Science.gov

Sample records for operational wrf forecasts

  1. Operational forecast products and applications based on WRF/Chem

    NASA Astrophysics Data System (ADS)

    Hirtl, Marcus; Flandorfer, Claudia; Langer, Matthias; Mantovani, Simone; Olefs, Marc; Schellander-Gorgas, Theresa

    2015-04-01

    The responsibilities of the national weather service of Austria (ZAMG) include the support of the federal states and the public in questions connected to the protection of the environment in the frame of advisory and counseling services as well as expert opinions. The ZAMG conducts daily Air-Quality forecasts using the on-line coupled model WRF/Chem. The mother domain expands over Europe, North Africa and parts of Russia. The nested domain includes the alpine region and has a horizontal resolution of 4 km. Local emissions (Austria) are used in combination with European inventories (TNO and EMEP) for the simulations. The modeling system is presented and the results from the evaluation of the assimilation of pollutants using the 3D-VAR software GSI is shown. Currently observational data (PM10 and O3) from the Austrian Air-Quality network and from European stations (EEA) are assimilated into the model on an operational basis. In addition PM maps are produced using Aerosol Optical Thickness (AOT) observations from MODIS in combination with model data using machine learning techniques. The modeling system is operationally evaluated with different data sets. The emphasis of the application is on the forecast of pollutants which are compared to the hourly values (PM10, O3 and NO2) of the Austrian Air-Quality network. As the meteorological conditions are important for transport and chemical processes, some parameters like wind and precipitation are automatically evaluated (SAL diagrams, maps, …) with other models (e.g. ECMWF, AROME, …) and ground stations via web interface. The prediction of the AOT is also important for operators of solar power plants. In the past Numerical Weather Prediction (NWP) models were used to predict the AOT based on cloud forecasts at the ZAMG. These models do not consider the spatial and temporal variation of the aerosol distribution in the atmosphere with a consequent impact on the accuracy of forecasts especially during clear-sky days when the influence of the aerosols can have a strong impact on the AOT. WRF/Chem forecasts of the atmospheric optical properties are used to add information on the incoming radiation during these days. The evaluation of the model with satellite data for different episodes with clear-sky conditions is presented.

  2. Operational on-line coupled chemical weather forecasts for Europe with WRF/Chem

    NASA Astrophysics Data System (ADS)

    Hirtl, Marcus; Mantovani, Simone; Krüger, Bernd C.; Flandorfer, Claudia; Langer, Matthias

    2014-05-01

    Air quality is a key element for the well-being and quality of life of European citizens. Air pollution measurements and modeling tools are essential for the assessment of air quality according to EU legislation. The responsibilities of ZAMG as the national weather service of Austria include the support of the federal states and the public in questions connected to the protection of the environment in the frame of advisory and counseling services as well as expert opinions. ZAMG conducts daily Air-Quality forecasts using the on-line coupled model WRF/Chem. Meteorology is simulated simultaneously with the emissions, turbulent mixing, transport, transformation, and fate of trace gases and aerosols. The emphasis of the application is on predicting pollutants over Austria. Two domains are used for the simulations: the mother domain covers Europe with a resolution of 12 km, the inner domain includes the alpine region with a horizontal resolution of 4 km; 45 model levels are used in the vertical direction. The model runs 2 times per day for a period of 72 hours and is initialized with ECMWF forecasts. On-line coupled models allow considering two-way interactions between different atmospheric processes including chemistry (both gases and aerosols), clouds, radiation, boundary layer, emissions, meteorology and climate. In the operational set-up direct-, indirect and semi-direct effects between meteorology and air chemistry are enabled. The model is running on the HPCF (High Performance Computing Facility) of the ZAMG. In the current set-up 1248 CPUs are used. As the simulations need a big amount of computing resources, a method to safe I/O-time was implemented. Every MPI task writes all its output into the shared memory filesystem of the compute nodes. Once the WRF/Chem integration is finished, all split NetCDF-files are merged and saved on the global file system. The merge-routine is based on parallel-NetCDF. With this method the model runs about 30% faster on the SGI-ICEX. Different additional external data sources can be used to improve the forecasts. Satellite measurements of the Aerosol Optical Thickness (AOT) and ground-based PM10-measurements are combined to highly-resolved initial fields using regression- and assimilation techniques. The available local emission inventories provided by the different Austrian regional governments were harmonized and are used for the model simulations. A model evaluation for a selected episode in February 2010 is presented with respect to PM10 forecasts. During that month exceedances of PM10-thresholds occurred at many measurement stations of the Austrian network. Different model runs (only model/only ground stations assimilated/satellite and ground stations assimilated) are compared to the respective measurements.

  3. Validation of WRF forecasts for the Chajnantor region

    NASA Astrophysics Data System (ADS)

    Pozo, Diana R.; Marín, J. C.; Illanes, L.; Curé, M.; Rabanus, D.

    2016-03-01

    This study assesses the performance of the Weather Research and Forecasting (WRF) model to represent the near-surface weather conditions and the precipitable water vapor (PWV) in the Chajnantor plateau, in the North of Chile, from April to December 2007. The WRF model shows a very good performance forecasting the near-surface temperature and zonal wind component, although it overestimates the 2m water vapor mixing ratio and underestimates the 10m meridional wind component. The model represents very well the seasonal, intraseasonal and the diurnal variation of PWV. However, the PWV errors increase after the 12 hours of simulation. Errors in the simulations are larger than 1.5 mm only during 10 % of the study period, they do not exceed 0.5 mm during 65 % of the time and they are below 0.25 mm more than 45 % of the time, which emphasizes the good performance of the model to forecast the PWV over the region. The misrepresentation of the near-surface humidity in the region by the WRF model may have a negative impact on the PWV forecasts. Thus, having accurate forecasts of humidity near the surface may result in more accurate PWV forecasts. Overall, results from this, as well as recent studies, supports the use of the WRF model to provide accurate weather forecasts for the region, particularly for the PWV, which can be of great benefit for astronomers in the planning of their scientific operations and observing time.

  4. Characterizing real-time forest disturbance in a dynamic land surface model with implications for operational streamflow forecasting using WRF-Hydro

    NASA Astrophysics Data System (ADS)

    Dugger, A. L.; Gochis, D. J.; McCreight, J. L.; Yu, W.

    2014-12-01

    Forest cover changes have potential to induce large shifts in water fluxes, including short-term responses like changing downstream flood risk and longer-term feedbacks between land surface water and the atmosphere that can influence local and regional weather patterns. Two main challenges in capturing vegetation changes within hydrological forecasting systems, however, are finding real-time data streams to characterize these changes and the ability to accurately translate readily observable qualities such as "greenness" to modelled biophysical and biochemical properties. Here we investigate the feasibility of using near real-time remote sensing products paired with a continuous-running atmosphere - land surface - hydrology model to characterize vegetation disturbance and regrowth dynamics, with the ultimate goal of improving short-range (hourly to daily) and mid-range (weekly to monthly) operational streamflow forecasts. We use WRF-Hydro, a framework for coupling components of land surface models, hydrologic models, and the Weather Research and Forecasting (WRF) atmospheric model. We take advantage of newly implemented data assimilation techniques in WRF-Hydro to assimilate MODIS near real-time observations into the NoahMP dynamic vegetation model to simulate disturbance and regrowth following (1) wildfire in the Southwest U.S. and (2) plantation forestry management in the Southeast U.S. Ranges of modeled vegetation structural (leaf and stem area, height, canopy organization) and biochemical (chlorophyll content) characteristics consistent with MODIS observations are used to estimate uncertainty ranges in downstream flow predictions. The WRF-Hydro framework allows us to characterize changes in streamflow behavior expected after these modeled forest disturbances, evaluate when and where assimilation of vegetation characteristics from remote sensing is most important to streamflow prediction, and quantify the role of recovering vegetation biophysical/biochemical state uncertainty in predicted streamflow probability distributions. These hindcast examples, by relying solely on near real-time data streams, provide a basis for improving representation of post-disturbance vegetation dynamics in future operational hydrological forecasting systems.

  5. A Multi-Season Study of the Effects of MODIS Sea-Surface Temperatures on Operational WRF Forecasts at NWS Miami, FL

    NASA Technical Reports Server (NTRS)

    Case, Jonathan L.; Santos, Pablo; Lazarus, Steven M.; Splitt, Michael E.; Haines, Stephanie L.; Dembek, Scott R.; Lapenta, William M.

    2008-01-01

    Studies at the Short-term Prediction Research and Transition (SPORT) Center have suggested that the use of Moderate Resolution Imaging Spectroradiometer (MODIS) sea-surface temperature (SST) composites in regional weather forecast models can have a significant positive impact on short-term numerical weather prediction in coastal regions. Recent work by LaCasse et al (2007, Monthly Weather Review) highlights lower atmospheric differences in regional numerical simulations over the Florida offshore waters using 2-km SST composites derived from the MODIS instrument aboard the polar-orbiting Aqua and Terra Earth Observing System satellites. To help quantify the value of this impact on NWS Weather Forecast Offices (WFOs), the SPORT Center and the NWS WFO at Miami, FL (MIA) are collaborating on a project to investigate the impact of using the high-resolution MODIS SST fields within the Weather Research and Forecasting (WRF) prediction system. The project's goal is to determine whether more accurate specification of the lower-boundary forcing within WRF will result in improved land/sea fluxes and hence, more accurate evolution of coastal mesoscale circulations and the associated sensible weather elements. The NWS MIA is currently running WRF in real-time to support daily forecast operations, using the National Centers for Environmental Prediction Nonhydrostatic Mesoscale Model dynamical core within the NWS Science and Training Resource Center's Environmental Modeling System (EMS) software. Twenty-seven hour forecasts are run dally initialized at 0300, 0900, 1500, and 2100 UTC on a domain with 4-km grid spacing covering the southern half of Florida and adjacent waters of the Gulf of Mexico and Atlantic Ocean. Each model run is initialized using the Local Analysis and Prediction System (LAPS) analyses available in AWIPS. The SSTs are initialized with the NCEP Real-Time Global (RTG) analyses at 1/12deg resolution (approx.9 km); however, the RTG product does not exhibit fine-scale details consistent with its grid resolution. SPORT is conducting parallel WRF EMS runs identical to the operational runs at NWS MIA except for the use of MODIS SST composites in place of the RTG product as the initial and boundary conditions over water, The MODIS SST composites for initializing the SPORT WRF runs are generated on a 2-km grid four times daily at 0400, 0700, 1600, and 1900 UTC, based on the times of the overhead passes of the Aqua and Terra satellites. The incorporation of the MODIS SST data into the SPORT WRF runs is staggered such that SSTs are updated with a new composite every six hours in each of the WRF runs. From mid-February to July 2007, over 500 parallel WRF simulations have been collected for analysis and verification. This paper will present verification results comparing the NWS MIA operational WRF runs to the SPORT experimental runs, and highlight any substantial differences noted in the predicted mesoscale phenomena for specific cases.

  6. Forecasting Lightning Threat Using WRF Proxy Fields

    NASA Technical Reports Server (NTRS)

    McCaul, E. W., Jr.

    2010-01-01

    Objectives: Given that high-resolution WRF forecasts can capture the character of convective outbreaks, we seek to: 1. Create WRF forecasts of LTG threat (1-24 h), based on 2 proxy fields from explicitly simulated convection: - graupel flux near -15 C (captures LTG time variability) - vertically integrated ice (captures LTG threat area). 2. Calibrate each threat to yield accurate quantitative peak flash rate densities. 3. Also evaluate threats for areal coverage, time variability. 4. Blend threats to optimize results. 5. Examine sensitivity to model mesh, microphysics. Methods: 1. Use high-resolution 2-km WRF simulations to prognose convection for a diverse series of selected case studies. 2. Evaluate graupel fluxes; vertically integrated ice (VII). 3. Calibrate WRF LTG proxies using peak total LTG flash rate densities from NALMA; relationships look linear, with regression line passing through origin. 4. Truncate low threat values to make threat areal coverage match NALMA flash extent density obs. 5. Blend proxies to achieve optimal performance 6. Study CAPS 4-km ensembles to evaluate sensitivities.

  7. WRF/Chem forecasting of wildfire smoke in Alaska

    NASA Astrophysics Data System (ADS)

    Stuefer, M.; Grell, G. A.; Freitas, S. R.; Kulchitsky, A. V.; Newby, G. B.

    2009-12-01

    We have been able to successfully predict the atmospheric effects and concentrations of smoke downwind from Alaska wildfires. The so-called UAFSmoke system includes detection of wild fire location and area using data from the Alaska Interagency Coordination Center and thermal anomalies from the MODIS instrument. Fire emissions are derived from above ground biomass fuel load data in one-kilometer resolution. WRF/Chem Version 3.1 with online plume dynamics represents the core of the UAFSmoke system. Besides wildfire emissions and NOAAs Global Forecast System meteorology, WRF/Chem initial and boundary conditions are updated with anthropogenic and biogenic data from various sources. System runs are performed in near real time at the Arctic Region Supercomputing Centers Sun Opteron cluster. Smoke and meteorological forecast products are shown at a dedicated webpage at http://smoke.arsc.edu. We present results and comparison of UAFSmoke forecasts with satellite derived imagery and ground based reference observations such as air quality measurements from the most recent Alaska fire season 2009. The daily smoke forecasts support the public and operational needs of fire management experts and the National Weather Service.

  8. WRF ensemble downscaling seasonal forecasts of China winter precipitation during 1982-2008

    NASA Astrophysics Data System (ADS)

    Yuan, Xing; Liang, Xin-Zhong; Wood, Eric F.

    2012-10-01

    The non-hydrostatic Weather Research and Forecasting model (WRF) was nested into NCEP's operational seasonal forecast model Climate Forecast System (CFS) to downscale seasonal prediction of winter precipitation over continental China. Using the same initial conditions, 16 ensemble downscaling forecasts configured with two alternative schemes of microphysics, cumulus, land surface and radiation in WRF were conducted at 30 km for 27-cold seasons (December-February) during 1982-2008. On average, WRF downscaling forecasts reduced wet bias of seasonal mean precipitation from CFS prediction by 25-71%, decreased errors by up to 33%, and increased equitable threat score by 0.1 for low threshold. With appropriate physical configurations, WRF could improve interannual variations over the region where CFS has correct anomaly signal. The spatial distribution of daily precipitation characteristics such as rainy frequency and extremes highlighted the sensitivity of downscaling forecasts to physical configurations, and the dominant uncertainties were introduced by land surface and radiation schemes. The differences in convective and resolved rainfall between alternative land surface and radiation schemes were consistent with differences of surface downwelling shortwave and longwave radiation through cloud-radiation feedback. Such feedback was strengthened in the land surface sensitivity experiments due to different parameterizations of surface albedo. As compared with CFS ensemble predictions with different initial conditions, the WRF ensemble downscaling forecasts with various physical schemes had larger spread, and some schemes could complement each other in different regions that provided a promising opportunity to enhance the prediction through optimization. The optimized WRF reduced error from the optimized CFS by 30% and increased pattern correlation by 0.12. Moreover, WRF physical configuration ensemble increased percentage of skillful probabilistic forecasts from CFS initial condition ensemble.

  9. The SPoRT-WRF: Evaluating the Impact of NASA Datasets on Convective Forecasts

    NASA Technical Reports Server (NTRS)

    Zavodsky, Bradley; Case, Jonathan; Kozlowski, Danielle; Molthan, Andrew

    2012-01-01

    The Short-term Prediction Research and Transition Center (SPoRT) is a collaborative partnership between NASA and operational forecasting entities, including a number of National Weather Service offices. SPoRT transitions real-time NASA products and capabilities to its partners to address specific operational forecast challenges. One challenge that forecasters face is applying convection-allowing numerical models to predict mesoscale convective weather. In order to address this specific forecast challenge, SPoRT produces real-time mesoscale model forecasts using the Weather Research and Forecasting (WRF) model that includes unique NASA products and capabilities. Currently, the SPoRT configuration of the WRF model (SPoRT-WRF) incorporates the 4-km Land Information System (LIS) land surface data, 1-km SPoRT sea surface temperature analysis and 1-km Moderate resolution Imaging Spectroradiometer (MODIS) greenness vegetation fraction (GVF) analysis, and retrieved thermodynamic profiles from the Atmospheric Infrared Sounder (AIRS). The LIS, SST, and GVF data are all integrated into the SPoRT-WRF through adjustments to the initial and boundary conditions, and the AIRS data are assimilated into a 9-hour SPoRT WRF forecast each day at 0900 UTC. This study dissects the overall impact of the NASA datasets and the individual surface and atmospheric component datasets on daily mesoscale forecasts. A case study covering the super tornado outbreak across the Ce ntral and Southeastern United States during 25-27 April 2011 is examined. Three different forecasts are analyzed including the SPoRT-WRF (NASA surface and atmospheric data), the SPoRT WRF without AIRS (NASA surface data only), and the operational National Severe Storms Laboratory (NSSL) WRF (control with no NASA data). The forecasts are compared qualitatively by examining simulated versus observed radar reflectivity. Differences between the simulated reflectivity are further investigated using convective parameters along with model soundings to determine the impacts of the various NASA datasets. Additionally, quantitative evaluation of select meteorological parameters is performed using the Meteorological Evaluation Tools model verification package to compare forecasts to in situ surface and upper air observations.

  10. Can High-resolution WRF Simulations Be Used for Short-term Forecasting of Lightning?

    NASA Technical Reports Server (NTRS)

    Goodman, S. J.; Lapenta, W.; McCaul, E. W., Jr.; LaCasse, K.; Petersen, W.

    2006-01-01

    A number of research teams have begun to make quasi-operational forecast simulations at high resolution with models such as the Weather Research and Forecast (WRF) model. These model runs have used horizontal meshes of 2-4 km grid spacing, and thus resolved convective storms explicitly. In the light of recent global satellite-based observational studies that reveal robust relationships between total lightning flash rates and integrated amounts of precipitation-size ice hydrometeors in storms, it is natural to inquire about the capabilities of these convection-resolving models in representing the ice hydrometeor fields faithfully. If they do, this might make operational short-term forecasts of lightning activity feasible. We examine high-resolution WRF simulations from several Southeastern cases for which either NLDN or LMA lightning data were available. All the WRF runs use a standard microphysics package that depicts only three ice species, cloud ice, snow and graupel. The realism of the WRF simulations is examined by comparisons with both lightning and radar observations and with additional even higher-resolution cloud-resolving model runs. Preliminary findings are encouraging in that they suggest that WRF often makes convective storms of the proper size in approximately the right location, but they also indicate that higher resolution and better hydrometeor microphysics would be helpful in improving the realism of the updraft strengths, reflectivity and ice hydrometeor fields.

  11. High-Resolution WRF Forecasts of Lightning Threat

    NASA Technical Reports Server (NTRS)

    Goodman, S. J.; McCaul, E. W., Jr.; LaCasse, K.

    2007-01-01

    Tropical Rainfall Measuring Mission (TRMM)lightning and precipitation observations have confirmed the existence of a robust relationship between lightning flash rates and the amount of large precipitating ice hydrometeors in storms. This relationship is exploited, in conjunction with the capabilities of the Weather Research and Forecast (WRF) model, to forecast the threat of lightning from convective storms using the output fields from the model forecasts. The simulated vertical flux of graupel at -15C is used in this study as a proxy for charge separation processes and their associated lightning risk. Initial experiments using 6-h simulations are conducted for a number of case studies for which three-dimensional lightning validation data from the North Alabama Lightning Mapping Array are available. The WRF has been initialized on a 2 km grid using Eta boundary conditions, Doppler radar radial velocity and reflectivity fields, and METAR and ACARS data. An array of subjective and objective statistical metrics is employed to document the utility of the WRF forecasts. The simulation results are also compared to other more traditional means of forecasting convective storms, such as those based on inspection of the convective available potential energy field.

  12. Intel Xeon Phi accelerated Weather Research and Forecasting (WRF) Goddard microphysics scheme

    NASA Astrophysics Data System (ADS)

    Mielikainen, J.; Huang, B.; Huang, A. H.-L.

    2014-12-01

    The Weather Research and Forecasting (WRF) model is a numerical weather prediction system designed to serve both atmospheric research and operational forecasting needs. The WRF development is a done in collaboration around the globe. Furthermore, the WRF is used by academic atmospheric scientists, weather forecasters at the operational centers and so on. The WRF contains several physics components. The most time consuming one is the microphysics. One microphysics scheme is the Goddard cloud microphysics scheme. It is a sophisticated cloud microphysics scheme in the Weather Research and Forecasting (WRF) model. The Goddard microphysics scheme is very suitable for massively parallel computation as there are no interactions among horizontal grid points. Compared to the earlier microphysics schemes, the Goddard scheme incorporates a large number of improvements. Thus, we have optimized the Goddard scheme code. In this paper, we present our results of optimizing the Goddard 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 Intel MIC is capable of executing a full operating system and entire programs rather than just kernels as the GPU does. The MIC coprocessor supports all important Intel development tools. Thus, the development environment is one familiar 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 discussed in this paper. The results show that the optimizations improved performance of Goddard microphysics scheme on Xeon Phi 7120P by a factor of 4.7×. In addition, the optimizations reduced the Goddard microphysics scheme's share of the total WRF processing time from 20.0 to 7.5%. Furthermore, the same optimizations improved performance on Intel Xeon E5-2670 by a factor of 2.8× compared to the original code.

  13. WRF Optimization for Forecasting Wet Microburst Potential

    NASA Astrophysics Data System (ADS)

    Carroll, D.

    2011-12-01

    Microbursts are strong localized winds from thunderstorms with speeds up to 168 miles per hour. Microbursts have a short life cycle and typically develop and dissipate in the span of a few minutes. The crashes of Eastern Air Lines flight 66 (1975), Pan Am flight 759 (1982), Delta Air lines flight 191 (1985) and USAIR flight 1016 (1994), just to name a few, have been linked to microburst. The sudden intense nature of microbursts poses many hazards not only to planes during take-off and landings, but to property as well. The detection of microbursts by forecasters has improved in the Doppler radar era, however, providing advanced warnings remains a difficult task. It is hoped that through high resolution modeling, optimal configurations can be determined to improve the forecasting of microburst activity. This study tests the sensitivity of four cloud microphysics schemes used in the Weather Research and Forecasting Environmental Model System. The ideal microphysics scheme is chosen by comparing simulated maximum surface wind gusts to observed thunderstorm wind damage reports.

  14. Development and Implementation of Dynamic Scripts to Execute Cycled GSI/WRF Forecasts

    NASA Technical Reports Server (NTRS)

    Zavodsky, Bradley; Srikishen, Jayanthi; Berndt, Emily; Li, Xuanli; Watson, Leela

    2014-01-01

    The Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model and Gridpoint Statistical Interpolation (GSI) data assimilation (DA) are the operational systems that make up the North American Mesoscale (NAM) model and the NAM Data Assimilation System (NDAS) analysis used by National Weather Service forecasters. The Developmental Testbed Center (DTC) manages and distributes the code for the WRF and GSI, but it is up to individual researchers to link the systems together and write scripts to run the systems, which can take considerable time for those not familiar with the code. The objective of this project is to develop and disseminate a set of dynamic scripts that mimic the unique cycling configuration of the operational NAM to enable researchers to develop new modeling and data assimilation techniques that can be easily transferred to operations. The current version of the SPoRT GSI/WRF Scripts (v3.0.1) is compatible with WRF v3.3 and GSI v3.0.

  15. Evaluation of the high resolution WRF-Chem air quality forecast and its comparison with statistical ozone predictions

    NASA Astrophysics Data System (ADS)

    abkar, R.; Honzak, L.; Skok, G.; Forkel, R.; Rakovec, J.; Ceglar, A.; agar, N.

    2015-02-01

    An integrated high resolution modelling system based on the regional on-line coupled meteorology-atmospheric chemistry WRF-Chem model has been applied for numerical weather prediction and for air quality forecast in Slovenia. In the study an evaluation of the air quality forecasting system has been performed for summer 2013. In the case of ozone (O3) daily maxima the first day and second day model predictions have been also compared to the operational statistical O3 forecast and to persistence. Results of discrete and categorical evaluations show that the WRF-Chem based forecasting system is able to produce reliable forecasts, which depending on monitoring site and the evaluation measure applied can outperform the statistical model. For example, correlation coefficient shows the highest skill for WRF-Chem model O3 predictions, confirming the significance of the non-linear processes taken into account in an on-line coupled Eulerian model. For some stations and areas biases were relatively high due to highly complex terrain and unresolved local meteorological and emission dynamics, which contributed to somewhat lower WRF-Chem skill obtained in categorical model evaluations. Applying a bias-correction could further improve WRF-Chem model forecasting skill in these cases.

  16. Numerical forecasting at ALMA observatory using WRF data assimilation

    NASA Astrophysics Data System (ADS)

    Pozo, Diana; Marin, Julio C.; Olivares, Manuel; Arevalo, Jorge; Cure, Michel

    2014-05-01

    The ALMA (Atacama Large Millimeter Array) observatory is located in the Chajnantor plateau in the North of Chile, at 5100 m of height. This is a region with scarce observations, clear skies and dry conditions during a large part of the year. The present study uses WRF data assimilation to improve the numerical weather forecasts of near-surface variables and precipitable water vapor (PWV) at ALMA observatory. Four days were selected, two of them showed large PWV values as a result of the influence of synoptic scale perturbations on the region, and the other two showed very low PWV values. A number of simulations with different data assimilation combinations and a Control simulation were performed for each of these 4 days to analyze their influence on the weather forecasts at the site. Assimilating data only in the innermost domain (1km horizontal resolution) seems to provide the best results.

  17. A methodological study on using weather research and forecasting (WRF) model outputs to drive a one-dimensional cloud model

    NASA Astrophysics Data System (ADS)

    Jin, Ling; Kong, Fanyou; Lei, Hengchi; Hu, Zhaoxia

    2014-01-01

    A new method for driving a One-Dimensional Stratiform Cold (1DSC) cloud model with Weather Research and Forecasting (WRF) model outputs was developed by conducting numerical experiments for a typical large-scale stratiform rainfall event that took place on 4-5 July 2004 in Changchun, China. Sensitivity test results suggested that, with hydrometeor profiles extracted from the WRF outputs as the initial input, and with continuous updating of soundings and vertical velocities (including downdraft) derived from the WRF model, the new WRF-driven 1DSC modeling system (WRF-1DSC) was able to successfully reproduce both the generation and dissipation processes of the precipitation event. The simulated rainfall intensity showed a time-lag behind that observed, which could have been caused by simulation errors of soundings, vertical velocities and hydrometeor profiles in the WRF output. Taking into consideration the simulated and observed movement path of the precipitation system, a nearby grid point was found to possess more accurate environmental fields in terms of their similarity to those observed in Changchun Station. Using profiles from this nearby grid point, WRF-1DSC was able to reproduce a realistic precipitation pattern. This study demonstrates that 1D cloud-seeding models do indeed have the potential to predict realistic precipitation patterns when properly driven by accurate atmospheric profiles derived from a regional shortrange forecasting system. This opens a novel and important approach to developing an ensemble-based rain enhancement prediction and operation system under a probabilistic framework concept.

  18. Toward Improved Land Surface Initialization in Support of Regional WRF Forecasts at the Kenya Meteorological Department

    NASA Technical Reports Server (NTRS)

    Case. Jonathan; Mungai, John; Sakwa, Vincent; Kabuchanga, Eric; Zavodsky, Bradley T.; Limaye, Ashutosh S.

    2014-01-01

    Flooding and drought are two key forecasting challenges for the Kenya Meteorological Department (KMD). Atmospheric processes leading to excessive precipitation and/or prolonged drought can be quite sensitive to the state of the land surface, which interacts with the boundary layer of the atmosphere providing a source of heat and moisture. The development and evolution of precipitation systems are affected by heat and moisture fluxes from the land surface within weakly-sheared environments, such as in the tropics and sub-tropics. These heat and moisture fluxes during the day can be strongly influenced by land cover, vegetation, and soil moisture content. Therefore, it is important to represent the land surface state as accurately as possible in numerical weather prediction models. Enhanced regional modeling capabilities have the potential to improve forecast guidance in support of daily operations and high-end events over east Africa. KMD currently runs a configuration of the Weather Research and Forecasting (WRF) model in real time to support its daily forecasting operations, invoking the Nonhydrostatic Mesoscale Model (NMM) dynamical core. They make use of the National Oceanic and Atmospheric Administration / National Weather Service Science and Training Resource Center's Environmental Modeling System (EMS) to manage and produce the WRF-NMM model runs on a 7-km regional grid over eastern Africa. Two organizations at the National Aeronautics and Space Administration Marshall Space Flight Center in Huntsville, AL, SERVIR and the Short-term Prediction Research and Transition (SPoRT) Center, have established a working partnership with KMD for enhancing its regional modeling capabilities. To accomplish this goal, SPoRT and SERVIR will provide experimental land surface initialization datasets and model verification capabilities to KMD. To produce a land-surface initialization more consistent with the resolution of the KMD-WRF runs, the NASA Land Information System (LIS) will be run at a comparable resolution to provide real-time, daily soil initialization data in place of interpolated Global Forecast System soil moisture and temperature data. Additionally, real-time green vegetation fraction data from the Visible Infrared Imaging Radiometer Suite will be incorporated into the KMD-WRF runs, once it becomes publicly available from the National Environmental Satellite Data and Information Service. Finally, model verification capabilities will be transitioned to KMD using the Model Evaluation Tools (MET) package, in order to quantify possible improvements in simulated temperature, moisture and precipitation resulting from the experimental land surface initialization. The transition of these MET tools will enable KMD to monitor model forecast accuracy in near real time. This presentation will highlight preliminary verification results of WRF runs over east Africa using the LIS land surface initialization.

  19. Evaluation of WRF Model Physics in Wind Forecasts over Complex Terrain

    NASA Astrophysics Data System (ADS)

    Cheng, W. Y.; Liu, Y.; Warner, T.

    2009-12-01

    Forecasting wind power is highly desired for power grid operation, and it is extremely challenging as well because of the sporadic temporal nature of atmospheric winds. Power companies must anticipate the magnitude and timing of wind power in order to balance their power load. Numerical weather prediction (NWP) models such as WRF are very useful tools in forecasting mesoscale weather and winds at wind farms. Nonetheless, it is well known that model physics can have a profound impact on the model solution. Furthermore, many of the WRF model physics packages were designed for idealized scenarios and over flat terrain, which may not be performed in the same way over complex terrain regions where many wind farms locate. Research Applications Laboratory (RAL) at National Center for Atmospheric Research (NCAR), in collaboration with Xcel Energy to develop a real time wind power forecast systems for various wind farms including those in Colorado. One of the core components of the system is a high-resolution NCAR/ATEC WRF-ARW-based Real-Time Four-Dimensional Data Assimilation (RTFDDA) and forecasting system (?x = 3.3 km). This study evaluated different physics parameterizations and combinations in cloud microphysics, cumulus parameterization, land surface model, surface layer scheme, and planetary boundary layer parameterization in forecasting wind speed in two summer and winter ramp-up events in a Northern Colorado wind farm, respectively. We will compare and contrast the performance of different physics parameterizations and combinations between the summer (convective storms) and winter (cold frontal passage) ramp-up cases.

  20. Evaluation and comparison of O3 forecasts of ALARO-CAMx and WRF-Chem

    NASA Astrophysics Data System (ADS)

    Flandorfer, Claudia; Hirtl, Marcus

    2015-04-01

    ZAMG runs two models for Air-Quality forecasts operationally: ALARO-CAMx and WRF-Chem. ALARO-CAMx is a combination of the meteorological model ALARO and the photochemical dispersion model CAMx and is operated at ZAMG by order of the regional governments since 2005. The emphasis of this modeling system is on predicting ozone peaks in the north-east Austrian flatlands. Two modeling domains are used with the highest resolution (5 km) in the alpine region. Various extensions with external data sources have been conducted in the past to improve the daily forecasts of the model, e.g. data assimilation of O3- and PM10 observations from the Austrian measurement network (with optimum interpolation technique); MACC-II boundary conditions; combination of high resolved emission inventories for Austria with TNO and EMEP data. The biogenic emissions are provided by the SMOKE model. The model runs 2 times per day for a period of 48 hours. The second model which is operational is the on-line coupled model WRF-Chem. Meteorology is simulated simultaneously with the emission, turbulent mixing, transport, transformation, and fate of trace gases and aerosols. 2 domains are used for the simulations. The mother domain covers Europe with a resolution of 12 km. The inner domain includes the alpine region with a horizontal resolution of 4km. 45 model levels are used in the vertical. The model runs 2 times per day for a period of 72 hours and is initialized with ECMWF forecasts. The evaluation of both models is conducted for summer 2014 with the main focus on the forecasts of ozone. The measurements of the Air-Quality stations are compared with the punctual forecasts at the sites of the stations and with the area forecasts for every province of Austria. Beside the evaluation a comparison of the forecasts of ALARO-CAMx and WRF-Chem is done. The summer 2014 was the coldest and the dullest in the last 9 years. Due to this only two exceedances of the information threshold were measured (June 2014). In addition, data from summer 2013 is used to compare the forecasts of both models. In July and August 2013 the information threshold has been exceeded several times during a heat wave.

  1. A high resolution WRF model for wind energy forecasting

    NASA Astrophysics Data System (ADS)

    Vincent, Claire Louise; Liu, Yubao

    2010-05-01

    The increasing penetration of wind energy into national electricity markets has increased the demand for accurate surface layer wind forecasts. There has recently been a focus on forecasting the wind at wind farm sites using both statistical models and numerical weather prediction (NWP) models. Recent advances in computing capacity and non-hydrostatic NWP models means that it is possible to nest mesoscale models down to Large Eddy Simulation (LES) scales over the spatial area of a typical wind farm. For example, the WRF model (Skamarock 2008) has been run at a resolution of 123 m over a wind farm site in complex terrain in Colorado (Liu et al. 2009). Although these modelling attempts indicate a great hope for applying such models for detailed wind forecasts over wind farms, one of the obvious challenges of running the model at this resolution is that while some boundary layer structures are expected to be modelled explicitly, boundary layer eddies into the inertial sub-range can only be partly captured. Therefore, the amount and nature of sub-grid-scale mixing that is required is uncertain. Analysis of Liu et al. (2009) modelling results in comparison to wind farm observations indicates that unrealistic wind speed fluctuations with a period of around 1 hour occasionally occurred during the two day modelling period. The problem was addressed by re-running the same modelling system with a) a modified diffusion constant and b) two-way nesting between the high resolution model and its parent domain. The model, which was run with horizontal grid spacing of 370 m, had dimensions of 505 grid points in the east-west direction and 490 points in the north-south direction. It received boundary conditions from a mesoscale model of resolution 1111 m. Both models had 37 levels in the vertical. The mesoscale model was run with a non-local-mixing planetary boundary layer scheme, while the 370 m model was run with no planetary boundary layer scheme. It was found that increasing the diffusion constant caused damping of the unrealistic fluctuations, but did not completely solve the problem. Using two-way nesting also mitigated the unrealistic fluctuations significantly. It can be concluded that for real case LES modelling of wind farm circulations, care should be taken to ensure the consistency between the mesoscale weather forcing and LES models to avoid exciting spurious noise along the forcing boundary. The development of algorithms that adequately model the sub-grid-scale mixing that cannot be resolved by LES models is an important area for further research. References Liu, Y. Y._W. Liu, W. Y.Y. Cheng, W. Wu, T. T. Warner and K. Parks, 2009: Simulating intra-farm wind variations with the WRF-RTFDDA-LES modeling system. 10th WRF Users' Workshop, Boulder, C, USA. June 23 - 26, 2009. Skamarock, W., J. Dudhia, D.O. Gill, D.M. Barker, M.G.Duda, X-Y. Huang, W. Wang and J.G. Powers, A Description of the Advanced Research WRF version 3, NCAR Technical Note TN-475+STR, NCAR, Boulder, Colorado, 2008.

  2. Toward Improved Land Surface Initialization in Support of Regional WRF Forecasts at the Kenya Meteorological Service (KMS)

    NASA Technical Reports Server (NTRS)

    Case, Jonathan L.; Mungai, John; Sakwa, Vincent; Kabuchanga, Eric; Zavodsky, Bradley T.; Limaye, Ashutosh S.

    2014-01-01

    SPoRT/SERVIR/RCMRD/KMS Collaboration: Builds off strengths of each organization. SPoRT: Transition of satellite, modeling and verification capabilities; SERVIR-Africa/RCMRD: International capacity-building expertise; KMS: Operational organization with regional weather forecasting expertise in East Africa. Hypothesis: Improved land-surface initialization over Eastern Africa can lead to better temperature, moisture, and ultimately precipitation forecasts in NWP models. KMS currently initializes Weather Research and Forecasting (WRF) model with NCEP/Global Forecast System (GFS) model 0.5-deg initial / boundary condition data. LIS will provide much higher-resolution land-surface data at a scale more representative to regional WRF configuration. Future implementation of real-time NESDIS/VIIRS vegetation fraction to further improve land surface representativeness.

  3. New features in WRF-SFIRE and the wildfire forecasting and danger system in Israel

    NASA Astrophysics Data System (ADS)

    Mandel, J.; Amram, S.; Beezley, J. D.; Kelman, G.; Kochanski, A. K.; Kondratenko, V. Y.; Lynn, B. H.; Regev, B.; Vejmelka, M.

    2014-02-01

    Recent advances in computational capabilities of computer clusters made operational deployments of coupled atmosphere-fire models feasible, as the weather and fire spread forecast can be nowadays generated faster than real time. This paper presents new developments in the coupled WRF-SFIRE model and related software in past two years, being a response to the needs of the community interested in operational testing of WRF-SFIRE. We describe a new concept of the fireline intensity intended to better inform about the local fire front properties and fire danger. We present a fuel moisture model and a fuel moisture data assimilation system based on the Remote Automated Weather Stations (RAWS) observations, allowing for fire simulations across landscapes and time scales of varying fuel moisture conditions. The paper also describes the implementation of a coupling with the atmospheric chemistry and aerosol schemes in WRF-Chem allowing for simulation of smoke dispersion and effects of fires on air quality, as well as a data assimilation method allowing for starting the fire simulations from an observed fire perimeters instead of ignition points. Finally, an example of an operational deployment and new visualization and the data management tools are presented.

  4. Forecast bias analysis using object-based verification of regional WRF summertime convective forecasts

    NASA Astrophysics Data System (ADS)

    Starzec, Mariusz

    Forecast verification remains a crucial component of improving model forecasts, but still remains a challenge to perform. An objective method is developed to verify simulated reflectivity against radar reflectivity at a 1 km altitude utilizing the Method for Object-based Diagnostic Evaluation (MODE) Tool. Comparing the reflectivity field allows for an instantaneous view of what is occurring in simulations without any averaging that may occur when analyzing fields such as accumulated precipitation. The objective method is applied to high resolution 3 km and 1 km local convective WRF summertime forecasts in the Northern Plains region. The bulk verification statistics reveal that forecasts generate too many objects, over-forecast the areal coverage of convection, and over-intensify convection. No noteworthy increases in skill are found when increasing to 1 km resolution and instead lead to a significant over-forecasting of small cells. A sensitivity study is performed to investigate the forecast biases found by varying the cloud droplet concentration, microphysical scheme, and horizontal resolution on a case day containing weakly forced convection mostly below the freezing level. Changing the cloud droplet concentration has a strong impact on the number of object and area biases. Increasing droplet counts to observed values generates a forecast that more closely resembles the observations in terms of area and object counts, but leads not enough rain generation. Changing the microphysical scheme produces the most pronounced effects on object counts and intensity, which is attributed to differences in autoconversion formulations. Coarsening the resolution from 3 km to 9 km leads to a decrease in skill, showing that 3 km simulations are more effective at convective forecasts. Increasing the resolution to 1 km results in amplifying the object count bias, and is found to not be worth the additional computational expense.

  5. Evaluation of the high resolution WRF-Chem (v3.4.1) air quality forecast and its comparison with statistical ozone predictions

    NASA Astrophysics Data System (ADS)

    Žabkar, R.; Honzak, L.; Skok, G.; Forkel, R.; Rakovec, J.; Ceglar, A.; Žagar, N.

    2015-07-01

    An integrated modelling system based on the regional online coupled meteorology-atmospheric chemistry WRF-Chem model configured with two nested domains with horizontal resolutions of 11.1 and 3.7 km has been applied for numerical weather prediction and for air quality forecasts in Slovenia. In the study, an evaluation of the air quality forecasting system has been performed for summer 2013. In the case of ozone (O3) daily maxima, the first- and second-day model predictions have been also compared to the operational statistical O3 forecast and to the persistence. Results of discrete and categorical evaluations show that the WRF-Chem-based forecasting system is able to produce reliable forecasts which, depending on monitoring site and the evaluation measure applied, can outperform the statistical model. For example, the correlation coefficient shows the highest skill for WRF-Chem model O3 predictions, confirming the significance of the non-linear processes taken into account in an online coupled Eulerian model. For some stations and areas biases were relatively high due to highly complex terrain and unresolved local meteorological and emission dynamics, which contributed to somewhat lower WRF-Chem skill obtained in categorical model evaluations. Applying a bias correction could further improve WRF-Chem model forecasting skill in these cases.

  6. Atmospheric and seeing forecast: WRF model validation with in situ measurements at ORM

    NASA Astrophysics Data System (ADS)

    Giordano, C.; Vernin, J.; Vázquez Ramió, H.; Muñoz-Tuñón, C.; Varela, A. M.; Trinquet, H.

    2013-04-01

    We present a comparison between in situ measurements and forecasted data at the Observatorio del Roque de Los Muchachos. Forecasting is obtained with the Weather Research and Forecasting (WRF) model associated with a turbulence parametrization which follows Trinquet-Vernin model. The purpose of this study is to validate the capability of the WRF model to forecast the atmospheric and optical conditions (seeing and related adaptive optics parameters). The final aim is to provide a tool to optimize the observing time in the observatories, the so-called flexible scheduling. More than 4500 h of simulations above Observatorio del Roque de Los Muchachos (ORM) site with WRF in 2009 were calculated, and compared with data acquired during 2009 with Automatic Weather Station, Differential Image Motion Monitor and Multiple Aperture Scintillation Sensor. Each simulation corresponds to a 24h in advance forecasting with one predicted value each hour. Comparison shows that WRF forecasting agrees well with the effective meteorological parameters at ground level, such as pressure (within a scatter σP = 1.1 hPa), temperature (σT = 2 K), wind speed (σ|V| = 3.9 m s-1) and relative humidity (σ _{R_h}=18.9 per cent). Median precipitable water vapour content above the ORM predicted by WRF in 2009 is 3 mm, close to 3.8 mm reported in the literature over the period 2001-2008. For what concern optical parameters (seeing, coherence time, isoplanatic angle), WRF forecasting are in good agreement on nightly or monthly basis, better than random or carbon-copy tries. We hope to improve these results with a better vertical and horizontal grid resolution. Our method is robust enough to be applied to potential astronomical sites, where no instruments are available.

  7. The SPoRT-WRF: Evaluating the Impact of NASA Datasets on Convective Forecasts

    NASA Technical Reports Server (NTRS)

    Zavodsky, Bradley; Kozlowski, Danielle; Case, Jonathan; Molthan, Andrew

    2012-01-01

    Short-term Prediction Research and Transition (SPoRT) seeks to improve short-term, regional weather forecasts using unique NASA products and capabilities SPoRT has developed a unique, real-time configuration of the NASA Unified Weather Research and Forecasting (WRF)WRF (ARW) that integrates all SPoRT modeling research data: (1) 2-km SPoRT Sea Surface Temperature (SST) Composite, (2) 3-km LIS with 1-km Greenness Vegetation Fraction (GVFs) (3) 45-km AIRS retrieved profiles. Transitioned this real-time forecast to NOAA's Hazardous Weather Testbed (HWT) as deterministic model at Experimental Forecast Program (EFP). Feedback from forecasters/participants and internal evaluation of SPoRT-WRF shows a cool, dry bias that appears to suppress convection likely related to methodology for assimilation of AIRS profiles Version 2 of the SPoRT-WRF will premier at the 2012 EFP and include NASA physics, cycling data assimilation methodology, better coverage of precipitation forcing, and new GVFs

  8. Development and Implementation of Dynamic Scripts to Execute Cycled WRF/GSI Forecasts

    NASA Technical Reports Server (NTRS)

    Zavodsky, Bradley; Srikishen, Jayanthi; Berndt, Emily; Li, Quanli; Watson, Leela

    2014-01-01

    Automating the coupling of data assimilation (DA) and modeling systems is a unique challenge in the numerical weather prediction (NWP) research community. In recent years, the Development Testbed Center (DTC) has released well-documented tools such as the Weather Research and Forecasting (WRF) model and the Gridpoint Statistical Interpolation (GSI) DA system that can be easily downloaded, installed, and run by researchers on their local systems. However, developing a coupled system in which the various preprocessing, DA, model, and postprocessing capabilities are all integrated can be labor-intensive if one has little experience with any of these individual systems. Additionally, operational modeling entities generally have specific coupling methodologies that can take time to understand and develop code to implement properly. To better enable collaborating researchers to perform modeling and DA experiments with GSI, the Short-term Prediction Research and Transition (SPoRT) Center has developed a set of Perl scripts that couple GSI and WRF in a cycling methodology consistent with the use of real-time, regional observation data from the National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center (EMC). Because Perl is open source, the code can be easily downloaded and executed regardless of the user's native shell environment. This paper will provide a description of this open-source code and descriptions of a number of the use cases that have been performed by SPoRT collaborators using the scripts on different computing systems.

  9. How important is getting the land surface energy exchange correct in WRF for wind energy forecasting?

    NASA Astrophysics Data System (ADS)

    Wharton, S.; Simpson, M.; Osuna, J. L.; Newman, J. F.; Biraud, S.

    2013-12-01

    Wind power forecasting is plagued with difficulties in accurately predicting the occurrence and intensity of atmospheric conditions at the heights spanned by industrial-scale turbines (~ 40 to 200 m above ground level). Better simulation of the relevant physics would enable operational practices such as integration of large fractions of wind power into power grids, scheduling maintenance on wind energy facilities, and deciding design criteria based on complex loads for next-generation turbines and siting. Accurately simulating the surface energy processes in numerical models may be critically important for wind energy forecasting as energy exchange at the surface strongly drives atmospheric mixing (i.e., stability) in the lower layers of the planetary boundary layer (PBL), which in turn largely determines wind shear and turbulence at heights found in the turbine rotor-disk. We hypothesize that simulating accurate a surface-atmosphere energy coupling should lead to more accurate predictions of wind speed and turbulence at heights within the turbine rotor-disk. Here, we tested 10 different land surface model configurations in the Weather Research and Forecasting (WRF) model including Noah, Noah-MP, SSiB, Pleim-Xiu, RUC, and others to evaluate (1) the accuracy of simulated surface energy fluxes to flux tower measurements, (2) the accuracy of forecasted wind speeds to observations at rotor-disk heights, and (3) the sensitivity of forecasting hub-height rotor disk wind speed to the choice of land surface model. WRF was run for four, two-week periods covering both summer and winter periods over the Southern Great Plains ARM site in Oklahoma. Continuous measurements of surface energy fluxes and lidar-based wind speed, direction and turbulence were also available. The SGP ARM site provided an ideal location for this evaluation as it centrally located in the wind-rich Great Plains and multi-MW wind farms are rapidly expanding in the area. We found significant differences in simulated wind speeds at rotor-disk heights from WRF which indicated, in part, the sensitivity of lower PBL winds to surface energy exchange. We also found significant differences in energy partitioning between sensible heat and latent energy depending on choice of land surface model. Overall, the most consistent, accurate model results were produced using Noah-MP. Noah-MP was most accurate at simulating energy fluxes and wind shear. Hub-height wind speed, however, was predicted with most accuracy with Pleim-Xiu. This suggests that simulating wind shear in the surface layer is consistent with accurately simulating surface energy exchange while the exact magnitudes of wind speed may be more strongly influenced by the PBL dynamics. As the nation is working towards a 20% wind energy goal by 2030, increasing the accuracy of wind forecasting at rotor-disk heights becomes more important considering that utilities require wind farms to estimate their power generation 24 to 36 hours ahead and face penalties for inaccuracies in those forecasts.

  10. Impact of MODIS High-Resolution Sea-Surface Temperatures on WRF Forecasts at NWS Miami, FL

    NASA Technical Reports Server (NTRS)

    Case, Jonathan L.; LaCasse, Katherine M.; Dembek, Scott R.; Santos, Pablo; Lapenta, William M.

    2007-01-01

    Over the past few years,studies at the Short-term Prediction Research and Transition (SPoRT) Center have suggested that the use of Moderate Resolution Imaging Spectroradiometer (MODIS) composite sea-surface temperature (SST) products in regional weather forecast models can have a significant positive impact on short-term numerical weather prediction in coastal regions. The recent paper by LaCasse et al. (2007, Monthly Weather Review) highlights lower atmospheric differences in regional numerical simulations over the Florida offshore waters using 2-km SST composites derived from the MODIS instrument aboard the polar-orbiting Aqua and Terra Earth Observing System satellites. To help quantify the value of this impact on NWS Weather Forecast Offices (WFOs), the SPoRT Center and the NWS WFO at Miami, FL (MIA) are collaborating on a project to investigate the impact of using the high-resolution MODIS SST fields within the Weather Research and Forecasting (WRF) prediction system. The scientific hypothesis being tested is: More accurate specification of the lower-boundary forcing within WRF will result in improved land/sea fluxes and hence, more accurate evolution of coastal mesoscale circulations and the associated sensible weather elements. The NWS MIA is currently running the WRF system in real-time to support daily forecast operations, using the National Centers for Environmental Prediction Nonhydrostatic Mesoscale Model dynamical core within the NWS Science and Training Resource Center's Environmental Modeling System (EMS) software; The EMS is a standalone modeling system capable of downloading the necessary daily datasets, and initializing, running and displaying WRF forecasts in the NWS Advanced Weather Interactive Processing System (AWIPS) with little intervention required by forecasters. Twenty-seven hour forecasts are run daily with start times of 0300,0900, 1500, and 2100 UTC on a domain with 4-km grid spacing covering the southern half of Florida and the far western portions of the Bahamas, the Florida Keys, the Straights of Florida, and adjacent waters of the Gulf of Mexico and Atlantic Ocean. Each model run is initialized using the Local Analysis and Prediction System (LAPS) analyses available in AWIPS, invoking the diabatic. "hot-start" capability. In this WRF model "hot-start", the LAPS-analyzed cloud and precipitation features are converted into model microphysics fields with enhanced vertical velocity profiles, effectively reducing the model spin-up time required to predict precipitation systems. The SSTs are initialized with the NCEP Real-Time Global (RTG) analyses at l/12 degree resolution (approx. 9 km); however, the RTG product does not exhibit fine-scale details consistent with its grid resolution. SPoRT is conducting parallel WRF EMS runs identical to the operational runs at NWS MIA in every respect except for the use of MODIS SST composites in place of the RTG product as the initial and boundary conditions over water. The MODIS SST composites for initializing the SPoRT WRF runs are generated on a 2-km grid four times daily at 0400, 0700, 1600, and 1900 UTC, based on the times of the overhead passes of the Aqua and Terra satellites. The incorporation of the MODIS SST composites into the SPoRTWRF runs is staggered such that the 0400UTC composite initializes the 0900 UTC WRF, the 0700 UTC composite initializes the 1500 UTC WRF, the 1600 UTC composite initializes the 2100 UTC WRF, and the 1900 UTC composite initializes the 0300 UTC WRF. A comparison of the SPoRT and Miami forecasts is underway in 2007, and includes quantitative verification of near-surface temperature, dewpoint, and wind forecasts at surface observation locations. In addition, particular days of interest are being analyzed to determine the impact of the MODIS SST data on the development and evolution of predicted sea/land-breeze circulations, clouds, and precipitation. This paper will present verification results comparing the NWS MIA forecasts the SPoRT experimental WRF forecasts, and highlight any substantial difference

  11. Use of High-resolution WRF Simulations to Forecast Lightning Threat

    NASA Technical Reports Server (NTRS)

    McCaul, William E.; LaCasse, K.; Goodman, S. J.

    2006-01-01

    Recent observational studies have confirmed the existence of a robust statistical relationship between lightning flash rates and the amount of large precipitating ice hydrometeors in storms. This relationship is exploited, in conjunction with the capabilities of recent forecast models such as WRF, to forecast the threat of lightning from convective storms using the output fields from the model forecasts. The simulated vertical flux of graupel at -15C is used in this study as a proxy for charge separation processes and their associated lightning risk. Six-h simulations are conducted for a number of case studies for which three-dimensional lightning validation data from the North Alabama Lightning Mapping Array are available. Experiments indicate that initialization of the WRF model on a 2 km grid using Eta boundary conditions, Doppler radar radial velocity and reflectivity fields, and METAR and ACARS data yield the most realistic simulations. An array of subjective and objective statistical metrics are employed to document the utility of the WRF forecasts. The simulation results are also compared to other more traditional means of forecasting convective storms, such as those based on inspection of the convective available potential energy field.

  12. Assessment of Two Planetary Boundary Layer Schemes (ACM2 and YSU) within the Weather Research and Forecasting (WRF) Model

    NASA Astrophysics Data System (ADS)

    Wolff, J.; Harrold, M.; Xu, M.

    2014-12-01

    The Weather Research and Forecasting (WRF) model is a highly configurable numerical weather prediction system used in both research and operational forecasting applications. Rigorously testing select configurations and evaluating the performance for specific applications is necessary due to the flexibility offered by the system. The Developmental Testbed Center (DTC) performed extensive testing and evaluation with the Advanced Research WRF (ARW) dynamic core for two physics suite configurations with a goal of assessing the impact that the planetary boundary layer (PBL) scheme had on the final forecast performance. The baseline configuration was run with the Air Force Weather Agency's physics suite, which includes the Yonsei University PBL scheme, while the second configuration was substituted with the Asymmetric Convective Model (ACM2) PBL scheme. This presentation will focus on assessing the forecast performance of the two configurations; both configurations were run over the same set of cases, allowing for a direct comparison of performance. The evaluation was performed over a 15 km CONUS domain for a testing period from September 2013 through August 2014. Simulations were initialized every 36 hours and run out to 48 hours; a 6-hour "warm start" spin-up, including data assimilation using the Gridpoint Statistical Interpolation system preceded each simulation. The extensive testing period allows for robust results as well as the ability to investigate seasonal and regional differences between the two configurations. Results will focus on the evaluation of traditional verification metrics for surface and upper air variables, along with an assessment of statistical and practical significance.

  13. Implementation of a lightning data assimilation technique in the Weather Research and Forecasting (WRF) model for improving precipitation prediction

    NASA Astrophysics Data System (ADS)

    Giannaros, Theodore; Kotroni, Vassiliki; Lagouvardos, Kostas

    2015-04-01

    Lightning data assimilation has been recently attracting increasing attention as a technique implemented in numerical weather prediction (NWP) models for improving precipitation forecasts. In the frame of TALOS project, we implemented a robust lightning data assimilation technique in the Weather Research and Forecasting (WRF) model with the aim to improve the precipitation prediction in Greece. The assimilation scheme employs lightning as a proxy for the presence or absence of deep convection. In essence, flash data are ingested in WRF to control the Kain-Fritsch (KF) convective parameterization scheme (CPS). When lightning is observed, indicating the occurrence of convective activity, the CPS is forced to attempt to produce convection, whereas the CPS may be optionally be prevented from producing convection when no lightning is observed. Eight two-day precipitation events were selected for assessing the performance of the lightning data assimilation technique. The ingestion of lightning in WRF was carried out during the first 6 h of each event and the evaluation focused on the consequent 24 h, constituting a realistic setup that could be used in operational weather forecasting applications. Results show that the implemented assimilation scheme can improve model performance in terms of precipitation prediction. Forecasts employing the assimilation of flash data were found to exhibit more skill than control simulations, particularly for the intense (>20 mm) 24 h rain accumulations. Analysis of results also revealed that the option not to suppress the KF scheme in the absence of observed lightning, leads to a generally better performance compared to the experiments employing the full control of the CPS' triggering. Overall, the implementation of the lightning data assimilation technique is found to improve the model's ability to represent convection, especially in situations when past convection has modified the mesoscale environment in ways that affect the occurrence and evolution of subsequent convection.

  14. Application of WRF - SWAT OpenMI 2.0 based models integration for real time hydrological modelling and forecasting

    NASA Astrophysics Data System (ADS)

    Bugaets, Andrey; Gonchukov, Leonid

    2014-05-01

    Intake of deterministic distributed hydrological models into operational water management requires intensive collection and inputting of spatial distributed climatic information in a timely manner that is both time consuming and laborious. The lead time of the data pre-processing stage could be essentially reduced by coupling of hydrological and numerical weather prediction models. This is especially important for the regions such as the South of the Russian Far East where its geographical position combined with a monsoon climate affected by typhoons and extreme heavy rains caused rapid rising of the mountain rivers water level and led to the flash flooding and enormous damage. The objective of this study is development of end-to-end workflow that executes, in a loosely coupled mode, an integrated modeling system comprised of Weather Research and Forecast (WRF) atmospheric model and Soil and Water Assessment Tool (SWAT 2012) hydrological model using OpenMI 2.0 and web-service technologies. Migration SWAT into OpenMI compliant involves reorganization of the model into a separate initialization, performing timestep and finalization functions that can be accessed from outside. To save SWAT normal behavior, the source code was separated from OpenMI-specific implementation into the static library. Modified code was assembled into dynamic library and wrapped into C# class implemented the OpenMI ILinkableComponent interface. Development of WRF OpenMI-compliant component based on the idea of the wrapping web-service clients into a linkable component and seamlessly access to output netCDF files without actual models connection. The weather state variables (precipitation, wind, solar radiation, air temperature and relative humidity) are processed by automatic input selection algorithm to single out the most relevant values used by SWAT model to yield climatic data at the subbasin scale. Spatial interpolation between the WRF regular grid and SWAT subbasins centroid (which are coinciding as virtual weather stations) realized as OpenMI AdaptedOutput. In order to make sure that SWAT-WRF integration technically sounds and preevaluate the impact of the climatic data resolution on the model parameters a number of test calculations were performed with different time-spatial aggregation of WRF output. Numerical experiments were carried out for the period of 2012-2013 on the Komarovka river watershed (former Primorskaya water-balance station) located in the small mountains landscapes in the western part of the Khankaiskaya plain. The watershed outlet is equipped with the automatic water level and rain gauging stations of Primorie Hydrometeorological Agency (Prigidromet http://primgidromet.ru) observation network. Spatial structure of SWAT simulation realized by ArcSWAT 2012 with 10m DEM resolution and 1:50000 soils and landuse cover. Sensitivity analysis and calibration are performed with SWAT CUP. WRF-SWAT composition is assembled in the GUI OpenMI. For the test basin in most cases the simulation results show that the predicted and measured water levels demonstrate acceptable agreement. Enforcing SWAT with WRF output avoids some semi-empirical model approximation, replaces a native weather generator for WRF forecast interval and improved upon the operational streamflow forecast. It is anticipated that leveraging direct use of the WRF variables (not only substituted standard SWAT input) will have good potential to make SWAT more physically sound.

  15. Toward Improved Land Surface Initialization in Support of Regional WRF Forecasts at the Kenya Meteorological Service (KMS)

    NASA Technical Reports Server (NTRS)

    Case, Johnathan L.; Mungai, John; Sakwa, Vincent; Kabuchanga, Eric; Zavodsky, Bradley T.; Limaye, Ashutosh S.

    2014-01-01

    Flooding and drought are two key forecasting challenges for the Kenya Meteorological Service (KMS). Atmospheric processes leading to excessive precipitation and/or prolonged drought can be quite sensitive to the state of the land surface, which interacts with the planetary boundary layer (PBL) of the atmosphere providing a source of heat and moisture. The development and evolution of precipitation systems are affected by heat and moisture fluxes from the land surface, particularly within weakly-sheared environments such as in the tropics and sub-tropics. These heat and moisture fluxes during the day can be strongly influenced by land cover, vegetation, and soil moisture content. Therefore, it is important to represent the land surface state as accurately as possible in land surface and numerical weather prediction (NWP) models. Enhanced regional modeling capabilities have the potential to improve forecast guidance in support of daily operations and high-impact weather over eastern Africa. KMS currently runs a configuration of the Weather Research and Forecasting (WRF) NWP model in real time to support its daily forecasting operations, making use of the NOAA/National Weather Service (NWS) Science and Training Resource Center's Environmental Modeling System (EMS) to manage and produce the KMS-WRF runs on a regional grid over eastern Africa. Two organizations at the NASA Marshall Space Flight Center in Huntsville, AL, SERVIR and the Shortterm Prediction Research and Transition (SPoRT) Center, have established a working partnership with KMS for enhancing its regional modeling capabilities through new datasets and tools. To accomplish this goal, SPoRT and SERVIR is providing enhanced, experimental land surface initialization datasets and model verification capabilities to KMS as part of this collaboration. To produce a land-surface initialization more consistent with the resolution of the KMS-WRF runs, the NASA Land Information System (LIS) is run at a comparable resolution to provide real-time, daily soil initialization data in place of data interpolated from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model soil moisture and temperature fields. Additionally, realtime green vegetation fraction (GVF) data from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (Suomi- NPP) satellite will be incorporated into the KMS-WRF runs, once it becomes publicly available from the National Environmental Satellite Data and Information Service (NESDIS). Finally, model verification capabilities will be transitioned to KMS using the Model Evaluation Tools (MET; Brown et al. 2009) package in conjunction with a dynamic scripting package developed by SPoRT (Zavodsky et al. 2014), to help quantify possible improvements in simulated temperature, moisture and precipitation resulting from the experimental land surface initialization. Furthermore, the transition of these MET tools will enable KMS to monitor model forecast accuracy in near real time. This paper presents preliminary efforts to improve land surface model initialization over eastern Africa in support of operations at KMS. The remainder of this extended abstract is organized as follows: The collaborating organizations involved in the project are described in Section 2; background information on LIS and the configuration for eastern Africa is presented in Section 3; the WRF configuration used in this modeling experiment is described in Section 4; sample experimental WRF output with and without LIS initialization data are given in Section 5; a summary is given in Section 6 followed by acknowledgements and references.

  16. Generalized Wind Turbine Actuator Disk Parameterization in the Weather Research and Forecasting (WRF) Model for Real-World Simulations

    NASA Astrophysics Data System (ADS)

    Marjanovic, N.; Mirocha, J. D.; Chow, F. K.

    2013-12-01

    In this work, we examine the performance of a generalized actuator disk (GAD) model embedded within the Weather Research and Forecasting (WRF) atmospheric model to study wake effects on successive rows of turbines at a North American wind farm. These wake effects are of interest as they can drastically reduce down-wind energy extraction and increase turbulence intensity. The GAD, which is designed for turbulence-resolving simulations, is used within downscaled large-eddy simulations (LES) forced with mesoscale simulations and WRF's grid nesting capability. The GAD represents the effects of thrust and torque created by a wind turbine on the atmosphere within a disk representing the rotor swept area. The lift and drag forces acting on the turbine blades are parameterized using blade-element theory and the aerodynamic properties of the blades. Our implementation permits simulation of turbine wake effects and turbine/airflow interactions within a realistic atmospheric boundary layer flow field, including resolved turbulence, time-evolving mesoscale forcing, and real topography. The GAD includes real-time yaw and pitch control to respond realistically to changing flow conditions. Simulation results are compared to SODAR data from operating wind turbines and an already existing WRF mesoscale turbine drag parameterization to validate the GAD parameterization.

  17. Use of High-Resolution WRF Simulations to Forecast Lightning Threat

    NASA Technical Reports Server (NTRS)

    McCaul, E. W., Jr.; LaCasse, K.; Goodman, S. J.; Cecil, D. J.

    2008-01-01

    Recent observational studies have confirmed the existence of a robust statistical relationship between lightning flash rates and the amount of large precipitating ice hydrometeors aloft in storms. This relationship is exploited, in conjunction with the capabilities of cloud-resolving forecast models such as WRF, to forecast explicitly the threat of lightning from convective storms using selected output fields from the model forecasts. The simulated vertical flux of graupel at -15C and the shape of the simulated reflectivity profile are tested in this study as proxies for charge separation processes and their associated lightning risk. Our lightning forecast method differs from others in that it is entirely based on high-resolution simulation output, without reliance on any climatological data. short [6-8 h) simulations are conducted for a number of case studies for which three-dmmensional lightning validation data from the North Alabama Lightning Mapping Array are available. Experiments indicate that initialization of the WRF model on a 2 km grid using Eta boundary conditions, Doppler radar radial velocity fields, and METAR and ACARS data y&eld satisfactory simulations. __nalyses of the lightning threat fields suggests that both the graupel flux and reflectivity profile approaches, when properly calibrated, can yield reasonable lightning threat forecasts, although an ensemble approach is probably desirable in order to reduce the tendency for misplacement of modeled storms to hurt the accuracy of the forecasts. Our lightning threat forecasts are also compared to other more traditional means of forecasting thunderstorms, such as those based on inspection of the convective available potential energy field.

  18. Dynamical downscaling precipitation over Southwest Asia: Impacts of radiance data assimilation on the forecasts of the WRF-ARW model

    NASA Astrophysics Data System (ADS)

    Xu, Jianjun; Powell, , Alfred M.

    2012-07-01

    Based on the dynamical downscaling with the Advanced Research Weather (WRF-ARW) mesoscale model, the accuracy of the precipitation forecasts in Southwest Asia has been assessed. Results show that the accuracy of the 24-h and 48-h forecasts for precipitation is closely related to the complex topography of the mountain areas. To understand the impacts of the initial condition uncertainties on accuracy of the dynamical downscaling, a series of data assimilation experiments has been performed. The Advanced Television and Infrared Observation Satellite Operational Vertical Sounder (ATOVS) radiance observations and a data assimilation system named the Gridpoint Statistical Interpolation (GSI), developed by the National Centers for Environmental Prediction (NCEP), were used in this study. The results show that the satellite data provides beneficial information for improving the initial conditions for the dynamical model system and the “forecast” errors are reduced for most locations within the 24-h hindcasts.

  19. Design of a WRF Ensemble for Improved Wind Forecasts at Turbine Height

    NASA Astrophysics Data System (ADS)

    Gallus, W. A.; Deppe, A. J.; Takle, E. S.

    2010-12-01

    The inherent variability of wind requires accurate forecasts to optimize wind power generation. The weather research and forecasting (WRF) model with 10-km horizontal resolution was used to explore improvements in wind speed forecasts at hub height (80m). Results were validated using wind speed measurements at 80 m from a meteorological tower at the Pomeroy wind farm in northwestern Iowa. An ensemble consisting of different planetary boundary layer (PBL) schemes showed little spread between the individual PBL members. A second configuration using three random perturbations of the Global Forecast System (GFS) model produced more spread in the wind speed forecasts, but less model skill. A third ensemble with members having different initialization times showed model spread comparable to that from the perturbation results, but model skill was not compromised. In addition, we examined post-processing techniques such as bias correction of the diurnal cycle, training of the model for the day 2 forecast based on day 1 results, and bias corrections based on both observed and simulated wind direction and speed. Early evaluation suggests that the ensemble mean of the first and third ensembles provides a more skillful wind forecast than any particular member, and further improvements occur when bias corrections are made. The best improvement occurs when bias is corrected based on forecasted wind speed. Tests are ongoing with a six member ensemble that tries to balance the best configurations in terms of both skill and spread.

  20. Investigating Anomalies in the Output Generated by the Weather Research and Forecasting (WRF) Model

    NASA Astrophysics Data System (ADS)

    Decicco, Nicholas; Trout, Joseph; Manson, J. Russell; Rios, Manny; King, David

    2015-04-01

    The Weather Research and Forecasting (WRF) model is an advanced mesoscale numerical weather prediction (NWP) model comprised of two numerical cores, the Numerical Mesoscale Modeling (NMM) core, and the Advanced Research WRF (ARW) core. An investigation was done to determine the source of erroneous output generated by the NMM core. In particular were the appearance of zero values at regularly spaced grid cells in output fields and the NMM core's evident (mis)use of static geographic information at a resolution lower than the nesting level for which the core is performing computation. A brief discussion of the high-level modular architecture of the model is presented as well as methods utilized to identify the cause of these problems. Presented here are the initial results from a research grant, ``A Pilot Project to Investigate Wake Vortex Patterns and Weather Patterns at the Atlantic City Airport by the Richard Stockton College of NJ and the FAA''.

  1. Impact of Irrigation Methods on LSM Spinup and Initialization of WRF Forecasts

    NASA Astrophysics Data System (ADS)

    Lawston, P.; Santanello, J. A.; Zaitchik, B. F.; Beaudoing, H.

    2013-12-01

    In the United States, irrigation represents the largest consumption of fresh water and accounts for approximately one-third of all water usage. Irrigation has been shown to modify local hydrology and regional climate through a repartitioning of water at the surface and through the atmosphere, and can in some cases drastically change the terrestrial energy budget in agricultural areas during the growing season. Vegetation cover and soil moisture primarily control water and energy fluxes from the surface so accurate representation of the land surface characteristics is key to determining and predicting atmospheric conditions. This study utilizes NASA's Land Information System (LIS) and the NASA Unified Weather Research and Forecasting (NU-WRF) model to investigate changes in land-atmosphere interactions resulting from drip, flood, and sprinkler irrigation methods. The study area encompasses a 500 km x 600 km region of the Central Great Plains including portions of Nebraska, Kansas, Iowa, and Missouri. This area provides a steep irrigation gradient, as much of the western region is heavily irrigated while minimal irrigation occurs in the eastern section. Five-year irrigated LIS spinups were used to initialize two-day, 1-km WRF forecasts. Two forecast periods were chosen, one in a drier than normal year (2006) and one in a wetter than normal year (2008) to evaluate the sensitivity of the irrigation approaches and impacts to the background climate conditions. The offline and coupled simulation results show that both LIS spinups and NU-WRF forecasts are sensitive to irrigation and irrigation methods, as exhibited by significant changes to temperature, soil moisture, boundary layer height, and the partitioning of latent and sensible heat fluxes. Dry year impacts are greater than those in the wet year suggesting that the magnitude of these changes is dependent on the existing precipitation regime. Sprinkler and flood irrigation schemes impact the NU-WRF forecast the most, while drip irrigation has a comparatively small effect. Evaluation of the irrigation schemes using observations of soil moisture, fluxes, and meteorological state variables shows that a realistic characterization of the land surface in terms of land cover classification, soil type, and soil moisture anomalies via a LSM spinup are critical to producing a proper simulation of irrigation in land surface and coupled models.

  2. An Operational Environmental Meteorology Forecasting system for Eastern China

    NASA Astrophysics Data System (ADS)

    Zhou, Guangqiang; Xu, Jianming; Xie, Ying; Wu, Jianbin; Yu, Zhongqi; Chang, Luyu

    2015-04-01

    Since 2012 an operational environmental meteorology forecasting system was setup to provide daily forecasts of environmental meteorology pollutants for the Eastern China region. Initialized with 0.5 degree GFS meteorological fields, the system uses the WRF-Chem model to provide daily 96-hour forecasts. Model forecasts for meteorological fields and pollutants concentrations (e.g. PM2.5 and O3) as well as haze conditions are displayed through an open platform. Verifications of the model results in terms of statistical and graphical products are also displayed at the website. Currently, the modeling system provides strong support for the daily AQI forecasting of Shanghai, and it also provides guidance products for other meteorological agencies in the Eastern China region. Here the modeling system design will be presented, together with long-term verification results for PM2.5 and O3forecasts.

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

    EPA Science Inventory

    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. Track prediction of very severe cyclone `Nargis' using high resolution weather research forecasting (WRF) model

    NASA Astrophysics Data System (ADS)

    Pattanaik, D. R.; Rama Rao, Y. V.

    2009-08-01

    The recent very severe cyclonic storm (VSCS) ‘Nargis’ over the Bay of Bengal caused widespread destruction over Myanmar after hitting the coast on 2 May 2008. The real time forecasting of the VSCS ‘Nargis’ was a very difficult task as it did not follow the normal westerly/northwesterly track. In the present study, a detailed diagnostic analysis of the system ‘Nargis’ is carried out initially to investigate the features associated with this unusual movement and subsequently the real time forecast of VSCS ‘Nargis’ using high resolution advanced version weather research forecasting (WRF) model is presented. The advanced research WRF model was run for 72 h at 27 km and 20 km resolutions with 28, 29, 30 April and 1 May as the initial conditions. The diagnostic study indicates that the recurvature of the system ‘Nargis’ was mainly associated with: • upper level southerly/southwesterly steering wind at 200 hPa level associated with anticyclonic circulation over southeastern sector of the centre of the system

  5. The efficiency of the Weather Research and Forecasting (WRF) model for simulating typhoons

    NASA Astrophysics Data System (ADS)

    Haghroosta, T.; Ismail, W. R.; Ghafarian, P.; Barekati, S. M.

    2014-08-01

    The Weather Research and Forecasting (WRF) model includes various configuration options related to physics parameters, which can affect the performance of the model. In this study, numerical experiments were conducted to determine the best combination of physics parameterization schemes for the simulation of sea surface temperatures, latent heat flux, sensible heat flux, precipitation rate, and wind speed that characterized typhoons. Through these experiments, several physics parameterization options within the Weather Research and Forecasting (WRF) model were exhaustively tested for typhoon Noul, which originated in the South China Sea in November 2008. The model domain consisted of one coarse domain and one nested domain. The resolution of the coarse domain was 30 km, and that of the nested domain was 10 km. In this study, model simulation results were compared with the Climate Forecast System Reanalysis (CFSR) data set. Comparisons between predicted and control data were made through the use of standard statistical measurements. The results facilitated the determination of the best combination of options suitable for predicting each physics parameter. Then, the suggested best combinations were examined for seven other typhoons and the solutions were confirmed. Finally, the best combination was compared with other introduced combinations for wind-speed prediction for typhoon Washi in 2011. The contribution of this study is to have attention to the heat fluxes besides the other parameters. The outcomes showed that the suggested combinations are comparable with the ones in the literature.

  6. Improving Regional Forecast by Assimilating Atmospheric InfraRed Sounder (AIRS) Profiles into WRF Model

    NASA Technical Reports Server (NTRS)

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

    2009-01-01

    In data sparse regions, remotely-sensed observations can be used to improve analyses and produce improved forecasts. One such source comes from the Atmospheric InfraRed Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), represents one of the most advanced space-based atmospheric sounding systems. The purpose of this paper is to describe a procedure to optimally assimilate high resolution AIRS profile data into a regional configuration of the Advanced Research WRF (ARW) version 2.2 using WRF-Var. The paper focuses on development of background error covariances for the regional domain and background type, and an optimal methodology for ingesting AIRS temperature and moisture profiles as separate overland and overwater retrievals with different error characteristics. The AIRS thermodynamic profiles are derived from the version 5.0 Earth Observing System (EOS) science team retrieval algorithm and contain information about the quality of each temperature layer. The quality indicators were used to select the highest quality temperature and moisture data for each profile location and pressure level. The analyses were then used to conduct a month-long series of regional forecasts over the continental U.S. The long-term impacts of AIRS profiles on forecast were assessed against verifying NAM analyses and stage IV precipitation data.

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

    SciTech Connect

    Lundquist, J; Glascoe, L; Obrecht, J

    2010-03-18

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

  8. Urban irrigation effects on WRF-UCM summertime forecast skill over the Los Angeles metropolitan area

    NASA Astrophysics Data System (ADS)

    Vahmani, P.; Hogue, T. S.

    2015-10-01

    In the current study, we explicitly address the impacts of urban irrigation on the local hydrological cycle by integrating a previously developed irrigation scheme within the coupled framework of the Weather Research and Forecasting-Urban Canopy Models (WRF-UCM) over the semiarid Los Angeles metropolitan area. We focus on the impacts of irrigation on the urban water cycle and atmospheric feedback. Our results demonstrate a significant sensitivity of WRF-UCM simulated surface turbulent fluxes to the incorporation of urban irrigation. Introducing anthropogenic moisture, vegetated pixels show a shift in the energy partitioning toward elevated latent heat fluxes. The cooling effects of irrigation on daily peak air temperatures are evident over all three urban types, with the largest influence over low-intensity residential areas (average cooling of 1.64°C). The evaluation of model performance via comparison against CIMIS (California Irrigation Management Information System) evapotranspiration (ET) estimates indicates that WRF-UCM, after adding irrigation, performs reasonably during the course of the month of July, tracking day-to-day variability of ET with notable consistency. In the nonirrigated case, CIMIS-based ET fluctuations are significantly underestimated by the model. Our analysis shows the importance of accurate representation of urban irrigation in modeling studies, especially over water-scarce regions such as the Los Angeles metropolitan area. We also illustrate that the impacts of irrigation on simulated energy and water cycles are more critical for longer-term simulations due to the interactions between irrigation and soil moisture fluctuations.

  9. Using the LAPS / WRF system to Analyze and Forecast Solar Radiation

    NASA Astrophysics Data System (ADS)

    Albers, S. C.; Jankov, I.

    2011-12-01

    The LAPS system is being used to produce rapid update, high resolution analyses and forecasts of solar radiation. The cloud analysis uses satellite, METARs, radar, aircraft and model first guess information to produce an hourly 3-D field of cloud fraction, cloud liquid, and cloud ice. The cloud analysis and satellite data together are used to produce a gridded analysis of total solar radiation. This is verified against solar radiation measurements that are independent (not used in the analysis). Two domains are being run and verified at present. The one with the most stations covers the Oklahoma mesonet with about 100 pyranometers. The total solar radiation forecast is being run on two domains, and is being initialized using the same cloud analysis package that drives the analysis fields mentioned above. The Colorado domain produces hourly forecasts, initialized every 6 hours. It is verified with about 20 Oklahoma mesonet stations. The HWT domain initializes WRF every 2 hours, with 15-minute output. Forecasts are being compared with the Oklahoma mesonet. Real-time verification of the analyses (including images of the analysis), and forecasts can be seen on our website 'laps.noaa.gov', and will be explored in this presentation.

  10. Verification of ECMWF, GFS and WRF forecast in coastal desert region of Middle East

    NASA Astrophysics Data System (ADS)

    Nechaj, Pavol; Bartokov, Ivana

    2014-05-01

    Forecast skill of different models over Middle East region is presented. ECMWF has 12.5 km resolution, while WRF with 16 km and nested 5 km grid is initialized by GFS. The comparison encompasses first half of year 2012 and 48-72 hours forecasts, which are evaluated by standard scores Bias, Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). In Dubai Emirate, the temperature RMSE of ECMWF is higher by 1.5 deg. C on average. As far as the desert terrain is flat and the station is 100km form the coast, the reason is not straightforward result of better resolution. More precise capturing of the diurnal variation especially the sea breeze phenomenon seems of higher importance. 9 other stations were examined.

  11. High Resolution Forecasting System for Mountain area based on KLAPS-WRF

    NASA Astrophysics Data System (ADS)

    Chun, Ji Min; Rang Kim, Kyu; Lee, Seon-Yong; Kang, Wee Soo; Park, Jong Sun; Yi, Chae Yeon; Choi, Young-jean; Park, Eun Woo; Hong, Soon Sung; Jung, Hyun-Sook

    2013-04-01

    This paper reviews the results of recent observations and simulations on the thermal belt and cold air drainage, which are outstanding in local climatic phenomena in mountain areas. In a mountain valley, cold air pool and thermal belt were simulated with the Weather and Research Forecast (WRF) model and the Korea Local Analysis and Prediction System (KLAPS) to determine the impacts of planetary boundary layer (PBL) schemes and topography resolution on model performance. Using the KLAPS-WRF models, an information system was developed for 12 hour forecasting of cold air damage in orchard. This system was conducted on a three level nested grid from 1 km to 111 m horizontal resolution. Results of model runs were verified by the data from automated weather stations, which were installed at twelve sites in a valley at Yeonsuri, Yangpyeonggun, Gyeonggido to measure temperature and wind speed and direction during March to May 2012. The potential of the numerical model to simulate these local features was found to be dependent on the planetary boundary layer schemes. Statistical verification results indicate that Mellor-Yamada-Janjic (MYJ) PBL scheme was in good agreement with night time temperature, while the no-PBL scheme produced predictions similar to the day time temperature observation. Although the KLAPS-WRF system underestimates temperature in mountain areas and overestimates wind speed, it produced an accurate description of temperature, with an RMSE of 1.67 ?C in clear daytime. Wind speed and direction were not forecasted well in precision (RMSE: 5.26 m/s and 10.12 degree). It might have been caused by the measurement uncertainty and spatial variability. Additionally, the performance of KLAPS-WRF was performed to evaluate for different terrain resolution: Topography data were improved from USGS (United States Geological Survey) 30" to NGII (National Geographic Information Institute) 10 m. The simulated results were quantitatively compared to observations and there was a significant improvement (RMSE: 2.06 ?C -> 1.73 ?C) in the temperature prediction in the study area. The results will provide useful guidance of grid size selection on high resolution simulation over the mountain regions in Korea.

  12. High resolution WRF ensemble forecasting for irrigation: Multi-variable evaluation

    NASA Astrophysics Data System (ADS)

    Kioutsioukis, Ioannis; de Meij, Alexander; Jakobs, Hermann; Katragkou, Eleni; Vinuesa, Jean-Francois; Kazantzidis, Andreas

    2016-01-01

    An ensemble of meteorological simulations with the WRF model at convection-allowing resolution (2 km) is analysed in a multi-variable evaluation framework over Europe. Besides temperature and precipitation, utilized variables are relative humidity, boundary layer height, shortwave radiation, wind speed, convective and large-scale precipitation in view of explaining some of the biases. Furthermore, the forecast skill of evapotranspiration and irrigation water need is ultimately assessed. It is found that the modelled temperature exhibits a small but significant negative bias during the cold period in the snow-covered northeast regions. Total precipitation exhibits positive bias during all seasons but autumn, peaking in the spring months. The varying physics configurations resulted in significant differences for the simulated minimum temperature, summer rainfall, relative humidity, solar radiation and planetary boundary layer height. The interaction of the temperature and moisture profiles with the different microphysics schemes, results in excess convective precipitation using MYJ/WSM6 compared to YSU/Thompson. With respect to evapotranspiration and irrigation need, the errors using the MYJ configuration were in opposite directions and eventually cancel out, producing overall smaller biases. WRF was able to dynamically downscale global forecast data into finer resolutions in space and time for hydro-meteorological applications such as the irrigation management. Its skill was sensitive to the geographical location and physical configuration, driven by the variable relative importance of evapotranspiration and rainfall.

  13. Tracking tropical cloud systems for the diagnosis of simulations by the weather research and forecasting (WRF) model

    SciTech Connect

    Vogelmann, A.M.; Lin, W.; Cialella, A.; Luke, E. P.; Jensen, M. P.; Zhang, M. H.; Boer, E.

    2010-06-27

    To aid in improving model parameterizations of clouds and convection, we examine the capability of models, using explicit convection, to simulate the life cycle of tropical cloud systems in the tropical warm pool. The cloud life cycle is determined using a satellite cloud tracking algorithm (Boer and Ramanathan, J. Geophys. Res., 1997), and the statistics are compared to those of simulations using the Weather Research and Forecasting (WRF) Model. Using New York Blue, a Blue Gene/L supercomputer that is co-operated by Brookhaven and Stony Brook, simulations are run at a resolution comparable to the observations. Initial results suggest that the organization of the mesoscale convective systems is particularly sensitive to the cloud microphysics parameterization used.

  14. Tracking tropical cloud systems - Observations for the diagnosis of simulations by the Weather Research and Forecasting (WRF) Model

    SciTech Connect

    Vogelmann, A.M.; Lin, W.; Cialella, A.; Luke, E.; Jensen, M.; Zhang, M.

    2010-03-15

    To aid in improving model parameterizations of clouds and convection, we examine the capability of models, using explicit convection, to simulate the life cycle of tropical cloud systems in the vicinity of the ARM Tropical Western Pacific sites. The cloud life cycle is determined using a satellite cloud tracking algorithm (Boer and Ramanathan, 1997), and the statistics are compared to those of simulations using the Weather Research and Forecasting (WRF) Model. Using New York Blue, a Blue Gene/L supercomputer that is co-operated by Brookhaven and Stony Brook, simulations are run at a resolution comparable to the observations. Initial results suggest a computational paradox where, even though the size of the simulated systems are about half of that observed, their longevities are still similar. The explanation for this seeming incongruity will be explored.

  15. Use of Vertically Integrated Ice in WRF-Based Forecasts of Lightning Threat

    NASA Technical Reports Server (NTRS)

    McCaul, E. W., jr.; Goodman, S. J.

    2008-01-01

    Previously reported methods of forecasting lightning threat using fields of graupel flux from WRF simulations are extended to include the simulated field of vertically integrated ice within storms. Although the ice integral shows less temporal variability than graupel flux, it provides more areal coverage, and can thus be used to create a lightning forecast that better matches the areal coverage of the lightning threat found in observations of flash extent density. A blended lightning forecast threat can be constructed that retains much of the desirable temporal sensitivity of the graupel flux method, while also incorporating the coverage benefits of the ice integral method. The graupel flux and ice integral fields contributing to the blended forecast are calibrated against observed lightning flash origin density data, based on Lightning Mapping Array observations from a series of case studies chosen to cover a wide range of flash rate conditions. Linear curve fits that pass through the origin are found to be statistically robust for the calibration procedures.

  16. A WRF and MM5-based four-dimensional data assimilation weather analysis and forecasting system for wind energy applications

    NASA Astrophysics Data System (ADS)

    Liu, Y.; Warner, T.; Wu, W.; Chen, F.; Boehnert, J.; Frehlich, R.; Swerdlin, S.

    2008-12-01

    Accurate high-resolution weather analyses and forecasts are very important for wind energy production and management. A Real-Time Four Dimensional Data Assimilation (RTFDDA) and forecasting system has been developed at NCAR to address meteorological needs for estimating wind- energy generation through downscaling with nested grids. The RTFDDA system is built around the Penn State/NCAR Mesoscale Model version 5 (MM5) and the Weather Research and Forecasting (WRF) model. It is capable of continuously collecting and ingesting diverse synoptic and asynoptic weather observations from conventional and unconventional platforms, and provides continuous 4-D synthetic weather analyses, nowcasts and short-term forecasts for mesoscale regions. Operational RTFDDA systems have been implemented at seven US Army test ranges and also have supported tens of other applications in military, public and private sectors in the last seven years, providing rapidly updated, multi-scale weather analyses and forecasts with the fine-mesh domain having 0.5 - 3 km grid increments. The observational data ingested by the system includes WMO standard upper-air and surface reports, wind profilers, satellite cloud-drift winds, commercial aircraft reports, all available mesonet data, radar observations, and any special instruments that report temperature, winds and moistures. Recently, the system has been expanded to include several new modeling and data assimilation capabilities that are highly valuable for wind energy applications: a) Ensemble RTFDDA, which is a multi-model, mesoscale data analysis and forecasting system that samples uncertainties in the major components of RTFDDA and predicts the uncertainties in the weather forecasts by performing an ensemble of RTFDDA analyses and forecasts; b) LES (Large Eddy Simulation) modeling, which is nested down from the RTFDDA mesoscale data assimilation and forecasts to LES models with grid sizes of ~100 m for wind farm regions using GIS 30-m resolution terrain; and c) HRLDAS (High- Resolution Land-Surface Data Assimilation System), which assimilates high-resolution satellite vegetation and soil data to generate high-resolution, accurate soil moisture and temperature, which is critical for the evolution of the boundary layer structure and thus improves turbine-height wind energy and turbulence predictions. The capability of the modeling system is demonstrated with a case study for a wind farm located in southwest New Mexico, where nested domains were used with grid increments from 30 km down to 0.123 km.

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

    NASA Astrophysics Data System (ADS)

    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.

  18. Inclusion of biomass burning in WRF-Chem: Impact of wildfires on weather forecasts

    SciTech Connect

    Grell, G. A.; Freitas, Saulo; Stuefer, Martin; Fast, Jerome D.

    2011-06-06

    A plume rise algorithm for wildfires was included in WRF-Chem, and applied to look at the impact of intense wildfires during the 2004 Alaska wildfire season on weather forecasts using model resolutions of 10km and 2km. Biomass burning emissions were estimated using a biomass burning emissions model. In addition, a 1-D, time-dependent cloud model was used online in WRF-Chem to estimate injection heights as well as the final emission rates. It was shown that with the inclusion of the intense wildfires of the 2004 fire season in the model simulations, the interaction of the aerosols with the atmospheric radiation led to significant modifications of vertical profiles of temperature and moisture in cloud-free areas. On the other hand, when clouds were present, the high concentrations of fine aerosol (PM2.5) and the resulting large numbers of Cloud Condensation Nuclei (CCN) had a strong impact on clouds and microphysics, with decreased precipitation coverage and precipitation amounts during the first 12 hours of the integration, but significantly stronger storms during the afternoon hours.

  19. Predicting lightning activity in Greece with the Weather Research and Forecasting (WRF) model

    NASA Astrophysics Data System (ADS)

    Giannaros, Theodore M.; Kotroni, Vassiliki; Lagouvardos, Konstantinos

    2015-04-01

    In recent years, significant progress has been made in the development and implementation of parameterizations for the prediction of lightning. In the present study, the commonly used Price and Rind lightning parameterization is evaluated. This parameterization has been recently introduced in the state-of-the-art Weather Research and Forecasting (WRF) model, allowing for the online simulation of lightning activity. The evaluation of the parameterization is conducted for ten different single-day events that took place in Greece during the period of years from 2010 to 2013. Results show that the WRF model could be used for real-time lightning prediction applications, given that the lightning parameterization is properly adapted. In particular, the analysis revealed that model-resolved variables related to the microphysics and thermodynamics are necessary for controlling the parameterization of lightning, which otherwise results to significant overprediction. The total ice content, the maximum vertical velocity and the convective available potential energy were found to be the storm parameters that, when combined together, improve the ability of the model to correctly predict lightning, significantly restricting false alarms. This was further highlighted by separately examining two example case studies, for which the numerical simulations successfully reproduced the spatial and temporal characteristics of lightning activity.

  20. Cloudiness forecast with WRF mesoscale model: Validation from BLLAST 2011 field campaign

    NASA Astrophysics Data System (ADS)

    Gonzlez-Zamora, ngel; Yage, Carlos; Romn-Cascon, Carlos; Sastre, Mariano

    2013-04-01

    Cloud cover is one of the most difficult meteorological variables to predict by weather forecasting meteorological models. However it is a very important element to determine because it has multiple applications, not only in weather forecasting but also in other issues as those related to renewable energy, and particularly to those related to solar radiation, as can be solar thermal or photovoltaic power, where the passage of a cloud across the fields of solar panels can reduced energy production. Cloudiness forecasting is clearly a challenge for this field, where we can achieve a significant reduction in production costs of this energy if an accurate cloud cover forecasting is available. The processes involved in the formation and organization of clouds and precipitation extend from physical and chemical processes involved in small-scale nucleation and growth of cloud particles to the large-scale dynamic processes that are associated with synoptic weather systems. It is important to consider an appropriate scale, not only in determining the effects of a particular phenomenon but also in planning experimental campaigns. The objective of this work is to analyze the ability of the a mesoscale prediction model (WRF) to simulate cloud cover for three different days of the BLLAST 2011 field campaign, recently performed at the south of France, near the Pyrenees: a day with clear skies, an overcast day, and finally a day with clouds of evolution including some scattered showers. Sensitivity experiments using different PBL, Microphysics and Cumulus parameterizations have been carried out, and the simulations have been analyzed in order to establish the best configuration to accurate forecast the cloudiness and meteorological variables associated to it (T2m, longwave and shortwave incoming radiation at surface).

  1. Performance tuning Weather Research and Forecasting (WRF) Goddard longwave radiative transfer scheme on Intel Xeon Phi

    NASA Astrophysics Data System (ADS)

    Mielikainen, Jarno; Huang, Bormin; Huang, Allen H.

    2015-10-01

    Next-generation mesoscale numerical weather prediction system, the Weather Research and Forecasting (WRF) model, is a designed for dual use for forecasting and research. WRF offers multiple physics options that can be combined in any way. One of the physics options is radiance computation. The major source for energy for the earth's climate is solar radiation. Thus, it is imperative to accurately model horizontal and vertical distribution of the heating. Goddard solar radiative transfer model includes the absorption duo to water vapor,ozone, ozygen, carbon dioxide, clouds and aerosols. The model computes the interactions among the absorption and scattering by clouds, aerosols, molecules and surface. Finally, fluxes are integrated over the entire longwave spectrum.In this paper, we present our results of optimizing the Goddard longwave radiative transfer 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 optimizations improved the performance of the original Goddard longwave radiative transfer scheme on Xeon Phi 7120P by a factor of 2.2x. Furthermore, the same optimizations improved the performance of the Goddard longwave radiative transfer scheme on a dual socket configuration of eight core Intel Xeon E5-2670 CPUs by a factor of 2.1x compared to the original Goddard longwave radiative transfer scheme code.

  2. An Operational Configuration of the ARPS Data Analysis System to Initialize WRF in the NM'S Environmental Modeling System

    NASA Technical Reports Server (NTRS)

    Case, Jonathan; Blottman, Pete; Hoeth, Brian; Oram, Timothy

    2006-01-01

    The Weather Research and Forecasting (WRF) model is the next generation community mesoscale model designed to enhance collaboration between the research and operational sectors. The NM'S as a whole has begun a transition toward WRF as the mesoscale model of choice to use as a tool in making local forecasts. Currently, both the National Weather Service in Melbourne, FL (NWS MLB) and the Spaceflight Meteorology Group (SMG) are running the Advanced Regional Prediction System (AIRPS) Data Analysis System (ADAS) every 15 minutes over the Florida peninsula to produce high-resolution diagnostics supporting their daily operations. In addition, the NWS MLB and SMG have used ADAS to provide initial conditions for short-range forecasts from the ARPS numerical weather prediction (NWP) model. Both NM'S MLB and SMG have derived great benefit from the maturity of ADAS, and would like to use ADAS for providing initial conditions to WRF. In order to assist in this WRF transition effort, the Applied Meteorology Unit (AMU) was tasked to configure and implement an operational version of WRF that uses output from ADAS for the model initial conditions. Both agencies asked the AMU to develop a framework that allows the ADAS initial conditions to be incorporated into the WRF Environmental Modeling System (EMS) software. Developed by the NM'S Science Operations Officer (S00) Science and Training Resource Center (STRC), the EMS is a complete, full physics, NWP package that incorporates dynamical cores from both the National Center for Atmospheric Research's Advanced Research WRF (ARW) and the National Centers for Environmental Prediction's Non-Hydrostatic Mesoscale Model (NMM) into a single end-to-end forecasting system. The EMS performs nearly all pre- and postprocessing and can be run automatically to obtain external grid data for WRF boundary conditions, run the model, and convert the data into a format that can be readily viewed within the Advanced Weather Interactive Processing System. The EMS has also incorporated the WRF Standard Initialization (SI) graphical user interface (GUT), which allows the user to set up the domain, dynamical core, resolution, etc., with ease. In addition to the SI GUT, the EMS contains a number of configuration files with extensive documentation to help the user select the appropriate input parameters for model physics schemes, integration timesteps, etc. Therefore, because of its streamlined capability, it is quite advantageous to configure ADAS to provide initial condition data to the EMS software. One of the biggest potential benefits of configuring ADAS for ingest into the EMS is that the analyses could be used to initialize either the ARW or NMM. Currently, the ARPS/ADAS software has a conversion routine only for the ARW dynamical core. However, since the NIvIM runs about 2.5 times faster than the ARW, it is quite advantageous to be able to run an ADAS/NMM configuration operationally due to the increased efficiency.

  3. Cluster Analysis of the Trajectories for Forecasted Transport of Air Pollutants using WRF and HYSPLIT Models over Istanbul for January and July, 2009

    NASA Astrophysics Data System (ADS)

    Acar, M.; Balli, C.; Caglar, F.; Tan, E.; Onol, B.; Karan, H.; Unal, Y.

    2012-04-01

    The objective of this study is to determine the risk areas which would be under the influence of particulates and gases released from a hypothetical source in Istanbul and transported by dominant atmospheric flows for months of January and July. Both January and July wind simulations are performed for the year of 2009 using the WRF model to distinguish the seasonal variations. For the initial and boundary conditions, ECMWF forecast data set is used and the results are compared to the ECMWF ERA-Interim data. Three nested domains are used over the Northwestern part of Turkey, Istanbul has been chosen as the centre point of the nested domains, which have 420x270, 385x352, and 400x310 grid points for the 9km, 3km, and 1km resolutions, respectively, and all domains have 45 vertical levels. WSM6 microphysics and YSU planetary boundary layer schemes are used for all domains. Grell-Devenyi cumulus parameterization scheme is used for the mother domain. 30s horizontal grid spaced MODIS land use data is preferred instead of USGS land use data. 24 hours forecasts are calculated starting from both the 00 UTC and 12 UTC for all days of January and July. In this study, HYSPLIT 24 hourly forward trajectory analyses are performed by using WRF results for thirteen height levels: 10m, 50m, 100m, 200m, 300m, 400m, 500m, 600m, 800m, 1000m, 1500m, 2000m, and 3000m. 5 clusters are determined using Total Spatial Variance (TSV) method for each January and July trajectory analyses. Only the trajectories for 10m, 50m, 500m, and 2000m levels are clustered in order to decide the predominant flow regime for each month. Moreover, the same cluster analyses are achieved for the WRF simulations for the mother domain, ECMWF operational data, and ERA-Interim to discuss the model performance versus observational data based on 5 cluster members. Comparisons of wind speeds for Istanbul between observations (surface/upper air), and simulations (ECMWF Interim/ECMWF forecast/WRF) revealed that both forecast and WRF simulations are closer to the observations below 850hPa level. These comparisons increase our confidence on WRF simulations and associated forward trajectories below 850hPa level. Our analysis shows that the WRF model results and ECMWF forecast data have in good agreement for both January and July clusters especially for the levels of 10m, 500m, and 2000m for January and 10m and 500m for July. The cluster analyses of forward trajectories indicate that the predominant flow regime is northeasterly in both January and July. On the other hand, the longest trajectory is southwesterly in January but it is northeasterly in July indicating that the stronger flows dominate these directions. It is also estimated that the trajectories extend longer in January than July to the Northern part of Turkey for each cluster because of the stronger winds prevailing during winter months in association with synoptic scale systems. The distance between the trajectories end points and the source location is shorter in July due to relatively weak winds and it is estimated that only the Southern part of Turkey might be under the influence of particulates and gases released from Istanbul. Key words: Turkey, cluster analysis, trajectory analysis, WRF, HYSPLIT models.

  4. WRF and Mass-Consistent Wind Model Applications for Wind Power Forecasting in California's Coastal Complex Terrain

    NASA Astrophysics Data System (ADS)

    Clifford, K. T.; Clements, C. B.; Voss, M.

    2010-12-01

    Coastal Californias complex terrain shows promising potential for wind energy capture given the consistent, moderately strong onshore flow of air over the Coastal Range. Coupling this background flow and the different mountain atmosphere phenomena that act to further accelerate and channel the flow, certain sections of the coastal range could be a viable location for wind turbine farms. For this region, high resolution WRF and mass-consistent wind models were constructed, run, and their results were compared. The validation of the WRF and mass-consistent models forecast ability for several above ground levels proved they aptly capture the local flow. Furthermore, the models application in real time regional wind energy forecasting was explored.

  5. Testing of Typhoon WRF (TWRF) Initial Field and Its Application to Operational Typhoon Prediction at Taiwan

    NASA Astrophysics Data System (ADS)

    Chen, D.-S.; Hsiao, L.-F.; Yeh, T.-C.; Guo, Y.-R.

    2012-04-01

    Typhoons are the most significant weather systems in Taiwan, and they cause considerable damage there every year. The associated rainfall of typhoons is also one of the most important water resources in Taiwan. The numerical prediction models provide necessary guidance on typhoon track forecast. However, for a numerical model to predict accurate rainfall and wind field are still a highly challenging task. In addition, the two major factors that lead to challenge on typhoon forecasting in the vicinity of Taiwan are resulted from the lack of observational data over the Northwest Pacific Ocean and the significant interaction between typhoon circulation and Taiwan Central Mountain Range. In order to provide subjective guidance for the forecast team in the Central Weather Bureau (CWB) on typhoon track and precipitation, the numerical typhoon model is needed for more accurate typhoon predictions. Improve the skill of typhoon prediction is the highest priority for Taiwan's Central Weather Bureau (CWB). To achieve this goal, one key component is to improve the accuracy of model initial condition. More recently, the community model such as Weather Research and Forecasting (WRF) modeling system is widely applied to tropical cyclone forecast. A version of WRF model, called TWRF (Typhoon WRF) in the Central Weather Bureau, was developed from 2010. In the TWRF system, including the partial cycling approach, typhoon initialization scheme, outer loops in WRF 3DVAR system are used to examine the ability on the typhoon prediction. The ultimate aim is the construction of real-time forecasting of typhoon track and rainfall prior to and affecting Taiwan, to improve the typhoon warnings and provide local officials with the comprehensive information in the hardest hit areas as soon as possible. The detail performance of TWRF during 2010, 2011 typhoon season and the improvement strategies in the near future will be presented in the conference.

  6. Latitude belt convection permitting simulation using the Weather Research and Forecasting (WRF) model

    NASA Astrophysics Data System (ADS)

    Warrach-Sagi, Kirsten; Schwitalla, Thomas; Wulfmeyer, Volker

    2015-04-01

    Extreme events like the heat wave in summer 2003 in Central Europe and in August 2010 in Russia (which was associated with floodings of the Odra an in Pakistan) and severe floodings in Germany were caused by persistent so-called omega and blocking Vb weather situations in Europe. They are caused when quasi-stationary, quasi-resonant enhanced and quasi-resonant Rossby waves develop in mid-latitudes. To simulate quasi-stationary Rossby waves in numerical weather prediction and climate models at least a resolution of 20 km is required, however, to simulate the associated extremes the simulations need to be convection permitting. Further the high resolution allows the small scale structures to feed back to the large scale systems. Most of the current limited area, high-resolution models apply a domain which is centered over the region of interest. Such limited area model applications may suffer from a deterioration of synoptic features like low pressure systems due to effects in the boundary relaxation zone when downscaling reanalysis or global model simulation data. For Europe this is mainly caused by the longitudinal boundaries. A way to overcome these types of difficulties is to run a latitude belt simulation model. We applied the Weather Research and Forecasting (WRF) model with 3 km horizontal resolution for July and August 2013 forcing the model 6-hourly with ECMWF analyses data at 20N and 65N and with daily sea surface temperature data from the OSTIA project of the UK Met Office at 6 km resolution. The model domain encompasses 12000*1500*57 grid cells. First results of this so far unique simulation will be presented.

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

    SciTech Connect

    Iacono, Michael J.

    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.

  8. Application of the CloudSat and NEXRAD Radars Toward Improvements in High Resolution Operational Forecasts

    NASA Technical Reports Server (NTRS)

    Molthan, A. L.; Haynes, J. A.; Case, J. L.; Jedlovec, G. L.; Lapenta, W. M.

    2008-01-01

    As computational power increases, operational forecast models are performing simulations with higher spatial resolution allowing for the transition from sub-grid scale cloud parameterizations to an explicit forecast of cloud characteristics and precipitation through the use of single- or multi-moment bulk water microphysics schemes. investments in space-borne and terrestrial remote sensing have developed the NASA CloudSat Cloud Profiling Radar and the NOAA National Weather Service NEXRAD system, each providing observations related to the bulk properties of clouds and precipitation through measurements of reflectivity. CloudSat and NEXRAD system radars observed light to moderate snowfall in association with a cold-season, midlatitude cyclone traversing the Central United States in February 2007. These systems are responsible for widespread cloud cover and various types of precipitation, are of economic consequence, and pose a challenge to operational forecasters. This event is simulated with the Weather Research and Forecast (WRF) Model, utilizing the NASA Goddard Cumulus Ensemble microphysics scheme. Comparisons are made between WRF-simulated and observed reflectivity available from the CloudSat and NEXRAD systems. The application of CloudSat reflectivity is made possible through the QuickBeam radiative transfer model, with cautious application applied in light of single scattering characteristics and spherical target assumptions. Significant differences are noted within modeled and observed cloud profiles, based upon simulated reflectivity, and modifications to the single-moment scheme are tested through a supplemental WRF forecast that incorporates a temperature dependent snow crystal size distribution.

  9. WRF forecast skill of the Great Plains low level jet and its correlation to forecast skill of mesoscale convective system precipitation

    NASA Astrophysics Data System (ADS)

    Squitieri, Brian Joseph

    One of the primary mechanisms for supporting summer nocturnal precipitation across the central United States is the Great Plains low-level Jet (LLJ). Mesoscale Convective Systems (MCSs) are organized storm complexes that can be supported from the upward vertical motion supplied at the terminus of the LLJ, which bring beneficial rains to farmers. As such, a need for forecasting these storm complexes exists. Correlating forecast skills of the LLJ and MCS precipitation in high spatial resolution modeling was the main goal of this research. STAGE IV data was used as observations for MCS precipitation and the 00-hr 13 km RUC analysis was employed for evaluation of the LLJ. The 4 km WRF was used for high resolution forecast simulations, with 2 microphysics and 3 planetary boundary layer schemes selected for a sensitivity study to see which model run best simulated reality. It was found that the forecast skill of the potential temperature and directional components of the geostrophic and ageostrophic winds within the LLJ correlated well with MCS precipitation, especially early during LLJ evolution. Since the 20 real cases sampled consisted of three LLJ types (synoptic, inertial oscillation and transition), forecast skill in other parameters such as deep layer and low level shear, convergence, frontogenesis and stability parameters were compared to MCS forecast skill to see if consistent signals outside of the LLJ influenced MCS evolution in forecasts. No correlations were found among these additional parameters. Given the variety of synoptic setups present, the lack of forecast skill correlations between several variables and MCSs resulted as different synoptic or mesoscale mechanisms played varying roles if importance in different cases.

  10. WRF4G: The Weather Research Forecasting model workflow for the GRID

    NASA Astrophysics Data System (ADS)

    Fernandez-Quiruelas, Valvanuz; Fernandez Fernandez, Jesus; Cofino, Antonio S.; Fita, Lluis

    2010-05-01

    Several application areas as high energy physics or bio-applications have benefited for years from GRID technologies. Applications from the Earth Science community are starting to take advantage of this technology (see e.g. www.eu-degree.eu) . Earth science applications and, in particular, a climate and meteorological models poses a great challenge to the GRID in terms of the computing and storage requirements. These models are resolved by numerical equations that are CPU intensive applications which usually require long walltimes and produce large amounts of data. WRF for GRID (WRF4G) is a port of the WRF Modeling System to GRID environments. Small modifications to the source code of the model allow the monitoring and output data management in a flexible way. In addition to the model, the WRF Grid Enabling Layer (WRFGEL) is an interface between the model and the GRID, allowing the model to inform about its status, get the required input data and save the output data to a Storage Element (SE) in the GRID. Finally, a set of user scripts permits a flexible design of experiments consisting of realizations which can span different physics/parameters and/or a sequence of independent hindcasts. Currently, the heterogenous GRID infrastructure is subject to common failures and intermittent availability of resources the numerical weather models are not prepared for. For those reasons, in this contribution we present a new execution framework providing a software wrapper for a numerical prediction model. Since multi-site parallelism cannot be used due to latency, the GRID is best suited for large amounts of independent and relatively short simulations (ensembles). WRF4G is able to benefit from intrasite parallelism where available, though. The WRF4G framework has been adapted for the gLite middleware developed in the EGEE project (http://eu-egee.org), and used in EELA2 project .

  11. Intel Many Integrated Core (MIC) architecture optimization strategies for a memory-bound Weather Research and Forecasting (WRF) Goddard microphysics scheme

    NASA Astrophysics Data System (ADS)

    Mielikainen, Jarno; Huang, Bormin; Huang, Allen H.

    2014-10-01

    The Goddard cloud microphysics scheme is a sophisticated cloud microphysics scheme in the Weather Research and Forecasting (WRF) model. The WRF is a widely used weather prediction system in the world. It development is a done in collaborative around the globe. The Goddard microphysics scheme is very suitable for massively parallel computation as there are no interactions among horizontal grid points. Compared to the earlier microphysics schemes, the Goddard scheme incorporates a large number of improvements. Thus, we have optimized the code of this important part of WRF. In this paper, we present our results of optimizing the Goddard 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 Intel MIC is capable of executing a full operating system and entire programs rather than just kernels as the GPU do. The MIC 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 optimizations improved performance of the original code on Xeon Phi 7120P by a factor of 4.7x. Furthermore, the same optimizations improved performance on a dual socket Intel Xeon E5-2670 system by a factor of 2.8x compared to the original code.

  12. Tailoring seasonal climate forecasts for hydropower operations

    NASA Astrophysics Data System (ADS)

    Block, P.

    2011-04-01

    Integration of seasonal precipitation forecasts into water resources operations and planning is practically nonexistent, even in regions of scarcity. This is often attributable to water manager's tendency to act in a risk averse manner, preferring to avoid consequences of poor forecasts, at the expense of unrealized benefits. Convincing demonstrations of forecast value are therefore desirable to support assimilation into practice. A dynamically linked system, including forecast, rainfall-runoff, and hydropower models, is applied to the upper Blue Nile basin in Ethiopia to compare benefits and reliability generated by actual forecasts against a climatology-based approach, commonly practiced in most water resources systems. Processing one hundred decadal sequences demonstrates superior forecast-based benefits in 68 cases, a respectable advancement, however benefits in a few forecast-based sequences are noticeably low, likely to dissuade manager's adoption. A hydropower sensitivity test reveals a propensity toward poor-decision making when forecasts over-predict wet conditions. Tailoring the precipitation forecast to highlight critical dry forecasts minimizes this inclination, resulting in 97% of the sequences favoring the forecast-based approach. Considering managerial risk preferences for the system, even risk-averse actions, if coupled with forecasts, exhibit superior benefits and reliability compared with risk-taking tendencies conditioned on climatology.

  13. operational modelling and forecasting of the Iberian shelves ecosystem

    NASA Astrophysics Data System (ADS)

    Marta-Almeida, M.; Reboreda, R.; Rocha, C.; Dubert, J.; Nolasco, R.; Cordeiro, N.; Luna, T.; Rocha, A.; Silva, J. Lencart e.; Queiroga, H.; Peliz, A.; Ruiz-Villarreal, M.

    2012-04-01

    There is a growing interest on physical and biogeochemical oceanic hindcasts and forecasts from a wide range of users and businesses. In this contribution we present an operational biogeochemical forecast system for the Portuguese and Galician oceanographic regions, where atmospheric, hydrodynamic and biogeochemical variables are integrated. The ocean model ROMS, with a horizontal resolution of 3 km, is forced by the atmospheric model WRF and includes a NPZD biogeochemical module. In addition to oceanographic variables, the system predicts the concentration of nitrate, phytoplankton, zooplankton and detritus (mmolN m-3). Model results are compared against radar currents and remote sensed SST and chlorophyll. Quantitative skill assessment during a summer upwelling period shows that our modelling system adequately represents the surface circulation over the shelf including the observed spatial variability and trends of temperature and chlorophyll concentration. Additionally, the skill assessment also shows some deficiencies like the overestimation of upwelling circulation and consequently, of the duration and intensity of the phytoplankton blooms. These and other departures from the observations are discussed, their origins identified and future improvements suggested. The forecast system is the first of its kind in the region and provides free online distribution of model input and output, as well as comparisons of model results with satellite imagery for qualitative operational assessment of model skill.

  14. Towards Operational Modeling and Forecasting of the Iberian Shelves Ecosystem

    PubMed Central

    Marta-Almeida, Martinho; Reboreda, Rosa; Rocha, Carlos; Dubert, Jesus; Nolasco, Rita; Cordeiro, Nuno; Luna, Tiago; Rocha, Alfredo; Lencart e Silva, João D.; Queiroga, Henrique; Peliz, Alvaro; Ruiz-Villarreal, Manuel

    2012-01-01

    There is a growing interest on physical and biogeochemical oceanic hindcasts and forecasts from a wide range of users and businesses. In this contribution we present an operational biogeochemical forecast system for the Portuguese and Galician oceanographic regions, where atmospheric, hydrodynamic and biogeochemical variables are integrated. The ocean model ROMS, with a horizontal resolution of 3 km, is forced by the atmospheric model WRF and includes a Nutrients-Phytoplankton-Zooplankton-Detritus biogeochemical module (NPZD). In addition to oceanographic variables, the system predicts the concentration of nitrate, phytoplankton, zooplankton and detritus (mmol N m−3). Model results are compared against radar currents and remote sensed SST and chlorophyll. Quantitative skill assessment during a summer upwelling period shows that our modelling system adequately represents the surface circulation over the shelf including the observed spatial variability and trends of temperature and chlorophyll concentration. Additionally, the skill assessment also shows some deficiencies like the overestimation of upwelling circulation and consequently, of the duration and intensity of the phytoplankton blooms. These and other departures from the observations are discussed, their origins identified and future improvements suggested. The forecast system is the first of its kind in the region and provides free online distribution of model input and output, as well as comparisons of model results with satellite imagery for qualitative operational assessment of model skill. PMID:22666349

  15. Development of Operational Hydrologic Forecasting Capabilities

    NASA Astrophysics Data System (ADS)

    Clark, M. P.; Hay, L. E.; Whitaker, J. S.

    2001-12-01

    Two obstacles limit the use of Numerical Weather Prediction (NWP) model output in hydrologic prediction systems. First, meteorological forecasts from current-day NWP models are laden with biases. Secondly, NWP model forecasts at the space/time scales used in hydrologic models are unreliable. Both of these obstacles can be overcome through statistical downscaling using Model Output Statistics (MOS), where the development of empirical relationships between grid point values of NWP output (e.g., vertical velocity, total column precipitable water, static stability) and observed data (e.g., maximum temperature at a point location) provide a statistical correction of NWP forecasts. However, statistical intervention using MOS is difficult to apply in practice because operational modeling centers continually update ("improve") forecast models. Such frequent updates ensures a state-of-the-art forecasting system, but severely degrades the utility of archived forecasts from previous versions of the NWP models. The National Oceanic and Atmospheric Administration (NOAA) Climate Diagnostics Center (CDC), in collaboration with the Climate Research Division of SCRIPPS, is generating a re-forecast data set using a fixed version (circa 1998) of the NCEP operational NWP model. In this study, we statistically downscale the forecast archive to improve model forecasts of precipitation and temperature, and assess the benefits of a fixed version of the NWP model for hydrologic predictions. Results from cross-validated prediction experiments show that statistically downscaled forecasts of precipitation and temperature are free of systematic biases, and of higher skill than the raw NWP output. These downscaled NWP forecasts are used as input to hydrologic models in select river basins in the contiguous United States, and the performance of the NWP-based forecasts is compared against the National Weather Service (NWS) Extended Streamflow Prediction (ESP) procedure. Hydrologic forecasts made using statistically downscaled fixed NWP output were significantly more accurate, both in terms of deterministic and probabilistic forecast skill, than hydrologic forecasts made using the NWS ESP approach. Forecast improvements were most pronounced in snowmelt-dominated river basins, where short-term variations in runoff are more strongly influenced by variations in temperature than variations in precipitation. Hydrologic forecasts based on raw (uncorrected) NWP output were of similar accuracy, and in some cases worse, than the NWS ESP forecasts. For the purposes of hydrologic prediction, it is preferable to use an older fixed version of the NWP model with a long archive of forecasts than to have a current state-of-the-art NWP model that includes no forecast archive at all.

  16. Using Forecast Information in Reservoir Operation

    NASA Astrophysics Data System (ADS)

    Faber, B.

    2011-12-01

    Reservoir operation is a series of decisions made over time. We choose whether to release water for various downstream purposes, or store it for later use. We choose whether to detain high flows to protect downstream areas, or pass that flow to retain space to store imminent higher flows. These decisions are driven by current and future inflows to the reservoir, and yet those inflows are uncertain and extremely variable. Conceptually, more information provides opportunity for better decisions, and so information about future inflows can improve reservoir operations. However, uncertain information must be used carefully, with awareness of the uncertainty and the likely consequence of "wrong" decisions (i.e., those with consequences worse than decisions that might otherwise have been made.) The historical streamflow record offers one source of information on the range and timing of streamflows. Streamflow forecasting provides additional valuable information on coming reservoir inflows, both at short and longer time scales. For example, 5-day flow forecasts based on forecasted precipitation can inform rain-flood operations, while seasonal snowmelt forecasts can aid snowmelt-flood operation, reservoir refill, and seasonal allocation of water supply. Forecast information can aid our decision-making greatly, but too much reliance on an incorrect forecast can make operation worse. Informed use of forecasts requires an understanding of the expected range of the actual streamflow (the error distribution). Forecast products must therefore be provided with a description of skill and error distribution understood by the producers and users of that information. Using forecasts wisely, with an understanding of their uncertainty, is an important aspect of the operation of our nation's Federal reservoirs.

  17. Tsunami Forecast: Connecting Science with Warning Operations

    NASA Astrophysics Data System (ADS)

    Titov, V. V.

    2014-12-01

    Tsunami modeling capability had been rapidly developing even before the watershed event of the 2004 Sumatra tsunami. During 1990-2000, the International Decade for Natural Disaster Reduction, the tsunami scientific community took on the difficult task of developing the modeling capability that would provide accuracy needed for long-term tsunami forecast tsunami hazard maps. After exhaustive field, laboratory and modeling efforts by the international scientific community, the modeling capability has been achieved with accuracy deemed sufficient for operational use. Several real-time model forecast tools started to be used at TWCs in the US and Japan. In parallel, the observational component of tsunami warning systems had been improving, including updated existing seismic and coastal sea-level stations array. New early detection and measurement system (DART) has been developed specifically for tsunami forecast applications. The 2004 Sumatra tsunami has triggered the efforts of intensive implementation of science results into operational tsunami warning capabilities. At present, several tsunami forecast systems, based on various modeling and detection capabilities, are operational. Since 2004, over 40 tsunamis, including the 2011 Japanese tsunami, provided real-time tests for the tsunami forecast system capabilities. Preliminary assessment of tsunami forecast performance will be presented based on the analysis of the U.S. operational tsunami inundation forecast. Assessing forecast performance is important to evaluate the needs for improvement and further research. Baseline of the tsunami forecast skills has now been established and will be presented based on the data from the tsunamis during the decade. Future improvements and future challenges will also be discussed.

  18. Explicitly-coupled cloud physics and radiation parameterizations and subsequent evaluation in WRF high-resolution convective forecasts

    NASA Astrophysics Data System (ADS)

    Thompson, Gregory; Tewari, Mukul; Ikeda, Kyoko; Tessendorf, Sarah; Weeks, Courtney; Otkin, Jason; Kong, Fanyou

    2016-02-01

    The impacts of various assumptions of cloud properties represented within a numerical weather prediction model's radiation scheme are demonstrated. In one approach, the model assumed the radiative effective radii of cloud water, cloud ice, and snow were represented by values assigned a priori, whereas a second, "coupled" approach utilized known cloud particle assumptions in the microphysics scheme that evolved during the simulations to diagnose the radii explicitly. This led to differences in simulated infrared (IR) brightness temperatures, radiative fluxes through clouds, and resulting surface temperatures that ultimately affect model-predicted diurnally-driven convection. The combined approach of evaluating simulated versus observed IR brightness temperatures, radiation reaching the ground, and surface temperature forecasts revealed the root model biases better than evaluating any single variable. This study found that the Weather Research and Forecasting (WRF) model predicted less overall clouds than was observed, particularly in the mid‑troposphere, but that properly connecting the assumptions of particle sizes in the microphysics scheme to the radiation scheme resulted in sensible cloud-radiation indirect effects and modest improvements in simulated IR brightness temperature, amount of solar radiation reaching the ground, and surface temperature.

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

    NASA Astrophysics Data System (ADS)

    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.

  20. Revisiting Intel Xeon Phi optimization of Thompson cloud microphysics scheme in Weather Research and Forecasting (WRF) model

    NASA Astrophysics Data System (ADS)

    Mielikainen, Jarno; Huang, Bormin; Huang, Allen

    2015-10-01

    The Thompson cloud microphysics scheme is a sophisticated cloud microphysics scheme in the Weather Research and Forecasting (WRF) model. The scheme is very suitable for massively parallel computation as there are no interactions among horizontal grid points. Compared to the earlier microphysics schemes, the Thompson scheme incorporates a large number of improvements. Thus, we have optimized the speed of this important part of WRF. Intel Many Integrated Core (MIC) ushers in a new era of supercomputing speed, performance, and compatibility. It allows the developers to run code at trillions of calculations per second using the familiar programming model. In this paper, we present our results of optimizing the Thompson microphysics scheme on Intel Many Integrated Core Architecture (MIC) hardware. The Intel Xeon Phi coprocessor is the first product based on Intel MIC architecture, and it consists of up to 61 cores connected by a high performance on-die bidirectional interconnect. The coprocessor supports all important Intel development tools. Thus, the development environment is familiar one to a vast number of CPU developers. Although, getting a maximum performance out of MICs will require using some novel optimization techniques. New optimizations for an updated Thompson scheme are discusses in this paper. The optimizations improved the performance of the original Thompson code on Xeon Phi 7120P by a factor of 1.8x. Furthermore, the same optimizations improved the performance of the Thompson on a dual socket configuration of eight core Intel Xeon E5-2670 CPUs by a factor of 1.8x compared to the original Thompson code.

  1. Operational seasonal forecasting of crop performance.

    PubMed

    Stone, Roger C; Meinke, Holger

    2005-11-29

    Integrated, interdisciplinary crop performance forecasting systems, linked with appropriate decision and discussion support tools, could substantially improve operational decision making in agricultural management. Recent developments in connecting numerical weather prediction models and general circulation models with quantitative crop growth models offer the potential for development of integrated systems that incorporate components of long-term climate change. However, operational seasonal forecasting systems have little or no value unless they are able to change key management decisions. Changed decision making through incorporation of seasonal forecasting ultimately has to demonstrate improved long-term performance of the cropping enterprise. Simulation analyses conducted on specific production scenarios are especially useful in improving decisions, particularly if this is done in conjunction with development of decision-support systems and associated facilitated discussion groups. Improved management of the overall crop production system requires an interdisciplinary approach, where climate scientists, agricultural scientists and extension specialists are intimately linked with crop production managers in the development of targeted seasonal forecast systems. The same principle applies in developing improved operational management systems for commodity trading organizations, milling companies and agricultural marketing organizations. Application of seasonal forecast systems across the whole value chain in agricultural production offers considerable benefits in improving overall operational management of agricultural production. PMID:16433097

  2. Operational seasonal forecasting of crop performance

    PubMed Central

    Stone, Roger C; Meinke, Holger

    2005-01-01

    Integrated, interdisciplinary crop performance forecasting systems, linked with appropriate decision and discussion support tools, could substantially improve operational decision making in agricultural management. Recent developments in connecting numerical weather prediction models and general circulation models with quantitative crop growth models offer the potential for development of integrated systems that incorporate components of long-term climate change. However, operational seasonal forecasting systems have little or no value unless they are able to change key management decisions. Changed decision making through incorporation of seasonal forecasting ultimately has to demonstrate improved long-term performance of the cropping enterprise. Simulation analyses conducted on specific production scenarios are especially useful in improving decisions, particularly if this is done in conjunction with development of decision-support systems and associated facilitated discussion groups. Improved management of the overall crop production system requires an interdisciplinary approach, where climate scientists, agricultural scientists and extension specialists are intimately linked with crop production managers in the development of targeted seasonal forecast systems. The same principle applies in developing improved operational management systems for commodity trading organizations, milling companies and agricultural marketing organizations. Application of seasonal forecast systems across the whole value chain in agricultural production offers considerable benefits in improving overall operational management of agricultural production. PMID:16433097

  3. Impacts of AMSU-A, MHS and IASI data assimilation on temperature and humidity forecasts with GSI-WRF over the western United States

    NASA Astrophysics Data System (ADS)

    Bao, Y.; Xu, J.; Powell, A. M., Jr.; Shao, M.; Min, J.; Pan, Y.

    2015-10-01

    Using NOAA's Gridpoint Statistical Interpolation (GSI) data assimilation system and NCAR's Advanced Research WRF (Weather Research and Forecasting) (ARW-WRF) regional model, six experiments are designed by (1) a control experiment (CTRL) and five data assimilation (DA) experiments with different data sets, including (2) conventional data only (CON); (3) microwave data (AMSU-A + MHS) only (MW); (4) infrared data (IASI) only (IR); (5) a combination of microwave and infrared data (MWIR); and (6) a combination of conventional, microwave and infrared observation data (ALL). One-month experiments in July 2012 and the impacts of the DA on temperature and moisture forecasts at the surface and four vertical layers over the western United States have been investigated. The four layers include lower troposphere (LT) from 800 to 1000 hPa, middle troposphere (MT) from 400 to 800 hPa, upper troposphere (UT) from 200 to 400 hPa, and lower stratosphere (LS) from 50 to 200 hPa. The results show that the regional GSI-WRF system is underestimating the observed temperature in the LT and overestimating in the UT and LS. The MW DA reduced the forecast bias from the MT to the LS within 30 h forecasts, and the CON DA kept a smaller forecast bias in the LT for 2-day forecasts. The largest root mean square error (RMSE) is observed in the LT and at the surface (SFC). Compared to the CTRL, the MW DA produced the most positive contribution in the UT and LS, and the CON DA mainly improved the temperature forecasts at the SFC. However, the IR DA gave a negative contribution in the LT. Most of the observed humidity in the different vertical layers is overestimated in the humidity forecasts except in the UT. The smallest bias in the humidity forecast occurred at the SFC and in the UT. The DA experiments apparently reduced the bias from the LT to UT, especially for the IR DA experiment, but the RMSEs are not reduced in the humidity forecasts. Compared to the CTRL, the IR DA experiment has a larger RMSE in the moisture forecast, although the smallest bias is found in the LT and MT.

  4. Experiments of the WRF three-/four-dimensional variational (3/4DVAR) data assimilation in the forecasting of Antarctic cyclones

    NASA Astrophysics Data System (ADS)

    Chu, Kekuan; Xiao, Qingnong; Liu, Chengsi

    2013-05-01

    The three-/four-dimensional variational data assimilation systems (3/4DVAR) of the Weather Research and Forecasting (WRF) model were explored in the forecasting of two Antarctic synoptic cyclones, which had large influence on the Ross Sea/Ross Ice Shelf region in October 2007. A suite of variational data assimilation experiments, including regular 3DVAR, high-resolution 3DVAR, and 4DVAR experiments, were designed to evaluate their performances in weather analysis and forecasting in Antarctica. In general, both 4DVAR and high-resolution 3DVAR experiments showed better forecasting skill than regular 3DVAR experiments. High-resolution 3DVAR experiments were the most efficient in reducing the analysis errors of surface winds and temperature, and had the best performance during the first 24 h of forecasting. However, during the following forecast period, 4DVAR experiments showed either better or about comparable performance to high-resolution 3DVAR experiments. These results indicate that increasing the spatial resolution during 3DVAR is an economical approach to improving the weather analysis and forecasting over Antarctica. At the same time, the 4DVAR approach had a longer impact on forecasting than the high-resolution 3DVAR approach. Understandably, both of the variational assimilation approaches are promising techniques toward improving the regional analysis and forecasting over Antarctica.

  5. Optimizing the updated Goddard shortwave radiation Weather Research and Forecasting (WRF) scheme for Intel Many Integrated Core (MIC) architecture

    NASA Astrophysics Data System (ADS)

    Mielikainen, Jarno; Huang, Bormin; Huang, Allen H.-L.

    2015-05-01

    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 updated Goddard shortwave radiation Weather Research and Forecasting (WRF) 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 co-processor 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 Xeon Phi will require using some novel optimization techniques. Those optimization techniques are discusses in this paper. The results show that the optimizations improved performance of the original code on Xeon Phi 7120P by a factor of 1.3x.

  6. Timetable of an operational flood forecasting system

    NASA Astrophysics Data System (ADS)

    Liechti, Katharina; Jaun, Simon; Zappa, Massimiliano

    2010-05-01

    At present a new underground part of Zurich main station is under construction. For this purpose the runoff capacity of river Sihl, which is passing beneath the main station, is reduced by 40%. If a flood is to occur the construction site is evacuated and gates can be opened for full runoff capacity to prevent bigger damages. However, flooding the construction site, even if it is controlled, is coupled with costs and retardation. The evacuation of the construction site at Zurich main station takes about 2 to 4 hours and opening the gates takes another 1 to 2 hours each. In the upper part of the 336 km2 Sihl catchment the Sihl lake, a reservoir lake, is situated. It belongs and is used by the Swiss Railway Company for hydropower production. This lake can act as a retention basin for about 46% of the Sihl catchment. Lowering the lake level to gain retention capacity, and therewith safety, is coupled with direct loss for the Railway Company. To calculate the needed retention volume and the water to be released facing unfavourable weather conditions, forecasts with a minimum lead time of 2 to 3 days are needed. Since the catchment is rather small, this can only be realised by the use of meteorological forecast data. Thus the management of the construction site depends on accurate forecasts to base their decisions on. Therefore an operational hydrological ensemble prediction system (HEPS) was introduced in September 2008 by the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). It delivers daily discharge forecasts with a time horizon of 5 days. The meteorological forecasts are provided by MeteoSwiss and stem from the operational limited-area COSMO-LEPS which downscales the ECMWF ensemble prediction system to a spatial resolution of 7 km. Additional meteorological data for model calibration and initialisation (air temperature, precipitation, water vapour pressure, global radiation, wind speed and sunshine duration) and radar data are also provided by MeteoSwiss. Additional meteorological and hydrological observations are provided by a hydropower company, the Canton of Zurich and the Federal Office for the Environment (FOEN). The hydrological forecasting is calculated by the semi-distributed hydrological model PREVAH (Precipitation-Runoff-EVapotranspiration-HRU-related Model) and is further processed by the hydraulic model FLORIS. Finally the forecasts and alerts along with additional meteorological and hydrological observations and forecasts from collaborating institution are sent to a webserver accessible for decision makers. We will document the setup of our operational flood forecasting system, evaluate its performance and show how the collaboration and communication between science and practice, including all the different interests, works for this particular example.

  7. Impacts of AMSU-A/MHS and IASI data assimilation on temperature and humidity forecasts with GSI/WRF over the Western United States

    NASA Astrophysics Data System (ADS)

    Bao, Y.; Xu, J.; Powell, A. M., Jr.; Shao, M.

    2015-06-01

    Using NOAA's Gridpoint Statistical Interpolation (GSI) data assimilation system and NCAR's Advanced Research WRF (ARW-WRF) regional model, six experiments are designed by (1) control experiment (CTRL) and five data assimilation (DA) experiments with different data sets including (2) conventional data only (CON), (3) microwave data (AMSU-A + MHS) only (MW), (4) infrared data (IASI) only (IR), (5) combination of microwave and infrared data (MWIR), (6) combination of conventional, microwave and infrared observation data (ALL). One month experiments in July 2012 and impacts of the DA on temperature and moisture forecasts at the surface and four vertical layers, which over the western United States have been investigated. The four layers include lower troposphere (LT) from 800 to 1000 hPa}, middle troposphere (MT) from 400 to 800 hPa, upper troposphere (UT) from 200 to 400 hPa and lower stratosphere (LS) from 50 to 200 hPa. The results show that the regional GSI/WRF system is underestimating the observed temperature in the LT and overestimating in the UT and LS. The MW DA reduced the forecast bias from the MT to the LS within 30 h forecasts, and the CON DA kept a smaller forecast bias in the LT for 2-day forecasts. The largest RMS error is observed in the LT and at the surface (SFC). Compared to the CTRL, the MW DA made the most positive contribution in the UT and LS, and the CON DA mainly improved the temperature forecasts at the SFC. However, the IR DA made a negative contribution in the LT. Most of the observed humidity in the different vertical layers is overestimated in the humidity forecasts except in the UT. The smallest bias in the humidity forecast occurred at the SFC and UT. The DA experiments apparently reduced the bias from the LT to UT, especially for the IR DA experiment, but the RMS errors are not reduced in the humidity forecasts. Compared to the CTRL, the IR DA experiment has a larger RMS error in the moisture forecast although the smallest bias is found in the LT and MT.

  8. Initial results on computational performance of Intel Many Integrated Core (MIC) architecture: implementation of the Weather and Research Forecasting (WRF) Purdue-Lin microphysics scheme

    NASA Astrophysics Data System (ADS)

    Mielikainen, Jarno; Huang, Bormin; Huang, Allen H.

    2014-10-01

    Purdue-Lin scheme is a relatively sophisticated microphysics scheme in the Weather Research and Forecasting (WRF) model. The scheme includes six classes of hydro meteors: water vapor, cloud water, raid, cloud ice, snow and graupel. The scheme is very suitable for massively parallel computation as there are no interactions among horizontal grid points. In this paper, we accelerate the Purdue Lin scheme using Intel Many Integrated Core Architecture (MIC) hardware. The Intel Xeon Phi is a high performance coprocessor consists of up to 61 cores. The Xeon Phi is connected to a CPU via the PCI Express (PICe) bus. In this paper, we will discuss in detail the code optimization issues encountered while tuning the Purdue-Lin microphysics Fortran code for Xeon Phi. In particularly, getting a good performance required utilizing multiple cores, the wide vector operations and make efficient use of memory. The results show that the optimizations improved performance of the original code on Xeon Phi 5110P by a factor of 4.2x. Furthermore, the same optimizations improved performance on Intel Xeon E5-2603 CPU by a factor of 1.2x compared to the original code.

  9. Operational aerosol and dust storm forecasting

    NASA Astrophysics Data System (ADS)

    Westphal, D. L.; Curtis, C. A.; Liu, M.; Walker, A. L.

    2009-03-01

    The U. S. Navy now conducts operational forecasting of aerosols and dust storms on global and regional scales. The Navy Aerosol Analysis and Prediction System (NAAPS) is run four times per day and produces 6-day forecasts of sulfate, smoke, dust and sea salt aerosol concentrations and visibility for the entire globe. The Coupled Ocean Atmosphere Mesoscale Prediction System (COAMPS) is run twice daily for Southwest Asia and produces 3-day forecasts of dust, smoke, and visibility. The graphical output from these models is available on the Internet (www.nrlmry.navy.mil/aerosol/). The aerosol optical properties are calculated for each specie for each forecast output time and used for sea surface temperature (SST) retrieval corrections, regional electro-optical (EO) propagation assessments, and the development of satellite algorithms. NAAPS daily aerosol optical depth (AOD) values are compared with the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) AOD values. Visibility forecasts are compared quantitatively with surface synoptic reports.

  10. Operational air quality forecasting system for Spain: CALIOPE

    NASA Astrophysics Data System (ADS)

    Baldasano, J. M.; Piot, M.; Jorba, O.; Goncalves, M.; Pay, M.; Pirez, C.; Lopez, E.; Gasso, S.; Martin, F.; García-Vivanco, M.; Palomino, I.; Querol, X.; Pandolfi, M.; Dieguez, J. J.; Padilla, L.

    2009-12-01

    The European Commission (EC) and the United States Environmental Protection Agency (US-EPA) have shown great concerns to understand the transport and dynamics of pollutants in the atmosphere. According to the European directives (1996/62/EC, 2002/3/EC, 2008/50/EC), air quality modeling, if accurately applied, is a useful tool to understand the dynamics of air pollutants, to analyze and forecast the air quality, and to develop programs reducing emissions and alert the population when health-related issues occur. The CALIOPE project, funded by the Spanish Ministry of the Environment, has the main objective to establish an air quality forecasting system for Spain. A partnership of four research institutions composes the CALIOPE project: the Barcelona Supercomputing Center (BSC), the center of investigation CIEMAT, the Earth Sciences Institute ‘Jaume Almera’ (IJA-CSIC) and the CEAM Foundation. CALIOPE will become the official Spanish air quality operational system. This contribution focuses on the recent developments and implementation of the integrated modelling system for the Iberian Peninsula (IP) and Canary Islands (CI) with a high spatial and temporal resolution (4x4 sq. km for IP and 2x2 sq. km for CI, 1 hour), namely WRF-ARW/HERMES04/CMAQ/BSC-DREAM. The HERMES04 emission model has been specifically developed as a high-resolution (1x1 sq. km, 1 hour) emission model for Spain. It includes biogenic and anthropogenic emissions such as on-road and paved-road resuspension production, power plant generation, ship and plane traffic, airports and ports activities, industrial and agricultural sectors as well as domestic and commercial emissions. The qualitative and quantitative evaluation of the model was performed for a reference year (2004) using data from ground-based measurement networks. The products of the CALIOPE system will provide 24h and 48h forecasts for O3, NO2, SO2, CO, PM10 and PM2.5 at surface level. An operational evaluation system has been developed to provide near real-time evaluation products for the Spanish territory. For this purpose, more than 130 surface stations, 2 ozonesondes and the OMI satellite retrieval information are introduced to the system on a daily basis. A web-based visualization system allows a straightforward access to all the evaluation products. The present contribution will describe the main characteristics of the operational system and results of the operational evaluation.

  11. A regional ocean current forecast operational system for the sea around Taiwan

    NASA Astrophysics Data System (ADS)

    Yu, Hao-Cheng; Yu, Jason C. S.; Chu, Chi-Hao; Teyr, Terng-Chuen

    2014-05-01

    Ocean current prediction is an important and a challenging task on marine operational forecasting system. This has been a widely developed subject in recent year internationally. The system can provide information to various applications, i.e. coastal structure design, environment management, navigation operation, fishery and recreations. Another potential application of the current prediction is to provide information for marine rescue and emergency response. Through the aid from high performance computing techniques, ocean current forecasting can be efficiently operated within a feasible time by covering a wider domain of operation and with higher resolution. A multi-scale Regional Ocean Current Forecast Operational System (ROCFOS) is developed at Central Weather Bureau (CWB), Taiwan, since 2008. The system has coupled 4 different scales of model domains together, from the Pacific to the seas around Taiwan. The modeling system has been constructed based on ROMS and SELFE and implemented for daily operation. The system is re-initialized with HYCOM and RTOFS daily forecast and driven by meteorological predictions from NCEP GFS and WRF developed at CWB. Satellite data from GHRSST and AVISO are used the calibration and the verification of model results. An NCAR/ncl tool was also developed to process both structured and unstructured data. The modeling system and the analysis of the operational results will be presented.

  12. Operational data flow between hydrological forecasting systems

    NASA Astrophysics Data System (ADS)

    Davids, Femke; de Kleermaeker, Simone; van Loenen, Arnejan; Gijsbers, Peter; Bogaard, Tom; Twigt, Daniel; Heynert, Karel

    2014-05-01

    One of the major challenges in operational forecasting is organizing and controlling the flow of data. In the Water Management Centre for the Netherlands several FEWS (Flood Early Warning Systems) have been set up for operational use. The six systems specialize in different areas, namely i) fluvial flooding, ii) water distribution during droughts, iii) real time control of canal water levels and gauges, iv) coastal flooding, v) lake management and flooding and vi) water management in the delta area. These systems obtain data partly from the same but also from different data sources. Each individual system uses (different) models and pre and post processing steps that have been optimized for the most important parameters. It is crucial to exchange data and forecasts in an efficient way between the systems, for example to use as boundaries in model runs. This paper will describe the methods and challenges that we face in organizing the data flow between these systems.

  13. Operational earthquake forecasting can enhance earthquake preparedness

    USGS Publications Warehouse

    Jordan, T.H.; Marzocchi, W.; Michael, A.J.; Gerstenberger, M.C.

    2014-01-01

    We cannot yet predict large earthquakes in the short term with much reliability and skill, but the strong clustering exhibited in seismic sequences tells us that earthquake probabilities are not constant in time; they generally rise and fall over periods of days to years in correlation with nearby seismic activity. Operational earthquake forecasting (OEF) is the dissemination of authoritative information about these time?dependent probabilities to help communities prepare for potentially destructive earthquakes. The goal of OEF is to inform the decisions that people and organizations must continually make to mitigate seismic risk and prepare for potentially destructive earthquakes on time scales from days to decades. To fulfill this role, OEF must provide a complete description of the seismic hazardground?motion exceedance probabilities as well as short?term rupture probabilitiesin concert with the long?term forecasts of probabilistic seismic?hazard analysis (PSHA).

  14. The Experimental Regional Ensemble Forecast System (ExREF): Its Use in NWS Forecast Operations and Preliminary Verification

    NASA Technical Reports Server (NTRS)

    Reynolds, David; Rasch, William; Kozlowski, Daniel; Burks, Jason; Zavodsky, Bradley; Bernardet, Ligia; Jankov, Isidora; Albers, Steve

    2014-01-01

    The Experimental Regional Ensemble Forecast (ExREF) system is a tool for the development and testing of new Numerical Weather Prediction (NWP) methodologies. ExREF is run in near-realtime by the Global Systems Division (GSD) of the NOAA Earth System Research Laboratory (ESRL) and its products are made available through a website, an ftp site, and via the Unidata Local Data Manager (LDM). The ExREF domain covers most of North America and has 9-km horizontal grid spacing. The ensemble has eight members, all employing WRF-ARW. The ensemble uses a variety of initial conditions from LAPS and the Global Forecasting System (GFS) and multiple boundary conditions from the GFS ensemble. Additionally, a diversity of physical parameterizations is used to increase ensemble spread and to account for the uncertainty in forecasting extreme precipitation events. ExREF has been a component of the Hydrometeorology Testbed (HMT) NWP suite in the 2012-2013 and 2013-2014 winters. A smaller domain covering just the West Coast was created to minimize band-width consumption for the NWS. This smaller domain has and is being distributed to the National Weather Service (NWS) Weather Forecast Office and California Nevada River Forecast Center in Sacramento, California, where it is ingested into the Advanced Weather Interactive Processing System (AWIPS I and II) to provide guidance on the forecasting of extreme precipitation events. This paper will review the cooperative effort employed by NOAA ESRL, NASA SPoRT (Short-term Prediction Research and Transition Center), and the NWS to facilitate the ingest and display of ExREF data utilizing the AWIPS I and II D2D and GFE (Graphical Software Editor) software. Within GFE is a very useful verification software package called BoiVer that allows the NWS to utilize the River Forecast Center's 4 km gridded QPE to compare with all operational NWP models 6-hr QPF along with the ExREF mean 6-hr QPF so the forecasters can build confidence in the use of the ExREF in preparing their rainfall forecasts. Preliminary results will be presented.

  15. High-resolution evapotranspiration estimates for California using satellite imagery and weather station measurements and the Weather Research Forecasting (WRF) model

    NASA Astrophysics Data System (ADS)

    Matthias, F.; Hart, Q. J.; Ustin, S. L.

    2007-12-01

    Spatially distributed potential Evapotranspiration, ET0, has been calculated to produce daily and hourly ET0 maps for the State of California at 2 km2 resolution. Hourly NOAA GOES imager satellite visible data are used to predict daily radiation. These are combined with interpolated California Irrigation Management Information System (CIMIS) weather station meteorological data for temperature, wind speed and humidity to satisfy the Penman-Monteith ET0 equation. In the next step, we investigate the use of the Weather Research Forecasting (WRF) model to improve the spatial estimates of daily evapotranspiration for the state of California. CIMIS real-time weather station and real-time satellite data are integrated into a prognostic version of the WRF model using its data nudging scheme. This paper we compares spatially interpolated climate parameters and evapotranspiration to the output of WRF with and without data assimilation of CIMIS data. The research assists California's Department of Water Resources to better monitor water use and water management. In addition to the scientific advances in understanding short- term weather systems and their impacts on plant resources, there is considerable societal importance, given impacts of current droughts and predictions for significantly reduced winter snow packs in California under some climate change scenarios.

  16. Incorporate Hydrologic Forecast for Real-Time Reservoir Operations

    NASA Astrophysics Data System (ADS)

    Zhao, T.; Cai, X.; Zhao, J.

    2011-12-01

    Advances in weather forecasting, hydrologic modeling, and hydro-climatic teleconnection relationships have significantly improved streamflow forecast precision and lead-time. The advances provide great opportunities to improve the operation rules of water resources systems, for example, updating reservoir operation curves using long-term forecast, or even replacing operation rules by real-time optimization and simulation models utilizing various streamflow forecast products. However, incorporation of forecast for real-time optimization of reservoir operation needs more understanding of the forecast uncertainty (FU) evolution with forecast horizon (FH, the advance time of a forecast) and the complicating effect of FU and FH. Increasing horizon may provide more information for decision making in a long time framework but with increasing error and less reliable information. This presentation addresses the challenges on the use of hydrologic forecast for real-time reservoir operations through the following two particular studies: 1) Evaluating the effectiveness of the various hydrological forecast products for reservoir operation with an explicit simulation of dynamic evolution of uncertainties involved in those products. A hypothetical example shows that optimal reservoir operation varies with the hydrologic forecast products. The utility of the reservoir operation with ensemble or probabilistic streamflow forecast (with a probabilistic uncertainty distribution) is the highest compared to deterministic streamflow forecast (DSF) with the forecast uncertainty represented in the form of deterministic forecast errors and DSF-based probabilistic streamflow forecast with the forecast uncertainty represented by a conditional distribution of forecast uncertainty for a given DSF. 2) Identifying an effective forecast horizon (EFH) under a limited inflow forecast considering the complicating effect of FH and FU, as well as streamflow variability and reservoir characteristics. The complicating effect is illustrated and it is shown that 1) when FH is short, the reservoir needs more information to regulate the inflow and FH is the dominating factor; 2) when FH is long, the inflow information may be too uncertain to guide reservoir operation decisions and FU becomes the dominating factor; 3) at a medium FH with sufficient inflow information and an acceptable uncertainty, the effective forecast horizon (EFH) can be located. The length of EFH is short with a high FU but it depends on the reservoir capacity and inflow variability. Thus, with a given forecast technology available, an EFH exists and it can be obtained through Monte-Carlo simulations with forecast uncertainty and inflow variability statistical characteristics.

  17. Forecasting Bz at Earth - an Operational Perspective

    NASA Astrophysics Data System (ADS)

    Pizzo, V. J.

    2014-12-01

    Forecasting the magnetic structure of an Earth-directed CME remains a difficult challenge, even with all the observational and modeling assets available today. Coronagraph and heliospheric imager data provide the only tangible information on CME structure near the Sun, but they specifically measure the mass distribution and offer only vague hints at the magnetic configuration. As input to models of the interplanetary medium, this information currently enables prediction of the arrival time at 1 AU within a statistical 8-hour or so window, but no forecast of the magnetic content of the CME. We discuss how the introduction of time-dependent ambient flows may impact the estimation of magnetic draping fields at the front of a CME, and we examine how the interplanetary evolution of a CME with an embedded magnetic cloud (MC) differs from that with a purely hydrodynamic driver, as in current operational models. In both cases the driver represents a localized injection of momentum, which dominates the dynamics. However, the MC case presents two additional dynamic elements: (1) the magnetic tension and high Alfven speeds in the cloud provide a rigidity that tends to preserve the initial shape of the driver; (2) the edges of the MC interact directly with the swept-up spiral magnetic ambient field, leading to erosion of the internal fields. In both cases, the hypersonic flow conditions and the geometric spreading of the predominantly radial motion tends to keep the interactions local, such that different parts of the structure may experience quite different evolution with heliocentric distance. The resulting localized deformations make the interpretation of the true configuration of such structures difficult to infer from in situ observations and severely complicate our ability to forecast accurately the magnetic structure expected at Earth. A key issue confronting any purported forecast scheme for CME magnetic content is the definition of a "good fit" between prediction and observation - this is not at all a straightforward task and real progress in this area hinges upon sensible and honest evaluation forecast performance.

  18. The New Era in Operational Forecasting

    NASA Astrophysics Data System (ADS)

    Tobiska, W.; Schunk, R. W.; Sojka, J. J.; Carlson, H. C.; Gardner, L. C.; Scherliess, L.; Zhu, L.; Eccles, J. V.; Rice, D. D.; Bouwer, D.; Bailey, J. J.; Knipp, D. J.; Blake, J. B.; Rex, J.; Fuschino, R.; Mertens, C. J.; Gersey, B.; Wilkins, R.; Atwell, W.

    2012-12-01

    Space weather's effects upon the near-Earth environment are due to dynamic changes in the energy transfer processes from the Sun's photons, particles, and fields. Of the space environment domains that are affected by space weather, the ionosphere, thermosphere, and even troposphere are key regions that are affected. The Utah State University (USU) Space Weather Center (SWC) and Space Environment Technologies (SET) are developing and producing commercial space weather applications. Key systems for providing timely information about the effects of space weather are SWC's Global Assimilation of Ionospheric Measurements (GAIM) system, SET's Magnetosphere Alert and Prediction System (MAPS), and SET's Automated Radiation Measurements for Aviation Safety (ARMAS) system. GAIM, operated by SWC, improves real-time communication and navigation systems by continuously ingesting up to 10,000 slant TEC measurements every 15-minutes from approximately 500 stations. Ionosonde data from several dozen global stations is ingested every 15 minutes to improve the vertical profiles within GAIM. These operational runs enable the reporting of global radio high frequency (HF) signal strengths and near vertical incidence skywave (NVIS) maps used by amateur radio operators and emergency responders via the http://q-upnow.com website. MAPS provides a forecast Dst index out to 6 days through the data-driven Anemomilos algorithm. Anemomilos uses observational proxies for the magnitude, location, and velocity of solar ejecta events. This forecast index is used by satellite operations to characterize upcoming geomagnetic storms, for example. ARMAS is demonstrating a prototype flight of microdosimeters on aircraft to capture the "weather" of the radiation environment for air-crew and passenger safety. It assimilates real-time radiation dose and dose rate data into the global NAIRAS radiation system to correct the global climatology for more accurate radiation fields along flight tracks. This team also provides the space weather smartphone app called SpaceWx for iPhone, iPad, iPod, and Android for professional users and public space weather education. SpaceWx displays the real-time solar, heliosphere, magnetosphere, thermosphere, and ionosphere drivers to changes in the total electron content, for example, as well as global NVIS maps. We describe recent forecasting advances for moving space weather information through automated systems into operational, derivative products for communications, aviation, and satellite operations uses.

  19. Data Assimilation of Lightning using 1D+3D/4D WRF Var Assimilation Schemes with Non-Linear Observation Operators

    NASA Astrophysics Data System (ADS)

    Navon, M. I.; Stefanescu, R.; Fuelberg, H. E.; Marchand, M.

    2012-12-01

    NASA's launch of the GOES-R Lightning Mapper (GLM) in 2015 will provide continuous, full disc, high resolution total lightning (IC + CG) data. The data will be available at a horizontal resolution of approximately 9 km. Compared to other types of data, the assimilation of lightning data into operational numerical models has received relatively little attention. Previous efforts of lightning assimilation mostly have employed nudging. This paper will describe the implementation of 1D+3D/4D Var assimilation schemes of existing ground-based WTLN (Worldwide Total Lightning Network) lightning observations using non-linear observation operators in the incremental WRFDA system. To mimic the expected output of GLM, the WTLN data were used to generate lightning super-observations characterized by flash rates/81 km2/20 min. A major difficulty associated with variational approaches is the complexity of the observation operator that defines the model equivalent of lightning. We use Convective Available Potential Energy (CAPE) as a proxy between lightning data and model variables. This operator is highly nonlinear. Marecal and Mahfouf (2003) have shown that nonlinearities can prevent direct assimilation of rainfall rates in the ECMWF 4D-VAR (using the incremental formulation proposed by Courtier et al. (1994)) from being successful. Using data from the 2011 Tuscaloosa, AL tornado outbreak, we have proved that the direct assimilation of lightning data into the WRF 3D/4D - Var systems is limited due to this incremental approach. Severe threshold limits must be imposed on the innovation vectors to obtain an improved analysis. We have implemented 1D+3D/4D Var schemes to assimilate lightning observations into the WRF model. Their use avoids innovation vector constrains from preventing the inclusion of a greater number of lightning observations Their use also minimizes the problem that nonlinearities in the moist convective scheme can introduce discontinuities in the cost function between inner and outer loops of the incremental 3-D/4-D VAR minimization. The first part of this paper will describe the methodology and performance analysis of the 1D-Var retrieval scheme that adjusts the WRF temperature profiles closer to an observed value as in Mahfouf et al. (2005). The second part will show the positive impact of these 1D-Var pseudo - temperature observations on both model 3D/4D-Var WRF analyses and short-range forecasts for three cases - the Tuscaloosa tornado outbreak (April 27, 2011) with intense but localized lightning, a second severe storm outbreak with more widespread but less intense lightning (June 27, 2011), and a northeaster containing much less lightning.

  20. A dynamical model for forecasting operational losses

    NASA Astrophysics Data System (ADS)

    Bardoscia, M.; Bellotti, R.

    2012-04-01

    A novel dynamical model for the study of operational risk in banks and suitable for the calculation of the Value at Risk (VaR) is proposed. The equation of motion takes into account the interactions among different bank's processes, the spontaneous generation of losses via a noise term and the efforts made by the bank to avoid their occurrence. Since the model is very general, it can be tailored on the internal organizational structure of a specific bank by estimating some of its parameters from historical operational losses. The model is exactly solved in the case in which there are no causal loops in the matrix of couplings and it is shown how the solution can be exploited to estimate also the parameters of the noise. The forecasting power of the model is investigated by using a fraction f of simulated data to estimate the parameters, showing that for f=0.75 the VaR can be forecast with an error ≃10-3.

  1. Coupling WRF Double-Moment 6-class (WDM6) microphysics scheme to RRTMG radiation scheme in Weather Research Forecasting (WRF) model

    NASA Astrophysics Data System (ADS)

    Bae, Soo Ya; Hong, Song-You

    2015-04-01

    Radiative fluxes are mainly affected by the amount and radius of hydrometeors. Since a double-moment microphysics scheme predicts the number and volume concentrations, the effective radius of hydrometeors is easily calculated. However, WDM6 does not include the computation process for the effective radius of hydrometeors. To examine the effect of the effective radius in WDM6 on RRTMG radiative flux and meteorological phenomena in the WRF model, we adapt the method of calculating effective radius of cloud, ice, and snow in WDM6, then link between WDM6 and RRTMG schemes. For cloud, we develop the equation based on cloud size distribution used in WDM6. The shape of ice is assumed to be simple bullet and the number concentration and maximum dimension of ice are calculated with ice mixing ratio (Hong et al., 2004). Under these ice characteristics of WDM6, we adopt the equation of Mitchell et al. (1996), which is only as a function of maximum dimension of ice, to effective radius equation of ice. For snow, diameter is the same with the inverse of the slope parameter of snow. The slope parameter takes into account air temperature as well as snow mixing ratio. The effective radius of modified WDM6 is found to be smaller than that of simulation using Thompson's equations except for clouds. The combined package of the WDM6-RRTMG reduces the amount of hydrometeors, which leads to the increase of shortwave reaching ground. A comparison of simulated precipitation with TRMM (Tropical Rainfall Measuring Mission) Multi-satellite Precipitation Analysis (TMPA) observation shows better agreement when the WDM6-RRTMG with the hydrometeor linkage is introduced.

  2. Towards operational flood forecasting using Data Assimilation

    NASA Astrophysics Data System (ADS)

    Piacentini, A.; Ricci, S. M.; Le Pape, E.; Habert, J.; Jonville, G.; Goutal, N.; Barthlmy, S.; Morel, T.; Duchaine, F.; Thual, O.

    2012-12-01

    Over the last few years, a collaborative work between CERFACS, LNHE (EDF R&D), SCHAPI and CETMEF resulted in the implementation of a Data Assimilation (DA) method on top of MASCARET, in the framework of real-time forecasting. This prototype named DAMP (Data Assimilation with MASCARET Prototype) showed promising results on the Adour and Marne catchments as it improves the forecast skills of the hydraulic model using water level and discharge in-situ observations (Ricci et al, 2011) as show in Figure 1. In the existing prototype, data assimilation was implemented with the OpenPalm coupler following two different and sequentially applied approaches based on the Kalman Filter algorithm: the correction of the upstream and lateral inflow to the model and the direct correction of the water level and discharge. As of today both technical and research developments on DAMP are on going. The implementation of DAMP for operational use at SCHAPI is on going within the modeling plateform POM (Plateforme Oprationnelle pour la Modlisation) that will provide integrated numerical models for the major French catchments. The DAMP will also benefits from numerical developments by LNHE on MASCARET that was recently instrumented with interface commands (API) and formulated as an IRF module (Initialize-Run-Finalize). These solutions allow to minimize the interlocking of the DA algorithm and MASCARET sources codes. In addition, the Palm-Parasol functionality in Open-Palm is now used to efficiently spawn an ensemble of MASCARET integrations used to formulate the DA algorithm. Along with these technical aspects, the DA algorithm is also being improved. Sensitivity study carried out: the control vector should be extended, especially to include the Strickler coefficients. An ensemble based DA algorithm (EnKF) is also currently being implemented to improve the modelling of the background error covariance matrix used to distribute the correction to the water level and discharge states when observations are assimilated from observation points to the entire state. Building on the existing prototype and by methodological and theoretical advances, the operational use of the DAMP offers great perspective for the use of DA for flood forecasting with direct application at the French SPC (Service de Prvision des Crues).

  3. The Value of Humans in the Operational River Forecasting Enterprise

    NASA Astrophysics Data System (ADS)

    Pagano, T. C.

    2012-04-01

    The extent of human control over operational river forecasts, such as by adjusting model inputs and outputs, varies from nearly completely automated systems to those where forecasts are generated after discussion among a group of experts. Historical and realtime data availability, the complexity of hydrologic processes, forecast user needs, and forecasting institution support/resource availability (e.g. computing power, money for model maintenance) influence the character and effectiveness of operational forecasting systems. Automated data quality algorithms, if used at all, are typically very basic (e.g. checks for impossible values); substantial human effort is devoted to cleaning up forcing data using subjective methods. Similarly, although it is an active research topic, nearly all operational forecasting systems struggle to make quantitative use of Numerical Weather Prediction model-based precipitation forecasts, instead relying on the assessment of meteorologists. Conversely, while there is a strong tradition in meteorology of making raw model outputs available to forecast users via the Internet, this is rarely done in hydrology; Operational river forecasters express concerns about exposing users to raw guidance, due to the potential for misinterpretation and misuse. However, this limits the ability of users to build their confidence in operational products through their own value-added analyses. Forecasting agencies also struggle with provenance (i.e. documenting the production process and archiving the pieces that went into creating a forecast) although this is necessary for quantifying the benefits of human involvement in forecasting and diagnosing weak links in the forecasting chain. In hydrology, the space between model outputs and final operational products is nearly unstudied by the academic community, although some studies exist in other fields such as meteorology.

  4. Validation of snow cover simulated by the Weather Research and Forecasting (WRF) model using in-situ snow survey data in the Altai Mountains

    NASA Astrophysics Data System (ADS)

    Sugiura, K.; Kitabata, H.; Kadota, T.

    2014-12-01

    Seasonal snow water equivalent (SWE) is important for an estimate of water storage in the hydrological cycle, prediction of snow disaster, water resources, and so on. Satellite observation technique is valuable but has the coarse footprint such as 25 km spacing generally. In this study, numerical simulation model is applied to estimate the SWE in the presence of spatial variability. To begin with, we examined validity of the SWE simulated by the Weather Research and Forecasting (WRF) model. Snow surveys in the Altai Mountain were carried out, and the in-situ snow data were manually collected during the maximum SWE season each year. We compared the SWE simulated by the WRF model (ver. 3.3) with that collected by the in-situ snow surveys in the Altai Mountains during four years (2008, 2011, 2012, and 2013). The results show that the agreement with in-situ snow survey data is good. Based on this, this presentation will also describe characteristics of the simulated SWE distribution in the Altai Mountains.

  5. WRF-ARW Physics Parameterizations Influence on Mesoscale Convective System (MCS) Forecasts and Development of Process-Oriented Verification for WRF-ARW Output

    NASA Astrophysics Data System (ADS)

    Sines, T. R.; Arritt, R. W.

    2014-12-01

    Several methods of verification such as statistical analysis of precipitation estimate performance, but don't reveal if the model is reproducing physical storms occurring in nature. The goal of this procedure is to assess whether accurate precipitation forecasts reflect sound physics. We have developed an algorithm for mesoscale convective system (MCS) detection and tracking as a step toward process-oriented verification of regional climate models. The algorithm detects MCSs as contiguous regions of precipitation exceeding 1.5mm/hr co-located with 925-700hPa thickness change of <5 m/h. The latter criterion reflects mesoscale evaporative cooling. When tested with 3-hourly North American Regional Reanalysis (NARR) data from Apr.-Sept. 1993, the algorithm detected 98 MCSs. Of these, 10 were determined as false alarms after comparing precipitation and infrared satellite imagery. The end result of this research will be a regional climate model configured with parameterization schemes that most accurately reproduce MCSs over the central United States and a tool which is applicable to the detection and tracking of MCSs in model output.

  6. Skill assessment for an operational algal bloom forecast system

    PubMed Central

    Stumpf, Richard P.; Tomlinson, Michelle C.; Calkins, Julie A.; Kirkpatrick, Barbara; Fisher, Kathleen; Nierenberg, Kate; Currier, Robert; Wynne, Timothy T.

    2010-01-01

    An operational forecast system for harmful algal blooms (HABs) in southwest Florida is analyzed for forecasting skill. The HABs, caused by the toxic dinoflagellate, Karenia brevis, lead to shellfish toxicity and to respiratory irritation. In addition to predicting new blooms and their extent, HAB forecasts are made twice weekly during a bloom event, using a combination of satellite derived image products, wind predictions, and a rule-based model derived from previous observations and research. These forecasts include: identification, intensification, transport, extent, and impact; the latter being the most significant to the public. Identification involves identifying new blooms as HABs and is validated against an operational monitoring program involving water sampling. Intensification forecasts, which are much less frequently made, can only be evaluated with satellite data on mono-specific blooms. Extent and transport forecasts of HABs are also evaluated against the water samples. Due to the resolution of the forecasts and available validation data, skill cannot be resolved at scales finer than 30 km. Initially, respiratory irritation forecasts were analyzed using anecdotal information, the only available data, which had a bias toward major respiratory events leading to a forecast accuracy exceeding 90%. When a systematic program of twice-daily observations from lifeguards was implemented, the forecast could be meaningfully assessed. The results show that the forecasts identify the occurrence of respiratory events at all lifeguard beaches 70% of the time. However, a high rate (80%) of false positive forecasts occurred at any given beach. As the forecasts were made at half to whole county level, the resolution of the validation data was reduced to county level, reducing false positives to 22% (accuracy of 78%). The study indicates the importance of systematic sampling, even when using qualitative descriptors, the use of validation resolution to evaluate forecast capabilities, and the need to match forecast and validation resolutions. PMID:20628532

  7. Operational Considerations for Geomorphological and Ecological Forecasting

    NASA Astrophysics Data System (ADS)

    Costanza, Katelyn

    2015-04-01

    Applying predictive models beyond weather and water has become a relatively new topic of research in the operational setting. It has become increasingly important to provide answers related to: • fate and transport of pollutants and hazardous wastes • shoaling and impacts to navigation • water quality and its potential impacts to ecology • deltaic processes. The Water Institute and Deltares are currently working on a pilot project to develop a system that will potentially answer these questions. The Mississippi River Delta is the area of focus for this pilot project. This project is utilizing and enhancing the capabilities of the Flood Early Warning System (FEWS). The Mississippi River Delta has been devastated by anthropogenic influences over the last century. These influences in conjunction with subsidence and sea level rise have caused astounding land loss rates. Government agencies are in the process of developing innovative ways to reconnect the river with the dying delta. One of the alternatives being planned is a system of sediment diversion projects. These diversions are much like flood water diversions which already exist along the river today. These planned diversions provide Deltares and The Water Institute of the Gulf the perfect case scenario to test both morphology and ecological models within an operational system. In order to build an operational system such as this, it was necessary to use FEWS as a platform to analyze multivariate and disparate sources of environmental data. This was necessary for monitoring the delta and providing boundary conditions to the models. Applying morphological models in a predictive manner is a new concept. Researchers from Deltares and The Water Institute have had to develop new methods to provide predictive boundaries and warm states to the models. It is intended that this system will ultimately be used to provide forecasted guidance on the optimal operation of the diversions to reduce the impacts to ecological health and increase the volume of sediment diverted to the receiving areas.

  8. Optimizing zonal advection of the Advanced Research WRF (ARW) dynamics for Intel MIC

    NASA Astrophysics Data System (ADS)

    Mielikainen, Jarno; Huang, Bormin; Huang, Allen H.

    2014-10-01

    The Weather Research and Forecast (WRF) model is the most widely used community weather forecast and research model in the world. There are two distinct varieties of WRF. The Advanced Research WRF (ARW) is an experimental, advanced research version featuring very high resolution. The WRF Nonhydrostatic Mesoscale Model (WRF-NMM) has been designed for forecasting operations. WRF consists of dynamics code and several physics modules. The WRF-ARW core is based on an Eulerian solver for the fully compressible nonhydrostatic equations. In the paper, we will use Intel Intel Many Integrated Core (MIC) architecture to substantially increase the performance of a zonal advection subroutine for optimization. It is of the most time consuming routines in the ARW dynamics core. Advection advances the explicit perturbation horizontal momentum equations by adding in the large-timestep tendency along with the small timestep pressure gradient tendency. We will describe the challenges we met during the development of a high-speed dynamics code subroutine for MIC architecture. Furthermore, lessons learned from the code optimization process will be discussed. The results show that the optimizations improved performance of the original code on Xeon Phi 5110P by a factor of 2.4x.

  9. Optimizing meridional advection of the Advanced Research WRF (ARW) dynamics for Intel Xeon Phi coprocessor

    NASA Astrophysics Data System (ADS)

    Mielikainen, Jarno; Huang, Bormin; Huang, Allen H.-L.

    2015-05-01

    The most widely used community weather forecast and research model in the world is the Weather Research and Forecast (WRF) model. Two distinct varieties of WRF exist. The one we are interested is the Advanced Research WRF (ARW) is an experimental, advanced research version featuring very high resolution. The WRF Nonhydrostatic Mesoscale Model (WRF-NMM) has been designed for forecasting operations. WRF consists of dynamics code and several physics modules. The WRF-ARW core is based on an Eulerian solver for the fully compressible nonhydrostatic equations. In the paper, we optimize a meridional (north-south direction) advection subroutine for Intel Xeon Phi coprocessor. Advection is of the most time consuming routines in the ARW dynamics core. It advances the explicit perturbation horizontal momentum equations by adding in the large-timestep tendency along with the small timestep pressure gradient tendency. We will describe the challenges we met during the development of a high-speed dynamics code subroutine for MIC architecture. Furthermore, lessons learned from the code optimization process will be discussed. The results show that the optimizations improved performance of the original code on Xeon Phi 7120P by a factor of 1.2x.

  10. Evaluation of two Operational Weather Forecasting Systems for the Mediterranean Region

    NASA Astrophysics Data System (ADS)

    Papadopoulos, A.; Katsafados, P.

    2012-04-01

    This paper presents an intercomparison and evaluation of two weather forecasting systems for the Mediterranean Sea and the surrounding countries. The POSEIDON weather forecasting system has been developed in the framework of the project "Monitoring, forecasting and information system for the Greek Seas" at the Hellenic Centre for Marine Research (HCMR) in 1999. In the current HCMR's operational procedures the system issues high-resolution (~5 km) weather forecasts for 5 days ahead. It is based on an advanced version of the non-hydrostatic atmospheric Eta/NCEP model and is forced by the GFS model. To achieve better initialization a meteorological data assimilation package, the LAPS, has been implemented which employs all available real-time observations. Likewise, the Weather Research and Forecasting (WRF) limited area model with the embedded Non-hydrostatic Mesoscale Model (NMM) dynamical core became operational at the Department of Geography at Harokopio University of Athens in 2008. It provides daily 120-hour weather forecasts in a single domain covering the entire Mediterranean basin and the Black Sea at a resolution of 0.09 x0.09. The performance of the two operational systems has been assessed across the Mediterranean region and the surrounding countries using as reference the surface measurements available from the World Meteorological Organization (WMO) network unevenly distributed over the domain of integration. Surface observations from more than 900 conventional stations were used to verify and compare categorical forecasts of the 10-m wind field, 2-m air temperature and sea level pressure every 3 hours and the accumulated 6-h precipitation. The verification of the operational systems is based on the point-to-point comparison between the model generated variables and the relevant surface observations. Therefore, a verification procedure has been developed based on the estimation of traditional objective verification techniques such as bias, RMSE and threat scores for both continuous and discrete predictants. Preliminary results indicated that the model errors are highly dependent on the diurnal cycle, the seasonality, the forecast time and the station location especially over areas with complex physiographic characteristics.

  11. The air quality forecast about PM2.5 before and during APEC 2014 in Beijing by WRF-CMAQ model system

    NASA Astrophysics Data System (ADS)

    Wu, Qizhong; Xu, Wenshuai; Wang, Zifa

    2015-04-01

    In the past year 2014, the APEC meeting was hold in Beijing, where the particulate matter (PM2.5) concentration is high and worried. In such a heavily air-polluted environment, people want access to reasonable air quality predictions, that the government can take necessary short-term emissions reduction measures to improve air quality. According to Wu et al. (2014), the enhanced model domain and the updated emissions inventory will improve the model performance of particulate matter concentration obviously, a new model system, with the enhanced 9km-domain and latest emission inventory in WRF-SMOKE-CMAQ model, was established in October 2014, before APEC. As a result, the model system plays good performance in the whole October: 1) the model catches four air pollution episodes in October, and has a high correlation coefficient of 0.89, 2) the daily forecast of PM2.5 concentration reaches 277 \\unit{{?}g m-3} and close to the observed value (320 \\unit{{?}g m-3}), but still a little underestimated, 3) the mean bias(MB) of the forecast to observed is 1.03 \\unit{{?}g m-3} and the normalized mean bias(NMB) is 24.9{%}, 4) the normalized mean square error (NMSE) between the forecast and observed is 0.137 in October. The forecast results, with well performance, indicate the emissions inventory used in the model system is reasonable as baseline scenario, which scenario without any emission-sources reduction. From 3 to 12 November, the emission-sources reduction measures(e.g. the traffic restriction, factory cut production and closures) are carried step by step in Beijing and its surrounding areas. Those measures information is collected and used in the SMOKE model with growth/project module, to prepared as a reduced emissions inventory as APEC scenario. The same WRF-CMAQ model system, but be driven by the emission inventory of APEC scenario, was added from 3 November, to forecast the air quality under such emission-sources reduction measures, and evaluate the effect of emission-sources reduction during APEC 2014 in Beijing. According to the forecast results, the daily PM2.5 concentration will reduce from 107 \\unit{{?}g m-3} in the baseline to 72 \\unit{{?}g m-3} in the APEC scenario, while the observed is 69 \\unit{{?}g m-3} on 8 November. From 6 to 10 November, the observation is lower than the forecast results in the baseline scenario, that indicates the emission-sources reduction measures effects in the air quality in Beijing, especially PM2.5 concentration, if we trust the model. We also found that the observation on 7-8 November even lower than the forecast results in the APEC scenario, which indicates the emission reduction efforts more than expected.

  12. Solar forecasting for operational support of SEGS plants

    SciTech Connect

    Cerni, T.A.; Price, H.

    1997-12-31

    This paper describes the first successful demonstration of quantitative direct normal irradiance (DNI) solar forecasting, for operational support of the SEGS (Solar Electric Generating Systems) plants near Kramer Junction, CA. As such, this effort likely represents the first demonstration of its kind for any solar thermal power plant. Real time, multi-spectral GOES-9 satellite imagery and related meteorological data were utilized for generating one and two day quantitative DNI solar forecasts. In the absence of professional forecasts, SEGS plant operators often resort to the use of climatology and persistence for generating DNI solar forecasts. Analysis of 6 months of Kramer Junction solar data demonstrates that climatology and persistence produce unreliable solar forecasts, because they are fundamentally incapable of predicting large day-to-day variations. Accurate, reliable, DNI solar forecasts can increase the annual energy production of SEGS plants, through more prudent use of the limited natural gas allotment and through improved maintenance planning.

  13. Development of a short-term irradiance prediction system using post-processing tools on WRF-ARW meteorological forecasts in Spain

    NASA Astrophysics Data System (ADS)

    Rincón, A.; Jorba, O.; Baldasano, J. M.

    2010-09-01

    The increased contribution of solar energy in power generation sources requires an accurate estimation of surface solar irradiance conditioned by geographical, temporal and meteorological conditions. The knowledge of the variability of these factors is essential to estimate the expected energy production and therefore help stabilizing the electricity grid and increase the reliability of available solar energy. The use of numerical meteorological models in combination with statistical post-processing tools may have the potential to satisfy the requirements for short-term forecasting of solar irradiance for up to several days ahead and its application in solar devices. In this contribution, we present an assessment of a short-term irradiance prediction system based on the WRF-ARW mesoscale meteorological model (Skamarock et al., 2005) and several post-processing tools in order to improve the overall skills of the system in an annual simulation of the year 2004 in Spain. The WRF-ARW model is applied with 4 km x 4 km horizontal resolution and 38 vertical layers over the Iberian Peninsula. The hourly model irradiance is evaluated against more than 90 surface stations. The stations are used to assess the temporal and spatial fluctuations and trends of the system evaluating three different post-processes: Model Output Statistics technique (MOS; Glahn and Lowry, 1972), Recursive statistical method (REC; Boi, 2004) and Kalman Filter Predictor (KFP, Bozic, 1994; Roeger et al., 2003). A first evaluation of the system without post-processing tools shows an overestimation of the surface irradiance, due to the lack of atmospheric absorbers attenuation different than clouds not included in the meteorological model. This produces an annual BIAS of 16 W m-2 h-1, annual RMSE of 106 W m-2 h-1 and annual NMAE of 42%. The largest errors are observed in spring and summer, reaching RMSE of 350 W m-2 h-1. Results using Kalman Filter Predictor show a reduction of 8% of RMSE, 83% of BIAS, and NMAE decreases down to 32%. The REC method shows a reduction of 6% of RMSE, 79% of BIAS, and NMAE decreases down to 28%. When comparing stations at different altitudes, the overestimation is enhanced at coastal stations (less than 200m) up to 900 W m-2 h-1. The results allow us to analyze strengths and drawbacks of the irradiance prediction system and its application in the estimation of energy production from photovoltaic system cells. References Boi, P.: A statistical method for forecasting extreme daily temperatures using ECMWF 2-m temperatures and ground station measurements, Meteorol. Appl., 11, 245-251, 2004. Bozic, S.: Digital and Kalman filtering, John Wiley, Hoboken, New Jersey, 2nd edn., 1994. Glahn, H. and Lowry, D.: The use of Model Output Statistics (MOS) in Objective Weather Forecasting, Applied Meteorology, 11, 1203-1211, 1972. Roeger, C., Stull, R., McClung, D., Hacker, J., Deng, X., and Modzelewski, H.: Verification of Mesoscale Numerical Weather Forecasts in Mountainous Terrain for Application to Avalanche Prediction, Weather and forecasting, 18, 1140-1160, 2003. Skamarock, W., Klemp, J., Dudhia, J., Gill, D., Barker, D. M., Wang, W., and Powers, J. G.: A Description of the Advanced Research WRF Version 2, Tech. Rep. NCAR/TN-468+STR, NCAR Technical note, 2005.

  14. WRF4G: WRF experiment management made simple

    NASA Astrophysics Data System (ADS)

    Fernndez-Quiruelas, V.; Fernndez, J.; Cofio, A. S.; Blanco, C.; Garca-Dez, M.; Magario, M.; Fita, L.; Gutirrez, J. M.

    2015-08-01

    This work presents a framework, WRF4G, to manage the experiment workflow of the Weather Research and Forecasting (WRF) modelling system. WRF4G provides a flexible design, execution and monitoring for a general class of scientific experiments. It has been designed with the aim of facilitating the management and reproducibility of complex experiments. Furthermore, the concepts behind the design of this framework can be straightforwardly extended to other models. We describe the user interface and the new concepts required to design parameter-sweep, hindcast and climate simulation experiments. A number of examples are provided, based on the design used for existing (published) WRF experiments. This software is open-source and publicly available http://www.meteo.unican.es/software/wrf4g).

  15. Accuracy of the Operational NOAA-EPA National Air Quality Forecasting System during Summer 2005 and 2006 in Philadelphia

    NASA Astrophysics Data System (ADS)

    Huff, A. K.; Ryan, W. F.; Bahrmann, C. P.

    2007-12-01

    The NOAA-EPA National Air Quality Forecasting System (NAQFS) is a numerical ozone forecast model that consists of a coupled version of NOAA's North American Mesoscale (NAM)-12 model and EPA's Community Multiscale Air Quality model (CMAQ). NAQFS runs twice daily at 0600 UTC and 1200 UTC and predicts hourly ozone abundances as mixing ratios in units of parts per billion (ppb). Model output is typically processed to provide 1-hour and 8-hour average ozone forecasts that correspond to the national ambient air quality standards (NAAQS) for ozone. Now in its third season as an operational model, NAQFS is designed to assist air quality meteorologists by providing accurate and dependable forecast guidance. Before air quality meteorologists are willing to rely upon a new tool like NAQFS to assist them in preparing forecasts for the public, however, the model must be evaluated in the typical operational setting for air quality forecasting - the metropolitan scale. To address this need, results will be presented from a pilot statistical study of Summer 2005 and 2006 NAQFS performance in the Philadelphia forecast area. The accuracy, bias, and skill of the model has been determined by comparing 8-hour average ozone forecasts from the 1200 UTC run of NAQFS to corresponding observed ozone values. Overall, the model shows the best skill in suburban areas, where ozone levels tend to peak across the metropolitan region. NAQFS was not reliable in 2005 during Code Orange and Red events, when ozone levels were greater than 84 ppb. In 2006, NAQFS accuracy increased during these cases, which was most likely a result of the NAM's changeover from the Eta model to the Weather and Forecasting (WRF) model. The most probable sources of error that impacted NAQFS performance in 2005 and 2006 will be discussed, including shortcomings in emissions databases and poorly characterized atmospheric chemistry. Preliminary results from the 2007 summer ozone season will also be provided.

  16. The Figure of Merit in Space (FMS) and Probability Analyses of the Concentrations for Forecasted Transport of Particles using the WRF and HYSPLIT Models over Istanbul for January and July, 2009.

    NASA Astrophysics Data System (ADS)

    Ball?, C.; Acar, M.; Caglar, F.; Tan, E.; Onol, B.; Karan, H.; Unal, Y. S.

    2012-04-01

    The main focus of this study is to compare the 24 hourly WRF model and HYSPLIT performances to the observations in terms of concentrations using FMS technique and to determine the probabilities of the spread of the modeled concentrations. In this study, 0.25-degree grid size ECMWF operational model data set is used to generate 24-hour forecasts of atmospheric fields by the WRF model. Each daily forecast is started for both 00 UTC and 12 UTC for the months of January and July of 2009. The interested model area is downscaled by the ratio of 3, starting from 9km resolution to the 1km resolution. 45 vertical levels were structured for the 3 nested domains of which Istanbul is centered. After the WRF model was used for these four sets of simulations, the dispersions of particles are analyzed by using HYSPLIT model. 30,000 particulates with the initial delivery of 5,000 particles to the atmosphere are released at 10m over Istanbul. The concentration analyses are performed for the nested domains in the order of one mother domain only, domain 1 and 2, and three nested-domains, which are named as WRFD1, WRFD12, and WRFD123, respectively. The Figure of Merit in Space (FMS) method is applied to the HYSPLIT results, which are obtained from the WRF model in order to perform the space analysis to be able to compare them to the concentrations calculated by ECMWF Interim data. FMS can be counted as the statistical coefficient of this space analysis, so one can expect that high FMS values can show high agreement between observations and model results. Since FMS is a ratio between the intersections of the areas to their union, it is not possible to deduce whether the model over predicts or under predicts, but it is a good indicator for the spread of the concentration in space. In this study, we have used percentage values of FMS for the fixed time as January and July 2009 and for a fixed concentration level. FMS analysis is applied to the three domain structures as defined above, WRFD1, WRFD12, and WRFD123. FMS values are calculated for the threshold value of 1 pgm-3. The FMS results verify that WRF model wind velocity results are in good agreement with ECMWF ERA Interim data for the level of 10m. FMS values show us that probabilities of 13 days are higher than 50% for July average. Whereas, in January, only 4 days pass over 50%. Consequently, this indicate that July model forecasts may give better results than January forecasts. Moreover, we have calculated the probabilities of the concentration spread for both July and January and detected different spreads between 12 UTC and 00 UTC initialization. Therefore, 12 UTC results show higher probabilities than 00 UTC. According to January 00 UTC and 12 UTC model results, dominant direction of particles' spread is southwesterly. Consistently, the higher probability concentrations can be seen in the Black Sea region extending to the Northern neighbors of Turkey with the probability of approximately 20%. We also observed secondary dominant particles dispersion in the northeast direction with the probability of 25% extending to the Northern Aegean Sea and to the coast of Greece. Since Istanbul is the hypothetical origin location of particle release, the highest probability of concentrations is seen in this location. In July, for 00 UTC, the highest probability spread is toward to the south. Because the predominant wind direction in summer is northeasterly in the northwestern part of Turkey, north Aegean and Marmara Seas are affected by particles with 40% chance. Although, for further south, this probability is decreased to 25 to 30%, Central and Western Anatolia and the border of Greece are still at higher risk. As a result, our analyses indicate that if there is an explosion in Istanbul area, high-risk regions depend on the season. If it occurs in winter, the transported hazardous particles might affect the northern part of Turkey and its neighbors, while in summer the southern and western part of Turkey is under the threat. Key words: Turkey, FMS and probability analyses, concentration analysis, WRF, HYSPLIT models.

  17. Evaluating the performance in the Swedish operational hydrological forecasting systems

    NASA Astrophysics Data System (ADS)

    Pechlivanidis, Ilias; Bosshard, Thomas; Spångmyr, Henrik; Lindström, Göran; Olsson, Jonas; Arheimer, Berit

    2014-05-01

    The production of hydrological forecasts generally involves the selection of model(s) and setup, calibration and initialization, verification and updating, generation and evaluation of forecasts. Although, field data are commonly used to calibrate and initiate hydrological models, technological advancements have allowed the use of additional information, i.e. remote sensing data and meteorological ensemble forecasts, to improve hydrological forecasts. However, the precision of hydrological forecasts is often subject to uncertainty related to various components of the production chain and data used. The Swedish Meteorological and Hydrological Institute (SMHI) operationally produces hydrological medium-range forecasts in Sweden using two modeling systems based on the HBV and S-HYPE hydrological models. The hydrological forecasts use both deterministic and ensemble (in total 51 ensemble members which are further reduced to 5 statistical members; 2, 25, 50, 75, 98% percentiles) meteorological forecasts from ECMWF to add information on the uncertainty of the predicted values. In this study, we evaluate the performance of the two operational hydrological forecasting systems and identify typical uncertainties in the forecasting production chain and ways to reduce them. In particular, we investigate the effect of autoregressive updating of the forecasted discharge, and of using the median of the ensemble instead of deterministic forecasts. Medium-range (10 days) hydrological forecasts across 71 selected indicator stations are used. The Kling-Gupta Efficiency and its decomposed terms are used to analyse the performance in different characteristics of the flow signal. Results show that the HBV and S-HYPE models with AR updating are both capable of producing adequate forecasts for a short lead time (1 to 2 days), and the performance steadily decreases in lead time. The autoregressive updating method can improve the performance of the two systems by 30 to 40% in terms of the KGE. This is mainly because the method has a significant impact on the improvement of discharge volume. S-HYPE seems to perform slightly better than HBV in the longer lead time, probably because the S-HYPE system is capable of updating the lake water level, which has an impact on the longer lead times. Moreover, the deterministic and ensemble HBV systems with AR updating perform fairly similar for all lead times. Keywords: Hydrological forecasting, S-HYPE, HBV, Operational production, Kling-Gupta Efficiency, Uncertainty.

  18. Tropical Cyclones: Forecasting Advances, Science Opportunities and Operational Challenges

    NASA Astrophysics Data System (ADS)

    Bosart, L. F.

    2014-12-01

    Although skill in forecasting the tracks of tropical cyclones (TCs) by operational forecast centers have improved steadily over the last 25 years, corresponding forecasts of TC intensity have shown little improvement until recently. These recent improvements in TC intensity forecasts appear to be related to a combination of better data assimilation, improved physics, and increased resolution in global operational numerical weather prediction models and new knowledge gained from a variety of recent TC-related field programs such as BGRIP, IFEX,and PREDICT. The first part of this presentation will briefly review the state of the art of TC track and intensity forecasting. The bulk of this presentation will address important TC-related science and operational challenges. These challenges include: 1) determining the physical processes that govern TC clustering, mutually interacting TCs, and the existence of different TC genesis pathways, 2) establishing how tropical-midlatitude interactions associated with recurving and transitioning (extratropical transition) TCs can trigger downstream baroclinic development, the subsequent formation of eastward-propagating Rossby wave trains, and the ensuing occurrence of extreme weather events well downstream, and 3) identifying critical TC-related forecast problems such as forecasts of the timing and extent of coastal storm surges and inland flooding associated with landfalling TCs). These important science and operational challenges will be illustrated with brief case studies.

  19. Model Combination and Weighting Methods in Operational Flood Forecasting

    NASA Astrophysics Data System (ADS)

    Bogner, Konrad; Pappenberger, Florian; Cloke, Hannah L.

    2013-04-01

    In order to get maximum benefits from operational forecast systems based on different model approaches, it is necessary to find an optimal way to combine the forecasts in real-time and to derive the predictive probability distribution by assigning different weights to the different actual forecasts according to the forecast performance of the previous days. In the European Flood Alert System (EFAS) a Bayesian Forecast System has been implemented in order to derive the overall predictive probability distribution. The EFAS is driven by different numerical weather prediction systems like the deterministic forecasts from the German Weather Service and from the ECMWF, as well as Ensemble Prediction Systems from the ECMWS and COSMO-LEPS. In this study the effect of combining these different forecast systems in respect of the total predictive uncertainty are investigated by applying different weighting methods like the Non-homogenous Gaussian Regression (NGR) model, the Bayesian Model Averaging (BMA) and an empirical method. Besides that different methods of bias removal are applied, namely additive and regression based ones, and the applicability in operational forecast is tested. One of the problems identified is the difficulty in optimizing the weight parameters for each lead-time separately resulting in highly inconsistent forecasts, especially for regression based bias removal methods. Therefore in operational use methods with only sub-optimal skill score results, could be preferable showing more realistic shapes of uncertainty bands for the predicted future stream-flow values. Another possible approach could be the optimization of the weighting parameters not for each lead-time separately, but to look at different levels of aggregations over expanding windows of time ranges. First results indicate the importance of the proper choice of the model combination method in view of reliability and sharpness of the forecast system.

  20. Ensemble Streamflow Forecast Improvements in NYC's Operations Support Tool

    NASA Astrophysics Data System (ADS)

    Wang, L.; Weiss, W. J.; Porter, J.; Schaake, J. C.; Day, G. N.; Sheer, D. P.

    2013-12-01

    Like most other water supply utilities, New York City's Department of Environmental Protection (DEP) has operational challenges associated with drought and wet weather events. During drought conditions, DEP must maintain water supply reliability to 9 million customers as well as meet environmental release requirements downstream of its reservoirs. During and after wet weather events, DEP must maintain turbidity compliance in its unfiltered Catskill and Delaware reservoir systems and minimize spills to mitigate downstream flooding. Proactive reservoir management - such as release restrictions to prepare for a drought or preventative drawdown in advance of a large storm - can alleviate negative impacts associated with extreme events. It is important for water managers to understand the risks associated with proactive operations so unintended consequences such as endangering water supply reliability with excessive drawdown prior to a storm event are minimized. Probabilistic hydrologic forecasts are a critical tool in quantifying these risks and allow water managers to make more informed operational decisions. DEP has recently completed development of an Operations Support Tool (OST) that integrates ensemble streamflow forecasts, real-time observations, and a reservoir system operations model into a user-friendly graphical interface that allows its water managers to take robust and defensible proactive measures in the face of challenging system conditions. Since initial development of OST was first presented at the 2011 AGU Fall Meeting, significant improvements have been made to the forecast system. First, the monthly AR1 forecasts ('Hirsch method') were upgraded with a generalized linear model (GLM) utilizing historical daily correlations ('Extended Hirsch method' or 'eHirsch'). The development of eHirsch forecasts improved predictive skill over the Hirsch method in the first week to a month from the forecast date and produced more realistic hydrographs on the tail end of high flow periods. These improvements allowed DEP to more effectively manage water quality control and spill mitigation operations immediately after storm events. Later on, post-processed hydrologic forecasts from the National Weather Service (NWS) including the Advanced Hydrologic Prediction Service (AHPS) and the Hydrologic Ensemble Forecast Service (HEFS) were implemented into OST. These forecasts further increased the predictive skill over the initial statistical models as current basin conditions (e.g. soil moisture, snowpack) and meteorological forecasts (with HEFS) are now explicitly represented. With the post-processed HEFS forecasts, DEP may now truly quantify impacts associated with wet weather events on the horizon, rather than relying on statistical representations of current hydrologic trends. This presentation will highlight the benefits of the improved forecasts using examples from actual system operations.

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

    NASA Technical Reports Server (NTRS)

    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.

  2. Research in Support of Operational Space Weather Forecasting

    NASA Astrophysics Data System (ADS)

    Viereck, R. A.

    2014-12-01

    Space Weather Operations can be defined as the specification and forecasting of the space environment for societal benefit. It is the potential benefit to society that is the justification for much space physics research and it helps to highlight the importance of the NASA Sun Earth Connections and Heliophysics programs. Scientific advances in the last few years have brought the first physics-base space weather forecast model into operations. Observations of the sun at higher cadences and improved spectral and temporal resolution have opened new windows on the processes that drive space weather. However, there are still some fundamental scientific questions, relevant to space weather forecasting, the answers to which still elude us. In this presentation I will review some of the most pressing needs of the space weather forecasters and the scientific advances and new observations that would greatly enhance the accuracy of space weather products and predictions.

  3. Subhourly wind forecasting techniques for wind turbine operations

    SciTech Connect

    Wegley, H.L.; Kosorok, M.R.; Formica, W.J.

    1984-08-01

    Three models for making automated forecasts of subhourly wind and wind power fluctuations were examined to determine the models' appropriateness, accuracy, and reliability in wind forecasting for wind turbine operation. Such automated forecasts appear to have value not only in wind turbine control and operating strategies, but also in improving individual wind turbine control and operating strategies, but also in improving individual wind turbine operating strategies (such as determining when to attempt startup). A simple persistence model, an autoregressive model, and a generalized equivalent Markhov (GEM) model were developed and tested using spring season data from the WKY television tower located near Oklahoma City, Oklahoma. The three models represent a pure measurement approach, a pure statistical method and a statistical-dynamical model, respectively. Forecasting models of wind speed means and measures of deviations about the mean were developed and tested for all three forecasting techniques for the 45-meter level and for the 10-, 30- and 60-minute time intervals. The results of this exploratory study indicate that a persistence-based approach, using onsite measurements, will probably be superior in the 10-minute time frame. The GEM model appears to have the most potential in 30-minute and longer time frames, particularly when forecasting wind speed fluctuations. However, several improvements to the GEM model are suggested. In comparison to the other models, the autoregressive model performed poorly at all time frames; but, it is recommended that this model be upgraded to an autoregressive moving average (ARMA or ARIMA) model. The primary constraint in adapting the forecasting models to the production of wind turbine cluster power output forecasts is the lack of either actual data, or suitable models, for simulating wind turbine cluster performance.

  4. Sensitivity of WRF short-term forecasts to different soil moisture initializations from the GLDAS database over South America in March 2009

    NASA Astrophysics Data System (ADS)

    Dillon, María E.; Collini, Estela A.; Ferreira, Lorena J.

    2016-01-01

    In Numerical Weather Prediction models it is essential to properly describe both the atmosphere and the surface initial conditions. With respect to the last, a major issue is the difficulty to attain a correct representation of soil moisture due to the lack of a measurement network established. This fact is crucial in South America. One alternative is the information given by the Land Surface Models (LSM), for example those provided by the Global Land Data Assimilation System (GLDAS). Our main concern is to investigate the sensitivity of short-term numerical weather prediction to soil moisture initializations. The analysis is focused in precipitation mainly to the second forecast day, and other variables related to the atmospheric water balance. To accomplish this, we perform five experiments including some of the GLDAS databases (NOAH, VIC and MOSAIC) in the initialization of the Weather Research and Forecasting (WRF) model, during a test period of one month (March 2009). An initial field normalization procedure using one of the soil models as reference is also evaluated. We show that the ambiguity of the soil models, given by their spatial and temporal variability as well as the forcing atmospheric fields, is transferred to the weather prediction model coupling, all over the month considered. Particularly, we show that the normalized percentage bias (NBIAS) of daily precipitation calculated for the second forecast day does not present well-defined patterns of over or underestimations: all the experiments show a wide range of variation. With respect to the normalized root mean square error (NRMSE) calculated for the same variable, we find that the values are generally low. In addition, the mean values of each statistic measure (NBIAS, BIAS, NRMSE and RMSE) do not show significant differences among the experiments (at 99% of significance). Nonetheless, it was shown that using the MOSAIC LSM for the initial conditions leads to minor NRMSE and RMSE maximums. Finally, while analyzing both moisture fluxes and precipitable water at different periods of the month, we find sensitive areas where the impact is mostly important, as Southeastern South America, central Argentina and Northeastern Brazil.

  5. Recent advances and applications of WRF-SFIRE

    NASA Astrophysics Data System (ADS)

    Mandel, J.; Amram, S.; Beezley, J. D.; Kelman, G.; Kochanski, A. K.; Kondratenko, V. Y.; Lynn, B. H.; Regev, B.; Vejmelka, M.

    2014-10-01

    Coupled atmosphere-fire models can now generate forecasts in real time, owing to recent advances in computational capabilities. WRF-SFIRE consists of the Weather Research and Forecasting (WRF) model coupled with the fire-spread model SFIRE. This paper presents new developments, which were introduced as a response to the needs of the community interested in operational testing of WRF-SFIRE. These developments include a fuel-moisture model and a fuel-moisture-data-assimilation system based on the Remote Automated Weather Stations (RAWS) observations, allowing for fire simulations across landscapes and time scales of varying fuel-moisture conditions. The paper also describes the implementation of a coupling with the atmospheric chemistry and aerosol schemes in WRF-Chem, which allows for a simulation of smoke dispersion and effects of fires on air quality. There is also a data-assimilation method, which provides the capability of starting the fire simulations from an observed fire perimeter, instead of an ignition point. Finally, an example of operational deployment in Israel, utilizing some of the new visualization and data-management tools, is presented.

  6. Operational Earthquake Forecasting: Proposed Guidelines for Implementation (Invited)

    NASA Astrophysics Data System (ADS)

    Jordan, T. H.

    2010-12-01

    The goal of operational earthquake forecasting (OEF) is to provide the public with authoritative information about how seismic hazards are changing with time. During periods of high seismic activity, short-term earthquake forecasts based on empirical statistical models can attain nominal probability gains in excess of 100 relative to the long-term forecasts used in probabilistic seismic hazard analysis (PSHA). Prospective experiments are underway by the Collaboratory for the Study of Earthquake Predictability (CSEP) to evaluate the reliability and skill of these seismicity-based forecasts in a variety of tectonic environments. How such information should be used for civil protection is by no means clear, because even with hundredfold increases, the probabilities of large earthquakes typically remain small, rarely exceeding a few percent over forecasting intervals of days or weeks. Civil protection agencies have been understandably cautious in implementing formal procedures for OEF in this sort of “low-probability environment.” Nevertheless, the need to move more quickly towards OEF has been underscored by recent experiences, such as the 2009 L’Aquila earthquake sequence and other seismic crises in which an anxious public has been confused by informal, inconsistent earthquake forecasts. Whether scientists like it or not, rising public expectations for real-time information, accelerated by the use of social media, will require civil protection agencies to develop sources of authoritative information about the short-term earthquake probabilities. In this presentation, I will discuss guidelines for the implementation of OEF informed by my experience on the California Earthquake Prediction Evaluation Council, convened by CalEMA, and the International Commission on Earthquake Forecasting, convened by the Italian government following the L’Aquila disaster. (a) Public sources of information on short-term probabilities should be authoritative, scientific, open, and timely, and they need to convey the epistemic uncertainties in the operational forecasts. (b) Earthquake probabilities should be based on operationally qualified, regularly updated forecasting systems. All operational procedures should be rigorously reviewed by experts in the creation, delivery, and utility of earthquake forecasts. (c) The quality of all operational models should be evaluated for reliability and skill by retrospective testing, and the models should be under continuous prospective testing in a CSEP-type environment against established long-term forecasts and a wide variety of alternative, time-dependent models. (d) Short-term models used in operational forecasting should be consistent with the long-term forecasts used in PSHA. (e) Alert procedures should be standardized to facilitate decisions at different levels of government and among the public, based in part on objective analysis of costs and benefits. (f) In establishing alert procedures, consideration should also be made of the less tangible aspects of value-of-information, such as gains in psychological preparedness and resilience. Authoritative statements of increased risk, even when the absolute probability is low, can provide a psychological benefit to the public by filling information vacuums that can lead to informal predictions and misinformation.

  7. Operational pollution forecast for the region of Bulgaria

    NASA Astrophysics Data System (ADS)

    Syrakov, D.; Etropolska, I.; Prodanova, M.; Ganev, K.; Miloshev, N.; Slavov, K.

    2012-10-01

    An operational prototype of the Integrated Bulgarian Chemical Weather Forecasting and Information System is presented. This version of the system is limited to relatively low resolution (10 km) but covers all Bulgaria. It is based on the US EPA Models-3 System (MM5, SMOKE and CMAQ). The meteorological input to the system is the Bulgarian operational numerical weather forecast. The boundary conditions are taken from analogous Greek system (Aristotle University of Thessaloniki). Bulgarian system runs automatically twice a day (00 and 12 UTC) and produces 48-hour forecast. The part of the results of each System's run is post-processed in a way to be visualized and uploaded to a respective web site. In the paper, description of the System is given together with a demonstration of its products. In addition highlights of Systems upgrade will be given.

  8. Lightning Initiation Forecasting: An Operational Dual-Polarimetric Radar Technique

    NASA Technical Reports Server (NTRS)

    Woodard, Crystal J.; Carey, L. D.; Petersen, W. A.; Roeder, W. P.

    2011-01-01

    The objective of this NASA MSFC and NOAA CSTAR funded study is to develop and test operational forecast algorithms for the prediction of lightning initiation utilizing the C-band dual-polarimetric radar, UAHuntsville's Advanced Radar for Meteorological and Operational Research (ARMOR). Although there is a rich research history of radar signatures associated with lightning initiation, few studies have utilized dual-polarimetric radar signatures (e.g., Z(sub dr) columns) and capabilities (e.g., fuzzy-logic particle identification [PID] of precipitation ice) in an operational algorithm for first flash forecasting. The specific goal of this study is to develop and test polarimetric techniques that enhance the performance of current operational radar reflectivity based first flash algorithms. Improving lightning watch and warning performance will positively impact personnel safety in both work and leisure environments. Advanced warnings can provide space shuttle launch managers time to respond appropriately to secure equipment and personnel, while they can also provide appropriate warnings for spectators and players of leisure sporting events to seek safe shelter. Through the analysis of eight case dates, consisting of 35 pulse-type thunderstorms and 20 non-thunderstorm case studies, lightning initiation forecast techniques were developed and tested. The hypothesis is that the additional dual-polarimetric information could potentially reduce false alarms while maintaining high probability of detection and increasing lead-time for the prediction of the first lightning flash relative to reflectivity-only based techniques. To test the hypothesis, various physically-based techniques using polarimetric variables and/or PID categories, which are strongly correlated to initial storm electrification (e.g., large precipitation ice production via drop freezing), were benchmarked against the operational reflectivity-only based approaches to find the best compromise between forecast skill and lead-time. Forecast skill is determined by statistical analysis of probability of detection (POD), false alarm ratio (FAR), Operational Utility Index (OUI), and critical success index (CSI).

  9. Analysis of 2000 Financial Forecasts and Annual Operating Statements. Report.

    ERIC Educational Resources Information Center

    Higher Education Funding Council for England, Bristol.

    This report provides a summary of financial projections for the higher education sector in England covering 1999-2000 to 2003-2004 and a summary of the sector's annual operating statements for 1999-2000 and 2000-2001. It is based on information provided by higher education institutions in July 2000. These forecasts were prepared before the outcome…

  10. Use of wind power forecasting in operational decisions.

    SciTech Connect

    Botterud, A.; Zhi, Z.; Wang, J.; Bessa, R.J.; Keko, H.; Mendes, J.; Sumaili, J.; Miranda, V.

    2011-11-29

    The rapid expansion of wind power gives rise to a number of challenges for power system operators and electricity market participants. The key operational challenge is to efficiently handle the uncertainty and variability of wind power when balancing supply and demand in ths system. In this report, we analyze how wind power forecasting can serve as an efficient tool toward this end. We discuss the current status of wind power forecasting in U.S. electricity markets and develop several methodologies and modeling tools for the use of wind power forecasting in operational decisions, from the perspectives of the system operator as well as the wind power producer. In particular, we focus on the use of probabilistic forecasts in operational decisions. Driven by increasing prices for fossil fuels and concerns about greenhouse gas (GHG) emissions, wind power, as a renewable and clean source of energy, is rapidly being introduced into the existing electricity supply portfolio in many parts of the world. The U.S. Department of Energy (DOE) has analyzed a scenario in which wind power meets 20% of the U.S. electricity demand by 2030, which means that the U.S. wind power capacity would have to reach more than 300 gigawatts (GW). The European Union is pursuing a target of 20/20/20, which aims to reduce greenhouse gas (GHG) emissions by 20%, increase the amount of renewable energy to 20% of the energy supply, and improve energy efficiency by 20% by 2020 as compared to 1990. Meanwhile, China is the leading country in terms of installed wind capacity, and had 45 GW of installed wind power capacity out of about 200 GW on a global level at the end of 2010. The rapid increase in the penetration of wind power into power systems introduces more variability and uncertainty in the electricity generation portfolio, and these factors are the key challenges when it comes to integrating wind power into the electric power grid. Wind power forecasting (WPF) is an important tool to help efficiently address this challenge, and significant efforts have been invested in developing more accurate wind power forecasts. In this report, we document our work on the use of wind power forecasting in operational decisions.

  11. Operational aspects of asynchronous filtering for flood forecasting

    NASA Astrophysics Data System (ADS)

    Rakovec, O.; Weerts, A. H.; Sumihar, J.; Uijlenhoet, R.

    2015-06-01

    This study investigates the suitability of the asynchronous ensemble Kalman filter (AEnKF) and a partitioned updating scheme for hydrological forecasting. The AEnKF requires forward integration of the model for the analysis and enables assimilation of current and past observations simultaneously at a single analysis step. The results of discharge assimilation into a grid-based hydrological model (using a soil moisture error model) for the Upper Ourthe catchment in the Belgian Ardennes show that including past predictions and observations in the data assimilation method improves the model forecasts. Additionally, we show that elimination of the strongly non-linear relation between the soil moisture storage and assimilated discharge observations from the model update becomes beneficial for improved operational forecasting, which is evaluated using several validation measures.

  12. The potential of archive functionality in operational forecasting

    NASA Astrophysics Data System (ADS)

    Davids, Femke; Verkade, Jan

    2015-04-01

    One aspect of making good predictions is a good forecasting system (data and models) and another essential part of making good predictions is a well-trained forecaster. Good data management practices and training protocols are important in reaching these goals. Among other reasons, this led to the development of a new archive functionality within the forecasting software Delft-FEWS. This open archive is based on standards and supports different data access protocols. Options have been developed to archive specific hazard events either via automatic protocols or manually. Data and events can be accessed from within Delft-FEWS but also other software programs. The data is converted to NetCDF and metadata is added. These archived events can be retrieved in the live operational forecasting system to compare to a current situation to aid interpretation and decision making or used stand alone for training purposes. In this presentation we would like to demonstrate an application of this new functionality and the opportunities that it provides in a Dutch fluvial forecasting system.

  13. An operational global ocean forecast system and its applications

    NASA Astrophysics Data System (ADS)

    Mehra, A.; Tolman, H. L.; Rivin, I.; Rajan, B.; Spindler, T.; Garraffo, Z. D.; Kim, H.

    2012-12-01

    A global Real-Time Ocean Forecast System (RTOFS) was implemented in operations at NCEP/NWS/NOAA on 10/25/2011. This system is based on an eddy resolving 1/12 degree global HYCOM (HYbrid Coordinates Ocean Model) and is part of a larger national backbone capability of ocean modeling at NWS in strong partnership with US Navy. The forecast system is run once a day and produces a 6 day long forecast using the daily initialization fields produced at NAVOCEANO using NCODA (Navy Coupled Ocean Data Assimilation), a 3D multi-variate data assimilation methodology. As configured within RTOFS, HYCOM has a horizontal equatorial resolution of 0.08 degrees or ~9 km. The HYCOM grid is on a Mercator projection from 78.64 S to 47 N and north of this it employs an Arctic dipole patch where the poles are shifted over land to avoid a singularity at the North Pole. This gives a mid-latitude (polar) horizontal resolution of approximately 7 km (3.5 km). The coastline is fixed at 10 m isobath with open Bering Straits. This version employs 32 hybrid vertical coordinate surfaces with potential density referenced to 2000 m. Vertical coordinates can be isopycnals, often best for resolving deep water masses, levels of equal pressure (fixed depths), best for the well mixed unstratified upper ocean and sigma-levels (terrain-following), often the best choice in shallow water. The dynamic ocean model is coupled to a thermodynamic energy loan ice model and uses a non-slab mixed layer formulation. The forecast system is forced with 3-hourly momentum, radiation and precipitation fluxes from the operational Global Forecast System (GFS) fields. Results include global sea surface height and three dimensional fields of temperature, salinity, density and velocity fields used for validation and evaluation against available observations. Several downstream applications of this forecast system will also be discussed which include search and rescue operations at US Coast Guard, navigation safety information provided by OPC using real time ocean model guidance from Global RTOFS surface ocean currents, operational guidance on radionuclide dispersion near Fukushima using 3D tracers, boundary conditions for various operational coastal ocean forecast systems (COFS) run by NOS etc.

  14. Geostatistical Interpolation Using Copulas and its Use in Operational Forecasts

    NASA Astrophysics Data System (ADS)

    Alam, M.

    2012-12-01

    Extreme meteorological and hydrological events have the potential to affect the daily life in multiple ways if proper and timely steps are not taken to deal with such events. For appropriate steps to be taken for mitigating the effects of extreme events it is very necessary that reliable forecast are available for important meteorological variables. Operational forecasts (whether deterministic or ensemble) made by any state institution are strongly based on data obtained from observational network stations of the state. Given that observational networks are not usually very dense it is always very important to have comprehensive spatial estimates of important meteorological variables at unobserved locations. A rather new geosatistical approach Copula is used in this study for spatial interpolation of important meteorological variables such as precipitation, temperature and wind gusts. Observational network of gauging stations under operational use of Deutscher Wetterdeinst (DWD, German weather services) are used to calculate empirical Copulas for precipitation, temperature and wind gusts. Using the concept of spatial theoretical Copulas, relationships are developed between each variable for different separating distances and finally interpolation is made for unobserved points. The advantage of using Copulas over other interpolation methods is that instead of one averaged value of variable, complete distribution of variable is interpolated to unobserved location. This gives better opportunity to make uncertainty analysis by studying the confidence intervals of the interpolated values. Extreme events can thus be specifically considered when forecasts or forecast verifications are made.

  15. Efficient tools for marine operational forecast and oil spill tracking.

    PubMed

    Marta-Almeida, Martinho; Ruiz-Villarreal, Manuel; Pereira, Janini; Otero, Pablo; Cirano, Mauro; Zhang, Xiaoqian; Hetland, Robert D

    2013-06-15

    Ocean forecasting and oil spill modelling and tracking are complex activities requiring specialised institutions. In this work we present a lighter solution based on the Operational Ocean Forecast Python Engine (OOFε) and the oil spill model General NOAA Operational Modelling Environment (GNOME). These two are robust relocatable and simple to implement and maintain. Implementations of the operational engine in three different regions with distinct oceanic systems, using the ocean model Regional Ocean Modelling System (ROMS), are described, namely the Galician region, the southeastern Brazilian waters and the Texas-Louisiana shelf. GNOME was able to simulate the fate of the Prestige oil spill (Galicia) and compared well with observations of the Krimsk accident (Texas). Scenarios of hypothetical spills in Campos Basin (Brazil) are illustrated, evidencing the sensitiveness to the dynamical system. OOFε and GNOME are proved to be valuable, efficient and low cost tools and can be seen as an intermediate stage towards more complex operational implementations of ocean forecasting and oil spill modelling strategies. PMID:23643409

  16. Evaluation of a seven-year air quality simulation using the Weather Research and Forecasting (WRF)/Community Multiscale Air Quality (CMAQ) models in the eastern United States.

    PubMed

    Zhang, Hongliang; Chen, Gang; Hu, Jianlin; Chen, Shu-Hua; Wiedinmyer, Christine; Kleeman, Michael; Ying, Qi

    2014-03-01

    The performance of the Weather Research and Forecasting (WRF)/Community Multi-scale Air Quality (CMAQ) system in the eastern United States is analyzed based on results from a seven-year modeling study with a 4-km spatial resolution. For 2-m temperature, the monthly averaged mean bias (MB) and gross error (GE) values are generally within the recommended performance criteria, although temperature is over-predicted with MB values up to 2K. Water vapor at 2-m is well-predicted but significant biases (>2 g kg(-1)) were observed in wintertime. Predictions for wind speed are satisfactory but biased towards over-prediction with 0

  17. Application of Weather Research and Forecasting Model with Chemistry (WRF/Chem) over northern China: Sensitivity study, comparative evaluation, and policy implications

    NASA Astrophysics Data System (ADS)

    Wang, Litao; Zhang, Yang; Wang, Kai; Zheng, Bo; Zhang, Qiang; Wei, Wei

    2016-01-01

    An extremely severe and persistent haze event occurred over the middle and eastern China in January 2013, with the record-breaking high concentrations of fine particulate matter (PM2.5). In this study, an online-coupled meteorology-air quality model, the Weather Research and Forecasting Model with Chemistry (WRF/Chem), is applied to simulate this pollution episode over East Asia and northern China at 36- and 12-km grid resolutions. A number of simulations are conducted to examine the sensitivities of the model predictions to various physical schemes. The results show that all simulations give similar predictions for temperature, wind speed, wind direction, and humidity, but large variations exist in the prediction for precipitation. The concentrations of PM2.5, particulate matter with aerodynamic diameter of 10 μm or less (PM10), sulfur dioxide (SO2), and nitrogen dioxide (NO2) are overpredicted partially due to the lack of wet scavenging by the chemistry-aerosol option with the 1999 version of the Statewide Air Pollution Research Center (SAPRC-99) mechanism with the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) and the Volatility Basis Set (VBS) for secondary organic aerosol formation. The optimal set of configurations with the best performance is the simulation with the Gorddard shortwave and RRTM longwave radiation schemes, the Purdue Lin microphysics scheme, the Kain-Fritsch cumulus scheme, and a nudging coefficient of 1 × 10-5 for water vapor mixing ratio. The emission sensitivity simulations show that the PM2.5 concentrations are most sensitive to nitrogen oxide (NOx) and SO2 emissions in northern China, but to NOx and ammonia (NH3) emissions in southern China. 30% NOx emission reductions may result in an increase in PM2.5 concentrations in northern China because of the NH3-rich and volatile organic compound (VOC) limited conditions over this area. VOC emission reductions will lead to a decrease in PM2.5 concentrations in eastern China. However, 30% reductions in the emissions of SO2, NOx, NH3, and VOC, individually or collectively, are insufficient to effectively mitigate the severe pollution over northern China. More aggressive emission controls, which needs to be identified in further studies, are needed in this area to reach the objective of 25% PM2.5 concentration reduction in 2017 proposed in the Action Plan for Air Pollution Prevention and Control by the State Council in 2013.

  18. Retrospective tests of hybrid operational earthquake forecasting models for Canterbury

    NASA Astrophysics Data System (ADS)

    Rhoades, D. A.; Liukis, M.; Christophersen, A.; Gerstenberger, M. C.

    2016-01-01

    The Canterbury, New Zealand, earthquake sequence, which began in September 2010, occurred in a region of low crustal deformation and previously low seismicity. Because, the ensuing seismicity in the region is likely to remain above previous levels for many years, a hybrid operational earthquake forecasting model for Canterbury was developed to inform decisions on building standards and urban planning for the rebuilding of Christchurch. The model estimates occurrence probabilities for magnitudes M ≥ 5.0 in the Canterbury region for each of the next 50 yr. It combines two short-term, two medium-term and four long-term forecasting models. The weight accorded to each individual model in the operational hybrid was determined by an expert elicitation process. A retrospective test of the operational hybrid model and of an earlier informally developed hybrid model in the whole New Zealand region has been carried out. The individual and hybrid models were installed in the New Zealand Earthquake Forecast Testing Centre and used to make retrospective annual forecasts of earthquakes with magnitude M > 4.95 from 1986 on, for time-lags up to 25 yr. All models underpredict the number of earthquakes due to an abnormally large number of earthquakes in the testing period since 2008 compared to those in the learning period. However, the operational hybrid model is more informative than any of the individual time-varying models for nearly all time-lags. Its information gain relative to a reference model of least information decreases as the time-lag increases to become zero at a time-lag of about 20 yr. An optimal hybrid model with the same mathematical form as the operational hybrid model was computed for each time-lag from the 26-yr test period. The time-varying component of the optimal hybrid is dominated by the medium-term models for time-lags up to 12 yr and has hardly any impact on the optimal hybrid model for greater time-lags. The optimal hybrid model is considerably more informative than the operational hybrid model at long time-lags, but less so when the period of the Canterbury earthquakes is excluded from the tests. The results highlight the value of including medium-term models and a range of long-term models in operational forecasting. Based on the tests carried out here, the operational hybrid model is expected to outperform most of the individual models in the next 25 yr.

  19. Operational monitoring and forecasting in the Aegean Sea: system limitations and forecasting skill evaluation.

    PubMed

    Nittis, K; Zervakis, V; Perivoliotis, L; Papadopoulos, A; Chronis, G

    2001-01-01

    The POSEIDON system, based on a network of 11 oceanographic buoys and a system of atmospheric/oceanic models, provides real-time observations and forecasts of the marine environmental conditions in the Aegean Sea. The buoy network collects meteorological, sea state and upper-ocean physical and biochemical data. The efficiency and functionality of the various system components are being evaluated during the present pre-operational phase and discussed in this paper. The problem of bio-fouling on optical and chemical sensors is found to be a main limitation factor on the quality of data. Possible solutions to this problem as well as quality control methods that are being developed are also described. Finally, an evaluation of the numerical models is presented through the estimation of their forecasting skill for selected periods. PMID:11760182

  20. Satellite freeze forecast system. Operating/troubleshooting manual

    NASA Technical Reports Server (NTRS)

    Martsolf, J. D. (Principal Investigator)

    1983-01-01

    Examples of operational procedures are given to assist users of the satellites freeze forecasting system (SFFS) in logging in on to the computer, executing the programs in the menu, logging off the computer, and setting up the automatic system. Directions are also given for displaying, acquiring, and listing satellite maps; for communicating via terminal and monitor displays; and for what to do when the SFFS doesn't work. Administrative procedures are included.

  1. Operational application and improvements of the disease risk forecast model PROCULTURE to optimize fungicides spray for the septoria leaf blotch disease in winter wheat in Luxembourg

    NASA Astrophysics Data System (ADS)

    Junk, J.; Grgen, K.; El Jarroudi, M.; Delfosse, P.; Pfister, L.; Hoffmann, L.

    2008-05-01

    The model PROCULTURE has been developed by the Universit Catholique de Louvain - UCL (Belgium) to simulate the progress of the septoria leaf blotch disease on winter wheat during the cropping season. The model has been validated in Luxembourg for four years at four distinct representative sites. It is able to identify infection periods due to the causal agent Mycosphaerella graminicola on the last five leaf layers by combining meteorological data with phenological data from PROCULTURE's crop growth model component. The meteorological forcing consists of hourly time-series of air temperature, relative humidity and cumulative rainfall since the time of sowing, retrieved from automatic weather stations for hindcast and numerical weather prediction model outputs for the forecast periods. In order to improve the model, leaf wetness - which is one of the most important drivers for the spread of the disease - shall be added as an additional predictor. Therefore leaf wetness sensors were set up at four test sites during the 2007 growing season. To get a continuous spatial coverage of the country, it is planned to couple the PROCULTURE model offline to 12-hourly operational weather forecasts from an implementation of the Weather Research and Forecasting (WRF) model for Luxembourg at 1 km resolution. Because the WRF model does not provide leaf wetness directly, an artificial neural network (ANN) is used to model this parameter.

  2. Weather Research and Forecasting Model with the Immersed Boundary Method

    Energy Science and Technology Software Center (ESTSC)

    2012-05-01

    The Weather Research and Forecasting (WRF) Model with the immersed boundary method is an extension of the open-source WRF Model available for wwww.wrf-model.org. The new code modifies the gridding procedure and boundary conditions in the WRF model to improve WRF's ability to simutate the atmosphere in environments with steep terrain and additionally at high-resolutions.

  3. Addressing the Challenges of Distributed Hydrologic Modeling for Operational Forecasting

    NASA Astrophysics Data System (ADS)

    Butts, M. B.; Yamagata, K.; Kobor, J.; Fontenot, E.

    2008-05-01

    Operational forecasting systems must provide reliable, accurate and timely flood forecasts for a range of catchments from small rapidly responding mountain catchments and urban areas to large, complex but more slowly responding fluvial systems. Flood forecasting systems have evolved from simple forecasting for flood mitigation to real-time decision support systems for real-time reservoir operations for water supply, navigation, hydropower, for managing environmental flows and habitat protection, cooling water and water quality forecasting. These different requirements lead to a number of challenges in applying distributed modelling in an operational context. These challenges include, the often short time available for forecasting that requires a trade-off between model complexity and accuracy on the one hand and on the other hand the need for efficient calculations to reduce the computation times. Limitations in the data available in real-time require modelling tools that can not only operate on a minimum of data but also take advantage of new data sources such as weather radar, satellite remote sensing, wireless sensors etc. Finally, models must not only accurately predict flood peaks but also forecast low flows and surface water-groundwater interactions, water quality, water temperature, optimal reservoir levels, and inundated areas. This paper shows how these challenges are being addressed in a number of case studies. The central strategy has been to develop a flexible modelling framework that can be adapted to different data sources, different levels of complexity and spatial distribution and different modelling objectives. The resulting framework allows amongst other things, optimal use of grid-based precipitation fields from weather radar and numerical weather models, direct integration of satellite remote sensing, a unique capability to treat a range of new forecasting problems such as flooding conditioned by surface water-groundwater interactions. Results from flood modelling on the Odra River in Poland show that this model system can perform as well as traditional models and gives good predictions in mountainous catchments. By allowing different process representations to be applied within the same framework, it is possible to develop hydrological models in a phased manner. This phased approach was used for example in the Napa Valley, California where it is important to balance water demands for urban areas, agriculture, and ecosystem preservation while maintaining flood protection and water quality. A first regional model was developed with a detailed description of the surface process and a simple linear reservoir was used to simulate the groundwater component. Then a more detailed fully-distributed finite-difference groundwater model was constructed within the same framework while maintaining the surface water components. In the DMIP case study, Blue River, Oklahoma, this flexibility has been used to evaluate the performance of different model structures, and to determine the impact of grid resolution on model accuracy. The results show clear limits to the benefit attained by increasing model complexity and resolution. In contrast, detailed flood mapping using high resolution topography carried out with this tool in South Boulder Creek, Colorado show that very detailed description of the topography and flows paths are required for accurate flood mapping and determination of the flood risk. This framework is now being used to develop a flood forecasting system for the Big Cypress Basin in Florida.

  4. Seeing and ground meteorology forecast for site quality and observatory operations

    NASA Astrophysics Data System (ADS)

    Giordano, C.; Vernin, J.; Muñoz-Tuñon, C.; Trinquet, H.

    2014-08-01

    The quality of astronomical observations is strongly related to the quality properties of the atmosphere. This parameter is important for the determination of the observation modes, and for observation program, the socalled flexible scheduling. We propose to present the implementation of the WRF model in order to routinely and automatically forecast the optical conditions. The purpose of our study is to predict 24 hours ahead the optical conditions above an observatory to optimize the observation time, not only the meteorological conditions at ground level, but also the vertical distribution of the optical turbulence and the wind speed, i.e the so-called astronomical seeing. The seeing is computed using the Trinquet-Vernin model coupled with the vertical profiles of the wind shear and the potential temperature predicted by the WRF model. We made a comparison between the WRF output and the in situ measurements made with the DIMM and an automatic weather station above the Observatorio del Roque de los Muchachos, Canary Island. Here we show that the increase of resolution in both the terrain model and 3D grid yields better forecast when compared with in situ optical and meteorological observations.

  5. Theoretical basis for operational ensemble forecasting of coronal mass ejections

    NASA Astrophysics Data System (ADS)

    Pizzo, V. J.; Koning, C.; Cash, M.; Millward, G.; Biesecker, D. A.; Puga, L.; Codrescu, M.; Odstrcil, D.

    2015-10-01

    We lay out the theoretical underpinnings for the application of the Wang-Sheeley-Arge-Enlil modeling system to ensemble forecasting of coronal mass ejections (CMEs) in an operational environment. In such models, there is no magnetic cloud component, so our results pertain only to CME front properties, such as transit time to Earth. Within this framework, we find no evidence that the propagation is chaotic, and therefore, CME forecasting calls for different tactics than employed for terrestrial weather or hurricane forecasting. We explore a broad range of CME cone inputs and ambient states to flesh out differing CME evolutionary behavior in the various dynamical domains (e.g., large, fast CMEs launched into a slow ambient, and the converse; plus numerous permutations in between). CME propagation in both uniform and highly structured ambient flows is considered to assess how much the solar wind background affects the CME front properties at 1 AU. Graphical and analytic tools pertinent to an ensemble approach are developed to enable uncertainties in forecasting CME impact at Earth to be realistically estimated. We discuss how uncertainties in CME pointing relative to the Sun-Earth line affects the reliability of a forecast and how glancing blows become an issue for CME off-points greater than about the half width of the estimated input CME. While the basic results appear consistent with established impressions of CME behavior, the next step is to use existing records of well-observed CMEs at both Sun and Earth to verify that real events appear to follow the systematic tendencies presented in this study.

  6. Effect of streamflow forecast uncertainty on real-time reservoir operation

    NASA Astrophysics Data System (ADS)

    Zhao, Tongtiegang; Cai, Ximing; Yang, Dawen

    2011-04-01

    Various hydrological forecast products have been applied to real-time reservoir operation, including deterministic streamflow forecast ( DSF), DSF-based probabilistic streamflow forecast ( pseudo-PSF, pPSF), and ensemble or probabilistic streamflow forecast (denoted as real-PSF, rPSF). DSF represents forecast uncertainty in the form of deterministic forecast errors, pPSF a conditional distribution of forecast uncertainty for a given DSF, and rPSF a probabilistic uncertainty distribution. Compared to previous studies that treat the forecast products as ad hoc inputs for reservoir operation models, this paper attempts to model the dynamic evolution of uncertainties involved in the various forecast products and explores their effect on real-time reservoir operation decisions. Through a hypothetical example of a single-objective real-time reservoir operation model, the results illustrate that forecast uncertainty exerts significant effects. Reservoir operation efficiency, as measured by a utility function, decreases as the forecast uncertainty increases but the magnitude depends on the forecast products used. In general, the utility of the reservoir operation with rPSF is nearly as high as the utility obtained with a perfect forecast. Meanwhile, the utilities of DSF and pPSF are similar to each other but not as high as rPSF. Moreover, streamflow variability and reservoir capacity can change the magnitude of the effects of forecast uncertainty, but not the relative merit of DSF, pPSF, and rPSF.

  7. Characteristics of Operational Space Weather Forecasting: Observations and Models

    NASA Astrophysics Data System (ADS)

    Berger, Thomas; Viereck, Rodney; Singer, Howard; Onsager, Terry; Biesecker, Doug; Rutledge, Robert; Hill, Steven; Akmaev, Rashid; Milward, George; Fuller-Rowell, Tim

    2015-04-01

    In contrast to research observations, models and ground support systems, operational systems are characterized by real-time data streams and run schedules, with redundant backup systems for most elements of the system. We review the characteristics of operational space weather forecasting, concentrating on the key aspects of ground- and space-based observations that feed models of the coupled Sun-Earth system at the NOAA/Space Weather Prediction Center (SWPC). Building on the infrastructure of the National Weather Service, SWPC is working toward a fully operational system based on the GOES weather satellite system (constant real-time operation with back-up satellites), the newly launched DSCOVR satellite at L1 (constant real-time data network with AFSCN backup), and operational models of the heliosphere, magnetosphere, and ionosphere/thermosphere/mesophere systems run on the Weather and Climate Operational Super-computing System (WCOSS), one of the worlds largest and fastest operational computer systems that will be upgraded to a dual 2.5 Pflop system in 2016. We review plans for further operational space weather observing platforms being developed in the context of the Space Weather Operations Research and Mitigation (SWORM) task force in the Office of Science and Technology Policy (OSTP) at the White House. We also review the current operational model developments at SWPC, concentrating on the differences between the research codes and the modified real-time versions that must run with zero fault tolerance on the WCOSS systems. Understanding the characteristics and needs of the operational forecasting community is key to producing research into the coupled Sun-Earth system with maximal societal benefit.

  8. Impact of Gas-Phase Mechanisms on Weather Research Forecasting Model with Chemistry (WRF/Chem) Predictions: Mechanism Implementation and Comparative Evaluation

    EPA Science Inventory

    Gas-phase mechanisms provide important oxidant and gaseous precursors for secondary aerosol formation. Different gas-phase mechanisms may lead to different predictions of gases, aerosols, and aerosol direct and indirect effects. In this study, WRF/Chem-MADRID simulations are cond...

  9. AN OPERATIONAL EVALUATION OF THE ETA-CMAQ AIR QUALITY FORECAST MODEL

    EPA Science Inventory

    The National Oceanic and Atmospheric Administration (NOAA), in collaboration with the Environmental Protection Agency (EPA), are developing an Air Quality Forecasting Program that will eventually result in an operational Nationwide Air Quality Forecasting System. The initial pha...

  10. Impacts of Improved Day-Ahead Wind Forecasts on Power Grid Operations: September 2011

    SciTech Connect

    Piwko, R.; Jordan, G.

    2011-11-01

    This study analyzed the potential benefits of improving the accuracy (reducing the error) of day-ahead wind forecasts on power system operations, assuming that wind forecasts were used for day ahead security constrained unit commitment.

  11. The WRF nested within the CESM: Simulations of a midlatitude cyclone over the Southern Great Plains

    NASA Astrophysics Data System (ADS)

    He, Juanxiong; Zhang, Minghua; Lin, Wuyin; Colle, Brian; Liu, Ping; Vogelmann, Andrew M.

    2013-07-01

    This paper describes an integrated modeling system in which the Weather Research and Forecasting model (WRF) is nested within the Community Earth System Model (CESM). This system is validated for the simulation of a midlatitude cyclongesis event over the Southern Great Plains of the United States. The global atmospheric model CAM4 at T42 resolution in the CESM has missed this cyclogenesis, while the nested WRF at 30 km grid spacing (or finer) that is initialized with the CAM4 condition and laterally forced by the CAM4 successfully simulated the deepening midtropospheric trough and associated cyclogenesis. An analysis of the potential velocity evolution and sensitivity experiments show that it is the higher WRF resolution that allowed the realistic sharpening of the Ertel's Potential Vorticity (EPV) gradient and the ensuing cyclogenesis. The terrain resolution and the physical parameterizations, however, play little role in the difference between the CAM4 and the WRF in the CESM. The integrated WRF/CESM system is intended as one method of global climate modeling with regional simulation capabilities. The present case study also serves as a verification of the system by comparing with standalone WRF simulations forced by operational analyses.

  12. Simulating atmosphere flow for wind energy applications with WRF-LES

    SciTech Connect

    Lundquist, J K; Mirocha, J D; Chow, F K; Kosovic, B; Lundquist, K A

    2008-01-14

    Forecasts of available wind energy resources at high spatial resolution enable users to site wind turbines in optimal locations, to forecast available resources for integration into power grids, to schedule maintenance on wind energy facilities, and to define design criteria for next-generation turbines. This array of research needs implies that an appropriate forecasting tool must be able to account for mesoscale processes like frontal passages, surface-atmosphere interactions inducing local-scale circulations, and the microscale effects of atmospheric stability such as breaking Kelvin-Helmholtz billows. This range of scales and processes demands a mesoscale model with large-eddy simulation (LES) capabilities which can also account for varying atmospheric stability. Numerical weather prediction models, such as the Weather and Research Forecasting model (WRF), excel at predicting synoptic and mesoscale phenomena. With grid spacings of less than 1 km (as is often required for wind energy applications), however, the limits of WRF's subfilter scale (SFS) turbulence parameterizations are exposed, and fundamental problems arise, associated with modeling the scales of motion between those which LES can represent and those for which large-scale PBL parameterizations apply. To address these issues, we have implemented significant modifications to the ARW core of the Weather Research and Forecasting model, including the Nonlinear Backscatter model with Anisotropy (NBA) SFS model following Kosovic (1997) and an explicit filtering and reconstruction technique to compute the Resolvable Subfilter-Scale (RSFS) stresses (following Chow et al, 2005).We are also modifying WRF's terrain-following coordinate system by implementing an immersed boundary method (IBM) approach to account for the effects of complex terrain. Companion papers presenting idealized simulations with NBA-RSFS-WRF (Mirocha et al.) and IBM-WRF (K. A. Lundquist et al.) are also presented. Observations of flow through the Altamont Pass (Northern California) wind farm are available for validation of the WRF modeling tool for wind energy applications. In this presentation, we use these data to evaluate simulations using the NBA-RSFS-WRF tool in multiple configurations. We vary nesting capabilities, multiple levels of RSFS reconstruction, SFS turbulence models (the new NBA turbulence model versus existing WRF SFS turbulence models) to illustrate the capabilities of the modeling tool and to prioritize recommendations for operational uses. Nested simulations which capture both significant mesoscale processes as well as local-scale stable boundary layer effects are required to effectively predict available wind resources at turbine height.

  13. Model averaging methods to merge operational statistical and dynamic seasonal streamflow forecasts in Australia

    NASA Astrophysics Data System (ADS)

    Schepen, Andrew; Wang, Q. J.

    2015-03-01

    The Australian Bureau of Meteorology produces statistical and dynamic seasonal streamflow forecasts. The statistical and dynamic forecasts are similarly reliable in ensemble spread; however, skill varies by catchment and season. Therefore, it may be possible to optimize forecasting skill by weighting and merging statistical and dynamic forecasts. Two model averaging methods are evaluated for merging forecasts for 12 locations. The first method, Bayesian model averaging (BMA), applies averaging to forecast probability densities (and thus cumulative probabilities) for a given forecast variable value. The second method, quantile model averaging (QMA), applies averaging to forecast variable values (quantiles) for a given cumulative probability (quantile fraction). BMA and QMA are found to perform similarly in terms of overall skill scores and reliability in ensemble spread. Both methods improve forecast skill across catchments and seasons. However, when both the statistical and dynamical forecasting approaches are skillful but produce, on special occasions, very different event forecasts, the BMA merged forecasts for these events can have unusually wide and bimodal distributions. In contrast, the distributions of the QMA merged forecasts for these events are narrower, unimodal and generally more smoothly shaped, and are potentially more easily communicated to and interpreted by the forecast users. Such special occasions are found to be rare. However, every forecast counts in an operational service, and therefore the occasional contrast in merged forecasts between the two methods may be more significant than the indifference shown by the overall skill and reliability performance.

  14. Near Real Time Data for Operational Space Weather Forecasting

    NASA Astrophysics Data System (ADS)

    Berger, T. E.

    2014-12-01

    Space weather operations presents unique challenges for data systems and providers. Space weather events evolve more quickly than terrestrial weather events. While terrestrial weather occurs on timescales of minutes to hours, space weather storms evolve on timescales of seconds to minutes. For example, the degradation of the High Frequency Radio communications between the ground and commercial airlines is nearly instantaneous when a solar flare occurs. Thus the customer is observing impacts at the same time that the operational forecast center is seeing the event unfold. The diversity and spatial scale of the space weather system is such that no single observation can capture the salient features. The vast space that encompasses space weather and the scarcity of observations further exacerbates the situation and make each observation even more valuable. The physics of interplanetary space, through which many major storms propagate, is very different from the physics of the ionosphere where most of the impacts are felt. And while some observations can be made from ground-based observatories, many of the most critical data comes from satellites, often in unique orbits far from Earth. In this presentation, I will describe some of the more important sources and types of data that feed into the operational alerts, watches, and warnings of space weather storms. Included will be a discussion of some of the new space weather forecast models and the data challenges that they bring forward.

  15. Lessons of L'Aquila for Operational Earthquake Forecasting

    NASA Astrophysics Data System (ADS)

    Jordan, T. H.

    2012-12-01

    The L'Aquila earthquake of 6 Apr 2009 (magnitude 6.3) killed 309 people and left tens of thousands homeless. The mainshock was preceded by a vigorous seismic sequence that prompted informal earthquake predictions and evacuations. In an attempt to calm the population, the Italian Department of Civil Protection (DPC) convened its Commission on the Forecasting and Prevention of Major Risk (MRC) in L'Aquila on 31 March 2009 and issued statements about the hazard that were widely received as an "anti-alarm"; i.e., a deterministic prediction that there would not be a major earthquake. On October 23, 2012, a court in L'Aquila convicted the vice-director of DPC and six scientists and engineers who attended the MRC meeting on charges of criminal manslaughter, and it sentenced each to six years in prison. A few weeks after the L'Aquila disaster, the Italian government convened an International Commission on Earthquake Forecasting for Civil Protection (ICEF) with the mandate to assess the status of short-term forecasting methods and to recommend how they should be used in civil protection. The ICEF, which I chaired, issued its findings and recommendations on 2 Oct 2009 and published its final report, "Operational Earthquake Forecasting: Status of Knowledge and Guidelines for Implementation," in Aug 2011 (www.annalsofgeophysics.eu/index.php/annals/article/view/5350). As defined by the Commission, operational earthquake forecasting (OEF) involves two key activities: the continual updating of authoritative information about the future occurrence of potentially damaging earthquakes, and the officially sanctioned dissemination of this information to enhance earthquake preparedness in threatened communities. Among the main lessons of L'Aquila is the need to separate the role of science advisors, whose job is to provide objective information about natural hazards, from that of civil decision-makers who must weigh the benefits of protective actions against the costs of false alarms and failures-to-predict. The best way to achieve this separation is to use probabilistic rather than deterministic statements in characterizing short-term changes in seismic hazards. The ICEF recommended establishing OEF systems that can provide the public with open, authoritative, and timely information about the short-term probabilities of future earthquakes. Because the public needs to be educated into the scientific conversation through repeated communication of probabilistic forecasts, this information should be made available at regular intervals, during periods of normal seismicity as well as during seismic crises. In an age of nearly instant information and high-bandwidth communication, public expectations regarding the availability of authoritative short-term forecasts are rapidly evolving, and there is a greater danger that information vacuums will spawn informal predictions and misinformation. L'Aquila demonstrates why the development of OEF capabilities is a requirement, not an option.

  16. Global operational hydrological forecasts through eWaterCycle

    NASA Astrophysics Data System (ADS)

    van de Giesen, Nick; Bierkens, Marc; Donchyts, Gennadii; Drost, Niels; Hut, Rolf; Sutanudjaja, Edwin

    2015-04-01

    Central goal of the eWaterCycle project (www.ewatercycle.org) is the development of an operational hyper-resolution hydrological global model. This model is able to produce 14 day ensemble forecasts based on a hydrological model and operational weather data (presently NOAA's Global Ensemble Forecast System). Special attention is paid to prediction of situations in which water related issues are relevant, such as floods, droughts, navigation, hydropower generation, and irrigation stress. Near-real time satellite data will be assimilated in the hydrological simulations, which is a feature that will be presented for the first time at EGU 2015. First, we address challenges that are mainly computer science oriented but have direct practical hydrological implications. An important feature in this is the use of existing standards and open-source software to the maximum extent possible. For example, we use the Community Surface Dynamics Modeling System (CSDMS) approach to coupling models (Basic Model Interface (BMI)). The hydrological model underlying the project is PCR-GLOBWB, built by Utrecht University. This is the motor behind the predictions and state estimations. Parts of PCR-GLOBWB have been re-engineered to facilitate running it in a High Performance Computing (HPC) environment, run parallel on multiple nodes, as well as to use BMI. Hydrological models are not very CPU intensive compared to, say, atmospheric models. They are, however, memory hungry due to the localized processes and associated effective parameters. To accommodate this memory need, especially in an ensemble setting, a variation on the traditional Ensemble Kalman Filter was developed that needs much less on-chip memory. Due to the operational nature, the coupling of the hydrological model with hydraulic models is very important. The idea is not to run detailed hydraulic routing schemes over the complete globe but to have on-demand simulation prepared off-line with respect to topography and parameterizations. This allows for very detailed simulations at hectare to meter scales, where and when this is needed. At EGU 2015, the operational global eWaterCycle model will be presented for the first time, including forecasts at high resolution, the innovative data assimilation approach, and on-demand coupling with hydraulic models.

  17. Optimal Operation Planning of Wind Farm Installed BESS Using Wind Power Forecast Data of Wind Turbine Generators Considering Forecast Error

    NASA Astrophysics Data System (ADS)

    Ogimi, Kazuki; Kamiyama, Shota; Palmer, Michael; Yona, Atsushi; Senju, Tomonobu; Funabashi, Toshihisa

    2013-06-01

    In order to solve the problems of global warming and depletion of energy resource, renewable energy systems such as wind generation are getting attention. However, wind power fluctuates due to variation of wind speed, and it is difficult to perfectly forecast wind power. This paper describes a method to use power forecast data of wind turbine generators considering wind power forecast error for optimal operation. The purpose in this paper is to smooth the output power fluctuation of a wind farm and to obtain more beneficial electrical power for selling.

  18. Sensitivity of the forecast skill to the combination of physical parameterizations in the WRF/Chem model: A study in the Metropolitan Region of São Paulo (MRSP)

    NASA Astrophysics Data System (ADS)

    Silva Junior, R. S.; Rocha, R. P.; Andrade, M. F.

    2007-05-01

    The Planetary Boundary Layer (PBL) is the region of the atmosphere that suffers the direct influence of surface processes and the evolution of their characteristics during the day is of great importance for the pollutants dispersion. The aim of the present work is to analyze the most efficient combination of PBL, cumulus convection and cloud microphysics parameterizations for the forecast of the vertical profile of wind speed over Metropolitan Region of São Paulo (MRSP) that presents serious problems of atmospheric pollution. The model used was the WRF/Chem that was integrated for 48 h forecasts during one week of observational experiment that take place in the MRSP during October-November of 2006. The model domain has 72 x 48 grid points, with 18 km of resolution, centered in the MRSP. Considering a mixed-physics ensemble approach the forecasts used a combination of the parameterizations: (a) PBL the schemes of Mellor-Yamada-Janjic (MYJ) and Yonsei University Scheme (YSU); (b) cumulus convections schemes of Grell-Devenyi ensemble (GDE) and Betts-Miller-Janjic (BMJ); (c) cloud microphysics schemes of Purdue Lin (MPL) and NCEP 5-class (MPN). The combinations tested were the following: MYJ-BMJ-MPL, MYJ-BMJ-MPN, MYJ-GDE-MPL, MYJ-GDE-MPN, YSU-BMJ-MPL, YSU-BMJ-MPN, YSU-GDE-MPL, YSU-GDE-MPN, i.e., a set of 8 previsions for day. The model initial and boundary conditions was obtained of the AVN-NCEP model. Besides this data set, the MRSP observed soundings were used to verify the WRF results. The statistical analysis considered the correlation coefficient, root mean square error, mean error between forecasts and observed wind profiles. The results showed that the most suitable combination is the YSU-GDE-MPL. This can be associated to the GDE cumulus convection scheme, which takes into consideration the entrainment process in the clouds, and also the MPL scheme that considers a larger number of classes of water phase, including the ice and mixed phases. For PBL the YSU presents the better approaches to represent the wind speed, where the atmospheric gradients are stronger and the atmosphere is less mixed.

  19. Operational aspects of asynchronous filtering for improved flood forecasting

    NASA Astrophysics Data System (ADS)

    Rakovec, Oldrich; Weerts, Albrecht; Sumihar, Julius; Uijlenhoet, Remko

    2014-05-01

    Hydrological forecasts can be made more reliable and less uncertain by recursively improving initial conditions. A common way of improving the initial conditions is to make use of data assimilation (DA), a feedback mechanism or update methodology which merges model estimates with available real world observations. The traditional implementation of the Ensemble Kalman Filter (EnKF; e.g. Evensen, 2009) is synchronous, commonly named a three dimensional (3-D) assimilation, which means that all assimilated observations correspond to the time of update. Asynchronous DA, also called four dimensional (4-D) assimilation, refers to an updating methodology, in which observations being assimilated into the model originate from times different to the time of update (Evensen, 2009; Sakov 2010). This study investigates how the capabilities of the DA procedure can be improved by applying alternative Kalman-type methods, e.g., the Asynchronous Ensemble Kalman Filter (AEnKF). The AEnKF assimilates observations with smaller computational costs than the original EnKF, which is beneficial for operational purposes. The results of discharge assimilation into a grid-based hydrological model for the Upper Ourthe catchment in Belgian Ardennes show that including past predictions and observations in the AEnKF improves the model forecasts as compared to the traditional EnKF. Additionally we show that elimination of the strongly non-linear relation between the soil moisture storage and assimilated discharge observations from the model update becomes beneficial for an improved operational forecasting, which is evaluated using several validation measures. In the current study we employed the HBV-96 model built within a recently developed open source modelling environment OpenStreams (2013). The advantage of using OpenStreams (2013) is that it enables direct communication with OpenDA (2013), an open source data assimilation toolbox. OpenDA provides a number of algorithms for model calibration and assimilation and is suitable to be connected to any kind of environmental model. This setup is embedded in the Delft Flood Early Warning System (Delft-FEWS, Werner et al., 2013) for making all simulations and forecast runs and handling of all hydrological and meteorological data. References: Evensen, G. (2009), Data Assimilation: The Ensemble Kalman Filter, Springer, doi:10.1007/978-3-642-03711-5. OpenDA (2013), The OpenDA data-assimilation toolbox, www.openda.org, (last access: 1 November 2013). OpenStreams (2013), OpenStreams, www.openstreams.nl, (last access: 1 November 2013). Sakov, P., G. Evensen, and L. Bertino (2010), Asynchronous data assimilation with the EnKF, Tellus, Series A: Dynamic Meteorology and Oceanography, 62(1), 24-29, doi:10.1111/j.1600-0870.2009.00417.x. Werner, M., J. Schellekens, P. Gijsbers, M. van Dijk, O. van den Akker, and K. Heynert (2013), The Delft-FEWS flow forecasting system, Environ. Mod. & Soft., 40(0), 65-77, doi: http://dx.doi.org/10.1016/j.envsoft.2012.07.010.

  20. Value of seasonal flow forecast to reservoir operation for water supply in snow-dominated catchments

    NASA Astrophysics Data System (ADS)

    Anghileri, Daniela; Voisin, Nathalie; Castelletti, Andrea; Pianosi, Francesca; Nijssen, Bart; Lettenmaier, Dennis

    2014-05-01

    The recursive application of forecasting and optimization can make management strategies more flexible and efficient by improving the potential for anticipating, and thus adapting, to adverse events. In the field of reservoir operation, this means enriching the information base on which release decisions are made. At a minimum, this includes the available reservoir storage, but reservoir management can greatly benefit from consideration of other pieces of information as, for instance, weather and flow forecasts. However, the utility or value of inflow forecasts is directly related to forecast quality. In this work, we focus on snow-dominated water resource systems, where the prediction of the volume and timing of snowmelt can greatly enhance the operational performance. We use the Oroville-Thermalito reservoir complex in the Feather River Basin, California, as a case study to explore the effect of forecast quality on optimal release strategies. We use Deterministic Dynamic Programming to optimize medium-range and seasonal reservoir operation based on different forecasts of reservoir inflows. We determine maximum reservoir operation performance by forcing the optimization with observed inflows, which is equivalent to a perfect forecast. The forecast quality is then progressively degraded to relate forecast skill to changes in release decisions and to determine the minimum forecast skill that is required to affect decision-making. We generate forecasted inflow sequences using the Variable Infiltration Capacity (VIC) hydrology model. Forecast initial conditions are created using observed meteorology, while inflow forecasts are based on seasonal climate forecasts. Although the forecast skill level is specific to the Feather River basin, the methodology should be transferable to other systems with strong seasonal runoff regimes. We assess the transferability of the case study results to other systems using alternative reservoir characteristics of the Oroville-Thermalito reservoir system as a surrogate for alternate reservoir configurations. Specifically, we explore the sensitivity of reservoir operation performance to the ratio of reservoir mean inflow volume to reservoir capacity and downstream demand requirements.

  1. WMOP: The SOCIB Western Mediterranean Sea OPerational forecasting system

    NASA Astrophysics Data System (ADS)

    Renault, Lionel; Juza, Mélanie; Garau, Bartolomé; Sayol, Juan Manuel; Orfila, Alejandro; Tintoré, Joaquín

    2013-04-01

    Development of science based ocean-forecasting systems at global, regional, sub-regional and local scales is needed to increase our understanding of ocean processes and to support knowledge based management of the marine environment. In this context, WMOP (Western Mediterranean sea /Balearic OPerational system) is the forecasting subsystem component of SOCIB, the new Balearic Islands Coastal Observing and Forecasting System. The WMOP system is operational since the end of 2010. The ROMS model is forced every 3 hours with atmospheric forcing derived from AEMET/Hirlam and daily boundary conditions provided by MFS2 from MyOcean/MOON. Model domain is implemented over an area extending from Gibraltar strait to Corsica/Sardinia (from 6°W to 9°E and from 35°N to 44.5°N), including Balearic Sea and Gulf of Lion. The grid is 631 x 539 points with a resolution of ~1.5km, which allows good representation of mesoscale and submesoscale features (first baroclinic Rossby radius ~10-15 km) of key relevance in this region. The model has 30 sigma levels, and the vertical s coordinate is stretched for boundary layer resolution, also essential to capture extreme events water masses formation and dynamical effects. Bottom topography is derived from a 2' resolution database. Online validation procedures based on inter-comparison of model outputs against observing systems and reference models such as MFS and Mercator are used to assess at what level the numerical models are able to reproduce the features observed from in-situ systems and remote sensing. The intrinsic three-dimensional variability of the coastal ocean and open-ocean exchanges imply the need of muti-plaform observing systems covering a variety of scales. Fixed moorings provide a good temporal resolution but poor spatial coverage, while satellite products provide a good spatial coverage but just on the surface layer. Gliders can provide a reasonable spatial variability in both horizontal and vertical axes. Thus, inter-comparison with products from different types of sources provides a good view of how well the model is performing and reproducing the dynamics of the basin. Additionally, this present study aims at assessing WMOP simulations quantitatively against complementary observational databases, i.e. to identify well-simulated physical features and to characterize the structure of model biases. The simulations are evaluated against hydrographic observations (temperature/salinity profiles from the ENACT-ENSEMBLES database), buoys, gliders and satellite data. We compare various simulations (WMOP, MFS, Mercator) to quantify the impact of the (sub)mesoscale on the large scale circulation.

  2. MOCASSIM - an operational forecast system for the Portuguese coastal waters.

    NASA Astrophysics Data System (ADS)

    Vitorino, J.; Soares, C.; Almeida, S.; Rusu, E.; Pinto, J.

    2003-04-01

    An operational system for the forecast of oceanographic conditions off the Portuguese coast is presently being implemented at Instituto Hidrográfico (IH), in the framework of project MOCASSIM. The system is planned to use a broad range of observations provided both from IH observational networks (wave buoys, tidal gauges) and programs (hydrographic surveys, moorings) as well as from external sources. The MOCASSIM system integrates several numerical models which, combined, are intended to cover the relevant physical processes observed in the geographical areas of interest. At the present stage of development the system integrates a circulation module and a wave module. The circulation module is based on the Harvard Ocean Prediction System (HOPS), a primitive equation model formulated under the rigid lid assumption, which includes a data assimilation module. The wave module is based on the WaveWatch3 (WW3) model, which provides wave conditions in the North Atlantic basin, and on the SWAN model which is used to improve the wave forecasts on coastal or other specific areas of interest. The models use the meteorological forcing fields of a limited area model (ALADIN model) covering the Portuguese area, which are being provided in the framework of a close colaboration with Instituto de Meteorologia. Although still under devellopment, the MOCASSIM system has already been used in several operationnal contexts. These included the operational environmental assessment during both national and NATO navy exercises and, more recently, the monitoring of the oceanographic conditions in the NW Iberian area affected by the oil spill of MV "Prestige". The system is also a key component of ongoing research on the oceanography of the Portuguese continental margin, which is presently being conducted at IH in the framework of national and European funded projects.

  3. Operational flood forecasting system of Umbria Region "Functional Centre

    NASA Astrophysics Data System (ADS)

    Berni, N.; Pandolfo, C.; Stelluti, M.; Ponziani, F.; Viterbo, A.

    2009-04-01

    The hydrometeorological alert office (called "Decentrate Functional Centre" - CFD) of Umbria Region, in central Italy, is the office that provides technical tools able to support decisions when significant flood/landslide events occur, furnishing 24h support for the whole duration of the emergency period, according to the national directive DPCM 27 February 2004 concerning the "Operating concepts for functional management of national and regional alert system during flooding and landslide events for civil protection activities purposes" that designs, within the Italian Civil Defence Emergency Management System, a network of 21 regional Functional Centres coordinated by a central office at the National Civil Protection Department in Rome. Due to its "linking" role between Civil Protection "real time" activities and environmental/planning "deferred time" ones, the Centre is in charge to acquire and collect both real time and quasi-static data: quantitative data from monitoring networks (hydrometeorological stations, meteo radar, ...), meteorological forecasting models output, Earth Observation data, hydraulic and hydrological simulation models, cartographic and thematic GIS data (vectorial and raster type), planning studies related to flooding areas mapping, dam managing plans during flood events, non instrumental information from direct control of "territorial presidium". A detailed procedure for the management of critical events was planned, also in order to define the different role of various authorities and institutions involved. Tiber River catchment, of which Umbria region represents the main upper-medium portion, includes also regional trans-boundary issues very important to cope with, especially for what concerns large dam behavior and management during heavy rainfall. The alert system is referred to 6 different warning areas in which the territory has been divided into and based on a threshold system of three different increasing critical levels according to the expected ground effects: ordinary, moderate and high. Particularly, hydrometric and rainfall thresholds for both floods and landslides alarms were assessed. Based on these thresholds, at the Umbria Region Functional Centre an automatic phone-call and SMS alert system is operating. For a real time flood forecasting system, at the CFD several hydrological and hydraulic models were developed. Three rainfall-runoff hydrological models, using different quantitative meteorological forecasts, are available: the event based models X-Nash (based on the Nash theory) and Mike-Drift coupled with the hydraulic model Mike-11 (developed by the Danish Hydraulic Institute - DHI); and the physically-based continuous model Mobidic (MOdello di Bilancio Idrologico DIstribuito e Continuo - Distributed and Continuous Model for the Hydrological Balance, developed by the University of Florence in cooperation with the Functional Centre of Tuscany Region). Other two hydrological models, using observed data of the real time hydrometeorological network, were implemented: the first one is the rainfall-runoff hydrological model Hec-Hms coupled with the hydraulic model Hec-Ras (United States Army Corps of Engineers - USACE). Moreover, Hec-Hms, is coupled also with a continuous soil moisture model for a more precise evaluation of the antecedent moisture condition of the basin, which is a key factor for a correct runoff volume evaluation. The second one is the routing hydrological model Stafom (STage FOrecasting Model, developed by the Italian Research Institute for Geo-Hydrological Protection of the National Research Council - IRPI-CNR). This model is an adaptive model for on-line stage forecasting for river branches where significant lateral inflow contributions occur and, up to now, it is implemented for the main Tiber River branch and it allows a forecasting lead time up to 10 hours for the downstream river section. Recently, during the period between December the 4th and the 16th 2008, Umbria region territory was interested by a severe rainfall event causing many floods and landslides. During the mainly critical phases the CFD furnished an immediate, significant 24h support for the decision support activities. The official web site (www.cfumbria.it), entirely developed with open source tools, represented a very useful device furnishing good performances for the monitoring and data dissemination to all the subjects involved, especially to the National/Regional Civil Protection offices and territorial presidium. Thresholds presented good accordance with non instrumental observations and automatic alert system was very effective. At last, during the flooding event a continuous link with the National Department, regional Civil Protection offices, territorial presidium and local public services, together with real time instrumental monitoring and now-casting hydrological activities performed by available models, represented a suitable junction between practice and science in CFD operational forecasting system at local, regional and national scale.

  4. Operational hydrological ensemble forecasts in France, taking into account rainfall and hydrological model uncertainties.

    NASA Astrophysics Data System (ADS)

    Mathevet, T.; Garavaglia, F.; Gailhard, J.; Garon, R.; Dubus, L.

    2009-09-01

    In operational conditions, the actual quality of meteorological and hydrological forecasts do not allow decision-making in a certain future. In this context, meteorological and hydrological ensemble forecasts allow a better representation of forecasts uncertainties. Compared to classical deterministic forecasts, ensemble forecasts improve the human expertise of hydrological forecasts, which is essential to synthesize available informations, coming from different meteorological and hydrological models and human experience. In this paper, we present a hydrological ensemble forecasting system under development at EDF (French Hydropower Company). Our results were updated, taking into account a longer rainfall forecasts archive. Our forecasting system both takes into account rainfall forecasts uncertainties and hydrological model forecasts uncertainties. Hydrological forecasts were generated using the MORDOR model (Andreassian et al., 2006), developed at EDF and used on a daily basis in operational conditions on a hundred of watersheds. Two sources of rainfall forecasts were used : one is based on ECMWF forecasts, another is based on an analogues approach (Obled et al., 2002). Two methods of hydrological model forecasts uncertainty estimation were used : one is based on the use of equifinal parameter sets (Beven & Binley, 1992), the other is based on the statistical modelisation of the hydrological forecast empirical uncertainty (Montanari et al., 2004 ; Schaefli et al., 2007). Daily operational hydrological 7-day ensemble forecasts during 4 years (from 2005 to 2008) in few alpine watersheds were evaluated. Finally, we present a way to combine rainfall and hydrological model forecast uncertainties to achieve a good probabilistic calibration. Our results show that the combination of ECMWF and analogues-based rainfall forecasts allow a good probabilistic calibration of rainfall forecasts. They show also that the statistical modeling of the hydrological forecast empirical uncertainty has a better probabilistic calibration, than the equifinal parameter set approach. Andreassian et al., 2006. Catalogue of the models used in MOPEX 2004/2005. Large sample basin experiments for hydrological mode parameterisation : results of the Model Parameter Experiment, IAHS Publ. 307, 41-94. Beven & Binley, 1992. The future of distributed models : model calibration and uncertainty prediction. Hydrological Processes, 6, 279-298. Obled, C., Bontron, G., Garon, R., 2002. Quantitative precipitation forecasts: a statistical adaptation of model outputs though an analogues sorting approach. Atmospheric Research, 63, 303-324. Montanari, A. and Brath, A., (2004). A stochastic approach for assessing the uncertainty of rainfall-runoff simulations. Water Resources Research, 40, W01106, doi:10.1029/2003WR002540. Schaefli, B., Balin Talamba, D. and Musy, A., (2007). Quantifying hydrological modeling errors through a mixture of normal distributions. Journal of Hydrology, 332, 303-315.

  5. Wildland fire simulation by WRF-Fire

    NASA Astrophysics Data System (ADS)

    Mandel, J.; Beezley, J. D.; Kochanski, A.; Kondratenko, V. Y.; Sousedik, B.

    2010-12-01

    This presentation will give an overview of the principles, algorithms, and features of the coupled atmosphere-wildland fire software WRF-Fire. WRF-Fire consists of a fire-spread model, based on a modified Rothermel's formula implemented by the level-set method, coupled with the Weather Research and Forecasting model (WRF). The code has been publicly released with WRF and it is supported by the developers. The WRF infrastructure is used for parallel execution, with additional improvements. In addition to the input of standard atmospheric data, the WRF Preprocessing System (WPS) has been extended for the input of high-resolution topography and fuel data. The fuel models can be easily modified by the user. The components of the wind and of the terrain gradient are interpolated to the fire model mesh by accurate formulas which respect grid staggering. Ignition models include point, drip-torch line, and, in near future, a developed fire perimeter from standard web sources, with an atmosphere spin-up. Companion presentations will describe a validation on the FireFlux experiment, and a simulation of a real wildland fire in a terrain with sharp gradients. This work was supported by NSF grants CNS-0719641 and ATM-0835579. Simulation of the FireFlux grass fire experiment (Clements et al., 2007) in WRF-Fire.

  6. DEVELOPING MCIP TO PROCESS WRF-EM OUTPUT

    EPA Science Inventory

    This presentation describes modifications that were made to the Community Multiscale Air Quality (CMAQ) Modeling System's Meteorology-Chemistry Interface Processor (MCIP) to ingest a new meteorological model, the Weather Research and Forecasting (WRF) Model. This presentation al...

  7. Evaluation of WRF-Urban Canopy Model over Seoul, Korea

    NASA Astrophysics Data System (ADS)

    Byon, J.; Seo, B.; Choi, Y.

    2008-12-01

    Numerical models with a fine grid can be a useful tool for investigation of urban forecast which provide input to air dispersion and pollution model. Simulation for urban forecast may be conducted using CFD model or mesoscale model. A small domain of the CFD model limits for the study of larger scale forcing to the urban environment. Improvement of computational environment and physics in mesoscale model allows urban scale prediction with a larger domain using mososcale model. It is implemented the parameterization of urban effect in the WRF mesoscale model which is developed in NCAR. NCAR coupled an urban canopy model (UCM) with Noah land surface model in the WRF model to realistically represent the urban by high resolution of land-use and building information. This study is focus on evaluation of WRF-UCM over the urban region of Seoul, South Korea during July 1-10 and October 6-12, 2007. WRF-UCM is conducted with 1km resolution and a 10km WRF model result which is forecasted at Korea Meteorological Administration numerical weather prediction center employed as initial and boundary condition. The urban land-use is remapped using data from Korean Ministry of Environment(KME). The KME land-use data is retrieved from Landsat satellite which has a 30-m resolution. The air temperature of WRF model is lower than observation, while wind speed increase in the model forecast. The temperature from the WRF-UCM is higher than that from the standard WRF over Seoul. The coupled WRF-UCM represents increase of urban heat which is caused from urban effects such as anthropogenic heat and building, etc. The performance of the WRF-UCM results over Seoul, South Korea would be presented in the conference. The WRF-UCM results will contribute to the study of urban heat and air flow in the city.

  8. eWaterCycle: A global operational hydrological forecasting model

    NASA Astrophysics Data System (ADS)

    van de Giesen, Nick; Bierkens, Marc; Donchyts, Gennadii; Drost, Niels; Hut, Rolf; Sutanudjaja, Edwin

    2015-04-01

    Development of an operational hyper-resolution hydrological global model is a central goal of the eWaterCycle project (www.ewatercycle.org). This operational model includes ensemble forecasts (14 days) to predict water related stress around the globe. Assimilation of near-real time satellite data is part of the intended product that will be launched at EGU 2015. The challenges come from several directions. First, there are challenges that are mainly computer science oriented but have direct practical hydrological implications. For example, we aim to make use as much as possible of existing standards and open-source software. For example, different parts of our system are coupled through the Basic Model Interface (BMI) developed in the framework of the Community Surface Dynamics Modeling System (CSDMS). The PCR-GLOBWB model, built by Utrecht University, is the basic hydrological model that is the engine of the eWaterCycle project. Re-engineering of parts of the software was needed for it to run efficiently in a High Performance Computing (HPC) environment, and to be able to interface using BMI, and run on multiple compute nodes in parallel. The final aim is to have a spatial resolution of 1km x 1km, which is currently 10 x 10km. This high resolution is computationally not too demanding but very memory intensive. The memory bottleneck becomes especially apparent for data assimilation, for which we use OpenDA. OpenDa allows for different data assimilation techniques without the need to build these from scratch. We have developed a BMI adaptor for OpenDA, allowing OpenDA to use any BMI compatible model. To circumvent memory shortages which would result from standard applications of the Ensemble Kalman Filter, we have developed a variant that does not need to keep all ensemble members in working memory. At EGU, we will present this variant and how it fits well in HPC environments. An important step in the eWaterCycle project was the coupling between the hydrological and hydrodynamic models. The hydrological model will run operationally for the whole globe. Once special situations are predicted, such as floods, navigation hindrances, or water shortages, a detailed local hydraulic model will start to predict the exact local consequences. In Vienna, we will show for the first time the operational global eWaterCycle model, including high resolution forecasts, our new data assimilation technique, and coupled hydrological/hydraulic models.

  9. Sensitivity of the Community Multiscale Air Quality (CMAQ) Model v4.7 Results for the Eastern United States to MM5 and WRF Meteorological Drivers

    EPA Science Inventory

    This paper presents a comparison of the operational performance of two Community Multiscale Air Quality (CMAQ) model v4.7 simulations that utilize input data from the 5th generation Mesoscale Model MM5 and the Weather Research and Forecasting (WRF) meteorological models.

  10. AFWA Space Weather Operations: Specifying and Forecasting the Operational Environment for the Warfighter

    NASA Astrophysics Data System (ADS)

    Reich, J. P.; Davis, B. L.; Sattler, M. P.

    2008-12-01

    The effects of space weather on modern military systems can range from mere annoyances to catastrophic system outages and failures. Knowing the timing and severity of mission-impacting space weather is crucial to mitigating and overcoming its effects. The 2nd Weather Squadron's Space Weather Flight at the Air Force Weather Agency (AFWA) faces the daily challenge of forecasting space weather to support US warfighters and other DoD customers. The forecaster's success depends on the combined efforts of other AFWA personnel involved in technology transition and space weather program management to deliver the latest technology to operations. As the DoD lead for space weather, AFWA provides timely, accurate, and relevant space weather data and forecasts. This presentation will give an overview of infrastructure that makes this possible, highlight the current state of the GAIM and HAF models, and explore future required capabilities. A new delivery of the Global Assimilation of Ionospheric Measurements (GAIM) model is on the horizon, as well as the delivery of the Space Weather Modeling System (SWMS), the first coupled solar wind-ionospheric model in operations. This will enhance the quality of the GAIM forecast out to 5 days.

  11. Short-term ensemble streamflow forecasting using operationally-produced single-valued streamflow forecasts - A Hydrologic Model Output Statistics (HMOS) approach

    NASA Astrophysics Data System (ADS)

    Regonda, Satish Kumar; Seo, Dong-Jun; Lawrence, Bill; Brown, James D.; Demargne, Julie

    2013-08-01

    We present a statistical procedure for generating short-term ensemble streamflow forecasts from single-valued, or deterministic, streamflow forecasts produced operationally by the U.S. National Weather Service (NWS) River Forecast Centers (RFCs). The resulting ensemble streamflow forecast provides an estimate of the predictive uncertainty associated with the single-valued forecast to support risk-based decision making by the forecasters and by the users of the forecast products, such as emergency managers. Forced by single-valued quantitative precipitation and temperature forecasts (QPF, QTF), the single-valued streamflow forecasts are produced at a 6-h time step nominally out to 5 days into the future. The single-valued streamflow forecasts reflect various run-time modifications, or "manual data assimilation", applied by the human forecasters in an attempt to reduce error from various sources in the end-to-end forecast process. The proposed procedure generates ensemble traces of streamflow from a parsimonious approximation of the conditional multivariate probability distribution of future streamflow given the single-valued streamflow forecast, QPF, and the most recent streamflow observation. For parameter estimation and evaluation, we used a multiyear archive of the single-valued river stage forecast produced operationally by the NWS Arkansas-Red River Basin River Forecast Center (ABRFC) in Tulsa, Oklahoma. As a by-product of parameter estimation, the procedure provides a categorical assessment of the effective lead time of the operational hydrologic forecasts for different QPF and forecast flow conditions. To evaluate the procedure, we carried out hindcasting experiments in dependent and cross-validation modes. The results indicate that the short-term streamflow ensemble hindcasts generated from the procedure are generally reliable within the effective lead time of the single-valued forecasts and well capture the skill of the single-valued forecasts. For smaller basins, however, the effective lead time is significantly reduced by short basin memory and reduced skill in the single-valued QPF.

  12. Operational river discharge forecasting in poorly gauged basins: the Kavango River basin case study

    NASA Astrophysics Data System (ADS)

    Bauer-Gottwein, P.; Jensen, I. H.; Guzinski, R.; Bredtoft, G. K. T.; Hansen, S.; Michailovsky, C. I.

    2015-03-01

    Operational probabilistic forecasts of river discharge are essential for effective water resources management. Many studies have addressed this topic using different approaches ranging from purely statistical black-box approaches to physically based and distributed modeling schemes employing data assimilation techniques. However, few studies have attempted to develop operational probabilistic forecasting approaches for large and poorly gauged river basins. The objective of this study is to develop open-source software tools to support hydrologic forecasting and integrated water resources management in Africa. We present an operational probabilistic forecasting approach which uses public-domain climate forcing data and a hydrologic-hydrodynamic model which is entirely based on open-source software. Data assimilation techniques are used to inform the forecasts with the latest available observations. Forecasts are produced in real time for lead times of 0-7 days. The operational probabilistic forecasts are evaluated using a selection of performance statistics and indicators and the performance is compared to persistence and climatology benchmarks. The forecasting system delivers useful forecasts for the Kavango River, which are reliable and sharp. Results indicate that the value of the forecasts is greatest for intermediate lead times between 4 and 7 days.

  13. Short-term optimal operation of water systems using ensemble forecasts

    NASA Astrophysics Data System (ADS)

    Raso, L.; Schwanenberg, D.; van de Giesen, N. C.; van Overloop, P. J.

    2014-09-01

    Short-term water system operation can be realized using Model Predictive Control (MPC). MPC is a method for operational management of complex dynamic systems. Applied to open water systems, MPC provides integrated, optimal, and proactive management, when forecasts are available. Notwithstanding these properties, if forecast uncertainty is not properly taken into account, the system performance can critically deteriorate. Ensemble forecast is a way to represent short-term forecast uncertainty. An ensemble forecast is a set of possible future trajectories of a meteorological or hydrological system. The growing ensemble forecasts availability and accuracy raises the question on how to use them for operational management. The theoretical innovation presented here is the use of ensemble forecasts for optimal operation. Specifically, we introduce a tree based approach. We called the new method Tree-Based Model Predictive Control (TB-MPC). In TB-MPC, a tree is used to set up a Multistage Stochastic Programming, which finds a different optimal strategy for each branch and enhances the adaptivity to forecast uncertainty. Adaptivity reduces the sensitivity to wrong forecasts and improves the operational performance. TB-MPC is applied to the operational management of Salto Grande reservoir, located at the border between Argentina and Uruguay, and compared to other methods.

  14. Charactering biomass burning aerosol in the Weather Research and Forecasting model with Chemistry (WRF-Chem), with evaluation against SAMBBA flight data.

    NASA Astrophysics Data System (ADS)

    Archer-Nicholls, S.; Lowe, D.; Darbyshire, E.; Morgan, W.; Freitas, S. R.; Longo, K.; Coe, H.; McFiggans, G.

    2014-12-01

    The burning of forests in the Amazonia region is a globally significant source of carbonaceous aerosol, containing both absorbing and scattering components. Biomass burning aerosol (BBA) are efficient CCN, modifying cloud properties and influencing atmospheric circulation and precipitation tendencies. The impacts of BBA are highly dependent on their size distribution and composition. Studies in this region can therefore benefit greatly from the use of state-of-the-art sectional aerosol representations. A bottom-up fire emissions inventory, 3BEM, has been developed by Longo et al.1. It uses satellite products to identify fire locations, applying the emissions factors of Andrei and Merlot3 to generate daily emission maps. Flaming emissions are very buoyant, and a method for injecting emissions at altitude is needed to accurately describe the vertical profile of BBA. A parameterisation has been developed to simulate this sub-grid process4, and previously implemented in WRF-Chem using a modal aerosol scheme5. For this work we have modified the WRF-Chem model to simulate 3BEM emissions using the MOSAIC sectional aerosol scheme6. This modified version of WRF-Chem v3.4.1 has been run for September 2012 over South America (25km grid-spacing). We will present model results evaluating the modelled aerosol vertical distribution, size distribution, composition and optical properties against measurements taken by the FAAM BAe-146 research aircraft during the SAMBBA field campaign. The plume-rise parameterisation was found to inject flaming emissions too high over most fires, resulting in high modelled aerosol loadings at high altitude. We probed the behaviour of the parameterisation by developing a new SAMBBA-tuned 3BEM emissions scenario, which uses more realistic estimates of fire size. Results from high-resolution (5 and 1km) nested simulations will also be presented, in order to evaluate the impacts of explicit aerosol-cloud interactions in non-parameterised clouds. 1. K. Longo et al., 2010, Atmos. Chem. Phys., 10, 5785-5795. 2. M. O. Andreae and P. Merlot, 2001, Global Biogeochem. Cy., 15(4), 955-966. 3. S. Freitas et al., 2007, Atmos. Chem. Phys., 7, 3385-3398. 4. G. Grell et al., 2011, Atmos. Chem. Phys., 11, 5289-5303. 5. R. Zavari et al., 2008, J. Geophys. Res., 113, D132024.

  15. Compute unified device architecture (CUDA)-based parallelization of WRF Kessler cloud microphysics scheme

    NASA Astrophysics Data System (ADS)

    Mielikainen, Jarno; Huang, Bormin; Wang, Jun; Allen Huang, H.-L.; Goldberg, Mitchell D.

    2013-03-01

    In recent years, graphics processing units (GPUs) have emerged as a low-cost, low-power and a very high performance alternative to conventional central processing units (CPUs). The latest GPUs offer a speedup of two-to-three orders of magnitude over CPU for various science and engineering applications. The Weather Research and Forecasting (WRF) model is the latest-generation numerical weather prediction model. It has been designed to serve both operational forecasting and atmospheric research needs. It proves useful for a broad spectrum of applications for domain scales ranging from meters to hundreds of kilometers. WRF computes an approximate solution to the differential equations which govern the air motion of the whole atmosphere. Kessler microphysics module in WRF is a simple warm cloud scheme that includes water vapor, cloud water and rain. Microphysics processes which are modeled are rain production, fall and evaporation. The accretion and auto-conversion of cloud water processes are also included along with the production of cloud water from condensation. In this paper, we develop an efficient WRF Kessler microphysics scheme which runs on Graphics Processing Units (GPUs) using the NVIDIA Compute Unified Device Architecture (CUDA). The GPU-based implementation of Kessler microphysics scheme achieves a significant speedup of 70 over its CPU based single-threaded counterpart. When a 4 GPU system is used, we achieve an overall speedup of 132 as compared to the single thread CPU version.

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

    NASA Astrophysics Data System (ADS)

    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.

  17. Operational river discharge forecasting in poorly gauged basins: the Kavango River Basin case study

    NASA Astrophysics Data System (ADS)

    Bauer-Gottwein, P.; Jensen, I. H.; Guzinski, R.; Bredtoft, G. K. T.; Hansen, S.; Michailovsky, C. I.

    2014-10-01

    Operational probabilistic forecasts of river discharge are essential for effective water resources management. Many studies have addressed this topic using different approaches ranging from purely statistical black-box approaches to physically-based and distributed modelling schemes employing data assimilation techniques. However, few studies have attempted to develop operational probabilistic forecasting approaches for large and poorly gauged river basins. This study is funded by the European Space Agency under the TIGER-NET project. The objective of TIGER-NET is to develop open-source software tools to support integrated water resources management in Africa and to facilitate the use of satellite earth observation data in water management. We present an operational probabilistic forecasting approach which uses public-domain climate forcing data and a hydrologic-hydrodynamic model which is entirely based on open-source software. Data assimilation techniques are used to inform the forecasts with the latest available observations. Forecasts are produced in real time for lead times of 0 to 7 days. The operational probabilistic forecasts are evaluated using a selection of performance statistics and indicators. The forecasting system delivers competitive forecasts for the Kavango River, which are reliable and sharp. Results indicate that the value of the forecasts is greatest for intermediate lead times between 4 and 7 days.

  18. Tailoring seasonal climate forecasts for hydropower operations in Ethiopia's upper Blue Nile basin

    NASA Astrophysics Data System (ADS)

    Block, P.

    2010-06-01

    Explicit integration of seasonal precipitation forecasts into water resources operations and planning is practically nonexistent, even in regions of scarcity. This is often attributable to water manager's tendency to act in a risk averse manner, preferring to avoid consequences of poor forecasts, at the expense of unrealized benefits. Convincing demonstrations of forecast value are therefore desirable to support assimilation into practice. A dynamic coupled system, including forecast, rainfall-runoff, and hydropower models, is applied to the upper Blue Nile basin in Ethiopia to compare benefits and reliability generated by actual forecasts against a climatology-based approach, commonly practiced in most water resources systems. Processing one hundred decadal sequences demonstrates superior forecast-based benefits in 68 cases, a respectable advancement, however benefits in a few forecast-based sequences are noticeably low, likely to dissuade manager's adoption. A hydropower sensitivity test reveals a propensity toward poor-decision making when forecasts over-predict wet conditions. Tailoring the precipitation forecast to highlight critical dry predictions minimizes this inclination, resulting in 97% of the sequences favoring the forecast-based approach. Considering managerial risk preferences for the system, even risk-averse actions, if coupled with forecasts, exhibits superior benefits and reliability compared with risk-taking tendencies relying on climatology.

  19. Science and Engineering of an Operational Tsunami Forecasting System

    SciTech Connect

    Gonzalez, Frank

    2009-04-06

    After a review of tsunami statistics and the destruction caused by tsunamis, a means of forecasting tsunamis is discussed as part of an overall program of reducing fatalities through hazard assessment, education, training, mitigation, and a tsunami warning system. The forecast is accomplished via a concept called Deep Ocean Assessment and Reporting of Tsunamis (DART). Small changes of pressure at the sea floor are measured and relayed to warning centers. Under development is an international modeling network to transfer, maintain, and improve tsunami forecast models.

  20. Science and Engineering of an Operational Tsunami Forecasting System

    ScienceCinema

    Gonzalez, Frank

    2010-01-08

    After a review of tsunami statistics and the destruction caused by tsunamis, a means of forecasting tsunamis is discussed as part of an overall program of reducing fatalities through hazard assessment, education, training, mitigation, and a tsunami warning system. The forecast is accomplished via a concept called Deep Ocean Assessment and Reporting of Tsunamis (DART). Small changes of pressure at the sea floor are measured and relayed to warning centers. Under development is an international modeling network to transfer, maintain, and improve tsunami forecast models.

  1. Operational Solar Forecasting System for DNI and GHI for Horizons Covering 5 Minutes to 72 Hours

    NASA Astrophysics Data System (ADS)

    Coimbra, C. F.

    2014-12-01

    I will describe the methodology used to develop and deploy operationally a comprehensive solar forecasting system for both concentrated and non-concentrated solar technologies. This operational forecasting system ingests data from local telemetry, remote sensing and Numerical Weather Prediction (NWP) models, processes all the diferent types of data (time series, sky images, satellite images, gridded data, etc.) to produce concatenated solar forecasts from 5 minutes out to 72 hours into the future. Each forecast is optimized with stochastic learning techniques that include input selection, model topology optimization, model output statistics, metric fitness optimization and machine learning. These forecasts are used by solar generators (plant managers), utilities and independent system operators for operations, scheduling, dispatching and market participation.

  2. Operational model for forecasting location specific quantitative precipitation and probability of precipitation over India

    NASA Astrophysics Data System (ADS)

    Maini, P.; Kumar, Ashok; Singh, S. V.; Rathore, L. S.

    2004-03-01

    The National Centre for Medium Range Weather Forecasting was established in early 1988 with the major objective to develop operational medium range weather forecasting capability and agricultural meteorological advisory services (AAS) for each of the 127 agricultural climatic zones for the farming community in India. At present, medium range weather forecast of six surface weather parameters namely, average cloud cover, 24 h accumulated precipitation, average wind speed, predominant wind direction, maximum/minimum temperature trends (up to 4 days) is provided to 83 units in different agricultural climatic zones. In addition the forecast of weekly cumulative rainfall is also provided. An objective system for obtaining location specific forecast, in the medium range, of surface weather elements is evolved at NCMRWF. The basic information used for this is the output from the general circulation model (GCM). A T80L18 model operational at the centre since 1994 has been recently upgraded to a T170L28 model. However, it is well known that in spite of higher resolution, the global models are unable to account for the small-scale effects (e.g. of topography, local environmental features) important in predicting surface weather parameters like rainfall, temperature etc. This necessitates the development of statistical-dynamical models. Hence an operational system for forecasting rainfall (quantitative, probability of precipitation (PoP)) has been developed at the centre and implemented since 1994. A Perfect Prog Method (PPM) approach is followed for statistical interpretation (SI) of Numerical Weather Prediction (NWP) products. PPM model equations are developed by using analysis data obtained from European Centre for Medium Range Weather Forecasts (ECMWF) for a period of six years (1985-1990). Rainfall forecasts are subsequently obtained from these equations by using T80 model output. A comparative study of the skill of SI forecast and the direct model output (DMO) forecast has indicated that SI forecast improves over the DMO considerably and hence can be developed as a fully automatic operational weather forecasting system.

  3. Operational Ensemble River Forecasting in the United States and Australia: Practices and Challenges

    NASA Astrophysics Data System (ADS)

    Pagano, T. C.

    2012-04-01

    Operational river forecasts have been long produced to support water resources management in the United States and Australia. These forecasts cover a range of timescales from flash flooding (e.g. minutes to hours ahead) to seasonal (e.g. months ahead) and are generated by a range of statistical (e.g. regression-based) and dynamical (e.g. rainfall-runoff) model based techniques. Forecast uncertainty is commonly estimated operationally by using an ensemble of future precipitation scenarios and/or a measure of historical model error. Retrospective ensemble forecasting and the use of reforecasts for bias-adjustment and post-processing have become popular research topics and a few successful demonstration projects exist in both countries. Practical methods of post-processing, such as ensemble dressing, have been used to improve the probabilistic reliability of forecasts. The translation of predictions of probability distributions of streamflow into temporally and spatially consistent ensemble hydrographs remains an area for further development. However, probabilistic forecast communication and use remains a stumbling block for many. Furthermore, ensemble generation and post-processing typically require completely automated systems, making it difficult for humans to contribute their expertise to the forecasting process. This talk draws on ten years of experience as an operational forecaster with the US Department of Agriculture and as a developer of short-term flood forecasting systems to support the Australian Bureau of Meteorology.

  4. Verification of Ensemble Forecasts for the New York City Operations Support Tool

    NASA Astrophysics Data System (ADS)

    Day, G.; Schaake, J. C.; Thiemann, M.; Draijer, S.; Wang, L.

    2012-12-01

    The New York City water supply system operated by the Department of Environmental Protection (DEP) serves nine million people. It covers 2,000 square miles of portions of the Catskill, Delaware, and Croton watersheds, and it includes nineteen reservoirs and three controlled lakes. DEP is developing an Operations Support Tool (OST) to support its water supply operations and planning activities. OST includes historical and real-time data, a model of the water supply system complete with operating rules, and lake water quality models developed to evaluate alternatives for managing turbidity in the New York City Catskill reservoirs. OST will enable DEP to manage turbidity in its unfiltered system while satisfying its primary objective of meeting the City's water supply needs, in addition to considering secondary objectives of maintaining ecological flows, supporting fishery and recreation releases, and mitigating downstream flood peaks. The current version of OST relies on statistical forecasts of flows in the system based on recent observed flows. To improve short-term decision making, plans are being made to transition to National Weather Service (NWS) ensemble forecasts based on hydrologic models that account for short-term weather forecast skill, longer-term climate information, as well as the hydrologic state of the watersheds and recent observed flows. To ensure that the ensemble forecasts are unbiased and that the ensemble spread reflects the actual uncertainty of the forecasts, a statistical model has been developed to post-process the NWS ensemble forecasts to account for hydrologic model error as well as any inherent bias and uncertainty in initial model states, meteorological data and forecasts. The post-processor is designed to produce adjusted ensemble forecasts that are consistent with the DEP historical flow sequences that were used to develop the system operating rules. A set of historical hindcasts that is representative of the real-time ensemble forecasts is needed to verify that the post-processed forecasts are unbiased, statistically reliable, and preserve the skill inherent in the "raw" NWS ensemble forecasts. A verification procedure and set of metrics will be presented that provide an objective assessment of ensemble forecasts. The procedure will be applied to both raw ensemble hindcasts and to post-processed ensemble hindcasts. The verification metrics will be used to validate proper functioning of the post-processor and to provide a benchmark for comparison of different types of forecasts. For example, current NWS ensemble forecasts are based on climatology, using each historical year to generate a forecast trace. The NWS Hydrologic Ensemble Forecast System (HEFS) under development will utilize output from both the National Oceanic Atmospheric Administration (NOAA) Global Ensemble Forecast System (GEFS) and the Climate Forecast System (CFS). Incorporating short-term meteorological forecasts and longer-term climate forecast information should provide sharper, more accurate forecasts. Hindcasts from HEFS will enable New York City to generate verification results to validate the new forecasts and further fine-tune system operating rules. Project verification results will be presented for different watersheds across a range of seasons, lead times, and flow levels to assess the quality of the current ensemble forecasts.

  5. Tailoring seasonal climate forecasts for hydropower operations in Ethiopias upper Blue Nile basin

    NASA Astrophysics Data System (ADS)

    Block, P. J.

    2009-12-01

    Explicit integration of seasonal precipitation forecasts into water resources operations and planning is practically nonexistent, even in regions of scarcity. This is often attributable to water managers tendency to act in a risk averse manner, preferring to avoid consequences of poor forecasts, at the expense of unrealized benefits. Convincing demonstrations of forecast value are therefore desirable to support assimilation into practice. A dynamic coupled system, including forecast, rainfall-runoff, and hydropower models, is applied to the upper Blue Nile basin in Ethiopia to compare benefits generated by actual forecasts against a climatology-based approach, commonly practiced in most water resources systems. Processing one hundred decadal sequences demonstrates superior forecast-based benefits in 68 cases, a respectable advancement, however benefits in a few forecast-based sequences are noticeably low, likely to dissuade managers adoption. A hydropower sensitivity test reveals a propensity toward poor-decision making when forecasts over-predict wet conditions. The forecast is therefore tailored to dampen precipitation projections in the above normal tercile while retaining critical near normal and dry predictions, subsequently improving reliability to 96-percent. Such tailoring potentially provides strong incentive to risk-adverse water managers cautious to embrace forecast technology.

  6. Theoretical error convergence of limited forecast horizon in optimal reservoir operating decisions

    NASA Astrophysics Data System (ADS)

    You, Gene Jiing-Yun; Yu, Cheng-Wei

    2013-03-01

    This study proposes a method of analyzing the error bound of optimal reservoir operation based on an inflow forecast with a limited horizon. This is a practical approach to real-world applications because current weather forecasts and climate predictions cannot necessarily achieve the "perfect forecast" required for optimal solutions. This study proposes a method to measure the error and error bound according to terminal stage boundary conditions, for which a theoretical convergence rate is derived. Our results suggest that convergence can be attained at a rate faster than the inverse of the extension of the study horizon. This demonstrates that the application of rolling horizons can improve the quality of decision making by exploiting available forecasts/information. When a perfect forecast is unavailable, the rolling decision procedure with regularly updated forecast information could help to avoid serious losses due to shortsighted policies.

  7. Forecasting Wind and Solar Generation: Improving System Operations, Greening the Grid

    SciTech Connect

    2016-01-01

    This document discusses improving system operations with forecasting and solar generation. By integrating variable renewable energy (VRE) forecasts into system operations, power system operators can anticipate up- and down-ramps in VRE generation in order to cost-effectively balance load and generation in intra-day and day-ahead scheduling. This leads to reduced fuel costs, improved system reliability, and maximum use of renewable resources.

  8. A comparative verification of forecasts from two operational solar wind models

    NASA Astrophysics Data System (ADS)

    Norquist, Donald C.; Meeks, Warner C.

    2010-12-01

    The solar wind (SW) and interplanetary magnetic field (IMF) have a significant influence on the near-Earth space environment. In this study we evaluate and compare forecasts from two models that predict SW and IMF conditions: the Hakamada-Akasofu-Fry (HAF) version 2, operational at the Air Force Weather Agency, and Wang-Sheeley-Arge (WSA) version 1.6, executed routinely at the Space Weather Prediction Center. SW speed (Vsw) and IMF polarity (Bpol) forecasts at L1 were compared with Wind and Advanced Composition Explorer satellite observations. Verification statistics were computed by study year and forecast day. Results revealed that both models' mean Vsw are slower than observed. The HAF slow bias increases with forecast duration. WSA had lower Vsw forecast-observation difference (F-O) absolute means and standard deviations than HAF. HAF and WSA Vsw forecast standard deviations were less than observed. Vsw F-O mean square skill rarely exceeds that of recurrence forecasts. Bpol is correctly predicted 65%-85% of the time in both models. Recurrence beats the models in Bpol skill in nearly every year forecast day category. Verification by "event" (flare events ?5 days before forecast start) and "nonevent" (no flares) forecasts showed that most HAF Vsw bias growth, F-O standard deviation decrease, and forecast standard deviation decrease were due to the event forecasts. Analysis of single time step Vsw increases of ?20% in the nonevent forecasts indicated that both models predicted too many occurrences and missed many observed incidences. Neither model had skill above a random guess in predicting Vsw increase arrival time at L1.

  9. Operational data assimilation for improving hydrologic, hydrodynamic, and water quality forecasting using open tools

    NASA Astrophysics Data System (ADS)

    Weerts, Albrecht; Kockx, Arno; Sumihar, Julius; Verlaan, Martin; Hummel, Stef; Kramer, Werner; de Klaermaker, Simone

    2014-05-01

    Data assimilation holds considerable potential for improving water quantity (hydrologic/ hydraulic) and water quality predictions. However, advances in hydrologic DA research have not been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. In contrast to most operational weather (related) forecast centers operational hydrologic forecast centers often are unable to support & maintain or lack the required computing support to implement such intensive DA calculations. Moreover, it remains difficult to achieve coupling of models, data, DA techniques and exploitation of high performance computing solutions in the operational forecasting process. Several potential components of a future solution have been or are being developed, one of those being the open source project OpenDA (www.openda.org). The objective of this poster is to highlight the development of OpenDA for operational forecasting and its integration with Delft-FEWS that is being used by more than 40 operational forecast centres around the world. Several applications of OpenDA using open source (and available) model codes from various fields will be highlighted.

  10. Forecasting the mixed-layer depth in the Northeast Atlantic: an ensemble approach, with uncertainties based on data from operational ocean forecasting systems

    NASA Astrophysics Data System (ADS)

    Drillet, Y.; Lellouche, J. M.; Levier, B.; Drévillon, M.; Le Galloudec, O.; Reffray, G.; Regnier, C.; Greiner, E.; Clavier, M.

    2014-12-01

    Operational systems operated by Mercator Ocean provide daily ocean forecasts, and combining these forecasts we can produce ensemble forecast and uncertainty estimates. This study focuses on the mixed-layer depth in the Northeast Atlantic near the Porcupine Abyssal Plain for May 2013. This period is of interest for several reasons: (1) four Mercator Ocean operational systems provide daily forecasts at a horizontal resolution of 1/4, 1/12 and 1/36° with different physics; (2) glider deployment under the OSMOSIS project provides observation of the changes in mixed-layer depth; (3) the ocean stratifies in May, but mixing events induced by gale force wind are observed and forecast by the systems. Statistical scores and forecast error quantification for each system and for the combined products are presented. Skill scores indicate that forecasts are consistently better than persistence, and temporal correlations between forecast and observations are greater than 0.8 even for the 4-day forecast. The impact of atmospheric forecast error, and for the wind field in particular (miss or time delay of a wind burst forecast), is also quantified in terms of occurrence and intensity of mixing or stratification events.

  11. Comparative Evaluation of the Impact of WRF-NMM and WRF-ARW Meteorology on CMAQ Simulations for O3 and Related Species During the 2006 TexAQS/GoMACCS Campaign

    EPA Science Inventory

    In this paper, impact of meteorology derived from the Weather, Research and Forecasting (WRF) Nonhydrostatic Mesoscale Model (NMM) and WRFAdvanced Research WRF (ARW) meteorological models on the Community Multiscale Air Quality (CMAQ) simulations for ozone and its related prec...

  12. Recent Operational Innovations and Future Developments at the Flood Forecasting Centre

    NASA Astrophysics Data System (ADS)

    Millard, Jon; Pilling, Charlie

    2015-04-01

    The Flood Forecasting Centre (FFC) was established in 2009 to give an overview of flood risk across England and Wales and is a partnership between the UK Met Office, the Environment Agency and Natural Resources Wales. Primarily serving the emergency response community, the FFC aims to provide trusted guidance to help protect lives and livelihoods from flooding across England and Wales from its base at the Met Office in Exeter. The flood forecasts consist of an assessment of the likelihood as well as the expected level of impacts of flood events during the next five days. The FFC provide forecasts for all natural sources of flooding, namely; fluvial, coastal, surface water and groundwater but liaise closely with meteorologists at the Met Office and local flood forecasters at the Environment Agency and Natural Resources Wales. Key challenges include providing; forecasts with longer lead times especially for fluvial and coastal events, forecasts at shorter timescales and with more spatial focus for rapid response catchments and surface water events, and also clear communications of forecast uncertainties. As well as operational activities, the FFC run a significant development and improvement programme and are linked in with Met Office and Environment Agency science projects in order to bring new science into operations to try and meet these challenges and improve performance. Latest developments which are now being applied operationally to provide an enhanced flood warning service will be presented. Examples include; the use of the national hydrological model Grid to Grid (G2G) for both fluvial and surface water flooding, extended surge ensembles for coastal flooding, enhancements in the surface water forecasting tool, and improvements to products communicating these forecasts. An overview of the current projects under development will also be provided, including; improvements to data within G2G, surface water hazard impact modelling, 7 day wave ensemble forecasts, regime analysis for longer lead time coastal flood forecasting and improving products and communications.

  13. How good do seasonal streamflow forecasts need to be to improve reservoir operation?

    NASA Astrophysics Data System (ADS)

    Anghileri, D.; Voisin, N.; Pianosi, F.; Castelletti, A.; Nijssen, B.; Lettenmaier, D. P.

    2013-12-01

    Reservoir operating rules inform release decisions based on competing demands, priorities, available storage, and reservoir characteristics. Reservoir inflow forecasts over a range of forecast lead times (from days or less to seasons or longer) can improve release decisions and lead to more efficient use of reservoir storage. However, the utility or value of inflow forecasts is directly related to forecast quality. Through a case study of the Oroville-Thermalito reservoir complex in the Feather River Basin, California, we explore the effect of forecast quality on optimal release strategies. Streamflow in the Upper Feather Basin is strongly seasonal signal, with most of the flow occurring during the winter (mostly from rainfall at lower elevations) and spring (from melt of the previous winter's snow accumulation). Accurate prediction of the volume and timing of snowmelt (which is possible via various means, including monitoring of accumulated winter precipitation, and measurements of high elevation snowpack) has the potential to improve reservoir operation. In this study, we use Deterministic Dynamic Programming to optimize medium-range and seasonal reservoir operation based on different forecasts of reservoir inflows. We determine maximum reservoir performance by forcing the optimization with observed inflows, which is equivalent to a perfect forecast. The forecast quality is then progressively degraded to allow forecast skill to be related to changes in release decisions and to determine the minimum forecast skill that is required to affect decision-making. We generate forecasted inflow sequences using the Variable Infiltration Capacity (VIC) hydrology model. Forecast initial conditions are created using observed meteorology, including streamflow and snow data assimilation, while inflow forecasts are based on seasonal climate forecasts. Although the specific forecast skill level is specific to the Feather River basin, the methodology should be transferable to other systems, especially elsewhere in the western U.S., and other locations with strongly seasonal runoff regimes. We assess the transferability of the case study results to other systems using alternative reservoir characteristics of the Oroville-Thermalito reservoir system as a surrogate for alternate reservoir configurations. Specifically, we explore the sensitivity of reservoir operation performance to the ratio of reservoir mean inflow volume to reservoir capacity and downstream demand requirements.

  14. Advancing Data Assimilation in Operational Hydrologic Forecasting: Progresses, Challenges, and Emerging Opportunities

    NASA Technical Reports Server (NTRS)

    Liu, Yuqiong; Weerts, A.; Clark, M.; Hendricks Franssen, H.-J; Kumar, S.; Moradkhani, H.; Seo, D.-J.; Schwanenberg, D.; Smith, P.; van Dijk, A. I. J. M.; van Velzen, N.; He, M.; Lee, H.; Noh, S. J.; Rakovec, O.; Restrepo, P.

    2012-01-01

    Data assimilation (DA) holds considerable potential for improving hydrologic predictions as demonstrated in numerous research studies. However, advances in hydrologic DA research have not been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. This is due in part to a lack of mechanisms to properly quantify the uncertainty in observations and forecast models in real-time forecasting situations and to conduct the merging of data and models in a way that is adequately efficient and transparent to operational forecasters. The need for effective DA of useful hydrologic data into the forecast process has become increasingly recognized in recent years. This motivated a hydrologic DA workshop in Delft, the Netherlands in November 2010, which focused on advancing DA in operational hydrologic forecasting and water resources management. As an outcome of the workshop, this paper reviews, in relevant detail, the current status of DA applications in both hydrologic research and operational practices, and discusses the existing or potential hurdles and challenges in transitioning hydrologic DA research into cost-effective operational forecasting tools, as well as the potential pathways and newly emerging opportunities for overcoming these challenges. Several related aspects are discussed, including (1) theoretical or mathematical aspects in DA algorithms, (2) the estimation of different types of uncertainty, (3) new observations and their objective use in hydrologic DA, (4) the use of DA for real-time control of water resources systems, and (5) the development of community-based, generic DA tools for hydrologic applications. It is recommended that cost-effective transition of hydrologic DA from research to operations should be helped by developing community-based, generic modeling and DA tools or frameworks, and through fostering collaborative efforts among hydrologic modellers, DA developers, and operational forecasters.

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

    EPA Science Inventory

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

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

    EPA Science Inventory

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

  17. Operational early warning of shallow landslides in Norway: Evaluation of landslide forecasts and associated challenges

    NASA Astrophysics Data System (ADS)

    Dahl, Mads-Peter; Colleuille, Hervé; Boje, Søren; Sund, Monica; Krøgli, Ingeborg; Devoli, Graziella

    2015-04-01

    The Norwegian Water Resources and Energy Directorate (NVE) runs a national early warning system (EWS) for shallow landslides in Norway. Slope failures included in the EWS are debris slides, debris flows, debris avalanches and slush flows. The EWS has been operational on national scale since 2013 and consists of (a) quantitative landslide thresholds and daily hydro-meteorological prognosis; (b) daily qualitative expert evaluation of prognosis / additional data in decision to determine warning levels; (c) publication of warning levels through various custom build internet platforms. The effectiveness of an EWS depends on both the quality of forecasts being issued, and the communication of forecasts to the public. In this analysis a preliminary evaluation of landslide forecasts from the Norwegian EWS within the period 2012-2014 is presented. Criteria for categorizing forecasts as correct, missed events or false alarms are discussed and concrete examples of forecasts falling into the latter two categories are presented. The evaluation show a rate of correct forecasts exceeding 90%. However correct forecast categorization is sometimes difficult, particularly due to poorly documented landslide events. Several challenges has to be met in the process of further lowering rates of missed events of false alarms in the EWS. Among others these include better implementation of susceptibility maps in landslide forecasting, more detailed regionalization of hydro-meteorological landslide thresholds, improved prognosis on precipitation, snowmelt and soil water content as well as the build-up of more experience among the people performing landslide forecasting.

  18. An Operational Flood Forecast System for the Indus Valley

    NASA Astrophysics Data System (ADS)

    Shrestha, K.; Webster, P. J.

    2012-12-01

    The Indus River is central to agriculture, hydroelectric power, and the potable water supply in Pakistan. The ever-present risk of drought - leading to poor soil conditions, conservative dam practices, and higher flood risk - amplifies the consequences of abnormally large precipitation events during the monsoon season. Preparation for the 2010 and 2011 floods could have been improved by coupling quantitative precipitation forecasts to a distributed hydrological model. The nature of slow-rise discharge on the Indus and overtopping of riverbanks in this basin indicate that medium-range (1-10 day) probabilistic weather forecasts can be used to assess flood risk at critical points in the basin. We describe a process for transforming these probabilities into an alert system for supporting flood mitigation and response decisions on a daily basis. We present a fully automated two-dimensional flood forecast methodology based on meteorological variables from the European Centre for Medium-Range Weather Forecasts (ECMWF) Variable Ensemble Prediction System (VarEPS). Energy and water fluxes are calculated in 25km grid cells using macroscale hydrologic parameterizations from the UW Variable Infiltration Capacity (VIC) model. A linear routing model transports grid cell surface runoff and baseflow within each grid cell to the outlet and into the stream network. The overflow points are estimated using flow directions, flow velocities, and maximum discharge thresholds from each grid cell. Flood waves are then deconvolved from the in-channel discharge time series and propagated into adjacent cells until a storage criterion based on average grid cell elevation is met. Floodwaters are drained back into channels as a continuous process, thus simulating spatial extent, depth, and persistence on the plains as the ensemble forecast evolves with time.

  19. AN OPERATIONAL EVALUATION OF THE ETA - CMAQ AIR QUALITY FORECAST MODEL

    EPA Science Inventory

    The National Oceanic and Atmospheric Administration (NOAA), in partnership with the United States Environmental Protection Agency (EPA), are developing an operational, nationwide Air Quality Forecasting (AQF) system. An experimental phase of this program, which couples NOAA's Et...

  20. Weather Research and Forecasting Model's Community Variational/Ensemble Data Assimilation System: WRFDA

    SciTech Connect

    Barker, D.; Huang, X. Y.; Liu, Z. Q.; Auligne, T.; Zhang, X.; Rugg, S.; Ajjaji, R.; Bourgeois, A.; Bray, J.; Chen, Y. S.; Demirtas, M.; Guo, Y. R.; Henderson, T.; Huang, W.; Lin, H. C.; Michalakes, J.; Rizvi, S.; Zhang, X. Y.

    2012-06-01

    Data assimilation is the process by which observations are combined with short-range NWP model output to produce an analysis of the state of the atmosphere at a specified time. Since its inception in the late 1990s, the multiagency Weather Research and Forecasting (WRF) model effort has had a strong data assimilation component, dedicating two working groups to the subject. This article documents the history of the WRF data assimilation effort, and discusses the challenges associated with balancing academic, research, and operational data assimilation requirements in the context of the WRF effort to date. The WRF Model's Community Variational/Ensemble Data Assimilation System (WRFDA) has evolved over the past 10 years, and has resulted in over 30 refereed publications to date, as well as implementation in a wide range of real-time and operational NWP systems.

  1. Impact of Improved Solar Forecasts on Bulk Power System Operations in ISO-NE (Presentation)

    SciTech Connect

    Brancucci Martinez-Anido, C.; Florita, A.; Hodge, B.M.

    2014-11-01

    The diurnal nature of solar power is made uncertain by variable cloud cover and the influence of atmospheric conditions on irradiance scattering processes. Its forecasting has become increasingly important to the unit commitment and dispatch process for efficient scheduling of generators in power system operations. This presentation is an overview of a study that examines the value of improved solar forecasts on Bulk Power System Operations.

  2. Impact of Improved Solar Forecasts on Bulk Power System Operations in ISO-NE: Preprint

    SciTech Connect

    Brancucci Martinez-Anido, C.; Florita, A.; Hodge, B. M.

    2014-09-01

    The diurnal nature of solar power is made uncertain by variable cloud cover and the influence of atmospheric conditions on irradiance scattering processes. Its forecasting has become increasingly important to the unit commitment and dispatch process for efficient scheduling of generators in power system operations. This study examines the value of improved solar power forecasting for the Independent System Operator-New England system. The results show how 25% solar power penetration reduces net electricity generation costs by 22.9%.

  3. Simulating urban flow and dispersion in Beijing by coupling a CFD model with the WRF model

    NASA Astrophysics Data System (ADS)

    Miao, Yucong; Liu, Shuhua; Chen, Bicheng; Zhang, Bihui; Wang, Shu; Li, Shuyan

    2013-11-01

    The airflow and dispersion of a pollutant in a complex urban area of Beijing, China, were numerically examined by coupling a Computational Fluid Dynamics (CFD) model with a mesoscale weather model. The models used were Open Source Field Operation and Manipulation (OpenFOAM) software package and Weather Research and Forecasting (WRF) model. OpenFOAM was firstly validated against wind-tunnel experiment data. Then, the WRF model was integrated for 42 h starting from 0800 LST 08 September 2009, and the coupled model was used to compute the flow fields at 1000 LST and 1400 LST 09 September 2009. During the WRF-simulated period, a high pressure system was dominant over the Beijing area. The WRF-simulated local circulations were characterized by mountain valley winds, which matched well with observations. Results from the coupled model simulation demonstrated that the airflows around actual buildings were quite different from the ambient wind on the boundary provided by the WRF model, and the pollutant dispersion pattern was complicated under the influence of buildings. A higher concentration level of the pollutant near the surface was found in both the step-down and step-up notches, but the reason for this higher level in each configurations was different: in the former, it was caused by weaker vertical flow, while in the latter it was caused by a downward-shifted vortex. Overall, the results of this study suggest that the coupled WRF-OpenFOAM model is an important tool that can be used for studying and predicting urban flow and dispersions in densely built-up areas.

  4. Towards integrated error estimation and lag-aware data assimilation for operational streamflow forecasting

    NASA Astrophysics Data System (ADS)

    Li, Y.; Ryu, D.; Western, A. W.; Wang, Q.; Robertson, D.; Crow, W. T.

    2013-12-01

    Timely and reliable streamflow forecasting with acceptable accuracy is fundamental for flood response and risk management. However, streamflow forecasting models are subject to uncertainties from inputs, state variables, model parameters and structures. This has led to an ongoing development of methods for uncertainty quantification (e.g. generalized likelihood and Bayesian approaches) and methods for uncertainty reduction (e.g. sequential and variational data assimilation approaches). These two classes of methods are distinct yet related, e.g., the validity of data assimilation is essentially determined by the reliability of error specification. Error specification has been one of the most challenging areas in hydrologic data assimilation and there is a major opportunity for implementing uncertainty quantification approaches to inform both model and observation uncertainties. In this study, ensemble data assimilation methods are combined with the maximum a posteriori (MAP) error estimation approach to construct an integrated error estimation and data assimilation scheme for operational streamflow forecasting. We contrast the performance of two different data assimilation schemes: a lag-aware ensemble Kalman smoother (EnKS) and the conventional ensemble Kalman filter (EnKF). The schemes are implemented for a catchment upstream of Myrtleford in the Ovens river basin, Australia to assimilate real-time discharge observations into a conceptual catchment model, modle du Gnie Rural 4 paramtres Horaire (GR4H). The performance of the integrated system is evaluated in both a synthetic forecasting scenario with observed precipitation and an operational forecasting scenario with Numerical Weather Prediction (NWP) forecast rainfall. The results show that the error parameters estimated by the MAP approach generates a reliable spread of streamflow prediction. Continuous state updating reduces uncertainty in initial states and thereby improves the forecasting accuracy significantly. The EnKS streamflow forecasts are more accurate and reliable than the EnKF for the synthetic scenario. They also alleviate instability in the EnKF due to overcorrection of current state variables. For the operational forecasting case, the forecasts benefit less from state updating and the difference between the EnKS and EnKF becomes less significant. This is because the uncertainty in the NWP rainfall forecasts becomes dominant with increasing lead time. Forecast discharge in 2010. Solid curves are observations and gray areas indicate 95% of probabilistic forecasts. (a) openloop ensemble spread based on the error parameters estimated by the MAP; (b) 60-h lead time forecasts based on the EnKS.

  5. Real-Time Operational Forecasting on Shipboard of the Iceland-Faeroe Frontal Variability.

    NASA Astrophysics Data System (ADS)

    Robinson, A. R.; Arango, H. G.; Leslie, W. G.; Miller, A. J.; Warn-Varnas, A.; Poulain, P.-M.

    1996-02-01

    Real-time operational shipboard forecasts of Iceland-Faeroe frontal variability were executed for the first time with a primitive equation mode. High quality, intensive hydrographic surveys during August 1993 were used for initialization, updating, and validation of the forecasts. Vigorous and rapid synoptic events occurred over several-day timescales including a southeastward reorientation of the Iceland-Faeroe Front and the development of a strong, cold deep-sock meander. A qualitative and quantitative assessment of the skill of these forecasts shows they captured the essential features of both events. The anomaly pattern correlation coefficient and the rms error between forecast and observed fields are particularly impressive (and substantially superior to persistence) for the forecast of the cold meander.

  6. Flood forecasting with DDD-application of a parsimonious hydrological model in operational flood forecasting in Norway

    NASA Astrophysics Data System (ADS)

    Skaugen, Thomas; Haddeland, Ingjerd

    2014-05-01

    A new parameter-parsimonious rainfall-runoff model, DDD (Distance Distribution Dynamics) has been run operationally at the Norwegian Flood Forecasting Service for approximately a year. DDD has been calibrated for, altogether, 104 catchments throughout Norway, and provide runoff forecasts 8 days ahead on a daily temporal resolution driven by precipitation and temperature from the meteorological forecast models AROME (48 hrs) and EC (192 hrs). The current version of DDD differs from the standard model used for flood forecasting in Norway, the HBV model, in its description of the subsurface and runoff dynamics. In DDD, the capacity of the subsurface water reservoir M, is the only parameter to be calibrated whereas the runoff dynamics is completely parameterised from observed characteristics derived from GIS and runoff recession analysis. Water is conveyed through the soils to the river network by waves with celerities determined by the level of saturation in the catchment. The distributions of distances between points in the catchment to the nearest river reach and of the river network give, together with the celerities, distributions of travel times, and, consequently unit hydrographs. DDD has 6 parameters less to calibrate in the runoff module than the HBV model. Experiences using DDD show that especially the timing of flood peaks has improved considerably and in a comparison between DDD and HBV, when assessing timeseries of 64 years for 75 catchments, DDD had a higher hit rate and a lower false alarm rate than HBV. For flood peaks higher than the mean annual flood the median hit rate is 0.45 and 0.41 for the DDD and HBV models respectively. Corresponding number for the false alarm rate is 0.62 and 0.75 For floods over the five year return interval, the median hit rate is 0.29 and 0.28 for the DDD and HBV models, respectively with false alarm rates equal to 0.67 and 0.80. During 2014 the Norwegian flood forecasting service will run DDD operationally at a 3h temporal resolution. Running DDD at a 3h resolution will give a better prediction of flood peaks in small catchments, where the averaging over 24 hrs will lead to a underestimation of high events, and we can better describe the progress floods in larger catchments. Also, at a 3h temporal resolution we make better use of the meteorological forecasts that for long have been provided at a very detailed temporal resolution.

  7. Evaluation of and Suggested Improvements to the WSM6 Microphysics in WRF- ARW Using Synthetic and Observed GOES-13 Imagery

    SciTech Connect

    Grasso, Lewis; Lindsey, Daniel T.; Lim, Kyo-Sun; Clark, Adam; Bikos, Dan; Dembek, Scott R.

    2014-10-01

    Synthetic satellite imagery can be employed to evaluate simulated cloud fields. Past studies have revealed that the Weather Research and Forecasting (WRF) WRF Single-Moment 6-class (WSM6) microphysics in WRF-ARW produces less upper level ice clouds within synthetic images compared to observations. Synthetic Geostationary Operational Environmental Satellite (GOES)-13 imagery at 10.7 μm of simulated cloud fields from the 4 km National Severe Storms Laboratory (NSSL) WRF-ARW is compared to observed GOES-13 imagery. Histograms suggest that too few points contain upper level simulated ice clouds. In particular, side-by-side examples are shown of synthetic and observed convective anvils. Such images illustrate the lack of anvil cloud associated with convection produced by the NSSL WRF-ARW. A vertical profile of simulated hydrometeors suggests that too much cloud water mass may be converted into graupel mass, effectively reducing the main source of ice mass in a simulated anvil. Further, excessive accretion of ice by snow removes ice from an anvil by precipitation settling. Idealized sensitivity tests reveal that a 50% reduction of the conversion of cloud water mass to graupel and a 50% reduction of the accretion rate of ice by snow results in a significant increase in anvil ice of a simulated storm. Such results provide guidance as to which conversions could be reformulated, in a more physical manner, to increase simulated ice mass in the upper troposphere.

  8. Assimilation of GPM GMI Rainfall Product with WRF GSI

    NASA Technical Reports Server (NTRS)

    Li, Xuanli; Mecikalski, John; Zavodsky, Bradley

    2015-01-01

    The Global Precipitation Measurement (GPM) is an international mission to provide next-generation observations of rain and snow worldwide. The GPM built on Tropical Rainfall Measuring Mission (TRMM) legacy, while the core observatory will extend the observations to higher latitudes. The GPM observations can help advance our understanding of precipitation microphysics and storm structures. Launched on February 27th, 2014, the GPM core observatory is carrying advanced instruments that can be used to quantify when, where, and how much it rains or snows around the world. Therefore, the use of GPM data in numerical modeling work is a new area and will have a broad impact in both research and operational communities. The goal of this research is to examine the methodology of assimilation of the GPM retrieved products. The data assimilation system used in this study is the community Gridpoint Statistical Interpolation (GSI) system for the Weather Research and Forecasting (WRF) model developed by the Development Testbed Center (DTC). The community GSI system runs in independently environment, yet works functionally equivalent to operational centers. With collaboration with the NASA Short-term Prediction Research and Transition (SPoRT) Center, this research explores regional assimilation of the GPM products with case studies. Our presentation will highlight our recent effort on the assimilation of the GPM product 2AGPROFGMI, the retrieved Microwave Imager (GMI) rainfall rate data for initializing a real convective storm. WRF model simulations and storm scale data assimilation experiments will be examined, emphasizing both model initialization and short-term forecast of precipitation fields and processes. In addition, discussion will be provided on the development of enhanced assimilation procedures in the GSI system with respect to other GPM products. Further details of the methodology of data assimilation, preliminary result and test on the impact of GPM data and the influence on precipitation forecast will be presented at the conference.

  9. A strategy for representing the effects of convective momentum transport in multiscale models: Evaluation using a new superparameterized version of the Weather Research and Forecast model (SP-WRF)

    NASA Astrophysics Data System (ADS)

    Tulich, S. N.

    2015-06-01

    This paper describes a general method for the treatment of convective momentum transport (CMT) in large-scale dynamical solvers that use a cyclic, two-dimensional (2-D) cloud-resolving model (CRM) as a "superparameterization" of convective-system-scale processes. The approach is similar in concept to traditional parameterizations of CMT, but with the distinction that both the scalar transport and diagnostic pressure gradient force are calculated using information provided by the 2-D CRM. No assumptions are therefore made concerning the role of convection-induced pressure gradient forces in producing up or down-gradient CMT. The proposed method is evaluated using a new superparameterized version of the Weather Research and Forecast model (SP-WRF) that is described herein for the first time. Results show that the net effect of the formulation is to modestly reduce the overall strength of the large-scale circulation, via "cumulus friction." This statement holds true for idealized simulations of two types of mesoscale convective systems, a squall line, and a tropical cyclone, in addition to real-world global simulations of seasonal (1 June to 31 August) climate. In the case of the latter, inclusion of the formulation is found to improve the depiction of key synoptic modes of tropical wave variability, in addition to some aspects of the simulated time-mean climate. The choice of CRM orientation is also found to importantly affect the simulated time-mean climate, apparently due to changes in the explicit representation of wide-spread shallow convective regions.

  10. Recent development of the Met Office operational ocean forecasting system: an overview and assessment of the new Global FOAM forecasts

    NASA Astrophysics Data System (ADS)

    Blockley, E. W.; Martin, M. J.; McLaren, A. J.; Ryan, A. G.; Waters, J.; Lea, D. J.; Mirouze, I.; Peterson, K. A.; Sellar, A.; Storkey, D.

    2014-11-01

    The Forecast Ocean Assimilation Model (FOAM) is an operational ocean analysis and forecast system run daily at the Met Office. FOAM provides modelling capability in both deep ocean and coastal shelf sea regimes using the NEMO (Nucleus for European Modelling of the Ocean) ocean model as its dynamical core. The FOAM Deep Ocean suite produces analyses and 7-day forecasts of ocean tracers, currents and sea ice for the global ocean at 1/4 resolution. Satellite and in situ observations of temperature, salinity, sea level anomaly and sea ice concentration are assimilated by FOAM each day over a 48 h observation window. The FOAM Deep Ocean configurations have recently undergone a major upgrade which has involved the implementation of a new variational, first guess at appropriate time (FGAT) 3D-Var, assimilation scheme (NEMOVAR); coupling to a different, multi-thickness-category, sea ice model (CICE); the use of coordinated ocean-ice reference experiment (CORE) bulk formulae to specify the surface boundary condition; and an increased vertical resolution for the global model. In this paper the new FOAM Deep Ocean system is introduced and details of the recent changes are provided. Results are presented from 2-year reanalysis integrations of the Global FOAM configuration including an assessment of short-range ocean forecast accuracy. Comparisons are made with both the previous FOAM system and a non-assimilative FOAM system. Assessments reveal considerable improvements in the new system to the near-surface ocean and sea ice fields. However there is some degradation to sub-surface tracer fields and in equatorial regions which highlights specific areas upon which to focus future improvements.

  11. Operational water management of Rijnland water system and pilot of ensemble forecasting system for flood control

    NASA Astrophysics Data System (ADS)

    van der Zwan, Rene

    2013-04-01

    The Rijnland water system is situated in the western part of the Netherlands, and is a low-lying area of which 90% is below sea-level. The area covers 1,100 square kilometres, where 1.3 million people live, work, travel and enjoy leisure. The District Water Control Board of Rijnland is responsible for flood defence, water quantity and quality management. This includes design and maintenance of flood defence structures, control of regulating structures for an adequate water level management, and waste water treatment. For water quantity management Rijnland uses, besides an online monitoring network for collecting water level and precipitation data, a real time control decision support system. This decision support system consists of deterministic hydro-meteorological forecasts with a 24-hr forecast horizon, coupled with a control module that provides optimal operation schedules for the storage basin pumping stations. The uncertainty of the rainfall forecast is not forwarded in the hydrological prediction. At this moment 65% of the pumping capacity of the storage basin pumping stations can be automatically controlled by the decision control system. Within 5 years, after renovation of two other pumping stations, the total capacity of 200 m3/s will be automatically controlled. In critical conditions there is a need of both a longer forecast horizon and a probabilistic forecast. Therefore ensemble precipitation forecasts of the ECMWF are already consulted off-line during dry-spells, and Rijnland is running a pilot operational system providing 10-day water level ensemble forecasts. The use of EPS during dry-spells and the findings of the pilot will be presented. Challenges and next steps towards on-line implementation of ensemble forecasts for risk-based operational management of the Rijnland water system will be discussed. An important element in that discussion is the question: will policy and decision makers, operator and citizens adapt this Anticipatory Water management, including temporary lower storage basin levels and a reduction in extra investments for infrastructural measures.

  12. Transition from Research to Operations: Assessing Value of Experimental Forecast Products within the NWSFO Environment

    NASA Technical Reports Server (NTRS)

    Lapenta, William M.; Wohlman, Richard; Bradshaw, Tom; Burks, Jason; Jedlovec, Gary; Goodman, Steve; Darden, Chris; Meyer, Paul

    2003-01-01

    The NASA Short-term Prediction Research and Transition (SPoRT) Center seeks to accelerate the infusion of NASA Earth Science Enterprise (ESE) observations, data assimilation and modeling research into NWS forecast operations and decision-making. To meet long-term program expectations, it is not sufficient simply to give forecasters sophisticated workstations or new forecast products without fully assessing the ways in which they will be utilized. Close communication must be established between the research and operational communities so that developers have a complete understanding of user needs. In turn, forecasters must obtain a more comprehensive knowledge of the modeling and sensing tools available to them. A major goal of the SPoRT Program is to develop metrics and conduct assessment studies with NWS forecasters to evaluate the impacts and benefits of ESE experimental products on forecast skill. At a glance the task seems relatively straightforward. However, performing assessment of experimental products in an operational environment is demanding. Given the tremendous time constraints placed on NWS forecasters, it is imperative that forecaster input be obtained in a concise unobtrusive manor. Great care must also be taken to ensure that forecasters understand their participation will eventually benefit them and WFO operations in general. Two requirements of the assessment plan developed under the SPoRT activity are that it 1) Can be implemented within the WFO environment; and 2) Provide tangible results for BOTH the research and operational communities. Supplemental numerical quantitative precipitation forecasts (QPF) were chosen as the first experimental SPoRT product to be evaluated during a Pilot Assessment Program conducted 1 May 2003 within the Huntsville AL National Weather Service Forecast Office. Forecast time periods were broken up into six- hour bins ranging from zero to twenty-four hours. Data were made available for display in AWIPS on an operational basis so they could be efficiently incorporated into the forecast process. The methodology used to assess the value of experimental QPFs compared to available operational products is best described as a three-tier approach involving both forecasters and research scientists. Tier-one is a web-based survey completed by duty forecasters on the aviation and public desks. The survey compiles information on how the experimental product was used in the forecast decision making process. Up to 6 responses per twenty-four hours can be compiled during a precipitation event. Tier-two consists of an event post mortem and experimental product assessment performed daily by the NASA/NWS Liaison. Tier-three is a detailed breakdown/analysis of specific events targeted by either the NWS SO0 or SPoRT team members. The task is performed by both NWS and NASA research scientists and may be conducted once every couple of months. The findings from the Pilot Assessment Program will be reported at the meeting.

  13. Development of RGB Composite Imagery for Operational Weather Forecasting Applications

    NASA Technical Reports Server (NTRS)

    Molthan, Andrew L.; Fuell, Kevin K.; Oswald, Hayden, K; Knaff, John A.

    2012-01-01

    The NASA Short-term Prediction Research and Transition (SPoRT) Center, in collaboration with the Cooperative Institute for Research in the Atmosphere (CIRA), is providing red-green-blue (RGB) color composite imagery to several of NOAA s National Centers and National Weather Service forecast offices as a demonstration of future capabilities of the Advanced Baseline Imager (ABI) to be implemented aboard GOES-R. Forecasters rely upon geostationary satellite imagery to monitor conditions over their regions of responsibility. Since the ABI will provide nearly three times as many channels as the current GOES imager, the volume of data available for analysis will increase. RGB composite imagery can aid in the compression of large data volumes by combining information from multiple channels or paired channel differences into single products that communicate more information than provided by a single channel image. A standard suite of RGB imagery has been developed by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), based upon the Spinning Enhanced Visible and Infrared Imager (SEVIRI). The SEVIRI instrument currently provides visible and infrared wavelengths comparable to the future GOES-R ABI. In addition, the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the NASA Terra and Aqua satellites can be used to demonstrate future capabilities of GOES-R. This presentation will demonstrate an overview of the products currently disseminated to SPoRT partners within the GOES-R Proving Ground, and other National Weather Service forecast offices, along with examples of their application. For example, CIRA has used the channels of the current GOES sounder to produce an "air mass" RGB originally designed for SEVIRI. This provides hourly imagery over CONUS for looping applications while demonstrating capabilities similar to the future ABI instrument. SPoRT has developed similar "air mass" RGB imagery from MODIS, and through a case study example, synoptic-scale features evident in single-channel water vapor imagery are shown in the context of the air mass product. Other products, such as the "nighttime microphysics" RGB, are useful in the detection of low clouds and fog. Nighttime microphysics products from MODIS offer some advantages over single-channel or spectral difference techniques and will be discussed in the context of a case study. Finally, other RGB products from SEVIRI are being demonstrated as precursors to GOES-R within the GOES-R Proving Ground. Examples of "natural color" and "dust" imagery will be shown with relevant applications.

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

    NASA Astrophysics Data System (ADS)

    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 informative climate indices for the region of interest.

  15. Utilization of Precipitation and Moisture Products Derived from Satellites to Support NOAA Operational Precipitation Forecasts

    NASA Astrophysics Data System (ADS)

    Ferraro, R.; Zhao, L.; Kuligowski, R. J.; Kusselson, S.; Ma, L.; Kidder, S. Q.; Forsythe, J. M.; Jones, A. S.; Ebert, E. E.; Valenti, E.

    2012-12-01

    NOAA/NESDIS operates a constellation of polar and geostationary orbiting satellites to support weather forecasts and to monitor the climate. Additionally, NOAA utilizes satellite assets from other U.S. agencies like NASA and the Department of Defense, as well as those from other nations with similar weather and climate responsibilities (i.e., EUMETSAT and JMA). Over the past two decades, through joint efforts between U.S. and international government researchers, academic partners, and private sector corporations, a series of "value added" products have been developed to better serve the needs of weather forecasters and to exploit the full potential of precipitation and moisture products generated from these satellites. In this presentation, we will focus on two of these products - Ensemble Tropical Rainfall Potential (eTRaP) and Blended Total Precipitable Water (bTPW) - and provide examples on how they contribute to hydrometeorological forecasts. In terms of passive microwave satellite products, TPW perhaps is most widely used to support real-time forecasting applications, as it accurately depicts tropospheric water vapor and its movement. In particular, it has proven to be extremely useful in determining the location, timing, and duration of "atmospheric rivers" which contribute to and sustain flooding events. A multi-sensor approach has been developed and implemented at NESDIS in which passive microwave estimates from multiple satellites and sensors are merged to create a seamless, bTPW product that is more efficient for forecasters to use. Additionally, this product is being enhanced for utilization for television weather forecasters. Examples will be shown to illustrate the roll of atmospheric rivers and contribution to flooding events, and how the bTPW product was used to improve the forecast of these events. Heavy rains associated with land falling tropical cyclones (TC) frequently trigger floods that cause millions of dollars of damage and tremendous loss of lives. To provide observations-based forecast guidance for TC heavy rain, the Tropical Rainfall Potential (TRaP), an extrapolation forecast generated by accumulating rainfall estimates from satellites with microwave sensors as the storm is translated along the forecast track, was originally developed to predict the maximum rainfall at landfall, as well as the spatial pattern of precipitation. More recently, an enhancement has been made to combine the TRaP forecasts from multiple sensors and various start times into an ensemble (eTRaP). The ensemble approach provides not only more accurate quantitative precipitation forecasts, including more skillful maximum rainfall amount and location, it also produces probabilistic forecasts of rainfall exceeding various thresholds that decision makers can use to make critical risk assessments. Examples of the utilization and performance of eTRaP will be given in the presentation.

  16. A Statistical Comparison of the Blossom Blight Forecasts of MARYBLYT and Cougarblight with Receiver Operating Characteristic Curve Analysis

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Blossom blight forecasting is an important aspect of fire blight, caused by Erwinia amylovora, management for both apple and pear. A comparison of the forecast accuracy of two common fire blight forecasters, MARYBLYT and Cougarblight, was performed with receiver operating characteristic (ROC) curve ...

  17. Low-Level Wind Forecast over the La Plata River Region with a Mesoscale Boundary-Layer Model Forced by Regional Operational Forecasts

    NASA Astrophysics Data System (ADS)

    Sraibman, L.; Berri, G. J.

    2009-03-01

    A mesoscale boundary-layer model (BLM) is used for running 12-h low-level wind forecasts for the La Plata River region. Several experiments are performed with different boundary conditions that include operational forecasts of the Eta/CPTEC model, local observations, as well as a combination of both. The BLM wind forecasts are compared to the surface wind observations of five weather stations during the period November 2003-April 2004. Two accuracy measures are used: the hit rate or percentage of cases with agreement in the wind direction sector, and the root-mean-squared error (RMSE) of the horizontal wind components. The BLM surface wind forecasts are always more accurate, since its averaged hit rate is three times greater and its averaged RMSE is one half smaller than the Eta forecasts. Despite the large errors in the surface winds displayed by the Eta forecasts, its 850 hPa winds and surface temperature forecasts are able to drive the BLM model to obtain surface winds forecasts with smaller errors than the Eta model. An additional experiment demonstrates that the advantage of using the BLM model for forecasting low-level winds over the La Plata River region is the result of a more appropriate definition of the land-river surface temperature contrast. The particular formulation that the BLM model has for the geometry of the river coasts is fundamental for resolving the smaller scale details of the low-level local circulation. The main conclusion of the study is that operational low-level wind forecasts for the La Plata River region can be improved by running the BLM model forced by the Eta operational forecasts.

  18. Improved Weather and Power Forecasts for Energy Operations - the German Research Project EWeLiNE

    NASA Astrophysics Data System (ADS)

    Lundgren, Kristina; Siefert, Malte; Hagedorn, Renate; Majewski, Detlev

    2014-05-01

    The German energy system is going through a fundamental change. Based on the energy plans of the German federal government, the share of electrical power production from renewables should increase to 35% by 2020. This means that, in the near future at certain times renewable energies will provide a major part of Germany's power production. Operating a power supply system with a large share of weather-dependent power sources in a secure way requires improved power forecasts. One of the most promising strategies to improve the existing wind power and PV power forecasts is to optimize the underlying weather forecasts and to enhance the collaboration between the meteorology and energy sectors. Deutscher Wetterdienst addresses these challenges in collaboration with Fraunhofer IWES within the research project EWeLiNE. The overarching goal of the project is to improve the wind and PV power forecasts by combining improved power forecast models and optimized weather forecasts. During the project, the numerical weather prediction models COSMO-DE and COSMO-DE-EPS (Ensemble Prediction System) by Deutscher Wetterdienst will be generally optimized towards improved wind power and PV forecasts. For instance, it will be investigated whether the assimilation of new types of data, e.g. power production data, can lead to improved weather forecasts. With regard to the probabilistic forecasts, the focus is on the generation of ensembles and ensemble calibration. One important aspect of the project is to integrate the probabilistic information into decision making processes by developing user-specified products. In this paper we give an overview of the project and present first results.

  19. Wind Power Forecasting Error Frequency Analyses for Operational Power System Studies: Preprint

    SciTech Connect

    Florita, A.; Hodge, B. M.; Milligan, M.

    2012-08-01

    The examination of wind power forecasting errors is crucial for optimal unit commitment and economic dispatch of power systems with significant wind power penetrations. This scheduling process includes both renewable and nonrenewable generators, and the incorporation of wind power forecasts will become increasingly important as wind fleets constitute a larger portion of generation portfolios. This research considers the Western Wind and Solar Integration Study database of wind power forecasts and numerical actualizations. This database comprises more than 30,000 locations spread over the western United States, with a total wind power capacity of 960 GW. Error analyses for individual sites and for specific balancing areas are performed using the database, quantifying the fit to theoretical distributions through goodness-of-fit metrics. Insights into wind-power forecasting error distributions are established for various levels of temporal and spatial resolution, contrasts made among the frequency distribution alternatives, and recommendations put forth for harnessing the results. Empirical data are used to produce more realistic site-level forecasts than previously employed, such that higher resolution operational studies are possible. This research feeds into a larger work of renewable integration through the links wind power forecasting has with various operational issues, such as stochastic unit commitment and flexible reserve level determination.

  20. Operational Coupled Forecasting of Waves, Currents and Coastal Inundation in Cook Inlet, Alaska

    NASA Astrophysics Data System (ADS)

    Sharma, A.; Panchang, V. G.

    2013-12-01

    Prediction of reliable ocean weather conditions is critical for ship navigation, offshore oil and gas operations, proper management of nearshore resources, studies related to oil-spill and pollutant transport, etc. The Cook Inlet (Alaska) region exhibits the largest tidal fluctuations in the United States, and hence exhibits significant flooding and drying which poses threats to a variety of activities in coastal regions. A coupled wind-wave-current system is developed to obtain forecasts of waves and circulation pattern for a 36 h forecast period. A sophisticated wave transformation model and a three-dimensional circulation model are considered, and the forecasted high-resolution winds from different sources are utilized. The coupled system also predicts the extent of 'wet' and 'dry' regions during a particular forecast cycle. The effect of grid resolution on the overall results is studied by using nested grid approach with high-resolution grid for two separate regions. The forecasted results of different modeled quantities are compared with data available from various sources such as satellite images, field observations and other relevant models. It is found that the coupling of different components is required for better estimates of 'wet' and 'dry' nearshore regions. Good agreement between data and model results demonstrate the efficiency of this coupled system for operational forecasting.

  1. Multi-Model Long-Range Ensemble Forecast for Decision Support in Hydroelectric Operations

    NASA Astrophysics Data System (ADS)

    Kunkel, M. L.; Parkinson, S.; Blestrud, D.; Holbrook, V. P.

    2014-12-01

    Idaho Power Company (IPC) is a hydroelectric based utility serving over a million customers in southern Idaho and eastern Oregon. Hydropower makes up ~50% of our power generation and accurate predictions of streamflow and precipitation drive our long-term planning and decision support for operations. We investigate the use of a multi-model ensemble approach for mid and long-range streamflow and precipitation forecasts throughout the Snake River Basin. Forecast are prepared using an Idaho Power developed ensemble forecasting technique for 89 locations throughout the Snake River Basin for periods of 3 to 18 months in advance. A series of multivariable linear regression, multivariable non-linear regression and multivariable Kalman filter techniques are combined in an ensemble forecast based upon two data types, historical data (streamflow, precipitation, climate indices [i.e. PDO, ENSO, AO, etc…]) and single value decomposition derived values based upon atmospheric heights and sea surface temperatures.

  2. An Enhanced Convective Forecast (ECF) for the New York TRACON Area

    NASA Technical Reports Server (NTRS)

    Wheeler, Mark; Stobie, James; Gillen, Robert; Jedlovec, Gary; Sims, Danny

    2008-01-01

    In an effort to relieve summer-time congestion in the NY Terminal Radar Approach Control (TRACON) area, the FAA is testing an enhanced convective forecast (ECF) product. The test began in June 2008 and is scheduled to run through early September. The ECF is updated every two hours, right before the Air Traffic Control System Command Center (ATCSCC) national planning telcon. It is intended to be used by traffic managers throughout the National Airspace System (NAS) and airlines dispatchers to supplement information from the Collaborative Convective Forecast Product (CCFP) and the Corridor Integrated Weather System (CIWS). The ECF begins where the current CIWS forecast ends at 2 hours and extends out to 12 hours. Unlike the CCFP it is a detailed deterministic forecast with no aerial coverage limits. It is created by an ENSCO forecaster using a variety of guidance products including, the Weather Research and Forecast (WRF) model. This is the same version of the WRF that ENSCO runs over the Florida peninsula in support of launch operations at the Kennedy Space Center. For this project, the WRF model domain has been shifted to the Northeastern US. Several products from the NASA SPoRT group are also used by the ENSCO forecaster. In this paper we will provide examples of the ECF products and discuss individual cases of traffic management actions using ECF guidance.

  3. Verification of Advances in a Coupled Snow-runoff Modeling Framework for Operational Streamflow Forecasts

    NASA Astrophysics Data System (ADS)

    Barik, M. G.; Hogue, T. S.; Franz, K. J.; He, M.

    2011-12-01

    The National Oceanic and Atmospheric Administration's (NOAA's) River Forecast Centers (RFCs) issue hydrologic forecasts related to flood events, reservoir operations for water supply, streamflow regulation, and recreation on the nation's streams and rivers. The RFCs use the National Weather Service River Forecast System (NWSRFS) for streamflow forecasting which relies on a coupled snow model (i.e. SNOW17) and rainfall-runoff model (i.e. SAC-SMA) in snow-dominated regions of the US. Errors arise in various steps of the forecasting system from input data, model structure, model parameters, and initial states. The goal of the current study is to undertake verification of potential improvements in the SNOW17-SAC-SMA modeling framework developed for operational streamflow forecasts. We undertake verification for a range of parameters sets (i.e. RFC, DREAM (Differential Evolution Adaptive Metropolis)) as well as a data assimilation (DA) framework developed for the coupled models. Verification is also undertaken for various initial conditions to observe the influence of variability in initial conditions on the forecast. The study basin is the North Fork America River Basin (NFARB) located on the western side of the Sierra Nevada Mountains in northern California. Hindcasts are verified using both deterministic (i.e. Nash Sutcliffe efficiency, root mean square error, and joint distribution) and probabilistic (i.e. reliability diagram, discrimination diagram, containing ratio, and Quantile plots) statistics. Our presentation includes comparison of the performance of different optimized parameters and the DA framework as well as assessment of the impact associated with the initial conditions used for streamflow forecasts for the NFARB.

  4. Role of climate forecasts and initial land-surface conditions in developing operational streamflow and soil moisture forecasts in a rainfall-runoff regime: skill assessment

    NASA Astrophysics Data System (ADS)

    Sinha, T.; Sankarasubramanian, A.

    2012-04-01

    Skillful seasonal streamflow forecasts obtained from climate and land surface conditions could significantly improve water and energy management. Since climate forecasts are updated on monthly basis, we evaluate the potential in developing operational monthly streamflow forecasts on a continuous basis throughout the year. Further, basins in the rainfall-runoff regime critically depend on the forecasted precipitation in the upcoming months as opposed to snowmelt regimes where initial hydrological conditions (IHC) play a critical role. The goal of this study is to quantify the role of monthly updated precipitation forecasts and IHC in forecasting 6-month lead monthly streamflow for a rainfall-runoff mechanism dominated basin - Apalachicola River at Chattahoochee, FL. The Variable Infiltration Capacity (VIC) land surface model is implemented with two forcings: (a) monthly updated precipitation forecasts from ECHAM4.5 Atmospheric General Circulation Model (AGCM) forced with sea surface temperature forecasts and (b) daily climatological ensemble. The difference in skill between the above two quantifies the improvements that could be attainable using the AGCM forecasts. Monthly retrospective streamflow forecasts are developed from 1981 to 2010 and streamflow forecasts estimated from the VIC model are also compared with those predicted by using the principal component regression (PCR) model. Mean square error (MSE) in predicting monthly streamflow using the above VIC model are compared with the MSE of streamflow climatology under ENSO conditions as well as under normal years. Results indicate that VIC forecasts, at 1-2 month lead time, obtained using ECHAM4.5 are significantly better than VIC forecasts obtained using climatological ensemble over all the seasons except forecasts issued in fall and the PCR models perform better during the fall months. Over longer lead times (3-6 months), VIC forecasts derived using ECHAM4.5 forcings alone performed better compared to the MSE of streamflow climatology during winter and spring seasons. During ENSO years, streamflow forecasts exhibit better skill even up to six month lead time. Comparison of the seasonal soil moisture forecasts developed using ECHAM4.5 forcings with seasonal streamflow also show significant skill at 1-3 month lead time over the all four seasons.

  5. Impact of Kalpana-1 retrieved atmospheric motion vectors on mesoscale model forecast during summer monsoon 2011

    NASA Astrophysics Data System (ADS)

    Kaur, Inderpreet; Kumar, Prashant; Deb, S. K.; Kishtawal, C. M.; Pal, P. K.; Kumar, Raj

    2015-05-01

    The atmospheric motion vectors (AMVs) retrieved from multi-spectral geostationary satellites form a very crucial input to improve the initial conditions of numerical weather prediction (NWP) models at all operational agencies throughout the globe. With the recent update of operational AMV retrieval algorithm using infrared, water vapor, and visible channels of Indian geostationary meteorological satellite Kalpana-1, an attempt has been made to assess the impact of AMVs in the NWP models. In this study, the impact of Kalpana-1 AMVs is assessed by assimilating them in the Weather Research and Forecasting (WRF) model using three-dimensional variational data assimilation method during the entire month of July 2011 over the Indian Ocean region. Apart from Kalpana-1 AMVs, the other AMVs available from Global Telecommunications System (GTS) are also assimilated to generate the WRF model analyses. After the initial verification of WRF model analyses, the 12-h wind forecasts from the WRF model are compared with National Centers for Environmental Prediction Global Data Assimilation System final analyses. The assimilation of Kalpana-1 AMVs shows positive impact in 12-h wind forecast over the tropical region in the upper troposphere. Similar results are obtained when other AMVs available through GTS are used for assimilation, though the magnitude of positive impact of Kalpana-1 AMVs is slightly higher over tropical region. The 24-h rainfall forecasts are also improved over the Western India and the Bay of Bengal region, when Kalpana-1 AMVs are used for assimilation against control experiments.

  6. Risk and Reward: Adaptive Reservoir Operations using Seasonal Climate Forecasts

    NASA Astrophysics Data System (ADS)

    Brown, C.; Souza Filho, F. A.

    2007-12-01

    A typical water manager's objectives consist of retaining enough water in the reservoir to meet urban demand over the decision period, and releasing remaining water to agriculture in accordance with demand. The goal is to release as much water as possible, but not so much that the urban demand is not met. As a result, there are tradeoffs between releasing more water from the reservoir, and improving the yield. The decision hinges on the assumption the water manager holds regarding the future inflows to the reservoir and the risk of an urban water shortfall that the manager is willing to bear. Here we explore the effects of the future inflows to the reservoir that are assumed in making reservoir release decisions. With a conservative assumption of inflows, the reservoir releases will be limited by the continuous need to conserve water for the future. This results in very high reservoir levels that are prepared to be drawn down when the chosen drought scenario occurs. An alternative to these approaches is one that embraces the known year to year variability in the probability of drought at a particular location. We term these approaches "dynamic" risk management and provide an example using real interannual streamflow forecasts applied to reservoir release decision making for Oros Reservoir, NE Brazil.

  7. Operational perspective of remote sensing-based forest fire danger forecasting systems

    NASA Astrophysics Data System (ADS)

    Chowdhury, Ehsan H.; Hassan, Quazi K.

    2015-06-01

    Forest fire is a natural phenomenon in many ecosystems across the world. One of the most important components of forest fire management is the forecasting of fire danger conditions. Here, our aim was to critically analyse the following issues, (i) current operational forest fire danger forecasting systems and their limitations; (ii) remote sensing-based fire danger monitoring systems and usefulness in operational perspective; (iii) remote sensing-based fire danger forecasting systems and their functional implications; and (iv) synergy between operational forecasting systems and remote sensing-based methods. In general, the operational systems use point-based measurements of meteorological variables (e.g., temperature, wind speed and direction, relative humidity, precipitations, cloudiness, solar radiation, etc.) and generate danger maps upon employing interpolation techniques. Theoretically, it is possible to overcome the uncertainty associated with the interpolation techniques by using remote sensing data. During the last several decades, efforts were given to develop fire danger condition systems, which could be broadly classified into two major groups: fire danger monitoring and forecasting systems. Most of the monitoring systems focused on determining the danger during and/or after the period of image acquisition. A limited number of studies were conducted to forecast fire danger conditions, which could be adaptable. Synergy between the operational systems and remote sensing-based methods were investigated in the past but too much complex in nature. Thus, the elaborated understanding about these developments would be worthwhile to advance research in the area of fire danger in the context of making them operational.

  8. Seasonal Water Resources Management and Probabilistic Operations Forecast in the San Juan Basin

    NASA Astrophysics Data System (ADS)

    Daugherty, L.; Zagona, E. A.; Rajagopalan, B.; Grantz, K.; Miller, W. P.; Werner, K.

    2013-12-01

    Projections of reservoir conditions and operations of major water resources systems in the Colorado River Basin are generated each month for a 2-year period by the Bureau of Reclamation (Reclamation) using the 24-Month Study (24MS) model. This is a monthly timestep deterministic model that incorporates a single streamflow forecast trace produced by the National Weather Service (NWS) Colorado Basin River Forecast Center (CBRFC), resulting in the most probable reservoir operations projection. Using an Extended Streamflow Prediction (ESP) method and a physically based hydrologic model, the CBRFC produces an ensemble of streamflow forecasts by sampling historical weather sequences conditioned on 3-7 month seasonal climate forecasts starting from the model's current initial conditions. Using the 24MS model with the most probable forecast from the ESP ensemble, Reclamation manually inputs projected operations, adjusting the operations to meet system objectives. The result is a single most probable operations forecast that does not quantify the uncertainty associated with the ensemble flow projections. In addition, the variability in the ESP method is limited by the flows that result from the historical meteorological record. This research addresses these shortcomings by using an alternative method of generating an ensemble of forecasts with greater variability and applies these to a rulebased operations model to produce a probabilistic projection of operations. To accomplish this, we combined an enhanced ESP with a probabilistic version of the 24MS model known as the Mid-Term Operations Model (MTOM). The MTOM has captured the operating policies in a set of rules that are designed to meet system objectives for a wide range of hydrologic conditions, thus can be used to simulate operations for many hydrologic scenarios. For each year, stochastic weather sequences are generated conditioned on probabilistic seasonal climate forecasts which are coupled with the SAC-SMA model within the NWS Community Hydrologic Prediction System (CHPS) to produce an ensemble streamflow forecast. The ensemble traces are used to drive the MTOM with the initial conditions of the water resources system and the operating rules, to provide ensembles of water resources management and operation metrics. We applied this integrated approach to forecasting in the San Juan River Basin (SJRB) using a portion of the Colorado River MTOM. The management objectives in the basin include water supply for irrigation, tribal water rights, environmental flows, and flood control. The spring streamflow ensembles were issued at four different lead times on the first of each month from January - April, and are incorporated into the MTOM for the period 2002-2010. Ensembles of operational performance metrics for the SJRB such as Navajo Reservoir releases, end of water year storage, environmental flows and water supply for irrigation were computed and their skills evaluated against variables obtained in a baseline simulation using historical streamflow. Preliminary results indicate that thus obtained probabilistic forecasts may produce increased skill especially at long lead time (e.g., on Jan and Feb 1st). The probabilistic information for water management variables provide risks of system vulnerabilities and thus enables risk-based efficient planning and operations.

  9. Evaluation of an operational streamflow forecasting system driven by ensemble precipitation forecasts : a case study for the Gatineau watershed

    NASA Astrophysics Data System (ADS)

    Boucher, M.-A.; Perreault, L.; Tremblay, D.; Gaudet, J.; Minville, M.; Anctil, F.

    2009-04-01

    Among the various sources of uncertainty for hydrological forecasts, the uncertainty linked to meteorological inputs prevail. Precipitation is particularly difficult to forecast and observed values are often poor representation of the true precipitation field. In order to account for the uncertainty related to precipitation data, it can be interesting to produce ensemble streamflow forecasts by feeding a hydrological model with ensemble precipitation forecasts issued by atmospheric models. In this study, we use ensemble precipitation forecasts to drive Hydrotel, a distributed hydrological model. We concentrate on the Gatineau watershed, which serves as an experimental watershed for Hydro-Qubec, the major hydropower producer in Quebec. The main goal of this study is to demonstrate that ensemble precipitation forecasts can improve streamflow forecasting for the watershed of interest. The ensemble precipitation forecasts were produced by Environnement Canada from march first of 2002 to december 31st of 2003. They were obtained using two atmospheric models, SEF (8 members plus the control deterministic forecast) and GEM (8 members). The corresponding deterministic precipitation forecast issued by SEF model is also used with Hydrotel in order to compare ensemble streamflow forecasts with their deterministic counterparts. The quality of the precipitation forecasts is first assessed, using the continuous ranked probability score (CRPS), the logarithmic score, rank histograms and reliability diagrams. The performance of the corresponding streamflow forecasts obtained at the end of the process is also evaluated using the same quality assessment tools.

  10. Evaluation of surface ozone simulated by the WRF/CMAQ online modelling system

    NASA Astrophysics Data System (ADS)

    Marougianni, Garyfalia; Katragkou, Eleni; Giannaros, Theodoros; Poupkou, Anastasia; Melas, Dimitris; Zanis, Prodromos; Feidas, Haralambos

    2013-04-01

    In this work we evaluate the online model WRF/CMAQ with respect to surface ozone and compare its performance with an off-line modelling system (WRF/CAMx) that has been operationally used by Aristotle University of Thessaloniki (AUTH) for chemical weather forecasting in the Mediterranean. The online model consists of the mesoscale meteorological model WRF3.3 and the air quality model CMAQ5.0.1 which are coupled in every time-step. The modelling domain covers Europe with a resolution of 30 Km (identical projection for meteorological and chemistry simulations to avoid interpolation errors) and CMAQ has 17 vertical layers extending up to 15 Km. Anthropogenic emissions are prepared according to the SNAP nomenclature and the biogenic emissions are provided by the Natural Emission Model (NEMO) developed by AUTH. A 2-month simulation is performed by WRF/CMAQ covering the time period of June-July 2010. Average monthly concentration values obtained from the MACCII service (IFS-Mozart) are used as chemical boundary conditions for the simulations. For the WRF simulations boundary conditions are provided by the ECMWF. The same boundaries, chemical mechanism (CBV), emissions and model set up is used in the off-line WRF/CAMx in order to allow a more direct comparison of model results. To evaluate the performance of the WRF/CMAQ online model, simulated ozone concentrations are compared against near surface ozone measurements from the EMEP network. Τhe model has been validated with the climatic observational database that has been compiled in the framework of the GEOCLIMA project (http://www.geoclima.eu/). In the evaluation analysis only those stations that fulfill the criterion of 75% data availability for near surface ozone are used. Various statistical metrics are used for the model evaluation, including correlation coefficient (R), normalized standard deviation (NSD) and modified normalized mean bias (MNMB). The final aim is to investigate whether the state-of-the-art WRF/CMAQ online model is successful in representing in an acceptable way a key atmospheric pollutant like ozone. Preliminary results indicate that WRF/CMAQ captures relatively well the spatial patterns of surface ozone over Europe. Its results are compared to the extensively tested offline modelling system WRF/CAMx, which runs with similar configuration in an identical domain over the same time slice. The aim is to assess the differences in surface ozone between the off-line and online model and try to find the mechanisms underlying these differences. Conclusively, this study aims in quantifying the differences in the results of the off-line WRF/CAMx and the online WRF/CMAQ modelling systems, in order to decide which can more adequately address the needs of emerging assessment for air quality-climate interactions and provide dynamically consistent predictions, ultimately justifying the choice of online versus off-line approaches.This work has been developed in the framework of the NSRF project: Development of a Geographical Information System for Climate information (Geoclima).

  11. FOGCAST: Probabilistic fog forecasting based on operational (high-resolution) NWP models

    NASA Astrophysics Data System (ADS)

    Masbou, M.; Hacker, M.; Bentzien, S.

    2013-12-01

    The presence of fog and low clouds in the lower atmosphere can have a critical impact on both airborne and ground transports and is often connected with serious accidents. The improvement of localization, duration and variations in visibility therefore holds an immense operational value. Fog is generally a small scale phenomenon and mostly affected by local advective transport, radiation, turbulent mixing at the surface as well as its microphysical structure. Sophisticated three-dimensional fog models, based on advanced microphysical parameterization schemes and high vertical resolution, have been already developed and give promising results. Nevertheless, the computational time is beyond the range of an operational setup. Therefore, mesoscale numerical weather prediction models are generally used for forecasting all kinds of weather situations. In spite of numerous improvements, a large uncertainty of small scale weather events inherent in deterministic prediction cannot be evaluated adequately. Probabilistic guidance is necessary to assess these uncertainties and give reliable forecasts. In this study, fog forecasts are obtained by a diagnosis scheme similar to Fog Stability Index (FSI) based on COSMO-DE model outputs. COSMO-DE I the German-focused high-resolution operational weather prediction model of the German Meteorological Service. The FSI and the respective fog occurrence probability is optimized and calibrated with statistical postprocessing in terms of logistic regression. In a second step, the predictor number of the FOGCAST model has been optimized by use of the LASSO-method (Least Absolute Shrinkage and Selection Operator). The results will present objective out-of-sample verification based on the Brier score and is performed for station data over Germany. Furthermore, the probabilistic fog forecast approach, FOGCAST, serves as a benchmark for the evaluation of more sophisticated 3D fog models. Several versions have been set up based on different numerical weather prediction systems: 1- COSMO-DE operational forecasts (50 vertical layers, dz_min=20m), 2- COSMO-DE forecasts with different vertical grid setups, 3- COSMO-DE forecasts with fog microphysics of the one dimensional fog forecast model, PAFOG 4- COSMO-FOG forecasts with a very high vertical resolution (60 layers, dz_min=4m) and an one-moment fog microphysics based on the PAFOG model. The results will quantify the impact of vertical grid resolution, and the importance of detailed cloud microphysics, considering explicitly cloud droplet distribution and sedimentation processes.

  12. Assimilation of Dual-Polarimetric Radar Observations with WRF GSI

    NASA Technical Reports Server (NTRS)

    Li, Xuanli; Mecikalski, John; Fehnel, Traci; Zavodsky, Bradley; Srikishen, Jayanthi

    2014-01-01

    Dual-polarimetric (dual-pol) radar typically transmits both horizontally and vertically polarized radio wave pulses. From the two different reflected power returns, more accurate estimate of liquid and solid cloud and precipitation can be provided. The upgrade of the traditional NWS WSR-88D radar to include dual-pol capabilities will soon be completed for the entire NEXRAD network. Therefore, the use of dual-pol radar network will have a broad impact in both research and operational communities. The assimilation of dual-pol radar data is especially challenging as few guidelines have been provided by previous research. It is our goal to examine how to best use dual-pol radar data to improve forecast of severe storm and forecast initialization. In recent years, the Development Testbed Center (DTC) has released the community Gridpoint Statistical Interpolation (GSI) DA system for the Weather Research and Forecasting (WRF) model. The community GSI system runs in independently environment, yet works functionally equivalent to operational centers. With collaboration with the NASA Short-term Prediction Research and Transition (SPoRT) Center, this study explores regional assimilation of the dual-pol radar variables from the WSR-88D radars for real case storms. Our presentation will highlight our recent effort on incorporating the horizontal reflectivity (ZH), differential reflectivity (ZDR), specific differential phase (KDP), and radial velocity (VR) data for initializing convective storms, with a significant focus being on an improved representation of hydrometeor fields. In addition, discussion will be provided on the development of enhanced assimilation procedures in the GSI system with respect to dual-pol variables. Beyond the dual-pol variable assimilation procedure developing within a GSI framework, highresolution (=1 km) WRF model simulations and storm scale data assimilation experiments will be examined, emphasizing both model initialization and short-term forecast of precipitation fields and processes. Further details of the methodology of data assimilation, the impact of different dual-pol variables, the influence on precipitation forecast will be presented at the conference.

  13. Simulation of the Meadow Creek fire using WRF-Fire

    NASA Astrophysics Data System (ADS)

    Beezley, J. D.; Kochanski, A.; Kondratenko, V. Y.; Mandel, J.; Sousedik, B.

    2010-12-01

    The Meadow Creek fire burned nearly 1,500 acres in the mountainous region of northwestern Colorado in the summer of 2010. We simulate this fire using WRF-Fire and compare the output to estimated fire perimeters and the atmospheric conditions detected by nearby weather stations. WRF-Fire is a fire spread model coupled with the Weather Research and Forecasting model (WRF). Because WRF is a mesoscale weather forecasting model, it was not designed to handle a high resolution computational grid necessary for wildfire simulations. Consequently, we apply various techniques such as terrain smoothing and diffusive damping in order to achieve numerical stability. The data used to initialize our simulation necessarily comes from higher resolution sources than most standard weather simulations. We have chosen to use topographical and vegetation datasets available publicly from the U.S. Geological Survey (USGS) at resolutions of about 10 meters, which must first be converted to a format that is read by the WRF Preprocessing System (WPS). Finally, because atmospheric data is typically only available at grid resolutions of greater than 10 kilometers, we have used WRF's grid nesting infrastructure to provide reasonable initial and boundary conditions for fire simulation. Supported by NSF grants CNS-0719641 and ATM-0835579 The topography of the computational domain with altitudes annotated in meters. The final observed fire perimeter is displayed as a dotted line.

  14. WRF model sensitivity to choice of parameterization: a study of the `York Flood 1999'

    NASA Astrophysics Data System (ADS)

    Remesan, Renji; Bellerby, Tim; Holman, Ian; Frostick, Lynne

    2015-10-01

    Numerical weather modelling has gained considerable attention in the field of hydrology especially in un-gauged catchments and in conjunction with distributed models. As a consequence, the accuracy with which these models represent precipitation, sub-grid-scale processes and exceptional events has become of considerable concern to the hydrological community. This paper presents sensitivity analyses for the Weather Research Forecast (WRF) model with respect to the choice of physical parameterization schemes (both cumulus parameterisation (CPSs) and microphysics parameterization schemes (MPSs)) used to represent the `1999 York Flood' event, which occurred over North Yorkshire, UK, 1st-14th March 1999. The study assessed four CPSs (Kain-Fritsch (KF2), Betts-Miller-Janjic (BMJ), Grell-Devenyi ensemble (GD) and the old Kain-Fritsch (KF1)) and four MPSs (Kessler, Lin et al., WRF single-moment 3-class (WSM3) and WRF single-moment 5-class (WSM5)] with respect to their influence on modelled rainfall. The study suggests that the BMJ scheme may be a better cumulus parameterization choice for the study region, giving a consistently better performance than other three CPSs, though there are suggestions of underestimation. The WSM3 was identified as the best MPSs and a combined WSM3/BMJ model setup produced realistic estimates of precipitation quantities for this exceptional flood event. This study analysed spatial variability in WRF performance through categorical indices, including POD, FBI, FAR and CSI during York Flood 1999 under various model settings. Moreover, the WRF model was good at predicting high-intensity rare events over the Yorkshire region, suggesting it has potential for operational use.

  15. Risk Analysis of Multipurpose Reservoir Real-time Operation based on Probabilistic Hydrologic Forecasting

    NASA Astrophysics Data System (ADS)

    Liu, P.

    2011-12-01

    Quantitative analysis of the risk for reservoir real-time operation is a hard task owing to the difficulty of accurate description of inflow uncertainties. The ensemble-based probabilistic hydrologic forecasting, which outputs a lot of inflow scenarios or traces, does well in depicting the inflow not only the marginal distribution but also their corrections. This motivates us to analyze the reservoir operating risk by inputting probabilistic hydrologic forecasting into reservoir real-time operation. The proposed procedure involves: (1) based upon the Bayesian inference, two alternative techniques, the generalized likelihood uncertainty estimation (GLUE) and Markov chain Monte Carlo (MCMC), are implemented for producing probabilistic hydrologic forecasting, respectively, (2) the reservoir risk is defined as the ratio of the number of traces that excessive (or below) the critical value to the total number of traces, and (3) a multipurpose reservoir operation model is build to produce Pareto solutions for trade-offs between risks and profits with the inputted probabilistic hydrologic forecasting. With a case study of the China's Three Gorges Reservoir, it is found that the reservoir real-time operation risks can be estimated and minimized based on the proposed methods, and this is great potential benefit in decision and choosing the most realistic one.

  16. An operational real-time flood forecasting system in Southern Italy

    NASA Astrophysics Data System (ADS)

    Ortiz, Enrique; Coccia, Gabriele; Todini, Ezio

    2015-04-01

    A real-time flood forecasting system has been operating since year 2012 as a non-structural measure for mitigating the flood risk in Campania Region (Southern Italy), within the Sele river basin (3.240 km2). The Sele Flood Forecasting System (SFFS) has been built within the FEWS (Flood Early Warning System) platform developed by Deltares and it assimilates the numerical weather predictions of the COSMO LAM family: the deterministic COSMO-LAMI I2, the deterministic COSMO-LAMI I7 and the ensemble numerical weather predictions COSMO-LEPS (16 members). Sele FFS is composed by a cascade of three main models. The first model is a fully continuous physically based distributed hydrological model, named TOPKAPI-eXtended (Idrologia&Ambiente s.r.l., Naples, Italy), simulating the dominant processes controlling the soil water dynamics, runoff generation and discharge with a spatial resolution of 250 m. The second module is a set of Neural-Networks (ANN) built for forecasting the river stages at a set of monitored cross-sections. The third component is a Model Conditional Processor (MCP), which provides the predictive uncertainty (i.e., the probability of occurrence of a future flood event) within the framework of a multi-temporal forecast, according to the most recent advancements on this topic (Coccia and Todini, HESS, 2011). The MCP provides information about the probability of exceedance of a maximum river stage within the forecast lead time, by means of a discrete time function representing the variation of cumulative probability of exceeding a river stage during the forecast lead time and the distribution of the time occurrence of the flood peak, starting from one or more model forecasts. This work shows the Sele FFS performance after two years of operation, evidencing the added-values that can provide to a flood early warning and emergency management system.

  17. An Integrated Risk Approach for Assessing the Use of Ensemble Streamflow Forecasts in Hydroelectric Reservoir Operations

    NASA Astrophysics Data System (ADS)

    Lowry, T. S.; Wigmosta, M.; Barco, J.; Voisin, N.; Bier, A.; Coleman, A.; Skaggs, R.

    2012-12-01

    This paper presents an integrated risk approach using ensemble streamflow forecasts for optimizing hydro-electric power generation. Uncertainty in the streamflow forecasts are translated into integrated risk by calculating the deviation of an optimized release schedule that simultaneously maximizes power generation and environmental performance from release schedules that maximize the two objectives individually. The deviations from each target are multiplied by the probability of occurrence and then summed across all probabilities to get the integrated risk. The integrated risk is used to determine which operational scheme exposes the operator to the least amount of risk or conversely, what are the consequences of basing future operations on a particular prediction. Decisions can be made with regards to the tradeoff between power generation, environmental performance, and exposure to risk. The Hydropower Seasonal Concurrent Optimization for Power and Environment (HydroSCOPE) model developed at Sandia National Laboratories (SNL) is used to model the flow, temperature, and power generation and is coupled with the DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) optimization package to identify the maximum potential power generation, the maximum environmental performance, and the optimal operational scheme that maximizes both for each instance of the ensemble forecasts. The ensemble forecasts were developed in a collaborative effort between the Pacific Northwest National Laboratory (PNNL) and the University of Washington to develop an Enhanced Hydrologic Forecasting System (EHFS) that incorporates advanced ensemble forecasting approaches and algorithms, spatiotemporal datasets, and automated data acquisition and processing. Both the HydroSCOPE model and the EHFS forecast tool are being developed as part of a larger, multi-laboratory water-use optimization project funded through the US Department of Energy. The simulations were based on the three-reservoir Aspinall Unit on the Gunnison River in Colorado for a hypothetical, 6-month time span running from April through September. The results indicate that using ensemble forecasts within a risk-based framework enables construction of a Pareto front that depicts the trade-offs between hydropower production, environmental effects, and integrated risk. By better understanding these trade-offs, operators can make more informed decisions and develop more robust reservoir operation strategies.

  18. Multi-platform operational validation of the Western Mediterranean SOCIB forecasting system

    NASA Astrophysics Data System (ADS)

    Juza, Mélanie; Mourre, Baptiste; Renault, Lionel; Tintoré, Joaquin

    2014-05-01

    The development of science-based ocean forecasting systems at global, regional, and local scales can support a better management of the marine environment (maritime security, environmental and resources protection, maritime and commercial operations, tourism, ...). In this context, SOCIB (the Balearic Islands Coastal Observing and Forecasting System, www.socib.es) has developed an operational ocean forecasting system in the Western Mediterranean Sea (WMOP). WMOP uses a regional configuration of the Regional Ocean Modelling System (ROMS, Shchepetkin and McWilliams, 2005) nested in the larger scale Mediterranean Forecasting System (MFS) with a spatial resolution of 1.5-2km. WMOP aims at reproducing both the basin-scale ocean circulation and the mesoscale variability which is known to play a crucial role due to its strong interaction with the large scale circulation in this region. An operational validation system has been developed to systematically assess the model outputs at daily, monthly and seasonal time scales. Multi-platform observations are used for this validation, including satellite products (Sea Surface Temperature, Sea Level Anomaly), in situ measurements (from gliders, Argo floats, drifters and fixed moorings) and High-Frequency radar data. The validation procedures allow to monitor and certify the general realism of the daily production of the ocean forecasting system before its distribution to users. Additionally, different indicators (Sea Surface Temperature and Salinity, Eddy Kinetic Energy, Mixed Layer Depth, Heat Content, transports in key sections) are computed every day both at the basin-scale and in several sub-regions (Alboran Sea, Balearic Sea, Gulf of Lion). The daily forecasts, validation diagnostics and indicators from the operational model over the last months are available at www.socib.es.

  19. Running WRF on various distributed computing infrastructures through a standard-based Science Gateway

    NASA Astrophysics Data System (ADS)

    Barbera, Roberto; Bruno, Riccardo; La Rocca, Giuseppe; Markussen Lunde, Torleif; Pehrson, Bjorn

    2014-05-01

    The Weather Research and Forecasting (WRF) modelling system is a widely used meso-scale numerical weather prediction system designed to serve both atmospheric research and operational forecasting needs. WRF has a large worldwide community counting more than 20,000 users in 130 countries and it has been specifically designed to be the state-of-the-art atmospheric simulation system being portable and running efficiently on available parallel computing platforms. Although WRF can be executed in many different environments ranging form the single core inside a stand-alone machine up to the most sophisticated HPC platforms, there are no solutions yet to match the e-Science paradigm where software, data and users are "linked" together by the network as components of distributed computing infrastructures. The topmost component of the typical e-Science model consists of Science Gateways, defined as community-developed sets of tools, applications, and data collections that normally are integrated via a portal to get access to a distributed infrastructure. One of the many available Science Gateway solutions is the Catania Science Gateway Framework (CSGF - www.catania-science-gateways.it) whose most descriptive keywords are: standard adoption, interoperability and standard adoption. The support of standards such as SAGA and SAML allows any CSGF user to seamlessly access and use both Grid and Cloud-based resources. In this work we present the CSGF and how it has been used in the context of the eI4frica project (www.ei4africa.eu) to implement the Africa Grid Science Gateway (http://sgw.africa-grid.org), which allows to execute WRF simulations on various kinds of distributed computing infrastructures at the same time, including the EGI Federated Cloud.

  20. Forecasting the Economic Impact of Future Space Station Operations

    NASA Technical Reports Server (NTRS)

    Summer, R. A.; Smolensky, S. M.; Muir, A. H.

    1967-01-01

    Recent manned and unmanned Earth-orbital operations have suggested great promise of improved knowledge and of substantial economic and associated benefits to be derived from services offered by a space station. Proposed application areas include agriculture, forestry, hydrology, public health, oceanography, natural disaster warning, and search/rescue operations. The need for reliable estimates of economic and related Earth-oriented benefits to be realized from Earth-orbital operations is discussed and recent work in this area is reviewed. Emphasis is given to those services based on remote sensing. Requirements for a uniform, comprehensive and flexible methodology are discussed. A brief review of the suggested methodology is presented. This methodology will be exercised through five case studies which were chosen from a gross inventory of almost 400 user candidates. The relationship of case study results to benefits in broader application areas is discussed, Some management implications of possible future program implementation are included.

  1. Review of Wind Energy Forecasting Methods for Modeling Ramping Events

    SciTech Connect

    Wharton, S; Lundquist, J K; Marjanovic, N; Williams, J L; Rhodes, M; Chow, T K; Maxwell, R

    2011-03-28

    Tall onshore wind turbines, with hub heights between 80 m and 100 m, can extract large amounts of energy from the atmosphere since they generally encounter higher wind speeds, but they face challenges given the complexity of boundary layer flows. This complexity of the lowest layers of the atmosphere, where wind turbines reside, has made conventional modeling efforts less than ideal. To meet the nation's goal of increasing wind power into the U.S. electrical grid, the accuracy of wind power forecasts must be improved. In this report, the Lawrence Livermore National Laboratory, in collaboration with the University of Colorado at Boulder, University of California at Berkeley, and Colorado School of Mines, evaluates innovative approaches to forecasting sudden changes in wind speed or 'ramping events' at an onshore, multimegawatt wind farm. The forecast simulations are compared to observations of wind speed and direction from tall meteorological towers and a remote-sensing Sound Detection and Ranging (SODAR) instrument. Ramping events, i.e., sudden increases or decreases in wind speed and hence, power generated by a turbine, are especially problematic for wind farm operators. Sudden changes in wind speed or direction can lead to large power generation differences across a wind farm and are very difficult to predict with current forecasting tools. Here, we quantify the ability of three models, mesoscale WRF, WRF-LES, and PF.WRF, which vary in sophistication and required user expertise, to predict three ramping events at a North American wind farm.

  2. Outcomes of 2002 Financial Forecasts and Annual Operating Statements.

    ERIC Educational Resources Information Center

    Higher Education Funding Council for England, Bristol.

    This report provides a summary of the English higher education sector's annual operating statements (AOSs) for 2001-2002 and gives financial projections for the sector covering 2001-2002 to 2005-2006. It is based on information provided by higher education institutions in July 2002. The AOS information relates to Higher Education Funding Council…

  3. Storm surge forecasting for operating the Venice Flood Barrier with minimal impact on port activities

    NASA Astrophysics Data System (ADS)

    Cecconi, Giovanni

    2015-04-01

    The operation of the Venice storm barrier, due to enter into operation by the end of 2017 , is particularly demanding in terms of the required accuracy of the forecast of the max water level for the time lead of 3-6 hours. With present sea level and safeguard level established at 1.1 m a.s.l. of 1895 the barrier is expected to be operated 10 times a year to cope with an average of 5 storms with around 15 redirections of the navigation through the locks. The 5 extra closures and the 10 extra interferences with navigation are needed for compensating the present forecast uncertainty of 10 cm in the maximum storm high for the required time lead of three hours, the time needed to stop navigation before the closures of the lagoon inlets. A decision support system based on these rules have been tested along the last four year with satisfactory results in term of reliability easy of operations. The forecast is presently based on a statistical model associated with a deterministic local model; the main source of uncertainty is related to the prediction of the local wind. Due to delays in the completion of Venice local protection till 1.1 m it is expected that the population will urge a reduction of the safeguard level from 1.1m to 0.9m with an exponential increase in the number of closures with greater impact on navigation. The present acceleration in sea level rise will also contribute to the increase in the number of closures. To reduce the impact on port activity, better forecast accuracy is required together with experimenting new operational closures : e.g. activating only the northern barriers. The paper evaluate the problem and the possible solutions in terms of improving storm surge forecast and developing new schemes for partial operation of the barriers for predicted limited floods not requiring complete closures.

  4. Application of a Coupled WRF-Hydro Model for Extreme Flood Events in the Mediterranean Basins

    NASA Astrophysics Data System (ADS)

    Fredj, Erick; Givati, Amir

    2015-04-01

    More accurate simulation of precipitation and streamflow is a challenge that can be addressed by using the Weather Research and Forecasting Model (WRF) in conjunction with the hydrological model coupling extension package (WRF-Hydro).This is demonstrated for the country of Israel and surrounding regions. Simulations from the coupled WRF/WRF-Hydro system were verified against measurements from rain gauges and hydrometric stations in the domain for the 2012-2013 and 2013-2014 winters (wet seasons). These periods were characterized by many punctuated hydrometeorological and hydroclimatic events, including both severe drought and extreme floods events. The WRF model simulations were initialized with 0.5 degree NOAA/NCEP GFS model data. The model domain was set up with 3 domains, up to 3km grid spacing resolution. The model configuration used here constitutes a fully distributed, 3-dimensional, variably-saturated surface and subsurface flow model. Application of terrain routing and, subsequently, channel and reservoir routing functions, to the uni-dimensional NOAA land surface model was motivated by the need to account for increased complexity in land surface states and fluxes and to provide a more physically-realistic conceptualization of terrestrial hydrologic processes. The simulation results indicated a good agreement with actual peak discharges for extreme flood events and for full hydrographs. Specifically the coupled WRF/WRF-Hydro model as configured in this study shows improvement in simulated precipitation over one way WRF precipitation simulations. The correlation between the observed and the simulated precipitation using the fully coupled WRF/WRF-Hydro system was higher than the standalone WRF model, especially for convective precipitation events that affect arid regions in the domain. The results suggest that the coupled WRF/WRF-Hydro system has potential for flood forecasting and flood warning purposes at 0-72 hour lead times for large cool season storm events and also for convective storms that impact Israel during the transition seasons.

  5. Performance Evaluation of Emerging High Performance Computing Technologies using WRF

    NASA Astrophysics Data System (ADS)

    Newby, G. B.; Morton, D.

    2008-12-01

    The Arctic Region Supercomputing Center (ARSC) has evaluated multicore processors and other emerging processor technologies for a variety of high performance computing applications in the earth and space sciences, especially climate and weather applications. A flagship effort has been to assess dual core processor nodes on ARSC's Midnight supercomputer, in which two-socket systems were compared to eight-socket systems. Midnight is utilized for ARSC's twice-daily weather research and forecasting (WRF) model runs, available at weather.arsc.edu. Among other findings on Midnight, it was found that the Hypertransport system for interconnecting Opteron processors, memory, and other subsystems does not scale as well on eight-socket (sixteen processor) systems as well as two-socket (four processor) systems. A fundamental limitation is the cache snooping operation performed whenever a computational thread accesses main memory. This increases memory latency as the number of processor sockets increases. This is particularly noticeable on applications such as WRF that are primarily CPU-bound, versus applications that are bound by input/output or communication. The new Cray XT5 supercomputer at ARSC features quad core processors, and will host a variety of scaling experiments for WRF, CCSM4, and other models. Early results will be presented, including a series of WRF runs for Alaska with grid resolutions under 2km. ARSC will discuss a set of standardized test cases for the Alaska domain, similar to existing test cases for CONUS. These test cases will provide different configuration sizes and resolutions, suitable for single processors up to thousands. Beyond multi-core Opteron-based supercomputers, ARSC has examined WRF and other applications on additional emerging technologies. One such technology is the graphics processing unit, or GPU. The 9800-series nVidia GPU was evaluated with the cuBLAS software library. While in-socket GPUs might be forthcoming in the future, current generations of GPUs lack a sufficient balance of computational resources to replace the general-purpose microprocessor found in most traditional supercomputer architectures. ARSC has also worked with the Cell Broadband Engine in a small Playstation3 cluster, as well as a 24-processor system based on IBM's QS22 blades. The QS22 system, called Quasar, features the PowerXCell 8i processor found in the RoadRunner system, along with an InfiniBand network and high performance storage. Quasar overcomes the limitations of the small memory and relatively slow network of the PS3 systems. The presentation will include system-level benchmarks on Quasar, as well as evaluation of the WRF test cases. Another technology evaluation focused on Sun's UltraSPARC T2+ processor, which ARSC evaluated in a two-way system. Each T2+ provides eight processor cores, each of which provides eight threads, for a total of 128 threads in a single system. WRF scalability was good up to the number of cores, but multiple threads per core did not scale as well. Throughout the discussion, practical findings from ARSC will be summarized. While multicore general-purpose microprocessors are anticipated to remain important for large computers running earth and space science applications, the role of other potentially disruptive technologies is less certain. Limitations of current and future technologies will be discussed. class="ab'>

  6. Towards guided data assimilation for operational hydrologic forecasting in the US Tennessee River basin

    NASA Astrophysics Data System (ADS)

    Weerts, Albrecht; Wood, Andy; Carney, Shaun; Day, Jay; Lemans, Matthijs; Sumihar, Julius; Verkade, Jan; Newman, Andy

    2015-04-01

    In the US, the forecasting approach used by the NWS River Forecast Centers and other regional organizations such as the Bonneville Power Administration (BPA) or Tennessee Valley Authority (TVA) has traditionally involved manual model input and state modifications made by forecasters in real-time. This process is time consuming and requires expert knowledge and experience. The benefits of automated data assimilation (DA) as a strategy for avoiding manual modification approaches have been demonstrated in research studies (eg. Seo et al., 2009). This study explores the usage of various ensemble DA algorithms within the operational platform used by TVA. The final goal is to identify a DA algorithm that will guide the manual modification process used by TVA forecasters and realize considerable time gains (without loss of quality or even enhance the quality) within the forecast process. We evaluate the usability of various popular algorithms for DA that have been applied on a limited basis for operational hydrology. To this end, Delft-FEWS was wrapped (via piwebservice) in OpenDA to enable execution of FEWS workflows (and the chained models within these workflows, including SACSMA, UNITHG and LAGK) in a DA framework. Within OpenDA, several filter methods are available. We considered 4 algorithms: particle filter (RRF), Ensemble Kalman Filter and Asynchronous Ensemble Kalman and Particle filter. The initial results are promising. We will present verification results for these methods (and possible more) for a variety of sub basins in the Tennessee River basin. Finally, we will offer recommendations for guided DA based on our results. References Seo, D.-J., L. Cajina, R. Corby and T. Howieson, 2009: Automatic State Updating for Operational Streamflow Forecasting via Variational Data Assimilation, 367, Journal of Hydrology, 255-275.

  7. Research to Operations of Ionospheric Scintillation Detection and Forecasting

    NASA Astrophysics Data System (ADS)

    Jones, J.; Scro, K.; Payne, D.; Ruhge, R.; Erickson, B.; Andorka, S.; Ludwig, C.; Karmann, J.; Ebelhar, D.

    Ionospheric Scintillation refers to random fluctuations in phase and amplitude of electromagnetic waves caused by a rapidly varying refractive index due to turbulent features in the ionosphere. Scintillation of transionospheric UHF and L-Band radio frequency signals is particularly troublesome since this phenomenon can lead to degradation of signal strength and integrity that can negatively impact satellite communications and navigation, radar, or radio signals from other systems that traverse or interact with the ionosphere. Although ionospheric scintillation occurs in both the equatorial and polar regions of the Earth, the focus of this modeling effort is on equatorial scintillation. The ionospheric scintillation model is data-driven in a sense that scintillation observations are used to perform detection and characterization of scintillation structures. These structures are then propagated to future times using drift and decay models to represent the natural evolution of ionospheric scintillation. The impact on radio signals is also determined by the model and represented in graphical format to the user. A frequency scaling algorithm allows for impact analysis on frequencies other than the observation frequencies. The project began with lab-grade software and through a tailored Agile development process, deployed operational-grade code to a DoD operational center. The Agile development process promotes adaptive promote adaptive planning, evolutionary development, early delivery, continuous improvement, regular collaboration with the customer, and encourage rapid and flexible response to customer-driven changes. The Agile philosophy values individuals and interactions over processes and tools, working software over comprehensive documentation, customer collaboration over contract negotiation, and responding to change over following a rigid plan. The end result was an operational capability that met customer expectations. Details of the model and the process of operational integration are discussed as well as lessons learned to improve performance on future projects.

  8. Operational value of ensemble streamflow forecasts for hydropower production: A Canadian case study

    NASA Astrophysics Data System (ADS)

    Boucher, Marie-Amlie; Tremblay, Denis; Luc, Perreault; Franois, Anctil

    2010-05-01

    Ensemble and probabilistic forecasts have many advantages over deterministic ones, both in meteorology and hydrology (e.g. Krzysztofowicz, 2001). Mainly, they inform the user on the uncertainty linked to the forecast. It has been brought to attention that such additional information could lead to improved decision making (e.g. Wilks and Hamill, 1995; Mylne, 2002; Roulin, 2007), but very few studies concentrate on operational situations involving the use of such forecasts. In addition, many authors have demonstrated that ensemble forecasts outperform deterministic forecasts in terms of performance (e.g. Jaun et al., 2005; Velazquez et al., 2009; Laio and Tamea, 2007). However, such performance is mostly assessed on the basis of numerical scoring rules, which compare the forecasts to the observations, and seldom in terms of management gains. The proposed case study adopts an operational point of view, on the basis that a novel forecasting system has value only if it leads to increase monetary and societal gains (e.g. Murphy, 1994; Laio and Tamea, 2007). More specifically, Environment Canada operational ensemble precipitation forecasts are used to drive the HYDROTEL distributed hydrological model (Fortin et al., 1995), calibrated on the Gatineau watershed located in Qubec, Canada. The resulting hydrological ensemble forecasts are then incorporated into Hydro-Qubec SOHO stochastic management optimization tool that automatically search for optimal operation decisions for the all reservoirs and hydropower plants located on the basin. The timeline of the study is the fall season of year 2003. This period is especially relevant because of high precipitations that nearly caused a major spill, and forced the preventive evacuation of a portion of the population located near one of the dams. We show that the use of the ensemble forecasts would have reduced the occurrence of spills and flooding, which is of particular importance for dams located in populous area, and increased hydropower production. The ensemble precipitation forecasts extend from March 1st of 2002 to December 31st of 2003. They were obtained using two atmospheric models, SEF (8 members plus the control deterministic forecast) and GEM (8 members). The corresponding deterministic precipitation forecast issued by SEF model is also used within HYDROTEL in order to compare ensemble streamflow forecasts with their deterministic counterparts. Although this study does not incorporate all the sources of uncertainty, precipitation is certainly the most important input for hydrological modeling and conveys a great portion of the total uncertainty. References: Fortin, J.P., Moussa, R., Bocquillon, C. and Villeneuve, J.P. 1995: HYDROTEL, un modle hydrologique distribu pouvant bnficier des donnes fournies par la tldtection et les systmes d'information gographique, Revue des Sciences de l'Eau, 8(1), 94-124. Jaun, S., Ahrens, B., Walser, A., Ewen, T. and Schaer, C. 2008: A probabilistic view on the August 2005 floods in the upper Rhine catchment, Natural Hazards and Earth System Sciences, 8 (2), 281-291. Krzysztofowicz, R. 2001: The case for probabilistic forecasting in hydrology, Journal of Hydrology, 249, 2-9. Murphy, A.H. 1994: Assessing the economic value of weather forecasts: An overview of methods, results and issues, Meteorological Applications, 1, 69-73. Mylne, K.R. 2002: Decision-Making from probability forecasts based on forecast value, Meteorological Applications, 9, 307-315. Laio, F. and Tamea, S. 2007: Verification tools for probabilistic forecasts of continuous hydrological variables, Hydrology and Earth System Sciences, 11, 1267-1277. Roulin, E. 2007: Skill and relative economic value of medium-range hydrological ensemble predictions, Hydrology and Earth System Sciences, 11, 725-737. Velazquez, J.-A., Petit, T., Lavoie, A., Boucher, M.-A., Turcotte, R., Fortin, V. and Anctil, F. 2009: An evaluation of the Canadian global meteorological ensemble prediction system for short-term hydrological forecasting, Hydrology and Earth System Sciences, 13(11), 2221-2231. Wilks, D.S. and Hamill, T.M. 1995: Potential economic value of ensemble-based surface weather forecasts, Monthly Weather Review, 123(12), 3565-3575.

  9. Delft FEWS: an open interface that connects models and data streams for operational forecasting systems

    NASA Astrophysics Data System (ADS)

    de Rooij, Erik; Werner, Micha

    2010-05-01

    Many of the operational forecasting systems that are in use today are centred around a single modelling suite. Over the years these systems and the required data streams have been tailored to provide a closed-knit interaction with their underlying modelling components. However, as time progresses it becomes a challenge to integrate new technologies into these model centric operational systems. Often the software used to develop these systems is out of date, or the original designers of these systems are no longer available. Additionally, the changing of the underlying models may requiring the complete system to be changed. This then becomes an extensive effort, not only from a software engineering point of view, but also from a training point of view. Due to significant time and resources being committed to re-training the forecasting teams that interact with the system on a daily basis. One approach to reducing the effort required in integrating new models and data is through an open interface architecture, and through the use of defined interfaces and standards in data exchange. This approach is taken by the Delft-FEWS operational forecasting shell, which has now been applied in some 40 operational forecasting centres across the world. The Delft-FEWS framework provides several interfaces that allow models and data in differing formats to be flexibly integrated with the system. The most common approach to the integration of modes is through the Delft-FEWS Published Interface. This is an XML based data exchange format that supports the exchange of time series data, as well as vector and gridded data formats. The Published Interface supports standardised data formats such as GRIB and the NetCDF-CF standard. A wide range of models has been integrated with the system through this approach, and these are used operationally across the forecasting centres using Delft FEWS. Models can communicate directly with the interface of Delft-FEWS, or through a SOAP service. This giving the flexibility required for a state-of-the-art operational forecasting service. While Delft-FEWS comes with a user-friendly GIS based interface, a time series viewer and editor, and a wide range of tools for visualization, analysis, validation and data conversion, the available graphical display can be extended. New graphical components can be seamlessly integrated with the system through the SOAP service. Thanks to this open infrastructure, new models can easily be incorporated into an operational system without having to change the operational process. This allows the forecaster to focus on the science instead of having to worry about model details and data formats. Furthermore all model formats introduced to the Delft-FEWS framework will in principle become available to the Delft-FEWS community (in some cases subject to the licence conditions of the model supplier). Currently a wide range of models has been integrated and is being used operationally; Mike 11, HEC-RAS & HEC-RESSIM, HBV, MODFLOW, SOBEK and more. In this way Delft-FEWS not only provides a modelling interface but also a platform for model inter-comparison or multi-model ensembles, as well as a knowledge interface that allows forecasters throughout the world to exchange their views and ideas on operational forecasting. Keywords: FEWS; forecasting; modelling; timeseries; data; XML; NetCDF; interface; SOAP

  10. The importance of short-term forecasting of thunderstorms to launch operations at Cape Canaveral

    NASA Technical Reports Server (NTRS)

    Cooper, Harry J.; Smith, Eric A.

    1993-01-01

    The local meteorological events leading up to the launch of the space shuttle Atlantis on 2 August 1991 were captured in full-resolution GOES visible data being archived for the Convection and Precipitation/Electrification Experiment. The postponement of the launch on 1 August, and the successful lift-off on the following day provide a good example of the important role played by nowcasting and short-term forecasting at Cape Canaveral. In this brief article, we discuss the local weather conditions prior to, during, and after the launch and demonstrate the importance of short-term forecasting capabilities around the cape during launch operations.

  11. Operational Earthquake Forecasting and Decision-Making in a Low-Probability Environment

    NASA Astrophysics Data System (ADS)

    Jordan, T. H.; the International Commission on Earthquake ForecastingCivil Protection

    2011-12-01

    Operational earthquake forecasting (OEF) is the dissemination of authoritative information about the time dependence of seismic hazards to help communities prepare for potentially destructive earthquakes. Most previous work on the public utility of OEF has anticipated that forecasts would deliver high probabilities of large earthquakes; i.e., deterministic predictions with low error rates (false alarms and failures-to-predict) would be possible. This expectation has not been realized. An alternative to deterministic prediction is probabilistic forecasting based on empirical statistical models of aftershock triggering and seismic clustering. During periods of high seismic activity, short-term earthquake forecasts can attain prospective probability gains in excess of 100 relative to long-term forecasts. The utility of such information is by no means clear, however, because even with hundredfold increases, the probabilities of large earthquakes typically remain small, rarely exceeding a few percent over forecasting intervals of days or weeks. Civil protection agencies have been understandably cautious in implementing OEF in this sort of "low-probability environment." The need to move more quickly has been underscored by recent seismic crises, such as the 2009 L'Aquila earthquake sequence, in which an anxious public was confused by informal and inaccurate earthquake predictions. After the L'Aquila earthquake, the Italian Department of Civil Protection appointed an International Commission on Earthquake Forecasting (ICEF), which I chaired, to recommend guidelines for OEF utilization. Our report (Ann. Geophys., 54, 4, 2011; doi: 10.4401/ag-5350) concludes: (a) Public sources of information on short-term probabilities should be authoritative, scientific, open, and timely, and need to convey epistemic uncertainties. (b) Earthquake probabilities should be based on operationally qualified, regularly updated forecasting systems. (c) All operational models should be evaluated for reliability and skill by retrospective testing, and the models should be under continuous prospective testing against long-term forecasts and alternative time-dependent models. (d) Short-term models used in operational forecasting should be consistent with the long-term forecasts used in probabilistic seismic hazard analysis. (e) Alert procedures should be standardized to facilitate decisions at different levels of government, based in part on objective analysis of costs and benefits. (f) In establishing alert protocols, consideration should also be given to the less tangible aspects of value-of-information, such as gains in psychological preparedness and resilience. Authoritative statements of increased risk, even when the absolute probability is low, can provide a psychological benefit to the public by filling information vacuums that lead to informal predictions and misinformation. Formal OEF procedures based on probabilistic forecasting appropriately separate hazard estimation by scientists from the decision-making role of civil protection authorities. The prosecution of seven Italian scientists on manslaughter charges stemming from their actions before the L'Aquila earthquake makes clear why this separation should be explicit in defining OEF protocols.

  12. Advanced Research WRF (ARW) Modeled Low-level Jet Climatology Compared to Observed Climatologies

    NASA Astrophysics Data System (ADS)

    Storm, B.; Basu, S.; Dudhia, J.

    2007-05-01

    Nocturnal low-level jets (LLJs) are common features observed in the Great Plains region of the United States. LLJs play a key factor in initiating and sustaining mesoscale convective systems and other severe convective storm modes in the Great Plains. The LLJ in the Great Plains can also be a key source of moisture transport into the region which is crucial for severe weather development and shown important for widespread flooding. Knowing the climatology of such events is important so an understanding of the importance of the LLJ in severe weather can be furthered. Several observational studies have been conducted to determine the climatology of LLJs over the Great Plains. However these studies are limited due to the spatial restraints. Using point measurements makes it nearly impossible to determine the spatial structure of LLJs. Using a NWP model lessens this restraint, though grid spacing and frequency of the model output is still problematic using operational forecasts. This study investigates how well the operational Advanced Research WRF (ARW) forecasts represent the LLJ climatology of the region centered on the ARM site along the Kansas/Oklahoma border using 6 months (June- Sept.) of 3 hourly outputs with 12 km grid spacing. The NCAR's operational ARW is run in 36/12 km two-way nested configuration and provides a 48 h forecast from 00 Z initialization utilizing 40 km Eta fields. Preliminary results indicate that the ARW has similar climatology characteristics (i.e. frequency, max time occurrence, dominant direction) to the previous observational studies. To forecast LLJs, accurate representation of the PBL is crucial, which is also important for being able to forecast many high impact events. If the WRF can be shown to produce a similar climatology to that what has been observed, we gain more confidence in WRF and its PBL parameterizations. Since the ARW shows promise of representing the LLJ climatology of the ARM site closely to what has been found in the observational studies, the ARW could be used to get a better understanding on the frequency of the LLJ over other sites within the Great Plains. This information could be further used to understand the importance of the LLJ in moisture transportation. It is also possible for the ARW to be used to further investigate the forcing mechanisms of LLJs which is yet not fully understood. A better understanding of LLJ development could lead to greater improvement in forecasts and severe weather initiation.

  13. A stochastic operational forecasting system of the Black Sea: Technique and validation

    NASA Astrophysics Data System (ADS)

    Vandenbulcke, Luc; Barth, Alexander

    2015-09-01

    In this article, we present the latest version of an ensemble forecasting system of the hydrodynamics of the Black Sea, based on the GHER model. The system includes the Weakly Constrained Ensembles algorithm to generate random, but physically balanced perturbations to initialize members of the ensemble. On top of initial conditions, the ensemble accounts also for uncertainty on the atmospheric forcing fields, and on some scalar parameters such as river flows or model diffusion coefficients. The forecasting system also includes the Ocean Assimilation Kit, a sequential data assimilation package implementing the SEEK and Ensemble Kalman filters. A novel aspect of the forecasting system is that not only our best estimate of the future ocean state is provided, but also the associated error estimated from the ensemble of models. The primary goal of this paper is to quantitatively show that the ensemble variability is a good estimation of the model error, regardless of the magnitude of the forecast errors themselves. In order for this estimation to be meaningful, the model itself should also be well validated. Therefore, we describe the model validation against general circulation patterns. Some particular aspects critical for the Black Sea circulation are validated as well: the mixed layer depth and the shelfopen sea exchanges. The model forecasts are also compared with observed sea surface temperature, and errors are compared to those of another operational model as well.

  14. An operational forecasting system for the meteorological and marine conditions in Mediterranean regional and coastal areas

    NASA Astrophysics Data System (ADS)

    Casaioli, M.; Catini, F.; Inghilesi, R.; Lanucara, P.; Malguzzi, P.; Mariani, S.; Orasi, A.

    2014-05-01

    The coupling of a suite of meteorological limited area models with a wave prediction system based on the nesting of different wave models provides for medium-range sea state forecasts at the Mediterranean, regional and coastal scale. The new system has been operational at ISPRA since September 2012, after the upgrade of both the meteorological BOLAM model and large-scale marine components of the original SIMM forecasting system and the implementation of the new regional and coastal (WAM-SWAN coupling) chain of models. The coastal system is composed of nine regional-scale high-resolution grids, covering all Italian seas and six coastal grids at very high resolution, capable of accounting for the effects of the interaction between the incoming waves and the bathymetry. A preliminary analysis of the performance of the system is discussed here focusing on the ability of the system to simulate the mean features of the wave climate at the regional and sub-regional scale. The results refer to two different verification studies. The first is the comparison of the directional distribution of almost one year of wave forecasts against the known wave climate in northwestern Sardinia and central Adriatic Sea. The second is a sensitivity test on the effect on wave forecasts of the spatial resolution of the wind forcing, being the comparison between wave forecast and buoy data at two locations in the northern Adriatic and Ligurian Sea during several storm episodes in the period autumn 2012-winter 2013.

  15. Comparative analysis of operational forecasts versus actual weather conditions in airline flight planning, volume 4

    NASA Technical Reports Server (NTRS)

    Keitz, J. F.

    1982-01-01

    The impact of more timely and accurate weather data on airline flight planning with the emphasis on fuel savings is studied. This volume of the report discusses the results of Task 4 of the four major tasks included in the study. Task 4 uses flight plan segment wind and temperature differences as indicators of dates and geographic areas for which significant forecast errors may have occurred. An in-depth analysis is then conducted for the days identified. The analysis show that significant errors occur in the operational forecast on 15 of the 33 arbitrarily selected days included in the study. Wind speeds in an area of maximum winds are underestimated by at least 20 to 25 kts. on 14 of these days. The analysis also show that there is a tendency to repeat the same forecast errors from prog to prog. Also, some perceived forecast errors from the flight plan comparisons could not be verified by visual inspection of the corresponding National Meteorological Center forecast and analyses charts, and it is likely that they are the result of weather data interpolation techniques or some other data processing procedure in the airlines' flight planning systems.

  16. Volcanic ash transport integrated in the WRF-Chem model: a description of the application and verification results from the 2010 Eyjafjallajkull eruption.

    NASA Astrophysics Data System (ADS)

    Stuefer, Martin; Webley, Peter; Grell, Georg; Freitas, Saulo; Kim, Chang Ki; Egan, Sean

    2013-04-01

    Regional volcanic ash dispersion models are usually offline decoupled from the numerical weather prediction model. Here we describe a new functionality using an integrated modeling system that allows simulating emission, transport, and sedimentation of pollutants released during volcanic activities. A volcanic preprocessor tool has been developed to initialize the Weather Research Forecasting model with coupled Chemistry (WRF-Chem) with volcanic ash and sulphur dioxide emissions. Volcanic ash variables were added into WRF-Chem, and the model was applied to study the 2010 eruption of Eyjafjallajkull. We evaluate our results using WRF-Chem with available ash detection data from satellite and airborne sensors, and from ground based Lidar measurements made available through the AeroCom project. The volcanic ash was distributed into 10 different bins according to the particle size ranging from 2 mm to less than 3.9 ?m; different particle size distributions derived from historic eruptions were tested. An umbrella shaped initial ash cloud and an empirical relationship between mass eruption rates and eruption heights were used to initialize WRF-Chem. We show WRF-Chem model verification for the Eyjafjallajkull eruptions, which occurred during the months of April and May 2010. The volcanic ash plume dispersed extensively over Europe. Comparisons with satellite remote sensing volcanic ash retrievals showed good agreement during the events, also ground-based LIDAR compared well to the model simulations. The model sensitivity analysis of the Eyjafjallajkull event showed a considerable bias of ass mass concentrations afar from the volcano depending on initial ash size and eruption rate assumptions. However the WRF-Chem model initialized with reliable eruption source parameters produced good quality forecasts, and will be tested for operational volcanic ash transport predictions.

  17. Forecast-Based Operations Support Tool for the New York City Water Supply System

    NASA Astrophysics Data System (ADS)

    Pyke, G.; Porter, J.

    2012-12-01

    The NYC water supply system serves 9 million people with over 1 BGD of water drawn from 19 reservoirs. To support operation of the system to meet multiple objectives (e.g. supply reliability, water quality, environmental releases, hydropower, peak flow mitigation), the New York City Department of Environmental Protection (DEP) is developing an Operations Support Tool (OST), a forecast-based decision support system that provides a probabilistic foundation for water supply operations and planning. Key features of OST include: the ability to run both long-term simulations and short-term probabilistic simulations on the same model platform; automated processing of near-real-time (NRT) data sources; use of inflow forecasts to support look-ahead operational simulations; and water supply-water quality model linkage to account for feedback and tradeoffs between supply and quality objectives. OST supports two types of simulations. Long-term runs execute the system model over an extended historical record and are used to evaluate reservoir operating rules, infrastructure modifications, and climate change scenarios (with inflows derived from downscaled GCM data). Short-term runs for operational guidance consist of multiple (e.g. 80+) short (e.g. one year) runs, all starting from the same initial conditions (typically those of the current day). Ensemble reservoir inflow forecast traces are used to drive the model for the duration of the simulation period. The result of these runs is a distribution of potential future system states. DEP managers analyze the distributions for alternate scenarios and make operations decisions using risk-based metrics such as probability of refill or the likelihood of a water quality event. For operational simulations, the OST data system acquires NRT data from DEP internal sources (SCADA operations data, keypoint water quality, in-stream/in-reservoir water quality, meteorological and snowpack monitoring sites). OST acquires streamflow data from USGS and ensemble inflow forecasts from the National Weather Service (NWS). Incoming data passes through an automated flagging/filling process, and data is presented to operators for approval prior to use as model input. OST allows the user to drive operational runs with two types of ensemble inflow forecasts. Statistical forecasts are based on historical inflows that are conditioned on antecedent hydrology. The statistical algorithm is relatively simple and versatile and is useful for longer-term projections. For improved short-term skill, OST will rely on NWS meteorologically-based ensemble forecasts. A post-processor within OST will provide bias correction for the NWS ensembles. OST applications to date have included routine short-term operational projections to support release decisions, analysis of tradeoffs between water supply and water quality during turbidity events, facility outage planning, development of operating rules and release policies, long-term water supply planning, and climate change assessment. The structure and capabilities of OST are expected to be a useful template for drinking water utilities and water system managers seeking to integrate forecasts into system operations and balance tradeoffs between competing objectives in both near-term operations and long-term planning.

  18. Report on the First International Symposium on Operational Weather Forecasting in Antarctica.

    NASA Astrophysics Data System (ADS)

    Turner, John; Pendlebury, Stephen; Cowled, Lance; Jacka, Kieran; Jones, Marjorie; Targett, Philip

    2000-01-01

    The First International Symposium on Operational Weather Forecasting in Antarctica was held in Hobart, Australia, from 31 August to 3 September 1998. There were 40 attendees at the meeting from Australia, Belgium, Brazil, China, France, Italy, Russia, and the United Kingdom. In recent years there has been considerable growth in the requirement for weather forecasts for the Antarctic because of the increases in complex scientific research activities and the rapid growth of tourism to the continent. At many of the research stations there are now sophisticated forecasting operations that make use of the data available from drifting buoys and automatic weather stations, the output from numerical weather prediction systems, and high resolution satellite imagery. The models have considerable success at predicting the synoptic-scale depressions that occur over the ocean and in the coastal region. However, the many mesoscale systems that occur, which are very important for forecasting local conditions, are not well represented in the model fields and their movement is mainly predicted via the satellite data. In the future it is anticipated that high resolution, limited-area models will be run for selected parts of the continent. The symposium showed that great advances had been made during recent years in forecasting for the Antarctic as a result of our better understanding of atmospheric processes at high latitudes, along with the availability of high resolution satellite imagery and the output of numerical models. Outstanding problems include the difficulty of getting all of the observations to the main analysis centers outside the Antarctic in a timely fashion, the lack of upper air data from the Antarctic Peninsula and the interior of the continent, and the poor representation of the Antarctic orography and high latitude processes in numerical models. An outcome of the symposium will be a weather forecasting handbook dealing with the entire continent.

  19. “ How Reliable is the Couple of WRF & VIC Models”

    EPA Science Inventory

    The ability of the fully coupling of Weather Research & Forecasting Model (WRF) and Variable Infiltration Capacity (VIC) model to produce hydrological and climate variables was evaluated. First, the VIC model was run by using observed meteorological data and calibrated in the Upp...

  20. Using a Coupled Lake Model with WRF for Dynamical Downscaling

    EPA Science Inventory

    The Weather Research and Forecasting (WRF) model is used to downscale a coarse reanalysis (National Centers for Environmental PredictionDepartment of Energy Atmospheric Model Intercomparison Project reanalysis, hereafter R2) as a proxy for a global climate model (GCM) to examine...

  1. FULLY COUPLED "ONLINE" CHEMISTRY WITHIN THE WRF MODEL

    EPA Science Inventory

    A fully coupled "online" Weather Research and Forecasting/Chemistry (WRF/Chem) model has been developed. The air quality component of the model is fully consistent with the meteorological component; both components use the same transport scheme (mass and scalar preserving), the s...

  2. Using a Coupled Lake Model with WRF for Dynamical Downscaling

    EPA Science Inventory

    The Weather Research and Forecasting (WRF) model is used to downscale a coarse reanalysis (National Centers for Environmental Prediction–Department of Energy Atmospheric Model Intercomparison Project reanalysis, hereafter R2) as a proxy for a global climate model (GCM) to examine...

  3. How Reliable is the Couple of WRF & VIC Models

    EPA Science Inventory

    The ability of the fully coupling of Weather Research & Forecasting Model (WRF) and Variable Infiltration Capacity (VIC) model to produce hydrological and climate variables was evaluated. First, the VIC model was run by using observed meteorological data and calibrated in the Upp...

  4. UPDATE ON DEVELOPMENT OF NUDGING FDDA FOR ADVANCED RESEARCH WRF

    EPA Science Inventory

    A nudging-based four-dimensional data assimilation (FDDA) system is being developed for the Weather Research and Forecasting (WRF) Model. This effort represents a collaboration between The Pennsylvania State University (i.e., Penn State), the National Center for Atmospheric Rese...

  5. Error discrimination of an operational hydrological forecasting system at a national scale

    NASA Astrophysics Data System (ADS)

    Jordan, F.; Brauchli, T.

    2010-09-01

    The use of operational hydrological forecasting systems is recommended for hydropower production as well as flood management. However, the forecast uncertainties can be important and lead to bad decisions such as false alarms and inappropriate reservoir management of hydropower plants. In order to improve the forecasting systems, it is important to discriminate the different sources of uncertainties. To achieve this task, reanalysis of past predictions can be realized and provide information about the structure of the global uncertainty. In order to discriminate between uncertainty due to the weather numerical model and uncertainty due to the rainfall-runoff model, simulations assuming perfect weather forecast must be realized. This contribution presents the spatial analysis of the weather uncertainties and their influence on the river discharge prediction of a few different river basins where an operational forecasting system exists. The forecast is based on the RS 3.0 system [1], [2], which is also running the open Internet platform www.swissrivers.ch [3]. The uncertainty related to the hydrological model is compared to the uncertainty related to the weather prediction. A comparison between numerous weather prediction models [4] at different lead times is also presented. The results highlight an important improving potential of both forecasting components: the hydrological rainfall-runoff model and the numerical weather prediction models. The hydrological processes must be accurately represented during the model calibration procedure, while weather prediction models suffer from a systematic spatial bias. REFERENCES [1] Garcia, J., Jordan, F., Dubois, J. & Boillat, J.-L. 2007. "Routing System II, Modélisation d'écoulements dans des systèmes hydrauliques", Communication LCH n° 32, Ed. Prof. A. Schleiss, Lausanne [2] Jordan, F. 2007. Modèle de prévision et de gestion des crues - optimisation des opérations des aménagements hydroélectriques à accumulation pour la réduction des débits de crue, thèse de doctorat n° 3711, Ecole Polytechnique Fédérale, Lausanne [3] Keller, R. 2009. "Le débit des rivières au peigne fin", Revue Technique Suisse, N°7/8 2009, Swiss engineering RTS, UTS SA, Lausanne, p. 11 [4] Kaufmann, P., Schubiger, F. & Binder, P. 2003. Precipitation forecasting by a mesoscale numerical weather prediction (NWP) model : eight years of experience, Hydrology and Earth System

  6. The potential of remotely sensed soil moisture for operational flood forecasting

    NASA Astrophysics Data System (ADS)

    Wanders, N.; Karssenberg, D.; de Roo, A.; de Jong, S.; Bierkens, M. F.

    2013-12-01

    Nowadays, remotely sensed soil moisture is readily available from multiple space born sensors. The high temporal resolution and global coverage make these products very suitable for large-scale land-surface applications. The potential to use these products in operational flood forecasting has thus far not been extensively studied. In this study, we evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the timing and height of the flood peak and low flows. EFAS is used for operational flood forecasting in Europe and uses a distributed hydrological model for flood predictions for lead times up to 10 days. Satellite-derived soil moisture from ASCAT, AMSR-E and SMOS is assimilated into the EFAS system for the Upper Danube basin and results are compared to assimilation of only discharge observations. Discharge observations are available at the outlet and at six additional locations throughout the catchment. To assimilate soil moisture data into EFAS, an Ensemble Kalman Filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, derived from a detailed model-satellite soil moisture comparison study, is included to ensure optimal performance of the EnKF. For the validation, additional discharge observations not used in the EnKF are used as an independent validation dataset. Our results show that the accuracy of flood forecasts is increased when more discharge observations are used in that the Mean Absolute Error (MAE) of the ensemble mean is reduced by 65%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of base flows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the Continuous Ranked Probability Score (CRPS) shows a performance increase of 10-15% on average, compared to assimilation of discharge only. The rank histograms show that the forecast is not biased. The timing errors in the flood predictions are decreased when soil moisture data is used and imminent floods can be forecasted with skill one day earlier. In conclusion, our study shows that assimilation of satellite soil moisture increases the performance of flood forecasting systems for large catchments, like the Upper Danube. The additional gain is highest when discharge observations from both upstream and downstream areas are used in combination with the soil moisture data. These results show the potential of future soil moisture missions with a higher spatial resolution like SMAP to improve near-real time flood forecasting in large catchments.

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

    NASA Technical Reports Server (NTRS)

    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) at Johnson Space Center in Houston, TX have used a local data integration system (LDIS) as part of their forecast and warning operations. The original LDIS was developed by NASA's Applied Meteorology Unit (AMU; Bauman et ai, 2004) in 1998 (Manobianco and Case 1998) and has undergone subsequent improvements. Each has benefited from three-dimensional (3-D) 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 understanding of evolving fine-scale weather features

  8. Application of probabilistic hydrologic forecasting for risk analysis of multipurpose reservoir real-time operation

    NASA Astrophysics Data System (ADS)

    Liu, P.

    2012-12-01

    Quantitative analysis of the risk for reservoir real-time operation is a hard task owing to the difficulty of accurate description of inflow uncertainties. The ensemble-based probabilistic hydrologic forecasting depicts the inflow not only the marginal distributions but also their corrections by producing inflow scenarios. This motivates us to analyze the reservoir real-time operating risk with ensemble-based hydrologic forecasting inputs. The proposed procedure involves: (1) based upon the Bayesian inference, the Markov Chain Monte Carlo (MCMC) is implemented to produce ensemble-based probabilistic hydrologic forecasting, (2) the reservoir risk is defined as the ratio of the number of scenarios that excessive the critical value to the total number of scenarios, (3) a multipurpose reservoir operation model is built and solved using scenario optimization to produce Pareto solutions for trade-offs between risks and profits. With a case study of the China's Three Gorges Reservoir (TGR) for the 2010 and 2012 floods, it is found that the reservoir real-time operation risks can be estimated directly and minimized based on the proposed methods, and is easy of implementation by the reservoir operators.

  9. WRF Simulation over the Eastern Africa by use of Land Surface Initialization

    NASA Astrophysics Data System (ADS)

    Sakwa, V. N.; Case, J.; Limaye, A. S.; Zavodsky, B.; Kabuchanga, E. S.; Mungai, J.

    2014-12-01

    The East Africa region experiences severe weather events associated with hazards of varying magnitude. It receives heavy precipitation which leads to wide spread flooding and lack of sufficient rainfall in some parts results into drought. Cases of flooding and drought are two key forecasting challenges for the Kenya Meteorological Service (KMS). The source of heat and moisture depends on the state of the land surface which interacts with the boundary layer of the atmosphere to produce excessive precipitation or lack of it that leads to severe drought. The development and evolution of precipitation systems are affected by heat and moisture fluxes from the land surface within weakly-sheared environments, such as in the tropics and sub-tropics. These heat and moisture fluxes during the day can be strongly influenced by land cover, vegetation, and soil moisture content. Therefore, it is important to represent the land surface state as accurately as possible in numerical weather prediction models. Improved modeling capabilities within the region have the potential to enhance forecast guidance in support of daily operations and high-impact weather over East Africa. KMS currently runs a configuration of the Weather Research and Forecasting (WRF) model in real time to support its daily forecasting operations, invoking the Non-hydrostatic Mesoscale Model (NMM) dynamical core. They make use of the National Oceanic and Atmospheric Administration / National Weather Service Science and Training Resource Center's Environmental Modeling System (EMS) to manage and produce the WRF-NMM model runs on a 7-km regional grid over Eastern Africa.SPoRT and SERVIR provide land surface initialization datasets and model verification tool. The NASA Land Information System (LIS) provide real-time, daily soil initialization data in place of interpolated Global Forecast System soil moisture and temperature data. Model verification is done using the Model Evaluation Tools (MET) package, in order to quantify possible improvements in simulated temperature, moisture and precipitation resulting from the experimental land surface initialization. These MET tools enable KMS to monitor model forecast accuracy in near real time. This study highlights verification results of WRF runs over East Africa using the LIS land surface initialization.

  10. Development of On-line Wildfire Emissions for the Operational Canadian Air Quality Forecast System

    NASA Astrophysics Data System (ADS)

    Pavlovic, R.; Menard, S.; Chen, J.; Anselmo, D.; Paul-Andre, B.; Gravel, S.; Moran, M. D.; Davignon, D.

    2013-12-01

    An emissions processing system has been developed to incorporate near-real-time emissions from wildfires and large prescribed burns into Environment Canada's real-time GEM-MACH air quality (AQ) forecast system. Since the GEM-MACH forecast domain covers Canada and most of the USA, including Alaska, fire location information is needed for both of these large countries. Near-real-time satellite data are obtained and processed separately for the two countries for organizational reasons. Fire location and fuel consumption data for Canada are provided by the Canadian Forest Service's Canadian Wild Fire Information System (CWFIS) while fire location and emissions data for the U.S. are provided by the SMARTFIRE (Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation) system via the on-line BlueSky Gateway. During AQ model runs, emissions from individual fire sources are injected into elevated model layers based on plume-rise calculations and then transport and chemistry calculations are performed. This 'on the fly' approach to the insertion of emissions provides greater flexibility since on-line meteorology is used and reduces computational overhead in emission pre-processing. An experimental wildfire version of GEM-MACH was run in real-time mode for the summers of 2012 and 2013. 48-hour forecasts were generated every 12 hours (at 00 and 12 UTC). Noticeable improvements in the AQ forecasts for PM2.5 were seen in numerous regions where fire activity was high. Case studies evaluating model performance for specific regions, computed objective scores, and subjective evaluations by AQ forecasters will be included in this presentation. Using the lessons learned from the last two summers, Environment Canada will continue to work towards the goal of incorporating near-real-time intermittent wildfire emissions within the operational air quality forecast system.

  11. Development of an Operational Hydrological Monitoring and Seasonal Forecast System for China

    NASA Astrophysics Data System (ADS)

    Tang, Q.; Zhang, X.

    2014-12-01

    Hydrological monitoring and forecast are critical for disaster mitigation and water resources management. Although large investments have been made in climate forecasting and in related monitoring of land surface conditions, the experimental streamflow monitoring and forecast system is yet to be developed for China. We propose a frame to collect near-real-time meteorological forcings from various sources, to apply land surface hydrological model to simulate hydrological states and fluxes, and to generate ensemble seasonal forecasts of river discharge and soil moisture over China. A retrospective land surface hydrologic fluxes and states dataset with a 0.25° spatial resolution and a 3-hourly time step was developed using the Variable Infiltration Capacity (VIC) model as driven by gridded observation-based meteorological forcings in 1952-2012. The VIC simulations were carefully calibrated against the available streamflow observations and the simulated river discharge matched well with the observed monthly streamflow at the large river basins in China. The Tropical Rainfall Measuring Mission (TRMM) based near-real-time satellite precipitation product was adjusted at each grid to match the daily precipitation distribution with the ground observations during the period of 2000-2010. The adjusted satellite precipitation was used to simulate hydrological states and fluxes in a near-real-time manner and to provide initial hydrological conditions for seasonal forecast. The performance of hydrological monitoring and skill of seasonal streamflow prediction were assessed. The potential and challenges of using the operational monitoring and forecast system for improved flooding and drought management are discussed.

  12. Short-term Operating Strategy with Consideration of Load Forecast and Generating Unit Uncertainty

    NASA Astrophysics Data System (ADS)

    Sarjiya; Eua-Arporn, Bundhit; Yokoyama, Akihiko

    One of the common problems faced by many electric utilities concernes with the uncertainty from both load forecast error and generating unit unavailability. This uncertainty might lead to uneconomic operation if it is not managed properly in the planning stage. Utilities may have many operational tools, e.g. unit commitment, economic dispatch. However, they require a proper operating strategy, taking into account uncertainties. This paper explicitly demonstrates how to include the uncertainties to obtain the best operating strategy for any power systems. The uncertainty of the load forecast is handled using decision analysis method, meanwhile the uncertainty of the generating unit is approached by inclusion of risk cost to the total cost. In addition, three spinning reserve strategies based on deterministic criteria are incorporated in the development of scenario. Meanwhile, Mixed Integer Linear Programming method is utilized to generate unit commitment decision in each created scenario. The best strategy which gives the minimum total cost is selected among the developed scenarios. The proposed method has been tested using a modified of IEEE 24-bus system. Sensitivity analysis with respect to the number of unit, expected unserved energy price, standard deviation of load forecast, and probability of load level is reported.

  13. UQ -- Fast Surrogates Key to New Methodologies in an Operational and Research Volcanic Hazard Forecasting System

    NASA Astrophysics Data System (ADS)

    Hughes, C. G.; Stefanescu, R. E. R.; Patra, A. K.; Bursik, M. I.; Madankan, R.; Pouget, S.; Jones, M.; Singla, P.; Singh, T.; Pitman, E. B.; Morton, D.; Webley, P.

    2014-12-01

    As the decision to construct a hazard map is frequently precipitated by the sudden initiation of activity at a volcano that was previously considered dormant, timely completion of the map is imperative. This prohibits the calculation of probabilities through direct sampling of a numerical ash-transport and dispersion model. In developing a probabilistic forecast for ash cloud locations following an explosive volcanic eruption, we construct a number of possible meta-models (a model of the simulator) to act as fast surrogates for the time-expensive model. We will illustrate the new fast surrogates based on both polynomial chaos and multilevel sparse representations that have allowed us to conduct the Uncertainty Quantification (UQ) in a timely fashion. These surrogates allow orders of magnitude improvement in cost associated with UQ, and are likely to have a major impact in many related domains.This work will be part of an operational and research volcanic forecasting system (see the Webley et al companion presentation) moving towards using ensembles of eruption source parameters and Numerical Weather Predictions (NWPs), rather than single deterministic forecasts, to drive the ash cloud forecasting systems. This involves using an Ensemble Prediction System (EPS) as input to an ash transport and dispersion model, such as PUFF, to produce ash cloud predictions, which will be supported by a Decision Support System. Simulation ensembles with different input volcanic source parameters are intelligently chosen to predict the average and higher-order moments of the output correctly.

  14. Enhanced Short-Term Wind Power Forecasting and Value to Grid Operations: Preprint

    SciTech Connect

    Orwig, K.; Clark, C.; Cline, J.; Benjamin, S.; Wilczak, J.; Marquis, M.; Finley, C.; Stern, A.; Freedman, J.

    2012-09-01

    The current state of the art of wind power forecasting in the 0- to 6-hour time frame has levels of uncertainty that are adding increased costs and risk on the U.S. electrical grid. It is widely recognized within the electrical grid community that improvements to these forecasts could greatly reduce the costs and risks associated with integrating higher penetrations of wind energy. The U.S. Department of Energy has sponsored a research campaign in partnership with the National Oceanic and Atmospheric Administration (NOAA) and private industry to foster improvements in wind power forecasting. The research campaign involves a three-pronged approach: 1) a 1-year field measurement campaign within two regions; 2) enhancement of NOAA's experimental 3-km High-Resolution Rapid Refresh (HRRR) model by assimilating the data from the field campaign; and 3) evaluation of the economic and reliability benefits of improved forecasts to grid operators. This paper and presentation provides an overview of the regions selected, instrumentation deployed, data quality and control, assimilation of data into HRRR, and preliminary results of HRRR performance analysis.

  15. Assessing Risk in Operational Decisions Using Great Lakes Probabilistic Water Level Forecasts

    PubMed

    LEE; CLITES; KEILLOR

    1997-01-01

    / A method adapted from the National Weather Service's Extended Streamflow Prediction technique is applied retrospectively to three Great Lakes case studies to show how risk assessment using probabilistic monthly water level forecasts could have contributed to the decision-mak-ing process. The first case study examines the 1985 International Joint Commission (IJC) decision to store water in Lake Superior to reduce high levels on the downstream lakes. Probabilistic forecasts are generated for Lake Superior and Lakes Michigan-Huron and used with riparian inundation value functions to assess the relative impacts of the IJC's decision on riparian interests for both lakes. The second case study evaluates the risk of flooding at Milwaukee, Wisconsin, and the need to implement flood-control projects if Lake Michigan levels were to continue to rise above the October 1986 record. The third case study quantifies the risks of impaired municipal water works operation during the 1964-1965 period of extreme low water levels on Lakes Huron, St. Clair, Erie, and Ontario. Further refinements and other potential applications of the probabilistic forecast technique are discussed.KEY WORDS: Great Lakes; Water levels; Forecasting; Risk; Decision making PMID:8939784

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

    NASA Technical Reports Server (NTRS)

    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/Meteorological Assimilation Data Ingest System (MADIS), as well as the Kennedy Space Center ICape Canaveral Air Force Station wind tower network. The scripts provide NWS MLB and SMG with several options for setting a desirable runtime configuration of the LDIS to account for adjustments in grid spacing, domain location, choice of observational data sources, and selection of background model fields, among others. The utility of an improved LDIS will be demonstrated through postanalysis warm and cool season case studies that compare high-resolution model output with and without the ADAS analyses. Operationally, these upgrades will result in more accurate depictions of the current local environment to help with short-range weather forecasting applications, while also offering an improved initialization for local versions of the Weather Research and Forecasting model.

  17. Integrated Forecast and Reservoir Management for Northern California

    NASA Astrophysics Data System (ADS)

    Georgakakos, K. P.; Graham, N.; Georgakakos, A. P.; Yao, H.

    2011-12-01

    The INFORM (Integrated Forecast and Reservoir Management) Demonstration Project was created to demonstrate the utility of climate, weather and hydrologic predictions for water resources management in Northern California (includes Trinity River, the Sacramento River, the Feather River, the American River, the San Joaquin River, and the Sacramento-San Joaquin Delta). The INFORM system integrates climate-weather-hydrology forecasting and adaptive reservoir management methods, explicitly accounting for system input and model uncertainties. Operational ensemble forecasts from the Global Forecast System (GFS) and the Climate Forecast System (CFS) of the National Centers of Environmental Prediction (NCEP) are used to drive the WRF model and an Intermediate Complexity Regional Model (ICRM) to produce ensemble precipitation and temperature forecasts with a 10km x 10km resolution and from 6 hours to 30 days. These forecasts feed hydrologic models and provide ensemble inflow forecasts for the major reservoirs of Northern California. The ensemble inflow forecasts are input to a multiobjective and multisite adaptive decision support system designed to support the planning and management processes by deriving real time trade-offs among all relevant water management objectives (i.e., water supply and conservation, hydroelectric power production, flood control, and fisheries and environmental management) at user preferred risk levels. Operational tests over an initial three-year demonstration phase showed good operational performance both for wet and dry years. The presentation focuses on (1) modeling aspects of the current forecast and reservoir components and recent tests and (2) use of recent forecasts for the generation of applicable operational tradeoffs. The test results corroborate the operational value of the integrated forecast-management system.

  18. Development and Testing of Operational Dual-Polarimetric Radar Based Lightning Initiation Forecast Techniques

    NASA Technical Reports Server (NTRS)

    Woodard, Crystal; Carey, Lawrence D.; Petersen, Walter A.; Felix, Mariana; Roeder, William P.

    2011-01-01

    Lightning is one of Earth s natural dangers, destructive not only to life but also physical property. According to the National Weather Service, there are on average 58 lightning fatalities each year, with over 300 related injuries (NWS 2010). The ability to forecast lightning is critical to a host of activities ranging from space vehicle launch operations to recreational sporting events. For example a single lightning strike to a Space Shuttle could cause billions of dollars of damage and possible loss of life. While forecasting that provides longer lead times could provide sporting officials with more time to respond to possible threatening weather events, thus saving the lives of player and bystanders. Many researchers have developed and tested different methods and tools of first flash forecasting, however few have done so using dual-polarimetric radar variables and products on an operational basis. The purpose of this study is to improve algorithms for the short-term prediction of lightning initiation through development and testing of operational techniques that rely on parameters observed and diagnosed using C-band dual-polarimetric radar.

  19. On Improving the Operational Performance of the Cyprus Coastal Ocean Forecasting System

    NASA Astrophysics Data System (ADS)

    Radhakrishnan, H.; Moulitsas, I.; Hayes, D.; Zodiatis, G.; Georgiou, G.

    2012-04-01

    Modeling oceans is computationally expensive. Rising demands for speedier and higher resolution forecasts, better estimations of prediction uncertainty, and need for additional modules further increase the costs of computation. Parallel processing provides a viable solution to satisfy these demands without sacrificing accuracy or omitting any physical phenomena. Our objective is to develop and implement a parallel version of Cyprus Coastal Ocean Forecasting and Observing System (CYCOFOS) hydrodynamic model for the Eastern Mediterranean Levantine Sea using Message Passing Interface (MPI) that runs on commodity computing clusters running open source software. The parallel software is constructed in a modular fashion to make it easy to integrate end-user applications in the future. Parallelizing CYCOFOS also enables us to run multiple simulations using different parameters, and initial and boundary conditions to improve the accuracy of the model forecasts, and reduce uncertainty. The Cyprus Coastal Ocean Forecasting and Observing System (CYCOFOS) was developed within the broad frame of EuroGOOS (European GOOS) and MedGOOS (Mediterranean GOOS), to provide operational oceanographic forecast and monitoring on local and sub-regional scales in the Eastern Mediterranean Basin. The system has been operational since early 2002, consists of several forecasting, observing, and end-user modules, and has been enriched and improved in recent years. The system provides daily forecasting data to end-users, necessary for operational application in marine safety, such as the Mediterranean oil spill and trajectory modeling system. Like many coastal and sub-regional operational hydrodynamic forecasting systems in the Mediterranean, CYCOFOS is based on the Princeton Ocean Model (POM). There have been a number of attempts to parallelize the Princeton Ocean Model, on which the CYCOFOS is based, such as MP-POM. However, existing parallel code models rely on the use of specific outdated hardware architectures and associated software. Additionally, all reported works seem to be one-off attempts with no further development, the emphasis being given to the high performance computing aspect rather than to accurate ocean forecasting and end-user applications. The goal of producing a distributed memory parallel version of POM based on the Message Passing Interface (MPI) paradigm is done in three stages producing three versions of the code. In the first version, we take advantage of the Cartesian nature of the POM mesh, and use the built-in functionality of MPI routines to split the mesh uniformly along longitude and latitude among the processors. This version is the least efficient version because the processors whose meshes contain a lot of land regions will have a lower computational load. Therefore the overall computational load balance is poor and significant portion of the time is spent idling. The objective of this version is to produce a parallel version of the code that can replicate the results of the serial version of the POM code used in CYCOFOS for validation and verification purposes Results from the first parallel version of CYCOFOS will be presented during the conference, and speedup will be discussed. We will also present our parallelization strategies for the second and third versions which will improve the load balancing and speedup by using a weighted distribution of the grids among the processors.

  20. Forecasting the precipitable water vapour content: validation for astronomical observatories using radiosoundings

    NASA Astrophysics Data System (ADS)

    Prez-Jordn, G.; Castro-Almazn, J. A.; Muoz-Tun, C.; Codina, B.; Vernin, J.

    2015-09-01

    The atmospheric precipitable water vapour content (PWV) strongly affects astronomical observations in the infrared (IR). We have validated the Weather Research and Forecasting (WRF) mesoscale numerical weather prediction (NWP) model as an operational forecasting tool for PWV. In the validation, we used atmospheric radiosounding data obtained directly at the Roque de los Muchachos Observatory [ORM: ?2200 metres above sea level (masl)] during three intensive runs and an aditional verification sample of 1 yr of radiosonde data from World Meteorological Organization (WMO) station 60018 in Gmar (Tenerife, TFE: ?105 masl). These data sets allowed us to calibrate the model at the observatory site and to validate it under different PWV and atmospheric conditions. The ability of the WRF model in forecasting the PWV at astronomical observatories and the effects of horizontal model grid size on the computed PWV and vertical profiles of humidity are discussed. An excellent agreement between model forecasts and observations was found at both locations, with correlations above 0.9 in all cases. Subtle but significant differences between model horizontal resolutions have been found, the 3 km grid size being the most accurate and the one selected for future work. Absolute calibrations are given for the lowest and finest grid resolutions. The median PWV values obtained were 3.8 and 18.3 mm at ORM and TFE, respectively. WRF forecasts will complement the PWV measured by the GPS monitoring system at the Canarian Observatories.

  1. Coupling fast all-season soil strength land surface model with weather research and forecasting model to assess low-level icing in complex terrain

    NASA Astrophysics Data System (ADS)

    Sines, Taleena R.

    Icing poses as a severe hazard to aircraft safety with financial resources and even human lives hanging in the balance when the decision to ground a flight must be made. When analyzing the effects of ice on aviation, a chief cause for danger is the disruption of smooth airflow, which increases the drag force on the aircraft therefore decreasing its ability to create lift. The Weather Research and Forecast (WRF) model Advanced Research WRF (WRF-ARW) is a collaboratively created, flexible model designed to run on distributed computing systems for a variety of applications including forecasting research, parameterization research, and real-time numerical weather prediction. Land-surface models, one of the physics options available in the WRF-ARW, output surface heat and moisture flux given radiation, precipitation, and surface properties such as soil type. The Fast All-Season Soil STrength (FASST) land-surface model was developed by the U.S. Army ERDC-CRREL in Hanover, New Hampshire. Designed to use both meteorological and terrain data, the model calculates heat and moisture within the surface layer as well as the exchange of these parameters between the soil, surface elements (such as snow and vegetation), and atmosphere. Focusing on the Presidential Mountain Range of New Hampshire under the NASA Experimental Program to Stimulate Competitive Research (EPSCoR) Icing Assessments in Cold and Alpine Environments project, one of the main goals is to create a customized, high resolution model to predict and assess ice accretion in complex terrain. The purpose of this research is to couple the FASST land-surface model with the WRF to improve icing forecasts in complex terrain. Coupling FASST with the WRF-ARW may improve icing forecasts because of its sophisticated approach to handling processes such as meltwater, freezing, thawing, and others that would affect the water and energy budget and in turn affect icing forecasts. Several transformations had to take place in order for the FASST land-surface model and WRF-ARW to work together as fully coupled models. Changes had to be made to the WRF-ARW build mechanisms (Chapter 1, section a) so that FASST would be recognized as a new option that could be chosen through the namelist and compiled with other modules. Similarly, FASST had to be altered to no longer read meteorological data from a file, but accept input from WRF-ARW at each time step in a way that did not alter the integrity or run-time processes of the model. Several icing events were available to test the newly coupled model as well as the performance of other available land-surface models from the WRF-ARW. A variation of event intensities and durations from these events were chosen to give a broader view of the land-surface models' abilities to accurately predict icing in complex terrain. Non- icing events were also used in testing to ensure the land-surface models were not predicting ice in the events where none occurred. When compared to the other land-surface models and observations FASST showed a warm bias in several regions. As the forecasts progressed, FASST appeared to attempt to correct this bias and performed similarly to the other land-surface models and at times better than these land-surface models in areas of the domain not affected by this bias. To correct this warm bias, future investigation should be conducted into the reasoning behind this warm bias, including but not limited to: FASST operation and elevation modeling, WRF-ARW variables and forecasting methods, as well as allowing for spin-up prior to forecast times. Following the correction to the warm bias, FASST can be parallelized to allow for operational forecast performance and included in the WRF-ARW forecasting suite for future software releases. (Abstract shortened by UMI.).

  2. An operational coupled wave-current forecasting system for the northern Adriatic Sea

    NASA Astrophysics Data System (ADS)

    Russo, A.; Coluccelli, A.; Deserti, M.; Valentini, A.; Benetazzo, A.; Carniel, S.

    2012-04-01

    Since 2005 an Adriatic implementation of the Regional Ocean Modeling System (AdriaROMS) is being producing operational short-term forecasts (72 hours) of some hydrodynamic properties (currents, sea level, temperature, salinity) of the Adriatic Sea at 2 km horizontal resolution and 20 vertical s-levels, on a daily basis. The main objective of AdriaROMS, which is managed by the Hydro-Meteo-Clima Service (SIMC) of ARPA Emilia Romagna, is to provide useful products for civil protection purposes (sea level forecasts, outputs to run other forecasting models as for saline wedge, oil spills and coastal erosion). In order to improve the forecasts in the coastal area, where most of the attention is focused, a higher resolution model (0.5 km, again with 20 vertical s-levels) has been implemented for the northern Adriatic domain. The new implementation is based on the Coupled-Ocean-Atmosphere-Wave-Sediment Transport Modeling System (COAWST)and adopts ROMS for the hydrodynamic and Simulating WAve Nearshore (SWAN) for the wave module, respectively. Air-sea fluxes are computed using forecasts produced by the COSMO-I7 operational atmospheric model. At the open boundary of the high resolution model, temperature, salinity and velocity fields are provided by AdriaROMS while the wave characteristics are provided by an operational SWAN implementation (also managed by SIMC). Main tidal components are imposed as well, derived from a tidal model. Work in progress is oriented now on the validation of model results by means of extensive comparisons with acquired hydrographic measurements (such as CTDs or XBTs from sea-truth campaigns), currents and waves acquired at observational sites (including those of SIMC, CNR-ISMAR network and its oceanographic tower, located off the Venice littoral) and satellite-derived wave-heights data. Preliminary results on the forecast waves denote how, especially during intense storms, the effect of coupling can lead to significant variations in the wave heights. Part of the activity has been funded by the EU FP VII program (project "MICORE", contract n. 202798) and by the Regione Veneto regional law 15/2007 (Progetto "MARINA").

  3. Towards guided data assimilation for operational hydrologic forecasting in the US Tennessee River basin

    NASA Astrophysics Data System (ADS)

    Weerts, A.; Wood, A. W.; Clark, M. P.; Carney, S.; Day, G. N.; Lemans, M.; Sumihar, J.; Newman, A. J.

    2014-12-01

    In the US, the forecasting approach used by the NWS River Forecast Centers and other regional organizations such as the Bonneville Power Administration (BPA) or Tennessee Valley Authority (TVA) has traditionally involved manual model input and state modifications made by forecasters in real-time. This process is time consuming and requires expert knowledge and experience. The benefits of automated data assimilation (DA) as a strategy for avoiding manual modification approaches have been demonstrated in research studies (eg. Seo et al., 2009). This study explores the usage of various ensemble DA algorithms within the operational platform used by TVA. The final goal is to identify a DA algorithm that will guide the manual modification process used by TVA forecasters and realize considerable time gains (without loss of quality or even enhance the quality) within the forecast process. We evaluate the usability of various popular algorithms for DA that have been applied on a limited basis for operational hydrology. To this end, Delft-FEWS was wrapped (via piwebservice) in OpenDA to enable execution of FEWS workflows (and the chained models within these workflows, including SACSMA, UNITHG and LAGK) in a DA framework. Within OpenDA, several filter methods are available. We considered 4 algorithms: particle filter (RRF), Ensemble Kalman Filter and Asynchronous Ensemble Kalman and Particle filter. Retrospective simulation results for one location and algorithm (AEnKF) are illustrated in Figure 1. The initial results are promising. We will present verification results for these methods (and possible more) for a variety of sub basins in the Tennessee River basin. Finally, we will offer recommendations for guided DA based on our results. References Seo, D.-J., L. Cajina, R. Corby and T. Howieson, 2009: Automatic State Updating for Operational Streamflow Forecasting via Variational Data Assimilation, 367, Journal of Hydrology, 255-275. Figure 1. Retrospectively simulated streamflow for the headwater basin above Powell River at Jonesville (red is observed flow, blue is simulated flow without DA, black is simulated flow with DA)

  4. Forecasting Propagation and Evolution of CMEs in an Operational Setting: What Has Been Learned

    NASA Technical Reports Server (NTRS)

    Zheng, Yihua; Macneice, Peter; Odstrcil, Dusan; Mays, M. L.; Rastaetter, Lutz; Pulkkinen, Antti; Taktakishvili, Aleksandre; Hesse, Michael; Kuznetsova, M. Masha; Lee, Hyesook; Chulaki, Anna

    2013-01-01

    One of the major types of solar eruption, coronal mass ejections (CMEs) not only impact space weather, but also can have significant societal consequences. CMEs cause intense geomagnetic storms and drive fast mode shocks that accelerate charged particles, potentially resulting in enhanced radiation levels both in ions and electrons. Human and technological assets in space can be endangered as a result. CMEs are also the major contributor to generating large amplitude Geomagnetically Induced Currents (GICs), which are a source of concern for power grid safety. Due to their space weather significance, forecasting the evolution and impacts of CMEs has become a much desired capability for space weather operations worldwide. Based on our operational experience at Space Weather Research Center at NASA Goddard Space Flight Center (http://swrc.gsfc.nasa.gov), we present here some of the insights gained about accurately predicting CME impacts, particularly in relation to space weather operations. These include: 1. The need to maximize information to get an accurate handle of three-dimensional (3-D) CME kinetic parameters and therefore improve CME forecast; 2. The potential use of CME simulation results for qualitative prediction of regions of space where solar energetic particles (SEPs) may be found; 3. The need to include all CMEs occurring within a 24 h period for a better representation of the CME interactions; 4. Various other important parameters in forecasting CME evolution in interplanetary space, with special emphasis on the CME propagation direction. It is noted that a future direction for our CME forecasting is to employ the ensemble modeling approach.

  5. Development and evaluation of the operational Air-Quality forecast model for Austria ALARO-CAMx

    NASA Astrophysics Data System (ADS)

    Flandorfer, Claudia; Hirtl, Marcus; Krger, Bernd C.

    2014-05-01

    The Air-Quality model for Austria (AQA) is operated at ZAMG in cooperation with the University of Natural Resources and Life Sciences (BOKU) in Vienna by order of the regional governments since 2005. The modeling system is currently a combination of the meteorological model ALARO and the photochemical dispersion model CAMx. Two modeling domains are used with the highest resolution (5 km) in the alpine region. Various extensions with external data sources have been conducted in the past to improve the daily forecasts of the model. Since 2013 O3- and PM10-observations from the Austrian measurement network have been assimilated daily using optimum interpolation. Dynamic chemical boundary conditions are obtained from Air-Quality forecasts provided by ECMWF in the frame of MACC-II. Additionally the latest available high resolved emission inventories for Austria are combined with TNO and EMEP data. The biogenic emissions are provided by the SMOKE model. ZAMG provides daily forecasts of O3, PM10 and NO2 to the regional governments of Austria. The evaluation of these forecasts is done for the summer 2013 with the main focus on the forecasts of ozone. The measurements of the Air-Quality stations are compared with the punctual forecasts at the sites of the station and with the area forecasts for every province of Austria. In the summer of 2013, two heat waves occurred. The first very short heat wave was in June 2013. During this period one exceedance of the alert threshold value for ozone occurred. The second heat wave took place from the end of July to the mid of August. Due to very high temperatures (new temperature record for Austria measured in Bad Deutsch-Altenburg with 40.5C) and long dryness episodes the information threshold value has been exceeded several times in the eastern regions of Austria. The alert threshold value has been exceeded one time in this period. For the evaluation, the results for the second heat wave episode in Eastern Austria will be discussed in detail.

  6. Comparative Evaluation of the Impact of WRF-NMM and WRF-ARW Meteorology on CMAQ Simulations for O3 and Related Species During the 2006 TexAQS/GoMACCS Campaign

    EPA Science Inventory

    In this paper, impact of meteorology derived from the Weather, Research and Forecasting (WRF)– Non–hydrostatic Mesoscale Model (NMM) and WRF–Advanced Research WRF (ARW) meteorological models on the Community Multiscale Air Quality (CMAQ) simulations for ozone and its related prec...

  7. Implementation of aerosol assimilation in Gridpoint Statistical Interpolation (v. 3.2) and WRF-Chem (v. 3.4.1)

    NASA Astrophysics Data System (ADS)

    Pagowski, M.; Liu, Z.; Grell, G. A.; Hu, M.; Lin, H.-C.; Schwartz, C. S.

    2014-08-01

    Gridpoint Statistical Interpolation (GSI) is an assimilation tool that is used at the National Centers for Environmental Prediction (NCEP) in operational weather forecasting in the USA. In this article, we describe implementation of an extension to the GSI for assimilating surface measurements of PM2.5, PM10, and MODIS aerosol optical depth at 550 nm with WRF-Chem (Weather Research and Forecasting model coupled with Chemistry). We also present illustrative results. In the past, the aerosol assimilation system has been employed to issue daily PM2.5 forecasts at NOAA/ESRL (Earth System Research Laboratory) and, we believe, it is well tested and mature enough to be made available for wider use. We provide a package that, in addition to augmented GSI, consists of software for calculating background error covariance statistics and for converting in situ and satellite data to BUFR (Binary Universal Form for the Representation of meteorological data) format, and sample input files for an assimilation exercise. Thanks to flexibility in the GSI and coupled meteorology-chemistry of WRF-Chem, assimilating aerosol observations can be carried out simultaneously with meteorological data assimilation. Both GSI and WRF-Chem are well documented with user guides available online. This article is primarily intended to be a technical note on the implementation of the aerosol assimilation. Its purpose is also to provide guidance for prospective users of the computer code. Scientific aspects of aerosol assimilation are also briefly discussed.

  8. Impact of assimilating met-tower, turbine nacelle anemometer and other intensified wind farm observation systems on 0 - 12h wind energy prediction using the NCAR WRF-RTFDDA model

    NASA Astrophysics Data System (ADS)

    Liu, Y.; Cheng, W.; Liu, Y. W.; Wiener, G.; Frehlich, R.; Mahoney, W.; Warner, T.; Himelic, J.; Parks, K.; Early, S.

    2010-09-01

    In collaboration with Xcel Energy and Vasaila Inc., the National Center for Atmospheric Research (NCAR) conducts modeling study to evaluate the existing and the enhanced intensive observation systems for wind power nowcasting and short-range forecasting at a northern Colorado wind farm. The NCAR WRF (Weather Research and Forecasting model) based Real-Time Four-Dimensional Data Assimilation (RTFDDA) and forecasting system, which has been employed to support Xcel Energy operational wind forecast, was used in this study. The observational data include ten met-towers, a 915Hz wind profiler, a sodar and a Windcube Doppler lidar, besides the in-farm met-towers and wind speed and power reports from more than 300 of wind turbines. The WRF-RTFDDA 4-dimensioanl data assimilation algorithm allows to spread and propagate observation information in the WRF model space (x, y, z and time) with weighting functions built according to the observation location and time. The WRF-RTFDDA was set up to run with four nested domains with grid increments of 30, 10, 3.333 and 1.111km respectively. The standard and diverse non-conventional observations are assimilated on coarse grid domains along with the special wind farm observations. In this study, we investigate a) spread of surface observations in PBL according to PBL depth and regimes, b) optimization of horizontal influence radii and steep-terrain adjustment, and c) impact of different observation platforms and data types on 0 - 12 h wind prediction . It is found that PBL mixing and thermodynamic structures are greatly influenced by the PBL parameterization formulation. The range of the data assimilation effect on forecasts relies on weather and PBL regimes. In most cases, assimilation of in-farm and near-farm observations improves up to 12-hour wind power prediction and assimilation of in-farm data can significantly improves 0 - 6 hour forecasts.

  9. Analyzing the Eyjafjallajkull 2010 eruption using satellite remote sensing, lidar and WRF-Chem dispersion and tracking model

    NASA Astrophysics Data System (ADS)

    Webley, P. W.; Steensen, T.; Stuefer, M.; Grell, G.; Freitas, S.; Pavolonis, M.

    2012-10-01

    Volcanic ash forecasting is a critical tool in hazard assessment and operational volcano monitoring. The use of volcanic ash transport and dispersion models allows analysts to determine the future location of ash clouds. In April-May 2010, Eyjafjallajkull in Iceland erupted explosively. Presented here is an evaluation of the volcanic application of the weather research and forecasting in-line chemistry model (WRF-Chem) applied to Eyjafjallajkull. The analysis focuses on the first few days of the explosive events, April 14-19. The model simulations are presented along with multiple satellite and ground based tools to compare and validate the results. The WRF-Chem results showed the ash cloud dispersing toward mainland Europe, with concentrations crossing Europe between 0.5-2.0 mg/m3, centered at 5 km ASL, +/-1 km. Comparisons with satellite volcanic ash retrievals showed a good agreement and ground-based Light Detection And Ranging (LIDAR) data compared well to the model simulations. The analysis in this manuscript has illustrated the use of WRF-Chem for volcanic eruptions, with the coupled numerical weather simulation and ash forecasting important to understand the local atmospheric conditions as well as the ash cloud distribution. We show that to fully forecast ash concentrations, to the level of mg's per m3, there is a need for accurate knowledge of the plume height; mass eruption rate; particle size distribution and duration along with a fusion of all data. Then accurate hazard assessments can be performed to limit the impact that dispersing clouds have on the aviation community and population.

  10. Comparative analysis of operational forecasts versus actual weather conditions in airline flight planning: Summary report

    NASA Technical Reports Server (NTRS)

    Keitz, J. F.

    1982-01-01

    The impact of more timely and accurate weather data on airline flight planning with the emphasis on fuel savings is studied. This summary report discusses the results of each of the four major tasks of the study. Task 1 compared airline flight plans based on operational forecasts to plans based on the verifying analyses and found that average fuel savings of 1.2 to 2.5 percent are possible with improved forecasts. Task 2 consisted of similar comparisons but used a model developed for the FAA by SRI International that simulated the impact of ATc diversions on the flight plans. While parts of Task 2 confirm the Task I findings, inconsistency with other data and the known impact of ATC suggests that other Task 2 findings are the result of errors in the model. Task 3 compares segment weather data from operational flight plans with the weather actually observed by the aircraft and finds the average error could result in fuel burn penalties (or savings) of up to 3.6 percent for the average 8747 flight. In Task 4 an in-depth analysis of the weather forecast for the 33 days included in the study finds that significant errors exist on 15 days. Wind speeds in the area of maximum winds are underestimated by 20 to 50 kts., a finding confirmed in the other three tasks.

  11. Evaluation of cumulus cloud radiation interaction effects on air quality relevant meteorological variables from WRF, from a regional climate perspective

    EPA Science Inventory

    Aware only of the resolved, grid-scale clouds, the Weather Research & Forecasting model (WRF) does not consider the interactions between subgrid-scale convective clouds and radiation. One consequence of this omission may be WRFs overestimation of surface precipitation during sum...

  12. Performance Assessment of New Land-Surface and Planetary Boundary Layer Physics in the WRF-ARW

    EPA Science Inventory

    The Pleim-Xiu land surface model, Pleim surface layer scheme, and Asymmetric Convective Model (version 2) are now options in version 3.0 of the Weather Research and Forecasting model (WRF) Advanced Research WRF (ARW) core. These physics parameterizations were developed for the f...

  13. Integration of Wind Generation and Load Forecast Uncertainties into Power Grid Operations

    SciTech Connect

    Makarov, Yuri V.; Etingov, Pavel V.; Huang, Zhenyu; Ma, Jian; Chakrabarti, Bhujanga B.; Subbarao, Krishnappa; Loutan, Clyde; Guttromson, Ross T.

    2010-04-20

    In this paper, a new approach to evaluate the uncertainty ranges for the required generation performance envelope, including the balancing capacity, ramping capability and ramp duration is presented. The approach includes three stages: statistical and actual data acquisition, statistical analysis of retrospective information, and prediction of future grid balancing requirements for specified time horizons and confidence intervals. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on a histogram analysis incorporating all sources of uncertainty and parameters of a continuous (wind forecast and load forecast errors) and discrete (forced generator outages and failures to start up) nature. Preliminary simulations using California Independent System Operator (CAISO) real life data have shown the effectiveness and efficiency of the proposed approach.

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

    PubMed

    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. PMID:18651182

  15. The Wind Forecast Improvement Project (WFIP). A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations -- the Northern Study Area

    SciTech Connect

    Finley, Cathy

    2014-04-30

    This report contains the results from research aimed at improving short-range (0-6 hour) hub-height wind forecasts in the NOAA weather forecast models through additional data assimilation and model physics improvements for use in wind energy forecasting. Additional meteorological observing platforms including wind profilers, sodars, and surface stations were deployed for this study by NOAA and DOE, and additional meteorological data at or near wind turbine hub height were provided by South Dakota State University and WindLogics/NextEra Energy Resources over a large geographical area in the U.S. Northern Plains for assimilation into NOAA research weather forecast models. The resulting improvements in wind energy forecasts based on the research weather forecast models (with the additional data assimilation and model physics improvements) were examined in many different ways and compared with wind energy forecasts based on the current operational weather forecast models to quantify the forecast improvements important to power grid system operators and wind plant owners/operators participating in energy markets. Two operational weather forecast models (OP_RUC, OP_RAP) and two research weather forecast models (ESRL_RAP, HRRR) were used as the base wind forecasts for generating several different wind power forecasts for the NextEra Energy wind plants in the study area. Power forecasts were generated from the wind forecasts in a variety of ways, from very simple to quite sophisticated, as they might be used by a wide range of both general users and commercial wind energy forecast vendors. The error characteristics of each of these types of forecasts were examined and quantified using bulk error statistics for both the local wind plant and the system aggregate forecasts. The wind power forecast accuracy was also evaluated separately for high-impact wind energy ramp events. The overall bulk error statistics calculated over the first six hours of the forecasts at both the individual wind plant and at the system-wide aggregate level over the one year study period showed that the research weather model-based power forecasts (all types) had lower overall error rates than the current operational weather model-based power forecasts, both at the individual wind plant level and at the system aggregate level. The bulk error statistics of the various model-based power forecasts were also calculated by season and model runtime/forecast hour as power system operations are more sensitive to wind energy forecast errors during certain times of year and certain times of day. The results showed that there were significant differences in seasonal forecast errors between the various model-based power forecasts. The results from the analysis of the various wind power forecast errors by model runtime and forecast hour showed that the forecast errors were largest during the times of day that have increased significance to power system operators (the overnight hours and the morning/evening boundary layer transition periods), but the research weather model-based power forecasts showed improvement over the operational weather model-based power forecasts at these times.

  16. Extreme rainfall in Serbia, May 2014, simulation using WRF NMM and RainFARM: DRIHM project

    NASA Astrophysics Data System (ADS)

    Deki?, Ljiljana; Mihalovi?, Ana; Dimitrijevi?, Vladimir; Rebora, Nicola; Parodi, Antonio

    2015-04-01

    Extreme rainfall in Serbia, May 2014, simulation using WRF NMM and RainFARM: DRIHM project Ljiljana Deki? (1), Ana Mihalovi? (1), Vladimir Dimitrijevi? (1), Nicola Rebora (2), Antonio Parodi (2) (1)Republic HydroMeteorological Service of Serbia, Belgrade, Serbia, (2)CIMA Research Foundation, Savona, Italy In May 2014 Balkan region was affected with the continuous heavy rainfall, the heaviest in 120 years of recording observation, causing extensive flooding. Serbia suffered human casualties, huge infrastructure and industrial destruction and agricultural damage. Cyclone development and trajectory was very well predicted by RHMSS operational WRF NMM numerical model but extreme precipitation was not possible to predict with sufficient precision. Simulation of extreme rainfall situations using different numerical weather prediction models can indicate weakness of the model and point out importance of specified physical approach and parameterization schemes. The FP7 Distributed Research Infrastructure for Hydro-Meteorology DRIHM project gives a framework for using different models in forecasting extreme weather events. One of the DRIHM component is Rainfall Filtered Autoregressive Model RainFARM for stochastic rainfall downscaling. Objective of the DRIHM project was developing of standards and conversion of the data for seamless use of meteorological and hydrological models in flood prediction. This paper describes numerical tests and results of WRF NMM nonhydrostatic model and RainFARM downscaling applied on WRF NMM outputs. Different physics options in WRF NMM and their influence on precipitation amount were investigated. RainFARM was applied on every physical option with downscaling from 4km to 500m and 100m horizontal resolution and 100 ensemble members. We analyzed locations on the catchments in Serbia where flooding was the strongest and the most destructive. Statistical evaluation of ensemble output gives new insight into the sub scale precipitation distribution. Comparison of predicted WRF NMM precipitation amount against the observation shows good agreement. Applying RainFARM is evaluation, some of the ensemble members give an indication of extreme precipitation closer to observed values. Results are encouraging and give a space for further investigation of stochastic rainfall downscaling.

  17. Weather Research and Forecasting Model Sensitivity Comparisons for Warm Season Convective Initiation

    NASA Technical Reports Server (NTRS)

    Watson, Leela R.; Hoeth, Brian; Blottman, Peter F.

    2007-01-01

    Mesoscale weather conditions can significantly affect the space launch and landing operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). During the summer months, land-sea interactions that occur across KSC and CCAFS lead to the formation of a sea breeze, which can then spawn deep convection. These convective processes often last 60 minutes or less and pose a significant challenge to the forecasters at the National Weather Service (NWS) Spaceflight Meteorology Group (SMG). The main challenge is that a "GO" forecast for thunderstorms and precipitation is required at the 90 minute deorbit decision for End Of Mission (EOM) and at the 30 minute Return To Launch Site (RTLS) decision at the Shuttle Landing Facility. Convective initiation, timing, and mode also present a forecast challenge for the NWS in Melbourne, FL (MLB). The NWS MLB issues such tactical forecast information as Terminal Aerodrome Forecasts (TAFs), Spot Forecasts for fire weather and hazardous materials incident support, and severe/hazardous weather Watches, Warnings, and Advisories. Lastly, these forecasting challenges can also affect the 45th Weather Squadron (45 WS), which provides comprehensive weather forecasts for shuttle launch, as well as ground operations, at KSC and CCAFS. The need for accurate mesoscale model forecasts to aid in their decision making is crucial. Both the SMG and the MLB are currently implementing the Weather Research and Forecasting Environmental Modeling System (WRF EMS) software into their operations. The WRF EMS software allows users to employ both 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, provides SMG and NWS MLB with a lot of flexibility. It also creates challenges, 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 to determine which configuration will best predict warm season convective initiation in East-Central Florida. Four different combinations of WRF initializations will be run (ADAS-ARW, ADAS-NMM, LAPS-ARW, and LAPS-NMM) at a 4-km resolution over the Florida peninsula and adjacent coastal waters. Five candidate convective initiation days using three different flow regimes over East-Central Florida will be examined, as well as two null cases (non-convection days). Each model run will be integrated 12 hours with three runs per day, at 0900, 1200, and 1500 UTe. ADAS analyses will be generated every 30 minutes using Level II Weather Surveillance Radar-1988 Doppler (WSR-88D) data from all Florida radars to verify the convection forecast. These analyses will be run on the same domain as the four model configurations. To quantify model performance, model output will be subjectively compared to the ADAS analyses of convection to determine forecast accuracy. In addition, a subjective comparison of the performance of the ARW using a high-resolution local grid with 2-way nesting, I-way nesting, and no nesting will be made for select convective initiation cases. The inner grid will cover the East-Central Florida region at a resolution of 1.33 km. The authors will summarize the relative skill of the various WRF configurations and how each configuration behaves relative to the others, as well as determine the best model configuration for predicting warm season convective initiation over East-Central Florida.

  18. Design of a next-generation regional weather research and forecast model.

    SciTech Connect

    Michalakes, J.

    1999-01-13

    The Weather Research and Forecast (WRF) model is a new model development effort undertaken jointly by the National Center for Atmospheric Research (NCAR), the National Oceanic and Atmospheric Administration (NOAA), and a number of collaborating institutions and university scientists. The model is intended for use by operational NWP and university research communities, providing a common framework for idealized dynamical studies, fill physics numerical weather prediction, air-quality simulation, and regional climate. It will eventually supersede large, well-established but aging regional models now maintained by the participating institutions. The WRF effort includes re-engineering the underlying software architecture to produce a modular, flexible code designed from the outset to provide portable performance across diverse computing architectures. This paper outlines key elements of the WRF software design.

  19. Generating Real-Time Tsunami Forecast Animations for Tsunami Warning Operations

    NASA Astrophysics Data System (ADS)

    Becker, N. C.; Wang, D.; Fryer, G. J.; Weinstein, S.

    2012-12-01

    The complex calculations inherent in tsunami forecast models once required supercomputers to solve and could only be deployed in an operational setting as a database of precomputed best-guess solutions for likely future tsunamis. More recently scientists at the Pacific Tsunami Warning Center (PTWC) developed a tsunami forecast model, RIFT, that takes an earthquake's centroid moment tensor solutioneither from nearby historic events or rapidly determined by W-phase analysisand solves the linear shallow water equations in real time with commercial off-the-shelf computer servers and open-source software tools (Wang et al., 2009). RIFT not only rapidly calculates tsunami forecasts in real time, but also generates and archives data grids easily ingested by other software packages to generate maps and animations in a variety of image, video, and geobrowser file formats (e.g., KML). These graphical products aid both operational and outreach efforts as they help PTWC scientists to rapidly ingest and comprehend large, complex data sets, to share these data with emergency managers, and to educate the general public about the behavior of tsunamis. Prior to developing animation capability PTWC used tsunami travel time contour maps to show expected arrival times of the first tsunami waves. Though useful to expert users, such maps can mislead a nonexpert as they do not show amplitude information and give the impression that tsunami waves have constant amplitudes throughout an ocean basin. A tsunami forecast "energy map" improves tsunami hazard communication by showing the variability in maximum wave heights, but does not show the timing of the maximum wave arrivals. A tsunami forecast animation, however, shows both how fast the tsunami will move and the distribution of its amplitudes over time, thus communicating key concepts about tsunami behavior such as reflection and refraction of waves, that the first arriving wave is not necessarily the largest wave, and that tsunami wave oscillations can last for hours or days. Tsunami wave propagation animations are not new, but the speed of the RIFT calculations and modern computer hardware allow PTWC to generate a global-domain animation with 4-arc-minute resolution in less than two hours of real time, fast enough to provide decision support in tsunami warning operations and to share these animations with emergency managers and the public before the tsunami impacts threatened coastlines in the far field.

  20. Intercomparison of Operational Ocean Forecasting Systems in the framework of GODAE

    NASA Astrophysics Data System (ADS)

    Hernandez, F.

    2009-04-01

    One of the main benefits of the GODAE 10-year activity is the implementation of ocean forecasting systems in several countries. In 2008, several systems are operated routinely, at global or basin scale. Among them, the BLUElink (Australia), HYCOM (USA), MOVE/MRI.COM (Japan), Mercator (France), FOAM (United Kingdom), TOPAZ (Norway) and C-NOOFS (Canada) systems offered to demonstrate their operational feasibility by performing an intercomparison exercise during a three months period (February to April 2008). The objectives were: a) to show that operational ocean forecasting systems are operated routinely in different countries, and that they can interact; b) to perform in a similar way a scientific validation aimed to assess the quality of the ocean estimates, the performance, and forecasting capabilities of each system; and c) to learn from this intercomparison exercise to increase inter-operability and collaboration in real time. The intercomparison relies on the assessment strategy developed for the EU MERSEA project, where diagnostics over the global ocean have been revisited by the GODAE contributors. This approach, based on metrics, allow for each system: a) to verify if ocean estimates are consistent with the current general knowledge of the dynamics; and b) to evaluate the accuracy of delivered products, compared to space and in-situ observations. Using the same diagnostics also allows one to intercompare the results from each system consistently. Water masses and general circulation description by the different systems are consistent with WOA05 Levitus climatology. The large scale dynamics (tropical, subtropical and subpolar gyres ) are also correctly reproduced. At short scales, benefit of high resolution systems can be evidenced on the turbulent eddy field, in particular when compared to eddy kinetic energy deduced from satellite altimetry of drifter observations. Comparisons to high resolution SST products show some discrepancies on ocean surface representation, either due to model and forcing fields errors, or assimilation scheme efficiency. Comparisons to sea-ice satellite products also evidence discrepancies linked to model, forcing and assimilation strategies of each forecasting system. Key words: Intercomparison, ocean analysis, operational oceanography, system assessment, metrics, validation GODAE Intercomparison Team: L. Bertino (NERSC/Norway), G. Brassington (BMRC/Australia), E. Chassignet (FSU/USA), J. Cummings (NRL/USA), F. Davidson (DFO/Canda), M. Drévillon (CERFACS/France), P. Hacker (IPRC/USA), M. Kamachi (MRI/Japan), J.-M. Lellouche (CERFACS/France), K. A. Lisæter (NERSC/Norway), R. Mahdon (UKMO/UK), M. Martin (UKMO/UK), A. Ratsimandresy (DFO/Canada), and C. Regnier (Mercator Ocean/France)

  1. Forecast Verification for North American Mesoscale (NAM) Operational Model over Karst/Non-Karst regions

    NASA Astrophysics Data System (ADS)

    Sullivan, Z.; Fan, X.

    2014-12-01

    Karst is defined as a landscape that contains especially soluble rocks such as limestone, gypsum, and marble in which caves, underground water systems, over-time sinkholes, vertical shafts, and subterranean river systems form. The cavities and voids within a karst system affect the hydrology of the region and, consequently, can affect the moisture and energy budget at surface, the planetary boundary layer development, convection, and precipitation. Carbonate karst landscapes comprise about 40% of land areas over the continental U.S east of Tulsa, Oklahoma. Currently, due to the lack of knowledge of the effects karst has on the atmosphere, no existing weather model has the capability to represent karst landscapes and to simulate its impact. One way to check the impact of a karst region on the atmosphere is to check the performance of existing weather models over karst and non-karst regions. The North American Mesoscale (NAM) operational forecast is the best example, of which historical forecasts were archived. Variables such as precipitation, maximum/minimum temperature, dew point, evapotranspiration, and surface winds were taken into account when checking the model performance over karst versus non-karst regions. The forecast verification focused on a five-year period from 2007-2011. Surface station observations, gridded observational dataset, and North American Regional Reanalysis (for certain variables with insufficient observations) were used. Thirteen regions of differing climate, size, and landscape compositions were chosen across the Contiguous United States (CONUS) for the investigation. Equitable threat score (ETS), frequency bias (fBias), and root-mean-square error (RMSE) scores were calculated and analyzed for precipitation. RMSE and mean bias (Bias) were analyzed for other variables. ETS, fBias, and RMSE scores show generally a pattern of lower forecast skills, a greater magnitude of error, and a greater under prediction of precipitation over karst than non-karst regions. In addition, standardized data was used to eliminate differences from varying climates across CONUS. The metrics derived from the standardized data shows further evidence that the NAM forecast had lower forecast skills and an overall higher magnitude of error over karst than non-karst regions.

  2. Examining Interior Grid Nudging Techniques Using Two-Way Nesting in the WRF Model for Regional Climate Modeling

    EPA Science Inventory

    This study evaluates interior nudging techniques using the Weather Research and Forecasting (WRF) model for regional climate modeling over the conterminous United States (CONUS) using a two-way nested configuration. NCEP–Department of Energy Atmospheric Model Intercomparison Pro...

  3. Examining Interior Grid Nudging Techniques Using Two-Way Nesting in the WRF Model for Regional Climate Modeling

    EPA Science Inventory

    This study evaluates interior nudging techniques using the Weather Research and Forecasting (WRF) model for regional climate modeling over the conterminous United States (CONUS) using a two-way nested configuration. NCEPDepartment of Energy Atmospheric Model Intercomparison Pro...

  4. An Observation-base investigation of nudging in WRF for downscaling surface climate information to 12-km Grid Spacing

    EPA Science Inventory

    Previous research has demonstrated the ability to use the Weather Research and Forecast (WRF) model and contemporary dynamical downscaling methods to refine global climate modeling results to a horizontal resolution of 36 km. Environmental managers and urban planners have expre...

  5. Triumphs and Tribulations of WRF-Chem Development and Use

    SciTech Connect

    Gustafson, William I.; Fast, Jerome D.; Easter, Richard C.; Ghan, Steven J.

    2005-06-27

    In order to address scientific questions related to aerosol chemistry and meteorological-aerosol-radiation-cloud feedbacks at the urban to regional scale, scientists at the Pacific Northwest National Laboratory (PNNL) have made substantial contributions to the chemistry version of the Weather Research and Forecasting model (WRF-Chem) during the past one and a half years. These contributions include an additional gas-phase chemistry mechanism, a sectional aerosol module, an additional photolysis module, feedbacks between aerosols and radiation, and extending the nesting capability of WRF to include the chemistry scalars. During the development process, a number of limitations in WRF have been identified that complicate adding all the desired chemistry capabilities as originally planned. These issues will be discussed along with changes that have been made to help mitigate some of them. Mechanisms currently in development will also be discussed including a secondary organic aerosol (SOA) mechanism for the sectional aerosol module, aqueous chemistry, and the aerosol indirect effect.

  6. A multi-model Python wrapper for operational oil spill transport forecasts

    NASA Astrophysics Data System (ADS)

    Hou, X.; Hodges, B. R.; Negusse, S.; Barker, C.

    2015-01-01

    The Hydrodynamic and oil spill modeling system for Python (HyosPy) is presented as an example of a multi-model wrapper that ties together existing models, web access to forecast data and visualization techniques as part of an adaptable operational forecast system. The system is designed to automatically run a continual sequence of hindcast/forecast hydrodynamic models so that multiple predictions of the time-and-space-varying velocity fields are already available when a spill is reported. Once the user provides the estimated spill parameters, the system runs multiple oil spill prediction models using the output from the hydrodynamic models. As new wind and tide data become available, they are downloaded from the web, used as forcing conditions for a new instance of the hydrodynamic model and then applied to a new instance of the oil spill model. The predicted spill trajectories from multiple oil spill models are visualized through Python methods invoking Google MapTM and Google EarthTM functions. HyosPy is designed in modules that allow easy future adaptation to new models, new data sources or new visualization tools.

  7. Evaluation of WRF Planetary Boundary Layer Schemes over the Coastal Waters of Southern New England

    NASA Astrophysics Data System (ADS)

    Sienkiewicz, Matthew J.

    Winds, temperatures and moisture in the planetary boundary layer (PBL) are often difficult for operational models to predict given the relatively sparse observations and that most model PBL parameterizations were developed over inland locations. Coastal marine layer forecasts are important for the forecasting of severe storms and wind energy resources in the highly populated coastal marine environment of the Northeast U.S. (NEUS). Mesoscale models are known to have large biases in wind speeds and temperatures at these lower levels over coastal waters. The goal of this project is to evaluate the performance of six PBL schemes in the Weather Research and Forecasting (WRF-ARW) model version 3.4.1 in the coastal marine environment of the NEUS. This study region, stretching from the south shore of Long Island out to Cape Cod is an ideal location for an offshore wind energy grid based on such factors as regional energy demand, water depth, and available wind resource. Verification of six WRF PBL schemes (two non-local, first-order schemes and four local, TKE-order schemes) was performed using a dataset of observations at multiple levels from the Cape Wind tower in Nantucket Sound from 2003 to 2011, as well as surrounding NDBC and ASOS stations. A series of 30-hour WRF runs were conducted for 90 randomly selected days between 2003 and 2011, with initial and boundary conditions supplied by the North American Regional Reanalysis (NARR). All schemes generally displayed negative wind speed biases over the water. The cool season displayed the largest negative biases as well as a shear profile indicative of an over-mixed boundary layer. It is hypothesized that errors in the model SST field in Nantucket Sound aided in the too-stable (unstable) model MABL structures during the warm (cool) seasons and the resultant under-mixed (over-mixed) wind shear profiles. Additional model verification from three Long-EZ aircraft flights during the Improving the Mapping and Prediction of Offshore Wind Resources (IMPOWR) field campaign during strong southwesterly flow and a developing low-level jet (LLJ) supported the hypotheses. WRF simulations show that most schemes underestimated the height and magnitude of the LLJ, while overestimating the static stability below the LLJ in the vicinity of Nantucket Sound. A warmer SST field was found to improve the near-surface thermal and moisture profiles. Model runs were forced with a variety of analyses, and it was found that even for long simulations the results were more sensitive to the boundary conditions than to the PBL schemes.

  8. An exceptionally heavy snowfall in Northeast china: large-scale circulation anomalies and hindcast of the NCAR WRF model

    NASA Astrophysics Data System (ADS)

    Wang, Huijun; Yu, Entao; Yang, Song

    2011-06-01

    In Northeast China (NEC), snowfalls usually occur during winter and early spring, from mid-October to late March, and strong snowfalls rarely occur in middle spring. During 12-13 April 2010, an exceptionally strong snowfall occurred in NEC, with 26.8 mm of accumulated water-equivalent snow over Harbin, the capital of the most eastern province in NEC. In this study, the major features of the snowfall and associated large-scale circulation and the predictability of the snowfall are analyzed using both observations and models. The Siberia High intensified and shifted southeastward from 10 days before the snowfall, resulting in intensifying the low-pressure system over NEC and strengthening the East Asian Trough during 12-13 April. Therefore, large convergence of water vapor and strong rising motion appeared over eastern NEC, resulting in heavy snowfall. Hindcast experiments were carried out using the NCAR Weather Research and Forecasting (WRF) model in a two-way nesting approach, forced by NCEP Global Forecast System data sets. Many observed features including the large-scale and regional circulation anomalies and snowfall amount can be reproduced reasonably well, suggesting the feasibility of the WRF model in forecasting extreme weather events over NEC. A quantitative analysis also shows that the nested NEC domain simulation is even better than mother domain simulation in simulating the snowfall amount and spatial distribution, and that both simulations are more skillful than the NCEP Global Forecast System output. The forecast result from the nested forecast system is very promising for an operational purpose.

  9. Application of data assimilation in portable operational forecasting systemsthe DATools assimilation environment

    NASA Astrophysics Data System (ADS)

    El Serafy, Ghada Y.; Gerritsen, Herman; Hummel, Stef; Weerts, Albrecht H.; Mynett, Arthur E.; Tanaka, Masahiro

    2007-10-01

    The first part of the paper describes a portable and flexible data assimilation environment (DATools) for easy application of data assimilation and calibration techniques to models that are used in smaller-scale engineering applications, for example to guide temporary offshore construction works, marine surveying and salvage operations. These applications are characterized by the need for detailed forecasts of often strongly non-linear marine behaviour in shallow-shelf seas under constraints of limited field measurements and absence of large-scale computing facilities. The applications are often prepared, and their results are interpreted by end-user engineers in field offices, not well acquainted with the data assimilation theory. The DATools data assimilation environment has been designed to facilitate such applications. It presently features an ensemble Kalman filter and two particle filters. These can be coupled to any process model in a standardized way through the so-called Published Interface that is used in an increasing number of flood-forecasting applications across Europe. The design of the system and its main modules are discussed, looking at the system from a non-specialist user perspective and focusing on modularity, transparency, user guidance, intuitive and flexible uncertainty prescription. The second part of the paper describes a typical example application of data assimilation in an engineering environment for which the modelling environment has been developed: the daily forecast of current and salinity profiles to guide construction works in Osaka Bay. An ensemble Kalman filter-based steady-state Kalman filter is developed for assimilation of salinity and horizontal currents into an existing three-dimensional flow model for the highly non-linear stratified shallow bay. Calibration results are summarised for both hindcast mode and forecast mode. After assessment of the results, a second, improved model setup is described, including now assimilation of temperature and water level as well as currents and salinity. The calibration results are shown to improve further. Both applications show that significant forecast improvements can be possible with data assimilation in typical engineering applications, notwithstanding operational constraints such as limited measurement data and restricted computational infrastructure. It is argued that the described portable data assimilation environment is well suited for temporary engineering applications as it provides typical end-user functionalities for data assimilation and will so allow the user to focus on the characteristics of the physical processes and interpretation of the results instead of on details of the mathematical techniques. This should make practical data assimilation accessible for a much wider (end) user community in oceanography and offshore.

  10. Validating the dynamic downscaling ability of WRF for East Asian summer climate

    NASA Astrophysics Data System (ADS)

    Gao, Jiangbo; Hou, Wenjuan; Xue, Yongkang; Wu, Shaohong

    2015-12-01

    To better understand the regional climate model (RCM) performance for East Asian summer climate and the influencing factors, this study evaluated the dynamic downscaling ability of the Weather Research Forecast (WRF) RCM. According to the comprehensive comparison studies on different physical processes and experimental settings, the optimal combination of WRF model setups can be obtained for East Asian precipitation and temperature simulations. Furthermore, based on the optimal combination, when compared with climate observations, WRF shows high ability to downscale NCEP DOE Reanalysis-2, which provided initial and lateral boundary conditions for the WRF, especially for the precipitation simulation due to the better simulated low-level water vapor flux. However, the strengthened Western North Pacific Subtropical High (WPSH) from WRF simulation results in the positive anomaly for summer rainfall.

  11. Selected issues related to heat storage tank modelling and optimisation aimed at forecasting its operation

    NASA Astrophysics Data System (ADS)

    Badyda, Krzysztof; Bujalski, Wojciech; Niewi?ski, Grzegorz; Warcho?, Micha?

    2011-12-01

    The paper presents results of research focused on modelling heat storage tank operation used for forecasting purposes. It presents selected issues related to mathematical modelling of heat storage tanks and related equipment and discusses solution process of the optimisation task. Presented detailed results were obtained during real-life industrial implementation of the optimisation process at the Siekierki combined heat and power (CHP) plant in Warsaw owned by Vattenfall Heat Poland S.A. (currently by Polish Oil & Gas Company - PGNiG SA) carried out by the Academic Research Centre of Power Industry and Environment Protection, Warsaw University of Technology in collaboration with Transition Technologies S.A. company.

  12. Operational Forecast of Runoff from Large Scale Basins using Satellite-Gravimetry and Remote Sensing

    NASA Astrophysics Data System (ADS)

    Riegger, Johannes; Tourian, Mohammad

    2015-04-01

    The forecast of river runoff is a major issue in hydrology and of considerable economic importance with respect to the management of floods and droughts. However an accurate and reliable forecast is a major challenge as runoff depends on climatic and physiographic conditions and on different driving forces such as present recharge, water storage in liquid and solid form, etc. Specifically the quantification of the solid and liquid water storage components and their transition has a major impact on the accuracy of runoff forcasts especially during melting periods. As groundbased measurements of groundwater levels, snow water equivalent and soil moisture are point measurements the determination of water storage is still is quite inaccurate and unreliable on large spatial scales. GRACE gravimetry provides a direct measure of water storage anomalies and thus a determination of runoff - storage (R-S) relationships on large scales catchments. For fully humid tropic regions the system behaviour can be described as a linear time invariant (LTI) system between runoff and total mass with a phase shift due to runoff routing time lag. For boreal regions runoff and liquid mass quantified on the basis of GRACE and MODIS snow coverage also behave as a LTI system. This allows for a direct determination of runoff from GRACE gravity measurements and remote sensing based on an adaption of the parameters time lag, hydraulic time constant and mass offset between the time series of runoff and liquid mass (Riegger & Tourian, 2014). Even though there are no operational GRACE measurements available at the moment, an approach for short term runoff forecasts using operational data is investigated here in order to explore the prediction potential of operational data. The approach is based on the R-S relationship for liquid storage components with the respective parameters taken from previous runoff, recharge, mass and snow coverage time series in a training period. These are used to predict runoff for the subsequent month based on the values of present recharge, snow coverage and liquid mass. The forecast results are compared to measured runoff during the prediction period. Our investigations on large scale catchments emphasize the considerable potential for the use of operational GRACE and remote sensing data in runoff predictions. Future improvements in spatial and temporal resolution will tremendously increase the number of catchments for which this method can be applied. References: Riegger, J., and M. J. Tourian (2014), Characterization of runoff-storage relationships by satellite gravimetry and remote sensing. Water Resour. Res., 50, doi:10.1002/2013WR013847.

  13. Application of data assimilation for improved operational water level forecasting on the northwest European shelf and North Sea

    NASA Astrophysics Data System (ADS)

    Zijl, Firmijn; Sumihar, Julius; Verlaan, Martin

    2015-11-01

    For the Netherlands, accurate water level forecasting in the coastal region is crucial, since large areas of the land lie below sea level. During storm surges, detailed and timely water level forecasts provided by an operational storm surge forecasting system are necessary to support, for example, the decision to close the movable storm surge barriers in the Eastern Scheldt and the Rotterdam Waterway. In the past years, a new generation operational tide-surge model (Dutch Continental Shelf Model version 6) has been developed covering the northwest European continental shelf. In a previous study, a large effort has been put in representing relevant physical phenomena in this process model as well as reducing parameter uncertainty over a wide area. While this has resulted in very accurate water level representation (root-mean-square error (RMSE) 7-8 cm), during severe storm surges, the errors in the meteorological model forcing are generally non-negligible and can cause forecast errors of several decimetres. By integrating operationally available observational data in the forecast model by means of real-time data assimilation, the errors in the meteorological forcing are prevented from propagating to the hydrodynamic tide-surge model forecasts. This paper discusses the development of a computationally efficient steady-state Kalman filter to enhance the predictive quality for the shorter lead times by improving the system state at the start of the forecast. Besides evaluating the model quality against shelf-wide tide gauge observations for a year-long hindcast simulation, the predictive value of the Kalman filter is determined by comparing the forecast quality for various lead time intervals against the model without a steady-state Kalman filter. This shows that, even though the process model has a water level representation that is substantially better than that of other comparable operational models of this scale, substantial improvements in predictive quality in the first few hours are possible in an actual operational setting.

  14. Application of data assimilation for improved operational water level forecasting on the northwest European shelf and North Sea

    NASA Astrophysics Data System (ADS)

    Zijl, Firmijn; Sumihar, Julius; Verlaan, Martin

    2015-12-01

    For the Netherlands, accurate water level forecasting in the coastal region is crucial, since large areas of the land lie below sea level. During storm surges, detailed and timely water level forecasts provided by an operational storm surge forecasting system are necessary to support, for example, the decision to close the movable storm surge barriers in the Eastern Scheldt and the Rotterdam Waterway. In the past years, a new generation operational tide-surge model (Dutch Continental Shelf Model version 6) has been developed covering the northwest European continental shelf. In a previous study, a large effort has been put in representing relevant physical phenomena in this process model as well as reducing parameter uncertainty over a wide area. While this has resulted in very accurate water level representation (root-mean-square error (RMSE) ˜7-8 cm), during severe storm surges, the errors in the meteorological model forcing are generally non-negligible and can cause forecast errors of several decimetres. By integrating operationally available observational data in the forecast model by means of real-time data assimilation, the errors in the meteorological forcing are prevented from propagating to the hydrodynamic tide-surge model forecasts. This paper discusses the development of a computationally efficient steady-state Kalman filter to enhance the predictive quality for the shorter lead times by improving the system state at the start of the forecast. Besides evaluating the model quality against shelf-wide tide gauge observations for a year-long hindcast simulation, the predictive value of the Kalman filter is determined by comparing the forecast quality for various lead time intervals against the model without a steady-state Kalman filter. This shows that, even though the process model has a water level representation that is substantially better than that of other comparable operational models of this scale, substantial improvements in predictive quality in the first few hours are possible in an actual operational setting.

  15. Skill of regional and global model forecast over Indian region

    NASA Astrophysics Data System (ADS)

    Kumar, Prashant; Kishtawal, C. M.; Pal, P. K.

    2016-02-01

    The global model analysis and forecast have a significant impact on the regional model predictions, as global model provides the initial and lateral boundary condition to regional model. This study addresses an important question whether the regional model can improve the short-range weather forecast as compared to the global model. The National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) and the Weather Research and Forecasting (WRF) model are used in this study to evaluate the performance of global and regional models over the Indian region. A 24-h temperature and specific humidity forecast from the NCEP GFS model show less error compared to WRF model forecast. Rainfall prediction is improved over the Indian landmass when WRF model is used for rainfall forecast. Moreover, the results showed that high-resolution global model analysis (GFS4) improved the regional model forecast as compared to low-resolution global model analysis (GFS3).

  16. The suitability of remotely sensed soil moisture for improving operational flood forecasting

    NASA Astrophysics Data System (ADS)

    Wanders, N.; Karssenberg, D.; de Roo, A.; de Jong, S. M.; Bierkens, M. F. P.

    2014-06-01

    We evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the prediction of the timing and height of the flood peak and low flows. EFAS is an operational flood forecasting system for Europe and uses a distributed hydrological model (LISFLOOD) for flood predictions with lead times of up to 10 days. For this study, satellite-derived soil moisture from ASCAT (Advanced SCATterometer), AMSR-E (Advanced Microwave Scanning Radiometer - Earth Observing System) and SMOS (Soil Moisture and Ocean Salinity) is assimilated into the LISFLOOD model for the Upper Danube Basin and results are compared to assimilation of discharge observations only. To assimilate soil moisture and discharge data into the hydrological model, an ensemble Kalman filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, is included to ensure increased performance of the EnKF. For the validation, additional discharge observations not used in the EnKF are used as an independent validation data set. Our results show that the accuracy of flood forecasts is increased when more discharge observations are assimilated; the mean absolute error (MAE) of the ensemble mean is reduced by 35%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of baseflows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the continuous ranked probability score (CRPS) shows a performance increase of 5-10% on average, compared to assimilation of discharge only. When soil moisture data is used, the timing errors in the flood predictions are decreased especially for shorter lead times and imminent floods can be forecasted with more skill. The number of false flood alerts is reduced when more observational data is assimilated into the system. The added values of the satellite data is largest when these observations are assimilated in combination with distributed discharge observations. These results show the potential of remotely sensed soil moisture observations to improve near-real time flood forecasting in large catchments.

  17. The Transition of High-Resolution NASA MODIS Sea Surface Temperatures into the WRF Environmental Modeling System

    NASA Technical Reports Server (NTRS)

    Case, Jonathan L.; Jedlove, Gary J.; Santos, Pablo; Medlin, Jeffrey M.; Rozumalski, Robert A.

    2009-01-01

    The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed a Moderate Resolution Imaging Spectroradiometer (MODIS) sea surface temperature (SST) composite at 2-km resolution that has been implemented in version 3 of the National Weather Service (NWS) Weather Research and Forecasting (WRF) Environmental Modeling System (EMS). The WRF EMS is a complete, full physics numerical weather prediction package that incorporates dynamical cores from both the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM). The installation, configuration, and execution of either the ARW or NMM models is greatly simplified by the WRF EMS to encourage its use by NWS Weather Forecast Offices (WFOs) and the university community. The WRF EMS is easy to run on most Linux workstations and clusters without the need for compilers. Version 3 of the WRF EMS contains the most recent public release of the WRF-NMM and ARW modeling system (version 3 of the ARW is described in Skamarock et al. 2008), the WRF Pre-processing System (WPS) utilities, and the WRF Post-Processing program. The system is developed and maintained by the NWS National Science Operations Officer Science and Training Resource Coordinator. To initialize the WRF EMS with high-resolution MODIS SSTs, SPoRT developed the composite product consisting of MODIS SSTs over oceans and large lakes with the NCEP Real-Time Global (RTG) filling data over land points. Filling the land points is required due to minor inconsistencies between the WRF land-sea mask and that used to generate the MODIS SST composites. This methodology ensures a continuous field that adequately initializes all appropriate arrays in WRF. MODIS composites covering the Gulf of Mexico, western Atlantic Ocean and the Caribbean are generated daily at 0400, 0700, 1600, and 1900 UTC corresponding to overpass times of the NASA Aqua and Terra polar orbiting satellites. The MODIS SST product is output in gridded binary-1 (GRIB-1) data format for a seamless incorporation into WRF via the WPS utilities. The full-resolution, 1-km MODIS product is sub-sampled to 2-km grid spacing due to limitations in handling very large dimensions in the GRIB-1 data format. The GRIB-1 files are posted online at ftp://ftp.nsstc.org/sstcomp/WRF/, which is directly accessed by the WRF EMS scripts. The MODIS SST composites are also downloaded to the EMS data server, which is accessible by the WRF EMS users and NWS WFOs. The SPoRT MODIS SST composite provides the model with superior detail of the ocean gradients around Florida and surrounding waters, whereas the operational RTG SST typically depicts a relatively smooth field and is not able to capture sharp horizontal gradients in SST. Differences of 2-3 C are common over small horizontal distances, leading to enhanced SST gradients on either side of the Gulf Stream and along the edges of the cooler shelf waters. These sharper gradients can in turn produce atmospheric responses in simulated temperature and wind fields as depicted in LaCasse et al. Differences in atmospheric verification statistics over a several month study were generally small in the vicinity of south Florida; however, the validation of SSTs at specific buoy locations revealed important improvements in the biases and RMS errors, especially in the vicinity of the cooler shelf waters off the east-central Florida coast. A current weakness in the MODIS SST product is the occurrence of occasional discontinuities caused by high latency in SST coverage due to persistent cloud cover. An enhanced method developed by Jedlovec et al. (2009, GHRSST User Symposium) reduces the occurrence of these problems by adding Advanced Microwave Scanning Radiometer -- EOS (AMSR-E) SST data to the compositing process. Enhanced SST composites are produced over the ocean regions surrounding the Continental U.S. at four times each day corresponding to Terra and Aqua equator crossing times. For a given day and overpass time, both MODInd AMSR-E data from the previous seven days form a collection used in the compositing. At each MODIS pixel, cloud-free SST values from the collection are used to form a weighted average based on their latency (number of days from the current day). In this way, recent SST data are given more weight than older data. One of the primary issues involved in incorporating the AMSR-E microwave data in the composites is the tradeoff between the decreased spatial resolution of the AMSR-E data (25 km) and the increased coverage due to its near all-weather capability. Currently, the AMSR-E is given a weight of 20% compared to MODIS data, thereby preserving the spatial structure observed in the MODIS data. Day-time (night-time) AMSR-E SST data from Aqua are used with both Terra and Aqua MODIS day-time (night-time) SST data sets.

  18. Inflow forecasting model construction with stochastic time series for coordinated dam operation

    NASA Astrophysics Data System (ADS)

    Kim, T.; Jung, Y.; Kim, H.; Heo, J. H.

    2014-12-01

    Dam inflow forecasting is one of the most important tasks in dam operation for an effective water resources management and control. In general, dam inflow forecasting with stochastic time series model is possible to apply when the data is stationary because most of stochastic process based on stationarity. However, recent hydrological data cannot be satisfied the stationarity anymore because of climate change. Therefore a stochastic time series model, which can consider seasonality and trend in the data series, named SARIMAX(Seasonal Autoregressive Integrated Average with eXternal variable) model were constructed in this study. This SARIMAX model could increase the performance of stochastic time series model by considering the nonstationarity components and external variable such as precipitation. For application, the models were constructed for four coordinated dams on Han river in South Korea with monthly time series data. As a result, the models of each dam have similar performance and it would be possible to use the model for coordinated dam operation.Acknowledgement This research was supported by a grant 'Establishing Active Disaster Management System of Flood Control Structures by using 3D BIM Technique' [NEMA-NH-12-57] from the Natural Hazard Mitigation Research Group, National Emergency Management Agency of Korea.

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

    NASA Technical Reports Server (NTRS)

    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.

  20. Boundary-Layer Phenomena in the Vicinity of an Isolated Mountain: A Climatography Based on an Operational High-Resolution Forecast System

    NASA Astrophysics Data System (ADS)

    Serafin, S.; De Wekker, S.; Knievel, J. C.

    2013-12-01

    Granite Peak, located in the Dugway Proving Ground (DPG) in western Utah, is an isolated mountain rising ~800 m above the surrounding terrain. It has an approximately ellipsoidal shape oriented in the NNW-SSE direction and its main axes are respectively ~10- and ~6-km long. A flat dry lake (playa) lies west and northwest of the peak, while a NW-sloping plain covered by herbaceous vegetation extends to the eastern part of DPG. Because of these topography and land-use features, a variety of different flow phenomena are expected to occur over and around Granite Peak. These include upslope and drainage winds, local breeze systems, gap flows, dynamically accelerated downslope winds and potentially boundary layer separation and the formation of wakes. Consequently, the area is an ideal location for studying the interaction between mountain flows and the atmospheric boundary layer. Since the 1990s, DPG has used a continuously operating meso-gamma-scale analysis and forecast system (4DWX) developed by the NCAR's Research Applications Laboratory (RAL). The system is based on WRF, runs with a grid spacing of 1.1-km in its innermost domain, applies observational nudging in a three-hour cycle, and provides weather analyses and forecasts at hourly intervals. In this study, model output from the 4DWX system is used to build a short-term climatography (2010-2012) of the prevailing boundary layer flow regimes in DPG. Measurements from the network of Surface Area Mesonet Stations (SAMS) operative at DPG are used to verify the quality of 4DWX simulations and their ability to reproduce the dominant flow patterns. The study then focuses on boundary-layer separation (BLS) events: near-surface wind, temperature and pressure fields from 4DWX are analysed in order to identify the most favorable regions for the onset of separation. A limited set of events, identified by means of an objective procedure, is then studied in detail in order to understand the preferred conditions for the development of the phenomenon. S-SW flows with considerable near-surface veering and an embedded low-level jet are found to be the most common scenario leading to leeside boundary-layer separation. Example of a BLS event in the lee of Granite Peak (near gridpoints x=12, y=15). Near-surface wind speed (in m/s) and vectors are displayed on the 4DWX model grid (Δx: 1.1 km).

  1. Applications of data assimilation methodologies in wind power forecasting

    NASA Astrophysics Data System (ADS)

    Zupanski, Dusanka; Paquin, Kurt; Kelly, Robert; Nelson, Stacey; Zupanski, Milija; Jankov, Isidora; Mallapragada, Padma

    2010-05-01

    Wind energy is one form of clean energy that is expected to play a significant role in power generation in many countries. Accurate wind forecasts are essential for balancing wind energy production and hence ensuring reliable grid operations, as well as for reducing the cost of wind power integration. One of the most effective ways to improve weather forecasts, including the wind forecasts, is through data assimilation. Data assimilation methods are routinely used in operational weather forecasting centers and in research at the universities. However, the use of data assimilation in wind power forecasting has been limited so far. The situation is changing now as the community is beginning to realize that, in this era of more abundant wind observations from met-towers, radars, lidars, sodars and satellites, data assimilation could play a significant role in the integration of wind energy onto the electric grid. Precision Wind LLC and Colorado State University (CSU) joined together in exploring data assimilation methods for wind power forecasting. We use a data assimilation method called Maximum Likelihood Ensemble Filter (MLEF), developed at CSU, and a complex numerical weather prediction model, the Weather Research and Forecasting (WRF) model. We assimilate wind and power production site data to improve wind and power forecasts. We pay a special attention to reducing forecast errors of significant ramp events, which are recognized as the biggest challenge for the wind power forecast utility to the system operators. Results from a couple of pilot projects performed in real time for system operators over multiple months will be presented.

  2. A high resolution Adriatic-Ionian Sea circulation model for operational forecasting

    NASA Astrophysics Data System (ADS)

    Ciliberti, Stefania Angela; Pinardi, Nadia; Coppini, Giovanni; Oddo, Paolo; Vukicevic, Tomislava; Lecci, Rita; Verri, Giorgia; Kumkar, Yogesh; Creti', Sergio

    2015-04-01

    A new numerical regional ocean model for the Italian Seas, with focus on the Adriatic-Ionian basin, has been implemented within the framework of Technologies for Situational Sea Awareness (TESSA) Project. The Adriatic-Ionian regional model (AIREG) represents the core of the new Adriatic-Ionian Forecasting System (AIFS), maintained operational by CMCC since November 2014. The spatial domain covers the Adriatic and the Ionian Seas, extending eastward until the Peloponnesus until the Libyan coasts; it includes also the Tyrrhenian Sea and extends westward, including the Ligurian Sea, the Sardinia Sea and part of the Algerian basin. The model is based on the NEMO-OPA (Nucleus for European Modeling of the Ocean - Ocean PArallelise), version 3.4 (Madec et al. 2008). NEMO has been implemented for AIREG at 1/45° resolution model in horizontal using 121 vertical levels with partial steps. It solves the primitive equations using the time-splitting technique for solving explicitly the external gravity waves. The model is forced by momentum, water and heat fluxes interactively computed by bulk formulae using the 6h-0.25° horizontal-resolution operational analysis and forecast fields from the European Centre for Medium-Range Weather Forecast (ECMWF) (Tonani et al. 2008, Oddo et al. 2009). The atmospheric pressure effect is included as surface forcing for the model hydrodynamics. The evaporation is derived from the latent heat flux, while the precipitation is provided by the Climate Prediction Centre Merged Analysis of Precipitation (CMAP) data. Concerning the runoff contribution, the model considers the estimate of the inflow discharge of 75 rivers that flow into the Adriatic-Ionian basin, collected by using monthly means datasets. Because of its importance as freshwater input in the Adriatic basin, the Po River contribution is provided using daily average observations from ARPA Emilia Romagna observational network. AIREG is one-way nested into the Mediterranean Forecasting System (MFS, http://medforecast.bo.ingv.it/) using daily means fields computed from daily outputs of the 1/16° general circulation model. One-way nesting is done by a novel pre-processing tool for an on-the-fly computation of boundary datasets compatible with BDY module provided by NEMO. It imposes the interpolation constraint and correction as in Pinardi et al. (2003) on the total velocity, ensuring that the total volume transport across boundaries is preserved after the interpolation procedures. In order to compute the lateral open boundary conditions, the model applies the Flow Relaxation Scheme (Engerdhal, 1995) for temperature, salinity and velocities and the Flather's radiation condition (Flather, 1976) for the depth-mean transport. Concerning the forecasting production cycle, AIFS produces 9-days forecast every day, producing hourly and daily means of temperature, salinity, surface currents, heat flux, water flux and shortwave radiation fields. AIREG model performances have been verified by using statistics (root mean square errors and BIAS) with respect to observed data (ARGO and CDT datasets)

  3. An Improvement of Fine Scale Wind Field Prediction using WRF/MMIF Models for CALPUFF Application.

    NASA Astrophysics Data System (ADS)

    Kim, A. L.; Koo, Y. S.

    2014-12-01

    Accurate simulation of CALPUFF dispersion modeling is largely dependent on the data sets which are properly resolved in the spatial and temporal evolution of meteorological field on a wide range of scales. The fine scale field wind of 100 m spatial resolution is required for the CALPUFF modeling in the complex terrain near the coastal area. The objective of this paper is to provide information how to calculate the fine scale wind field using recent advances in the meteorological model. The diagnostic model of CALMET has been used to generate fine grid scale wind field by interpolating output of mesoscale prognostic weather models of MM5 (short for Fifth-Generation Penn State/NCAR Mesoscale Model) and WRF (Weather Research and Forecast). The MMIF(The Mesoscale Model Interface Program) interfacial program directly converting WRF meteorological output to formats appropriate for CALPUFF modeling without diagnostic interpolations is recently developed. The modeling comparison between WRF/CALMET and WRF/MMIF was carried out to find out a best way in generating fine wind field in the complex geological conditions. For the WRF/CALMET modeling, WRF model output of 900m grid resolution was provided to CALMET model and CALMET then calculated the fine grid resolution of 100m by diagnostically interpolating the WRF output. For the WRF/MMIF modeling, the WRF model directly calculate the fine grid of 100m and the MMIF program was used to convert WRF data. In order to validate model performance of two methods, simulated variables of meteorological fields were compared with observations at the landfill site near the coast in KOREA. It is found that WRF/MMIF is in better agreement with observations than CALWRF/CALMET in respect to the statics of RMSE and IOA. CALPUFF modeling with landfill emission data of H2S was performed and compared with monitoring data to identify effects on meteorological data on the final outcome of CALPUFF dispersion modeling.

  4. An Improved Multi-Scale Modeling Framework for WRF over Complex Terrain

    NASA Astrophysics Data System (ADS)

    Wiersema, D. J.; Lundquist, K. A.; Chow, F. K.

    2014-12-01

    Atmospheric modelers continue to push towards higher resolution simulations of the planetary boundary layer. As resolution is refined, the resolved terrain slopes increase. Atmospheric models using terrain-following coordinates, such as the Weather Research and Forecasting (WRF) model, suffer from numerical errors since steep terrain slopes lead to grid skewness, resulting in model failure. One solution to this problem is the use of an immersed boundary method, which uses a non-conforming grid, for simulations over complex terrain. Our implementation of an immersed boundary method in WRF, known as WRF-IBM, was developed for use at the micro-scale and has been shown to accurately simulate flow around complex topography, such as urban environments or mountainous terrain. The research presented here describes our newly developed framework to enable concurrently run multi-scale simulations using the WRF model at the meso-scale and the WRF-IBM model at the micro-scale. WRF and WRF-IBM use different vertical coordinates therefore it is not possible to use the existing nesting framework to pass lateral boundary conditions from a WRF parent domain to a WRF-IBM nested domain. Nesting between WRF and WRF-IBM requires "vertical grid nesting", meaning the ability to pass information between domains with different vertical levels. Our newly implemented method for vertical grid nesting, available in the public release of WRFv3.6.1, allows nested domains to utilize different vertical levels. Using our vertical grid nesting code, we are currently developing the ability to nest a domain using IBM within a domain using terrain-following coordinates. Here we present results from idealized cases displaying the functionality of the multi-scale nesting framework and the advancement towards multi-scale meteorological simulations over complex terrain.

  5. Operational earthquake forecasting in California: A prototype system combining UCERF3 and CyberShake

    NASA Astrophysics Data System (ADS)

    Milner, K. R.; Jordan, T. H.; Field, E. H.

    2014-12-01

    Operational earthquake forecasting (OEF) is the dissemination of authoritative information about time-dependent earthquake probabilities to help communities prepare for potentially destructive earthquakes. The goal of OEF is to inform the decisions that people and organizations must continually make to mitigate seismic risk and prepare for potentially destructive earthquakes on time scales from days to decades. To attain this goal, OEF must provide a complete description of the seismic hazardground motion exceedance probabilities as well as short-term rupture probabilitiesin concert with the long-term forecasts of probabilistic seismic hazard analysis. We have combined the Third Uniform California Earthquake Rupture Forecast (UCERF3) of the Working Group on California Earthquake Probabilities (Field et al., 2014) with the CyberShake ground-motion model of the Southern California Earthquake Center (Graves et al., 2011; Callaghan et al., this meeting) into a prototype OEF system for generating time-dependent hazard maps. UCERF3 represents future earthquake activity in terms of fault-rupture probabilities, incorporating both Reid-type renewal models and Omori-type clustering models. The current CyberShake model comprises approximately 415,000 earthquake rupture variations to represent the conditional probability of future shaking at 285 geographic sites in the Los Angeles region (~236 million horizontal-component seismograms). This combination provides significant probability gains relative to OEF models based on empirical ground-motion prediction equations (GMPEs), primarily because the physics-based CyberShake simulations account for the rupture directivity, basin effects, and directivity-basin coupling that are not represented by the GMPEs.

  6. A statistical comparison of the reliability of the blossom blight forecasts of MARYBLYT and cougarblight with receiver operating characteristic (ROC) curve analysis

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Blossom blight forecasting is an important aspect of fire blight, caused by Erwinia amylovora, management for both apple and pear. A comparison of the forecast accuracy of two common fire blight forecasters, MARYBLYT and Cougarblight, was performed with receiver operating characteristic (ROC) curve...

  7. Evaluating the Impact of Atmospheric Infrared Sounder (AIRS) Data On Convective Forecasts

    NASA Technical Reports Server (NTRS)

    Kozlowski, Danielle; Zavodsky, Bradley

    2011-01-01

    The Short-term Prediction Research and Transition Center (SPoRT) is a collaborative partnership between NASA and operational forecasting partners, including a number of National Weather Service (NWS) offices. SPoRT provides real-time NASA products and capabilities to its partners to address specific operational forecast challenges. The mission of SPoRT is to transition observations and research capabilities into operations to help improve short-term weather forecasts on a regional scale. Two areas of focus are data assimilation and modeling, which can to help accomplish SPoRT's programmatic goals of transitioning NASA data to operational users. Forecasting convective weather is one challenge that faces operational forecasters. Current numerical weather prediction (NWP) models that operational forecasters use struggle to properly forecast location, timing, intensity and/or mode of convection. Given the proper atmospheric conditions, convection can lead to severe weather. SPoRT's partners in the National Oceanic and Atmospheric Administration (NOAA) have a mission to protect the life and property of American citizens. This mission has been tested as recently as this 2011 severe weather season, which has seen more than 300 fatalities and injuries and total damages exceeding $10 billion. In fact, during the three day period from 25-27 April, 1,265 storms reports (362 tornado reports) were collected making this three day period one of most active in American history. To address the forecast challenge of convective weather, SPoRT produces a real-time NWP model called the SPoRT Weather Research and Forecasting (SPoRT-WRF), which incorporates unique NASA data sets. One of the NASA assets used in this unique model configuration is retrieved profiles from the Atmospheric Infrared Sounder (AIRS).The goal of this project is to determine the impact that these AIRS profiles have on the SPoRT-WRF forecasts by comparing to a current operational model and a control SPoRT-WRF model that does not contain AIRS profiles.

  8. Forecasting atmospheric angular momentum and length-of-day using operational meteorological models

    NASA Technical Reports Server (NTRS)

    Rosen, R. D.; Salstein, D. A.; Nehrkorn, T.; Dickey, J. O.; Eubanks, T. M.; Steppe, J. A.; Mccalla, M. R. P.; Miller, A. J.

    1990-01-01

    Forecasts of zonal wind fields produced by the medium-range forecast model of the U.S. National Meteorological Center are used to create predictions of the atmosphere's angular momentum at lead times of 1-10 days. The skill of these forecasts, which are of interest to those concerned with monitoring changes in the length-of-day for navigational purposes, is assessed, and the regions in the atmosphere that contribute most importantly to forecast errors are identified.

  9. Observation of a tropopause fold by MARA VHF wind-profiler radar and ozonesonde at Wasa, Antarctica: comparison with ECMWF analysis and a WRF model simulation

    NASA Astrophysics Data System (ADS)

    Mihalikova, M.; Kirkwood, S.; Arnault, J.; Mikhaylova, D.

    2012-09-01

    Tropopause folds are one of the mechanisms of stratosphere-troposphere exchange, which can bring ozone rich stratospheric air to low altitudes in the extra-tropical regions. They have been widely studied at northern mid- or high latitudes, but so far almost no studies have been made at mid- or high southern latitudes. The Moveable Atmospheric Radar for Antarctica (MARA), a 54.5 MHz wind-profiler radar, has operated at the Swedish summer station Wasa, Antarctica (73 S, 13.5 W) during austral summer seasons from 2007 to 2011 and has observed on several occasions signatures similar to those caused by tropopause folds at comparable Arctic latitudes. Here a case study is presented of one of these events when an ozonesonde successfully sampled the fold. Analysis from European Center for Medium Range Weather Forecasting (ECMWF) is used to study the circumstances surrounding the event, and as boundary conditions for a mesoscale simulation using the Weather Research and Forecasting (WRF) model. The fold is well resolved by the WRF simulation, and occurs on the poleward side of the polar jet stream. However, MARA resolves fine-scale layering associated with the fold better than the WRF simulation.

  10. Implementation of aerosol assimilation in Gridpoint Statistical Interpolation v. 3.2 and WRF-Chem v. 4.3.1

    NASA Astrophysics Data System (ADS)

    Pagowski, M.; Liu, Z.; Grell, G. A.; Hu, M.; Lin, H.-C.; Schwartz, C. S.

    2014-04-01

    Gridpoint Statistical Interpolation (GSI) is an assimilation tool that is used at the National Centers for Environmental Prediction in operational weather forecasting. In this article we describe implementation of an extension to the GSI for assimilating surface measurements of PM2.5, PM10, and MODIS Aerosol Optical Depth at 550 nm with WRF-Chem. We also present illustrations of the results. In the past the aerosol assimilation system has been employed to issue daily PM2.5 forecasts at NOAA/ESRL and, in our belief, is well tested and mature enough to make available for wider use. We provide a package that, in addition to augmented GSI, consists of software for calculating background error covariance statistics and for converting in-situ and satellite data to BUFR format, plus sample input files for an assimilation exercise. Thanks to flexibility in the GSI and coupled meteorology-chemistry of WRF-Chem, assimilating aerosol observations can be carried out simultaneously with meteorological data assimilation. Both GSI and WRF-Chem are well documented with user guides available on-line. This article is primarily intended as a technical note on the implementation of the aerosol assimilation. Its purpose is also to provide guidance for prospective users of the computer code. Limited space is devoted to scientific aspects of aerosol assimilation.

  11. Assessment and forecasting of lightning potential and its effect on launch operations at Cape Canaveral Air Force Station and John F. Kennedy Space Center

    NASA Technical Reports Server (NTRS)

    Weems, J.; Wyse, N.; Madura, J.; Secrist, M.; Pinder, C.

    1991-01-01

    Lightning plays a pivotal role in the operation decision process for space and ballistic launches at Cape Canaveral Air Force Station and Kennedy Space Center. Lightning forecasts are the responsibility of Detachment 11, 4th Weather Wing's Cape Canaveral Forecast Facility. These forecasts are important to daily ground processing as well as launch countdown decisions. The methodology and equipment used to forecast lightning are discussed. Impact on a recent mission is summarized.

  12. Climatological analysis of the real-time NSSL 4km WRF-ARW

    NASA Astrophysics Data System (ADS)

    Goines, D. C.; Kennedy, A. D.

    2014-12-01

    In recent years, the Weather Research and Forecasting (WRF) model has been used for dynamic downscaling ofGlobal Climate Models (GCMs) to forecast smaller scale phenomena that GCMs cannot resolve at coarseresolutions. High resolution convection-allowing (CA) WRF simulations have gained popularity in recent yearsdue to their ability to resolve the structure of high impact phenomena such as topographically inducedprecipitation, mesoscale convective systems, and supercell thunderstorms. An accurate representation of theseextreme events is important for climate mitigation and adaptation strategies by policy makers. With the usage ofdownscaling and fine resolutions of WRF simulations becoming more recurrent, the question still remains: dohigh resolution CA WRF simulations correctly represent climatological precipitation? This study examines theclimatology of precipitation over the U.S. Central Plains produced for 7 years (2007-2013) by the NationalSevere Storms Laboratory (NSSL) high resolution (4km) CA WRF model. Precipitation forecasts for variousforecast hours are analyzed to determine whether the model climatology is similar to observations. TheMeteorological Evaluation Tool (MET) Method for Object-Based Diagnostic Evaluation (MODE) is utilized tocompare the precipitation forecasts to observations. The National Centers for Environmental Prediction (NCEP)Stage IV multi-sensor precipitation analysis is used as the truth for model assessment. Model performance isinvestigated for a variety of synoptic regimes using self-organizing maps (SOMs).

  13. High-resolution summer rainfall prediction in the JHWC real-time WRF system

    NASA Astrophysics Data System (ADS)

    Lee, Dong-Kyou; Eom, Dae-Yong; Kim, Joo-Wan; Lee, Jae-Bok

    2010-08-01

    The WRF-based real-time forecast system (http://jhwc.snu.ac.kr/weather) of the Joint Center for High-impact Weather and Climate Research (JHWC) has been in operation since November 2006; this system has three nested model domains using GFS (Global Forecast System) data for its initial and boundary conditions. In this study, we evaluate the improvement in daily and hourly weather prediction, particularly the prediction of summer rainfall over the Korean Peninsula, in the JHWC WRF (Weather Research and Forecasting) model system by 3DVAR (three-Dimensional Variational) data assimilation using the data obtained from KEOP (Korea Enhanced Observation Program). KEOP was conducted during the period June 15 to July 15, 2007, and the data obtained included GTS (Global Telecommunication System) upper-air sounding, AWS (Automatic Weather System), wind profiler, and radar observation data. Rainfall prediction and its characteristics should be verified by using the precipitation observation and the difference field of each experiment. High-resolution (3 km in domain 3) summer rainfall prediction over the Korean peninsula is substantially influenced by improved synoptic-scale prediction in domains 1 (27 km) and 2 (9 km), in particular by data assimilation using the sounding and wind profiler data. The rainfall prediction in domain 3 was further improved by radar and AWS data assimilation in domain 3. The equitable threat score and bias score of the rainfall predicted in domain 3 indicated improvement for the threshold values of 0.1, 1, and 2.5 mm with data assimilation. For cases of occurrence of heavy rainfall (7 days), the equitable threat score and bias score improved considerably at all threshold values as compared to the entire period of KEOP. Radar and AWS data assimilation improved the temporal and spatial distributions of diurnal rainfall over southern Korea, and AWS data assimilation increased the predicted rainfall amount by approximately 0.3 mm 3hr-1.

  14. WRF4G project: Advances in running climate simulations on the EGI Infrastructure

    NASA Astrophysics Data System (ADS)

    Blanco, Carlos; Cofino, Antonio S.; Fernndez Quiruelas, Valvanuz; Garca, Markel; Fernndez, Jess

    2014-05-01

    The Weather Research and Forecasting For Grid (WRF4G) project is a two-year Spanish National R&D project, which has started in 2011. It is now a well established project, involving scientists and technical staff from several institutions, which contribute results to international initiatives such as CORDEX and European FP7 projects such as SPECS and EUPORIAS. The aim of the WRF4G project is to homogenize access hybrid Distributed Computer Infrastructures (DCIs), such as HPC and Grid infrastructures, for climate researchers. Additionally, it provides a productive interface to accomplish ambitious climate experiments such as regional hind-cast/forecast and sensitivity studies. Although Grid infrastructures are very powerful, they have some drawbacks for executing climate applications such as the WRF model. This makes necessary to encapsulate the applications in a middleware in order to provide the appropriate services for monitoring and management. Therefore, the challenge of the WRF4G project is to develop a generic adaptation framework (WRF4G framework) to disseminate it to the scientific community. The framework aims at simplifying the model access by releasing climate scientists from technical and computational aspects. In this contribution, we present some new advances of the WRF4G framework, including new components for designing experiments, simulation monitoring and data management. Additionally, we will show how WRF4G makes possible to run complex experiments on EGI infrastructures concurrently over several VOs such as esr and earth.vo.ibergrid. http://www.meteo.unican.es/software/wrf4g This work has been partially funded by the European Regional Development Fund (ERDF) and the Spanish National R&D Plan 2008-2011 (CGL2011-28864, WRF4G)

  15. Impact of an improved WRF-urban canopy model on diurnal air temperature simulation over northern Taiwan

    NASA Astrophysics Data System (ADS)

    Lin, C.-Y.; Su, C.-J.; Kusaka, H.; Akimoto, Y.; Sheng, Y. F.; Huang, J.-C.; Hsu, H.-H.

    2015-10-01

    This study evaluated the impact of urbanization over northern Taiwan using the Weather Research and Forecasting (WRF) model coupled with the Noah land-surface model and a modified Urban Canopy Model (WRF-UCM2D). In the original UCM coupled in WRF (WRF-UCM), when the land use in the model grid net is identified as "urban", the urban fraction value is fixed. Similarly, the UCM assumes the distribution of anthropogenic heat (AH) to be constant. Such not only may lead to over- or underestimation, the temperature difference between urban and non-urban areas has also been neglected. To overcome the above-mentioned limitations and to improve the performance of the original UCM model, WRF-UCM is modified to consider the 2-D urban fraction and AH (WRF-UCM2D). The two models were found to have comparable simulation performance for urban areas but large differences in simulated results were observed for non-urban, especially at nighttime. WRF-UCM2D yielded a higher R2 than WRF-UCM (0.72 vs. 0.48, respectively), while bias and RMSE achieved by WRF-UCM2D were both significantly smaller than those attained by WRF-UCM (0.27 and 1.27 vs. 1.12 and 1.89, respectively). In other words, the improved model not only enhanced correlation but also reduced bias and RMSE for the nighttime data of non-urban areas. WRF-UCM2D performed much better than WRF-UCM at non-urban stations with low urban fraction during nighttime. The improved simulation performance of WRF-UCM2D at non-urban area is attributed to the energy exchange which enables efficient turbulence mixing at low urban fraction. The achievement of this study has a crucial implication for assessing the impacts of urbanization on air quality and regional climate.

  16. WRF's wind power ensembles for a wind farm located in a coastal area of Turkey

    NASA Astrophysics Data System (ADS)

    Kirkil, Gokhan; Ezber, Yasemin; Kaytanci, Tarik

    2015-04-01

    Short-term wind forecasts are obtained for a wind farm located in a coastal area of Turkey. The simulated month is March when the plant is under strong south-westerly gusts. We performed multi-scale simulations using WRF's different Planetary Boundary Layer (PBL) parameterizations as well as Large Eddy Simulation (LES). WRF ensembles with different PBL parameterizations showed little spread for wind speed forecasts. LES models improved the forecasts. Statistical error analysis is performed and ramp events are analyzed. Model forecasts for ramps in general were poor. Complex topography of the study area also affects PBL and LES parameterizations' performance, especially the accuracy of wind forecasts were poor in late afternoons.

  17. Pre-operational short-term forecasts for Mediterranean Sea biogeochemistry

    NASA Astrophysics Data System (ADS)

    Lazzari, P.; Teruzzi, A.; Salon, S.; Campagna, S.; Calonaci, C.; Colella, S.; Tonani, M.; Crise, A.

    2010-01-01

    Operational prediction of the marine environment is recognised as a fundamental research issue in Europe. We present a pre-operational implementation of a biogeochemical model for the pelagic waters of the Mediterranean Sea, developed within the framework of the MERSEA-IP European project. The OPATM-BFM coupled model is the core of a fully automatic system that delivers weekly analyses and forecast maps for the Mediterranean Sea biogeochemistry. The system has been working in its current configuration since April 2007 with successful execution of the fully automatic operational chain in 87% of the cases while in the remaining cases the runs were successfully accomplished after operator intervention. A description of the system developed and also a comparison of the model results with satellite data are presented, together with a measure of the model skill evaluated by means of seasonal target diagrams. Future studies will address the implementation of a data assimilation scheme for the biogeochemical compartment in order to increase the skill of the model's performance.

  18. Community Coordinated Modeling Center: Addressing Needs of Operational Space Weather Forecasting

    NASA Technical Reports Server (NTRS)

    Kuznetsova, M.; Maddox, M.; Pulkkinen, A.; Hesse, M.; Rastaetter, L.; Macneice, P.; Taktakishvili, A.; Berrios, D.; Chulaki, A.; Zheng, Y.; Mullinix, R.

    2012-01-01

    Models are key elements of space weather forecasting. The Community Coordinated Modeling Center (CCMC, http://ccmc.gsfc.nasa.gov) hosts a broad range of state-of-the-art space weather models and enables access to complex models through an unmatched automated web-based runs-on-request system. Model output comparisons with observational data carried out by a large number of CCMC users open an unprecedented mechanism for extensive model testing and broad community feedback on model performance. The CCMC also evaluates model's prediction ability as an unbiased broker and supports operational model selections. The CCMC is organizing and leading a series of community-wide projects aiming to evaluate the current state of space weather modeling, to address challenges of model-data comparisons, and to define metrics for various user s needs and requirements. Many of CCMC models are continuously running in real-time. Over the years the CCMC acquired the unique experience in developing and maintaining real-time systems. CCMC staff expertise and trusted relations with model owners enable to keep up to date with rapid advances in model development. The information gleaned from the real-time calculations is tailored to specific mission needs. Model forecasts combined with data streams from NASA and other missions are integrated into an innovative configurable data analysis and dissemination system (http://iswa.gsfc.nasa.gov) that is accessible world-wide. The talk will review the latest progress and discuss opportunities for addressing operational space weather needs in innovative and collaborative ways.

  19. Impact of radar data assimilation for the simulation of a heavy rainfall case in central Italy using WRF-3DVAR

    NASA Astrophysics Data System (ADS)

    Maiello, I.; Ferretti, R.; Gentile, S.; Montopoli, M.; Picciotti, E.; Marzano, F. S.; Faccani, C.

    2014-09-01

    The aim of this study is to investigate the role of the assimilation of Doppler weather radar (DWR) data in a mesoscale model for the forecast of a heavy rainfall event that occurred in Italy in the urban area of Rome from 19 to 22 May 2008. For this purpose, radar reflectivity and radial velocity acquired from Monte Midia Doppler radar are assimilated into the Weather Research Forecasting (WRF) model, version 3.4.1. The general goal is to improve the quantitative precipitation forecasts (QPF): with this aim, several experiments are performed using the three-dimensional variational (3DVAR) technique. Moreover, sensitivity tests to outer loops are performed to include non-linearity in the observation operators. In order to identify the best initial conditions (ICs), statistical indicators such as forecast accuracy, frequency bias, false alarm rate and equitable threat score for the accumulated precipitation are used. The results show that the assimilation of DWR data has a large impact on both the position of convective cells and on the rainfall forecast of the analyzed event. A positive impact is also found if they are ingested together with conventional observations. Sensitivity to the use of two or three outer loops is also found if DWR data are assimilated together with conventional data.

  20. Demonstrating the Operational Value of Thermodynamic Hyperspectral Profiles in the Pre-Convective Environment

    NASA Technical Reports Server (NTRS)

    Kozlowski, Danielle; Zavodsky, Bradley T.; Jedlovec, Gary J.

    2011-01-01

    The Short-term Prediction Research and Transition Center (SPoRT) is a collaborative partnership between NASA and operational forecasting partners, including a number of National Weather Service (NWS) Weather Forecasting Offices (WFO). As a part of the transition to operations process, SPoRT attempts to identify possible limitations in satellite observations and provide operational forecasters a product that will result in the most impact on their forecasts. One operational forecast challenge that some NWS offices face, is forecasting convection in data-void regions such as large bodies of water. The Atmospheric Infrared Sounder (AIRS) is a sounding instrument aboard NASA's Aqua satellite that provides temperature and moisture profiles of the atmosphere. This paper will demonstrate an approach to assimilate AIRS profile data into a regional configuration of the WRF model using its three-dimensional variational (3DVAR) assimilation component to be used as a proxy for the individual profiles.

  1. Integration of RGB "Dust" Imagery to Operations at the Albuqueque Forecast Office

    NASA Technical Reports Server (NTRS)

    Fuell, Kevin; Guyer, Brian

    2014-01-01

    The NASA/Short-term Prediction, Research, and Transition (SPoRT) Program has been providing unique Red-Green-Blue (RGB) composite imagery to its operational partners since 2005. In the early years of activity these RGB products were related to a True Color RGB, showing what one's own eyes would see if looking down at earth from space, as well as a Snow-Cloud RGB (i.e. False Color), separating clouds from snow on the ground. More recently SPoRT has used the EUMETSAT Best Practices standards for RGB composites to transition a wide array of imagery for multiple uses. A "Dust" RGB product has had particular use at the Albuquerque, New Mexico WFO. Several cases have occurred where users were able to isolate dust plume locations for mesoscale and microscale events during day and night time conditions. In addition the "Dust" RGB can be used for more than just detection of dust as it is sensitive to the changes in density due to atmospheric moisture content. Hence low-level dry boundaries can often be discriminated. This type of imagery is a large change from the single channel imagery typically used by operational forecast staff and hence, can be a challenge to interpret. This presentation aims to discuss the integration of such new imagery into operational use as well as the benefits assessed by the Albuquerque WFO over several documented events.

  2. Integration of RGB "Dust" Imagery to Operations at the Albuquerque Forecast Office

    NASA Technical Reports Server (NTRS)

    Fuell, Kevin; Guyer, Brian

    2014-01-01

    The NASA/Short-term Prediction, Research, and Transition (SPoRT) Program has been providing unique Red-Green-Blue (RGB) composite imagery to its operational partners since 2005. In the early years of activity these RGB products were related to a True Color RGB, showing what one's own eyes would see if looking down at earth from space, as well as a Snow-Cloud RGB (i.e. False Color), separating clouds from snow on the ground. More recently SPoRT has used the EUMETSAT Best Practices standards for RGB composites to transition a wide array of imagery for multiple uses. A "Dust" RGB product has had particular use at the Albuquerque, New Mexico WFO. Several cases have occurred where users were able to isolate dust plume locations for mesoscale and microscale events during day and night time conditions. In addition the "Dust" RGB can be used for more than just detection of dust as it is sensitive to the changes in density due to atmospheric moisture content. Hence low-level dry boundaries can often be discriminated. This type of imagery is a large change from the single channel imagery typically used by operational forecast staff and hence, can be a challenge to interpret. This presentation aims to discuss the integration of such new imagery into operational use as well as the benefits assessed by the Albuquerque WFO over several documented events.

  3. Evaluation of Improved Pushback Forecasts Derived from Airline Ground Operations Data

    NASA Technical Reports Server (NTRS)

    Carr, Francis; Theis, Georg; Feron, Eric; Clarke, John-Paul

    2003-01-01

    Accurate and timely predictions of airline pushbacks can potentially lead to improved performance of automated decision-support tools for airport surface traffic, thus reducing the variability and average duration of costly airline delays. One factor which affects the realization of these benefits is the level of uncertainty inherent in the turn processes. To characterize this inherent uncertainty, three techniques are developed for predicting time-to-go until pushback as a function of available ground-time; elapsed ground-time; and the status (not-started/in-progress/completed) of individual turn processes (cleaning, fueling, etc.). These techniques are tested against a large and detailed dataset covering approximately l0(exp 4) real-world turn operations obtained through collaboration with Deutsche Lufthansa AG. Even after the dataset is filtered to obtain a sample of turn operations with minimal uncertainty, the standard deviation of forecast error for all three techniques is lower-bounded away from zero, indicating that turn operations have a significant stochastic component. This lower-bound result shows that decision-support tools must be designed to incorporate robust mechanisms for coping with pushback demand stochasticity, rather than treating the pushback demand process as a known deterministic input.

  4. Offline tracer transport modeling with global WRF model data

    NASA Astrophysics Data System (ADS)

    Belikov, Dmitry; Maksytov, Shamil; Zaripov, Radomir; Bart, Andrey; Starchenko, Alexander

    2013-04-01

    This work describes the one-way coupling between a global configuration of the Weather Research and Forecasting (WRF) weather prediction model (http://wrf-model.org/) and the National Institute for Environmental Studies (NIES) three-dimensional offline chemical transport model (version NIES-08.1i). The primary motivation for developing this coupled model has been to reduce transport errors in global-scale simulation of greenhouse gases through a more detailed description of the meteorological conditions. We have implemented a global configuration of WRF model (version 3.4.1, ARW core) with 2.5 degree horizontal resolution and 32 vertical levels. The WRF model was driving with NCEP Final Analysis (FNL) reanalysis using combined techniques: FDDA + Cyclic Incremental Correction (like in intermittent data assimilation). Time-averaged mass-coupled horizontal velocities on sigma levels with approach supposed by Nehrkorn et al. (2010) are calculated to drive NIES TM. The NIES TM is designed to simulate natural and anthropogenic synoptic-scale variations in atmospheric constituents at diurnal, seasonal and interannual timescales. The model uses a mass-conservative flux-form formulation that consists of a third-order van Leer advection scheme and a horizontal dry-air mass flux correction. The horizontal latitude-longitude grid is a reduced rectangular grid (i.e., the grid size is doubled several times approaching the poles), with an initial spatial resolution of 2.5 deg x 2.5 deg and 32 vertical levels from the surface up to the level of 3 hPa. A simulations of the atmospheric tracer are used to evaluate the performance of the coupled WRF-NIES model. Simulated distributions are validated against in situ observations and compared with output from "standard" version of NIES TM driven by the Japanese 25-year Reanalysis/the Japan Meteorological Agency Climate Data Assimilation System (JRA-25/JCDAS) dataset. Fields calculated by WRF and used to drive NIES TM were also evaluated by comparing it with JRA-25/JCDAS reanalysis distributed on Gaussian horizontal grid T106 (320 x 160) with 40 hybrid ?-p levels and the 6-hourly time step. Nehrkorn, T., Eluszkiewicz, J., Wofsy, S. C., Lin, J. C., Gerbig, C., Longo, M. and Freitas, S.: Coupled weather research and forecasting - stochastic time-inverted lagrangian transport (WRF-STILT) model, Meteorol. Atmos. Phys., 107, 51-64, doi:10.1007/s00703-010-0068-x, 2010.

  5. Assimilation of soil moisture observations from remote sensing in operational flood forecasting

    NASA Astrophysics Data System (ADS)

    Mazzoleni, Maurizio; Alfonso, Leonardo; Ferri, Michele; Monego, Martina; Norbiato, Daniele; Solomatine, Dimitri P.

    2014-05-01

    Flooding and the resulting damages occurred in Europe in recent decades showed that the need of a preparation to critical events can be considered as a key factor in reducing their impact on society. It has been shown that early warning systems may reduce significantly the direct and indirect damages and costs of a flood impact. In order to improve the forecasting systems, data assimilation methods were proposed in the last years to integrate real-time observations into hydrological and hydrodynamic models. The aim of this work is to assimilate observations of soil moisture into an operational flood forecasting system in Italy in order to evaluate the effect on the water level along the main river channel. The methodology is applied in the Bacchiglione catchment, located in the North of Italy, having a drainage area of about 1400 km2, length of main reach of 118km and average discharge of 30m3/s at Padova. In order to represent this system, the Bacchiglione basin was considered as a set of different sub-basins characterized by its own hydrologic response and connected each other mainly by propagation phenomena. A 1D hydrodynamic model was then used to estimate water level along the main channel. The assimilation of the soil moisture observations was carried out using a variant of the Kalman filter-based technique. The main idea of this study was to update the model state (the soil water capacity) as response of the distributed information of soil moisture, and then estimate the flow hydrograph at the basin outlet. As a basis we used the approach by Brocca et al.(2012), using a different model structure and with adaption allowing for real-time use. The results of this work show how the added value of soil moisture into the hydrological model can improve the forecast of the flow hydrograph and the consequent water level in the main channel. This study is part of the FP7 European Project WeSenseIt. [1] Brocca, L., Moramarco, T., Melone, F., Wagner, W., Hasenauer, S., and Hahn, S. (2012) Assimilation of Surface- and Root-Zone ASCAT Soil Moisture Products Into Rainfall-Runoff Modeling, IEEE Transactions on Geoscience and Remote Sensing, 50(7), 2542-2555

  6. An Upper Ocean Model for Operational Forecasts During MaudNESS

    NASA Astrophysics Data System (ADS)

    McPhee, M. G.

    2006-12-01

    The MaudNESS experiment required onboard assimilation of weather data and forecasts, along with remote sensing of ice concentration, into real-time models in order to (i) determine most likely regions for encountering marginal upper ocean stability; (ii) forecast ice trajectories during passive drifts; and (iii) aid in determining optimum ship orientation to minimize "watch circle" problems with up to four instrument systems deployed from the vessel. In planning the experiment, a number of different approaches were suggested and implemented. Here I describe combining relatively simple ice and upper ocean models with ice concentration and 5-day weather forecasts provided daily by the Antarctic Mesoscale Prediction System (AMPS) at NCAR. The model was used as an operational tool during the project. The upper ocean model is an adaptation of local turbulence closure (LTC) based on similarity scaling and modified to approximate local in situ density gradients using finite difference of the density (including pressure) across two points on the mean value grid (in a staggered grid system) at pressure evaluated at the midpoint. This provides at least a rough approximation of the thermobaric and other equation-of-state characteristics dependent on pressure, which are thought to be important in regions of low static stability in the eastern Weddell Sea. Model diffusivities are determined by a combination of local scale velocity and buoyancy flux. In the relatively well mixed layer above the pycnocline, LTC turbulence scales are determined by the scale velocity and buoyancy flux at the ice-ocean interface, where the turbulent scale velocity is determined mainly by wind speed and buoyancy flux depends on interface conditions, involving mixed-layer temperature and salinity, conductive heat flux in the ice cover, ice salinity, and interface friction velocity. Ice concentration modifies surface fluxes by apportioning buoyancy flux between ice covered and open ocean conditions. Conductive heat flux in the ice is related to air-water temperature difference through an ice layer with constant thermal conductivity overlain by a thin snow cover. Examples of how the model was used during the experiment will be shown along with "hindcast" results obtained since.

  7. Forecasting the mixed layer depth in the north east Atlantic: an ensemble approach, with uncertainties based on data from operational oceanic systems

    NASA Astrophysics Data System (ADS)

    Drillet, Y.; Lellouche, J. M.; Levier, B.; Drévillon, M.; Le Galloudec, O.; Reffray, G.; Regnier, C.; Greiner, E.; Clavier, M.

    2014-06-01

    Operational systems operated by Mercator Océan provide daily ocean forecasts, and combining these forecasts we can produce ensemble forecast and uncertainty estimates. This study focuses on the mixed layer depth in the North East Atlantic near the Porcupine Abyssal Plain for May 2013. This period is of interest for several reasons: (1) four Mercator Océan operational systems provide daily forecasts at a horizontal resolution of 1/4°, 1/12° and 1/36° with different physics; (2) glider deployment under the OSMOSIS project provides observation of the changes in mixed layer depth; (3) the ocean stratifies in May, but mixing events induced by gale force wind are observed and forecasted by the systems. A statistical approach and forecast error quantification for each system and for the combined products are presented. Skill scores indicate that forecasts are in any case better than persistence, and temporal correlations between forecast and observations are greater than 0.8 even for the 4 day forecast. The impact of atmospheric forecast error, and for the wind field in particular, is also quantified in terms of the forecast time delay and the intensity of mixing or stratification events.

  8. Operational forecasting of daily temperatures in the Valencia Region. Part I: maximum temperatures in summer.

    NASA Astrophysics Data System (ADS)

    Gómez, I.; Estrela, M.

    2009-09-01

    Extreme temperature events have a great impact on human society. Knowledge of summer maximum temperatures is very useful for both the general public and organisations whose workers have to operate in the open, e.g. railways, roadways, tourism, etc. Moreover, summer maximum daily temperatures are considered a parameter of interest and concern since persistent heat-waves can affect areas as diverse as public health, energy consumption, etc. Thus, an accurate forecasting of these temperatures could help to predict heat-wave conditions and permit the implementation of strategies aimed at minimizing the negative effects that high temperatures have on human health. The aim of this work is to evaluate the skill of the RAMS model in determining daily maximum temperatures during summer over the Valencia Region. For this, we have used the real-time configuration of this model currently running at the CEAM Foundation. To carry out the model verification process, we have analysed not only the global behaviour of the model for the whole Valencia Region, but also its behaviour for the individual stations distributed within this area. The study has been performed for the summer forecast period of 1 June - 30 September, 2007. The results obtained are encouraging and indicate a good agreement between the observed and simulated maximum temperatures. Moreover, the model captures quite well the temperatures in the extreme heat episodes. Acknowledgement. This work was supported by "GRACCIE" (CSD2007-00067, Programa Consolider-Ingenio 2010), by the Spanish Ministerio de Educación y Ciencia, contract number CGL2005-03386/CLI, and by the Regional Government of Valencia Conselleria de Sanitat, contract "Simulación de las olas de calor e invasiones de frío y su regionalización en la Comunidad Valenciana" ("Heat wave and cold invasion simulation and their regionalization at Valencia Region"). The CEAM Foundation is supported by the Generalitat Valenciana and BANCAIXA (Valencia, Spain).

  9. Operational forecasting of daily temperatures in the Valencia Region. Part II: minimum temperatures in winter.

    NASA Astrophysics Data System (ADS)

    Gómez, I.; Estrela, M.

    2009-09-01

    Extreme temperature events have a great impact on human society. Knowledge of minimum temperatures during winter is very useful for both the general public and organisations whose workers have to operate in the open, e.g. railways, roadways, tourism, etc. Moreover, winter minimum temperatures are considered a parameter of interest and concern since persistent cold-waves can affect areas as diverse as public health, energy consumption, etc. Thus, an accurate forecasting of these temperatures could help to predict cold-wave conditions and permit the implementation of strategies aimed at minimizing the negative effects that low temperatures have on human health. The aim of this work is to evaluate the skill of the RAMS model in determining daily minimum temperatures during winter over the Valencia Region. For this, we have used the real-time configuration of this model currently running at the CEAM Foundation. To carry out the model verification process, we have analysed not only the global behaviour of the model for the whole Valencia Region, but also its behaviour for the individual stations distributed within this area. The study has been performed for the winter forecast period from 1 December 2007 - 31 March 2008. The results obtained are encouraging and indicate a good agreement between the observed and simulated minimum temperatures. Moreover, the model captures quite well the temperatures in the extreme cold episodes. Acknowledgement. This work was supported by "GRACCIE" (CSD2007-00067, Programa Consolider-Ingenio 2010), by the Spanish Ministerio de Educación y Ciencia, contract number CGL2005-03386/CLI, and by the Regional Government of Valencia Conselleria de Sanitat, contract "Simulación de las olas de calor e invasiones de frío y su regionalización en la Comunidad Valenciana" ("Heat wave and cold invasion simulation and their regionalization at Valencia Region"). The CEAM Foundation is supported by the Generalitat Valenciana and BANCAIXA (Valencia, Spain).

  10. Integrated Flood Forecast and Virtual Dam Operation System for Water Resources and Flood Risk Management

    NASA Astrophysics Data System (ADS)

    Shibuo, Yoshihiro; Ikoma, Eiji; Lawford, Peter; Oyanagi, Misa; Kanauchi, Shizu; Koudelova, Petra; Kitsuregawa, Masaru; Koike, Toshio

    2014-05-01

    While availability of hydrological- and hydrometeorological data shows growing tendency and advanced modeling techniques are emerging, such newly available data and advanced models may not always be applied in the field of decision-making. In this study we present an integrated system of ensemble streamflow forecast (ESP) and virtual dam simulator, which is designed to support river and dam manager's decision making. The system consists of three main functions: real time hydrological model, ESP model, and dam simulator model. In the real time model, the system simulates current condition of river basins, such as soil moisture and river discharges, using LSM coupled distributed hydrological model. The ESP model takes initial condition from the real time model's output and generates ESP, based on numerical weather prediction. The dam simulator model provides virtual dam operation and users can experience impact of dam control on remaining reservoir volume and downstream flood under the anticipated flood forecast. Thus the river and dam managers shall be able to evaluate benefit of priori dam release and flood risk reduction at the same time, on real time basis. Furthermore the system has been developed under the concept of data and models integration, and it is coupled with Data Integration and Analysis System (DIAS) - a Japanese national project for integrating and analyzing massive amount of observational and model data. Therefore it has advantage in direct use of miscellaneous data from point/radar-derived observation, numerical weather prediction output, to satellite imagery stored in data archive. Output of the system is accessible over the web interface, making information available with relative ease, e.g. from ordinary PC to mobile devices. We have been applying the system to the Upper Tone region, located northwest from Tokyo metropolitan area, and we show application example of the system in recent flood events caused by typhoons.

  11. Operational hydrological ensemble forecasts in France. Recent development of the French Hydropower Company (EDF), taking into account rainfall and hydrological model uncertainties.

    NASA Astrophysics Data System (ADS)

    Mathevet, T.; Garavaglia, F.; Garon, R.; Gailhard, J.; Paquet, E.

    2009-04-01

    In operational conditions, the actual quality of meteorological and hydrological forecasts do not allow decision-making in a certain future. In this context, meteorological and hydrological ensemble forecasts allow a better representation of forecasts uncertainties. Compared to classical deterministic forecasts, ensemble forecasts improve the human expertise of hydrological forecasts, which is essential to synthesize available informations, coming from different meteorological and hydrological models and human experience. In this paper, we present a hydrological ensemble forecasting system under development at EDF (French Hydropower Company). This forecasting system both takes into account rainfall forecasts uncertainties and hydrological model forecasts uncertainties. Hydrological forecasts were generated using the MORDOR model (Andreassian et al., 2006), developed at EDF and used on a daily basis in operational conditions on a hundred of watersheds. Two sources of rainfall forecasts were used : one is based on ECMWF forecasts, another is based on an analogues approach (Obled et al., 2002). Two methods of hydrological model forecasts uncertainty estimation were used : one is based on the use of equifinal parameter sets (Beven & Binley, 1992), the other is based on the statistical modelisation of the hydrological forecast empirical uncertainty (Montanari et al., 2004 ; Schaefli et al., 2007). Daily operational hydrological 7-day ensemble forecasts during 2 years in 3 alpine watersheds were evaluated. Finally, we present a way to combine rainfall and hydrological model forecast uncertainties to achieve a good probabilistic calibration. Our results show that the combination of ECMWF and analogues-based rainfall forecasts allow a good probabilistic calibration of rainfall forecasts. They show also that the statistical modeling of the hydrological forecast empirical uncertainty has a better probabilistic calibration, than the equifinal parameter set approach. Andreassian et al., 2006. Catalogue of the models used in MOPEX 2004/2005. Large sample basin experiments for hydrological mode parameterisation : results of the Model Parameter Experiment, IAHS Publ. 307, 41-94. Beven & Binley, 1992. The future of distributed models : model calibration and uncertainty prediction. Hydrological Processes, 6, 279-298. Obled, C., Bontron, G., Garon, R., 2002. Quantitative precipitation forecasts: a statistical adaptation of model outputs though an analogues sorting approach. Atmospheric Research, 63, 303-324. Montanari, A. and Brath, A., 2004. A stochastic approach for assessing the uncertainty of rainfall-runoff simulations. Water Resources Research, 40, W01106, doi:10.1029/2003WR002540. Schaefli, B., Balin Talamba, D. and Musy, A., 2007. Quantifying hydrological modeling errors through a mixture of normal distributions. Journal of Hydrology, 332, 303-315.

  12. An operational medium range local weather forecasting system developed in India

    NASA Astrophysics Data System (ADS)

    Kumar, Ashok; Maini, Parvinder; Rathore, L. S.; Singh, S. V.

    2000-01-01

    A forecasting system for objective medium range location specific forecasts of surface weather elements was evolved at the National Centre for Medium Range Weather Forecasting (NCMRWF). The basic information used for this is the output from a general circulation model (GCM). The two essential components of the system are statistical interpretation (SI) forecast and direct model output (DMO) forecast. These are explained in brief. The SI forecast is obtained by using dynamical-statistical methods like model output statistics (MOS) and the perfect prog method (PPM) in which prediction of upper air circulation from a GCM around the location of interest is used. The DMO forecast is obtained from the prediction of surface weather elements from the GCM. The procedure for preparation of final forecast by using these two components and prevailing synoptic conditions is also explained. This is essentially a man-machine-mix approach. Finally, an evaluation of the forecast skill for the 1996 monsoon and some of the future plans are presented.

  13. Real-time operative earthquake forecasting: the case of L'Aquila sequence

    NASA Astrophysics Data System (ADS)

    Marzocchi, W.; Lombardi, A.

    2009-12-01

    A reliable earthquake forecast is one of the fundamental components required for reducing seismic risk. Despite very recent efforts devoted to test the validity of available models, the present skill at forecasting the evolution of seismicity is still largely unknown. The recent Mw 6.3 earthquake - that struck near the city of L'Aquila, Italy on April 6, 2009, causing hundreds of deaths and vast damages - offered to scientists a unique opportunity to test for the first time the forecasting capability in a real-time application. Here, we describe the results of this first prospective experiment. Immediately following the large event, we began producing daily one-day earthquake forecasts for the region, and we provided these forecasts to Civil Protection - the agency responsible for managing the emergency. The forecasts are based on a stochastic model that combines the Gutenberg-Richter distribution of earthquake magnitudes and power-law decay in space and time of triggered earthquakes. The results from the first month following the L'Aquila earthquake exhibit a good fit between forecasts and observations, indicating that accurate earthquake forecasting is now a realistic goal. Our experience with this experiment demonstrates an urgent need for a connection between probabilistic forecasts and decision-making in order to establish - before crises - quantitative and transparent protocols for decision support.

  14. Sea Level Rise as Covariate for Extreme Value Analysis and Forecasting at the Operational Level

    NASA Astrophysics Data System (ADS)

    Toilliez, J. O.; Fay, S.

    2012-12-01

    A time-dependent statistical model to predict the probability of certain extreme water levels (WL) is presented. The model uses a time-dependent extreme-value distribution (GEVD) to fit annual maxima series from select locations in the continental United States. The objectives are two-fold. First, the model integrates sea-level rise as covariate to yield the best fit for the variability observed in the historical data. Conversely, the inclusion of sea-level rise (SLR) allows projections to be made about the probability of future occurrences of extreme water levels using state, federal and international guidance on SLR forecasted values. Secondly, the model is compared with commonly used operational methods (such as ranking, steady-state extreme value analysis) to project extreme water levels, which rely on the assumption of stationarity of the signal. We show that in certain areas, this assumption is violated, thereby potentially invalidating the conclusions of such methods. The paper focuses on the application of extreme value models at the operational level. It is part of an on-going effort from the marine civil infrastructure industry to understand, deploy and apply judicious methods to effectively integrate with long-term, climate-related variability in their marine structure design.

  15. Ensemble Data Assimilation for Channel Flow Routing to Improve Operational Hydrologic Forecasting

    NASA Astrophysics Data System (ADS)

    Liu, Y.; Lee, H.; Seo, D.; Brown, J.; Corby, R.; Howieson, T.

    2008-12-01

    Channel flow routing, which predicts hydrograph transformation as water moves downstream, is a critical step in operational forecasting of floods and water resources. Like hydrologic modeling for headwater basins, routing modeling involves many kinds of uncertainties arising from observational data and the model itself. In addition to in-channel transformations, routing must also consider uncertainties from less-than-well-known sources and sinks along the channel. Data assimilation holds large potential in accounting for these different uncertainties in a dynamically and statistically consistent way. In this presentation, we describe an application of ensemble data assimilation for a hydrologic channel routing model based on the variable three-parameter Muskingum method, in which we consider errors in the inflow and outflow observations, and uncertainties in the initial conditions and Muskingum parameters. For data assimilation, we adopt the Maximum Likelihood Ensemble Filter (or MLEF, Zupanski 2005), which combines the strengths of variational data assimilation and ensemble filtering techniques. Results from applications to selected river sections in Texas in the WGRFC's service area will be presented, along with issues from research and operational perspectives.

  16. Toward long-lead operational forecasts of drought: An experimental study in the Murray-Darling River Basin

    NASA Astrophysics Data System (ADS)

    Barros, Ana P.; Bowden, Gavin J.

    2008-08-01

    SummaryResiliency and effectiveness in water resources management of drought is strongly depend on advanced knowledge of drought onset, duration and severity. The motivation of this work is to extend the lead time of operational drought forecasts. The research strategy is to explore the predictability of drought severity from space-time varying indices of large-scale climate phenomena relevant to regional hydrometeorology (e.g. ENSO) by integrating linear and non-linear statistical data models, specifically self-organizing maps (SOM) and multivariate linear regression analysis. The methodology is demonstrated through the step-by-step development of a model to forecast monthly spatial patterns of the standard precipitation index (SPI) within the Murray-Darling Basin (MDB) in Australia up to 12 months in advance. First, the rationale for the physical hypothesis and the exploratory data analysis including principal components, wavelet and partial mutual information analysis to identify and select predictor variables are presented. The focus is on spatial datasets of precipitation, sea surface temperature anomaly (SSTA) patterns over the Indian and Pacific Oceans, temporal and spatial gradients of outgoing longwave radiation (OLR) in the Pacific Ocean, and the far western Pacific wind-stress anomaly. Second, the process of model construction, calibration and evaluation is described. The experimental forecasts show that there is ample opportunity to increase the lead time of drought forecasts for decision support using parsimonious data models that capture the governing climate processes at regional scale. OLR gradients proved to be dispensable predictors, whereas SPI-based predictors appear to control predictability when the SSTA in the region [87.5N-87.5S; 27.5E-67.5W] and eastward wind-stress anomalies in the region [4N-4S; 130E-160E) are small, respectively, 1 and 0.01 dyne/cm 2, that is when ENSO activity is weak. The areal averaged 12-month lead-time forecasts of SPI in the MDB explain up to 60% of the variance in the observations ( r > 0.7). Based on a threshold SPI of -0.5 for severe drought at the regional scale and for a nominal 12-month lead time, the forecast of the timing of onset is within 0-2 months of the actual threshold being met by the observations, thus effectively a 10-month lead time forecast at a minimum. Spatial analysis suggests that forecast errors can be attributed in part to a mismatch between the spatial heterogeneity of rainfall and raingauge density in the observational network. Forecast uncertainty on the other hand appears associated with the number of redundant predictors used in the forecast model.

  17. Impact of Model Resolution and Snow Cover Modification on the Performance of Weather Forecasting and Research (WRF) Models of Winter Conditions that Contribute to Ozone Pollution in the Uintah Basin, Eastern Utah, Winter 2013. Trang T. Tran, Marc Mansfield and Seth Lyman Bingham Research Center, Utah State University

    NASA Astrophysics Data System (ADS)

    Tran, T. T.; Mansfield, M. L.; Lyman, S.

    2013-12-01

    The Uintah Basin of Eastern Utah, USA, has experienced winter ozone pollution events with ozone concentrations exceeding the National Ambient Air Quality Standard of 75 ppb. With a total of four winter seasons of ozone sampling, winter 2013 is the worst on record for ozone pollution in the basin. Emissions of volatile organic compounds (VOCs) and nitrogen oxides (NOx) from oil and gas industries and other activities provide the precursors for ozone formation. The chemical mechanism of ozone formation is non-linear and complicated depending on the availability of VOCs and NOx. Moreover, meteorological conditions also play an important role in triggering ozone pollution. In the Uintah Basin, high albedo due to snow cover, a 'bowl-shaped' terrain, and strong inversions that trap precursors within the boundary layer are important factors contributing to ozone pollution. However, these local meteorological phenomena have been misrepresented by recent numerical modeling studies, probably due to misrepresenting the snow cover and complex terrain of the basin. In this study, Weather Research and Forecasting (WRF) simulations are performed on a model domain covering the entire Uintah Basin for winter 2013 (Dec 2012 - Mar 2013) to test the impacts of several grid resolutions (e.g., 4000, 1300 and 800m) and snow cover modification on performance of models of the local weather conditions of the basin. These sensitivity tests help to determine the best model configurations to produce appropriate meteorological input for air-quality simulations.

  18. The Use of Operational Short and Long Lead-time Hydrologic Forecasts by Water Resources Decision Makers in the Ohio River Valley

    NASA Astrophysics Data System (ADS)

    Adams, T. E.

    2012-12-01

    The need for hydroclimatic forecasts for water resources systems operations is significant and is clearly growing. Hydroclimatic forecasts consist of two components: first, forecasts of hydrometeorological forcings used to drive hydrologic models and, second, the resulting streamflow and stage forecasts or derivative quantities, such as reservoir inflow volumes or time above (or below) some threshold value. These forecast range from hourly to annual lead-times and include both deterministic and probabilistic formats. In the Ohio River Valley, forecasts are made available by the NOAA/NWS Ohio River Forecast Center to decision makers. These include the general public, local and state emergency managers and other officials, federal agencies, utilities, the navigation industry, and agricultural sector, and others. Hydrologic forecasts are utilized by end-users for widely varying purposes including flood warning and mitigation, reservoir management, and decision making for construction projects, to name a few. This paper will illustrate the range of NWS hydrologic streamflow and stage products that are made publicly available and how some of the forecasts are used during drought or low-flow periods and during episodes of flooding. The methodologies used to generate hydroclimatic forecasts and the complexities found in large-scale operational systems and their impact on forecast robustness will also be discussed.

  19. Downscaling seasonal to centennial simulations on distributed computing infrastructures using WRF model. The WRF4G project

    NASA Astrophysics Data System (ADS)

    Cofino, A. S.; Fernndez Quiruelas, V.; Blanco Real, J. C.; Garca Dez, M.; Fernndez, J.

    2013-12-01

    Nowadays Grid Computing is powerful computational tool which is ready to be used for scientific community in different areas (such as biomedicine, astrophysics, climate, etc.). However, the use of this distributed computing infrastructures (DCI) is not yet common practice in climate research, and only a few teams and applications in this area take advantage of this infrastructure. Thus, the WRF4G project objective is to popularize the use of this technology in the atmospheric sciences area. In order to achieve this objective, one of the most used applications has been taken (WRF; a limited- area model, successor of the MM5 model), that has a user community formed by more than 8000 researchers worldwide. This community develop its research activity on different areas and could benefit from the advantages of Grid resources (case study simulations, regional hind-cast/forecast, sensitivity studies, etc.). The WRF model is used by many groups, in the climate research community, to carry on downscaling simulations. Therefore this community will also benefit. However, Grid infrastructures have some drawbacks for the execution of applications that make an intensive use of CPU and memory for a long period of time. This makes necessary to develop a specific framework (middleware). This middleware encapsulates the application and provides appropriate services for the monitoring and management of the simulations and the data. Thus,another objective of theWRF4G project consists on the development of a generic adaptation of WRF to DCIs. It should simplify the access to the DCIs for the researchers, and also to free them from the technical and computational aspects of the use of theses DCI. Finally, in order to demonstrate the ability of WRF4G solving actual scientific challenges with interest and relevance on the climate science (implying a high computational cost) we will shown results from different kind of downscaling experiments, like ERA-Interim re-analysis, CMIP5 models, or seasonal. WRF4G is been used to run WRF simulations which are contributing to the CORDEX initiative and others projects like SPECS and EUPORIAS. This work is been partially funded by the European Regional Development Fund (ERDF) and the Spanish National R&D Plan 2008-2011 (CGL2011-28864)

  20. Operational reservoir inflow forecasting with radar altimetry: the Zambezi case study

    NASA Astrophysics Data System (ADS)

    Michailovsky, C. I.; Bauer-Gottwein, P.

    2014-03-01

    River basin management can greatly benefit from short-term river discharge predictions. In order to improve model produced discharge forecasts, data assimilation allows for the integration of current observations of the hydrological system to produce improved forecasts and reduce prediction uncertainty. Data assimilation is widely used in operational applications to update hydrological models with in situ discharge or level measurements. In areas where timely access to in situ data is not possible, remote sensing data products can be used in assimilation schemes. While river discharge itself cannot be measured from space, radar altimetry can track surface water level variations at crossing locations between the satellite ground track and the river system called virtual stations (VS). Use of radar altimetry versus traditional monitoring in operational settings is complicated by the low temporal resolution of the data (between 10 and 35 days revisit time at a VS depending on the satellite) as well as the fact that the location of the measurements is not necessarily at the point of interest. However, combining radar altimetry from multiple VS with hydrological models can help overcome these limitations. In this study, a rainfall runoff model of the Zambezi River basin is built using remote sensing data sets and used to drive a routing scheme coupled to a simple floodplain model. The extended Kalman filter is used to update the states in the routing model with data from 9 Envisat VS. Model fit was improved through assimilation with the Nash-Sutcliffe model efficiencies increasing from 0.19 to 0.62 and from 0.82 to 0.88 at the outlets of two distinct watersheds, the initial NSE (Nash-Sutcliffe efficiency) being low at one outlet due to large errors in the precipitation data set. However, model reliability was poor in one watershed with only 58 and 44% of observations falling in the 90% confidence bounds, for the open loop and assimilation runs respectively, pointing to problems with the simple approach used to represent model error.

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

    SciTech Connect

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

    1995-09-11

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

  2. Towards an operational system for oil-spill forecast over Spanish waters: initial developments and implementation test.

    PubMed

    Sotillo, M G; Fanjul, E Alvarez; Castanedo, S; Abascal, A J; Menendez, J; Emelianov, M; Olivella, R; García-Ladona, E; Ruiz-Villarreal, M; Conde, J; Gómez, M; Conde, P; Gutierrez, A D; Medina, R

    2008-04-01

    The ESEOO Project, launched after the Prestige crisis, has boosted operational oceanography capacities in Spain, creating new operational oceanographic services and increasing synergies between these new operational tools and already existing systems. In consequence, the present preparedness to face an oil-spill crisis is enhanced, significantly improving the operational response regarding ocean, meteorological and oil-spill monitoring and forecasting. A key aspect of this progress has been the agreement between the scientific community and the Spanish Search and Rescue Institution (SASEMAR), significantly favoured within the ESEOO framework. Important achievements of this collaboration are: (1) the design of protocols that at the crisis time provide operational state-of-the-art information, derived from both forecasting and observing systems; (2) the establishment, in case of oil-spill crisis, of a new specialized unit, named USyP, to monitor and forecast the marine oceanographic situation, providing the required met-ocean and oil-spill information for the crisis managers. The oil-spill crisis scenario simulated during the international search and rescue Exercise "Gijón-2006", organized by SASEMAR, represented an excellent opportunity to test the capabilities and the effectiveness of this USyP unit, as well as the protocols established to analyze and transfer information. The results presented in this work illustrate the effectiveness of the operational approach, and constitute an encouraging and improved base to face oil-spill crisis. PMID:18321533

  3. Numerical simulation of circulation in Kara and Pechora Seas using the system of operational diagnosis and forecast of the marine dynamics

    NASA Astrophysics Data System (ADS)

    Diansky, Nikolay; Fomin, Vladimir; Kabatchenko, Ilya; Gusev, Anatoly

    2015-04-01

    The system of operational diagnosis and forecast (SODaF) is presented for hydrometeorological characteristics of Kara and Pechora Seas, which is implemented in the N.N.Zubov State Oceanography Institute (SOI). It includes the computation of atmospheric forcing using the WRF model, computation of currents, sea level, temperature, salinity and sea ice using the model INMOM, and computation of wind wave parameters using Russian Wind Wave Model (RWWM).The results of the verification are presented including simulated hydrometeocharacteristics obtained by SODaF for Kara and Pechora Seas. As well, the retrospective simulation was performed for thermohydrodynamical characteristics of these seas for the ice-free period of 2003-2012. The important features of circulation in Kara and Pechora Seas and the structure of water exchange between them in the ice-free period are shown. The use of non-hydrostatic atmospheric model WRF allows one to reproduce katabatic winds formed over the glaciers. In general, the direction and speed of katabatic winds are fairly permanent. In accordance with the nature of katabatic winds, they are intensified from warm to cold period that is well manifested in the wind map for August. The basis of the Kara Sea circulation is NewLand, Yamal and Ob-Yenisey currents, which are well reproduced with the INMOM. It is shown that the main contribution to the monthly mean circulation of Kara and Pechora seas is made by wind currents. In the western part of the Kara Sea between the mainland and the New Land in the fall the pronounced cyclonic circulation is formed that is typical for closed seas. The main components of the circulation are the NewLand and Yamal currents flowing respectively along the eastern coast of NewLand and the western coast of the Yamal Peninsula.It is caused by regional winds directed from the "cold" land to the "warm" sea. In summer,such a circulation is broken along the coast of the mainland, so that the Yamal flow is reduced. This happensbecause hte significant contribution to the wind circulation is made by movement of air masses from the"cold" sea to the"warm" land.The same effect influences the intensification of the Ob-Yenisei current in autumn and its weakening in the summer. Katabatic winds form anticyclonic (in relation to the islands) circulation around NewLand and NorthLand islands. The most prominent structure of the circulation is NewLand current. Water exchange through the Kara Gate in the ice-free period is proceeding so that the outflow from Cara to Pechora Sea occurs along the New Land, and at the opposite coast of the Kara Gate, on the contrary, the inflow in the Kara Sea appears. Outflow is formed by NewLand current. This occurs not only in the surface layer, but also over the whole depth, as indicated by the se level distribution. At the same time, outflow from the Kara Sea in the ice-free period prevails over inflow.

  4. Forecasting Lake-Effect Precipitation in the Great Lakes Region Using NASA Enhanced-Satellite Data

    NASA Technical Reports Server (NTRS)

    Cipullo, Michelle; Molthan, Andrew; Shafer, Jackie; Case, Jonathan; Jedlovec, Gary

    2011-01-01

    Lake-effect precipitation is common in the Great Lakes region, particularly during the late fall and winter. The synoptic processes of lake-effect precipitation are well understood by operational forecasters, but individual forecast events still present a challenge. Locally run, high resolution models can assist the forecaster in identifying the onset and duration of precipitation, but model results are sensitive to initial conditions, particularly the assumed surface temperature of the Great Lakes. The NASA Short-term Prediction Research and Transition (SPoRT) Center has created a Great Lakes Surface Temperature (GLST) composite, which uses infrared estimates of water temperatures obtained from the MODIS instrument aboard the Aqua and Terra satellites, other coarser resolution infrared data when MODIS is not available, and ice cover maps produced by the NOAA Great Lakes Environmental Research Lab (GLERL). This product has been implemented into the Weather Research and Forecast (WRF) model Environmental Modeling System (WRF-EMS), used within forecast offices to run local, high resolution forecasts. The sensitivity of the model forecast to the GLST product was analyzed with a case study of the Lake Effect Storm Echinacea, which produced 10 to 12 inches of snowfall downwind of Lake Erie, and 8 to 18 inches downwind of Lake Ontario from 27-29 January 2010. This research compares a forecast using the default Great Lakes surface temperatures from the Real Time Global sea surface temperature (RTG SST), in the WRF-EMS model to the enhanced NASA SPoRT GLST product to study forecast impacts. Results from this case study show that the SPoRT GLST contained less ice cover over Lake Erie and generally cooler water temperatures over Lakes Erie and Ontario. Latent and sensible heat fluxes over Lake Ontario were decreased in the GLST product. The GLST product decreased the quantitative precipitation forecast (QPF), which can be correlated to the decrease in temperatures and heat fluxes. A slight increase in precipitation coverage was noted over Lake Erie due to a decrease in ice cover. Both the RTG SST and the GLST products predicted the precipitation south of the actual location of precipitation. This single case study is the first part of an examination to determine how MODIS data can be applied to improve model forecasts in the Great Lakes region.

  5. A modeling study on Sumatra squall lines using WRF

    NASA Astrophysics Data System (ADS)

    LO, C. F.

    2014-12-01

    Sumatra Squall Line (SSL) is a tropical squall line system that usually forms over Sumatra at night and then sweeps towards the west coast of Peninsular Malaysia and Singapore in the early morning. SSL is one of the dominant rain-bearing systems in the region, particularly between April and November each year. In this study, we examine a series of short term (<24 hours) numerical experiments using Weather Research and Forecasting (WRF) model, conducted under a monthly long high occurrences period in August 2013. We examine the predictability and evaluate the WRF performance. By examining over 20 SSL cases, we identify the general features of SSL and discuss its formation and propagation mechanisms. Through inter-comparison of simulations driven by GFS and ECMWF re-analyses, we also discuss the triggering effects of large scale atmospheric conditions on SSL.

  6. The HFIP High Resolution Hurricane Forecast Test

    NASA Astrophysics Data System (ADS)

    Nance, L. B.; Bernardet, L.; Bao, S.; Brown, B.; Carson, L.; Fowler, T.; Halley Gotway, J.; Harrop, C.; Szoke, E.; Tollerud, E. I.; Wolff, J.; Yuan, H.

    2010-12-01

    Tropical cyclones are a serious concern for the nation, causing significant risk to life, property and economic vitality. The National Oceanic and Atmospheric Administration (NOAA) National Weather Service has a mission of issuing tropical cyclone forecasts and warnings, aimed at protecting life and property and enhancing the national economy. In the last 10 years, the errors in hurricane track forecasts have been reduced by about 50% through improved model guidance, enhanced observations, and forecaster expertise. However, little progress has been made during this period toward reducing forecasted intensity errors. To address this shortcoming, NOAA established the Hurricane Forecast Improvement Project (HFIP) in 2007. HFIP is a 10-year plan to improve one to five day tropical cyclone forecasts, with a focus on rapid intensity change. Recent research suggests that prediction models with grid spacing less than 1 km in the inner core of the hurricane may provide a substantial improvement in intensity forecasts. The 2008-09 staging of the High Resolution Hurricane (HRH) Test focused on quantifying the impact of increased horizontal resolution in numerical models on hurricane intensity forecasts. The primary goal of this test was an evaluation of the effect of increasing horizontal resolution within a given model across a variety of storms with different intensity, location and structure. The test focused on 69 retrospectives cases from the 2005 and 2007 hurricane seasons. Six modeling groups participated in the HRH test utilizing a variety of models, including three configurations of the Weather Research and Forecasting (WRF) model, the operational GFDL model, the Navys tropical cyclone model, and a model developed at the University of Wisconsin-Madison (UWM). The Development Testbed Center (DTC) was tasked with providing objective verification statistics for a variety of metrics. This presentation provides an overview of the HRH Test and a summary of the standard verification results, as well as results obtained by applying new verification tools developed at the DTC that assess changes in forecast skill for Rapid Intensification (RI) and Rapid Weakening (RW) events and forecast consistency.

  7. Aviation & Space Weather Policy Research: Integrating Space Weather Observations & Forecasts into Operations

    NASA Astrophysics Data System (ADS)

    Fisher, G.; Jones, B.

    2006-12-01

    The American Meteorological Society and SolarMetrics Limited are conducting a policy research project leading to recommendations that will increase the safety, reliability, and efficiency of the nation's airline operations through more effective use of space weather forecasts and information. This study, which is funded by a 3-year National Science Foundation grant, also has the support of the Federal Aviation Administration and the Joint Planning and Development Office (JPDO) who is planning the Next Generation Air Transportation System. A major component involves interviewing and bringing together key people in the aviation industry who deal with space weather information. This research also examines public and industrial strategies and plans to respond to space weather information. The focus is to examine policy issues in implementing effective application of space weather services to the management of the nation's aviation system. The results from this project will provide government and industry leaders with additional tools and information to make effective decisions with respect to investments in space weather research and services. While space weather can impact the entire aviation industry, and this project will address national and international issues, the primary focus will be on developing a U.S. perspective for the airlines.

  8. Operational evaluation of forecast and diagnostic atmospheric models in a forest canopy environment

    SciTech Connect

    Atchison, M.K.; Dean, D.; Lambert, W.C.; Seely, S.

    1996-12-31

    For many years, simple Gaussian diffusion models have been used by the operational community for real-time air pollution analysis. These models generally produce quick, reasonable results for short distances and simple meteorological situations. However model simulations involving complex topography or weather patterns may produce very unreliable results. In order to aid the user in model selection, a study is currently underway to evaluate several mesoscale and trajectory-diffusion type models for source to sampler distances of up to 150 km in various environments. The models chosen for this study are the Regional Atmospheric Modeling System (RAMS), the Higher Order Turbulence Model for Atmospheric Circulation (HOTMAC) and the Short-Range Layered Atmospheric Model (SLAM). RAMS and HOTMAC are traditional meteorological forecast models while SLAM is a diagnostic trajectory and diffusion model. These models are undergoing an evaluation using ground-based tracer data from meteorological experiments representing desert, forest, complex topography, and lands/sea breeze physiographic environments. The focus of the present work will be in the forest canopy environment using data from the Short Range Experiment (SRE) conducted at the Savannah River Plant from March 1975 through September 1977.

  9. Impact of an improved WRF urban canopy model on diurnal air temperature simulation over northern Taiwan

    NASA Astrophysics Data System (ADS)

    Lin, Chuan-Yao; Su, Chiung-Jui; Kusaka, Hiroyuki; Akimoto, Yuko; Sheng, Yang-Fan; Huang, -Chuan, Jr.; Hsu, Huang-Hsiung

    2016-02-01

    This study evaluates the impact of urbanization over northern Taiwan using the Weather Research and Forecasting (WRF) Model coupled with the Noah land-surface model and a modified urban canopy model (WRF-UCM2D). In the original UCM coupled to WRF (WRF-UCM), when the land use in the model grid is identified as "urban", the urban fraction value is fixed. Similarly, the UCM assumes the distribution of anthropogenic heat (AH) to be constant. This may not only lead to over- or underestimation of urban fraction and AH in urban and non-urban areas, but spatial variation also affects the model-estimated temperature. To overcome the abovementioned limitations and to improve the performance of the original UCM model, WRF-UCM is modified to consider the 2-D urban fraction and AH (WRF-UCM2D).The two models were found to have comparable temperature simulation performance for urban areas, but large differences in simulated results were observed for non-urban areas, especially at nighttime. WRF-UCM2D yielded a higher correlation coefficient (R2) than WRF-UCM (0.72 vs. 0.48, respectively), while bias and RMSE achieved by WRF-UCM2D were both significantly smaller than those attained by WRF-UCM (0.27 and 1.27 vs. 1.12 and 1.89, respectively). In other words, the improved model not only enhanced correlation but also reduced bias and RMSE for the nighttime data of non-urban areas. WRF-UCM2D performed much better than WRF-UCM at non-urban stations with a low urban fraction during nighttime. The improved simulation performance of WRF-UCM2D in non-urban areas is attributed to the energy exchange which enables efficient turbulence mixing at a low urban fraction. The result of this study has a crucial implication for assessing the impacts of urbanization on air quality and regional climate.

  10. The water-bearing numerical model and its operational forecasting experiments part I: the water-bearing numerical model

    NASA Astrophysics Data System (ADS)

    Xia, Daqing; Xu, Youping

    1998-06-01

    In first paper of articles, the physical and calculating schemes of the water-bearing numerical model are described. The model is developed by bearing all species of hydrometeors in a conventional numerical model in which the dynamic framework of hydrostatic equilibrium is taken. The main contributions are: the mixing ratios of all species of hydrometeors are added as the prognostic variables of model, the prognostic equations of these hydrometeors are introduced, the cloud physical framework is specially designed, some technical measures are used to resolve a series of physical, mathematical and computational problems arising from water-bearing; and so on. The various problems (in such aspects as the designs of physical and calculating schemes and the composition of computational programme) which are exposed in feasibility test, in sensibility test, and especially in operational forecasting experiments are successfully resolved using a lot of technical measures having been developed from researches and tests. Finally, the operational forecasting running of the water-bearing numerical model and its forecasting system is realized stably and reliably, and the fine forecasts are obtained. All of these mentioned above will be described in second paper.

  11. Real-Time Ocean Forecasting System in support of U.S. Coast Guard's Search and Rescue Operations

    NASA Astrophysics Data System (ADS)

    Schoch, C.; Chao, Y.; Howlett, E.; Allen, A. A.

    2012-12-01

    This talk will describe a real-time ocean forecasting system off the U.S. west coast developed to enhance U.S. Coast Guard (USCG) decision support tools for search and rescue operations. The forecasting model is based on the Regional Ocean Modeling System (ROMS) with multi-domain nested configurations. A multi-scale 3-dimensional variational (3DVAR) data assimilation scheme is used to assimilate both in situ (e.g., gliders) and remotely sensed data from both satellite and land-based platforms (e.g., high-frequency (HF) radars). The performance of this real-time ocean forecasting system was evaluated during a two-week field experiment during July-August 2009 in Prince William Sound, Alaska. The 72-hour ocean forecast fields in Alaska's Prince William Sound and California coastal ocean are now produced in real-time and accessible by the USCG's decision support tool during search and rescue operations. Recent test results using the independent data collected by the USCG will be discussed.

  12. Evaluating the Contribution of NASA Remotely-Sensed Data Sets on a Convection-Allowing Forecast Model

    NASA Technical Reports Server (NTRS)

    Zavodsky, Bradley T.; Case, Jonathan L.; Molthan, Andrew L.

    2012-01-01

    The Short-term Prediction Research and Transition (SPoRT) Center is a collaborative partnership between NASA and operational forecasting partners, including a number of National Weather Service forecast offices. SPoRT provides real-time NASA products and capabilities to help its partners address specific operational forecast challenges. One challenge that forecasters face is using guidance from local and regional deterministic numerical models configured at convection-allowing resolution to help assess a variety of mesoscale/convective-scale phenomena such as sea-breezes, local wind circulations, and mesoscale convective weather potential on a given day. While guidance from convection-allowing models has proven valuable in many circumstances, the potential exists for model improvements by incorporating more representative land-water surface datasets, and by assimilating retrieved temperature and moisture profiles from hyper-spectral sounders. In order to help increase the accuracy of deterministic convection-allowing models, SPoRT produces real-time, 4-km CONUS forecasts using a configuration of the Weather Research and Forecasting (WRF) model (hereafter SPoRT-WRF) that includes unique NASA products and capabilities including 4-km resolution soil initialization data from the Land Information System (LIS), 2-km resolution SPoRT SST composites over oceans and large water bodies, high-resolution real-time Green Vegetation Fraction (GVF) composites derived from the Moderate-resolution Imaging Spectroradiometer (MODIS) instrument, and retrieved temperature and moisture profiles from the Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI). NCAR's Model Evaluation Tools (MET) verification package is used to generate statistics of model performance compared to in situ observations and rainfall analyses for three months during the summer of 2012 (June-August). Detailed analyses of specific severe weather outbreaks during the summer will be presented to assess the potential added-value of the SPoRT datasets and data assimilation methodology compared to a WRF configuration without the unique datasets and data assimilation.

  13. Technical Challenges and Solutions in Representing Lakes when using WRF in Downscaling Applications

    EPA Science Inventory

    The Weather Research and Forecasting (WRF) model is commonly used to make high resolution future projections of regional climate by downscaling global climate model (GCM) outputs. Because the GCM fields are typically at a much coarser spatial resolution than the target regional ...

  14. Operational Irrigation Scheduling for Citrus Trees with Soil Moisture Data Assimilation and Weather Forecast

    NASA Astrophysics Data System (ADS)

    Han, Xujun; Hendricks Franssen, Harrie-Jan; Martínez Alzamora, Fernando; Ángel Jiménez Bello, Miguel; Chanzy, André; Vereecken, Harry

    2015-04-01

    Agricultural areas in the Mediterranean are expected to face more drought stress in the future due to climate change and human activities. Irrigation scheduling is necessary to allocate the optimal water amount at the right time period to avoid unnecessary water losses. An operational data assimilation framework was set-up to combine model predictions and soil moisture measurements in an optimal way for characterizing the soil water status of the root zone. Irrigation amounts for the next days are optimized on the basis of the soil water status of the root zone and meteorological ensemble predictions. In these experiments, the uncertainties of atmospheric forcings and soil properties were considered. The uncertain model forcings were taken from an ensemble of weather forecasts by ECMWF, and delivered by MeteoFrance in this project. The improved soil moisture profile was used to calculate the irrigation requirement taking into account the root distribution of citrus trees in the subsurface. The approach was tested operationally for the experimental site near Picassent, Valencia, Spain. Three fields were irrigated according to our approach in the years 2013 and 2014. Three others were irrigated traditionally, based on FAO-criteria. Soil moisture was measured by FDR probes at 10 cm and 30 cm depth at various fields and these real time data were assimilated by the Local Ensemble Transform Kalman Filter (LETKF) into the Community Land Model (CLM) to improve the estimation of the soil moisture profile. The measured soil moisture was assimilated five times per day before the start of the next drip irrigation. The final results (total amount of irrigated water, stem water potential and citrus production) show that our strategy resulted in significantly less irrigated water compared to the FAO-irrigated fields, but without indications of increased water stress. Soil moisture contents did not decline over time in our approach, stem water potential measurements did not indicate water stress and citrus production was not affected by less irrigation.

  15. A two-stage method of quantitative flood risk analysis for reservoir real-time operation using ensemble-based hydrologic forecasts

    NASA Astrophysics Data System (ADS)

    Liu, P.

    2013-12-01

    Quantitative analysis of the risk for reservoir real-time operation is a hard task owing to the difficulty of accurate description of inflow uncertainties. The ensemble-based hydrologic forecasts directly depict the inflows not only the marginal distributions but also their persistence via scenarios. This motivates us to analyze the reservoir real-time operating risk with ensemble-based hydrologic forecasts as inputs. A method is developed by using the forecast horizon point to divide the future time into two stages, the forecast lead-time and the unpredicted time. The risk within the forecast lead-time is computed based on counting the failure number of forecast scenarios, and the risk in the unpredicted time is estimated using reservoir routing with the design floods and the reservoir water levels of forecast horizon point. As a result, a two-stage risk analysis method is set up to quantify the entire flood risks by defining the ratio of the number of scenarios that excessive the critical value to the total number of scenarios. The China's Three Gorges Reservoir (TGR) is selected as a case study, where the parameter and precipitation uncertainties are implemented to produce ensemble-based hydrologic forecasts. The Bayesian inference, Markov Chain Monte Carlo, is used to account for the parameter uncertainty. Two reservoir operation schemes, the real operated and scenario optimization, are evaluated for the flood risks and hydropower profits analysis. With the 2010 flood, it is found that the improvement of the hydrologic forecast accuracy is unnecessary to decrease the reservoir real-time operation risk, and most risks are from the forecast lead-time. It is therefore valuable to decrease the avarice of ensemble-based hydrologic forecasts with less bias for a reservoir operational purpose.

  16. Comparative analysis of operational forecasts versus actual weather conditions in airline flight planning, volume 3

    NASA Technical Reports Server (NTRS)

    Keitz, J. F.

    1982-01-01

    The impact of more timely and accurate weather data on airline flight planning with the emphasis on fuel savings is studied. This volume of the report discusses the results of Task 3 of the four major tasks included in the study. Task 3 compares flight plans developed on the Suitland forecast with actual data observed by the aircraft (and averaged over 10 degree segments). The results show that the average difference between the forecast and observed wind speed is 9 kts. without considering direction, and the average difference in the component of the forecast wind parallel to the direction of the observed wind is 13 kts. - both indicating that the Suitland forecast underestimates the wind speeds. The Root Mean Square (RMS) vector error is 30.1 kts. The average absolute difference in direction between the forecast and observed wind is 26 degrees and the temperature difference is 3 degree Centigrade. These results indicate that the forecast model as well as the verifying analysis used to develop comparison flight plans in Tasks 1 and 2 is a limiting factor and that the average potential fuel savings or penalty are up to 3.6 percent depending on the direction of flight.

  17. Demonstration of successful malaria forecasts for Botswana using an operational seasonal climate model

    NASA Astrophysics Data System (ADS)

    MacLeod, Dave A.; Jones, Anne; Di Giuseppe, Francesca; Caminade, Cyril; Morse, Andrew P.

    2015-04-01

    The severity and timing of seasonal malaria epidemics is strongly linked with temperature and rainfall. Advance warning of meteorological conditions from seasonal climate models can therefore potentially anticipate unusually strong epidemic events, building resilience and adapting to possible changes in the frequency of such events. Here we present validation of a process-based, dynamic malaria model driven by hindcasts from a state-of-the-art seasonal climate model from the European Centre for Medium-Range Weather Forecasts. We validate the climate and malaria models against observed meteorological and incidence data for Botswana over the period 1982-2006 the longest record of observed incidence data which has been used to validate a modeling system of this kind. We consider the impact of climate model biases, the relationship between climate and epidemiological predictability and the potential for skillful malaria forecasts. Forecast skill is demonstrated for upper tercile malaria incidence for the Botswana malaria season (January-May), using forecasts issued at the start of November; the forecast system anticipates six out of the seven upper tercile malaria seasons in the observational period. The length of the validation time series gives confidence in the conclusion that it is possible to make reliable forecasts of seasonal malaria risk, forming a key part of a health early warning system for Botswana and contributing to efforts to adapt to climate change.

  18. Operational specification and forecasting advances for Dst, LEO thermospheric densities, and aviation radiation dose and dose rate

    NASA Astrophysics Data System (ADS)

    Tobiska, W. Kent

    Space weathers effects upon the near-Earth environment are due to dynamic changes in the energy transfer processes from the Suns photons, particles, and fields. Of the space environment domains that are affected by space weather, the magnetosphere, thermosphere, and even troposphere are key regions that are affected. Space Environment Technologies (SET) has developed and is producing innovative space weather applications. Key operational systems for providing timely information about the effects of space weather on these domains are SETs Magnetosphere Alert and Prediction System (MAPS), LEO Alert and Prediction System (LAPS), and Automated Radiation Measurements for Aviation Safety (ARMAS) system. MAPS provides a forecast Dst index out to 6 days through the data-driven, redundant data stream Anemomilos algorithm. Anemomilos uses observational proxies for the magnitude, location, and velocity of solar ejecta events. This forecast index is used by satellite operations to characterize upcoming geomagnetic storms, for example. In addition, an ENLIL/Rice Dst prediction out to several days has also been developed and will be described. LAPS is the SET fully redundant operational system providing recent history, current epoch, and forecast solar and geomagnetic indices for use in operational versions of the JB2008 thermospheric density model. The thermospheric densities produced by that system, driven by the LAPS data, are forecast to 72-hours to provide the global mass densities for satellite operators. ARMAS is a project that has successfully demonstrated the operation of a micro dosimeter on aircraft to capture the real-time radiation environment due to Galactic Cosmic Rays and Solar Energetic Particles. The dose and dose-rates are captured on aircraft, downlinked in real-time via the Iridium satellites, processed on the ground, incorporated into the most recent NAIRAS global radiation climatology data runs, and made available to end users via the web and smart phone apps. ARMAS provides the weather of the radiation environment to improve air-crew and passenger safety. Many of the data products from MAPS, LAPS, and ARMAS are available on the SpaceWx smartphone app for iPhone, iPad, iPod, and Android professional users and public space weather education. We describe recent forecasting advances for moving the space weather information from these automated systems into operational, derivative products for communications, aviation, and satellite operations uses.

  19. Operational specification and forecasting advances for Dst, LEO thermospheric densities, and aviation radiation dose and dose rate

    NASA Astrophysics Data System (ADS)

    Tobiska, W.; Knipp, D. J.; Burke, W. J.; Bouwer, D.; Bailey, J. J.; Hagan, M. P.; Didkovsky, L. V.; Garrett, H. B.; Bowman, B. R.; Gannon, J. L.; Atwell, W.; Blake, J. B.; Crain, W.; Rice, D.; Schunk, R. W.; Fulgham, J.; Bell, D.; Gersey, B.; Wilkins, R.; Fuschino, R.; Flynn, C.; Cecil, K.; Mertens, C. J.; Xu, X.; Crowley, G.; Reynolds, A.; Azeem, S. I.; Wiley, S.; Holland, M.; Malone, K.

    2013-12-01

    Space weather's effects upon the near-Earth environment are due to dynamic changes in the energy transfer processes from the Sun's photons, particles, and fields. Of the space environment domains that are affected by space weather, the magnetosphere, thermosphere, and even troposphere are key regions that are affected. Space Environment Technologies (SET) has developed and is producing innovative space weather applications. Key operational systems for providing timely information about the effects of space weather on these domains are SET's Magnetosphere Alert and Prediction System (MAPS), LEO Alert and Prediction System (LAPS), and Automated Radiation Measurements for Aviation Safety (ARMAS) system. MAPS provides a forecast Dst index out to 6 days through the data-driven, redundant data stream Anemomilos algorithm. Anemomilos uses observational proxies for the magnitude, location, and velocity of solar ejecta events. This forecast index is used by satellite operations to characterize upcoming geomagnetic storms, for example. LAPS is the SET fully redundant operational system providing recent history, current epoch, and forecast solar and geomagnetic indices for use in operational versions of the JB2008 thermospheric density model. The thermospheric densities produced by that system, driven by the LAPS data, are forecast to 72-hours to provide the global mass densities for satellite operators. ARMAS is a project that has successfully demonstrated the operation of a micro dosimeter on aircraft to capture the real-time radiation environment due to Galactic Cosmic Rays and Solar Energetic Particles. The dose and dose-rates are captured on aircraft, downlinked in real-time via the Iridium satellites, processed on the ground, incorporated into the most recent NAIRAS global radiation climatology data runs, and made available to end users via the web and smart phone apps. ARMAS provides the 'weather' of the radiation environment to improve air-crew and passenger safety. Many of the data products from MAPS, LAPS, and ARMAS are available on the SpaceWx smartphone app for iPhone, iPad, iPod, and Android professional users and public space weather education. We describe recent forecasting advances for moving the space weather information from these automated systems into operational, derivative products for communications, aviation, and satellite operations uses.

  20. Community Coordinated Modeling Center: Paving the Way for Progress in Space Science Research to Operational Space Weather Forecasting

    NASA Astrophysics Data System (ADS)

    Kuznetsova, M. M.; Maddox, M. M.; Mays, M. L.; Mullinix, R.; MacNeice, P. J.; Pulkkinen, A. A.; Rastaetter, L.; Shim, J.; Taktakishvili, A.; Zheng, Y.; Wiegand, C.

    2013-12-01

    Community Coordinated Modeling Center (CCMC) was established at the dawn of the millennium as an essential element on the National Space Weather Program. One of the CCMC goals was to pave the way for progress in space science research to operational space weather forecasting. Over the years the CCMC acquired the unique experience in preparing complex models and model chains for operational environment, in developing and maintaining powerful web-based tools and systems ready to be used by space weather service providers and decision makers as well as in space weather prediction capabilities assessments. The presentation will showcase latest innovative solutions for space weather research, analysis, forecasting and validation and review on-going community-wide initiatives enabled by CCMC applications.

  1. Development of an Operation Control System for Photovoltaics and Electric Storage Heaters for Houses Based on Information in Weather Forecasts

    NASA Astrophysics Data System (ADS)

    Obara, Shin'ya

    An all-electric home using an electric storage heater with safety and cleaning is expanded. However, the general electric storage heater leads to an unpleasant room temperature and energy loss by the overs and shorts of the amount of heat radiation when the climate condition changes greatly. Consequently, the operation of the electric storage heater introduced into an all-electric home, a storage type electric water heater, and photovoltaics was planned using weather forecast information distributed by a communication line. The comfortable evaluation (the difference between a room-temperature target and a room-temperature result) when the proposed system was employed based on the operation planning, purchase electric energy, and capacity of photovoltaics was investigated. As a result, comfortable heating operation was realized by using weather forecast data; furthermore, it is expected that the purchase cost of the commercial power in daytime can be reduced by introducing photovoltaics. Moreover, when the capacity of the photovoltaics was increased, the surplus power was stored in the electric storage heater, but an extremely unpleasant room temperature was not shown in the investigation ranges of this paper. By obtaining weather information from the forecast of the day from an external service using a communication line, the heating system of the all-electric home with low energy loss and comfort temperature is realizable.

  2. A Study of Fog Characteristics Using a Coupled WRF-COBEL Model Over Thessaloniki Airport, Greece

    NASA Astrophysics Data System (ADS)

    Stolaki, Stavroula; Pytharoulis, Ioannis; Karacostas, Theodore

    2012-05-01

    An attempt is made to couple the one dimensional COBEL-ISBA (Code de Brouillard l'chelle Locale-Interactions Soil Biosphere Atmosphere) model with the WRF (Weather Research and Forecasting)-ARW (Advanced Research WRF) numerical weather prediction model to study a fog event that formed on 20 January 2008 over Thessaloniki Airport, Greece. It is the first time that the coupling of COBEL and WRF models is achieved and applied to a fog event over an airport. At first, the performance of the integrated WRF-COBEL system is investigated, by validating it against the available surface observations. The temperature and humidity vertical profiles were used for initializing the model. The performance of WRF-COBEL is considered successful, since it realistically simulated the fog onset and dissipation better than the WRF alone. The COBEL's sensitivity to initial conditions such as temperature and specific humidity perturbations was also tested. It is found that a small increase of temperature (~1C) counteracts fog development and results in less fog density. On the other hand, a small decrease of temperature results in much denser fog formation. It is concluded that the integrated model approach for aviation applications can be useful to study fog impact on local traffic and aviation.

  3. Development of a methodology for probable maximum precipitation estimation over the American River watershed using the WRF model

    NASA Astrophysics Data System (ADS)

    Tan, Elcin

    A new physically-based methodology for probable maximum precipitation (PMP) estimation is developed over the American River Watershed (ARW) using the Weather Research and Forecast (WRF-ARW) model. A persistent moisture flux convergence pattern, called Pineapple Express, is analyzed for 42 historical extreme precipitation events, and it is found that Pineapple Express causes extreme precipitation over the basin of interest. An average correlation between moisture flux convergence and maximum precipitation is estimated as 0.71 for 42 events. The performance of the WRF model is verified for precipitation by means of calibration and independent validation of the model. The calibration procedure is performed only for the first ranked flood event 1997 case, whereas the WRF model is validated for 42 historical cases. Three nested model domains are set up with horizontal resolutions of 27 km, 9 km, and 3 km over the basin of interest. As a result of Chi-square goodness-of-fit tests, the hypothesis that "the WRF model can be used in the determination of PMP over the ARW for both areal average and point estimates" is accepted at the 5% level of significance. The sensitivities of model physics options on precipitation are determined using 28 microphysics, atmospheric boundary layer, and cumulus parameterization schemes combinations. It is concluded that the best triplet option is Thompson microphysics, Grell 3D ensemble cumulus, and YSU boundary layer (TGY), based on 42 historical cases, and this TGY triplet is used for all analyses of this research. Four techniques are proposed to evaluate physically possible maximum precipitation using the WRF: 1. Perturbations of atmospheric conditions; 2. Shift in atmospheric conditions; 3. Replacement of atmospheric conditions among historical events; and 4. Thermodynamically possible worst-case scenario creation. Moreover, climate change effect on precipitation is discussed by emphasizing temperature increase in order to determine the physically possible upper limits of precipitation due to climate change. The simulation results indicate that the meridional shift in atmospheric conditions is the optimum method to determine maximum precipitation in consideration of cost and efficiency. Finally, exceedance probability analyses of the model results of 42 historical extreme precipitation events demonstrate that the 72-hr basin averaged probable maximum precipitation is 21.72 inches for the exceedance probability of 0.5 percent. On the other hand, the current operational PMP estimation for the American River Watershed is 28.57 inches as published in the hydrometeorological report no. 59 and a previous PMP value was 31.48 inches as published in the hydrometeorological report no. 36. According to the exceedance probability analyses of this proposed method, the exceedance probabilities of these two estimations correspond to 0.036 percent and 0.011 percent, respectively.

  4. Climate forecasts in disaster management: Red Cross flood operations in West Africa, 2008.

    PubMed

    Braman, Lisette Martine; van Aalst, Maarten Krispijn; Mason, Simon J; Suarez, Pablo; Ait-Chellouche, Youcef; Tall, Arame

    2013-01-01

    In 2008, the International Federation of Red Cross and Red Crescent Societies (IFRC) used a seasonal forecast for West Africa for the first time to implement an Early Warning, Early Action strategy for enhanced flood preparedness and response. Interviews with disaster managers suggest that this approach improved their capacity and response. Relief supplies reached flood victims within days, as opposed to weeks in previous years, thereby preventing further loss of life, illness, and setbacks to livelihoods, as well as augmenting the efficiency of resource use. This case demonstrates the potential benefits to be realised from the use of medium-to-long-range forecasts in disaster management, especially in the context of potential increases in extreme weather and climate-related events due to climate variability and change. However, harnessing the full potential of these forecasts will require continued effort and collaboration among disaster managers, climate service providers, and major humanitarian donors. PMID:23066755

  5. Wind Energy Management System EMS Integration Project: Incorporating Wind Generation and Load Forecast Uncertainties into Power Grid Operations

    SciTech Connect

    Makarov, Yuri V.; Huang, Zhenyu; Etingov, Pavel V.; Ma, Jian; Guttromson, Ross T.; Subbarao, Krishnappa; Chakrabarti, Bhujanga B.

    2010-01-01

    The power system balancing process, which includes the scheduling, real time dispatch (load following) and regulation processes, is traditionally based on deterministic models. Since the conventional generation needs time to be committed and dispatched to a desired megawatt level, the scheduling and load following processes use load and wind and solar power production forecasts to achieve future balance between the conventional generation and energy storage on the one side, and system load, intermittent resources (such as wind and solar generation), and scheduled interchange on the other side. Although in real life the forecasting procedures imply some uncertainty around the load and wind/solar forecasts (caused by forecast errors), only their mean values are actually used in the generation dispatch and commitment procedures. Since the actual load and intermittent generation can deviate from their forecasts, it becomes increasingly unclear (especially, with the increasing penetration of renewable resources) whether the system would be actually able to meet the conventional generation requirements within the look-ahead horizon, what the additional balancing efforts would be needed as we get closer to the real time, and what additional costs would be incurred by those needs. To improve the system control performance characteristics, maintain system reliability, and minimize expenses related to the system balancing functions, it becomes necessary to incorporate the predicted uncertainty ranges into the scheduling, load following, and, in some extent, into the regulation processes. It is also important to address the uncertainty problem comprehensively by including all sources of uncertainty (load, intermittent generation, generators’ forced outages, etc.) into consideration. All aspects of uncertainty such as the imbalance size (which is the same as capacity needed to mitigate the imbalance) and generation ramping requirement must be taken into account. The latter unique features make this work a significant step forward toward the objective of incorporating of wind, solar, load, and other uncertainties into power system operations. Currently, uncertainties associated with wind and load forecasts, as well as uncertainties associated with random generator outages and unexpected disconnection of supply lines, are not taken into account in power grid operation. Thus, operators have little means to weigh the likelihood and magnitude of upcoming events of power imbalance. In this project, funded by the U.S. Department of Energy (DOE), a framework has been developed for incorporating uncertainties associated with wind and load forecast errors, unpredicted ramps, and forced generation disconnections into the energy management system (EMS) as well as generation dispatch and commitment applications. A new approach to evaluate the uncertainty ranges for the required generation performance envelope including balancing capacity, ramping capability, and ramp duration has been proposed. The approach includes three stages: forecast and actual data acquisition, statistical analysis of retrospective information, and prediction of future grid balancing requirements for specified time horizons and confidence levels. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on a histogram analysis, incorporating all sources of uncertainties of both continuous (wind and load forecast errors) and discrete (forced generator outages and start-up failures) nature. A new method called the “flying brick” technique has been developed to evaluate the look-ahead required generation performance envelope for the worst case scenario within a user-specified confidence level. A self-validation algorithm has been developed to validate the accuracy of the confidence intervals.

  6. Assimilation of Dual-Polarimetric Radar Observations with WRF 3DVAR and its Impact on Ice Microphysics

    NASA Astrophysics Data System (ADS)

    Li, X.; Mecikalski, J. R.; Fehnel, T.; Posselt, D. J.

    2013-12-01

    Studies have shown that ra