WorldWideScience

Sample records for numerical weather forecasting

  1. 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...

  2. Ensemble flare forecasting: using numerical weather prediction techniques to improve space weather operations

    Science.gov (United States)

    Murray, S.; Guerra, J. A.

    2017-12-01

    One essential component of operational space weather forecasting is the prediction of solar flares. Early flare forecasting work focused on statistical methods based on historical flaring rates, but more complex machine learning methods have been developed in recent years. A multitude of flare forecasting methods are now available, however it is still unclear which of these methods performs best, and none are substantially better than climatological forecasts. Current operational space weather centres cannot rely on automated methods, and generally use statistical forecasts with a little human intervention. Space weather researchers are increasingly looking towards methods used in terrestrial weather to improve current forecasting techniques. Ensemble forecasting has been used in numerical weather prediction for many years as a way to combine different predictions in order to obtain a more accurate result. It has proved useful in areas such as magnetospheric modelling and coronal mass ejection arrival analysis, however has not yet been implemented in operational flare forecasting. Here we construct ensemble forecasts for major solar flares by linearly combining the full-disk probabilistic forecasts from a group of operational forecasting methods (ASSA, ASAP, MAG4, MOSWOC, NOAA, and Solar Monitor). Forecasts from each method are weighted by a factor that accounts for the method's ability to predict previous events, and several performance metrics (both probabilistic and categorical) are considered. The results provide space weather forecasters with a set of parameters (combination weights, thresholds) that allow them to select the most appropriate values for constructing the 'best' ensemble forecast probability value, according to the performance metric of their choice. In this way different forecasts can be made to fit different end-user needs.

  3. Verification of Forecast Weather Surface Variables over Vietnam Using the National Numerical Weather Prediction System

    Directory of Open Access Journals (Sweden)

    Tien Du Duc

    2016-01-01

    Full Text Available 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 Forecasting using lateral conditions from GSM and GFS. The model performance is evaluated by comparing model output against observations of precipitation, wind speed, and temperature at 168 weather stations, with daily data from 2010 to 2014. In general, the global models provide more accurate forecasts than the regional models, probably due to the low horizontal resolution in the regional model. Also, the model performance is poorer for stations with altitudes greater than 500 meters above sea level (masl. For tropical cyclone performance validations, the maximum wind surface forecast from global and regional models is also verified against the best track of Joint Typhoon Warning Center. Finally, the model forecast skill during a recent extreme rain event in northeast Vietnam is evaluated.

  4. 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.

  5. 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

  6. 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....

  7. 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

  8. Probabilistic forecasting of shallow, rainfall-triggered landslides using real-time numerical weather predictions

    Directory of Open Access Journals (Sweden)

    J. Schmidt

    2008-04-01

    Full Text Available A project established at the National Institute of Water and Atmospheric Research (NIWA in New Zealand is aimed at developing a prototype of a real-time landslide forecasting system. The objective is to predict temporal changes in landslide probability for shallow, rainfall-triggered landslides, based on quantitative weather forecasts from numerical weather prediction models. Global weather forecasts from the United Kingdom Met Office (MO Numerical Weather Prediction model (NWP are coupled with a regional data assimilating NWP model (New Zealand Limited Area Model, NZLAM to forecast atmospheric variables such as precipitation and temperature up to 48 h ahead for all of New Zealand. The weather forecasts are fed into a hydrologic model to predict development of soil moisture and groundwater levels. The forecasted catchment-scale patterns in soil moisture and soil saturation are then downscaled using topographic indices to predict soil moisture status at the local scale, and an infinite slope stability model is applied to determine the triggering soil water threshold at a local scale. The model uses uncertainty of soil parameters to produce probabilistic forecasts of spatio-temporal landslide occurrence 48~h ahead. The system was evaluated for a damaging landslide event in New Zealand. Comparison with landslide densities estimated from satellite imagery resulted in hit rates of 70–90%.

  9. Spatial bias and uncertainty in numerical weather predictions for urban runoff forecasts with long time horizons

    DEFF Research Database (Denmark)

    Pedersen, Jonas Wied; Courdent, Vianney Augustin Thomas; Vezzaro, Luca

    2017-01-01

    Numerical Weather Predictions (NWP) can be used to forecast urban runoff with long lead times. However, NWP exhibit large spatial uncertainties and using forecasted precipitation directly above the catchment might therefore not be an ideal approach in an online setup. We use the Danish...... Meteorological Institute’s NWP ensemble and investigate a large spatial neighborhood around the catchment over a two-year period. When compared against in-sewer observations, runoff forecasts forced with precipitation from north-east of the catchment are most skillful. This highlights spatial biases...... in the coupled hydro-meteorological setup, which a forecaster should be aware of....

  10. Numerical Weather Forecasting at the Savannah River Site

    International Nuclear Information System (INIS)

    Buckley, R.L.

    1999-01-01

    Facilities such as the Savannah River Site (SRS), which contain the potential for hazardous atmospheric releases, rely on the predictive capabilities of dispersion models to assess possible emergency response actions. The operational design in relation to domain size and forecast time is presented, along with verification of model results over extended time periods with archived surface observations

  11. Numerical Weather Forecasting at the Savannah River Site

    Energy Technology Data Exchange (ETDEWEB)

    Buckley, R.L.

    1999-01-26

    Facilities such as the Savannah River Site (SRS), which contain the potential for hazardous atmospheric releases, rely on the predictive capabilities of dispersion models to assess possible emergency response actions. The operational design in relation to domain size and forecast time is presented, along with verification of model results over extended time periods with archived surface observations.

  12. Short period forecasting of catchment-scale precipitation. Part I: the role of Numerical Weather Prediction

    Directory of Open Access Journals (Sweden)

    M. A. Pedder

    2000-01-01

    Full Text Available A deterministic forecast of surface precipitation involves solving a time-dependent moisture balance equation satisfying conservation of total water substance. A realistic solution needs to take into account feedback between atmospheric dynamics and the diabatic sources of heat energy associated with phase changes, as well as complex microphysical processes controlling the conversion between cloud water (or ice and precipitation. Such processes are taken into account either explicitly or via physical parameterisation schemes in many operational numerical weather prediction models; these can therefore generate precipitation forecasts which are fully consistent with the predicted evolution of the atmospheric state as measured by observations of temperature, wind, pressure and humidity. This paper reviews briefly the atmospheric moisture balance equation and how it may be solved in practice. Solutions are obtained using the Meteorological Office Mesoscale version of its operational Unified Numerical Weather Prediction (NWP model; they verify predicted precipitation rates against catchment-scale values based on observations collected during an Intensive Observation Period (IOP of HYREX. Results highlight some limitations of an operational NWP forecast in providing adequate time and space resolution, and its sensitivity to initial conditions. The large-scale model forecast can, nevertheless, provide important information about the moist dynamical environment which could be incorporated usefully into a higher resolution, ‘storm-resolving’ prediction scheme. Keywords: Precipitation forecasting; moisture budget; numerical weather prediction

  13. Initial weather regimes as predictors of numerical 30-day mean forecast accuracy

    Science.gov (United States)

    Colucci, Stephen J.; Baumhefner, David P.

    1992-01-01

    Thirty 30-day mean 500-mb-height anomaly forecasts generated by the NCAR Community Climate Model (CCM) for the year 1978 are examined in order to determine if the forecast accuracy can be estimated with the initial conditions. The initial weather regimes were defined in such a way that the regimes could discriminate between the best and the worst 30-day mean forecasts run from the initial fields in this data set. On the basis of the CCM experiments, it is suggested that the accuracy of numerical 30-day mean forecasts may depend upon the accuracy with which the cyclones and their interactions with the planetary scale are predicted early in the forecast cycle, and that this accuracy may depend upon the initial conditions.

  14. 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.

  15. On the dynamic estimation of relative weights for observation and forecast in numerical weather prediction

    Science.gov (United States)

    Wahba, Grace; Deepak, A. (Editor)

    1988-01-01

    The problem of merging direct and remotely sensed (indirect) data with forecast data to get an estimate of the present state of the atmosphere for the purpose of numerical weather prediction is examined. To carry out this merging optimally, it is necessary to provide an estimate of the relative weights to be given to the observations and forecast. It is possible to do this dynamically from the information to be merged, if the correlation structure of the errors from the various sources is sufficiently different. Some new statistical approaches to doing this are described, and conditions quantified in which such estimates are likely to be good.

  16. Real-time dynamic control of the Three Gorges Reservoir by coupling numerical weather rainfall prediction and flood forecasting

    DEFF Research Database (Denmark)

    Wang, Y.; Chen, H.; Rosbjerg, Dan

    2013-01-01

    In reservoir operation improvement of the accuracy of forecast flood inflow and extension of forecast lead-time can effectively be achieved by using rainfall forecasts from numerical weather predictions with a hydrological catchment model. In this study, the Regional Spectrum Model (RSM), which i...

  17. Application of numerical weather prediction in wind power forecasting: Assessment of the diurnal cycle

    Directory of Open Access Journals (Sweden)

    Tobias Heppelmann

    2017-06-01

    Full Text Available For a secure integration of weather dependent renewable energies in Germany's mixed power supply, precise forecasts of expected wind power are indispensable. These in turn are heavily dependent on numerical weather prediction (NWP. With this relevant area of application, NWP models need to be evaluated concerning new variables such as wind speed at hub heights of wind power plants. This article presents verification results of the deterministic NWP forecasts of the global ICON model, its ICON-EU nest, the COSMO-EU, and the COSMO-DE as well as of the ensemble prediction system COSMO-DE-EPS of the German National Weather Service (DWD, against wind mast observations. The focus is on the diurnal cycle in the Planetary Boundary Layer as wind power forecasts for Germany exhibit pronounced systematic amplitude and phase errors in the morning and evening hours. NWP forecasts with lead times up to 48 hours are examined. All considered NWP models reveal shortcomings concerning the representation of the diurnal cycle. Especially in summertime at onshore locations, when Low-Level Jets form, nocturnal wind speeds at hub height are underestimated. In the COSMO model, stable conditions are not sufficiently reflected in the first part of the night and the vertical mixing after sunrise establishes too late. The verification results of the COSMO-DE-EPS confirm the deficiencies of the deterministic forecasts. The deficiencies are present in all ensemble members and thus indicate potential for improvement not only in the model physics parameterization but also concerning the physical ensemble perturbations.

  18. Sensitivity analysis of numerical weather prediction radiative schemes to forecast direct solar radiation over Australia

    Science.gov (United States)

    Mukkavilli, S. K.; Kay, M. J.; Taylor, R.; Prasad, A. A.; Troccoli, A.

    2014-12-01

    The Australian Solar Energy Forecasting System (ASEFS) project requires forecasting timeframes which range from nowcasting to long-term forecasts (minutes to two years). As concentrating solar power (CSP) plant operators are one of the key stakeholders in the national energy market, research and development enhancements for direct normal irradiance (DNI) forecasts is a major subtask. This project involves comparing different radiative scheme codes to improve day ahead DNI forecasts on the national supercomputing infrastructure running mesoscale simulations on NOAA's Weather Research & Forecast (WRF) model. ASEFS also requires aerosol data fusion for improving accurate representation of spatio-temporally variable atmospheric aerosols to reduce DNI bias error in clear sky conditions over southern Queensland & New South Wales where solar power is vulnerable to uncertainities from frequent aerosol radiative events such as bush fires and desert dust. Initial results from thirteen years of Bureau of Meteorology's (BOM) deseasonalised DNI and MODIS NASA-Terra aerosol optical depth (AOD) anomalies demonstrated strong negative correlations in north and southeast Australia along with strong variability in AOD (~0.03-0.05). Radiative transfer schemes, DNI and AOD anomaly correlations will be discussed for the population and transmission grid centric regions where current and planned CSP plants dispatch electricity to capture peak prices in the market. Aerosol and solar irradiance datasets include satellite and ground based assimilations from the national BOM, regional aerosol researchers and agencies. The presentation will provide an overview of this ASEFS project task on WRF and results to date. The overall goal of this ASEFS subtask is to develop a hybrid numerical weather prediction (NWP) and statistical/machine learning multi-model ensemble strategy that meets future operational requirements of CSP plant operators.

  19. Case studies of NOAA 6/TIROS N data impact on numerical weather forecasts

    Science.gov (United States)

    Druyan, L. M.; Alperson, Z.; Ben-Amram, T.

    1984-01-01

    The impact of satellite temperatures from systems which predate the launching of the third generation of vertical sounding instruments aboard TIROS N (13 Oct 1978) and NOAA 6 (27 June 1979) is reported. The first evaluation of soundings from TIROS N found that oceanic, cloudy retrievals over NH mid latitudes show a cold bias in winter. It is confirmed for both satellite systems using a larger data base. It is shown that RMS differences between retrievals and colocated radiosonde observations within the swath 30-60N during the 1979-80 winter were generally 2-3K in clear air and higher for cloudy columns. A positive impact of TIROS N temperatures on the analysis of synoptic weather systems is shown. Analyses prepared from only satellite temperatures seemed to give a better definition to weather systems' thermal structure than that provided by corresponding NMC analyses without satellite data. The results of a set of 14 numerical forecast experiments performed with the PE model of the Israel Meteorological Service (IMS) are summarized; these were designed to test the impact of TIROS N and NOAA 6 temperatures within the IMS analysis and forecast cycle. The satellite data coverage over the NH, the mean area/period S1 and RMS verification scores and the spatial distribution of SAT versus NO SAT forecast differences are discussed and it is concluded that positive forecast impact occurs over ocean areas where the extra data improve the specification which is otherwise available from conventional observations. The forecast impact for three cases from the same set of experiments was examined and it is found that satellite temperatures, observed over the Atlantic Ocean contribute to better forecasts over Iceland and central Europe although a worse result was verified over Spain. It is also shown that the better scores of a forecast based also on satellite data and verified over North America actually represent a mixed impact on the forecast synoptic patterns. A superior 48 hr

  20. Integrated Land Data Assimilation System for Numerical Weather Prediction at the European Center for Medium-Range Weather Forecasts

    Science.gov (United States)

    de Rosnay, Patricia; Hólm, Elias; Bonavita, Massimo; English, Steve

    2017-04-01

    The European Centre for Medium-Range Weather Forecasts (ECMWF) system relies on an Earth System approach focusing on atmosphere, ocean, waves, land, and sea ice. Different data assimilation methods are used for the each component of the Earth System. A hybrid 4D-Var is used for the atmosphere, a simplified sea-surface temperature (SST) and sea ice analysis is used for medium-range forecasts and for the reanalyses (ERA-Interim and ERA5). The ECMWF land and atmosphere data assimilation systems are weakly coupled, using a coupled land-atmosphere background forecast and separate analyses for the atmosphere and for the surface (soil moisture and snow). Conventional and satellite observations that inform on the state of both subsystems are assimilated. They are located at the land-atmosphere interface and include two-metre temperature and relative humidity, snow depth, and soil moisture. In this presentation we present the land-atmosphere weakly coupled assimilation currently used at ECMWF for Numerical Weather Prediction (NWP) purpose. Perspectives of coupling enhancement using Ensemble Data Assimilaton (EDA) and EDA-based cross correlation estimates with coupling at the outer loop level of the atmospheric 4D-Var are discussed.

  1. Numerical simulation of birch pollen dispersion with an operational weather forecast system.

    Science.gov (United States)

    Vogel, Heike; Pauling, Andreas; Vogel, Bernhard

    2008-11-01

    We included a parameterisation of the emissions of pollen grains into the comprehensive model system COSMO-ART. In addition, a detailed density distribution of birch trees within Switzerland was derived. Based on these new developments, we carried out numerical simulations of the dispersion of pollen grains for an episode that occurred in April 2006 over Switzerland and the adjacent regions. Since COSMO-ART is based on the operational forecast model of the German Weather Service, we are presenting a feasibility study of daily pollen forecast based on methods which have been developed during the last two decades for the treatment of anthropogenic aerosol. A comparison of the model results and very detailed pollen counts documents the current possibilities and the shortcomings of the method and gives hints for necessary improvements.

  2. 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

  3. Cloud-Based Numerical Weather Prediction for Near Real-Time Forecasting and Disaster Response

    Science.gov (United States)

    Molthan, Andrew; Case, Jonathan; Venners, Jason; Schroeder, Richard; Checchi, Milton; Zavodsky, Bradley; Limaye, Ashutosh; O'Brien, Raymond

    2015-01-01

    The use of cloud computing resources continues to grow within the public and private sector components of the weather enterprise as users become more familiar with cloud-computing concepts, and competition among service providers continues to reduce costs and other barriers to entry. Cloud resources can also provide capabilities similar to high-performance computing environments, supporting multi-node systems required for near real-time, regional weather predictions. Referred to as "Infrastructure as a Service", or IaaS, the use of cloud-based computing hardware in an on-demand payment system allows for rapid deployment of a modeling system in environments lacking access to a large, supercomputing infrastructure. Use of IaaS capabilities to support regional weather prediction may be of particular interest to developing countries that have not yet established large supercomputing resources, but would otherwise benefit from a regional weather forecasting capability. Recently, collaborators from NASA Marshall Space Flight Center and Ames Research Center have developed a scripted, on-demand capability for launching the NOAA/NWS Science and Training Resource Center (STRC) Environmental Modeling System (EMS), which includes pre-compiled binaries of the latest version of the Weather Research and Forecasting (WRF) model. The WRF-EMS provides scripting for downloading appropriate initial and boundary conditions from global models, along with higher-resolution vegetation, land surface, and sea surface temperature data sets provided by the NASA Short-term Prediction Research and Transition (SPoRT) Center. This presentation will provide an overview of the modeling system capabilities and benchmarks performed on the Amazon Elastic Compute Cloud (EC2) environment. In addition, the presentation will discuss future opportunities to deploy the system in support of weather prediction in developing countries supported by NASA's SERVIR Project, which provides capacity building

  4. Forecasting severe ice storms using numerical weather prediction: the March 2010 Newfoundland event

    Directory of Open Access Journals (Sweden)

    J. Hosek

    2011-02-01

    Full Text Available The northeast coast of North America is frequently hit by severe ice storms. These freezing rain events can produce large ice accretions that damage structures, frequently power transmission and distribution infrastructure. For this reason, it is highly desirable to model and forecast such icing events, so that the consequent damages can be prevented or mitigated. The case study presented in this paper focuses on the March 2010 ice storm event that took place in eastern Newfoundland. We apply a combination of a numerical weather prediction model and an ice accretion algorithm to simulate a forecast of this event.

    The main goals of this study are to compare the simulated meteorological variables to observations, and to assess the ability of the model to accurately predict the ice accretion load for different forecast horizons. The duration and timing of the freezing rain event that occurred between the night of 4 March and the morning of 6 March was simulated well in all model runs. The total precipitation amounts in the model, however, differed by up to a factor of two from the observations. The accuracy of the model air temperature strongly depended on the forecast horizon, but it was acceptable for all simulation runs. The simulated accretion loads were also compared to the design values for power delivery structures in the region. The results indicated that the simulated values exceeded design criteria in the areas of reported damage and power outages.

  5. Evaluating the use of high-resolution numerical weather forecast for debris flow prediction.

    Science.gov (United States)

    Nikolopoulos, Efthymios I.; Bartsotas, Nikolaos S.; Borga, Marco; Kallos, George

    2015-04-01

    The sudden occurrence combined with the high destructive power of debris flows pose a significant threat to human life and infrastructures. Therefore, developing early warning procedures for the mitigation of debris flows risk is of great economical and societal importance. Given that rainfall is the predominant factor controlling debris flow triggering, it is indisputable that development of effective debris flows warning procedures requires accurate knowledge of the properties (e.g. duration, intensity) of the triggering rainfall. Moreover, efficient and timely response of emergency operations depends highly on the lead-time provided by the warning systems. Currently, the majority of early warning systems for debris flows are based on nowcasting procedures. While the latter may be successful in predicting the hazard, they provide warnings with a relatively short lead-time (~6h). Increasing the lead-time is necessary in order to improve the pre-incident operations and communication of the emergency, thus coupling warning systems with weather forecasting is essential for advancing early warning procedures. In this work we evaluate the potential of using high-resolution (1km) rainfall fields forecasted with a state-of-the-art numerical weather prediction model (RAMS/ICLAMS), in order to predict the occurrence of debris flows. Analysis is focused over the Upper Adige region, Northeast Italy, an area where debris flows are frequent. Seven storm events that generated a large number (>80) of debris flows during the period 2007-2012 are analyzed. Radar-based rainfall estimates, available from the operational C-band radar located at Mt Macaion, are used as the reference to evaluate the forecasted rainfall fields. Evaluation is mainly focused on assessing the error in forecasted rainfall properties (magnitude, duration) and the correlation in space and time with the reference field. Results show that the forecasted rainfall fields captured very well the magnitude and

  6. 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...

  7. Verification of global numerical weather forecasting systems in polar regions using TIGGE data

    OpenAIRE

    Jung, Thomas; Matsueda, Mio

    2016-01-01

    High-latitude climate change is expected to increase the demand for reliable weather and environmental forecasts in polar regions. In this study, a quantitative assessment of the skill of state-of-the-art global weather prediction systems in polar regions is given using data from the THORPEX Interactive Grand Global Ensemble (TIGGE) for the period 2006/2007 – 2012/2013. Forecast skill in the Arctic is comparable to that found in the North- ern Hemisphere ...

  8. Feasibility Study of Short-Term Storm Forecasting Over the Gulf of Mexico by Blending Satellite-Based Extrapolation Forecasts with Numerical Weather Prediction Results

    Science.gov (United States)

    Cai, H.; Kessinger, C.; Rehak, N.; Pinto, J. O.; Megenhardt, D.; Albo, D.; Phillips, C.; Bankert, R.; Hawkins, J.

    2012-12-01

    Deep convection over the ocean poses a potentially great danger for trans-oceanic flights, as tragically demonstrated by the Air France Flight 447 accident of 2009. This paper describes a forecasting system that will produce 0-12 hr convective forecasts over the Gulf of Mexico domain using a blending technique that combines satellite-based extrapolation forecasts with Numerical Weather Prediction (NWP) model forecasts. Closely following the steps of the Federal Aviation Administration (FAA) Aviation Weather Research Program (AWRP) CoSPA development, a forecasting system is being developed to blend satellite-derived rain rate and cloud top height with their corresponding fields derived from the Global Forecasting System (GFS) NWP model. Forecasts will be computed over the 0-12 hr time frame within a domain that encompasses the greater Gulf of Mexico and parts of the continental United States. Tests of various extrapolation techniques have been completed and an optimum technique has been selected. Both the extrapolated and the GFS rain rate forecast performance statistics have been compiled. Considering the relative strength of the NWP model and the satellite-based extrapolation forecasts, a dynamical-weighting technique, similar to what is being used in CoSPA, has been tested. The weights are determined by past performance of extrapolation and model forecasts as a function of forecast lead time. A prototype blended forecasting system for oceanic convection using dynamical-weighting techniques has been developed and preliminary results of the blended forecasting system will be reported at the conference.

  9. Real-time dynamic control of the Three Gorges Reservoir by coupling numerical weather rainfall prediction and flood forecasting

    DEFF Research Database (Denmark)

    Wang, Y.; Chen, H.; Rosbjerg, Dan

    2013-01-01

    In reservoir operation improvement of the accuracy of forecast flood inflow and extension of forecast lead-time can effectively be achieved by using rainfall forecasts from numerical weather predictions with a hydrological catchment model. In this study, the Regional Spectrum Model (RSM), which...... is developed by the Japan Meteorological Agency, was used to forecast rainfall with 5 days lead-time in the upper region of the Three Gorges Reservoir (TGR). A conceptual hydrological model, the Xinanjiang Model, has been set up to forecast the inflow flood of TGR by the Ministry of Water Resources Information...... Center. Here, the flood forecast model coupled with the rainfall forecast from RSM has been employed to carry out real-time dynamic control of the Flood Limiting Water Level (FLWL) of TGR in order to improve the hydropower generation without increasing the flood risk. Taking the flood events of the flood...

  10. 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.

  11. 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.

  12. Assessing the Impact of Observations on Numerical Weather Forecasts Using the Adjoint Method

    Science.gov (United States)

    Gelaro, Ronald

    2012-01-01

    The adjoint of a data assimilation system provides a flexible and efficient tool for estimating observation impacts on short-range weather forecasts. The impacts of any or all observations can be estimated simultaneously based on a single execution of the adjoint system. The results can be easily aggregated according to data type, location, channel, etc., making this technique especially attractive for examining the impacts of new hyper-spectral satellite instruments and for conducting regular, even near-real time, monitoring of the entire observing system. This talk provides a general overview of the adjoint method, including the theoretical basis and practical implementation of the technique. Results are presented from the adjoint-based observation impact monitoring tool in NASA's GEOS-5 global atmospheric data assimilation and forecast system. When performed in conjunction with standard observing system experiments (OSEs), the adjoint results reveal both redundancies and dependencies between observing system impacts as observations are added or removed from the assimilation system. Understanding these dependencies may be important for optimizing the use of the current observational network and defining requirements for future observing systems

  13. 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.

  14. 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...

  15. 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...

  16. 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...

  17. 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...

  18. Space Weather Forecasting at IZMIRAN

    Science.gov (United States)

    Gaidash, S. P.; Belov, A. V.; Abunina, M. A.; Abunin, A. A.

    2017-12-01

    Since 1998, the Institute of Terrestrial Magnetism, Ionosphere, and Radio Wave Propagation (IZMIRAN) has had an operating heliogeophysical service—the Center for Space Weather Forecasts. This center transfers the results of basic research in solar-terrestrial physics into daily forecasting of various space weather parameters for various lead times. The forecasts are promptly available to interested consumers. This article describes the center and the main types of forecasts it provides: solar and geomagnetic activity, magnetospheric electron fluxes, and probabilities of proton increases. The challenges associated with the forecasting of effects of coronal mass ejections and coronal holes are discussed. Verification data are provided for the center's forecasts.

  19. 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.

  20. Now, Here's the Weather Forecast...

    Science.gov (United States)

    Richardson, Mathew

    2013-01-01

    The Met Office has a long history of weather forecasting, creating tailored weather forecasts for customers across the world. Based in Exeter, the Met Office is also home to the Met Office Hadley Centre, a world-leading centre for the study of climate change and its potential impacts. Climate information from the Met Office Hadley Centre is used…

  1. 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)

  2. 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

  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. Severe Weather Forecast Decision Aid

    Science.gov (United States)

    Bauman, William H., III; Wheeler, Mark M.; Short, David A.

    2005-01-01

    This report presents a 15-year climatological study of severe weather events and related severe weather atmospheric parameters. Data sources included local forecast rules, archived sounding data, Cloud-to-Ground Lightning Surveillance System (CGLSS) data, surface and upper air maps, and two severe weather event databases covering east-central Florida. The local forecast rules were used to set threat assessment thresholds for stability parameters that were derived from the sounding data. The severe weather events databases were used to identify days with reported severe weather and the CGLSS data was used to differentiate between lightning and non-lightning days. These data sets provided the foundation for analyzing the stability parameters and synoptic patterns that were used to develop an objective tool to aid in forecasting severe weather events. The period of record for the analysis was May - September, 1989 - 2003. The results indicate that there are certain synoptic patterns more prevalent on days with severe weather and some of the stability parameters are better predictors of severe weather days based on locally tuned threat values. The results also revealed the stability parameters that did not display any skill related to severe weather days. An interactive web-based Severe Weather Decision Aid was developed to assist the duty forecaster by providing a level of objective guidance based on the analysis of the stability parameters, CGLSS data, and synoptic-scale dynamics. The tool will be tested and evaluated during the 2005 warm season.

  5. 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.

  6. 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...

  7. 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.

  8. Weather Forecasting From Woolly Art to Solid Science

    Science.gov (United States)

    Lynch, P.

    THE PREHISTORY OF SCIENTIFIC FORECASTING Vilhelm Bjerknes Lewis Fry Richardson Richardson's Forecast THE BEGINNING OF MODERN NUMERICAL WEATHER PREDICTION John von Neumann and the Meteorology Project The ENIAC Integrations The Barotropic Model Primitive Equation Models NUMERICAL WEATHER PREDICTION TODAY ECMWF HIRLAM CONCLUSIONS REFERENCES

  9. 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

  10. Weather and climate forecasting : chronicle of a revolution

    OpenAIRE

    Lynch, Peter

    2010-01-01

    Remarkable advances in weather forecasts during the past half-century have brought great benefits to humanity. Accurate forecasts save many lives, and early warnings mitigate the worst effects of extreme weather events, when they are available. Detailed, accurate forecasts are of huge economic value, with numerous studies showing that the benefits of forecasts outweigh the costs many times over. Advances in climate modeling over the past fifty years hav...

  11. Road weather forecast quality analysis : project summary

    Science.gov (United States)

    2006-03-01

    The purpose of this research is to enhance the use of KDOTs Roadway Weather : Information System by improving the weather forecasts themselves and raising the level of : confidence in these forecasts.

  12. Improving weather forecasts for wind energy applications

    Science.gov (United States)

    Kay, Merlinde; MacGill, Iain

    2010-08-01

    Weather forecasts play an important role in the energy industry particularly because of the impact of temperature on electrical demand. Power system operation requires that this variable and somewhat unpredictable demand be precisely met at all times and locations from available generation. As wind generation makes up a growing component of electricity supply around the world, it has become increasingly important to be able to provide useful forecasting for this highly variable and uncertain energy resource. Of particular interest are forecasts of weather events that rapidly change wind energy production from one or more wind farms. In this paper we describe work underway to improve the wind forecasts currently available from standard Numerical Weather Prediction (NWP) through a bias correction methodology. Our study has used the Australian Bureau of Meteorology MesoLAPS 5 km limited domain model over the Victoria/Tasmania region, providing forecasts for the Woolnorth wind farm, situated in Tasmania, Australia. The accuracy of these forecasts has been investigated, concentrating on the key wind speed ranges 5 - 15 ms-1 and around 25 ms-1. A bias correction methodology was applied to the NWP hourly forecasts to help account for systematic issues such as the NWP grid point not being at the exact location of the wind farm. An additional correction was applied for timing issues by using meteorological data from the wind farm. Results to date show a reduction in spread of forecast error for hour ahead forecasts by as much as half using this double correction methodology - a combination of both bias correction and timing correction.

  13. Improving weather forecasts for wind energy applications

    International Nuclear Information System (INIS)

    Kay, Merlinde; MacGill, Iain

    2010-01-01

    Weather forecasts play an important role in the energy industry particularly because of the impact of temperature on electrical demand. Power system operation requires that this variable and somewhat unpredictable demand be precisely met at all times and locations from available generation. As wind generation makes up a growing component of electricity supply around the world, it has become increasingly important to be able to provide useful forecasting for this highly variable and uncertain energy resource. Of particular interest are forecasts of weather events that rapidly change wind energy production from one or more wind farms. In this paper we describe work underway to improve the wind forecasts currently available from standard Numerical Weather Prediction (NWP) through a bias correction methodology. Our study has used the Australian Bureau of Meteorology MesoLAPS 5 km limited domain model over the Victoria/Tasmania region, providing forecasts for the Woolnorth wind farm, situated in Tasmania, Australia. The accuracy of these forecasts has been investigated, concentrating on the key wind speed ranges 5 - 15 ms -1 and around 25 ms -1 . A bias correction methodology was applied to the NWP hourly forecasts to help account for systematic issues such as the NWP grid point not being at the exact location of the wind farm. An additional correction was applied for timing issues by using meteorological data from the wind farm. Results to date show a reduction in spread of forecast error for hour ahead forecasts by as much as half using this double correction methodology - a combination of both bias correction and timing correction.

  14. Improving Local Weather Forecasts for Agricultural Applications

    NARCIS (Netherlands)

    Doeswijk, T.G.; Keesman, K.J.

    2005-01-01

    For controlling agricultural systems, weather forecasts can be of substantial importance. Studies have shown that forecast errors can be reduced in terms of bias and standard deviation using forecasts and meteorological measurements from one specific meteorological station. For agricultural systems

  15. Assessing the Impact of Surface and Upper-Air Observations on the Forecast Skill of the ACCESS Numerical Weather Prediction Model over Australia

    Directory of Open Access Journals (Sweden)

    Sergei Soldatenko

    2018-01-01

    Full Text Available The impact of the Australian Bureau of Meteorology’s in situ observations (land and sea surface observations, upper air observations by radiosondes, pilot balloons, wind profilers, and aircraft observations on the short-term forecast skill provided by the ACCESS (Australian Community Climate and Earth-System Simulator global numerical weather prediction (NWP system is evaluated using an adjoint-based method. This technique makes use of the adjoint perturbation forecast model utilized within the 4D-Var assimilation system, and is able to calculate the individual impact of each assimilated observation in a cycling NWP system. The results obtained show that synoptic observations account for about 60% of the 24-h forecast error reduction, with the remainder accounted for by aircraft (12.8%, radiosondes (10.5%, wind profilers (3.9%, pilot balloons (2.8%, buoys (1.7% and ships (1.2%. In contrast, the largest impact per observation is from buoys and aircraft. Overall, all observation types have a positive impact on the 24-h forecast skill. Such results help to support the decision-making process regarding the evolution of the observing network, particularly at the national level. Consequently, this 4D-Var-based approach has great potential as a tool to assist the design and running of an efficient and effective observing network.

  16. 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.

  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. 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)...

  19. Enhanced road weather forecasting : Clarus regional demonstrations.

    Science.gov (United States)

    2011-01-01

    The quality of road weather forecasts : has major impacts on users of surface : transportation systems and managers : of those systems. Improving the quality : involves the ability to provide accurate, : route-specific road weather information : (e.g...

  20. Urban runoff forecasting with ensemble weather predictions

    DEFF Research Database (Denmark)

    Pedersen, Jonas Wied; Courdent, Vianney Augustin Thomas; Vezzaro, Luca

    This research shows how ensemble weather forecasts can be used to generate urban runoff forecasts up to 53 hours into the future. The results highlight systematic differences between ensemble members that needs to be accounted for when these forecasts are used in practice.......This research shows how ensemble weather forecasts can be used to generate urban runoff forecasts up to 53 hours into the future. The results highlight systematic differences between ensemble members that needs to be accounted for when these forecasts are used in practice....

  1. 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.

  2. 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...

  3. Probabilistic Space Weather Forecasting: a Bayesian Perspective

    Science.gov (United States)

    Camporeale, E.; Chandorkar, M.; Borovsky, J.; Care', A.

    2017-12-01

    Most of the Space Weather forecasts, both at operational and research level, are not probabilistic in nature. Unfortunately, a prediction that does not provide a confidence level is not very useful in a decision-making scenario. Nowadays, forecast models range from purely data-driven, machine learning algorithms, to physics-based approximation of first-principle equations (and everything that sits in between). Uncertainties pervade all such models, at every level: from the raw data to finite-precision implementation of numerical methods. The most rigorous way of quantifying the propagation of uncertainties is by embracing a Bayesian probabilistic approach. One of the simplest and most robust machine learning technique in the Bayesian framework is Gaussian Process regression and classification. Here, we present the application of Gaussian Processes to the problems of the DST geomagnetic index forecast, the solar wind type classification, and the estimation of diffusion parameters in radiation belt modeling. In each of these very diverse problems, the GP approach rigorously provide forecasts in the form of predictive distributions. In turn, these distributions can be used as input for ensemble simulations in order to quantify the amplification of uncertainties. We show that we have achieved excellent results in all of the standard metrics to evaluate our models, with very modest computational cost.

  4. Uncertainty Forecasts Improve Weather-Related Decisions and Attenuate the Effects of Forecast Error

    Science.gov (United States)

    Joslyn, Susan L.; LeClerc, Jared E.

    2012-01-01

    Although uncertainty is inherent in weather forecasts, explicit numeric uncertainty estimates are rarely included in public forecasts for fear that they will be misunderstood. Of particular concern are situations in which precautionary action is required at low probabilities, often the case with severe events. At present, a categorical weather…

  5. Earth Remote Sensing for Weather Forecasting and Disaster Applications

    Science.gov (United States)

    Molthan, Andrew; Bell, Jordan; Case, Jonathan; Cole, Tony; Elmer, Nicholas; McGrath, Kevin; Schultz, Lori; Zavodsky, Brad

    2016-01-01

    NASA's constellation of current missions provide several opportunities to apply satellite remote sensing observations to weather forecasting and disaster response applications. Examples include: Using NASA's Terra and Aqua MODIS, and the NASA/NOAA Suomi-NPP VIIRS missions to prepare weather forecasters for capabilities of GOES-R; Incorporating other NASA remote sensing assets for improving aspects of numerical weather prediction; Using NASA, NOAA, and international partner resources (e.g. ESA/Sentinel Series); and commercial platforms (high-res, or UAV) to support disaster mapping.

  6. 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...

  7. Satellite Sounder Data Assimilation for Improving Alaska Region Weather Forecast

    Science.gov (United States)

    Zhu, Jiang; Stevens, E.; Zavodsky, B. T.; Zhang, X.; Heinrichs, T.; Broderson, D.

    2014-01-01

    Data assimilation has been demonstrated very useful in improving both global and regional numerical weather prediction. Alaska has very coarser surface observation sites. On the other hand, it gets much more satellite overpass than lower 48 states. How to utilize satellite data to improve numerical prediction is one of hot topics among weather forecast community in Alaska. The Geographic Information Network of Alaska (GINA) at University of Alaska is conducting study on satellite data assimilation for WRF model. AIRS/CRIS sounder profile data are used to assimilate the initial condition for the customized regional WRF model (GINA-WRF model). Normalized standard deviation, RMSE, and correlation statistic analysis methods are applied to analyze one case of 48 hours forecasts and one month of 24-hour forecasts in order to evaluate the improvement of regional numerical model from Data assimilation. The final goal of the research is to provide improved real-time short-time forecast for Alaska regions.

  8. School Science Inspired by Improving Weather Forecasts

    Science.gov (United States)

    Reid, Heather; Renfrew, Ian A.; Vaughan, Geraint

    2014-01-01

    High winds and heavy rain are regular features of the British weather, and forecasting these events accurately is a major priority for the Met Office and other forecast providers. This is the challenge facing DIAMET, a project involving university groups from Manchester, Leeds, Reading, and East Anglia, together with the Met Office. DIAMET is part…

  9. Economic Impact of Fire Weather Forecasts

    Science.gov (United States)

    Don Gunasekera; Graham Mills; Mark Williams

    2006-01-01

    Southeastern Australia, where the State of Victoria is located is regarded as one of the most fire prone areas in the world. The Australian Bureau of Meteorology provides fire weather services in Victoria as part of a national framework for the provision of such services. These services range from fire weather warnings to special forecasts for hazard reduction burns....

  10. How MAG4 Improves Space Weather Forecasting

    Science.gov (United States)

    Falconer, David; Khazanov, Igor; Barghouty, Nasser

    2013-01-01

    Dangerous space weather is driven by solar flares and Coronal Mass Ejection (CMEs). Forecasting flares and CMEs is the first step to forecasting either dangerous space weather or All Clear. MAG4 (Magnetogram Forecast), developed originally for NASA/SRAG (Space Radiation Analysis Group), is an automated program that analyzes magnetograms from the HMI (Helioseismic and Magnetic Imager) instrument on NASA SDO (Solar Dynamics Observatory), and automatically converts the rate (or probability) of major flares (M- and X-class), Coronal Mass Ejections (CMEs), and Solar Energetic Particle Events.

  11. Recent Progress of Solar Weather Forecasting at Naoc

    Science.gov (United States)

    He, Han; Wang, Huaning; Du, Zhanle; Zhang, Liyun; Huang, Xin; Yan, Yan; Fan, Yuliang; Zhu, Xiaoshuai; Guo, Xiaobo; Dai, Xinghua

    The history of solar weather forecasting services at National Astronomical Observatories, Chinese Academy of Sciences (NAOC) can be traced back to 1960s. Nowadays, NAOC is the headquarters of the Regional Warning Center of China (RWC-China), which is one of the members of the International Space Environment Service (ISES). NAOC is responsible for exchanging data, information and space weather forecasts of RWC-China with other RWCs. The solar weather forecasting services at NAOC cover short-term prediction (within two or three days), medium-term prediction (within several weeks), and long-term prediction (in time scale of solar cycle) of solar activities. Most efforts of the short-term prediction research are concentrated on the solar eruptive phenomena, such as flares, coronal mass ejections (CMEs) and solar proton events, which are the key driving sources of strong space weather disturbances. Based on the high quality observation data of the latest space-based and ground-based solar telescopes and with the help of artificial intelligence techniques, new numerical models with quantitative analyses and physical consideration are being developed for the predictions of solar eruptive events. The 3-D computer simulation technology is being introduced for the operational solar weather service platform to visualize the monitoring of solar activities, the running of the prediction models, as well as the presenting of the forecasting results. A new generation operational solar weather monitoring and forecasting system is expected to be constructed in the near future at NAOC.

  12. Space weather forecasting: Past, Present, Future

    Science.gov (United States)

    Lanzerotti, L. J.

    2012-12-01

    There have been revolutionary advances in electrical technologies over the last 160 years. The historical record demonstrates that space weather processes have often provided surprises in the implementation and operation of many of these technologies. The historical record also demonstrates that as the complexity of systems increase, including their interconnectedness and interoperability, they can become more susceptible to space weather effects. An engineering goal, beginning during the decades following the 1859 Carrington event, has been to attempt to forecast solar-produced disturbances that could affect technical systems, be they long grounded conductor-based or radio-based or required for exploration, or the increasingly complex systems immersed in the space environment itself. Forecasting of space weather events involves both frontier measurements and models to address engineering requirements, and industrial and governmental policies that encourage and permit creativity and entrepreneurship. While analogies of space weather forecasting to terrestrial weather forecasting are frequently made, and while many of the analogies are valid, there are also important differences. This presentation will provide some historical perspectives on the forecast problem, a personal assessment of current status of several areas including important policy issues, and a look into the not-too-distant future.

  13. Improved Local Weather Forecasts Using Artificial Neural Networks

    DEFF Research Database (Denmark)

    Wollsen, Morten Gill; Jørgensen, Bo Nørregaard

    2015-01-01

    Solar irradiance and temperature forecasts are used in many different control systems. Such as intelligent climate control systems in commercial greenhouses, where the solar irradiance affects the use of supplemental lighting. This paper proposes a novel method to predict the forthcoming weather...... using an artificial neural network. The neural network used is a NARX network, which is known to model non-linear systems well. The predictions are compared to both a design reference year as well as commercial weather forecasts based upon numerical modelling. The results presented in this paper show...

  14. Interactive Forecasting with the National Weather Service River Forecast System

    Science.gov (United States)

    Smith, George F.; Page, Donna

    1993-01-01

    The National Weather Service River Forecast System (NWSRFS) consists of several major hydrometeorologic subcomponents to model the physics of the flow of water through the hydrologic cycle. The entire NWSRFS currently runs in both mainframe and minicomputer environments, using command oriented text input to control the system computations. As computationally powerful and graphically sophisticated scientific workstations became available, the National Weather Service (NWS) recognized that a graphically based, interactive environment would enhance the accuracy and timeliness of NWS river and flood forecasts. Consequently, the operational forecasting portion of the NWSRFS has been ported to run under a UNIX operating system, with X windows as the display environment on a system of networked scientific workstations. In addition, the NWSRFS Interactive Forecast Program was developed to provide a graphical user interface to allow the forecaster to control NWSRFS program flow and to make adjustments to forecasts as necessary. The potential market for water resources forecasting is immense and largely untapped. Any private company able to market the river forecasting technologies currently developed by the NWS Office of Hydrology could provide benefits to many information users and profit from providing these services.

  15. 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.

  16. 14 CFR 135.213 - Weather reports and forecasts.

    Science.gov (United States)

    2010-01-01

    ... 14 Aeronautics and Space 3 2010-01-01 2010-01-01 false Weather reports and forecasts. 135.213... Operating Limitations and Weather Requirements § 135.213 Weather reports and forecasts. (a) Whenever a person operating an aircraft under this part is required to use a weather report or forecast, that person...

  17. Visualizing Uncertainty for Probabilistic Weather Forecasting based on Reforecast Analogs

    Science.gov (United States)

    Pelorosso, Leandro; Diehl, Alexandra; Matković, Krešimir; Delrieux, Claudio; Ruiz, Juan; Gröeller, M. Eduard; Bruckner, Stefan

    2016-04-01

    Numerical weather forecasts are prone to uncertainty coming from inaccuracies in the initial and boundary conditions and lack of precision in numerical models. Ensemble of forecasts partially addresses these problems by considering several runs of the numerical model. Each forecast is generated with different initial and boundary conditions and different model configurations [GR05]. The ensembles can be expressed as probabilistic forecasts, which have proven to be very effective in the decision-making processes [DE06]. The ensemble of forecasts represents only some of the possible future atmospheric states, usually underestimating the degree of uncertainty in the predictions [KAL03, PH06]. Hamill and Whitaker [HW06] introduced the "Reforecast Analog Regression" (RAR) technique to overcome the limitations of ensemble forecasting. This technique produces probabilistic predictions based on the analysis of historical forecasts and observations. Visual analytics provides tools for processing, visualizing, and exploring data to get new insights and discover hidden information patterns in an interactive exchange between the user and the application [KMS08]. In this work, we introduce Albero, a visual analytics solution for probabilistic weather forecasting based on the RAR technique. Albero targets at least two different type of users: "forecasters", who are meteorologists working in operational weather forecasting and "researchers", who work in the construction of numerical prediction models. Albero is an efficient tool for analyzing precipitation forecasts, allowing forecasters to make and communicate quick decisions. Our solution facilitates the analysis of a set of probabilistic forecasts, associated statistical data, observations and uncertainty. A dashboard with small-multiples of probabilistic forecasts allows the forecasters to analyze at a glance the distribution of probabilities as a function of time, space, and magnitude. It provides the user with a more

  18. Visually Comparing Weather Features in Forecasts.

    Science.gov (United States)

    Quinan, P Samuel; Meyer, Miriah

    2016-01-01

    Meteorologists process and analyze weather forecasts using visualization in order to examine the behaviors of and relationships among weather features. In this design study conducted with meteorologists in decision support roles, we identified and attempted to address two significant common challenges in weather visualization: the employment of inconsistent and often ineffective visual encoding practices across a wide range of visualizations, and a lack of support for directly visualizing how different weather features relate across an ensemble of possible forecast outcomes. In this work, we present a characterization of the problems and data associated with meteorological forecasting, we propose a set of informed default encoding choices that integrate existing meteorological conventions with effective visualization practice, and we extend a set of techniques as an initial step toward directly visualizing the interactions of multiple features over an ensemble forecast. We discuss the integration of these contributions into a functional prototype tool, and also reflect on the many practical challenges that arise when working with weather data.

  19. Forecasting the space weather impact

    DEFF Research Database (Denmark)

    Crosby, N. B.; Veronig, A.; Robbrecht, E.

    2012-01-01

    The FP7 COronal Mass Ejections and Solar Energetic Particles (COMESEP) project is developing tools for forecasting geomagnetic storms and solar energetic particle (SEP) radiation storms. By analysis of historical data, complemented by the extensive data coverage of solar cycle 23, the key ingredi...

  20. Combining Spatial Statistical and Ensemble Information in Probabilistic Weather Forecasts

    National Research Council Canada - National Science Library

    Berrocal, Veronica J; Raftery, Adrian E; Gneiting, Tilmann

    2006-01-01

    .... Bayesian model averaging (BMA) is a statistical postprocessing method for forecast ensembles that generates calibrated probabilistic forecast products for weather quantities at individual sites...

  1. WOD - Weather On Demand forecasting system

    Science.gov (United States)

    Rognvaldsson, Olafur; Ragnarsson, Logi; Stanislawska, Karolina

    2017-04-01

    The backbone of the Belgingur forecasting system (called WOD - Weather On Demand) is the WRF-Chem atmospheric model, with a number of in-house customisations. Initial and boundary data are taken from the Global Forecasting System, operated by the National Oceanic and Atmospheric Administration (NOAA). Operational forecasts use cycling of a number of parameters, mainly deep soil and surface fields. This is done to minimise spin-up effects and to ensure proper book-keeping of hydrological fields such as snow accumulation and runoff, as well as the constituents of various chemical parameters. The WOD system can be used to create conventional short- to medium-range weather forecasts for any location on the globe. The WOD system can also be used for air quality purposes (e.g. dispersion forecasts from volcanic eruptions) and as a tool to provide input to other modelling systems, such as hydrological models. A wide variety of post-processing options are also available, making WOD an ideal tool for creating highly customised output that can be tailored to the specific needs of individual end-users. The most recent addition to the WOD system is an integrated verification system where forecasts can be compared to surface observations from chosen locations. Forecast visualisation, such as weather charts, meteograms, weather icons and tables, is done via number of web components that can be configured to serve the varying needs of different end-users. The WOD system itself can be installed in an automatic way on hardware running a range of Linux based OS. System upgrades can also be done in semi-automatic fashion, i.e. upgrades and/or bug-fixes can be pushed to the end-user hardware without system downtime. Importantly, the WOD system requires only rudimentary knowledge of the WRF modelling, and the Linux operating systems on behalf of the end-user, making it an ideal NWP tool in locations with limited IT infrastructure.

  2. Road weather forecast quality analysis

    Science.gov (United States)

    2006-03-01

    It is just as important to keep the highways functioning in a safe and efficient manner as it is to construct them in : the first place. Our economy is built around an efficient transportation system. Winter weather plays an important role : in highw...

  3. Tomorrow's Forecast: Oceans and Weather.

    Science.gov (United States)

    Smigielski, Alan

    1995-01-01

    This issue of "Art to Zoo" focuses on weather and climate and is tied to the traveling exhibition Ocean Planet from the Smithsonian's National Museum of Natural History. The lessons encourage students to think about the profound influence the oceans have on planetary climate and life on earth. Sections of the lesson plan include: (1)…

  4. The Quest for the Perfect Weather Forecaster

    Science.gov (United States)

    Kahl, Jonathan; Horwitz, Kevin; Berg, Craig; Gruhl, Mary

    2004-01-01

    It is said that meteorology is the only profession where a person can be wrong half the time and still keep his or her job. The truth is not quite so bleak, but one can still ask, "Just how accurate are weather forecasters, anyway?" This article presents two projects for middle level students to investigate this issue in a hands-on,…

  5. Magnetogram Forecast: An All-Clear Space Weather Forecasting System

    Science.gov (United States)

    Barghouty, Nasser; Falconer, David

    2015-01-01

    Solar flares and coronal mass ejections (CMEs) are the drivers of severe space weather. Forecasting the probability of their occurrence is critical in improving space weather forecasts. The National Oceanic and Atmospheric Administration (NOAA) currently uses the McIntosh active region category system, in which each active region on the disk is assigned to one of 60 categories, and uses the historical flare rates of that category to make an initial forecast that can then be adjusted by the NOAA forecaster. Flares and CMEs are caused by the sudden release of energy from the coronal magnetic field by magnetic reconnection. It is believed that the rate of flare and CME occurrence in an active region is correlated with the free energy of an active region. While the free energy cannot be measured directly with present observations, proxies of the free energy can instead be used to characterize the relative free energy of an active region. The Magnetogram Forecast (MAG4) (output is available at the Community Coordinated Modeling Center) was conceived and designed to be a databased, all-clear forecasting system to support the operational goals of NASA's Space Radiation Analysis Group. The MAG4 system automatically downloads nearreal- time line-of-sight Helioseismic and Magnetic Imager (HMI) magnetograms on the Solar Dynamics Observatory (SDO) satellite, identifies active regions on the solar disk, measures a free-energy proxy, and then applies forecasting curves to convert the free-energy proxy into predicted event rates for X-class flares, M- and X-class flares, CMEs, fast CMEs, and solar energetic particle events (SPEs). The forecast curves themselves are derived from a sample of 40,000 magnetograms from 1,300 active region samples, observed by the Solar and Heliospheric Observatory Michelson Doppler Imager. Figure 1 is an example of MAG4 visual output

  6. Convective Weather Forecast Accuracy Analysis at Center and Sector Levels

    Science.gov (United States)

    Wang, Yao; Sridhar, Banavar

    2010-01-01

    This paper presents a detailed convective forecast accuracy analysis at center and sector levels. The study is aimed to provide more meaningful forecast verification measures to aviation community, as well as to obtain useful information leading to the improvements in the weather translation capacity models. In general, the vast majority of forecast verification efforts over past decades have been on the calculation of traditional standard verification measure scores over forecast and observation data analyses onto grids. These verification measures based on the binary classification have been applied in quality assurance of weather forecast products at the national level for many years. Our research focuses on the forecast at the center and sector levels. We calculate the standard forecast verification measure scores for en-route air traffic centers and sectors first, followed by conducting the forecast validation analysis and related verification measures for weather intensities and locations at centers and sectors levels. An approach to improve the prediction of sector weather coverage by multiple sector forecasts is then developed. The weather severe intensity assessment was carried out by using the correlations between forecast and actual weather observation airspace coverage. The weather forecast accuracy on horizontal location was assessed by examining the forecast errors. The improvement in prediction of weather coverage was determined by the correlation between actual sector weather coverage and prediction. observed and forecasted Convective Weather Avoidance Model (CWAM) data collected from June to September in 2007. CWAM zero-minute forecast data with aircraft avoidance probability of 60% and 80% are used as the actual weather observation. All forecast measurements are based on 30-minute, 60- minute, 90-minute, and 120-minute forecasts with the same avoidance probabilities. The forecast accuracy analysis for times under one-hour showed that the errors in

  7. Flare forecasting at the Met Office Space Weather Operations Centre

    Science.gov (United States)

    Murray, S. A.; Bingham, S.; Sharpe, M.; Jackson, D. R.

    2017-04-01

    The Met Office Space Weather Operations Centre produces 24/7/365 space weather guidance, alerts, and forecasts to a wide range of government and commercial end-users across the United Kingdom. Solar flare forecasts are one of its products, which are issued multiple times a day in two forms: forecasts for each active region on the solar disk over the next 24 h and full-disk forecasts for the next 4 days. Here the forecasting process is described in detail, as well as first verification of archived forecasts using methods commonly used in operational weather prediction. Real-time verification available for operational flare forecasting use is also described. The influence of human forecasters is highlighted, with human-edited forecasts outperforming original model results and forecasting skill decreasing over longer forecast lead times.

  8. Resolution of Probabilistic Weather Forecasts with Application in Disease Management.

    Science.gov (United States)

    Hughes, G; McRoberts, N; Burnett, F J

    2017-02-01

    Predictive systems in disease management often incorporate weather data among the disease risk factors, and sometimes this comes in the form of forecast weather data rather than observed weather data. In such cases, it is useful to have an evaluation of the operational weather forecast, in addition to the evaluation of the disease forecasts provided by the predictive system. Typically, weather forecasts and disease forecasts are evaluated using different methodologies. However, the information theoretic quantity expected mutual information provides a basis for evaluating both kinds of forecast. Expected mutual information is an appropriate metric for the average performance of a predictive system over a set of forecasts. Both relative entropy (a divergence, measuring information gain) and specific information (an entropy difference, measuring change in uncertainty) provide a basis for the assessment of individual forecasts.

  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. Communicating Environmental Uncertainty: The Nature of Weather Forecasts.

    Science.gov (United States)

    Travis, Richard W.; Riebsame, William E.

    1979-01-01

    Traces the path of weather forecasts from the time they are made by the National Oceanic and Atmospheric Administration until the time they are received by the public through the mass media. The purpose of the article is to provide geography teachers with basic information on weather forecasts, interpretation of forecast terms, and indications…

  13. 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.

  14. Basic Diagnosis and Prediction of Persistent Contrail Occurrence using High-resolution Numerical Weather Analyses/Forecasts and Logistic Regression. Part I: Effects of Random Error

    Science.gov (United States)

    Duda, David P.; Minnis, Patrick

    2009-01-01

    Straightforward application of the Schmidt-Appleman contrail formation criteria to diagnose persistent contrail occurrence from numerical weather prediction data is hindered by significant bias errors in the upper tropospheric humidity. Logistic models of contrail occurrence have been proposed to overcome this problem, but basic questions remain about how random measurement error may affect their accuracy. A set of 5000 synthetic contrail observations is created to study the effects of random error in these probabilistic models. The simulated observations are based on distributions of temperature, humidity, and vertical velocity derived from Advanced Regional Prediction System (ARPS) weather analyses. The logistic models created from the simulated observations were evaluated using two common statistical measures of model accuracy, the percent correct (PC) and the Hanssen-Kuipers discriminant (HKD). To convert the probabilistic results of the logistic models into a dichotomous yes/no choice suitable for the statistical measures, two critical probability thresholds are considered. The HKD scores are higher when the climatological frequency of contrail occurrence is used as the critical threshold, while the PC scores are higher when the critical probability threshold is 0.5. For both thresholds, typical random errors in temperature, relative humidity, and vertical velocity are found to be small enough to allow for accurate logistic models of contrail occurrence. The accuracy of the models developed from synthetic data is over 85 percent for both the prediction of contrail occurrence and non-occurrence, although in practice, larger errors would be anticipated.

  15. Verification of the AFWA 3-Element Severe Weather Forecast Algorithm

    OpenAIRE

    Pagliaro, Daniel E.

    2008-01-01

    Accurate severe thunderstorm forecasts are critical to providing sufficient leadtime to protect lives and property. The Air Force Weather Agency has developed a 3-Element Severe Weather Forecast Algorithm that when applied to model forecasts gives and outlook region for severe thunderstorms. Improvements were made in this study to enhance the algorithm's forecast skill, reduce its "false alarm" rate, and thereby increase the amount of lead-time for installation commanders to take decisive act...

  16. Singular vectors, predictability and ensemble forecasting for weather and climate

    International Nuclear Information System (INIS)

    Palmer, T N; Zanna, Laure

    2013-01-01

    The local instabilities of a nonlinear dynamical system can be characterized by the leading singular vectors of its linearized operator. The leading singular vectors are perturbations with the greatest linear growth and are therefore key in assessing the system’s predictability. In this paper, the analysis of singular vectors for the predictability of weather and climate and ensemble forecasting is discussed. An overview of the role of singular vectors in informing about the error growth rate in numerical models of the atmosphere is given. This is followed by their use in the initialization of ensemble weather forecasts. Singular vectors for the ocean and coupled ocean–atmosphere system in order to understand the predictability of climate phenomena such as ENSO and meridional overturning circulation are reviewed and their potential use to initialize seasonal and decadal forecasts is considered. As stochastic parameterizations are being implemented, some speculations are made about the future of singular vectors for the predictability of weather and climate for theoretical applications and at the operational level. This article is part of a special issue of Journal of Physics A: Mathematical and Theoretical devoted to ‘Lyapunov analysis: from dynamical systems theory to applications’. (review)

  17. Visualizing uncertainty : Towards a better understanding of weather forecasts

    NARCIS (Netherlands)

    Toet, A.; Tak, S.; Erp, J.B.F. van

    2016-01-01

    Uncertainty visualizations are increasingly used in communications to the general public. A well-known example is the weather forecast. Rather than providing an exact temperature value, weather forecasts often show the range in which the temperature will lie. But uncertainty visualizations are also

  18. Skill prediction of local weather forecasts based on the ECMWF ensemble

    Directory of Open Access Journals (Sweden)

    C. Ziehmann

    2001-01-01

    Full Text Available Ensemble Prediction has become an essential part of numerical weather forecasting. In this paper we investigate the ability of ensemble forecasts to provide an a priori estimate of the expected forecast skill. Several quantities derived from the local ensemble distribution are investigated for a two year data set of European Centre for Medium-Range Weather Forecasts (ECMWF temperature and wind speed ensemble forecasts at 30 German stations. The results indicate that the population of the ensemble mode provides useful information for the uncertainty in temperature forecasts. The ensemble entropy is a similar good measure. This is not true for the spread if it is simply calculated as the variance of the ensemble members with respect to the ensemble mean. The number of clusters in the C regions is almost unrelated to the local skill. For wind forecasts, the results are less promising.

  19. 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)

  20. 72-Hour Forecast of Air Temperatures from the National Weather Service's National Digital Forecast Database (NDFD)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Digital Forecast Database (NDFD) contains a seamless mosaic of the National Weather Service's (NWS) digital forecasts of air temperature. In...

  1. 24-Hour Forecast of Air Temperatures from the National Weather Service's National Digital Forecast Database (NDFD)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Digital Forecast Database (NDFD) contains a seamless mosaic of the National Weather Service's (NWS) digital forecasts of air temperature. In...

  2. 48-Hour Forecast of Air Temperatures from the National Weather Service's National Digital Forecast Database (NDFD)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The National Digital Forecast Database (NDFD) contains a seamless mosaic of the National Weather Service's (NWS) digital forecasts of air temperature. In...

  3. Flare forecasting at the Met Office Space Weather Operations Centre

    OpenAIRE

    Murray, Sophie A.; Bingham, Suzy; Sharpe, Michael; Jackson, David R.

    2017-01-01

    The Met Office Space Weather Operations Centre produces 24/7/365 space weather guidance, alerts, and forecasts to a wide range of government and commercial end users across the United Kingdom. Solar flare forecasts are one of its products, which are issued multiple times a day in two forms; forecasts for each active region on the solar disk over the next 24 hours, and full-disk forecasts for the next four days. Here the forecasting process is described in detail, as well as first verification...

  4. 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...

  5. Evaluation and Economic Value of Winter Weather Forecasts

    OpenAIRE

    Snyder, Derrick William

    2014-01-01

    State and local highway agencies spend millions of dollars each year to deploy winter operation teams to plow snow and de-ice roadways. Accurate and timely weather forecast information is critical for effective decision making. Students from Purdue University partnered with the Indiana Department of Transportation to create an experimental winter weather forecast service for the 2012-2013 winter season in Indiana to assist in achieving these goals. One forecast product, an hourly timeline of ...

  6. New Approach To Hour-By-Hour Weather Forecast

    Science.gov (United States)

    Liao, Q. Q.; Wang, B.

    2017-12-01

    Fine hourly forecast in single station weather forecast is required in many human production and life application situations. Most previous MOS (Model Output Statistics) which used a linear regression model are hard to solve nonlinear natures of the weather prediction and forecast accuracy has not been sufficient at high temporal resolution. This study is to predict the future meteorological elements including temperature, precipitation, relative humidity and wind speed in a local region over a relatively short period of time at hourly level. By means of hour-to-hour NWP (Numeral Weather Prediction)meteorological field from Forcastio (https://darksky.net/dev/docs/forecast) and real-time instrumental observation including 29 stations in Yunnan and 3 stations in Tianjin of China from June to October 2016, predictions are made of the 24-hour hour-by-hour ahead. This study presents an ensemble approach to combine the information of instrumental observation itself and NWP. Use autoregressive-moving-average (ARMA) model to predict future values of the observation time series. Put newest NWP products into the equations derived from the multiple linear regression MOS technique. Handle residual series of MOS outputs with autoregressive (AR) model for the linear property presented in time series. Due to the complexity of non-linear property of atmospheric flow, support vector machine (SVM) is also introduced . Therefore basic data quality control and cross validation makes it able to optimize the model function parameters , and do 24 hours ahead residual reduction with AR/SVM model. Results show that AR model technique is better than corresponding multi-variant MOS regression method especially at the early 4 hours when the predictor is temperature. MOS-AR combined model which is comparable to MOS-SVM model outperform than MOS. Both of their root mean square error and correlation coefficients for 2 m temperature are reduced to 1.6 degree Celsius and 0.91 respectively. The

  7. Basic Diagnosis and Prediction of Persistent Contrail Occurrence using High-resolution Numerical Weather Analyses/Forecasts and Logistic Regression. Part II: Evaluation of Sample Models

    Science.gov (United States)

    Duda, David P.; Minnis, Patrick

    2009-01-01

    Previous studies have shown that probabilistic forecasting may be a useful method for predicting persistent contrail formation. A probabilistic forecast to accurately predict contrail formation over the contiguous United States (CONUS) is created by using meteorological data based on hourly meteorological analyses from the Advanced Regional Prediction System (ARPS) and from the Rapid Update Cycle (RUC) as well as GOES water vapor channel measurements, combined with surface and satellite observations of contrails. Two groups of logistic models were created. The first group of models (SURFACE models) is based on surface-based contrail observations supplemented with satellite observations of contrail occurrence. The second group of models (OUTBREAK models) is derived from a selected subgroup of satellite-based observations of widespread persistent contrails. The mean accuracies for both the SURFACE and OUTBREAK models typically exceeded 75 percent when based on the RUC or ARPS analysis data, but decreased when the logistic models were derived from ARPS forecast data.

  8. A numerical forecast model for road meteorology

    Science.gov (United States)

    Meng, Chunlei

    2017-05-01

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

  9. Advances in sequential data assimilation and numerical weather forecasting: An Ensemble Transform Kalman-Bucy Filter, a study on clustering in deterministic ensemble square root filters, and a test of a new time stepping scheme in an atmospheric model

    Science.gov (United States)

    Amezcua, Javier

    This dissertation deals with aspects of sequential data assimilation (in particular ensemble Kalman filtering) and numerical weather forecasting. In the first part, the recently formulated Ensemble Kalman-Bucy (EnKBF) filter is revisited. It is shown that the previously used numerical integration scheme fails when the magnitude of the background error covariance grows beyond that of the observational error covariance in the forecast window. Therefore, we present a suitable integration scheme that handles the stiffening of the differential equations involved and doesn't represent further computational expense. Moreover, a transform-based alternative to the EnKBF is developed: under this scheme, the operations are performed in the ensemble space instead of in the state space. Advantages of this formulation are explained. For the first time, the EnKBF is implemented in an atmospheric model. The second part of this work deals with ensemble clustering, a phenomenon that arises when performing data assimilation using of deterministic ensemble square root filters in highly nonlinear forecast models. Namely, an M-member ensemble detaches into an outlier and a cluster of M-1 members. Previous works may suggest that this issue represents a failure of EnSRFs; this work dispels that notion. It is shown that ensemble clustering can be reverted also due to nonlinear processes, in particular the alternation between nonlinear expansion and compression of the ensemble for different regions of the attractor. Some EnSRFs that use random rotations have been developed to overcome this issue; these formulations are analyzed and their advantages and disadvantages with respect to common EnSRFs are discussed. The third and last part contains the implementation of the Robert-Asselin-Williams (RAW) filter in an atmospheric model. The RAW filter is an improvement to the widely popular Robert-Asselin filter that successfully suppresses spurious computational waves while avoiding any distortion

  10. Use of EOS Data in AWIPS for Weather Forecasting

    Science.gov (United States)

    Jedlovec, Gary J.; Haines, Stephanie L.; Suggs, Ron J.; Bradshaw, Tom; Darden, Chris; Burks, Jason

    2003-01-01

    Operational weather forecasting relies heavily on real time data and modeling products for forecast preparation and dissemination of significant weather information to the public. The synthesis of this information (observations and model products) by the meteorologist is facilitated by a decision support system to display and integrate the information in a useful fashion. For the NWS this system is called Advanced Weather Interactive Processing System (AWIPS). Over the last few years NASA has launched a series of new Earth Observation Satellites (EOS) for climate monitoring that include several instruments that provide high-resolution measurements of atmospheric and surface features important for weather forecasting and analysis. The key to the utilization of these unique new measurements by the NWS is the real time integration of the EOS data into the AWIPS system. This is currently being done in the Huntsville and Birmingham NWS Forecast Offices under the NASA Short-term Prediction Research and Transition (SPORT) Program. This paper describes the use of near real time MODIS and AIRS data in AWIPS to improve the detection of clouds, moisture variations, atmospheric stability, and thermal signatures that can lead to significant weather development. The paper and the conference presentation will focus on several examples where MODIS and AIRS data have made a positive impact on forecast accuracy. The results of an assessment of the utility of these products for weather forecast improvement made at the Huntsville NWS Forecast Office will be presented.

  11. Is It Going to Rain Today? Understanding the Weather Forecast.

    Science.gov (United States)

    Allsopp, Jim; And Others

    1996-01-01

    Presents a resource for science teachers to develop a better understanding of weather forecasts, including outlooks, watches, warnings, advisories, severe local storms, winter storms, floods, hurricanes, nonprecipitation hazards, precipitation probabilities, sky condition, and UV index. (MKR)

  12. Seasonal Forecasting of Fire Weather Based on a New Global Fire Weather Database

    Science.gov (United States)

    Dowdy, Andrew J.; Field, Robert D.; Spessa, Allan C.

    2016-01-01

    Seasonal forecasting of fire weather is examined based on a recently produced global database of the Fire Weather Index (FWI) system beginning in 1980. Seasonal average values of the FWI are examined in relation to measures of the El Nino-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). The results are used to examine seasonal forecasts of fire weather conditions throughout the world.

  13. Operational Numerical Weather Prediction systems based on Linux cluster architectures

    International Nuclear Information System (INIS)

    Pasqui, M.; Baldi, M.; Gozzini, B.; Maracchi, G.; Giuliani, G.; Montagnani, S.

    2005-01-01

    The progress in weather forecast and atmospheric science has been always closely linked to the improvement of computing technology. In order to have more accurate weather forecasts and climate predictions, more powerful computing resources are needed, in addition to more complex and better-performing numerical models. To overcome such a large computing request, powerful workstations or massive parallel systems have been used. In the last few years, parallel architectures, based on the Linux operating system, have been introduced and became popular, representing real high performance-low cost systems. In this work the Linux cluster experience achieved at the Laboratory far Meteorology and Environmental Analysis (LaMMA-CNR-IBIMET) is described and tips and performances analysed

  14. 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 ...

  15. Impact of AIRS Thermodynamic Profile on Regional Weather Forecast

    Science.gov (United States)

    Chou, Shih-Hung; Zavodsky, Brad; Jedlovee, Gary

    2010-01-01

    Prudent assimilation of AIRS thermodynamic profiles and quality indicators can improve initial conditions for regional weather models. AIRS-enhanced analysis has warmer and moister PBL. Forecasts with AIRS profiles are generally closer to NAM analyses than CNTL. Assimilation of AIRS leads to an overall QPF improvement in 6-h accumulated precipitation forecasts. Including AIRS profiles in assimilation process enhances the moist instability and produces stronger updrafts and a better precipitation forecast than the CNTL run.

  16. 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 ...

  17. The weather forecasting in Colombia: Science plus Art

    International Nuclear Information System (INIS)

    Gonzalez Marentes, Humberto

    2006-01-01

    The presentation intends to show briefly and rapidly the progress weather forecasting science has undergone times until today. Undoubtedly, there has been an impressive technological advances, more data better models, better representations of the physics of the atmosphere; however for the case of the low latitude countries, there are still some problems to resolve concerning the local prediction that deserve more research and more data to be included in the models. As these limitations subsist, the subjective knowledge and the experience of the duty forecaster remain valuable. The presentation is also useful to summarize how IDEAM prepares short weather forecasts

  18. An abridged history of federal involvement in space weather forecasting

    Science.gov (United States)

    Caldwell, Becaja; McCarron, Eoin; Jonas, Seth

    2017-10-01

    Public awareness of space weather and its adverse effects on critical infrastructure systems, services, and technologies (e.g., the electric grid, telecommunications, and satellites) has grown through recent media coverage and scientific research. However, federal interest and involvement in space weather dates back to the decades between World War I and World War II when the National Bureau of Standards led efforts to observe, forecast, and provide warnings of space weather events that could interfere with high-frequency radio transmissions. The efforts to observe and predict space weather continued through the 1960s during the rise of the Cold War and into the present with U.S. government efforts to prepare the nation for space weather events. This paper provides a brief overview of the history of federal involvement in space weather forecasting from World War II, through the Apollo Program, and into the present.

  19. Reducing probabilistic weather forecasts to the worst-case scenario: anchoring effects.

    Science.gov (United States)

    Joslyn, Susan; Savelli, Sonia; Nadav-Greenberg, Limor

    2011-12-01

    Many weather forecast providers believe that forecast uncertainty in the form of the worst-case scenario would be useful for general public end users. We tested this suggestion in 4 studies using realistic weather-related decision tasks involving high winds and low temperatures. College undergraduates, given the statistical equivalent of the worst-case scenario (1 boundary of the 80% predictive interval), demonstrated biased understanding of future weather conditions compared with those given both bounds or no uncertainty information. We argue that this was due to an anchoring effect on numeric estimates, which were closer to the worst-case scenario than was warranted and increased linearly as the anchor became more extreme. In many situations tested here, anchoring in numeric estimates also extended to subsequent binary decisions, leading participants with the worst-case scenario to take action more often than did other participants. These results suggest that worst-case scenario forecasts can mislead the user. They appear to convince people that wind speeds will be higher and temperatures will be lower than what are indicated by the forecast. In addition, participants systematically "corrected" the forecast they were given. This effect was most prominent in the condition in which no uncertainty was provided, suggesting that people feel compelled to take uncertainty into account, even when it is not acknowledged by the forecast. Both the anchoring and correction biases were least evident when both bounds were provided, suggesting that balanced uncertainty leads to the best understanding of future weather conditions.

  20. Cost-Loss Analysis of Ensemble Solar Wind Forecasting: Space Weather Use of Terrestrial Weather Tools

    Science.gov (United States)

    Henley, E. M.; Pope, E. C. D.

    2017-12-01

    This commentary concerns recent work on solar wind forecasting by Owens and Riley (2017). The approach taken makes effective use of tools commonly used in terrestrial weather—notably, via use of a simple model—generation of an "ensemble" forecast, and application of a "cost-loss" analysis to the resulting probabilistic information, to explore the benefit of this forecast to users with different risk appetites. This commentary aims to highlight these useful techniques to the wider space weather audience and to briefly discuss the general context of application of terrestrial weather approaches to space weather.

  1. 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

  2. From Richardson to early numerical weather prediction

    OpenAIRE

    Lynch, Peter

    2010-01-01

    The development of computer models for numerical simulation of the atmosphere and oceans is one of the great scientific triumphs of the past fifty years. These models have added enormously to our understanding of the complex processes in the atmosphere and oceans. The consequences for humankind of ongoing climate change will be far-reaching. Earth system models are the best means we have of predicting the future of our climate. The basic ideas of numerical forecasting and c...

  3. Temperature sensitivity of a numerical pollen forecast model

    Science.gov (United States)

    Scheifinger, Helfried; Meran, Ingrid; Szabo, Barbara; Gallaun, Heinz; Natali, Stefano; Mantovani, Simone

    2016-04-01

    Allergic rhinitis has become a global health problem especially affecting children and adolescence. Timely and reliable warning before an increase of the atmospheric pollen concentration means a substantial support for physicians and allergy suffers. Recently developed numerical pollen forecast models have become means to support the pollen forecast service, which however still require refinement. One of the problem areas concerns the correct timing of the beginning and end of the flowering period of the species under consideration, which is identical with the period of possible pollen emission. Both are governed essentially by the temperature accumulated before the entry of flowering and during flowering. Phenological models are sensitive to a bias of the temperature. A mean bias of -1°C of the input temperature can shift the entry date of a phenological phase for about a week into the future. A bias of such an order of magnitude is still possible in case of numerical weather forecast models. If the assimilation of additional temperature information (e.g. ground measurements as well as satellite-retrieved air / surface temperature fields) is able to reduce such systematic temperature deviations, the precision of the timing of phenological entry dates might be enhanced. With a number of sensitivity experiments the effect of a possible temperature bias on the modelled phenology and the pollen concentration in the atmosphere is determined. The actual bias of the ECMWF IFS 2 m temperature will also be calculated and its effect on the numerical pollen forecast procedure presented.

  4. Verification of space weather forecasts at the UK Met Office

    Science.gov (United States)

    Bingham, S.; Sharpe, M.; Jackson, D.; Murray, S.

    2017-12-01

    The UK Met Office Space Weather Operations Centre (MOSWOC) has produced space weather guidance twice a day since its official opening in 2014. Guidance includes 4-day probabilistic forecasts of X-ray flares, geomagnetic storms, high-energy electron events and high-energy proton events. Evaluation of such forecasts is important to forecasters, stakeholders, model developers and users to understand the performance of these forecasts and also strengths and weaknesses to enable further development. Met Office terrestrial near real-time verification systems have been adapted to provide verification of X-ray flare and geomagnetic storm forecasts. Verification is updated daily to produce Relative Operating Characteristic (ROC) curves and Reliability diagrams, and rolling Ranked Probability Skill Scores (RPSSs) thus providing understanding of forecast performance and skill. Results suggest that the MOSWOC issued X-ray flare forecasts are usually not statistically significantly better than a benchmark climatological forecast (where the climatology is based on observations from the previous few months). By contrast, the issued geomagnetic storm activity forecast typically performs better against this climatological benchmark.

  5. 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

  6. Space Weather Monitoring and Forecasting Activity in NICT

    Science.gov (United States)

    Nagatsuma, Tsutomu; Watari, Shinichi; T. Murata, Ken

    Disturbances of Space environment around the Earth (geospace) is controlled by the activity of the Sun and the solar wind. Disturbances in geospace sometimes cause serious problems to satellites, astronauts, and telecommunications. To minimize the effect of the problems, space weather forecasting is necessary. In Japan, NICT (National Institute of Information and Communications Technology) is in charge of space weather forecasting services as a regional warning center of International Space Environment Service. With help of geospace environment data exchanging among the international cooperation, NICT operates daily space weather forecast service every day to provide information on nowcasts and forecasts of solar flare, geomagnetic disturbances, solar proton event, and radio-wave propagation conditions in the ionosphere. For prompt reporting of space weather information, we also conduct our original observation networks from the Sun to the upper atmosphere: Hiraiso solar observatory, domestic ionosonde networks, magnetometer & HF radar observations in far-east Siberia and Alaska, and south-east Asia low-latitude ionospheric network (SEALION). ACE (Advanced Composition Explorer) and STEREO (Solar TErrestrial RElations Observatory) real-time beacon data are received using our antenna facilities to monitor the solar and solar wind conditions in near real-time. Our current activities and future perspective of space weather monitoring and forecasting will be introduced in this report.

  7. Developing a Seamless Hydrologic Forecast System: Integrating weather and climate prediction

    Science.gov (United States)

    Yuan, Xing; Wood, Eric; Liang, Miaoling

    2014-05-01

    Skilful and reliable forecasts of land surface hydrologic conditions from daily to seasonal scales will facilitate the management of reservoirs, agriculture and urban water resources, and provide early warning of flooding and droughts. With the improvement of numerical weather and climate predictions, dynamical model-based short-term or seasonal hydrologic forecasts have been widely implemented. However, limited dialogue exists between the hydrometeorological forecasting and the hydroclimate prediction communities. Given that the weather-climate prediction problem is seamless, and phenomena often occur at all time-scales, atmospheric scientists have been developing seamless prediction system in recent years using unified modeling systems to predict both weather and climate. Therefore, it is now time to develop seamless hydro-meteorological forecast systems that can provide forecast capability from flash flooding to seasonal droughts within a common system. Such a system would also allow one to investigate the interaction of hydroclimatic processes across scales and should enhance hydrologic predictability. In this presentation, several decades of 16-day reforecasts from NCEP's latest Global Forecast System (GFS) and 9-month reforecasts from its Climate Forecast System version 2 (CFSv2) will be used to investigate how the two-week weather forecast that has higher resolution and more observations in its data assimilation contributes to seasonal hydrologic predictability, and whether the seasonal climate forecast model that fully resolve the ocean-atmosphere-land coupling system is useful to extend the 1-2 week short-term hydrologic forecast up to 3-4 weeks. The Ohio basin in mid-western United States will be used as a case study.

  8. 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

  9. Time Relevance of Convective Weather Forecast for Air Traffic Automation

    Science.gov (United States)

    Chan, William N.

    2006-01-01

    The Federal Aviation Administration (FAA) is handling nearly 120,000 flights a day through its Air Traffic Management (ATM) system and air traffic congestion is expected to increse substantially over the next 20 years. Weather-induced impacts to throughput and efficiency are the leading cause of flight delays accounting for 70% of all delays with convective weather accounting for 60% of all weather related delays. To support the Next Generation Air Traffic System goal of operating at 3X current capacity in the NAS, ATC decision support tools are being developed to create advisories to assist controllers in all weather constraints. Initial development of these decision support tools did not integrate information regarding weather constraints such as thunderstorms and relied on an additional system to provide that information. Future Decision Support Tools should move towards an integrated system where weather constraints are factored into the advisory of a Decision Support Tool (DST). Several groups such at NASA-Ames, Lincoln Laboratories, and MITRE are integrating convective weather data with DSTs. A survey of current convective weather forecast and observation data show they span a wide range of temporal and spatial resolutions. Short range convective observations can be obtained every 5 mins with longer range forecasts out to several days updated every 6 hrs. Today, the short range forecasts of less than 2 hours have a temporal resolution of 5 mins. Beyond 2 hours, forecasts have much lower temporal. resolution of typically 1 hour. Spatial resolutions vary from 1km for short range to 40km for longer range forecasts. Improving the accuracy of long range convective forecasts is a major challenge. A report published by the National Research Council states improvements for convective forecasts for the 2 to 6 hour time frame will only be achieved for a limited set of convective phenomena in the next 5 to 10 years. Improved longer range forecasts will be probabilistic

  10. Space Climate and Space Weather: Exploration and Forecasts

    Science.gov (United States)

    Lundstedt, H.; Wintoft, P.; Jensen, J. M.; Boberg, F.; Wik, M.

    2004-12-01

    A new approach of exploring and forecasting solar activity was recently introduced. The Lund Solar Activity Model (LSAM) uses as input solar activity indicators: For exploration of the long-term space climate C14 proxy. For short-term (space weather) flare forecasts solar magnetic field and helioseismic data. The Lund Group also operates the Region Warning Center (RWC) Sweden of ISES. Real-time forecasts of the space weather and effects are offered. New real-time forecasts of the local geomagnetic activity have been developed, as part of the ESA GIC pilot project. A third workshop on Artificial Intelligence Applications in Solar-Terrestrial Physics is planned to be held in Lund, September 21-23, 2005.

  11. Weather forecasts, Weather derivatives, Black-Scholes, Feynmann-Kac and Fokker-Planck

    OpenAIRE

    Jewson, Stephen

    2003-01-01

    We investigate the relationships between weather forecasting, weather derivatives, the Black-Scholes equation, Feynmann-Kac theory and the Fokker-Planck equation. There is one useful result, but on the whole the relations we present seem to be more interesting than practically useful.

  12. GPS Estimates of Integrated Precipitable Water Aid Weather Forecasters

    Science.gov (United States)

    Moore, Angelyn W.; Gutman, Seth I.; Holub, Kirk; Bock, Yehuda; Danielson, David; Laber, Jayme; Small, Ivory

    2013-01-01

    Global Positioning System (GPS) meteorology provides enhanced density, low-latency (30-min resolution), integrated precipitable water (IPW) estimates to NOAA NWS (National Oceanic and Atmospheric Adminis tration Nat ional Weather Service) Weather Forecast Offices (WFOs) to provide improved model and satellite data verification capability and more accurate forecasts of extreme weather such as flooding. An early activity of this project was to increase the number of stations contributing to the NOAA Earth System Research Laboratory (ESRL) GPS meteorology observing network in Southern California by about 27 stations. Following this, the Los Angeles/Oxnard and San Diego WFOs began using the enhanced GPS-based IPW measurements provided by ESRL in the 2012 and 2013 monsoon seasons. Forecasters found GPS IPW to be an effective tool in evaluating model performance, and in monitoring monsoon development between weather model runs for improved flood forecasting. GPS stations are multi-purpose, and routine processing for position solutions also yields estimates of tropospheric zenith delays, which can be converted into mm-accuracy PWV (precipitable water vapor) using in situ pressure and temperature measurements, the basis for GPS meteorology. NOAA ESRL has implemented this concept with a nationwide distribution of more than 300 "GPSMet" stations providing IPW estimates at sub-hourly resolution currently used in operational weather models in the U.S.

  13. 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

  14. Activity to Forecast Weather Using a Computer and GMS Pictures

    OpenAIRE

    榊原, 保志; 池本, 博司

    2003-01-01

    This paper presents an activity to forecast weather using a computer and Geostationary Meteorology Satellite (GMS) images. Students obtained visual images and infrared images scanned by GMS from web pages and investigated the distribution of cumulonimbus and nimbostratus, which often bring precipitation, using functions of digital imaging solutions such as color balance, posterization and layer. Two trial lesson at a high school suggested that this activity helped students think weather forec...

  15. 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.

  16. The effort to increase the space weather forecasting accuracy in KSWC

    Science.gov (United States)

    Choi, J. S.

    2017-12-01

    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 as the Regional Warning Center of the International Space Environment Service (ISES). 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. Recently, KSWC are focusing on increasing the accuracy of space weather forecasting results and verifying the model generated results. The forecasting accuracy will be calculated based on the probability statistical estimation so that the results can be compared numerically. Regarding the cosmic radiation does, we are gathering the actual measured data of radiation does using the instrument by cooperation with the domestic airlines. Based on the measurement, we are going to verify the reliability of SAFE system which was developed by KSWC to provide the cosmic radiation does information with the airplane cabin crew and public users.

  17. 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.

  18. Verification of Weather Running Estimate-Nowcast (WRE-N) Forecasts Using a Spatial-Categorical Method

    Science.gov (United States)

    2017-07-01

    NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) John W Raby 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7...forecast, verification, categorical forecast, weather impacts, thresholds, numerical weather prediction, observations, Model Evaluation Tools , MET...Unclassified c. THIS PAGE Unclassified 19b. TELEPHONE NUMBER (Include area code) 575-678-2004 Standard Form 298 (Rev. 8/98) Prescribed by ANSI

  19. A Weather Analysis and Forecasting System for Baja California, Mexico

    Science.gov (United States)

    Farfan, L. M.

    2006-05-01

    The weather of the Baja California Peninsula, part of northwestern Mexico, is mild and dry most of the year. However, during the summer, humid air masses associated with tropical cyclones move northward in the eastern Pacific Ocean. Added features that create a unique meteorological situation include mountain ranges along the spine of the peninsula, warm water in the Gulf of California, and the cold California Current in the Pacific. These features interact with the environmental flow to induce conditions that play a role in the occurrence of localized, convective systems during the approach of tropical cyclones. Most of these events occur late in the summer, generating heavy precipitation, strong winds, lightning, and are associated with significant property damage to the local populations. Our goal is to provide information on the characteristics of these weather systems by performing an analysis of observations derived from a regional network. This includes imagery from radar and geostationary satellite, and data from surface stations. A set of real-time products are generated in our research center and are made available to a broad audience (researchers, students, and business employees) by using an internet site. Graphical products are updated anywhere from one to 24 hours and includes predictions from numerical models. Forecasts are derived from an operational model (GFS) and locally generated simulations based on a mesoscale model (MM5). Our analysis and forecasting system has been in operation since the summer of 2005 and was used as a reference for a set of discussions during the development of eastern Pacific tropical cyclones. This basin had 15 named storms and none of them made landfall on the west coast of Mexico; however, four systems were within 800 km from the area of interest, resulting in some convective activity. During the whole season, a group of 30 users from our institution, government offices, and local businesses received daily information

  20. SWIFF: Space weather integrated forecasting framework

    Czech Academy of Sciences Publication Activity Database

    Lapenta, G.; Pierrard, V.; Keppens, R.; Markidis, S.; Poedts, S.; Šebek, Ondřej; Trávníček, Pavel M.; Henri, P.; Califano, F.; Pegoraro, F.; Faganello, M.; Olshevsky, V.; Restante, A. L.; Nordlund, A.; Frederiksen, J. T.; Mackay, D. H.; Parnell, C. E.; Bemporad, A.; Susino, R.; Borremans, K.

    2013-01-01

    Roč. 3, 18 February (2013), A05/1-A05/17 ISSN 2115-7251 Institutional support: RVO:68378289 Keywords : space weather * modelling * high performance computing Subject RIV: BL - Plasma and Gas Discharge Physics Impact factor: 2.519, year: 2013 http://www.swsc- journal .org/ articles /swsc/pdf/2013/01/swsc120033.pdf

  1. 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.

  2. Convective Weather Forecast Quality Metrics for Air Traffic Management Decision-Making

    Science.gov (United States)

    Chatterji, Gano B.; Gyarfas, Brett; Chan, William N.; Meyn, Larry A.

    2006-01-01

    Since numerical weather prediction models are unable to accurately forecast the severity and the location of the storm cells several hours into the future when compared with observation data, there has been a growing interest in probabilistic description of convective weather. The classical approach for generating uncertainty bounds consists of integrating the state equations and covariance propagation equations forward in time. This step is readily recognized as the process update step of the Kalman Filter algorithm. The second well known method, known as the Monte Carlo method, consists of generating output samples by driving the forecast algorithm with input samples selected from distributions. The statistical properties of the distributions of the output samples are then used for defining the uncertainty bounds of the output variables. This method is computationally expensive for a complex model compared to the covariance propagation method. The main advantage of the Monte Carlo method is that a complex non-linear model can be easily handled. Recently, a few different methods for probabilistic forecasting have appeared in the literature. A method for computing probability of convection in a region using forecast data is described in Ref. 5. Probability at a grid location is computed as the fraction of grid points, within a box of specified dimensions around the grid location, with forecast convection precipitation exceeding a specified threshold. The main limitation of this method is that the results are dependent on the chosen dimensions of the box. The examples presented Ref. 5 show that this process is equivalent to low-pass filtering of the forecast data with a finite support spatial filter. References 6 and 7 describe the technique for computing percentage coverage within a 92 x 92 square-kilometer box and assigning the value to the center 4 x 4 square-kilometer box. This technique is same as that described in Ref. 5. Characterizing the forecast, following

  3. Verification of operational weather forecasts from the POSEIDON system across the Eastern Mediterranean

    Directory of Open Access Journals (Sweden)

    A. Papadopoulos

    2009-07-01

    Full Text Available The POSEIDON weather forecasting system became operational at the Hellenic Centre for Marine Research (HCMR in October 1999. The system with its nesting capability provided 72-h forecasts in two different model domains, i.e. 25- and 10-km grid spacing. The lower-resolution domain covered an extended area that included most of Europe, Mediterranean Sea and N. Africa, while the higher resolution domain focused on the Eastern Mediterranean. A major upgrade of the system was recently implemented in the framework of the POSEIDON-II project (2005–2008. The aim was to enhance the forecasting skill of the system through improved model parameterization schemes and advanced numerical techniques for assimilating available observations to produce high resolution analysis fields. The configuration of the new system is applied on a horizontal resolution of 1/20°×1/20° (~5 km covering the Mediterranean basin, Black Sea and part of North Atlantic providing up to 5-day forecasts. This paper reviews and compares the current with the previous weather forecasting systems at HCMR presenting quantitative verification statistics from the pre-operational period (from mid-November 2007 to October 2008. The statistics are based on verification against surface observations from the World Meteorological Organization (WMO network across the Eastern Mediterranean region. The results indicate that the use of the new system can significantly improve the weather forecasts.

  4. Epidemic Forecasting is Messier Than Weather Forecasting: The Role of Human Behavior and Internet Data Streams in Epidemic Forecast.

    Science.gov (United States)

    Moran, Kelly R; Fairchild, Geoffrey; Generous, Nicholas; Hickmann, Kyle; Osthus, Dave; Priedhorsky, Reid; Hyman, James; Del Valle, Sara Y

    2016-12-01

    Mathematical models, such as those that forecast the spread of epidemics or predict the weather, must overcome the challenges of integrating incomplete and inaccurate data in computer simulations, estimating the probability of multiple possible scenarios, incorporating changes in human behavior and/or the pathogen, and environmental factors. In the past 3 decades, the weather forecasting community has made significant advances in data collection, assimilating heterogeneous data steams into models and communicating the uncertainty of their predictions to the general public. Epidemic modelers are struggling with these same issues in forecasting the spread of emerging diseases, such as Zika virus infection and Ebola virus disease. While weather models rely on physical systems, data from satellites, and weather stations, epidemic models rely on human interactions, multiple data sources such as clinical surveillance and Internet data, and environmental or biological factors that can change the pathogen dynamics. We describe some of similarities and differences between these 2 fields and how the epidemic modeling community is rising to the challenges posed by forecasting to help anticipate and guide the mitigation of epidemics. We conclude that some of the fundamental differences between these 2 fields, such as human behavior, make disease forecasting more challenging than weather forecasting. Published by Oxford University Press for the Infectious Diseases Society of America 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.

  5. Improvements in medium range weather forecasting system of India

    Indian Academy of Sciences (India)

    8851, doi: 10.1029/2002JD003296. Environmental Modeling Centre 2003 The GFS Atmospheric. Model; NCEP Office Note 442 12. Han J and Pan H-L 2006 Sensitivity of hurricane intensity forecast to convective momentum transport parameteri- sation; Mon. Weather Rev. 134 664–674. Han J and Pan H-L 2010 Revision ...

  6. SWIFF: Space weather integrated forecasting framework

    Czech Academy of Sciences Publication Activity Database

    Lapenta, G.; Pierrard, V.; Keppens, R.; Markidis, S.; Poedts, S.; Šebek, Ondřej; Trávníček, Pavel M.; Henri, P.; Califano, F.; Pegoraro, F.; Faganello, M.; Olshevsky, V.; Restante, A. L.; Nordlund, A.; Frederiksen, J. T.; Mackay, D. H.; Parnell, C. E.; Bemporad, A.; Susino, R.; Borremans, K.

    2013-01-01

    Roč. 3, February (2013), A05/1-A05/17 ISSN 2115-7251 R&D Projects: GA MŠk(CZ) 7E11053 EU Projects: European Commission(XE) 263340 - SWIFF Institutional support: RVO:67985815 ; RVO:68378289 Keywords : space weather * modelling * high performance computing Subject RIV: BN - Astronomy, Celestial Mechanics, Astrophysics; BL - Plasma and Gas Discharge Physics (UFA-U) Impact factor: 2.519, year: 2013

  7. Forecasting space weather: Can new econometric methods improve accuracy?

    Science.gov (United States)

    Reikard, Gordon

    2011-06-01

    Space weather forecasts are currently used in areas ranging from navigation and communication to electric power system operations. The relevant forecast horizons can range from as little as 24 h to several days. This paper analyzes the predictability of two major space weather measures using new time series methods, many of them derived from econometrics. The data sets are the A p geomagnetic index and the solar radio flux at 10.7 cm. The methods tested include nonlinear regressions, neural networks, frequency domain algorithms, GARCH models (which utilize the residual variance), state transition models, and models that combine elements of several techniques. While combined models are complex, they can be programmed using modern statistical software. The data frequency is daily, and forecasting experiments are run over horizons ranging from 1 to 7 days. Two major conclusions stand out. First, the frequency domain method forecasts the A p index more accurately than any time domain model, including both regressions and neural networks. This finding is very robust, and holds for all forecast horizons. Combining the frequency domain method with other techniques yields a further small improvement in accuracy. Second, the neural network forecasts the solar flux more accurately than any other method, although at short horizons (2 days or less) the regression and net yield similar results. The neural net does best when it includes measures of the long-term component in the data.

  8. The MST radar technique: Requirements for operational weather forecasting

    Science.gov (United States)

    Larsen, M. F.

    1983-01-01

    There is a feeling that the accuracy of mesoscale forecasts for spatial scales of less than 1000 km and time scales of less than 12 hours can be improved significantly if resources are applied to the problem in an intensive effort over the next decade. Since the most dangerous and damaging types of weather occur at these scales, there are major advantages to be gained if such a program is successful. The interest in improving short term forecasting is evident. The technology at the present time is sufficiently developed, both in terms of new observing systems and the computing power to handle the observations, to warrant an intensive effort to improve stormscale forecasting. An assessment of the extent to which the so-called MST radar technique fulfills the requirements for an operational mesoscale observing network is reviewed and the extent to which improvements in various types of forecasting could be expected if such a network is put into operation are delineated.

  9. The primacy of doubt: Evolution of numerical weather prediction from determinism to probability

    Science.gov (United States)

    Palmer, Tim

    2017-06-01

    Over the last 25 years, the focus of operational numerical weather prediction has evolved from that of estimating the most likely evolution of weather to that of estimating probability distributions of future weather associated with inevitable uncertainties in both initial conditions and model equations. This evolution from determinism to uncertainty has not only increased the scientific rigor of weather prediction, it has also increased the value of weather forecasts for users. In addition, it has opened up a new approach to solving the equations of motion, likely to be of importance for both weather and climate prediction in an age where high-performance computing is limited by power consumption. However, despite all this, the numerical weather prediction community has yet to embrace fully the concept of the primacy of doubt. It is now time to take the final step in this direction.

  10. Online short-term forecast of greenhouse heat load using a weather forecast service

    DEFF Research Database (Denmark)

    Vogler-Finck, P. J.C.; Bacher, P.; Madsen, Henrik

    2017-01-01

    In some district heating systems, greenhouses represent a significant share of the total load, and can lead to operational challenges. Short term load forecast of such consumers has a strong potential to contribute to the improvement of the overall system efficiency. This work investigates...... the performance of recursive least squares for predicting the heat load of individual greenhouses in an online manner. Predictor inputs (weekly curves terms and weather forecast inputs) are selected in an automated manner using a forward selection approach. Historical load measurements from 5 Danish greenhouses...... with different operational characteristics were used, together with weather measurements and a weather forecast service. It was found that these predictors of reduced complexity and computational load performed well at capturing recurring load profiles, but not fast frequency random changes. Overall, the root...

  11. Weather forecast-based optimization of integrated energy systems.

    Energy Technology Data Exchange (ETDEWEB)

    Zavala, V. M.; Constantinescu, E. M.; Krause, T.; Anitescu, M.

    2009-03-01

    In this work, we establish an on-line optimization framework to exploit detailed weather forecast information in the operation of integrated energy systems, such as buildings and photovoltaic/wind hybrid systems. We first discuss how the use of traditional reactive operation strategies that neglect the future evolution of the ambient conditions can translate in high operating costs. To overcome this problem, we propose the use of a supervisory dynamic optimization strategy that can lead to more proactive and cost-effective operations. The strategy is based on the solution of a receding-horizon stochastic dynamic optimization problem. This permits the direct incorporation of economic objectives, statistical forecast information, and operational constraints. To obtain the weather forecast information, we employ a state-of-the-art forecasting model initialized with real meteorological data. The statistical ambient information is obtained from a set of realizations generated by the weather model executed in an operational setting. We present proof-of-concept simulation studies to demonstrate that the proposed framework can lead to significant savings (more than 18% reduction) in operating costs.

  12. Time-Hierarchical Clustering and Visualization of Weather Forecast Ensembles.

    Science.gov (United States)

    Ferstl, Florian; Kanzler, Mathias; Rautenhaus, Marc; Westermann, Rudiger

    2017-01-01

    We propose a new approach for analyzing the temporal growth of the uncertainty in ensembles of weather forecasts which are started from perturbed but similar initial conditions. As an alternative to traditional approaches in meteorology, which use juxtaposition and animation of spaghetti plots of iso-contours, we make use of contour clustering and provide means to encode forecast dynamics and spread in one single visualization. Based on a given ensemble clustering in a specified time window, we merge clusters in time-reversed order to indicate when and where forecast trajectories start to diverge. We present and compare different visualizations of the resulting time-hierarchical grouping, including space-time surfaces built by connecting cluster representatives over time, and stacked contour variability plots. We demonstrate the effectiveness of our visual encodings with forecast examples of the European Centre for Medium-Range Weather Forecasts, which convey the evolution of specific features in the data as well as the temporally increasing spatial variability.

  13. Verification of Space Weather Forecasts Issued by the Met Office Space Weather Operations Centre

    Science.gov (United States)

    Sharpe, M. A.; Murray, S. A.

    2017-10-01

    The Met Office Space Weather Operations Centre was founded in 2014 and part of its remit is a daily Space Weather Technical Forecast to help the UK build resilience to space weather impacts; guidance includes 4 day geomagnetic storm forecasts (GMSF) and X-ray flare forecasts (XRFF). It is crucial for forecasters, users, modelers, and stakeholders to understand the strengths and weaknesses of these forecasts; therefore, it is important to verify against the most reliable truth data source available. The present study contains verification results for XRFFs using Geo-Orbiting Earth Satellite 15 satellite data and GMSF using planetary K-index (Kp) values from the GFZ Helmholtz Centre. To assess the value of the verification results, it is helpful to compare them against a reference forecast and the frequency of occurrence during a rolling prediction period is used for this purpose. An analysis of the rolling 12 month performance over a 19 month period suggests that both the XRFF and GMSF struggle to provide a better prediction than the reference. However, a relative operating characteristic and reliability analysis of the full 19 month period reveals that although the GMSF and XRFF possess discriminatory skill, events tend to be overforecast.

  14. [50 years of the methodology of weather forecasting for medicine].

    Science.gov (United States)

    Grigor'ev, K I; Povazhnaia, E L

    2014-01-01

    The materials reported in the present article illustrate the possibility of weather forecasting for the medical purposes in the historical aspect. The main characteristics of the relevant organizational and methodological approaches to meteoprophylaxis based of the standard medical forecasts are presented. The emphasis is laid on the priority of the domestic medical school in the development of the principles of diagnostics and treatment of meteosensitivity and meteotropic complications in the patients presenting with various diseases with special reference to their age-related characteristics.

  15. Expert Systems and Weather Forecasting in the 4th and 5th Grade.

    Science.gov (United States)

    Kirkwood, James J.; Gimblett, Randy H.

    1992-01-01

    Fourth and fifth graders built weather measuring instruments, entered data into a computer program that forecasted weather, and compared the resultant forecast with actual weather. As a result of their activities, students took a greater interest in weather phenomena, understood the computer program, and learned to think more logically. (LB)

  16. Fifty Years of Space Weather Forecasting from Boulder

    Science.gov (United States)

    Berger, T. E.

    2015-12-01

    The first official space weather forecast was issued by the Space Disturbances Laboratory in Boulder, Colorado, in 1965, ushering in an era of operational prediction that continues to this day. Today, the National Oceanic and Atmospheric Administration (NOAA) charters the Space Weather Prediction Center (SWPC) as one of the nine National Centers for Environmental Prediction (NCEP) to provide the nation's official watches, warnings, and alerts of space weather phenomena. SWPC is now integral to national and international efforts to predict space weather events, from the common and mild, to the rare and extreme, that can impact critical technological infrastructure. In 2012, the Strategic National Risk Assessment included extreme space weather events as low-to-medium probability phenomena that could, unlike any other meteorogical phenomena, have an impact on the government's ability to function. Recognizing this, the White House chartered the Office of Science and Technology Policy (OSTP) to produce the first comprehensive national strategy for the prediction, mitigation, and response to an extreme space weather event. The implementation of the National Strategy is ongoing with NOAA, its partners, and stakeholders concentrating on the goal of improving our ability to observe, model, and predict the onset and severity of space weather events. In addition, work continues with the research community to improve our understanding of the physical mechanisms - on the Sun, in the heliosphere, and in the Earth's magnetic field and upper atmosphere - of space weather as well as the effects on critical infrastructure such as electrical power transmission systems. In fifty years, people will hopefully look back at the history of operational space weather prediction and credit our efforts today with solidifying the necessary developments in observational systems, full-physics models of the entire Sun-Earth system, and tools for predicting the impacts to infrastructure to protect

  17. The Weather Forecast Using Data Mining Research Based on Cloud Computing.

    Science.gov (United States)

    Wang, ZhanJie; Mazharul Mujib, A. B. M.

    2017-10-01

    Weather forecasting has been an important application in meteorology and one of the most scientifically and technologically challenging problem around the world. In my study, we have analyzed the use of data mining techniques in forecasting weather. This paper proposes a modern method to develop a service oriented architecture for the weather information systems which forecast weather using these data mining techniques. This can be carried out by using Artificial Neural Network and Decision tree Algorithms and meteorological data collected in Specific time. Algorithm has presented the best results to generate classification rules for the mean weather variables. The results showed that these data mining techniques can be enough for weather forecasting.

  18. Fine Forecasts: Encouraging the Media to Include Ultraviolet Radiation Information in Summertime Weather Forecasts

    Science.gov (United States)

    Richards, R.; Reeder, A. I.; Bulliard, J.-L.

    2004-01-01

    Melanoma and skin cancer are largely attributable to over-exposure to solar ultraviolet radiation (UVR). Reports of UVR levels within media weather forecasts appear to be well received by the public and have good potential to communicate the need for appropriate sun protection to a broad audience. This study describes provision of UVR messages by…

  19. Anvil Forecast Tool in the Advanced Weather Interactive Processing System

    Science.gov (United States)

    Barrett, Joe H., III; Hood, Doris

    2009-01-01

    Meteorologists from the 45th Weather Squadron (45 WS) and National Weather Service Spaceflight Meteorology Group (SMG) have identified anvil forecasting as one of their most challenging tasks when predicting the probability of violations of the Lightning Launch Commit Criteria and Space Shuttle Flight Rules. As a result, the Applied Meteorology Unit (AMU) was tasked to create a graphical overlay tool for the Meteorological Interactive Data Display System (MIDDS) that indicates the threat of thunderstorm anvil clouds, using either observed or model forecast winds as input. The tool creates a graphic depicting the potential location of thunderstorm anvils one, two, and three hours into the future. The locations are based on the average of the upper level observed or forecasted winds. The graphic includes 10 and 20 n mi standoff circles centered at the location of interest, as well as one-, two-, and three-hour arcs in the upwind direction. The arcs extend outward across a 30 sector width based on a previous AMU study that determined thunderstorm anvils move in a direction plus or minus 15 of the upper-level wind direction. The AMU was then tasked to transition the tool to the Advanced Weather Interactive Processing System (AWIPS). SMG later requested the tool be updated to provide more flexibility and quicker access to model data. This presentation describes the work performed by the AMU to transition the tool into AWIPS, as well as the subsequent improvements made to the tool.

  20. Impact of High Resolution SST Data on Regional Weather Forecasts

    Science.gov (United States)

    Jedlovec, Gary J.; Case, Jonathon; LaFontaine, Frank; Vazquez, Jorge; Mattocks, Craig

    2010-01-01

    Past studies have shown that the use of coarse resolution SST products such as from the real-time global (RTG) SST analysis[1] or other coarse resolution once-a-day products do not properly portray the diurnal variability of fluxes of heat and moisture from the ocean that drive the formation of low level clouds and precipitation over the ocean. For example, the use of high resolution MODIS SST composite [2] to initialize the Advanced Research Weather Research and Forecast (WRF) (ARW) [3] has been shown to improve the prediction of sensible weather parameters in coastal regions [4][5}. In an extend study, [6] compared the MODIS SST composite product to the RTG SST analysis and evaluated forecast differences for a 6 month period from March through August 2007 over the Florida coastal regions. In a comparison to buoy data, they found that that the MODIS SST composites reduced the bias and standard deviation over that of the RTG data. These improvements led to significant changes in the initial and forecasted heat fluxes and the resulting surface temperature fields, wind patterns, and cloud distributions. They also showed that the MODIS composite SST product, produced for the Terra and Aqua satellite overpass times, captured a component of the diurnal cycle in SSTs not represented in the RTG or other one-a-day SST analyses. Failure to properly incorporate these effects in the WRF initialization cycle led to temperature biases in the resulting short term forecasts. The forecast impact was limited in some situations however, due to composite product inaccuracies brought about by data latency during periods of long-term cloud cover. This paper focuses on the forecast impact of an enhanced MODIS/AMSR-E composite SST product designed to reduce inaccuracies due data latency in the MODIS only composite product.

  1. High resolution numerical weather prediction over the Indian ...

    Indian Academy of Sciences (India)

    In this study, the Florida State University Global Spectral Model (FSUGSM), in association with a high-resolution nested regional spectral model (FSUNRSM), is used for short-range weather forecasts over the Indian domain. Three-day forecasts for each day of August 1998 were performed using different versions of the ...

  2. 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

  3. 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...

  4. Using fire-weather forecasts and local weather observations in predicting burning index for individual fire-danger stations.

    Science.gov (United States)

    Owen P. Cramer

    1958-01-01

    Any agency engaged in forest-fire control needs accurate weather forecasts and systematic procedures for making the best use of predicted and reported weather information. This study explores the practicability of using several tabular and graphical aids for converting area forecasts and local observations of relative humidity and wind speed into predicted values for...

  5. Engaging Earth- and Environmental-Science Undergraduates through Weather Discussions and an eLearning Weather Forecasting Contest

    Science.gov (United States)

    Schultz, David M.; Anderson, Stuart; Seo-Zindy, Ryo

    2013-01-01

    For students who major in meteorology, engaging in weather forecasting can motivate learning, develop critical-thinking skills, improve their written communication, and yield better forecasts. Whether such advances apply to students who are not meteorology majors has been less demonstrated. To test this idea, a weather discussion and an eLearning…

  6. Evaluation of short-term weather forecasts in South Africa | Banitz ...

    African Journals Online (AJOL)

    In this paper a brief overview will be given for the reasons for doing evaluations of short-term weather forecasts as well as the methodology thereof. Short-term weather forecasts are defined as a forecast valid for the current day as well as the next day. In other words up to 48 h ahead. Results are given for South African ...

  7. Assessments of Total Lightning Data Utility in Weather Forecasting

    Science.gov (United States)

    Buechler, Dennis E.; Goodman, Steve; LaCasse, Katherine; Blakeslee, Richard; Darden, Chris

    2005-01-01

    National Weather Service forecasters in Huntsville, Alabama have had access to total lightning data from the North Alabama Lightning Mapping Array (LMA) since 2003. Forecasters can monitor real-time total lightning observations on their AWIPS (Advanced Weather Interactive Processing System (AWIPS) workstations. The lightning data is used to supplement other observations such as radar and satellite data. The lightning data is updated every 2 min, providing more timely evidence of storm growth or decay than is available from 5 min radar scans. Total lightning observations have been used to positively impact warning decisions in a number of instances. A number of approaches are being pursued to assess the usefulness of total lightning measurements to the operational forecasting community in the warning decision process. These approaches, which include both qualitative and quantitative assessment methods, will be discussed. submitted to the American Meteorological Society (AMS) Conference on Meteorological Applications of Lightning Data to be held in San Diego, CA January 9-13,2005. This will be a presentation and an extended abstract will be published on a CD available from the AMS.

  8. WEATHER FORECAST DATA SEMANTIC ANALYSIS IN F-LOGIC

    Directory of Open Access Journals (Sweden)

    Ana Meštrović

    2007-06-01

    Full Text Available This paper addresses the semantic analysis problem in a spoken dialog system developed for the domain of weather forecasts. The main goal of semantic analysis is to extract the meaning from the spoken utterances and to transform it into a domain database format. In this work a semantic database for the domain of weather forecasts is represented using the F-logic formalism. Semantic knowledge is captured through semantic categories a semantic dictionary using phrases and output templates. Procedures for semantic analysis of Croatian weather data combine parsing techniques for Croatian language and slot filling approach. Semantic analysis is conducted in three phases. In the first phase the main semantic category for the input utterance is determined. The lattices are used for hierarchical semantic relation representation and main category derivation. In the second phase semantic units are analyzed and knowledge slots in the database are filled. Since some slot values of input data are missing in the third phase, incomplete data is updated with missing values. All rules for semantic analysis are defined in the F-logic and implemented using the FLORA-2 system. The results of semantic analysis evaluation in terms of frame and slot error rates are presented.

  9. Hourly weather forecasts for gas turbine power generation

    Directory of Open Access Journals (Sweden)

    G. Giunta

    2017-06-01

    Full Text Available An hourly short-term weather forecast can optimize processes in Combined Cycle Gas Turbine (CCGT plants by helping to reduce imbalance charges on the national power grid. Consequently, a reliable meteorological prediction for a given power plant is crucial for obtaining competitive prices for the electric market, better planning and stock management, sales and supplies of energy sources. The paper discusses the short-term hourly temperature forecasts, at lead time day+1 and day+2, over a period of thirteen months in 2012 and 2013 for six Italian CCGT power plants of 390 MW each (260 MW from the gas turbine and 130 MW from the steam turbine. These CCGT plants are placed in three different Italian climate areas: the Po Valley, the Adriatic coast, and the North Tyrrhenian coast. The meteorological model applied in this study is the eni-Kassandra Meteo Forecast (e‑kmf™, a multi-model approach system to provide probabilistic forecasts with a Kalman filter used to improve accuracy of local temperature predictions. Performance skill scores, computed by the output data of the meteorological model, are compared with local observations, and used to evaluate forecast reliability. In the study, the approach has shown good overall scores encompassing more than 50,000 hourly temperature values. Some differences from one site to another, due to local meteorological phenomena, can affect the short-term forecast performance, with consequent impacts on gas-to-power production and related negative imbalances. For operational application of the methodology in CCGT power plant, the benefits and limits have been successfully identified.

  10. Analysis of Numerical Weather Predictions of Reference Evapotranspiration and Precipitation

    Science.gov (United States)

    Bughici, Theodor; Lazarovitch, Naftali; Fredj, Erick; Tas, Eran

    2017-04-01

    This study attempts to improve the forecast skill of the evapotranspiration (ET0) and Precipitation for the purpose of crop irrigation management over Israel using the Weather Research and Forecasting (WRF) Model. Optimized crop irrigation, in term of timing and quantities, decreases water and agrochemicals demand. Crop water demands depend on evapotranspiration and precipitation. The common method for computing reference evapotranspiration, for agricultural needs, ET0, is according to the FAO Penman-Monteith equation. The weather variables required for ET0 calculation (air temperature, relative humidity, wind speed and solar irradiance) are estimated by the WRF model. The WRF Model with two-way interacting domains at horizontal resolutions of 27, 9 and 3 km is used in the study. The model prediction was performed in an hourly time resolution and a 3 km spatial resolution, with forecast lead-time of up to four days. The WRF prediction of these variables have been compared against measurements from 29 meteorological stations across Israel for the year 2013. The studied area is small but with strong climatic gradient, diverse topography and variety of synoptic conditions. The forecast skill that was used for forecast validation takes into account the prediction bias, mean absolute error and root mean squared error. The forecast skill of the variables was almost robust to lead time, except for precipitation. The forecast skill was tested across stations with respect to topography and geographic location and for all stations with respect to seasonality and synoptic weather system determined by employing a semi-objective synoptic systems classification to the forecasted days. It was noticeable that forecast skill of some of the variables was deteriorated by seasonality and topography. However, larger impacts in the ET0 skill scores on the forecasted day are achieved by a synoptic based forecast. These results set the basis for increasing the robustness of ET0 to

  11. Forecasting Safe or Dangerous Space Weather from HMI Magnetograms

    Science.gov (United States)

    Falconer, David; Barghouty, Abdulnasser F.; Khazanov, Igor; Moore, Ron

    2011-01-01

    We have developed a space-weather forecasting tool using an active-region free-energy proxy that was measured from MDI line-of-sight magnetograms. To develop this forecasting tool (Falconer et al 2011, Space Weather Journal, in press), we used a database of 40,000 MDI magnetograms of 1300 active regions observed by MDI during the previous solar cycle (cycle 23). From each magnetogram we measured our free-energy proxy and for each active region we determined its history of major flare, CME and Solar Particle Event (SPE) production. This database determines from the value of an active region s free-energy proxy the active region s expected rate of production of 1) major flares, 2) CMEs, 3) fast CMEs, and 4) SPEs during the next few days. This tool was delivered to NASA/SRAG in 2010. With MDI observations ending, we have to be able to use HMI magnetograms instead of MDI magnetograms. One of the difficulties is that the measured value of the free-energy proxy is sensitive to the spatial resolution of the measured magnetogram: the 0.5 /pixel resolution of HMI gives a different value for the free-energy proxy than the 2 /pixels resolution of MDI. To use our MDI-database forecasting curves until a comparably large HMI database is accumulated, we smooth HMI line-of-sight magnetograms to MDI resolution, so that we can use HMI to find the value of the free-energy proxy that MDI would have measured, and then use the forecasting curves given by the MDI database. The new version for use with HMI magnetograms was delivered to NASA/SRAG (March 2011). It can also use GONG magnetograms, as a backup.

  12. 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

  13. Origins of forecast skill of weather and climate events on verifiable time scales

    CSIR Research Space (South Africa)

    Landman, WA

    2012-07-01

    Full Text Available Verification of weather and seasonal forecasts, as well as the statistical analysis of the spatial and temporal description of forecast and observed fields, are necessary to improve on our understanding of the capabilities of models to describe...

  14. The Scientific Foundations of Forecasting Magnetospheric Space Weather

    Science.gov (United States)

    Eastwood, J. P.; Nakamura, R.; Turc, L.; Mejnertsen, L.; Hesse, M.

    2017-11-01

    The magnetosphere is the lens through which solar space weather phenomena are focused and directed towards the Earth. In particular, the non-linear interaction of the solar wind with the Earth's magnetic field leads to the formation of highly inhomogenous electrical currents in the ionosphere which can ultimately result in damage to and problems with the operation of power distribution networks. Since electric power is the fundamental cornerstone of modern life, the interruption of power is the primary pathway by which space weather has impact on human activity and technology. Consequently, in the context of space weather, it is the ability to predict geomagnetic activity that is of key importance. This is usually stated in terms of geomagnetic storms, but we argue that in fact it is the substorm phenomenon which contains the crucial physics, and therefore prediction of substorm occurrence, severity and duration, either within the context of a longer-lasting geomagnetic storm, but potentially also as an isolated event, is of critical importance. Here we review the physics of the magnetosphere in the frame of space weather forecasting, focusing on recent results, current understanding, and an assessment of probable future developments.

  15. Utilizing Probability Distribution Functions and Ensembles to Forecast lonospheric and Thermosphere Space Weather

    Science.gov (United States)

    2016-04-26

    Functions and Ensembles to Forecast lonospheric and Thermosphere Space Weather 5a. CONTRACT NUMBER 5b. GRANT NUMBER FA9550-12-1-0265 5c. PROGRAM... weather forecasting community. They cause important geomagnetic storms that can eventually affect systems in orbit and on the ground. Therefore, the...Ionosphere Storm Forecasts . Space Weather , 13, 125129. doi: 10.1002/2014SW001125. 5. Zou, S., M. B. Moldwin, A. J. Ridley, M. J. Nicolls, A. J. Coster, E. G

  16. 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.

  17. Downscaling Global Weather Forecast Outputs Using ANN for Flood Prediction

    Directory of Open Access Journals (Sweden)

    Nam Do Hoai

    2011-01-01

    Full Text Available Downscaling global weather prediction model outputs to individual locations or local scales is a common practice for operational weather forecast in order to correct the model outputs at subgrid scales. This paper presents an empirical-statistical downscaling method for precipitation prediction which uses a feed-forward multilayer perceptron (MLP neural network. The MLP architecture was optimized by considering physical bases that determine the circulation of atmospheric variables. Downscaled precipitation was then used as inputs to the super tank model (runoff model for flood prediction. The case study was conducted for the Thu Bon River Basin, located in Central Vietnam. Study results showed that the precipitation predicted by MLP outperformed that directly obtained from model outputs or downscaled using multiple linear regression. Consequently, flood forecast based on the downscaled precipitation was very encouraging. It has demonstrated as a robust technology, simple to implement, reliable, and universal application for flood prediction through the combination of downscaling model and super tank model.

  18. Engaging Earth- and Environmental-Science Undergraduates Through Weather Discussions and an eLearning Weather Forecasting Contest

    Science.gov (United States)

    Schultz, David M.; Anderson, Stuart; Seo-Zindy, Ryo

    2013-06-01

    For students who major in meteorology, engaging in weather forecasting can motivate learning, develop critical-thinking skills, improve their written communication, and yield better forecasts. Whether such advances apply to students who are not meteorology majors has been less demonstrated. To test this idea, a weather discussion and an eLearning weather forecasting contest were devised for a meteorology course taken by third-year undergraduate earth- and environmental-science students. The discussion consisted of using the recent, present, and future weather to amplify the topics of the week's lectures. Then, students forecasted the next day's high temperature and the probability of precipitation for Woodford, the closest official observing site to Manchester, UK. The contest ran for 10 weeks, and the students received credit for participation. The top students at the end of the contest received bonus points on their final grade. A Web-based forecast contest application was developed to register the students, receive their forecasts, and calculate weekly standings. Students who were successful in the forecast contest were not necessarily those who achieved the highest scores on the tests, demonstrating that the contest was possibly testing different skills than traditional learning. Student evaluations indicate that the weather discussion and contest were reasonably successful in engaging students to learn about the weather outside of the classroom, synthesize their knowledge from the lectures, and improve their practical understanding of the weather. Therefore, students taking a meteorology class, but not majoring in meteorology, can derive academic benefits from weather discussions and forecast contests. Nevertheless, student evaluations also indicate that better integration of the lectures, weather discussions, and the forecasting contests is necessary.

  19. 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...

  20. Forecasting Space Weather Events for a Neighboring World

    Science.gov (United States)

    Zheng, Yihua; Mason, Tom; Wood, Erin L.

    2015-01-01

    Shortly after NASA's Mars Atmosphere and Volatile EvolutioN mission (MAVEN) spacecraft entered Mars' orbit on 21 September 2014, scientists glimpsed the Martian atmosphere's response to a front of solar energetic particles (SEPs) and an associated coronal mass ejection (CME). In response to some solar flares and CMEs, streams of SEPs burst from the solar atmosphere and are further accelerated in the interplanetary medium between the Sun and the planets. These particles deposit their energy and momentum into anything in their path, including the Martian atmosphere and MAVEN particle detectors. MAVEN scientists had been alerted to the likely CME-Mars encounter by a space weather prediction system that had its origins in space weather forecasting for Earth but now forecasts space weather for Earth's neighboring planets. The two Solar Terrestrial Relations Observatory spacecraft and Solar Heliospheric Observatory observed a CME on 26 September, with a trajectory that suggested a Mars intercept. A computer model developed for solar wind prediction, the Wang-Sheeley-Arge-Enlil cone model [e.g., Zheng et al., 2013; Parsons et al., 2011], running in real time at the Community Coordinated Modeling Center (CCMC) located at NASA Goddard since 2006, showed the CME propagating in the direction of Mars (Figure 1). According to MAVEN particle detectors, the disturbance and accompanying SEP enhancement at the leading edge of the CME reached Mars at approximately 17 hours Universal Time on 29 September 2014. Such SEPs may have a profound effect on atmospheric escape - they are believed to be a possible means for driving atmospheric loss. SEPs can cause loss of Mars' upper atmosphere through several loss mechanisms including sputtering of the atmosphere. Sputtering occurs when atoms are ejected from the atmosphere due to impacts with energetic particles.

  1. Weather Stations as Educational and Hazard-Forecasting Tools

    Science.gov (United States)

    Bowman, L. J.; Gierke, J. S.; Gochis, E. E.; Dominguez, R.; Mayer, A. S.

    2014-12-01

    Small, relatively inexpensive (grade levels, while also facilitating compilation of climate data for longer term research. Weather stations and networks of stations have been installed both locally and abroad in mostly rural and resource-limited settings. The data are being used either in the classroom to engage students in place-based, scientific investigations and/or research to improve hydrometeorological hazard forecasting, including water scarcity. The San Vicente (El Salvador) Network of six stations monitors rainfall to aid warning and evacuations for landslide and flooding hazards. Other parameters are used in modeling the watershed hydrology. A station installed in Hermosillo, Mexico is used in both Geography and Ecology Classes. Trends in temperature and rainfall are graphed and compared to historic data gathered over the last 30 years by CONAGUA. These observations are linked to local water-related problems, including well salinization, diminished agriculture, depleted aquifers, and social conflict regarding access to water. Two weather stations were installed at the Hannahville Indian Community School (Nah Tah Wahsh) in Michigan for educational purposes of data collection, analysis, and presentation. Through inquiry-based explorations of local hydrological processes, students are introduced to how meteorological data are used in understanding watershed hydrology and the sustainable management of groundwater resources. Several Michigan Technological University Peace Corps Masters International students have deployed weather stations in and around the communities where they serve, and the data are used in research to help in understanding water resource availability and irrigation needs.

  2. Distributed Sensor Network for meteorological observations and numerical weather Prediction Calculations

    Directory of Open Access Journals (Sweden)

    Á. Vas

    2013-06-01

    Full Text Available The prediction of weather generally means the solution of differential equations on the base of the measured initial conditions where the data of close and distant neighboring points are used for the calculations. It requires the maintenance of expensive weather stations and supercomputers. However, if weather stations are not only capable of measuring but can also communicate with each other, then these smart sensors can also be applied to run forecasting calculations. This applies the highest possible level of parallelization without the collection of measured data into one place. Furthermore, if more nodes are involved, the result becomes more accurate, but the computing power required from one node does not increase. Our Distributed Sensor Network for meteorological sensing and numerical weather Prediction Calculations (DSN-PC can be applied in several different areas where sensing and numerical calculations, even the solution of differential equations, are needed.

  3. Numerical simulation of heavy precipitation events using mesoscale weather forecast models. Validation with radar data and diagnosis of the atmospheric moisture budget; Numerische Simulation von Starkniederschlagsereignissen mit mesoskaligen Wettervorhersagemodellen. Ueberpruefung mit Radar-Daten und Diagnose der atmosphaerischen Wasserbilanz

    Energy Technology Data Exchange (ETDEWEB)

    Keil, C.

    2000-07-01

    Convective precipitation systems contribute substantially to the summertime rainfall maximum in the northern Alpine region. The capability of mesoscale weather forecast models in capturing such heavy precipitation events is investigated. The complementary application of so far hardly used areal radar data and conventional rain gauge observations enables a case-study-type evaluation of summertime precipitation episodes. Different rainfall episodes are simulated with the former operational model (DM, meshsize 14 km) of Deutscher Wetterdienst (DWD). The influence of the horizontal resolution and the parameterization of moist convection is subsequently studied with a higher resolution atmospheric model (MC2, meshsize 2 km). Diagnostic studies on the atmospheric water budget regarding the rainfall episode, which instigated the Oder-flood in summer 1997, allow an examination of the origin of the moisture and the genesis of the copious precipitation. (orig.) [German] Konvektive Niederschlagssysterne tragen im Nordalpenraum wesentlich zum sommerlichen Niederschlagsmaximum bei. Die Faehigkeit mesoskaliger Wettervorhersagemodelle, solche Starkniederschlagsereignisse zu erfassen, wird in dieser Arbeit untersucht. Durch den komplementaeren Gebrauch von, bisher kaum genutzten, flaechendeckenden Radardaten und konventionellen Niederschlagsmessungen des Bodenmessnetzes werden Modellergebnisse sommerlicher Niederschlagssysteme fallstudienhaft detailliert ueberprueft. Fuer verschiedene Starkniederschlagsereignisse werden dazu Modellsimulationen mit dem in den 90er Jahren operationellen Modell (DM, Maschenweite 14 km) des Deutschen Wetterdienstes (DWD) durchgefuehrt. Zur Untersuchung des Einflusses der horizontalen Maschenweite und der Niederschlagsparametrisierung werden ferner numerische Simulationen mit einem hoeher aufloesdenden Atmosphaerenmodell (MC2, Maschenweite 2 km) behandelt. Anhand diagnostischer Untersuchungen der atmosphaerischen Wasserbilanz laesst sich ausserdem die

  4. 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

  5. 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.

  6. Types of Forecast and Weather-Related Information Used among Tourism Businesses in Coastal North Carolina

    Science.gov (United States)

    Ayscue, Emily P.

    This study profiles the coastal tourism sector, a large and diverse consumer of climate and weather information. It is crucial to provide reliable, accurate and relevant resources for the climate and weather-sensitive portions of this stakeholder group in order to guide them in capitalizing on current climate and weather conditions and to prepare them for potential changes. An online survey of tourism business owners, managers and support specialists was conducted within the eight North Carolina oceanfront counties asking respondents about forecasts they use and for what purposes as well as why certain forecasts are not used. Respondents were also asked about their perceived dependency of their business on climate and weather as well as how valuable different forecasts are to their decision-making. Business types represented include: Agriculture, Outdoor Recreation, Accommodations, Food Services, Parks and Heritage, and Other. Weekly forecasts were the most popular forecasts with Monthly and Seasonal being the least used. MANOVA and ANOVA analyses revealed outdoor-oriented businesses (Agriculture and Outdoor Recreation) as perceiving themselves significantly more dependent on climate and weather than indoor-oriented ones (Food Services and Accommodations). Outdoor businesses also valued short-range forecasts significantly more than indoor businesses. This suggests a positive relationship between perceived climate and weather dependency and forecast value. The low perceived dependency and value of short-range forecasts of indoor businesses presents an opportunity to create climate and weather information resources directed at how they can capitalize on positive climate and weather forecasts and how to counter negative effects with forecasted adverse conditions. The low use of long-range forecasts among all business types can be related to the low value placed on these forecasts. However, these forecasts are still important in that they are used to make more

  7. Results of the Clarus demonstrations : evaluation of enhanced road weather forecasting enabled by Clarus.

    Science.gov (United States)

    2011-06-14

    This document is the final report of an evaluation of Clarus-enabled enhanced road weather forecasting used in the Clarus Demonstrations. : This report examines the use of Clarus data to enhance four types of weather models and forecasts: The Local A...

  8. Reducing Probabilistic Weather Forecasts to the Worst-Case Scenario: Anchoring Effects

    Science.gov (United States)

    Joslyn, Susan; Savelli, Sonia; Nadav-Greenberg, Limor

    2011-01-01

    Many weather forecast providers believe that forecast uncertainty in the form of the worst-case scenario would be useful for general public end users. We tested this suggestion in 4 studies using realistic weather-related decision tasks involving high winds and low temperatures. College undergraduates, given the statistical equivalent of the…

  9. Coupling between the JULES land-surface scheme and the CCATT-BRAMS atmospheric chemistry model (JULES-CCATT-BRAMS1.0: applications to numerical weather forecasting and the CO2 budget in South America

    Directory of Open Access Journals (Sweden)

    D. S. Moreira

    2013-08-01

    Full Text Available This article presents the coupling of the JULES surface model to the CCATT-BRAMS atmospheric chemistry model. This new numerical system is denominated JULES-CCATT-BRAMS. We demonstrate the performance of this new model system in relation to several meteorological variables and the CO2 mixing ratio over a large part of South America, focusing on the Amazon basin. The evaluation was conducted for two time periods, the wet (March and dry (September seasons of 2010. The model errors were calculated in relation to meteorological observations at conventional stations in airports and automatic stations. In addition, CO2 mixing ratios in the first model level were compared with meteorological tower measurements and vertical CO2 profiles were compared with observations obtained with airborne instruments. The results of this study show that the JULES-CCATT-BRAMS modeling system provided a significant gain in performance for the considered atmospheric fields relative to those simulated by the LEAF (version 3 surface model originally employed by CCATT-BRAMS. In addition, the new system significantly increases the ability to simulate processes involving air–surface interactions, due to the ability of JULES to simulate photosynthesis, respiration and dynamic vegetation, among other processes. We also discuss a wide range of numerical studies involving coupled atmospheric, land surface and chemistry processes that could be done with the system introduced here. Thus, this work presents to the scientific community a free modeling tool, with good performance in comparison with observational data and reanalysis model data, at least for the region and time period discussed here. Therefore, in principle, this model is able to produce atmospheric hindcast/forecast simulations at different spatial resolutions for any time period and any region of the globe.

  10. 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.

  11. Snowfall Rate Retrieval Using Passive Microwave Measurements and Its Applications in Weather Forecast and Hydrology

    Science.gov (United States)

    Meng, Huan; Ferraro, Ralph; Kongoli, Cezar; Yan, Banghua; Zavodsky, Bradley; Zhao, Limin; Dong, Jun; Wang, Nai-Yu

    2015-01-01

    (AMSU), Microwave Humidity Sounder (MHS) and Advance Technology Microwave Sounder (ATMS). ATMS is the follow-on sensor to AMSU and MHS. Currently, an AMSU and MHS based land snowfall rate (SFR) product is running operationally at NOAA/NESDIS. Based on the AMSU/MHS SFR, an ATMS SFR algorithm has also been developed. The algorithm performs retrieval in three steps: snowfall detection, retrieval of cloud properties, and estimation of snow particle terminal velocity and snowfall rate. The snowfall detection component utilizes principal component analysis and a logistic regression model. It employs a combination of temperature and water vapor sounding channels to detect the scattering signal from falling snow and derives the probability of snowfall. Cloud properties are retrieved using an inversion method with an iteration algorithm and a two-stream radiative transfer model. A method adopted to calculate snow particle terminal velocity. Finally, snowfall rate is computed by numerically solving a complex integral. The SFR products are being used mainly in two communities: hydrology and weather forecast. Global blended precipitation products traditionally do not include snowfall derived from satellites because such products were not available operationally in the past. The ATMS and AMSU/MHS SFR now provide the winter precipitation information for these blended precipitation products. Weather forecasters mainly rely on radar and station observations for snowfall forecast. The SFR products can fill in gaps where no conventional snowfall data are available to forecasters. The products can also be used to confirm radar and gauge snowfall data and increase forecasters' confidence in their prediction.

  12. 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.

  13. Weather factors in the short-term forecasting of daily ambulance calls.

    Science.gov (United States)

    Wong, Ho-Ting; Lai, Poh-Chin

    2014-07-01

    The daily ambulance demand for Hong Kong is rising, and it has been shown that weather factors (temperature and humidity) play a role in the demand for ambulance services. This study aimed at developing short-term forecasting models of daily ambulance calls using the 7-day weather forecast data as predictors. We employed the autoregressive integrated moving average (ARIMA) method to analyze over 1.3 million cases of emergency attendance in May 2006 through April 2009 and the 7-day weather forecast data for the same period. Our results showed that the ARIMA model could offer reasonably accurate forecasts of daily ambulance calls at 1-7 days ahead of time and with improved accuracy by including weather factors. Specifically, the inclusion of average temperature alone in our ARIMA model improved the predictability of the 1-day forecast when compared to that of a simple ARIMA model (8.8% decrease in the root mean square error, RMSE=53 vs 58). The improvement in the 7-day forecast with average temperature as a predictor was more pronounced, with a 10% drop in prediction error (RMSE=62 vs 69). These findings suggested that weather forecast data can improve the 1- to 7-day forecasts of daily ambulance demand. As weather forecast data are readily accessible from Hong Kong Observatory's official website, there is virtually no cost to including them in the ARIMA models, which yield better prediction for forward planning and deployment of ambulance manpower.

  14. Influence of Met-Ocean Condition Forecasting Uncertainties on Weather Window Predictions for Offshore Operations

    DEFF Research Database (Denmark)

    Gintautas, Tomas; Sørensen, John Dalsgaard

    2017-01-01

    The article briefly presents a novel methodology of weather window estimation for offshore operations and mainly focuses on effects of met-ocean condition forecasting uncertainties on weather window predictions when using the proposed methodology. It is demonstrated that the proposed methodology...... to include stochastic variables, representing met-ocean forecasting uncertainties and the results of such modification are given in terms of predicted weather windows for a selected test case....

  15. Opportunities and challenges of indigenous biotic weather forecasting among the Borena herders of southern Ethiopia.

    Science.gov (United States)

    Ayal, Desalegn Yayeh; Desta, Solomon; Gebru, Getachew; Kinyangi, James; Recha, John; Radeny, Maren

    2015-01-01

    The practical utilization of available modern as well as traditional weather forecasting systems builds herders' resiliency capacity to climatic shocks. The precision and reliability of the forecasting system determines its creditability and acceptance by the users to be proactive in the decisions they make based on the forecasted information. It has been postulated that traditional weather forecasting systems are becoming less reliable due to repeated faulty forecasts. The study assesses the current status of the Borana traditional weather forecasting system and how traditional experts make weather forecasts based on biotic indicators such as intestinal readings, changes in plant and animal body languages. Questionnaire survey, field observations, focus group discussions and interviews with relevant key informants were employed to obtain data. Collected field data was compared with National Metrological Service Agency instrumental data for consistency. Results reveal that herders made short term weather forecasts using intestinal readings, and observed changes in plant and animal body languages. The study shows the extent how public confidence in the accuracy of indigenous weather forecasting skills has been gradually eroded overtime due to faulty forecasts. The precision and credibility of the traditional weather forecast steadily declined and led to repeated faulty predictions. Poor documentation, oral based knowledge transfer system, influence of religion and modern education, aging and extinction of traditional experts were identified as the major causes undermining the vitality of traditional climate forecast. Traditional weather foresting knowledge and skill could have some utility and also serve as a starting point to scientifically study the relationship between various signs and implied climatic events. This article recommends before traditional Borana weather forecasting system completely disappears, a remedial action should be carried out to rescue this

  16. Distinguishing high and low flow domains in urban drainage systems 2 days ahead using numerical weather prediction ensembles

    DEFF Research Database (Denmark)

    Courdent, Vianney Augustin Thomas; Grum, Morten; Mikkelsen, Peter Steen

    2018-01-01

    to transform the forecasted rainfall into forecasted flow series and evaluate three different approaches to establishing the relative operating characteristics (ROC) diagram of the forecast, which is a plot of POD against POFD for each fraction of concordant ensemble members and can be used to select......Precipitation constitutes a major contribution to the flow in urban storm- and wastewater systems. Forecasts of the anticipated runoff flows, created from radar extrapolation and/or numerical weather predictions, can potentially be used to optimize operation in both wet and dry weather periods...... prediction (NWP) model subject to three different ensemble post-processing approaches can be used to forecast flow exceedance in a combined sewer for a wide range of ratios between the probability of detection (POD) and the probability of false detection (POFD). We use a hydrological rainfall-runoff model...

  17. Verification of a Real Time Weather Forecasting System in Southern Italy

    OpenAIRE

    Tiriolo, Luca; Torcasio, Rosa Claudia; Montesanti, Stefania; Federico, Stefano

    2015-01-01

    This paper shows the performance of an operational forecasting system, based on the regional atmospheric modeling system (RAMS), at 3 km horizontal resolution over southern Italy. The model is initialized from the 12 UTC operational analysis/forecasting cycle of the European Centre for Medium range Weather Forecasts (ECMWF). The forecast is issued for the following three days. The performance is evaluated for a whole year for the surface parameters: temperature, relative humidity, wind speed ...

  18. Applications of Kalman filters based on non-linear functions to numerical weather predictions

    Directory of Open Access Journals (Sweden)

    G. Galanis

    2006-10-01

    Full Text Available This paper investigates the use of non-linear functions in classical Kalman filter algorithms on the improvement of regional weather forecasts. The main aim is the implementation of non linear polynomial mappings in a usual linear Kalman filter in order to simulate better non linear problems in numerical weather prediction. In addition, the optimal order of the polynomials applied for such a filter is identified. This work is based on observations and corresponding numerical weather predictions of two meteorological parameters characterized by essential differences in their evolution in time, namely, air temperature and wind speed. It is shown that in both cases, a polynomial of low order is adequate for eliminating any systematic error, while higher order functions lead to instabilities in the filtered results having, at the same time, trivial contribution to the sensitivity of the filter. It is further demonstrated that the filter is independent of the time period and the geographic location of application.

  19. 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.

  20. Future Missions for Space Weather Specifications and Forecasts

    Science.gov (United States)

    Onsager, T. G.; Biesecker, D. A.; Anthes, R. A.; Maier, M. W.; Gallagher, F. W., III; St Germain, K.

    2017-12-01

    The progress of technology and the global integration of our economic and security infrastructures have introduced vulnerabilities to space weather that demand a more comprehensive ability to specify and to predict the dynamics of the space environment. This requires a comprehensive network of real-time space-based and ground-based observations with long-term continuity. In order to determine the most cost effective space architectures for NOAA's weather, space weather, and environmental missions, NOAA conducted the NOAA Satellite Observing System Architecture (NSOSA) study. This presentation will summarize the process used to document the future needs and the relative priorities for NOAA's operational space-based observations. This involves specifying the most important observations, defining the performance attributes at different levels of capability, and assigning priorities for achieving the higher capability levels. The highest priority observations recommended by the Space Platform Requirements Working Group (SPRWG) for improvement above a minimal capability level will be described. Finally, numerous possible satellite architectures have been explored to assess the costs and benefits of various architecture configurations.

  1. The communicative process of weather forecasts issued in the probabilistic form

    Directory of Open Access Journals (Sweden)

    Alessio Raimondi

    2009-03-01

    Full Text Available One of the main purposes of weather forecasting is that of protecting weather-sensitive human activities. Forecasts issued in the probabilistic form have a higher informative content, as opposed to deterministic one, since they bear information that give also a measure of their own uncertainty. However, in order to make an appropriate and effective use of this kind of forecasts in an operational setting, communication becomes significatively relevant.The present paper, after having briefly examined the weather forecasts concerning Hurricane Charley (August 2004, tackles the issue of the communicative process in detail.The bottom line of this study is that for the weather forecast to achieve its best predictive potential, an in-depth analysis of communication issues is necessary.

  2. The communicative process of weather forecasts issued in the probabilistic form (Italian original version

    Directory of Open Access Journals (Sweden)

    Alessio Raimondi

    2009-03-01

    Full Text Available One of the main purposes of weather forecasting is that of protecting weather-sensitive human activities. Forecasts issued in the probabilistic form have a higher informative content, as opposed to deterministic one, since they bear information that give also a measure of their own uncertainty. However, in order to make an appropriate and effective use of this kind of forecasts in an operational setting, communication becomes significatively relevant.The present paper, after having briefly examined the weather forecasts concerning Hurricane Charley (August 2004, tackles the issue of the communicative process in detail.The bottom line of this study is that for the weather forecast to achieve its best predictive potential, an in-depth analysis of communication issues is necessary.

  3. Using weather forecasts for predicting forest-fire danger

    Science.gov (United States)

    H. T. Gisborne

    1925-01-01

    Three kinds of weather control the fluctuations of forest-fire danger-wet weather, dry weather, and windy weather. Two other conditions also contribute to the fluctuation of fire danger. These are the occurrence of lightning and the activities of man. But neither of these fire-starting agencies is fully effective unless the weather has dried out the forest materials so...

  4. 3-D visualization of ensemble weather forecasts - Part 2: Forecasting warm conveyor belt situations for aircraft-based field campaigns

    Science.gov (United States)

    Rautenhaus, M.; Grams, C. M.; Schäfler, A.; Westermann, R.

    2015-02-01

    We present the application of interactive 3-D visualization of ensemble weather predictions to forecasting warm conveyor belt situations during aircraft-based atmospheric research campaigns. Motivated by forecast requirements of the T-NAWDEX-Falcon 2012 campaign, a method to predict 3-D probabilities of the spatial occurrence of warm conveyor belts has been developed. Probabilities are derived from Lagrangian particle trajectories computed on the forecast wind fields of the ECMWF ensemble prediction system. Integration of the method into the 3-D ensemble visualization tool Met.3D, introduced in the first part of this study, facilitates interactive visualization of WCB features and derived probabilities in the context of the ECMWF ensemble forecast. We investigate the sensitivity of the method with respect to trajectory seeding and forecast wind field resolution. Furthermore, we propose a visual analysis method to quantitatively analyse the contribution of ensemble members to a probability region and, thus, to assist the forecaster in interpreting the obtained probabilities. A case study, revisiting a forecast case from T-NAWDEX-Falcon, illustrates the practical application of Met.3D and demonstrates the use of 3-D and uncertainty visualization for weather forecasting and for planning flight routes in the medium forecast range (three to seven days before take-off).

  5. Linear and nonlinear post-processing of numerically forecasted surface temperature

    Directory of Open Access Journals (Sweden)

    M. Casaioli

    2003-01-01

    Full Text Available In this paper we test different approaches to the statistical post-processing of gridded numerical surface air temperatures (provided by the European Centre for Medium-Range Weather Forecasts onto the temperature measured at surface weather stations located in the Italian region of Puglia. We consider simple post-processing techniques, like correction for altitude, linear regression from different input parameters and Kalman filtering, as well as a neural network training procedure, stabilised (i.e. driven into the absolute minimum of the error function over the learning set by means of a Simulated Annealing method. A comparative analysis of the results shows that the performance with neural networks is the best. It is encouraging for systematic use in meteorological forecast-analysis service operations.

  6. How to judge the quality and value of weather forecast products

    Science.gov (United States)

    Thornes, John E.; Stephenson, David B.

    2001-09-01

    In order to decide whether or not a weather service supplier is giving good value for money we need to monitor the quality of the forecasts and the use that is made of the forecasts to estimate their value. A number of verification statistics are examined to measure the quality of forecasts - including Miss Rate, False Alarm Rate, the Peirce Skill Score and the Odds Ratio Skill Score - and a means of testing the significance of these values is presented. In order to assess the economic value of the forecasts a value index is suggested that takes into account the cost-loss ratio and forecast errors. It is suggested that a combination of these quality and value statistics could be used by weather forecast customers to choose the best forecast provider and to set limits for performance related contracts.

  7. Evaluation of Wind Power Forecasts from the Vermont Weather Analytics Center and Identification of Improvements

    Energy Technology Data Exchange (ETDEWEB)

    Optis, Michael [National Renewable Energy Lab. (NREL), Golden, CO (United States); Scott, George N. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Draxl, Caroline [National Renewable Energy Lab. (NREL), Golden, CO (United States)

    2018-02-02

    The goal of this analysis was to assess the wind power forecast accuracy of the Vermont Weather Analytics Center (VTWAC) forecast system and to identify potential improvements to the forecasts. Based on the analysis at Georgia Mountain, the following recommendations for improving forecast performance were made: 1. Resolve the significant negative forecast bias in February-March 2017 (50% underprediction on average) 2. Improve the ability of the forecast model to capture the strong diurnal cycle of wind power 3. Add ability for forecast model to assess internal wake loss, particularly at sites where strong diurnal shifts in wind direction are present. Data availability and quality limited the robustness of this forecast assessment. A more thorough analysis would be possible given a longer period of record for the data (at least one full year), detailed supervisory control and data acquisition data for each wind plant, and more detailed information on the forecast system input data and methodologies.

  8. Investigation of the Usability of Mobile Sensors for Weather Forecasting

    Directory of Open Access Journals (Sweden)

    Semih Dalğın

    2015-08-01

    Full Text Available Crowd sourcing is a popular method for providing data from people by the use of mobile sensor, internet and communication technologies. However efficient use of the raw data provided by the sensors with different characteristics in order to obtain accurate results is not investigated in detail. This study aims to investigate the data collected by mobile sensors integrated in the smartphones for scientific purposes such as weather forecasting. In this context, accuracy of the data provided mobile humidity, pressure and temperature sensors was examined in this study. Data provided by 5 smart phones and 3 Bluetooth sensors were tested in this context. Accuracy assessment process was performed by calculating the Root Mean Square Errors of the data with respect to reference data collected by TST Sensor simultaneously. This study shows that accuracy of the data collected with the mobile sensors is affected by several external parameters such as climatic conditions, handling habits of the user, and etc. Although it is possible to calculate correction constant for each sensor separately, it is not possible to calculate a unique and universal correction constant in order to increase the accuracy of the raw data collected by the mobile sensors. Therefore further studies should be executed for improving the accuracy of the mobile sensor data for scientific purposes.

  9. Visualizing Confidence in Cluster-Based Ensemble Weather Forecast Analyses.

    Science.gov (United States)

    Kumpf, Alexander; Tost, Bianca; Baumgart, Marlene; Riemer, Michael; Westermann, Rudiger; Rautenhaus, Marc

    2018-01-01

    In meteorology, cluster analysis is frequently used to determine representative trends in ensemble weather predictions in a selected spatio-temporal region, e.g., to reduce a set of ensemble members to simplify and improve their analysis. Identified clusters (i.e., groups of similar members), however, can be very sensitive to small changes of the selected region, so that clustering results can be misleading and bias subsequent analyses. In this article, we - a team of visualization scientists and meteorologists-deliver visual analytics solutions to analyze the sensitivity of clustering results with respect to changes of a selected region. We propose an interactive visual interface that enables simultaneous visualization of a) the variation in composition of identified clusters (i.e., their robustness), b) the variability in cluster membership for individual ensemble members, and c) the uncertainty in the spatial locations of identified trends. We demonstrate that our solution shows meteorologists how representative a clustering result is, and with respect to which changes in the selected region it becomes unstable. Furthermore, our solution helps to identify those ensemble members which stably belong to a given cluster and can thus be considered similar. In a real-world application case we show how our approach is used to analyze the clustering behavior of different regions in a forecast of "Tropical Cyclone Karl", guiding the user towards the cluster robustness information required for subsequent ensemble analysis.

  10. The Impact of the Assimilation of AIRS Radiance Measurements on Short-term Weather Forecasts

    Science.gov (United States)

    McCarty, Will; Jedlovec, Gary; Miller, Timothy L.

    2009-01-01

    Advanced spaceborne instruments have the ability to improve the horizontal and vertical characterization of temperature and water vapor in the atmosphere through the explicit use of hyperspectral thermal infrared radiance measurements. The incorporation of these measurements into a data assimilation system provides a means to continuously characterize a three-dimensional, instantaneous atmospheric state necessary for the time integration of numerical weather forecasts. Measurements from the National Aeronautics and Space Administration (NASA) Atmospheric Infrared Sounder (AIRS) are incorporated into the gridpoint statistical interpolation (GSI) three-dimensional variational (3D-Var) assimilation system to provide improved initial conditions for use in a mesoscale modeling framework mimicking that of the operational North American Mesoscale (NAM) model. The methodologies for the incorporation of the measurements into the system are presented. Though the measurements have been shown to have a positive impact in global modeling systems, the measurements are further constrained in this system as the model top is physically lower than the global systems and there is no ozone characterization in the background state. For a study period, the measurements are shown to have positive impact on both the analysis state as well as subsequently spawned short-term (0-48 hr) forecasts, particularly in forecasted geopotential height and precipitation fields. At 48 hr, height anomaly correlations showed an improvement in forecast skill of 2.3 hours relative to a system without the AIRS measurements. Similarly, the equitable threat and bias scores of precipitation forecasts of 25 mm (6 hr)-1 were shown to be improved by 8% and 7%, respectively.

  11. Extreme scaling for global weather forecasts at O(1km) horizontal resolution

    Science.gov (United States)

    Wedi, Nils; Düben, Peter

    2017-04-01

    We report on recent experimentation towards improved scalability of high resolution simulations with the Integrated Forecast System of the European Centre for Medium-Range Weather Forecasts (ECMWF). A significant step towards further savings both in terms of throughput and speed-up is provided by the impact on simulations if numerical precision is selectively reduced in high resolution simulations from double to single precision. However, while higher horizontal resolution evidently increases the cost of simulations, there are other computational cost drivers arising from increasing model complexity through coupling of ocean waves, and including the ocean circulation and its interaction with the atmosphere. The cost/benefit ratios of these different modelling aspects are evaluated and illustrated with global simulations for the "Medicane" Trixie, a rare, high-impact weather event in the Mediterranean with a tropical-like cyclone structure that was observed in October/November 2016. High resolution simulations with IFS are performed as part of the ESiWACE project (www.esiwace.eu).

  12. 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...

  13. 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.

  14. 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.

  15. How is the weather? Forecasting inpatient glycemic control.

    Science.gov (United States)

    Saulnier, George E; Castro, Janna C; Cook, Curtiss B; Thompson, Bithika M

    2017-11-01

    Apply methods of damped trend analysis to forecast inpatient glycemic control. Observed and calculated point-of-care blood glucose data trends were determined over 62 weeks. Mean absolute percent error was used to calculate differences between observed and forecasted values. Comparisons were drawn between model results and linear regression forecasting. The forecasted mean glucose trends observed during the first 24 and 48 weeks of projections compared favorably to the results provided by linear regression forecasting. However, in some scenarios, the damped trend method changed inferences compared with linear regression. In all scenarios, mean absolute percent error values remained below the 10% accepted by demand industries. Results indicate that forecasting methods historically applied within demand industries can project future inpatient glycemic control. Additional study is needed to determine if forecasting is useful in the analyses of other glucometric parameters and, if so, how to apply the techniques to quality improvement.

  16. Accuracy of National Weather Service wind-direction forecasts at Macon and Augusta, Georgia

    Science.gov (United States)

    Leonidas G. Lavdas

    1997-01-01

    National Weather Service wind forecasts and observations over a nine-year period (1985 to 1993) were analyzed to determine the usefulness of these forecasts for forestry smoke management. Data from Macon, GA indicated that forecasts were accurate to within plus or minus 22.5E about 38 percent of the time. When a wider plus or minus 67.5E window was used, accuracy...

  17. Improved Impact of Atmospheric Infrared Sounder (AIRS) Radiance Assimilation in Numerical Weather Prediction

    Science.gov (United States)

    Zavodsky, Bradley; Chou, Shih-Hung; Jedlovec, Gary

    2012-01-01

    Improvements to global and regional numerical weather prediction (NWP) have been demonstrated through assimilation of data from NASA s Atmospheric Infrared Sounder (AIRS). Current operational data assimilation systems use AIRS radiances, but impact on regional forecasts has been much smaller than for global forecasts. Retrieved profiles from AIRS contain much of the information that is contained in the radiances and may be able to reveal reasons for this reduced impact. Assimilating AIRS retrieved profiles in an identical analysis configuration to the radiances, tracking the quantity and quality of the assimilated data in each technique, and examining analysis increments and forecast impact from each data type can yield clues as to the reasons for the reduced impact. By doing this with regional scale models individual synoptic features (and the impact of AIRS on these features) can be more easily tracked. This project examines the assimilation of hyperspectral sounder data used in operational numerical weather prediction by comparing operational techniques used for AIRS radiances and research techniques used for AIRS retrieved profiles. Parallel versions of a configuration of the Weather Research and Forecasting (WRF) model with Gridpoint Statistical Interpolation (GSI) that mimics the analysis methodology, domain, and observational datasets for the regional North American Mesoscale (NAM) model run at the National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center (EMC) are run to examine the impact of each type of AIRS data set. The first configuration will assimilate the AIRS radiance data along with other conventional and satellite data using techniques implemented within the operational system; the second configuration will assimilate AIRS retrieved profiles instead of AIRS radiances in the same manner. Preliminary results of this study will be presented and focus on the analysis impact of the radiances and profiles for selected cases.

  18. Combining traditional weather forecasting, science in Kenya | CRDI ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    24 févr. 2012 ... Kenyan farmers have relied on the indigenous weather prediction methods of the Nganyi rainmakers for generations. But extreme weather caused by climate change is affecting the natural signs that rainmakers use to predict weather. Many fear traditional methods are therefore becoming redundant and ...

  19. Combining traditional weather forecasting, science in Kenya | IDRC ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    2012-02-24

    Feb 24, 2012 ... Kenyan farmers have relied on the indigenous weather prediction methods of the Nganyi rainmakers for generations. But extreme weather caused by climate change is affecting the natural signs that rainmakers use to predict weather. Many fear traditional methods are therefore becoming redundant and ...

  20. Numerical air quality forecasting over eastern China: An operational application of WRF-Chem

    Science.gov (United States)

    Zhou, Guangqiang; Xu, Jianming; Xie, Ying; Chang, Luyu; Gao, Wei; Gu, Yixuan; Zhou, Ji

    2017-03-01

    The Regional Atmospheric Environmental Modeling System for eastern China (RAEMS) is an operational numerical system to forecast near surface atmospheric pollutants such as PM2.5 and O3 over the eastern China region. This system was based on the fully online coupled weather research and forecasting/chemistry (WRF-Chem) model. Anthropogenic emissions were based on the multi-resolution emission inventory for China (MEIC), and biogenic emissions were online calculated using model of emissions of gases and aerosols from nature (MEGAN2). Authorized by the China Meteorological Administration (CMA), this system started to provide operational forecast in 2013. With a large domain covering eastern China, the system produces daily 72-hr forecast. In this work, a comprehensive evaluation was carried out against measurements for two full years (2014-2015). Evaluation results show that the RAEMS is skillful in forecasting temporal variation and spatial distribution of major air pollutants over the eastern China region. The performance is consistent in different forecast length of 24 h, 48 h, and 72 h. About half of cities have correlation coefficients greater than 0.6 for PM2.5 and 0.7 for daily maximum 8-h averaged (DM8H) ozone. The forecasted PM2.5 is generally in good agreement with observed concentrations, with most cities having normalized mean biases (NMB) within ±25%. Forecasted ozone diurnal variation is very similar to that of observed, and makes small peak time error for DM8H ozone. It also shows good capability in capturing ozone pollution as indicated by high critical success indexes (CSI). The modeling system also exhibits acceptable performance for PM10, NO2, SO2, and CO. Meanwhile, degraded performance for PM2.5 is found under heavy polluted conditions, and there is a general over estimation in ozone concentrations.

  1. 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.

  2. Three-dimensional visualization of ensemble weather forecasts – Part 1: The visualization tool Met.3D (version 1.0

    Directory of Open Access Journals (Sweden)

    M. Rautenhaus

    2015-07-01

    Full Text Available We present "Met.3D", a new open-source tool for the interactive three-dimensional (3-D visualization of numerical ensemble weather predictions. The tool has been developed to support weather forecasting during aircraft-based atmospheric field campaigns; however, it is applicable to further forecasting, research and teaching activities. Our work approaches challenging topics related to the visual analysis of numerical atmospheric model output – 3-D visualization, ensemble visualization and how both can be used in a meaningful way suited to weather forecasting. Met.3D builds a bridge from proven 2-D visualization methods commonly used in meteorology to 3-D visualization by combining both visualization types in a 3-D context. We address the issue of spatial perception in the 3-D view and present approaches to using the ensemble to allow the user to assess forecast uncertainty. Interactivity is key to our approach. Met.3D uses modern graphics technology to achieve interactive visualization on standard consumer hardware. The tool supports forecast data from the European Centre for Medium Range Weather Forecasts (ECMWF and can operate directly on ECMWF hybrid sigma-pressure level grids. We describe the employed visualization algorithms, and analyse the impact of the ECMWF grid topology on computing 3-D ensemble statistical quantities. Our techniques are demonstrated with examples from the T-NAWDEX-Falcon 2012 (THORPEX – North Atlantic Waveguide and Downstream Impact Experiment campaign.

  3. Integrated system of visualization of the meteorological information for the weather forecast - SIPROT

    International Nuclear Information System (INIS)

    Leon Aristizabal, Gloria Esperanza

    2006-01-01

    The SIPROT is an operating system in real time for the handling of weather data through of a tool; it gathers together GIS and geodatabases. The SIPROT has the capacity to receive, to analyze and to exhibit weather charts of many national and international weather data in alphanumeric and binary formats from meteorological stations and satellites, as well as the results of the simulations of global and regional meteorological and wave models. The SIPROT was developed by the IDEAM to facilitate the handling of million weather dataset that take place daily and are required like elements of judgment for the inherent workings to the analyses and weather forecast

  4. Forecast Jointed Rock Mass Compressive Strength Using a Numerical Model

    Directory of Open Access Journals (Sweden)

    Protosenya Anatoliy

    2016-01-01

    Full Text Available The method of forecasting the strength of the jointed rock mass by numerical modeling of finite element method in ABAQUS was described. The paper presents advantages of this method to solve the problem of determining the mechanical characteristics of jointed rock mass and the basic steps of creating a numerical geomechanical model of jointed rock mass and numerical experiment. Numerical simulation was carried out with jointed rock mass in order to obtain the ratio of strain and stress while loading the numerical model, determining parameters of quantitative assessment of the impact of the discontinuities orientation on the value of the compressive strength, compressive strength anisotropy. The results of the numerical experiment are compared with the data of experimental studies investigations. Innovative materials and structures are analyzed in this paper. The results that were obtained by calculation show qualitative agreement with the results of laboratory experiments of jointed rock mass.

  5. A combined road weather forecast system to prevent road ice formation in the Adige Valley (Italy)

    Science.gov (United States)

    Di Napoli, Claudia; Piazza, Andrea; Antonacci, Gianluca; Todeschini, Ilaria; Apolloni, Roberto; Pretto, Ilaria

    2016-04-01

    Road ice is a dangerous meteorological hazard to a nation's transportation system and economy. By reducing the pavement friction with vehicle tyres, ice formation on pavements increases accident risk and delays travelling times thus posing a serious threat to road users' safety and the running of economic activities. Keeping roads clear and open is therefore essential, especially in mountainous areas where ice is likely to form during the winter period. Winter road maintenance helps to restore road efficiency and security, and its benefits are up to 8 times the costs sustained for anti-icing strategies [1]. However, the optimization of maintenance costs and the reduction of the environmental damage from over-salting demand further improvements. These can be achieved by reliable road weather forecasts, and in particular by the prediction of road surface temperatures (RSTs). RST is one of the most important parameters in determining road surface conditions. It is well known from literature that ice forms on pavements in high-humidity conditions when RSTs are below 0°C. We have therefore implemented an automatic forecast system to predict critical RSTs on a test route along the Adige Valley complex terrain, in the Italian Alps. The system considers two physical models, each computing heat and energy fluxes between the road and the atmosphere. One is Reuter's radiative cooling model, which predicts RSTs at sunrise as a function of surface temperatures at sunset and the time passed since then [2]. One is METRo (Model of the Environment and Temperature of Roads), a road weather forecast software which also considers heat conduction through road material [3]. We have applied the forecast system to a network of road weather stations (road weather information system, RWIS) installed on the test route [4]. Road and atmospheric observations from RWIS have been used as initial conditions for both METRo and Reuter's model. In METRo observations have also been coupled to

  6. Incorporating Ensemble-based Probabilistic Forecasts into a Campaign Simulation in the Weather Impact Assessment Tool (WIAT)

    Science.gov (United States)

    2010-06-01

    STW Strike Warfare UAV Unmanned Aerial Vehicle VBA Visual Basic for Applications WIAT Weather Impact Assessment Tool WIAT* Weather Impact...increased budget and research resources will be devoted to the continued development of probabilistic forecasting techniques and products. Deterministic...managing resources . The ability of the forecaster to accurately predict the most likely evolution of weather parameters and to communicate a qualitative

  7. Weather monitoring and forecasting over eastern Attica (Greece) in the frame of FLIRE project

    Science.gov (United States)

    Kotroni, Vassiliki; Lagouvardos, Konstantinos; Chrysoulakis, Nektarios; Makropoulos, Christtos; Mimikou, Maria; Papathanasiou, Chrysoula; Poursanidis, Dimitris

    2015-04-01

    In the frame of FLIRE project a Decision Support System has been built with the aim to support decision making of Civil Protection Agencies and local stakeholders in the area of east Attica (Greece), in the cases of forest fires and floods. In this presentation we focus on a specific action that focuses on the provision of high resolution short-term weather forecasting data as well as of dense meteorological observations over the study area. Both weather forecasts and observations serve as an input in the Weather Information Management Tool (WIMT) of the Decision Support System. We focus on: (a) the description of the adopted strategy for setting-up the operational weather forecasting chain that provides the weather forecasts for the FLIRE project needs, (b) the presentation of the surface network station that provides real-time weather monitoring of the study area and (c) the strategy adopted for issuing smart alerts for thunderstorm forecasting based of real-time lightning observations as well as satellite observations.

  8. Sharing wind power forecasts in electricity markets: A numerical analysis

    International Nuclear Information System (INIS)

    Exizidis, Lazaros; Kazempour, S. Jalal; Pinson, Pierre; Greve, Zacharie de; Vallée, François

    2016-01-01

    Highlights: • Information sharing among different agents can be beneficial for electricity markets. • System cost decreases by sharing wind power forecasts between different agents. • Market power of wind producer may increase by sharing forecasts with market operator. • Extensive out-of-sample analysis is employed to draw reliable conclusions. - Abstract: In an electricity pool with significant share of wind power, all generators including conventional and wind power units are generally scheduled in a day-ahead market based on wind power forecasts. Then, a real-time market is cleared given the updated wind power forecast and fixed day-ahead decisions to adjust power imbalances. This sequential market-clearing process may cope with serious operational challenges such as severe power shortage in real-time due to erroneous wind power forecasts in day-ahead market. To overcome such situations, several solutions can be considered such as adding flexible resources to the system. In this paper, we address another potential solution based on information sharing in which market players share their own wind power forecasts with others in day-ahead market. This solution may improve the functioning of sequential market-clearing process through making more informed day-ahead schedules, which reduces the need for balancing resources in real-time operation. This paper numerically evaluates the potential value of sharing forecasts for the whole system in terms of system cost reduction. Besides, its impact on each market player’s profit is analyzed. The framework of this study is based on a stochastic two-stage market setup and complementarity modeling, which allows us to gain further insights into information sharing impacts.

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

    Indian Academy of Sciences (India)

    hazard evaluation; Canadian Geotechnical Journal 7. 414–419. Sharma S S and Ganju A 2000 Complexities of avalanche forecasting in Western Himalaya: An overview; Cold. Regions Science and Technology 31 95–102. Singh A and Ganju A 2004 A supplement to nearest neigh- bour method for avalanche forecasting; ...

  10. Hit Rate of Space Weather Forecasts of the Japanese Forecast Center and Analysis of Problematic Events on the Forecasts between June 2014 and March 2015

    Science.gov (United States)

    Watari, S.; Kato, H.; Yamamoto, K.

    2015-12-01

    The hit rate of space weather forecasts issued by the Japanese forecast center in the National Institute of Information and Communications Technology (NICT) between June 2014 and March 2015 are compared with that by the persistence method. It is shown that the hit rate of the forecasts by the Japanese center is better than that by the persistence method. Several problematic events on the space weather forecasts during the same period are analyzed. Those events are (1) geomagnetic storms associated with coronal mass ejections (CMEs) on 9 September 2014 and on 15 March 2015 with different durations of southward interplanetary magnetic field (IMF), (2) a large active region, AR 12192 without CMEs, solar energetic particle events, and geomagnetic storms, (3) a geomagnetic storm on 7 January 2015 caused by a faint CME, and (4) disagreement between the in-situ observation at 1 AU and the prediction of the Potential Field Source Surface (PFSS) model on timing of sector crossing in January 2015.

  11. A Space Weather Forecasting System with Multiple Satellites Based on a Self-Recognizing Network

    Science.gov (United States)

    Tokumitsu, Masahiro; Ishida, Yoshiteru

    2014-01-01

    This paper proposes a space weather forecasting system at geostationary orbit for high-energy electron flux (>2 MeV). The forecasting model involves multiple sensors on multiple satellites. The sensors interconnect and evaluate each other to predict future conditions at geostationary orbit. The proposed forecasting model is constructed using a dynamic relational network for sensor diagnosis and event monitoring. The sensors of the proposed model are located at different positions in space. The satellites for solar monitoring equip with monitoring devices for the interplanetary magnetic field and solar wind speed. The satellites orbit near the Earth monitoring high-energy electron flux. We investigate forecasting for typical two examples by comparing the performance of two models with different numbers of sensors. We demonstrate the prediction by the proposed model against coronal mass ejections and a coronal hole. This paper aims to investigate a possibility of space weather forecasting based on the satellite network with in-situ sensing. PMID:24803190

  12. Hybrid dynamical-statistical S2S forecasts with weather types over North America

    Science.gov (United States)

    Johnson, N. C.; Munoz, A. G.; Vecchi, G. A.; Gudgel, R.

    2017-12-01

    Dynamical forecast models provide a foundation for S2S forecast systems, but systematic errors may arise for various reasons, including insufficient spatial resolution, insufficient ensemble size, and errors in physical parameterizations. Despite these flaws, the ability of dynamical models to simulate sources of prediction skill and their large-scale circulation responses allows us to draw from empirical predictor/large-scale circulation relationships to compensate for these shortcomings. In this study we use the framework known as weather types (WTs) to act as the mediator for a hybrid dynamical-statistical S2S forecast system. WTs are large-scale, quasi-stationary circulation patterns that, in this application, are determined by k-means clustering of geopotential height. We generate both subseasonal (weeks 3-4) and seasonal forecasts for December - February over eastern North America by taking dynamical model forecasts of WTs and then using empirical relationships to translate these WT forecasts into probabilistic temperature and precipitation forecasts. We generate and evaluate this forecast system with hindcasts from the Geophysical Fluid Dynamics Laboratory (GFDL) Forecast-oriented Low Ocean Resolution (FLOR) dynamical model for the period of 1981-2016. Preliminary results suggest that this hybrid dynamical-statistical approach substantially reduces seasonal precipitation forecast error over the raw model forecasts. Pattern-dependent circulation biases and sources of WT forecast skill are investigated.

  13. Space Weather - A Socio-Economic Impact and Forecast Benefit Study

    Science.gov (United States)

    Gibbs, M.; Burnett, C. M.; Bisi, M. M.; Hapgood, M. A.; Biffis, E.; Eastwood, J. P.; McKinnell, L. A.; Green, L.; Bentley, R.; Trichas, M.; Wicks, R. T.

    2016-12-01

    Space Weather is getting increasing attention from governments and major industry sectors around the world. Increasingly they look to science to better understand the potential impacts of severe events and to operational forecast centres to help them mitigate the risk posed. However in comparison to terrestrial weather forecasting, space weather forecasting and the science that underpins it relies on a relatively small number of ground and space based observations. To overcome this limitation there is an increasing need for economic assessment to allow evidence based judgements for these organisations to decide upon investment decisions between mitigation for space weather instead of other more traditional risks such as flooding. A major study, funded by the UK Space Agency has sought to address these issues by mapping the socio-economic costs to different scales of space weather event and assessing the benefit forecasting might provide given the current and improved level of observations or how that might deteriorate if existing satellite data was missing. The results of the study increase the available body of evidence needed for future investment in space weather mitigation, whether that be improved observation, scientific understanding or services covering both extreme events and also `background' space weather variability.

  14. Activities of the Japanese space weather forecast center at Communications Research Laboratory.

    Science.gov (United States)

    Watari, Shinichi; Tomita, Fumihiko

    2002-12-01

    The International Space Environment Service (ISES) is an international organization for space weather forecasts and belongs to the International Union of Radio Science (URSI). There are eleven ISES forecast centers in the world, and Communications Research Laboratory (CRL) runs the Japanese one. We make forecasts on the space environment and deliver them over the phones and through the Internet. Our forecasts could be useful for human activities in space. Currently solar activity is near maximum phase of the solar cycle 23. We report the several large disturbances of space environment occurred in 2001, during which low-latitude auroras were observed several times in Japan.

  15. InFlight Weather Forecasts at Your Fingertips

    Science.gov (United States)

    2003-01-01

    A new information system is delivering real-time weather reports to pilots where they need it the most - inside their aircraft cockpits. Codeveloped by NASA and ViGYAN, Inc., the WSI InFlight(trademark) Cockpit Weather System provides a continuous, satellite-based broadcast of weather information to a portable or panel-mounted display inside the cockpit. With complete coverage and content for the continental United States at any altitude, the system is specifically designed for inflight use.

  16. Impact of prescribed diabatic heating on short range weather forecasts

    Science.gov (United States)

    Marx, L.; Shukla, J.

    1984-01-01

    Using the 9 layer general circulation model developed at the Goddard Laboratory for Atmospheric Sciences (GLAS), several 4 to 5 day integrations were made to assess the impact that latent heating processes (supersaturation and moist convective) have on the model forecasts. In an earlier study by Shukla (1981) it was hypothesized that because of strong interaction between dynamics and moist convection, small initial errors grow very fast and make short range forecasting difficult. The purpose of this study was to examine if prescribed heating rates can improve the forecasts for a few days.

  17. Real Time Data in Synoptic Meteolorolgy and Weather Forecasting Education

    Science.gov (United States)

    Campetella, C. M.; Gassmann, M. I.

    2006-05-01

    The Department of Atmospheric and Oceanographic Sciences (DAOS) of the University of Buenos Aires is the university component of the World Meteorological Organization (WMO) Regional Meteorological Training Center (RMTC) in Region III. In January, 2002 our RMTC was invited to take part in the MeteoForum pilot project that was developed jointly by the COMET and Unidata programs of the University Corporation for Atmospheric Research (UCAR). MeteoForum comprises an international network of WMO Region III and IV RMTCs working collaboratively with universities to enhance their roles of training and education through information technologies and multilingual collections of resources. The DAOS undertook to improve its infrastructure to be able to access hydro-meteorological information in real-time as part of the Unidata community. In 2003, the DAOS received some Unidata equipment grant funds to update its computer infrastructure, improving communications with an operationally quicker system. Departmental networking was upgraded to 100 Mb/s capability while, at the same time, new computation resources were purchased that increased the number of computers available for student use from 5 to 8. This upgrade has also resulted in more and better computers being available for student and faculty research. A video projection system, purchased with funds provided by the COMET program as part of Meteoforum, is used in classrooms with Internet connections for a variety of educational activities. The upgraded computing and networking facilities have contributed to the development of educational modules using real-time hydro-meteorological and other digital data for the classroom. With the aid of Unidata personal, the Unidata Local Data Management (LDM) software was installed and configured to request and process real-time feeds of global observational data; global numerical model output from the US National Centers for Environmental Prediction (NCEP) models; and all imager channels

  18. Sensitivity of Short-Term Weather Forecasts to Assimilated AIRS Data: Implications for NPOESS Applications

    Science.gov (United States)

    Zavodsky, Bradley; McCarty, Will; Chou, Shih-Hung; Jedlovec, Gary

    2009-01-01

    The Atmospheric Infrared Sounder (AIRS) is acting as a heritage and risk reduction instrument for the Cross-track lnfrared Sounder (CrIS) to be flown aboard the NPP and NPOESS satellites. The hyperspectral nature of AIRS and CrIS provides high-quality soundings that, along with their asynoptic observation time over North America, make them attractive sources to fill the spatial and temporal data voids in upper air temperature and moisture measurements for use in data assimilation and numerical weather prediction. Observations from AlRS can be assimilated either as direct radiances or retrieved thermodynamic profiles, and the Short-Term Prediction Research and Transition (SPORT) Center at NASA's Marshall Space Flight Center has used both data types to improve short-term (0-48h), regional forecasts. The purpose of this paper is to share SPORT'S experiences using AlRS radiances and retrieved profiles in regional data assimilation activities by showing that proper handling of issues-including cloud contamination and land emissivity characterization-are necessary to produce optimal analyses and forecasts.

  19. 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...

  20. 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...

  1. Improved Weather Forecasting for the Dynamic Scheduling System of the Green Bank Telescope

    Science.gov (United States)

    Henry, Kari; Maddalena, Ronald

    2018-01-01

    The Robert C Byrd Green Bank Telescope (GBT) uses a software system that dynamically schedules observations based on models of vertical weather forecasts produced by the National Weather Service (NWS). The NWS provides hourly forecasted values for ~60 layers that extend into the stratosphere over the observatory. We use models, recommended by the Radiocommunication Sector of the International Telecommunications Union, to derive the absorption coefficient in each layer for each hour in the NWS forecasts and for all frequencies over which the GBT has receivers, 0.1 to 115 GHz. We apply radiative transfer models to derive the opacity and the atmospheric contributions to the system temperature, thereby deriving forecasts applicable to scheduling radio observations for up to 10 days into the future. Additionally, the algorithms embedded in the data processing pipeline use historical values of the forecasted opacity to calibrate observations. Until recently, we have concentrated on predictions for high frequency (> 15 GHz) observing, as these need to be scheduled carefully around bad weather. We have been using simple models for the contribution of rain and clouds since we only schedule low-frequency observations under these conditions. In this project, we wanted to improve the scheduling of the GBT and data calibration at low frequencies by deriving better algorithms for clouds and rain. To address the limitation at low frequency, the observatory acquired a Radiometrics Corporation MP-1500A radiometer, which operates in 27 channels between 22 and 30 GHz. By comparing 16 months of measurements from the radiometer against forecasted system temperatures, we have confirmed that forecasted system temperatures are indistinguishable from those measured under good weather conditions. Small miss-calibrations of the radiometer data dominate the comparison. By using recalibrated radiometer measurements, we looked at bad weather days to derive better models for forecasting the

  2. Development of GNSS PWV information management system for very short-term weather forecast in the Korean Peninsula

    Science.gov (United States)

    Park, Han-Earl; Yoon, Ha Su; Yoo, Sung-Moon; Cho, Jungho

    2017-04-01

    Over the past decade, Global Navigation Satellite System (GNSS) was in the spotlight as a meteorological research tool. The Korea Astronomy and Space Science Institute (KASI) developed a GNSS precipitable water vapor (PWV) information management system to apply PWV to practical applications, such as very short-term weather forecast. The system consists of a DPR, DRS, and TEV, which are divided functionally. The DPR processes GNSS data using the Bernese GNSS software and then retrieves PWV from zenith total delay (ZTD) with the optimized mean temperature equation for the Korean Peninsula. The DRS collects data from eighty permanent GNSS stations in the southern part of the Korean Peninsula and provides the PWV retrieved from GNSS data to a user. The TEV is in charge of redundancy of the DPR. The whole process is performed in near real-time where the delay is ten minutes. The validity of the GNSS PWV was proved by means of a comparison with radiosonde data. In the experiment of numerical weather prediction model, the GNSS PWV was utilized as the initial value of the Weather Research & Forecasting (WRF) model for heavy rainfall event. As a result, we found that the forecasting capability of the WRF is improved by data assimilation of GNSS PWV.

  3. Forecasting Space Weather-Induced GPS Performance Degradation Using Random Forest

    Science.gov (United States)

    Filjar, R.; Filic, M.; Milinkovic, F.

    2017-12-01

    Space weather and ionospheric dynamics have a profound effect on positioning performance of the Global Satellite Navigation System (GNSS). However, the quantification of that effect is still the subject of scientific activities around the world. In the latest contribution to the understanding of the space weather and ionospheric effects on satellite-based positioning performance, we conducted a study of several candidates for forecasting method for space weather-induced GPS positioning performance deterioration. First, a 5-days set of experimentally collected data was established, encompassing the space weather and ionospheric activity indices (including: the readings of the Sudden Ionospheric Disturbance (SID) monitors, components of geomagnetic field strength, global Kp index, Dst index, GPS-derived Total Electron Content (TEC) samples, standard deviation of TEC samples, and sunspot number) and observations of GPS positioning error components (northing, easting, and height positioning error) derived from the Adriatic Sea IGS reference stations' RINEX raw pseudorange files in quiet space weather periods. This data set was split into the training and test sub-sets. Then, a selected set of supervised machine learning methods based on Random Forest was applied to the experimentally collected data set in order to establish the appropriate regional (the Adriatic Sea) forecasting models for space weather-induced GPS positioning performance deterioration. The forecasting models were developed in the R/rattle statistical programming environment. The forecasting quality of the regional forecasting models developed was assessed, and the conclusions drawn on the advantages and shortcomings of the regional forecasting models for space weather-caused GNSS positioning performance deterioration.

  4. 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)

  5. FORECASTING PILE SETTLEMENT ON CLAYSTONE USING NUMERICAL AND ANALYTICAL METHODS

    Directory of Open Access Journals (Sweden)

    Ponomarev Andrey Budimirovich

    2016-06-01

    Full Text Available In the article the problem of designing pile foundations on claystones is reviewed. The purpose of this paper is comparative analysis of the analytical and numerical methods for forecasting the settlement of piles on claystones. The following tasks were solved during the study: 1 The existing researches of pile settlement are analyzed; 2 The characteristics of experimental studies and the parameters for numerical modeling are presented, methods of field research of single piles’ operation are described; 3 Calculation of single pile settlement is performed using numerical methods in the software package Plaxis 2D and analytical method according to the requirements SP 24.13330.2011; 4 Experimental data is compared with the results of analytical and numerical calculations; 5 Basing on these results recommendations for forecasting pile settlement on claystone are presented. Much attention is paid to the calculation of pile settlement considering the impacted areas in ground space beside pile and the comparison with the results of field experiments. Basing on the obtained results, for the prediction of settlement of single pile on claystone the authors recommend using the analytical method considered in SP 24.13330.2011 with account for the impacted areas in ground space beside driven pile. In the case of forecasting the settlement of single pile on claystone by numerical methods in Plaxis 2D the authors recommend using the Hardening Soil model considering the impacted areas in ground space beside the driven pile. The analyses of the results and calculations are presented for examination and verification; therefore it is necessary to continue the research work of deep foundation at another experimental sites to improve the reliability of the calculation of pile foundation settlement. The work is of great interest for geotechnical engineers engaged in research, design and construction of pile foundations.

  6. Realtime Space Weather Forecasts Via Android Phone App

    Science.gov (United States)

    Crowley, G.; Haacke, B.; Reynolds, A.

    2010-12-01

    For the past several years, ASTRA has run a first-principles global 3-D fully coupled thermosphere-ionosphere model in real-time for space weather applications. The model is the Thermosphere-Ionosphere Mesosphere Electrodynamics General Circulation Model (TIMEGCM). ASTRA also runs the Assimilative Mapping of Ionospheric Electrodynamics (AMIE) in real-time. Using AMIE to drive the high latitude inputs to the TIMEGCM produces high fidelity simulations of the global thermosphere and ionosphere. These simulations can be viewed on the Android Phone App developed by ASTRA. The SpaceWeather app for the Android operating system is free and can be downloaded from the Google Marketplace. We present the current status of realtime thermosphere-ionosphere space-weather forcasting and discuss the way forward. We explore some of the issues in maintaining real-time simulations with assimilative data feeds in a quasi-operational setting. We also discuss some of the challenges of presenting large amounts of data on a smartphone. The ASTRA SpaceWeather app includes the broadest and most unique range of space weather data yet to be found on a single smartphone app. This is a one-stop-shop for space weather and the only app where you can get access to ASTRA’s real-time predictions of the global thermosphere and ionosphere, high latitude convection and geomagnetic activity. Because of the phone's GPS capability, users can obtain location specific vertical profiles of electron density, temperature, and time-histories of various parameters from the models. The SpaceWeather app has over 9000 downloads, 30 reviews, and a following of active users. It is clear that real-time space weather on smartphones is here to stay, and must be included in planning for any transition to operational space-weather use.

  7. 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...

  8. Influence of Met-Ocean Condition Forecasting Uncertainties on Weather Window Predictions for Offshore Operations

    DEFF Research Database (Denmark)

    Gintautas, Tomas; Sørensen, John Dalsgaard

    2017-01-01

    The article briefly presents a novel methodology of weather window estimation for offshore operations and mainly focuses on effects of met-ocean condition forecasting uncertainties on weather window predictions when using the proposed methodology. It is demonstrated that the proposed methodology...... to include stochastic variables, representing met-ocean forecasting uncertainties and the results of such modification are given in terms of predicted weather windows for a selected test case....... has the capacity to retain the uncertainties of met-ocean condition forecasting and transfer them into uncertainties of probability of operation failure. In addition to that, improvements to the failure function, used to define operation failure are presented. The failure function is modified...

  9. Weather forecasts, users' economic expenses and decision strategies

    Science.gov (United States)

    Carter, G. M.

    1972-01-01

    Differing decision models and operational characteristics affecting the economic expenses (i.e., the costs of protection and losses suffered if no protective measures have been taken) associated with the use of predictive weather information have been examined.

  10. COST ES0602: towards a European network on chemical weather forecasting and information systems

    Directory of Open Access Journals (Sweden)

    J. Kukkonen

    2009-04-01

    Full Text Available The COST ES0602 action provides a forum for benchmarking approaches and practices in data exchange and multi-model capabilities for chemical weather forecasting and near real-time information services in Europe. The action includes approximately 30 participants from 19 countries, and its duration is from 2007 to 2011 (http://www.chemicalweather.eu/. Major efforts have been dedicated in other actions and projects to the development of infrastructures for data flow. We have therefore aimed for collaboration with ongoing actions towards developing near real-time exchange of input data for air quality forecasting. We have collected information on the operational air quality forecasting models on a regional and continental scale in a structured form, and inter-compared and evaluated the physical and chemical structure of these models. We have also constructed a European chemical weather forecasting portal that includes links to most of the available chemical weather forecasting systems in Europe. The collaboration also includes the examination of the case studies that have been organized within COST-728, in order to inter-compare and evaluate the models against experimental data. We have also constructed an operational model forecasting ensemble. Data from a representative set of regional background stations have been selected, and the operational forecasts for this set of sites will be inter-compared and evaluated. The Action has investigated, analysed and reviewed existing chemical weather information systems and services, and will provide recommendations on best practices concerning the presentation and dissemination of chemical weather information towards the public and decision makers.

  11. Verification of operational weather forecasts from the POSEIDON system across the Eastern Mediterranean

    OpenAIRE

    A. Papadopoulos; P. Katsafados

    2009-01-01

    The POSEIDON weather forecasting system became operational at the Hellenic Centre for Marine Research (HCMR) in October 1999. The system with its nesting capability provided 72-h forecasts in two different model domains, i.e. 25- and 10-km grid spacing. The lower-resolution domain covered an extended area that included most of Europe, Mediterranean Sea and N. Africa, while the higher resolution domain focused on the Eastern Mediterranean. A major upgrade of the system was recently implemented...

  12. Anvil Forecast Tool in the Advanced Weather Interactive Processing System, Phase II

    Science.gov (United States)

    Barrett, Joe H., III

    2008-01-01

    Meteorologists from the 45th Weather Squadron (45 WS) and Spaceflight Meteorology Group have identified anvil forecasting as one of their most challenging tasks when predicting the probability of violations of the Lightning Launch Commit Criteria and Space Light Rules. As a result, the Applied Meteorology Unit (AMU) created a graphical overlay tool for the Meteorological Interactive Data Display Systems (MIDDS) to indicate the threat of thunderstorm anvil clouds, using either observed or model forecast winds as input.

  13. Study on The Extended Range Weather Forecast of Low Frequency Signal Based on Period Analysis Method

    Science.gov (United States)

    Li, X.

    2016-12-01

    Although many studies have explored the MJO and its application for weather forecasting, low-frequency oscillation has been insufficiently studied for the extend range weather forecasting over middle and high latitudes. In China, low-frequency synoptic map is a useful tool for meteorological operation department to forecast extend range weather. It is therefore necessary to develop objective methods to serve the need for finding low-frequency signal, interpretation and application of this signal in the extend range weather forecasting. In this paper, method of Butterworth band pass filter was applied to get low-frequency height field at 500hPa from 1980 to 2014 by using NCEP/NCAR daily grid data. Then period analysis and optimal subset regression methods were used to process the low frequency data of 150 days before the first forecast day and extend the low frequency signal of 500hPa low-frequency high field to future 30 days in the global from June to August during 2011-2014. Finally, the results were test. The main results are as follows: (1) In general, the fitting effect of low frequency signals of 500hPa low-frequency height field by period analysis in the northern hemisphere was better than that in the southern hemisphere, and was better in the low latitudes than that in the high latitudes. The fitting accuracy gradually reduced with the increase of forecast time length, which tended to be stable during the late forecasting period. (2) The fitting effects over the 6 key regions in China showed that except filtering result over Xinjiang area in the first 10 days and 30 days, filtering results over the other 5 key regions throughout the whole period have passed reliability test with level more than 95%. (3) The center and scope of low and high low frequency systems can be fitted well by using the methods mentioned above, which is consist with the corresponding use of the low-frequency synoptic map for the prediction of the extended period. Application of the

  14. The Art and Science of Long-Range Space Weather Forecasting

    Science.gov (United States)

    Hathaway, David H.; Wilson, Robert M.

    2006-01-01

    Long-range space weather forecasts are akin to seasonal forecasts of terrestrial weather. We don t expect to forecast individual events but we do hope to forecast the underlying level of activity important for satellite operations and mission pl&g. Forecasting space weather conditions years or decades into the future has traditionally been based on empirical models of the solar cycle. Models for the shape of the cycle as a function of its amplitude become reliable once the amplitude is well determined - usually two to three years after minimum. Forecasting the amplitude of a cycle well before that time has been more of an art than a science - usually based on cycle statistics and trends. Recent developments in dynamo theory -the theory explaining the generation of the Sun s magnetic field and the solar activity cycle - have now produced models with predictive capabilities. Testing these models with historical sunspot cycle data indicates that these predictions may be highly reliable one, or even two, cycles into the future.

  15. 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

  16. Current problems in communication from the weather forecast in the prevention of hydraulic and hydrogeological risk

    Science.gov (United States)

    Fazzini, Massimiliano; Vaccaro, Carmela

    2014-05-01

    The Italian territory is one of the most fragile hydraulic and hydro geologic of the world, due to its complexity physiographic, lithological and above meteo-climatic too. Moreover, In recent years, the unhappy urbanization, the abandonment of mountain areas and countryside have fostered hydro geological instability, ever more devastating, in relation to the extremes of meteorological events. After the dramatic floods and landscapes of the last 24 months - in which more than 50 people died - it is actually open a public debate on the issues related to prevention, forecasting and management of hydro-meteorological risk. Aim of the correct weather forecasting at different spatial and temporal scales is to avoid or minimize the potential occurrence of damage or human losses resulting from the increasingly of frequent extreme weather events. In Italy, there are two major complex problems that do not allow for effective dissemination of the correct weather forecasting. First, the absence of a national meteorological service - which can ensure the quality of information. In this regard, it is at an advanced stage the establishment of a unified national weather service - formed by technicians to national and regional civil protection and the Meteorological Service of the Air Force, which will ensure the quality of the prediction, especially through exclusive processing of national and local weather forecasting and hydro geological weather alert. At present, however, this lack favors the increasing diffusion of meteorological sites more or less professional - often totally not "ethical" - which, at different spatial scales, tend to amplify the signals from the weather prediction models, describing them the users of the web such as exceptional or rare phenomena and often causing unjustified alarmism. This behavior is almost always aimed at the desire of give a forecast before other sites and therefore looking for new commercial sponsors, with easy profits. On the other hand

  17. 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.

  18. Microcontroller-based network for meteorological sensing and weather forecast calculations

    Directory of Open Access Journals (Sweden)

    A. Vas

    2012-06-01

    Full Text Available Weather forecasting needs a lot of computing power. It is generally accomplished by using supercomputers which are expensive to rent and to maintain. In addition, weather services also have to maintain radars, balloons and pay for worldwide weather data measured by stations and satellites. Weather forecasting computations usually consist of solving differential equations based on the measured parameters. To do that, the computer uses the data of close and distant neighbor points. Accordingly, if small-sized weather stations, which are capable of making measurements, calculations and communication, are connected through the Internet, then they can be used to run weather forecasting calculations like a supercomputer does. It doesn’t need any central server to achieve this, because this network operates as a distributed system. We chose Microchip’s PIC18 microcontroller (μC platform in the implementation of the hardware, and the embedded software uses the TCP/IP Stack v5.41 provided by Microchip.

  19. Verifying Operational and Developmental Air Force Weather Cloud Analysis and Forecast Products Using Lidar Data from Department of Energy Atmospheric Radiation Measurement (ARM) Sites

    Science.gov (United States)

    Hildebrand, E. P.

    2017-12-01

    Air Force Weather has developed various cloud analysis and forecast products designed to support global Department of Defense (DoD) missions. A World-Wide Merged Cloud Analysis (WWMCA) and short term Advected Cloud (ADVCLD) forecast is generated hourly using data from 16 geostationary and polar-orbiting satellites. Additionally, WWMCA and Numerical Weather Prediction (NWP) data are used in a statistical long-term (out to five days) cloud forecast model known as the Diagnostic Cloud Forecast (DCF). The WWMCA and ADVCLD are generated on the same polar stereographic 24 km grid for each hemisphere, whereas the DCF is generated on the same grid as its parent NWP model. When verifying the cloud forecast models, the goal is to understand not only the ability to detect cloud, but also the ability to assign it to the correct vertical layer. ADVCLD and DCF forecasts traditionally have been verified using WWMCA data as truth, but this might over-inflate the performance of those models because WWMCA also is a primary input dataset for those models. Because of this, in recent years, a WWMCA Reanalysis product has been developed, but this too is not a fully independent dataset. This year, work has been done to incorporate data from external, independent sources to verify not only the cloud forecast products, but the WWMCA data itself. One such dataset that has been useful for examining the 3-D performance of the cloud analysis and forecast models is Atmospheric Radiation Measurement (ARM) data from various sites around the globe. This presentation will focus on the use of the Department of Energy (DoE) ARM data to verify Air Force Weather cloud analysis and forecast products. Results will be presented to show relative strengths and weaknesses of the analyses and forecasts.

  20. Predictability of extreme weather events for NE U.S.: improvement of the numerical prediction using a Bayesian regression approach

    Science.gov (United States)

    Yang, J.; Astitha, M.; Anagnostou, E. N.; Hartman, B.; Kallos, G. B.

    2015-12-01

    Weather prediction accuracy has become very important for the Northeast U.S. given the devastating effects of extreme weather events in the recent years. Weather forecasting systems are used towards building strategies to prevent catastrophic losses for human lives and the environment. Concurrently, weather forecast tools and techniques have evolved with improved forecast skill as numerical prediction techniques are strengthened by increased super-computing resources. In this study, we examine the combination of two state-of-the-science atmospheric models (WRF and RAMS/ICLAMS) by utilizing a Bayesian regression approach to improve the prediction of extreme weather events for NE U.S. The basic concept behind the Bayesian regression approach is to take advantage of the strengths of two atmospheric modeling systems and, similar to the multi-model ensemble approach, limit their weaknesses which are related to systematic and random errors in the numerical prediction of physical processes. The first part of this study is focused on retrospective simulations of seventeen storms that affected the region in the period 2004-2013. Optimal variances are estimated by minimizing the root mean square error and are applied to out-of-sample weather events. The applicability and usefulness of this approach are demonstrated by conducting an error analysis based on in-situ observations from meteorological stations of the National Weather Service (NWS) for wind speed and wind direction, and NCEP Stage IV radar data, mosaicked from the regional multi-sensor for precipitation. The preliminary results indicate a significant improvement in the statistical metrics of the modeled-observed pairs for meteorological variables using various combinations of the sixteen events as predictors of the seventeenth. This presentation will illustrate the implemented methodology and the obtained results for wind speed, wind direction and precipitation, as well as set the research steps that will be

  1. 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.

  2. Evaluating National Weather Service Seasonal Forecast Products in Reservoir Operation Case Studies

    Science.gov (United States)

    Nielson, A.; Guihan, R.; Polebistki, A.; Palmer, R. N.; Werner, K.; Wood, A. W.

    2014-12-01

    Forecasts of future weather and streamflow can provide valuable information for reservoir operations and water management. A challenge confronting reservoir operators today is how to incorporate both climate and streamflow products into their operations and which of these forecast products are most informative and useful for optimized water management. This study incorporates several reforecast products provided by the Colorado Basin River Forecast Center (CBRFC) which allows a complete retrospective analysis of climate forecasts, resulting in an evaluation of each product's skill in the context of water resources management. The accuracy and value of forecasts generated from the Climate Forecast System version 2 (CFSv2) are compared to the accuracy and value of using an Ensemble Streamflow Predictions (ESP) approach. Using the CFSv2 may offer more insight when responding to climate driven extremes than the ESP approach because the CFSv2 incorporates a fully coupled climate model into its forecasts rather than using all of the historic climate record as being equally probable. The role of forecast updating frequency will also be explored. Decision support systems (DSS) for both Salt Lake City Parley's System and the Snohomish County Public Utility Department's (SnoPUD) Jackson project will be used to illustrate the utility of forecasts. Both DSS include a coupled simulation and optimization model that will incorporate system constraints, operating policies, and environmental flow requirements. To determine the value of the reforecast products, performance metrics meaningful to the managers of each system are to be identified and quantified. Without such metrics and awareness of seasonal operational nuances, it is difficult to identify forecast improvements in meaningful ways. These metrics of system performance are compared using the different forecast products to evaluate the potential benefits of using CFSv2 seasonal forecasts in systems decision making.

  3. Integrating indigenous knowledge with conventional science: Enhancing localised climate and weather forecasts in Nessa, Mulanje, Malawi

    Science.gov (United States)

    Kalanda-Joshua, Miriam; Ngongondo, Cosmo; Chipeta, Lucy; Mpembeka, F.

    Subsistence rain fed agriculture underpins rural livelihoods in the Sub Saharan Africa. The overdependence on rainfall suggests the need for more reliable climate and weather forecasts to guide farm level decision making. Traditionally, African farmers have used indigenous knowledge (IK) to understand weather and climate patterns and make decisions about crops and farming practices. However, increased rainfall variability in recent years associated with climate change has reduced their confidence in indigenous knowledge, hence reducing their adaptive capacity and increasing their vulnerability to climate change. To address this problem, researchers are advocating the integration of indigenous knowledge into scientific climate forecasts at the local level, where it can be used to enhance the resilience of communities vulnerable to climate change. A study was therefore conducted to establish commonly used IK indicators in weather and climate forecasting and people’s perceptions of climate change and variability in Nessa Village, Southern Malawi. We further compared the people’s perceptions on climate change and variability with empirical evidence from a nearby weather station during 1971-2003 and the major constraints that the people face to fully utilise conventional weather and climate forecasts. Our results show various forms of traditional indicators that have been used to predict weather and climate for generations. These include certain patterns and behaviour of flora and fauna as well as environmental conditions. We further established that the peoples documentation of major climatic events over the years in the area agreed with the empirical evidence from the temperature and rainfall data. Overall, rainfall in the area has reduced since 1971 with increasing temperatures. The people were however of the view that current scientific weather and climate predictions in Malawi were not that useful at village level because they do not incorporate IK.

  4. The Use of Interactive Graphics Processing in Short-Range Terminal Weather Forecasting: An Initial Assessment.

    Science.gov (United States)

    1983-03-31

    need to move from a manual, labor -intensive weather support function to provide more timely re- sponse to operational needs, the USAF has embarked on a...namely from the Air Force Global Weather Central (AFGVC) and the Automated Weather Network (AWN). The AWN collects, edits, reforma :-s and transmits...n wkillI affect thle kasa aUI ArICa late in the 1 2-h forecast period. Thus, showers <car be explected in \\k ausan after 2000 GAI~lT wvith steady

  5. 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.

  6. Advanced approach to numerical forecasting using artificial neural networks

    Directory of Open Access Journals (Sweden)

    Michael Štencl

    2009-01-01

    Full Text Available Current global market is driven by many factors, such as the information age, the time and amount of information distributed by many data channels it is practically impossible analyze all kinds of incoming information flows and transform them to data with classical methods. New requirements could be met by using other methods. Once trained on patterns artificial neural networks can be used for forecasting and they are able to work with extremely big data sets in reasonable time. The patterns used for learning process are samples of past data. This paper uses Radial Basis Functions neural network in comparison with Multi Layer Perceptron network with Back-propagation learning algorithm on prediction task. The task works with simplified numerical time series and includes forty observations with prediction for next five observations. The main topic of the article is the identification of the main differences between used neural networks architectures together with numerical forecasting. Detected differences then verify on practical comparative example.

  7. From the weather forecast to the prognostic moisture content of dry agricultural crops

    NARCIS (Netherlands)

    Atzema, A.J.

    1994-01-01

    Part 1

    The aim of the study of grass is to forecast the drying of cut grass up to five days ahead, hourly. The first investigated problem is the response of the drying of cut grass to the weather elements. Next a simple model and an advanced model for the drying of cut

  8. A Confidence Index Approach Based on ERA-40 Data for Numerical Short Range Forecasts

    Directory of Open Access Journals (Sweden)

    Thomas Prenosil

    2014-09-01

    Full Text Available Critical weather related missions increasingly rely on highly automated numerical products, even if only limited computer capacities are available to generate them. This holds true especially for military tactical decision aids but also for civil requirements from firebrigades, the Red Cross or technical relief organizations. With respect to inherent atmospheric indeterminateness, a systematic quality control of numerical input turns out to become more and more essential for the users. As an economical alternative to the complex and expensive ensemble prediction method, the German Bundeswehr Geoinformation Centre has decided in favour of an analogue approach called similar synoptic situations (3s, which is based on ECMWF's ERA-40 re-analysis archive. Similarity is defined by a special distance measure for synoptic fields. The typical range of interest is 2500km×2500km$2500\\,\\text{km}\\times2500\\,\\text{km}$ in space with approximately one degree of horizontal resolution and up to 36 hours of forecast time. Historical 12, 24 and 36 hours ERA-40 forecast qualities are merged by 3s into a confidence index, indicating current anomalies of numerical quality versus monthly means in special areas of interest. As the results from the ERA-40 archive are used without any statistical adaption, this assessment is exclusively valid for trouble-free synoptic model runs in the short range. For a better understanding of the estimated anomalies in numerical forecast quality, the involved synoptic conditions are classified by a well established weather type classification. The overall method has been verified from 45 years of ERA-40 data and 10 years of GME forecasts from the Deutscher Wetterdienst. The 3s technique is highly flexible all over the globe with the exception of the tropics, because the present version includes the geostrophic approximation. At present, 3s runs operationally within four geographic areas: (1 Central Europe, (2 Kosovo with

  9. Verification of a Real Time Weather Forecasting System in Southern Italy

    Directory of Open Access Journals (Sweden)

    Luca Tiriolo

    2015-01-01

    Full Text Available This paper shows the performance of an operational forecasting system, based on the regional atmospheric modeling system (RAMS, at 3 km horizontal resolution over southern Italy. The model is initialized from the 12 UTC operational analysis/forecasting cycle of the European Centre for Medium range Weather Forecasts (ECMWF. The forecast is issued for the following three days. The performance is evaluated for a whole year for the surface parameters: temperature, relative humidity, wind speed and direction, and precipitation. The verification has been performed against SYNOP stations over southern Italy. A dense non-GTS network over Calabria is used for precipitation. Results show that RMSE is about 2-3 K for temperature, 12–16% for relative humidity, 2.0–2.8 m/s for wind speed, and 55–75° for wind direction, the performance varying with the season and with the forecasting time. The error increases between the first and third forecast days. The verification of the rainfall forecast shows that the model underestimates the area of the precipitation. The model output statistics (MOS is applied to all parameters but precipitation. Results show that the MOS reduces the RMSE by 0–30%, depending on the forecasting time, on the season and on the meteorological parameter.

  10. The impact of scatterometer wind data on global weather forecasting

    Science.gov (United States)

    Atlas, D.; Baker, W. E.; Kalnay, E.; Halem, M.; Woiceshyn, P. M.; Peteherych, S.

    1984-01-01

    The impact of SEASAT-A scatterometer (SASS) winds on coarse resolution atmospheric model forecasts was assessed. The scatterometer provides high resolution winds, but each wind can have up to four possible directions. One wind direction is correct; the remainder are ambiguous or "aliases'. In general, the effect of objectively dealiased-SASS data was found to be negligible in the Northern Hemisphere. In the Southern Hemisphere, the impact was larger and primarily beneficial when vertical temperature profile radiometer (VTPR) data was excluded. However, the inclusion of VTPR data eliminates the positive impact, indicating some redundancy between the two data sets.

  11. The Impact of Atmospheric InfraRed Sounder (AIRS) Profiles on Short-term Weather Forecasts

    Science.gov (United States)

    Chou, Shih-Hung; Zavodsky, Brad; Jedlovec, Gary J.; Lapenta, William

    2007-01-01

    The Atmospheric Infrared Sounder (AIRS), together with the Advanced Microwave Sounding Unit (AMSU), represents one of the most advanced spacebased atmospheric sounding systems. The combined AlRS/AMSU system provides radiance measurements used to retrieve temperature profiles with an accuracy of 1 K over 1 km layers under both clear and partly cloudy conditions, while the accuracy of the derived humidity profiles is 15% in 2 km layers. Critical to the successful use of AIRS profiles for weather and climate studies is the use of profile quality indicators and error estimates provided with each profile Aside form monitoring changes in Earth's climate, one of the objectives of AIRS is to provide sounding information of sufficient accuracy such that the assimilation of the new observations, especially in data sparse region, will lead to an improvement in weather forecasts. The purpose of this paper is to describe a procedure to optimally assimilate highresolution AIRS profile data in a regional analysis/forecast model. The paper will focus on the impact of AIRS profiles on a rapidly developing east coast storm and will also discuss preliminary results for a 30-day forecast period, simulating a quasi-operation environment. Temperature and moisture profiles were obtained from the prototype version 5.0 EOS science team retrieval algorithm which includes explicit error information for each profile. The error profile information was used to select the highest quality temperature and moisture data for every profile location and pressure level for assimilation into the ARPS Data Analysis System (ADAS). The AIRS-enhanced analyses were used as initial fields for the Weather Research and Forecast (WRF) system used by the SPORT project for regional weather forecast studies. The ADASWRF system will be run on CONUS domain with an emphasis on the east coast. The preliminary assessment of the impact of the AIRS profiles will focus on quality control issues associated with AIRS

  12. Anvil Forecast Tool in the Advanced Weather Interactive Processing System (AWIPS)

    Science.gov (United States)

    Barrett, Joe H., III; Hood, Doris

    2009-01-01

    Launch Weather Officers (LWOs) from the 45th Weather Squadron (45 WS) and forecasters from the National Weather Service (NWS) Spaceflight Meteorology Group (SMG) have identified anvil forecasting as one of their most challenging tasks when predicting the probability of violating the Lightning Launch Commit Criteria (LLCC) (Krider et al. 2006; Space Shuttle Flight Rules (FR), NASA/JSC 2004)). As a result, the Applied Meteorology Unit (AMU) developed a tool that creates an anvil threat corridor graphic that can be overlaid on satellite imagery using the Meteorological Interactive Data Display System (MIDDS, Short and Wheeler, 2002). The tool helps forecasters estimate the locations of thunderstorm anvils at one, two, and three hours into the future. It has been used extensively in launch and landing operations by both the 45 WS and SMG. The Advanced Weather Interactive Processing System (AWIPS) is now used along with MIDDS for weather analysis and display at SMG. In Phase I of this task, SMG tasked the AMU to transition the tool from MIDDS to AWIPS (Barrett et aI., 2007). For Phase II, SMG requested the AMU make the Anvil Forecast Tool in AWIPS more configurable by creating the capability to read model gridded data from user-defined model files instead of hard-coded files. An NWS local AWIPS application called AGRID was used to accomplish this. In addition, SMG needed to be able to define the pressure levels for the model data, instead of hard-coding the bottom level as 300 mb and the top level as 150 mb. This paper describes the initial development of the Anvil Forecast Tool for MIDDS, followed by the migration of the tool to AWIPS in Phase I. It then gives a detailed presentation of the Phase II improvements to the AWIPS tool.

  13. Future sea ice conditions and weather forecasts in the Arctic: Implications for Arctic shipping.

    Science.gov (United States)

    Gascard, Jean-Claude; Riemann-Campe, Kathrin; Gerdes, Rüdiger; Schyberg, Harald; Randriamampianina, Roger; Karcher, Michael; Zhang, Jinlun; Rafizadeh, Mehrad

    2017-12-01

    The ability to forecast sea ice (both extent and thickness) and weather conditions are the major factors when it comes to safe marine transportation in the Arctic Ocean. This paper presents findings focusing on sea ice and weather prediction in the Arctic Ocean for navigation purposes, in particular along the Northeast Passage. Based on comparison with the observed sea ice concentrations for validation, the best performing Earth system models from the Intergovernmental Panel on Climate Change (IPCC) program (CMIP5-Coupled Model Intercomparison Project phase 5) were selected to provide ranges of potential future sea ice conditions. Our results showed that, despite a general tendency toward less sea ice cover in summer, internal variability will still be large and shipping along the Northeast Passage might still be hampered by sea ice blocking narrow passages. This will make sea ice forecasts on shorter time and space scales and Arctic weather prediction even more important.

  14. Proposed Use of the NASA Ames Nebula Cloud Computing Platform for Numerical Weather Prediction and the Distribution of High Resolution Satellite Imagery

    Science.gov (United States)

    Limaye, Ashutosh S.; Molthan, Andrew L.; Srikishen, Jayanthi

    2010-01-01

    The development of the Nebula Cloud Computing Platform at NASA Ames Research Center provides an open-source solution for the deployment of scalable computing and storage capabilities relevant to the execution of real-time weather forecasts and the distribution of high resolution satellite data to the operational weather community. Two projects at Marshall Space Flight Center may benefit from use of the Nebula system. The NASA Short-term Prediction Research and Transition (SPoRT) Center facilitates the use of unique NASA satellite data and research capabilities in the operational weather community by providing datasets relevant to numerical weather prediction, and satellite data sets useful in weather analysis. SERVIR provides satellite data products for decision support, emphasizing environmental threats such as wildfires, floods, landslides, and other hazards, with interests in numerical weather prediction in support of disaster response. The Weather Research and Forecast (WRF) model Environmental Modeling System (WRF-EMS) has been configured for Nebula cloud computing use via the creation of a disk image and deployment of repeated instances. Given the available infrastructure within Nebula and the "infrastructure as a service" concept, the system appears well-suited for the rapid deployment of additional forecast models over different domains, in response to real-time research applications or disaster response. Future investigations into Nebula capabilities will focus on the development of a web mapping server and load balancing configuration to support the distribution of high resolution satellite data sets to users within the National Weather Service and international partners of SERVIR.

  15. Wind laws for shockless initialization. [numerical forecasting model

    Science.gov (United States)

    Ghil, M.; Shkoller, B.

    1976-01-01

    A system of diagnostic equations for the velocity field, or wind laws, was derived for each of a number of models of large-scale atmospheric flow. The derivation in each case is mathematically exact and does not involve any physical assumptions not already present in the prognostic equations, such as nondivergence or vanishing of derivatives of the divergence. Therefore, initial states computed by solving these diagnostic equations should be compatible with the type of motion described by the prognostic equations of the model and should not generate initialization shocks when inserted into the model. Numerical solutions of the diagnostic system corresponding to a barotropic model are exhibited. Some problems concerning the possibility of implementing such a system in operational numerical weather prediction are discussed.

  16. 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.

  17. 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.

  18. Communicating Climate Change - Weather Forecast Need Assessment and Information Dissemination Mechanism to Farmers in Nepal

    Science.gov (United States)

    Panthi, J., Sr.

    2014-12-01

    Climate Change is becoming one of the major threats to the fragile Himalayan ecosystem. It is affecting all sectors mainly fresh water, agriculture, forest, biodiversity and species. The subsistence agriculture system of Nepal is mainly rain-fed; therefore, climate change and climate extremes do have direct impacts on it. Weather extremes like droughts, floods and landslides long-lasting fog, hot and cold waves are affecting the agriculture sectors of Nepal. As human-induced climate change has already showing its impacts and it is going to be there for a long time to come, it is paramount importance to move towards the adaptation. Early warning system is an effective way for reducing the impacts of disasters. Forecasting of weather parameters (temperature, precipitation, and wind) helps farmers for their preparedness activities. With consultation with farmers and other relevant institutions, a research project was carried out, for the first time in Nepal, to identify the forecast information need to farmers and their dissemination mechanism. Community consultation workshops, key informant survey, and field observations were the techniques used for this research. Two ecological locations: Bageshwori VDC in Banke (plain) and Dhaibung VDC in Rasuwa (mountain) were taken as the pilot sites for this assessment. People in both the districts are dependent highly on agriculture and the weather extremes like hailstone, untimely rainfall; droughts are affecting their agriculture practices. They do not have confidence in the weather forecast information disseminated by the government of Nepal currently being done because it is a general forecast not done for a smaller domain and the forecast is valid only for 24 hours. The weather forecast need to the farmers in both the sites are: rainfall (intensity, duration and time), drought, and hailstone but in Banke, people wished to have the information of heat and cold waves too as they are affecting their wheat and tomato crops

  19. High Impact Weather Forecasts and Warnings with the GOES-R Geostationary Lightning Mapper (GLM)

    Science.gov (United States)

    Goodman, Steven; Blakeslee, Richard; Koshak, William; Mach, Douglas

    2011-01-01

    The Geostationary Operational Environmental Satellite (GOES-R) is the next series to follow the existing GOES system currently operating over the Western Hemisphere. A major advancement over the current GOES include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM). The GLM will operate continuously day and night with near-uniform spatial resolution of 8 km with a product refresh rate of less than 20 sec over the Americas and adjacent oceanic regions. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency. In parallel with the instrument development, a GOES-R Risk Reduction Science Team and Algorithm Working Group Lightning Applications Team have begun to develop cal/val performance monitoring tools and new applications using the GLM alone, in conjunction with other instruments, and merged or blended integrated observing system products combining satellite, radar, in-situ and numerical models. Proxy total lightning data from the NASA Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional ground-based lightning networks are being used to develop the pre-launch algorithms, test data sets, and applications, as well as improve our knowledge of thunderstorm initiation and evolution. In this presentation we review the planned implementation of the instrument and suite of operational algorithms.

  20. 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.

  1. 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

  2. Technology Reconciliation in the Remote Sensing ERA of United States Civilian Weather Forecasting: 1957 -1987.

    Science.gov (United States)

    Courain, Margaret Eileen

    This dissertation seeks to advance an understanding of the management of a major technological change in meteorology. The study examines the connection between changes in production and real-time use of data products derived from remote -sensing data collection and the evolution of U.S. civilian weather forecasting 1957-1987. The role of data collection in weather forecasting throughout history is examined, giving most attention to the 1957-1987 period. Critical to the real-time use of remote-sensing data was technology reconciliation. As defined by the author, it is the function or process by which data products and information derived from a new technology are made consistent or congruent with the existing data representations of a science in order to be used effectively. No model had been developed for a technology reconciliation process, or definition of the major role technology reconciliators played in the 30-year evolution of the science of weather forecasting. In order to assess the new remote-sensing data resource and its use in U.S. civilian weather forecasting, a Data Accountability and Review Technique (DART) was developed by the author in 1989. This technique was used to identify 16 of the technology reconciliators who developed and reconciled 25 new remote-sensing data products with the weather charts, maps and computer models of the National Weather Service. In five separate program teams, they were responsible for 15 improvements in the products--forecasts--and 18 improvement in the process of weather forecasting. A model of the technology reconciliation is proposed which can be applied to understanding the contemporary history of other sciences. The model, as well as the methods developed by the author to recognize the process of technology reconciliation has a much more general applicability beyond the sciences. Any field implementing new technology that promises to improve its whole way of working will be faced with the task of technology

  3. From Early Exploration to Space Weather Forecasts: Canada's Geomagnetic Odyssey

    Science.gov (United States)

    Lam, Hing-Lan

    2011-05-01

    Canada is a region ideally suited for the study of space weather: The north magnetic pole is encompassed within its territory, and the auroral oval traverses its vast landmass from east to west. Magnetic field lines link the country directly to the outer magnetosphere. In light of this geographic suitability, it has been a Canadian tradition to install ground monitors to remotely sense the space above Canadian territory. The beginning of this tradition dates back to 1840, when Edward Sabine, a key figure in the “magnetic crusade” to establish magnetic observatories throughout the British Empire in the nineteenth century, founded the first Canadian magnetic observatory on what is now the campus of the University of Toronto, 27 years before the birth of Canada. This observatory, which later became the Toronto Magnetic and Meteorological Observatory, marked the beginning of the Canadian heritage of installing magnetic stations and other ground instruments in the years to come. This extensive network of ground-based measurement devices, coupled with space-based measurements in more modern times, has enabled Canadian researchers to contribute significantly to studies related to space weather.

  4. Three-dimensional visualization of ensemble weather forecasts - Part 2: Forecasting warm conveyor belt situations for aircraft-based field campaigns

    Science.gov (United States)

    Rautenhaus, M.; Grams, C. M.; Schäfler, A.; Westermann, R.

    2015-07-01

    We present the application of interactive three-dimensional (3-D) visualization of ensemble weather predictions to forecasting warm conveyor belt situations during aircraft-based atmospheric research campaigns. Motivated by forecast requirements of the T-NAWDEX-Falcon 2012 (THORPEX - North Atlantic Waveguide and Downstream Impact Experiment) campaign, a method to predict 3-D probabilities of the spatial occurrence of warm conveyor belts (WCBs) has been developed. Probabilities are derived from Lagrangian particle trajectories computed on the forecast wind fields of the European Centre for Medium Range Weather Forecasts (ECMWF) ensemble prediction system. Integration of the method into the 3-D ensemble visualization tool Met.3D, introduced in the first part of this study, facilitates interactive visualization of WCB features and derived probabilities in the context of the ECMWF ensemble forecast. We investigate the sensitivity of the method with respect to trajectory seeding and grid spacing of the forecast wind field. Furthermore, we propose a visual analysis method to quantitatively analyse the contribution of ensemble members to a probability region and, thus, to assist the forecaster in interpreting the obtained probabilities. A case study, revisiting a forecast case from T-NAWDEX-Falcon, illustrates the practical application of Met.3D and demonstrates the use of 3-D and uncertainty visualization for weather forecasting and for planning flight routes in the medium forecast range (3 to 7 days before take-off).

  5. Three-dimensional visualization of ensemble weather forecasts – Part 2: Forecasting warm conveyor belt situations for aircraft-based field campaigns

    Directory of Open Access Journals (Sweden)

    M. Rautenhaus

    2015-07-01

    Full Text Available We present the application of interactive three-dimensional (3-D visualization of ensemble weather predictions to forecasting warm conveyor belt situations during aircraft-based atmospheric research campaigns. Motivated by forecast requirements of the T-NAWDEX-Falcon 2012 (THORPEX – North Atlantic Waveguide and Downstream Impact Experiment campaign, a method to predict 3-D probabilities of the spatial occurrence of warm conveyor belts (WCBs has been developed. Probabilities are derived from Lagrangian particle trajectories computed on the forecast wind fields of the European Centre for Medium Range Weather Forecasts (ECMWF ensemble prediction system. Integration of the method into the 3-D ensemble visualization tool Met.3D, introduced in the first part of this study, facilitates interactive visualization of WCB features and derived probabilities in the context of the ECMWF ensemble forecast. We investigate the sensitivity of the method with respect to trajectory seeding and grid spacing of the forecast wind field. Furthermore, we propose a visual analysis method to quantitatively analyse the contribution of ensemble members to a probability region and, thus, to assist the forecaster in interpreting the obtained probabilities. A case study, revisiting a forecast case from T-NAWDEX-Falcon, illustrates the practical application of Met.3D and demonstrates the use of 3-D and uncertainty visualization for weather forecasting and for planning flight routes in the medium forecast range (3 to 7 days before take-off.

  6. Economic consequences of improved temperature forecasts: An experiment with the Florida citrus growers (control group results). Executive summary. [weather forecasting

    Science.gov (United States)

    1977-01-01

    A demonstration experiment is being planned to show that frost and freeze prediction improvements are possible utilizing timely Synchronous Meteorological Satellite temperature measurements and that this information can affect Florida citrus grower operations and decisions so as to significantly reduce the cost for frost and freeze protection and crop losses. The design and implementation of the first phase of an economic experiment which will monitor citrus growers decisions, actions, costs and losses, and meteorological forecasts and actual weather events was carried out. The economic experiment was designed to measure the change in annual protection costs and crop losses which are the direct result of improved temperature forecasts. To estimate the benefits that may result from improved temperature forecasting capability, control and test groups were established with effective separation being accomplished temporally. The control group, utilizing current forecasting capability, was observed during the 1976-77 frost season and the results are reported. A brief overview is given of the economic experiment, the results obtained to date, and the work which still remains to be done.

  7. 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

  8. Using discrete online weather forecasts for building services applications and load management; Methoden zum Einsatz diskreter, webbasierter Wetterprognosen in Gebaeudetechnik und Lastmanagement

    Energy Technology Data Exchange (ETDEWEB)

    Seerig, Axel; Sagerschnig, Carina [Gruner AG, Basel (Switzerland)

    2009-02-15

    Usually, commercially used hourly weather forecasts of national weather institutes are implemented for predictive control strategies. Energy demand and energy loads are calculated by utilizing adequate models with predicted air temperatures. However, on the internet, numerous providers offer freely available weather forecasts. Mostly forecasts of maximum and minimum outside air temperatures are available for five to nine days in advance. Many applications in building services do require hourly or quarter-hourly data. This paper describes a method for generating weather data of any resolution for freely available weather forecasts issued by online services. Ice storage and load prediction of a building are cited as examples of predictive control strategies using web-based weather forecasts. (Abstract Copyright [2009], Wiley Periodicals, Inc.) [German] Prognosegefuehrte Regelungen verwenden zumeist kostenpflichtige stuendliche Wetterprognosen, die auf der momentan gemessenen Aussentemperatur basieren (z. B. Verschiebemethode) oder stuendliche Prognosen eines nationalen meteorologischen Dienstes fuer den jeweiligen Standort. Mit Hilfe der prognostizierten Werte werden unter Verwendung geeigneter Modelle die jeweils benoetigten Lastgaenge vorausberechnet und weiterverarbeitet. Das Internet hingegen bietet die Moeglichkeit, von vielen Anbietern kostenfrei Wetterprognosen fuer beliebige Standorte zu erhalten. Gewoehnlich sind diese Prognosen zumindest fuer die maximale und minimale Aussentemperatur fuer fuenf bis maximal neun Tage im Voraus verfuegbar. Fuer die meisten Anwendungen in der Gebaeude- und Energietechnik werden jedoch Werte in stuendlicher bzw. viertelstuendlicher Aufloesung benoetigt. Gegenstand dieses Aufsatzes ist die Beschreibung eines Verfahrens zur Erstellung von zeitlich beliebig aufgeloesten Wetterdaten auf Basis von frei verfuegbaren Wetterprognosen aus dem Internet. Als Beispiele fuer den Einsatz der darauf basierenden prognosegefuehrten Regelung

  9. Energy operations and planning decision support for systems using weather forecast information

    International Nuclear Information System (INIS)

    Altalo, M.G.

    2004-01-01

    Hydroelectric utilities deal with uncertainties on a regular basis. These include uncertainties in weather, policy and markets. This presentation outlined regional studies to define uncertainty, sources of uncertainty and their affect on power managers, power marketers, power insurers and end users. Solutions to minimize uncertainties include better forecasting and better business processes to mobilize action. The main causes of uncertainty in energy operations and planning include uncaptured wind, precipitation and wind events. Load model errors also contribute to uncertainty in energy operations. This presentation presented the results of a 2002-2003 study conducted by the National Oceanic and Atmospheric Administration (NOAA) on the impact uncertainties in northeast energy weather forecasts. The study demonstrated the cost of seabreeze error on transmission and distribution. The impact of climate scale events were also presented along with energy demand implications. It was suggested that energy planners should incorporate climate change parameters into planning, and that models should include probability distribution forecasts and ensemble forecasting methods that incorporate microclimate estimates. It was also suggested that seabreeze, lake effect, fog, afternoon thunderstorms and frontal passage should be incorporated into forecasts. tabs., figs

  10. A Space Weather Forecasting System with Multiple Satellites Based on a Self-Recognizing Network

    Directory of Open Access Journals (Sweden)

    Masahiro Tokumitsu

    2014-05-01

    Full Text Available This paper proposes a space weather forecasting system at geostationary orbit for high-energy electron flux (>2 MeV. The forecasting model involves multiple sensors on multiple satellites. The sensors interconnect and evaluate each other to predict future conditions at geostationary orbit. The proposed forecasting model is constructed using a dynamic relational network for sensor diagnosis and event monitoring. The sensors of the proposed model are located at different positions in space. The satellites for solar monitoring equip with monitoring devices for the interplanetary magnetic field and solar wind speed. The satellites orbit near the Earth monitoring high-energy electron flux. We investigate forecasting for typical two examples by comparing the performance of two models with different numbers of sensors. We demonstrate the prediction by the proposed model against coronal mass ejections and a coronal hole. This paper aims to investigate a possibility of space weather forecasting based on the satellite network with in-situ sensing.

  11. A space weather forecasting system with multiple satellites based on a self-recognizing network.

    Science.gov (United States)

    Tokumitsu, Masahiro; Ishida, Yoshiteru

    2014-05-05

    This paper proposes a space weather forecasting system at geostationary orbit for high-energy electron flux (>2 MeV). The forecasting model involves multiple sensors on multiple satellites. The sensors interconnect and evaluate each other to predict future conditions at geostationary orbit. The proposed forecasting model is constructed using a dynamic relational network for sensor diagnosis and event monitoring. The sensors of the proposed model are located at different positions in space. The satellites for solar monitoring equip with monitoring devices for the interplanetary magnetic field and solar wind speed. The satellites orbit near the Earth monitoring high-energy electron flux. We investigate forecasting for typical two examples by comparing the performance of two models with different numbers of sensors. We demonstrate the prediction by the proposed model against coronal mass ejections and a coronal hole. This paper aims to investigate a possibility of space weather forecasting based on the satellite network with in-situ sensing.

  12. Space Weather Products and Tools Used in Auroral Monitoring and Forecasting at CCMC/SWRC

    Science.gov (United States)

    Zheng, Yihua; Rastaetter, Lutz

    2015-01-01

    Key points discussed in this chapter are (1) the importance of aurora research to scientific advances and space weather applications, (2) space weather products at CCMC that are relevant to aurora monitoring and forecasting, and (3) the need for more effort from the whole community to achieve a better and long-lead-time forecast of auroral activity. Aurora, as manifestations of solar wind-magnetosphere-ionosphere coupling that occurs in a region of space that is relatively easy to access for sounding rockets, satellites, and other types of observational platforms, serves as a natural laboratory for studying the underlying physics of the complex system. From a space weather application perspective, auroras can cause surface charging of technological assets passing through the region, result in scintillation effects affecting communication and navigation, and cause radar cluttering that hinders military and civilian applications. Indirectly, an aurora and its currents can induce geomagnetically induced currents (GIC) on the ground, which poses major concerns for the wellbeing and operation of power grids, particularly during periods of intense geomagnetic activity. In addition, accurate auroral forecasting is desired for auroral tourism. In this chapter, we first review some of the existing auroral models and discuss past validation efforts. Such efforts are crucial in transitioning a model(s) from research to operations and for further model improvement and development that also benefits scientific endeavors. Then we will focus on products and tools that are used for auroral monitoring and forecasting at the Space Weather Research Center (SWRC). As part of the CCMC (Community Coordinated Modeling Center), SWRC has been providing space weather services since 2010.

  13. Impact of Moist Physics Complexity on Tropical Cyclone Simulations from the Hurricane Weather Research and Forecast System

    Science.gov (United States)

    Kalina, E. A.; Biswas, M.; Newman, K.; Grell, E. D.; Bernardet, L.; Frimel, J.; Carson, L.

    2017-12-01

    The parameterization of moist physics in numerical weather prediction models plays an important role in modulating tropical cyclone structure, intensity, and evolution. The Hurricane Weather Research and Forecast system (HWRF), the National Oceanic and Atmospheric Administration's operational model for tropical cyclone prediction, uses the Scale-Aware Simplified Arakawa-Schubert (SASAS) cumulus scheme and a modified version of the Ferrier-Aligo (FA) microphysics scheme to parameterize moist physics. The FA scheme contains a number of simplifications that allow it to run efficiently in an operational setting, which includes prescribing values for hydrometeor number concentrations (i.e., single-moment microphysics) and advecting the total condensate rather than the individual hydrometeor species. To investigate the impact of these simplifying assumptions on the HWRF forecast, the FA scheme was replaced with the more complex double-moment Thompson microphysics scheme, which individually advects cloud ice, cloud water, rain, snow, and graupel. Retrospective HWRF forecasts of tropical cyclones that occurred in the Atlantic and eastern Pacific ocean basins from 2015-2017 were then simulated and compared to those produced by the operational HWRF configuration. Both traditional model verification metrics (i.e., tropical cyclone track and intensity) and process-oriented metrics (e.g., storm size, precipitation structure, and heating rates from the microphysics scheme) will be presented and compared. The sensitivity of these results to the cumulus scheme used (i.e., the operational SASAS versus the Grell-Freitas scheme) also will be examined. Finally, the merits of replacing the moist physics schemes that are used operationally with the alternatives tested here will be discussed from a standpoint of forecast accuracy versus computational resources.

  14. Maintaining a Local Data Integration System in Support of Weather Forecast Operations

    Science.gov (United States)

    Watson, Leela R.; Blottman, Peter F.; Sharp, David W.; Hoeth, Brian

    2010-01-01

    Since 2000, both the National Weather Service in Melbourne, FL (NWS MLB) and the Spaceflight Meteorology Group (SMG) have used a local data integration system (LDIS) as part of their forecast and warning operations. Each has benefited from 3-dimensional analyses that are delivered to forecasters every 15 minutes across the peninsula of Florida. The intent is to generate products that enhance short-range weather forecasts issued in support of NWS MLB and SMG operational requirements within East Central Florida. The current LDIS uses the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS) package as its core, which integrates a wide variety of national, regional, and local observational data sets. It assimilates all available real-time data within its domain and is run at a finer spatial and temporal resolution than current national- or regional-scale analysis packages. As such, it provides local forecasters with a more comprehensive and complete understanding of evolving fine-scale weather features. Recent efforts have been undertaken to update the LDIS through the formal tasking process of NASA's Applied Meteorology Unit. The goals include upgrading LDIS with the latest version of ADAS, incorporating new sources of observational data, and making adjustments to shell scripts written to govern the system. A series of scripts run a complete modeling system consisting of the preprocessing step, the main model integration, and the post-processing step. The preprocessing step prepares the terrain, surface characteristics data sets, and the objective analysis for model initialization. Data ingested through ADAS include (but are not limited to) Level II Weather Surveillance Radar- 1988 Doppler (WSR-88D) data from six Florida radars, Geostationary Operational Environmental Satellites (GOES) visible and infrared satellite imagery, surface and upper air observations throughout Florida from NOAA's Earth System Research Laboratory/Global Systems Division

  15. Natural priors, CMSSM fits and LHC weather forecasts

    International Nuclear Information System (INIS)

    Allanach, Benjamin C.; Cranmer, Kyle; Lester, Christopher G.; Weber, Arne M.

    2007-01-01

    Previous LHC forecasts for the constrained minimal supersymmetric standard model (CMSSM), based on current astrophysical and laboratory measurements, have used priors that are flat in the parameter tan β, while being constrained to postdict the central experimental value of M Z . We construct a different, new and more natural prior with a measure in μ and B (the more fundamental MSSM parameters from which tan β and M Z are actually derived). We find that as a consequence this choice leads to a well defined fine-tuning measure in the parameter space. We investigate the effect of such on global CMSSM fits to indirect constraints, providing posterior probability distributions for Large Hadron Collider (LHC) sparticle production cross sections. The change in priors has a significant effect, strongly suppressing the pseudoscalar Higgs boson dark matter annihilation region, and diminishing the probable values of sparticle masses. We also show how to interpret fit information from a Markov Chain Monte Carlo in a frequentist fashion; namely by using the profile likelihood. Bayesian and frequentist interpretations of CMSSM fits are compared and contrasted

  16. Forecast for nuclear energy: Clear skies or stormy weather?

    Science.gov (United States)

    Ferguson, Charles D.

    2018-01-01

    During the last decade many people in the nuclear industry were forecasting a renaissance in construction of nuclear power plants, especially in light of the near-zero greenhouse gas emissions of nuclear power and the global need for such cleaner electricity sources. While the accident in March 2011 at the Fukushima Daiichi Nuclear Power Station in Japan resulted in dozens of reactor shutdowns in Japan and reconsideration of new nuclear power plants in several countries, other countries are continuing to build new plants but not at a fast enough rate yet to make a significant further reduction in greenhouse gas emissions. Even before this accident, the prospects for major growth in nuclear power were dim. To explicate the present situation and potential future scenarios for nuclear power, this paper examines the issue of who bears the financial risk especially during the construction phase, the roles of governments in financial interventions such as loan guarantees, tax credits, and prices on greenhouse gas emissions, the effects of regulated versus market-based utility systems, the competition with relatively cheap natural gas, the roles of various governments around the world in determining the use of nuclear power, the interdependent nature of the nuclear industry with companies both competing and cooperating with each other, and the issue of whether small modular reactors or advanced nuclear reactors could result in many more plants being constructed in the United States and worldwide.

  17. Statistical Correction of Air Temperature Forecasts for City and Road Weather Applications

    Science.gov (United States)

    Mahura, Alexander; Petersen, Claus; Sass, Bent; Gilet, Nicolas

    2014-05-01

    The method for statistical correction of air /road surface temperatures forecasts was developed based on analysis of long-term time-series of meteorological observations and forecasts (from HIgh Resolution Limited Area Model & Road Conditions Model; 3 km horizontal resolution). It has been tested for May-Aug 2012 & Oct 2012 - Mar 2013, respectively. The developed method is based mostly on forecasted meteorological parameters with a minimal inclusion of observations (covering only a pre-history period). Although the st iteration correction is based taking into account relevant temperature observations, but the further adjustment of air and road temperature forecasts is based purely on forecasted meteorological parameters. The method is model independent, e.g. it can be applied for temperature correction with other types of models having different horizontal resolutions. It is relatively fast due to application of the singular value decomposition method for matrix solution to find coefficients. Moreover, there is always a possibility for additional improvement due to extra tuning of the temperature forecasts for some locations (stations), and in particular, where for example, the MAEs are generally higher compared with others (see Gilet et al., 2014). For the city weather applications, new operationalized procedure for statistical correction of the air temperature forecasts has been elaborated and implemented for the HIRLAM-SKA model runs at 00, 06, 12, and 18 UTCs covering forecast lengths up to 48 hours. The procedure includes segments for extraction of observations and forecast data, assigning these to forecast lengths, statistical correction of temperature, one-&multi-days statistical evaluation of model performance, decision-making on using corrections by stations, interpolation, visualisation and storage/backup. Pre-operational air temperature correction runs were performed for the mainland Denmark since mid-April 2013 and shown good results. Tests also showed

  18. Forecasting challenges during the severe weather outbreak in Central Europe on 25 June 2008

    Science.gov (United States)

    Púčik, Tomáš; Francová, Martina; Rýva, David; Kolář, Miroslav; Ronge, Lukáš

    2011-06-01

    On 25 June 2008, severe thunderstorms caused widespread damage and two fatalities in the Czech Republic. Significant features of the storms included numerous downbursts on a squall line that exhibited a bow echo reflectivity pattern, with sustained wind gusts over 32 m/s at several reporting stations. Moreover, a tornado and several downbursts of F2 intensity occurred within the convective system, collocated with the development of mesovortices within the larger scale bow echo. The extent of the event was sufficient to call it a derecho, as the windstorm had affected Eastern Germany, Southern Poland, Slovakia, Austria and Northern Hungary as well. Ahead of the squall line, several well-organized isolated cells occurred, exhibiting supercellular characteristics, both from a radar and visual perspective. These storms produced large hail and also isolated severe wind gusts. This paper deals mostly with the forecasting challenges that were experienced by the meteorologist on duty during the evolution of this convective scenario. The main challenge of the day was to identify the region that would be most affected by severe convection, especially as the numerical weather prediction failed to anticipate the extent and the progress of the derecho-producing mesoscale convective systems (MCSs). Convective storms developed in an environment conducive to severe thunderstorms, with strong wind shear confined mostly to the lower half of the troposphere. These developments also were strongly influenced by mesoscale factors, especially a mesolow centered over Austria and its trough stretching to Eastern Bohemia. The paper demonstrates how careful mesoscale analysis could prove useful in dealing with such convective situations. Remote-sensing methods are also shown to be useful in such situations, especially when they can offer sufficient lead time to issue a warning, which is not always the case.

  19. 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.

  20. Flow intake control using dry-weather forecast

    Science.gov (United States)

    Icke, Otto; van Schagen, Kim; Huising, Christian; Wuister, Jasper; van Dijk, Edward; Budding, Arjan

    2017-08-01

    Level-based control of the influent flow causes peak discharges at a waste water treatment plant (WWTP) after rainfall events. Furthermore, the capacity of the post-treatment is in general smaller than the maximum hydraulic capacity of the WWTP. This results in a significant bypass of the post-treatment during peak discharge. The optimisation of influent flow reduces peak discharge, and increases the treatment efficiency of the whole water cycle, which benefits the surface water quality. In this paper, it is shown that half of the bypasses of the post-treatment can be prevented by predictive control. A predictive controller for influent flow is implemented using the Aquasuitetext">® Advanced Monitoring and Control platform. Based on real-time measured water levels in the sewerage and both rainfall and dry-weather flow (DWF) predictions, a discharge limitation is determined by a volume optimisation technique. For the analysed period (February-September 2016) results at WWTP Bennekom show that about 50 % of bypass volume can be prevented. Analysis of single rainfall events shows that the used approach is still conservative and that the bypass can be even further decreased by allowing discharge limitation during precipitation.

  1. Quantitative impact of aerosols on numerical weather prediction. Part II: Impacts to IR radiance assimilation

    Science.gov (United States)

    Marquis, J. W.; Campbell, J. R.; Oyola, M. I.; Ruston, B. C.; Zhang, J.

    2017-12-01

    This is part II of a two-part series examining the impacts of aerosol particles on weather forecasts. In this study, the aerosol indirect effects on weather forecasts are explored by examining the temperature and moisture analysis associated with assimilating dust contaminated hyperspectral infrared radiances. The dust induced temperature and moisture biases are quantified for different aerosol vertical distribution and loading scenarios. The overall impacts of dust contamination on temperature and moisture forecasts are quantified over the west coast of Africa, with the assistance of aerosol retrievals from AERONET, MPL, and CALIOP. At last, methods for improving hyperspectral infrared data assimilation in dust contaminated regions are proposed.

  2. Integration of Space Weather Forecasts into Space Protection

    Science.gov (United States)

    Reeves, G.

    2012-09-01

    How would the US respond to a clandestine attack that disabled one of our satellites? How would we know that it was an attack, not a natural failure? The goal of space weather programs as applied to space protection are simple: Provide a rapid and reliable assessment of the probability that satellite or system failure was caused by the space environment. Achieving that goal is not as simple. However, great strides are being made on a number of fronts. We will report on recent successes in providing rapid, automated anomaly/attack assessment for the penetrating radiation environment in the Earth's radiation belts. We have previously reported on the Dynamic Radiation Environment Assimilation Model (DREAM) that was developed at Los Alamos National Laboratory to assess hazards posed by the natural and by nuclear radiation belts. This year we will report on recent developments that are moving this program from the research, test, and evaluation phases to real-time implementation and application. We will discuss the challenges of leveraging space environment data sets for applications that are beyond the scope of mission requirements, the challenges of moving data from where they exist to where they are needed, the challenges of turning data into actionable information, and how those challenges were overcome. We will discuss the state-of-the-art as it exists in 2012 including the new capabilities that have been enabled and the limitations that still exist. We will also discuss how currently untapped data resources could advance the state-of-the-art and the future steps for implementing automatic real-time anomaly forensics.

  3. Economic consequences of improved temperature forecasts: An experiment with the Florida citrus growers (control group results). [weather forecasting

    Science.gov (United States)

    1977-01-01

    A demonstration experiment is being planned to show that frost and freeze prediction improvements are possible utilizing timely Synchronous Meteorological Satellite temperature measurements and that this information can affect Florida citrus grower operations and decisions. An economic experiment was carried out which will monitor citrus growers' decisions, actions, costs and losses, and meteorological forecasts and actual weather events and will establish the economic benefits of improved temperature forecasts. A summary is given of the economic experiment, the results obtained to date, and the work which still remains to be done. Specifically, the experiment design is described in detail as are the developed data collection methodology and procedures, sampling plan, data reduction techniques, cost and loss models, establishment of frost severity measures, data obtained from citrus growers, National Weather Service, and Federal Crop Insurance Corp., resulting protection costs and crop losses for the control group sample, extrapolation of results of control group to the Florida citrus industry and the method for normalization of these results to a normal or average frost season so that results may be compared with anticipated similar results from test group measurements.

  4. 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.

  5. 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.

  6. Operational Numerical Weather Prediction at the Met Office and potential ways forward for operational space weather prediction systems

    Science.gov (United States)

    Jackson, David

    NICT (National Institute of Information and Communications Technology) has been in charge of space weather forecast service in Japan for more than 20 years. The main target region of the space weather is the geo-space in the vicinity of the Earth where human activities are dominant. In the geo-space, serious damages of satellites, international space stations and astronauts take place caused by energetic particles or electromagnetic disturbances: the origin of the causes is dynamically changing of solar activities. Positioning systems via GPS satellites are also im-portant recently. Since the most significant effect of positioning error comes from disturbances of the ionosphere, it is crucial to estimate time-dependent modulation of the electron density profiles in the ionosphere. NICT is one of the 13 members of the ISES (International Space Environment Service), which is an international assembly of space weather forecast centers under the UNESCO. With help of geo-space environment data exchanging among the member nations, NICT operates daily space weather forecast service every day to provide informa-tion on forecasts of solar flare, geomagnetic disturbances, solar proton event, and radio-wave propagation conditions in the ionosphere. The space weather forecast at NICT is conducted based on the three methodologies: observations, simulations and informatics (OSI model). For real-time or quasi real-time reporting of space weather, we conduct our original observations: Hiraiso solar observatory to monitor the solar activity (solar flare, coronal mass ejection, and so on), domestic ionosonde network, magnetometer HF radar observations in far-east Siberia, and south-east Asia low-latitude ionosonde network (SEALION). Real-time observation data to monitor solar and solar-wind activities are obtained through antennae at NICT from ACE and STEREO satellites. We have a middle-class super-computer (NEC SX-8R) to maintain real-time computer simulations for solar and solar

  7. Numerical Weather Prediction and Relative Economic Value framework to improve Integrated Urban Drainage- Wastewater management

    DEFF Research Database (Denmark)

    Courdent, Vianney Augustin Thomas

    to evaluate when acting on the forecast is beneficial or not. Rainfall forecasts are extremely valuable for estimating near future storm-water-related impacts on the IUDWS. Therefore, weather radar extrapolation “nowcasts” provide valuable predictions for RTC. However, radar nowcasts are limited...... by their prediction horizon of 1 to 2 hours and RTC of IUDWS could benefit from longer forecast horizons. The development of NWP models in parallel to the increase in computational power has led to limited area models (LAM) with increasingly finer spatial-temporal resolution, opening the possibility to use...... such weather forecast products in urban water management. NWPs are complementary to radar forecasts, providing predictions on a longer time scale (days). However, atmospheric motions are chaotic and highly nonlinear. Applying NWP to urban catchments, which often have a similar size to a single NWP grid cell...

  8. 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.

  9. 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.

  10. Ultra-Short-Term Forecast of Photovoltaic Output Power under Fog and Haze Weather

    Directory of Open Access Journals (Sweden)

    Weiliang Liu

    2018-02-01

    Full Text Available Fog and haze (F-H weather has been occurring frequently in China since 2012, which affects the output power of photovoltaic (PV generation dramatically by directly weakening solar irradiance and aggravating dust deposition on PV panels. The ultra-short-term forecast method presented in this study would help to fully reflect the dual effects of F-H on PV output power. Aiming at the weakening effect on solar irradiance, estimation models of atmospheric aerosol optical depth (AOD based on particle matter (PM concentration were established with machine learning (ML method, and the total irradiance received by PV panels was calculated based on simplified REST2 model. Aiming at the aggravating effect on dust deposition on PV panels, sample set of “cumulative PM concentration—efficiency reduction” was constructed through special measurement experiments, then the efficiency reduction under certain dust deposition state was estimated with similar-day choosing method. Based on photoelectric conversion model, PM concentration prediction and weather forecast information, ultra-short-term forecast of PV output power was realized. Experimental results proved the validity and feasibility of the presented forecast method.

  11. 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.

  12. Beyond Climate and Weather Science: Expanding the Forecasting Family to Serve Societal Needs

    Science.gov (United States)

    Barron, E. J.

    2009-05-01

    The ability to "anticipate" the future is what makes information from the Earth sciences valuable to society - whether it is the prediction of severe weather or the future availability of water resources in response to climate change. An improved ability to anticipate or forecast has the potential to serve society by simultaneously improving our ability to (1) promote economic vitality, (2) enable environmental stewardship, (3) protect life and property, as well as (4) improve our fundamental knowledge of the earth system. The potential is enormous, yet many appear ready to move quickly toward specific mitigation and adaptation strategies assuming that the science is settled. Five important weakness must be addressed first: (1) the formation of a true "climate services" function and capability, (2) the deliberate investment in expanding the family of forecasting elements to incorporate a broader array of environmental factors and impacts, (3) the investment in the sciences that connect climate to society, (4) a deliberate focus on the problems associated with scale, in particular the difference between the scale of predictive models and the scale associated with societal decisions, and (5) the evolution from climate services and model predictions to the equivalent of "environmental intelligence centers." The objective is to bring the discipline of forecasting to a broader array of environmental challenges. Assessments of the potential impacts of global climate change on societal sectors such as water, human health, and agriculture provide good examples of this challenge. We have the potential to move from a largely reactive mode in addressing adverse health outcomes, for example, to one in which the ties between climate, land cover, infectious disease vectors, and human health are used to forecast and predict adverse human health conditions. The potential exists for a revolution in forecasting, that entrains a much broader set of societal needs and solutions. The

  13. Multivariate Climate-Weather Forecasting System: An Integrated Approach for Mitigating Agricultural Risks in Punjab

    Science.gov (United States)

    Ravindranath, A.; Devineni, N.

    2015-12-01

    While India has a long history of prediction of the All India Monsoon, work on spatially specific attributes of the monsoon, as well as monsoon break periods has only recently emerged. However, from a risk management context, prognostic information of a single variable such as total precipitation or average temperature will be of less utility especially for specific operational purposes. An integrated regional climate-weather forecast system covering precipitation, temperature and humidity etc. over the year will benefit the farmers in the context of a specific decision time table for irrigation scheduling as well as for pre-season crop choices. Hence, contrary to the existing forecasting methods that develop multi time scale information of a single variable at a time, in this paper, we introduce an integrated regional multivariate climate-weather forecasting system that directly relates to agricultural decision making and risk mitigation. These multi-scale risk attributes include mutually dependent, spatially disaggregated statistics such as total rainfall, average temperature, growing degree days, relative humidity, total number of rainfall days/dry spell length, and cumulative water deficits that inform the potential irrigation water requirements for crops. Given that these attributes exhibit mutual dependence across space and time, we propose to explore common ocean-atmospheric conditions from the observations and the state of the art Global Circulation Models (GCMs) that can be utilized as the predictor variables for the forecasting system. Hierarchical Bayesian methods are be used to develop the integrated forecast system. The developed multivariate forecasts will be adapted and disseminated as decision tools for the farmers under the extension projects in Punjab region of India.

  14. Resilient Sensor Networks with Spatiotemporal Interpolation of Missing Sensors: An Example of Space Weather Forecasting by Multiple Satellites

    Science.gov (United States)

    Tokumitsu, Masahiro; Hasegawa, Keisuke; Ishida, Yoshiteru

    2016-01-01

    This paper attempts to construct a resilient sensor network model with an example of space weather forecasting. The proposed model is based on a dynamic relational network. Space weather forecasting is vital for a satellite operation because an operational team needs to make a decision for providing its satellite service. The proposed model is resilient to failures of sensors or missing data due to the satellite operation. In the proposed model, the missing data of a sensor is interpolated by other sensors associated. This paper demonstrates two examples of space weather forecasting that involves the missing observations in some test cases. In these examples, the sensor network for space weather forecasting continues a diagnosis by replacing faulted sensors with virtual ones. The demonstrations showed that the proposed model is resilient against sensor failures due to suspension of hardware failures or technical reasons. PMID:27092508

  15. Resilient Sensor Networks with Spatiotemporal Interpolation of Missing Sensors: An Example of Space Weather Forecasting by Multiple Satellites.

    Science.gov (United States)

    Tokumitsu, Masahiro; Hasegawa, Keisuke; Ishida, Yoshiteru

    2016-04-15

    This paper attempts to construct a resilient sensor network model with an example of space weather forecasting. The proposed model is based on a dynamic relational network. Space weather forecasting is vital for a satellite operation because an operational team needs to make a decision for providing its satellite service. The proposed model is resilient to failures of sensors or missing data due to the satellite operation. In the proposed model, the missing data of a sensor is interpolated by other sensors associated. This paper demonstrates two examples of space weather forecasting that involves the missing observations in some test cases. In these examples, the sensor network for space weather forecasting continues a diagnosis by replacing faulted sensors with virtual ones. The demonstrations showed that the proposed model is resilient against sensor failures due to suspension of hardware failures or technical reasons.

  16. Resilient Sensor Networks with Spatiotemporal Interpolation of Missing Sensors: An Example of Space Weather Forecasting by Multiple Satellites

    Directory of Open Access Journals (Sweden)

    Masahiro Tokumitsu

    2016-04-01

    Full Text Available This paper attempts to construct a resilient sensor network model with an example of space weather forecasting. The proposed model is based on a dynamic relational network. Space weather forecasting is vital for a satellite operation because an operational team needs to make a decision for providing its satellite service. The proposed model is resilient to failures of sensors or missing data due to the satellite operation. In the proposed model, the missing data of a sensor is interpolated by other sensors associated. This paper demonstrates two examples of space weather forecasting that involves the missing observations in some test cases. In these examples, the sensor network for space weather forecasting continues a diagnosis by replacing faulted sensors with virtual ones. The demonstrations showed that the proposed model is resilient against sensor failures due to suspension of hardware failures or technical reasons.

  17. 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.

  18. Short Term Weather Forecasting and Long Term Climate Predictions in Mesoamerica

    Science.gov (United States)

    Hardin, D. M.; Daniel, I.; Mecikalski, J.; Graves, S.

    2008-05-01

    The SERVIR project utilizes several predictive models to support regional monitoring and decision support in Mesoamerica. Short term forecasts ranging from a few hours to several days produce more than 30 data products that are used daily by decision makers, as well as news organizations in the region. The forecast products can be visualized in both two and three dimensional viewers such as Google Maps and Google Earth. Other viewers developed specifically for the Mesoamerican region by the University of Alabama in Huntsville and the Institute for the Application of Geospatial Technologies in Auburn New York can also be employed. In collaboration with the NASA Short Term Prediction Research and Transition (SpoRT) Center SERVIR utilizes the Weather Research and Forecast (WRF) model to produce short-term (24 hr) regional weather forecasts twice a day. Temperature, precipitation, wind, and other variables are forecast in 10km and 30km grids over the Mesoamerica region. Using the PSU/NCAR Mesoscale Model, known as MM5, SERVIR produces 48 hour- forecasts of soil temperature, two meter surface temperature, three hour accumulated precipitation, winds at different heights, and other variables. These are forecast hourly in 9km grids. Working in collaboration with the Atmospheric Science Department of the University of Alabama in Huntsville produces a suite of short-term (0-6 hour) weather prediction products are generated. These "convective initiation" products predict the onset of thunderstorm rainfall and lightning within a 1-hour timeframe. Models are also employed for long term predictions. The SERVIR project, under USAID funding, has developed comprehensive regional climate change scenarios of Mesoamerica for future years: 2010, 2015, 2025, 2050, and 2099. These scenarios were created using the Pennsylvania State University/National Center for Atmospheric Research (MM5) model and processed on the Oak Ridge National Laboratory Cheetah supercomputer. The goal of these

  19. Determining optimal clothing ensembles based on weather forecasts, with particular reference to outdoor winter military activities.

    Science.gov (United States)

    Morabito, Marco; Pavlinic, Daniela Z; Crisci, Alfonso; Capecchi, Valerio; Orlandini, Simone; Mekjavic, Igor B

    2011-07-01

    Military and civil defense personnel are often involved in complex activities in a variety of outdoor environments. The choice of appropriate clothing ensembles represents an important strategy to establish the success of a military mission. The main aim of this study was to compare the known clothing insulation of the garment ensembles worn by soldiers during two winter outdoor field trials (hike and guard duty) with the estimated optimal clothing thermal insulations recommended to maintain thermoneutrality, assessed by using two different biometeorological procedures. The overall aim was to assess the applicability of such biometeorological procedures to weather forecast systems, thereby developing a comprehensive biometeorological tool for military operational forecast purposes. Military trials were carried out during winter 2006 in Pokljuka (Slovenia) by Slovene Armed Forces personnel. Gastrointestinal temperature, heart rate and environmental parameters were measured with portable data acquisition systems. The thermal characteristics of the clothing ensembles worn by the soldiers, namely thermal resistance, were determined with a sweating thermal manikin. Results showed that the clothing ensemble worn by the military was appropriate during guard duty but generally inappropriate during the hike. A general under-estimation of the biometeorological forecast model in predicting the optimal clothing insulation value was observed and an additional post-processing calibration might further improve forecast accuracy. This study represents the first step in the development of a comprehensive personalized biometeorological forecast system aimed at improving recommendations regarding the optimal thermal insulation of military garment ensembles for winter activities.

  20. 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.

  1. Impact of Atmospheric Infrared Sounder (AIRS) Thermodynamic Profiles on Regional Weather Forecasting

    Science.gov (United States)

    Chou, Shih-Hung; Zavodsky, Bradley T.; Jedlovee, Gary J.

    2010-01-01

    In data sparse regions, remotely-sensed observations can be used to improve analyses and lead to better forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), provides temperature and moisture profiles with accuracy comparable to that of radiosondes. The purpose of this paper is to describe a procedure to assimilate AIRS thermodynamic profile data into a regional configuration of the Advanced Research Weather Research and Forecasting (WRF-ARW) model using its three-dimension variational (3DVAR) analysis component (WRF-Var). Quality indicators are used to select only the highest quality temperature and moisture profiles for assimilation in both clear and partly cloudy regions. Separate error characteristics for land and water profiles are also used in the assimilation process. Assimilation results indicate that AIRS profiles produce an analysis closer to in situ observations than the background field. Forecasts from a 37-day case study period in the winter of 2007 show that AIRS profile data can lead to improvements in 6-h cumulative precipitation forecasts due to instability added in the forecast soundings by the AIRS profiles. Additionally, in a convective heavy rainfall event from February 2007, assimilation of AIRS profiles produces a more unstable boundary layer resulting in enhanced updrafts in the model. These updrafts produce a squall line and precipitation totals that more closely reflect ground-based observations than a no AIRS control forecast. The location of available high-quality AIRS profiles ahead of approaching storm systems is found to be of paramount importance to the amount of impact the observations will have on the resulting forecasts.

  2. Using forecast and observed weather data to assess performance of forecast products in identifying heat waves and estimating heat wave effects on mortality.

    Science.gov (United States)

    Zhang, Kai; Chen, Yeh-Hsin; Schwartz, Joel D; Rood, Richard B; O'Neill, Marie S

    2014-09-01

    Heat wave and health warning systems are activated based on forecasts of health-threatening hot weather. We estimated heat-mortality associations based on forecast and observed weather data in Detroit, Michigan, and compared the accuracy of forecast products for predicting heat waves. We derived and compared apparent temperature (AT) and heat wave days (with heat waves defined as ≥ 2 days of daily mean AT ≥ 95th percentile of warm-season average) from weather observations and six different forecast products. We used Poisson regression with and without adjustment for ozone and/or PM10 (particulate matter with aerodynamic diameter ≤ 10 μm) to estimate and compare associations of daily all-cause mortality with observed and predicted AT and heat wave days. The 1-day-ahead forecast of a local operational product, Revised Digital Forecast, had about half the number of false positives compared with all other forecasts. On average, controlling for heat waves, days with observed AT = 25.3°C were associated with 3.5% higher mortality (95% CI: -1.6, 8.8%) than days with AT = 8.5°C. Observed heat wave days were associated with 6.2% higher mortality (95% CI: -0.4, 13.2%) than non-heat wave days. The accuracy of predictions varied, but associations between mortality and forecast heat generally tended to overestimate heat effects, whereas associations with forecast heat waves tended to underestimate heat wave effects, relative to associations based on observed weather metrics. Our findings suggest that incorporating knowledge of local conditions may improve the accuracy of predictions used to activate heat wave and health warning systems.

  3. Towards Assimilating GOES-R Infrared Brightness Temperatures for the Analysis and Forecast of Tropical Cyclones and Severe Weather

    Science.gov (United States)

    Zhang, F.; Minamide, M.; Clothiaux, E. E.

    2015-12-01

    An ensemble data assimilation system is used to assess the impact of assimilating satellite infrared radiance data in both clear and cloudy skies on the analysis and forecast of severe weather and tropical cyclones. The new generation geostationary satellite infrared radiance data, including those from AHI onboard Himawara-8 launched in October 2014 and the Advanced Baseline Imager (ABI) on GOES-R to be launched in 2016, have or will have near global coverage at all times with high spatial and temporal resolution. GOES-R will provide two times higher spatial and temporal resolution radiance data than the satellites currently in orbit. Both contain 10 infrared channels with 2 km x 2 km spatial resolution with images produced every 15 minutes. The assimilation of such high-resolution satellite observations in both clear and cloudy skies is challenging given their strong non-linear relationships to the underlying model fields, the general lack of effective quality control on them, the need to apply bias corrections to them, and the necessity of data synthesizing and thinning for their application to regional scale numerical weather prediction. These difficulties are especially relevant to the cloudy radiances. For the current study we couple the Community Radiative Transfer Model (CRTM) to the ensemble Kalman filter (EnKF) data assimilation system developed at Penn State University (PSU) and built around the Weather Research and Forecasting model (WRF). This new framework, together with our assimilation strategies that include superobbing and effective data quality control, enables us to directly assimilate multiple channel brightness temperatures with high temporal and spatial resolution into the EnKF. The impact of assimilating brightness temperatures from these new advanced imagers is assessed through both examining the dynamical covariance between the satellite radiances and the state variables estimated from an ensemble and performing extensive observing system

  4. Sharing wind power forecasts in electricity markets: A numerical analysis

    DEFF Research Database (Denmark)

    Exizidis, Lazaros; Pinson, Pierre; Kazempour, Jalal

    2016-01-01

    In an electricity pool with significant share of wind power, all generators including conventional and wind power units are generally scheduled in a day-ahead market based on wind power forecasts. Then, a real-time market is cleared given the updated wind power forecast and fixed day-ahead decisi...

  5. Experiments in Objective Aviation Weather Forecasting Using Upper-Level Steering.

    Science.gov (United States)

    1983-12-13

    Estoque 2 and Reed 3 developed graphical 1000- mb forecast techniques, applying baroclinic theory, and these techniques steered thermal patterns using upper...none. 42 References 1. Fjbrtoft. R. (1952) On a numerical method of integrating the barotropic vorti- city equation. Tellus 4:179-194. 2. Estoque . M.A

  6. 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).

  7. 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.

  8. 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

  9. The Impact of Weather Forecasts of Various Lead Times on Snowmaking Decisions Made for the 2010 Vancouver Olympic Winter Games

    Science.gov (United States)

    Doyle, Chris

    2014-01-01

    The Vancouver 2010 Winter Olympics were held from 12 to 28 February 2010, and the Paralympic events followed 2 weeks later. During the Games, the weather posed a grave threat to the viability of one venue and created significant complications for the event schedule at others. Forecasts of weather with lead times ranging from minutes to days helped organizers minimize disruptions to sporting events and helped ensure all medal events were successfully completed. Of comparable importance, however, were the scenarios and forecasts of probable weather for the winter in advance of the Games. Forecasts of mild conditions at the time of the Games helped the Games' organizers mitigate what would have been very serious potential consequences for at least one venue. Snowmaking was one strategy employed well in advance of the Games to prepare for the expected conditions. This short study will focus on how operational decisions were made by the Games' organizers on the basis of both climatological and snowmaking forecasts during the pre-Games winter. An attempt will be made to quantify, economically, the value of some of the snowmaking forecasts made for the Games' operators. The results obtained indicate that although the economic value of the snowmaking forecast was difficult to determine, the Games' organizers valued the forecast information greatly. This suggests that further development of probabilistic forecasts for applications like pre-Games snowmaking would be worthwhile.

  10. Ensemble Flow Forecasts for Risk Based Reservoir Operations of Lake Mendocino in Mendocino County, California: A Framework for Objectively Leveraging Weather and Climate Forecasts in a Decision Support Environment

    Science.gov (United States)

    Delaney, C.; Hartman, R. K.; Mendoza, J.; Whitin, B.

    2017-12-01

    Forecast informed reservoir operations (FIRO) is a methodology that incorporates short to mid-range precipitation and flow forecasts to inform the flood operations of reservoirs. The Ensemble Forecast Operations (EFO) alternative is a probabilistic approach of FIRO that incorporates ensemble streamflow predictions (ESPs) made by NOAA's California-Nevada River Forecast Center (CNRFC). With the EFO approach, release decisions are made to manage forecasted risk of reaching critical operational thresholds. A water management model was developed for Lake Mendocino, a 111,000 acre-foot reservoir located near Ukiah, California, to evaluate the viability of the EFO alternative to improve water supply reliability but not increase downstream flood risk. Lake Mendocino is a dual use reservoir, which is owned and operated for flood control by the United States Army Corps of Engineers and is operated for water supply by the Sonoma County Water Agency. Due to recent changes in the operations of an upstream hydroelectric facility, this reservoir has suffered from water supply reliability issues since 2007. The EFO alternative was simulated using a 26-year (1985-2010) ESP hindcast generated by the CNRFC. The ESP hindcast was developed using Global Ensemble Forecast System version 10 precipitation reforecasts processed with the Hydrologic Ensemble Forecast System to generate daily reforecasts of 61 flow ensemble members for a 15-day forecast horizon. Model simulation results demonstrate that the EFO alternative may improve water supply reliability for Lake Mendocino yet not increase flood risk for downstream areas. The developed operations framework can directly leverage improved skill in the second week of the forecast and is extendable into the S2S time domain given the demonstration of improved skill through a reliable reforecast of adequate historical duration and consistent with operationally available numerical weather predictions.

  11. A review of multimodel superensemble forecasting for weather, seasonal climate, and hurricanes

    Science.gov (United States)

    Krishnamurti, T. N.; Kumar, V.; Simon, A.; Bhardwaj, A.; Ghosh, T.; Ross, R.

    2016-06-01

    This review provides a summary of work in the area of ensemble forecasts for weather, climate, oceans, and hurricanes. This includes a combination of multiple forecast model results that does not dwell on the ensemble mean but uses a unique collective bias reduction procedure. A theoretical framework for this procedure is provided, utilizing a suite of models that is constructed from the well-known Lorenz low-order nonlinear system. A tutorial that includes a walk-through table and illustrates the inner workings of the multimodel superensemble's principle is provided. Systematic errors in a single deterministic model arise from a host of features that range from the model's initial state (data assimilation), resolution, representation of physics, dynamics, and ocean processes, local aspects of orography, water bodies, and details of the land surface. Models, in their diversity of representation of such features, end up leaving unique signatures of systematic errors. The multimodel superensemble utilizes as many as 10 million weights to take into account the bias errors arising from these diverse features of multimodels. The design of a single deterministic forecast models that utilizes multiple features from the use of the large volume of weights is provided here. This has led to a better understanding of the error growths and the collective bias reductions for several of the physical parameterizations within diverse models, such as cumulus convection, planetary boundary layer physics, and radiative transfer. A number of examples for weather, seasonal climate, hurricanes and sub surface oceanic forecast skills of member models, the ensemble mean, and the superensemble are provided.

  12. The Effect of NEXRAD Image Looping and National Convective Weather Forecast Product on Pilot Decision Making in the Use of a Cockpit Weather Information Display

    Science.gov (United States)

    Burgess, Malcolm A.; Thomas, Rickey P.

    2004-01-01

    This experiment investigated improvements to cockpit weather displays to better support the hazardous weather avoidance decision-making of general aviation pilots. Forty-eight general aviation pilots were divided into three equal groups and presented with a simulated flight scenario involving embedded convective activity. The control group had access to conventional sources of pre-flight and in-flight weather products. The two treatment groups were provided with a weather display that presented NEXRAD mosaic images, graphic depiction of METARs, and text METARs. One treatment group used a NEXRAD image looping feature and the second group used the National Convective Weather Forecast (NCWF) product overlaid on the NEXRAD display. Both of the treatment displays provided a significant increase in situation awareness but, they provided incomplete information required to deal with hazardous convective weather conditions, and would require substantial pilot training to permit their safe and effective use.

  13. Numerical Weather Predictions Evaluation Using Spatial Verification Methods

    Science.gov (United States)

    Tegoulias, I.; Pytharoulis, I.; Kotsopoulos, S.; Kartsios, S.; Bampzelis, D.; Karacostas, T.

    2014-12-01

    During the last years high-resolution numerical weather prediction simulations have been used to examine meteorological events with increased convective activity. Traditional verification methods do not provide the desired level of information to evaluate those high-resolution simulations. To assess those limitations new spatial verification methods have been proposed. In the present study an attempt is made to estimate the ability of the WRF model (WRF -ARW ver3.5.1) to reproduce selected days with high convective activity during the year 2010 using those feature-based verification methods. Three model domains, covering Europe, the Mediterranean Sea and northern Africa (d01), the wider area of Greece (d02) and central Greece - Thessaly region (d03) are used at horizontal grid-spacings of 15km, 5km and 1km respectively. By alternating microphysics (Ferrier, WSM6, Goddard), boundary layer (YSU, MYJ) and cumulus convection (Kain-­-Fritsch, BMJ) schemes, a set of twelve model setups is obtained. The results of those simulations are evaluated against data obtained using a C-Band (5cm) radar located at the centre of the innermost domain. Spatial characteristics are well captured but with a variable time lag between simulation results and radar data. Acknowledgements: This research is co­financed by the European Union (European Regional Development Fund) and Greek national funds, through the action "COOPERATION 2011: Partnerships of Production and Research Institutions in Focused Research and Technology Sectors" (contract number 11SYN_8_1088 - DAPHNE) in the framework of the operational programme "Competitiveness and Entrepreneurship" and Regions in Transition (OPC II, NSRF 2007-­-2013).

  14. Taking Risks for the Future of Space Weather Forecasting, Research, and Operations

    Science.gov (United States)

    Jaynes, A. N.; Baker, D. N.; Kanekal, S. G.; Li, X.; Turner, D. L.

    2017-12-01

    Taking Risks for the Future of Space Weather Forecasting, Research, and Operations The need for highly improved space weather modeling and monitoring is quickly becoming imperative as our society depends ever more on the sensitive technology that builds and connects our world. Instead of relying primarily on tried and true concepts, academic institutions and funding agencies alike should be focusing on truly new and innovative ways to solve this pressing problem. In this exciting time, where student-led groups can launch CubeSats for under a million dollars and companies like SpaceX are actively reducing the cost-cap of access to space, the space physics community should be pushing the boundaries of what is possible to enhance our understanding of the space environment. Taking great risks in instrumentation, mission concepts, operational development, collaborations, and scientific research is the best way to move our field forward to where it needs to be for the betterment of science and society.

  15. Medical weather forecast as the risk management facilities of meteopathia with population

    Science.gov (United States)

    Efimenko, Natalya; Chalaya, Elena; Povolotskaia, Nina; Senik, Irina; Topuriya, David

    2013-04-01

    Frequent cases of extreme deviations of weather conditions and anthropogenic press on the Earth atmosphere are external stressors and provoke the development of meteopathic reactions (DMR) with people suffering from dysadaptation (DA). [EGU2011-6740-3; EGU2012-6103]. The influence of weather factors on the person is multivariate which complicates the search of physiological indicators of this exposure. The results of long-term researches of meteodependence and risks development of weather-conditional pathologic reactions with people suffering from DA (1640 observed people) in various systems and human body subsystems (thermal control, cardiovascular, respiratory, vegetative and central nervous systems) were taken as a principle of calculation methodology of estimation of weather pathogenicity (EWP). This estimation is used in the system of medical weather forecast (MWF) in the resorts of Caucasian Mineral Waters and is marked as an organized structure in prevention of DMR risks. Nowadays MWF efficiency is from 78% to 95% as it depends not only on the performance of models of dynamic, synoptic, heliogeophysical forecasts, but also on the underestimation of environmental factors which often act as dominating stressors. The program of atmospheric global system monitoring and real-time forecasts doesn`t include atmospheric electricity factors, ionization factors, range and chemistry factors of aerosol particles and organic volatile plant matters in atmospheric boundary layer. New fractality researches of control mechanisms processes providing adaptation to external and internal environmental conditions with patients suffering from DA allowed us to understand the meaning of the phenomenon of structural similarity and similarity of physiological response processes to the influence of weather types with similar dominating environmental factors. Particularly, atmospheric conditions should be regarded as stressor natural factors that create deionization conditions of the

  16. Optimal Use of Space-Borne Advanced Infrared and Microwave Soundings for Regional Numerical Weather Prediction

    Directory of Open Access Journals (Sweden)

    Chian-Yi Liu

    2016-09-01

    Full Text Available Satellite observations can either be assimilated as radiances or as retrieved physical parameters to reduce error in the initial conditions used by the Numerical Weather Prediction (NWP model. Assimilation of radiances requires a radiative transfer model to convert atmospheric state in model space to that in radiance space, thus requiring a lot of computational resources especially for hyperspectral instruments with thousands of channels. On the other hand, assimilating the retrieved physical parameters is computationally more efficient as they are already in thermodynamic states, which can be compared with NWP model outputs through the objective analysis scheme. A microwave (MW sounder and an infrared (IR sounder have their respective observational limitation due to the characteristics of adopted spectra. The MW sounder observes at much larger field-of-view (FOV compared to an IR sounder. On the other hand, MW has the capability to reveal the atmospheric sounding when the clouds are presented, but IR observations are highly sensitive to clouds, The advanced IR sounder is able to reduce uncertainties in the retrieved atmospheric temperature and moisture profiles due to its higher spectral-resolution than the MW sounder which has much broader spectra bands. This study tries to quantify the optimal use of soundings retrieved from the microwave sounder AMSU and infrared sounder AIRS onboard the AQUA satellite in the regional Weather and Research Forecasting (WRF model through three-dimensional variational (3D-var data assimilation scheme. Four experiments are conducted by assimilating soundings from: (1 clear AIRS single field-of-view (SFOV; (2 retrieved from using clear AMSU and AIRS observations at AMSU field-of-view (SUP; (3 all SFOV soundings within AMSU FOVs must be clear; and (4 SUP soundings which must have all clear SFOV soundings within the AMSU FOV. A baseline experiment assimilating only conventional data is generated for comparison

  17. Application of Suomi-NPP Green Vegetation Fraction and NUCAPS for Improving Regional Numerical Weather Prediction

    Science.gov (United States)

    Case, Jonathan L.; Berndt, Emily B.; Srikishen, Jayanthi; Zavodsky, Bradley T.

    2014-01-01

    The NASA SPoRT Center is working to incorporate Suomi-NPP products into its research and transition activities to improve regional numerical weather prediction (NWP). Specifically, SPoRT seeks to utilize two data products from NOAA/NESDIS: (1) daily global VIIRS green vegetation fraction (GVF), and (2) NOAA Unique CrIS and ATMS Processing System (NUCAPS) temperature and moisture retrieved profiles. The goal of (1) is to improve the representation of vegetation in the Noah land surface model (LSM) over existing climatological GVF datasets in order to improve the land-atmosphere energy exchanges in NWP models and produce better temperature, moisture, and precipitation forecasts. The goal of (2) is to assimilate NUCAPS retrieved profiles into the Gridpoint Statistical Interpolation (GSI) data assimilation system to assess the impact on a summer pre-frontal convection case. Most regional NWP applications make use of a monthly GVF climatology for use in the Noah LSM within the Weather Research and Forecasting (WRF) model. The GVF partitions incoming energy into direct surface heating/evaporation over bare soil versus evapotranspiration processes over vegetated surfaces. Misrepresentations of the fractional coverage of vegetation during anomalous weather/climate regimes (e.g., early/late bloom or freeze; drought) can lead to poor NWP model results when land-atmosphere feedback is important. SPoRT has been producing a daily MODIS GVF product based on the University of Wisconsin Direct Broadcast swaths of Normalized Difference Vegetation Index (NDVI). While positive impacts have been demonstrated in the WRF model for some cases, the reflectances composing these NDVI do not correct for atmospheric aerosols nor satellite view angle, resulting in temporal noisiness at certain locations (especially heavy vegetation). The method behind the NESDIS VIIRS GVF is expected to alleviate the issues seen in the MODIS GVF real-time product, thereby offering a higher-quality dataset for

  18. Urban fine-scale forecasting reveals weather conditions with unprecedented detail

    NARCIS (Netherlands)

    Ronda, R.J.; Steeneveld, G.J.; Heusinkveld, B.G.; Attema, Jisk; Holtslag, A.A.M.

    2017-01-01

    Feasibility of Numerical Weather Prediction at urban neighborhood and street scales demonstrated for summer conditions in the Amsterdam metropolitan region (Netherlands). As the number of urban dwellers increases from an estimated 4 billion in 2014 to an expected 6.5 billion by 2050 (UN 2014),

  19. 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.

  20. 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.

  1. Interactions of physical, chemical, and biological weather calling for an integrated approach to assessment, forecasting, and communication of air quality.

    Science.gov (United States)

    Klein, Thomas; Kukkonen, Jaakko; Dahl, Aslög; Bossioli, Elissavet; Baklanov, Alexander; Vik, Aasmund Fahre; Agnew, Paul; Karatzas, Kostas D; Sofiev, Mikhail

    2012-12-01

    This article reviews interactions and health impacts of physical, chemical, and biological weather. Interactions and synergistic effects between the three types of weather call for integrated assessment, forecasting, and communication of air quality. Today's air quality legislation falls short of addressing air quality degradation by biological weather, despite increasing evidence for the feasibility of both mitigation and adaptation policy options. In comparison with the existing capabilities for physical and chemical weather, the monitoring of biological weather is lacking stable operational agreements and resources. Furthermore, integrated effects of physical, chemical, and biological weather suggest a critical review of air quality management practices. Additional research is required to improve the coupled modeling of physical, chemical, and biological weather as well as the assessment and communication of integrated air quality. Findings from several recent COST Actions underline the importance of an increased dialog between scientists from the fields of meteorology, air quality, aerobiology, health, and policy makers.

  2. 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

  3. 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.

  4. Seismic imaging of the Sun's far hemisphere and its applications in space weather forecasting.

    Science.gov (United States)

    Lindsey, Charles; Braun, Douglas

    2017-06-01

    The interior of the Sun is filled acoustic waves with periods of about 5 min. These waves, called " p modes," are understood to be excited by convection in a thin layer beneath the Sun's surface. The p modes cause seismic ripples, which we call "the solar oscillations." Helioseismic observatories use Doppler observations to map these oscillations, both spatially and temporally. The p modes propagate freely throughout the solar interior, reverberating between the near and far hemispheres. They also interact strongly with active regions at the surfaces of both hemispheres, carrying the signatures of said interactions with them. Computational analysis of the solar oscillations mapped in the Sun's near hemisphere, applying basic principles of wave optics to model the implied p modes propagating through the solar interior, gives us seismic maps of large active regions in the Sun's far hemisphere. These seismic maps are useful for space weather forecasting. For the past decade, NASA's twin STEREO spacecraft have given us full coverage of the Sun's far hemisphere in electromagnetic (EUV) radiation from the far side of Earth's orbit about the Sun. We are now approaching a decade during which the STEREO spacecraft will lose their farside vantage. There will occur significant periods from thence during which electromagnetic coverage of the Sun's far hemisphere will be incomplete or nil. Solar seismology will make it possible to continue our monitor of large active regions in the Sun's far hemisphere for the needs of space weather forecasters during these otherwise blind periods.

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

    Science.gov (United States)

    Dreher, Joseph G.

    2009-01-01

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

  6. 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

  7. 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.

  8. Seasonal forecast of French Mediterranean heavy precipitating events linked to weather regimes

    Directory of Open Access Journals (Sweden)

    J.-F. Guérémy

    2012-07-01

    Full Text Available Seasonal predictability of local precipitation is rather weak in the mid-latitudes. This is the case when assessing the skill of the seasonal forecast of Heavy Precipitating Event (HPE extreme occurrence over the French Mediterranean coast during the fall season. Tropics to extra-tropics teleconnection patterns do appear when averaging analyzed fields over the years characterised by a frequency of HPE occurrence in the upper 17% of the distribution. A methodology taking weather regime occurrence into account as an intermediate step to forecast HPE extreme occurrence is presented. For the period 1960 to 2001 and four different sets of seasonal forecast, the Economical Value is doubled, compared to the score obtained with the simulated local precipitation data, when using a linear model (Linear Discriminant Analysis in this case taking simulated 200 hPa velocity potential–stream function regime occurrences as predictors. Interestingly, larger scores are shown for this couple of fields over a large-scale domain including the tropics than for the 500 hPa geopotential height over an Euro–Atlantic domain, despite a tighter link of the latter field to the local precipitation.

  9. Stochastic weather inputs for improved urban water demand forecasting: application of nonlinear input variable selection and machine learning methods

    Science.gov (United States)

    Quilty, J.; Adamowski, J. F.

    2015-12-01

    Urban water supply systems are often stressed during seasonal outdoor water use as water demands related to the climate are variable in nature making it difficult to optimize the operation of the water supply system. Urban water demand forecasts (UWD) failing to include meteorological conditions as inputs to the forecast model may produce poor forecasts as they cannot account for the increase/decrease in demand related to meteorological conditions. Meteorological records stochastically simulated into the future can be used as inputs to data-driven UWD forecasts generally resulting in improved forecast accuracy. This study aims to produce data-driven UWD forecasts for two different Canadian water utilities (Montreal and Victoria) using machine learning methods by first selecting historical UWD and meteorological records derived from a stochastic weather generator using nonlinear input variable selection. The nonlinear input variable selection methods considered in this work are derived from the concept of conditional mutual information, a nonlinear dependency measure based on (multivariate) probability density functions and accounts for relevancy, conditional relevancy, and redundancy from a potential set of input variables. The results of our study indicate that stochastic weather inputs can improve UWD forecast accuracy for the two sites considered in this work. Nonlinear input variable selection is suggested as a means to identify which meteorological conditions should be utilized in the forecast.

  10. 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

  11. A biometeorological procedure for weather forecast to assess the optimal outdoor clothing insulation.

    Science.gov (United States)

    Morabito, Marco; Crisci, Alfonso; Cecchi, Lorenzo; Modesti, Pietro Amedeo; Maracchi, Giampiero; Gensini, Gian Franco; Orlandini, Simone

    2008-09-01

    Clothing insulation represents an important parameter strongly dependent on climate/weather variability and directly involved in the assessment of the human energy balance. Few studies tried to explore the influence of climate changes on the optimal clothing insulation for outdoor spaces. For this reason, the aim of this work was to investigate mainly the optimal outdoor minimum clothing insulation value required to reach the thermal neutrality (min_clo) related to climate change on a seasonal basis. Subsequently, we developed an example of operational biometeorological procedure to provide 72-hour forecast maps concerning the min_clo. Hourly meteorological data were provided by three Italian weather stations located in Turin, Rome and Palermo, for the period 1951-1995. Environmental variables and subjective characteristics referred to an average adult young male at rest and at a very high metabolic rate were used as input variables to calculate the min_clo by using a thermal index based on the human energy balance. Trends of min_clo were assessed by a non-parametric statistical method. Results showed a lower magnitude of trends in a subject at a very high metabolic rate than at rest. Turin always showed a decrease of min_clo during the study period and prevalently negative trends were also observed in Palermo. On the other hand, an opposite situation was observed in Rome, especially during the morning in all seasons. The development of a daily operational procedure to forecast customized min_clo could provide useful information for the outdoor clothing fitting that might help to reduce the weather-related human health risk.

  12. Verification of Pre-Monsoon Temperature Forecasts over India during 2016 with focus on Heat Wave Prediction

    OpenAIRE

    Singh, Harvir; Arora, Kopal; Ashrit, Raghvendra; Rajagopal, En

    2016-01-01

    The operational medium-range weather forecasting based on Numerical Weather Prediction (NWP) models are complemented by the forecast products based on Ensemble Prediction Systems (EPS). This change has been recognized as an essentially useful tool for the medium range forecasting and is now finding its place in forecasting the extreme events. Here we investigate the extreme events (Heat waves) using a high-resolution numerical weather prediction and its ensemble forecast in union with the c...

  13. Evaluating the skill of seasonal weather forecasts in predicting aflatoxin contamination of groundnut in Senegal

    Science.gov (United States)

    Brak, B.; Challinor, A.

    2011-12-01

    skill in seasonal weather forecasting in West Africa (Senegal) sufficient to predict the occurrence of high (median) aflatoxin concentrations in groundnut at harvest and after some period of storage? For multiple locations in Senegal, aflatoxin contamination (AC) indices estimated using observed weather data from 1999-2010 were compared with AC indices based on gridded seasonal weather forecasts for the same location and year. Pearson correlation coefficients for ACobs and ACpred indices were calculated using all locations combined and, if sufficient weather years without missing values were available, for individual locations to test for regional differences in skill.

  14. 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.

  15. 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.

  16. Transition of Suomi National Polar-Orbiting Partnership (S-NPP) Data Products for Operational Weather Forecasting Applications

    Science.gov (United States)

    Smith, Matthew R.; Molthan, Andrew L.; Fuell, Kevin K.; Jedlovec, Gary J.

    2012-01-01

    SPoRT is a team of NASA/NOAA scientists focused on demonstrating the utility of NASA and future NOAA data and derived products on improving short-term weather forecasts. Work collaboratively with a suite of unique products and selected WFOs in an end-to-end transition activity. Stable funding from NASA and NOAA. Recognized by the science community as the "go to" place for transitioning experimental and research data to the operational weather community. Endorsed by NWS ESSD/SSD chiefs. Proven paradigm for transitioning satellite observations and modeling capabilities to operations (R2O). SPoRT s transition of NASA satellite instruments provides unique or higher resolution data products to complement the baseline suite of geostationary data available to forecasters. SPoRT s partnership with NWS WFOs provides them with unique imagery to support disaster response and local forecast challenges. SPoRT has years of proven experience in developing and transitioning research products to the operational weather community. SPoRT has begun work with CONUS and OCONUS WFOs to determine the best products for maximum benefit to forecasters. VIIRS has already proven to be another extremely powerful tool, enhancing forecasters ability to handle difficult forecasting situations.

  17. Operational use of VIIRS Multispectral Imagery and NUCAPS Soundings in Short-term Weather Forecasting

    Science.gov (United States)

    Molthan, A.; Fuell, K. K.; Berndt, E.; Schultz, L. A.

    2016-12-01

    The NASA/SPoRT Program supports the NOAA/JPSS program through the transition of S-NPP VIIRS and CrIS/ATMS products to prepare users for the upcoming JPSS-1/-2 missions. Several multispectral (i.e. RGB) imagery products can be created from VIIRS based on internationally-accepted recipes developed by EUMETSAT. Initial transition of a Nighttime Microphysics RGB to operations revealed improved distinction between low clouds and fog compared with legacy satellite imagery, and hence, improvement in short-term aviation and public forecasts. An increased number of S-NPP passes at high latitude combined with other instruments led to a series of "microphysical" RGBs to be introduced to NWS forecasters in Alaska at both local weather offices as well as regional aviation centers. Forecasters in Alaska also applied VIIRS microphysical RGBs to identify small scale features such as valley/coastal fog, volcanic ash, and convective precipitation. Further use of a "Dust" RGB in the U.S. southwest led to changes in NWS forecast products due to improvements in detection and monitoring of dust aloft. As multispectral imagery has gained operational acceptance, additional work has begun to develop quantitative products to assist users with their interpretation of RGB imagery. For example, National Center forecasters often use an "Air Mass" RGB to differentiate between possible stratospheric /tropospheric interactions, moist tropical air masses, and cool, continental/maritime air masses. Research was done to demonstrate how the NUCAPS CrIS/ATMS infrared retrieved temperature, moisture, and ozone profiles can aid Air Mass RGB imagery interpretation as well as how these quantitative values are important for anticipating tropical to extratropical transition events. In addition, an enhanced stratospheric depth product was developed to identify the dynamic tropopause from the NUCAPS retrieved ozone profiles to aid identification of stratospheric air influence. Forecasters from National Centers

  18. Preparing for Operational Use of High Priority Products from the Joint Polar Satellite System (JPSS) in Numerical Weather Prediction

    Science.gov (United States)

    Nandi, S.; Layns, A. L.; Goldberg, M.; Gambacorta, A.; Ling, Y.; Collard, A.; Grumbine, R. W.; Sapper, J.; Ignatov, A.; Yoe, J. G.

    2017-12-01

    This work describes end to end operational implementation of high priority products from National Oceanic and Atmospheric Administration's (NOAA) operational polar-orbiting satellite constellation, to include Suomi National Polar-orbiting Partnership (S-NPP) and the Joint Polar Satellite System series initial satellite (JPSS-1), into numerical weather prediction and earth systems models. Development and evaluation needed for the initial implementations of VIIRS Environmental Data Records (EDR) for Sea Surface Temperature ingestion in the Real-Time Global Sea Surface Temperature Analysis (RTG) and Polar Winds assimilated in the National Weather Service (NWS) Global Forecast System (GFS) is presented. These implementations ensure continuity of data in these models in the event of loss of legacy sensor data. Also discussed is accelerated operational implementation of Advanced Technology Microwave Sounder (ATMS) Temperature Data Records (TDR) and Cross-track Infrared Sounder (CrIS) Sensor Data Records, identified as Key Performance Parameters by the National Weather Service. Operational use of SNPP after 28 October, 2011 launch took more than one year due to the learning curve and development needed for full exploitation of new remote sensing capabilities. Today, ATMS and CrIS data positively impact weather forecast accuracy. For NOAA's JPSS initial satellite (JPSS-1), scheduled for launch in late 2017, we identify scope and timelines for pre-launch and post-launch activities needed to efficiently transition these capabilities into operations. As part of these alignment efforts, operational readiness for KPPs will be possible as soon as 90 days after launch. The schedule acceleration is possible because of the experience with S-NPP. NOAA operational polar-orbiting satellite constellation provides continuity and enhancement of earth systems observations out to 2036. Program best practices and lessons learned will inform future implementation for follow-on JPSS-3 and -4

  19. An Analysis of Numerical Weather Prediction of the Diabatic Rossby Vortex

    Science.gov (United States)

    2014-06-01

    NUMERICAL WEATHER PREDICTION OF THE DIABATIC ROSSBY VORTEX by Matthew W. McKenzie June 2014 Thesis Advisor: Richard W. Moore Second Reader...TYPE AND DATES COVERED Master’s Thesis 4. TITLE AND SUBTITLE AN ANALYSIS OF NUMERICAL WEATHER PREDICTION OF THE DIABATIC ROSSBY VORTEX 5...public release; distribution is unlimited 12b. DISTRIBUTION CODE 13. ABSTRACT (maximum 200 words) This work examines diabatic Rossby vortex

  20. Diabatic heating rate estimates from European Centre for Medium-Range Weather Forecasts analyses

    Science.gov (United States)

    Christy, John R.

    1991-01-01

    Vertically integrated diabatic heating rate estimates (H) calculated from 32 months of European Center for Medium-Range Weather Forecasts daily analyses (May 1985-December 1987) are determined as residuals of the thermodynamic equation in pressure coordinates. Values for global, hemispheric, zonal, and grid point H are given as they vary over the time period examined. The distribution of H is compared with previous results and with outgoing longwave radiation (OLR) measurements. The most significant negative correlations between H and OLR occur for (1) tropical and Northern-Hemisphere mid-latitude oceanic areas and (2) zonal and hemispheric mean values for periods less than 90 days. Largest positive correlations are seen in periods greater than 90 days for the Northern Hemispheric mean and continental areas of North Africa, North America, northern Asia, and Antarctica. The physical basis for these relationships is discussed. An interyear comparison between 1986 and 1987 reveals the ENSO signal.

  1. Coupling of weather forecasts and smart grid-control of wastewater inlet to Kolding WWTP (Denmark)

    DEFF Research Database (Denmark)

    Evald Bjerg, Julie; Grum, Morten; Courdent, Vianney Augustin Thomas

    2015-01-01

    four strategies for controlling the wastewater flow to Kolding Central wastewater treatment plant (WWTP) based on the Smart Grid concept are investigated. The control strategies use the storage volume in the pipe system upstream the WWTP to detain water during hours with high electricity prices...... and emitted CO2 equivalents. Weather forecasts were used to empty out the system prior to a rain event, ensuring that the control strategies did not lead to increases in combined sewer overflow. The largest savings obtained were 833 EUR/month and 3909 kg CO2 equivalents/month, which were achieved by only...... sending wastewater to the treatment plant during the six cheapest hours of the day. The savings achieved with the other control strategies were however in the ranges 65–300 EUR/month and 196–910 kg CO2 equivalents/month. These evaluations were generally done with limited storage space of just around 20...

  2. Distinguishing high and low flow domains in urban drainage systems 2 days ahead using numerical weather prediction ensembles

    Science.gov (United States)

    Courdent, Vianney; Grum, Morten; Mikkelsen, Peter Steen

    2018-01-01

    Precipitation constitutes a major contribution to the flow in urban storm- and wastewater systems. Forecasts of the anticipated runoff flows, created from radar extrapolation and/or numerical weather predictions, can potentially be used to optimize operation in both wet and dry weather periods. However, flow forecasts are inevitably uncertain and their use will ultimately require a trade-off between the value of knowing what will happen in the future and the probability and consequence of being wrong. In this study we examine how ensemble forecasts from the HIRLAM-DMI-S05 numerical weather prediction (NWP) model subject to three different ensemble post-processing approaches can be used to forecast flow exceedance in a combined sewer for a wide range of ratios between the probability of detection (POD) and the probability of false detection (POFD). We use a hydrological rainfall-runoff model to transform the forecasted rainfall into forecasted flow series and evaluate three different approaches to establishing the relative operating characteristics (ROC) diagram of the forecast, which is a plot of POD against POFD for each fraction of concordant ensemble members and can be used to select the weight of evidence that matches the desired trade-off between POD and POFD. In the first approach, the rainfall input to the model is calculated for each of 25 ensemble members as a weighted average of rainfall from the NWP cells over the catchment where the weights are proportional to the areal intersection between the catchment and the NWP cells. In the second approach, a total of 2825 flow ensembles are generated using rainfall input from the neighbouring NWP cells up to approximately 6 cells in all directions from the catchment. In the third approach, the first approach is extended spatially by successively increasing the area covered and for each spatial increase and each time step selecting only the cell with the highest intensity resulting in a total of 175 ensemble

  3. 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

  4. Verification of ECMWF seasonal forecasts using Euro-Atlantic weather regimes

    Science.gov (United States)

    Cortesi, Nicola; González-Reviriego, Nube; Torralba, Veronica; Marcos, Raül; Lee, Doo Young; Doblas-Reyes, Francisco Javier

    2017-04-01

    This research aims at assessing the goodness of the ECMWF seasonal forecasting system S4 in simulating the four observed Euro-Atlantic weather regimes (NAO+, NAO-, blocking and Atlantic ridge) classified with the ERA-Interim reference reanalysis. Interannual frequencies of occurrence, persistence and transition probability, as well as their impact on 2-m temperature and 10-m wind speed, were compared at monthly time scale. Results show that S4 is highly skilled at reproducing, for all lead times (up to six months in advance), the spatial structure of the observed regime anomalies, their average interannual frequencies, persistences, transition probabilities and impact on wind speed and temperature, except during autumn months. Spring and summer months perform almost as well as the winter season. However, S4 shows almost no skill in reproducing the interannual monthly variability of the frequencies of the regimes. Furthermore, S4 forecasts tend to underestimate the monthly frequency of occurrence and persistence of the NAO+ and NAO- regimes, and to overestimate the monthly frequency of blocking and Atlantic ridge regimes. Finally, S4 greatly underestimates the transition probabilities of NAO+ regime to NAO- regime and vice versa, and overestimates the transition probability from NAO+/NAO- regimes to blocking or Atlantic ridge.

  5. Evaluation of Radiation Belt Space Weather Forecasts for Internal Charging Analyses

    Science.gov (United States)

    Minow, Joseph I.; Coffey, Victoria N.; Jun, Insoo; Garrett, Henry B.

    2007-01-01

    A variety of static electron radiation belt models, space weather prediction tools, and energetic electron datasets are used by spacecraft designers and operations support personnel as internal charging code inputs to evaluate electrostatic discharge risks in space systems due to exposure to relativistic electron environments. Evaluating the environment inputs is often accomplished by comparing whether the data set or forecast tool reliability predicts measured electron flux (or fluence over a given period) for some chosen period. While this technique is useful as a model metric, it does not provide the information necessary to evaluate whether short term deviances of the predicted flux is important in the charging evaluations. In this paper, we use a 1-D internal charging model to compute electric fields generated in insulating materials as a function of time when exposed to relativistic electrons in the Earth's magnetosphere. The resulting fields are assumed to represent the "true" electric fields and are compared with electric field values computed from relativistic electron environments derived from a variety of space environment and forecast tools. Deviances in predicted fields compared to the "true" fields which depend on insulator charging time constants will be evaluated as a potential metric for determining the importance of predicted and measured relativistic electron flux deviations over a range of time scales.

  6. An industry perspective on the use of seasonal forecasts and weather information for evaluating sensitivities in traded commodity supply chains

    Science.gov (United States)

    Domeisen, Daniela; Slavov, Georgi

    2015-04-01

    Weather information on seasonal timescales is crucial to various end users, from the level of subsistence farming to the government level. Also the financial industry is ever more aware of and interested in the benefits that early and correctly interpreted forecast information provides. Straight forward and often cited applications include the estimation of rainfall and temperature anomalies for drought - prone agricultural areas producing traded commodities, as well as some of the rather direct impacts of weather on energy production. Governments, weather services, as well as both academia and private companies are working on tailoring climate and weather information to a growing number of customers. However, also other large markets, such as coal, iron ore, and gas, are crucially dependent on seasonal weather information and forecasts, while the needs are again very dependent on the direction of the predicted signal. So far, relatively few providers in climate services address these industries. All of these commodities show a strong seasonal and weather dependence, and an unusual winter or summer can crucially impact their demand and supply. To name a few impacts, gas is crucially driven by heating demand, iron ore excavation is dependent on the available water resources, and coal mining is dependent on winter temperatures and rainfall. This contribution will illustrate and provide an inside view of the type of climate and weather information needed for the various large commodity industries.

  7. 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.

  8. Patients' and staffs' experiences of an automated telephone weather forecasting service.

    Science.gov (United States)

    Cooper, Richard; O'Hara, Rachel

    2010-04-01

    Patients with chronic obstructive pulmonary disease (COPD) have recently been offered severe weather warnings and medication reminders using an automated telephone service and interactive voice recognition technology. Our aim was to explore patients' and health care staffs' perceptions and experiences of the technologies, their contribution to the management of COPD and implementation issues. Qualitative semi-structured telephone interviews were undertaken with 18 patients and six staff from five primary care centres in the Bradford area, England. Interview transcripts were thematically analysed. Patients considered the telephone service was an appropriate way to deliver information but there was some variation in perceived usefulness. Many patients praised the service, valuing reassurance and medication reminders, but others were indifferent and even critical. Criticism tended to reflect scepticism over the reliability of weather forecasts information rather than the automated telephone service itself. There was limited impact on the management strategies of patients apart from some patients ordering medication. Primary care staff considered the service a success but some felt that it lacked participation by hard-to-reach groups (non-English speaking, mild COPD patients). Our concerns about the resource implications of successful implementation were also raised. An automated telephone service was generally acceptable to patients but changes in COPD management were limited, possibly because the patients already had a good understanding of their condition and self-management strategies. Implications for practice include the need for strategies to target hard-to-reach groups which may need more resources.

  9. 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

  10. 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

  11. Performance of the operational high-resolution numerical weather predictions of the Daphne project

    Science.gov (United States)

    Tegoulias, Ioannis; Pytharoulis, Ioannis; Karacostas, Theodore; Kartsios, Stergios; Kotsopoulos, Stelios; Bampzelis, Dimitrios

    2015-04-01

    In the framework of the DAPHNE project, the Department of Meteorology and Climatology (http://meteo.geo.auth.gr) of the Aristotle University of Thessaloniki, Greece, utilizes the nonhydrostatic Weather Research and Forecasting model with the Advanced Research dynamic solver (WRF-ARW) in order to produce high-resolution weather forecasts over Thessaly in central Greece. The aim of the DAPHNE project is to tackle the problem of drought in this area by means of Weather Modification. Cloud seeding assists the convective clouds to produce rain more efficiently or reduce hailstone size in favour of raindrops. The most favourable conditions for such a weather modification program in Thessaly occur in the period from March to October when convective clouds are triggered more frequently. Three model domains, using 2-way telescoping nesting, cover: i) Europe, the Mediterranean sea and northern Africa (D01), ii) Greece (D02) and iii) the wider region of Thessaly (D03; at selected periods) at horizontal grid-spacings of 15km, 5km and 1km, respectively. This research work intents to describe the atmospheric model setup and analyse its performance during a selected period of the operational phase of the project. The statistical evaluation of the high-resolution operational forecasts is performed using surface observations, gridded fields and radar data. Well established point verification methods combined with novel object based upon these methods, provide in depth analysis of the model skill. Spatial characteristics are adequately captured but a variable time lag between forecast and observation is noted. Acknowledgments: This research work has been co-financed by the European Union (European Regional Development Fund) and Greek national funds, through the action "COOPERATION 2011: Partnerships of Production and Research Institutions in Focused Research and Technology Sectors" (contract number 11SYN_8_1088 - DAPHNE) in the framework of the operational programme "Competitiveness

  12. Streamlining On-Demand Access to Joint Polar Satellite System (JPSS) Data Products for Weather Forecasting

    Science.gov (United States)

    Evans, J. D.; Tislin, D.

    2017-12-01

    Observations from the Joint Polar Satellite System (JPSS) support National Weather Service (NWS) forecasters, whose Advanced Weather Interactive Processing System (AWIPS) Data Delivery (DD) will access JPSS data products on demand from the National Environmental Satellite, Data, and Information Service (NESDIS) Product Distribution and Access (PDA) service. Based on the Open Geospatial Consortium (OGC) Web Coverage Service, this on-demand service promises broad interoperability and frugal use of data networks by serving only the data that a user needs. But the volume, velocity, and variety of JPSS data products impose several challenges to such a service. It must be efficient to handle large volumes of complex, frequently updated data, and to fulfill many concurrent requests. It must offer flexible data handling and delivery, to work with a diverse and changing collection of data, and to tailor its outputs into products that users need, with minimal coordination between provider and user communities. It must support 24x7 operation, with no pauses in incoming data or user demand; and it must scale to rapid changes in data volume, variety, and demand as new satellites launch, more products come online, and users rely increasingly on the service. We are addressing these challenges in order to build an efficient and effective on-demand JPSS data service. For example, on-demand subsetting by many users at once may overload a server's processing capacity or its disk bandwidth - unless alleviated by spatial indexing, geolocation transforms, or pre-tiling and caching. Filtering by variable (/ band / layer) may also alleviate network loads, and provide fine-grained variable selection; to that end we are investigating how best to provide random access into the variety of spatiotemporal JPSS data products. Finally, producing tailored products (derivatives, aggregations) can boost flexibility for end users; but some tailoring operations may impose significant server loads

  13. The main pillar: Assessment of space weather observational asset performance supporting nowcasting, forecasting, and research to operations.

    Science.gov (United States)

    Posner, A; Hesse, M; St Cyr, O C

    2014-04-01

    Space weather forecasting critically depends upon availability of timely and reliable observational data. It is therefore particularly important to understand how existing and newly planned observational assets perform during periods of severe space weather. Extreme space weather creates challenging conditions under which instrumentation and spacecraft may be impeded or in which parameters reach values that are outside the nominal observational range. This paper analyzes existing and upcoming observational capabilities for forecasting, and discusses how the findings may impact space weather research and its transition to operations. A single limitation to the assessment is lack of information provided to us on radiation monitor performance, which caused us not to fully assess (i.e., not assess short term) radiation storm forecasting. The assessment finds that at least two widely spaced coronagraphs including L4 would provide reliability for Earth-bound CMEs. Furthermore, all magnetic field measurements assessed fully meet requirements. However, with current or even with near term new assets in place, in the worst-case scenario there could be a near-complete lack of key near-real-time solar wind plasma data of severe disturbances heading toward and impacting Earth's magnetosphere. Models that attempt to simulate the effects of these disturbances in near real time or with archival data require solar wind plasma observations as input. Moreover, the study finds that near-future observational assets will be less capable of advancing the understanding of extreme geomagnetic disturbances at Earth, which might make the resulting space weather models unsuitable for transition to operations. Manuscript assesses current and near-future space weather assetsCurrent assets unreliable for forecasting of severe geomagnetic stormsNear-future assets will not improve the situation.

  14. 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.

  15. Gps Real -Time Estimation Of Precipitable Water Vapor And Its Application On Weather Forecast In Hong Kong

    Science.gov (United States)

    Wang, X. Y.; Chen, Y. Q.; Li, P. W.

    2003-04-01

    To use the Precipitable Water Vapor (PWV) derived from ground-based GPS surveys in meteorological forecasting application, the real time or near time estimates must be available with an accuracy comparable with that obtained with existing meteorological measurement techniques. Authors have developed a real time GPS data processing system, which can perform data transfer, data merging, estimation of the Total Zenith Delay (TZD), the separation of the Zenith Wet Delay (ZWD) and the transformation to ZWD to PWV. The system is developed based on GAMIT software, and using a sliding-window processing approach. The IGS ultra-rapid GPS orbits are employed. The system was tested using the real time GPS observations from Hong Kong GPS network and two GPS sites in Shanghai. The near real-time PWV can be obtained with 20-30 minutes and accuracy of 2 mm. The GPS derived PWVs were imputed into numerical weather prediction models and the results with and without GPS derived PWV are compared.

  16. 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.

  17. Large Scale GPU Accelerated PPMLR-MHD Simulations for Space Weather Forecast

    OpenAIRE

    Guo, Xiangyu; Tang, Binbin; Tao, Jian; Huang, Zhaohui; Du, Zhihui

    2016-01-01

    PPMLR-MHD is a new magnetohydrodynamics (MHD) model used to simulate the interactions of the solar wind with the magnetosphere, which has been proved to be the key element of the space weather cause-and-effect chain process from the Sun to Earth. Compared to existing MHD methods, PPMLR-MHD achieves the advantage of high order spatial accuracy and low numerical dissipation. However, the accuracy comes at a cost. On one hand, this method requires more intensive computation. On the other hand, m...

  18. Data Assimilation of SMAP Observations and the Impact on Weather Forecasts and Heat Stress

    Science.gov (United States)

    Zavodsky, Bradley; Case, Jonathan; Blankenship, Clay; Crosson, William; White, Khristopher

    2014-01-01

    SPoRT produces real-time LIS soil moisture products for situational awareness and local numerical weather prediction over CONUS, Mesoamerica, and East Africa ?Currently interact/collaborate with operational partners on evaluation of soil moisture products ?Drought/fire ?Extreme heat ?Convective initiation ?Flood and water borne diseases ?Initial efforts to assimilate L2 soil moisture observations from SMOS (as a precursor for SMAP) have been successful ?Active/passive blended product from SMAP will be assimilated similarly and higher spatial resolution should improve on local-scale processes

  19. 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

  20. On the sensitivity of numerical weather prediction to remotely sensed marine surface wind data - A simulation study

    Science.gov (United States)

    Cane, M. A.; Cardone, V. J.; Halem, M.; Halberstam, I.

    1981-01-01

    The reported investigation has the objective to assess the potential impact on numerical weather prediction (NWP) of remotely sensed surface wind data. Other investigations conducted with similar objectives have not been satisfactory in connection with a use of procedures providing an unrealistic distribution of initial errors. In the current study, care has been taken to duplicate the actual distribution of information in the conventional observing system, thus shifting the emphasis from accuracy of the data to the data coverage. It is pointed out that this is an important consideration in assessing satellite observing systems since experience with sounder data has shown that improvements in forecasts due to satellite-derived information is due less to a general error reduction than to the ability to fill data-sparse regions. The reported study concentrates on the evaluation of the observing system simulation experimental design and on the assessment of the potential of remotely sensed marine surface wind data.

  1. 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.

  2. 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.

  3. 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.

  4. Quantitative precipitation estimation based on high-resolution numerical weather prediction and data assimilation with WRF – a performance test

    Directory of Open Access Journals (Sweden)

    Hans-Stefan Bauer

    2015-04-01

    Full Text Available Quantitative precipitation estimation and forecasting (QPE and QPF are among the most challenging tasks in atmospheric sciences. In this work, QPE based on numerical modelling and data assimilation is investigated. Key components are the Weather Research and Forecasting (WRF model in combination with its 3D variational assimilation scheme, applied on the convection-permitting scale with sophisticated model physics over central Europe. The system is operated in a 1-hour rapid update cycle and processes a large set of in situ observations, data from French radar systems, the European GPS network and satellite sensors. Additionally, a free forecast driven by the ECMWF operational analysis is included as a reference run representing current operational precipitation forecasting. The verification is done both qualitatively and quantitatively by comparisons of reflectivity, accumulated precipitation fields and derived verification scores for a complex synoptic situation that developed on 26 and 27 September 2012. The investigation shows that even the downscaling from ECMWF represents the synoptic situation reasonably well. However, significant improvements are seen in the results of the WRF QPE setup, especially when the French radar data are assimilated. The frontal structure is more defined and the timing of the frontal movement is improved compared with observations. Even mesoscale band-like precipitation structures on the rear side of the cold front are reproduced, as seen by radar. The improvement in performance is also confirmed by a quantitative comparison of the 24-hourly accumulated precipitation over Germany. The mean correlation of the model simulations with observations improved from 0.2 in the downscaling experiment and 0.29 in the assimilation experiment without radar data to 0.56 in the WRF QPE experiment including the assimilation of French radar data.

  5. 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.

  6. SWIFTER - Space Weather Informatics, Forecasting, and Technology through Enabling Research and Virtual Organizations

    Science.gov (United States)

    Schaefer, R. K.; Morrison, D.; Paxton, L.; Holm, J.; Weiss, M.; Hsieh, S.

    2009-05-01

    SWIFTER will build a virtual organization to enable collaboration among research, military, and commercial communities to find new ways to understand, characterize, and forecast space weather to meet the needs of our technology based society. In this paper we discuss how knowledge is shared in organizations and how a virtual organization can be formed. A key element of a "virtual" organization is that it is a fluid collection of members that share some means of communicating relevant information among some of its members. The members also share ideas in evolution (such as analysis, new technologies, and predictive trending). As concepts mature they can be matured or discarded more quickly as the power of the network is brought to bear early and often. Space weather, the changes in the near-Earth space environment, is important to a wide range of users as well as the public. The public is interested in a variety of phenomena including meteors, solar flares, the aurora, noctilucent clouds and climate change. Industry focus tends to be on more concrete problems such as ground-induced currents in power lines and communications with aircraft in transpolar routes as well as geolocation (i.e. the use of GPS systems to precisely map a function to a position). Other government-oriented users service specialized communities who may be more or less unaware of the research and development upon which the forecasts or nowcasts rely for accuracy. The basic research community may be more or less unaware of the details of the applications, or potential applications of their research. The problem, then, is that each of these constituencies may share elements in common but there is no umbrella organization that ties them together, nor is there likely to be such an organization. Our goal in this paper is to outline a scheme for a virtual organization, delineate the functions of that VO and illustrate how it might be formed. We also will assess the barriers to knowledge transfer that

  7. 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

  8. The New Data Assimilation System at the Italian Air Force Weather Service: Design and Preliminary Results

    National Research Council Canada - National Science Library

    Bonavita, Massimo

    2002-01-01

    ...) in order to improve its numerical weather prediction capabilities and provide more accurate guidance to operational forecasters, The system, which is undergoing testing before eventual operational...

  9. Probabilistic online runoff forecasting for urban catchments using inputs from rain gauges as well as statically and dynamically adjusted weather radar

    DEFF Research Database (Denmark)

    Löwe, Roland; Thorndahl, Søren; Mikkelsen, Peter Steen

    2014-01-01

    We investigate the application of rainfall observations and forecasts from rain gauges and weather radar as input to operational urban runoff forecasting models. We apply lumped rainfall runoff models implemented in a stochastic grey-box modelling framework. Different model structures are conside......We investigate the application of rainfall observations and forecasts from rain gauges and weather radar as input to operational urban runoff forecasting models. We apply lumped rainfall runoff models implemented in a stochastic grey-box modelling framework. Different model structures...

  10. The Power of Weather: Some Empirical Evidence on Predicting Day-ahead Power Prices through Day-ahead Weather Forecasts

    NARCIS (Netherlands)

    F. Ravazzolo (Francesco); C. Zhou (Chen); C. Huurman

    2007-01-01

    textabstractIn the literature the effects of weather on electricity sales are well-documented. However, studies that have investigated the impact of weather on electricity prices are still scarce (e.g. Knittel and Roberts, 2005), partly because the wholesale power markets have only recently been

  11. Numerical Forecasting Experiment of the Wave Energy Resource in the China Sea

    Directory of Open Access Journals (Sweden)

    Chong Wei Zheng

    2016-01-01

    Full Text Available The short-term forecasting of wave energy is important to provide guidance for the electric power operation and power transmission system and to enhance the efficiency of energy capture and conversion. This study produced a numerical forecasting experiment of the China Sea wave energy using WAVEWATCH-III (WW3, the latest version 4.18 wave model driven by T213 (WW3-T213 and T639 (WW3-T639 wind data separately. Then the WW3-T213 and WW3-T639 were verified and compared to build a short-term wave energy forecasting structure suited for the China Sea. Considering the value of wave power density (WPD, “wave energy rose,” daily and weekly total storage and effective storage of wave energy, this study also designed a series of short-term wave energy forecasting productions. Results show that both the WW3-T213 and WW3-T639 exhibit a good skill on the numerical forecasting of the China Sea WPD, while the result of WW3-T639 is much better. Judging from WPD and daily and weekly total storage and effective storage of wave energy, great wave energy caused by cold airs was found. As there are relatively frequent cold airs in winter, early spring, and later autumn in the China Sea and the surrounding waters, abundant wave energy ensues.

  12. 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

  13. [On the development of a system of medical weather forecast for the Caucasian Mineral Waters spa-and-resort complex].

    Science.gov (United States)

    Povolotskaia, N P; Efimova, N V; Zherlitsina, L I; Kirilenko, A A; Kortunova, Z V; Golitsin, G S; Senik, I A; Rubinshteĭn, K G

    2010-01-01

    A system of medical weather forecast for the Caucasian Mineral Waters spa-and-resort complex has been modified and updated based on the results of long-term observations of weather conditions in the region of interest with special reference to the bioclimatic regime, atmospheric circulation, aerosol pollution of the near-ground air, ultraviolet radiation, heliomagnetic activity, and meteopathic effects. This system provides a basis for the timely emergency meteopreventive treatment of meteodependent patients and therefore can be instrumental in enhancing efficiency of spa-and-resort rehabilitative therapy.

  14. Optimizing Weather and Research Forecast (WRF) Thompson cloud microphysics on Intel Many Integrated Core (MIC)

    Science.gov (United States)

    Mielikainen, Jarno; Huang, Bormin; Huang, Allen

    2014-05-01

    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. Those optimization techniques are discusses in this paper. The results show that the optimization improved MIC performance by 3.4x. Furthermore, the optimized MIC code is 7.0x faster than the optimized multi-threaded code on the four CPU cores of a single socket Intel Xeon E5-2603 running at 1.8 GHz.

  15. 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.

  16. 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.

  17. 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.

  18. Development of the Chen Magnetic Cloud Prediction Algorithm for Real-Time Space Weather Forecasting

    Science.gov (United States)

    Bain, H. M.; Biesecker, D. A.; Cash, M. D.; Reinard, A.; Chen, J.

    2017-12-01

    We present details of a space weather forecasting tool which attempts to accurately predict the occurrence and severity of large geomagnetic storms caused by prolonged periods of south directed magnetic field components associated with magnetic clouds. The algorithm takes the work of Chen et al. (1996, 1997) and modifies it to run in a real-time operational environment, with input solar wind data from the Deep Space Climate Observatory (DSCOVR) spacecraft at L1. From the real-time magnetic field measurements, the algorithm identifies the initial magnetic field rotation signature assuming it represents the initial phase of a magnetic cloud. Fitting the field rotation, an estimate of the solar wind profile upstream of the spacecraft is determined, in particular the expected event duration (time to the next zero crossing of Bz) and maximum Bz field strength. Using Bayesian statistics, the tool returns the probability of a large geomagnetic storm occurring and a measure of its geoeffectiveness, with an expected warning time of several hours to possibly more than 10 hours (Arge et al. 2002). We discuss the current algorithm performance as well the limitations of the model.

  19. General Relativity Explains the Shnoll Effect and Makes Possible Forecasting Earthquakes and Weather Cataclysms

    Science.gov (United States)

    Rabounski, Dmitri; Borissova, Larissa

    2014-03-01

    The Shnoll effect is manifested in the fine structure of the noise registered in stable processes, wherein as the magnitude of signal and the average noise remain unchanged. It is periodic fluctuation of the fine structure of the noise according to the cosmic cycles connected with stars, the Sun, and the Moon. The Shnoll effect is explained herein according to General Relativity, as the twin/entangled synchronization states of the observer's reference frame. The states are repeated while the observer travels, in common with the Earth, through the cosmic grid of the geodesic synchronization paths that connect his local reference frame with the reference frames of the other cosmic bodies. These synchronization periods are expected to be existing in the noise of natural processes of any type (physics, biology, social, etc.) and such artificial processes as the random number generation by a computer software. These periods match with the periods of the Shnoll effect. The theory gives not only to explain the Shnoll effect, but also allows forecasting the fluctuations in the stock exchange market, the fluctuations of weather, earthquakes, and other cataclysms.

  20. 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 ...

  1. 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

  2. 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.

  3. The Main Pillar: Assessment of Space Weather Observational Asset Performance Supporting Nowcasting, Forecasting and Research to Operations

    Science.gov (United States)

    Posner, Arik; Hesse, Michael; SaintCyr, Chris

    2014-01-01

    Space weather forecasting critically depends upon availability of timely and reliable observational data. It is therefore particularly important to understand how existing and newly planned observational assets perform during periods of severe space weather. Extreme space weather creates challenging conditions under which instrumentation and spacecraft may be impeded or in which parameters reach values that are outside the nominal observational range. This paper analyzes existing and upcoming observational capabilities for forecasting, and discusses how the findings may impact space weather research and its transition to operations. A single limitation to the assessment is lack of information provided to us on radiation monitor performance, which caused us not to fully assess (i.e., not assess short term) radiation storm forecasting. The assessment finds that at least two widely spaced coronagraphs including L4 would provide reliability for Earth-bound CMEs. Furthermore, all magnetic field measurements assessed fully meet requirements. However, with current or even with near term new assets in place, in the worst-case scenario there could be a near-complete lack of key near-real-time solar wind plasma data of severe disturbances heading toward and impacting Earth's magnetosphere. Models that attempt to simulate the effects of these disturbances in near real time or with archival data require solar wind plasma observations as input. Moreover, the study finds that near-future observational assets will be less capable of advancing the understanding of extreme geomagnetic disturbances at Earth, which might make the resulting space weather models unsuitable for transition to operations.

  4. 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.

  5. 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.

  6. P161 Improved Impact of Atmospheric Infrared Sounder (AIRS) Radiance Assimilation in Numerical Weather Prediction

    Science.gov (United States)

    Zavodsky, Bradley T.; Chou, Shih-Hung; Jedlovec, Gary J.

    2012-01-01

    For over 6 years, AIRS radiances have been assimilated operationally into National (e.g. Environmental Modeling Center (EMC)) and International (e.g. European Centre for Medium-Range Weather Forecasts (ECMWF)), operational centers; assimilated in the North American Mesoscale (NAM) since 2008. Due partly to data latency and operational constraints, hyperspectral radiance assimilation has had less impact on the Gridpoint Statistical Interpolation (GSI) system used in the NAM and GFS. Objective of this project is to use AIRS retrieved profiles as a proxy for the AIRS radiances in situations where AIRS radiances are unable to be assimilated in the current operational system by evaluating location and magnitude of analysis increments.

  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. 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

  9. 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

  10. Towards Experimental Operational Fire Weather Prediction at Subseasonal to Seasonal Scales for Alaska Using the NMME Hindcasts and Realtime Forecasts.

    Science.gov (United States)

    Sampath, A.; Bhatt, U. S.; Bieniek, P.; York, A.; Peng, P.; Brettschneider, B.; Thoman, R.; Jandt, R.; Ziel, R.; Branson, G.; Strader, M. H.; Alden, M. S.

    2017-12-01

    The summer 2004 and 2015 wildfires in Alaska were the two largest fire seasons on record since 1950 where approximately the land area of Massachusetts burned. The record fire year of 2004 resulted in 6.5 million acres burned while the 2015 wildfire season resulted in 5.2 million acres burned. In addition to the logistical cost of fighting fires and the loss of infrastructure, wildfires also lead to dangerous air quality in Alaska. Fires in Alaska result from lightning strikes coupled with persistent (extreme) dry warm conditions in remote areas with limited fire management and the seasonal climate/weather determine the extent of the fire season in Alaska. Advanced weather/climate outlooks for allocating staff and resources from days to a season are particularly needed by fire managers. However, there are no operational seasonal products currently for the Alaska region. Probabilistic forecasts of the expected seasonal climate/weather would aid tremendously in the planning process. Earlier insight of both lightening and fuel conditions would assist fire managers in planning resource allocation for the upcoming season. For fuel conditions, the state-of-the-art NMME (1982-2017) climate predictions were used to compute the Canadian Forest Fire Weather Index System (CFFWIS). The CFFWIS is used by fire managers to forecast forest fires in Alaska. NMME forecast (March and May) based Buildup Index (BUI) values were underestimated compared to BUI based on reanalysis and station data, demonstrating the necessity for bias correction. Post processing of NMME data will include bias correction using the quantile mapping technique. This study will provide guidance as to the what are the best available products for anticipating the fire season.

  11. ECMWF SSW forecast evaluation using infrasound

    NARCIS (Netherlands)

    Smets, P.S.M.; Assink, J. D.; le Pichon, A; Evers, L.G.

    2016-01-01

    Accurate prediction of Sudden Stratospheric Warming (SSW) events is important for the performance of numerical weather prediction due to significant stratosphere-Troposphere coupling. In this study, for the first time middle atmospheric numerical weather forecasts are evaluated using infrasound.

  12. 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.

  13. Meteorological observation with Doppler and Raman lidars and comparison with numerical weather simulations

    Science.gov (United States)

    Tamura, Hidetoshi; Kihara, Naoto; Fujii, Takashi; Fukuchi, Tetsuo; Wada, Koji; Hirakuchi, Hiromaru

    2012-11-01

    Meteorological observation data such as temperature, humidity, wind speed and wind direction are important for validating and improving numerical weather simulation models. Lidar is an effective method for acquiring such data with high range resolution and short time intervals. In this study, we carried out a field observation with coherent Doppler Lidar and Raman Lidar systems at the coastal area of Yokosuka, Japan, and compared the observed data with the results of numerical weather simulations. We obtained the vertical profiles of horizontal wind speeds and wind directions by Doppler Lidar with 65 m vertical range resolution, and the vertical profiles of the water vapor mixing ratio by Raman Lidar with 20 m vertical range resolution at the lower atmospheric boundary layer (200-600 m height from ground level). These data were acquired at time intervals of 10 minutes. We found an interesting phenomenon from observed data indicating that, under weak wind conditions, water vapor in the atmosphere significantly increased just after a definite change in wind direction from land breeze to sea breeze. A similar phenomenon was also predicted by the numerical weather simulation with the same meteorological and terrestrial conditions. We analyzed the numerical results and found that the change in water vapor mentioned above is mainly caused by the difference between the evaporation from land and sea surfaces, which were located upwind of the land and sea breezes, respectively.

  14. The impact of weather and ocean forecasting on hydrocarbon production and pollution management in the Gulf of Mexico

    International Nuclear Information System (INIS)

    Kaiser, Mark J.; Pulsipher, Allan G.

    2007-01-01

    Over the past 2 years, the vulnerability of offshore production in the Gulf of Mexico (GOM) has been brought to light by extensive damage to oil and gas facilities and pipelines resulting from Hurricanes Ivan, Katrina, and Rita. The occurrences of extreme weather regularly force operators to shut-down production, cease drilling and construction activities, and evacuate personnel. Loop currents and eddies can also impact offshore operations and delay installation and drilling activities and reduce the effectiveness of oil spill response strategies. The purpose of this paper is to describe how weather and ocean forecasting impact production activities and pollution management in the GOM. Physical outcome and decision models in support of production and development activities and oil spill response management are presented, and the expected economic benefits that may result from the implementation of an integrated ocean observation network in the region are summarized. Improved ocean observation systems are expected to reduce the uncertainty of forecasting and to enhance the value of ocean/weather information throughout the Gulf region. The source of benefits and the size of activity from which improved ocean observation benefits may be derived are estimated for energy development and production activities and oil spill response management

  15. The impact of weather and ocean forecasting on hydrocarbon production and pollution management in the Gulf of Mexico

    Energy Technology Data Exchange (ETDEWEB)

    Kaiser, Mark J. [Center for Energy Studies, Louisiana State University, Energy Coast and Environment Building, Nicholson Extension Drive, Baton Rouge, LA 70803 (United States)]. E-mail: mkaiser@lsu.edu; Pulsipher, Allan G. [Center for Energy Studies, Louisiana State University, Energy Coast and Environment Building, Nicholson Extension Drive, Baton Rouge, LA 70803 (United States)

    2007-02-15

    Over the past 2 years, the vulnerability of offshore production in the Gulf of Mexico (GOM) has been brought to light by extensive damage to oil and gas facilities and pipelines resulting from Hurricanes Ivan, Katrina, and Rita. The occurrences of extreme weather regularly force operators to shut-down production, cease drilling and construction activities, and evacuate personnel. Loop currents and eddies can also impact offshore operations and delay installation and drilling activities and reduce the effectiveness of oil spill response strategies. The purpose of this paper is to describe how weather and ocean forecasting impact production activities and pollution management in the GOM. Physical outcome and decision models in support of production and development activities and oil spill response management are presented, and the expected economic benefits that may result from the implementation of an integrated ocean observation network in the region are summarized. Improved ocean observation systems are expected to reduce the uncertainty of forecasting and to enhance the value of ocean/weather information throughout the Gulf region. The source of benefits and the size of activity from which improved ocean observation benefits may be derived are estimated for energy development and production activities and oil spill response management.

  16. Ethno-meteorology and scientific weather forecasting: Small farmers and scientists’ perspectives on climate variability in the Okavango Delta, Botswana

    Directory of Open Access Journals (Sweden)

    Oluwatoyin Dare Kolawole

    2014-01-01

    Full Text Available Recent trends in abrupt weather changes continue to pose a challenge to agricultural production most especially in sub-Saharan Africa. The paper specifically addresses the questions on how local farmers read and predict the weather; and how they can collaborate with weather scientists in devising adaptation strategies for climate variability (CV in the Okavango Delta of Botswana. Recent trends in agriculture-related weather variables available from country’s climate services, as well as in freely available satellite rainfall products were analysed. The utility of a seasonal hydrological forecasting system for the study area in the context of supporting farmer’s information needs were assessed. Through a multi-stage sampling procedure, a total of 592 households heads in 8 rural communities in the Okavango Delta were selected and interviewed using open and close-ended interview schedules. Also, 19 scientists were purposively selected and interviewed using questionnaires. Key informant interviews, focus group and knowledge validation workshops were used to generate qualitative information from both farmers and scientists. Descriptive and inferential statistics were used in summarising the data. Analysis of satellite rainfall products indicated that there was a consistent increase in total annual rainfall throughout the region in the last 10 years, accompanied by an increase in number of rain days, and reduction of duration of dry spells. However, there is a progressive increase in the region’s temperatures leading to increase in potential evaporation. Findings from social surveys show that farmers’ age, education level, number of years engaged in farming, sources of weather information, knowledge of weather forecasting and decision on farming practices either had a significant relationship or correlation with their perceptions about the nature of both local [ethno-meteorological] and scientific weather knowledge. Nonetheless, there was a

  17. Limited Area Forecasting and Statistical Modelling for Wind Energy Scheduling

    DEFF Research Database (Denmark)

    Rosgaard, Martin Haubjerg

    forecast accuracy for operational wind power scheduling. Numerical weather prediction history and scales of atmospheric motion are summarised, followed by a literature review of limited area wind speed forecasting. Hereafter, the original contribution to research on the topic is outlined. The quality...... control of wind farm data used as forecast reference is described in detail, and a preliminary limited area forecasting study illustrates the aggravation of issues related to numerical orography representation and accurate reference coordinates at ne weather model resolutions. For the o shore and coastal...... sites studied limited area forecasting is found to deteriorate wind speed prediction accuracy, while inland results exhibit a steady forecast performance increase with weather model resolution. Temporal smoothing of wind speed forecasts is shown to improve wind power forecast performance by up to almost...

  18. Forecasting distributions of large federal-lands fires utilizing satellite and gridded weather information

    Science.gov (United States)

    H.K. Preisler; R.E. Burgan; J.C. Eidenshink; J.M. Klaver; R.W. Klaver

    2009-01-01

    The current study presents a statistical model for assessing the skill of fire danger indices and for forecasting the distribution of the expected numbers of large fires over a given region and for the upcoming week. The procedure permits development of daily maps that forecast, for the forthcoming week and within federal lands, percentiles of the distributions of (i)...

  19. Extending to seasonal scales the current usage of short range weather forecasts and climate projections for water management in Spain

    Science.gov (United States)

    Rodriguez-Camino, Ernesto; Voces, José; Sánchez, Eroteida; Navascues, Beatriz; Pouget, Laurent; Roldan, Tamara; Gómez, Manuel; Cabello, Angels; Comas, Pau; Pastor, Fernando; Concepción García-Gómez, M.°; José Gil, Juan; Gil, Delfina; Galván, Rogelio; Solera, Abel

    2016-04-01

    This presentation, first, briefly describes the current use of weather forecasts and climate projections delivered by AEMET for water management in Spain. The potential use of seasonal climate predictions for water -in particular dams- management is then discussed more in-depth, using a pilot experience carried out by a multidisciplinary group coordinated by AEMET and DG for Water of Spain. This initiative is being developed in the framework of the national implementation of the GFCS and the European project, EUPORIAS. Among the main components of this experience there are meteorological and hydrological observations, and an empirical seasonal forecasting technique that provides an ensemble of water reservoir inflows. These forecasted inflows feed a prediction model for the dam state that has been adapted for this purpose. The full system is being tested retrospectively, over several decades, for selected water reservoirs located in different Spanish river basins. The assessment includes an objective verification of the probabilistic seasonal forecasts using standard metrics, and the evaluation of the potential social and economic benefits, with special attention to drought and flooding conditions. The methodology of implementation of these seasonal predictions in the decision making process is being developed in close collaboration with final users participating in this pilot experience.

  20. Effects of Parameterized Orographic Drag on Weather Forecasting and Simulated Climatology Over East Asia During Boreal Summer

    Science.gov (United States)

    Choi, Hyun-Joo; Choi, Suk-Jin; Koo, Myung-Seo; Kim, Jung-Eun; Kwon, Young Cheol; Hong, Song-You

    2017-10-01

    The impact of subgrid orographic drag on weather forecasting and simulated climatology over East Asia in boreal summer is examined using two parameterization schemes in a global forecast model. The schemes consider gravity wave drag (GWD) with and without lower-level wave breaking drag (LLWD) and flow-blocking drag (FBD). Simulation results from sensitivity experiments verify that the scheme with LLWD and FBD improves the intensity of a summertime continental high over the northern part of the Korean Peninsula, which is exaggerated with GWD only. This is because the enhanced lower tropospheric drag due to the effects of lower-level wave breaking and flow blocking slows down the wind flowing out of the high-pressure system in the lower troposphere. It is found that the decreased lower-level divergence induces a compensating weakening of middle- to upper-level convergence aloft. Extended experiments for medium-range forecasts for July 2013 and seasonal simulations for June to August of 2013-2015 are also conducted. Statistical skill scores for medium-range forecasting are improved not only in low-level winds but also in surface pressure when both LLWD and FBD are considered. A simulated climatology of summertime monsoon circulation in East Asia is also realistically reproduced.

  1. Evaluation of high-resolution forecasts with the non-hydrostaticnumerical weather prediction model Lokalmodell for urban air pollutionepisodes in Helsinki, Oslo and Valencia

    Directory of Open Access Journals (Sweden)

    B. Fay

    2006-01-01

    Full Text Available The operational numerical weather prediction model Lokalmodell LM with 7,km horizontal resolution was evaluated for forecasting meteorological conditions during observed urban air pollution episodes. The resolution was increased to experimental 2.8 km and 1.1 km resolution by one-way interactive nesting without introducing urbanisation of physiographic parameters or parameterisations. The episodes examined are two severe winter inversion-induced episodes in Helsinki in December 1995 and Oslo in January 2003, three suspended dust episodes in spring and autumn in Helsinki and Oslo, and a late-summer photochemical episode in the Valencia area. The evaluation was basically performed against observations and radiosoundings and focused on the LM skill at forecasting the key meteorological parameters characteristic for the specific episodes. These included temperature inversions, atmospheric stability and low wind speeds for the Scandinavian episodes and the development of mesoscale recirculations in the Valencia area. LM forecasts often improved due to higher model resolution especially in mountainous areas like Oslo and Valencia where features depending on topography like temperature, wind fields and mesoscale valley circulations were better described. At coastal stations especially in Helsinki, forecast gains were due to the improved physiographic parameters (land fraction, soil type, or roughness length. The Helsinki and Oslo winter inversions with extreme nocturnal inversion strengths of 18°C were not sufficiently predicted with all LM resolutions. In Helsinki, overprediction of surface temperatures and low-level wind speeds basically led to underpredicted inversion strength. In the Oslo episode, the situation was more complex involving erroneous temperature advection and mountain-induced effects for the higher resolutions. Possible explanations include the influence of the LM treatment of snow cover, sea ice and stability-dependence of transfer

  2. Great Historical Events That Were Significantly Affected by the Weather: Part 8, Germany's War on the Soviet Union, 1941-45. II. Some Important Weather Forecasts, 1942-45.

    Science.gov (United States)

    Neumann, J.; Flohn, H.

    1988-07-01

    Short- to medium-range weather forecasts were prepared by Soviet meteorologists for the Battle of Stalingrad. These included forecasts for days suitable for massing troops and equipment and for starting the Soviet offensive in November 1942 that resulted in the encirclement of the German 6th Army. Another forecast was connected with the operation of artificial thickening of the ice cover of the Volga River in the Stalingrad area that made it possible to drive tanks from the cast bank to the west bank of the river (width: about 1 km).In January 1943 a German Panzer army had to be withdrawn from the Caucasus. To accelerate the retreat, light elements of that army crossed some 42 km of the ice cover of the Gulf of Taganrog (Sea of Azov). The crossing was authorized after a meteorologist proved his estimate of the ice-cover thickness by landing in a light plane on the ice.In January 1945 weather forecasts played an important role in the major Soviet (2 200 000 troops and 5 000 warplanes) Oder-Vistula offensive. Marshal Koney writes with appreciation of the correct weather forecasts.In the Appendix, considerations that led German meteorologists to formulate a forecast for a minimum of five days of fog or low clouds from the Ardennes to southern England are reviewed. This forecast was used by the German High Command for the start of the Battle of the Bulge in December 1944.

  3. Conditional Monthly Weather Resampling Procedure for Operational Seasonal Water Resources Forecasting

    Science.gov (United States)

    Beckers, J.; Weerts, A.; Tijdeman, E.; Welles, E.; McManamon, A.

    2013-12-01

    To provide reliable and accurate seasonal streamflow forecasts for water resources management several operational hydrologic agencies and hydropower companies around the world use the Extended Streamflow Prediction (ESP) procedure. The ESP in its original implementation does not accommodate for any additional information that the forecaster may have about expected deviations from climatology in the near future. Several attempts have been conducted to improve the skill of the ESP forecast, especially for areas which are affected by teleconnetions (e,g. ENSO, PDO) via selection (Hamlet and Lettenmaier, 1999) or weighting schemes (Werner et al., 2004; Wood and Lettenmaier, 2006; Najafi et al., 2012). A disadvantage of such schemes is that they lead to a reduction of the signal to noise ratio of the probabilistic forecast. To overcome this, we propose a resampling method conditional on climate indices to generate meteorological time series to be used in the ESP. The method can be used to generate a large number of meteorological ensemble members in order to improve the statistical properties of the ensemble. The effectiveness of the method was demonstrated in a real-time operational hydrologic seasonal forecasts system for the Columbia River basin operated by the Bonneville Power Administration. The forecast skill of the k-nn resampler was tested against the original ESP for three basins at the long-range seasonal time scale. The BSS and CRPSS were used to compare the results to those of the original ESP method. Positive forecast skill scores were found for the resampler method conditioned on different indices for the prediction of spring peak flows in the Dworshak and Hungry Horse basin. For the Libby Dam basin however, no improvement of skill was found. The proposed resampling method is a promising practical approach that can add skill to ESP forecasts at the seasonal time scale. Further improvement is possible by fine tuning the method and selecting the most

  4. 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

  5. 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

  6. The “Weather Intelligence for Renewable Energies” Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation

    DEFF Research Database (Denmark)

    Sperati, Simone; Alessandrini, Stefano; Pinson, Pierre

    2015-01-01

    A benchmarking exercise was organized within the framework of the European Action Weather Intelligence for Renewable Energies (“WIRE”) with the purpose of evaluating the performance of state of the art models for short-term renewable energy forecasting. The exercise consisted in forecasting...... the power output of two wind farms and two photovoltaic power plants, in order to compare the merits of forecasts based on different modeling approaches and input data. It was thus possible to obtain a better knowledge of the state of the art in both wind and solar power forecasting, with an overview...... and comparison of the principal and the novel approaches that are used today in the field, and to assess the evolution of forecast performance with respect to previous benchmarking exercises. The outcome of this exercise consisted then in proposing new challenges in the renewable power forecasting field...

  7. The “Weather Intelligence for Renewable Energies” Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation

    Directory of Open Access Journals (Sweden)

    Simone Sperati

    2015-09-01

    Full Text Available A benchmarking exercise was organized within the framework of the European Action Weather Intelligence for Renewable Energies (“WIRE” with the purpose of evaluating the performance of state of the art models for short-term renewable energy forecasting. The exercise consisted in forecasting the power output of two wind farms and two photovoltaic power plants, in order to compare the merits of forecasts based on different modeling approaches and input data. It was thus possible to obtain a better knowledge of the state of the art in both wind and solar power forecasting, with an overview and comparison of the principal and the novel approaches that are used today in the field, and to assess the evolution of forecast performance with respect to previous benchmarking exercises. The outcome of this exercise consisted then in proposing new challenges in the renewable power forecasting field and identifying the main areas for improving accuracy in the future.

  8. Numerical forecast test on local wind fields at Qinshan Nuclear Power Plant

    International Nuclear Information System (INIS)

    Chen Xiaoqiu

    2005-01-01

    Non-hydrostatic, full compressible atmospheric dynamics model is applied to perform numerical forecast test on local wind fields at Qinshan nuclear power plant, and prognostic data are compared with observed data for wind fields. The results show that the prognostic of wind speeds is better than that of wind directions as compared with observed results. As the whole, the results of prognostic wind field are consistent with meteorological observation data, 54% of wind speeds are within a factor of 1.5, about 61% of the deviation of wind direction within the 1.5 azimuth (≤33.75 degrees) in the first six hours. (authors)

  9. 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.

  10. Multi-step-ahead Method for Wind Speed Prediction Correction Based on Numerical Weather Prediction and Historical Measurement Data

    Science.gov (United States)

    Wang, Han; Yan, Jie; Liu, Yongqian; Han, Shuang; Li, Li; Zhao, Jing

    2017-11-01

    Increasing the accuracy of wind speed prediction lays solid foundation to the reliability of wind power forecasting. Most traditional correction methods for wind speed prediction establish the mapping relationship between wind speed of the numerical weather prediction (NWP) and the historical measurement data (HMD) at the corresponding time slot, which is free of time-dependent impacts of wind speed time series. In this paper, a multi-step-ahead wind speed prediction correction method is proposed with consideration of the passing effects from wind speed at the previous time slot. To this end, the proposed method employs both NWP and HMD as model inputs and the training labels. First, the probabilistic analysis of the NWP deviation for different wind speed bins is calculated to illustrate the inadequacy of the traditional time-independent mapping strategy. Then, support vector machine (SVM) is utilized as example to implement the proposed mapping strategy and to establish the correction model for all the wind speed bins. One Chinese wind farm in northern part of China is taken as example to validate the proposed method. Three benchmark methods of wind speed prediction are used to compare the performance. The results show that the proposed model has the best performance under different time horizons.

  11. 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.

  12. 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.

  13. 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.

  14. 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

  15. Multi-Scale Enviro-HIRLAM Forecasting of Weather and Atmospheric Composition over China and its Megacities

    Science.gov (United States)

    Mahura, Alexander; Amstrup, Bjarne; Nuterman, Roman; Yang, Xiaohua; Baklanov, Alexander

    2017-04-01

    Air pollution is a serious problem in different regions of China and its continuously growing megacities. Information on air quality, and especially, in urbanized areas is important for decision making, emergency response and population. In particular, the metropolitan areas of Shanghai, Beijing, and Pearl River Delta are well known as main regions having serious air pollution problems. The on-line integrated meteorology-chemistry-aerosols Enviro-HIRLAM (Environment - HIgh Resolution Limited Area Model) model adapted for China and selected megacities is applied for forecasting of weather and atmospheric composition (with focus on aerosols). The model system is running in downscaling chain from regional to urban scales at subsequent horizontal resolutions of 15-5-2.5 km. The model setup includes also the urban Building Effects Parameterization module, describing different types of urban districts (industrial commercial, city center, high density and residential) with its own morphological and aerodynamical characteristics. The effects of urbanization are important for atmospheric transport, dispersion, deposition, and chemical transformations, in addition to better quality emission inventories for China and selected urban areas. The Enviro-HIRLAM system provides meteorology and air quality forecasts at regional-subregional-urban scales (China - East China - selected megacities). In particular, such forecasting is important for metropolitan areas, where formation and development of meteorological and chemical/aerosol patterns are especially complex. It also provides information for evaluation impact on selected megacities of China as well as for investigation relationship between air pollution and meteorology.

  16. 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.

  17. 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.

  18. Forecast Based Financing for Managing Weather and Climate Risks to Reduce Potential Disaster Impacts

    Science.gov (United States)

    Arrighi, J.

    2017-12-01

    There is a critical window of time to reduce potential impacts of a disaster after a forecast for heightened risk is issued and before an extreme event occurs. The concept of Forecast-based Financing focuses on this window of opportunity. Through advanced preparation during system set-up, tailored methodologies are used to 1) analyze a range of potential extreme event forecasts, 2) identify emergency preparedness measures that can be taken when factoring in forecast lead time and inherent uncertainty and 3) develop standard operating procedures that are agreed on and tied to guaranteed funding sources to facilitate emergency measures led by the Red Cross or government actors when preparedness measures are triggered. This presentation will focus on a broad overview of the current state of theory and approaches used in developing a forecast-based financing systems - with a specific focus on hydrologic events, case studies of success and challenges in various contexts where this approach is being piloted, as well as what is on the horizon to be further explored and developed from a research perspective as the application of this approach continues to expand.

  19. Results of the Clarus Regional Demonstrations : Evaluation of Enhanced Road Weather Forecasting

    Science.gov (United States)

    2012-01-01

    The Clarus Initiative is a research effort : of the U.S. Department of Transportation : Intelligent Transportation Systems Joint : Program Office and the Federal Highway : Administrations Road Weather : Management Program to develop and : demonstr...

  20. Developing a Global, Short-Term Fire Weather Forecasting Tool Using NWP Input Meteorology and Satellite Fire Data

    Science.gov (United States)

    Peterson, D. A.; Hyer, E. J.; Wang, J.

    2011-12-01

    In order to meet the emerging need for better estimates of biomass burning emissions in air quality and climate models, a statistical model is developed to characterize the effect of a given set of meteorological conditions on the following day's fire activity, including ignition and spread potential. Preliminary tests are conducted within several spatial domains of the North American boreal forest by investigating a wide range of meteorological information, including operational fire weather forecasting indices, such as the Canadian Forest Fire Danger Rating System (CFFDRS). However, rather than using local noon surface station data, the six components of the CFFDRS are modified to use inputs from the North America Regional Reanalysis (NARR) and the Navy's Operational Global Atmospheric Prediction System Model (NOGAPS). The Initial Spread Index (ISI) and the Fire Weather Index (FWI) are shown to be the most relevant components of the CFFDRS for short-term changes in fire activity. However, both components are found to be highly sensitive to variations in relative humidity and wind speed input data. Several variables related to fire ignition from dry lighting, such as instability and the synoptic pattern, are also incorporated. Cases of fire ignition, growth, decay, and extinction are stratified using satellite fire observations from the Geostationary Operational Environmental Satellites (GOES) and the MODerate Resolution Imaging Spectroradiometer (MODIS) and compared to the available suite of meteorological information. These comparisons reveal that combinations of meteorological variables, such as the FWI, ISI, and additional indices developed for this study, produce the greatest separability between major fire growth and decay cases, which are defined by the observed change in fire counts and fire radiative power. This information is used to derive statistical relationships affecting the short-term changes in fire activity and subsequently applied to other

  1. The Worldwide Interplanetary Scintillation (IPS) Stations (WIPSS) Network in support of Space-Weather Science and Forecasting

    Science.gov (United States)

    Bisi, Mario Mark; Americo Gonzalez-Esparza, J.; Jackson, Bernard; Aguilar-Rodriguez, Ernesto; Tokumaru, Munetoshi; Chashei, Igor; Tyul'bashev, Sergey; Manoharan, Periasamy; Fallows, Richard; Chang, Oyuki; Yu, Hsiu-Shan; Fujiki, Ken'ichi; Shishov, Vladimir; Barnes, David

    2017-04-01

    The phenomenon of space weather - analogous to terrestrial weather which describes the changing low-altitude atmospheric conditions on Earth - is essentially a description of the changes in the plasma environment at and near the Earth. Some key parameters for space-weather purposes driving space weather at the Earth include velocity, density, magnetic field, high-energy particles, and radiation coming into and within the near-Earth space environment. Interplanetary scintillation (IPS) can be used to provide a global measure of velocity and density as well as indications of changes in the plasma and magnetic-field rotations along each observational line of sight. If the observations are formally inverted into a three-dimensional (3-D) tomographic reconstruction (such as using the University of California, San Diego - UCSD - kinematic model and reconstruction technique), then source-surface magnetic fields can also be propagated out to the Earth (and beyond) as well as in-situ data also being incorporated into the reconstruction. Currently, this has been done using IPS data only from the Institute for Space-Earth Environmental (ISEE) and has been scientifically since the 1990s, and in a forecast mode since around 2000. There is now a defined (and updated) IPS Common Data Format (IPSCDFv1.1) which is being implemented by the majority of the IPS community (this also feeds into the UCSD tomography). The Worldwide IPS Stations (WIPSS) Network aims to bring together, using IPSCDFv1.1, the worldwide real-time capable IPS observatories with well-developed and tested analyses techniques being unified across all single-site systems (such as MEXART, Pushchino, and Ooty) and cross-calibrated to the multi-site ISEE system (as well as learning from the scientific-based systems such as EISCAT, LOFAR, and the MWA), into the UCSD 3-D tomography to improve the accuracy, spatial and temporal data coverage, and both the spatial and temporal resolution for improved space-weather science

  2. Observations of interplanetary scintillation and their application to the space weather forecast

    International Nuclear Information System (INIS)

    Kojima, Masayoshi; Kakinuma, Takakiyo

    1989-01-01

    The interplanetary scintillation (IPS) method using natural radio sources can observe the solar wind near the sun and at high latitudes that have never been accessible to any spacecraft. Therefore, the IPS has been the most powerful method to observe the solar wind in three-dimensional space. Although the IPS method cannot predict when a flare will occur or when a filament will disappear, it can be used to forecast the propagation of interplanetary disturbances and to warn when they will attack the earth. Thus, the IPS method can be used to forecast recurrent interplanetary phenomena as well as transient phenomena. (author)

  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. Qualitative and quantitative descriptions of temperature: a study of the terminology used by local television weather forecasters to describe thermal sensation.

    Science.gov (United States)

    Brunskill, Jeffrey C

    2010-03-01

    This paper presents a study of the relationship between quantitative and qualitative descriptions of temperature. Online weather forecast narratives produced by local television forecasters were collected from affiliates in 23 cities throughout the northeastern, central and southern portions of the United States from August 2007 to July 2008. The narratives were collected to study the terminology and reference frames that local forecasters use to describe predicted temperatures for the following day. The main objectives were to explore the adjectives used to describe thermal conditions and the impact that geographical and seasonal variations in thermal conditions have on these descriptions. The results of this empirical study offer some insights into the structure of weather narratives and suggest that spatiotemporal variations in the weather impact how forecasters describe the temperature to their local audiences. In a broader sense, this investigation builds upon research in biometeorology, urban planning and linguistics that has explored the physiological and psychological factors that influence subjective assessments of thermal sensation and comfort. The results of this study provide a basis to reason about how thermal comfort is conveyed in meteorological communications and how experiential knowledge derived from daily observations of the weather influence how we think about and discuss the weather.

  5. Assessing the value of post-processed state-of-the-art long-term weather forecast ensembles for agricultural water management mediated by farmers' behaviours

    Science.gov (United States)

    Li, Yu; Giuliani, Matteo; Castelletti, Andrea

    2016-04-01

    Recent advances in modelling of coupled ocean-atmosphere dynamics significantly improved skills of long-term climate forecast from global circulation models (GCMs). These more accurate weather predictions are supposed to be a valuable support to farmers in optimizing farming operations (e.g. crop choice, cropping and watering time) and for more effectively coping with the adverse impacts of climate variability. Yet, assessing how actually valuable this information can be to a farmer is not straightforward and farmers' response must be taken into consideration. Indeed, in the context of agricultural systems potentially useful forecast information should alter stakeholders' expectation, modify their decisions, and ultimately produce an impact on their performance. Nevertheless, long-term forecast are mostly evaluated in terms of accuracy (i.e., forecast quality) by comparing hindcast and observed values and only few studies investigated the operational value of forecast looking at the gain of utility within the decision-making context, e.g. by considering the derivative of forecast information, such as simulated crop yields or simulated soil moisture, which are essential to farmers' decision-making process. In this study, we contribute a step further in the assessment of the operational value of long-term weather forecasts products by embedding these latter into farmers' behavioral models. This allows a more critical assessment of the forecast value mediated by the end-users' perspective, including farmers' risk attitudes and behavioral patterns. Specifically, we evaluate the operational value of thirteen state-of-the-art long-range forecast products against climatology forecast and empirical prediction (i.e. past year climate and historical average) within an integrated agronomic modeling framework embedding an implicit model of the farmers' decision-making process. Raw ensemble datasets are bias-corrected and downscaled using a stochastic weather generator, in

  6. A Wind Forecasting System for Energy Application

    Science.gov (United States)

    Courtney, Jennifer; Lynch, Peter; Sweeney, Conor

    2010-05-01

    Accurate forecasting of available energy is crucial for the efficient management and use of wind power in the national power grid. With energy output critically dependent upon wind strength there is a need to reduce the errors associated wind forecasting. The objective of this research is to get the best possible wind forecasts for the wind energy industry. To achieve this goal, three methods are being applied. First, a mesoscale numerical weather prediction (NWP) model called WRF (Weather Research and Forecasting) is being used to predict wind values over Ireland. Currently, a gird resolution of 10km is used and higher model resolutions are being evaluated to establish whether they are economically viable given the forecast skill improvement they produce. Second, the WRF model is being used in conjunction with ECMWF (European Centre for Medium-Range Weather Forecasts) ensemble forecasts to produce a probabilistic weather forecasting product. Due to the chaotic nature of the atmosphere, a single, deterministic weather forecast can only have limited skill. The ECMWF ensemble methods produce an ensemble of 51 global forecasts, twice a day, by perturbing initial conditions of a 'control' forecast which is the best estimate of the initial state of the atmosphere. This method provides an indication of the reliability of the forecast and a quantitative basis for probabilistic forecasting. The limitation of ensemble forecasting lies in the fact that the perturbed model runs behave differently under different weather patterns and each model run is equally likely to be closest to the observed weather situation. Models have biases, and involve assumptions about physical processes and forcing factors such as underlying topography. Third, Bayesian Model Averaging (BMA) is being applied to the output from the ensemble forecasts in order to statistically post-process the results and achieve a better wind forecasting system. BMA is a promising technique that will offer calibrated

  7. Can Weather Radars Help Monitoring and Forecasting Wind Power Fluctuations at Large Offshore Wind Farms?

    DEFF Research Database (Denmark)

    Trombe, Pierre-Julien; Pinson, Pierre; Madsen, Henrik

    2011-01-01

    The substantial impact of wind power fluctuations at large offshore wind farms calls for the development of dedicated monitoring and prediction approaches. Based on recent findings, a Local Area Weather Radar (LAWR) was installed at Horns Rev with the aim of improving predictability, controlability...... and potentially maintenance planning. Additional images are available from a Doppler radar covering the same area. The parallel analysis of rain events detection and of regime sequences in wind (and power) fluctuations demonstrates the interest of employing weather radars for a better operation and management...... of offshore wind farms....

  8. Characterization of Lightning Occurrence in Alaska Using Various Weather Indices for Lightning Forecasting

    OpenAIRE

    Farukh, Murad Ahmed; Hayasaka, Hiroshi; Kimura, Keiji

    2011-01-01

    Alaska lost 10% of its forest area due to vigorous forest fires in 2004 and 2005. Repeated lightning-caused forest fires annoy residents and influencing earth’s atmosphere in every fire season. The authors have reported on the weather conditions of Alaska’s most severe lightning occurrence in mid June 2005. This paper examines a range of weather indices like soar, instability, ‘dry lightning’ and others to the factors that could clearly explain lightning characteristics in Alaska. First, ligh...

  9. Multigrid methods for improving the variational data assimilation in numerical weather prediction

    Directory of Open Access Journals (Sweden)

    Youn-Hee Kang

    2014-07-01

    Full Text Available Two conditions are needed to solve numerical weather prediction models: initial condition and boundary condition. The initial condition has an especially important bearing on the model performance. To get a good initial condition, many data assimilation techniques have been developed for the meteorological and the oceanographical fields. Currently, the most commonly used technique for operational applications is the 3 dimensional (3-D or 4 dimensional variational data assimilation method. The numerical method used for the cost function minimising process is usually an iterative method such as the conjugate gradient. In this paper, we use the multigrid method based on the cell-centred finite difference on the variational data assimilation to improve the performance of the minimisation procedure for 3D-Var data assimilation.

  10. Evaluation of numerical weather predictions performed in the context of the project DAPHNE

    Science.gov (United States)

    Tegoulias, Ioannis; Pytharoulis, Ioannis; Bampzelis, Dimitris; Karacostas, Theodore

    2014-05-01

    The region of Thessaly in central Greece is one of the main areas of agricultural production in Greece. Severe weather phenomena affect the agricultural production in this region with adverse effects for farmers and the national economy. For this reason the project DAPHNE aims at tackling the problem of drought by means of weather modification through the development of the necessary tools to support the application of a rainfall enhancement program. In the present study the numerical weather prediction system WRF-ARW is used, in order to assess its ability to represent extreme weather phenomena in the region of Thessaly. WRF is integrated in three domains covering Europe, Eastern Mediterranean and Central-Northern Greece (Thessaly and a large part of Macedonia) using telescoping nesting with grid spacing of 15km, 5km and 1.667km, respectively. The cases examined span throughout the transitional and warm period (April to September) of the years 2008 to 2013, including days with thunderstorm activity. Model results are evaluated against all available surface observations and radar products, taking into account the spatial characteristics and intensity of the storms. Preliminary results indicate a good level of agreement between the simulated and observed fields as far as the standard parameters (such as temperature, humidity and precipitation) are concerned. Moreover, the model generally exhibits a potential to represent the occurrence of the convective activity, but not its exact spatiotemporal characteristics. Acknowledgements This research work has been co-financed by the European Union (European Regional Development Fund) and Greek national funds, through the action "COOPERATION 2011: Partnerships of Production and Research Institutions in Focused Research and Technology Sectors" (contract number 11SYN_8_1088 - DAPHNE) in the framework of the operational programme "Competitiveness and Entrepreneurship" and Regions in Transition (OPC II, NSRF 2007-2013)

  11. "Big Data Assimilation" for 30-second-update 100-m-mesh Numerical Weather Prediction

    Science.gov (United States)

    Miyoshi, Takemasa; Lien, Guo-Yuan; Kunii, Masaru; Ruiz, Juan; Maejima, Yasumitsu; Otsuka, Shigenori; Kondo, Keiichi; Seko, Hiromu; Satoh, Shinsuke; Ushio, Tomoo; Bessho, Kotaro; Kamide, Kazumi; Tomita, Hirofumi; Nishizawa, Seiya; Yamaura, Tsuyoshi; Ishikawa, Yutaka

    2017-04-01

    A typical lifetime of a single cumulonimbus is within an hour, and radar observations often show rapid changes in only a 5-minute period. For precise prediction of such rapidly-changing local severe storms, we have developed what we call a "Big Data Assimilation" (BDA) system that performs 30-second-update data assimilation cycles at 100-m grid spacing. The concept shares that of NOAA's Warn-on-Forecast (WoF), in which rapidly-updated high-resolution NWP will play a central role in issuing severe-storm warnings even only minutes in advance. The 100-m resolution and 30-second update frequency are a leap above typical recent research settings, and it was possible by the fortunate combination of Japan's most advanced supercomputing and sensing technologies: the 10-petaflops K computer and the Phased Array Weather Radar (PAWR). The X-band PAWR is capable of a dense three-dimensional volume scan at 100-m range resolution with 100 elevation angles and 300 azimuth angles, up to 60-km range within 30 seconds. The PAWR data show temporally-smooth evolution of convective rainstorms. This gives us a hope that we may assume the Gaussian error distribution in 30-second forecasts before strong nonlinear dynamics distort the error distribution for rapidly-changing convective storms. With this in mind, we apply the Local Ensemble Transform Kalman Filter (LETKF) that considers flow-dependent error covariance explicitly under the Gaussian-error assumption. The flow-dependence would be particularly important in rapidly-changing convective weather. Using a 100-member ensemble at 100-m resolution, we have tested the Big Data Assimilation system in real-world cases of sudden local rainstorms, and obtained promising results. However, the real-time application is a big challenge, and currently it takes 10 minutes for a cycle. We explore approaches to accelerating the computations, such as using single-precision arrays in the model computation and developing an efficient I/O middleware for

  12. The ARISE project: multi-instrument observations in the middle atmosphere for improving extreme event monitoring and weather forecasts

    Science.gov (United States)

    Blanc, Elisabeth

    2017-04-01

    The ARISE project integrates different station networks providing observations from ground to the lower thermosphere, including the infrasound network developed for the Comprehensive Nuclear-Test-Ban Treaty (CTBT) verification regime augmented by national stations, the Network for the Detection of Atmospheric Composition Changes (NDACC) providing Lidar measurements, complementary Mesosphere-Stratosphere-Troposphere (MST) and meteor radars, wind radiometers, ionospheric sounders and satellites. The main objective is to recover the vertical structure of the atmospheric disturbances over broad spatial and temporal scales with unprecedented resolution in both space and time. The poster highlights recent results obtained in the main project applications which focus on weather and climate forecasting, remote observations of extreme events such as thunderstorms or volcanic eruptions, and characterisation of large scale disturbances such as gravity waves and sudden stratospheric warming events.

  13. 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.

  14. NOAA/NCEP Global Forecast System (GFS) Atmospheric Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — U.S. National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) numerical weather...

  15. Combined use of weather forecasting and satellite remote sensing information for fire risk, fire and fire impact monitoring

    Directory of Open Access Journals (Sweden)

    Wolfgang Knorr

    2011-06-01

    Full Text Available The restoration of fire-affected forest areas needs to be combined with their future protection from renewed catastrophic fires, such as those that occurred in Greece during the 2007 summer season. The present work demonstrates that the use of various sources of satellite data in conjunction with weather forecast information is capable of providing valuable information for the characterization of fire danger with the purpose of protecting the Greek national forest areas. This study shows that favourable meteorological conditions have contributed to the fire outbreak during the days of the unusually damaging fires in Peloponnese as well as Euboia (modern Greek: Evia at the end of August 2007. During those days, Greece was located between an extended high pressure system in Central Europe and a low pressure system in the Middle East. Their combination resulted in strong north-northeasterly winds in the Aegean Sea. As a consequence, strong winds were also observed in the regions of Evia and Peloponnese, especially in mountainous areas. The analysis of satellite images showing smoke emitted from the fires corroborates the results from the weather forecasts. A further analysis using the Fraction of Absorbed Photosyntetically Active Radiation (FAPAR as an indicator of active vegetation shows the extent of the destruction caused by the fire. The position of the burned areas coincides with that of the active fires detected in the earlier satellite image. Using the annual maximum FAPAR as an indicator of regional vegetation density, it was found that only regions with relatively high FAPAR were burned.

  16. Hydrological now- and forecasting : Integration of operationally available remotely sensed and forecasted hydrometeorological variables into distributed hydrological models

    NARCIS (Netherlands)

    Schuurmans, J.M.

    2008-01-01

    Keywords: hydrology, models, soil moisture, rainfall, radar, rain gauge, remote sensing, evapotranspiration, forecasting, numerical weather prediction, Netherlands, Langbroekerwetering, Lopikerwaard. Computer simulation models are an important tool for hydrologists. With these models they can

  17. 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

  18. Knowing Their Place: The Blue Hill Observatory and the Value of Local Knowledge in an Era of Synoptic Weather Forecasting, 1884-1894.

    Science.gov (United States)

    Bergman, James

    2016-09-01

    Argument The history of meteorology has focused a great deal on the "scaling up" of knowledge infrastructures through the development of national and global observation networks. This article argues that such efforts to scale up were paralleled by efforts to define a place for local knowledge. By examining efforts of the Blue Hill Meteorological Observatory, near Boston, Massachusetts, to issue local weather forecasts that competed with the centralized forecasts of the U.S. Signal Service, this article finds that Blue Hill, as a user of the Signal Service's observation network, developed a new understanding of local knowledge by combining local observations of the weather with the synoptic maps afforded by the nationwide telegraph network of the U.S. Signal Service. Blue Hill used these forecasts not only as a service, but also as evidence of the superiority of its model of local forecasting over the Signal Service's model, and in the process opened up larger questions about the value of a weather forecast and the value of different kinds of knowledge in meteorology.

  19. 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.

  20. New Weather Sensing and Forecasting Capabilities for Ground-to-Space Operations.

    Science.gov (United States)

    1987-02-01

    i -t. " ’ ----- •. - .1.. NOE = 3.’ . 0. " S1CURIY CLASSIFICATION OF T141S PAGUM(3MINa EDO & NO ’-e’Certaln weather variables exercise an... orbiting satellites. It determines winds by sensing the motion of natural aerosols. The satellites, including both the next- generation polar- orbiting and...surface winds were a consideration for the first Strategic Defense Initiative laser test using the orbiting Discovery space shuttle on 22 June 1985. It

  1. Forecasting the Solar Drivers of Severe Space Weather from Active-Region Magnetograms

    Science.gov (United States)

    Falconer, David A.; Moore, Ronald L.; Barghouty, Abdulnasser F.; Khazanov, Igor

    2012-01-01

    Solar drivers of severe space weather can be predicted from line-of-sight magnetograms, via a free-energy proxy measured from the neutral lines. This can be done in near real time. In addition to depending strongly on the free magnetic energy, an active region's chance of having a major eruption depends strongly on other aspects of the evolving magnetic field (e.g., its complexity and flux emergence).

  2. Model predictive control for a smart solar tank based on weather and consumption forecasts

    DEFF Research Database (Denmark)

    Halvgaard, Rasmus; Bacher, Peder; Perers, Bengt

    2012-01-01

    and the heat consumption in a residential house. The storage tank provides heat in periods where there is low solar radiation and stores heat when there is surplus solar heat. The forecasts of consumption patterns were based on data obtained from meters in a group of single-family houses in Denmark. The tank...... the storage tank to serve a smart energy system in which flexible consumers are expected to help balance fluctuating renewable energy sources like wind and solar. Through simulations, the impact of applying Economic MPC shows annual electricity cost savings up to 25-30%....

  3. Effects of using a posteriori methods for the conservation of integral invariants. [for weather forecasting

    Science.gov (United States)

    Takacs, Lawrence L.

    1988-01-01

    The nature and effect of using a posteriori adjustments to nonconservative finite-difference schemes to enforce integral invariants of the corresponding analytic system are examined. The method of a posteriori integral constraint restoration is analyzed for the case of linear advection, and the harmonic response associated with the a posteriori adjustments is examined in detail. The conservative properties of the shallow water system are reviewed, and the constraint restoration algorithm applied to the shallow water equations are described. A comparison is made between forecasts obtained using implicit and a posteriori methods for the conservation of mass, energy, and potential enstrophy in the complete nonlinear shallow-water system.

  4. Incorporating Medium-Range Weather Forecasts in Seasonal Crop Scenarios over the Greater Horn of Africa to Support National/Regional/Local Decision Makers

    Science.gov (United States)

    Shukla, S.; Husak, G. J.; Funk, C. C.; Verdin, J. P.

    2015-12-01

    The USAID's Famine Early Warning Systems Network (FEWS NET) provides seasonal assessments of crop conditions over the Greater Horn of Africa (GHA) and other food insecure regions. These assessments and current livelihood, nutrition, market conditions and conflicts are used to generate food security scenarios that help national, regional and local decision makers target their resources and mitigate socio-economic losses. Among the various tools that FEWS NET uses is the FAO's Water Requirement Satisfaction Index (WRSI). The WRSI is a simple yet powerful crop assessment model that incorporates current moisture conditions (at the time of the issuance of forecast), precipitation scenarios, potential evapotranspiration and crop parameters to categorize crop conditions into different classes ranging from "failure" to "very good". The WRSI tool has been shown to have a good agreement with local crop yields in the GHA region. At present, the precipitation scenarios used to drive the WRSI are based on either a climatological forecast (that assigns equal chances of occurrence to all possible scenarios and has no skill over the forecast period) or a sea-surface temperature anomaly based scenario (which at best have skill at the seasonal scale). In both cases, the scenarios fail to capture the skill that can be attained by initial atmospheric conditions (i.e., medium-range weather forecasts). During the middle of a cropping season, when a week or two of poor rains can have a devastating effect, two weeks worth of skillful precipitation forecasts could improve the skill of the crop scenarios. With this working hypothesis, we examine the value of incorporating medium-range weather forecasts in improving the skill of crop scenarios in the GHA region. We use the NCEP's Global Ensemble Forecast system (GEFS) weather forecasts and examine the skill of crop scenarios generated using the GEFS weather forecasts with respect to the scenarios based solely on the climatological forecast

  5. A revised radiation package of G-packed McICA and two-stream approximation: Performance evaluation in a global weather forecasting model

    Science.gov (United States)

    Baek, Sunghye

    2017-07-01

    For more efficient and accurate computation of radiative flux, improvements have been achieved in two aspects, integration of the radiative transfer equation over space and angle. First, the treatment of the Monte Carlo-independent column approximation (MCICA) is modified focusing on efficiency using a reduced number of random samples ("G-packed") within a reconstructed and unified radiation package. The original McICA takes 20% of CPU time of radiation in the Global/Regional Integrated Model systems (GRIMs). The CPU time consumption of McICA is reduced by 70% without compromising accuracy. Second, parameterizations of shortwave two-stream approximations are revised to reduce errors with respect to the 16-stream discrete ordinate method. Delta-scaled two-stream approximation (TSA) is almost unanimously used in Global Circulation Model (GCM) but contains systematic errors which overestimate forward peak scattering as solar elevation decreases. These errors are alleviated by adjusting the parameterizations of each scattering element—aerosol, liquid, ice and snow cloud particles. Parameterizations are determined with 20,129 atmospheric columns of the GRIMs data and tested with 13,422 independent data columns. The result shows that the root-mean-square error (RMSE) over the all atmospheric layers is decreased by 39% on average without significant increase in computational time. Revised TSA developed and validated with a separate one-dimensional model is mounted on GRIMs for mid-term numerical weather forecasting. Monthly averaged global forecast skill scores are unchanged with revised TSA but the temperature at lower levels of the atmosphere (pressure ≥ 700 hPa) is slightly increased (< 0.5 K) with corrected atmospheric absorption.

  6. Integrating interannual climate variability forecasts into weather-indexed crop insurance. The case of Malawi, Kenya and Tanzania

    Science.gov (United States)

    Vicarelli, M.; Giannini, A.; Osgood, D.

    2009-12-01

    In this study we explore the potential for re-insurance schemes built on regional climatic forecasts. We focus on micro-insurance contracts indexed on precipitation in 9 villages in Kenya, Tanzania (Eastern Africa) and Malawi (Southern Africa), and analyze the precipitation patterns and payouts resulting from El Niño Southern Oscillation (ENSO). The inability to manage future climate risk represents a “poverty trap” for several African regions. Weather shocks can potentially destabilize not only household, but also entire countries. Governments in drought-prone countries, donors and relief agencies are becoming aware of the importance to develop an ex-ante risk management framework for weather risk. Joint efforts to develop innovative mechanisms to spread and pool risk such as microinsurance and microcredit are currently being designed in several developing countries. While ENSO is an important component in modulating the rainfall regime in tropical Africa, the micro-insurance experiments currently under development to address drought risk among smallholder farmers in this region do not take into account ENSO monitoring or forecasting yet. ENSO forecasts could be integrated in the contracts and reinsurance schemes could be designed at the continental scale taking advantage of the different impact of ENSO on different regions. ENSO is associated to a bipolar precipitation pattern in Southern and Eastern Africa. La Niña years (i.e. Cold ENSO Episodes) are characterized by dry climate in Eastern Africa and wet climate in Southern Africa. During El Niño (or Warm Episode) the precipitation dipole is inverted, and Eastern Africa experiences increased probability for above normal rainfall (Halpert and Ropelewski, 1992, Journal of Climate). Our study represents the first exercise in trying to include ENSO forecasts in micro weather index insurance contract design. We analyzed the contracts payouts with respect to climate variability. In particular (i) we simulated

  7. Simulating infectious disease risk based on climatic drivers: from numerical weather prediction to long term climate change scenario

    Science.gov (United States)

    Caminade, C.; Ndione, J. A.; Diallo, M.; MacLeod, D.; Faye, O.; Ba, Y.; Dia, I.; Medlock, J. M.; Leach, S.; McIntyre, K. M.; Baylis, M.; Morse, A. P.

    2012-04-01

    Climate variability is an important component in determining the incidence of a number of diseases with significant health and socioeconomic impacts. In particular, vector born diseases are the most likely to be affected by climate; directly via the development rates and survival of both the pathogen and the vector, and indirectly through changes in the surrounding environmental conditions. Disease risk models of various complexities using different streams of climate forecasts as inputs have been developed within the QWeCI EU and ENHanCE ERA-NET project frameworks. This work will present two application examples, one for Africa and one for Europe. First, we focus on Rift Valley fever over sub-Saharan Africa, a zoonosis that affects domestic animals and humans by causing an acute fever. We show that the Rift Valley fever outbreak that occurred in late 2010 in the northern Sahelian region of Mauritania might have been anticipated ten days in advance using the GFS numerical weather prediction system. Then, an ensemble of regional climate projections is employed to model the climatic suitability of the Asian tiger mosquito for the future over Europe. The Asian tiger mosquito is an invasive species originally from Asia which is able to transmit West Nile and Chikungunya Fever among others. This species has spread worldwide during the last decades, mainly through the shipments of goods from Asia. Different disease models are employed and inter-compared to achieve such a task. Results show that the climatic conditions over southern England, central Western Europe and the Balkans might become more suitable for the mosquito (including the proviso that the mosquito has already been introduced) to establish itself in the future.

  8. Building the ensemble flood prediction system by using numerical weather prediction data: Case study in Kinu river basin, Japan

    Science.gov (United States)

    Ishitsuka, Y.; Yoshimura, K.

    2016-12-01

    Floods have a potential to be a major source of economic or human damage caused by natural disasters. Flood prediction systems were developed all over the world and to treat the uncertainty of the prediction ensemble simulation is commonly adopted. In this study, ensemble flood prediction system using global scale land surface and hydrodynamic model was developed. The system requests surface atmospheric forcing and Land Surface Model, MATSIRO, calculates runoff. Those generated runoff is inputted to hydrodynamic model CaMa-Flood to calculate discharge and flood inundation. CaMa-Flood can simulate flood area and its fraction by introducing floodplain connected to river channel. Forecast leadtime was set 39hours according to forcing data. For the case study, the flood occurred at Kinu river basin, Japan in 2015 was hindcasted. In a 1761 km² Kinu river basin, 3-days accumulated average rainfall was 384mm and over 4000 people was left in the inundated area. Available ensemble numerical weather prediction data at that time was inputted to the system in a resolution of 0.05 degrees and 1hour time step. As a result, the system predicted the flood occurrence by 45% and 84% at 23 and 11 hours before the water level exceeded the evacuation threshold, respectively. Those prediction lead time may provide the chance for early preparation for the floods such as levee reinforcement or evacuation. Adding to the discharge, flood area predictability was also analyzed. Although those models were applied for Japan region, this system can be applied easily to other region or even global scale. The areal flood prediction in meso to global scale would be useful for detecting hot zones or vulnerable areas over each region.

  9. Evaluation of Enhanced High Resolution MODIS/AMSR-E SSTs and the Impact on Regional Weather Forecast

    Science.gov (United States)

    Schiferl, Luke D.; Fuell, Kevin K.; Case, Jonathan L.; Jedlovec, Gary J.

    2010-01-01

    Over the last few years, the NASA Short-term Prediction Research and Transition (SPoRT) Center has been generating a 1-km sea surface temperature (SST) composite derived from retrievals of the Moderate Resolution Imaging Spectroradiometer (MODIS) for use in operational diagnostics and regional model initialization. With the assumption that the day-to-day variation in the SST is nominal, individual MODIS passes aboard the Earth Observing System (EOS) Aqua and Terra satellites are used to create and update four composite SST products each day at 0400, 0700, 1600, and 1900 UTC, valid over the western Atlantic and Caribbean waters. A six month study from February to August 2007 over the marine areas surrounding southern Florida was conducted to compare the use of the MODIS SST composite versus the Real-Time Global SST analysis to initialize the Weather Research and Forecasting (WRF) model. Substantial changes in the forecast heat fluxes were seen at times in the marine boundary layer, but relatively little overall improvement was measured in the sensible weather elements. The limited improvement in the WRF model forecasts could be attributed to the diurnal changes in SST seen in the MODIS SST composites but not accounted for by the model. Furthermore, cloud contamination caused extended periods when individual passes of MODIS were unable to update the SSTs, leading to substantial SST latency and a cool bias during the early summer months. In order to alleviate the latency problems, the SPoRT Center recently enhanced its MODIS SST composite by incorporating information from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) instruments as well as the Operational Sea Surface Temperature and Sea Ice Analysis. These enhancements substantially decreased the latency due to cloud cover and improved the bias and correlation of the composites at available marine point observations. While these enhancements improved upon the modeled cold bias using the original MODIS SSTs

  10. Impacts of Amazonia biomass burning aerosols assessed from short-range weather forecasts

    Directory of Open Access Journals (Sweden)

    S. R. Kolusu

    2015-11-01

    Full Text Available The direct radiative impacts of biomass burning aerosols (BBA on meteorology are investigated using short-range forecasts from the Met Office Unified Model (MetUM over South America during the South American Biomass Burning Analysis (SAMBBA. The impacts are evaluated using a set of three simulations: (i no aerosols, (ii with monthly mean aerosol climatologies and (iii with prognostic aerosols modelled using the Coupled Large-scale Aerosol Simulator for Studies In Climate (CLASSIC scheme. Comparison with observations show that the prognostic CLASSIC scheme provides the best representation of BBA. The impacts of BBA are quantified over central and southern Amazonia from the first and second day of 2-day forecasts during 14 September–3 October 2012. On average, during the first day of the forecast, including prognostic BBA reduces the clear-sky net radiation at the surface by 15 ± 1 W m−2 and reduces net top-of-atmosphere (TOA radiation by 8 ± 1 W m−2, with a direct atmospheric warming of 7 ± 1 W m−2. BBA-induced reductions in all-sky radiation are smaller in magnitude: 9.0 ± 1 W m−2 at the surface and 4.0 ± 1 W m−2 at TOA. In this modelling study the BBA therefore exert an overall cooling influence on the Earth–atmosphere system, although some levels of the atmosphere are directly warmed by the absorption of solar radiation. Due to the reduction of net radiative flux at the surface, the mean 2 m air temperature is reduced by around 0.1 ± 0.02 °C. The BBA also cools the boundary layer (BL but warms air above by around 0.2 °C due to the absorption of shortwave radiation. The overall impact is to reduce the BL depth by around 19 ± 8 m. These differences in heating lead to a more anticyclonic circulation at 700 hPa, with winds changing by around 0.6 m s−1. Inclusion of climatological or prognostic BBA in the MetUM makes a small but significant improvement in forecasts of temperature and relative humidity, but improvements were

  11. Short-Term Forecasting Models for Photovoltaic Plants: Analytical versus Soft-Computing Techniques

    OpenAIRE

    Monteiro, Claudio; Fernandez-Jimenez, L. Alfredo; Ramirez-Rosado, Ignacio J.; Muñoz-Jimenez, Andres; Lara-Santillan, Pedro M.

    2013-01-01

    We present and compare two short-term statistical forecasting models for hourly average electric power production forecasts of photovoltaic (PV) plants: the analytical PV power forecasting model (APVF) and the multiplayer perceptron PV forecasting model (MPVF). Both models use forecasts from numerical weather prediction (NWP) tools at the location of the PV plant as well as the past recorded values of PV hourly electric power production. The APVF model consists of an original modeling for adj...

  12. 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.

  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. 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.

  15. GEOS-5 seasonal forecast system

    Science.gov (United States)

    Borovikov, Anna; Cullather, Richard; Kovach, Robin; Marshak, Jelena; Vernieres, Guillaume; Vikhliaev, Yury; Zhao, Bin; Li, Zhao

    2017-09-01

    Ensembles of numerical forecasts based on perturbed initial conditions have long been used to improve estimates of both weather and climate forecasts. The Goddard Earth Observing System (GEOS) Atmosphere-Ocean General Circulation Model, Version 5 (GEOS-5 AOGCM) Seasonal-to-Interannual Forecast System has been used routinely by the GMAO since 2008, the current version since 2012. A coupled reanalysis starting in 1980 provides the initial conditions for the 9-month experimental forecasts. Once a month, sea surface temperature from a suite of 11 ensemble forecasts is contributed to the North American Multi-Model Ensemble (NMME) consensus project, which compares and distributes seasonal forecasts of ENSO events. Since June 2013, GEOS-5 forecasts of the Arctic sea-ice distribution were provided to the Sea-Ice Outlook project. The seasonal forecast output data includes surface fields, atmospheric and ocean fields, as well as sea ice thickness and area, and soil moisture variables. The current paper aims to document the characteristics of the GEOS-5 seasonal forecast system and to highlight forecast biases and skills of selected variables (sea surface temperature, air temperature at 2 m, precipitation and sea ice extent) to be used as a benchmark for the future GMAO seasonal forecast systems and to facilitate comparison with other global seasonal forecast systems.

  16. 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.

  17. 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).

  18. Large-Eddy Simulations of Atmospheric Flows Over Complex Terrain Using the Immersed-Boundary Method in the Weather Research and Forecasting