Sample records for operational wrf forecasts

  1. An analysis of the operational GFS simplified Arakawa Schubert parameterization within a WRF framework: A Hurricane Sandy (2012) long-term track forecast perspective

    NASA Astrophysics Data System (ADS)

    Bassill, Nick P.

    2015-01-01

    Sandy (2012) is known as an incredibly destructive storm and one defined meteorologically by its large size, and its significant forecast track spreads among various operational models roughly 1 week before landfall. While the operational European Centre for Medium-Range Weather Forecasts model accurately depicted a northeastern United States landfall, the Global Forecasting System (GFS) model consistently forecast a track toward the North Atlantic Ocean. Using a Weather Research and Forecasting (WRF) model framework, Bassill suggested that these differences were primarily a function of differences between the two models' cumulus parameterization (CP). This study also uses a WRF model framework to examine the simplified Arakawa Schubert CP used in the GFS model. It is found that increasing the deep convective entrainment coefficient produces more realistic forecast tracks for forecasts initialized roughly 1 week before landfall. This occurs through a reorientation of the precipitation (and associated latent heating) around Sandy during a critical time period in which it was interacting with a series of upper troughs to its west and northwest. Reorienting the latent heating reshapes the upper tropospheric steering pattern toward the one that is more negatively tilted and consistent with observations.

  2. 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.

  3. 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.

  4. 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 NOAA’s 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 Center’s 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.

  5. Evaluation of year 2007 operational WRF-NMM on Italy

    Microsoft Academic Search

    B. Gozzini; G. Bartolini; D. Grifoni; G. Messeri; F. Pasi; F. Piani; M. Rossi; C. Tei

    2009-01-01

    The verification of numerical weather forecasts is an essential part of every forecasting system especially when dealing with Civil Protection warnings. The LaMMA Consortium (Laboratory for Meteorology and Environmental Modelling) being the regional weather forecasting service of Tuscany Region (Italy) is responsible for issuing the meteorological warnings. To support this kind of activities LaMMa is running operationally the WRF-NMM model

  6. 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.

  7. Air pollution forecasting by coupled atmosphere-fire model WRF and SFIRE with WRF-Chem

    E-print Network

    Kochanski, Adam K; Mandel, Jan; Clements, Craig B

    2013-01-01

    Atmospheric pollution regulations have emerged as a dominant obstacle to prescribed burns. Thus, forecasting the pollution caused by wildland fires has acquired high importance. WRF and SFIRE model wildland fire spread in a two-way interaction with the atmosphere. The surface heat flux from the fire causes strong updrafts, which in turn change the winds and affect the fire spread. Fire emissions, estimated from the burning organic matter, are inserted in every time step into WRF-Chem tracers at the lowest atmospheric layer. The buoyancy caused by the fire then naturally simulates plume dynamics, and the chemical transport in WRF-Chem provides a forecast of the pollution spread. We discuss the choice of wood burning models and compatible chemical transport models in WRF-Chem, and demonstrate the results on case studies.

  8. 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.

  9. Lightning forecasting in southeastern Brazil using the WRF model

    NASA Astrophysics Data System (ADS)

    Zepka, G. S.; Pinto, O.; Saraiva, A. C. V.

    2014-01-01

    This paper introduces a lightning forecasting method called Potential Lightning Region (PLR), which is the probability of the occurrence of lightning over a region of interest. The PLR was calculated using a combination of meteorological variables obtained from high-resolution Weather Research and Forecasting (WRF) model simulations during the summer season in southeastern Brazil. The model parameters used in the PLR definition were: surface-based Convective Available Potential Energy (SBCAPE), Lifted Index (LI), K-Index (KI), average vertical velocity between 850 and 700 hPa (w), and integrated ice-mixing ratio from 700 to 500 hPa (QICE). Short-range runs of twelve non-severe thunderstorm cases were performed with the WRF model, using different convective and microphysical schemes. Through statistical evaluations, the WRF cloud parameterizations that best described the convective thunderstorms with lightning in southeastern Brazil were the combination of Grell-Devenyi and Thompson schemes. Two calculation methods were proposed: the Linear PLR and Normalized PLR. The difference between them is basically how they deal with the influence of lightning flashes over the WRF domain's grid points for the twelve thunderstorms analyzed. Three case studies were used to test both methods. A statistical evaluation lowering the spatial resolution of the WRF grid into larger areas was performed to study the behavior and accuracy of the PLR methods. The Normalized PLR presented the most suitable one, predicting flash occurrence appropriately.

  10. 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.

  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. 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.

  13. Evaluation of the high resolution WRF-Chem 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.

    2015-02-01

    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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. A WRF Ensemble for Improved Wind Speed Forecasts at Turbine Height ADAM J. DEPPE AND WILLIAM A. GALLUS JR.

    E-print Network

    McCalley, James D.

    . 1. Introduction In recent years, wind energy production has under- gone rapid growth, and the UA WRF Ensemble for Improved Wind Speed Forecasts at Turbine Height ADAM J. DEPPE AND WILLIAM A in wind speed forecasts at a typical wind turbine hub height (80 m). An ensemble consisting of WRF model

  19. 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

  20. 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.

  1. 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.

  2. 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

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. Coupled weather research and forecasting-stochastic time-inverted lagrangian transport (WRF-STILT) model

    NASA Astrophysics Data System (ADS)

    Nehrkorn, Thomas; Eluszkiewicz, Janusz; Wofsy, Steven C.; Lin, John C.; Gerbig, Christoph; Longo, Marcos; Freitas, Saulo

    2010-06-01

    This paper describes the coupling between a mesoscale numerical weather prediction model, the Weather Research and Forecasting (WRF) model, and a Lagrangian Particle Dispersion Model, the Stochastic Time-Inverted Lagrangian Transport (STILT) model. The primary motivation for developing this coupled model has been to reduce transport errors in continental-scale top-down estimates of terrestrial greenhouse gas fluxes. Examples of the model’s application are shown here for backward trajectory computations originating at CO2 measurement sites in North America. Owing to its unique features, including meteorological realism and large support base, good mass conservation properties, and a realistic treatment of convection within STILT, the WRF-STILT model offers an attractive tool for a wide range of applications, including inverse flux estimates, flight planning, satellite validation, emergency response and source attribution, air quality, and planetary exploration.

  9. Evaluation of DNI forecast based on the WRF mesoscale atmospheric model for CPV applications

    NASA Astrophysics Data System (ADS)

    Lara-Fanego, V.; Ruiz-Arias, J. A.; Pozo-Vázquez, A. D.; Gueymard, C. A.; Tovar-Pescador, J.

    2012-10-01

    The integration of large-scale solar electricity production into the energy supply structures depends es-sentially on the precise advance knowledge of the available resource. Numerical weather prediction (NWP) models provide a reliable and comprehensive tool for short-and medium-range solar radiation forecasts. The methodology followed here is based on the WRF model. For CPV systems the primary energy source is the direct normal irradi-ance (DNI), which is dramatically affected by the presence of clouds. Therefore, the reliability of DNI forecasts is directly related to the accuracy of cloud information. Two aspects of this issue are discussed here: (i) the effect of the model's horizontal spatial resolution; and (ii) the effect of the spatial aggregation of the predicted irradiance. Results show that there is no improvement in DNI forecast skill at high spatial resolutions, except under clear-sky conditions. Furthermore, the spatial averaging of the predicted irradiance noticeably reduces their initial error.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. The Impact of Improved Cloud Characterization in the Weather Research & Forecasting (WRF) Model on Air Quality Simulations

    NASA Astrophysics Data System (ADS)

    Pour Biazar, A.; McNider, R. T.; Doty, K.; Park, Y. H.; Khan, M. N.; Dornblaser, B.

    2013-12-01

    In air quality simulations, clouds have a significant role as they modulate photolysis rates, impact boundary-layer development, lead to deep vertical mixing of pollutants and precursors, and induce aqueous phase chemistry. Unfortunately, numerical meteorological models still have difficulty in creating clouds in the right place and time compared to observed clouds. This is especially the case when synoptic-scale forcing is weak, as often is the case during air pollution episodes in the Southeast United States. In turn, a poor representation of clouds impacts the photochemical model's ability in simulating the air quality. In the current activity the Geostationary Operational Environmental Satellite (GOES) derived cloud fields are assimilated within Weather Research and Forecasting (WRF) model to improve simulated clouds. A technique was developed to dynamically support cloud formation/dissipation within WRF based on GOES observations. Satellites provide the best observational platform for defining the formation and location of clouds. The basic assumption in the technique is that model clouds on average are associated with positive vertical motion and clear areas with negative vertical motion. Thus, the technique uses observations to identify model cloud errors, estimates a target vertical velocity and moisture to create/remove clouds, and adjust the flow field accordingly. The technique was implemented and tested in WRF for a month-long simulation during August 2006. The results show 7-10% improvement in model cloud simulation. The technique proved to be effective regardless of the convective parameterization scheme used. Furthermore, the impact of these improvements on air quality simulations was investigated. Preliminary results from this activity will be presented.

  15. OPERATIONAL FLOOD FORECAST IN BAVARIA

    Microsoft Academic Search

    Christine Hangen-Brodersen; Alfons Vogelbacher; Franz-Klemens Holle

    The structure and organisation of the Bavarian flood information service is introduced with focus on the operational flood forecast. Five flood forecast centres corresponding to the main river basins (Main, Danube, Inn) and tributary basins where large reservoirs have to be operated (Iller-Lech, Isar) are responsible for the operational flood forecast. They closely co-operate with the co-ordinating main flood information

  16. An Improvement in high-resolution wind forecasting of the WRF Model by using a 3DVAR analysis with radar data for Istanbul/Turkey

    NASA Astrophysics Data System (ADS)

    Acar, Merve; Balli, Ceren; Tan, Elcin; Aksoy, Altug; Unal, Yurdanur

    2013-04-01

    The systematic verification of the forecast products is a crucial part of any forecasting system. In this study, we attempt to address the question whether high-resolution forecasts increase deterministic skills in the wind field beyond what can be accomplished with a coarser-resolution model, and additionally, how 3DVAR analyses improve these high-resolution wind forecasts for Istanbul. The Weather Research and Forecasting (WRF-ARW) model is used to produce 24-hr forecasts over a domain centered on Istanbul, extending to Ukraine in the north, northern Africa in the South, Tyyhrenian Sea in the west and Caspian Sea in the east. A three-nested domain layout is chosen: the coarsest domain with 9-km, finer domain with 3-km, and finest domain with 1-km grid resolution. All domains have 45 vertical levels. The model is initialized and forced at the boundaries by ECMWF operational forecast data at both 00UTC and 12UTC for January and July 2009 to obtain 24-hr forecasts. Thus, four sets of simulations are obtained. The relationship between forecast quality and horizontal grid spacing has been mainly carried out using the traditional objective verification metric of point-wise root-mean squared errors. The forecast grid closest to the observation location is selected for verification. First, the forecasted wind field at the surface and different pressure levels is compared to ECMWF-ERA Interim reanalysis for the largest domain to examine the areal limits of forecast accuracy. Second, five radiosonde observations taken from Istanbul, Izmir, Ankara, Isparta, and Athens are compared to the forecasts at the surface and standard pressure levels. Third, verifications against nine surface station observations in Istanbul are performed. Comparisons of 24-hr wind forecasts with the data observed at 5 radiosonde stations suggest that ECMWF operational forecast model produces wind fields closer to observations than ERA Interim near the surface. High resolution WRF model driven by operational forecast data improves the operational forecast near the surface up to 700 hPa. However, above 700 hPa, the root mean square errors dramatically increase with height, and they are at their extreme at the jet level. When hourly 10-m surface wind speeds are compared with the nearest grid point forecasts at 9 stations located in the city of Istanbul, it is found that WRF overpredicted the wind speed compared to the observations. However, approximately 60% of the errors in speed lie in the +/- 1.5m/sec range. This shows that although high-resolution wind direction is predicted with less error, a 3DVAR analysis might be needed to improve the wind speed forecasts. We applied a 3DVAR analysis approach by using radar data for selected days of January and July, 2009. Forecast accuracy of the results for these selected days will be presented.

  17. 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.

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

    NASA Astrophysics Data System (ADS)

    Albers, S. C.; Xie, Y.; Jiang, H.; Toth, Z.

    2012-12-01

    The Local Analysis and Prediction System (LAPS) is being used to produce rapid update, high resolution analyses and forecasts of solar radiation (Global Horizontal Irradiance or GHI). LAPS is highly portable and can be run onsite, particularly when high-resolution and rapid updating is needed. This allows the user to assimilate their own observational data merged with centrally available observations and to set up the analysis/forecast configuration to their liking. The cloud analysis uses satellite (including IR and 1-km resolution visible imagery, updated every 15-min), 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 a dense network of real-time solar radiation measurements that are independent (not used in the analysis). We are focusing mainly on a two nested domains covering the Southern Plains states that encompass networks of pyranometers located in Oklahoma and Texas. The GHI forecast is being run on the outer domain, and is being initialized using the same cloud analysis package that drives the analysis fields mentioned above. The HWT domain initializes WRF every hour with 15-minute output. Real-time verification of the analyses (including images of the analysis), and forecasts can be seen on our website, and updated results will be explored in this presentation.

  19. 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.

  20. Evaluation of Wrf Real-Time Forecast during MC3E Period: Sensitivity of Model Configuration for Diurnal Precipitation Variation

    NASA Astrophysics Data System (ADS)

    Wu, D.; Matsui, T.; Tao, W.; Peters-Lidard, C. D.; Rienecker, M. M.; Hou, A. Y.

    2011-12-01

    The WRF-ARW model with high resolution was employed for the real-time forecast during the MC3E field campaign period (April 22 - June 6, 2011) over the SGP region. The model features new Goddard microphysics (Lang et al. 2011) and Goddard radiation schemes, and runs twice a day with 00Z and 12Z forecast cycle. Our primary goal is to examine the model's ability to simulate diurnal variation of precipitation and to identify physical processes that are essential for improving the forecast skills. The studies consisted with the comparisons among a composite of the WRF simulations during the campaign period with NLDAS (North-American Land Data Assimilation Systems) and NAM (North America Mesoscale Model) forecast. A set of the WRF simulations with different physics parameterization schemes and with different horizontal resolutions are also conducted to investigate effects of the model resolution and physics schemes on the propagating rainfall system over the SGP site. Results showed that the WRF simulation with fine (2km of grid spacing) and intermediate (6 ~ 10km of grid spacing) resolution with parameterized convective schemes could reproduce reasonable MCS propagation, thus diurnal rainfall cycles over the SGP site. However, even if using the same convective parameterization, with the coarse-resolution (18~30km of grid spacing) configuration, the WRF simulation do not capture the MCS propagation reasonably. This means that model effective resolution (10 times of grid spacing) needs to be less than 100km (i.e., 10km of grid spacing), which is close to the typical Rossby Radius of deformation in the Mid-latitude summertime disturbance (100~150km distance). In addition, hail option in the Goddard microphysics appears to be an effective option to reproduce a more realistic continental MCS structure in the WRF simulations.

  1. Evaluation of WRF Model Output for Severe Weather Forecasting from the 2008 NOAA Hazardous Weather Testbed Spring Experiment

    E-print Network

    Xue, Ming

    Evaluation of WRF Model Output for Severe Weather Forecasting from the 2008 NOAA Hazardous Weather/OAR/National Severe Storms Laboratory, Norman, Oklahoma STEVEN J. WEISS NOAA/NCEP/Storm Prediction Center, Norman, Oklahoma MING XUE Center for Analysis and Prediction of Storms, and School of Meteorology, University

  2. Evaluation of WRF model output for severe-weather forecasting from the 2008 NOAA Hazardous Weather Testbed Spring Experiment

    E-print Network

    Xue, Ming

    1 Evaluation of WRF model output for severe-weather forecasting from the 2008 NOAA Hazardous/OAR/National Severe Storms Laboratory, Norman, OK Steven J. Weiss NOAA/NCEP/Storm Prediction Center, Norman, OK Ming Xue Center for the Analysis and Prediction of Storms and School of Meteorology, University of Oklahoma

  3. 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.

  4. 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.

  5. 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.

  6. Evaluation of the Weather Research and Forecasting (WRF) Model over Portugal: Case study

    NASA Astrophysics Data System (ADS)

    Rodrigues, Mónica; Rocha, Alfredo; Monteiro, Ana

    2013-04-01

    Established in 1756 the Demarcated Douro Region, became the first viticulturist region to be delimited and regulated under worldwide scale. The region has an area of 250000 hectares, from which 45000 are occupied by continuous vineyards (IVDP, 2010). It stretches along the Douro river valleys and its main streams, from the region of Mesão Frio, about 100 kilometers east from Porto town where this river discharges till attaining the frontier with Spain in the east border. Due to its stretching and extension in the W-E direction accompanying the Douro Valley, it is not strange that the region is not homogeneous having, therefore, three sub-regions: Baixo Corgo, Cima Corgo and Douro Superior. The Baixo Corgo the most western region is the "birthplace" of the viticulturalist region. The main purpose of this work is to evaluate and test the quality of a criterion developed to determine the occurrence of frost. This criterion is to be used latter by numerical weather forecasts (WRF-ARW) and put into practice in 16 meteorological stations in the Demarcated Douro Region. Firstly, the criterion was developed to calculate the occurrence of frost based on the meteorological data observed in those 16 stations. Time series of temperatures and precipitation were used for a period of approximately 20 years. It was verified that the meteorological conditions associated to days with frost (SG) and without frost (CG) are different in each station. Afterwards, the model was validated, especially in what concerns the simulation of the daily minimal temperature. Correcting functions were applied to the data of the model, having considerably diminished the errors of simulation. Then the criterion of frost estimate was applied do the output of the model for a period of 2 frost seasons. The results show that WRF simulates successfully the appearance of frost episodes and so can be used in the frost forecasting.

  7. P1.61 FORECASTER TRAINING ON THE NCEP NORTH AMERICAN MESOSCALE (NAM) WEATHER RESEARCH AND FORECASTING (WRF) MODEL

    E-print Network

    ) following the method of Janjic (2001). Details are still evolving, but generally speaking, the operational in order to allow its most effective utilization as a forecast tool in the human forecast process. al., 1993) and had to account for many changes (NCEP, 2004 and COMET, 2005) to the model physics

  8. 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.

  9. How reliable is the offline linkage of Weather Research & Forecasting Model (WRF) and Variable Infiltration Capacity (VIC) model?

    NASA Astrophysics Data System (ADS)

    Tang, Chunling; Dennis, Robin L.

    2014-05-01

    The aim for this research is to evaluate the ability of the offline linkage of Weather Research & Forecasting Model (WRF) and Variable Infiltration Capacity (VIC) model to produce hydrological, e.g. evaporation (ET), soil moisture (SM), runoff, and baseflow. First, the VIC model was run by using observed meteorological data and calibrated in the Upper Mississippi River Basin (UMRB) from 1980 to 2010. Subsequently, a simulation based on an offline linkage of WRF and VIC was performed in the UMRB with the calibrated parameters established above from 2006 to 2009. Standard measured meteorological inputs to VIC were replaced by WRF meteorological variables. A spatiotemporal comparison of offline simulated ET, SM, runoff, and baseflow produced by the VIC calibrated run (base data) and by the offline linkage run was conducted. The results showed that the offline linkage of VIC with WRF was able to achieve good agreement in the simulation of monthly and daily soil moisture, and monthly evaporation. This suggests the VIC linkage should function without causing a large change in the moisture budget. However, the offline linkage showed most disagreement in daily and monthly runoff, and baseflow which is related to errors in WRF precipitation.

  10. Towards a forecasting system of air quality for Asia using the WRF-Chem model

    NASA Astrophysics Data System (ADS)

    Katinka Petersen, Anna; Kumar, Rajesh; Brasseur, Guy; Granier, Claire

    2013-04-01

    The degradation of air quality in Asia resulting from the intensification of human activities, and the related impacts on the health of billions of people have become an urgent matter of concern. The World Health Organization states that each year nearly 3.3 million people die worldwide prematurely because of air pollution. The situation is particularly acute in Asia. Improving air quality over the Asian continent has become a major challenge for national, regional and local authorities. A prerequisite for air quality improvement is the development of a reliable monitoring system with surface instrumentation and space platforms as well as an analysis and prediction system based on an advanced chemical-meteorological model. The aim is to use the WRF-Chem model for the prediction of daily air quality for the Asian continent with spatial resolution that will be increased in densely populated areas by grid nesting. The modeling system covers a nearly the entire Asian continent so that transport processes of chemical compounds within the continent are simulated and analyzed. To additionally account for the long-range effects and assess their relative importance against regional emissions, the regional chemical transport modeling system uses information from a global modeling system as boundary conditions. The first steps towards a forecasting system over Asia are to test the model performance over this large model domain and the different emissions inventories available for Asia. In this study, the WRF-Chem model was run for a domain covering 60°E to 150°E, 5°S to 50°N at a resolution of 60 km x 60 km for January 2006 with three alternative emission inventories available for Asia (MACCITY, INTEX-B and REAS). We present an intercomparison of the three different simulations and evaluate the simulations with satellite and in situ observations, with focus on ozone, particulate matter, nitrogen oxides and carbon monoxide. The differences between the simulations using different emission inventories are discussed.

  11. An operational ocean forecast system for the South China Sea

    NASA Astrophysics Data System (ADS)

    Wang, Yonggang; Liu, Haixing; Wei, Zexun

    2013-04-01

    A multi-grid regional ocean circulation model is established on the basis of ROMS to develop the operational forecasting system for the South China Sea. The outer region is covering from 15°S to 44°N and from 99°E to 150°E with horizontal resolution (1/8)°×(1/8)°. The inner one is covering the South China Sea with horizontal resolution (1/32)°×(1/32)°. The model is first spun up through integration for 15 years with annually cyclic sea surface forcing condition to reach a stationary annually cyclic circulation fields. Then the model is further integrated from February 2006 to September 2012 driven by the NCEP reanalysis 6 hourly mean dataset. In the second stage a Projection-OI data assimilation method is applied with assimilating satellite sea surface height anomaly to adjust the sea temperature and a Nudging-based data assimilation technique is used to incorporate satellite data of sea surface temperature. After being assessed by in-situ CTD, ADCP and ARGO data, an operational forecast system is developed and run since October 2012 combining with an atmosphere forecast model (based on WRF) and an ocean wave forecast model (based on MASNUM Wave Model). For each day, the forecast system starts at 24 hours in advance for assimilating process, then runs 72 hours forecast. The surface wave induced vertical mixing is incorporated to KPP mixing scheme during these 96 hours run to improve the upper ocean temperature. The operational forecast system is assessed regularly and improved gradually.

  12. Implementation and evaluation of cloud analysis with WSR-88D reflectivity data for GSI and WRF-ARW

    E-print Network

    Xue, Ming

    Implementation and evaluation of cloud analysis with WSR-88D reflectivity data for GSI and WRF Interpolation (GSI) and the Advanced Research WRF (WRF-ARW). The case of 23 May 2005 Central Plains storm WSR-88D radars within the proposed operational configuration on 6-h forecast of the storm cluster

  13. Implementation of new sub-grid runoff parameterization within the Weather Research and Forecasting (WRF) modeling system

    NASA Astrophysics Data System (ADS)

    Khodamorad poor, M.; Irannejad, P.

    2012-04-01

    Runoff is an important component of the water cycle in land surface parameterization schemes, whose estimation is very difficult because of its dependence on rainfall, soil moisture, and topography, which vary temporally and spatially. In this study, two different methods of sub-grid parameterization of runoff are tested within the WRF numerical weather forecast model. The land surface scheme originally used in WRF is NOAH, in which runoff is parameterized based on the probably distributed function (PDF) of soil infiltration capacity. The river discharge calculated from WRF-NOAH simulated runoff and routed using total runoff integrating pathways (TRIP) model for three sub-basins of Karoon River, in the southwestern Iran, including Soosan, Harmaleh and Farseat is compared with observations for the winter 2006. WRF-NOAH extremely underestimates the discharge in the Karoon River basin, probably because of uncertainties in the runoff parameterization, which is in turn due to unavailability of soil infiltration data needed to estimate the shape and parameters of the PDF of the infiltration capacity. For this reason, we modified NOAH (NOAH-SIM) by substituting the infiltration capacity dependent runoff parameterization with a parameterization based on the PDF of the topographic index, following the philosophy used in the simplified TOPMODEL. As the topographic index is scale dependent, high resolution of topographic indices (10 m) are derived from digital elevation data model in low resolution (1000 m) by using a downscaling method. Evaluation of stimulated discharge by the two land surface schemes (NOAH-SIM, NOAH) coupled in WRF, with observed discharge proves improved runoff simulation by NOAH-SIM in all the three sub-basins. Compared to NOAH, NOAH-SIM simulated discharge has lower bias, smaller mean absolute error, higher efficiency coefficient, and a standard deviation closer to that observed. Coupling NOAH-SIM with WRF not only improves runoff simulations, but also feeds back to the atmosphere and changes the simulated precipitation. The mean and variations of precipitation simulated by the WRF is closer to that observed at selected stations in the basin. The main reason for the increased precipitation, despite the decreased surface evaporation, can be the increase in the calculated surface temperature and hence stronger instability and enhancement of surface moisture flux convergence.

  14. Prediction of Landfalling Hurricanes with the Advanced Hurricane WRF Model

    Microsoft Academic Search

    Christopher Davis; Wei Wang; Shuyi S. Chen; Yongsheng Chen; Kristen Corbosiero; Mark Demaria; Jimy Dudhia; Greg Holland; Joe Klemp; John Michalakes; Heather Reeves; Richard Rotunno; Chris Snyder; Qingnong Xiao

    2008-01-01

    Real-time forecasts of five landfalling Atlantic hurricanes during 2005 using the Advanced Research Weather Research and Forecasting (WRF) (ARW) Model at grid spacings of 12 and 4 km revealed performance generally competitive with, and occasionally superior to, other operational forecasts for storm position and intensity. Recurring errors include 1) excessive intensification prior to landfall, 2) insufficient momentum exchange with the

  15. WRF-NMM Mesoscale Weather Forecast Model and CALMET Meteorological Preprocessor Wind Simulations over the Mountaneous Region

    NASA Astrophysics Data System (ADS)

    Radonjic, Zivorad; Telenta, Bosko; Chambers, Doug, ,, Dr.; Janjic, Zavisa, ,, Dr.

    2010-05-01

    An advanced mesoscale WRF- NMM (Weather Research and Forecasting - Nonhydrostatic Mesoscale Model), was used in this application. The model was performed on a fine scale resolution (3 by 3 km) over large modelling domain ~ 300 by 300 km for one year of data (2004). Based on this resolution the areas with elevated wind speeds are determined. Each area identified with high wind speeds is processed with the U.S. EPA's meteorological preprocessor CALMET (part of the CALMET/CALPUFF long range regulatory system) with a fine resolution of 100 by 100 m to capture dynamic effects over the mountain region. Some limited data were available for validation. The application of the CALMET preprocessor demonstrated kinematic effects that result in increaed wind speeds above the mountains. This effect was confirmed by the measeurments with the sonic anemometers mounted on a TV tower in the study area. In addition, it was concluded that in the ridged terrain, the standard power low profile is not applicable. In addition, the WRF-NMM was tested in the same application on the resolution of 100 by 100m. The model simulation was limited for one month, because of the computer time requirement. Although of limited duration, this test suggests that WRF-NMM can be applied directly, without re-processing the data through the CALMET.

  16. Climatology for the Operational Forecaster

    NSDL National Science Digital Library

    COMET

    2013-04-18

    Climate information can be used as guidance for a range of weather-dependent operations. This module summarizes the Climate Analysis Process, a series of steps for determining which climatological products and data will be most useful for a specified application. The Climate Analysis Process is followed in the context of preparing a climatological brief for a ship deployment across multiple ocean basins. Though the focus is on Department of Defense data sources, including the Advanced Climate Analysis and Forecasting (ACAF) system, information on other sources is also provided. Products from the various sources are used to assemble a final climatological brief relevant to naval operations.

  17. Using the WRF Mesoscale Model

    NSDL National Science Digital Library

    COMET

    2006-11-01

    This module provides insights on how to best use WRF mesoscale model guidance in the forecast process. Using two cases in southwest Asia where AFWA WRF is currently in use, it examines improvements offered by the WRF for forecasting fronts, topographic impacts, precipitation type, and hazards to aviation. The module also discusses some mesoscale model limitations, and offers strategies for transitioning between using mesoscale and global NWP guidance for medium-range forecasts, even when the models differ significantly.

  18. 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.

  19. Toward Constructing Operational Geomagnetic Activity Forecast Model

    NASA Astrophysics Data System (ADS)

    Nagatsuma, T.; Kunitake, M.; Murata, K. T.

    2010-12-01

    Prediction of geomagnetic activity is one of the fundamental issues of space weather forecast. We are developing geomagnetic activity forecasting model based on the solar wind - magnetosphere - ionosphere (SW-M-I) coupling. We are operating daily space weather forecast as Regional Warning Center of Japan in International Space Environment Service (ISES). The key point of our forecasting model is ionosphereic conductivity dependence of the coupling function. We have found that the efficiency of SW-M-I coupling is not constant but has a dependence of ionospheric conductivity within the polar cap. Therefore, operational forecasting model of geomagnetic activity should take into account these variations and dependence. Our model can explain the diurnal and semiannual and solar cycle variations of geomagnetic activity from solar wind parameter and F10.7 index. We also examine the possibility of using inner heliospheric solar wind data such as STEREO data for a few days advance of geomagnetic activity forecast. Based on the comparison between ACE and STEREO data, we have found that the solar wind velocity can be predicted from the STEREO data well, but the Bz component of interplanetary magnetic field (IMF) is difficult to predict rather than the magnitude of IMF. This suggests that the probabilistic approach is needed for the mid-term geomagnetic forecast. We will introduce the future direction of our geomagnetic activity forecasting model in our presentation.

  20. Online-coupled modeling of volcanic ash and SO2 dispersion with WRF-Chem

    NASA Astrophysics Data System (ADS)

    Stuefer, Martin; Egan, Sean; Webley, Peter; Grell, Georg; Freitas, Saulo; Pavolonis, Mike; Dehn, Jonathan

    2014-05-01

    We included a volcanic emission and plume model into the Weather Research Forecast Model with inline Chemistry (WRF-Chem). The volcanic emission model with WRF-Chem has been tested and evaluated with historic eruptions, and the volcanic application was included into the official release of WRF-Chem beginning with WRF version 3.3 in 2011. Operational volcanic WRF-Chem runs have been developed using different domains centered on main volcanoes of the Aleutian chain and Popocatépetl Volcano, Mexico. The Global Forecast System (GFS) is used for the meteorological initialization of WRF-Chem, and default eruption source parameters serve as initial source data for the runs. We report on the model setup, and the advantages to treat the volcanic ash and sulphur dioxide emissions inline within the numerical weather prediction model. In addition we outline possibilities to initialize WRF-Chem with a fully automated algorithm to retrieve volcanic ash cloud properties from satellite data. WRF-Chem runs from recent volcanic eruptions resulted in atmospheric ash loadings, which compared well with the satellite data taking into account that satellite retrieval data represent only a limited amount of the actually emitted source due to detection thresholds. In addition particle aggregative effects are not included in the WRF-Chem model to date.

  1. 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

  2. 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

  3. 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.

  4. 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.

  5. 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.

  6. Wind Power Forecasting andWind Power Forecasting and Electricity Market Operations

    E-print Network

    Kemner, Ken

    Wind Power Forecasting andWind Power Forecasting and Electricity Market Operations Audun Botterud://www.dis.anl.gov/projects/windpowerforecasting.html IAWind 2010 Ames, IA, April 6, 2010 #12;Outline Background Using wind power forecasts in market operations ­ Current status in U.S. markets ­ Handling uncertainties in system operations ­ Wind power

  7. 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.

  8. Subhourly wind forecasting techniques for wind turbine operations

    Microsoft Academic Search

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

    1984-01-01

    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

  9. VIIRS in AWIPS: Supporting Operational Forecasters

    NASA Astrophysics Data System (ADS)

    Strabala, K.; Gumley, L.; Huang, H.; Heinrichs, T. A.; Hungershöfer, K.

    2012-12-01

    The Joint Polar Satellite System (JPSS) project has funded the inclusion of Soumi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) data in the Advanced Weather Interactive Processing System (AWIPS) in support of operational National Weather Service (NWS) Forecasters. The focus of this effort is to provide VIIRS data to high latitude regions (Alaska), where there are more frequent polar overpasses, and where the geostationary data large view angles make it less effective in monitoring small scale events. Because the Suomi NPP data is available via direct broadcast (DB), it can be acquired by X/L band antennas and processed in near-real time using the free Community Satellite Processing Package (CSPP), which transforms VIIRS raw data into SDRs identical to the IDPS VIIRS SDRs. Working closely with the University of Alaska - Fairbanks Geographic Information Network of Alaska (GINA) team, the CSPP software is running operationally with products remapped and fed to the forecast offices for display in AWIPS. Along with the installation, forecaster training was provided to help operations personnel understand the kinds of events where the high resolution data will be most useful. The high quality of the VIIRS data, the improved spatial resolution and coverage as well as the new day/night band, point to operational use of the data over all AWIPS domains. Examples are provided from different domains using direct broadcast data over Alaska, CONUS (collected and processed at SSEC), as well as Hawaii (antenna installation summer 2012).;

  10. 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.

  11. OPERATIONAL EARTHQUAKE FORECASTING State of Knowledge and Guidelines for Utilization

    E-print Network

    .................................................................................................................................... 323 II. SCIENCE OF EARTHQUAKE FORECASTING AND PREDICTION 325 A. Definitions and Concepts....................................................................................................................................... 325 B. Research on Earthquake PredictabilityOPERATIONAL EARTHQUAKE FORECASTING State of Knowledge and Guidelines for Utilization Report

  12. WRF-EMS Aviation Products

    NSDL National Science Digital Library

    2014-09-14

    This lesson illustrates how numerical guidance from the Weather Research and Forecasting Model - Environmental Modeling System (WRF-EMS) can be added to surface observations, satellite graphics, and conceptual models of important aviation phenomena, to produce TAFs. Specifically, the lesson describes how visibility, cloud ceilings, and the flight categories variables provide values for aviation forecasts in Africa.

  13. Real-time air quality forecasting over the southeastern United States using WRF/Chem-MADRID: Multiple-year assessment and sensitivity studies

    NASA Astrophysics Data System (ADS)

    Yahya, Khairunnisa; Zhang, Yang; Vukovich, Jeffrey M.

    2014-08-01

    An air quality forecasting system is a tool for protecting public health by providing an early warning system against harmful air pollutants. In this work, the online-coupled Weather Research and Forecasting Model with Chemistry with the Model of Aerosol Dynamics, Reaction, Ionization and Dissolution (WRF/Chem-MADRID) is used to forecast ozone (O3) and fine particles (PM2.5) concentrations over the southeastern U.S. for three O3 seasons from May to September in 2009, 2010, and 2011 and three winters from December to February during 2009-2010, 2010-2011, and 2011-2012. The forecasted chemical concentrations and meteorological variables are evaluated with observations from networks data in terms of spatial distribution, temporal variation, and discrete and categorical performance statistics. The model performs well for O3 and satisfactorily for PM2.5 in terms of both discrete and categorical evaluations but larger biases exist in PM species. The model biases are due to uncertainties in meteorological predictions, emissions, boundary conditions, chemical reactions, as well as uncertainties/differences in the measurement data used for evaluation. Sensitivity simulations show that using MEGAN online biogenic emissions and satellite-derived wildfire emissions result in improved performance for PM2.5 despite a degraded performance for O3. A combination of both can reduce normalize mean bias of PM2.5 from -18.3% to -11.9%. This work identifies a need to improve the accuracy of emissions by using dynamic biogenic and fire emissions that are dependent on meteorological conditions, in addition to the needs for more accurate anthropogenic emissions for urban areas and more accurate meteorological forecasts.

  14. Using HPC within an operational forecasting configuration

    NASA Astrophysics Data System (ADS)

    Jagers, H. R. A.; Genseberger, M.; van den Broek, M. A. F. H.

    2012-04-01

    Various natural disasters are caused by high-intensity events, for example: extreme rainfall can in a short time cause major damage in river catchments, storms can cause havoc in coastal areas. To assist emergency response teams in operational decisions, it's important to have reliable information and predictions as soon as possible. This starts before the event by providing early warnings about imminent risks and estimated probabilities of possible scenarios. In the context of various applications worldwide, Deltares has developed an open and highly configurable forecasting and early warning system: Delft-FEWS. Finding the right balance between simulation time (and hence prediction lead time) and simulation accuracy and detail is challenging. Model resolution may be crucial to capture certain critical physical processes. Uncertainty in forcing conditions may require running large ensembles of models; data assimilation techniques may require additional ensembles and repeated simulations. The computational demand is steadily increasing and data streams become bigger. Using HPC resources is a logical step; in different settings Delft-FEWS has been configured to take advantage of distributed computational resources available to improve and accelerate the forecasting process (e.g. Montanari et al, 2006). We will illustrate the system by means of a couple of practical applications including the real-time dynamic forecasting of wind driven waves, flow of water, and wave overtopping at dikes of Lake IJssel and neighboring lakes in the center of The Netherlands. Montanari et al., 2006. Development of an ensemble flood forecasting system for the Po river basin, First MAP D-PHASE Scientific Meeting, 6-8 November 2006, Vienna, Austria.

  15. Forecasting urban PM10 and PM2.5 pollution episodes in very stable nocturnal conditions and complex terrain using WRF-Chem CO tracer model

    NASA Astrophysics Data System (ADS)

    Saide, Pablo E.; Carmichael, Gregory R.; Spak, Scott N.; Gallardo, Laura; Osses, Axel E.; Mena-Carrasco, Marcelo A.; Pagowski, Mariusz

    2011-05-01

    This study presents a system to predict high pollution events that develop in connection with enhanced subsidence due to coastal lows, particularly in winter over Santiago de Chile. An accurate forecast of these episodes is of interest since the local government is entitled by law to take actions in advance to prevent public exposure to PM10 concentrations in excess of 150 ?g m -3 (24 h running averages). The forecasting system is based on accurately simulating carbon monoxide (CO) as a PM10/PM2.5 surrogate, since during episodes and within the city there is a high correlation (over 0.95) among these pollutants. Thus, by accurately forecasting CO, which behaves closely to a tracer on this scale, a PM estimate can be made without involving aerosol-chemistry modeling. Nevertheless, the very stable nocturnal conditions over steep topography associated with maxima in concentrations are hard to represent in models. Here we propose a forecast system based on the WRF-Chem model with optimum settings, determined through extensive testing, that best describe both meteorological and air quality available measurements. Some of the important configurations choices involve the boundary layer (PBL) scheme, model grid resolution (both vertical and horizontal), meteorological initial and boundary conditions and spatial and temporal distribution of the emissions. A forecast for the 2008 winter is performed showing that this forecasting system is able to perform similarly to the authority decision for PM10 and better than persistence when forecasting PM10 and PM2.5 high pollution episodes. Problems regarding false alarm predictions could be related to different uncertainties in the model such as day to day emission variability, inability of the model to completely resolve the complex topography and inaccuracy in meteorological initial and boundary conditions. Finally, according to our simulations, emissions from previous days dominate episode concentrations, which highlights the need for 48 h forecasts that can be achieved by the system presented here. This is in fact the largest advantage of the proposed system.

  16. 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.

  17. 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.

  18. Data Assimilation of Lightning in WRF 3/4-D VAR Using Observation Operators

    E-print Network

    Navon, Michael

    . In our case we also proved that the direct assimilation of lightning into the WRF 3D - VAR schemes tornado outbreak in a domain of 406 Ã? 305 km2 with a mesh resolution of 1 km in each horizontal direction scheme and large-scale condensation) closer to an observed value as in Mahfouf et al. (2005). In our case

  19. 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.

  20. 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.; Barthélémy, 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 Opérationnelle pour la Modélisation) 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 Prévision des Crues).

  1. Polar Satellite Products for the Operational Forecaster

    NSDL National Science Digital Library

    Patrick Dills

    This web page offers a module on polar satellite meteorology which provides an overview of the current operational polar orbiting environmental satellites (POES) and a small sample of the many meteorological products and their uses in operational weather forecasting. The module begins with a comparison of key polar orbiting environmental satellites and geostationary operational environmental satellite (GOES) characteristics and capabilities. Next, an overview of instrument configurations and their respective meteorological observing capabilities onboard both civilian and military spacecraft is presented. A preview of polar orbiting environmental satellite imagery and selected products is included. A history offers a closer look at the development of civilian and military meteorological polar orbiting environmental satellites in the United States from the very first flight of a polar orbiter in April of 1960. Content assumes student has at least an undergraduate background in basic atmospheric or environmental science and physics. A self-test is provided at the end of the module to help the student evaluate what he or she has learned by completing this module.

  2. 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.

  3. Forecasting Flash Floods with an Operational Model

    Microsoft Academic Search

    P. A. Ayral; S. Sauvagnargues-Lesage; S. Gay; F. Bressand

    The flash flood forecasting model ALHTAÏR (“Alarme Hydrologique Territoriale Automatisée par Indicateur de Risque”) has been\\u000a developed during the last five years by the flood-warning service of the Gard Region (SAC-30), in the South-East of France.\\u000a A spatial version for the flash flood forecasting model is described in this paper. This flash flood forecasting model is\\u000a divided in three separate

  4. 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

  5. Waves in Ice Forecasting for Arctic Operators

    NASA Astrophysics Data System (ADS)

    Dumont, D.; Williams, T.; Bennetts, L.

    2013-12-01

    Sea ice cover is becoming increasingly weak and fragmented during the summer in the Arctic Ocean. This presents new opportunities for offshore engineering activities and shipping routes. However, operational forecasting models do not include waves in the partially ice-covered ocean, or their impact on the ice cover, which severely compromises the safety of potential human activities in these regions. Wave-ice interactions are composed of two coupled processes. First, ice floes cause wave energy to attenuate. Second, wave motion imposes strains on the ice cover, which can fracture the ice into small floes. We have developed the first model that incorporates both wave attenuation and ice fracture. The model predicts the evolution of the wave spectrum in the ice-covered ocean and the floe size distribution in the initial 10s to 100s of kilometers of ice-covered ocean, where waves control the maximum floe size allowable. The model is currently being nested in areas of operational interest in a pan-Artic ice-ocean model.

  6. Wind Speed Forecasting for Power System Operation

    E-print Network

    Zhu, Xinxin

    2013-07-22

    In order to support large-scale integration of wind power into current electric energy system, accurate wind speed forecasting is essential, because the high variation and limited predictability of wind pose profound challenges to the power system...

  7. Utilizing Operational and Improved Remote Sensing Measurements to Assess Air Quality Monitoring Model Forecasts

    NASA Astrophysics Data System (ADS)

    Gan, Chuen-Meei

    Air quality model forecasts from Weather Research and Forecast (WRF) and Community Multiscale Air Quality (CMAQ) are often used to support air quality applications such as regulatory issues and scientific inquiries on atmospheric science processes. In urban environments, these models become more complex due to the inherent complexity of the land surface coupling and the enhanced pollutants emissions. This makes it very difficult to diagnose the model, if the surface parameter forecasts such as PM2.5 (particulate matter with aerodynamic diameter less than 2.5 microm) are not accurate. For this reason, getting accurate boundary layer dynamic forecasts is as essential as quantifying realistic pollutants emissions. In this thesis, we explore the usefulness of vertical sounding measurements on assessing meteorological and air quality forecast models. In particular, we focus on assessing the WRF model (12km x 12km) coupled with the CMAQ model for the urban New York City (NYC) area using multiple vertical profiling and column integrated remote sensing measurements. This assessment is helpful in probing the root causes for WRF-CMAQ overestimates of surface PM2.5 occurring both predawn and post-sunset in the NYC area during the summer. In particular, we find that the significant underestimates in the WRF PBL height forecast is a key factor in explaining this anomaly. On the other hand, the model predictions of the PBL height during daytime when convective heating dominates were found to be highly correlated to lidar derived PBL height with minimal bias. Additional topics covered in this thesis include mathematical method using direct Mie scattering approach to convert aerosol microphysical properties from CMAQ into optical parameters making direct comparisons with lidar and multispectral radiometers feasible. Finally, we explore some tentative ideas on combining visible (VIS) and mid-infrared (MIR) sensors to better separate aerosols into fine and coarse modes.

  8. 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.

  9. Coupling the high-complexity land surface model ACASA to the mesoscale model WRF

    E-print Network

    Pyles, R. D.

    In this study, the Weather Research and Forecasting (WRF) model is coupled with the Advanced Canopy–Atmosphere–Soil Algorithm (ACASA), a high-complexity land surface model. Although WRF is a state-of-the-art regional ...

  10. Evaluating Forecasts in Reservoir Operations: The Role of Reforecast Products

    NASA Astrophysics Data System (ADS)

    Guihan, R.; Polebitski, A.; Palmer, R. N.; Werner, K.; Nielson, A.

    2013-12-01

    Forecasts of future weather can provide valuable information for reservoir operations. A challenge confronting reservoir operators today is whether to incorporate new climate products into their operations or to use historic data, perhaps Ensemble Streamflow Predictions (ESP), to guide them. This research evaluates the quality and value of forecasts generated from the Climate Forecast System version 2 (CFSv2) using the operations of Bear Lake, a multi-purpose reservoir owned by Pacific Corps, and compares it to the quality and value of using an ESP approach. Streamflow reforecasts are generated and used to evaluate the predictive skill of the CFSv2 in the context of decision making and reservoir operations. For the Bear Lake system, forecasts are most critical during the April through September period, when releases are being made for irrigation purposes. Snowpack data, available from April to June, are a determining factor in streamflow runoff during the later spring and early summer. The CFSv2 reforecast data makes use of this information and the approach used this research also uses snowpack data to select appropriate analog years in the ESP estimations. The streamflow forecasts are used as input for a decision support system. The decision support system for this study includes a simulation model that incorporates system constraints and operating policies. To determine the value of the reforecast products, performance metrics meaningful to managers 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. Some of the important operational metrics formulated for the Bear Lake Project are maximizing release irrigation allocations and reliably meeting set allocations. These metrics of system performance are compared for the reforecast, climatology, and observed scenarios to evaluate the potential benefits of using CFSv2 seasonal forecasts in systems decision making.

  11. 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.

  12. New Operational Tsunami Forecast: Accuracy Assessment of Tsunami Amplitude Predictions

    NASA Astrophysics Data System (ADS)

    Titov, V.

    2013-12-01

    NOAA has accepted a new tsunami forecast method in operational use to predict tsunami flooding, amplitudes and other tsunami parameters in real-time, while tsunami is still propagating. The method (called Short-term Inundation Forecast for Tsunamis -- SIFT) uses DART real-time data to improve the accuracy of coastal tsunami forecast, when compared with just the seismic data-based assessment. The main goal of the forecast system is to forecast flooding due to tsunami wave at specific coastal locations. Other tsunami parameters are also computed to estimate overall hazard at a given location for a specific tsunami event. Knowing the accuracy of the forecast is extremely important for making right decisions throughout tsunami warnings procedures. During operational testing of the system a comprehensive analysis of accuracy of the system has been performed. The presentation will present the accuracy analysis of the tsunami forecast and implications for future development and improvements of tsunami forecasting.The rapid development of computing technology allowed us to look into the tsunami impact caused by above hypotheses using high-resolution models with large coverage of Pacific Northwest. With the slab model of MaCrory et al. (2012) (as part of the USGS slab 1.0 model) for the Cascadia earthquake, we tested the above hypotheses to assess the tsunami hazards along the entire U.S. West Coast. The modeled results indicate these hypothetical scenarios may cause runup heights very similar to those observed along Japan's coastline during the 2011 Japan tsunami,. Comparing to a long rupture, the Tohoku-type rupture may cause more serious impact at the adjacent coastline, independent of where it would occur in the Cascadia subduction zone. These findings imply that the Cascadia tsunami hazard may be greater than originally thought.

  13. 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

  14. ADVANCED SYSTEMS FOR OPERATIONAL OCEAN FORECASTING OF INTERDISCIPLINARY FIELDS AND UNCERTAINTIES

    E-print Network

    Robinson, Allan R.

    ocean forecasting for naval and maritime operations, pollution control, fisheriesADVANCED SYSTEMS FOR OPERATIONAL OCEAN FORECASTING OF INTERDISCIPLINARY FIELDS AND UNCERTAINTIES-mail: robinson@pacific.deas.harvard.edu Advanced integrated systems for the prediction of operational activities

  15. 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.

  16. 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.

  17. 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.; Görgen, K.; El Jarroudi, M.; Delfosse, P.; Pfister, L.; Hoffmann, L.

    2008-05-01

    The model PROCULTURE has been developed by the Université 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.

  18. 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).

  19. Recent progress in the operational forecasting of summer severe weather

    Microsoft Academic Search

    Paul Joe; Cliff Crozier; Norman Donaldson; Dave Etkin; Erik Brun; Steve Clodman; Jim Abraham; Stan Siok; Mike Leduc; Phil Chadwick; Steve Knott; Jamie Archibald; Glenn Vickers; Steve Blackwell; Rick Drouillard; Alan Whitman; Harold Brooks; Nick Kouwen; Richard Verret; Gilles Fournier; Bob Kochtubajda

    1995-01-01

    Summer severe weather (SSW) can strike suddenly and unexpectedly with disastrous consequences for human activity. Considerable progress has been made in the past ten years in the operational forecasting of SSW. Traditionally, SSW was defined to consist of tornadoes, strong winds, hail, lightning and heavy rain. Hazardous types of strong winds have recently been expanded to include microbursts, macrobursts and

  20. 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. (Decision and Information Sciences); (INESC Porto)

    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.

  1. 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...

  2. Operational aspects of asynchronous filtering for hydrological forecasting

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

    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 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.

  3. 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.

  4. 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.

  5. 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...

  6. 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 (OOF?) 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. OOF? 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

  7. Operational Hydrologic Forecasts in the Columbia River Basin

    NASA Astrophysics Data System (ADS)

    Shrestha, K. Y.; Curry, J. A.; Webster, P. J.; Toma, V. E.; Jelinek, M.

    2013-12-01

    The Columbia River Basin (CRB) covers an area of ~670,000 km2 and stretches across parts of seven U.S. states and one Canadian province. The basin is subject to a variable climate, and moisture stored in snowpack during the winter is typically released in spring and early summer. These releases contribute to rapid increases in flow. A number of impoundments have been constructed on the Columbia River main stem and its tributaries for the purposes of flood control, navigation, irrigation, recreation, and hydropower. Storage reservoirs allow water managers to adjust natural flow patterns to benefit water and energy demands. In the past decade, the complexity of water resource management issues in the basin has amplified the importance of streamflow forecasting. Medium-range (1-10 day) numerical weather forecasts of precipitation and temperature can be used to drive hydrological models. In this work, probabilistic meteorological variables from the European Center for Medium Range Weather Forecasting (ECMWF) are used to force the Variable Infiltration Capacity (VIC) model. Soil textures were obtained from FAO data; vegetation types / land cover information from UMD land cover data; stream networks from USGS HYDRO1k; and elevations from CGIAR version 4 SRTM data. The surface energy balance in 0.25° (~25 km) cells is closed through an iterative process operating at a 6 hour timestep. Output fluxes from a number of cells in the basin are combined through one-dimensional flow routing predicated on assumptions of linearity and time invariance. These combinations lead to daily mean streamflow estimates at key locations throughout the basin. This framework is suitable for ingesting daily numerical weather prediction data, and was calibrated using USGS mean daily streamflow data at the Dalles Dam (TDA). Operational streamflow forecasts in the CRB have been active since October 2012. These are 'naturalized' or unregulated forecasts. In 2013, increases of ~2600 m3/s (~48% of average discharge for water years 1879-2012) or greater were observed at TDA during the following periods: 29 March to 12 April, 5 May to 11 May, and 19 June to 29 June. Precipitation and temperature forecasts during these periods are shown along with changes in the model simulated snowpack. We evaluate the performance of the ensemble mean 10 days in advance of each of these three events, and comment on how the distribution of ensemble members affected forecast confidence in each situation.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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...

  14. PWV forecast validation at ALMA site

    NASA Astrophysics Data System (ADS)

    Pozo, D.; Illanes, L.; Caneo, M.; Curé, M.

    2011-11-01

    In this study, the WRF (Weather Research and Forecasting) model was implemented to predict the atmospheric conditions, particularly the precipitable water vapor (PWV) in the North of Chile. Its performance was evaluated over the ALMA (Atacama Large Millimeter/submillimeter Array) site. Five WRF configurations with different physical options for boundary layer, soil model and microphysics were compared with observations from a radiometer and a weather station from April to December 2007. The results show that all the simulations overestimate PWV values, particularly in summer months. In addition, the microphysics parameterization changes do not notably affect the forecast, observing improved results with the soil model Noah. The errors were smallest with the YSU-Noah configuration, suggesting that it is appropriate to be used in operational forecasting of PWV in ALMA.

  15. Coupling the Goddard Aerosol Transport Model, Cloud Microphysics, and Radiation Schemes in the NASA-Unifed Weather Research and Forecasting (NU-WRF) Model

    NASA Astrophysics Data System (ADS)

    Shi, J. J.; Matsui, T.; Tao, W.; Peters-Lidard, C. D.; Chin, M.; Tan, Q.; Kemp, E. M.

    2011-12-01

    It is well known that aerosols in the atmosphere often serve as condensation nuclei in the formation of cloud droplets and ice particles. As a result, these aerosols acting as condensation nuclei exert influence on the microphysical properties of both warm-, mixed- and ice-phase clouds. Recent research efforts have led to notable progress in increasing our understanding of their microphysical properties and the factors that enable them to act as cloud condensation nuclei and ice nuclei and therefore the indirect effects on cloud formation. On the other hand, these same aerosols also have a direct effect on how longwave amd shortwave radiations are absorbed in the atmosphere and consequently the heating in the atmosphere and at the surface. In this latest model development, the Goddard microphysics and longwave/shortwave schemes in WRF are coupled inline with the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model in the WRF-Chem to account for the direct (radiation) and the indirect (microphysics) efffects.

  16. 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...

  17. 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...

  18. 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.

  19. A Wind Forecasting System for Energy Application

    NASA Astrophysics Data System (ADS)

    Courtney, Jennifer; Lynch, Peter; Sweeney, Conor

    2010-05-01

    Accurate forecasting of available energy is crucial for the efficient management and use of wind power in the national power grid. With energy output critically dependent upon wind strength there is a need to reduce the errors associated wind forecasting. The objective of this research is to get the best possible wind forecasts for the wind energy industry. To achieve this goal, three methods are being applied. First, a mesoscale numerical weather prediction (NWP) model called WRF (Weather Research and Forecasting) is being used to predict wind values over Ireland. Currently, a gird resolution of 10km is used and higher model resolutions are being evaluated to establish whether they are economically viable given the forecast skill improvement they produce. Second, the WRF model is being used in conjunction with ECMWF (European Centre for Medium-Range Weather Forecasts) ensemble forecasts to produce a probabilistic weather forecasting product. Due to the chaotic nature of the atmosphere, a single, deterministic weather forecast can only have limited skill. The ECMWF ensemble methods produce an ensemble of 51 global forecasts, twice a day, by perturbing initial conditions of a 'control' forecast which is the best estimate of the initial state of the atmosphere. This method provides an indication of the reliability of the forecast and a quantitative basis for probabilistic forecasting. The limitation of ensemble forecasting lies in the fact that the perturbed model runs behave differently under different weather patterns and each model run is equally likely to be closest to the observed weather situation. Models have biases, and involve assumptions about physical processes and forcing factors such as underlying topography. Third, Bayesian Model Averaging (BMA) is being applied to the output from the ensemble forecasts in order to statistically post-process the results and achieve a better wind forecasting system. BMA is a promising technique that will offer calibrated probabilistic wind forecasts which will be invaluable in wind energy management. In brief, this method turns the ensemble forecasts into a calibrated predictive probability distribution. Each ensemble member is provided with a 'weight' determined by its relative predictive skill over a training period of around 30 days. Verification of data is carried out using observed wind data from operational wind farms. These are then compared to existing forecasts produced by ECMWF and Met Eireann in relation to skill scores. We are developing decision-making models to show the benefits achieved using the data produced by our wind energy forecasting system. An energy trading model will be developed, based on the rules currently used by the Single Electricity Market Operator for energy trading in Ireland. This trading model will illustrate the potential for financial savings by using the forecast data generated by this research.

  20. WRF4SG: A Scientific Gateway for climate experiment workflows

    NASA Astrophysics Data System (ADS)

    Blanco, Carlos; Cofino, Antonio S.; Fernandez-Quiruelas, Valvanuz

    2013-04-01

    The Weather Research and Forecasting model (WRF) is a community-driven and public domain model widely used by the weather and climate communities. As opposite to other application-oriented models, WRF provides a flexible and computationally-efficient framework which allows solving a variety of problems for different time-scales, from weather forecast to climate change projection. Furthermore, WRF is also widely used as a research tool in modeling physics, dynamics, and data assimilation by the research community. Climate experiment workflows based on Weather Research and Forecasting (WRF) are nowadays among the one of the most cutting-edge applications. These workflows are complex due to both large storage and the huge number of simulations executed. In order to manage that, we have developed a scientific gateway (SG) called WRF for Scientific Gateway (WRF4SG) based on WS-PGRADE/gUSE and WRF4G frameworks to ease achieve WRF users needs (see [1] and [2]). WRF4SG provides services for different use cases that describe the different interactions between WRF users and the WRF4SG interface in order to show how to run a climate experiment. As WS-PGRADE/gUSE uses portlets (see [1]) to interact with users, its portlets will support these use cases. A typical experiment to be carried on by a WRF user will consist on a high-resolution regional re-forecast. These re-forecasts are common experiments used as input data form wind power energy and natural hazards (wind and precipitation fields). In the cases below, the user is able to access to different resources such as Grid due to the fact that WRF needs a huge amount of computing resources in order to generate useful simulations: * Resource configuration and user authentication: The first step is to authenticate on users' Grid resources by virtual organizations. After login, the user is able to select which virtual organization is going to be used by the experiment. * Data assimilation: In order to assimilate the data sources, the user has to select them browsing through LFC Portlet. * Design Experiment workflow: In order to configure the experiment, the user will define the type of experiment (i.e. re-forecast), and its attributes to simulate. In this case the main attributes are: the field of interest (wind, precipitation, ...), the start and end date simulation and the requirements of the experiment. * Monitor workflow: In order to monitor the experiment the user will receive notification messages based on events and also the gateway will display the progress of the experiment. * Data storage: Like Data assimilation case, the user is able to browse and view the output data simulations using LFC Portlet. The objectives of WRF4SG can be described by considering two goals. The first goal is to show how WRF4SG facilitates to execute, monitor and manage climate workflows based on the WRF4G framework. And the second goal of WRF4SG is to help WRF users to execute their experiment workflows concurrently using heterogeneous computing resources such as HPC and Grid. [1] Kacsuk, P.: P-GRADE portal family for grid infrastructures. Concurrency and Computation: Practice and Experience. 23, 235-245 (2011). [2] http://www.meteo.unican.es/software/wrf4g

  1. The Establishment of an Operational Earthquake Forecasting System in Italy

    NASA Astrophysics Data System (ADS)

    Marzocchi, Warner; Lombardi, Anna Maria; Casarotti, Emanuele

    2014-05-01

    Just after the Mw 6.2 earthquake that hit L'Aquila, on April 6 2009, the Civil Protection nominated an International Commission on Earthquake Forecasting (ICEF) that paved the way to the development of the Operational Earthquake Forecasting (OEF), defined as the "procedures for gathering and disseminating authoritative information about the time dependence of seismic hazards to help communities prepare for potentially destructive earthquakes". In this paper we introduce the first official OEF system in Italy that has been developed by the new-born Centro di Pericolosità Sismica at the Istituto Nazionale di Geofisica e Vulcanologia. The system provides every day an update of the weekly probabilities of ground shaking over the whole Italian territory. In this presentation, we describe in detail the philosophy behind the system, the scientific details, and the output format that has been preliminary defined in agreement with Civil Protection. To our knowledge, this is the first operational system that fully satisfies the ICEF guidelines. Probably, the most sensitive issue is related to the communication of such a kind of message to the population. Acknowledging this inherent difficulty, in agreement with Civil Protection we are planning pilot tests to be carried out in few selected areas in Italy; the purpose of such tests is to check the effectiveness of the message and to receive feedbacks.

  2. 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.

  3. 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.

  4. 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.

  5. Using Heliospheric Imaging for Storm Forecasting - SMEI CME Observations as a Tool for Operational Forecasting at AFWA

    NASA Astrophysics Data System (ADS)

    Webb, D. F.; Johnston, J. C.; Fry, C. D.; Kuchar, T. A.

    2008-12-01

    Observations of coronal mass ejections (CMEs) from heliospheric imagers such as the Solar Mass Ejection Imager (SMEI) can lead to significant improvements in operational space weather forecasting. We are working with the Air Force Weather Agency (AFWA) to ingest SMEI all-sky imagery with appropriate tools to help forecasters improve their operational space weather forecasts. We describe two approaches: 1) Near- real time analysis of propagating CMEs from SMEI images alone combined with near-Sun observations of CME onsets and, 2) Using these calculations of speed as a mid-course correction to the HAFv2 solar wind model forecasts. HAFv2 became operational at AFWA in late 2006. The objective is to determine a set of practical procedures that the duty forecaster can use to update or correct a solar wind forecast using heliospheric imager data. SMEI observations can be used inclusively to make storm forecasts, as recently discussed in Webb et al. (Space Weather, in press, 2008). We have developed a point-and-click analysis tool for use with SMEI images and are working with AFWA to ensure that timely SMEI images are available for analyses. When a frontside solar eruption occurs, especially if within about 45 deg. of Sun center, a forecaster checks for an associated CME observed by a coronagraph within an appropriate time window. If found, especially if the CME is a halo type, the forecaster checks SMEI observations about a day later, depending on the apparent initial CME speed, for possibly associated CMEs. If one is found, then the leading edge is measured over several successive frames and an elongation-time plot constructed. A minimum of three data points, i.e., over 3-4 orbits or about 6 hours, are necessary for such a plot. Using the solar source location and onset time of the CME from, e.g., SOHO observations, and assuming radial propagation, a distance-time relation is calculated and extrapolated to the 1 AU distance. As shown by Webb et al., the storm onset time is then expected to be about 3 hours after this 1 AU arrival time (AT). The prediction program is updated as more SMEI data become available. Currently when an appropriate solar event occurs, AFWA routinely runs the HAFv2 model to make a forecast of the shock and ejecta arrival times at Earth. SMEI data can be used to improve this prediction. The HAFv2 model can produce synthetic sky maps of predicted CME brightness for comparison with SMEI images. The forecaster uses SMEI imagery to observe and track the CME. The forecaster then measures the CME location and speed using the SMEI imagery and the HAFv2 synthetic sky maps. After comparing the SMEI and HAFv2 results, the forecaster can adjust a key input to HAFv2, such as the initial speed of the disturbance at the Sun or the mid-course speed. The forecaster then iteratively runs HAFv2 until the observed and forecast sky maps match. The final HAFv2 solution becomes the new forecast. When the CME/shock arrives at (or does not reach) Earth, the forecaster verifies the forecast and updates the forecast skill statistics. Eventually, we plan to develop a more automated version of this procedure.

  6. 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

  7. Operational Water Resources Forecasting System for The Netherlands

    NASA Astrophysics Data System (ADS)

    Weerts, A.; Prinsen, G.; Patzke, S.; van Verseveld, W.; Berger, H.; Kroon, T.

    2011-12-01

    During periods of low flows of the Rhine and Meuse Rivers and/or agricultural drought the National Coordinating Committee for Water Distribution of the Netherlands has to decide how the available surface water is used and allocated between different functions like safety (e.g. peat-levee stability), reduction of salt water intrusion, drinking water and agriculture. Since 2009, a real time forecasting system is operational and provides daily nationwide forecasts on the total fresh surface water supply, groundwater levels and saturation of the root zone at 250x250 meters using a surface water model coupled with a MODFLOW-MetaSWAP model of the saturated-unsaturated zone and with a lead-time of 10-30 days. In 2011, new forecasts products like a spatial groundwater anomaly plots for the weekly drought bulletin were introduced. Besides this product, a no rain scenario with a leadtime of 30 days and schematic status displays were also introduced. These products turned out to provide usefull information to support decision making and inform the public during the low period and unusal dry start of 2011 in the Netherlands and Rhine and Meuse basin. The changing patterns in groundwater anomaly give good insight into the hydrological behaviour of the Netherlands. The no-rain scenario provided usefull information to decide on maintaining increased target levels of Lake IJssel and Lake Marker (e.g. the main fresh water supply basins in the Netherlands). Displays of water quality infomation (chloride concentrations) helped to gain insight on the extend of salt water intrusion in the South-Western part of the Netherlands. The schematic status displays provide the water managers a quick and easy to understand overview of the hydrological status cumulating all the underlying detailed information.

  8. Operational Water Resources Forecasting System for The Netherlands

    NASA Astrophysics Data System (ADS)

    Weerts, A. H.; Prinsen, G.; Patzke, S.; van Verseveld, W.; Berger, H.; Kroon, T.

    2012-04-01

    During periods of low flows of the Rhine and Meuse Rivers and/or agricultural drought the National Coordinating Committee for Water Distribution of the Netherlands has to decide how the available surface water is used and allocated between different functions like safety (e.g. peat-levee stability), reduction of salt water intrusion, drinking water and agriculture. Since 2009, a real time forecasting system is operational and provides daily nationwide forecasts on the total fresh surface water supply, groundwater levels and saturation of the root zone at 250x250 meters using a surface water model coupled with a MODFLOW-MetaSWAP model of the saturated-unsaturated zone and with a lead-time of 10-30 days. In 2011, new forecasts products like a spatial groundwater anomaly plots for the weekly drought bulletin were introduced. Besides this product, a no rain scenario with a leadtime of 30 days and schematic status displays were also introduced. These products turned out to provide usefull information to support decision making and inform the public during the low period and unusal dry start of 2011 in the Netherlands and Rhine and Meuse basin. The changing patterns in groundwater anomaly give good insight into the hydrological behaviour of the Netherlands. The no-rain scenario provided usefull information to decide on maintaining increased target levels of Lake IJssel and Lake Marker (e.g. the main fresh water supply basins in the Netherlands). Displays of water quality infomation (chloride concentrations) helped to gain insight on the extend of salt water intrusion in the South-Western part of the Netherlands. The schematic status displays provide the water managers a quick and easy to understand overview of the hydrological status cumulating all the underlying detailed information.

  9. 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 ?m 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.

  10. 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.

  11. 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.

  12. Forecasting urban PM10 and PM2.5 pollution episodes in very stable nocturnal conditions and complex terrain using WRF–Chem CO tracer model

    Microsoft Academic Search

    Pablo E. Saide; Gregory R. Carmichael; Scott N. Spak; Laura Gallardo; Axel E. Osses; Marcelo A. Mena-Carrasco; Mariusz Pagowski

    2011-01-01

    This study presents a system to predict high pollution events that develop in connection with enhanced subsidence due to coastal lows, particularly in winter over Santiago de Chile. An accurate forecast of these episodes is of interest since the local government is entitled by law to take actions in advance to prevent public exposure to PM10 concentrations in excess of

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. Probability forecasts for weather-dependent agricultural operations using generalized estimating equations

    NASA Astrophysics Data System (ADS)

    Detlefsen, Nina K.

    2006-10-01

    In agricultural production many operations depend on the weather. In this paper, a model is investigated that calculates the probability for the execution of a given operation which depends on several meteorological parameters. The model is based on a 48-hr numerical weather forecast with hourly resolution. The probability forecasts are compared to the numeric forecasts for the operation based on the numeric weather forecast. The model is a logistic regression model with generalized estimating equations. The Brier skill score, sharpness and reliability diagrams and relative operating characteristic curves are used to evaluate the model. The model setup described is dynamic in the sense that on a given date, parameters are estimated based on history and these parameter estimates are used for calculating the probability forecasts. This means that parameter estimates adapt automatically to seasonal changes in weather and to changes in numerical weather forecasts following developments in the forecast models. In this paper, we perform model output statistics, which tune the numeric weather forecast to an operation that depends on several meteorological parameters rather than only tuning a single weather parameter. Although some problems occurred, the model developed showed that the numerical forecast for such an operation could be improved.

  18. 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.

  19. 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.

  20. Feedbacks of the use of two uncertainty assessment techniques by operational flood forecasters

    NASA Astrophysics Data System (ADS)

    Berthet, Lionel; Bourgin, François; Perrin, Charles; Andréassian, Vazken

    2014-05-01

    In 2013, forecasters working in the French flood forecasting services tested two automatic techniques for forecast uncertainty assessment in their operational context. These techniques were expected to characterize predictive uncertainty, and provide forecasters with confidence intervals (for example, 80% central intervals) associated to their forecasts (forecast intervals) and estimates of the probability of exceeding some warning thresholds. The first technique was the quantile regression method (Weerts et al., 2011), while the second one was a data-based and non-parametric method. These techniques were applied to a forecasting rainfall-runoff model (GRP) and to two hydraulic models (HYDRA and MASCARET). Both techniques are based on the statistical analysis of past forecast errors. In the case of the hydrological model, the past forecast errors were estimated using a 'perfect' rainfall scenario (corresponding to a posteriori observed rainfall). The forecasters pointed out that the approaches are simple enough to be easily understood, which was stressed as a clear advantage over "black-box" tools. The feedbacks showed that many operational forecasters enjoyed the fact that these automatic assessments brought out the qualities and the defaults of the model (e.g., bias) of which they were aware... or not. Therefore these results clearly helped them to better know the limits of their models. The forecast intervals (80%) produced by the methods were often found too large by the forecasters to be very helpful in their decision-making. Moreover, forecasters thought they were able to give narrower intervals (still being reliable) based on their experience. The methods were considered as providing very good starting points by the forecasters, encouraging them to build their own forecast intervals. Forecasters use the probability of exceeeding a threshold as one piece of information (among others) to decide whether to issue a warning or not. It is considered as very informative and valuable by the forecasters, even in the case different future precipitation scenarios would be used. Operational perspectives are the combination of ensemble precipitation forecasts and these techniques.

  1. 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.

  2. 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. ?he 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).

  3. 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.

  4. THE PREV AIR SYSTEM, AN OPERATIONAL SYSTEM FOR LARGE SCALE AIR QUALITY FORECASTS OVER EUROPE; APPLICATIONS

    E-print Network

    Paris-Sud XI, Université de

    THE PREV AIR SYSTEM, AN OPERATIONAL SYSTEM FOR LARGE SCALE AIR QUALITY FORECASTS OVER EUROPE air quality forecasts over Europe. This is the visible part of a wider collaborative project, in the framework of negotiations on trans-boundary air pollution". (2) Providing large scale national air quality

  5. Tailoring seasonal climate forecasts for hydropower operations in Ethiopia’s 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 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 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. 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. 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.

  7. 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.

  8. 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.

  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. 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.

  12. Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities

    NASA Astrophysics Data System (ADS)

    Liu, Y.; Weerts, A. H.; 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.

    2012-10-01

    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.

  13. 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.

  14. Evaluating the impact of parameterization choice on WRF-Chemistry simulations

    NASA Astrophysics Data System (ADS)

    Peckham, S. E.; Grell, G. A.; McKeen, S. A.; Wilczak, J. M.; Fast, J. D.

    2005-12-01

    The accurate prediction of air quality using numerical models involves correctly simulating both the meteorology and chemical processes. With the recent increases in computing power, complex three-dimensional air quality models have become a cost effective tool to study physical processes and their interaction with air chemistry, as well as different model formulations to treat the formation and evolution of aerosols. Some of these models are also used now to forecast air quality on an operational or semi-operational basis. One such tool is the Weather Research and Forecast (WRF)/Chemistry model (WRF-Chem). This model is unusual in that the transport and transformation of all chemical and aerosol components are calculated online, or in lock-step with the meteorological and thermodynamic calculations. Among the physical processes that are most critical for air pollution modeling are the parameterization of the planetary boundary layer (PBL) and cloud processes. Several fundamentally different PBL parameterizations - also used for air quality applications - are available within the WRF framework. In addition, the simulated PBL growth and behavior is dependent upon the parameterization of the land surface, clouds, and shortwave radiation, and how they interact with the PBL parameterization. The presentation will cover the results from the approach taken to determine the optimal model configuration through evaluating the influence that each parameterization or module has on the meteorological and air quality forecasts, using a systematic evaluation against data collected during the ICARTT/NEAQS-2004 field study (http://www.al.noaa.gov/2004). With so many possible combinations of physical and chemical modules, it was necessary to first determine which meteorological model configurations best reproduce the observed meteorological conditions for a select 2-week period from the NEAQS2004 field experiment.

  15. 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'>

  16. Volcanic ash transport integrated in the WRF-Chem model: a description of the application and verification results from the 2010 Eyjafjallajökull 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 Eyjafjallajökull. 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 Eyjafjallajökull 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 Eyjafjallajökull 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. RESERVOIR RELEASE FORECAST MODEL FOR FLOOD OPERATION OF THE FOLSOM PROJECT INCLUDING PRE-RELEASES

    E-print Network

    Bowles, David S.

    the operational capabilities created by a modification to increase outlet capacity and by improved weather forecasts based on the Advanced Hydrologic Prediction System (AHPS) of the National Weather Service (NWS

  18. 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...

  19. Real-Time Data Assimilation for Operational Ensemble Streamflow Forecasting JASPER A. VRUGT

    E-print Network

    Vrugt, Jasper A.

    Real-Time Data Assimilation for Operational Ensemble Streamflow Forecasting JASPER A. VRUGT Earth values must be specified (Table 1). Corresponding author address: Jasper Vrugt, Earth and Envi- ronmental

  20. WRF4G project: Adaptation of WRF Model to Distributed Computing Infrastructures

    NASA Astrophysics Data System (ADS)

    Cofino, Antonio S.; Fernández Quiruelas, Valvanuz; García Díez, Markel; Blanco Real, Jose C.; Fernández, Jesús

    2013-04-01

    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 first objective of this project 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 been used as input by many energy and natural hazards community, therefore those 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 jobs and the data. Thus, the second objective of the project consists on the development of a generic adaptation of WRF for Grid (WRF4G), to be distributed as open-source and to be integrated in the official WRF development cycle. The use of this WRF adaptation should be transparent and useful to face any of the previously described studies, and avoid any of the problems of the Grid infrastructure. Moreover it should simplify the access to the Grid infrastructures for the research teams, and also to free them from the technical and computational aspects of the use of the Grid. Finally, in order to demonstrate the ability of Grid infrastructures in solving a scientific problem with interest and relevance on the meteorology area (implying a high computational cost) we will perform a high resolution hindcast on Southwestern Europe with ERA-Interim re-analysis as boundary and initial conditions. The production of an atmospheric hindcast at high resolution, will provide an appropriate assessment of the possibilities and uncertainties of the WRF model for the evaluation and forecasting of weather, energy and natural hazards. [1] http://www.meteo.unican.es/software/wrf4g

  1. Predictability of European air quality: Assessment of 3 years of operational forecasts and analyses by the PREV'AIR system

    E-print Network

    Menut, Laurent

    Predictability of European air quality: Assessment of 3 years of operational forecasts and analyses time, the long-term evaluation of an operational real-time air quality forecasting and analysis system between several public organizations. The system forecasts and analyzes air quality throughout Europe

  2. 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.

  3. 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%.

  4. 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...

  5. 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...

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

    NASA Astrophysics Data System (ADS)

    Boucher, Marie-Amélie; Tremblay, Denis; Luc, Perreault; François, 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 Québec, Canada. The resulting hydrological ensemble forecasts are then incorporated into Hydro-Québec 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 modèle hydrologique distribué pouvant bénéficier des données fournies par la télédétection et les systèmes d'information géographique, 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(1

  7. Operational forecasts of algae blooms in the Baltic Sea

    Microsoft Academic Search

    Iréne Lake; Lennart Funkquist

    2008-01-01

    During the fall of 2007 a project was initiated with the main aim to set up a test system for algae forecasts. Secondly the project was to, within its scope, perform a crude validation of the results. The final task for the project, which lasted until the end of December, was to present the results on the internal web. The

  8. 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.

  9. 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.

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

    Microsoft Academic Search

    L. Sraibman; G. J. Berri

    2009-01-01

    A mesoscale boundary-layer model (BLM) is used for running 12-h low-level wind forecasts for the La Plata River region. Several\\u000a experiments are performed with different boundary conditions that include operational forecasts of the Eta\\/CPTEC model, local\\u000a observations, as well as a combination of both. The BLM wind forecasts are compared to the surface wind observations of five\\u000a weather stations during

  11. 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.

  12. Polar Satellite Products for the Operational Forecaster: Microwave Analysis of Tropical Cyclones

    NSDL National Science Digital Library

    COMET

    2004-11-10

    This module introduces forecasters to the use of microwave image products for observing and analyzing tropical cyclones. Microwave data from polar-orbiting satellites is crucial to today’s operational forecasters, and particularly for those with maritime forecasting responsibilities where in situ observations are sparse. This module includes information on storm structure and techniques for improved storm positioning using the 37 and 85-91 GHz channels from several satellite sensors. Information on current sensors and on the product availability in the NPOESS era is also presented.

  13. 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.

  14. Evaluation of Mekong River Commission operational flood forecasts, 2000-2012

    NASA Astrophysics Data System (ADS)

    Pagano, T. C.

    2013-11-01

    This study created a 13 yr historical archive of operational flood forecasts issued by the Regional Flood Management and Mitigation Center (RFMMC) of the Mekong River Commission. The RFMMC issues 1 to 5 day-ahead daily deterministic river height forecasts for 22 locations throughout the wet season (June-October). When these forecasts reach near Flood Level, government agencies and the public are encouraged to take protective action against damages. When measured by standard skill scores, the forecasts perform exceptionally well (e.g. 1 day-ahead Nash-Sutcliffe > 0.99) although much of this apparent skill is due to the strong seasonal cycle and the narrow natural range of variability at certain locations. 5 day-ahead forecasts upstream of Phnom Penh typically have 0.8 m error standard deviation, whereas below Phnom Penh the error is typically 0.3 m. The Coefficients of Persistence for 1 day-ahead forecasts are typically 0.4-0.8 and 5 day-ahead forecasts are typically 0.1-0.7. RFMMC uses a series of benchmarks to define a metric of Percentage Satisfactory forecasts. As the benchmarks were derived based on the average error, certain locations and lead-times consistently appear less satisfactory than others. Instead, different benchmarks were proposed and derived based on the 70th percentile of absolute error over the 13 yr period. There are no obvious trends in the Percentage of Satisfactory forecasts from 2002-2012, regardless of the benchmark chosen. Finally, when evaluated from a categorical "crossing above/not-crossing above flood level" perspective, the forecasts have a moderate probability of detection (48% at 1 day-ahead, 31% at 5 day-ahead) and false alarm rate (13% at 1 day-ahead, 74% at 5 days-ahead).

  15. Evaluation of Mekong River commission operational flood forecasts, 2000-2012

    NASA Astrophysics Data System (ADS)

    Pagano, T. C.

    2014-07-01

    This study created a 13-year historical archive of operational flood forecasts issued by the Regional Flood Management and Mitigation Center (RFMMC) of the Mekong River Commission. The RFMMC issues 1- to 5-day daily deterministic river height forecasts for 22 locations throughout the wet season (June-October). When these forecasts reach near flood level, government agencies and the public are encouraged to take protective action against damages. When measured by standard skill scores, the forecasts perform exceptionally well (e.g., 1 day-ahead Nash-Sutcliffe > 0.99) although much of this apparent skill is due to the strong seasonal cycle and the narrow natural range of variability at certain locations. Five-day forecasts upstream of Phnom Penh typically have 0.8 m error standard deviation, whereas below Phnom Penh the error is typically 0.3 m. The coefficients of persistence for 1-day forecasts are typically 0.4-0.8 and 5-day forecasts are typically 0.1-0.7. RFMMC uses a series of benchmarks to define a metric of percentage satisfactory forecasts. As the benchmarks were derived based on the average error, certain locations and lead times consistently appear less satisfactory than others. Instead, different benchmarks were proposed and derived based on the 70th percentile of absolute error over the 13-year period. There are no obvious trends in the percentage of satisfactory forecasts from 2002 to 2012, regardless of the benchmark chosen. Finally, when evaluated from a categorical "crossing above/not-crossing above flood level" perspective, the forecasts have a moderate probability of detection (48% at 1 day ahead, 31% at 5 days ahead) and false alarm rate (13% at 1 day ahead, 74% at 5 days ahead).

  16. 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.

  17. 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 ...

  18. 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.

  19. Assessing uncertainties in flood forecasts for decision making: prototype of an operational flood management system integrating ensemble predictions

    Microsoft Academic Search

    J. Dietrich; A. H. Schumann; M. Redetzky; J. Walther; M. Denhard; Y. Wang; B. Pfützner; U. Büttner

    2009-01-01

    Ensemble forecasts aim at framing the uncertainties of the potential future development of the hydro-meteorological situation. A probabilistic evaluation can be used to communicate forecast uncertainty to decision makers. Here an operational system for ensemble based flood forecasting is presented, which combines forecasts from the European COSMO-LEPS, SRNWP-PEPS and COSMO-DE prediction systems. A multi-model lagged average super-ensemble is generated by

  20. 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.

  1. 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.

  2. A study on WRF radar data assimilation for hydrological rainfall prediction

    NASA Astrophysics Data System (ADS)

    Liu, J.; Bray, M.; Han, D.

    2013-08-01

    Mesoscale numerical weather prediction (NWP) models are gaining more attention in providing high-resolution rainfall forecasts at the catchment scale for real-time flood forecasting. The model accuracy is however negatively affected by the "spin-up" effect and errors in the initial and lateral boundary conditions. Synoptic studies in the meteorological area have shown that the assimilation of operational observations, especially the weather radar data, can improve the reliability of the rainfall forecasts from the NWP models. This study aims at investigating the potential of radar data assimilation in improving the NWP rainfall forecasts that have direct benefits for hydrological applications. The Weather Research and Forecasting (WRF) model is adopted to generate 10 km rainfall forecasts for a 24 h storm event in the Brue catchment (135.2 km2) located in southwest England. Radar reflectivity from the lowest scan elevation of a C-band weather radar is assimilated by using the three-dimensional variational (3D-Var) data-assimilation technique. Considering the unsatisfactory quality of radar data compared to the rain gauge observations, the radar data are assimilated in both the original form and an improved form based on a real-time correction ratio developed according to the rain gauge observations. Traditional meteorological observations including the surface and upper-air measurements of pressure, temperature, humidity and wind speed are also assimilated as a bench mark to better evaluate and test the potential of radar data assimilation. Four modes of data assimilation are thus carried out on different types/combinations of observations: (1) traditional meteorological data; (2) radar reflectivity; (3) corrected radar reflectivity; (4) a combination of the original reflectivity and meteorological data; and (5) a combination of the corrected reflectivity and meteorological data. The WRF rainfall forecasts before and after different modes of data assimilation are evaluated by examining the rainfall temporal variations and total amounts which have direct impacts on rainfall-runoff transformation in hydrological applications. It is found that by solely assimilating radar data, the improvement of rainfall forecasts are not as obvious as assimilating meteorological data; whereas the positive effect of radar data can be seen when combined with the traditional meteorological data, which leads to the best rainfall forecasts among the five modes. To further improve the effect of radar data assimilation, limitations of the radar correction ratio developed in this study are discussed and suggestions are made on more efficient utilisation of radar data in NWP data assimilation.

  3. From Predicting Solar Activity to Forecasting Space Weather: Practical Examples of Research-to-Operations and Operations-to-Research

    NASA Astrophysics Data System (ADS)

    Steenburgh, R. A.; Biesecker, D. A.; Millward, G. H.

    2014-02-01

    The successful transition of research to operations (R2O) and operations to research (O2R) requires, above all, interaction between the two communities. We explore the role that close interaction and ongoing communication played in the successful fielding of three separate developments: an observation platform, a numerical model, and a visualization and specification tool. Additionally, we will examine how these three pieces came together to revolutionize interplanetary coronal mass ejection (ICME) arrival forecasts. A discussion of the importance of education and training in ensuring a positive outcome from R2O activity follows. We describe efforts by the meteorological community to make research results more accessible to forecasters and the applicability of these efforts to the transfer of space-weather research. We end with a forecaster "wish list" for R2O transitions. Ongoing, two-way communication between the research and operations communities is the thread connecting it all.

  4. 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...

  5. 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 WRF?s overestimation of surface precipitation during sum...

  6. 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.

  7. 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.

  8. Operational coastal ocean forecasting in the Eastern Mediterranean: implementation and evaluation

    NASA Astrophysics Data System (ADS)

    Zodiatis, G.; Lardner, R.; Hayes, D. R.; Georgiou, G.; Sofianos, S.; Skliris, N.; Lascaratos, A.

    2006-06-01

    The Cyprus Coastal Ocean Forecasting and Observing System (CYCOFOS) has been producing operational flow forecasts of the northeastern Levantine Basin since 2002 and has been substantially improved in 2005. It is the first system in the Mediterranean to produce a forecast every day at the coastal scale. CYCOFOS uses a the POM (Princeton Ocean Model) flow model, and recently, within the frame of the MFSTEP project (Mediterranean Forecasting System, Toward Environmental Prediction), the flow model was upgraded to use the hourly SKIRON atmospheric forcing, and its resolution was increased from 2.5 km to 1.8 km. The CYCOFOS model is now nested in the ALERMO (Aegean Levantine Eddy Resolving Model) regional model from the University of Athens, which is nested within the MFS (Mediterranean Forecasting System) basin model. The Variational Initialization and FOrcing Platform (VIFOP) has been implemented to reduce the numerical transient processes following initialization. Moreover, a five-day forecast is repeated every day, providing more detailed and more accurate information, of particular value to coastal end users. Forecast results are posted on the web page http://www.ucy.ac.cy/cyocean. The new, daily, high-resolution forecasts agree exceptionally well with the ALERMO regional model. The agreement is better and results more reasonable when VIFOP is used. Active and slave experiments suggest that a four-week active period produces realistic results with more small-scale features. Bias with respect to the slave mode is negligible and there is no detectable bias with remote sensing images (for September, 2004). In situ observed hydrographic data from south of Cyprus are similar in many ways to the corresponding forecast fields. Plans for further model improvement include assimilation of observed temperature profiles (XBT), conductivity-temperature-depth (CTD) profiles from drifters or research cruises, and CT data from the CYCOFOS ocean observatory.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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...

  14. 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...

  15. Data assimilation of fuel moisture in WRF-SFIRE

    E-print Network

    Vejmelka, Martin; Mandel, Jan

    2013-01-01

    Fuel moisture is a major influence on the behavior of wildland fires and an important underlying factor in fire risk. We present a method to assimilate spatially sparse fuel moisture observations from remote automatic weather stations (RAWS) into the moisture model in WRF-SFIRE. WRF-SFIRE is a coupled atmospheric and fire behavior model which simulates the evolution of fuel moisture in idealized fuel species based on atmospheric state. The proposed method uses a modified trend surface model to estimate the fuel moisture field and its uncertainty based on currently available observations. At each grid point of WRF-SFIRE, this information is combined with the model forecast using a nonlinear Kalman filter, leading to an updated estimate of fuel moisture. We demonstrate the effectiveness of the method with tests in two real-world situations: a region in Southern California, where two large Santa Ana fires occurred recently, and on a domain enclosing Colorado.

  16. Navigating a Path Toward Operational, Short-term, Ensemble Based, Probablistic Streamflow Forecasts

    NASA Astrophysics Data System (ADS)

    Hartman, R. K.; Schaake, J.

    2004-12-01

    The National Weather Service (NWS) has federal responsibility for issuing public flood warnings in the United States. Additionally, the NWS has been engaged in longer range water resources forecasts for many years, particularly in the Western U.S. In the past twenty years, longer range forecasts have increasingly incorporated ensemble techniques. Ensemble techniques are attractive because they allow a great deal of flexibility, both temporally and in content. This technique also provides for the influence of additional forcings (i.e. ENSO), through either pre or post processing techniques. More recently, attention has turned to the use of ensemble techniques in the short-term streamflow forecasting process. While considerably more difficult, the development of reliable short-term probabilistic streamflow forecasts has clear application and value for many NWS customers and partners. During flood episodes, expensive mitigation actions are initialed or withheld and critical reservoir management decisions are made in the absence of uncertainty and risk information. Limited emergency services resources and the optimal use of water resources facilities necessitates the development of a risk-based decision making process. The development of reliable short-term probabilistic streamflow forecasts are an essential ingredient in the decision making process. This paper addresses the utility of short-term ensemble streamflow forecasts and the considerations that must be addressed as techniques and operational capabilities are developed. Verification and validation information are discussed from both a scientific and customer perspective. Education and training related to the interpretation and use of ensemble products are also addressed.

  17. 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.

  18. 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.

  19. 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-Québec, 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.

  20. A CyberShake-Based System for Operational Forecasting of Earthquake Ground Motions

    NASA Astrophysics Data System (ADS)

    Milner, K.; Jordan, T. H.; Graves, R. W.; Callaghan, S.; Maechling, P. J.; Field, E. H.; Small, P.; Cybershake Working Group

    2010-12-01

    The goal of operational earthquake forecasting (OEF) is to provide authoritative information about the time dependence of seismic hazard to help communities prepare for earthquakes. Statistical and physical models of earthquake interactions have begun to capture many features of natural seismicity, such as aftershock triggering and the clustering of seismic sequences. In some situations, seismicity-based forecasting methods can achieve short-term probability gain factors of 100-1000 relative to long-term forecasts. Unifying long-term and short-term earthquake probability models into a single time-dependent forecast (UCERF3) is the goal of the current Working Group on California Earthquake Probabilities. The UCERF models forecast fault ruptures. However, from an OEF perspective, forecasts are better represented in terms of the strong ground motions that constitute the primary seismic hazard. Moreover, the prospective testing of ground motion forecasts—an essential requirement for OEF—has certain advantages relative to the more indirect testing of rupture forecasts. This approach has been applied in the STEP model, which forecasts exceedance probabilities at the intensity-VI shaking level. A major limitation is that empirical attenuation relations used by STEP do not properly account for the directivity and basin effects for individual fault ruptures. This limitation can be overcome by the coupling of probabilistic rupture forecasting models with large ensembles of ground motion simulations. In particular, we develop a conceptual framework for OEF based on SCEC’s CyberShake simulation platform, which can simulate ground motions in geologically complex environments for rupture ensembles large enough (~106) to sample adequately the statistical variability represented in the UCERF forecasts. We show how local increases in rupture probabilities can be mapped into ground motion probabilities using a CyberShake model for the LA region. Maps of exceedance probabilities for 3-second spectral acceleration at 0.2 g are illustrated for short-term probability variations calculated using Agnew-Jones foreshock statistics. Significant gains in the ground motion probabilities relative to a STEP-type model are obtained, primarily because this calculation accounts for the rupture directivity and basin effects associated with all individual ruptures in the CyberShake model; e.g., the strong directivity-basin coupling previously inferred from the TeraShake and ShakeOut simulations.

  1. 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.

  2. 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.

  3. Evaluation of WRF Radiation and Microphysics Parameterizations for Use in the Polar Regions

    NASA Astrophysics Data System (ADS)

    Seefeldt, M. W.; Tice, M.; Cassano, J. J.

    2012-12-01

    The Weather Research and Forecasting (WRF) model is being used for applications in the polar regions ranging from real-time forecasting to regional climate simulations. A key to the performance of WRF in the polar regions is the evaluation and identification of an ideal suite of WRF physics parameterizations that best represent the polar atmosphere. This study evaluates an extensive set of combinations of WRF v3.4 shortwave radiation, longwave radiation, and microphysics parameterizations in month-long regional climate simulations. The results of the simulations are statistically compared against surface and upper-air meteorology and cloud observations from the Barrow, Alaska and Summit, Greenland. The conclusion of the study is the identification of a preferred combination(s) of radiation and microphysics parameterizations for the use in the polar regions.

  4. The european flood alert system EFAS - Part 2: Statistical skill assessment of probabilistic and deterministic operational forecasts

    NASA Astrophysics Data System (ADS)

    Bartholmes, J. C.; Thielen, J.; Ramos, M. H.; Gentilini, S.

    2009-02-01

    Since 2005 the European Flood Alert System (EFAS) has been producing probabilistic hydrological forecasts in pre-operational mode at the Joint Research Centre (JRC) of the European Commission. EFAS aims at increasing preparedness for floods in trans-national European river basins by providing medium-range deterministic and probabilistic flood forecasting information, from 3 to 10 days in advance, to national hydro-meteorological services. This paper is Part 2 of a study presenting the development and skill assessment of EFAS. In Part 1, the scientific approach adopted in the development of the system has been presented, as well as its basic principles and forecast products. In the present article, two years of existing operational EFAS forecasts are statistically assessed and the skill of EFAS forecasts is analysed with several skill scores. The analysis is based on the comparison of threshold exceedances between proxy-observed and forecasted discharges. Skill is assessed both with and without taking into account the persistence of the forecasted signal during consecutive forecasts. Skill assessment approaches are mostly adopted from meteorology and the analysis also compares probabilistic and deterministic aspects of EFAS. Furthermore, the utility of different skill scores is discussed and their strengths and shortcomings illustrated. The analysis shows the benefit of incorporating past forecasts in the probability analysis, for medium-range forecasts, which effectively increases the skill of the forecasts.

  5. 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 MO

  6. NOAA Harmful Algal Bloom Operational Forecast System Southwest Florida Forecast Region Maps

    E-print Network

    COLLIER LEE PASCO MONROE MANATEE CHARLOTTE SARASOTA PINELLAS NOAA Harmful Algal Bloom Operational-N Pinellas Bay-N Manatee N Pinellas S Pasco Bay-N Pinellas N Pasco S Pinellas S Manatee NOAA Harmful Algal-S Manatee Bay-N Sarasota N Sarasota S Manatee Bay-S Sarasota S Sarasota Bay-N Charlotte N Charlotte NOAA

  7. Forecasting the Coastal Ocean: Resolution, Tide, and Operational Data in the South Atlantic Bight

    E-print Network

    Forecasting the Coastal Ocean: Resolution, Tide, and Operational Data in the South Atlantic Bight D motion on the shelf occurs in this mode, in the tide- and weather-bands. The former is completely agreement with oceanic tides over the whole East Coast; (ADCIRC 1995); and quality data along the coast (NOS

  8. Forecasting the Coastal Ocean: Resolution, Tide, and Operational Data in the South Atlantic Bight

    E-print Network

    Forecasting the Coastal Ocean: Resolution, Tide, and Operational Data in the South Atlantic Bight D on the shelf occurs in this mode, in the tide- and weather-bands. The former is completely a remotely agreement with oceanic tides over the whole East Coast; (ADCIRC 1995); and quality data along the coast (NOS

  9. FEASIBILITY STUDY ON EARTHQUAKE EARLY WARNING AND OPERATIONAL EARTHQUAKE FORECASTING FOR RISK

    E-print Network

    1 FEASIBILITY STUDY ON EARTHQUAKE EARLY WARNING AND OPERATIONAL EARTHQUAKE FORECASTING FOR RISK Within the framework of the EC-funded project REAKT (Strategies and Tools for Real Time Earthquake Risk and initial implementation of Earthquake Early Warning (EEW) and time- dependent seismic hazard analyses aimed

  10. 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.

  11. Data impact of pre-GPM constellation microwave radiances in the Goddard WRF ensemble data assimilation system

    NASA Astrophysics Data System (ADS)

    Zhang, S. Q.; Chambon, P.; Lin, X.; Hou, A. Y.

    2012-12-01

    The forthcoming Global Precipitation Measurement (GPM) mission will make precipitation observations available from a constellation of space-borne observing systems. Assimilation of precipitation-affected radiances into numerical forecast models has shown promising potential in improving atmospheric analyses and forecasts. In the meantime it also raises new challenges to data assimilation systems. In order to effectively use these observations, a data assimilation system needs to have a forecast error covariance capturing temporal and spatial variability of precipitation and clouds, and an observation operator adequately representing non-linear microphysics and radiative transfer in presence of clouds and precipitation. We present a data impact study of microwave radiance observations in precipitating areas using Goddard WRF ensemble data assimilation system (Goddard-EDAS). This regional data assimilation system is designed to assimilate precipitation information into WRF model at high resolution, with a flow-dependent forecast error covariance and a non-linear all-sky radiance observation operator. A series of experiments are carried out assimilating microwave radiances from a pre-GPM constellation (SSMIS/DMSP-F16, -F17, -F18; AMSR-E/AQUA; MHS/NOAA-18, -19, Metop-A and TMI). Sensitivities to observation error specifications, number of ensemble members and selected channel of observations are examined through "single observation" assimilation experiments. A bias correction scheme for precipitation-affected radiance is developed based on innovation statistics and scattering index over land. The data impact is assessed in case studies of storms occurred over Western Europe and a tropical storm after landfall in the US. Results show that the assimilation of multiple-instrument radiances in precipitating areas has a positive impact on the accumulated rain forecasts verified by ground-based radar rain estimates, and a profound influence to the distribution of microphysical variables.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. Application of a statistical post-processing technique to a gridded, operational, air quality forecast

    NASA Astrophysics Data System (ADS)

    Neal, L. S.; Agnew, P.; Moseley, S.; Ordóñez, C.; Savage, N. H.; Tilbee, M.

    2014-12-01

    An automated air quality forecast bias correction scheme based on the short-term persistence of model bias with respect to recent observations is described. The scheme has been implemented in the operational Met Office five day regional air quality forecast for the UK. It has been evaluated against routine hourly pollution observations for a year-long hindcast. The results demonstrate the value of the scheme in improving performance. For the first day of the forecast the post-processing reduces the bias from 7.02 to 0.53 ?g m-3 for O3, from -4.70 to -0.63 ?g m-3 for NO2, from -4.00 to -0.13 ?g m-3 for PM2.5 and from -7.70 to -0.25 ?g m-3 for PM10. Other metrics also improve for all species. An analysis of the variation of forecast skill with lead-time is presented and demonstrates that the post-processing increases forecast skill out to five days ahead.

  17. Coupling the high complexity land surface model ACASA to the mesoscale model WRF

    NASA Astrophysics Data System (ADS)

    Xu, L.; Pyles, R. D.; Paw U, K. T.; Chen, S. H.; Monier, E.

    2014-05-01

    In this study, the Weather Research and Forecasting Model (WRF) is coupled with the Advanced Canopy-Atmosphere-Soil Algorithm (ACASA), a high complexity land surface model. Although WRF is a state-of-the-art regional atmospheric model with high spatial and temporal resolutions, the land surface schemes available in WRF are simple and lack the capability to simulate carbon dioxide, for example, the popular NOAH LSM. ACASA is a complex multilayer land surface model with interactive canopy physiology and full surface hydrological processes. It allows microenvironmental variables such as air and surface temperatures, wind speed, humidity, and carbon dioxide concentration to vary vertically. Simulations of surface conditions such as air temperature, dew point temperature, and relative humidity from WRF-ACASA and WRF-NOAH are compared with surface observation from over 700 meteorological stations in California. Results show that the increase in complexity in the WRF-ACASA model not only maintains model accuracy, it also properly accounts for the dominant biological and physical processes describing ecosystem-atmosphere interactions that are scientifically valuable. The different complexities of physical and physiological processes in the WRF-ACASA and WRF-NOAH models also highlight the impacts of different land surface and model components on atmospheric and surface conditions.

  18. Coupling the high-complexity land surface model ACASA to the mesoscale model WRF

    NASA Astrophysics Data System (ADS)

    Xu, L.; Pyles, R. D.; Paw U, K. T.; Chen, S. H.; Monier, E.

    2014-12-01

    In this study, the Weather Research and Forecasting (WRF) model is coupled with the Advanced Canopy-Atmosphere-Soil Algorithm (ACASA), a high-complexity land surface model. Although WRF is a state-of-the-art regional atmospheric model with high spatial and temporal resolutions, the land surface schemes available in WRF, such as the popular NOAH model, are simple and lack the capability of representing the canopy structure. In contrast, ACASA is a complex multilayer land surface model with interactive canopy physiology and high-order turbulence closure that allows for an accurate representation of heat, momentum, water, and carbon dioxide fluxes between the land surface and the atmosphere. It allows for microenvironmental variables such as surface air temperature, wind speed, humidity, and carbon dioxide concentration to vary vertically within and above the canopy. Surface meteorological conditions, including air temperature, dew point temperature, and relative humidity, simulated by WRF-ACASA and WRF-NOAH are compared and evaluated with observations from over 700 meteorological stations in California. Results show that the increase in complexity in the WRF-ACASA model not only maintains model accuracy but also properly accounts for the dominant biological and physical processes describing ecosystem-atmosphere interactions that are scientifically valuable. The different complexities of physical and physiological processes in the WRF-ACASA and WRF-NOAH models also highlight the impact of different land surface models on atmospheric and surface conditions.

  19. High-resolution rainfall variability simulated by the WRF RCM: application to eastern France

    NASA Astrophysics Data System (ADS)

    Marteau, Romain; Richard, Yves; Pohl, Benjamin; Smith, Carmela Chateau; Castel, Thierry

    2015-02-01

    The Weather Research and Forecasting (WRF) model, driven laterally by ERA-Interim reanalyses, is used here to downscale rainfall, at relatively high resolution (~8 km) over Burgundy (eastern France), during the period 1989-2009. Regional simulations are compared to the Météo-France Station Network (MFSN; 127 daily rain-gauge records), at various temporal scales, including interannual variability, the annual cycle, and weather types. Results show that the spatial distribution of WRF-simulated rainfall climatology is consistent with MFSN observation data, but WRF tends to overestimate annual rainfall by ~+15 %. At the interannual scale, WRF also performs very well (r ~ 0.8), despite almost constant, systematic overestimation. Only the average annual rainfall cycle is not accurately reproduced by WRF (r ~ 0.5), with rainfall overestimation in spring and summer, when convective rainfall prevails. During the winter season (October-March), when stratiform rainfall is prevalent, WRF performs better. Despite the biases for summertime convective events, these results suggest that high-resolution WRF simulations could successfully be used to document present and future climate variability at a regional scale. Nevertheless, because of overestimated convective rainfall, WRF-simulated rainfall should probably not be used directly to feed impact models, especially during the vegetative summer period.

  20. 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.

  1. 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.

  2. 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.

  3. 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

  4. 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

  5. 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.

  6. Use of Numerical Weather Research and Forecasting Specifications in Infrasound Propagation Modeling of Local and Regional Sources - Preliminary Investigations

    NASA Astrophysics Data System (ADS)

    Nava, S.; Masters, S. E.; Norris, D.

    2009-12-01

    High resolution characterization of the lower atmosphere is an important aspect of infrasound propagation modeling of local and regional sources. Rawinsonde weather balloons can be used to obtain such information, but may be impractical or unavailable at the time and location of interest, and do not capture spatial variability that may be important over regional ranges. In this study, we explore the utility of the Weather Research and Forecasting (WRF) Model, a state-of-the-science mesoscale numerical weather prediction system used in operational forecasting and atmospheric research (http://wrf-model.org). A ground truth database of analyst-confirmed mining and military disposal explosions recorded on an infrasound array located near Salt Lake City, Utah (USA), with source-to-receiver distances ranges from 15-200 km, forms the basis of this study. Of primary interest is infrasound propagation within the so-called zone of silence. Cases were identified in which infrasound detections were and were not observed from the same source location. It is assumed that the method of source detonation was similar and the dynamic atmosphere was the only variable affecting the observability. The WRF-model was executed to produce high resolution spatial and temporal wind and temperature fields for input into infrasound propagation models. The WRF simulations extended to 16-20 km altitude, and were configured using nested domains with horizontal spatial resolution of approximately 1.8 km and temporal output resolution of 15 minutes. Each simulation was initialized with the Global Forecast System (GFS) analysis approximately 12-18 hours before the infrasound event of interest and calculations continued for 24 hours. Local observed surface, upper air, radar, and rawinsonde data were used to judge if the WRF model fields were reasonable and matched the actual weather conditions. Ray trace, parabolic equation, and time-domain parabolic equation propagation predictions were computed using WRF-generated atmospheric specifications for the times of these events. Propagation model results successfully matched waveform observations for some of cases studied, predicting when WRF-predicted local atmospheric conditions did and did not support ducting along the source-receiver propagation path. Results were also compared to model predictions using range-independent, local rawinsonde profiles of the atmosphere. This study was a first attempt at using minimally tuned WRF-generated atmospheric specifications in propagation modeling of infrasound signals from local ground truth events. Additional work is needed to quantify the WRF prediction utility for use in high-fidelity infrasound propagation modeling over local and regional ranges.

  7. WRF4G project: Advances in running climate simulations on the EGI Infrastructure

    NASA Astrophysics Data System (ADS)

    Blanco, Carlos; Cofino, Antonio S.; Fernández Quiruelas, Valvanuz; García, Markel; Fernández, Jesús

    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)

  8. 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.

  9. Determining and exploiting the distribution function of wind power forecasting error for the economic operation of autonomous power systems

    Microsoft Academic Search

    Antonis G. Tsikalakis; Yiannis A. Katsigiannis; Pavlos S. Georgilakis; Nikos D. Hatziargyriou

    2006-01-01

    Many efforts have been presented in the bibliography for wind power forecasting in power systems and few of them have been used for autonomous power systems. The impact of knowing the distribution function of wind power forecasting error in the economic operation of a power system is studied in this paper. The papers proposes that the distribution of the wind

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

    NASA Astrophysics Data System (ADS)

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

    2015-01-01

    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).

  11. Polar Satellite Products for the Operational Forecaster (POES) Module 2: Microwave Products and Applications

    NSDL National Science Digital Library

    COMET

    1999-07-23

    This Web-based module is a component of the Integrated Sensor Training (IST) Professional Development Series (PDS) Professional Competency Unit #6-Satellite Data and Products. This module provides a closer look at the capabilities, products, and applications available to operational weather forecasting with the present suite of microwave instruments onboard both NOAA and DMSP satellites. If you wish, you may launch the module from this page.

  12. 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.

  13. Building and Evaluating an Operational SST-based Fire Season Forecast Model in the Southern Amazon

    NASA Astrophysics Data System (ADS)

    Chen, Y.; Randerson, J. T.; Morton, D. C.

    2013-12-01

    Base on time-lagged correlation analyses between satellite-observed active fire counts and two ocean climate indices (OCIs) that represent mean sea surface temperature anomalies over the tropical Pacific and Atlantic, we developed an operational model to forecast fire season severities (FSSs) in different regions of southern Amazon. The forecasts for FSS in each fire year were performed and reported at every month between November in previous year and the beginning of the fire season (June). Each prediction was derived from an optimized regression model that uses historical fire observations and OCI data at or before the prediction month only. Although the model performance generally decreased as the lead time for prediction increased, the rate of decline varied in different regions of the southern Amazon. In some regions such as Rondonia and Para, high-quality forecasts can be made as early as in November and December of previous year. We predicted that the 2012 fire season severity was below average across the southern Amazon because of strong La Nina conditions in the Pacific and below average sea surface temperatures in the North Atlantic. This prediction was validated by comparing with observed fire season severity. In several months before the 2013 fire season, we also predicted that the FSS in this year will be considerably higher than in 2011 or 2012 and average or above average relative to the long term mean in all regions. Specific points are recommended for future improvement of the forecast model.

  14. Effect of flow forecasting quality on benefits of reservoir operation - a case study for the Geheyan reservoir (China)

    NASA Astrophysics Data System (ADS)

    Dong, X.; Dohmen-Janssen, C. M.; Booij, M.; Hulscher, S.

    2006-12-01

    This paper presents a methodology to determine the effect of flow forecasting quality on the benefits of reservoir operation. The benefits are calculated in terms of the electricity generated, and the quality of the flow forecasting is defined in terms of lead time and accuracy of the forecasts. In order to determine such an effect, an optimization model for reservoir operation was developed which consists of two sub-models: a long-term (monthly) and a short-term (daily) optimization sub-model. A methodology was developed to couple these two sub-models, so that both short-term benefits (time span in the order of the flow forecasting lead time) and long-term benefits (one year) were considered and balanced. Both sub-models use Discretized Dynamic Programming (DDP) as their optimization algorithms. The Geheyan reservoir on the Qingjiang River in China was taken as case study. Observed (from the 1997 hydrological year) and forecasted flow series were used to calculate the benefits. Forecasted flow series were created by adding noises to the observed series. Different magnitudes of noise reflected different levels of forecasting accuracies. The results reveal, first of all, a threshold lead time of 33 days, beyond which further extension of the forecasting lead time will not lead to a significant increase in benefits. Secondly, for lead times shorter than 33 days, a longer lead time will generally lead to a higher benefit. Thirdly, a perfect inflow forecasting with a lead time of 4 days will realize 87% of the theoretical maximum electricity generated in one year. Fourthly, for a certain lead time, more accurate forecasting leads to higher benefits. For inflow forecasting with a fixed lead time of 4 days and different forecasting accuracies, the benefits can increase by 5 to 9% compared to the actual operation results. It is concluded that the definition of the appropriate lead time will depend mainly on the physical conditions of the basin and on the characteristics of the reservoir. The derived threshold lead time (33 days) gives a theoretical upper limit for the extension of forecasting lead time. Criteria for the appropriate forecasting accuracy for a specific feasible lead-time should be defined from the benefit-accuracy relationship, starting from setting a preferred benefit level, in terms of percentage of the theoretical maximum. Inflow forecasting with a higher accuracy does not always increase the benefits, because these also depend on the operation strategies of the reservoir.

  15. Evaluation of a climate simulation in Europe based on the WRF-NOAH model system: precipitation in Germany

    NASA Astrophysics Data System (ADS)

    Warrach-Sagi, Kirsten; Schwitalla, Thomas; Wulfmeyer, Volker; Bauer, Hans-Stefan

    2013-08-01

    The Weather Research and Forecast (WRF) model with its land surface model NOAH was set up and applied as regional climate model over Europe. It was forced with the latest ERA-interim reanalysis data from 1989 to 2008 and operated with 0.33° and 0.11° resolution. This study focuses on the verification of monthly and seasonal mean precipitation over Germany, where a high quality precipitation dataset of the German Weather Service is available. In particular, the precipitation is studied in the orographic terrain of southwestern Germany and the dry lowlands of northeastern Germany. In both regions precipitation data is very important for end users such as hydrologists and farmers. Both WRF simulations show a systematic positive precipitation bias not apparent in ERA-interim and an overestimation of wet day frequency. The downscaling experiment improved the annual cycle of the precipitation intensity, which is underestimated by ERA-interim. Normalized Taylor diagrams, i.e., those discarding the systematic bias by normalizing the quantities, demonstrate that downscaling with WRF provides a better spatial distribution than the ERA interim precipitation analyses in southwestern Germany and most of the whole of Germany but degrades the results for northeastern Germany. At the applied model resolution of 0.11°, WRF shows typical systematic errors of RCMs in orographic terrain such as the windward-lee effect. A convection permitting case study set up for summer 2007 improved the precipitation simulations with respect to the location of precipitation maxima in the mountainous regions and the spatial correlation of precipitation. This result indicates the high value of regional climate simulations on the convection-permitting scale.

  16. 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.

  17. 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.

  18. Forecasting propagation and evolution of CMEs in an operational setting: What has been learned

    NASA Astrophysics Data System (ADS)

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

    2013-10-01

    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.

  19. 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.

  20. Towards an operational system for oil-spill forecast over Spanish waters: Initial developments and implementation test

    Microsoft Academic Search

    M. G. Sotillo; E. Alvarez Fanjul; S. Castanedo; A. J. Abascal; J. Menendez; M. Emelianov; R. Olivella; E. García-Ladona; M. Ruiz-Villarreal; J. Conde; M. Gómez; P. Conde; A. D. Gutierrez; R. Medina

    2008-01-01

    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

  1. 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.

  2. 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.

  3. Using a neural network to make operational forecasts of ionospheric variations and storms at Kokubunji, Japan

    NASA Astrophysics Data System (ADS)

    Nakamura, M. I.; Maruyama, T.; Shidama, Y.

    2007-12-01

    An operational model was developed for forecasting ionospheric variations and storms at Kokubunji (35°N, 139°E), 24 hours in advance, by using a neural network. The ionospheric critical frequency (foF2) shows periodic variabilities from days to the solar cycle length and also shows sporadic changes known as ionospheric storms caused by geomagnetic storms (of solar disturbance origin). The neural network was trained for the target parameter of foF2 at each local time and input parameters of solar flux, sunspot number, day of the year, K-index at Kakioka. The training was conducted using the data obtained for the period from 1960 to 1984. The method was validated for the period from 1985 to 2003. The trained network can be used for daily forecasting ionospheric variations including storms using prompt daily reports of K-index, sunspot number, and solar flux values available on-line.

  4. Comparative analysis of operational forecasts versus actual weather conditions in airline flight planning, volume 1

    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 1 of the four major tasks included in the study. Task 1 compares flight plans based on forecasts with plans based on the verifying analysis from 33 days during the summer and fall of 1979. The comparisons show that: (1) potential fuel savings conservatively estimated to be between 1.2 and 2.5 percent could result from using more timely and accurate weather data in flight planning and route selection; (2) the Suitland forecast generally underestimates wind speeds; and (3) the track selection methodology of many airlines operating on the North Atlantic may not be optimum resulting in their selecting other than the optimum North Atlantic Organized Track about 50 percent of the time.

  5. PlutoWRF: a new general circulation model for Pluto's atmosphere

    NASA Astrophysics Data System (ADS)

    Toigo, A. D.; French, R. G.; Gierasch, P. J.

    2012-12-01

    We present initial simulations using a new general circulation model (GCM) for the atmosphere of Pluto, PlutoWRF. PlutoWRF is the Pluto-specific implementation of the planetWRF generalized planetary atmospheric model, itself an extension and enhancement of the terresterial National Center for Atmospheric Research (NCAR) Weather Research and Forecasting (WRF) model. In addition to utilizing orbital, geophysical, and atmospheric constants appropriate to Pluto, the model includes changes to physical parameterizations for turbulent and molecular transport, radiative heating and cooling of the atmosphere and surface (based on Strobel et al. [1996], with later improvements), and condensation and sublimation of ices on the surface. Sublimation and thermal forcing drive steady and seasonal flows, but also induce periodic (tidal and wave) components, which are the focus of our initial studies, and are thought to be detectable in stellar occultation temperature profiles.

  6. Downscaling seasonal to centennial simulations on distributed computing infrastructures using WRF model. The WRF4G project

    NASA Astrophysics Data System (ADS)

    Cofino, A. S.; Fernández Quiruelas, V.; Blanco Real, J. C.; García Díez, M.; Fernández, 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)

  7. 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)

  8. 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 solution—either from nearby historic events or rapidly determined by W-phase analysis—and 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.

  9. Forecasting Coastal Ice Decay and Break-up in Northern Alaska

    NASA Astrophysics Data System (ADS)

    Petrich, C.; Eicken, H.; Zhang, J.; Krieger, J.

    2009-12-01

    An operational forecast was developed for the break-up of coastal landfast sea ice in the Chukchi Sea off of Barrow, Alaska. The break-up process was systematically analyzed from 2000 to 2009 based on local observations of snow and ice conditions, climate records, image sequences obtained from web cameras, coastal X-band marine RADAR, and satellite imagery (mostly MODIS). Two fundamentally different break-up modes are distinguished. With the exception of 2003, 2004, and 2007, break-up of landfast sea ice was a two-step process. First, the near-shore ice cover disintegrated with ice fragments moving along shore, sheltered by grounded pressure ridges. Then, these confining ridges, typically around 1 km off-shore, broke out or melted in place. The timing of break-up of the near-shore ice cover is correlated with the downwelling solar radiation in June and July as measured at the Atmospheric Radiation Measurement (ARM) Program’s site over the nearby tundra. Break-up in 2009 was correctly predicted two weeks in advance based on the net shortwave radiation output of a 16-day regional forecast model (Weather Research and Forecasting (WRF) Model). The WRF output was converted to equivalent ARM observations based on the ground albedo assumed by the WRF model, the albedo measured at the ARM site, and the expected clear sky radiation. The downwelling shortwave radiation was also estimated from local cloud cover observations (NOAA aviation routine weather report (METAR)).

  10. Energy demand forecasting model, technical appendix. Computer program users guide and operative manual, data base users guide, and Pacific Northwest energy data base

    Microsoft Academic Search

    W. M. McHugh; J. M. Storie; J. W. Lockett; S. G. Scott; E. A. Holt

    1977-01-01

    Operating instructions and system documentation for a computerized energy demand forecasting model are presented. The model has the capability to forecast energy demand for four fuel types for the three Northwest states, in five-year steps, from 1980 through the year 2000. The forecasts were further broken down into the residential, commercial, industrial, transportation, and other sectors. The model written in

  11. 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...

  12. 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.

  13. 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.

  14. 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.

    2013-11-01

    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 for flood predictions with lead times up to 10 days. For this study, 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 discharge observations only. To assimilate soil moisture and discharge data into EFAS, an Ensemble Kalman Filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, 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 assimilated; 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 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 data is assimilated into the system and the best performance is achieved with the assimilation of both discharge and satellite observations. 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 remotely sensed soil moisture observations to improve near-real time flood forecasting in large catchments.

  15. 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.

  16. 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.

  17. 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.

  18. How reliable is the fully couple of WRF and VIC model?

    NASA Astrophysics Data System (ADS)

    Tang, C.; Dennis, R. L.

    2013-12-01

    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 Upper Mississippi River Basin (UMRB) from 1980 to 2010. Subsequently, a simulation based on an offline linkage of WRF and VIC was performed in the UMRB with the calibrated parameters established above from 2006 to 2009. Lastly, a simulation of fully coupled WRF and VIC through a coupler was performed in the UMRB. The performance of the fully couple of WRF and VIC was assessed with respect to key simulated variables through a comparison with the offline couple of WRF and VIC models, and well calibrated VIC model. Spatiotemporal comparisons of the simulated evaporation (ET), soil moisture (SM), runoff, and baseflow produced by the VIC calibrated run (base data), offline coupling, and fully coupling run were conducted. The results showed that: 1) the fully couple of VIC with WRF was able to achieve good agreement in the simulation of soil moisture and evaporation, 2) The fully coupling has significant improvement in simulation of runoff and baseflow in compare with the results from offline coupling. These suggest the VIC coupling should function without causing a large change in the moisture budget.

  19. 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.

  20. 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.

  1. 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 [WindLogics

    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. A comprehensive analysis of wind energy forecast errors for the various model-based power forecasts was presented for a suite of wind energy ramp definitions. The results compiled over the year-long study period showed that the power forecasts based on the research models (ESRL_RAP, HRRR) more accurately predict wind energy ramp events than the current operational forecast models, both at the system aggregate level and at the local wind plant level. At the system level, the ESRL_RAP-based forecasts most accurately predict both the total number of ramp events and the occurrence of the events themselves, but the HRRR-based forecasts more accurately predict the ramp rate. At the individual site level, the HRRR-based forecasts most accurately predicted the actual ramp occurrence, the total number of ramps and the ramp rates (40-60% improvement in ramp rates over the coarser resolution forecast

  2. Operational hydro-meteorological warning and real-time flood forecasting:the Piemonte region case study Hydrology and Earth System Sciences, 9(4), 457466 (2005) EGU

    E-print Network

    Paris-Sud XI, Université de

    Operational hydro-meteorological warning and real-time flood forecasting:the Piemonte region case study 457 Hydrology and Earth System Sciences, 9(4), 457466 (2005) © EGU Operational hydro-meteorological forecasting system in the context of the Piemonte Regions hydro-meteorological operational alert procedure

  3. The representation of the TTL in a tropical channel version of the WRF model

    NASA Astrophysics Data System (ADS)

    Hassler, B.; Evan, S.; Rosenlof, K. H.; Dudhia, J.; Davis, S. M.

    2012-12-01

    The NCAR Weather Research Forecast (WRF) model was initially developed and tested for regional simulations and weather forecasting in the troposphere. Little has been reported on WRF performance in simulations of the Tropical Tropopause Layer (TTL) and the lower stratosphere (LS). To address WRF ability to resolve the temperature and water vapor distribution in the TTL, we conducted a series of numerical experiments in the tropics for the boreal winter 2006 (December-January-February). The model domain is configured as a tropical channel with a horizontal grid-spacing of 37km, a vertical grid-spacing of 500m and a top at 0.1hPa. The European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis Interim (ERA-I) data provide initial and boundary conditions. The model is forced with an ozone distribution that corresponds to the climatology of observed ozone developed by Hassler et al. (2008). The model performance for TTL temperature variability is evaluated via comparisons with radiosonde data in the Western Pacific/South America and the MERRA and ERA-I reanalyses. The MLS water vapor is also used to evaluate WRF simulated water vapor in the TTL. The model is shown to have a realistic representation of tropical precipitation variability, mean tropical ascent, and evolution of the TTL zonal mean wind and temperature. The WRF simulations of the cold point tropopause show reasonable agreement with the reanalyses. The model captures the location of TTL water vapor minimum in the Western Pacific. However, the model simulation is drier than the MLS observations. Therefore we discuss the sensitivity of the WRF model in simulating TTL water vapor and temperature to the choice of the cumulus convection parameterization schemes.

  4. Disaggregating Operating and Financing Activities: Implications for Forecasts of Future Profitability*

    E-print Network

    : Implications for Forecasts of Future Profitability ABSTRACT Academic research and financial statement analysis Profitability* ADAM ESPLIN, Indiana University MAX HEWITT, Indiana University MARLENE PLUMLEE, University activities is useful for forecasting profitability and valuation. Consistent with this notion, the accounting

  5. Analysis of the Arctic atmospheric energy budget in WRF: A comparison with reanalyses and satellite observations

    NASA Astrophysics Data System (ADS)

    Porter, David F.; Cassano, John J.; Serreze, Mark C.

    2011-11-01

    The Weather Research and Forecasting (WRF) model is used to dynamically downscale the regional climate of the Arctic, an area undergoing rapid climate changes. Because the WRF model is increasingly being run over larger spatial and temporal scales, an assessment of its ability to reconstruct basic properties of regional climates, such as terms in the energy budget, is crucial. Estimates of the Arctic energy budget from WRF are compared with estimates from reanalyses and satellite observations. The WRF model was run on a large pan-Arctic domain continuously from 2000 to 2008. Apart from a few systematic shortcomings, WRF sufficiently captures the Arctic energy budget. The major deficiency, with differences from reanalyses and satellite observations as large as 40 W m-2 in summer months, is in the shortwave radiative fluxes at both the surface and top of atmosphere (TOA). WRF's positive bias in upwelling shortwave radiation is due to a specified constant sea ice albedo of 0.8, which is too high during the summer. When sea ice albedo in WRF is allowed to vary in a more realistic manner in a test simulation, both surface and TOA energy budget components improve, while showing little impact on the atmospheric energy convergence and storage. A second, similar WRF simulation was performed but with gridded nudging enabled. When the large-scale circulation is constrained to the forcing data, the two energy budget terms that are most dependent on weather patterns, the convergence of atmospheric energy transport and the tendency of column-integrated energy, closely resemble their reanalysis counterparts.

  6. An immersed boundary method in WRF for high resolution urban air quality modeling

    NASA Astrophysics Data System (ADS)

    Wiersema, D. J.; Lundquist, K. A.; Martien, P. T.; Rivard, T.; Chow, F. K.

    2012-12-01

    Urban air quality modeling at the neighborhood scale has the potential to become an important tool for long term exposure studies, regulation, and urban planning. Current generation models for urban flow or air quality are limited by laborious mesh creation, terrain slope restrictions due to coordinate transformations, lack of atmospheric physics, and/or omission of regional meteorological effects. To avoid these limitations we have extended the functionality of an existing model, IBM-WRF, a modified version of the Weather Research and Forecasting model (WRF) which uses an immersed boundary method (IBM) (Lundquist et al. 2010, 2012). The immersed boundary method used in our model allows for the evaluation of flow over complex urban geometries including vertical surfaces, sharp corners, and local topographic variations. Lateral boundaries in IBM-WRF can be prescribed using output from the standard WRF model, allowing for realistic meteorological input. IBM-WRF is being used to investigate the transport and trapping of vehicle emissions around a proposed affordable housing development located adjacent to a major freeway which transports 250,000+ vehicles per day. Urban topography is developed using high-resolution airborne LIDAR building data combined with ground elevation data. Meteorological input can be created using the WRF model configured to use several nested domains allowing for synoptic scale phenomena to affect the neighborhood scale IBM-WRF domain, with a horizontal resolution on the order of one meter. Initial results from IBM-WRF are presented here and will ultimately be used to assist planning efforts to reduce local air pollution exposure and minimize related associated adverse health effects.

  7. Nesting operational forecasting models in the Eastern Mediterranean: active and slave mode

    NASA Astrophysics Data System (ADS)

    Sofianos, S. S.; Skliris, N.; Mantziafou, A.; Lascaratos, A.; Zodiatis, G.; Lardner, R.; Hayes, D.; Georgiou, G.

    2006-08-01

    Modern ocean operational systems involve different groups and tools, in different regions and scales. Blending all these in a unique system with reliable forecasting capabilities is an important task. The efficiency of nesting procedures between different scale and resolution models are crucial in determining whether the dynamics at the different scales are well represented at each level or the nesting technique suppresses the dynamical features emerging from individual modelling components. In the present work, we investigate the role of the initialization of telescopically nested and with double horizontal resolution forecasting systems in the Eastern Mediterranean, comparing the results between weekly initialized experiments ("slave'' mode) and "free'' runs ("active'' mode) at the regional (Aegean-Levantine area) and shelf (Cyprus) scale. It is found that, although the main circulation pattern remains similar, the differences in the domain mean kinetic energy between the "slave'' and the "active'' experiments in the Aegean-Levantine region are large in both September 2004 and January 2005, with the "active'' being much more energetic, while in the Cyprus area differences are significantly smaller. The most pronounced differences in the circulation and sea surface temperature and salinity fields are observed in the Aegean Sea, during September 2004, related to the inflow and spreading of the Black Sea Water, and the Rhodes Gyre, during January 2005, related to small-scale eddy activity developed and surviving in the "active'' mode experiment that decreases the area of the gyre.

  8. Parameters Optimization for Operational Storm Surge/Tide Forecast Model using a Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Lee, W.; You, S.; Ryoo, S.; Global Environment System Research Laboratory

    2010-12-01

    Typhoons generated in northwestern Pacific Ocean annually affect the Korean Peninsula and storm surges generated by strong low pressure and sea winds often cause serious damage to property in the coastal region. To predict storm surges, a lot of researches have been conducted by using numerical models for many years. Various parameters used for calculation of physics process are used in numerical models based on laws of physics, but they are not accurate values. Because those parameters affect to the model performance, these uncertain values can sensitively operate results of the model. Therefore, optimization of these parameters used in numerical model is essential for accurate storm surge predictions. A genetic algorithm (GA) is recently used to estimate optimized values of these parameters. The GA is a stochastic exploration modeling natural phenomenon named genetic heritance and competition for survival. To realize breeding of species and selection, the groups which may be harmed are kept and use genetic operators such as inheritance, mutation, selection and crossover. In this study, we have improved operational storm surge/tide forecast model(STORM) of NIMR/KMA (National Institute of Meteorological Research/Korea Meteorological Administration) that covers 115E - 150E, 20N - 52N based on POM (Princeton Ocean Model) with 8km horizontal resolutions using the GA. Optimized values have been estimated about main 4 parameters which are bottom drag coefficient, background horizontal diffusivity coefficient, Smagoranski’s horizontal viscosity coefficient and sea level pressure scaling coefficient within STORM. These optimized parameters were estimated on typhoon MAEMI in 2003 and 9 typhoons which have affected to Korea peninsula from 2005 to 2007. The 4 estimated parameters were also used to compare one-month predictions in February and August 2008. During the 48h forecast time, the mean and median model accuracies improved by 25 and 51%, respectively.

  9. Forcing a distributed hydrological model with ensemble precipitation forecasts to support dam operation during floods

    Microsoft Academic Search

    O. C. Saavedra; T. Koike; K. Yang; T. Graf; X. Li; L. Wang; X. Han

    2010-01-01

    This study attempts to generate ensemble precipitation considering the accuracy of the quantitative precipitation forecast (QPF) within previous time steps. The combination of the forecasts with real time in situ measurements is used to determine the forecast error. A penalty weighting approach is suggested to account the spatial variability of the error. Underestimated or overestimated intensities of the QPF might

  10. Using a coupled lake model with WRF for dynamical downscaling

    NASA Astrophysics Data System (ADS)

    Mallard, Megan S.; Nolte, Christopher G.; Bullock, O. Russell; Spero, Tanya L.; Gula, Jonathan

    2014-06-01

    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 the consequences of using different methods for setting lake temperatures and ice on predicted 2 m temperature and precipitation in the Great Lakes region. A control simulation is performed where lake surface temperatures and ice coverage are interpolated from the GCM proxy. Because the R2 represents the five Great Lakes with only three grid points, ice formation is poorly represented, with large, deep lakes freezing abruptly. Unrealistic temperature gradients appear in areas where the coarse-scale fields have no inland water points nearby and lake temperatures on the finer grid are set using oceanic points from the GCM proxy. Using WRF coupled with the Freshwater Lake (FLake) model reduces errors in lake temperatures and significantly improves the timing and extent of ice coverage. Overall, WRF-FLake increases the accuracy of 2 m temperature compared to the control simulation where lake variables are interpolated from R2. However, the decreased error in FLake-simulated lake temperatures exacerbates an existing wet bias in monthly precipitation relative to the control run because the erroneously cool lake temperatures interpolated from R2 in the control run tend to suppress overactive precipitation.

  11. 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.

  12. Cite as: Lundquist, J.K., F. K. Chow, J. D. Mirocha, and K.A. Lundquist, 2007: An Improved WRF for Urban-Scale and Complex-Terrain Applications. American Meteorological Society's 7

    E-print Network

    Chow, Fotini Katopodes

    .1 Dynamic Reconstruction of Subfilter-scale Turbulent Stresses Recent advances in computational capabilities processes. Numerical weather prediction models, such as the Weather and Research Forecasting model (WRF by current turbulence models. Furthermore, WRF's terrain-following coordinate system is inappropriate

  13. Assessing uncertainties in flood forecasts for decision making: prototype of an operational flood management system integrating ensemble predictions

    NASA Astrophysics Data System (ADS)

    Dietrich, J.; Schumann, A. H.; Redetzky, M.; Walther, J.; Denhard, M.; Wang, Y.; Pfützner, B.; Büttner, U.

    2009-08-01

    Ensemble forecasts aim at framing the uncertainties of the potential future development of the hydro-meteorological situation. A probabilistic evaluation can be used to communicate forecast uncertainty to decision makers. Here an operational system for ensemble based flood forecasting is presented, which combines forecasts from the European COSMO-LEPS, SRNWP-PEPS and COSMO-DE prediction systems. A multi-model lagged average super-ensemble is generated by recombining members from different runs of these meteorological forecast systems. A subset of the super-ensemble is selected based on a priori model weights, which are obtained from ensemble calibration. Flood forecasts are simulated by the conceptual rainfall-runoff-model ArcEGMO. Parameter uncertainty of the model is represented by a parameter ensemble, which is a priori generated from a comprehensive uncertainty analysis during model calibration. The use of a computationally efficient hydrological model within a flood management system allows us to compute the hydro-meteorological model chain for all members of the sub-ensemble. The model chain is not re-computed before new ensemble forecasts are available, but the probabilistic assessment of the output is updated when new information from deterministic short range forecasts or from assimilation of measured data becomes available. For hydraulic modelling, with the desired result of a probabilistic inundation map with high spatial resolution, a replacement model can help to overcome computational limitations. A prototype of the developed framework has been applied for a case study in the Mulde river basin. However these techniques, in particular the probabilistic assessment and the derivation of decision rules are still in their infancy. Further research is necessary and promising.

  14. 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.

  15. Prediction and uncertainty of Hurricane Sandy (2012) explored through a real-time cloud-permitting ensemble analysis and forecast system assimilating airborne Doppler radar observations

    NASA Astrophysics Data System (ADS)

    Munsell, Erin B.; Zhang, Fuqing

    2014-03-01

    the Pennsylvania State University (PSU) real-time convection-permitting hurricane analysis and forecasting system (WRF-EnKF) that assimilates airborne Doppler radar observations, the sensitivity and uncertainty of forecasts initialized several days prior to landfall of Hurricane Sandy (2012) are assessed. The performance of the track and intensity forecasts of both the deterministic and ensemble forecasts by the PSU WRF-EnKF system show significant skill and are comparable to or better than forecasts produced by operational dynamical models, even at lead times of 4-5 days prior to landfall. Many of the ensemble members correctly capture the interaction of Sandy with an approaching midlatitude trough, which precedes Sandy's forecasted landfall in the Mid-Atlantic region of the United States. However, the ensemble reveals considerable forecast uncertainties in the prediction of Sandy. For example, in the ensemble forecast initialized at 0000 UTC 26 October 2012, 10 of the 60 members do not predict a United States landfall. Using ensemble composite and sensitivity analyses, the essential dynamics and initial condition uncertainties that lead to forecast divergence among the members in tracks and precipitation are examined. It is observed that uncertainties in the environmental steering flow are the most impactful factor on the divergence of Sandy's track forecasts, and its subsequent interaction with the approaching midlatitude trough. Though the midlatitude system does not strongly influence the final position of Sandy, differences in the timing and location of its interactions with Sandy lead to considerable differences in rainfall forecasts, especially with respect to heavy precipitation over land.

  16. Ensemble Volcanic Aerosol Forecasting in Hawaii using a Parallelized version of the Hysplit Dispersion Model

    NASA Astrophysics Data System (ADS)

    Pattantyus, A.; Businger, S.

    2013-12-01

    A transition from deterministic to probabilistic forecasts of the dispersion of emissions from the Kilauea Volcano on the Island of Hawaii is under way. Operational forecasts of volcanic smog (vog) have been produced for 3 years by a custom version of NOAA's Hysplit dispersion model (vog model hereafter), a Lagrangian transport model that uses high-resolution WRF-ARW model output for initial conditions run at the University of Hawaii at Manoa. The vog model has been successful in predicting which locations in the State of Hawaii will be impacted by the vog plume. Initial concentrations of emissions from the volcano are set empirically based on downstream observations provided by the Hawaiian Volcano Observatory. Fast changing meteorological conditions and/or rapid variations in emissions rates cause forecast errors to increase. Recent efforts aim to leverage the parallelism of Hysplit to run ensemble forecasts with various initial condition configurations to better quantify the forecast uncertainty. The ensemble will contain 28 members each with perturbed heights and locations of initial aerosol concentrations. Forecast sulfur dioxide and sulfate aerosol concentrations follow Air Resources Laboratory's Air Quality Index (AQI). The resulting probabilistic forecasts will provide probability of exceedance plots and concentration-probability plots for each AQI level. Because some people are extremely sensitive to low concentrations of sulfate aerosols, the lowest AQI levels will be distinguished in the exceedance map output. Downstream observations at Pahala and Kona will be used to validate the ensemble results, which will also be compared to the results of deterministic forecasts.

  17. 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.

  18. 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.

  19. 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.

  20. THE PREVAIR SYSTEM, AN OPERATIONAL SYSTEM FOR LARGE SCALE AIR QUALITY FORECASTS OVER EUROPE; APPLICATIONS AT THE LOCAL SCALE

    E-print Network

    Paris-Sud XI, Université de

    THE PREVAIR SYSTEM, AN OPERATIONAL SYSTEM FOR LARGE SCALE AIR QUALITY FORECASTS OVER EUROPE-boundary air pollution; (2) providing large scale national air quality information based on numerical information delivered by thé French qualified associations in charge of régional air quality monitoring (AASQA

  1. 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

  2. The efficiency of the WRF model for simulating typhoons

    NASA Astrophysics Data System (ADS)

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

    2014-01-01

    The Weather Research Forecast (WRF) model includes various configuration options related to physics parameters, which can affect the performance of the model. In this study, different 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 WRF model were exhaustively tested for typhoon Noul, which had 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 (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.

  3. An Immersed Boundary Method in WRF for High Resolution Urban Air Quality Modeling

    NASA Astrophysics Data System (ADS)

    Wiersema, D. J.; Lundquist, K. A.; Martien, P. T.; Rivard, T.; Chow, F. K.

    2013-12-01

    Urban air quality modeling at the neighborhood scale has potential to become an important tool for long term exposure studies, regulation, and urban planning. Current generation models for urban flow or air quality are limited by laborious mesh creation, terrain slope restrictions due to coordinate transformations, lack of atmospheric physics, and/or omission of regional meteorological effects. To avoid these limitations we have extended the functionality of an existing model, IBM-WRF, a modified version of the Weather Research and Forecasting model (WRF) which uses an immersed boundary method (IBM) (Lundquist et al. 2010, 2012). The immersed boundary method used in our model allows for the evaluation of flow over complex urban geometries including vertical surfaces, sharp corners, and local topographic variations. Lateral boundaries in IBM-WRF can be prescribed using output from the standard WRF model, allowing for realistic meteorological input. IBM-WRF is being used to investigate transport and trapping of vehicle emissions around a proposed affordable housing development located adjacent to a major freeway which transports 250,000+ vehicles per day. Urban topography is created using high-resolution airborne LIDAR building data combined with ground elevation data. Emission locations and strengths are assigned using data provided by the Bay Area Air Quality Management District. Development is underway to allow for meteorological input to be created using the WRF model configured to use nested domains. This will allow for synoptic scale phenomena to affect the neighborhood scale IBM-WRF domain, which has a horizontal resolution on the order of one meter. Initial results from IBM-WRF are presented here and will ultimately be used to assist planning efforts to reduce local air pollution exposure and minimize related associated adverse health effects. Lundquist, K., F. Chow, and J. Lundquist, 2010: An immersed boundary method for the weather research and forecasting model. Monthly Weather Review, 138 (3), 796-817. Lundquist, K., F. Chow, and J. Lundquist, 2012: An immersed boundary method enabling large-eddy simulations of flow over complex terrain in the wrf-model. In press.

  4. 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.

  5. Evaluation of a regional assimilation system coupled with the WRF-chem model

    NASA Astrophysics Data System (ADS)

    Liu, Yan-an; Gao, Wei; Huang, Hung-lung; Strabala, Kathleen; Liu, Chaoshun; Shi, Runhe

    2013-09-01

    Air quality has become a social issue that is causing great concern to humankind across the globe, but particularly in developing countries. Even though the Weather Research and Forecasting with Chemistry (WRF-Chem) model has been applied in many regions, the resolution for inputting meteorology field analysis still impacts the accuracy of forecast. This article describes the application of the CIMSS Regional Assimilation System (CRAS) in East China, and its capability to assimilate the direct broadcast (DB) satellite data for obtaining more detailed meteorological information, including cloud top pressure (CTP) and total precipitation water (TPW) from MODIS. Performance evaluation of CRAS is based on qualitative and quantitative analyses. Compared with data collected from ERA-Interim, Radiosonde, and the Tropical Rainfall Measuring Mission (TRMM) precipitation measurements using bias and Root Mean Square Error (RMSE), CRAS has a systematic error due to the impact of topography and other factors; however, the forecast accuracy of all elements in the model center area is higher at various levels. The bias computed with Radiosonde reveals that the temperature and geopotential height of CRAS are better than ERA-Interim at first guess. Moreover, the location of the 24 h accumulated precipitation forecast are highly consistent with the TRMM retrieval precipitation, which means that the performance of CRAS is excellent. In summation, the newly built Vtable can realize the function of inputting the meteorology field from CRAS output into WRF, which couples the CRAS with WRF-Chem. Therefore, this study not only provides for forecast accuracy of CRAS, but also increases the capability of running the WRF-Chem model at higher resolutions in the future.

  6. Assimilation of Atmospheric InfraRed Sounder (AIRS) Profiles using WRF-Var

    NASA Technical Reports Server (NTRS)

    Zavodsky, Brad; Jedlovec, Gary J.; Lapenta, William

    2008-01-01

    The Weather Research and Forecasting (WRF) model contains a three-dimensional variational (3DVAR) assimilation system (WRF-Var), which allows a user to join data from multiple sources into one coherent analysis. WRF-Var combines observations with a background field traditionally generated using a previous model forecast through minimization of a cost function. In data sparse regions, remotely-sensed observations may be able 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 combined AIRS/AMSU system provides radiance measurements used as input to a sophisticated retrieval scheme which has been shown to produce temperature profiles with an accuracy of 1 K over 1 km layers and humidity profiles with accuracy of 15% in 2 km layers in both clear and partly cloudy conditions. The retrieval algorithm also provides estimates of the accuracy of the retrieved values at each pressure level, allowing the user to select profiles based on the required error tolerances of the application. 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 field type using gen_be and an optimal methodology for ingesting AIRS temperature and moisture profiles as separate overland and overwater retrievals with different error characteristics in the WRF-Var. The AIRS thermodynamic profiles are obtained 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 are used to select the highest quality temperature and moisture data for each profile location and pressure level. Analyses are run to produce quasi-real-time regional weather forecasts over the continental U.S. The preliminary assessment of the impact of the AIRS profiles will focus on intelligent use of the quality indicators, optimized tuning of the WRF-Var, and comparison of analysis soundings to radiosondes.

  7. Forecast-skill-based simulation of streamflow forecasts

    NASA Astrophysics Data System (ADS)

    Zhao, Tongtiegang; Zhao, Jianshi

    2014-09-01

    Streamflow forecasts are updated periodically in real time, thereby facilitating forecast evolution. This study proposes a forecast-skill-based model of forecast evolution that is able to simulate dynamically updated streamflow forecasts. The proposed model applies stochastic models that deal with streamflow variability to generate streamflow scenarios, which represent cases without forecast skill of future streamflow. The model then employs a coefficient of prediction to determine forecast skill and to quantify the streamflow variability ratio explained by the forecast. By updating the coefficients of prediction periodically, the model efficiently captures the evolution of streamflow forecast. Simulated forecast uncertainty increases with increasing lead time; and simulated uncertainty during a specific future period decreases over time. We combine the statistical model with an optimization model and design a hypothetical case study of reservoir operation. The results indicate the significance of forecast skill in forecast-based reservoir operation. Shortage index reduces as forecast skill increases and ensemble forecast outperforms deterministic forecast at a similar forecast skill level. Moreover, an effective forecast horizon exists beyond which more forecast information does not contribute to reservoir operation and higher forecast skill results in longer effective forecast horizon. The results illustrate that the statistical model is efficient in simulating forecast evolution and facilitates analysis of forecast-based decision making.

  8. Climatological features of WRF-simulated tropical cyclones over the western North Pacific

    NASA Astrophysics Data System (ADS)

    Kim, Dasol; Jin, Chun-Sil; Ho, Chang-Hoi; Kim, Jinwon; Kim, Joo-Hong

    2014-11-01

    Tropical cyclones (TCs) over the western North Pacific (WNP) are simulated for the 29 TC seasons of July-October from 1982 to 2010 using the regional Weather Research and Forecasting (WRF) model nested within global WRF model simulations. Averaged over the entire 29-year period, the nested global-regional WRF has reasonably simulated the climatology of key TC features such as the location/frequency of genesis and tracks. The dynamical and thermal structures of the simulated TCs are weaker than observations owing to the coarse spatial resolution of the regional WRF (50 km × 50 km). TC frequencies are somewhat underestimated over the East China Sea but are substantially overestimated over the South China Sea and the Philippine Sea with neighboring oceans between 10°N and 15°N. Categorization of the simulated TCs into six clusters based on the observed TC clusters and the associated large-scale circulation show that the nested simulation depicts the observed TC characteristics well except for two clusters associated with TCs traveling from the Philippine Sea to the East China Sea. Errors in the simulated TC genesis and tracks are mostly related to these two clusters. In the simulation, the monsoon confluent zone over the Philippine Sea is too strong, and the mid-latitude jet stream expands farther south than that in the observations. Overall results from this study suggest that the nested global-regional WRF can be useful for studying the TC climatology over the WNP.

  9. 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.

  10. 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 at the Shuttle Landing Facility is required at the 90 minute deorbit decision for End Of Mission (EOM) and at the 30 minute Return To Launch Site (RTLS) decision. 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 (TAF5), 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. This study specifically addresses the skill of different model configurations in forecasting warm season convective initiation. Numerous factors influence the development of convection over the Florida peninsula. These factors include sea breezes, river and lake breezes, the prevailing low-level flow, and convergent flow due to convex coastlines that enhance the sea breeze. The interaction of these processes produces the warm season convective patterns seen over the Florida peninsula. However, warm season convection remains one of the most poorly forecast meteorological parameters. To determine which configuration options are best to address this specific forecast concern, the Weather Research and Forecasting (WRF) model, which has two dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM) was employed. In addition to the two dynamical cores, there are also two options for a "hot-start" initialization of the WRF model - the Local Analysis and Prediction System (LAPS; McGinley 1995) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS; Brewster 1996). Both LAPS and ADAS are 3- dimensional weather analysis systems that integrate multiple meteorological data sources into one consistent analysis over the user's domain of interest. This allows mesoscale models to benefit from the addition of highresolution data sources. Having a series of initialization options and WRF cores, as well as many options within each core, provides SMG and MLB with considerable flexibility as well as challenges. It is the goal of this study to assess the different configurations available and to determine which configuration will best predict warm season convective initiation.

  11. Evaluation of WRF-CHEM Model: A case study of Air Pollution Episode in Istanbul Metropolitan

    NASA Astrophysics Data System (ADS)

    Ayd?nöz, Esra; Gürer, Kemal; Toros, Hüseyin

    2014-05-01

    Istanbul is the largest city in Europe with a population of about 14 million and nearly 3.2 million registered vehicles. Considering that the city is at the junction of major transportation routes on both land and sea, emissions from all motor vehicles operating in the city and those that are in transit is the major source of pollution. The natural gas is used as the major heat source and the impact of other heating sources on the pollution episodes is not clearly known. During 19-29 December 2013 ?stanbul metropolitan area experienced a severe PM10 episode with average episode concentration of 127µgm-3 . The episode was associated with a high pressure system with center pressure of 1030 mb residing over Balkans and north of Black Sea and thereby influencing Istanbul. We carried out simulations using the Weather Research and Forecasting model with Chemistry (WRF-CHEM) v3.5 to examine the meteorological conditions and to produce estimates of PM10 over Istanbul for 17-31 December 2013. The three nested domains was setup using 18, 6 and 2 km horizontal grid spacing with (90x90), (115x115) and (130x130) grid points in 1st, 2nd and 3rd domains, respectively. The each domain was run using one way nesting option after preparing the results from the mother domain as an input to subsequent inner domain. 34 vertical levels were used with the lowest layer depth of 15 m above the surface and extending to 15 km at the model top. The model was configured using the model options after many tests to find optimal model parameters and was initialized using global emissions data available publicly. The local emissions database is still in works and is not available to use in the model instead of global data. The estimated PM10 concentrations were compared against the observed conditions. This work shows the first attempt of using WRF-CHEM in Turkey to estimate the pollutant concentrations instead of using other air pollution models such as WRF/CMAQ combination. At the time of constructing this abstract, the model runs were still being conducted and the results will be discussed at the conference. Acknowledgements The authors are grateful to Istanbul Metropolitan Municipality for the air quality data. This study is a background of the online integrated air quality and meteorology modeling project funding by the TUBITAK (Project No: 111Y319) and COST Action ES1004.

  12. Spatiotemporal differences in nitrogen fate and transport with application of NCDC and WRF precipitation data in the SWAT watershed model

    NASA Astrophysics Data System (ADS)

    Gabriel, M. C.; Knightes, C. D.; Cooter, E. J.; Dennis, R. L.

    2011-12-01

    Watershed fate and transport models are widely used within the US Environmental Protection Agency's (USEPA) Office of Research and Development (ORD) as tools to forecast ecosystem services and evaluate future scenarios associated with land use, climate change and emissions regulation. A critical step in applying fate and transport models is understanding model sensitivity and function, particularly as new and innovative methods become available to apply forcing function data, e.g. precipitation data. Currently, multiple precipitation data sources are available for use in watershed modeling, two of which include National Climactic Data Center (NCDC) and Weather Research and Forecasting (WRF) data. As there are clear distinctions in how precipitation is determined for these precipitation sources (gauge vs. model simulated), there can also exist significant differences in precipitation frequency on a site-by-site basis. These differences may translate to large contrasts in nitrogen transport due to the sensitivity of surface biogeochemical processes to precipitation characteristics, namely those influenced by soil moisture content. The objective of this study is to investigate potential differences in the fate and transport of reactive nitrogen for two watersheds in the Neuse Basin, North Carolina, USA, after separately applying NCDC and WRF precipitation data sources into the Soil and Water Assessment Tool (SWAT) watershed model. The spatiotemporal variation of several nitrogen transport processes will be compared, e.g. reactive nitrogen fixation, plant uptake, overland delivery to streams, denitrification. Results from this research will advance exposure science by providing a greater understanding of the operation and function of watershed fate and transport models, which are primary tools used to assess ecosystem exposure.

  13. 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.

  14. THE NOAA HAZARDOUS WEATHER TESTBED: COLLABORATIVE TESTING OF ENSEMBLE AND CONVECTION-ALLOWING WRF MODELS AND SUBSEQUENT

    E-print Network

    Xue, Ming

    THE NOAA HAZARDOUS WEATHER TESTBED: COLLABORATIVE TESTING OF ENSEMBLE AND CONVECTION-ALLOWING WRF NOAA's Hazardous Weather Testbed (HWT) is a joint facility managed by the National Severe Storms Laboratory (NSSL), the Storm Prediction Center (SPC), and the NWS Oklahoma City/Norman Weather Forecast

  15. Development and validation of a hurricane nature run using the joint OSSE nature run and the WRF model

    E-print Network

    Nolan, David S.

    Development and validation of a hurricane nature run using the joint OSSE nature run and the WRF; accepted 8 April 2013. [1] A nature run is a critical component of an observing system simulation forecasts. The nature run is a period of simulated weather generated by a research-quality numerical model

  16. Comparison of Spatial and Temporal Rainfall Characteristics in WRF-Simulated Precipitation to Gauge and Radar Observations

    EPA Science Inventory

    Weather Research and Forecasting (WRF) meteorological data are used for USEPA multimedia air and water quality modeling applications, within the CMAQ modeling system to estimate wet deposition and to evaluate future climate and land-use scenarios. While it is not expected that hi...

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

    NASA Technical Reports Server (NTRS)

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

    2014-01-01

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

  18. 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.

  19. Comparative analysis of operational forecasts versus actual weather conditions in airline flight planning, volume 2

    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 2 of the four major tasks included in the study. Task 2 compares various catagories of flight plans and flight tracking data produced by a simulation system developed for the Federal Aviation Administrations by SRI International. (Flight tracking data simulate actual flight tracks of all aircraft operating at a given time and provide for rerouting of flights as necessary to resolve traffic conflicts.) The comparisons of flight plans on the forecast to flight plans on the verifying analysis confirm Task 1 findings that wind speeds are generally underestimated. Comparisons involving flight tracking data indicate that actual fuel burn is always higher than planned, in either direction, and even when the same weather data set is used. Since the flight tracking model output results in more diversions than is known to be the case, it was concluded that there is an error in the flight tracking algorithm.

  20. Forecast indices from ground-based microwave radiometer for operational meteorology

    NASA Astrophysics Data System (ADS)

    Cimini, D.; Nelson, M.; Güldner, J.; Ware, R.

    2014-07-01

    Today, commercial microwave radiometers profilers (MWRP) are robust and unattended instruments providing real time accurate atmospheric observations at ~ 1 min temporal resolution under nearly all-weather conditions. Common commercial units operate in the 20-60 GHz frequency range and are able to retrieve profiles of temperature, vapour density, and relative humidity. Temperature and humidity profiles retrieved from MWRP data are used here to feed tools developed for processing radiosonde observations to obtain values of forecast indices (FI) commonly used in operational meteorology. The FI considered here include K index, Total Totals, KO index, Showalter index, T1 Gust, Fog Threat, Lifted Index, S Index (STT), Jefferson Index, MDPI, Thompson Index, TQ Index, and CAPE. Values of FI computed from radiosonde and MWRP-retrieved temperature and humidity profiles are compared in order to quantitatively demonstrate the level of agreement and the value of continuous FI updates. This analysis is repeated for two sites at midlatitude, the first one located at low altitude in Central Europe (Lindenberg, Germany), while the second one located at high altitude in North America (Whistler, Canada). It is demonstrated that FI computed from MWRP well correlate with those computed from radiosondes, with the additional advantage of nearly continuous update. The accuracy of MWRP-derived FI is tested against radiosondes, taken as a reference, showing different performances depending upon index and environmental situation. Overall, FI computed from MWRP retrievals agree well with radiosonde values, with correlation coefficients usually above 0.8 (with few exceptions). We conclude that MWRP retrievals can be used to produce meaningful FI, with the advantage (with respect to radiosondes) of nearly continuous update.

  1. Forecast indices from a ground-based microwave radiometer for operational meteorology

    NASA Astrophysics Data System (ADS)

    Cimini, D.; Nelson, M.; Güldner, J.; Ware, R.

    2015-01-01

    Today, commercial microwave radiometer profilers (MWRPs) are robust and unattended instruments providing real-time, accurate atmospheric observations at ~ 1 min temporal resolution under nearly all weather conditions. Common commercial units operate in the 20-60 GHz frequency range and are able to retrieve profiles of temperature, vapour density, and relative humidity. Temperature and humidity profiles retrieved from MWRP data are used here to feed tools developed for processing radiosonde observations to obtain values of forecast indices (FIs) commonly used in operational meteorology. The FIs considered here include K index, total totals, KO index, Showalter index, T1 gust, fog threat, lifted index, S index (STT), Jefferson index, microburst day potential index (MDPI), Thompson index, TQ index, and CAPE (convective available potential energy). Values of FIs computed from radiosonde and MWRP-retrieved temperature and humidity profiles are compared in order to quantitatively demonstrate the level of agreement and the value of continuous FI updates. This analysis is repeated for two sites at midlatitude, the first one located at low altitude in central Europe (Lindenberg, Germany) and the second one located at high altitude in North America (Whistler, Canada). It is demonstrated that FIs computed from MWRPs well correlate with those computed from radiosondes, with the additional advantage of nearly continuous updates. The accuracy of MWRP-derived FIs is tested against radiosondes, taken as a reference, showing different performances depending upon index and environmental situation. Overall, FIs computed from MWRP retrievals agree well with radiosonde values, with correlation coefficients usually above 0.8 (with few exceptions). We conclude that MWRP retrievals can be used to produce meaningful FIs, with the advantage (with respect to radiosondes) of nearly continuous updates.

  2. 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.

  3. 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 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. 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.

  4. 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.

  5. A Module for Assimilating Hyperspectral Infrared Retrieved Profiles into the Gridpoint Statistical Interpolation System for Unique Forecasting Applications

    NASA Technical Reports Server (NTRS)

    Berndt, Emily; Zavodsky, Bradley; Srikishen, Jayanthi; Blankenship, Clay

    2015-01-01

    Hyperspectral infrared sounder radiance data are assimilated into operational modeling systems however the process is computationally expensive and only approximately 1% of available data are assimilated due to data thinning as well as the fact that radiances are restricted to cloud-free fields of view. In contrast, the number of hyperspectral infrared profiles assimilated is much higher since the retrieved profiles can be assimilated in some partly cloudy scenes due to profile coupling other data, such as microwave or neural networks, as first guesses to the retrieval process. As the operational data assimilation community attempts to assimilate cloud-affected radiances, it is possible that the use of retrieved profiles might offer an alternative methodology that is less complex and more computationally efficient to solve this problem. The NASA Short-term Prediction Research and Transition (SPoRT) Center has assimilated hyperspectral infrared retrieved profiles into Weather Research and Forecasting Model (WRF) simulations using the Gridpoint Statistical Interpolation (GSI) System. Early research at SPoRT demonstrated improved initial conditions when assimilating Atmospheric Infrared Sounder (AIRS) thermodynamic profiles into WRF (using WRF-Var and assigning more appropriate error weighting to the profiles) to improve regional analysis and heavy precipitation forecasts. Successful early work has led to more recent research utilizing WRF and GSI for applications including the assimilation of AIRS profiles to improve WRF forecasts of atmospheric rivers and assimilation of AIRS, Cross-track Infrared and Microwave Sounding Suite (CrIMSS), and Infrared Atmospheric Sounding Interferometer (IASI) profiles to improve model representation of tropopause folds and associated non-convective wind events. Although more hyperspectral infrared retrieved profiles can be assimilated into model forecasts, one disadvantage is the retrieved profiles have traditionally been assigned the same error values as the rawinsonde observations when assimilated with GSI. Typically, satellitederived profile errors are larger and more difficult to quantify than traditional rawinsonde observations (especially in the boundary layer), so it is important to appropriately assign observation errors within GSI to eliminate potential spurious innovations and analysis increments that can sometimes arise when using retrieved profiles. The goal of this study is to describe modifications to the GSI source code to more appropriately assimilate hyperspectral infrared retrieved profiles and outline preliminary results that show the differences between a model simulation that assimilated the profiles as rawinsonde observations and one that assimilated the profiles in a module with the appropriate error values.

  6. Weather forecasting expert system study

    NASA Technical Reports Server (NTRS)

    1985-01-01

    Weather forecasting is critical to both the Space Transportation System (STS) ground operations and the launch/landing activities at NASA Kennedy Space Center (KSC). The current launch frequency places significant demands on the USAF weather forecasters at the Cape Canaveral Forecasting Facility (CCFF), who currently provide the weather forecasting for all STS operations. As launch frequency increases, KSC's weather forecasting problems will be great magnified. The single most important problem is the shortage of highly skilled forecasting personnel. The development of forecasting expertise is difficult and requires several years of experience. Frequent personnel changes within the forecasting staff jeopardize the accumulation and retention of experience-based weather forecasting expertise. The primary purpose of this project was to assess the feasibility of using Artificial Intelligence (AI) techniques to ameliorate this shortage of experts by capturing aria incorporating the forecasting knowledge of current expert forecasters into a Weather Forecasting Expert System (WFES) which would then be made available to less experienced duty forecasters.

  7. Assessment of vertical aerosol profiles simulation capability of Global Climate Models in different regions using the WRF

    NASA Astrophysics Data System (ADS)

    Park, S.; Allen, R. J.; Kafle, D. N.; Bahreini, R.; Amiri-Farahani, A.

    2013-12-01

    The various influences of aerosols on climate are currently receiving much attention, with emphasis on vertical distribution that has significant contribution to radiative forcing. Many studies have vertical aerosol distribution using diverse models. Model simulations yield large uncertainties, spanning several orders of magnitude. Simulations also disagree with observed profiles, with models generally placing too much aerosol aloft. Possible reasons for poor model performance include coarse model resolution, deficient transport and removal processes, including convection and wet and dry deposition. In addition, sources of aerosol play a pivotal role in controlling vertical distribution. For better understanding of aerosol vertical distribution and its influence on radiative forcing, model simulation need to be evaluated and corrected by using observational data and regional scale models. In this study, investigates the sensitivity of simulated vertical aerosol profiles to geophysical processes, using the Weather Research and Forecasting (WRF). The WRF model is used for mesoscale numerical weather prediction and is appropriate for a broad range of meteorological applications across scales ranging from meters to thousands of kilometers. In addition, WRF has the capability of applying other models schemes into the WRF process. For instance another model's convection scheme can be run in placed that in the WRF. Hence, other model schemes can also be evaluated. WRF simulations of the vertical aerosol profile will be compared to those based on lidar measurements at selected Atmospheric Radiation Measurement (ARM) sites.

  8. Basin-scale water-balance estimates of terrestrial water storage variations from ECMWF operational forecast analysis

    NASA Astrophysics Data System (ADS)

    Hirschi, Martin; Viterbo, Pedro; Seneviratne, Sonia I.

    2006-11-01

    In recent publications, a new basin-scale dataset of monthly variations in terrestrial water storage (BSWB) was derived for the ERA40 time period (1958-2002) using an atmospheric-terrestrial water-balance approach (Seneviratne et al., 2004; Hirschi et al., 2006). Here, we test the feasibility of using ECMWF operational forecast analyses - available for the recent time period in near real time - instead of reanalysis data for the derivation of these estimates. Our results suggest that the moisture flux convergence from the ECMWF operational forecast analysis is generally consistent with ERA40 in the investigated regions, including 35 mid-latitude river basins and domains. For ten domains with recent streamflow measurements, water-balance estimates of monthly terrestrial water storage variations derived using the ECMWF operational forecast data are compared with estimates from the Gravity Recovery and Climate Experiment (GRACE). In general the atmospheric-terrestrial water-balance estimates show more geographical detail than the analyzed standard resolution GRACE products.

  9. Influence of Forecast Accuracy of Photovoltaic Power Output on Facility Planning and Operation of Microgrid under 30 min Power Balancing Control

    NASA Astrophysics Data System (ADS)

    Kato, Takeyoshi; Sone, Akihito; Shimakage, Toyonari; Suzuoki, Yasuo

    A microgrid (MG) is one of the measures for enhancing the high penetration of renewable energy (RE)-based distributed generators (DGs). For constructing a MG economically, the capacity optimization of controllable DGs against RE-based DGs is essential. By using a numerical simulation model developed based on the demonstrative studies on a MG using PAFC and NaS battery as controllable DGs and photovoltaic power generation system (PVS) as a RE-based DG, this study discusses the influence of forecast accuracy of PVS output on the capacity optimization and daily operation evaluated with the cost. The main results are as follows. The required capacity of NaS battery must be increased by 10-40% against the ideal situation without the forecast error of PVS power output. The influence of forecast error on the received grid electricity would not be so significant on annual basis because the positive and negative forecast error varies with days. The annual total cost of facility and operation increases by 2-7% due to the forecast error applied in this study. The impact of forecast error on the facility optimization and operation optimization is almost the same each other at a few percentages, implying that the forecast accuracy should be improved in terms of both the number of times with large forecast error and the average error.

  10. A study of the connection between tropical cyclone track and intensity errors in the WRF model

    NASA Astrophysics Data System (ADS)

    Tien, Du Duc; Ngo-Duc, Thanh; Mai, Hoang Thi; Kieu, Chanh

    2013-10-01

    This study examines the dependence of the tropical cyclone (TC) intensity errors on the track errors in the Weather Research and Forecasting (WRF-ARW) model. By using the National Centers for Environmental Prediction global final analysis as the initial and boundary conditions for cloud-resolving simulations of TC cases that have small track errors, it is found that the 2- and 3-day intensity errors in the North Atlantic basin can be reduced to 15 and 19 % when the track errors decrease to 55 and 76 %, respectively, whereas the 1-day intensity error shows no significant reduction despite more than 30 % decrease of the 1-day track error. For the North-Western Pacific basin, the percentage of intensity reduction is somewhat similar with the 2- and 3-day intensity errors improved by about 15 and 19 %, respectively. This suggests that future improvement of the TC track forecast skill in the WRF-ARW model will be beneficial to the intensity forecast. However, the substantially smaller percentages of intensity improvement than those of the track error improvement indicate that ambient environment tends to play a less important role in determining the TC intensity as compared to other factors related to the vortex initialization or physics representations in the WRF-ARW model.

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

    NASA Technical Reports Server (NTRS)

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

    2012-01-01

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

  12. Operational earthquake forecasting in the South Iceland Seismic Zone: improving the earthquake catalogue

    NASA Astrophysics Data System (ADS)

    Panzera, Francesco; Vogfjörd, Kristin; Zechar, J. Douglas; Eberhard, David

    2014-05-01

    A major earthquake sequence is ongoing in the South Iceland Seismic Zone (SISZ), where experts expect earthquakes of up to MW = 7.1 in the coming years to decades. The historical seismicity in this region is well known and many major faults here and on Reykjanes Peninsula (RP) have already been mapped. The faults are predominantly N-S with right-lateral strike-slip motion, while the overall motion in the SISZ is E-W oriented left-lateral motion. The area that we propose for operational earthquake forecasting(OEF) contains both the SISZ and the RP. The earthquake catalogue considered for OEF, called the SIL catalogue, spans the period from 1991 until September 2013 and contains more than 200,000 earthquakes. Some of these events have a large azimuthal gap between stations, and some have large horizontal and vertical uncertainties. We are interested in building seismicity models using high-quality data, so we filter the catalogue using the criteria proposed by Gomberg et al. (1990) and Bondar et al. (2004). The resulting filtered catalogue contains around 130,000 earthquakes. Magnitude estimates in the Iceland catalogue also require special attention. The SIL system uses two methods to estimate magnitude. The first method is based on an empirical local magnitude (ML) relationship. The other magnitude scale is a so-called "local moment magnitude" (MLW), originally constructed by Slunga et al. (1984) to agree with local magnitude scales in Sweden. In the SIL catalogue, there are two main problems with the magnitude estimates and consequently it is not immediately possible to convert MLW to moment magnitude (MW). These problems are: (i) immediate aftershocks of large events are assigned magnitudes that are too high; and (ii) the seismic moment of large earthquakes is underestimated. For this reason the magnitude values in the catalogue must be corrected before developing an OEF system. To obtain a reliable MW estimate, we calibrate a magnitude relationship based on attenuation relations derived for earthquakes in Iceland (Pétursson and Vogfjörd, 2010) and use this relationship to address the problem of underestimating seismic moment for larger earthquakes (>3.0). Finally, to solve the problem related with the overestimation of aftershock magnitude of large earthquakes about 150 earthquakes were checked. All such passages demonstrate the importance of carefully checking the catalogue before proceeding with the operational earthquake forecasting. References Bondar, I., S.C. Myers, E.R. Engdahl, and E.A. Bergman (2004). Epicentre accuracy based on seismicnetwork criteria, Geophys. J. Int., 156, 483-496. Gomberg, J.S., K.M. Shedlock, and S.W. Roecker (1990). The effect of S-Wave arrival times on the accuracy of hypocenter estimation, Bull. Seism. Soc. Am., 80, 1605-1628. Pétursson and Vogfjörd (2010). Attenuation relations for near- and far field peak ground motion (PGV, PGA)and new magnitude estimatesfor large earthquakes in SW-Iceland. Report n° VI 2009-012, pp. 43, ISSN 1670-8261. Slunga, R., P. Norrman and A. Glans (1984). Seismicity of Southern Sweden - Stockholm: Försvarets Forskningsanstalt, July 1984. FOA Report, C2 C20543-T1, 106 p.

  13. Design and Impacts of Land-Biogenic-Atmosphere Coupling in the NASA-Unified WRF (NU-WRF) Modeling System

    NASA Technical Reports Server (NTRS)

    Tan, Qian; Santanello, Joseph A., Jr.; Zhou, Shujia; Tao, Zhining; Peters-Lidard, Christa d.; Chn, Mian

    2011-01-01

    Land-Atmosphere coupling is typically designed and implemented independently for physical (e.g. water and energy) and chemical (e.g. biogenic emissions and surface depositions)-based models and applications. Differences in scale, data requirements, and physics thus limit the ability of Earth System models to be fully coupled in a consistent manner. In order for the physical-chemical-biological coupling to be complete, treatment of the land in terms of surface classification, condition, fluxes, and emissions must be considered simultaneously and coherently across all components. In this study, we investigate a coupling strategy for the NASA-Unified Weather Research and Forecasting (NU-WRF) model that incorporates the traditionally disparate fluxes of water and energy through NASA's LIS (Land Information System) and biogenic emissions through BEIS (Biogenic Emissions Inventory System) and MEGAN (Model of Emissions of Gases and Aerosols from Nature) into the atmosphere. In doing so, inconsistencies across model inputs and parameter data are resolved such that the emissions from a particular plant species are consistent with the heat and moisture fluxes calculated for that land cover type. In turn, the response of the atmospheric turbulence and mixing in the planetary boundary layer (PBL) acts on the identical surface type, fluxes, and emissions for each. In addition, the coupling of dust emission within the NU-WRF system is performed in order to ensure consistency and to maximize the benefit of high-resolution land representation in LIS. The impacts of those self-consistent components on' the simulation of atmospheric aerosols are then evaluated through the WRF-Chem-GOCART (Goddard Chemistry Aerosol Radiation and Transport) model. Overall, this ambitious project highlights the current difficulties and future potential of fully coupled. components. in Earth System models, and underscores the importance of the iLEAPS community in supporting improved knowledge of processes and innovative approaches for models and observations.

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

    NASA Technical Reports Server (NTRS)

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

    2014-01-01

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

  15. An evaluation of the real-time tropical cyclone forecast skill of the Navy Operational Global Atmospheric Prediction System in the western North Pacific

    NASA Technical Reports Server (NTRS)

    Fiorino, Michael; Goerss, James S.; Jensen, Jack J.; Harrison, Edward J., Jr.

    1993-01-01

    The paper evaluates the meteorological quality and operational utility of the Navy Operational Global Atmospheric Prediction System (NOGAPS) in forecasting tropical cyclones. It is shown that the model can provide useful predictions of motion and formation on a real-time basis in the western North Pacific. The meterological characteristics of the NOGAPS tropical cyclone predictions are evaluated by examining the formation of low-level cyclone systems in the tropics and vortex structure in the NOGAPS analysis and verifying 72-h forecasts. The adjusted NOGAPS track forecasts showed equitable skill to the baseline aid and the dynamical model. NOGAPS successfully predicted unusual equatorward turns for several straight-running cyclones.

  16. Comparison of a coupled atmosphere-ocean (WRF-ROMS) model with an atmosphere only model (WRF) of two North Atlantic hurricanes

    NASA Astrophysics Data System (ADS)

    Mooney, P.; Mulligan, F. J.; Bruyere, C. L.; Bonnlander, B.

    2013-12-01

    We investigate the ability of a coupled regional atmosphere-ocean modeling system to simulate two extreme events in the North Atlantic. In this study we use the Coupled-Ocean-Atmosphere-Wave-Sediment Transport (COAWST; Warner et al., 2010) modeling system with only the atmosphere and ocean models activated. COAWST couples the atmosphere model (Weather Research and Forecasting model; WRF) to the ocean model (Regional Ocean Modeling System; ROMS) with the Model Coupling Toolkit. Results from the coupled system are compared with atmosphere only simulations of North Atlantic storms to evaluate the performance of the coupled modeling system. Two extreme events (Hurricane Katia and Hurricane Irene) were chosen to assess the level of improvement (or otherwise) arising from coupling WRF with ROMS. These two hurricanes involve different dynamics and present different challenges to the modeling system. Modelled storm tracks, storm intensities and sea surface temperatures are compared with observations to appraise the coupled modeling system's simulation of these two extreme events.

  17. Wind Energy Management System 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.

    2010-09-01

    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 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 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. In order 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. In this report, a new methodology to predict the uncertainty ranges for the required balancing capacity, ramping capability and ramp duration is presented. Uncertainties created by system load forecast errors, wind and solar forecast errors, generation forced outages are taken into account. The uncertainty ranges are evaluated for different confidence levels of having the actual generation requirements within the corresponding limits. The methodology helps to identify system balancing reserve requirement based on a desired system performance levels, identify system “breaking points”, where the generation system becomes unable to follow the generation requirement curve with the user-specified probability level, and determine the time remaining to these potential events. 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 (California ISO) real life data have shown the effectiveness of the proposed approach. A tool developed based on the new methodology described in this report will be integrated with the California ISO systems. Contractual work is currently in place to integrate the tool with the AREVA EMS system.

  18. On the potential of numerical short range fog forecast and low clouds with three-dimensional fog forecast models

    NASA Astrophysics Data System (ADS)

    Masbou, M.; Müller, M. D.; Steeneveld, G. J.; Cermak, J.; Bott, A.

    2010-07-01

    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. An improvement of localisation, duration and variations in visibility therefore holds an immense operational value for the field of transportation in conditions of low visibility. However, fog is generally a small scale phenomenon which is mostly affected by local advective transport, radiation, turbulent mixing at the surface as well as its microphysical structure. Therefore, a detailed description of the microphysical processes within the three-dimensional dynamical core of the forecast model is necessary. For this purpose, three-dimensional fog forecast models with a high vertical resolution with different microphysical complexity have been developed. COSMO-FOG and NMMFOG include a new microphysical parameterisation based on the one-dimensional fog forecast model. The implementation of the cloud water droplets as a new prognostic variable allows a detailed definition of the sedimentation processes and the variations in visibility. Also, we compare WRF mesoscale model results using different boundary-layer schemes that ignore or account for specific fog microphysics. In some realistic fog situations (radiative fog) the potential of these three-dimensional fog models will be presented. The fog spatial extension will be compared with MSG satellite products for fog and low cloud. It will be shown that the initialisation and the interaction between the earth’s surface and the atmosphere is one of the most important issues for reliable fog forecasts.

  19. On the added value and sensitivity of WRF to driving conditions over CORDEX-Africa domain

    NASA Astrophysics Data System (ADS)

    Lorente-Plazas, Raquel; García-Díez, Markel; Jimenez-Guerrero, Pedro; Fernández, Jesús; Montavez, Juan Pedro

    2014-05-01

    The assessment of the climate variability over Africa has recently attracted the interest of the regional climate downscaling research community. The main reasons are not only because Africa is a climate change hot-spot, but also due to the low capacity of this region for the adaptation and mitigation under negative impacts and its direct dependency on its socio-economic sustainability of the climate variability. Therefore, improvements in the understanding of the African climate could help the governments in decision-making. Under this umbrella, regional climate models (RCMs) are promising tools to assess the African regional climate. The main advantage of the RCMs, with respect to global reanalysis datasets, is the higher detail provided by the increased resolution which implies a better representation of land-surface interactions and atmospheric processes. However, the confidence on the RCMs strongly depends on the reduction/bounding of uncertainties. One of these sources of uncertainties is associated with the selection of the boundary conditions for driving the regional models. In this work, two identical CORDEX-compliant simulations have been performed over Africa with the unique difference of being driven by two different reanalyses. The reanalyses used were the European Centre for Medium Range Weather Forecasts Interim reanalysis (ERA-I) and the Japanese 25-year reanalysis (JRA-25) by the Japanese Meteorological Service. Both reanalyses have identical temporal resolution (6-hr) but different spatial grid resolution, 0.75 and 1.25 degrees, respectively. The regional model used was the Weather Research and Forecasting Model (WRF). The numerical experiments encompass the period 1989-2010 covering the Africa-CORDEX domain with a 50 km horizontal spatial resolution and 28 vertical levels up to 50 hPa. The WRF simulations are compared between them and against observations. For the mean and maximum temperature the CRU monthly time series (0.25deg) from Climatic Research Unit of the University of East Anglia are used. The precipitation is compared against the Tropical Rainfall Measuring Mission Project (TRMM) monthly data (0.25deg). The results depict that none of the reanalyses used outperforms the other in representing the African climate, since their performance depends on the variable, season and region assessed. The simulations show a noticeable disagreement for 2-m temperature in north-western Africa, where WRF-JRA tends to underestimate this variable mostly in winter and spring. For the monthly mean daily maximum temperature, WRF-JRA tends to overestimate the temperature in the Sahel in summer and in the border between Angola and Namibia in Winter. When comparing with CRU observations, there is a remarkably better spatial representation for the WRF-EI simulation in the North of Africa. However, the behaviour of WRF-EI and WRF-JRA is similar in the South of Africa. Intra-annual variability is well represented except in Atlas mountains where WRF-JRA underestimates temperature. Regarding precipitation, the main differences appear over the Sahel region in JAS and in the Congo area during JFM. The comparison with the TRMM data shows a better agreement with the WRF-JRA simulation except during summer in the Sahel region. The monthly annual cycle is well captured, except in Ethiopian highlands and Northern West Africa where WRF-JRA (WRF-EI) underestimate (overestimate) the annual cycle.

  20. SCADA-based Operator Support System for Power Plant Equipment Fault Forecasting

    NASA Astrophysics Data System (ADS)

    Mayadevi, N.; Ushakumari, S. S.; Vinodchandra, S. S.

    2014-12-01

    Power plant equipment must be monitored closely to prevent failures from disrupting plant availability. Online monitoring technology integrated with hybrid forecasting techniques can be used to prevent plant equipment faults. A self learning rule-based expert system is proposed in this paper for fault forecasting in power plants controlled by supervisory control and data acquisition (SCADA) system. Self-learning utilizes associative data mining algorithms on the SCADA history database to form new rules that can dynamically update the knowledge base of the rule-based expert system. In this study, a number of popular associative learning algorithms are considered for rule formation. Data mining results show that the Tertius algorithm is best suited for developing a learning engine for power plants. For real-time monitoring of the plant condition, graphical models are constructed by K-means clustering. To build a time-series forecasting model, a multi layer preceptron (MLP) is used. Once created, the models are updated in the model library to provide an adaptive environment for the proposed system. Graphical user interface (GUI) illustrates the variation of all sensor values affecting a particular alarm/fault, as well as the step-by-step procedure for avoiding critical situations and consequent plant shutdown. The forecasting performance is evaluated by computing the mean absolute error and root mean square error of the predictions.

  1. Pluto's Atmosphere and Surface Ices as Simulated by the PlutoWRF GCM

    NASA Astrophysics Data System (ADS)

    Toigo, A. D.; French, R. G.; Gierasch, P. J.; Richardson, M. I.; Guzewich, S.

    2013-12-01

    The PlutoWRF general circulation model (GCM) was built to examine the large-scale structure and dynamics of the atmosphere, the nature and propagation of waves within the atmosphere, and the exchanges of volatiles between the atmosphere and the surface. We seek to provide an comprehensive framework for the study of the increasingly rich observational data sets (including stellar occultations of the atmosphere) and to provide context and analysis of observations from the New Horizons mission. The PlutoWRF GCM is based on the planetary adaptation of the NCAR Weather Research and Forecasting (WRF) model. It is a compressible, nonhydrostatic model where we have added physics to treat radiative transfer following Zhu et al. (2013), a bulk nitrogen cycle including condensation of surface ice, and cycles of additional trace volatile species. Existing subsurface heat diffusion, surface layer exchange and boundary layer mixing schemes have been adapted to Pluto. Boundary conditions for initial ice distribution and surface pressure are taken from energy balance and non-GCM volatile transport models constrained by observations. In this work we focus on the performance of the PlutoWRF GCM compared with our linear tidal model (Toigo et al., 2010), and will examine the generation and propagation of large-scale gravity waves associated with diurnal sublimation and condensation.

  2. Coupled atmosphere-wildland fire modeling with WRF 3.3 and SFIRE 2011

    NASA Astrophysics Data System (ADS)

    Mandel, J.; Beezley, J. D.; Kochanski, A. K.

    2011-07-01

    We describe the physical model, numerical algorithms, and software structure of a model consisting of the Weather Research and Forecasting (WRF) model, coupled with the fire-spread model (SFIRE) module. In every time step, the fire model inputs the surface wind, which drives the fire, and outputs the heat flux from the fire into the atmosphere, which in turn influences the atmosphere. SFIRE is implemented by the level set method, which allows a submesh representation of the burning region and a flexible implementation of various kinds of ignition. The coupled model is capable of running on a cluster faster than real time even with fine resolution in dekameters. It is available as a part of the Open Wildland Fire Modeling (OpenWFM) environment at http://openwfm.org, which contains also utilities for visualization, diagnostics, and data processing, including an extended version of the WRF Preprocessing System (WPS). The SFIRE code with a subset of the features is distributed with WRF 3.3 as WRF-Fire.

  3. Fog/Low Visibility Forecasting from NCEP - Current Status and Performance

    NASA Astrophysics Data System (ADS)

    Zhou, B.; Dimego, G.; Gultepe, I.

    2010-07-01

    Low visibility(<1000m)/fog is very hazardous to air/land traffic and is beeing particularly emphasized at National Weather Service(NWS) of NOAA and in NextGen, a future Air Traffic Management System of Federal Aviation Administration (FAA), United States. As of now however, fog forecast is still not operational guidance from National Centers for Environment Prediction (NCEP), an official numerical weather prediction (NWP) center of NWS, due to its complexity and computational resource limitation. Instead, it is only diagnosed by local weather forecasters through either model output statistics (MOS) or other variables based upon their forecasting experience. Nevertheless, research on numerical fog prediction has been conducting at NCEP. Recently, in an effort to add it to NCEP’s operational guidance as a step to echo the requirement from NWS and the NextGen of FAA, low visibility/fog forecast was experimentally implemented and tested at NCEP. In this paper, predictions of fog and low visibility (< 1000 m) from various models and ensembles over North America as well as their evaluations are summarized. The involved models include North American Mesoscale (NAM) model, Rapid Updated Cycle (RUC) model, and Nonhydrostatic Mesoscale Model (NMM). NAM is NWS’s operational regional model to provide regular weather forecast guidance to local forecast offices nationally, RUC is an operational model specific for aviation weather guidance, and NMM is the NCEP’s version of Weather and Research Forecast (WRF) model, based on which NAM and other forecast systems are built. Besides from those single model forecast systems, low visibility/fog from two ensemble forecast systems are also presented. One is the Short Range Ensemble Forecast System (SREF), the other is the Very Sort Range Ensemble Forecast System (VSREF). Through verifications, deterministically and probabilistically from November 2009 to March 2010 on North America, the fog and low visibility predictabilities for various models and ensembles are compared and discussed. The results show that the general performances of fog and low visibility prediction from the single model forecast systems are still low, but the application of ensemble, either in low or high resolution, has shed light on its performance improvement. Furthermore through this study, where the efforts should be focused on in the models or methods are also suggested.

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

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

    Sporadically, the Sun unleashes severe magnetic activity into the heliosphere. The specific solar/heliospheric phenomena and their effects on humans, technology and the wider geospace environment include a) high-intensity emissions from the Sun causing radio blackouts and (surface) charging, b) particle acceleration in the solar corona leading to high dose rates of ionizing radiation in exposed materials that can trigger single event upsets in electronic components of space hardware, or temporal/permanent damage in tissue, c) arrivals of fast-moving coronal mass ejections with embedded enhancements of magnetic fields that can cause strong ionospheric disturbances affecting radio communications and induce out-of-spec currents in power lines near the surface. Many of the effects could now be forecast with higher fidelity than ever before. However, forecasting critically depends upon availability of timely and reliable observational data. It is therefore crucial to understand how observational assets perform during periods of severe space weather. This paper analyzes and documents the status of the existing and upcoming observational capabilities for forecasting, and discusses how the findings may impact space weather research and its transition to operations.

  5. Probability Forecasting in Meteorology

    Microsoft Academic Search

    Allan H. Murphy; Robert L. Winkler

    1984-01-01

    Efforts to quantify the uncertainty in weather forecasts began more than 75 years ago, and many studies and experiments involving objective and subjective probability forecasting have been conducted in meteorology in the intervening period. Moreover, the U.S. National Weather Service (NWS) initiated a nationwide program in 1965 in which precipitation probability forecasts were formulated on an operational basis and routinely

  6. "Developing a multi hazard air quality forecasting model for Santiago, Chile"

    NASA Astrophysics Data System (ADS)

    Mena, M. A.; Delgado, R.; Hernandez, R.; Saide, P. E.; Cienfuegos, R.; Pinochet, J. I.; Molina, L. T.; Carmichael, G. R.

    2013-05-01

    Santiago, Chile has reduced annual particulate matter from 69ug/m3 (in 1989) to 25ug/m3 (in 2012), mostly by forcing industry, the transport sector, and the residential heating sector to adopt stringent emission standards to be able to operate under bad air days. Statistical forecasting has been used to predict bad air days, and pollution control measures in Santiago, Chile, for almost two decades. Recently an operational PM2.5 deterministic model has been implemented using WRF-Chem. The model was developed by the University of Iowa and is run at the Chilean Meteorological Office. Model configuration includes high resolution emissions gridding (2km) and updated population distribution using 2008 data from LANDSCAN. The model is run using a 2 day spinup with a 5 day forecast. This model has allowed a preventive approach in pollution control measures, as episodes are the results of multiple days of bad dispersion. Decreeing air pollution control measures in advance of bad air days resulted in a reduction of 40% of alert days (80ug/m3 mean 24h PM2.5) and 66% of "preemergency days" (110ug/m3 mean 24h PM2.5) from 2011 to 2012, despite similar meteorological conditions. This model will be deployed under a recently funded Center for Natural Disaster Management, and include other meteorological hazards such as flooding, high temperature, storm waves, landslides, UV radiation, among other parameters. This paper will present the results of operational air quality forecasting, and the methodology that will be used to transform WRF-Chem into a multi hazard forecasting system.

  7. Moving towards operational forecasting of occurrence of L-band scintillations based on background ionospheric variability: a case study using GSAT platform

    NASA Astrophysics Data System (ADS)

    Bagiya, M. S.; Sridharan, R.; Sunda, S.

    2012-12-01

    An operational forecasting of L band scintillation is very vital for real time satellite based communication and navigation. A novel method based on GPS-TEC (GTEC) data several hours before the actual event has been worked out (Sridharan et al., 2012) and two of the many fundamental questions viz., 'when' and for 'how long' the scintillation patches are likely to be present have been answered conclusively. The close linkage between the perturbation features and the evolutionary pattern of the GPS L-band scintillation enables us to forecast 'when' and for 'how long' the L band scintillations could occur, in addition to their occurrence pattern. But as GPS is the moving system, a time lag is observed in forecasted evolutionary pattern of scintillation and actual observed scintillation which was expected also. To simplify this problem, the method has been updated with GSAT L1 - scintillation and ionosonde fof2 observations. Both these instruments are referred to fixed locations, but the only care has to be taken is that of the physical separation of the ionospheric regions referred by them. The perturbation features in terms of dfof2 now are used to forecast the evolutionary pattern of the scintillation couple of hours before the actual event. An excellent agreement between forecasted evolutionary pattern of scintillation and actual observed one take us one more step closer towards operational forecasting of L band scintillations. There are some occasions when scintillation was forecasted but it did not appear which attributed to the background conditions during that period.

  8. WRF Test on IBM BG/L:Toward High Performance Application to Regional Climate Research

    SciTech Connect

    Chin, H S

    2008-09-25

    The effects of climate change will mostly be felt on local to regional scales (Solomon et al., 2007). To develop better forecast skill in regional climate change, an integrated multi-scale modeling capability (i.e., a pair of global and regional climate models) becomes crucially important in understanding and preparing for the impacts of climate change on the temporal and spatial scales that are critical to California's and nation's future environmental quality and economical prosperity. Accurate knowledge of detailed local impact on the water management system from climate change requires a resolution of 1km or so. To this end, a high performance computing platform at the petascale appears to be an essential tool in providing such local scale information to formulate high quality adaptation strategies for local and regional climate change. As a key component of this modeling system at LLNL, the Weather Research and Forecast (WRF) model is implemented and tested on the IBM BG/L machine. The objective of this study is to examine the scaling feature of WRF on BG/L for the optimal performance, and to assess the numerical accuracy of WRF solution on BG/L.

  9. How much does simplification of probability forecasts reduce forecast quality?

    Microsoft Academic Search

    F. J. Doblas-Reyes; C. A. S. Coelho; D. B. Stephensonc

    2008-01-01

    Probability forecasts from an ensemble are often discretized into a small set of categories before being distributed to the users. This study investigates how such simplification can affect the forecast quality of probabilistic predictions as measured by the Brier score (BS). An example from the European Centre for Medium-Range Weather Forecasts (ECMWF) operational seasonal ensemble forecast system is used to

  10. Simulation of the dispersion of the Eyjafjallajökull plume over Europe with the German operational weather forecast system

    NASA Astrophysics Data System (ADS)

    Vogel, Heike; Förstner, Jochen; Vogel, Bernhard; Hanisch, Thomas; Mühr, Bernhard; Schättler, Ulrich

    2010-05-01

    After resting for 187 years the vulcano Eyjafjallajökull, Island wake up again at March 20th, 2010. Starting at April 14th massive emissions of volcanic ash occurred and finally lead to a shut down of civil aviation over entire Europe. We transferred the comprehensive online coupled model system COSMO-ART (Vogel et al., 2009) so far used for research purposes into the operational forecast mode at Deutscher Wetter-dienst (German Weather Service, DWD). COSMO-ART is the extension of the operational weather forecast model of DWD. Six individual size distributions were simulated starting from 1 ?m up to 35 ?m. Deposition, sedimentation, and below cloud scavenging were taken into account. Source heights were taken as published by the volcanic ash advisory centre London (VAAC), UK that is responsible for making the official forecast of ash coming from volcanoes in Island according to international agreements. During the first days of the eruption volcanic ash was injected into the atmosphere up to 11 km. Therefore, it was transported rapidly at higher levels towards Europe. A comparison of the simulated ash-plume with the satellite pictures shows that the model captures the horizontal distribution of the ash-plume quite well. Even the volcanic ash that was located above a narrow band of clouds is nicely reproduced. The temporal development can be also compared to Lidar measurements at different sites. These comparisons will be also presented. Our simulation results show the capability of an operational weather forecast model that is extended by aerosol processes to simulate the spatial and temporal distribution of volcanic ash qualitatively. As the source strength was not know and will not be known during future eruption events only a combination of ground based and satellite born remote sensing instruments together with in-situ observations and model results facilitates the work of decision makers during future events. Vogel, B., Vogel H., Bäumer, D., Bangert, M., Lundgren, K., Rinke, R., & Stanelle, T. (2009). Atmos. Chem. Phys., 9,8661-8680. VACC, www.metoffice.gov.uk/aviation/vaac/.

  11. NASA SPoRT Modeling and Data Assimilation Research and Transition Activities Using WRF, LIS and GSI

    NASA Technical Reports Server (NTRS)

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

    2014-01-01

    weather research and forecasting ===== The NASA Short-term Prediction Research and Transition (SPoRT) program has numerous modeling and data assimilation (DA) activities in which the WRF model is a key component. SPoRT generates realtime, research satellite products from the MODIS and VIIRS instruments, making the data available to NOAA/NWS partners running the WRF/EMS, including: (1) 2-km northwestern-hemispheric SST composite, (2) daily, MODIS green vegetation fraction (GVF) over CONUS, and (3) NASA Land Information System (LIS) runs of the Noah LSM over the southeastern CONUS. Each of these datasets have been utilized by specific SPoRT partners in local EMS model runs, with select offices evaluating the impacts using a set of automated scripts developed by SPoRT that manage data acquisition and run the NCAR Model Evaluation Tools verification package. SPoRT is engaged in DA research with the Gridpoint Statistical Interpolation (GSI) and Ensemble Kalman Filter in LIS for soil moisture DA. Ongoing DA projects using GSI include comparing the impacts of assimilating Atmospheric Infrared Sounder (AIRS) radiances versus retrieved profiles, and an analysis of extra-tropical cyclones with intense non-convective winds. As part of its Early Adopter activities for the NASA Soil Moisture Active Passive (SMAP) mission, SPoRT is conducting bias correction and soil moisture DA within LIS to improve simulations using the NASA Unified-WRF (NU-WRF) for both the European Space Agency's Soil Moisture Ocean Salinity and upcoming SMAP mission data. SPoRT has also incorporated real-time global GVF data into LIS and WRF from the VIIRS product being developed by NOAA/NESDIS. This poster will highlight the research and transition activities SPoRT conducts using WRF, NU-WRF, EMS, LIS, and GSI.

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

    NASA Technical Reports Server (NTRS)

    Posner, Arik; Hesse, Michael; SaintCyr, Chris

    2014-01-01

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

  13. Local refinement of RCM simulations based on the theory of Copulas: An application to bias correct WRF precipitation for Germany

    NASA Astrophysics Data System (ADS)

    Mao, Ganquan; Vogl, Stefanie; Laux, Patrick; Wagner, Sven; Kunstmann, Harald

    2014-05-01

    Precipitation information is crucial for regional hydrological and agricultural climate change impact studies. Regional climate models (RCMs) are suitable tools to provide high spatial resolution precipitation products at regional scales, however, they are usually biased not only in absolute values, but also in reproducing observed spatial patterns. Therefore, bias correction techniques are required to obtain suited meteorological information on regional scale. We present a Copula-based method to correct precipitation fields from the Weather Research and Forecasting (WRF) model by merging modelled fields with gridded observation data. Germany is selected as our research domain. High resolution (7km) WRF simulations are used in this study, which is driven by ERA40 reanalysis data for 1971-2000. REGNIE data from Germany Weather Service (DWD) were used as gridded observation data source (1km/daily) and rescaled to 7km for this application. The critical step of this proposed bias correction approach is the establishment of bivariate Copula models, each of them consists of two marginal distributions and one Copula function. The marginal distributions are used to describe the statistical properties of REGNIE and WRF-ERA40 data, while the theoretical Copula function represents the dependence structure between REGNIE and WRF-ERA40 data. Based on this Copula model, the conditional distribution of REGNIE conditioned on WRF-ERA40 can be derived. To generate bias corrected WRF-ERA40 precipitation, a random sample of possible outcomes is drawn from this conditional distribution. This also allows for a quantitative estimation of the inherent uncertainties. The expectation/median/mode value of the stochastic samples can be used as an estimation of the corrected value. For the application, a split-sampling approach is used. Results show that the marginal distributions of REGNIE and WRF-ERA40 are different which implies deficiencies of the WRF-ERA40 simulations to reproduce the statistics of precipitation properly. Copula functions vary in space and time, which indicates varying dependence structures for different seasons and locations. The corrected WRF-ERA40 data are compared with REGNIE in the validation period. It shows that the Copula-based approach successfully corrects for the errors in WRF-ERA40 precipitation. The range of the daily mean precipitation bias over Germany is reduced from -39%-84% to -29%-15%. Especially in winter time, the bias is reduced from -40%-111% to -33%-26%. The results are compared with two standard bias correction methods (linear scaling, quantile mapping) and discussed.

  14. The Lagrangian particle dispersion model FLEXPART-WRF VERSION 3.1

    SciTech Connect

    Brioude, J.; Arnold, D.; Stohl, A.; Cassiani, M.; Morton, Don; Seibert, P.; Angevine, W. M.; Evan, S.; Dingwell, A.; Fast, Jerome D.; Easter, Richard C.; Pisso, I.; Bukhart, J.; Wotawa, G.

    2013-11-01

    The Lagrangian particle dispersion model FLEXPART was originally designed for cal- culating long-range and mesoscale dispersion of air pollutants from point sources, such as after an accident in a nuclear power plant. In the meantime FLEXPART has evolved into a comprehensive tool for atmospheric transport modeling and analysis at different scales. This multiscale need from the modeler community has encouraged new developments in FLEXPART. In this document, we present a version that works with the Weather Research and Forecasting (WRF) mesoscale meteoro- logical model. Simple procedures on how to run FLEXPART-WRF are presented along with special options and features that differ from its predecessor versions. In addition, test case data, the source code and visualization tools are provided to the reader as supplementary material.

  15. Uncertainty in Lagrangian pollutant transport simulations due to meteorological uncertainty from a mesoscale WRF ensemble

    NASA Astrophysics Data System (ADS)

    Angevine, W. M.; Brioude, J.; McKeen, S.; Holloway, J. S.

    2014-12-01

    Lagrangian particle dispersion models require meteorological fields as input. Uncertainty in the driving meteorology is one of the major uncertainties in the results. The propagation of uncertainty through the system is not simple, and it has not been thoroughly explored. Here, we take an ensemble approach. Six different configurations of the Weather Research and Forecast (WRF) model drive otherwise identical simulations with FLEXPART-WRF for 49 days over eastern North America. The ensemble spreads of wind speed, mixing height, and tracer concentration are presented. Uncertainty of tracer concentrations due solely to meteorological uncertainty is 30-40%. Spatial and temporal averaging reduces the uncertainty marginally. Tracer age uncertainty due solely to meteorological uncertainty is 15-20%. These are lower bounds on the uncertainty, because a number of processes are not accounted for in the analysis.

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

    NASA Astrophysics Data System (ADS)

    Smith, M. R.; Fuell, K.; Molthan, A.; Jedlovec, G.

    2012-12-01

    The launch of the Suomi National Polar-Orbiting Partnership (S-NPP) satellite provides new and exciting opportunities for the application of remotely sensed data products in operational weather forecasting environments. The NASA Short-term Prediction Research and Transition (SPoRT) Center in Huntsville, Alabama is a NASA and NOAA-funded project to assist with the transition of experimental and research products to the operational weather community through partnership with NOAA/National Weather Service Weather Forecast Offices (NWS WFOs) throughout the United States. This presentation will provide the S-NPP community with an update on current and future SPoRT projects related to the dissemination of S-NPP derived data to NWS WFOs and highlight unique applications and value of data from the Visible Infrared Imaging Radiometer Suite (VIIRS), specifically applications of high resolution visible and infrared data, uses of the day-night (or near constant contrast) band, and multispectral composites. Other applications are envisioned through use of selected channels of the Cross-track Infrared Sounder (CrIS), the Advanced Technology Microwave Sounder (ATMS), and the Ozone Mapper Profiler Suite (OMPS). This presentation will also highlight opportunities for future collaboration with SPoRT and activities planned for participation in the NOAA Joint Polar Satellite Program (JPSS) Proving Ground.

  17. WRF-Var implementation for data assimilation experimentation at MIT

    E-print Network

    Williams, John K. (John Kenneth)

    2008-01-01

    The goal of this Masters project is to implement the WRF model with 3D variational assimilation (3DVAR) at MIT. A working version of WRF extends the scope of experimentation to mesoscale problems in both real and idealized ...

  18. Test and Sensitivity Analysis of Hydrological Modeling in the Coupled WRF-Urban Modeling System

    NASA Astrophysics Data System (ADS)

    Wang, Z.; yang, J.

    2013-12-01

    Rapid urbanization has emerged as the source of many adverse effects that challenge the environmental sustainability of cities under changing climatic patterns. One essential key to address these challenges is to physically resolve the dynamics of urban-land-atmospheric interactions. To investigate the impact of urbanization on regional climate, physically-based single layer urban canopy model (SLUCM) has been developed and implemented into the Weather Research and Forecasting (WRF) platform. However, due to the lack of realistic representation of urban hydrological processes, simulation of urban climatology by current coupled WRF-SLUCM is inevitably inadequate. Aiming at improving the accuracy of simulations, recently we implemented urban hydrological processes into the model, including (1) anthropogenic latent heat, (2) urban irrigation, (3) evaporation over impervious surface, and (4) urban oasis effect. In addition, we couple the green roof system into the model to verify its capacity in alleviating urban heat island effect at regional scale. Driven by different meteorological forcings, offline tests show that the enhanced model is more accurate in predicting turbulent fluxes arising from built terrains. Though the coupled WRF-SLUCM has been extensively tested against various field measurement datasets, accurate input parameter space needs to be specified for good model performance. As realistic measurements of all input parameters to the modeling framework are rarely possible, understanding the model sensitivity to individual parameters is essential to determine the relative importance of parameter uncertainty to model performance. Thus we further use an advanced Monte Carlo approach to quantify relative sensitivity of input parameters of the hydrological model. In particular, performance of two widely used soil hydraulic models, namely the van Genuchten model (based on generic soil physics) and an empirical model (viz. the CHC model currently adopted in WRF-SLUCM) is investigated. Results show that the CHC model requires a much finer time step for numerical stability in hydrological modeling and thus is more computationally expensive in the coupled WRF-SLUCM modeling environment.

  19. New, Improved Bulk-microphysical Schemes for Studying Precipitation Processes in WRF. Part 1; Comparisons with Other Schemes

    NASA Technical Reports Server (NTRS)

    Tao, W.-K.; Shi, J.; Chen, S. S> ; Lang, S.; Hong, S.-Y.; Thompson, G.; Peters-Lidard, C.; Hou, A.; Braun, S.; Simpson, J.

    2007-01-01

    Advances in computing power allow atmospheric prediction models to be mn at progressively finer scales of resolution, using increasingly more sophisticated physical parameterizations and numerical methods. The representation of cloud microphysical processes is a key component of these models, over the past decade both research and operational numerical weather prediction models have started using more complex microphysical schemes that were originally developed for high-resolution cloud-resolving models (CRMs). A recent report to the United States Weather Research Program (USWRP) Science Steering Committee specifically calls for the replacement of implicit cumulus parameterization schemes with explicit bulk schemes in numerical weather prediction (NWP) as part of a community effort to improve quantitative precipitation forecasts (QPF). An improved Goddard bulk microphysical parameterization is implemented into a state-of the-art of next generation of Weather Research and Forecasting (WRF) model. High-resolution model simulations are conducted to examine the impact of microphysical schemes on two different weather events (a midlatitude linear convective system and an Atllan"ic hurricane). The results suggest that microphysics has a major impact on the organization and precipitation processes associated with a summer midlatitude convective line system. The 31CE scheme with a cloud ice-snow-hail configuration led to a better agreement with observation in terms of simulated narrow convective line and rainfall intensity. This is because the 3ICE-hail scheme includes dense ice precipitating (hail) particle with very fast fall speed (over 10 m/s). For an Atlantic hurricane case, varying the microphysical schemes had no significant impact on the track forecast but did affect the intensity (important for air-sea interaction)

  20. The Tropical Cyclone Modeling Team (TCMT): Evaluation of Experimental Models for Tropical Cyclone Forecasting in Support of the NOAA Hurricane Forecast Improvement Project (HFIP)

    NASA Astrophysics Data System (ADS)

    Kucera, P. A.; Brown, B.; Nance, L. B.; Crosby, K. M.; Williams, C.; Jensen, T.

    2010-12-01

    In 2009, the National Center for Atmospheric Science (NCAR)/Research Applications Laboratory’s (RALs) Joint Numerical Testbed (JNT) Program formed a new entity called the Tropical Cyclone Modeling Team (TCMT). The focus of this team is testing and evaluation of experimental models with the goal of improving tropical cyclone forecasts. Much of this effort is sponsored by NOAA's Hurricane Forecast Improvement Project (HFIP). For HFIP, the TCMT designs model evaluation experiments and provides general testing and evaluation of the various forecast models included in the HFIP annual forecasting demonstrations and retrospective experiments. The TCMT is developing statistical approaches that are appropriate for evaluating a variety of tropical cyclone forecast attributes. These methods include new diagnostic tools to aid, for example, in the evaluation of track and intensity errors, precipitation and tropical cyclone structure forecasts. Currently, the TCMT is conducting an evaluation of a suite of experimental models that are candidates for future inclusion into the operational forecasting system at the National Hurricane Center (NHC). This 2010 retrospective analysis is being conducted using storms observed during the 2008 and 2009 hurricane seasons in the Eastern Pacific and Atlantic basins. The goals of the 2010 retrospective testing are to (1) provide adequate statistics for assessing the skill of the model candidates, (2) help identify modeling systems to could be included in future operational forecast guidance, and (3) provide information that may help to calibrate the multi-model ensemble forecasts. The retrospective testing focused on a representative sample of 27 storms from the 2008 and 2009 hurricane seasons. Four modeling groups participated in the retrospective testing. The models included two configurations of the Weather Research and Forecasting (WRF) model, a new version of the NOAA Geophysical Fluid Dynamic Laboratory’s (GFDL) model, and the Navy’s tropical cyclone model. This presentation will provide an overview of the 2010 retrospective testing, a summary of the differences in track and intensity errors for the comparisons of the individual participating models, and an evaluation of the impact of including the experimental model in the NHC conventional consensus forecast. The presentation will also provide an overview of future plans for the evaluation of tropical cyclone forecasts.

  1. The PRESSCA operational early warning system for landslide forecasting: the 11-12 November 2013 rainfall event in Central Italy.

    NASA Astrophysics Data System (ADS)

    Ciabatta, Luca; Brocca, Luca; Ponziani, Francesco; Berni, Nicola; Stelluti, Marco; Moramarco, Tommaso

    2014-05-01

    The Umbria Region, located in Central Italy, is one of the most landslide risk prone area in Italy, almost yearly affected by landslides events at different spatial scales. For early warning procedures aimed at the assessment of the hydrogeological risk, the rainfall thresholds represent the main tool for the Italian Civil Protection System. As shown in previous studies, soil moisture plays a key-role in landslides triggering. In fact, acting on the pore water pressure, soil moisture influences the rainfall amount needed for activating a landslide. In this work, an operational physically-based early warning system, named PRESSCA, that takes into account soil moisture for the definition of rainfall thresholds is presented. Specifically, the soil moisture conditions are evaluated in PRESSCA by using a distributed soil water balance model that is recently coupled with near real-time satellite soil moisture product obtained from ASCAT (Advanced SCATterometer) and from in-situ monitoring data. The integration of three different sources of soil moisture information allows to estimate the most accurate possible soil moisture condition. Then, both observed and forecasted rainfall data are compared with the soil moisture-based thresholds in order to obtain risk indicators over a grid of ~ 5 km. These indicators are then used for the daily hydrogeological risk evaluation and management by the Civil Protection regional service, through the sharing/delivering of near real-time landslide risk scenarios (also through an open source web platform: www.cfumbria.it). On the 11th-12th November, 2013, Umbria Region was hit by an exceptional rainfall event with up to 430mm/72hours that resulted in significant economic damages, but fortunately no casualties among the population. In this study, the results during the rainfall event of PRESSCA system are described, by underlining the model capability to reproduce, two days in advance, landslide risk scenarios in good spatial and temporal agreement with the occurred actual conditions. High-resolution risk scenarios (100mx100m), obtained by coupling PRESSCA forecasts with susceptibility and vulnerability layers, are also produced. The results show good relationship between the PRESSCA forecast and the reported landslides to the Civil Protection Service during the rainfall event, confirming the system robustness. The good forecasts of PRESSCA system have surely contributed to start well in advance the Civil Protection operations (alerting local authorities and population).

  2. Accurate Liquid Water Path Retrieval from Low-Cost Microwave Radiometers Using Additional Information from a Lidar Ceilometer and Operational Forecast Models

    E-print Network

    Hogan, Robin

    (NWP). In most operational NWP models, production of rain by collision and coalescence is parameterized by an autoconversion rate that depends upon the value of the cloud liquid water content in the model (e.g., Wil- son Information from a Lidar Ceilometer and Operational Forecast Models NICOLAS GAUSSIAT, ROBIN J. HOGAN

  3. SOLERAS - Solar-Powered Water Desalination Project at Yanbu: Forecasting models for operating and maintenance cost of the pilot plant

    SciTech Connect

    Al-Idrisi, M.; Hamad, G.

    1987-04-01

    This study was conducted in cooperation with the Department of Industrial Engineering of King Abdulaziz University. The main objective of this study is to meet some of the goals of the Solar Energy Water Desalination Plant (SEWDP) plan in the area of economic evaluation. The first part of this project focused on describing the existing trend in the operation and maintenance (OandM) cost for the SOLERAS Solar Energy Water Desalination Plant in Yanbu. The second part used the information obtained on existing trends to find suitable forecasting models. These models, which are found here, are sensitive to changes in costs trends. Nevertheless, the study presented here has established the foundation for (OandM) costs estimating in the plant. The methodologies used in this study should continue as more data on operation and maintenance costs become available, because, in the long run, the trend in costs will help determine where cost effectiveness might be improved. 7 refs., 24 figs., 15 tabs.

  4. AgI plumes in WRF LES simulations versus airborne measurements

    NASA Astrophysics Data System (ADS)

    Xue, L.; Rasmussen, R.; Breed, D. W.

    2011-12-01

    Inadequate or uncertain targeting of seedable clouds from silver iodide (AgI) ground-based generators has been a complex and hence a long-standing problem in winter orographic cloud seeding programs. To address this issue within the Wyoming Weather Modification Pilot Program (WWMPP), a focused field experiment was conducted between 9 February and 1 March 2011. Airborne measurements of AgI-generated ice nuclei (IN) plumes from ground-based generators were carried out by Weather Modification Inc. using a Piper Cheyenne II research aircraft equipped with an updated NCAR acoustic IN counter. The airborne data were collected over the Wyoming Medicine Bow and Sierra Madre mountain ranges on nine different days within the experimental period. This study explores the ability of the Weather Research and Forecast (WRF) model to reproduce reasonable AgI plumes by comparing the model results with these airborne measurements. A suite of WRF simulations, including 2.5 km and 500 m runs along with two 100-m resolution Large Eddy Simulations (LES), have been conducted for the 16 February case over the Medicine Bow range. Two different sets of gridded data, the North America Regional Reanalysis data and the WWMPP Real-Time Four-Dimensional Data Assimilation WRF forecast data, were used to drive the model independently. An AgI point-source module was applied to represent the release of AgI from the ground generators. A detailed description of the WRF LES results and comparisons with the airborne measurements will be presented at the conference.

  5. Comment on "Simulation of Surface Ozone Pollution in the Central Gulf Coast Region Using WRF/Chem Model: Sensitivity to PBL and Land Surface Physics"

    EPA Science Inventory

    A recently published meteorology and air quality modeling study has several serious deficiencies deserving comment. The study uses the weather research and forecasting/chemistry (WRF/Chem) model to compare and evaluate boundary layer and land surface modeling options. The most se...

  6. Comparison of mixed layer heights from airborne high spectral resolution lidar, ground-based measurements, and the WRF-Chem model during CalNex and CARES

    NASA Astrophysics Data System (ADS)

    Scarino, A. J.; Obland, M. D.; Fast, J. D.; Burton, S. P.; Ferrare, R. A.; Hostetler, C. A.; Berg, L. K.; Lefer, B.; Haman, C.; Hair, J. W.; Rogers, R. R.; Butler, C.; Cook, A. L.; Harper, D. B.

    2014-06-01

    The California Research at the Nexus of Air Quality and Climate Change (CalNex) and Carbonaceous Aerosol and Radiative Effects Study (CARES) field campaigns during May and June 2010 provided a data set appropriate for studying the structure of the atmospheric boundary layer (BL). The NASA Langley Research Center (LaRC) airborne high spectral resolution lidar (HSRL) was deployed to California onboard the NASA LaRC B-200 aircraft to aid in characterizing aerosol properties during these two field campaigns. Measurements of aerosol extinction (532 nm), backscatter (532 and 1064 nm), and depolarization (532 and 1064 nm) profiles during 31 flights, many in coordination with other research aircraft and ground sites, constitute a diverse data set for use in characterizing the spatial and temporal distribution of aerosols, as well as the depth and variability of the daytime mixed layer (ML) height. The paper describes the modified Haar wavelet covariance transform method used to derive the ML heights from HSRL backscatter profiles. HSRL ML heights are validated using ML heights derived from two radiosonde profile sites during CARES. Comparisons between ML heights from HSRL and a Vaisala ceilometer operated during CalNex were used to evaluate the representativeness of a fixed measurement over a larger region. In the Los Angeles basin, comparisons of ML heights derived from HSRL measurements and ML heights derived from the ceilometer result in a very good agreement (mean bias difference of 10 m and correlation coefficient of 0.89) up to 30 km away from the ceilometer site, but are essentially uncorrelated for larger distances, indicating that the spatial variability of the ML height is significant over these distances and not necessarily well captured by limited ground stations. The HSRL ML heights are also used to evaluate the performance in simulating the temporal and spatial variability of ML heights from the Weather Research and Forecasting Chemistry (WRF-Chem) community model. When compared to aerosol ML heights from HSRL, thermodynamic ML heights from WRF-Chem were underpredicted in the CalNex and CARES regions, shown by a bias difference value of -157 m and -29 m, respectively. Better agreement over the Central Valley than in mountainous regions suggests that some variability in the ML height is not well captured at the 4 km grid resolution of the model. A small but significant number of cases have poor agreement when WRF-Chem consistently overestimates the ML height in the late afternoon. Additional comparisons with WRF-Chem aerosol mixed layer heights show no significant improvement over thermodynamic ML heights, confirming that any differences between measurement and model are not due to the methodology of ML height determination.

  7. Forecasting forecast skill

    NASA Technical Reports Server (NTRS)

    Kalnay, Eugenia; Dalcher, Amnon

    1987-01-01

    It is shown that it is possible to predict the skill of numerical weather forecasts - a quantity which is variable from day to day and region to region. This has been accomplished using as predictor the dispersion (measured by the average correlation) between members of an ensemble of forecasts started from five different analyses. The analyses had been previously derived for satellite-data-impact studies and included, in the Northern Hemisphere, moderate perturbations associated with the use of different observing systems. When the Northern Hemisphere was used as a verification region, the prediction of skill was rather poor. This is due to the fact that such a large area usually contains regions with excellent forecasts as well as regions with poor forecasts, and does not allow for discrimination between them. However, when regional verifications were used, the ensemble forecast dispersion provided a very good prediction of the quality of the individual forecasts.

  8. Real-time flood forecasting with high-resolution NWP rainfall and dual data assimilation

    NASA Astrophysics Data System (ADS)

    Liu, Jia; Bray, Michaela; Han, Dawei

    2014-05-01

    Mesoscale Numerical Weather Prediction (NWP) models are nowadays gaining more and more attention in providing high-resolution rainfall forecasts for real-time flood forecasting. In this study, the newest generation NWP model, Weather Research & Forecasting (WRF) model, is integrated with the rainfall-runoff model in real-time to generate accurate flow forecasts at the catchment scale. The rainfall-runoff model is chosen as the Probability Distribution Model (PDM), which has widely been used for flood forecasting. Dual data assimilation is carried out for real-time updating of the flood forecasting system. The 3-Dimensional Variational (3DVar) data assimilation scheme is incorporated with WRF to assimilate meteorological observations and weather radar reflectivity data in order to improve the WRF rainfall forecasts; meanwhile real-time flow observations are assimilated by the Auto-Regressive Moving Average (ARMA) model to update the forecasted flow transformed by PDM. The Brue catchment located in Southwest England with a drainage area of 135.2 km2 is chosen to be the study area. A dense rain gauge network was set up during a project named HYREX (Hydrological radar experiment), which contains 49 rain gauges and a C-band weather radar, providing with sufficient hydrological and radar data for WRF model verification and data assimilation. Besides the radar reflectivity data, two types of NCAR archived data (SYNOP and SOUND, http://dss.ucar.edu) are also assimilated by 3DVar, which provide real-time surface and upper-level observations of pressure, temperature, humidity and wind from fixed and mobile stations. Four 24 hour storm events are selected from the HYREX project with different characteristics regarding storm formation and rainfall-runoff responses. Real-time flood forecasting is then carried out by the constructed forecasting system for the four storm events with a forecast lead time of 12 hours. The forecasting accuracy of the whole system is found to be largely improved by incorporating the WRF forecasted rainfall when the forecast lead time is beyond the catchment concentration time. The assimilation of real-time meteorological and radar data also show great advantage in improving the performance of the flood forecasting system. Key words: real-time flood forecasting; Weather Research & Forecasting (WRF) model; high-resolution rainfall forecasts; dual data assimilation.

  9. Norway and Cuba Continue Collaborating to Build Capacity to Improve Weather Forecasting

    NASA Astrophysics Data System (ADS)

    Antuña, Juan Carlos; Kalnay, Eugenia; Mesquita, Michel D. S.

    2014-06-01

    The Future of Climate Extremes in the Caribbean Extreme Cuban Climate (XCUBE) project, which is funded by the Norwegian Directorate for Civil Protection as part of an assignment for the Norwegian Ministry of Foreign Affairs to support scientific cooperation between Norway and Cuba, carried out a training workshop on seasonal forecasting, reanalysis data, and weather research and forecasting (WRF). The workshop was a follow-up to the XCUBE workshop conducted in Havana in 2013 and provided Cuban scientists with access to expertise on seasonal forecasting, the WRF model developed by the National Center for Atmospheric Research (NCAR) and the community, data assimilation, and reanalysis.

  10. Can a Regional Climate Model Improve the Ability to Forecast the North American Monsoon?

    E-print Network

    Castro, Christopher L.

    Can a Regional Climate Model Improve the Ability to Forecast the North American Monsoon. For the North American monsoon region, WRF adds value to the seasonally forecast temperature only in early to represent the North American monsoon, in terms of its cli- matology and interannual variability

  11. Evaluation of the regional climate model WRF over Svalbard

    NASA Astrophysics Data System (ADS)

    Lang, C.; Fettweis, X.; Erpicum, M.

    2012-04-01

    It is well known that high latitude zones are very sensitive to climate change. As a result of global warming, ice sheet melting has increased which in turn has an influence on climate through modifications of the thermohaline circulation, feedback of ice albedo, sea level rise, … Svalbard is an archipelago between 74 and 81°lat N and 60 percent of its area (62 248 km2) is covered with glaciers and ice sheets. The impact of global warming on the Svalbard cryosphere can be estimated with climate models. However, we need to use regional climate models as they offer the possibility of a higher resolution than general circulation models. We have evaluated here six different physics options available in the regional climate model WRF (Weather Research and Forecasting) forced by ERA-Interim reanalysis by comparing the Svalbard climate simulated over 2006-2009 at a 5 km resolution to near surface measurements at several weather stations through the archipelago. We have then carried out simulations of the Svalbard climate over the last 30 years (1979-2011) using the WRF configuration that gave the best results as well as simulations of the surface mass balance using a the land-surface model allowing to model the surface mass balance components through its multi-layer snow module. The results show a large interannual variability of the surface mass balance over Svalbard along with an increasing melting. The increase in temperature is responsible for the melting rate and the interannual variability is due to the variations of the mean summer temperature.

  12. Simulation of heavy rainfall events over Indian monsoon region using WRF-3DVAR data assimilation system

    NASA Astrophysics Data System (ADS)

    Routray, A.; Mohanty, U. C.; Niyogi, Dev; Rizvi, S. R. H.; Osuri, Krishna K.

    2010-02-01

    We present the results of the impact of the 3D variational data assimilation (3DVAR) system within the Weather Research and Forecasting (WRF) model to simulate three heavy rainfall events (25-28 June 2005, 29-31 July 2004, and 7-9 August 2002) over the Indian monsoon region. For each event, two numerical experiments were performed. In the first experiment, namely the control simulation (CNTL), the low-resolution global analyses are used as the initial and boundary conditions of the model. In the second experiment (3DV-ANA), the model integration was carried out by inserting additional observations in the model’s initial conditions using the 3DVAR scheme. The 3DVAR used surface weather stations, buoy, ship, radiosonde/rawinsonde, and satellite (oceanic surface wind, cloud motion wind, and cloud top temperature) observations obtained from the India Meteorological Department (IMD). After the successful inclusion of additional observational data using the 3DVAR data assimilation technique, the resulting reanalysis was able to successfully reproduce the structure of convective organization as well as prominent synoptic features associated with the mid-tropospheric cyclones (MTC). The location and intensity of the MTC were better simulated in the 3DV-ANA as compared to the CNTL. The results demonstrate that the improved initial conditions of the mesoscale model using 3DVAR enhanced the location and amount of rainfall over the Indian monsoon region. Model verification and statistical skill were assessed with the help of available upper-air sounding data. The objective verification further highlighted the efficiency of the data assimilation system. The improvements in the 3DVAR run are uniformly better as compared to the CNTL run for all the three cases. The mesoscale 3DVAR data assimilation system is not operational in the weather forecasting centers in India and a significant finding in this study is that the assimilation of Indian conventional and non-conventional observation datasets into numerical weather forecast models can help improve the simulation accuracy of meso-convective activities over the Indian monsoon region. Results from the control experiments also highlight that weather and regional climate model simulations with coarse analysis have high uncertainty in simulating heavy rain events over the Indian monsoon region and assimilation approaches, such as the 3DVAR can help reduce this uncertainty.

  13. Augmenting an operational forecasting system for the North and Baltic Seas by in situ T and S data assimilation

    NASA Astrophysics Data System (ADS)

    Losa, Svetlana; Danilov, Sergey; Schröter, Jens; Nerger, Lars; Maßmann, Silvia; Janssen, Frank

    2014-05-01

    In order to improve the hydrography forecast of the North and Baltic Seas, the operational circulation model of the German Federal Maritime and Hydrographic Agency (BSH) has been augmented by a data assimilation (DA) system. The DA system has been developed based on the Singular Evolution Interpolated Kalman (SEIK) filter algorithm (Pham, 1998) coded within the Parallel Data Assimilation Framework (Nerger et al., 2004, Nerger and Hiller, 2012). Previously the only data assimilated were sea surface temperature (SST) measurements obtained with the Advanced Very High Resolution Radiometer (AVHRR) aboard NOAA's polar orbiting satellites. While the quality of the forecast has been significantly improved by assimilating the satellite data (Losa et al., 2012, Losa et al., 2014), assimilation of in situ observational temperature (T) and salinity (S) profiles has allowed for further improvement. Assimilating MARNET time series and CTD and Scanfish measurements, however, required a careful calibration of the DA system with respect to local analysis. The study addresses the problem of the local SEIK analysis accounting for the data within a certain radius. The localisation radius is considered spatially variable and dependent on the system local dynamics. As such, we define the radius of the data influence based on the energy ratio of the baroclinic and barotropic flows. D. T. Pham, J. Verron, L. Gourdeau, 1998. Singular evolutive Kalman filters for data assimilation in oceanography, C. R. Acad. Sci. Paris, Earth and Planetary Sciences, 326, 255-260. L. Nerger, W. Hiller, J. Schröter, 2004. PDAF - The Parallel Data Assimilation Framework: Experiences with Kalman Filtering, In: Zwieflhofer, W., Mozdzynski, G. (Eds.), Use of high performance computing in meteorology: proceedings of the Eleventh ECMWF Workshop on the Use of High Performance Computing in Meteorology. Singapore: World Scientific, Reading, UK, 63-83. L. Nerger, W. Hiller, 2012. Software for Ensemble-based Data Assimilation Systems —Implementation Strategies and Scalability, Computers and Geosciences, 55, 110-118. S. N. Losa, S. Danilov, J. Schröter, L. Nerger, S. Maßmann, F. Janssen, 2012. Assimilating NOAA SST data into the BSH operational circulation model for the North and Baltic Seas: Inference about the data. Journal of Marine Systems, 105-108, 152-162. S. N. Losa, S. Danilov, J. Schröter, L. Nerger, S. Maßmann, F. Janssen, 2014. Assimilating NOAA SST data into the BSH operational circulation model for the North and Baltic Seas: Part.2 Sensitivity of the forecast's skill to the prior model error statistics. Journal of Marine Systems, 129, 259-270.

  14. Polar Satellite Products for the Operational Forecaster (POES) Module 1: POES Introduction

    NSDL National Science Digital Library

    COMET

    1999-03-25

    This Web-based module is a component of the Integrated Sensor Training (IST) Professional Development Series (PDS) Professional Competency Unit #6-Satellite Data and Products. Dr. Stan Kidder of the Cooperative Institute for Research in the Atmosphere (CIRA) at Colorado State University is the principal science advisor for this module with significant assistance from Dr. Gary Hufford (NWS Alaska Region). The module provides an overview of current polar satellite products and their applications in forecasting situations and also contains a summary of instruments currently in use and a short history of the U.S. polar satellite program. The module is the first in a series focusing on polar satellite products and applications.

  15. Detiding DART® Buoy Data for Real-Time Extraction of Source Coefficients for Operational Tsunami Forecasting

    NASA Astrophysics Data System (ADS)

    Percival, Donald B.; Denbo, Donald W.; Eblé, Marie C.; Gica, Edison; Huang, Paul Y.; Mofjeld, Harold O.; Spillane, Michael C.; Titov, Vasily V.; Tolkova, Elena I.

    2014-11-01

    U.S. Tsunami Warning Centers use real-time bottom pressure (BP) data transmitted from a network of buoys deployed in the Pacific and Atlantic Oceans to tune source coefficients of tsunami forecast models. For accurate coefficients and therefore forecasts, tides at the buoys must be accounted for. In this study, five methods for coefficient estimation are compared, each of which accounts for tides differently. The first three subtract off a tidal prediction based on (1) a localized harmonic analysis involving 29 days of data immediately preceding the tsunami event, (2) 68 pre-existing harmonic constituents specific to each buoy, and (3) an empirical orthogonal function fit to the previous 25 hrs of data. Method (4) is a Kalman smoother that uses method (1) as its input. These four methods estimate source coefficients after detiding. Method (5) estimates the coefficients simultaneously with a two-component harmonic model that accounts for the tides. The five methods are evaluated using archived data from eleven DART buoys, to which selected artificial tsunami signals are superimposed. These buoys represent a full range of observed tidal conditions and background BP noise in the Pacific and Atlantic, and the artificial signals have a variety of patterns and induce varying signal-to-noise ratios. The root-mean-square errors (RMSEs) of least squares estimates of sources coefficients using varying amounts of data are used to compare the five detiding methods. The RMSE varies over two orders of magnitude between detiding methods, generally decreasing in the order listed, with method (5) yielding the most accurate estimate of source coefficient. The RMSE is substantially reduced by waiting for the first full wave of the tsunami signal to arrive. As a case study, the five method are compared using data recorded from the devastating 2011 Japan tsunami.

  16. WRF Simulations of the 20-22 January 2007 Snow Events over Eastern Canada: Comparison with In-Situ and Satellite Observations

    NASA Technical Reports Server (NTRS)

    Shi, J. J.; Tao, W.-K.; Matsui, T.; Cifelli, R.; Huo, A.; Lang, S.; Tokay, A.; Peters-Lidard, C.; Jackson, G.; Rutledge, S.; Petersen, W.

    2009-01-01

    One of the grand challenges of the Global Precipitation Measurement (GPM) mission is to improve cold season precipitation measurements in middle and high latitudes through the use of high-frequency passive microwave radiometry. For this, the Weather Research and Forecasting (WRF) model with the Goddard microphysics scheme is coupled with a satellite data simulation unit (WRF-SDSU) that has been developed to facilitate over-land snowfall retrieval algorithms by providing a virtual cloud library and microwave brightness temperature (Tb) measurements consistent with the GPM Microwave Imager (GMI). This study tested the Goddard cloud microphysics scheme in WRF for two snowstorm events, a lake effect and a synoptic event, that occurred between 20 and 22 January 2007 over the Canadian CloudSAT/CALIPSO Validation Project (C3VP) site in Ontario, Canada. The 24h-accumulated snowfall predicted by the WRF model with the Goddard microphysics was comparable to the observed accumulated snowfall by the ground-based radar for both events. The model correctly predicted the onset and ending of both snow events at the CARE site. WRF simulations capture the basic cloud properties as seen by the ground-based radar and satellite (i.e., CloudSAT, AMSU-B) observations as well as the observed cloud streak organization in the lake event. This latter result reveals that WRF was able to capture the cloud macro-structure reasonably well.

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

    NASA Technical Reports Server (NTRS)

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

    2010-01-01

    Over the last few years, the NASA Short-term Prediction Research and Transition (SPoRT) Center has been generating a 1-km sea surface temperature (SST) composite derived from retrievals of the Moderate Resolution Imaging Spectroradiometer (MODIS) for use in operational diagnostics and regional model initialization. With the assumption that the day-to-day variation in the SST is nominal, individual MODIS passes aboard the Earth Observing System (EOS) Aqua and Terra satellites are used to create and update four composite SST products each day at 0400, 0700, 1600, and 1900 UTC, valid over the western Atlantic and Caribbean waters. A six month study from February to August 2007 over the marine areas surrounding southern Florida was conducted to compare the use of the MODIS SST composite versus the Real-Time Global SST analysis to initialize the Weather Research and Forecasting (WRF) model. Substantial changes in the forecast heat fluxes were seen at times in the marine boundary layer, but relatively little overall improvement was measured in the sensible weather elements. The limited improvement in the WRF model forecasts could be attributed to the diurnal changes in SST seen in the MODIS SST composites but not accounted for by the model. Furthermore, cloud contamination caused extended periods when individual passes of MODIS were unable to update the SSTs, leading to substantial SST latency and a cool bias during the early summer months. In order to alleviate the latency problems, the SPoRT Center recently enhanced its MODIS SST composite by incorporating information from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) instruments as well as the Operational Sea Surface Temperature and Sea Ice Analysis. These enhancements substantially decreased the latency due to cloud cover and improved the bias and correlation of the composites at available marine point observations. While these enhancements improved upon the modeled cold bias using the original MODIS SSTs, the discernable impacts on the WRF model were still somewhat limited. This paper explores several factors that may have contributed to this result. First, the original methodology to initialize the model used the most recent SST composite available in a hypothetical real ]time configuration, often matching the forecast initial time with an SST field that was 5-8 hours offset. To minimize the differences that result from the diurnal variations in SST, the previous day fs SST composite is incorporated at a time closest to the model initialization hour (e.g. 1600 UTC composite at 1500 UTC model initialization). Second, the diurnal change seen in the MODIS SST composites was not represented by the WRF model in previous simulations, since the SSTs were held constant throughout the model integration. To address this issue, we explore the use of a water skin-temperature diurnal cycle prediction capability within v3.1 of the WRF model to better represent fluctuations in marine surface forcing. Finally, the verification of the WRF model is limited to very few over-water sites, many of which are located near the coastlines. In order to measure the open ocean improvements from the AMSR-E, we could use an independent 2-dimensional, satellite-derived data set to validate the forecast model by applying an object-based verification method. Such a validation technique could aid in better understanding the benefits of the mesoscale SST spatial structure to regional models applications.

  18. Multi-spacecraft testing of time-dependent interplanetary MHD models for operational forecasting of geomagnetic storms

    NASA Astrophysics Data System (ADS)

    Dryer, M.; Smith, Z. K.

    1989-05-01

    An MHD 2-1/2D, time-dependent model is used, together with observations of six solar flares during February 3-7, 1986, to demonstrate global, large-scale, compound disturbances in the solar wind over a wide range of heliolongitudes. This scenario is one that is likely to occur many times during the cruise, possibly even encounter, phases of the Multi-Comet Mission. It is suggested that a model such as this one should be tested with multi-spacecraft data (such as the MCM and earth-based probes) with several goals in view: (1) utility of the model for operational real-time forecasting of geomagnetic storms, and (2) scientific interpretation of certain forms of cometary activities and their possible association with solar-generated activity.

  19. Regional Precipitation Forecast with Atmospheric InfraRed Sounder (AIRS) Profile Assimilation

    NASA Technical Reports Server (NTRS)

    Chou, S.-H.; Zavodsky, B. T.; Jedloved, G. J.

    2010-01-01

    Advanced technology in hyperspectral sensors such as the Atmospheric InfraRed Sounder (AIRS; Aumann et al. 2003) on NASA's polar orbiting Aqua satellite retrieve higher vertical resolution thermodynamic profiles than their predecessors due to increased spectral resolution. Although these capabilities do not replace the robust vertical resolution provided by radiosondes, they can serve as a complement to radiosondes in both space and time. These retrieved soundings can have a significant impact on weather forecasts if properly assimilated into prediction models. Several recent studies have evaluated the performance of specific operational weather forecast models when AIRS data are included in the assimilation process. LeMarshall et al. (2006) concluded that AIRS radiances significantly improved 500 hPa anomaly correlations in medium-range forecasts of the Global Forecast System (GFS) model. McCarty et al. (2009) demonstrated similar forecast improvement in 0-48 hour forecasts in an offline version of the operational North American Mesoscale (NAM) model when AIRS radiances were assimilated at the regional scale. Reale et al. (2008) showed improvements to Northern Hemisphere 500 hPa height anomaly correlations in NASA's Goddard Earth Observing System Model, Version 5 (GEOS-5) global system with the inclusion of partly cloudy AIRS temperature profiles. Singh et al. (2008) assimilated AIRS temperature and moisture profiles into a regional modeling system for a study of a heavy rainfall event during the summer monsoon season in Mumbai, India. This paper describes an approach to assimilate AIRS temperature and moisture profiles into a regional configuration of the Advanced Research Weather Research and Forecasting (WRF-ARW) model using its three-dimensional variational (3DVAR) assimilation system (WRF-Var; Barker et al. 2004). Section 2 describes the AIRS instrument and how the quality indicators are used to intelligently select the highest-quality data for assimilation. Section 3 presents an overall precipitation improvement with AIRS assimilation during a 37-day case study period, and Section 4 focuses on a single case study to further investigate the meteorological impact of AIRS profiles on synoptic scale models. Finally, Section 5 provides a summary of the paper.

  20. Using Customers' Reported Forecasts to Predict Future Sales

    E-print Network

    Gordon, Geoffrey J.

    Using Customers' Reported Forecasts to Predict Future Sales Nihat Altintas , Alan Montgomery orders using forecasts provided by their customers. Our goal is to improve the supplier's operations through a better un- derstanding of the customers's forecast behavior. Unfortunately, customer forecasts

  1. Transparent Grid Enablement of Weather Research and Forecasting

    E-print Network

    Sadjadi, S. Masoud

    and Subject Descriptors D.4.7 [Organization and Design]: Distributed Systems, Hierarchical Design; K.6 landfalls. The Weather Research and Forecasting (WRF) code is the latest numerical model that has been out of a single organization's local computing resources. However, the high resource requirements

  2. Development of the GEM-MACH-FireWork System: An Air Quality Model with On-line Wildfire Emissions within the Canadian Operational Air Quality Forecast System

    NASA Astrophysics Data System (ADS)

    Pavlovic, Radenko; Chen, Jack; Beaulieu, Paul-Andre; Anselmp, David; Gravel, Sylvie; Moran, Mike; Menard, Sylvain; Davignon, Didier

    2014-05-01

    A wildfire 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 U.S.A., including Alaska, fire location information is needed for both of these large countries. 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 the fire emissions provides flexibility and efficiency since on-line meteorology is used and computational overhead in emissions pre-processing is reduced. GEM-MACH-FireWork, an experimental wildfire version of GEM-MACH, was run in real-time mode for the summers of 2012 and 2013 in parallel with the normal operational version. 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 and computed objective scores 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 into the operational air quality forecast system.

  3. Forecasting inflation

    Microsoft Academic Search

    James H. Stock; Mark W. Watson

    1999-01-01

    This paper investigates forecasts of US inflation at the 12-month horizon. The starting point is the conventional unemployment rate Phillips curve, which is examined in a simulated out-of-sample forecasting framework. Inflation forecasts produced by the Phillips curve generally have been more accurate than forecasts based on other macroeconomic variables, including interest rates, money and commodity prices. These forecasts can however

  4. Toward operational forecasting and detection of weather-producing gravity waves

    NASA Technical Reports Server (NTRS)

    Koch, Steven E.

    1987-01-01

    Detecting (or nowcasting) mesoscale gravity waves whose horizontal wavelengths are 200 km or more is discussed. Synthesis of bandpass-filtered synoptic barograph data with geostationary satellite imagery sequences can accomplish this goal in an operational environment, but only if changes to the operational data collection and transmission systems are made.

  5. The Lagrangian particle dispersion model FLEXPART-WRF version 3.0

    NASA Astrophysics Data System (ADS)

    Brioude, J.; Arnold, D.; Stohl, A.; Cassiani, M.; Morton, D.; Seibert, P.; Angevine, W.; Evan, S.; Dingwell, A.; Fast, J. D.; Easter, R. C.; Pisso, I.; Burkhart, J.; Wotawa, G.

    2013-07-01

    The Lagrangian particle dispersion model FLEXPART was originally designed for calculating long-range and mesoscale dispersion of air pollutants from point sources, such as after an accident in a nuclear power plant. In the meantime FLEXPART has evolved into a comprehensive tool for atmospheric transport modeling and analysis at different scales. This multiscale need has encouraged new developments in FLEXPART. In this document, we present a FLEXPART version that works with the Weather Research and Forecasting (WRF) mesoscale meteorological model. We explain how to run and present special options and features that differ from its predecessor versions. For instance, a novel turbulence scheme for the convective boundary layer has been included that considers both the skewness of turbulence in the vertical velocity as well as the vertical gradient in the air density. To our knowledge, FLEXPART is the first model for which such a scheme has been developed. On a more technical level, FLEXPART-WRF now offers effective parallelization and details on computational performance are presented here. FLEXPART-WRF output can either be in binary or Network Common Data Form (NetCDF) format with efficient data compression. In addition, test case data and the source code are provided to the reader as Supplement. This material and future developments will be accessible at http://www.flexpart.eu.

  6. The Lagrangian particle dispersion model FLEXPART-WRF version 3.1

    NASA Astrophysics Data System (ADS)

    Brioude, J.; Arnold, D.; Stohl, A.; Cassiani, M.; Morton, D.; Seibert, P.; Angevine, W.; Evan, S.; Dingwell, A.; Fast, J. D.; Easter, R. C.; Pisso, I.; Burkhart, J.; Wotawa, G.

    2013-11-01

    The Lagrangian particle dispersion model FLEXPART was originally designed for calculating long-range and mesoscale dispersion of air pollutants from point sources, such that occurring after an accident in a nuclear power plant. In the meantime, FLEXPART has evolved into a comprehensive tool for atmospheric transport modeling and analysis at different scales. A need for further multiscale modeling and analysis has encouraged new developments in FLEXPART. In this paper, we present a FLEXPART version that works with the Weather Research and Forecasting (WRF) mesoscale meteorological model. We explain how to run this new model and present special options and features that differ from those of the preceding versions. For instance, a novel turbulence scheme for the convective boundary layer has been included that considers both the skewness of turbulence in the vertical velocity as well as the vertical gradient in the air density. To our knowledge, FLEXPART is the first model for which such a scheme has been developed. On a more technical level, FLEXPART-WRF now offers effective parallelization, and details on computational performance are presented here. FLEXPART-WRF output can either be in binary or Network Common Data Form (NetCDF) format, both of which have efficient data compression. In addition, test case data and the source code are provided to the reader as a Supplement. This material and future developments will be accessible at http://www.flexpart.eu.

  7. An Integrated High Resolution Hydrometeorological Modeling Testbed using LIS and WRF

    NASA Technical Reports Server (NTRS)

    Kumar, Sujay V.; Peters-Lidard, Christa D.; Eastman, Joseph L.; Tao, Wei-Kuo

    2007-01-01

    Scientists have made great strides in modeling physical processes that represent various weather and climate phenomena. Many modeling systems that represent the major earth system components (the atmosphere, land surface, and ocean) have been developed over the years. However, developing advanced Earth system applications that integrates these independently developed modeling systems have remained a daunting task due to limitations in computer hardware and software. Recently, efforts such as the Earth System Modeling Ramework (ESMF) and Assistance for Land Modeling Activities (ALMA) have focused on developing standards, guidelines, and computational support for coupling earth system model components. In this article, the development of a coupled land-atmosphere hydrometeorological modeling system that adopts these community interoperability standards, is described. The land component is represented by the Land Information System (LIS), developed by scientists at the NASA Goddard Space Flight Center. The Weather Research and Forecasting (WRF) model, a mesoscale numerical weather prediction system, is used as the atmospheric component. LIS includes several community land surface models that can be executed at spatial scales as fine as 1km. The data management capabilities in LIS enable the direct use of high resolution satellite and observation data for modeling. Similarly, WRF includes several parameterizations and schemes for modeling radiation, microphysics, PBL and other processes. Thus the integrated LIS-WRF system facilitates several multi-model studies of land-atmosphere coupling that can be used to advance earth system studies.

  8. Development of efficient GPU parallelization of WRF Yonsei University planetary boundary layer scheme

    NASA Astrophysics Data System (ADS)

    Huang, M.; Mielikainen, J.; Huang, B.; Chen, H.; Huang, H.-L. A.; Goldberg, M. D.

    2014-11-01

    The planetary boundary layer (PBL) is the lowest part of the atmosphere and where its character is directly affected by its contact with the underlying planetary surface. The PBL is responsible for vertical sub-grid-scale fluxes due to eddy transport in the whole atmospheric column. It determines the flux profiles within the well-mixed boundary layer and the more stable layer above. It thus provides an evolutionary model of atmospheric temperature, moisture (including clouds), and horizontal momentum in the entire atmospheric column. For such purposes, several PBL models have been proposed and employed in the weather research and forecasting (WRF) model of which the Yonsei University (YSU) scheme is one. To expedite weather research and prediction, we have put tremendous effort into developing an accelerated implementation of the entire WRF model using Graphics Processing Unit (GPU) massive parallel computing architecture whilst maintaining its accuracy as compared to its CPU-based implementation. This paper presents our efficient GPU-based design on WRF YSU PBL scheme. Using one NVIDIA Tesla K40 GPU, the GPU-based YSU PBL scheme achieves a speedup of 193× with respect to its Central Processing Unit (CPU) counterpart running on one CPU core, whereas the speedup for one CPU socket (4 cores) with respect to one CPU core is only 3.5×. We can even boost the speedup to 360× with respect to one CPU core as two K40 GPUs are applied.

  9. Annual Spring Experiments aim to accelerate the transfer of promising new concepts and tools from research to operations through intensive real-time forecasts and evaluations.

    E-print Network

    Xue, Ming

    Annual Spring Experiments aim to accelerate the transfer of promising new concepts and tools from research to operations through intensive real-time forecasts and evaluations. B ackground. Each spring of hazardous mesoscale weather. A primary goal is to accelerate the transfer of promising new tools from

  10. Case study of the operational usefulness of the Sharp Workstation in forecasting a mesocyclone-induced cold sector Tornado event in California. Technical memo

    Microsoft Academic Search

    Monteverdi

    1993-01-01

    An illustration of the operational usefulness of the SHARP Workstation in providing supplementary guidance to forecasters in a situation in which two tornadoes occurred in California's Sacramento Valley is presented. Use of the SHARP Workstation in analyzing the initial hodograph and in producing a bogus afternoon sounding and hodograph for the Sacramento Valley indicated that buoyancy and shear were in

  11. Medium-Range Air Quality Forecast During the Beijing Olympic Games

    NASA Astrophysics Data System (ADS)

    Zhang, Y.; Smith, J.; Wang, Z.; Luo, L.; Wu, Q.

    2008-12-01

    Prior to the XXIX Olympiad in Beijing, air quality was a major concern for many athletes and visitors to the Games. In response to the need for enhanced air quality forecasts, we explored and tested the capability of medium-range air quality forecasting in a multimodel ensemble system. The system consists of the Weather Research and Forecasting Model with Chemistry module (WRF-Chem), the Fifth-Generation NCAR/PennState Mesoscale Model (MM5), and the Nested Air Quality Prediction Modeling System (NAQPMS) developed at the Institute of Atmospheric Physics (IAP). Both MM5 and NAQPMS have been in operational use to produce short-term air quality forecasts. WRFChem is the major addition to the multimodel system. Forced with the forecast from the NCEP Global Ensemble Forecast System (GENS) at the lateral boundary, the multimodel system makes ensemble air quality forecasts out to 16 days with emission scenarios that reflect measures for the Olympics, including the closing down of factories around the city and beyond, a traffic control program that reduced the number of automobiles around the city by about half and elimination of all construction activities. Analyses of two forecasts are presented in this study. They were made on 5 August 2008 and 8 August 2008, both covering the entire Olympic period. Each forecast consists of three ensemble members that were produced with the same regional model but were forced by the control and two 'extremes' of the GENS forecast. The two extreme members were hand-picked to represent the best and worst case scenarios. The forecasts are evaluated with observations taken during the Olympic Games that include satellite observations, in-situ meteorological stations, LIDAR and air quality observations at the IAP tower site, 1 km away from the 'Bird Nest'. The analyses show good model skill in the first 3 days and generally satisfactory after 96 hours, with a successful forecast of potential pollution episode on 20 August 2008. The challenge has been posed to develop a method coupled with regional climatology and historical air quality data in narrowing the uncertainties in the medium-range air quality predictions in the future.

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

    NASA Astrophysics Data System (ADS)

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

    2009-12-01

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

  13. Roles of tropical overshooting convection in variation of stratospheric water vapor: A numerical study using the WRF Model

    NASA Astrophysics Data System (ADS)

    Kitagawa, Y.; Nakanishi, M.; Iwasaki, S.

    2013-12-01

    We simulate overshooting convection in the tropics and analyze its role in water vapor variation in the lower stratosphere using the Advanced Research Weather Research and Forecasting (ARW) model. The overshooting convection is a deep convection that penetrates into the stratosphere. We make a numerical experiment based on an observation, which suggested that the overshooting convection can increase water vapor in the lower stratosphere. A simulation with three nested domains is run for 18 hours; the horizontal grid spacing in the parent domain is 27 km and that in the third domain is 3 km. The vertical domain from the surface to 50 hPa is divided into 80 layers and the vertical resolution near the tropopause is approximately 300m. Initial and boundary conditions are given by Final Operational Global Analysis data (FNL) of the National Centers for Environmental Prediction (NCEP). The WRF model reproduce a number of overshooting convections particularly in the region between 165 and 166E and between 5 and 7S. Temporal variations of specific humidity averaged over that region suggest its increase in the lower stratosphere. To estimate the possibility of its constant increase, however, a long-term simulation is desired.

  14. Errors Characteristics of Two Grid Refinement Approaches in Aquaplanet Simulations: MPAS-A and WRF

    SciTech Connect

    Hagos, Samson M.; Leung, Lai-Yung R.; Rauscher, Sara; Ringler, Todd

    2013-09-01

    This study compares the error characteristics associated with two grid refinement approaches including global variable resolution and nesting for high resolution regional climate modeling. The global variable resolution model, Model for Prediction Across Scales-Atmosphere (MPAS-A), and the limited area model, Weather Research and Forecasting (WRF) model, are compared in an idealized aqua-planet context. For MPAS-A, simulations have been performed with a quasi-uniform resolution global domain at coarse (1°) and high (0.25°) resolution, and a variable resolution domain with a high resolution region at 0.25° configured inside a coarse resolution global domain at 1° resolution. Similarly, WRF has been configured to run on a coarse (1°) and high (0.25°) tropical channel domain as well as a nested domain with a high resolution region at 0.25° nested two-way inside the coarse resolution (1°) tropical channel. The variable resolution or nested simulations are compared against the high resolution simulations. Both models respond to increased resolution with enhanced precipitation. Limited and significant reduction in the ratio of convective to non-convective precipitation. The limited area grid refinement induces zonal asymmetry in precipitation (heating), accompanied by zonal anomalous Walker like circulations and standing Rossby wave signals. Within the high resolution limited area, the zonal distribution of precipitation is affected by advection in MPAS-A and by the nesting strategy in WRF. In both models, 20 day Kelvin waves propagate through the high-resolution domains fairly unaffected by the change in resolution (and the presence of a boundary in WRF) but increased resolution strengthens eastward propagating inertio-gravity waves.

  15. Development of a Regional Arctic Climate System model: Performance of Polar WRF for regional pan-Arctic atmospheric simulations

    NASA Astrophysics Data System (ADS)

    Higgins, Matthew; Cassano, John

    2010-05-01

    Efforts are currently underway to develop a regional Arctic climate system model (RACM), which will include atmosphere, ocean, sea ice, and land components. The atmospheric component of RACM is a version of Weather Research and Forecasting (WRF) model optimized for use in polar regions, known as Polar WRF. Extensive work has been completed to evaluate the performance of Polar WRF in the Arctic, with an emphasis on evaluating a variety of WRF's physical parameterizations and representation of ice covered surfaces. Using a pan-Arctic model domain, the sensitivity of atmospheric circulation, temperature, moisture, and precipitation is examined as a response to model physics options in boundary layer schemes, microphysics schemes, and longwave and shortwave radiation schemes. In addition, lower boundary land use properties (albedo, emissivity) for ice covered surfaces and fractional sea ice forcing options are examined. For each experiment, a three-member ensemble of January, April, July, and October one month simulations is used and compared with the NCEP/DOE Reanalysis II, with an emphasis on comparison with the broad features of the Arctic climate. Simulations with and without data assimilation will be presented. Significant biases in the circulation over the North Pacific are found for all model configurations that do not use data assimilation.

  16. An evaluation of WRF's ability to reproduce the surface wind over complex terrain based on typical circulation patterns

    NASA Astrophysics Data System (ADS)

    Jiménez, P. A.; Dudhia, J.; González-Rouco, J. F.; Montávez, J. P.; García-Bustamante, E.; Navarro, J.; Vilã-Guerau de Arellano, J.; Muñoz-Roldán, A.

    2013-07-01

    The performance of the Weather Research and Forecasting (WRF) model to reproduce the surface wind circulations over complex terrain is examined. The atmospheric evolution is simulated using two versions of the WRF model during an over 13 year period (1992 to 2005) over a complex terrain region located in the northeast of the Iberian Peninsula. A high horizontal resolution of 2km is used to provide an accurate representation of the terrain features. The multiyear evaluation focuses on the analysis of the accuracy displayed by the WRF simulations to reproduce the wind field of the six typical wind patterns (WPs) identified over the area in a previous observational work. Each pattern contains a high number of days which allows one to reach solid conclusions regarding the model performance. The accuracy of the simulations to reproduce the wind field under representative synoptic situations, or pressure patterns (PPs), of the Iberian Peninsula is also inspected in order to diagnose errors as a function of the large-scale situation. The evaluation is accomplished using daily averages in order to inspect the ability of WRF to reproduce the surface flow as a result of the interaction between the synoptic scale and the regional topography. Results indicate that model errors can originate from problems in the initial and lateral boundary conditions, misrepresentations at the synoptic scale, or the realism of the topographic features.

  17. Improving security of power system operation applying DG production forecasting tools

    Microsoft Academic Search

    Kurt Rohrig; Bernhard Lange

    2008-01-01

    The integration of renewable energy sources (RES) into the electric energy system has become an important challenge for the utilization and control of electric power systems, because of the fluctuating and intermittent behaviour of wind and solar power generation. The reliable operation of future energy supply structures with high share of distributed generation and renewable energy sources is an important

  18. Operational forecasting of daily temperatures in the Valencia Region. Part I: maximum temperatures in summer

    Microsoft Academic Search

    I. Gómez; M. Estrela

    2009-01-01

    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

  19. Forecasting Radiation Fog

    NSDL National Science Digital Library

    2014-09-14

    This is the second module in the Mesoscale Meteorology Primer series. This module starts with a forecast scenario that occurs during a winter radiation fog event in the Central Valley of California. After that, a conceptual section covers the physical processes of radiation fog through its life cycle. Operational sections addressing fog detection and forecasting conclude the module

  20. Progress toward forecasting of space weather effects on UHF SATCOM after Operation Anaconda

    NASA Astrophysics Data System (ADS)

    Kelly, Michael A.; Comberiate, Joseph M.; Miller, Ethan S.; Paxton, Larry J.

    2014-10-01

    Space weather impacts on communications are often presented as a raison d'etre for studying space weather (e.g., Solar and Space Physics: A Science for a Technological Society, 2013). Here we consider a communications outage during Operation Anaconda in Afghanistan that may have been related to ionospheric disturbances. Early military operations occurred during the peak of solar cycle 23 when ionospheric variability was enhanced. During Operation Anaconda, the Battle of Takur Ghar occurred at the summit of a 3191 m Afghan mountaintop on 4 March 2002 when the ionosphere was disturbed and could have affected UHF Satellite Communications (SATCOM). In this paper, we consider UHF SATCOM outages that occurred during repeated attempts to notify a Quick Reaction Force (QRF) on board an MH-47H Chinook to avoid a "hot" landing zone at the top of Takur Ghar. During a subsequent analysis of Operation Anaconda, these outages were attributed to poor performance of the UHF radios on the helicopters and to blockage by terrain. However, it is also possible that ionospheric anomalies together with multipath effects could have combined to decrease the signal-to-noise ratio of the communication links used by the QRF. A forensics study of Takur Ghar with data from the Global Ultraviolet Imager on the NASA Thermosphere Ionosphere Mesosphere Energetics and Dynamics mission showed the presence of ionospheric bubbles (regions of depleted electron density) along the line of sight between the Chinook and the UHF communications satellites in geostationary orbit that could have impacted communications. The events of 4 March 2002 motivated us to develop the Mesoscale Ionospheric Simulation Testbed model, which can be used to improve warnings of potential UHF outages during future military operations.

  1. Evaluation of Flood Forecast and Warning in Elbe river basin - Impact of Forecaster's Strategy

    Microsoft Academic Search

    Jan Danhelka; Tomas Vlasak

    2010-01-01

    Czech Hydrometeorological Institute (CHMI) is responsible for flood forecasting and warning in the Czech Republic. To meet that issue CHMI operates hydrological forecasting systems and publish flow forecast in selected profiles. Flood forecast and warning is an output of system that links observation (flow and atmosphere), data processing, weather forecast (especially NWP's QPF), hydrological modeling and modeled outputs evaluation and

  2. Comparison of Thunderstorm Simulations from WRF-NMM and WRF-ARW Models over East Indian Region

    PubMed Central

    Litta, A. J.; Mary Ididcula, Sumam; Mohanty, U. C.; Kiran Prasad, S.

    2012-01-01

    The thunderstorms are typical mesoscale systems dominated by intense convection. Mesoscale models are essential for the accurate prediction of such high-impact weather events. In the present study, an attempt has been made to compare the simulated results of three thunderstorm events using NMM and ARW model core of WRF system and validated the model results with observations. Both models performed well in capturing stability indices which are indicators of severe convective activity. Comparison of model-simulated radar reflectivity imageries with observations revealed that NMM model has simulated well the propagation of the squall line, while the squall line movement was slow in ARW. From the model-simulated spatial plots of cloud top temperature, we can see that NMM model has better captured the genesis, intensification, and propagation of thunder squall than ARW model. The statistical analysis of rainfall indicates the better performance of NMM than ARW. Comparison of model-simulated thunderstorm affected parameters with that of the observed showed that NMM has performed better than ARW in capturing the sharp rise in humidity and drop in temperature. This suggests that NMM model has the potential to provide unique and valuable information for severe thunderstorm forecasters over east Indian region. PMID:22645480

  3. Development and validation of a hurricane nature run using the joint OSSE nature run and the WRF model

    NASA Astrophysics Data System (ADS)

    Nolan, David S.; Atlas, Robert; Bhatia, Kieran T.; Bucci, Lisa R.

    2013-06-01

    A nature run is a critical component of an observing system simulation experiment (OSSE), which is a framework for evaluating the potential impact of additional observations, enhanced observing systems, or alternative data assimilation schemes toward improving numerical weather forecasts. The nature run is a period of simulated weather generated by a research-quality numerical model, from which synthetic observations are sampled and provided to the data assimilation system and forecast model. This paper describes the development and validation of a nature run that depicts the life cycle of a strong hurricane over the North Atlantic Ocean. For compatibility with related research projects, the hurricane nature run is generated by a regional model, the weather research and forecasting model (WRF), embedded within the Joint OSSE global nature run previously generated by the European Center for Medium-Range Weather Forecasting. The domain sizes, resolution, and physical parameterizations used in the WRF simulation are discussed, and the evolution of the storm from tropical wave to recurving hurricane is described. The realism of the simulated hurricane is evaluated by comparing the model output to composited data from real hurricanes obtained from both in situ and remotely sensed observations. These include the pressure-wind relationship, the kinematic and thermodynamic structure of the boundary layer, the size and outward slope of the radius of maximum winds, and contours of frequency by altitude diagrams of reflectivity and vertical velocity. The strengths and weaknesses of the nature run hurricane are discussed.

  4. Usefulness of high resolution coastal models for operational oil spill forecast: the "Full City" accident

    NASA Astrophysics Data System (ADS)

    Broström, G.; Carrasco, A.; Hole, L. R.; Dick, S.; Janssen, F.; Mattsson, J.; Berger, S.

    2011-11-01

    Oil spill modeling is considered to be an important part of a decision support system (DeSS) for oil spill combatment and is useful for remedial action in case of accidents, as well as for designing the environmental monitoring system that is frequently set up after major accidents. Many accidents take place in coastal areas, implying that low resolution basin scale ocean models are of limited use for predicting the trajectories of an oil spill. In this study, we target the oil spill in connection with the "Full City" accident on the Norwegian south coast and compare operational simulations from three different oil spill models for the area. The result of the analysis is that all models do a satisfactory job. The "standard" operational model for the area is shown to have severe flaws, but by applying ocean forcing data of higher resolution (1.5 km resolution), the model system shows results that compare well with observations. The study also shows that an ensemble of results from the three different models is useful when predicting/analyzing oil spill in coastal areas.

  5. WRF/Chem-MADRID: Incorporation of an Improved Aerosol Module into WRF/Chem and Its Initial Application to the TexAQS2000 Episode

    SciTech Connect

    Zhang, Yang; Pan, Ying; Wang, K.; Fast, Jerome D.; Grell, G. A.

    2010-09-17

    The Model of Aerosol Dynamics, Reaction, Ionization and Dissolution (MADRID) with three improved gas/particle mass transfer approaches (i.e., bulk equilibrium (EQUI), hybrid (HYBR), and kinetic (KINE)) has been incorporated into the Weather Research and Forecast/Chemistry Model (WRF/Chem) (referred to as WRF/Chem-MADRID) and evaluated with a 5-day episode from the 2000 Texas Air Quality Study (TexAQS2000). WRF/Chem-MADRID demonstrates an overall good skill in simulating surface/aloft meteorological parameters and chemical concentrations, tropospheric O3 residuals, and aerosol optical depths. The discrepancies can be attributed to inaccuracies in meteorological predictions (e.g., overprediction in mid-day boundary layer height), inaccurate total emissions or their hourly variations (e.g., HCHO, olefins, other inorganic aerosols), and uncertainties in initial and boundary conditions for some species (e.g., other inorganic aerosols and O3) at surface and aloft. Major differences in the results among the three gas/particle mass transfer approaches occur over coastal areas, where EQUI predicts higher PM2.5 than HYBR and KINE due to improperly redistributing condensed nitrate from chloride depletion process to fine PM mode. The net direct, semi-direct, and indirect effects of PM2.5 decreased domain wide shortwave radiation by 11.2-14.4 W m-2 (or 4.1-5.6%), decreased near-surface temperature by 0.06-0.14 °C (or 0.2-0.4%), led to 125 to 796 cm-3 cloud condensation nuclei at a supersaturation of 0.1%, produced cloud droplet numbers as high as 2064 cm-3, and reduced domain wide mean precipitation by 0.22-0.59 mm day-1.

  6. River water temperature and fish growth forecasting models

    NASA Astrophysics Data System (ADS)

    Danner, E.; Pike, A.; Lindley, S.; Mendelssohn, R.; Dewitt, L.; Melton, F. S.; Nemani, R. R.; Hashimoto, H.

    2010-12-01

    Water is a valuable, limited, and highly regulated resource throughout the United States. When making decisions about water allocations, state and federal water project managers must consider the short-term and long-term needs of agriculture, urban users, hydroelectric production, flood control, and the ecosystems downstream. In the Central Valley of California, river water temperature is a critical indicator of habitat quality for endangered salmonid species and affects re-licensing of major water projects and dam operations worth billions of dollars. There is consequently strong interest in modeling water temperature dynamics and the subsequent impacts on fish growth in such regulated rivers. However, the accuracy of current stream temperature models is limited by the lack of spatially detailed meteorological forecasts. To address these issues, we developed a high-resolution deterministic 1-dimensional stream temperature model (sub-hourly time step, sub-kilometer spatial resolution) in a state-space framework, and applied this model to Upper Sacramento River. We then adapted salmon bioenergetics models to incorporate the temperature data at sub-hourly time steps to provide more realistic estimates of salmon growth. The temperature model uses physically-based heat budgets to calculate the rate of heat transfer to/from the river. We use variables provided by the TOPS-WRF (Terrestrial Observation and Prediction System - Weather Research and Forecasting) model—a high-resolution assimilation of satellite-derived meteorological observations and numerical weather simulations—as inputs. The TOPS-WRF framework allows us to improve the spatial and temporal resolution of stream temperature predictions. The salmon growth models are adapted from the Wisconsin bioenergetics model. We have made the output from both models available on an interactive website so that water and fisheries managers can determine the past, current and three day forecasted water temperatures at any point along the river, and view various simulated alterations to the water discharge volume and discharge temperature. The subsequent impacts on fish growth will also be displayed so that managers can view how their operational decisions might impact salmon growth.

  7. Towards uncertainty estimation for operational forecast products - a multi-model-ensemble approach for the North Sea and the Baltic Sea

    NASA Astrophysics Data System (ADS)

    Golbeck, Inga; Li, Xin; Janssen, Frank

    2014-05-01

    Several independent operational ocean models provide forecasts of the ocean state (e.g. sea level, temperature, salinity and ice cover) in the North Sea and the Baltic Sea on a daily basis. These forecasts are the primary source of information for a variety of information and emergency response systems used e.g. to issue sea level warnings or carry out oil drift forecast. The forecasts are of course highly valuable as such, but often suffer from a lack of information on their uncertainty. With the aim of augmenting the existing operational ocean forecasts in the North Sea and the Baltic Sea by a measure of uncertainty a multi-model-ensemble (MME) system for sea surface temperature (SST), sea surface salinity (SSS) and water transports has been set up in the framework of the MyOcean-2 project. Members of MyOcean-2, the NOOS² and HIROMB/BOOS³ communities provide 48h-forecasts serving as inputs. Different variables are processed separately due to their different physical characteristics. Based on the so far collected daily MME products of SST and SSS, a statistical method, Empirical Orthogonal Function (EOF) analysis is applied to assess their spatial and temporal variability. For sea surface currents, progressive vector diagrams at specific points are consulted to estimate the performance of the circulation models especially in hydrodynamic important areas, e.g. inflow/outflow of the Baltic Sea, Norwegian trench and English Channel. For further versions of the MME system, it is planned to extend the MME to other variables like e.g. sea level, ocean currents or ice cover based on the needs of the model providers and their customers. It is also planned to include in-situ data to augment the uncertainty information and for validation purposes. Additionally, weighting methods will be implemented into the MME system to develop more complex uncertainty measures. The methodology used to create the MME will be outlined and different ensemble products will be presented. In addition, some preliminary results based on the statistical analysis of the uncertainty measures provide first estimates of the regional and temporal performance of the ocean models for each parameter. ²Northwest European Shelf Operational Oceanography System ³High-resolution Operational Model of the Baltic / Baltic Operational Oceanographic System

  8. Forecast Technical Document Forecast Types

    E-print Network

    ways of running a forecast on a forest component, depending on the source data and current land which details for the programmers how the Forecast System should handle, for each implemented Forecast the forecast are obtained from two sources: the Forestry Commission sub-compartment database (SCDB) for all

  9. Usefulness of high resolution coastal models for operational oil spill forecast: the Full City accident

    NASA Astrophysics Data System (ADS)

    Broström, G.; Carrasco, A.; Hole, L. R.; Dick, S.; Janssen, F.; Mattsson, J.; Berger, S.

    2011-06-01

    Oil spill modeling is considered to be an important decision support system (DeSS) useful for remedial action in case of accidents, as well as for designing the environmental monitoring system that is frequently set up after major accidents. Many accidents take place in coastal areas implying that low resolution basin scale ocean models is of limited use for predicting the trajectories of an oil spill. In this study, we target the oil spill in connection with the Full City accident on the Norwegian south coast and compare three different oil spill models for the area. The result of the analysis is that all models do a satisfactory job. The "standard" operational model for the area is shown to have severe flaws but including an analysis based on a higher resolution model (1.5 km resolution) for the area the model system show results that compare well with observations. The study also shows that an ensemble using three different models is useful when predicting/analyzing oil spill in coastal areas.

  10. Interpretation of Global Forecast Model 'Flipflops'

    NSDL National Science Digital Library

    2014-09-14

    All forecasters are familiar with occasional run-to-run changes in forecast direction that occur with medium-range (and sometimes even short-range) forecasts in the Global Forecast Model (aka AVN/MRF). This case describes two recent model "flipflops" in a pair of time-adjacent operational MRF runs, and shows how MRF ensemble forecasts shed light on what is actually going on in the operational MRF seasons.

  11. Use of Atmospheric Infrared Sounder clear-sky and cloud-cleared radiances in the Weather Research and Forecasting 3DVAR assimilation system for mesoscale weather predictions over the Indian region

    NASA Astrophysics Data System (ADS)

    Singh, Randhir; Kishtawal, C. M.; Pal, P. K.

    2011-11-01

    A set of assimilation experiments is conducted with the Three-Dimensional Variational (3DVAR) data assimilation system associated with the Weather Research and Forecasting (WRF) model. The purpose of the investigation is to assess the impact on forecast skill in response to assimilation of the Atmospheric Infrared Sounder (AIRS) clear-sky and cloud-cleared radiances over the Indian region. This is the first study that makes use of cloud-cleared radiances in the WRF system. Two sets of thirty-one 72 h forecasts are performed, all initialized at 00:00 UTC each day throughout the month of July 2010, to compare the model performance consequent to assimilation of clear-sky versus cloud-cleared radiances. A rigorous validation is produced against National Centers for Environmental Prediction analyzed wind, temperature, and moisture. In addition, the precipitation forecast skill is assessed against Tropical Rainfall Measuring Mission observations. The results show improvement in forecast skill consequent to the assimilation of cloud-cleared radiances (CCR). The implications of using CCR for operational weather forecasting appear to be significant. Since only a small fraction of AIRS channels are cloud-free, information obtained in cloudy regions, which is meteorologically very significant, is lost when assimilating only clear-sky radiances (CSR). On the contrary, assimilation of CCR allows a larger yield, which leads to improved model performance. The assimilation of CCR resulted in significantly improved rainfall prediction compared to that obtained from the use of CSR. The finding of this study clearly shows the advantage of CCR available from clear-sky as well as from partly cloudy regions as compared to CSR, which are available only in clear-sky regions.

  12. An integrated WRF/HYSPLIT modeling approach for the assessment of PM(2.5) source regions over the Mississippi Gulf Coast region.

    PubMed

    Yerramilli, Anjaneyulu; Dodla, Venkata Bhaskar Rao; Challa, Venkata Srinivas; Myles, Latoya; Pendergrass, William R; Vogel, Christoph A; Dasari, Hari Prasad; Tuluri, Francis; Baham, Julius M; Hughes, Robert L; Patrick, Chuck; Young, John H; Swanier, Shelton J; Hardy, Mark G

    2012-12-01

    Fine particulate matter (PM(2.5)) is majorly formed by precursor gases, such as sulfur dioxide (SO(2)) and nitrogen oxides (NO(x)), which are emitted largely from intense industrial operations and transportation activities. PM(2.5) has been shown to affect respiratory health in humans. Evaluation of source regions and assessment of emission source contributions in the Gulf Coast region of the USA will be useful for the development of PM(2.5) regulatory and mitigation strategies. In the present study, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model driven by the Weather Research & Forecasting (WRF) model is used to identify the emission source locations and transportation trends. Meteorological observations as well as PM(2.5) sulfate and nitric acid concentrations were collected at two sites during the Mississippi Coastal Atmospheric Dispersion Study, a summer 2009 field experiment along the Mississippi Gulf Coast. Meteorological fields during the campaign were simulated using WRF with three nested domains of 36, 12, and 4 km horizontal resolutions and 43 vertical levels and validated with North American Mesoscale Analysis. The HYSPLIT model was integrated with meteorological fields derived from the WRF model to identify the source locations using backward trajectory analysis. The backward trajectories for a 24-h period were plotted at 1-h intervals starting from two observation locations to identify probable sources. The back trajectories distinctly indicated the sources to be in the direction between south and west, thus to have origin from local Mississippi, neighboring Louisiana state, and Gulf of Mexico. Out of the eight power plants located within the radius of 300 km of the two monitoring sites examined as sources, only Watson, Cajun, and Morrow power plants fall in the path of the derived back trajectories. Forward dispersions patterns computed using HYSPLIT were plotted from each of these source locations using the hourly mean emission concentrations as computed from past annual emission strength data to assess extent of their contribution. An assessment of the relative contributions from the eight sources reveal that only Cajun and Morrow power plants contribute to the observations at the Wiggins Airport to a certain extent while none of the eight power plants contribute to the observations at Harrison Central High School. As these observations represent a moderate event with daily average values of 5-8 ?g m(-3) for sulfate and 1-3 ?g m(-3) for HNO(3) with differences between the two spatially varied sites, the local sources may also be significant contributors for the observed values of PM(2.5). PMID:23205159

  13. Weather Forecast Data an Important Input into Building Management Systems

    E-print Network

    Poulin, L.

    2013-01-01

    Lewis Poulin Implementation and Operational Services Section Canadian Meteorological Centre, Dorval, Qc National Prediction Operations Division ICEBO 2013, Montreal, Qc October 10 2013 Version 2013-09-27 Weather Forecast Data An Important... and weather information ? Numerical weather forecast production 101 ? From deterministic to probabilistic forecasts ? Some MSC weather forecast (NWP) datasets ? Finding the appropriate data for the appropriate forecast ? Preparing for probabilistic...

  14. Simulation of a supercell storm in clean and dirty atmosphere using weather research and forecast model with spectral bin microphysics

    Microsoft Academic Search

    Alexander Khain; Barry Lynn

    2009-01-01

    The development of supercell storms was simulated using a 2-km-resolution weather research and forecast (WRF) model with spectral (bin) microphysics (WRF-SBM) and a recent version of the Thompson bulk-parameterization scheme. The simulations were performed in clean, semipolluted, and dirty air under two values of relative humidity, conditionally referred to as low and high humidity. Both SBM and the Thompson scheme

  15. A Comparison Of Primitive Model Results Of The Short Term Wind Energy Prediction System (Sweps): WRF vs MM5

    NASA Astrophysics Data System (ADS)

    Unal, E.; Tan, E.; Mentes, S. S.; Caglar, F.; Turkmen, M.; Unal, Y. S.; Onol, B.; Ozdemir, E. T.

    2012-04-01

    Although discontinuous behavior of wind field makes energy production more difficult, wind energy is the fastest growing renewable energy sector in Turkey which is the 6th largest electricity market in Europe. Short-term prediction systems, which capture the dynamical and statistical nature of the wind field in spatial and time scales, need to be advanced in order to increase the wind power prediction accuracy by using appropriate numerical weather forecast models. Therefore, in this study, performances of the next generation mesoscale Numerical Weather Forecasting model, WRF, and The Fifth-Generation NCAR/Penn State Mesoscale Model, MM5, have been compared for the Western Part of Turkey. MM5 has been widely used by Turkish State Meteorological Service from which MM5 results were also obtained. Two wind farms of the West Turkey have been analyzed for the model comparisons by using two different model domain structures. Each model domain has been constructed by 3 nested domains downscaling from 9km to 1km resolution by the ratio of 3. Since WRF and MM5 models have no exactly common boundary layer, cumulus, and microphysics schemes, the similar physics schemes have been chosen for these two models in order to have reasonable comparisons. The preliminary results show us that, depending on the location of the wind farms, MM5 wind speed RMSE values are 1 to 2 m/s greater than that of WRF values. Since 1 to 2 m/s errors can be amplified when wind speed is converted to wind power; it is decided that the WRF model results are going to be used for the rest of the project.

  16. Comparison of Spatial and Temporal Rainfall Characteristics of WRF-Simulated Precipitation to gauge and radar observations

    NASA Astrophysics Data System (ADS)

    Price, K.; Purucker, T.; Andersen, T. K.; Knightes, C. D.; Cooter, E. J.; Otte, T.

    2012-12-01

    Weather Research and Forecasting (WRF) meteorological data are used for US EPA air and water quality modeling applications, within the CMAQ modeling system to estimate wet deposition and to evaluate future climate and land-use scenarios. While it is not expected that hindcasting applications of WRF simulations match observed rainfall on a day-to-day or individual event basis, it is important that the overall spatio-temporal structure of precipitation events represents reality. It has been shown that contaminant fate-and-transport is strongly event-dependent, and the temporal structure of precipitation (and subsequent streamflows) is a major driver of instream flows relating to habitat suitability, contaminant fluxes, dilution, water supply, etc. The spatial and temporal variability of WRF-simulations in the North Carolina Piedmont and Coastal Plain was compared to two observed precipitation datasets, interpolated National Climate Data Center (NCDC) gauge and Multisensor Precipitation Estimate (MPE) radar data. NCDC data are point data comprised of rain-gauge observations, which we interpolated to the 12 km WRF grid using co-kriging (with elevation as the covariate). MPE data, also known as Stage IV NEXRAD, are Doppler-radar derived, HADS-adjusted rainfall estimates at a 4 km resolution, which we resampled to match the 12 km WRF grid. Variographic properties were used to compare spatial structure of rainfall across daily, monthly, seasonal, and annual rainfall totals during the five-year study period. In addition, the variography of a sample of storm events stratified by intensity and type (e.g., frontal , local convective, and tropical cyclones) was compared. Three-way ANOVA was used to compare variographic parameters across the datasets. Temporal structure was compared using partial autocorrelation functions and seasonal decompositions. Evaluation of modeled precipitation spatial and temporal structure, as compared with two observed datasets, allows insights into errors that propagate through stages of water quality, future scenarios, etc. when using WRF simulations. These results will guide future improvements to WRF simulations used within the EPA air and water modeling programs. These results also demonstrate differences in the spatio-temporal structure of the two observed precipitation datasets and provides guidance for use of gauge and radar precipitation data in hindcasting modeling efforts for understanding watershed function and process.

  17. Effects of Incorporating a Climate Index into the Upper Klamath Operational Water Supply Forecast: Trans-Nino Index

    NASA Astrophysics Data System (ADS)

    Kennedy, A. M.; Garen, D. C.; Koch, R. W.

    2005-12-01

    This research investigates large-scale climate variables affecting inter-annual hydrologic variability of streams flowing into Upper Klamath Lake, Oregon. Six indexes - the Pacific North American Pattern, Southern Oscillation Index, Pacific Decadal Oscillation (PDO), Multivariate ENSO Index, Nino 3.4, and a revised Trans-Nino Index (TNI) - were evaluated independently for their ability to explain inter-annual variation of the Upper Williamson River, Sprague River, Upper Klamath Lake net inflow, and Crater Lake snow water equivalent (SWE). The TNI, which measures the sea surface temperature gradient between region Nino 1+2 and region Nino 4, was the only index to show significant correlations during the current warm phase of the PDO. During the warm PDO phase (1978-present), the averaged October through December TNI was strongly correlated (a = 0.05) with the following April through September Upper Williamson River discharge (r = 0.73), Sprague River discharge (r = 0.65), net inflow to Upper Klamath Lake (r = 0.68), and moderately correlated with observed Crater Lake April 1st SWE (r = 0.52). These results suggest that warm PDO phase equatorial sea surface temperature gradients, as opposed to mean sea surface temperature or sea-level pressure patterns, explain a large portion of hydrologic variability observed in the Upper Klamath basin. Furthermore, additional analysis indicated regional-scale correlations, which may extend the usefulness of the TNI outside of the Upper Klamath basin. Thus, the TNI may prove useful for long lead stream flow forecast operations, ecosystem scale modeling, and a variety of other environmental science applications.

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

    NASA Technical Reports Server (NTRS)

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

    2008-01-01

    NASA prefers to land the space shuttle at Kennedy Space Center (KSC). When weather conditions violate Flight Rules at KSC, NASA will usually divert the shuttle landing to Edwards Air Force Base (EAFB) in Southern California. But forecasting surface winds at EAFB is a challenge for the Spaceflight Meteorology Group (SMG) forecasters due to the complex terrain that surrounds EAFB, One particular phenomena identified by SMG is that makes it difficult to forecast the EAFB surface winds is called "wind cycling". This occurs when wind speeds and directions oscillate among towers near the EAFB runway leading to a challenging deorbit bum forecast for shuttle landings. The large-scale numerical weather prediction models cannot properly resolve the wind field due to their coarse horizontal resolutions, so a properly tuned high-resolution mesoscale model is needed. The Weather Research and Forecasting (WRF) model meets this requirement. The AMU assessed the different WRF model options to determine which configuration best predicted surface wind speed and direction at EAFB, To do so, the AMU compared the WRF model performance using two hot start initializations with the Advanced Research WRF and Non-hydrostatic Mesoscale Model dynamical cores and compared model performance while varying the physics options.

  19. Analysis of a heavy rainfall event over Beijing during 21-22 July 2012 based on high resolution model analyses and forecasts

    NASA Astrophysics Data System (ADS)

    Jiang, Xiaoman; Yuan, Huiling; Xue, Ming; Chen, Xi; Tan, Xiaoguang

    2014-02-01

    The heaviest rainfall over 61 yr hit Beijing during 21-22 July 2012. Characterized by great rainfall amount and intensity, wide range, and high impact, this record-breaking heavy rainfall caused dozens of deaths and extensive damage. Despite favorable synoptic conditions, operational forecasts underestimated the precipitation amount and were late at predicting the rainfall start time. To gain a better understanding of the performance of mesoscale models, verification of high-resolution forecasts and analyses from the WRF-based BJ-RUCv2.0 model with a horizontal grid spacing of 3 km is carried out. The results show that water vapor is very rich and a quasi-linear precipitation system produces a rather concentrated rain area. Moreover, model forecasts are first verified statistically using equitable threat score and BIAS score. The BJ-RUCv2.0 forecasts under-predict the rainfall with southwestward displacement error and time delay of the extreme precipitation. Further quantitative analysis based on the contiguous rain area method indicates that major errors for total precipitation (> 5 mm h-1) are due to inaccurate precipitation location and pattern, while forecast errors for heavy rainfall (> 20 mm h-1) mainly come from precipitation intensity. Finally, the possible causes for the poor model performance are discussed through diagnosing large-scale circulation and physical parameters (water vapor flux and instability conditions) of the BJ-RUCv2.0 model output.

  20. Application of WRF/Chem-MADRID and WRF/Polyphemus in Europe - Part 1: Model description, evaluation of meteorological predictions, and aerosol-meteorology interactions

    NASA Astrophysics Data System (ADS)

    Zhang, Y.; Sartelet, K.; Wu, S.-Y.; Seigneur, C.

    2013-07-01

    Comprehensive model evaluation and comparison of two 3-D air quality modeling systems (i.e., the Weather Research and Forecast model (WRF)/Polyphemus and WRF with chemistry and the Model of Aerosol Dynamics, Reaction, Ionization, and Dissolution (MADRID) (WRF/Chem-MADRID)) are conducted over Western Europe. Part 1 describes the background information for the model comparison and simulation design, the application of WRF for January and July 2001 over triple-nested domains in Western Europe at three horizontal grid resolutions: 0.5°, 0.125°, and 0.025°, and the effect of aerosol/meteorology interactions on meteorological predictions. Nine simulated meteorological variables (i.e., downward shortwave and longwave radiation fluxes (SWDOWN and LWDOWN), outgoing longwave radiation flux (OLR), temperature at 2 m (T2), specific humidity at 2 m (Q2), relative humidity at 2 m (RH2), wind speed at 10 m (WS10), wind direction at 10 m (WD10), and precipitation (Precip)) are evaluated using available observations in terms of spatial distribution, domainwide daily and site-specific hourly variations, and domainwide performance statistics. The vertical profiles of temperature, dew points, and wind speed/direction are also evaluated using sounding data. WRF demonstrates its capability in capturing diurnal/seasonal variations and spatial gradients and vertical profiles of major meteorological variables. While the domainwide performance of LWDOWN, OLR, T2, Q2, and RH2 at all three grid resolutions is satisfactory overall, large positive or negative biases occur in SWDOWN, WS10, and Precip even at 0.125° or 0.025° in both months and in WD10 in January. In addition, discrepancies between simulations and observations exist in T2, Q2, WS10, and Precip at mountain/high altitude sites and large urban center sites in both months, in particular, during snow events or thunderstorms. These results indicate the model's difficulty in capturing meteorological variables in complex terrain and subgrid-scale meteorological phenomena, due to inaccuracies in model initialization parameterization (e.g., lack of soil temperature and moisture nudging), limitations in the physical parameterizations (e.g., shortwave radiation, cloud microphysics, cumulus parameterizations, and ice nucleation treatments) as well as limitations in surface heat and moisture budget parameterizations (e.g., snow-related processes, subgrid-scale surface roughness elements, and urban canopy/heat island treatments and CO2 domes). While the use of finer grid resolutions of 0.125° and 0.025° shows some improvements for WS10, WD10, Precip, and some mesoscale events (e.g., strong forced convection and heavy precipitation), it does not significantly improve the overall statistical performance for all meteorological variables except for Precip. The WRF/Chem simulations with and without aerosols show that aerosols lead to reduced net shortwave radiation fluxes, 2 m temperature, 10 m wind speed, planetary boundary layer (PBL) height, and precipitation and increase aerosol optical depth, cloud condensation nuclei, cloud optical depth, and cloud droplet number concentrations over most of the domain. These results indicate a need to further improve the model representations of the above parameterizations as well as aerosol-meteorology interactions at all scales.

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

    NASA Technical Reports Server (NTRS)

    Watson, Leela R.

    2007-01-01

    This report describes the work done by the Applied Meteorology Unit (AMU) in assessing the success of different model configurations in predicting warm season convection over East-Central Florida. The Weather Research and Forecasting Environmental Modeling System (WRF EMS) software allows users to choose among two dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM). There are also data assimilation analysis packages available for the initialization of the WRF model - the Local Analysis and Prediction System (LAPS) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS). Besides model core and initialization options, the WRF model can be run with one- or two-way nesting. Having a series of initialization options and WRF cores, as well as many options within each core, creates challenges for local forecasters, such as determining which configuration options are best to address specific forecast concerns. This project assessed three different model intializations available to determine which configuration best predicts warm season convective initiation in East-Central Florida. The project also examined the use of one- and two-way nesting in predicting warm season convection.

  2. Aurora Forecast

    NSDL National Science Digital Library

    The Aurora Forecast from the Geophysical Institute at the University of Alaska, Fairbanks, provides aurora activity predictions for different locations around the world. Predictions are available as maps or as audio files. Users select a geographical area, and they are presented with a forecast map with the approximate Universal Time of greatest activity for the selected longitude about an hour before local geomagnetic midnight. Also included are links to information about the forecasts, how to interpret the forecasts, geomagnetic activity, and aurora links.

  3. Case study of the operational usefulness of the Sharp Workstation in forecasting a mesocyclone-induced cold sector Tornado event in California. Technical memo

    SciTech Connect

    Monteverdi, J.P.

    1993-03-01

    An illustration of the operational usefulness of the SHARP Workstation in providing supplementary guidance to forecasters in a situation in which two tornadoes occurred in California's Sacramento Valley is presented. Use of the SHARP Workstation in analyzing the initial hodograph and in producing a bogus afternoon sounding and hodograph for the Sacramento Valley indicated that buoyancy and shear were in the correct range for moderate to strong mesocyclone-induced tornadoes. Conventional wisdom would have suggested that weak funnel clouds and small hail were the chief threats in the weather pattern. However, forecasters, aware of the role of shear in inducing storm rotation and of the potential for the weather pattern to be associated with favorable buoyancy and shear parameters in certain regions of California, would have been alert to the possibility of damaging and potentially life-threatening tornadoes.

  4. Weather Forecasting

    NSDL National Science Digital Library

    2014-09-18

    Students consider how weather forecasting plays an important part in their daily lives. They learn about the history of weather forecasting — from old weather proverbs to modern forecasting equipment — and how improvements in weather technology have saved lives by providing advance warning of natural hazards.

  5. Some Practical Considerations Regarding Horizontal Resolution in the First Generation of Operational Convection-Allowing NWP

    Microsoft Academic Search

    John S. Kain; Steven J. Weiss; David R. Bright; Michael E. Baldwin; Jason J. Levit; Gregory W. Carbin; Craig S. Schwartz; Morris L. Weisman; Kelvin K. Droegemeier; Daniel B. Weber; Kevin W. Thomas

    2008-01-01

    During the 2005 NOAA Hazardous Weather Testbed Spring Experiment two different high-resolution configurations of the Weather Research and Forecasting-Advanced Research WRF (WRF-ARW) model were used to produce 30-h forecasts 5 days a week for a total of 7 weeks. These configurations used the same physical parameterizations and the same input dataset for the initial and boundary conditions, differing primarily in

  6. Assessing the Impact of Pre-gpm Microwave Precipitation Observations in the Goddard WRF Ensemble Data Assimilation System

    NASA Technical Reports Server (NTRS)

    Chambon, Philippe; Zhang, Sara Q.; Hou, Arthur Y.; Zupanski, Milija; Cheung, Samson

    2013-01-01

    The forthcoming Global Precipitation Measurement (GPM) Mission will provide next generation precipitation observations from a constellation of satellites. Since precipitation by nature has large variability and low predictability at cloud-resolving scales, the impact of precipitation data on the skills of mesoscale numerical weather prediction (NWP) is largely affected by the characterization of background and observation errors and the representation of nonlinear cloud/precipitation physics in an NWP data assimilation system. We present a data impact study on the assimilation of precipitation-affected microwave (MW) radiances from a pre-GPM satellite constellation using the Goddard WRF Ensemble Data Assimilation System (Goddard WRF-EDAS). A series of assimilation experiments are carried out in a Weather Research Forecast (WRF) model domain of 9 km resolution in western Europe. Sensitivities to observation error specifications, background error covariance estimated from ensemble forecasts with different ensemble sizes, and MW channel selections are examined through single-observation assimilation experiments. An empirical bias correction for precipitation-affected MW radiances is developed based on the statistics of radiance innovations in rainy areas. The data impact is assessed by full data assimilation cycling experiments for a storm event that occurred in France in September 2010. Results show that the assimilation of MW precipitation observations from a satellite constellation mimicking GPM has a positive impact on the accumulated rain forecasts verified with surface radar rain estimates. The case-study on a convective storm also reveals that the accuracy of ensemble-based background error covariance is limited by sampling errors and model errors such as precipitation displacement and unresolved convective scale instability.