascular mortality

    Science.gov (United States)

    Masselot, Pierre; Chebana, Fateh; Bélanger, Diane; St-Hilaire, André; Abdous, Belkacem; Gosselin, Pierre; Ouarda, Taha B. M. J.

    2018-01-01

    In a number of environmental studies, relationships between natural processes are often assessed through regression analyses, using time series data. Such data are often multi-scale and non-stationary, leading to a poor accuracy of the resulting regression models and therefore to results with moderate reliability. To deal with this issue, the present paper introduces the EMD-regression methodology consisting in applying the empirical mode decomposition (EMD) algorithm on data series and then using the resulting components in regression models. The proposed methodology presents a number of advantages. First, it accounts of the issues of non-stationarity associated to the data series. Second, this approach acts as a scan for the relationship between a response variable and the predictors at different time scales, providing new insights about this relationship. To illustrate the proposed methodology it is applied to study the relationship between weather and cardiovascular mortality in Montreal, Canada. The results shed new knowledge concerning the studied relationship. For instance, they show that the humidity can cause excess mortality at the monthly time scale, which is a scale not visible in classical models. A comparison is also conducted with state of the art methods which are the generalized additive models and distributed lag models, both widely used in weather-related health studies. The comparison shows that EMD-regression achieves better prediction performances and provides more details than classical models concerning the relationship.

  9. Application of global weather and climate model output to the design and operation of wind-energy systems

    Energy Technology Data Exchange (ETDEWEB)

    Curry, Judith [Climate Forecast Applications Network, Atlanta, GA (United States)

    2015-05-21

    This project addressed the challenge of providing weather and climate information to support the operation, management and planning for wind-energy systems. The need for forecast information is extending to longer projection windows with increasing penetration of wind power into the grid and also with diminishing reserve margins to meet peak loads during significant weather events. Maintenance planning and natural gas trading is being influenced increasingly by anticipation of wind generation on timescales of weeks to months. Future scenarios on decadal time scales are needed to support assessment of wind farm siting, government planning, long-term wind purchase agreements and the regulatory environment. The challenge of making wind forecasts on these longer time scales is associated with a wide range of uncertainties in general circulation and regional climate models that make them unsuitable for direct use in the design and planning of wind-energy systems. To address this challenge, CFAN has developed a hybrid statistical/dynamical forecasting scheme for delivering probabilistic forecasts on time scales from one day to seven months using what is arguably the best forecasting system in the world (European Centre for Medium Range Weather Forecasting, ECMWF). The project also provided a framework to assess future wind power through developing scenarios of interannual to decadal climate variability and change. The Phase II research has successfully developed an operational wind power forecasting system for the U.S., which is being extended to Europe and possibly Asia.

  10. The Planeterrella: an Analog Model for Teaching About the Invisible Electromagnetic Processes Driving Space Weather

    Science.gov (United States)

    Masongsong, E. V.; Glesener, G. B.; Angelopoulos, V.; Lilensten, J.; Bingley, L.

    2015-12-01

    The Planeterrella can be used as an analog to help students visualize and understand the electromagnetic processes driving space weather that affect our daily lives. Solar storms and solar wind charged particles (plasma) cause "space weather" via their interaction with Earth's protective magnetic shield, the magnetosphere. The Planeterrella uses magnetized spheres in a vacuum chamber to demonstrate solar wind energy transfer to Earth and planets, with polar localization of aurora due to charged particles traveling along geomagnetic field lines. The Planeterrella provides a unique opportunity to experience and manipulate plasma, the dominant form of matter in our universe, yet seldom observable on Earth. Severe space weather events produce spectacular auroral displays as well as devastating consequences: radiation exposure to air and space travelers, prolonged radio blackouts, and damage to critical GPS and communications satellites. We will (1) discuss ways in which the Planeterrella may be most useful in classroom settings, including large lecture halls, laboratories, and small discussion sessions; (2) provide information on how instructors can produce their own Planeterrella for their courses; and (3) invite meeting attendees to engage in a discussion on how we might be able to improve on the current design of the Planeterrella, and how to reach students in more parts of the world.

  11. A Mathematical Model of Hourly Solar Radiation in Varying Weather Conditions for a Dynamic Simulation of the Solar Organic Rankine Cycle

    Directory of Open Access Journals (Sweden)

    Taehong Sung

    2015-07-01

    Full Text Available A mathematical model of hourly solar radiation with weather variability is proposed based on the simple sky model. The model uses a superposition of trigonometric functions with short and long periods. We investigate the effects of the model variables on the clearness (kD and the probability of persistence (POPD indices and also evaluate the proposed model for all of the kD-POPD weather classes. A simple solar organic Rankine cycle (SORC system with thermal storage is simulated using the actual weather conditions, and then, the results are compared with the simulation results using the proposed model and the simple sky model. The simulation results show that the proposed model provides more accurate system operation characteristics than the simple sky model.

  12. Mirador - Weather

    Data.gov (United States)

    National Aeronautics and Space Administration — Earth Science data access made simple. Our weather system includes the dynamics of the atmosphere and its interaction with the oceans and land. The improvement of...

  13. Weather Research and Forecasting model simulation of an onshore wind farm: assessment against LiDAR and SCADA data

    Science.gov (United States)

    Santoni, Christian; Garcia-Cartagena, Edgardo J.; Zhan, Lu; Iungo, Giacomo Valerio; Leonardi, Stefano

    2017-11-01

    The integration of wind farm parameterizations into numerical weather prediction models is essential to study power production under realistic conditions. Nevertheless, recent models are unable to capture turbine wake interactions and, consequently, the mean kinetic energy entrainment, which are essential for the development of power optimization models. To address the study of wind turbine wake interaction, one-way nested mesoscale to large-eddy simulation (LES) were performed using the Weather Research and Forecasting model (WRF). The simulation contains five nested domains modeling the mesoscale wind on the entire North Texas Panhandle region to the microscale wind fluctuations and turbine wakes of a wind farm located at Panhandle, Texas. The wind speed, direction and boundary layer profile obtained from WRF were compared against measurements obtained with a sonic anemometer and light detection and ranging system located within the wind farm. Additionally, the power production were assessed against measurements obtained from the supervisory control and data acquisition system located in each turbine. Furthermore, to incorporate the turbines into very coarse LES, a modification to the implementation of the wind farm parameterization by Fitch et al. (2012) is proposed. This work was supported by the NSF, Grants No. 1243482 (WINDINSPIRE) and IIP 1362033 (WindSTAR), and TACC.

  14. US NOAA HRRR/RAP Model/Assimilation System 2016-17 Improvements for Aviation Weather Applications

    Science.gov (United States)

    Benjamin, Stan; Alexander, Curtis; Weygandt, Stephen; Hu, Ming; Smirnova, Tanya; Olson, Joseph; Brown, John; Kenyon, Jaymes; James, Eric; Jankov, Isidora; Ladwig, Terra

    2017-04-01

    To improve US short-range forecast guidance for aviation (and severe weather and energy applications), an operational upgrade of the Rapid Refresh (RAP, 13km) and High-Resolution Rapid Refresh (HRRR, 3km) model systems at NOAA's NCEP occurred in August 2016. This coordinated upgrade (RAP version 3 and HRRR version 2, RAPv3/HRRRv2) includes enhancements to the data assimilation, model, and post-processing formulations that result in significant improvements to aviation forecasts for upper-air, surface, cloud and precipitation, and thunderstorms. Key changes will be described toward the next NCEP operational implementation (RAPv4/HRRRv3), planned for early 2018. Additional work is focused testing and refinement in related areas, including a real-time prototype High Resolution Rapid Refresh Ensemble (HRRRE), a post-processing-based HRRR-time-lagged ensemble (HRRR-TLE), and a HRRR domain covering Alaska (HRRR-AK). In this presentation, a recap of the RAPv3/HRRRv2 upgrade and forecast improvements will be provided, followed by a description of the planned improvements for RAPv4/HRRRv3 and impacts for aviation guidance for winds (turbulence), clouds (ceiling and visibility) and near-surface (terminal) forecasts. ESRL is now showing strong further improvements from model and assimilation improvements from the new RAPv4/HRRRv3 including further enhancements to the model physics components (aerosol-aware Thompson microphysics, MYNN PBL scheme, Smirnova land-surface model), and testing of a new vertical coordinate). The interaction of the various physics modules has been a particular research focus area, with modifications in place that further reduce various physics-related model biases. HRRRv3/RAPv4 data assimilation enhancements include improved radar and cloud assimilation, addition of data from TAMDAR aircraft, radar radial velocity data, and GOES cloud-top cooling rate data). HRRR time-lagged ensemble products are now being produced in real-time for many variables

  15. An assessment of European synoptic variability in Hadley Centre Global Environmental models based on an objective classification of weather regimes

    Energy Technology Data Exchange (ETDEWEB)

    James, P.M. [Met Office, Hadley Centre for Climate Prediction and Research, Exeter (United Kingdom)

    2006-08-15

    The frequency of occurrence of persistent synoptic-scale weather patterns over the European and North-East Atlantic regions is examined in a hierarchy of climate model simulations and compared to observational re-analysed data. A new objective method, employing pattern correlation techniques, has been constructed for classifying daily-mean mean-sea-level pressure and 500 hPa geopotential height fields with respect to a set of 29 European weather regime types, based on the widely known subjective Grosswetterlagen (GWL) system of the German Weather Service. The objective method is described and applied initially to ERA40 and NCEP re-analysis data. While the resulting daily Objective-GWL catalogue shows some systematic differences with respect to the subjectively-derived original GWL series, the method is shown to be sufficiently robust for application to climate model output. Ensemble runs from the most recent development of the Hadley Centre's Global Environmental model, HadGEM1, in atmosphere-only, coupled and climate change scenario modes are analysed with regards to European synoptic variability. All simulations successfully exhibit a wide spread of GWL occurrences across all regime types, but some systematic differences in mean GWL frequencies are seen in spite of significant levels of interdecadal variability. These differences provide a basis for estimating local anomalies of surface temperature and precipitation over Europe, which would result from circulation changes alone, in each climate simulation. Comparison to observational re-analyses shows a clear and significant improvement in the simulation of realistic European synoptic variability with the development and resolution of the atmosphere-only models. (orig.)

  16. GNSS tropospheric tomography in Near-Real Time mode as a valuable data source for Numerical Weather Prediction models

    Science.gov (United States)

    Trzcina, Estera; Rohm, Witold; Dymarska, Natalia

    2017-04-01

    GNSS tropospheric tomography is a technique that aims to obtain spatial distribution of wet refractivity in the lower atmosphere based on satellite signal delay. These estimates, strictly related to the water vapor amount in atmosphere, can be assimilated in Numerical Weather Prediction (NWP) models. These observations are very valuable for the weather prediction process. Water vapor amount in the troposphere is one of the most important factors forming weather conditions. Moreover it is highly variable in time and space, thus should be monitored with high spatio-temporal resolution. Vertical distribution of the water vapor in the atmosphere is usually obtained by balloon-based radiosonde sounding. This approach is very common, but also expensive. Spatial and temporal resolutions of these measurements are rather poor in comparison to the NWP models. In contrast, resolution of the GNSS tomography can be similar to the NWP models with no additional costs, especially on the areas equipped with well-developed GNSS stations networks. Previous studies on GNSS tomography indicates that the accuracy of the results is satisfactory and might be applied in meteorology. Tropospheric tomography is a very promising technique for the weather prediction because of the slant satellite observations utilization - Slant Wet Delays (SWD) or Slant Integrated Water Vapor (SIWV). Due to the slant trajectories of the GNSS signals crossing atmosphere and tomography inverse processing the vertical profiles of humidity can be estimated. In this study an effort was made to meet two major preconditions for tomographic data assimilation in NWP: 1) adjusting tomography model to near-real time (NRT) observation and 2) reaching required accuracy of the solution. Moreover the first attempt of assimilation tomographic data in NWP model was made using refractivity profile operator (GPS_REF). GNSS tomography model TOMO2 was adjusted to use NRT troposphere observation by using predicted orbits, ZTDs and

  17. Enhancing Cloud Radiative Processes and Radiation Efficiency in the Advanced Research Weather Research and Forecasting (WRF) Model

    Energy Technology Data Exchange (ETDEWEB)

    Iacono, Michael J. [Atmospheric and Environmental Research, Lexington, MA (United States)

    2015-03-09

    The objective of this research has been to evaluate and implement enhancements to the computational performance of the RRTMG radiative transfer option in the Advanced Research version of the Weather Research and Forecasting (WRF) model. Efficiency is as essential as accuracy for effective numerical weather prediction, and radiative transfer is a relatively time-consuming component of dynamical models, taking up to 30-50 percent of the total model simulation time. To address this concern, this research has implemented and tested a version of RRTMG that utilizes graphics processing unit (GPU) technology (hereinafter RRTMGPU) to greatly improve its computational performance; thereby permitting either more frequent simulation of radiative effects or other model enhancements. During the early stages of this project the development of RRTMGPU was completed at AER under separate NASA funding to accelerate the code for use in the Goddard Space Flight Center (GSFC) Goddard Earth Observing System GEOS-5 global model. It should be noted that this final report describes results related to the funded portion of the originally proposed work concerning the acceleration of RRTMG with GPUs in WRF. As a k-distribution model, RRTMG is especially well suited to this modification due to its relatively large internal pseudo-spectral (g-point) dimension that, when combined with the horizontal grid vector in the dynamical model, can take great advantage of the GPU capability. Thorough testing under several model configurations has been performed to ensure that RRTMGPU improves WRF model run time while having no significant impact on calculated radiative fluxes and heating rates or on dynamical model fields relative to the RRTMG radiation. The RRTMGPU codes have been provided to NCAR for possible application to the next public release of the WRF forecast model.

  18. Space Weather Services of Korea

    Science.gov (United States)

    Yoon, K.; Hong, S.; Jangsuk, C.; Dong Kyu, K.; Jinyee, C.; Yeongoh, C.

    2016-12-01

    The Korean Space Weather Center (KSWC) of the National Radio Research Agency (RRA) is a government agency which is the official source of space weather information for Korean Government and the primary action agency of emergency measure to severe space weather condition. KSWC's main role is providing alerts, watches, and forecasts in order to minimize the space weather impacts on both of public and commercial sectors of satellites, aviation, communications, navigations, power grids, and etc. KSWC is also in charge of monitoring the space weather condition and conducting research and development for its main role of space weather operation in Korea. In this study, we will present KSWC's recent efforts on development of application-oriented space weather research products and services on user needs, and introduce new international collaborative projects, such as IPS-Driven Enlil model, DREAM model estimating electron in satellite orbit, global network of DSCOVR and STEREO satellites tracking, and ARMAS (Automated Radiation Measurement for Aviation Safety).

  19. Analysis of a severe weather event over Mecca, Kingdom of Saudi Arabia, using observations and high-resolution modelling

    KAUST Repository

    Dasari, Hari Prasad

    2017-08-10

    The dynamic and thermodynamic characteristics of a severe weather event that caused heavy wind and rainfall over Mecca, Kingdom of Saudi Arabia, on 11 September 2015 were investigated using available observations and the Weather Research and Forecasting model configured at 1 km resolution. Analysis of surface, upper air observations and model outputs reveals that the event was initiated by synoptic scale conditions that intensified by interaction with the local topography, triggering strong winds and high convective rainfall. The model predicted the observed characteristics of both rainfall and winds well, accurately predicting the maximum wind speed of 20–25 m s−1 that was sustained for about 2 h. A time series analysis of various atmospheric variables suggests a sudden fall in pressure, temperature and outgoing long wave radiation before the development of the storm, followed by a significant increase in wind speed, latent and moisture fluxes and change in wind direction during the mature stage of the storm. The model outputs suggest that the heavy rainfall was induced by a low-level moisture supply from the Red Sea combined with orographic lifting. Latent heat release from microphysical processes increased the vertical velocities in the mid-troposphere, further increasing the low-level convergence that strengthened the event.

  20. Solar Atmosphere to Earth's Surface: Long Lead Time dB/dt Predictions with the Space Weather Modeling Framework

    Science.gov (United States)

    Welling, D. T.; Manchester, W.; Savani, N.; Sokolov, I.; van der Holst, B.; Jin, M.; Toth, G.; Liemohn, M. W.; Gombosi, T. I.

    2017-12-01

    The future of space weather prediction depends on the community's ability to predict L1 values from observations of the solar atmosphere, which can yield hours of lead time. While both empirical and physics-based L1 forecast methods exist, it is not yet known if this nascent capability can translate to skilled dB/dt forecasts at the Earth's surface. This paper shows results for the first forecast-quality, solar-atmosphere-to-Earth's-surface dB/dt predictions. Two methods are used to predict solar wind and IMF conditions at L1 for several real-world coronal mass ejection events. The first method is an empirical and observationally based system to estimate the plasma characteristics. The magnetic field predictions are based on the Bz4Cast system which assumes that the CME has a cylindrical flux rope geometry locally around Earth's trajectory. The remaining plasma parameters of density, temperature and velocity are estimated from white-light coronagraphs via a variety of triangulation methods and forward based modelling. The second is a first-principles-based approach that combines the Eruptive Event Generator using Gibson-Low configuration (EEGGL) model with the Alfven Wave Solar Model (AWSoM). EEGGL specifies parameters for the Gibson-Low flux rope such that it erupts, driving a CME in the coronal model that reproduces coronagraph observations and propagates to 1AU. The resulting solar wind predictions are used to drive the operational Space Weather Modeling Framework (SWMF) for geospace. Following the configuration used by NOAA's Space Weather Prediction Center, this setup couples the BATS-R-US global magnetohydromagnetic model to the Rice Convection Model (RCM) ring current model and a height-integrated ionosphere electrodynamics model. The long lead time predictions of dB/dt are compared to model results that are driven by L1 solar wind observations. Both are compared to real-world observations from surface magnetometers at a variety of geomagnetic latitudes

  1. Surface Temperature Variation Prediction Model Using Real-Time Weather Forecasts

    Science.gov (United States)

    Karimi, M.; Vant-Hull, B.; Nazari, R.; Khanbilvardi, R.

    2015-12-01

    Combination of climate change and urbanization are heating up cities and putting the lives of millions of people in danger. More than half of the world's total population resides in cities and urban centers. Cities are experiencing urban Heat Island (UHI) effect. Hotter days are associated with serious health impacts, heart attaches and respiratory and cardiovascular diseases. Densely populated cities like Manhattan, New York can be affected by UHI impact much more than less populated cities. Even though many studies have been focused on the impact of UHI and temperature changes between urban and rural air temperature, not many look at the temperature variations within a city. These studies mostly use remote sensing data or typical measurements collected by local meteorological station networks. Local meteorological measurements only have local coverage and cannot be used to study the impact of UHI in a city and remote sensing data such as MODIS, LANDSAT and ASTER have with very low resolution which cannot be used for the purpose of this study. Therefore, predicting surface temperature in urban cities using weather data can be useful.Three months of Field campaign in Manhattan were used to measure spatial and temporal temperature variations within an urban setting by placing 10 fixed sensors deployed to measure temperature, relative humidity and sunlight. Fixed instrument shelters containing relative humidity, temperature and illumination sensors were mounted on lampposts in ten different locations in Manhattan (Vant-Hull et al, 2014). The shelters were fixed 3-4 meters above the ground for the period of three months from June 23 to September 20th of 2013 making measurements with the interval of 3 minutes. These high resolution temperature measurements and three months of weather data were used to predict temperature variability from weather forecasts. This study shows that the amplitude of spatial and temporal variation in temperature for each day can be predicted

  2. Severe weather during the North American monsoon and its response to rapid urbanization and a changing global climate within the context of high resolution regional atmospheric modeling

    Science.gov (United States)

    Luong, Thang Manh

    The North American monsoon (NAM) is the principal driver of summer severe weather in the Southwest U.S. With sufficient atmospheric instability and moisture, monsoon convection initiates during daytime in the mountains and later may organize, principally into mesoscale convective systems (MCSs). Most monsoon-related severe weather occurs in association with organized convection, including microbursts, dust storms, flash flooding and lightning. The overarching theme of this dissertation research is to investigate simulation of monsoon severe weather due to organized convection within the use of regional atmospheric modeling. A commonly used cumulus parameterization scheme has been modified to better account for dynamic pressure effects, resulting in an improved representation of a simulated MCS during the North American monsoon experiment and the climatology of warm season precipitation in a long-term regional climate model simulation. The effect of urbanization on organized convection occurring in Phoenix is evaluated in model sensitivity experiments using an urban canopy model (UCM) and urban land cover compared to pre-settlement natural desert land cover. The presence of vegetation and irrigation makes Phoenix a "heat sink" in comparison to its surrounding desert, and as a result the modeled precipitation in response to urbanization decreases within the Phoenix urban area and increase on its periphery. Finally, analysis of how monsoon severe weather is changing in association with observed global climate change is considered within the context of a series of retrospectively simulated severe weather events during the period 1948-2010 in a numerical weather prediction paradigm. The individual severe weather events are identified by favorable thermodynamic conditions of instability and atmospheric moisture (precipitable water). Changes in precipitation extremes are evaluated with extreme value statistics. During the last several decades, there has been

  3. Effective roughness calculated from satellite-derived land cover maps and hedge-information used in a weather forecasting model

    DEFF Research Database (Denmark)

    Hasager, C.B.; Nielsen, N.,W.; Jensen, N.O.

    2003-01-01

    In numerical weather prediction, climate and hydrological modelling, the grid cell size is typically larger than the horizontal length scales of variations in aerodynamic roughness, surface temperature and surface humidity. These local land cover variations give rise to sub-grid scale surface flux...... to be well-described in any large-scale model. A method of aggregating the roughness step changes in arbitrary real terrain has been applied in flat terrain (Denmark) where sub-grid scale vegetation-driven roughness variations are a dominant characteristic of the landscape. The aggregation model...... is a physical two-dimensional atmospheric flow model in the horizontal domain based on a linearized version of the Navier Stoke equation. The equations are solved by the Fast Fourier Transformation technique, hence the code is very fast. The new effective roughness maps have been used in the HIgh Resolution...

  4. Representation of the Saharan atmospheric boundary layer in the Weather and Research Forecast (WRF) model: A sensitivity analysis.

    Science.gov (United States)

    Todd, Martin; Cavazos, Carolina; Wang, Yi

    2013-04-01

    The Saharan atmospheric boundary layer (SABL) during summer is one of the deepest on Earth, and is crucial in controlling the vertical redistribution and long-range transport of dust in the Sahara. The SABL is typically made up of an actively growing convective layer driven by high sensible heating at the surface, with a deep, near-neutrally stratified Saharan residual layer (SRL) above it, which is mostly well mixed in humidity and temperature and reaches a height of ˜5-6km. These two layers are usually separated by a weak (≤1K) temperature inversion. Model representation of the SPBL structure and evolution is important for accurate weather/climate and aerosol prediction. In this work, we evaluate model performance of the Weather Research and Forecasting (WRF) to represent key multi-scale processes in the SABL during summer 2011, including depiction of the diurnal cycle. For this purpose, a sensitivity analysis is performed to examine the performance of seven PBL schemes (YSU, MYJ, QNSE, MYNN, ACM, Boulac and MRF) and two land-surface model (Noah and RUC) schemes. In addition, the sensitivity to the choice of lateral boundary conditions (ERA-Interim and NCEP) and land use classification maps (USGS and MODIS-based) is tested. Model outputs were confronted upper-air and surface observations from the Fennec super-site at Bordj Moktar and automatic weather station (AWS) in Southern Algeria Vertical profiles of wind speed, potential temperature and water vapour mixing ratio were examined to diagnose differences in PBL heights and model efficacy to reproduce the diurnal cycle of the SABL. We find that the structure of the model SABL is most sensitive the choice of land surface model and lateral boundary conditions and relatively insensitive to the PBL scheme. Overall the model represents well the diurnal cycle in the structure of the SABL. Consistent model biases include (i) a moist (1-2 gkg-1) and slightly cool (~1K) bias in the daytime convective boundary layer (ii

  5. Projected changes of extreme weather events in the eastern United States based on a high resolution climate modeling system

    International Nuclear Information System (INIS)

    Gao, Y; Fu, J S; Drake, J B; Liu, Y; Lamarque, J-F

    2012-01-01

    This study is the first evaluation of dynamical downscaling using the Weather Research and Forecasting (WRF) Model on a 4 km × 4 km high resolution scale in the eastern US driven by the new Community Earth System Model version 1.0 (CESM v1.0). First we examined the global and regional climate model results, and corrected an inconsistency in skin temperature during the downscaling process by modifying the land/sea mask. In comparison with observations, WRF shows statistically significant improvement over CESM in reproducing extreme weather events, with improvement for heat wave frequency estimation as high as 98%. The fossil fuel intensive scenario Representative Concentration Pathway (RCP) 8.5 was used to study a possible future mid-century climate extreme in 2057–9. Both the heat waves and the extreme precipitation in 2057–9 are more severe than the present climate in the Eastern US. The Northeastern US shows large increases in both heat wave intensity (3.05 °C higher) and annual extreme precipitation (107.3 mm more per year). (letter)

  6. Production of solar radiation bankable datasets from high-resolution solar irradiance derived with dynamical downscaling Numerical Weather prediction model

    Directory of Open Access Journals (Sweden)

    Yassine Charabi

    2016-11-01

    Full Text Available A bankable solar radiation database is required for the financial viability of solar energy project. Accurate estimation of solar energy resources in a country is very important for proper siting, sizing and life cycle cost analysis of solar energy systems. During the last decade an important progress has been made to develop multiple solar irradiance database (Global Horizontal Irradiance (GHI and Direct Normal Irradiance (DNI, using satellite of different resolution and sophisticated models. This paper assesses the performance of High-resolution solar irradiance derived with dynamical downscaling Numerical Weather Prediction model with, GIS topographical solar radiation model, satellite data and ground measurements, for the production of bankable solar radiation datasets. For this investigation, NWP model namely Consortium for Small-scale Modeling (COSMO is used for the dynamical downscaling of solar radiation. The obtained results increase confidence in solar radiation data base obtained from dynamical downscaled NWP model. The mean bias of dynamical downscaled NWP model is small, on the order of a few percents for GHI, and it could be ranked as a bankable datasets. Fortunately, these data are usually archived in the meteorological department and gives a good idea of the hourly, monthly, and annual incident energy. Such short time-interval data are valuable in designing and operating the solar energy facility. The advantage of the NWP model is that it can be used for solar radiation forecast since it can estimate the weather condition within the next 72–120 hours. This gives a reasonable estimation of the solar radiation that in turns can be used to forecast the electric power generation by the solar power plant.

  7. Extreme precipitation forecasting in the Chilean Andean region with complex topography using the Weather Research and Forecasting (WRF) model

    Science.gov (United States)

    Gironás, J.; Yáñez Morroni, G.; Caneo, M.; Delgado, R.

    2017-12-01

    The Weather Research and Forecasting (WRF) model is broadly used for weather forecasting, hindcasting and researching due to its good performance. However, the atmospheric conditions for simulating are not always optimal when it includes complex topographies: affecting WRF mathematical stability and convergence, therefore, its performance. As Chile is a country strongly characterized by a complex topography and high gradients of elevation, WRF is ineffective resolving Chilean mountainous terrain and foothills. The need to own an effective weather forecasting tool relies on that Chile's main cities are located in these regions. Furthermore, the most intense rainfall events take place here, commonly caused by the presence of cutoff lows. This work analyzes a microphysics scheme ensemble to enhance initial forecasts made by the Chilean Weather Agency (DMC). These forecasts were made over the Santiago piedmont, in Quebrada de Ramón watershed, located upstream an urban area highly populated. In this region a non-existing planning increases the potential damage of a flash flood. An initial testing was made over different vertical levels resolution (39 and 50 levels), and subsequently testing with land use and surface models, and finally with the initial and boundary condition data (GFS/FNL). Our task made emphasis in analyzing microphysics and lead time (3 to 5 days before the storm peak) in the computational simulations over three extreme rainfall events between 2015 and 2017. WRF shortcoming are also related to the complex configuration of the synoptic events, even when the steep topography difficult the rainfall event peak amount, and to a lesser degree, the exact rainfall event beginning prediction. No evident trend was found in the lead time, but as expected, better results in rainfall and zero isotherm height are obtained with smaller anticipation. We found that WRF do predict properly the N-hours with the biggest amount of rainfall (5 hours corresponding to

  8. Quantifying the VNIR Effects of Nanophase Iron Generated through the Space Weathering of Silicates: Reconciling Modeled Data with Laboratory Observations

    Science.gov (United States)

    Legett, C., IV; Glotch, T. D.; Lucey, P. G.

    2015-12-01

    Space weathering is a diverse set of processes that occur on the surfaces of airless bodies due to exposure to the space environment. One of the effects of space weathering is the generation of nanophase iron particles in glassy rims on mineral grains due to sputtering of iron-bearing minerals. These particles have a size-dependent effect on visible and near infrared (VNIR) reflectance spectra with smaller diameter particles (behavior), while larger particles (> 300 nm) darken without reddening. Between these two sizes, a gradual shift between these two behaviors occurs. In this work, we present results from the Multiple Sphere T-Matrix (MSTM) scattering model in combination with Hapke theory to explore the particle size and iron content parameter spaces with respect to VNIR (700-1700 nm) spectral slope. Previous work has shown that the MSTM-Hapke hybrid model offers improvements over Mie-Hapke models. Virtual particles are constructed out of an arbitrary number of spheres, and each sphere is assigned a refractive index and extinction coefficient for each wavelength of interest. The model then directly solves Maxwell's Equations at every wave-particle interface to predict the scattering, extinction and absorption efficiencies. These are then put into a simplified Hapke bidirectional reflectance model that yields a predicted reflectance. Preliminary results show an area of maximum slopes for iron particle diameters planned to better refine the extent of this region. Companion laboratory work using mixtures of powdered aerogel and nanophase iron particles provides a point of comparison to modeling efforts. The effects on reflectance and emissivity values due to particle size in a nearly ideal scatterer (aerogel) are also observed with comparisons to model data.

  9. The draft genome of whitefly Bemisia tabaci MEAM1, a global crop pest, provides novel insights into virus transmission, host adaptation, and insecticide resistance.

    Science.gov (United States)

    Chen, Wenbo; Hasegawa, Daniel K; Kaur, Navneet; Kliot, Adi; Pinheiro, Patricia Valle; Luan, Junbo; Stensmyr, Marcus C; Zheng, Yi; Liu, Wenli; Sun, Honghe; Xu, Yimin; Luo, Yuan; Kruse, Angela; Yang, Xiaowei; Kontsedalov, Svetlana; Lebedev, Galina; Fisher, Tonja W; Nelson, David R; Hunter, Wayne B; Brown, Judith K; Jander, Georg; Cilia, Michelle; Douglas, Angela E; Ghanim, Murad; Simmons, Alvin M; Wintermantel, William M; Ling, Kai-Shu; Fei, Zhangjun

    2016-12-14

    The whitefly Bemisia tabaci (Hemiptera: Aleyrodidae) is among the 100 worst invasive species in the world. As one of the most important crop pests and virus vectors, B. tabaci causes substantial crop losses and poses a serious threat to global food security. We report the 615-Mb high-quality genome sequence of B. tabaci Middle East-Asia Minor 1 (MEAM1), the first genome sequence in the Aleyrodidae family, which contains 15,664 protein-coding genes. The B. tabaci genome is highly divergent from other sequenced hemipteran genomes, sharing no detectable synteny. A number of known detoxification gene families, including cytochrome P450s and UDP-glucuronosyltransferases, are significantly expanded in B. tabaci. Other expanded gene families, including cathepsins, large clusters of tandemly duplicated B. tabaci-specific genes, and phosphatidylethanolamine-binding proteins (PEBPs), were found to be associated with virus acquisition and transmission and/or insecticide resistance, likely contributing to the global invasiveness and efficient virus transmission capacity of B. tabaci. The presence of 142 horizontally transferred genes from bacteria or fungi in the B. tabaci genome, including genes encoding hopanoid/sterol synthesis and xenobiotic detoxification enzymes that are not present in other insects, offers novel insights into the unique biological adaptations of this insect such as polyphagy and insecticide resistance. Interestingly, two adjacent bacterial pantothenate biosynthesis genes, panB and panC, have been co-transferred into B. tabaci and fused into a single gene that has acquired introns during its evolution. The B. tabaci genome contains numerous genetic novelties, including expansions in gene families associated with insecticide resistance, detoxification and virus transmission, as well as numerous horizontally transferred genes from bacteria and fungi. We believe these novelties likely have shaped B. tabaci as a highly invasive polyphagous crop pest and

  10. Integration of Weather Avoidance and Traffic Separation

    Science.gov (United States)

    Consiglio, Maria C.; Chamberlain, James P.; Wilson, Sara R.

    2011-01-01

    This paper describes a dynamic convective weather avoidance concept that compensates for weather motion uncertainties; the integration of this weather avoidance concept into a prototype 4-D trajectory-based Airborne Separation Assurance System (ASAS) application; and test results from a batch (non-piloted) simulation of the integrated application with high traffic densities and a dynamic convective weather model. The weather model can simulate a number of pseudo-random hazardous weather patterns, such as slow- or fast-moving cells and opening or closing weather gaps, and also allows for modeling of onboard weather radar limitations in range and azimuth. The weather avoidance concept employs nested "core" and "avoid" polygons around convective weather cells, and the simulations assess the effectiveness of various avoid polygon sizes in the presence of different weather patterns, using traffic scenarios representing approximately two times the current traffic density in en-route airspace. Results from the simulation experiment show that the weather avoidance concept is effective over a wide range of weather patterns and cell speeds. Avoid polygons that are only 2-3 miles larger than their core polygons are sufficient to account for weather uncertainties in almost all cases, and traffic separation performance does not appear to degrade with the addition of weather polygon avoidance. Additional "lessons learned" from the batch simulation study are discussed in the paper, along with insights for improving the weather avoidance concept. Introduction

  11. Modeled Forecasts of Dengue Fever in San Juan, PR Using NASA Satellite Enhanced Weather Forecasts

    Science.gov (United States)

    Morin, Cory; Quattrochi, Dale; Zavodsky, Bradley; Case, Jonathan

    2015-01-01

    Dengue virus is transmitted between humans and mosquitoes of the genus Aedes and causes approximately 96 million cases of disease (dengue fever) each year (Bhatet al. 2013). Symptoms of dengue fever include fever, headache, nausea, vomiting, and eye, muscle and joint pain (CDC). More sever manifestations such as abdominal pain, bleeding from nose and gums, vomiting of blood, and clammy skin occur in rare cases of dengue hemorrhagic fever (CDC). Dengue fever occurs throughout tropical and sub-tropical regions worldwide, however, the geographical range and size of epidemics is increasing. Weather and climate are drivers of dengue virus transmission dynamics (Morin et al. 2013) by affecting mosquito proliferation and the virus extrinsic incubation period (i.e. required time for the virus to replicate and disseminate within the mosquito before it can retransmit the virus).

  12. Projecting Future Changes in Extreme Weather During the North American Monsoon in the Southwest with High Resolution, Convective-Permitting Regional Atmospheric Modeling

    Science.gov (United States)

    Chang, H. I.; Castro, C. L.; Luong, T. M.; Lahmers, T.; Jares, M.; Carrillo, C. M.

    2014-12-01

    Most severe weather during the North American monsoon in the Southwest U.S. occurs in association with organized convection, including microbursts, dust storms, flash flooding and lightning. Our objective is to project how monsoon severe weather is changing due to anthropogenic global warming. We first consider a dynamically downscaled reanalysis (35 km grid spacing), generated with the Weather Research and Forecasting (WRF) model during the period 1948-2010. Individual severe weather events, identified by favorable thermodynamic conditions of instability and precipitable water, are then simulated for short-term, numerical weather prediction-type simulations of 24h at a convective-permitting scale (2 km grid spacing). Changes in the character of severe weather events within this period likely reflect long-term climate change driven by anthropogenic forcing. Next, we apply the identical model simulation and analysis procedures to several dynamically downscaled CMIP3 and CMIP5 models for the period 1950-2100, to assess how monsoon severe weather may change in the future and if these changes correspond with what is already occurring per the downscaled renalaysis and available observational data. The CMIP5 models we are downscaling (HadGEM and MPI-ECHAM6) will be included as part of North American CORDEX. The regional model experimental design for severe weather event projection reasonably accounts for the known operational forecast prerequisites for severe monsoon weather. The convective-permitting simulations show that monsoon convection appears to be reasonably well captured with the use of the dynamically downscaled reanalysis, in comparison to Stage IV precipitation data. The regional model tends to initiate convection too early, though correctly simulates the diurnal maximum in convection in the afternoon and subsequent westward propagation of thunderstorms. Projected changes in extreme event precipitation will be described in relation to the long-term changes in

  13. Simulation of Flash-Flood-Producing Storm Events in Saudi Arabia Using the Weather Research and Forecasting Model

    KAUST Repository

    Deng, Liping

    2015-05-01

    The challenges of monitoring and forecasting flash-flood-producing storm events in data-sparse and arid regions are explored using the Weather Research and Forecasting (WRF) Model (version 3.5) in conjunction with a range of available satellite, in situ, and reanalysis data. Here, we focus on characterizing the initial synoptic features and examining the impact of model parameterization and resolution on the reproduction of a number of flood-producing rainfall events that occurred over the western Saudi Arabian city of Jeddah. Analysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) data suggests that mesoscale convective systems associated with strong moisture convergence ahead of a trough were the major initial features for the occurrence of these intense rain events. The WRF Model was able to simulate the heavy rainfall, with driving convective processes well characterized by a high-resolution cloud-resolving model. The use of higher (1 km vs 5 km) resolution along the Jeddah coastline favors the simulation of local convective systems and adds value to the simulation of heavy rainfall, especially for deep-convection-related extreme values. At the 5-km resolution, corresponding to an intermediate study domain, simulation without a cumulus scheme led to the formation of deeper convective systems and enhanced rainfall around Jeddah, illustrating the need for careful model scheme selection in this transition resolution. In analysis of multiple nested WRF simulations (25, 5, and 1 km), localized volume and intensity of heavy rainfall together with the duration of rainstorms within the Jeddah catchment area were captured reasonably well, although there was evidence of some displacements of rainstorm events.

  14. Forecasting Optimal Solar Energy Supply in Jiangsu Province (China: A Systematic Approach Using Hybrid of Weather and Energy Forecast Models

    Directory of Open Access Journals (Sweden)

    Xiuli Zhao

    2014-01-01

    Full Text Available The idea of aggregating information is clearly recognizable in the daily lives of all entities whether as individuals or as a group, since time immemorial corporate organizations, governments, and individuals as economic agents aggregate information to formulate decisions. Energy planning represents an investment-decision problem where information needs to be aggregated from credible sources to predict both demand and supply of energy. To do this there are varying methods ranging from the use of portfolio theory to managing risk and maximizing portfolio performance under a variety of unpredictable economic outcomes. The future demand for energy and need to use solar energy in order to avoid future energy crisis in Jiangsu province in China require energy planners in the province to abandon their reliance on traditional, “least-cost,” and stand-alone technology cost estimates and instead evaluate conventional and renewable energy supply on the basis of a hybrid of optimization models in order to ensure effective and reliable supply. Our task in this research is to propose measures towards addressing optimal solar energy forecasting by employing a systematic optimization approach based on a hybrid of weather and energy forecast models. After giving an overview of the sustainable energy issues in China, we have reviewed and classified the various models that existing studies have used to predict the influences of the weather influences and the output of solar energy production units. Further, we evaluate the performance of an exemplary ensemble model which combines the forecast output of two popular statistical prediction methods using a dynamic weighting factor.

  15. Forecasting optimal solar energy supply in Jiangsu Province (China): a systematic approach using hybrid of weather and energy forecast models.

    Science.gov (United States)

    Zhao, Xiuli; Asante Antwi, Henry; Yiranbon, Ethel

    2014-01-01

    The idea of aggregating information is clearly recognizable in the daily lives of all entities whether as individuals or as a group, since time immemorial corporate organizations, governments, and individuals as economic agents aggregate information to formulate decisions. Energy planning represents an investment-decision problem where information needs to be aggregated from credible sources to predict both demand and supply of energy. To do this there are varying methods ranging from the use of portfolio theory to managing risk and maximizing portfolio performance under a variety of unpredictable economic outcomes. The future demand for energy and need to use solar energy in order to avoid future energy crisis in Jiangsu province in China require energy planners in the province to abandon their reliance on traditional, "least-cost," and stand-alone technology cost estimates and instead evaluate conventional and renewable energy supply on the basis of a hybrid of optimization models in order to ensure effective and reliable supply. Our task in this research is to propose measures towards addressing optimal solar energy forecasting by employing a systematic optimization approach based on a hybrid of weather and energy forecast models. After giving an overview of the sustainable energy issues in China, we have reviewed and classified the various models that existing studies have used to predict the influences of the weather influences and the output of solar energy production units. Further, we evaluate the performance of an exemplary ensemble model which combines the forecast output of two popular statistical prediction methods using a dynamic weighting factor.

  16. Verification of the skill of numerical weather prediction models in forecasting rainfall from U.S. landfalling tropical cyclones

    Science.gov (United States)

    Luitel, Beda; Villarini, Gabriele; Vecchi, Gabriel A.

    2018-01-01

    The goal of this study is the evaluation of the skill of five state-of-the-art numerical weather prediction (NWP) systems [European Centre for Medium-Range Weather Forecasts (ECMWF), UK Met Office (UKMO), National Centers for Environmental Prediction (NCEP), China Meteorological Administration (CMA), and Canadian Meteorological Center (CMC)] in forecasting rainfall from North Atlantic tropical cyclones (TCs). Analyses focus on 15 North Atlantic TCs that made landfall along the U.S. coast over the 2007-2012 period. As reference data we use gridded rainfall provided by the Climate Prediction Center (CPC). We consider forecast lead-times up to five days. To benchmark the skill of these models, we consider rainfall estimates from one radar-based (Stage IV) and four satellite-based [Tropical Rainfall Measuring Mission - Multi-satellite Precipitation Analysis (TMPA, both real-time and research version); Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN); the CPC MORPHing Technique (CMORPH)] rainfall products. Daily and storm total rainfall fields from each of these remote sensing products are compared to the reference data to obtain information about the range of errors we can expect from "observational data." The skill of the NWP models is quantified: (1) by visual examination of the distribution of the errors in storm total rainfall for the different lead-times, and numerical examination of the first three moments of the error distribution; (2) relative to climatology at the daily scale. Considering these skill metrics, we conclude that the NWP models can provide skillful forecasts of TC rainfall with lead-times up to 48 h, without a consistently best or worst NWP model.

  17. Optimizing Weather Research and Forecasting model parameterizations for boundary-layer turbulence production and dissipation over the Southern Appalachians

    Science.gov (United States)

    Thaxton, C.; Sherman, J. P.; Krintz, I. A.; Scher, A.; Ross, D.; Schlesselman, D.

    2017-12-01

    Atmospheric aerosol and contaminant transport and mixing over complex terrain are influenced by a broad-spectrum of turbulence production and dissipation mechanisms that are not, at present, considered in the Weather Research and Forecasting (WRF) model v3.9 numerical schemes that are constrained to parameterize the dynamic effects of small-scale turbulent structures. Unresolved thermally-driven processes, such slope and valley flows and associated recirculations, as well as orographically-produced or enhanced mechanical turbulence structures, may express as systematic yet potentially predictable model biases in the diurnal evolution of measurables and diagnostic parameters such as planetary boundary layer (PBL) height. Herein, we present an assessment of the (non-LES) WRF PBL schemes - YSU, MYJ, MYNNx, and ACM2 - over a range of synoptic conditions in the warm months of 2013 through comparison to a subset of 76 radiosonde launches taken at various times throughout the day, as well as continuous ground weather station data and ground-based lidar-derived diagnostics. Preliminary results, many of which may be explained by known passive and active mechanisms in complex terrain, include an over-prediction of PBL heights for non-local PBL schemes; an enhanced surface layer cold bias and under-prediction of PBL heights for local PBL schemes; and peak variance in potential temperature, specific humidity, and wind speed for all schemes at or near the entrainment zone. Suppressed amplitudes in the diurnal lidar-derived PBL height time series also suggest enhanced turbulence production during a range of nocturnal flow conditions. The aim of this investigation is to develop a recommended suite of coupled WRF PBL-surface layer parameterizations optimized to support modeling of aerosol load dynamics, aerosol-meteorology coupling, and operational forecasting in the Southern Appalachians, as well as to inform future WRF PBL scheme use and development.

  18. Numerical simulation for regional ozone concentrations: A case study by weather research and forecasting/chemistry (WRF/Chem) model

    Energy Technology Data Exchange (ETDEWEB)

    Habib Al Razi, Khandakar Md; Hiroshi, Moritomi [Environmental and Renewable Energy System, Graduate School of Engineering, Gifu University, 1-1 Yanagido, Gifu City, 501-1193 (Japan)

    2013-07-01

    The objective of this research is to better understand and predict the atmospheric concentration distribution of ozone and its precursor (in particular, within the Planetary Boundary Layer (Within 110 km to 12 km) over Kasaki City and the Greater Tokyo Area using fully coupled online WRF/Chem (Weather Research and Forecasting/Chemistry) model. In this research, a serious and continuous high ozone episode in the Greater Tokyo Area (GTA) during the summer of 14–18 August 2010 was investigated using the observation data. We analyzed the ozone and other trace gas concentrations, as well as the corresponding weather conditions in this high ozone episode by WRF/Chem model. The simulation results revealed that the analyzed episode was mainly caused by the impact of accumulation of pollution rich in ozone over the Greater Tokyo Area. WRF/Chem has shown relatively good performance in modeling of this continuous high ozone episode, the simulated and the observed concentrations of ozone, NOx and NO2 are basically in agreement at Kawasaki City, with best correlation coefficients of 0.87, 0.70 and 0.72 respectively. Moreover, the simulations of WRF/Chem with WRF preprocessing software (WPS) show a better agreement with meteorological observations such as surface winds and temperature profiles in the ground level of this area. As a result the surface ozone simulation performances have been enhanced in terms of the peak ozone and spatial patterns, whereas WRF/Chem has been succeeded to generate meteorological fields as well as ozone, NOx, NO2 and NO.

  19. Performance of a coupled lagged ensemble weather and river runoff prediction model system for the Alpine Ammer River catchment

    Science.gov (United States)

    Smiatek, G.; Kunstmann, H.; Werhahn, J.

    2012-04-01

    The Ammer River catchment located in the Bavarian Ammergau Alps and alpine forelands, Germany, represents with elevations reaching 2185 m and annual mean precipitation between1100 and 2000 mm a very demanding test ground for a river runoff prediction system. Large flooding events in 1999 and 2005 motivated the development of a physically based prediction tool in this area. Such a tool is the coupled high resolution numerical weather and river runoff forecasting system AM-POE that is being studied in several configurations in various experiments starting from the year 2005. Corner stones of the coupled system are the hydrological water balance model WaSiM-ETH run at 100 m grid resolution, the numerical weather prediction model (NWP) MM5 driven at 3.5 km grid cell resolution and the Perl Object Environment (POE) framework. POE implements the input data download from various sources, the input data provision via SOAP based WEB services as well as the runs of the hydrology model both with observed and with NWP predicted meteorology input. The one way coupled system utilizes a lagged ensemble prediction system (EPS) taking into account combination of recent and previous NWP forecasts. Results obtained in the years 2005-2011 reveal that river runoff simulations depict high correlation with observed runoff when driven with monitored observations in hindcast experiments. The ability to runoff forecasts is depending on lead times in the lagged ensemble prediction and shows still limitations resulting from errors in timing and total amount of the predicted precipitation in the complex mountainous area. The presentation describes the system implementation, and demonstrates the application of the POE framework in networking, distributed computing and in the setup of various experiments as well as long term results of the system application in the years 2005 - 2011.

  20. Comparison of different models for ground-level atmospheric turbulence strength (C(n)(2)) prediction with a new model according to local weather data for FSO applications.

    Science.gov (United States)

    Arockia Bazil Raj, A; Arputha Vijaya Selvi, J; Durairaj, S

    2015-02-01

    Atmospheric parameters strongly affect the performance of free-space optical communication (FSOC) systems when the optical wave is propagating through the inhomogeneous turbulence transmission medium. Developing a model to get an accurate prediction of the atmospheric turbulence strength (C(n)(2)) according to meteorological parameters (weather data) becomes significant to understand the behavior of the FSOC channel during different seasons. The construction of a dedicated free-space optical link for the range of 0.5 km at an altitude of 15.25 m built at Thanjavur (Tamil Nadu) is described in this paper. The power level and beam centroid information of the received signal are measured continuously with weather data at the same time using an optoelectronic assembly and the developed weather station, respectively, and are recorded in a data-logging computer. Existing models that exhibit relatively fewer prediction errors are briefed and are selected for comparative analysis. Measured weather data (as input factors) and C(n)(2) (as a response factor) of size [177,147×4] are used for linear regression analysis and to design mathematical models more suitable in the test field. Along with the model formulation methodologies, we have presented the contributions of the input factors' individual and combined effects on the response surface and the coefficient of determination (R(2)) estimated using analysis of variance tools. An R(2) value of 98.93% is obtained using the new model, model equation V, from a confirmatory test conducted with a testing data set of size [2000×4]. In addition, the prediction accuracies of the selected and the new models are investigated during different seasons in a one-year period using the statistics of day, week-averaged, month-averaged, and seasonal-averaged diurnal Cn2 profiles, and are verified in terms of the sum of absolute error (SAE). A Cn2 prediction maximum average SAE of 2.3×10(-13)  m(-2/3) is achieved using the new model in

  1. Basic Concepts for Convection Parameterization in Weather Forecast and Climate Models: COST Action ES0905 Final Report

    Directory of Open Access Journals (Sweden)

    Jun–Ichi Yano

    2014-12-01

    Full Text Available The research network “Basic Concepts for Convection Parameterization in Weather Forecast and Climate Models” was organized with European funding (COST Action ES0905 for the period of 2010–2014. Its extensive brainstorming suggests how the subgrid-scale parameterization problem in atmospheric modeling, especially for convection, can be examined and developed from the point of view of a robust theoretical basis. Our main cautions are current emphasis on massive observational data analyses and process studies. The closure and the entrainment–detrainment problems are identified as the two highest priorities for convection parameterization under the mass–flux formulation. The need for a drastic change of the current European research culture as concerns policies and funding in order not to further deplete the visions of the European researchers focusing on those basic issues is emphasized.

  2. Accessing Space Weather Information

    Science.gov (United States)

    Morrison, D.; Weiss, M.; Immer, E. A.; Patrone, D.; Potter, M.; Barnes, R. J.; Colclough, C.; Holder, R.

    2009-12-01

    To meet the needs of our technology based society, space weather forecasting needs to be advanced and this will entail collaboration amongst research, military and commercial communities to find new ways to understand, characterize, and forecast. In this presentation VITMO, the Virtual Ionosphere-Thermosphere-Mesosphere Observatory will be used as a prototype for a generalized system as a means to bring together a set of tools to access data, models and online collaboration tools to enable rapid progress. VITMO, available at http://vitmo.jhuapl.edu/, currently provides a data access portal for researchers and scientists to enable finding data products as well as access to tools and models. To further the needs of space weather forecasters, the existing VITMO data holdings need to be expanded to provide additional datasets as well as integrating relevant models and model output. VITMO can easily be adapted for the Space Weather domain in its entirety. In this presentation, we will demonstrate how VITMO and the VITMO architecture can be utilized as a prototype in support of integration of Space Weather forecasting tools, models and data.

  3. DOE Workshop; Pan-Gass Conference on the Representation of Atmospheric Processes in Weather and Climate Models

    Energy Technology Data Exchange (ETDEWEB)

    Morrison, Hugh [National Center for Atmospheric Research, Boulder, CO (United States)

    2012-11-12

    This is the first meeting of the whole new GEWEX (Global Energy and Water Cycle Experiment) Atmospheric System Study (GASS) project that has been formed from the merger of the GEWEX Cloud System Study (GCSS) Project and the GEWEX Atmospheric Boundary Layer Studies (GABLS). As such, this meeting will play a major role in energizing GEWEX work in the area of atmospheric parameterizations of clouds, convection, stable boundary layers, and aerosol-cloud interactions for the numerical models used for weather and climate projections at both global and regional scales. The representation of these processes in models is crucial to GEWEX goals of improved prediction of the energy and water cycles at both weather and climate timescales. This proposal seeks funds to be used to cover incidental and travel expenses for U.S.-based graduate students and early career scientists (i.e., within 5 years of receiving their highest degree). We anticipate using DOE funding to support 5-10 people. We will advertise the availability of these funds by providing a box to check for interested participants on the online workshop registration form. We will also send a note to our participants' mailing lists reminding them that the funds are available and asking senior scientists to encourage their more junior colleagues to participate. All meeting participants are encouraged to submit abstracts for oral or poster presentations. The science organizing committee (see below) will base funding decisions on the relevance and quality of these abstracts, with preference given to under-represented populations (especially women and minorities) and to early career scientists being actively mentored at the meeting (e.g. students or postdocs attending the meeting with their adviser).

  4. Use of weather research and forecasting model outputs to obtain near-surface refractive index structure constant over the ocean.

    Science.gov (United States)

    Qing, Chun; Wu, Xiaoqing; Li, Xuebin; Zhu, Wenyue; Qiao, Chunhong; Rao, Ruizhong; Mei, Haipin

    2016-06-13

    The methods to obtain atmospheric refractive index structure constant (Cn2) by instrument measurement are limited spatially and temporally and they are more difficult and expensive over the ocean. It is useful to forecast Cn2 effectively from Weather Research and Forecasting Model (WRF) outputs. This paper introduces a method that WRF Model is used to forecast the routine meteorological parameters firstly, and then Cn2 is calculated based on these parameters by the Bulk model from the Monin-Obukhov similarity theory (MOST) over the ocean near-surface. The corresponding Cn2 values measured by the micro-thermometer which is placed on the ship are compared with the ones forecasted by WRF model to determine how this method performs. The result shows that the forecasted Cn2 is consistent with the measured Cn2 in trend and the order of magnitude as a whole, as well as the correlation coefficient is up to 77.57%. This method can forecast some essential aspects of Cn2 and almost always captures the correct magnitude of Cn2, which experiences fluctuations of two orders of magnitude. Thus, it seems to be a feasible and meaningful method that using WRF model to forecast near-surface Cn2 value over the ocean.

  5. Inconsistencies in the Weather Research and Forecasting Model of the Marine Boundary Layer Along the Coast of California

    Science.gov (United States)

    Fisher, Andrew M.

    The late spring and summer low-level wind field along the California coast is primarily controlled by the pressure gradient between the Pacific high and the thermal low over the desert southwest. Strong northwesterly winds within the marine boundary layer (MBL) are common and the flow is often described as a two-layer shallow water hydraulic system, capped above by subsidence and bounded laterally by high coastal topography. Hydraulic features such as an expansion fan can occur near major coastal headlands. Numerical simulations using the Weather Research and Forecasting (WRF) modeling system were conducted over a two-month period and compared to observations from several buoy stations and aircraft measurements from the Precision Atmospheric Marine Boundary Layer Experiment (PreAMBLE). Model performance of the atmospheric adjustment near the Point Arguello and Point Conception (PAPC) headlands and into the Santa Barbara Channel (SBC) is assessed. Substantial inconsistencies are revealed, especially in the SBC. The strength of the synoptic forcing impacts model performance upstream of PAPC. The model maintains stronger winds than observed under weak forcing regimes, inadequately representing periods of wind relaxation. The large-scale forcing has minimal impact on the flow in the SBC, where poor modeling of the MBL characteristics exists throughout the entire period. Similar results are found in the coarser North American Mesoscale (NAM) model. In general, WRF overestimates the wind speed around PAPC and the expansion fan extends too far into the SBC. Previous conceptual models were based on similar flawed model results and limited observations. PreAMBLE measurements reveal a more complex lower atmosphere in the SBC than the simulations can represent. Mischaracterization of surface wind stress in the SBC has implications for forcing ocean models with WRF. Understanding model biases of the vertical profile of temperature and humidity are also critical to several

  6. 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

  7. Chemical weathering of flat continents

    Science.gov (United States)

    Maffre, Pierre; Goddéris, Yves; Ladant, Jean-Baptiste; Carretier, Sébastien; Moquet, Jean-Sébastien; Donnadieu, Yannick; Labat, David; Vigier, Nathalie

    2017-04-01

    Mountain uplift is often cited as the main trigger of the end Cenozoic glacial state. Conversely, the absence of major uplift is invoked to explain the early Eocene warmth. This hypothesis relies on the fact that mountain uplift increases the supply of "fresh" silicate rocks through enhanced physical erosion, and boosts CO2 consumption by chemical weathering. Atmospheric CO2 —and therefore climate— then adjust to compensate for the changes in weatherability and keep the geological carbon cycle balanced (Walker's feedback). Yet, orography also strongly influences the global atmospheric and oceanic circulation. Consequently, building mountains does not only change the weathering regime in the restricted area of the orogen, but also modifies the worldwide distribution of the weathering flux. We conduct a numerical experiment in which we simulate the climate of the present day world, with all mountain ranges being removed. Up-to-date weathering and erosion laws (West, 2012; Carretier et al., 2014) are then used to quantify the global weathering for a "flat world". Specifically, the parameters of the weathering law are first carefully calculated such that the present day distribution of the weathering fluxes matches the riverine geochemical data. When removing mountains, we predict a warmer and wetter climate, especially in geographic spots located in the equatorial band. The calculated response of the global weathering flux ranges from an increase by 50% to a decrease by 70% (relative to the present day with mountains). These contrasted responses are pending on the parameterisation of the weathering model, that makes it more sensitive to reaction rate (kinetically-limited mode) or to rock supply by erosion (supply-limited mode). The most likely parameterisation —based on data-model comparison— predicts a decrease of CO2 consumption by weathering by 40% when mountains are removed. These results show that (1) the behaviour of the weathering engine depends on the

  8. GLOBE Atmosphere and AMS Diversity Program Content to Foster Weather and Climate Science Awareness at HBCUs: A Curriculum Enhancement Model

    Science.gov (United States)

    Padgett, D.

    2017-12-01

    Tennessee State University (TSU) is a member of the "Global Learning and Observations to Benefit the Environment (GLOBE) Mission Earth" project. The World Regional Geography (GEOG 1010/1020) courses are required for Education majors. Pre-service teachers must complete several exercises to be certified in the GLOBE Atmosphere Protocols. The pre-service teachers are required to develop GLOBE-based lessons to high school students. The exercise theme is "Exploring the Impacts of Urban Heat Islands (UHI) using Geospatial Technology." Surface temperature, ambient air temperature, and cloud cover data are collected. Sample point locations are logged using Garmin GPS receivers and then mapped using ArcGIS Online (http://arcg.is/1oiD379). The service learning outreach associated with this experience requires collegians to thoroughly understand the physical, social, and health science content associated with UHIs and then impart the information to younger learners. The precollegiate students are motivated due to their closeness in age and social context to the college students. All of the students have the advantage of engaging in hands-on problem-based learning of complex meteorology, climate science, and geospatial technology concepts. The optimal result is to have pre-service teachers enroll in the Weather and Climate (GEOG 3500) course, which is supported by the American Meteorological Society (AMS) Weather and Climate Studies Curriculum. Tennessee State University faculty have completed training to deliver the curriculum through the AMS Diversity Program. The AMS Weather Studies and Climate Studies programs have been institutionalized at Tennessee State University (TSU) since fall 2005. Approximately 250 undergraduate students have been exposed to the interactive AMS learning materials over the past 10-plus years. Non-STEM, and education majors are stimulated by the real-time course content and are encouraged to think critically about atmospheric systems science, and

  9. Regional-seasonal weather forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Abarbanel, H.; Foley, H.; MacDonald, G.; Rothaus, O.; Rudermann, M.; Vesecky, J.

    1980-08-01

    In the interest of allocating heating fuels optimally, the state-of-the-art for seasonal weather forecasting is reviewed. A model using an enormous data base of past weather data is contemplated to improve seasonal forecasts, but present skills do not make that practicable. 90 references. (PSB)

  10. Computer Modeling of Hydrology, Weathering, and Isotopic Fractionation in Andrews Creek, Rocky Mountain National Park, Colorado for Water Years 1992 through 2012

    Science.gov (United States)

    Webb, R. M. T.; Parkhurst, D. L.; Mast, A.; Clow, D. W.

    2014-12-01

    The U.S. Geological Survey's (USGS) Water, Energy, and Biogeochemical Model (WEBMOD) was used to simulate hydrology, weathering, and isotopic fractionation in the 1.7 square kilometer Andrews Creek alpine watershed. WEBMOD includes hydrologic modules derived from the USGS Precipitation Runoff Modeling System, the National Weather Service Hydro-17 snow model, and TOPMODEL. PHREEQC, a geochemical reaction model, is coupled with the hydrologic model to simulate the geochemical evolution of waters as they evaporate, mix, and react within the landscape. Major solute concentrations and δ18O were modeled over the period 1992-2012. Variations of chloride and inorganic nitrogen respond almost entirely to variations in atmospheric deposition and preferential elution of snowpack. Both evaporation and melting result in isotopic enrichment of heavy isotopes in the residual snowpack throughout the summer. Magnesium and potassium, derived mostly from weathering with some atmospheric inputs, vary seasonally with uptake during the growing season and release during the fall and winter. The weathering of granitic minerals—oligoclase, biotite, chlorite, pyrite, calcite, and formation of secondary minerals—kaolinite, goethite, gibbsite, and smectite-illite—were selected as primary reactions based on mole-balance modeling of basin outflows. The rates of these reactions were quantified by calibrating WEBMOD to match observed concentrations and loads. Exported annual loads of most weathering products are highly correlated with discharge, whereas silica loads are less correlated with discharge, suggesting a source that is more active during dry years and less active during wet years. Potential sources include net dissolution of kaolinite and smectite-illite or mineralization of colloids with high silica content. WEBMOD is a valuable tool for simulating water quality variations in response to climate change, acid mine drainage, acid rain, biological transformations, and other

  11. Fog prediction using the modified asymptotic liquid water content vertical distribution formulation with the Weather Research and Forecasting model

    Science.gov (United States)

    Kim, E.; Lee, S.; Kim, J.; Chae, D.

    2017-12-01

    Fog forecasts have difficulty in forecasting due to temporal and spatial resolution problems, high numerical computations, complicated mechanisms related to turbulence in order to analyze the fog in the model, and a lack of appropriate fog physical processes. Conventional fog prediction is based on the surface visibility threshold "water content (LWC) in a model post processer. The following multi-rule fog diagnosis method is based on the fog related variables near the surface, such as visibility, low stratus, relative humidity and wind speed but this method only predicts fog occurrence not fog intensity. To improve this, a new fog diagnostic scheme, based on an asymptotic analytical study of radiation fog (Zhou and Ferrier 2008, ZF08) is to increase the accuracy of fog prediction by calculating the vertical LWC considering cooling, turbulence and droplet settling, visibility, surface relative humidity and low stratus. In this study, we intend to improve fog prediction through the Weather Research and Forecasting (WRF) model using high-resolution data. Although the prediction accuracy can be improved by combining the WRF Planetary Boundary Layer (PBL) scheme and 1 dimension (1D) model, it is necessary to increase the vertical resolution in the boundary layer to implement the fog formation and persistence mechanism in the internal boundary layer in the PBL more accurately, we'll modify the algorithm to enhance the effects of turbulence and then compare the newly predicted fog and observations to determine the accuracy of the forecast of the fog occurring on the Korean peninsula.

  12. Analysis of Hurricane Irene’s Wind Field Using the Advanced Research Weather Research and Forecast (WRF-ARW Model

    Directory of Open Access Journals (Sweden)

    Alfred M. Klausmann

    2014-01-01

    Full Text Available Hurricane Irene caused widespread and significant impacts along the U.S. east coast during 27–29 August 2011. During this period, the storm moved across eastern North Carolina and then tracked northward crossing into Long Island and western New England. Impacts included severe flooding from the mid-Atlantic states into eastern New York and western New England, widespread wind damage and power outages across a large portion of southern and central New England, and a major storm surge along portions of the Long Island coast. The objective of this study was to conduct retrospective simulations using the Advanced Research Weather Research and Forecast (WRF-ARW model in an effort to reconstruct the storm’s surface wind field during the period of 27–29 August 2011. The goal was to evaluate how to use the WRF modeling system as a tool for reconstructing the surface wind field from historical storm events to support storm surge studies. The results suggest that, with even modest data assimilation applied to these simulations, the model was able to resolve the detailed structure of the storm, the storm track, and the spatial surface wind field pattern very well. The WRF model shows real potential for being used as a tool to analyze historical storm events to support storm surge studies.

  13. Characterization of weathering profile in granites and ...

    Indian Academy of Sciences (India)

    In granitic rocks, various models of weathering profile have been proposed, but never for the hard rocks of West Africa. Besides, in the literature there is no description of the weathering profile in volcanosed- imentrary rocks. Therefore, we propose three models describing the weathering profiles in granites, metasediments ...

  14. Evaluating Changes in Extreme Weather During the North American Monsoon in the Southwest U.S. Using High Resolution, Convective-Permitting Regional Atmospheric Modeling

    Science.gov (United States)

    Castro, C. L.; Chang, H. I.; Luong, T. M.; Lahmers, T.; Jares, M.; Mazon, J.; Carrillo, C. M.; Adams, D. K.

    2015-12-01

    The North American monsoon (NAM) is the principal driver of summer severe weather in the Southwest U.S. Monsoon convection typically initiates during daytime over the mountains and may organize into mesoscale convective systems (MCSs). Most monsoon-related severe weather occurs in association with organized convection, including microbursts, dust storms, flash flooding and lightning. A convective resolving grid spacing (on the kilometer scale) model is required to explicitly represent the physical characteristics of organized convection, for example the presence of leading convective lines and trailing stratiform precipitation regions. Our objective is to analyze how monsoon severe weather is changing in relation to anthropogenic climate change. We first consider a dynamically downscaled reanalysis during a historical period 1948-2010. Individual severe weather event days, identified by favorable thermodynamic conditions, are then simulated for short-term, numerical weather prediction-type simulations of 30h at a convective-permitting scale. Changes in modeled severe weather events indicate increases in precipitation intensity in association with long-term increases in atmospheric instability and moisture, particularly with organized convection downwind of mountain ranges. However, because the frequency of synoptic transients is decreasing during the monsoon, organized convection is less frequent and convective precipitation tends to be more phased locked to terrain. These types of modeled changes also similarly appear in observed CPC precipitation, when the severe weather event days are selected using historical radiosonde data. Next, we apply the identical model simulation and analysis procedures to several dynamically downscaled CMIP3 and CMIP5 models for the period 1950-2100, to assess how monsoon severe weather may change in the future with respect to occurrence and intensity and if these changes correspond with what is already occurring in the historical

  15. Improving Weather Research and Forecasting Model Initial Conditions via Surface Pressure Analysis

    Science.gov (United States)

    2015-09-01

    boundary layer (ABL). It predicts turbulent kinetic energy (TKE) and is a Mellor-Yamada Level 2.5 turbulence closure model. As in Lee et al. (2012...cumulus parameterization (Kain 2004) is employed. For radiation , the Rapid Radiative Transfer Model (RRTM) (Mlawer et al. 1997) is used for...longwave and the Dudhia scheme (Dudhia 1989) for shortwave . The Noah land surface model (Chen and Dudhia 2001) is used to represent land surface processes

  16. Validation of foF2 and TEC Modeling During Geomagnetic Disturbed Times: Preliminary Outcomes of International Forum for Space Weather Modeling Capabilities Assessment

    Science.gov (United States)

    Shim, J. S.; Tsagouri, I.; Goncharenko, L. P.; Kuznetsova, M. M.

    2017-12-01

    To address challenges of assessment of space weather modeling capabilities, the CCMC (Community Coordinated Modeling Center) is leading the newly established "International Forum for Space Weather Modeling Capabilities Assessment." This presentation will focus on preliminary outcomes of the International Forum on validation of modeled foF2 and TEC during geomagnetic storms. We investigate the ionospheric response to 2013 Mar. geomagnetic storm event using ionosonde and GPS TEC observations in North American and European sectors. To quantify storm impacts on foF2 and TEC, we first quantify quiet-time variations of foF2 and TEC (e.g., the median and the average of the five quietest days for the 30 days during quiet conditions). It appears that the quiet time variation of foF2 and TEC are about 10% and 20-30%, respectively. Therefore, to quantify storm impact, we focus on foF2 and TEC changes during the storm main phase larger than 20% and 50%, respectively, compared to 30-day median. We find that in European sector, both foF2 and TEC response to the storm are mainly positive phase with foF2 increase of up to 100% and TEC increase of 150%. In North America sector, however, foF2 shows negative effects (up to about 50% decrease), while TEC shows positive response (the largest increase is about 200%). To assess modeling capability of reproducing the changes of foF2 and TEC due to the storm, we use various model simulations, which are obtained from empirical, physics-based, and data assimilation models. The performance of each model depends on the selected metrics, therefore, only one metrics is not enough to evaluate the models' predictive capabilities in capturing the storm impact. The performance of the model also varies with latitude and longitude.

  17. Exploring the role of fire, succession, climate, and weather on landscape dynamics using comparative modeling

    Science.gov (United States)

    Robert E. Keane; Geoffrey J. Cary; Mike D. Flannigan; Russell A. Parsons; Ian D. Davies; Karen J. King; Chao Li; Ross A. Bradstock; Malcolm Gill

    2013-01-01

    An assessment of the relative importance of vegetation change and disturbance as agents of landscape change under current and future climates would (1) provide insight into the controls of landscape dynamics, (2) help inform the design and development of coarse scale spatially explicit ecosystem models such as Dynamic Global Vegetation Models (DGVMs), and (3) guide...

  18. Modelling of secondary sedimentation under wet-weather and filamentous bulking conditions

    DEFF Research Database (Denmark)

    Ramin, Elham

    with first-order type equations are widely used among researches and practitioners for dynamic WWTP simulations. Several drawbacks of the first-order models have however been reported in the literature, which have led to the development of more advanced second-order 1-D SST models. Unfortunately, the second......, the settling parameters were found to be among the most influential parameters for predicting the WWTP performance in terms of biogas production and quality of treated water. Importantly, it was observed that the choice of 1-D SST type model influences the sensitivity measures of the parameters...... and compression settling, was developed. In addition, a rheology model of activated sludge was selected and calibrated to high quality rheological measurements from the optimized batch experiments. New correlations between rheology model parameters and sludge concentration were identified. A 2-D axisymmetric CFD...

  19. Confronting Weather and Climate Models with Observational Data from Soil Moisture Networks over the United States

    Science.gov (United States)

    Dirmeyer, Paul A.; Wu, Jiexia; Norton, Holly E.; Dorigo, Wouter A.; Quiring, Steven M.; Ford, Trenton W.; Santanello, Joseph A., Jr.; Bosilovich, Michael G.; Ek, Michael B.; Koster, Randal Dean; hide

    2016-01-01

    Four land surface models in uncoupled and coupled configurations are compared to observations of daily soil moisture from 19 networks in the conterminous United States to determine the viability of such comparisons and explore the characteristics of model and observational data. First, observations are analyzed for error characteristics and representation of spatial and temporal variability. Some networks have multiple stations within an area comparable to model grid boxes; for those we find that aggregation of stations before calculation of statistics has little effect on estimates of variance, but soil moisture memory is sensitive to aggregation. Statistics for some networks stand out as unlike those of their neighbors, likely due to differences in instrumentation, calibration and maintenance. Buried sensors appear to have less random error than near-field remote sensing techniques, and heat dissipation sensors show less temporal variability than other types. Model soil moistures are evaluated using three metrics: standard deviation in time, temporal correlation (memory) and spatial correlation (length scale). Models do relatively well in capturing large-scale variability of metrics across climate regimes, but poorly reproduce observed patterns at scales of hundreds of kilometers and smaller. Uncoupled land models do no better than coupled model configurations, nor do reanalyses out perform free-running models. Spatial decorrelation scales are found to be difficult to diagnose. Using data for model validation, calibration or data assimilation from multiple soil moisture networks with different types of sensors and measurement techniques requires great caution. Data from models and observations should be put on the same spatial and temporal scales before comparison.

  20. Optimal Physics Parameterization Scheme Combination of the Weather Research and Forecasting Model for Seasonal Precipitation Simulation over Ghana

    Directory of Open Access Journals (Sweden)

    Richard Yao Kuma Agyeman

    2017-01-01

    Full Text Available Seasonal predictions of precipitation, among others, are important to help mitigate the effects of drought and floods on agriculture, hydropower generation, disasters, and many more. This work seeks to obtain a suitable combination of physics schemes of the Weather Research and Forecasting (WRF model for seasonal precipitation simulation over Ghana. Using the ERA-Interim reanalysis as forcing data, simulation experiments spanning eight months (from April to November were performed for two different years: a dry year (2001 and a wet year (2008. A double nested approach was used with the outer domain at 50 km resolution covering West Africa and the inner domain covering Ghana at 10 km resolution. The results suggest that the WRF model generally overestimated the observed precipitation by a mean value between 3% and 64% for both years. Most of the scheme combinations overestimated (underestimated precipitation over coastal (northern zones of Ghana for both years but estimated precipitation reasonably well over forest and transitional zones. On the whole, the combination of WRF Single-Moment 6-Class Microphysics Scheme, Grell-Devenyi Ensemble Cumulus Scheme, and Asymmetric Convective Model Planetary Boundary Layer Scheme simulated the best temporal pattern and temporal variability with the least relative bias for both years and therefore is recommended for Ghana.

  1. On the Skill of Numerical Weather Prediction Models to Forecast Atmospheric Rivers over the Central United States

    Science.gov (United States)

    Nayak, M. A.; Villarini, G.; Lavers, D. A.

    2013-12-01

    Atmospheric Rivers (ARs) are filamentary regions of enhanced moisture transport in the lower troposphere and occur in the pre-cold frontal region (within the warm sector) of extra-tropical cyclones. They can be responsible for heavy rainfall and flooding over large areas of the mid-latitude regions, with major impacts on public safety and economic activity. They also transport and deliver atmospheric moisture that is essential for water resources and supply. The focus of this work is examination of the skill of several global Numerical Weather Prediction (NWP) models in identifying the occurrence of ARs over the central United States. This is an area affected by extreme floods, many of which have recently been related to the occurrence of ARs. Although these catastrophic events cannot be prevented, it is possible to be better prepared. Improved readiness relies on the availability of information that would allow making better-informed decisions about the most suitable water management strategies. Assessment of the NWP forecast skill is also an important way of testing whether the processes responsible for these events are captured by NWP models. Analyses cover the period from October 2006 to present. Different identification algorithms are implemented based on column-integrated water vapor and integrated water vapor transport. Multiple lead times are examined, ranging from 1 to 16 days, and a number of skill scores are considered to verify the skill of these NWP models.

  2. Clouds, weather, climate, and modeling for K-12 and public audiences from the Center for Multi-scale Modeling of Atmospheric Processes

    Science.gov (United States)

    Foster, S. Q.; Johnson, R. M.; Randall, D. A.; Denning, A.; Russell, R. M.; Gardiner, L. S.; Hatheway, B.; Jones, B.; Burt, M. A.; Genyuk, J.

    2010-12-01

    The need for improving the representation of cloud processes in climate models has been one of the most important limitations of the reliability of climate-change simulations. Now in its fifth year, the National Science Foundation-funded Center for Multi-scale Modeling of Atmospheric Processes (CMMAP) at Colorado State University (CSU) is addressing this problem through a revolutionary new approach to representing cloud processes on their native scales, including the cloud-scale interaction processes that are active in cloud systems. CMMAP has set ambitious education and human-resource goals to share basic information about the atmosphere, clouds, weather, climate, and modeling with diverse K-12 and public audiences. This is accomplished through collaborations in resource development and dissemination between CMMAP scientists, CSU’s Little Shop of Physics (LSOP) program, and the Windows to the Universe (W2U) program at University Corporation for Atmospheric Research (UCAR). Little Shop of Physics develops new hands on science activities demonstrating basic science concepts fundamental to understanding atmospheric characteristics, weather, and climate. Videos capture demonstrations of children completing these activities which are broadcast to school districts and public television programs. CMMAP and LSOP educators and scientists partner in teaching a summer professional development workshops for teachers at CSU with a semester's worth of college-level content on the basic physics of the atmosphere, weather, climate, climate modeling, and climate change, as well as dozens of LSOP inquiry-based activities suitable for use in classrooms. The W2U project complements these efforts by developing and broadly disseminating new CMMAP-related online content pages, animations, interactives, image galleries, scientists’ biographies, and LSOP videos to K-12 and public audiences. Reaching nearly 20 million users annually, W2U is highly valued as a curriculum enhancement

  3. NOAA Office for Coastal Management Coastal Inundation Digital Elevation Model: Corpus Christi Weather Forecast Office (WFO)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This digital elevation model (DEM) is a part of a series of DEMs produced for the National Oceanic and Atmospheric Administration Office for Coastal Management's Sea...

  4. Satellite Cloud Assimilation in the Weather Research & Forecasting (WRF) Model and its Impact on Air Quality Simulations

    Science.gov (United States)

    Pour Biazar, Arastoo; White, Andrew; McNider, Richard; Khan, Maudood; Dornblaser, Bright; Wu, Yuling

    2017-04-01

    Clouds have a significant role in air quality simulations as they modulate biogenic hydrocarbon emissions and photolysis rates, impact boundary-layer development, lead to deep vertical mixing of pollutants and precursors, and induce aqueous phase chemistry. Unfortunately, numerical meteorological models still have difficulty in creating clouds in the right place and time compared to observed clouds. This is especially the case when synoptic-scale forcing is weak, as often is the case during air pollution episodes in the Southeast United States. Thus, poor representation of clouds impacts the photochemical model's ability in simulating the air quality. However, since satellites provide the best observational platform for defining the formation and location of clouds, satellite observations can be of great value in retrospective simulations. Here, we present results from a recent activity in which the Geostationary Operational Environmental Satellite (GOES) derived cloud fields are assimilated within Weather Research and Forecasting (WRF) model to improve simulated clouds. The assimilation technique dynamically support cloud formation/dissipation within WRF based on GOES observations. The technique uses observations to identify model cloud errors, estimates a target vertical velocity and moisture to create/remove clouds, and adjust the flow field accordingly. The technique was implemented and tested in WRF for a month-long simulation during August 2006, and was tested in an air quality simulation over the period of August-September 2013 (NASA's Discover-AQ field campaign). The cloud assimilation on the average improved model cloud simulation by 15%. The cloud correction not only improved the spatial and temporal distribution of clouds, it also improved boundary layer temperature, humidity, and wind speed. These improvements in meteorological fields directly impacted the air quality simulations and altered trace gas concentrations. For air quality simulations, WRF

  5. Evaluation of the Weather Research and Forecasting mesoscale model for GABLS3: Impact of boundary-layer schemes, boundary conditions and spin-up

    NARCIS (Netherlands)

    Kleczek, M.A.; Steeneveld, G.J.; Holtslag, A.A.M.

    2014-01-01

    We evaluated the performance of the three-dimensional Weather Research and Forecasting (WRF) mesoscale model, specifically the performance of the planetary boundary-layer (PBL) parametrizations. For this purpose, Cabauw tower observations were used, with the study extending beyond the third GEWEX

  6. Updated global soil map for the Weather Research and Forecasting model and soil moisture initialization for the Noah land surface model

    Science.gov (United States)

    DY, C. Y.; Fung, J. C. H.

    2016-08-01

    A meteorological model requires accurate initial conditions and boundary conditions to obtain realistic numerical weather predictions. The land surface controls the surface heat and moisture exchanges, which can be determined by the physical properties of the soil and soil state variables, subsequently exerting an effect on the boundary layer meteorology. The initial and boundary conditions of soil moisture are currently obtained via National Centers for Environmental Prediction FNL (Final) Operational Global Analysis data, which are collected operationally in 1° by 1° resolutions every 6 h. Another input to the model is the soil map generated by the Food and Agriculture Organization of the United Nations - United Nations Educational, Scientific and Cultural Organization (FAO-UNESCO) soil database, which combines several soil surveys from around the world. Both soil moisture from the FNL analysis data and the default soil map lack accuracy and feature coarse resolutions, particularly for certain areas of China. In this study, we update the global soil map with data from Beijing Normal University in 1 km by 1 km grids and propose an alternative method of soil moisture initialization. Simulations of the Weather Research and Forecasting model show that spinning-up the soil moisture improves near-surface temperature and relative humidity prediction using different types of soil moisture initialization. Explanations of that improvement and improvement of the planetary boundary layer height in performing process analysis are provided.

  7. A grid-based distributed flood forecasting model for use with weather radar data: Part 1. Formulation

    Directory of Open Access Journals (Sweden)

    V. A. Bell

    1998-01-01

    Full Text Available A practical methodology for distributed rainfall-runoff modelling using grid square weather radar data is developed for use in real-time flood forecasting. The model, called the Grid Model, is configured so as to share the same grid as used by the weather radar, thereby exploiting the distributed rainfall estimates to the full. Each grid square in the catchment is conceptualised as a storage which receives water as precipitation and generates water by overflow and drainage. This water is routed across the catchment using isochrone pathways. These are derived from a digital terrain model assuming two fixed velocities of travel for land and river pathways which are regarded as model parameters to be optimised. Translation of water between isochrones is achieved using a discrete kinematic routing procedure, parameterised through a single dimensionless wave speed parameter, which advects the water and incorporates diffusion effects through the discrete space-time formulation. The basic model routes overflow and drainage separately through a parallel system of kinematic routing reaches, characterised by different wave speeds but using the same isochrone-based space discretisation; these represent fast and slow pathways to the basin outlet, respectively. A variant allows the slow pathway to have separate isochrones calculated using Darcy velocities controlled by the hydraulic gradient as estimated by the local gradient of the terrain. Runoff production within a grid square is controlled by its absorption capacity which is parameterised through a simple linkage function to the mean gradient in the square, as calculated from digital terrain data. This allows absorption capacity to be specified differently for every grid square in the catchment through the use of only two regional parameters and a DTM measurement of mean gradient for each square. An extension of this basic idea to consider the distribution of gradient within the square leads analytically

  8. Release of potential instability by mesoscale triggering - An objective model simulation. [in precipitation numerical weather forecasting

    Science.gov (United States)

    Matthews, D. A.

    1978-01-01

    The effects of mesoscale triggering on organized nonsevere convective cloud systems in the High Plains are considered. Two experiments were conducted to determine if a one-dimensional quasi-time dependent model could (1) detect soundings which were sensitive to mesoscale triggering, and (2) discriminate between cases which had mesoscale organized convection and those with no organized convection. The MESOCU model was used to analyze the available potential instability and thermodynamic potential for cloud growth. It is noted that lifting is a key factor in the release of available potential instability on the High Plains.

  9. Wet weather water quality modelling of a Portuguese urban catchment: difficulties and benefits.

    Science.gov (United States)

    David, L M; Matos, R S

    2002-01-01

    This paper discusses the use of water quality deterministic modelling together with an integrated approach to assess the impact of urban stormwater discharges into ephemeral watercourses, based on the study of a Portuguese catchment. The description of the main aspects, difficulties and benefits found during data collection and model calibration and verification is presented, and the associated uncertainties and errors discussed. Experimental results showed a strong short- and long-term impact of sewer discharges on rivers, and confirmed deposition, resuspension and transport of pollutants as important processes for the water quality. However, the resuspension of riverbed sediment pollutants during storms was probably more significant than the direct impact of the urban discharges. The HydroWorks model was used since it allows for the calculation of pollutant build-up on catchment surfaces and in gully pots, their wash-off, and the deposition and erosion of sediments in sewers. However, it uses several constants, which could not be independently calibrated, increasing the uncertainty already associated with the data. River flows have quite different magnitude from the sewer system overflows, which, together with the difficulties in evaluating river flow rates, makes the integrated modelling approach rather complex and costly.

  10. Seamless Modeling for Research & Predictability of Severe Tropical Storms from Weather-to-Climate Timescales

    Science.gov (United States)

    Ramaswamy, V.; Chen, J. H.; Delworth, T. L.; Knutson, T. R.; Lin, S. J.; Murakami, H.; Vecchi, G. A.

    2017-12-01

    Damages from catastrophic tropical storms such as the 2017 destructive hurricanes compel an acceleration of scientific advancements to understand the genesis, underlying mechanisms, frequency, track, intensity, and landfall of these storms. The advances are crucial to provide improved early information for planners and responders. We discuss the development and utilization of a global modeling capability based on a novel atmospheric dynamical core ("Finite-Volume Cubed Sphere or FV3") which captures the realism of the recent tropical storms and is a part of the NOAA Next-Generation Global Prediction System. This capability is also part of an emerging seamless modeling system at NOAA/ Geophysical Fluid Dynamics Laboratory for simulating the frequency of storms on seasonal and longer timescales with high fidelity e.g., Atlantic hurricane frequency over the past decades. In addition, the same modeling system has also been employed to evaluate the nature of projected storms on the multi-decadal scales under the influence of anthropogenic factors such as greenhouse gases and aerosols. The seamless modeling system thus facilitates research into and the predictability of severe tropical storms across diverse timescales of practical interest to several societal sectors.

  11. Validation of Crop Weather Models for'Crop Assessment arid Yield ...

    African Journals Online (AJOL)

    R.W. 1997. CRPSM - Crop,growth and irrigation scheduling model. Software user manual. Biolog- ical & Irrigation Engineering Department. Utah. State University. Logan, UT 84322-4105, 204 p. Jensen. M.E. 1968. Water consumption by agricultural plants. 111: T. T. Kozlowski (Eds.), Vo1.2, Water deficits and plant growth.

  12. On the contribution of lakes in predicting near-surface temperature in a global weather forecasting model

    Directory of Open Access Journals (Sweden)

    T. Stockdale

    2012-02-01

    Full Text Available The impact of lakes in numerical weather prediction is investigated in a set of global simulations performed with the ECMWF Integrated Forecasting System (IFS. A Fresh shallow-water Lake model (FLake is introduced allowing the coupling of both resolved and subgrid lakes (those that occupy less than 50% of a grid-box to the IFS atmospheric model. Global fields for the lake ancillary conditions (namely lake cover and lake depth, as well as initial conditions for the lake physical state, have been derived to initialise the forecast experiments. The procedure for initialising the lake variables is described and verified with particular emphasis on the importance of surface water temperature and freezing conditions. The response of short-range near surface temperature to the representation of lakes is examined in a set of forecast experiments covering one full year. It is shown that the impact of subgrid lakes is beneficial, reducing forecast error over the Northern territories of Canada and over Scandinavia particularly in spring and summer seasons. This is mainly attributed to the lake thermal effect, which delays the temperature response to seasonal radiation forcing.

  13. Assessment of the Weather Research and Forecasting (WRF model for simulation of extreme rainfall events in the upper Ganga Basin

    Directory of Open Access Journals (Sweden)

    I. Chawla

    2018-02-01

    Full Text Available Reliable estimates of extreme rainfall events are necessary for an accurate prediction of floods. Most of the global rainfall products are available at a coarse resolution, rendering them less desirable for extreme rainfall analysis. Therefore, regional mesoscale models such as the advanced research version of the Weather Research and Forecasting (WRF model are often used to provide rainfall estimates at fine grid spacing. Modelling heavy rainfall events is an enduring challenge, as such events depend on multi-scale interactions, and the model configurations such as grid spacing, physical parameterization and initialization. With this background, the WRF model is implemented in this study to investigate the impact of different processes on extreme rainfall simulation, by considering a representative event that occurred during 15–18 June 2013 over the Ganga Basin in India, which is located at the foothills of the Himalayas. This event is simulated with ensembles involving four different microphysics (MP, two cumulus (CU parameterizations, two planetary boundary layers (PBLs and two land surface physics options, as well as different resolutions (grid spacing within the WRF model. The simulated rainfall is evaluated against the observations from 18 rain gauges and the Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA 3B42RT version 7 data. From the analysis, it should be noted that the choice of MP scheme influences the spatial pattern of rainfall, while the choice of PBL and CU parameterizations influences the magnitude of rainfall in the model simulations. Further, the WRF run with Goddard MP, Mellor–Yamada–Janjic PBL and Betts–Miller–Janjic CU scheme is found to perform best in simulating this heavy rain event. The selected configuration is evaluated for several heavy to extremely heavy rainfall events that occurred across different months of the monsoon season in the region. The model performance

  14. Assessment of the Weather Research and Forecasting (WRF) model for simulation of extreme rainfall events in the upper Ganga Basin

    Science.gov (United States)

    Chawla, Ila; Osuri, Krishna K.; Mujumdar, Pradeep P.; Niyogi, Dev

    2018-02-01

    Reliable estimates of extreme rainfall events are necessary for an accurate prediction of floods. Most of the global rainfall products are available at a coarse resolution, rendering them less desirable for extreme rainfall analysis. Therefore, regional mesoscale models such as the advanced research version of the Weather Research and Forecasting (WRF) model are often used to provide rainfall estimates at fine grid spacing. Modelling heavy rainfall events is an enduring challenge, as such events depend on multi-scale interactions, and the model configurations such as grid spacing, physical parameterization and initialization. With this background, the WRF model is implemented in this study to investigate the impact of different processes on extreme rainfall simulation, by considering a representative event that occurred during 15-18 June 2013 over the Ganga Basin in India, which is located at the foothills of the Himalayas. This event is simulated with ensembles involving four different microphysics (MP), two cumulus (CU) parameterizations, two planetary boundary layers (PBLs) and two land surface physics options, as well as different resolutions (grid spacing) within the WRF model. The simulated rainfall is evaluated against the observations from 18 rain gauges and the Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) 3B42RT version 7 data. From the analysis, it should be noted that the choice of MP scheme influences the spatial pattern of rainfall, while the choice of PBL and CU parameterizations influences the magnitude of rainfall in the model simulations. Further, the WRF run with Goddard MP, Mellor-Yamada-Janjic PBL and Betts-Miller-Janjic CU scheme is found to perform best in simulating this heavy rain event. The selected configuration is evaluated for several heavy to extremely heavy rainfall events that occurred across different months of the monsoon season in the region. The model performance improved through incorporation

  15. A Weather-Based Prediction Model of Malaria Prevalence in Amenfi West District, Ghana

    Directory of Open Access Journals (Sweden)

    Esther Love Darkoh

    2017-01-01

    Full Text Available This study investigated the effects of climatic variables, particularly, rainfall and temperature, on malaria incidence using time series analysis. Our preliminary analysis revealed that malaria incidence in the study area decreased at about 0.35% annually. Also, the month of November recorded approximately 21% more malaria cases than the other months while September had a decreased effect of about 14%. The forecast model developed for this investigation indicated that mean minimum (P=0.01928 and maximum (P=0.00321 monthly temperatures lagged at three months were significant predictors of malaria incidence while rainfall was not. Diagnostic tests using Ljung-Box and ARCH-LM tests revealed that the model developed was adequate for forecasting. Forecast values for 2016 to 2020 generated by our model suggest a possible future decline in malaria incidence. This goes to suggest that intervention strategies put in place by some nongovernmental and governmental agencies to combat the disease are effective and thus should be encouraged and routinely monitored to yield more desirable outcomes.

  16. Detection of Digital Elevation Model Errors Using X-band Weather Radar

    Science.gov (United States)

    Young, Steven D.; deHaag, Maatren Uijt

    2007-01-01

    Flight in Instrument Meteorological Conditions requires pilots to manipulate flight controls while referring to a Primary Flight Display. The Primary Flight Display indicates aircraft attitude along with, in some cases, many other state variables such as altitude, speed, and guidance cues. Synthetic Vision Systems have been proposed that overlay the traditional information provided on Primary Flight Displays onto a scene depicting the location of terrain and other geo-spatial features.Terrain models used by these displays must have sufficient quality to avoid providing misleading information. This paper describes how X-band radar measurements can be used as part of a monitor, and/or maintenance system, to quantify the integrity of terrain models that are used by systems such as Synthetic Vision. Terrain shadowing effects, as seen by the radar, are compared in a statistical manner against estimated shadow feature elements extracted from the stored terrain model from the perspective of the airborne observer. A test statistic is defined that enables detection of errors as small as the range resolution of the radar. Experimental results obtained from two aircraft platforms hosting certified commercial-off-the-shelf X-band radars test the premise and illustrate its potential.

  17. Modeling the radiative effects of desert dust on weather and regional climate

    Directory of Open Access Journals (Sweden)

    C. Spyrou

    2013-06-01

    Full Text Available Mineral dust aerosols exert a significant effect on both solar and terrestrial radiation. By absorbing and scattering, the solar radiation aerosols reduce the amount of energy reaching the surface. In addition, aerosols enhance the greenhouse effect by absorbing and emitting outgoing longwave radiation. Desert dust forcing exhibits large regional and temporal variability due to its short lifetime and diverse optical properties, further complicating the quantification of the direct radiative effect (DRE. The complexity of the links and feedbacks of dust on radiative transfer indicate the need for an integrated approach in order to examine these impacts. In order to examine these feedbacks, the SKIRON limited area model has been upgraded to include the RRTMG (Rapid Radiative Transfer Model – GCM radiative transfer model that takes into consideration the aerosol radiative effects. It was run for a 6 year period. Two sets of simulations were performed, one without the effects of dust and the other including the radiative feedback. The results were first evaluated using aerosol optical depth data to examine the capabilities of the system in describing the desert dust cycle. Then the aerosol feedback on radiative transfer was quantified and the links between dust and radiation were studied. The study has revealed a strong interaction between dust particles and solar and terrestrial radiation, with several implications on the energy budget of the atmosphere. A profound effect is the increased absorption (in the shortwave and longwave in the lower troposphere and the induced modification of the atmospheric temperature profile. These feedbacks depend strongly on the spatial distribution of dust and have more profound effects where the number of particles is greater, such as near their source.

  18. The Los Alamos dynamic radiation environment assimilation model (DREAM) for space weather specification and forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Reeves, Geoffrey D [Los Alamos National Laboratory; Friedel, Reiner H W [Los Alamos National Laboratory; Chen, Yue [Los Alamos National Laboratory; Koller, Josef [Los Alamos National Laboratory; Henderson, Michael G [Los Alamos National Laboratory

    2008-01-01

    The Dynamic Radiation Environment Assimilation Model (DREAM) was developed at Los Alamos National Laboratory to assess, quantify, and predict the hazards from the natural space environment and the anthropogenic environment produced by high altitude nuclear explosions (HANE). DREAM was initially developed as a basic research activity to understand and predict the dynamics of the Earth's Van Allen radiation belts. It uses Kalman filter techniques to assimilate data from space environment instruments with a physics-based model of the radiation belts. DREAM can assimilate data from a variety of types of instruments and data with various levels of resolution and fidelity by assigning appropriate uncertainties to the observations. Data from any spacecraft orbit can be assimilated but DREAM was designed to function with as few as two spacecraft inputs: one from geosynchronous orbit and one from GPS orbit. With those inputs, DREAM can be used to predict the environment at any satellite in any orbit whether space environment data are available in those orbits or not. Even with very limited data input and relatively simple physics models, DREAM specifies the space environment in the radiation belts to a high level of accuracy. DREAM has been extensively tested and evaluated as we transition from research to operations. We report here on one set of test results in which we predict the environment in a highly-elliptical polar orbit. We also discuss long-duration reanalysis for spacecraft design, using DREAM for real-time operations, and prospects for 1-week forecasts of the radiation belt environment.

  19. Modeling Weather Impact on Airport Arrival Miles-in-Trail Restrictions

    Science.gov (United States)

    Wang, Yao; Grabbe, Shon

    2013-01-01

    When the demand for either a region of airspace or an airport approaches or exceeds the available capacity, miles-in-trail (MIT) restrictions are the most frequently issued traffic management initiatives (TMIs) that are used to mitigate these imbalances. Miles-intrail operations require aircraft in a traffic stream to meet a specific inter-aircraft separation in exchange for maintaining a safe and orderly flow within the stream. This stream of aircraft can be departing an airport, over a common fix, through a sector, on a specific route or arriving at an airport. This study begins by providing a high-level overview of the distribution and causes of arrival MIT restrictions for the top ten airports in the United States. This is followed by an in-depth analysis of the frequency, duration and cause of MIT restrictions impacting the Hartsfield-Jackson Atlanta International Airport (ATL) from 2009 through 2011. Then, machine-learning methods for predicting (1) situations in which MIT restrictions for ATL arrivals are implemented under low demand scenarios, and (2) days in which a large number of MIT restrictions are required to properly manage and control ATL arrivals are presented. More specifically, these predictions were accomplished by using an ensemble of decision trees with Bootstrap aggregation (BDT) and supervised machine learning was used to train the BDT binary classification models. The models were subsequently validated using data cross validation methods. When predicting the occurrence of arrival MIT restrictions under low demand situations, the model was able to achieve over all accuracy rates ranging from 84% to 90%, with false alarm ratios ranging from 10% to 15%. In the second set of studies designed to predict days on which a high number of MIT restrictions were required, overall accuracy rates of 80% were achieved with false alarm ratios of 20%. Overall, the predictions proposed by the model give better MIT usage information than what has been

  20. Reducing prediction uncertainty of weather controlled systems

    NARCIS (Netherlands)

    Doeswijk, T.G.

    2007-01-01

    In closed agricultural systems the weather acts both as a disturbance and as a resource. By using weather forecasts in control strategies the effects of disturbances can be minimized whereas the resources can be utilized. In this situation weather forecast uncertainty and model based control are

  1. Detection of Weather Radar Clutter

    DEFF Research Database (Denmark)

    Bøvith, Thomas

    2008-01-01

    Weather radars provide valuable information on precipitation in the atmosphere but due to the way radars work, not only precipitation is observed by the weather radar. Weather radar clutter, echoes from non-precipitating targets, occur frequently in the data, resulting in lowered data quality....... Especially in the application of weather radar data in quantitative precipitation estimation and forecasting a high data quality is important. Clutter detection is one of the key components in achieving this goal. This thesis presents three methods for detection of clutter. The methods use supervised...... and precipitating and non-precipitating clouds. Another method uses the difference in the motion field of clutter and precipitation measured between two radar images. Furthermore, the direction of the wind field extracted from a weather model is used. The third method uses information about the refractive index...

  2. Modeling systemic autoimmune rheumatic disease in rats under the adverse weather conditions

    Directory of Open Access Journals (Sweden)

    Yegudina Ye.D.

    2017-04-01

    Full Text Available Changes in the lungs, heart and kidneys are found in all animals with experimental systemic autoimmune rheumatic disease and respectively in 47%, 47% and 40% of cases of intact rats in a hostile environment with xenobiotics air pollution (ammonia + benzene + formalin, herewith in every third or fourth individual lesions of visceral vessels developed. The negative environmental situation increases the frequency of morphological signs of the disease, such as proliferation of endothelial vessels of the heart by 68% and renal arterioles by 52%, in addition, there are direct correlations of angiopathy degree in individual organs; this depends on the nature of pathological process modeling and demonstrates air pollution as a risk factor of disease in humans. The impact of pulmonary vessels sclerosis on the development of bronhosclerosis, perivascular infiltration of the heart muscle on the lymphocyte-macrophage infiltration of the stroma of the myocardium and sclerosis of renal arterioles on the degree of nephroslerosis of stroma is directly associated, with the model of systemic autoimmune rheumatic diseases whereas air pollution by xenobiotics determines dependences of the degree of cellular infiltration of alveolar septa from perivascular pulmonary infiltration, the development of cardiomyocytes hypertrophy from proliferation of the heart endothelial vessels, increase of kidney mesangial matrix from the proliferation of endothelial glomerular capillaries.

  3. A Numerical Method to Generate High Temporal Resolution Precipitation Time Series by Combining Weather Radar Measurements with a Nowcast Model

    DEFF Research Database (Denmark)

    Nielsen, Jesper Ellerbæk; Thorndahl, Søren Liedtke; Rasmussen, Michael R.

    2014-01-01

    for vector field estimation already known from short-term weather radar nowcasting. However, instead of forecasting the weather radar rainfall, the proposed interpolation method exploits the advection of the rainfall in the interpolation. The interpolated rainfall fields are validated by measurements......The topic of this paper is temporal interpolation of precipitation observed by weather radars. Precipitation measurements with high spatial and temporal resolution are, in general, desired for urban drainage applications. An advection-based interpolation method is developed which uses methods...... at ground level from laser disdrometers. The proposed interpolation method performs better when compared to traditional interpolation of weather radar rainfall where the radar observation is considered constant in time between measurements. It is demonstrated that the advection-based interpolation method...

  4. GEM-AQ/EC, an on-line global multi-scale chemical weather modelling system: model development and evaluation of global aerosol climatology

    Directory of Open Access Journals (Sweden)

    S. L. Gong

    2012-09-01

    Full Text Available A global air quality modeling system GEM-AQ/EC was developed by implementing tropospheric chemistry and aerosol processes on-line into the Global Environmental Multiscale weather prediction model – GEM. Due to the multi-scale features of the GEM, the integrated model, GEM-AQ/EC, is able to investigate chemical weather at scales from global to urban domains. The current chemical mechanism is comprised of 50 gas-phase species, 116 chemical and 19 photolysis reactions, and is complemented by a sectional aerosol module CAM (The Canadian Aerosol Module with 5 aerosols types: sulphate, black carbon, organic carbon, sea-salt and soil dust. Monthly emission inventories of black carbon and organic carbon from boreal and temperate vegetation fires were assembled using the most reliable areas burned datasets by countries, from statistical databases and derived from remote sensing products of 1995–2004. The model was run for ten years from from 1995–2004 with re-analyzed meteorology on a global uniform 1° × 1° horizontal resolution domain and 28 hybrid levels extending up to 10 hPa. The simulating results were compared with various observations including surface network around the globe and satellite data. Regional features of global aerosols are reasonably captured including emission, surface concentrations and aerosol optical depth. For various types of aerosols, satisfactory correlations were achieved between modeled and observed with some degree of systematic bias possibly due to large uncertainties in the emissions used in this study. A global distribution of natural aerosol contributions to the total aerosols is obtained and compared with observations.

  5. GEM-AQ, an on-line global multiscale chemical weather modelling system: model description and evaluation of gas phase chemistry processes

    Directory of Open Access Journals (Sweden)

    J. W. Kaminski

    2008-06-01

    Full Text Available Tropospheric chemistry and air quality processes were implemented on-line in the Global Environmental Multiscale weather prediction model. The integrated model, GEM-AQ, was developed as a platform to investigate chemical weather at scales from global to urban. The current chemical mechanism is comprised of 50 gas-phase species, 116 chemical and 19 photolysis reactions, and is complemented by a sectional aerosol module with 5 aerosols types. All tracers are advected using the semi-Lagrangian scheme native to GEM. The vertical transport includes parameterized subgrid-scale turbulence and large scale deep convection. Dry deposition is included as a flux boundary condition in the vertical diffusion equation. Wet deposition of gas-phase species is treated in a simplified way, and only below-cloud scavenging is considered. The emissions used include yearly-averaged anthropogenic, and monthly-averaged biogenic, ocean, soil, and biomass burning emission fluxes, as well as NOx from lightning. In order to evaluate the ability to simulate seasonal variations and regional distributions of trace gases such as ozone, nitrogen dioxide and carbon monoxide, the model was run for a period of five years (2001–2005 on a global uniform 1.5°×1.5° horizontal resolution domain and 28 hybrid levels extending up to 10 hPa. Model results were compared with observations from satellites, aircraft measurement campaigns and balloon sondes. We find that GEM-AQ is able to capture the spatial details of the chemical fields in the middle and lower troposphere. The modelled ozone consistently shows good agreement with observations, except over tropical oceans. The comparison of carbon monoxide and nitrogen dioxide with satellite measurements emphasizes the need for more accurate, year-specific emissions fluxes for biomass burning and anthropogenic sources. Other species also compare well with available observations.

  6. Expanding the performance curve of different weather data sources for hydrologic modeling in central Texas: a comparison of ground observations and the Climate Forecast System Reanalysis as watershed model inputs

    Science.gov (United States)

    Fuka, D. R.; Collick, A.; Auerbach, D.; Kleinman, P. J. A.; Wagena, M. B.; Sommerlot, A.; Harmel, D.; Easton, Z. M.

    2015-12-01

    Obtaining location specific representative meteorological data can be difficult and time consuming, even though correctly representing the weather is critical to hydrological modeling and watershed management planning. The Climate Forecast System Reanalysis (CFSR) dataset provides continuous, globally-available records that offer a consistent baseline for assessments of candidate weather data, have produced satisfactory hydrological model performance in some temperate and monsoonal locations, as well as have been demonstrated as a solution for ungaged tropical and semi-tropical montane basins. Taking advantage of exceptionally high rainfall data density in USDA-ARS's Reisel experimental watershed. We compared model performance under alternative weather inputs: Climate Forecast System Reanalysis (CFSR) records, a standard public weather station dataset available from the Global Historical Climate Network (GHCN), and a the high density research quality dataset available from the USDA-ARS. Results show that utilizing the CFSR precipitation and temperature data to force a watershed model provides stream discharge simulations that are as good as or better than models forced using traditional weather gauging stations available from GHCN, especially when stations are more than 10-km from the watershed. These results further demonstrate that adding CFSR data to the suite of watershed modelling tools provides new opportunities for meeting the challenges of modelling ungauged watersheds and advancing real-time hydrological modelling.

  7. Key Parameters for Urban Heat Island Assessment in A Mediterranean Context: A Sensitivity Analysis Using the Urban Weather Generator Model

    Science.gov (United States)

    Salvati, Agnese; Palme, Massimo; Inostroza, Luis

    2017-10-01

    Although Urban Heat Island (UHI) is a fundamental effect modifying the urban climate, being widely studied, the relative weight of the parameters involved in its generation is still not clear. This paper investigates the hierarchy of importance of eight parameters responsible for UHI intensity in the Mediterranean context. Sensitivity analyses have been carried out using the Urban Weather Generator model, considering the range of variability of: 1) city radius, 2) urban morphology, 3) tree coverage, 4) anthropogenic heat from vehicles, 5) building’s cooling set point, 6) heat released to canyon from HVAC systems, 7) wall construction properties and 8) albedo of vertical and horizontal surfaces. Results show a clear hierarchy of significance among the considered parameters; the urban morphology is the most important variable, causing a relative change up to 120% of the annual average UHI intensity in the Mediterranean context. The impact of anthropogenic sources of heat such as cooling systems and vehicles is also significant. These results suggest that urban morphology parameters can be used as descriptors of the climatic performance of different urban areas, easing the work of urban planners and designers in understanding a complex physical phenomenon, such as the UHI.

  8. Using X-band Weather Radar Measurements to Monitor the Integrity of Digital Elevation Models for Synthetic Vision Systems

    Science.gov (United States)

    Young, Steve; UijtdeHaag, Maarten; Sayre, Jonathon

    2003-01-01

    Synthetic Vision Systems (SVS) provide pilots with displays of stored geo-spatial data representing terrain, obstacles, and cultural features. As comprehensive validation is impractical, these databases typically have no quantifiable level of integrity. Further, updates to the databases may not be provided as changes occur. These issues limit the certification level and constrain the operational context of SVS for civil aviation. Previous work demonstrated the feasibility of using a realtime monitor to bound the integrity of Digital Elevation Models (DEMs) by using radar altimeter measurements during flight. This paper describes an extension of this concept to include X-band Weather Radar (WxR) measurements. This enables the monitor to detect additional classes of DEM errors and to reduce the exposure time associated with integrity threats. Feature extraction techniques are used along with a statistical assessment of similarity measures between the sensed and stored features that are detected. Recent flight-testing in the area around the Juneau, Alaska Airport (JNU) has resulted in a comprehensive set of sensor data that is being used to assess the feasibility of the proposed monitor technology. Initial results of this assessment are presented.

  9. The 2009–2010 Arctic stratospheric winter – general evolution, mountain waves and predictability of an operational weather forecast model

    Directory of Open Access Journals (Sweden)

    A. Dörnbrack

    2012-04-01

    Full Text Available The relatively warm 2009–2010 Arctic winter was an exceptional one as the North Atlantic Oscillation index attained persistent extreme negative values. Here, selected aspects of the Arctic stratosphere during this winter inspired by the analysis of the international field experiment RECONCILE are presented. First of all, and as a kind of reference, the evolution of the polar vortex in its different phases is documented. Special emphasis is put on explaining the formation of the exceptionally cold vortex in mid winter after a sequence of stratospheric disturbances which were caused by upward propagating planetary waves. A major sudden stratospheric warming (SSW occurring near the end of January 2010 concluded the anomalous cold vortex period. Wave ice polar stratospheric clouds were frequently observed by spaceborne remote-sensing instruments over the Arctic during the cold period in January 2010. Here, one such case observed over Greenland is analysed in more detail and an attempt is made to correlate flow information of an operational numerical weather prediction model to the magnitude of the mountain-wave induced temperature fluctuations. Finally, it is shown that the forecasts of the ECMWF ensemble prediction system for the onset of the major SSW were very skilful and the ensemble spread was very small. However, the ensemble spread increased dramatically after the major SSW, displaying the strong non-linearity and internal variability involved in the SSW event.

  10. Space weather modeling using artificial neural network. (Slovak Title: Modelovanie kozmického počasia umelou neurónovou sietou)

    Science.gov (United States)

    Valach, F.; Revallo, M.; Hejda, P.; Bochníček, J.

    2010-12-01

    Our modern society with its advanced technology is becoming increasingly vulnerable to the Earth's system disorders originating in explosive processes on the Sun. Coronal mass ejections (CMEs) blasted into interplanetary space as gigantic clouds of ionized gas can hit Earth within a few hours or days and cause, among other effects, geomagnetic storms - perhaps the best known manifestation of solar wind interaction with Earth's magnetosphere. Solar energetic particles (SEP), accelerated to near relativistic energy during large solar storms, arrive at the Earth's orbit even in few minutes and pose serious risk to astronauts traveling through the interplanetary space. These and many other threats are the reason why experts pay increasing attention to space weather and its predictability. For research on space weather, it is typically necessary to examine a large number of parameters which are interrelated in a complex non-linear way. One way to cope with such a task is to use an artificial neural network for space weather modeling, a tool originally developed for artificial intelligence. In our contribution, we focus on practical aspects of the neural networks application to modeling and forecasting selected space weather parameters.

  11. High-Frequency and Low-Frequency Variability in Stochastic Daily Weather Generator and Its Effect on Agricultural and Hydrologic Modelling

    Czech Academy of Sciences Publication Activity Database

    Dubrovský, Martin; Buchtele, Josef; Žalud, Z.

    2004-01-01

    Roč. 63, 1-2 (2004), s. 145-179 ISSN 0165-0009 R&D Projects: GA ČR GA205/99/1561; GA AV ČR IAA3060002 Institutional research plan: CEZ:AV0Z3042911 Keywords : Weather Generator * Agricultural Modelling * Hydrologic Modelling Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 2.035, year: 2004

  12. The influence of synoptic weather regimes on UK air quality: regional model studies of tropospheric column NO2

    Science.gov (United States)

    Pope, R. J.; Savage, N. H.; Chipperfield, M. P.; Ordóñez, C.; Neal, L. S.

    2015-10-01

    Synoptic meteorology can have a significant influence on UK air quality. Cyclonic conditions lead to the dispersion of air pollutants away from source regions, while anticyclonic conditions lead to their accumulation over source regions. Meteorology also modifies atmospheric chemistry processes such as photolysis and wet deposition. Previous studies have shown a relationship between observed satellite tropospheric column NO2 and synoptic meteorology in different seasons. Here, we test whether the UK Met Office Air Quality in the Unified Model (AQUM) can reproduce these observations and then use the model to explore the relative importance of various factors. We show that AQUM successfully captures the observed relationships when sampled under the Lamb weather types, an objective classification of midday UK circulation patterns. By using a range of idealized NOx-like tracers with different e-folding lifetimes, we show that under different synoptic regimes the NO2 lifetime in AQUM is approximately 6 h in summer and 12 h in winter. The longer lifetime can explain why synoptic spatial tropospheric column NO2 variations are more significant in winter compared to summer, due to less NO2 photochemical loss. We also show that cyclonic conditions have more seasonality in tropospheric column NO2 than anticyclonic conditions as they result in more extreme spatial departures from the wintertime seasonal average. Within a season (summer or winter) under different synoptic regimes, a large proportion of the spatial pattern in the UK tropospheric column NO2 field can be explained by the idealized model tracers, showing that transport is an important factor in governing the variability of UK air quality on seasonal synoptic timescales.

  13. Thallium contamination of soils/vegetation as affected by sphalerite weathering: a model rhizospheric experiment.

    Science.gov (United States)

    Vaněk, Aleš; Grösslová, Zuzana; Mihaljevič, Martin; Ettler, Vojtěch; Chrastný, Vladislav; Komárek, Michael; Tejnecký, Václav; Drábek, Ondřej; Penížek, Vít; Galušková, Ivana; Vaněčková, Barbora; Pavlů, Lenka; Ash, Christopher

    2015-01-01

    The environmental stability of Tl-rich sphalerite in two contrasting soils was studied. Rhizospheric conditions were simulated to assess the risk associated with sulfide microparticles entering agricultural (top)soils. The data presented here clearly demonstrate a significant effect of 500 μM citric acid, a model rhizospheric solution, on ZnS alteration followed by enhanced Tl and Zn release. The relative ZnS mass loss after 28 days of citrate incubation reached 0.05 and 0.03 wt.% in Cambisol and Leptosol samples respectively, and was up to 4 times higher, compared to H2O treatments. Incongruent (i.e., substantially increased) mobilization of Tl from ZnS was observed during the incubation time. Generally higher (long-term) stability of ZnS with lower Tl release is predicted for soils enriched in carbonates. Furthermore, the important role of silicates (mainly illite) in the stabilization of mobilized Tl, linked with structural (inter)layer Tl-K exchange, is suggested. Thallium was highly bioavailable, as indicated by its uptake by white mustard; maximum Tl amounts were detected in biomass grown on the acidic Cambisol. Despite the fact that sulfides are thought as relatively stable phases in soil environments, enhanced sulfide dissolution and Tl/trace element release (and bioaccumulation) can be assumed in rhizosphere systems. Copyright © 2014 Elsevier B.V. All rights reserved.

  14. Implementation of a generalized actuator disk wind turbine model into the weather research and forecasting model for large-eddy simulation applications

    Energy Technology Data Exchange (ETDEWEB)

    Mirocha, J. D. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Kosovic, B. [National Center for Atmospheric Research, Boulder, CO (United States); Aitken, M. L. [Univ. of Colorado, Boulder, CO (United States); Lundquist, J. K. [Univ. of Colorado, Boulder, CO (United States); National Renewable Energy Lab., Golden, CO (United States)

    2014-01-10

    A generalized actuator disk (GAD) wind turbine parameterization designed for large-eddy simulation (LES) applications was implemented into the Weather Research and Forecasting (WRF) model. WRF-LES with the GAD model enables numerical investigation of the effects of an operating wind turbine on and interactions with a broad range of atmospheric boundary layer phenomena. Numerical simulations using WRF-LES with the GAD model were compared with measurements obtained from the Turbine Wake and Inflow Characterization Study (TWICS-2011), the goal of which was to measure both the inflow to and wake from a 2.3-MW wind turbine. Data from a meteorological tower and two light-detection and ranging (lidar) systems, one vertically profiling and another operated over a variety of scanning modes, were utilized to obtain forcing for the simulations, and to evaluate characteristics of the simulated wakes. Simulations produced wakes with physically consistent rotation and velocity deficits. Two surface heat flux values of 20 W m–2 and 100 W m–2 were used to examine the sensitivity of the simulated wakes to convective instability. Simulations using the smaller heat flux values showed good agreement with wake deficits observed during TWICS-2011, whereas those using the larger value showed enhanced spreading and more-rapid attenuation. This study demonstrates the utility of actuator models implemented within atmospheric LES to address a range of atmospheric science and engineering applications. In conclusion, validated implementation of the GAD in a numerical weather prediction code such as WRF will enable a wide range of studies related to the interaction of wind turbines with the atmosphere and surface.

  15. Surface Weather, Signal Service and Weather Bureau

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Surface Weather, Signal Service and Weather Bureau (SWSSWB) Records primarily created by the United States Army Signal Service from 1819 until the paid and voluntary...

  16. Modeling the variability of solar radiation data among weather stations by means of principal components analysis

    International Nuclear Information System (INIS)

    Zarzo, Manuel; Marti, Pau

    2011-01-01

    Research highlights: →Principal components analysis was applied to R s data recorded at 30 stations. → Four principal components explain 97% of the data variability. → The latent variables can be fitted according to latitude, longitude and altitude. → The PCA approach is more effective for gap infilling than conventional approaches. → The proposed method allows daily R s estimations at locations in the area of study. - Abstract: Measurements of global terrestrial solar radiation (R s ) are commonly recorded in meteorological stations. Daily variability of R s has to be taken into account for the design of photovoltaic systems and energy efficient buildings. Principal components analysis (PCA) was applied to R s data recorded at 30 stations in the Mediterranean coast of Spain. Due to equipment failures and site operation problems, time series of R s often present data gaps or discontinuities. The PCA approach copes with this problem and allows estimation of present and past values by taking advantage of R s records from nearby stations. The gap infilling performance of this methodology is compared with neural networks and alternative conventional approaches. Four principal components explain 66% of the data variability with respect to the average trajectory (97% if non-centered values are considered). A new method based on principal components regression was also developed for R s estimation if previous measurements are not available. By means of multiple linear regression, it was found that the latent variables associated to the four relevant principal components can be fitted according to the latitude, longitude and altitude of the station where data were recorded from. Additional geographical or climatic variables did not increase the predictive goodness-of-fit. The resulting models allow the estimation of daily R s values at any location in the area under study and present higher accuracy than artificial neural networks and some conventional approaches

  17. Winter Weather: Frostbite

    Science.gov (United States)

    ... Safety During Fire Cleanup Wildfires PSAs Related Links Winter Weather About Winter Weather Before a Storm Prepare Your Home Prepare Your Car Winter Weather Checklists During a Storm Indoor Safety During ...

  18. Monthly Weather Review

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Supplements to the Monthly Weather Review publication. The Weather Bureau published the Monthly weather review Supplement irregularly from 1914 to 1949. The...

  19. Revisiting Intel Xeon Phi optimization of Thompson cloud microphysics scheme in Weather Research and Forecasting (WRF) model

    Science.gov (United States)

    Mielikainen, Jarno; Huang, Bormin; Huang, Allen

    2015-10-01

    The Thompson cloud microphysics scheme is a sophisticated cloud microphysics scheme in the Weather Research and Forecasting (WRF) model. The scheme is very suitable for massively parallel computation as there are no interactions among horizontal grid points. Compared to the earlier microphysics schemes, the Thompson scheme incorporates a large number of improvements. Thus, we have optimized the speed of this important part of WRF. Intel Many Integrated Core (MIC) ushers in a new era of supercomputing speed, performance, and compatibility. It allows the developers to run code at trillions of calculations per second using the familiar programming model. In this paper, we present our results of optimizing the Thompson microphysics scheme on Intel Many Integrated Core Architecture (MIC) hardware. The Intel Xeon Phi coprocessor is the first product based on Intel MIC architecture, and it consists of up to 61 cores connected by a high performance on-die bidirectional interconnect. The coprocessor supports all important Intel development tools. Thus, the development environment is familiar one to a vast number of CPU developers. Although, getting a maximum performance out of MICs will require using some novel optimization techniques. New optimizations for an updated Thompson scheme are discusses in this paper. The optimizations improved the performance of the original Thompson code on Xeon Phi 7120P by a factor of 1.8x. Furthermore, the same optimizations improved the performance of the Thompson on a dual socket configuration of eight core Intel Xeon E5-2670 CPUs by a factor of 1.8x compared to the original Thompson code.

  20. The Chemistry CATT-BRAMS model (CCATT-BRAMS 4.5: a regional atmospheric model system for integrated air quality and weather forecasting and research

    Directory of Open Access Journals (Sweden)

    K. M. Longo

    2013-09-01

    Full Text Available Coupled Chemistry Aerosol-Tracer Transport model to the Brazilian developments on the Regional Atmospheric Modeling System (CCATT-BRAMS, version 4.5 is an on-line regional chemical transport model designed for local and regional studies of atmospheric chemistry from the surface to the lower stratosphere suitable both for operational and research purposes. It includes gaseous/aqueous chemistry, photochemistry, scavenging and dry deposition. The CCATT-BRAMS model takes advantage of BRAMS-specific development for the tropics/subtropics as well as the recent availability of preprocessing tools for chemical mechanisms and fast codes for photolysis rates. BRAMS includes state-of-the-art physical parameterizations and dynamic formulations to simulate atmospheric circulations down to the meter. This on-line coupling of meteorology and chemistry allows the system to be used for simultaneous weather and chemical composition forecasts as well as potential feedback between the two. The entire system is made of three preprocessing software tools for user-defined chemical mechanisms, aerosol and trace gas emissions fields and the interpolation of initial and boundary conditions for meteorology and chemistry. In this paper, the model description is provided along with the evaluations performed by using observational data obtained from ground-based stations, instruments aboard aircrafts and retrieval from space remote sensing. The evaluation accounts for model applications at different scales from megacities and the Amazon Basin up to the intercontinental region of the Southern Hemisphere.

  1. Assessment of the weather research and forecasting model generalized parameterization schemes for advancement of precipitation forecasting in monsoon-driven river basins

    Science.gov (United States)

    Sikder, Safat; Hossain, Faisal

    2016-09-01

    Some of the world's largest and flood-prone river basins experience a seasonal flood regime driven by the monsoon weather system. Highly populated river basins with extensive rain-fed agricultural productivity such as the Ganges, Indus, Brahmaputra, Irrawaddy, and Mekong are examples of monsoon-driven river basins. It is therefore appropriate to investigate how precipitation forecasts from numerical models can advance flood forecasting in these basins. In this study, the Weather Research and Forecasting model was used to evaluate downscaling of coarse-resolution global precipitation forecasts from a numerical weather prediction model. Sensitivity studies were conducted using the TOPSIS analysis to identify the likely best set of microphysics and cumulus parameterization schemes, and spatial resolution from a total set of 15 combinations. This identified best set can pinpoint specific parameterizations needing further development to advance flood forecasting in monsoon-dominated regimes. It was found that the Betts-Miller-Janjic cumulus parameterization scheme with WRF Single-Moment 5-class, WRF Single-Moment 6-class, and Thompson microphysics schemes exhibited the most skill in the Ganges-Brahmaputra-Meghna basins. Finer spatial resolution (3 km) without cumulus parameterization schemes did not yield significant improvements. The short-listed set of the likely best microphysics-cumulus parameterization configurations was found to also hold true for the Indus basin. The lesson learned from this study is that a common set of model parameterization and spatial resolution exists for monsoon-driven seasonal flood regimes at least in South Asian river basins.

  2. Reconstruction of Historical Weather by Assimilating Old Weather Diary Data

    Science.gov (United States)

    Neluwala, P.; Yoshimura, K.; Toride, K.; Hirano, J.; Ichino, M.; Okazaki, A.

    2017-12-01

    Climate can control not only human life style but also other living beings. It is important to investigate historical climate to understand the current and future climates. Information about daily weather can give a better understanding of past life on earth. Long-term weather influences crop calendar as well as the development of civilizations. Unfortunately, existing reconstructed daily weather data are limited to 1850s due to the availability of instrumental data. The climate data prior to that are derived from proxy materials (e.g., tree-ring width, ice core isotopes, etc.) which are either in annual or decadal scale. However, there are many historical documents which contain information about weather such as personal diaries. In Japan, around 20 diaries in average during the 16th - 19th centuries have been collected and converted into a digitized form. As such, diary data exist in many other countries. This study aims to reconstruct historical daily weather during the 18th and 19th centuries using personal daily diaries which have analogue weather descriptions such as `cloudy' or `sunny'. A recent study has shown the possibility of assimilating coarse weather data using idealized experiments. We further extend this study by assimilating modern weather descriptions similar to diary data in recent periods. The Global Spectral model (GSM) of National Centers for Environmental Prediction (NCEP) is used to reconstruct weather with the Local Ensemble Kalman filter (LETKF). Descriptive data are first converted to model variables such as total cloud cover (TCC), solar radiation and precipitation using empirical relationships. Those variables are then assimilated on a daily basis after adding random errors to consider the uncertainty of actual diary data. The assimilation of downward short wave solar radiation using weather descriptions improves RMSE from 64.3 w/m2 to 33.0 w/m2 and correlation coefficient (R) from 0.5 to 0.8 compared with the case without any

  3. Development of a Disaggregation Framework toward the Estimation of Subdaily Reference Evapotranspiration: 1- Performance Comparison of some Daily-to-subdaily Weather Data Disaggregation Models

    Directory of Open Access Journals (Sweden)

    Farzin Parchami-Araghi

    2017-02-01

    Full Text Available Introduction: In order to provide more realistic representation of processes governing the water and energy balances as well as water quality and plant physiological processes, weather data are needed at finer timescales than currently are available at most regions. In this study, a physically based framework was developed to disaggregate daily weather data needed for estimation of subdaily reference evapotranspiration, including air temperature, wind speed, dew point, actual vapour pressure, relative humidity, and solar radiation. In this paper, the results of performance comparison of the utilized disaggregation approaches are presented. Materials and Methods: In developed framework, missing daily weather data are filled by implementation of a search-optimization algorithm. Meanwhile, disaggregation models can be calibrated using Unified Particle Swarm Optimization (UPSO algorithm. Daily and subdaily solar radiation is estimated, using a general physically based model proposed by Yang et al. (YNG model. Long-term daily and three-hourly weather data obtained from Abadan (59 years and Ahvaz (50 years synoptic weather stations were used to evaluate the performance of the developed framework. In order to evaluate the accuracy of the different disaggregation models, the mean error (ME, mean absolute error (MAE, root mean square error (RMSE, and Pearson correlation coefficient (r, and model efficiency coefficient (EF statistics were calculated. Different contributions to the overall mean square error was decomposed, using a regression-based method. Results and Discussion: The results indicated that compared to the WAVE I, WAVE II, WCALC, ERBS, and ESRA models, the calibrated TM model had the best performance to disaggregate daily air temperature with a EF of 0.9775 to 0.9924. Compared to air temperature disaggregation models with an arbitrary value for the time of maximum and minimum air temperature, the models in which the above mentioned times are

  4. Climatology of the Iberia coastal low-level wind jet: weather research forecasting model high-resolution results

    Directory of Open Access Journals (Sweden)

    Pedro M. M. Soares

    2013-01-01

    Full Text Available Coastal low-level jets (CLLJ are a low-tropospheric wind feature driven by the pressure gradient produced by a sharp contrast between high temperatures over land and lower temperatures over the sea. This contrast between the cold ocean and the warm land in the summer is intensified by the impact of the coastal parallel winds on the ocean generating upwelling currents, sharpening the temperature gradient close to the coast and giving rise to strong baroclinic structures at the coast. During summertime, the Iberian Peninsula is often under the effect of the Azores High and of a thermal low pressure system inland, leading to a seasonal wind, in the west coast, called the Nortada (northerly wind. This study presents a regional climatology of the CLLJ off the west coast of the Iberian Peninsula, based on a 9 km resolution downscaling dataset, produced using the Weather Research and Forecasting (WRF mesoscale model, forced by 19 years of ERA-Interim reanalysis (1989–2007. The simulation results show that the jet hourly frequency of occurrence in the summer is above 30% and decreases to about 10% during spring and autumn. The monthly frequencies of occurrence can reach higher values, around 40% in summer months, and reveal large inter-annual variability in all three seasons. In the summer, at a daily base, the CLLJ is present in almost 70% of the days. The CLLJ wind direction is mostly from north-northeasterly and occurs more persistently in three areas where the interaction of the jet flow with local capes and headlands is more pronounced. The coastal jets in this area occur at heights between 300 and 400 m, and its speed has a mean around 15 m/s, reaching maximum speeds of 25 m/s.

  5. Future projections of synoptic weather types over the Arabian Peninsula during the twenty-first century using an ensemble of CMIP5 models

    Science.gov (United States)

    El Kenawy, Ahmed M.; McCabe, Matthew F.

    2017-10-01

    An assessment of future change in synoptic conditions over the Arabian Peninsula throughout the twenty-first century was performed using 20 climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) database. We employed the mean sea level pressure (SLP) data from model output together with NCEP/NCAR reanalysis data and compared the relevant circulation types produced by the Lamb classification scheme for the base period 1975-2000. Overall, model results illustrated good agreement with the reanalysis, albeit with a tendency to underestimate cyclonic (C) and southeasterly (SE) patterns and to overestimate anticyclones and directional flows. We also investigated future projections for each circulation-type during the rainy season (December-May) using three Representative Concentration Pathways (RCPs), comprising RCP2.6, RCP4.5, and RCP8.5. Overall, two scenarios (RCP4.5 and RCP 8.5) revealed a statistically significant increase in weather types favoring above normal rainfall in the region (e.g., C and E-types). In contrast, weather types associated with lower amounts of rainfall (e.g., anticyclones) are projected to decrease in winter but increase in spring. For all scenarios, there was consistent agreement on the sign of change (i.e., positive/negative) for the most frequent patterns (e.g., C, SE, E and A-types), whereas the sign was uncertain for less recurrent types (e.g., N, NW, SE, and W). The projected changes in weather type frequencies in the region can be viewed not only as indicators of change in rainfall response but may also be used to inform impact studies pertinent to water resource planning and management, extreme weather analysis, and agricultural production.

  6. Future projections of synoptic weather types over the Arabian Peninsula during the twenty-first century using an ensemble of CMIP5 models

    KAUST Repository

    El Kenawy, Ahmed M.

    2016-07-28

    An assessment of future change in synoptic conditions over the Arabian Peninsula throughout the twenty-first century was performed using 20 climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) database. We employed the mean sea level pressure (SLP) data from model output together with NCEP/NCAR reanalysis data and compared the relevant circulation types produced by the Lamb classification scheme for the base period 1975–2000. Overall, model results illustrated good agreement with the reanalysis, albeit with a tendency to underestimate cyclonic (C) and southeasterly (SE) patterns and to overestimate anticyclones and directional flows. We also investigated future projections for each circulation-type during the rainy season (December–May) using three Representative Concentration Pathways (RCPs), comprising RCP2.6, RCP4.5, and RCP8.5. Overall, two scenarios (RCP4.5 and RCP 8.5) revealed a statistically significant increase in weather types favoring above normal rainfall in the region (e.g., C and E-types). In contrast, weather types associated with lower amounts of rainfall (e.g., anticyclones) are projected to decrease in winter but increase in spring. For all scenarios, there was consistent agreement on the sign of change (i.e., positive/negative) for the most frequent patterns (e.g., C, SE, E and A-types), whereas the sign was uncertain for less recurrent types (e.g., N, NW, SE, and W). The projected changes in weather type frequencies in the region can be viewed not only as indicators of change in rainfall response but may also be used to inform impact studies pertinent to water resource planning and management, extreme weather analysis, and agricultural production.

  7. Implementation of new sub-grid runoff parameterization within the Weather Research and Forecasting (WRF) modeling system

    Science.gov (United States)

    Khodamorad poor, M.; Irannejad, P.

    2012-04-01

    Runoff is an important component of the water cycle in land surface parameterization schemes, whose estimation is very difficult because of its dependence on rainfall, soil moisture, and topography, which vary temporally and spatially. In this study, two different methods of sub-grid parameterization of runoff are tested within the WRF numerical weather forecast model. The land surface scheme originally used in WRF is NOAH, in which runoff is parameterized based on the probably distributed function (PDF) of soil infiltration capacity. The river discharge calculated from WRF-NOAH simulated runoff and routed using total runoff integrating pathways (TRIP) model for three sub-basins of Karoon River, in the southwestern Iran, including Soosan, Harmaleh and Farseat is compared with observations for the winter 2006. WRF-NOAH extremely underestimates the discharge in the Karoon River basin, probably because of uncertainties in the runoff parameterization, which is in turn due to unavailability of soil infiltration data needed to estimate the shape and parameters of the PDF of the infiltration capacity. For this reason, we modified NOAH (NOAH-SIM) by substituting the infiltration capacity dependent runoff parameterization with a parameterization based on the PDF of the topographic index, following the philosophy used in the simplified TOPMODEL. As the topographic index is scale dependent, high resolution of topographic indices (10 m) are derived from digital elevation data model in low resolution (1000 m) by using a downscaling method. Evaluation of stimulated discharge by the two land surface schemes (NOAH-SIM, NOAH) coupled in WRF, with observed discharge proves improved runoff simulation by NOAH-SIM in all the three sub-basins. Compared to NOAH, NOAH-SIM simulated discharge has lower bias, smaller mean absolute error, higher efficiency coefficient, and a standard deviation closer to that observed. Coupling NOAH-SIM with WRF not only improves runoff simulations, but also

  8. What is the benefit of driving a hydrological model with data from a multi-site weather generator compared to data from a simple delta change approach?"

    Science.gov (United States)

    Rössler, Ole; Keller, Denise; Fischer, Andreas

    2016-04-01

    . Recently, a multi-site weather generator was developed and tested for downscaling purposes over Switzerland. The weather generator is of type Richardson, that is run with spatially correlated random number streams to ensure spatial consistency. As a downside, multi-site weather generators are much more complex to develop, but they are a very promising alternative downscaling technique. A new multi-site-weather generator was developed for Switzerland in a previous study (Keller et al. 2014). In this study, we tested this new multi-site-weather generator against the "standard" delta change derived data in a hydrological impact assessment study that focused on runoff in the meso-scale catchment of the river Thur catchment. Two hydrological models of different complexity were run with the data sets under present (1980-2009) and under future conditions (2070-2099), assuming the SRES A1B emission2070-2100 scenario conditions. Eight meteorological stations were used to interpolate a meteorological field that served as input to calibrate and validate the two hydrological models against runoff. The downscaling intercomparison was done for We applied 10 GCM-RCM combinations simulations of the ENSEMBLES. In case of the weather generator, that allows for multiple synthetic realizations, we generated for which change factors for each station (delta change approach) were available and generated 25 realizations of multi-site weather. with each climate model projection. Results show that the delta change driven data constitutes only one appropriate representation compared to theof a bandwidth of runoff projections yielded by the multi-site weather generator data. Especially oOn average, differences between both the two approaches are small. Low and high runoff Runoff values to both extremes are however better reproduced with the weather generator driven data set. The stochastic representation of multiday rainfall events are considered as the main reason. Hence, tThere is a clear yet small

  9. Rainfall extremes, weather and climatic characterization over complex terrain: A data-driven approach based on signal enhancement methods and extreme value modeling

    Science.gov (United States)

    Pineda, Luis E.; Willems, Patrick

    2017-04-01

    Weather and climatic characterization of rainfall extremes is both of scientific and societal value for hydrometeorogical risk management, yet discrimination of local and large-scale forcing remains challenging in data-scarce and complex terrain environments. Here, we present an analysis framework that separate weather (seasonal) regimes and climate (inter-annual) influences using data-driven process identification. The approach is based on signal-to-noise separation methods and extreme value (EV) modeling of multisite rainfall extremes. The EV models use a semi-automatic parameter learning [1] for model identification across temporal scales. At weather scale, the EV models are combined with a state-based hidden Markov model [2] to represent the spatio-temporal structure of rainfall as persistent weather states. At climatic scale, the EV models are used to decode the drivers leading to the shift of weather patterns. The decoding is performed into a climate-to-weather signal subspace, built via dimension reduction of climate model proxies (e.g. sea surface temperature and atmospheric circulation) We apply the framework to the Western Andean Ridge (WAR) in Ecuador and Peru (0-6°S) using ground data from the second half of the 20th century. We find that the meridional component of winds is what matters for the in-year and inter-annual variability of high rainfall intensities alongside the northern WAR (0-2.5°S). There, low-level southerly winds are found as advection drivers for oceanic moist of the normal-rainy season and weak/moderate the El Niño (EN) type; but, the strong EN type and its unique moisture surplus is locally advected at lowlands in the central WAR. Moreover, the coastal ridges, south of 3°S dampen meridional airflows, leaving local hygrothermal gradients to control the in-year distribution of rainfall extremes and their anomalies. Overall, we show that the framework, which does not make any prior assumption on the explanatory power of the weather

  10. Coupling the Weather Research and Forecasting (WRF) model and Large Eddy Simulations with Actuator Disk Model: predictions of wind farm power production

    Science.gov (United States)

    Garcia Cartagena, Edgardo Javier; Santoni, Christian; Ciri, Umberto; Iungo, Giacomo Valerio; Leonardi, Stefano

    2015-11-01

    A large-scale wind farm operating under realistic atmospheric conditions is studied by coupling a meso-scale and micro-scale models. For this purpose, the Weather Research and Forecasting model (WRF) is coupled with an in-house LES solver for wind farms. The code is based on a finite difference scheme, with a Runge-Kutta, fractional step and the Actuator Disk Model. The WRF model has been configured using seven one-way nested domains where the child domain has a mesh size one third of its parent domain. A horizontal resolution of 70 m is used in the innermost domain. A section from the smallest and finest nested domain, 7.5 diameters upwind of the wind farm is used as inlet boundary condition for the LES code. The wind farm consists in six-turbines aligned with the mean wind direction and streamwise spacing of 10 rotor diameters, (D), and 2.75D in the spanwise direction. Three simulations were performed by varying the velocity fluctuations at the inlet: random perturbations, precursor simulation, and recycling perturbation method. Results are compared with a simulation on the same wind farm with an ideal uniform wind speed to assess the importance of the time varying incoming wind velocity. Numerical simulations were performed at TACC (Grant CTS070066). This work was supported by NSF, (Grant IIA-1243482 WINDINSPIRE).

  11. An integrated modeling framework for real-time irrigation scheduling: the benefit of spectroscopy and weather forecasts

    Science.gov (United States)

    Brook, Anna; Polinova, Maria; Housh, Mashor

    2016-04-01

    Agriculture and agricultural landscapes are increasingly under pressure to meet the demands of a constantly increasing human population and globally changing food patterns. At the same time, there is rising concern that climate change and food security will harm agriculture in many regions of the world (Nelson et al., 2009). Facing those treats, majority of Mediterranean countries had chosen irrigated agriculture. For crop plants water is one of the most important inputs, as it is responsible for crop growth, production and it ensures the efficiency of other inputs (e.g. seeds, fertilizers and pesticide) but its use is in competition with other local sectors (e.g. industry, urban human use). Thus, well-timed availability of water is vital to agriculture for ensured yields. The increasing demand for irrigation has necessitated the need for optimal irrigation scheduling techniques that coordinate the timing and amount of irrigation to optimally manage the water use in agriculture systems. The irrigation scheduling problem can be challenging as farmers try to deal with different conflicting objectives of maximizing their yield while minimizing irrigation water use. Another challenge in the irrigation scheduling problem is attributed to the uncertain factors involved in the plant growth process during the growing season. Most notable, the climatic factors such as evapotranspiration and rainfall, these uncertain factors add a third objective to the farmer perspective, namely, minimizing the risk associated with these uncertain factors. Nevertheless, advancements in weather forecasting reduced the uncertainty level associated with future climatic data. Thus, climatic forecasts can be reliably employed to guide optimal irrigation schedule scheme when coupled with stochastic optimization models (Housh et al., 2012). Many studies have concluded that optimal irrigation decisions can provide substantial economic value over conventional irrigation decisions (Wang and Cai 2009

  12. Tropical and Subtropical Cloud Transitions in Weather and Climate Prediction Models: The GCSS/WGNE Pacific Cross-Section Intercomparison (GPCI)

    Science.gov (United States)

    Teixeira, J.; Cardoso, S.; Bonazzola, M.; Cole, J.; DeGenio, A.; DeMott, C.; Franklin, C.; Hannay, C.; Jakob, C.; Jiao, Y.; hide

    2011-01-01

    A model evaluation approach is proposed in which weather and climate prediction models are analyzed along a Pacific Ocean cross section, from the stratocumulus regions off the coast of California, across the shallow convection dominated trade winds, to the deep convection regions of the ITCZ the Global Energy and Water Cycle Experiment Cloud System Study/Working Group on Numerical Experimentation (GCSS/ WGNE) Pacific Cross-Section Intercomparison (GPCI). The main goal of GPCI is to evaluate and help understand and improve the representation of tropical and subtropical cloud processes in weather and climate prediction models. In this paper, a detailed analysis of cloud regime transitions along the cross section from the subtropics to the tropics for the season June July August of 1998 is presented. This GPCI study confirms many of the typical weather and climate prediction model problems in the representation of clouds: underestimation of clouds in the stratocumulus regime by most models with the corresponding consequences in terms of shortwave radiation biases; overestimation of clouds by the 40-yr ECMWF Re-Analysis (ERA-40) in the deep tropics (in particular) with the corresponding impact in the outgoing longwave radiation; large spread between the different models in terms of cloud cover, liquid water path and shortwave radiation; significant differences between the models in terms of vertical cross sections of cloud properties (in particular), vertical velocity, and relative humidity. An alternative analysis of cloud cover mean statistics is proposed where sharp gradients in cloud cover along the GPCI transect are taken into account. This analysis shows that the negative cloud bias of some models and ERA-40 in the stratocumulus regions [as compared to the first International Satellite Cloud Climatology Project (ISCCP)] is associated not only with lower values of cloud cover in these regimes, but also with a stratocumulus-to-cumulus transition that occurs too

  13. Sensitivities of crop models to extreme weather conditions during flowering period demonstrated for maize and winter wheat in Austria

    DEFF Research Database (Denmark)

    Eitzinger, J; Thaler, S; Schmid, E

    2013-01-01

    the start of flowering. Two locations in Austria, representing different agro-climatic zones and soil conditions, were included in the simulations over 2 years, 2003 and 2004, exhibiting contrasting weather conditions. In addition, soil management was modified at both sites by following either ploughing...

  14. Management Strategies to Sustain Irrigated Agriculture with Combination of Remote Sensing, Weather Monitoring & Forecasting and SWAP Modeling

    Science.gov (United States)

    Ermolaeva, Olga; Zeyliger, Anatoly

    2017-04-01

    Today world's water systems face formidable threats due to climate change and increasing water withdraw for agriculture, industry and domestic use. Projected in many parts of the earth increases in temperature, evaporation, and drought frequency shrunk water availability and magnify water scarcity. Declining irrigation water supplies threaten the sustainability of irrigated agricultural production which plays a critical role in meeting global food needs. In irrigated agriculture there is a strong call for deep efforts in order on the one hand to improve water efficiency use and on the other to maximize yields. The aim of this research is to provide tool to optimize water application with crop irrigation by sprinkling in order to sustain irrigated agriculture under limited water supply by increasing net returns per unit of water. For this aim some field experimental results of 2012 year growing season of alfalfa, corn and soya irrigated by sprinkling machines crops at left bank of Volga River at Saratov Region of Russia. Additionally a combination of data sets was used which includes MODIS images, local meteorological station and results of SWAP (Soil-Water-Atmosphere-Plant) modeling. This combination was used to estimate crop water stress defined as ratio between actual (ETa) and potential (ETc) evapotranspiration. By this way it was determined the effect of applied irrigation scheduling and water application depths on evapotranspiration, crop productivity and water stress coefficient. Aggregation of actual values of crop water stress and biomass data predicted by SWAP agrohydrological model with weather forecasting and irrigation scheduling was used to indicate of both rational timing and amount of irrigation water allocation. This type of analysis facilitating an efficient water management can be extended to irrigated areas by developing maps of water efficiency application serving as an irrigation advice system for farmers at his fields and as a decision support

  15. Preliminary Results of a U.S. Deep South Modeling Experiment Using NASA SPoRT Initialization Datasets for Operational National Weather Service Local Model Runs

    Science.gov (United States)

    Wood, Lance; Medlin, Jeffrey M.; Case, Jon

    2012-01-01

    A joint collaborative modeling effort among the NWS offices in Mobile, AL, and Houston, TX, and NASA Short-term Prediction Research and Transition (SPoRT) Center began during the 2011-2012 cold season, and continued into the 2012 warm season. The focus was on two frequent U.S. Deep South forecast challenges: the initiation of deep convection during the warm season; and heavy precipitation during the cold season. We wanted to examine the impact of certain NASA produced products on the Weather Research and Forecasting Environmental Modeling System in improving the model representation of mesoscale boundaries such as the local sea-, bay- and land-breezes (which often leads to warm season convective initiation); and improving the model representation of slow moving, or quasi-stationary frontal boundaries (which focus cold season storm cell training and heavy precipitation). The NASA products were: the 4-km Land Information System, a 1-km sea surface temperature analysis, and a 4-km greenness vegetation fraction analysis. Similar domains were established over the southeast Texas and Alabama coastlines, each with an outer grid with a 9 km spacing and an inner nest with a 3 km grid spacing. The model was run at each NWS office once per day out to 24 hours from 0600 UTC, using the NCEP Global Forecast System for initial and boundary conditions. Control runs without the NASA products were made at the NASA SPoRT Center. The NCAR Model Evaluation Tools verification package was used to evaluate both the positive and negative impacts of the NASA products on the model forecasts. Select case studies will be presented to highlight the influence of the products.

  16. Using Haines Index coupled with fire weather model predicted from high resolution LAM forecasts to asses wildfire extreme behaviour in Southern Europe.

    Science.gov (United States)

    Gaetani, Francesco; Baptiste Filippi, Jean; Simeoni, Albert; D'Andrea, Mirko

    2010-05-01

    Haines Index (HI) was developed by USDA Forest Service to measure the atmosphere's contribution to the growth potential of a wildfire. The Haines Index combines two atmospheric factors that are known to have an effect on wildfires: Stability and Dryness. As operational tools, HI proved its ability to predict plume dominated high intensity wildfires. However, since HI does not take into account the fuel continuity, composition and moisture conditions and the effects of wind and topography on fire behaviour, its use as forecasting tool should be carefully considered. In this work we propose the use of HI, predicted from HR Limited Area Model forecasts, coupled with a Fire Weather model (i.e., RISICO system) fully operational in Italy since 2003. RISICO is based on dynamic models able to represent in space and in time the effects that environment and vegetal physiology have on fuels and, in turn, on the potential behaviour of wildfires. The system automatically acquires from remote databases a thorough data-set of input information both of in situ and spatial nature. Meteorological observations, radar data, Limited Area Model weather forecasts, EO data, and fuel data are managed by a Unified Interface able to process a wide set of different data. Specific semi-physical models are used in the system to simulate the dynamics of the fuels (load and moisture contents of dead and live fuel) and the potential fire behaviour (rate of spread and linear intensity). A preliminary validation of this approach will be provided with reference to Sardinia and Corsica Islands, two major islands of the Mediterranean See frequently affected by extreme plume dominated wildfires. A time series of about 3000 wildfires burnt in Sardinia and Corsica in 2007 and 2008 will be used to evaluate the capability of HI coupled with the outputs of the Fire Weather model to forecast the actual risk in time and in space.

  17. Modelling of 10 Gbps Free Space Optics Communication Link Using Array of Receivers in Moderate and Harsh Weather Conditions

    Science.gov (United States)

    Gupta, Amit; Shaina, Nagpal

    2017-08-01

    Intersymbol interference and attenuation of signal are two major parameters affecting the quality of transmission in Free Space Optical (FSO) Communication link. In this paper, the impact of these parameters on FSO communication link is analysed for delivering high-quality data transmission. The performance of the link is investigated under the influence of amplifier in the link. The performance parameters of the link like minimum bit error rate, received signal power and Quality factor are examined by employing erbium-doped fibre amplifier in the link. The effects of amplifier are visualized with the amount of received power. Further, the link is simulated for moderate weather conditions at various attenuation levels on transmitted signal. Finally, the designed link is analysed in adverse weather conditions by using high-power laser source for optimum performance.

  18. Sensitivity of Numerical Weather Prediction to the Choice of Variable for Atmospheric Moisture Analysis into the Brazilian Global Model Data Assimilation System

    Directory of Open Access Journals (Sweden)

    Thamiris B. Campos

    2018-03-01

    Full Text Available Due to the high spatial and temporal variability of atmospheric water vapor associated with the deficient methodologies used in its quantification and the imperfect physics parameterizations incorporated in the models, there are significant uncertainties in characterizing the moisture field. The process responsible for incorporating the information provided by observation into the numerical weather prediction is denominated data assimilation. The best result in atmospheric moisture depend on the correct choice of the moisture control variable. Normalized relative humidity and pseudo-relative humidity are the variables usually used by the main weather prediction centers. The objective of this study is to assess the sensibility of the Center for Weather Forecast and Climate Studies to choose moisture control variable in the data assimilation scheme. Experiments using these variables are carried out. The results show that the pseudo-relative humidity improves the variables that depend on temperature values but damage the moisture field. The opposite results show when the simulation used the normalized relative humidity. These experiments suggest that the pseudo-relative humidity should be used in the cyclical process of data assimilation and the normalized relative humidity should be used in non-cyclic process (e.g., nowcasting application in high resolution.

  19. Shaken and Stirred: A Combined Reaction-Diffusion and Random Rate Model for the Temporal Evolution and Earthquake-induced Hydrodynamics of Silicate Mineral Weathering

    Science.gov (United States)

    Evaristo, J. A.; Willenbring, J.

    2013-12-01

    The time dependency of silicate mineral weathering has been explored in the literature in terms of processes and features that are intrinsic and extrinsic to the mineral [1]. However, although the advent of sophisticated reactive transport models has allowed for coupling increasingly complex reaction and transport processes [2,3], a simple and fundamental understanding of the temporal evolution of weathering is lacking. Here, we propose that a purely deterministic approach may not be sufficient given the inherent differences in reactivity over space and time. Therefore, we explore how a combined reaction-diffusion and random rate model - informed by a stochastic distribution of weathering rates K (T-1) - might be able to explain not only the temporal evolution but also the hydrodynamics of weathering during earthquakes; the latter being purportedly described by time-dependent property permeability (L2). Preliminary model results show that (1) an increase in dimensionless quantity βrp, where β is the diffusion length (L-1) and rp is the distance between pores (L), leads to a decrease in minimum reaction rate with time from the relation Kmin ∝ e-βrp/rp ; (2) at a given porosity, a time-dependent decrease in reactivity arises as permeability decreases due to decreasing pore size (and therefore increasing rp), which in turn may be related to the time-dependent feedback between dissolution and precipitation; (3) while permeability is lower in older soils, transient stresses as during earthquakes [4], may induce more efficient "declogging" of pores in these soils than in younger soils due to higher hydrodynamic viscous shear stress, thereby, resulting in a coseismic change in stream discharge Q; and (4) subsequent weathering beyond t~Kmin-1 exhibits a fall in rates, marking the cessation of logarithmic decay possibly due to dissolution-precipitation feedback. [1] White and Brantley (2003), Chem. Geol. 202, 479. [2] Lichtner P.C. (1996), Mineralogical Society of

  20. Marine traffic model based on cellular automaton: Considering the change of the ship's velocity under the influence of the weather and sea

    Science.gov (United States)

    Qi, Le; Zheng, Zhongyi; Gang, Longhui

    2017-10-01

    It was found that the ships' velocity change, which is impacted by the weather and sea, e.g., wind, sea wave, sea current, tide, etc., is significant and must be considered in the marine traffic model. Therefore, a new marine traffic model based on cellular automaton (CA) was proposed in this paper. The characteristics of the ship's velocity change are taken into account in the model. First, the acceleration of a ship was divided into two components: regular component and random component. Second, the mathematical functions and statistical distribution parameters of the two components were confirmed by spectral analysis, curve fitting and auto-correlation analysis methods. Third, by combining the two components, the acceleration was regenerated in the update rules for ships' movement. To test the performance of the model, the ship traffic flows in the Dover Strait, the Changshan Channel and the Qiongzhou Strait were studied and simulated. The results show that the characteristics of ships' velocities in the simulations are consistent with the measured data by Automatic Identification System (AIS). Although the characteristics of the traffic flow in different areas are different, the velocities of ships can be simulated correctly. It proves that the velocities of ships under the influence of weather and sea can be simulated successfully using the proposed model.

  1. Satellite-based albedo, sea surface temperature and effective land roughness maps used in the HIRLAM model for weather and climate scenarios

    Science.gov (United States)

    Hasager, C. B.; Nielsen, N. W.; Christensen, J. H.; Soegaard, H.; Boegh, E.; Rasmussen, M. S.; Jensen, N. O.

    2001-12-01

    A study is conducted on the effect of introducing maps of geophysical parameters retrieved from satellite Earth Observation data into the atmospheric model HIRLAM (HIgh Resolution Limited Area Model). . The HIRLAM system was developed by the HIRLAM project group, a cooperative project of the national weather services in Denmark, Finland, Iceland, Ireland, the Netherlands, Norway and Sweden. It is currently used by weather services in several European countries. The exchanges of sensible heat, water vapour and momentum between the land- and ocean surface and the atmosphere are very important dynamical processes in this type of model. The results from the HIRLAM model when using the improved surface boundary conditions is validated from wind and temperature data at synoptic weather stations and surface flux data from land- and ocean meteorological masts in Denmark. The results from a set of scenarios covering the hurricane in Denmark in December 1999 and several springtime cases in 2000 show improved weather forecasts. The methodology on retrieving improved boundary conditions is based on satellite image data. Maps on the geophysical parameters albedo and sea surface temperature are retrieved at a 1 km spatial resolution from NOAA AVHRR. Furthermore, land cover maps based on Landsat TM satellite data are used to assess the regional roughness. The high-resolution land roughness map (Areal Systems Information in a 25 m pixel resolution) is area-averaged into effective roughness values (15 km grid) by using a non-linear aggregation technique (QJRMS 1999, vol 125, 2075-2102). The area-averaging is highly non-linear due to the turbulent physical processes involved. Thus the effective surface conditions cannot be obtained by simple averaging but only by a flow model taking horizontal advection into consideration. The effect of hedges in the landscape is included as a correction index based on a vector-based map. The land surface fluxes of heat and water vapour is also

  2. Characterization of weathering profile in granites and ...

    Indian Academy of Sciences (India)

    Therefore, we propose three models describing the weathering profiles in granites, metasediments, and volcanic rocks for hard rock formations located in West Africa. For each of these models proposed for granitic and volcano sedimentary rocks of the Dimbokro catchment, vertical layered weathering profiles are described, ...

  3. Adverse Weather Evokes Nostalgia.

    Science.gov (United States)

    van Tilburg, Wijnand A P; Sedikides, Constantine; Wildschut, Tim

    2018-03-01

    Four studies examined the link between adverse weather and the palliative role of nostalgia. We proposed and tested that (a) adverse weather evokes nostalgia (Hypothesis 1); (b) adverse weather causes distress, which predicts elevated nostalgia (Hypothesis 2); (c) preventing nostalgia exacerbates weather-induced distress (Hypothesis 3); and (d) weather-evoked nostalgia confers psychological benefits (Hypothesis 4). In Study 1, participants listened to recordings of wind, thunder, rain, and neutral sounds. Adverse weather evoked nostalgia. In Study 2, participants kept a 10-day diary recording weather conditions, distress, and nostalgia. We also obtained meteorological data. Adverse weather perceptions were positively correlated with distress, which predicted higher nostalgia. Also, adverse natural weather was associated with corresponding weather perceptions, which predicted elevated nostalgia. (Results were mixed for rain.) In Study 3, preventing nostalgia (via cognitive load) increased weather-evoked distress. In Study 4, weather-evoked nostalgia was positively associated with psychological benefits. The findings pioneer the relevance of nostalgia as source of comfort in adverse weather.

  4. WEATHER INDEX- THE BASIS OF WEATHER DERIVATIVES

    Directory of Open Access Journals (Sweden)

    Botos Horia Mircea

    2011-07-01

    Full Text Available This paper approaches the subject of Weather Derivatives, more exactly their basic element the weather index. The weather index has two forms, the Heating Degree Day (HDD and the Cooling Degree Day (CDD. We will try to explain their origin, use and the relationship between the two forms of the index. In our research we started from the analysis of the weather derivatives and what they are based on. After finding out about weather index, we were interested in understanding exactly how they work and how they influence the value of the contract. On the national level the research in the field is scares, but foreign materials available. The study for this paper was based firstly on reading about Weather Derivative, and then going in the meteorogical field and determining the way by which the indices were determined. After this, we went to the field with interest in the indices, such as the energy and gas industries, and figured out how they determined the weather index. For the examples we obtained data from the weather index database, and calculated the value for the period. The study is made on a period of five years, in 8 cities of the European Union. The result of this research is that we can now understand better the importance of the way the indices work and how they influence the value of the Weather Derivatives. This research has an implication on the field of insurance, because of the fact that weather derivative are at the convergence point of the stock markets and the insurance market. The originality of the paper comes from the personal touch given to the theoretical aspect and through the analysis of the HDD and CDD index in order to show their general behaviour and relationship.

  5. Weather Radar Stations

    Data.gov (United States)

    Department of Homeland Security — These data represent Next-Generation Radar (NEXRAD) and Terminal Doppler Weather Radar (TDWR) weather radar stations within the US. The NEXRAD radar stations are...

  6. Winter Weather Emergencies

    Science.gov (United States)

    Severe winter weather can lead to health and safety challenges. You may have to cope with Cold related health ... Although there are no guarantees of safety during winter weather emergencies, you can take actions to protect ...

  7. National Convective Weather Diagnostic

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Current convective hazards identified by the National Convective Weather Detection algorithm. The National Convective Weather Diagnostic (NCWD) is an automatically...

  8. Mariners Weather Log

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Mariners Weather Log (MWL) is a publication containing articles, news and information about marine weather events and phenomena, worldwide environmental impact...

  9. Radar Weather Observation

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Radar Weather Observation is a set of archived historical manuscripts stored on microfiche. The primary source of these radar weather observations manuscript records...

  10. Natural Weathering Exposure Station

    Data.gov (United States)

    Federal Laboratory Consortium — The Corps of Engineers' Treat Island Natural Weathering Exposure Station is a long-term natural weathering facility used to study concrete durability. Located on the...

  11. Surface Weather Observations

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Surface Weather Observation Collection consists primarily of hourly, synoptic, daily, and monthly forms submitted to the archive by the National Weather Service...

  12. Land Surface Weather Observations

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — METAR is the international standard code format for hourly surface weather observations. The acronym roughly translates from French as Aviation Routine Weather...

  13. Pilot Weather Reports

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Aviation weather reports relayed from pilots to FAA air traffic controllers or National Weather Service personnel. Elements include sky cover, turbulence, wind...

  14. Daily Weather Records

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — These daily weather records were compiled from a subset of stations in the Global Historical Climatological Network (GHCN)-Daily dataset. A weather record is...

  15. Surface Weather Observing Manuals

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Manuals and instructions for taking weather observations. Includes the annual Weather Bureau 'Instructions for Preparing Meteorological Forms...' and early airways...

  16. Surface Weather Observations Hourly

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Standard hourly observations taken at Weather Bureau/National Weather Service offices and airports throughout the United States. Hourly observations began during the...

  17. Internet Weather Source

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Weather Service (NWS) National Telecommunications Gateway provides weather, hydrologic, and climate forecasts and warnings for the United States, its...

  18. Space Weather in Operation

    Data.gov (United States)

    National Aeronautics and Space Administration — The “Space Weather in Operations” effort will provide on-demand and near-real time space weather event information to the Data Access Toolkit (DAT), which is the...

  19. 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

  20. Gap-filling of dry weather flow rate and water quality measurements in urban catchments by a time series modelling approach

    DEFF Research Database (Denmark)

    Sandoval, Santiago; Vezzaro, Luca; Bertrand-Krajewski, Jean-Luc

    2016-01-01

    solids (TSS) online measurements (year 2007, 2 minutes time-step, combined system, Ecully, Lyon, France). Results show up the potential of the method to fill gaps longer than 0.5 days, especially between 0.5 days and 1 day (mean NSE > 0.6) in the flow rate time series. TSS results still perform very...... seeks to evaluate the potential of the Singular Spectrum Analysis (SSA), a time-series modelling/gap-filling method, to complete dry weather time series. The SSA method is tested by reconstructing 1000 artificial discontinuous time series, randomly generated from real flow rate and total suspended...

  1. Hands-on, online, and workshop-based K-12 weather and climate education resources from the Center for Multi-scale Modeling of Atmospheric Processes

    Science.gov (United States)

    Foster, S. Q.; Johnson, R. M.; Randall, D. A.; Denning, A.; Burt, M. A.; Gardiner, L.; Genyuk, J.; Hatheway, B.; Jones, B.; La Grave, M. L.; Russell, R. M.

    2009-12-01

    The need for improving the representation of cloud processes in climate models has been one of the most important limitations of the reliability of climate-change simulations. Now in its fourth year, the National Science Foundation-funded Center for Multi-scale Modeling of Atmospheric Processes (CMMAP) at Colorado State University (CSU) is addressing this problem through a revolutionary new approach to representing cloud processes on their native scales, including the cloud-scale interaction processes that are active in cloud systems. CMMAP has set ambitious education and human-resource goals to share basic information about the atmosphere, clouds, weather, climate, and modeling with diverse K-12 and public audiences. This is accomplished through collaborations in resource development and dissemination between CMMAP scientists, CSU’s Little Shop of Physics (LSOP) program, and the Windows to the Universe (W2U) program at University Corporation for Atmospheric Research (UCAR). Little Shop of Physics develops new hands on science activities demonstrating basic science concepts fundamental to understanding atmospheric characteristics, weather, and climate. Videos capture demonstrations of children completing these activities which are broadcast to school districts and public television programs. CMMAP and LSOP educators and scientists partner in teaching a summer professional development workshops for teachers at CSU with a semester's worth of college-level content on the basic physics of the atmosphere, weather, climate, climate modeling, and climate change, as well as dozens of LSOP inquiry-based activities suitable for use in classrooms. The W2U project complements these efforts by developing and broadly disseminating new CMMAP-related online content pages, animations, interactives, image galleries, scientists’ biographies, and LSOP videos to K-12 and public audiences. Reaching nearly 20 million users annually, W2U is highly valued as a curriculum enhancement

  2. Weather Fundamentals: Meteorology. [Videotape].

    Science.gov (United States)

    1998

    The videos in this educational series, for grades 4-7, help students understand the science behind weather phenomena through dramatic live-action footage, vivid animated graphics, detailed weather maps, and hands-on experiments. This episode (23 minutes) looks at how meteorologists gather and interpret current weather data collected from sources…

  3. Convective Weather Avoidance with Uncertain Weather Forecasts

    Science.gov (United States)

    Karahan, Sinan; Windhorst, Robert D.

    2009-01-01

    Convective weather events have a disruptive impact on air traffic both in terminal area and in en-route airspaces. In order to make sure that the national air transportation system is safe and efficient, it is essential to respond to convective weather events effectively. Traffic flow control initiatives in response to convective weather include ground delay, airborne delay, miles-in-trail restrictions as well as tactical and strategic rerouting. The rerouting initiatives can potentially increase traffic density and complexity in regions neighboring the convective weather activity. There is a need to perform rerouting in an intelligent and efficient way such that the disruptive effects of rerouting are minimized. An important area of research is to study the interaction of in-flight rerouting with traffic congestion or complexity and developing methods that quantitatively measure this interaction. Furthermore, it is necessary to find rerouting solutions that account for uncertainties in weather forecasts. These are important steps toward managing complexity during rerouting operations, and the paper is motivated by these research questions. An automated system is developed for rerouting air traffic in order to avoid convective weather regions during the 20- minute - 2-hour time horizon. Such a system is envisioned to work in concert with separation assurance (0 - 20-minute time horizon), and longer term air traffic management (2-hours and beyond) to provide a more comprehensive solution to complexity and safety management. In this study, weather is dynamic and uncertain; it is represented as regions of airspace that pilots are likely to avoid. Algorithms are implemented in an air traffic simulation environment to support the research study. The algorithms used are deterministic but periodically revise reroutes to account for weather forecast updates. In contrast to previous studies, in this study convective weather is represented as regions of airspace that pilots

  4. Beyond the Weather Chart: Weathering New Experiences.

    Science.gov (United States)

    Huffman, Amy Bruno

    1996-01-01

    Describes an early childhood educator's approach to teaching children about rain, rainbows, clouds, precipitation, the sun, air, and wind. Recommends ways to organize study topics and describes experiments that can help children better understand the different elements of weather. (MOK)

  5. Weatherization and Intergovernmental Program - Weatherization Assistance Program

    Energy Technology Data Exchange (ETDEWEB)

    None

    2010-06-01

    The U.S. Department of Energy’s (DOE) Weatherization Assistance Program reduces energy costs for low-income households by increasing the energy efficiency of their homes, while ensuring their health and safety.

  6. Solar weather monitoring

    Directory of Open Access Journals (Sweden)

    J.-F. Hochedez

    2005-11-01

    Full Text Available Space Weather nowcasting and forecasting require solar observations because geoeffective disturbances can arise from three types of solar phenomena: coronal mass ejections (CMEs, flares and coronal holes. For each, we discuss their definition and review their precursors in terms of remote sensing and in-situ observations. The objectives of Space Weather require some specific instrumental features, which we list using the experience gained from the daily operations of the Solar Influences Data analysis Centre (SIDC at the Royal Observatory of Belgium. Nowcasting requires real-time monitoring to assess quickly and reliably the severity of any potentially geoeffective solar event. Both research and forecasting could incorporate more observations in order to feed case studies and data assimilation respectively. Numerical models will result in better predictions of geomagnetic storms and solar energetic particle (SEP events. We review the data types available to monitor solar activity and interplanetary conditions. They come from space missions and ground observatories and range from sequences of dopplergrams, magnetograms, white-light, chromospheric, coronal, coronagraphic and radio images, to irradiance and in-situ time-series. Their role is summarized together with indications about current and future solar monitoring instruments.

  7. Radiation Storm vs. The Magnetic Shield: Superheroes of Magnetism & Space Weather Education - A Model for Teacher Professional Development Workshops

    Science.gov (United States)

    Russell, R. M.; Johnson, R. M.

    2010-12-01

    Magnetic and electric fields and phenomena play important roles in various situations in astronomy, planetary science, and Earth science. Students often lack an intuitive sense of electromagnetic phenomena, and therefore struggle with the complexities of planetary and stellar magnetic fields. Hands-on magnetism activities can provide students with an intuitive grasp of the basics of magnetism, preparing them for more challenging conceptual studies of magnetic phenomena. For the past six years, we have been presenting a professional development workshop for teachers covering the topics of magnetism and space weather. The workshop, which has been conducted more than 20 times for a range of audiences, blends together several simple hands-on activities, background information on space weather and geomagnetism, a collection of images, animations, and interactives that illustrate important concepts, and guidance about specific links between these topics and national science education standards. These workshops have been very well-received, and have consistently been rated highly by participants in surveys. We believe the methods used in these workshops can be applied to other topics in science education and to astronomy and Earth science education specifically. In this presentation, we will describe our magnetism and space weather workshop, including some of the hands-on activities. We will describe successful aspects of the workshop and comment on ways we think this approach could be replicated for other topics. We will also display some of the interactives, graphics, and animations shown during the workshops. Resources have been added to the workshop over the years in response to recurring questions from teachers; we will comment on this process and how it might be applied to other topics. The activities and extensive background content used or referenced in the workshop are available for free on the Windows to the Universe web site (www.windows2universe.org). Hands on

  8. Turbulence Dissipation Rates in the Planetary Boundary Layer from Wind Profiling Radars and Mesoscale Numerical Weather Prediction Models during WFIP2

    Science.gov (United States)

    Bianco, L.; McCaffrey, K.; Wilczak, J. M.; Olson, J. B.; Kenyon, J.

    2016-12-01

    When forecasting winds at a wind plant for energy production, the turbulence parameterizations in the forecast models are crucial for understanding wind plant performance. Recent research shows that the turbulence (eddy) dissipation rate in planetary boundary layer (PBL) parameterization schemes introduces significant uncertainty in the Weather Research and Forecasting (WRF) model. Thus, developing the capability to measure dissipation rates in the PBL will allow for identification of weaknesses in, and improvements to the parameterizations. During a preliminary field study at the Boulder Atmospheric Observatory in spring 2015, a 915-MHz wind profiling radar (WPR) measured dissipation rates concurrently with sonic anemometers mounted on a 300-meter tower. WPR set-up parameters (e.g., spectral resolution), post-processing techniques (e.g., filtering for non-atmospheric signals), and spectral averaging were optimized to capture the most accurate Doppler spectra for measuring spectral widths for use in the computation of the eddy dissipation rates. These encouraging results lead to the implementation of the observing strategy on a 915-MHz WPR in Wasco, OR, operating as part of the Wind Forecasting Improvement Project 2 (WFIP2). These observations are compared to dissipation rates calculated from the High-Resolution Rapid Refresh model, a WRF-based mesoscale numerical weather prediction model run for WFIP2 at 3000 m horizontal grid spacing and with a nest, which has 750-meter horizontal grid spacing, in the complex terrain region of the Columbia River Gorge. The observed profiles of dissipation rates are used to evaluate the PBL parameterization schemes used in the HRRR model, which are based on the modeled turbulent kinetic energy and a tunable length scale.

  9. Medium-range fire weather forecasts

    Science.gov (United States)

    J.O. Roads; K. Ueyoshi; S.C. Chen; J. Alpert; F. Fujioka

    1991-01-01

    The forecast skill of theNational Meteorological Center's medium range forecast (MRF) numerical forecasts of fire weather variables is assessed for the period June 1,1988 to May 31,1990. Near-surface virtual temperature, relative humidity, wind speed and a derived fire weather index (FWI) are forecast well by the MRF model. However, forecast relative humidity has...

  10. Appraisal of Weather Research and Forecasting Model Downscaling of Hydro-meteorological Variables and their Applicability for Discharge Prediction: Prognostic Approach for Ungauged Basin

    Science.gov (United States)

    Srivastava, P. K.; Han, D.; Rico-Ramirez, M. A.; Bray, M.; Islam, T.; Petropoulos, G.; Gupta, M.

    2015-12-01

    Hydro-meteorological variables such as Precipitation and Reference Evapotranspiration (ETo) are the most important variables for discharge prediction. However, it is not always possible to get access to them from ground based measurements, particularly in ungauged catchments. The mesoscale model WRF (Weather Research & Forecasting model) can be used for prediction of hydro-meteorological variables. However, hydro-meteorologists would like to know how well the downscaled global data products are as compared to ground based measurements and whether it is possible to use the downscaled data for ungauged catchments. Even with gauged catchments, most of the stations have only rain and flow gauges installed. Measurements of other weather hydro-meteorological variables such as solar radiation, wind speed, air temperature, and dew point are usually missing and thus complicate the problems. In this study, for downscaling the global datasets, the WRF model is setup over the Brue catchment with three nested domains (D1, D2 and D3) of horizontal grid spacing of 81 km, 27 km and 9 km are used. The hydro-meteorological variables are downscaled using the WRF model from the National Centers for Enviromental Prediction (NCEP) reanalysis datasets and subsequently used for the ETo estimation using the Penman Monteith equation. The analysis of weather variables and precipitation are compared against the ground based datasets, which indicate that the datasets are in agreement with the observed datasets for complete monitoring period as well as during the seasons except precipitation whose performance is poorer in comparison to the measured rainfall. After a comparison, the WRF estimated precipitation and ETo are then used as a input parameter in the Probability Distributed Model (PDM) for discharge prediction. The input data and model parameter sensitivity analysis and uncertainty estimation are also taken into account for the PDM calibration and prediction following the Generalised

  11. Parametrization of the Richardson weather generator within the European Union

    NARCIS (Netherlands)

    Voet, van der P.; Kramer, K.; Diepen, van C.A.

    1996-01-01

    The Richardson model for mathematically generating daily weather data was parametrized. Thirty years' time-series of the 355 main meteorological stations in the European Union formed the database. Model parameters were derived from both observed weather station data and interpolated weather data on

  12. Landslides as weathering reactors; links between physical erosion and weathering in rapidly eroding mountain belts

    Science.gov (United States)

    Emberson, R.; Hovius, N.; Galy, A.

    2014-12-01

    The link between physical erosion and chemical weathering is generally modelled with a surface-blanketing weathering zone, where the supply of fresh minerals is tied to the average rate of denudation. In very fast eroding environments, however, sediment production is dominated by landsliding, which acts in a stochastic fashion across the landscape, contrasting strongly with more uniform denudation models. If physical erosion is a driver of weathering at the highest erosion rates, then an alternative weathering model is required. Here we show that landslides can be effective 'weathering reactors'. Previous work modelling the effect of landslides on chemical weathering (Gabet 2007) considered the fresh bedrock surfaces exposed in landslide scars. However, fracturing during the landslide motion generates fresh surfaces, the total surface area of which exceeds that of the exposed scar by many orders of magnitude. Moreover, landslides introduce concavity into hillslopes, which acts to catch precipitation. This is funnelled into a deposit of highly fragmented rock mass with large reactive surface area and limited hydraulic conductivity (Lo et al. 2007). This allows percolating water reaction time for chemical weathering; any admixture of macerated organic debris could yield organic acid to further accelerate weathering. In the South island of New Zealand, seepage from recent landslide deposits has systematically high solute concentrations, far outstripping concentration in runoff from locations where soils are present. River total dissolved load in the western Southern Alps is highly correlated with the rate of recent (<35yrs) landsliding, suggesting that landslides are the dominant locus of weathering in this rapidly eroding landscape. A tight link between landsliding and weathering implies that localized weathering migrates through the landscape with physical erosion; this contrasts with persistent and ubiquitous weathering associated with soil production. Solute

  13. Space Weathering of Rocks

    Science.gov (United States)

    Noble, Sarah

    2011-01-01

    Space weathering discussions have generally centered around soils but exposed rocks will also incur the effects of weathering. On the Moon, rocks make up only a very small percentage of the exposed surface and areas where rocks are exposed, like central peaks, are often among the least space weathered regions we find in remote sensing data. However, our studies of weathered Ap 17 rocks 76015 and 76237 show that significant amounts of weathering products can build up on rock surfaces. Because rocks have much longer surface lifetimes than an individual soil grain, and thus record a longer history of exposure, we can study these products to gain a deeper perspective on the weathering process and better assess the relative impo!1ance of various weathering components on the Moon. In contrast to the lunar case, on small asteroids, like Itokowa, rocks make up a large fraction of the exposed surface. Results from the Hayabusa spacecraft at Itokowa suggest that while the low gravity does not allow for the development of a mature regolith, weathering patinas can and do develop on rock surfaces, in fact, the rocky surfaces were seen to be darker and appear spectrally more weathered than regions with finer materials. To explore how weathering of asteroidal rocks may differ from lunar, a set of ordinary chondrite meteorites (H, L, and LL) which have been subjected to artificial space weathering by nanopulse laser were examined by TEM. NpFe(sup 0) bearing glasses were ubiquitous in both the naturally-weathered lunar and the artificially-weathered meteorite samples.

  14. Statistical Analysis of Model Data for Operational Space Launch Weather Support at Kennedy Space Center and Cape Canaveral Air Force Station

    Science.gov (United States)

    Bauman, William H., III

    2010-01-01

    The 12-km resolution North American Mesoscale (NAM) model (MesoNAM) is used by the 45th Weather Squadron (45 WS) Launch Weather Officers at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) to support space launch weather operations. The 45 WS tasked the Applied Meteorology Unit to conduct an objective statistics-based analysis of MesoNAM output compared to wind tower mesonet observations and then develop a an operational tool to display the results. The National Centers for Environmental Prediction began running the current version of the MesoNAM in mid-August 2006. The period of record for the dataset was 1 September 2006 - 31 January 2010. The AMU evaluated MesoNAM hourly forecasts from 0 to 84 hours based on model initialization times of 00, 06, 12 and 18 UTC. The MesoNAM forecast winds, temperature and dew point were compared to the observed values of these parameters from the sensors in the KSC/CCAFS wind tower network. The data sets were stratified by model initialization time, month and onshore/offshore flow for each wind tower. Statistics computed included bias (mean difference), standard deviation of the bias, root mean square error (RMSE) and a hypothesis test for bias = O. Twelve wind towers located in close proximity to key launch complexes were used for the statistical analysis with the sensors on the towers positioned at varying heights to include 6 ft, 30 ft, 54 ft, 60 ft, 90 ft, 162 ft, 204 ft and 230 ft depending on the launch vehicle and associated weather launch commit criteria being evaluated. These twelve wind towers support activities for the Space Shuttle (launch and landing), Delta IV, Atlas V and Falcon 9 launch vehicles. For all twelve towers, the results indicate a diurnal signal in the bias of temperature (T) and weaker but discernable diurnal signal in the bias of dewpoint temperature (T(sub d)) in the MesoNAM forecasts. Also, the standard deviation of the bias and RMSE of T, T(sub d), wind speed and wind

  15. Investigation of boundary-layer wind predictions during nocturnal low-level jet events using the Weather Research and Forecasting model

    Energy Technology Data Exchange (ETDEWEB)

    Mirocha, Jeff D.; Simpson, Matthew D.; Fast, Jerome D.; Berg, Larry K.; Baskett, R.

    2016-04-01

    Simulations of two periods featuring three consecutive low level jet (LLJ) events in the US Upper Great Plains during the autumn of 2011 were conducted to explore the impacts of various setup configurations and physical process models on simulated flow parameters within the lowest 200 m above the surface, using the Weather Research and Forecasting (WRF) model. Sensitivities of simulated flow parameters to the horizontal and vertical grid spacing, planetary boundary layer (PBL) and land surface model (LSM) physics options, were assessed. Data from a Light Detection and Ranging (lidar) system, deployed to the Weather Forecast Improvement Project (WFIP; Finley et al. 2013) were used to evaluate the accuracy of simulated wind speed and direction at 80 m above the surface, as well as their vertical distributions between 120 and 40 m, covering the typical span of contemporary tall wind turbines. All of the simulations qualitatively captured the overall diurnal cycle of wind speed and stratification, producing LLJs during each overnight period, however large discrepancies occurred at certain times for each simulation in relation to the observations. 54-member ensembles encompassing changes of the above discussed configuration parameters displayed a wide range of simulated vertical distributions of wind speed and direction, and potential temperature, reflecting highly variable representations of stratification during the weakly stable overnight conditions. Root mean square error (RMSE) statistics show that different ensemble members performed better and worse in various simulated parameters at different times, with no clearly superior configuration . Simulations using a PBL parameterization designed specifically for the stable conditions investigated herein provided superior overall simulations of wind speed at 80 m, demonstrating the efficacy of targeting improvements of physical process models in areas of known deficiencies. However, the considerable magnitudes of the

  16. A Modulated-Gradient Parametrization for the Large-Eddy Simulation of the Atmospheric Boundary Layer Using the Weather Research and Forecasting Model

    Science.gov (United States)

    Khani, Sina; Porté-Agel, Fernando

    2017-12-01

    The performance of the modulated-gradient subgrid-scale (SGS) model is investigated using large-eddy simulation (LES) of the neutral atmospheric boundary layer within the weather research and forecasting model. Since the model includes a finite-difference scheme for spatial derivatives, the discretization errors may affect the simulation results. We focus here on understanding the effects of finite-difference schemes on the momentum balance and the mean velocity distribution, and the requirement (or not) of the ad hoc canopy model. We find that, unlike the Smagorinsky and turbulent kinetic energy (TKE) models, the calculated mean velocity and vertical shear using the modulated-gradient model, are in good agreement with Monin-Obukhov similarity theory, without the need for an extra near-wall canopy model. The structure of the near-wall turbulent eddies is better resolved using the modulated-gradient model in comparison with the classical Smagorinsky and TKE models, which are too dissipative and yield unrealistic smoothing of the smallest resolved scales. Moreover, the SGS fluxes obtained from the modulated-gradient model are much smaller near the wall in comparison with those obtained from the regular Smagorinsky and TKE models. The apparent inability of the LES model in reproducing the mean streamwise component of the momentum balance using the total (resolved plus SGS) stress near the surface is probably due to the effect of the discretization errors, which can be calculated a posteriori using the Taylor-series expansion of the resolved velocity field. Overall, we demonstrate that the modulated-gradient model is less dissipative and yields more accurate results in comparison with the classical Smagorinsky model, with similar computational costs.

  17. Assessment of Planetary-Boundary-Layer Schemes in the Weather Research and Forecasting Model Within and Above an Urban Canopy Layer

    Science.gov (United States)

    Ferrero, Enrico; Alessandrini, Stefano; Vandenberghe, Francois

    2018-03-01

    We tested several planetary-boundary-layer (PBL) schemes available in the Weather Research and Forecasting (WRF) model against measured wind speed and direction, temperature and turbulent kinetic energy (TKE) at three levels (5, 9, 25 m). The Urban Turbulence Project dataset, gathered from the outskirts of Turin, Italy and used for the comparison, provides measurements made by sonic anemometers for more than 1 year. In contrast to other similar studies, which have mainly focused on short-time periods, we considered 2 months of measurements (January and July) representing both the seasonal and the daily variabilities. To understand how the WRF-model PBL schemes perform in an urban environment, often characterized by low wind-speed conditions, we first compared six PBL schemes against observations taken by the highest anemometer located in the inertial sub-layer. The availability of the TKE measurements allows us to directly evaluate the performances of the model; results of the model evaluation are presented in terms of quantile versus quantile plots and statistical indices. Secondly, we considered WRF-model PBL schemes that can be coupled to the urban-surface exchange parametrizations and compared the simulation results with measurements from the two lower anemometers located inside the canopy layer. We find that the PBL schemes accounting for TKE are more accurate and the model representation of the roughness sub-layer improves when the urban model is coupled to each PBL scheme.

  18. Very short-term rainfall forecasting by effectively using the ensemble outputs of numerical weather prediction models

    Science.gov (United States)

    Wu, Ming-Chang; Lin, Gwo-Fong; Feng, Lei; Hwang, Gong-Do

    2017-04-01

    In Taiwan, heavy rainfall brought by typhoons often causes serious disasters and leads to loss of life and property. In order to reduce the impact of these disasters, accurate rainfall forecasts are always important for civil protection authorities to prepare proper measures in advance. In this study, a methodology is proposed for providing very short-term (1- to 6-h ahead) rainfall forecasts in a basin-scale area. The proposed methodology is developed based on the use of analogy reasoning approach to effectively integrate the ensemble precipitation forecasts from a numerical weather prediction system in Taiwan. To demonstrate the potential of the proposed methodology, an application to a basin-scale area (the Choshui River basin located in west-central Taiwan) during five typhoons is conducted. The results indicate that the proposed methodology yields more accurate hourly rainfall forecasts, especially the forecasts with a lead time of 1 to 3 hours. On average, improvement of the Nash-Sutcliffe efficiency coefficient is about 14% due to the effective use of the ensemble forecasts through the proposed methodology. The proposed methodology is expected to be useful for providing accurate very short-term rainfall forecasts during typhoons.

  19. The impact of convection in the West African monsoon region on global weather forecasts - explicit vs. parameterised convection simulations using the ICON model

    Science.gov (United States)

    Pante, Gregor; Knippertz, Peter

    2017-04-01

    The West African monsoon is the driving element of weather and climate during summer in the Sahel region. It interacts with mesoscale convective systems (MCSs) and the African easterly jet and African easterly waves. Poor representation of convection in numerical models, particularly its organisation on the mesoscale, can result in unrealistic forecasts of the monsoon dynamics. Arguably, the parameterisation of convection is one of the main deficiencies in models over this region. Overall, this has negative impacts on forecasts over West Africa itself but may also affect remote regions, as waves originating from convective heating are badly represented. Here we investigate those remote forecast impacts based on daily initialised 10-day forecasts for July 2016 using the ICON model. One set of simulations employs the default setup of the global model with a horizontal grid spacing of 13 km. It is compared with simulations using the 2-way nesting capability of ICON. A second model domain over West Africa (the nest) with 6.5 km grid spacing is sufficient to explicitly resolve MCSs in this region. In the 2-way nested simulations, the prognostic variables of the global model are influenced by the results of the nest through relaxation. The nest with explicit convection is able to reproduce single MCSs much more realistically compared to the stand-alone global simulation with parameterised convection. Explicit convection leads to cooler temperatures in the lower troposphere (below 500 hPa) over the northern Sahel due to stronger evaporational cooling. Overall, the feedback of dynamic variables from the nest to the global model shows clear positive effects when evaluating the output of the global domain of the 2-way nesting simulation and the output of the stand-alone global model with ERA-Interim re-analyses. Averaged over the 2-way nested region, bias and root mean squared error (RMSE) of temperature, geopotential, wind and relative humidity are significantly reduced in

  20. Weather and emotional state

    Science.gov (United States)

    Spasova, Z.

    2010-09-01

    Introduction Given the proven effects of weather on the human organism, an attempt to examine its effects on a psychic and emotional level has been made. Emotions affect the bio-tonus, working ability and concentration, hence their significance in various domains of economic life, such as health care, education, transportation, tourism, etc. Data and methods The research has been made in Sofia City within a period of 8 months, using 5 psychological methods (Eysenck Personality Questionnaire (EPQ), State-Trait Anxiety Inventory (STAI), Test for Self-assessment of the emotional state (developed by Wessman and Ricks), Test for evaluation of moods and Test "Self-confidence - Activity - Mood" (developed by the specialists from the Military Academy in Saint Petersburg). The Fiodorov-Chubukov's complex-climatic method was used to characterize meteorological conditions because of the purpose to include in the analysis a maximal number of meteorological elements. 16 weather types are defined in dependence of the meteorological elements values according to this method. Abrupt weather changes from one day to another, defined by the same method, were considered as well. Results and discussions The results obtained by t-test show that the different categories of weather lead to changes in the emotional status, which indicates a character either positive or negative for the organism. The abrupt weather changes, according to expectations, have negative effect on human emotions but only when a transition to the cloudy weather or weather type, classified as "unfavourable" has been realized. The relationship between weather and human emotions is rather complicated since it depends on individual characteristics of people. One of these individual psychological characteristics, marked by the dimension "neuroticism", has a strong effect on emotional reactions in different weather conditions. Emotionally stable individuals are more "protected" to the weather influence on their emotions

  1. Enabling Philippine Farmers to Adapt to Climate Variability Using Seasonal Climate and Weather Forecast with a Crop Simulation Model in an SMS-based Farmer Decision Support System

    Science.gov (United States)

    Ebardaloza, J. B. R.; Trogo, R.; Sabido, D. J.; Tongson, E.; Bagtasa, G.; Balderama, O. F.

    2015-12-01

    Corn farms in the Philippines are rainfed farms, hence, it is of utmost importance to choose the start of planting date so that the critical growth stages that are in need of water will fall on dates when there is rain. Most farmers in the Philippines use superstitions and traditions as basis for farming decisions such as when to start planting [1]. Before climate change, superstitions like planting after a feast day of a saint has worked for them but with the recent progression of climate change, farmers now recognize that there is a need for technological intervention [1]. The application discussed in this paper presents a solution that makes use of meteorological station sensors, localized seasonal climate forecast, localized weather forecast and a crop simulation model to provide recommendations to farmers based on the crop cultivar, soil type and fertilizer type used by farmers. It is critical that the recommendations given to farmers are not generic as each farmer would have different needs based on their cultivar, soil, fertilizer, planting schedule and even location [2]. This application allows the farmer to inquire about whether it will rain in the next seven days, the best date to start planting based on the potential yield upon harvest, when to apply fertilizer and by how much, when to water and by how much. Short messaging service (SMS) is the medium chosen for this application because while mobile penetration in the Philippines is as high as 101%, the smart phone penetration is only at 15% [3]. SMS has been selected as it has been identified as the most effective way of reaching farmers with timely agricultural information and knowledge [4,5]. The recommendations while derived from making use of Automated Weather Station (AWS) sensor data, Weather Research Forecasting (WRF) models and DSSAT 4.5 [9], are translated into the local language of the farmers and in a format that is easily understood as recommended in [6,7,8]. A pilot study has been started

  2. Association between Empirically Estimated Monsoon Dynamics and Other Weather Factors and Historical Tea Yields in China: Results from a Yield Response Model

    Directory of Open Access Journals (Sweden)

    Rebecca Boehm

    2016-04-01

    Full Text Available Farmers in China’s tea-growing regions report that monsoon dynamics and other weather factors are changing and that this is affecting tea harvest decisions. To assess the effect of climate change on tea production in China, this study uses historical weather and production data from 1980 to 2011 to construct a yield response model that estimates the partial effect of weather factors on tea yields in China, with a specific focus on East Asian Monsoon dynamics. Tea (Camellia sinensis (L. Kunze has not been studied using these methods even though it is an important crop for human nutrition and the economic well-being of rural communities in many countries. Previous studies have approximated the monsoon period using historical average onset and retreat dates, which we believe limits our understanding of how changing monsoon patterns affect crop productivity. In our analysis, we instead estimate the monsoon season across China’s tea growing regions empirically by identifying the unknown breakpoints in the year-by-province cumulative precipitation. We find that a 1% increase in the monsoon retreat date is associated with 0.481%–0.535% reduction in tea yield. In the previous year, we also find that a 1% increase in the date of the monsoon retreat is associated with a 0.604% decrease in tea yields. For precipitation, we find that a 1% increase in average daily precipitation occurring during the monsoon period is associated with a 0.184%–0.262% reduction in tea yields. In addition, our models show that 1% increase in the average daily monsoon precipitation from the previous growing season is associated with 0.258%–0.327% decline in yields. We also find that a 1% decrease in solar radiation in the previous growing season is associated with 0.554%-0.864% decrease in tea yields. These findings suggest the need for adaptive management and harvesting strategies given climate change projections and the known negative association between excess

  3. Whitefly (Bemisia tabaci Genn. , Stern. : Aleyrodidae) infestation patterns as influenced by cotton, weather and Heliothis: hypotheses testing by using simulation models

    Energy Technology Data Exchange (ETDEWEB)

    Baumgaertner, J.; Delucchi, V.; Von Arx, R.; Rubli, D.

    1986-01-01

    The whitefly Bemisia tabaci Genn. is an important pest on cotton in the Sudan Gezira. A population model based on the age-specific and time-varying life table approach has been constructed and validated for B. tabaci and the cotton variety Barac. Simulation experiments indicate that weather conditions are favourable for whitefly development until late autumn, when the bolls of early sown Barac open. Host plant quality, as measured by leaf age structure, is a very important factor in the B. tabaci life system. Reduced photosynthesis decreases and delays yield formation, but appears to have little effect on whitefly infestation levels at the time the bolls open. Simulated damage caused by Heliothis larvae to fruiting structures alters the growth of the plants, changes the age structures of the leaves and suggests increased whitefly numbers at the time of boll opening. 27 refs., 6 figs.

  4. Assessment of the ClimGen stochastic weather generator at ...

    African Journals Online (AJOL)

    Simulation of agricultural risk assessment and environmental management requires long series of daily weather data for the area being modelled. Acquiring and formatting this data can be very complex and time-consuming. This has led to the development of weather generation procedures and tools. Weather generators ...

  5. Effects of Weather on Tourism and its Moderation

    Science.gov (United States)

    Park, J. H.; Kim, S.; Lee, D. K.

    2016-12-01

    Tourism is weather sensitive industry (Gómez Martín, 2005). As climate change has been intensifying, the concerns about negative effects of weather on tourism also have been increasing. This study attempted to find ways that mitigate the negative effects from weather on tourism, by analyzing a path of the effects of weather on intention to revisit and its moderation. The data of the study were collected by a self-recording online questionnaire survey of South Korean domestic tourists during August 2015, and 2,412 samples were gathered. A path model of effects of weather on intention to revisit that including moderating effects from physical attraction satisfaction and service satisfaction was ran. Season was controlled in the path model. The model fit was adequate (CMIN/DF=2.372(p=.000), CFI=.974, RMSEA=.024, SRMR=0.040), and the Model Comparison, which assumes that the base model to be correct with season constrained model, showed that there was a seasonal differences in the model ( DF=24, CMIN=32.430, P=.117). By the analysis, it was figured out that weather and weather expectation affected weather satisfaction, and the weather satisfaction affected intention to revisit (spring/fall: .167**, summer: .104**, and winter: .114**). Meanwhile physical attraction satisfaction (.200**), and service satisfaction (.210**) of tourism positively moderated weather satisfaction in summer, and weather satisfaction positively moderated physical attraction (.238**) satisfaction and service satisfaction (.339**). In other words, in summer, dissatisfaction from hot weather was moderated by satisfaction from physical attractions and services, and in spring/fall, comfort weather conditions promoted tourists to accept tourism experience and be satisfied from attractions and services positively. Based on the result, it was expected that if industries focus on offering the good attractions and services based on weather conditions, there would be positive effects to alleviate tourists

  6. Designing a Weather Station

    Science.gov (United States)

    Roman, Harry T.

    2012-01-01

    The collection and analysis of weather data is crucial to the location of alternate energy systems like solar and wind. This article presents a design challenge that gives students a chance to design a weather station to collect data in advance of a large wind turbine installation. Data analysis is a crucial part of any science or engineering…

  7. Fabulous Weather Day

    Science.gov (United States)

    Marshall, Candice; Mogil, H. Michael

    2007-01-01

    Each year, first graders at Kensington Parkwood Elementary School in Kensington, Maryland, look forward to Fabulous Weather Day. Students learn how meteorologists collect data about the weather, how they study wind, temperature, precipitation, basic types/characteristics of clouds, and how they forecast. The project helps the students grow in…

  8. Evaporation and weather

    NARCIS (Netherlands)

    Bruin, H.A.R. de; Feddes, R.A.; Holtslag, A.A.M.; Lablans, W.N.; Schuurmans, C.J.E.; Shuttleworth, W.J.

    1987-01-01

    Data on evaporation to be used in agriculture, hydrology, forestry, etc. are usually supplied by meteorologists. Meteorologists themselves also use evaporation data. Air mass properties determining weather are strongly dependent on the input of water vapour from the surface. So for weather

  9. Tales of future weather

    NARCIS (Netherlands)

    Hazeleger, W.; van den Hurk, B.J.J.M.; Min, E.; van Oldenborgh, G.J.; Wang, X.; Petersen, A.C.; Stainforth, D.A.; Vasileiadou, E.; Smith, L.A.

    2015-01-01

    Society is vulnerable to extreme weather events and, by extension, to human impacts on future events. As climate changes weather patterns will change. The search is on for more effective methodologies to aid decision-makers both in mitigation to avoid climate change and in adaptation to changes. The

  10. Space weather: European Space Agency perspectives

    Science.gov (United States)

    Daly, E. J.; Hilgers, A.

    Spacecraft and payloads have become steadily more sophisticated and therefore more susceptible to space weather effects. ESA has long been active in applying models and tools to the problems associated with such effects on its spacecraft. In parallel, ESA and European agencies have built a highly successful solar-terrestrial physics capability. ESA is now investigating the marriage of these technological and scientific capabilities to address perceived user needs for space weather products and services. Two major ESA-sponsored studies are laying the groundwork for a possible operational European space weather service. The wide-ranging activities of ESA in the Space Weather/Space Environment domain are summarized and recent important examples of space weather concerns given.

  11. Large-Eddy Simulations of Atmospheric Flows Over Complex Terrain Using the Immersed-Boundary Method in the Weather Research and Forecasting Model

    Science.gov (United States)

    Ma, Yulong; Liu, Heping

    2017-12-01

    Atmospheric flow over complex terrain, particularly recirculation flows, greatly influences wind-turbine siting, forest-fire behaviour, and trace-gas and pollutant dispersion. However, there is a large uncertainty in the simulation of flow over complex topography, which is attributable to the type of turbulence model, the subgrid-scale (SGS) turbulence parametrization, terrain-following coordinates, and numerical errors in finite-difference methods. Here, we upgrade the large-eddy simulation module within the Weather Research and Forecasting model by incorporating the immersed-boundary method into the module to improve simulations of the flow and recirculation over complex terrain. Simulations over the Bolund Hill indicate improved mean absolute speed-up errors with respect to previous studies, as well an improved simulation of the recirculation zone behind the escarpment of the hill. With regard to the SGS parametrization, the Lagrangian-averaged scale-dependent Smagorinsky model performs better than the classic Smagorinsky model in reproducing both velocity and turbulent kinetic energy. A finer grid resolution also improves the strength of the recirculation in flow simulations, with a higher horizontal grid resolution improving simulations just behind the escarpment, and a higher vertical grid resolution improving results on the lee side of the hill. Our modelling approach has broad applications for the simulation of atmospheric flows over complex topography.

  12. Weather radar rainfall data in urban hydrology

    DEFF Research Database (Denmark)

    Thorndahl, Søren; Einfalt, Thomas; Willems, Patrick

    2017-01-01

    Application of weather radar data in urban hydrological applications has evolved significantly during the past decade as an alternative to traditional rainfall observations with rain gauges. Advances in radar hardware, data processing, numerical models, and emerging fields within urban hydrology...

  13. Transcriptome analysis of the whitefly, Bemisia tabaci MEAM1 during feeding on tomato infected with the crinivirus, Tomato chlorosis virus, identifies a temporal shift in gene expression and differential regulation of novel orphan genes.

    Science.gov (United States)

    Kaur, Navneet; Chen, Wenbo; Zheng, Yi; Hasegawa, Daniel K; Ling, Kai-Shu; Fei, Zhangjun; Wintermantel, William M

    2017-05-11

    Whiteflies threaten agricultural crop production worldwide, are polyphagous in nature, and transmit hundreds of plant viruses. Little is known how whitefly gene expression is altered due to feeding on plants infected with a semipersistently transmitted virus. Tomato chlorosis virus (ToCV; genus Crinivirus, family Closteroviridae) is transmitted by the whitefly (Bemisia tabaci) in a semipersistent manner and infects several globally important agricultural and ornamental crops, including tomato. To determine changes in global gene regulation in whiteflies after feeding on tomato plants infected with a crinivirus (ToCV), comparative transcriptomic analysis was performed using RNA-Seq on whitefly (Bemisia tabaci MEAM1) populations after 24, 48, and 72 h acquisition access periods on either ToCV-infected or uninfected tomatoes. Significant differences in gene expression were detected between whiteflies fed on ToCV-infected tomato and those fed on uninfected tomato among the three feeding time periods: 447 up-regulated and 542 down-regulated at 24 h, 4 up-regulated and 7 down-regulated at 48 h, and 50 up-regulated and 160 down-regulated at 72 h. Analysis revealed differential regulation of genes associated with metabolic pathways, signal transduction, transport and catabolism, receptors, glucose transporters, α-glucosidases, and the uric acid pathway in whiteflies fed on ToCV-infected tomatoes, as well as an abundance of differentially regulated novel orphan genes. Results demonstrate for the first time, a specific and temporally regulated response by the whitefly to feeding on a host plant infected with a semipersistently transmitted virus, and advance the understanding of the whitefly vector-virus interactions that facilitate virus transmission. Whitefly transmission of semipersistent viruses is believed to require specific interactions between the virus and its vector that allow binding of virus particles to factors within whitefly mouthparts. Results provide a

  14. Weather and road capacity

    DEFF Research Database (Denmark)

    Jensen, Thomas Christian

    2014-01-01

    The paper presents estimations of the effect of bad weather on the observed speed on a Danish highway section; Køge Bugt Motorvejen. The paper concludes that weather, primarily precipitation and snow, has a clear negative effect on speed when the road is not in hypercongestion mode. Furthermore......, the capacity of the highway seems to be reduced in bad weather and there are indications that travel time variability is also increased, at least in free-flow conditions. Heavy precipitation reduces speed and capacity by around 5-8%, whereas snow primarily reduces capacity. Other weather variables......-parametrically against traffic density and in step 2 the residuals from step 1 are regressed linearly against the weather variables. The choice of a non-parametric method is made to avoid constricting ties from a parametric specification and because the focus here is not on the relationship between traffic flow...

  15. Satellite observations of weather and climate

    Science.gov (United States)

    Kellogg, W. W.

    1974-01-01

    The SEASAT-A program is viewed as a new way to obtain atmospheric observations for weather and climatic studies in the framework of the Global Atmospheric Research Program (GARP). Total information derived from SEASAT-A sensor package provides a synoptic picture of the upper parts of the world's oceans as a prerequisite to the development of dynamic ocean models and combined ocean/atmospheric models for weather forecasting requirements.

  16. Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data

    Science.gov (United States)

    Lee, Joseph C. Y.; Lundquist, Julie K.

    2017-11-01

    Forecasts of wind-power production are necessary to facilitate the integration of wind energy into power grids, and these forecasts should incorporate the impact of wind-turbine wakes. This paper focuses on a case study of four diurnal cycles with significant power production, and assesses the skill of the wind farm parameterization (WFP) distributed with the Weather Research and Forecasting (WRF) model version 3.8.1, as well as its sensitivity to model configuration. After validating the simulated ambient flow with observations, we quantify the value of the WFP as it accounts for wake impacts on power production of downwind turbines. We also illustrate with statistical significance that a vertical grid with approximately 12 m vertical resolution is necessary for reproducing the observed power production. Further, the WFP overestimates wake effects and hence underestimates downwind power production during high wind speed, highly stable, and low turbulence conditions. We also find the WFP performance is independent of the number of wind turbines per model grid cell and the upwind-downwind position of turbines. Rather, the ability of the WFP to predict power production is most dependent on the skill of the WRF model in simulating the ambient wind speed.

  17. Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1 with meteorological and turbine power data

    Directory of Open Access Journals (Sweden)

    J. C. Y. Lee

    2017-11-01

    Full Text Available Forecasts of wind-power production are necessary to facilitate the integration of wind energy into power grids, and these forecasts should incorporate the impact of wind-turbine wakes. This paper focuses on a case study of four diurnal cycles with significant power production, and assesses the skill of the wind farm parameterization (WFP distributed with the Weather Research and Forecasting (WRF model version 3.8.1, as well as its sensitivity to model configuration. After validating the simulated ambient flow with observations, we quantify the value of the WFP as it accounts for wake impacts on power production of downwind turbines. We also illustrate with statistical significance that a vertical grid with approximately 12 m vertical resolution is necessary for reproducing the observed power production. Further, the WFP overestimates wake effects and hence underestimates downwind power production during high wind speed, highly stable, and low turbulence conditions. We also find the WFP performance is independent of the number of wind turbines per model grid cell and the upwind–downwind position of turbines. Rather, the ability of the WFP to predict power production is most dependent on the skill of the WRF model in simulating the ambient wind speed.

  18. Predicting the outbreak of hand, foot, and mouth disease in Nanjing, China: a time-series model based on weather variability

    Science.gov (United States)

    Liu, Sijun; Chen, Jiaping; Wang, Jianming; Wu, Zhuchao; Wu, Weihua; Xu, Zhiwei; Hu, Wenbiao; Xu, Fei; Tong, Shilu; Shen, Hongbing

    2017-10-01

    Hand, foot, and mouth disease (HFMD) is a significant public health issue in China and an accurate prediction of epidemic can improve the effectiveness of HFMD control. This study aims to develop a weather-based forecasting model for HFMD using the information on climatic variables and HFMD surveillance in Nanjing, China. Daily data on HFMD cases and meteorological variables between 2010 and 2015 were acquired from the Nanjing Center for Disease Control and Prevention, and China Meteorological Data Sharing Service System, respectively. A multivariate seasonal autoregressive integrated moving average (SARIMA) model was developed and validated by dividing HFMD infection data into two datasets: the data from 2010 to 2013 were used to construct a model and those from 2014 to 2015 were used to validate it. Moreover, we used weekly prediction for the data between 1 January 2014 and 31 December 2015 and leave-1-week-out prediction was used to validate the performance of model prediction. SARIMA (2,0,0)52 associated with the average temperature at lag of 1 week appeared to be the best model (R 2 = 0.936, BIC = 8.465), which also showed non-significant autocorrelations in the residuals of the model. In the validation of the constructed model, the predicted values matched the observed values reasonably well between 2014 and 2015. There was a high agreement rate between the predicted values and the observed values (sensitivity 80%, specificity 96.63%). This study suggests that the SARIMA model with average temperature could be used as an important tool for early detection and prediction of HFMD outbreaks in Nanjing, China.

  19. Investigating Surface Bias Errors in the Weather Research and Forecasting (WRF) Model using a Geographic Information System (GIS)

    Science.gov (United States)

    2015-02-01

    Mlawer et al. 1997) is used for long wave radiation and the Dudhia (1989) scheme for shortwave radiation . The Noah land surface model (Chen and...decreases the background turbulent kinetic energy and alters the diagnosis of the boundary layer depth used for model output and data assimilation...Kain 2004) cumulus parameterization is used only on the 9-km outer domain. For radiation , the Rapid Radiative Transfer Model (RRTM) parameterization

  20. A Mechanistic Model of Botrytis cinerea on Grapevines That Includes Weather, Vine Growth Stage, and the Main Infection Pathways

    Science.gov (United States)

    González-Domínguez, Elisa; Caffi, Tito; Ciliberti, Nicola; Rossi, Vittorio

    2015-01-01

    A mechanistic model for Botrytis cinerea on grapevine was developed. The model, which accounts for conidia production on various inoculum sources and for multiple infection pathways, considers two infection periods. During the first period (“inflorescences clearly visible” to “berries groat-sized”), the model calculates: i) infection severity on inflorescences and young clusters caused by conidia (SEV1). During the second period (“majority of berries touching” to “berries ripe for harvest”), the model calculates: ii) infection severity of ripening berries by conidia (SEV2); and iii) severity of berry-to-berry infection caused by mycelium (SEV3). The model was validated in 21 epidemics (vineyard × year combinations) between 2009 and 2014 in Italy and France. A discriminant function analysis (DFA) was used to: i) evaluate the ability of the model to predict mild, intermediate, and severe epidemics; and ii) assess how SEV1, SEV2, and SEV3 contribute to epidemics. The model correctly classified the severity of 17 of 21 epidemics. Results from DFA were also used to calculate the daily probabilities that an ongoing epidemic would be mild, intermediate, or severe. SEV1 was the most influential variable in discriminating between mild and intermediate epidemics, whereas SEV2 and SEV3 were relevant for discriminating between intermediate and severe epidemics. The model represents an improvement of previous B. cinerea models in viticulture and could be useful for making decisions about Botrytis bunch rot control. PMID:26457808

  1. Environmental Education Tips: Weather Activities.

    Science.gov (United States)

    Brainard, Audrey H.

    1989-01-01

    Provides weather activities including questions, on weather, heating the earth's surface, air, tools of the meteorologist, clouds, humidity, wind, and evaporation. Shows an example of a weather chart activity. (RT)

  2. Verification of Forecast Weather Surface Variables over Vietnam Using the National Numerical Weather Prediction System

    OpenAIRE

    Du Duc, Tien; Hole, Lars Robert; Tran Anh, Duc; Hoang Duc, Cuong; Nguyen Ba, Thuy

    2016-01-01

    The national numerical weather prediction system of Vietnam is presented and evaluated. The system is based on three main models, namely, the Japanese Global Spectral Model, the US Global Forecast System, and the US Weather Research and Forecasting (WRF) model. The global forecast products have been received at 0.25- and 0.5-degree horizontal resolution, respectively, and the WRF model has been run locally with 16 km horizontal resolution at the National Center for Hydro-Meteorological Foreca...

  3. Trait-based representation of hydrological functional properties of plants in weather and ecosystem models

    Directory of Open Access Journals (Sweden)

    Ashley M. Matheny

    2017-02-01

    Full Text Available Land surface models and dynamic global vegetation models typically represent vegetation through coarse plant functional type groupings based on leaf form, phenology, and bioclimatic limits. Although these groupings were both feasible and functional for early model generations, in light of the pace at which our knowledge of functional ecology, ecosystem demographics, and vegetation-climate feedbacks has advanced and the ever growing demand for enhanced model performance, these groupings have become antiquated and are identified as a key source of model uncertainty. The newest wave of model development is centered on shifting the vegetation paradigm away from plant functional types (PFTs and towards flexible trait-based representations. These models seek to improve errors in ecosystem fluxes that result from information loss due to over-aggregation of dissimilar species into the same functional class. We advocate the importance of the inclusion of plant hydraulic trait representation within the new paradigm through a framework of the whole-plant hydraulic strategy. Plant hydraulic strategy is known to play a critical role in the regulation of stomatal conductance and thus transpiration and latent heat flux. It is typical that coexisting plants employ opposing hydraulic strategies, and therefore have disparate patterns of water acquisition and use. Hydraulic traits are deterministic of drought resilience, response to disturbance, and other demographic processes. The addition of plant hydraulic properties in models may not only improve the simulation of carbon and water fluxes but also vegetation population distributions.

  4. A short-range weather prediction system for South Africa based on a multi-model approach

    CSIR Research Space (South Africa)

    Landman, S

    2012-10-01

    Full Text Available Service, forecasters use deterministic model output data as guidance to produce subjective probabilistic rainfall forecasts. The aim of this research is to determine the skill of a new objective multi-model, multi-institute probabilistic ensemble forecast...

  5. Genetic programming-based mathematical modeling of influence of weather parameters in BOD5removal by Lemna minor.

    Science.gov (United States)

    Chandrasekaran, Sivapragasam; Sankararajan, Vanitha; Neelakandhan, Nampoothiri; Ram Kumar, Mahalakshmi

    2017-11-04

    This study, through extensive experiments and mathematical modeling, reveals that other than retention time and wastewater temperature (T w ), atmospheric parameters also play important role in the effective functioning of aquatic macrophyte-based treatment system. Duckweed species Lemna minor is considered in this study. It is observed that the combined effect of atmospheric temperature (T atm ), wind speed (U w ), and relative humidity (RH) can be reflected through one parameter, namely the "apparent temperature" (T a ). A total of eight different models are considered based on the combination of input parameters and the best mathematical model is arrived at which is validated through a new experimental set-up outside the modeling period. The validation results are highly encouraging. Genetic programming (GP)-based models are found to reveal deeper understandings of the wetland process.

  6. Seafloor weathering buffering climate: numerical experiments

    Science.gov (United States)

    Farahat, N. X.; Archer, D. E.; Abbot, D. S.

    2013-12-01

    Continental silicate weathering is widely held to consume atmospheric CO2 at a rate controlled in part by temperature, resulting in a climate-weathering feedback [Walker et al., 1981]. It has been suggested that weathering of oceanic crust of warm mid-ocean ridge flanks also has a CO2 uptake rate that is controlled by climate [Sleep and Zahnle, 2001; Brady and Gislason, 1997]. Although this effect might not be significant on present-day Earth [Caldeira, 1995], seafloor weathering may be more pronounced during snowball states [Le Hir et al., 2008], during the Archean when seafloor spreading rates were faster [Sleep and Zahnle, 2001], and on waterworld planets [Abbot et al., 2012]. Previous studies of seafloor weathering have made significant contributions using qualitative, generally one-box, models, and the logical next step is to extend this work using a spatially resolved model. For example, experiments demonstrate that seafloor weathering reactions are temperature dependent, but it is not clear whether the deep ocean temperature affects the temperature at which the reactions occur, or if instead this temperature is set only by geothermal processes. Our goal is to develop a 2-D numerical model that can simulate hydrothermal circulation and resulting alteration of oceanic basalts, and can therefore address such questions. A model of diffusive and convective heat transfer in fluid-saturated porous media simulates hydrothermal circulation through porous oceanic basalt. Unsteady natural convection is solved for using a Darcy model of porous media flow that has been extensively benchmarked. Background hydrothermal circulation is coupled to mineral reaction kinetics of basaltic alteration and hydrothermal mineral precipitation. In order to quantify seafloor weathering as a climate-weathering feedback process, this model focuses on hydrothermal reactions that influence carbon uptake as well as ocean alkalinity: silicate rock dissolution, calcium and magnesium leaching

  7. Expansion of the Real-Time SPoRT-Land Information System for NOAA/National Weather Service Situational Awareness and Local Modeling Applications

    Science.gov (United States)

    Case, Jonathan L; White, Kristopher D.

    2014-01-01

    The NASA Short-term Prediction Research and Transition (SPoRT) Center in Huntsville, AL is running a real-time configuration of the Noah land surface model (LSM) within the NASA Land Information System (LIS) framework (hereafter referred to as the "SPoRT-LIS"). Output from the real-time SPoRT-LIS is used for (1) initializing land surface variables for local modeling applications, and (2) displaying in decision support systems for situational awareness and drought monitoring at select NOAA/National Weather Service (NWS) partner offices. The experimental CONUS run incorporates hourly quantitative precipitation estimation (QPE) from the National Severe Storms Laboratory Multi- Radar Multi-Sensor (MRMS) which will be transitioned into operations at the National Centers for Environmental Prediction (NCEP) in Fall 2014.This paper describes the current and experimental SPoRT-LIS configurations, and documents some of the limitations still remaining through the advent of MRMS precipitation analyses in the SPoRT-LIS land surface model (LSM) simulations.

  8. From the Sun’s atmosphere to the Earth’s atmosphere: an overview of scientific models available for space weather developments

    Directory of Open Access Journals (Sweden)

    C. Lathuillère

    2002-07-01

    Full Text Available Space weather aims at setting operational numerical tools in order to nowcast, forecast and quantify the solar activity events, the magnetosphere, ionosphere and thermosphere responses and the consequences on our technological societies. These tools can be divided in two parts. The first has a geophysical base (Sun, interplanetary medium, magnetosphere, atmosphere. The second concerns technological applications (telecommunications, spacecraft orbits, power plants .... In this paper, we aim at giving an overview of the models that belong to the first class (geophysics that might serve in the future as a basis for building global operational codes. For each model, we consider the physics underneath, the input and output parameters, and whether it is already operational, whether it may become operational in the near future, or if it is an academic research tool. Relevant references are given in order to serve as a starting point for further readings.Key words. Interplanetary physics (general or miscellaneous, Ionosphere (modelling and forecasting, Magnetospheric physics (general or miscellaneous

  9. The Impact of IBM Cell Technology on the Programming Paradigm in the Context of Computer Systems for Climate and Weather Models

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, Shujia; Duffy, Daniel; Clune, Thomas; Suarez, Max; Williams, Samuel; Halem, Milton

    2009-01-10

    The call for ever-increasing model resolutions and physical processes in climate and weather models demands a continual increase in computing power. The IBM Cell processor's order-of-magnitude peak performance increase over conventional processors makes it very attractive to fulfill this requirement. However, the Cell's characteristics, 256KB local memory per SPE and the new low-level communication mechanism, make it very challenging to port an application. As a trial, we selected the solar radiation component of the NASA GEOS-5 climate model, which: (1) is representative of column physics components (half the total computational time), (2) has an extremely high computational intensity: the ratio of computational load to main memory transfers, and (3) exhibits embarrassingly parallel column computations. In this paper, we converted the baseline code (single-precision Fortran) to C and ported it to an IBM BladeCenter QS20. For performance, we manually SIMDize four independent columns and include several unrolling optimizations. Our results show that when compared with the baseline implementation running on one core of Intel's Xeon Woodcrest, Dempsey, and Itanium2, the Cell is approximately 8.8x, 11.6x, and 12.8x faster, respectively. Our preliminary analysis shows that the Cell can also accelerate the dynamics component (~;;25percent total computational time). We believe these dramatic performance improvements make the Cell processor very competitive as an accelerator.

  10. Sensitivity of Turbine-Height Wind Speeds to Parameters in Planetary Boundary-Layer and Surface-Layer Schemes in the Weather Research and Forecasting Model

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Ben; Qian, Yun; Berg, Larry K.; Ma, Po-Lun; Wharton, Sonia; Bulaevskaya, Vera; Yan, Huiping; Hou, Zhangshuan; Shaw, William J.

    2016-07-21

    We evaluate the sensitivity of simulated turbine-height winds to 26 parameters applied in a planetary boundary layer (PBL) scheme and a surface layer scheme of the Weather Research and Forecasting (WRF) model over an area of complex terrain during the Columbia Basin Wind Energy Study. An efficient sampling algorithm and a generalized linear model are used to explore the multiple-dimensional parameter space and quantify the parametric sensitivity of modeled turbine-height winds. The results indicate that most of the variability in the ensemble simulations is contributed by parameters related to the dissipation of the turbulence kinetic energy (TKE), Prandtl number, turbulence length scales, surface roughness, and the von Kármán constant. The relative contributions of individual parameters are found to be dependent on both the terrain slope and atmospheric stability. The parameter associated with the TKE dissipation rate is found to be the most important one, and a larger dissipation rate can produce larger hub-height winds. A larger Prandtl number results in weaker nighttime winds. Increasing surface roughness reduces the frequencies of both extremely weak and strong winds, implying a reduction in the variability of the wind speed. All of the above parameters can significantly affect the vertical profiles of wind speed, the altitude of the low-level jet and the magnitude of the wind shear strength. The wind direction is found to be modulated by the same subset of influential parameters. Remainder of abstract is in attachment.

  11. Development of a digital model of ground-water flow in deeply weathered crystalline rock, Chester County, Pennsylvania

    Science.gov (United States)

    McGreevy, Laurence J.; Sloto, Ronald A.

    1980-01-01

    The model developed in this study simulates .recharge to, flow through, and discharge from the water-table aquifer in the upper Pickering Creek basin, a 5.98-square-mile basin representative of most of Chester County, Pennsylvania. The two-dimensional finite-difference model of Trescott, Pinder, and Larson was used with slight modification. The way ground-water evapotranspiration varies with depth was modified, and a minimum transmissivity was used to prevent the model from "going dry" during water-table simulations.

  12. Improving the representation of clouds, radiation, and precipitation using spectral nudging in the Weather Research and Forecasting model

    Science.gov (United States)

    Spectral nudging – a scale-selective interior constraint technique – is commonly used in regional climate models to maintain consistency with large-scale forcing while permitting mesoscale features to develop in the downscaled simulations. Several studies have demonst...