Relevant bibliographies by topics / Weather Research And Forecasting Model (WRF)

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Weather Research And Forecasting Model (WRF)'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 September 2023

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Journal articles on the topic "Weather Research And Forecasting Model (WRF)"

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Liang, Xin-Zhong, Min Xu, Xing Yuan, Tiejun Ling, Hyun I. Choi, Feng Zhang, Ligang Chen, et al. "Regional Climate–Weather Research and Forecasting Model." Bulletin of the American Meteorological Society 93, no. 9 (September 1, 2012): 1363–87. http://dx.doi.org/10.1175/bams-d-11-00180.1.

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The CWRF is developed as a climate extension of the Weather Research and Forecasting model (WRF) by incorporating numerous improvements in the representation of physical processes and integration of external (top, surface, lateral) forcings that are crucial to climate scales, including interactions between land, atmosphere, and ocean; convection and microphysics; and cloud, aerosol, and radiation; and system consistency throughout all process modules. This extension inherits all WRF functionalities for numerical weather prediction while enhancing the capability for climate modeling. As such, CWRF can be applied seamlessly to weather forecast and climate prediction. The CWRF is built with a comprehensive ensemble of alternative parameterization schemes for each of the key physical processes, including surface (land, ocean), planetary boundary layer, cumulus (deep, shallow), microphysics, cloud, aerosol, and radiation, and their interactions. This facilitates the use of an optimized physics ensemble approach to improve weather or climate prediction along with a reliable uncertainty estimate. The CWRF also emphasizes the societal service capability to provide impactrelevant information by coupling with detailed models of terrestrial hydrology, coastal ocean, crop growth, air quality, and a recently expanded interactive water quality and ecosystem model. This study provides a general CWRF description and basic skill evaluation based on a continuous integration for the period 1979– 2009 as compared with that of WRF, using a 30-km grid spacing over a domain that includes the contiguous United States plus southern Canada and northern Mexico. In addition to advantages of greater application capability, CWRF improves performance in radiation and terrestrial hydrology over WRF and other regional models. Precipitation simulation, however, remains a challenge for all of the tested models.
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Powers, Jordan G., Joseph B. Klemp, William C. Skamarock, Christopher A. Davis, Jimy Dudhia, David O. Gill, Janice L. Coen, et al. "The Weather Research and Forecasting Model: Overview, System Efforts, and Future Directions." Bulletin of the American Meteorological Society 98, no. 8 (August 1, 2017): 1717–37. http://dx.doi.org/10.1175/bams-d-15-00308.1.

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Abstract Since its initial release in 2000, the Weather Research and Forecasting (WRF) Model has become one of the world’s most widely used numerical weather prediction models. Designed to serve both research and operational needs, it has grown to offer a spectrum of options and capabilities for a wide range of applications. In addition, it underlies a number of tailored systems that address Earth system modeling beyond weather. While the WRF Model has a centralized support effort, it has become a truly community model, driven by the developments and contributions of an active worldwide user base. The WRF Model sees significant use for operational forecasting, and its research implementations are pushing the boundaries of finescale atmospheric simulation. Future model directions include developments in physics, exploiting emerging compute technologies, and ever-innovative applications. From its contributions to research, forecasting, educational, and commercial efforts worldwide, the WRF Model has made a significant mark on numerical weather prediction and atmospheric science.
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El Afandi, Gamal, Mostafa Morsy, and Fathy El Hussieny. "Heavy Rainfall Simulation over Sinai Peninsula Using the Weather Research and Forecasting Model." International Journal of Atmospheric Sciences 2013 (January 28, 2013): 1–11. http://dx.doi.org/10.1155/2013/241050.

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Heavy rainfall is one of major severe weather over Sinai Peninsula and causes many flash floods over the region. The good forecasting of rainfall is very much necessary for providing early warning before the flash flood events to avoid or minimize disasters. In the present study using the Weather Research and Forecasting (WRF) Model, heavy rainfall events that occurred over Sinai Peninsula and caused flash flood have been investigated. The flash flood that occurred on January 18, 2010, over different parts of Sinai Peninsula has been predicted and analyzed using the Advanced Weather Research and Forecast (WRF-ARW) Model. The predicted rainfall in four dimensions (space and time) has been calibrated with the measurements recorded at rain gauge stations. The results show that the WRF model was able to capture the heavy rainfall events over different regions of Sinai. It is also observed that WRF model was able to predict rainfall in a significant consistency with real measurements. In this study, several synoptic characteristics of the depressions that developed during the course of study have been investigated. Also, several dynamic characteristics during the evolution of the depressions were studied: relative vorticity, thermal advection, and geopotential height.
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Lundquist, Katherine A., Fotini Katopodes Chow, and Julie K. Lundquist. "An Immersed Boundary Method for the Weather Research and Forecasting Model." Monthly Weather Review 138, no. 3 (March 1, 2010): 796–817. http://dx.doi.org/10.1175/2009mwr2990.1.

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Abstract This paper describes an immersed boundary method that facilitates the explicit resolution of complex terrain within the Weather Research and Forecasting (WRF) model. Mesoscale models, such as WRF, are increasingly used for high-resolution simulations, particularly in complex terrain, but errors associated with terrain-following coordinates degrade the accuracy of the solution. The use of an alternative-gridding technique, known as an immersed boundary method, alleviates coordinate transformation errors and eliminates restrictions on terrain slope that currently limit mesoscale models to slowly varying terrain. Simulations are presented for canonical cases with shallow terrain slopes, and comparisons between simulations with the native terrain-following coordinates and those using the immersed boundary method show excellent agreement. Validation cases demonstrate the ability of the immersed boundary method to handle both Dirichlet and Neumann boundary conditions. Additionally, realistic surface forcing can be provided at the immersed boundary by atmospheric physics parameterizations, which are modified to include the effects of the immersed terrain. Using the immersed boundary method, the WRF model is capable of simulating highly complex terrain, as demonstrated by a simulation of flow over an urban skyline.
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Sung, Kwangjae. "The Local Unscented Transform Kalman Filter for the Weather Research and Forecasting Model." Atmosphere 14, no. 7 (July 13, 2023): 1143. http://dx.doi.org/10.3390/atmos14071143.

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In this study, the local unscented transform Kalman filter (LUTKF) proposed in the previous study estimates the state of the Weather Research and Forecasting (WRF) model through local analysis. Real observations are assimilated to investigate the analysis performance of the WRF-LUTKF system. The WRF model as a regional numerical weather prediction (NWP) model is widely used to explain the atmospheric state for mesoscale meteorological fields, such as operational forecasting and atmospheric research applications. For the LUTKF based on the sigma-point Kalman filter (SPKF), the state of the nonlinear system is estimated by propagating ensemble members through the unscented transformation (UT) without making any linearization assumptions for nonlinear models. The main objective of this study is to examine the feasibility of mesoscale data assimilations for the LUTKF algorithm using the WRF model and real observations. Similar to the local ensemble transform Kalman filter (LETKF), by suppressing the impact of distant observations on model state variables through localization schemes, the LUTKF can eliminate spurious long-distance correlations in the background covariance, which are induced by the sampling error due to the finite ensemble size; therefore, the LUTKF used in the WRF-LUTKF system can efficiently execute the data assimilation with a small ensemble size. Data assimilation test results demonstrate that the LUTKF can provide reliable analysis performance in estimating the WRF model state with real observations. Experiments with various ensemble size show that the LETKF can provide better estimation results with a larger ensemble size, while the LUTKF can achieve accurate and reliable assimilation results even with a smaller ensemble size.
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Roomi, Thaer Obaid. "Evaluation of Weather Research and Forecasting Model (WRF) Simulations over Middle East." Al-Mustansiriyah Journal of Science 29, no. 2 (November 17, 2018): 26. http://dx.doi.org/10.23851/mjs.v29i2.227.

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The Weather Research and Forecasting model (WRF) is an atmospheric simulation system designed for both research and operational applications. This worldwide used model requires a sophisticated modeling experience and computing skills. In this study, WRF model was used to predict many atmospheric parameters based on the initial conditions extracted from NOMADS data sets. The study area is basically the region surrounded by the longitudes and latitudes: 15o-75o E and 10.5o-45o N which typically includes the Middle East region. The model was installed on Linux platform with a grid size of 10 km in the X and Y directions. A low pressure trough was tracked in its movement from west to east via the Middle East during the period from 1 to 7 January 2010 as a case study of the WRF model. MATLAB and NCAR Command Language (NCL) were used to display the model output. To evaluate the forecasted parameters and patterns, some comparisons were made between the predicted and actual weather charts. Wind speeds and directions in the prognostic and actual charts of 700 hPa were in agreement. However, the predicted values of geopotential heights in WRF are somewhat overestimate the actual ones. This may be attributed to the differences in the data sources and data analysis methods of the two data agencies, NOMADS and ECMWF.
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Yáñez-Morroni, Gonzalo, Jorge Gironás, Marta Caneo, Rodrigo Delgado, and René Garreaud. "Using the Weather Research and Forecasting (WRF) Model for Precipitation Forecasting in an Andean Region with Complex Topography." Atmosphere 9, no. 8 (August 2, 2018): 304. http://dx.doi.org/10.3390/atmos9080304.

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The Weather Research and Forecasting (WRF) model has been successfully used in weather prediction, but its ability to simulate precipitation over areas with complex topography is not optimal. Consequently, WRF has problems forecasting rainfall events over Chilean mountainous terrain and foothills, where some of the main cities are located, and where intense rainfall occurs due to cutoff lows. This work analyzes an ensemble of microphysics schemes to enhance initial forecasts made by the Chilean Weather Agency in the front range of Santiago. We first tested different vertical levels resolution, land use and land surface models, as well as meteorological forcing (GFS/FNL). The final ensemble configuration considered three microphysics schemes and lead times over three rainfall events between 2015 and 2017. Cutoff low complex meteorological characteristics impede the temporal simulation of rainfall properties. With three days of lead time, WRF properly forecasts the rainiest N-hours and temperatures during the event, although more accuracy is obtained when the rainfall is caused by a meteorological frontal system. Finally, the WSM6 microphysics option had the best performance, although further analysis using other storms and locations in the area are needed to strengthen this result.
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Huang, Jian, Wu Wang, Yuzhu Wang, Jinrong Jiang, Chen Yan, Lian Zhao, and Yidi Bai. "Performance Evaluation and Optimization of the Weather Research and Forecasting (WRF) Model Based on Kunpeng 920." Applied Sciences 13, no. 17 (August 30, 2023): 9800. http://dx.doi.org/10.3390/app13179800.

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The Weather Research and Forecasting (WRF) model is a mesoscale numerical weather prediction system, which is widely used in major high-performance server platforms. This study focuses on the performance evaluation and optimization of WRF on Huawei’s self-developed kunpeng 920 processor platform, aiming to improve the operational efficiency of WRF. The results of the study show that the scalability of WRF on kunpeng 920 processor is well performed; the performance of WRF on kunpeng 920 processor is improved by 32.6% after invoking the Fast Math Library and Domain Decomposition Core Tile Division optimization. In terms of IO, the main optimizations are parallel IO and asynchronous IO. Eventually, the single output time of WRF is reduced from 37.28 s in serial IO mode to 0.14 s in asynchronous IO mode, and the overall running time is reduced from 1078.80 s to 807.94 s.
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Cassano, John J., Matthew E. Higgins, and Mark W. Seefeldt. "Performance of the Weather Research and Forecasting Model for Month-Long Pan-Arctic Simulations." Monthly Weather Review 139, no. 11 (November 1, 2011): 3469–88. http://dx.doi.org/10.1175/mwr-d-10-05065.1.

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Abstract The performance of the Weather Research and Forecasting (WRF) model was evaluated for month-long simulations over a large pan-Arctic model domain. The evaluation of seven different WRF (version 3.1) configurations for four months (January, April, July, and October 2007) indicated that WRF produces reasonable simulations of the Arctic atmosphere. Ranking of the model error statistics, calculated relative to the NCEP/Department of Energy Global Reanalysis 2 (NCEP-2), for sea level pressure, 500- and 300-hPa geopotential height, 2-m air temperature, and precipitation identified the model configurations that consistently produced the best pan-Arctic simulations. For all WRF configurations considered, large errors in circulation are evident in the North Pacific. The errors in the North Pacific are manifested as an overly weak and westward-shifted Aleutian low and overly strong subtropical Pacific high simulated by WRF. These circulation errors are nearly barotropic, with a slight increase in magnitude with height, and they vary slightly in magnitude and position as the WRF physics options and domain size are changed. It is concluded that the circulation errors are likely due to errors in the treatment of the model-top boundary. The use of a higher model top (10 hPa rather than 50 hPa) or spectral nudging of wavenumbers 1–3 in the top half of the model domain results in significantly reduced circulation biases. Simulations with WRF version 3.2 also show reduced errors.
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Klemp, J. B. "Advances in the WRF model for convection-resolving forecasting." Advances in Geosciences 7 (January 23, 2006): 25–29. http://dx.doi.org/10.5194/adgeo-7-25-2006.

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Abstract. The Weather Research and Forecasting (WRF) Model has been designed to be an efficient and flexible simulation system for use across a broad range of weather-forecast and idealized-research applications. Of particular interest is the use of WRF in nonhydrostatic applications in which moist-convective processes are treated explicitly, thereby avoiding the ambiguities of cumulus parameterization. To evaluate the capabilities of WRF for convection-resolving applications, real-time forecasting experiments have been conducted with 4 km horizontal mesh spacing for both convective systems in the central U.S. and for hurricanes approaching landfall in the southeastern U.S. These forecasts demonstrate a good potential for improving the forecast accuracy of the timing and location of these systems, as well as providing more detailed information on their structure and evolution that is not available in current coarser resolution operational forecast models.
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Dissertations / Theses on the topic "Weather Research And Forecasting Model (WRF)"

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Lou, Mei Meng. "Weather simulation in Macao using the Weather Research and Forecasting (WRF) Model." Thesis, University of Macau, 2009. http://umaclib3.umac.mo/record=b1943035.

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Shepherd, Tristan James. "A Numerical Modelling Study of Tropical Cyclone Sidr (2007): Sensitivity Experiments Using the Weather Research and Forecasting (WRF) Model." Thesis, University of Canterbury. Geography, 2008. http://hdl.handle.net/10092/2611.

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The tropical cyclone is a majestic, yet violent atmospheric weather system occurring over tropical waters. Their majesty evolves from the significant range of spatial scales they operate over: from the mesoscale, to the larger synoptic-scale. Their associated violent winds and seas, however, are often the cause of damage and destruction for settlements in their path. Between 10/11/07 and 16/11/07, tropical cyclone Sidr formed and intensified into a category 5 hurricane over the southeast tropical waters of the northern Indian Ocean. Sidr tracked west, then north, during the course of its life, and eventually made landfall on 15/11/07, as a category 4 cyclone near the settlement of Barguna, Bangladesh. The storm affected approximately 2.7 million people in Bangladesh, and of that number 4234 were killed. In this study, the dynamics of tropical cyclone Sidr are simulated using version 2.2.1 of Advanced Weather Research and Forecasting — a non-hydrostatic, two-way interactive, triply-nested-grid mesoscale model. Three experiments were developed examining model sensitivity to ocean-atmosphere interaction; initialisation time; and choice of convective parameterisation scheme. All experiments were verified against analysed synoptic data. The ocean-atmosphere experiment involved one simulation of a cold sea surface temperature, fixed at 10 °C; and simulated using a 15 km grid resolution. The initialisation experiment involved three simulations of different model start time: 108-, 72-, and 48-hours before landfall respectively. These were simulated using a 15 km grid resolution. The convective experiment consisted of four simulations, with three of these using a different implicit convective scheme. The three schemes used were, the Kain-Fritsch, Betts-Miller-Janjic, and Grell-Devenyi ensemble. The fourth case simulated convection explicitly. A nested domain of 5km grid spacing was used in the convective experiment, for high resolution modelling. In all experiments, the Eta-Ferrier microphysics scheme, and the Mellor-Yamada-Janjic planetary boundary layer scheme were used. As verified against available observations, the model showed considerable sensitivity in each of the experiments. The model was found to be well suited for combining ocean-atmosphere interactions: a cool sea surface caused cyclone Sidr to dissipate within 24 hours. The initialisation simulations indicated moderate model sensitivity to initialisation time: variations were found for both cyclone track and intensity. Of the three simulations, an initialisation time 108 hours prior to landfall, was found to most accurately represent cyclone Sidr’s track and intensity. Finally, the convective simulations showed that considerable differences were found in cyclone track, intensity, and structure, when using different convective schemes. The Kain-Fritsch scheme produced the most accurate cyclone track and structure, but the rainfall rate was spurious on the sub-grid-scale. The Betts-Miller-Janjic scheme resolved realistic rainfall on both domains, but cyclone intensity was poor. Of particular significance, was that explicit convection produced a similar result to the Grell-Devenyi ensemble for both model domain resolutions. Overall, the results suggest that the modelled cyclone is highly sensitive to changes in initial conditions. In particular, in the context of other studies, it appears that the combination of convective scheme, microphysics scheme, and boundary layer scheme, are most significant for accurate track and intensity prediction.
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Haines, Wesley Adam. "Acceleration of the Weather Research & Forecasting (WRF) Model using OpenACC and Case Study of the August 2012 Great Arctic Cyclone." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1373472482.

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Gaines, Mitchell. "Application of the Weather Research and Forecasting (WRF) Model to Simulate a Squall Line: Implications of Choosing Parameterization Scheme Combinations and Model Initialization Data Sets." TopSCHOLAR®, 2012. http://digitalcommons.wku.edu/theses/1181.

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On January 29-30, 2008 a squall line of thunderstorms moved through the Ohio Valley resulting in four deaths and one injury. Such events highlight the importance of accurate forecasting for public safety. Mesoscale Modeling plays an important role in any forecast of a potential squall line. The focus of this study was to examine the performance of several parameterization scheme combinations in the Weather Research and Forecasting Model version three (WRF) as they related to this event. These examinations included cloud microphysics (WRF Single-Moment 3-class, 6-class, and Goddard), cumulus parameterization (Kain-Fritsch and Bets-Miller-Janjic) and planetary boundary layer schemes (Yonsei-University and Mellor-Yamada-Janjic). A total of 12 WRF simulations were conducted for all potential scheme combinations. Data from the WRF simulations for several locations in south central Kentucky were analyzed and compared using Kentucky Mesonet observations for four locations: Bowling Green, Russellville, Murray and Liberty, KY. A fine model resolution of 1 km was used over these locations. Coarser resolutions of 3 km and 9 km were used on the outer two domains, which encompassed the Ohio and Tennessee Valleys. The model simulation performance was assessed using established statistical measures for the above four locations and by visually comparing the North American Regional Reanalysis dataset (NARR) along with modeled simulations. The most satisfactory scheme combination was the WRF Single-Moment 3-class Microphysics scheme, Kain-Fritsch cumulus parameterization scheme and Yonsei University scheme for the planetary boundary layer. The planetary boundary layer schemes were noted to have the greatest influence in determining the most satisfactory model simulations. There was limited influence from different selections of microphysics and cumulus parameterization schemes. The preferred physics parameters from these simulations were then used in six additional simulations to analyze the affect different initialization data sets have with regards to model output. Data sets used in these simulations were the Final Operational Analysis global data, North American Regional Reanalysis (3 and 6 hour) and the North American Mesoscale Model at 1, 3 and 6 hour timesteps, for a total of six simulations. More timesteps or an increase in model resolution did not materially improve the model performance.
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Johansson, Sara. "Coupling of the Weather Research and Forecasting model (WRF) with the Community Multiscale Air Qualitymodel (CMAQ), and analysing the forecasted ozone and nitrogendioxide concentrations." Thesis, Uppsala universitet, Luft-, vatten och landskapslära, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-303924.

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Air quality forecasts are of great value since several pollutants in our environment effect both human health, global climate, vegetation, crop yields, animals, materials and acidification of forests and lakes. Air-quality forecasts help to make people aware of the presence and the quantity of pollutants, and give them a chance to protect themselves, their business and the Earth. Many different air-quality models are in daily use all over the world, providing citizens with forecasts of air quality and warnings of unhealthy air quality if recommended highest concentrations are exceeded. This study adapts the WRF meteorological model (Weather research and Forecasting model) to be a driver of the CMAQ air-quality model (models-3 Community Multiscale Air Quality model). Forecasts of ozone and nitrogen dioxide concentrations from this coupled WRF/CMAQ modelling system are tested against observed data during a four-day period in May, 2006. The Lower Fraser Valley study area is a fertile valley surrounded by mountain chains in southwest British Columbia, Canada. The valley stretches from the Pacific coast eastwards towards the Rocky Mountains. This valley hosts more than 2 million people and it is west Canada’s fastest growing region. The Lower Fraser Valley holds a big city, Vancouver, several suburbs, numerous industries and a widespread agricultural production. During the analysed four-day period in May, a synoptic high-pressure built over the region, favoring high concentrations of pollutants as ozone and nitrogen dioxide. The created WRF/CMAQ model forecasted an acceptable magnitude of nitrogen dioxide but the daily variations are not recreated properly by the model. The WRF/CMAQ model forecasts the daily variation of ozone in a satisfying way, but the forecasted concentrations are overestimated by between 20 and 30 ppb throughout the study. Factors that could contribute to the elevated ozone concentrations were investigated, and it was found that the weather forecasting model WRF was not generating fully reliable meteorological values, which in turn hurt the air-quality forecasts. As the WRF model usually is a good weather forecasting model, the short spin-up time for the model could be a probable cause for its poor performance.
Prognoser över luftkvaliteten är mycket värdefulla, då flera luftföroreningar i vår närmiljö påverkar människans hälsa, det globala klimatet, vegetation, djur, material och bidrar till försurning av skog och vattendrag. Luftkvalitetsprognoser gör människan mer medveten om närvaron av luftföroreningar och i vilken mängd de finns. De ger människan en chans att vidta skyddsåtgärder för att skydda sig själv, sitt eventuella levebröd, och Jorden. Många olika luftkvalitetsmodeller används idag dagligdags över hela världen och förser invånare med prognoser för luftkvaliteten och varningar om koncentrationerna av föroreningar överstiger rekommenderade värden. I denna studie används väderprognosmodellen WRF (Weather Research and Forecasting model) för att driva luftkvalitetsmodellen CMAQ (models-3 Community Multiscale Air Quality model). Prognoser av ozon- och kvävedioxidhalterna i luften från den kopplade WRF/CMAQ modellen analyseras mot observerade data under en fyra dagars period i maj, 2006. Studieområdet Lower Fraser Valley är en bördig dalgång som är omgiven av bergskedjor i sydvästra British Columbia, Kanada. Dalen sträcker sig från Stilla havskusten och österut mot Klippiga bergen. I denna dalgång bor mer än 2 miljoner människor och det är västra Kanadas snabbast växande region. Lower Fraser Valley rymmer en storstad, Vancouver, flera förorter, många industrier och även stora jordbruksområden. Den fyra dagars period i maj som analyseras karaktäriseras av ett högtrycksbetonat synoptiskt väderläge med lokala variationer, vilka tillsammans är gynnsamma för att uppmäta höga koncentrationer av luftföroreningar som ozon och kvävedioxid. Den skapade WRF/CMAQ modellen prognostiserar godtagbar magnitud hos kvävedioxid men den dagliga variationen återskapas inte av modellen. Modellen prognostiserar den dagliga variationen av ozonkoncentration på ett tillfredsställande sätt, men storleksmässigt ligger koncentrationerna en faktor 20-30 ppb för högt rakt av under hela studien. Kringliggande faktorer som kan påverka koncentrationen ozon studeras närmare och det framkommer att den meteorologiska prognosmodellen WRF inte genererar fullt tillförlitliga värden för en rättvisande luftkvalitetsprognos. Då WRF modellen vanligtvis är en bra prognosmodell kan den korta initialiseringstiden för modellen vara en trolig orsak till dess otillräckliga prestation.
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Schmid, Christina [Verfasser], Thomas [Akademischer Betreuer] Mölg, and Thomas [Gutachter] Mölg. "Implementierung eines Schneedriftmoduls in das Weather Research and Forecasting (WRF) Modell und eine erste Evaluation / Christina Schmid ; Gutachter: Thomas Mölg ; Betreuer: Thomas Mölg." Erlangen : Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 2021. http://nbn-resolving.de/urn:nbn:de:bvb:29-opus4-172361.

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García, León Manuel. "Coastal risk forecast system : fostering proactive management at the Catalan coast." Doctoral thesis, Universitat Politècnica de Catalunya, 2018. http://hdl.handle.net/10803/669662.

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The action of sea storms is one of the most complex littoral processes with deep management implications. Along the Catalan shoreline which is about 700 km long, 190 km are subject to erosion and/or flooding. Around one million people live in areas potentially affected. Sea Level Rise could exacerbate this problem in the near future. Reactive interventions have been the norm in coastal engineering and management. This dissertation proposes a pre-storm strategy that foster cost-effective eco-compatible measures, termed Quick Defence Measures (QDM). Pre-storm intervention requires to forecast the future post-storm state. Hence, the main objective of this thesis is to assess present coastal risk through a Coastal Early Warning System (CEWS), termed LIM-COPAS, that forecasts the more relevant episodic coastal hazards at the area. LIM-COPAS consists of four modules: (i) meteorological model; (ii) wave generation/propagation code; (iii) coupled morpho-hydrodynamic model and (iv) risk module via non-stationary multivariate probabilistic models. The performance of this suite of models has been tested with (i) a set of hindcast events and (ii) synthetic storm conditions. The hindcasted events have been: December 2008 (D-08); October-2015 (O-15); November 2015 (N-15); January 2016 (J-16); February 2016 (F-16); December 2016 (D-16) and January 2017 (J-17). In D-08, errors in nearshore spectral wave parameters have been about twice than those in the offshore area. The error was around 20% in hydrodynamics and 50% in morphodynamics. The post-storm response has been acceptably reproduced, with a Brier Skill Score near 0.4. LIM-COPAS has shown good accuracy with high return period events (i.e. Tr,waves > 10 yrs, D-16 and J-17), but lower agreement was found for milder storms (i.e. O-15 and F-16). The meteorological module provided wind fields that were systematically overestimated. The integrated Mean Bias (MB) was -1.52 ± 0.78 m/s. Tarragona (Coefficient of Efficiency, COE = 0.27 ± 0.13) and Begur (COE = 0.29 ± 0.17) had metrics above the average value (COE = 0.24 ± 0.14); but lower agreement was found at Mahón (COE = 0.13 ± 0.16) and Dragonera. Wave metrics were more accurate than for the wind fields. The integrated Hs COE was 0.52±0.12 and Tm02 COE was 0.36±0.14. At the central coast, Hs has presented good metrics: low MB (-0.06 ± 0.08 m) and high COE (0.58 ± 0.11). The northern coast metrics were the most stable. The newly developed risk module has been implemented at 79 beaches. Erosion has been estimated as a bounded cost, whereas flooding as a high upside cost. Dissipative beaches tend to exhibit higher costs than reflective beaches under high sea levels. Tr,waves < 10 yrs events joint with storm-surges can lead to significant damage costs. The estimated losses for the N-15 event (2510·10^3 euros) do not differ excessively from J-17 (3200·10^3 euros). Two types of QDM have been numerically tested: (i) sand dunes and (ii) geotextile detached breakwaters. The benefits from maintaining the sand volumes outperform the flooding cost reduction. In general terms, the detached breakwater can be a suitable option for beaches in an intermediate morphodynamic state against low to moderate sea levels and high wave return periods. At dissipative beaches, dunes are the best option, but they require a minimum beach width (around 30 m) that ensures their lifetime. QDM functionality can be enhanced with compatible long-term actions (nourishments, sand bypasses, submerged vegetation, etc.). A healthy beach state is paramount for the QDM effectiveness. A higher sustainable management under present and future climate can be reached with the joint combination of (i) CEWS as a short-term forecasting tool; (ii) QDM that mitigate storm impacts and (iii) long-term interventions that improves the beach health.
La acción de los temporales de mar es uno de los procesos litorales más complejos, con profundas implicaciones en la gestión del litoral. A lo largo de la línea de costa catalana, 190 km están sometidos a erosión y/o inundación. Cerca de un millón de personas viven en áreas potencialmente afectadas. La tradición en ingeniería y gestión costera han sido intervenciones reactivas. Esta tesis propone una estrategia pre-tormenta que fomente una serie de medidas eco-compatibles, denominadas Medidas de Acción Rápida (MAR). Las intervenciones pre-tormenta requieren predecir el estado post-temporal de la costa. Por tanto, el principal objetivo de esta tesis es evaluar el riesgo costero episódico mediante un Sistema de Alarma Temprana Costero (CEWS), denominado LIM-COPAS, que predice las peligrosidades costeras más relevantes en dicha área. LIM-COPAS consiste de cuatro módulos: (i) modelo meteorológico; (ii) código de generación/propagación del oleaje; (iii) modelo acoplado morfo-hidrodinámico y (iv) un módulo de riesgo vía modelos probabilísticos multivariantes y no-estacionarios. El comportamiento de estos módulos ha sido analizado mediante (i) una serie de eventos pasados y (ii) temporales sintéticos. Los eventos pasados han sido: Diciembre 2008 (D-08); Octubre 2015 (O-15); Noviembre 2015 (N-15); Enero 2016 (J-16); Febrero 2016 (F-16); Diciembre 2016 (D-16) y Enero 2017 (J-17). En D-08, los errores en los parámetros espectrales de oleaje costero han sido casi el doble que en mar abierto. El error ha sido del 20% en la hidrodinámica y del 50% en la morfodinámica. La respuesta post-temporal ha sido reproducida aceptablemente, con Brier Skill Score cercanos a 0.4. LIM-COPAS ha demostrado buena precisión con tormentas de alto período de retorno (i.e. Tr,waves _ 10 yrs, D-16 y J-17), pero menor concordancia fue encontrada para las tormentas moderadas (i.e. O-15 y F-16). El módulo meteorológico estimó campos de viento que fueron sistemáticamente sobreestimados. El Sesgo Medio (MB) integrado fue de −1,52 ± 0,78 m/s. Tarragona (Coeficiente de Eficiencia, COE = 0,27±0,13) y Begur (COE = 0,29±0,17) tuvieron métricas por encima de la media (COE = 0,24±0,14); no obstante, peor ajuste se encontró en Mahón (COE = 0,13 ± 0,16) y Dragonera. Las métricas de oleaje fueron más precisas que las del viento. Hs COE integrada fue 0,52±0,12 y Tm02 COE fue 0,36±0,14. En la costa central, Hs presentó buenas métricas: bajo MB (−0,06 ± 0,08 m) y alto COE (0,58 ± 0,11). Las métricas en la costa norte fueron las más estables. El módulo de riesgo ha sido implementado en 79 playas. La erosión se ha estimado como un coste acotado, mientras que la inundación como un coste con alta cota superior. Las playas disipativas tienden a exhibir mayores costes que las playas reflejantes bajo altos niveles del mar. Episodios con Tr,waves _ 10yrs, concomitantes a mareas meteorológicas pueden conllevar costes significantes. Las pérdidas estimadas para N-15 (2510 · 103euros) no difieren en exceso de J-17 (3200 · 103 euros). Dos tipos de MAR han sido testeadas numéricamente: (i) dunas y (ii) diques exentos constituídos por geotextiles llenos de arena. Los beneficios de mantener estables los volúmenes de arena superan la reducción de los costes por inundación. En términos generales, los diques exentos pueden ser una opción adecuada para playas de estado morfodinámico intermedio frente a oleaje de alto período de retorno y niveles del mar bajos a moderados. En playas disipativas, las dunas son la mejor opción, pero requieren un ancho mínimo de playa (cerca de 30 m) que garantice su vida útil. La funcionalidad de las MAR puede mejorarse mediante acciones compatibles a largo-plazo (alimentaciones, bypass de arena, vegetación sumergida, etc.). Un estado de playa saludable es esencial para la efectividad de las MAR. Una gestión más sostenible bajo clima presente y futuro puede ser alcanzada mediante (i) CEWS como herramienta de predicción a corto plazo; (ii) MAR que mitiguen los impactos de los temporales y (iii) intervenciones a largo-plazo que mejoren la salud de la costa.
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Ryerson, William R. "Evaluation of the AFWA WRF 4-km moving nest model predictions for Western North Pacific tropical cyclones." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2006. http://library.nps.navy.mil/uhtbin/hyperion/06Mar%5FRyerson.pdf.

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Bruno, Jack H. "Evaluating the Weather Research and Forecasting Model Fidelity for Forecasting Lake Breezes." Ohio University Honors Tutorial College / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ouhonors1556189524538244.

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Nissan, Hannah. "Modelling rainfall erosivity using the Weather Research and Forecasting model." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/24681.

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Soil erosion is a serious threat to agricultural productivity and the sustainable provision of food to a growing world population. Current erosion models employ simplistic treatments of rainfall. This thesis presents a new approach to erosion modelling, using the Weather Research and Forecasting model to simulate rainfall erosivity, an indicator of the erosive capacity of rain. Rainfall erosivity is modelled in the Caucasus region, an area vulnerable to erosion and climate change pressures. Low intensity rainfall (below 2 mmhr^{-1}) is found to contribute significantly to erosivity (23%), contrary to common assumptions. An exponential dependence of the fraction of erosivity from light rain on the proportion of light rain is found. Erosion models focus on storms, but results suggest that storm-based calculations may exclude up to 30% of erosivity. In the Universal Soil Loss Equation, this does not lead to errors in long term soil loss but could cause an underestimation of event erosion. Rainfall kinetic energy flux is an important variable in erosion prediction and is routinely parameterised from intensity. Here this is dynamically simulated from basic physics in a cloud resolving model, using four microphysics schemes. Results are within the range of observations and capture the observed variability in kinetic energy for a given intensity, where current methods fail. Large raindrops are shown to contribute disproportionately to total kinetic energy, and also to surface precipitation, compared with their number. No connection has hitherto been drawn between aerosols and soil erosion. The effect of aerosols on rainfall erosivity is investigated in a cloud resolving model. Aerosols can either enhance or suppress precipitation. In both these cases the response of erosivity to a rise in aerosols is in the same direction as, but amplified beyond, the change in total rain. It is also shown that aerosols can influence erosivity by changing raindrop sizes. These results suggest that anthropogenic aerosol emissions affect erosivity and thus may have important consequences for agricultural productivity.
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Books on the topic "Weather Research And Forecasting Model (WRF)"

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Korea (South). Kisangchʻŏng. Yeboguk. Dijitʻŏl Yebo Kaebalkwa., ed. MOS kion yebo model kaebal mit hyŏnŏp unyŏng chʻegye. [Seoul]: Kisangchʻŏng Yeboguk Dijitʻŏl Yebo Kaebalkwa, 2006.

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Saitō, Kazuo. Documentation of the Meteorological Research Institute Numerical Prediction Division unified nonhydrostatic model. Japan: Meteorological Research Institute, 2001.

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Lanicci, John M. A conceptual model of the severe-storm environment for inclusion into air weather service severe-storm analysis and forecast procedures. Hanscomb AFB, MA: Atmospheric Sciences Division, Air Force Geophysics Laboratory, 1985.

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Book chapters on the topic "Weather Research And Forecasting Model (WRF)"

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Jawaheer, B. O., A. Z. Dhunny, T. S. M. Cunden, N. Chandrasekaran, and M. R. Lollchund. "Modelling the Effects of Wind Farming on the Local Weather Using Weather Research and Forecasting (WRF) Model." In Advances in Intelligent Systems and Computing, 219–31. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3338-5_21.

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Li, Yanping, and Zhenhua Li. "High-Resolution Weather Research Forecasting (WRF) Modeling and Projection Over Western Canada, Including Mackenzie Watershed." In Arctic Hydrology, Permafrost and Ecosystems, 815–47. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50930-9_28.

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Schwitalla, Thomas, Kirsten Warrach-Sagi, and Volker Wulfmeyer. "High-Resolution Latitude Belt Simulation with the Weather Research and Forecasting Model." In Sustained Simulation Performance 2015, 185–94. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20340-9_15.

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Khalchenkov, Aleksander, and Ivan Kovalets. "Evaluation of Spectral/Grid Nudging Methods for Weather Analysis and Forecasting in Kyiv Region with the Use of WRF Mesoscale Meteorological Model." In Advances in Intelligent Systems and Computing, 13–23. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58124-4_2.

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Jandaghian, Zahra, and Umberto Berardi. "The Coupling of the Weather Research and Forecasting Model with the Urban Canopy Models for Climate Simulations." In Urban Microclimate Modelling for Comfort and Energy Studies, 223–40. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65421-4_11.

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Bacciu, Valentina, Maria Mirto, Sandro Luigi Fiore, Costantino Sirca, Josè Maria Costa Saura, Sonia Scardigno, Valentina Scardigno, et al. "An operational platform for fire danger prevention and monitoring: insights from the OFIDIA2 project." In Advances in Forest Fire Research 2022, 87–92. Imprensa da Universidade de Coimbra, 2022. http://dx.doi.org/10.14195/978-989-26-2298-9_13.

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The project OFIDIA2 (Operational FIre Danger preventIon plAtform 2), funded by the Interreg Greece-Italy 2014-2020 Programme, proposed a pragmatic approach to improve the operational capacity of the stakeholders to detect and fight forest wildfires. A data analytics system was designed and implemented within the project to manage, transform, and extract knowledge from heterogenous data sources, through forecasting models such as weather, fire danger, and fire behaviour models. The high-resolution weather forecasting network previously developed in OFIDIA1 was enhanced by using a mesoscale configuration of the WRF-ARW model over the Central Mediterranean Sea. A nested domain over the Southern Italy at ~2km horizontal resolution allows getting high-resolution weather forecasts (2x2km) and processing data into fire danger models. Fires, fuel, topography and weather data were collected from several sources and used to run and calibrate fire models (FlamMap and Wildfire Analyst) in Apulia region (Italy). Based on the analyses of recurrent weather conditions leading to large fires, fire metrics’ maps for prevention and fire-fighting activities were produced. Finally, a Decision Support System (DSS) was also developed to provide support for 1) the selection of fire behaviour scenarios by means of mathematical models; and 2) the prevention of emergencies thanks to weather forecast information with fire danger indices at high resolutions.
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García-Valdecasas-Ojeda, M., S. de Franciscis, S. R. Gámiz-Fortis, Y. Castro-Díez, and M. J. Esteban-Parra. "Coupling study of the Variable Infiltration Capacity (VIC) model with Weather Research and Forecasting (WRF) model to simulate the streamflow in the Guadalquivir Basin." In Clima, sociedad, riesgos y ordenación del territorio, 109–19. Servicio de Publicaciones de la UA, 2016. http://dx.doi.org/10.14198/xcongresoaecalicante2016-10.

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Jeffers, Jim, James Reinders, and Avinash Sodani. "Weather research and forecasting (WRF)." In Intel Xeon Phi Processor High Performance Programming, 499–510. Elsevier, 2016. http://dx.doi.org/10.1016/b978-0-12-809194-4.00022-3.

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Thanh, Cong, Dao Nguyen Quynh Hoa, and Tran Tan Tien. "Application of Kalman Filter and Breeding Ensemble Technique to Forecast the Tropical Cyclone Activity." In Weather Forecasting [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.97783.

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Tropical cyclone (TC) is one of the major meteorology disasters, as they lead to deaths, destroy the infrastructure and the environment. Therefore, how to improve the predictability of TC’s activities, such as formation, track, and intensity, is very important and is considered an important task for current operational predicting TC centers in many countries. However, predicting TC’s activities has remained a big challenge for meteorologists due to our incomplete understanding of the multiscale interaction of TCs with the ambient environment and the limitation of numerical weather forecast tools. Hence, this chapter will exhibit some techniques to improve the ability to predict the formation and track of TCs using an ensemble prediction system. Particularly, the Local Ensemble Transform Kalman Filter (LETKF) scheme and its implementation in the WRF Model, as well as the Vortex tracking method that has been applied for the forecast of TCs formation, will be presented in subSection 1. Application of Breeding Ensemble to Tropical Cyclone Track Forecasts using the Regional Atmospheric Modeling System (RAMS) model will be introduced in subSection 2.
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Adnan, Muhammad, Rana Muhammad Adnan, Shiyin Liu, Muhammad Saifullah, Yasir Latif, and Mudassar Iqbal. "Prediction of Relative Humidity in a High Elevated Basin of Western Karakoram by Using Different Machine Learning Models." In Weather Forecasting [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.98226.

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Accurate and reliable prediction of relative humidity is of great importance in all fields concerning global climate change. The current study has employed Multivariate Adaptive Regression Spline (MARS) and M5 Tree (M5T) models to predict the relative humidity in the Hunza River basin, Pakistan. Both the models provided the best prediction for the input scenario S6 (RHt-1, RHt-2, RHt-3, Tt-1, Tt-2, Tt-3). The statistical analysis displayed that the MARS model provided a better prediction of relative humidity as compared to M5T at all meteorological stations, especially, at Ziarat followed by Khunjerab and Naltar. The values of root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) were (5.98%, 5.43%, and 0.808) for Khunjerab; (6.58%, 5.08%, and 0.806) for Naltar; and (5.86%, 4.97%, 0.815) for Ziarat during the testing of MARS model whereas, the values were (6.14%, 5.56%, and 0.772) for Khunjerab; (6.19%, 5.58% and 0.762) for Naltar and (6.08%, 5.46%, 0.783) for Ziarat during the testing of M5T model. Both the models performed slightly better in training as compared to the testing stage. The current study encourages future research to be conducted at high altitude basins for the prediction of other meteorological variables using machine learning tools.
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Conference papers on the topic "Weather Research And Forecasting Model (WRF)"

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Mathiesen, Patrick J., Craig Collier, and Jan P. Kleissl. "Development and Validation of an Operational, Cloud-Assimilating Numerical Weather Prediction Model for Solar Irradiance Forecasting." In ASME 2012 6th International Conference on Energy Sustainability collocated with the ASME 2012 10th International Conference on Fuel Cell Science, Engineering and Technology. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/es2012-91408.

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For solar irradiance forecasting, the operational numerical weather prediction (NWP) models (e.g. the North American Model (NAM)) have excellent coverage and are easily accessible. However, their accuracy in predicting cloud cover and irradiance is largely limited by coarse resolutions (> 10 km) and generalized cloud-physics parameterizations. Furthermore, with hourly or longer temporal output, the operational NWP models are incapable of forecasting intra-hour irradiance variability. As irradiance ramp rates often exceed 80% of clear sky irradiance in just a few minutes, this deficiency greatly limits the applicability of the operational NWP models for solar forecasting. To address these shortcomings, a high-resolution, cloud-assimilating model was developed at the University of California, San Diego (UCSD) and Garrad-Hassan, America, Inc (GLGH). Based off of the Weather and Research Forecasting (WRF) model, an operational 1.3 km-gridded solar forecast is implemented for San Diego, CA that is optimized to simulate local meteorology (specifically, summertime marine layer fog and stratus conditions) and sufficiently resolved to predict intra-hour variability. To produce accurate cloud-field initializations, a direct cloud assimilation system (WRF-CLDDA) was also developed. Using satellite imagery and ground weather station reports, WRF-CLDDA statistically populates the initial conditions by directly modifying cloud hydrometeors (cloud water and water vapor content). When validated against the dense UCSD pyranometer network, WRF-CLDDA produced more accurate irradiance forecasts than the NAM and more frequently predicted marine layer fog and stratus cloud conditions.
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Gualan-Saavedra, Ronald M., Lizandro D. Solano-Quinde, and Brett M. Bode. "GPU Acceleration of the Horizontal Diffusion Method in the Weather Research and Forecasting (WRF) Model." In 2015 Asia-Pacific Conference on Computer-Aided System Engineering (APCASE). IEEE, 2015. http://dx.doi.org/10.1109/apcase.2015.57.

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Liu, N., P. O. Koons, J. Monk, S. G. Roy, B. Segee, and Y. F. Zhu. "Graphics processing units (GPU) acceleration of the weather research and forecasting (WRF) model for hurricane Sandy." In International Conference on Environmental Science and Biological Engineering. Southampton, UK: WIT Press, 2014. http://dx.doi.org/10.2495/esbe140901.

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Mielikainen, Jarno, Bormin Huang, and Allen Huang. "Revisiting Intel Xeon Phi optimization of Thompson cloud microphysics scheme in Weather Research and Forecasting (WRF) model." In SPIE Remote Sensing, edited by Bormin Huang, Sebastián López, Zhensen Wu, Jose M. Nascimento, Boris A. Alpatov, and Jordi Portell de Mora. SPIE, 2015. http://dx.doi.org/10.1117/12.2196526.

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Gong, W., F. Meyer, P. W. Webley, D. Morton, and S. Liu. "Performance analysis of atmospheric correction in InSAR data based on the Weather Research and Forecasting Model (WRF)." In IGARSS 2010 - 2010 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2010. http://dx.doi.org/10.1109/igarss.2010.5652267.

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Tabone, Ilaria, and Mario E. Bertaina. "The Weather Research and Forecasting (WRF) model contribution to the atmospheric conditions estimation during the EUSO-Balloo." In The 34th International Cosmic Ray Conference. Trieste, Italy: Sissa Medialab, 2016. http://dx.doi.org/10.22323/1.236.0642.

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Morton, Don, Oralee Nudson, Don Bahls, and Greg Newby. "The Weather Research and Forecasting (WRF) Model as a Tool for Evaluating HPCMP Assets and Capabilities in Grand Scale Numerical Weather Prediction." In 2010 DoD High Performance Computing Modernization Program Users Group Conference (HPCMP-UGC). IEEE, 2010. http://dx.doi.org/10.1109/hpcmp-ugc.2010.51.

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Polezhayeva, Antonina, and Antonina Polezhayeva. "NUMERICAL MODELING OF POLAR LOWS OVER THE BARENTS SEA: IMPACT OF WRF PARAMETRIZATIONS ON THE QUALITY OF FORECAST." In Managing risks to coastal regions and communities in a changing world. Academus Publishing, 2017. http://dx.doi.org/10.31519/conferencearticle_5b1b9387834ac4.45240165.

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Polar lows are generally characterized by severe weather in the form of strong winds, showers and occasionally heavy snow, which have sometimes resulted in the loss of life, especially at sea. Numerical simulations with mesoscale atmospheric models is a good alternative to investigate polar low phenomenon, because they produce temporally and spatially regular-spaced fields of atmospheric variables with high resolution. To describe the evolution of atmospheric processes the Advanced Weather Research and Forecasting (WRF-ARW) model was used. The principal objectives of this study were 1) the understanding of mesoscale WRF model and adapting the model for the Barents Sea region; 2) to conduct numerical experiments using WRF model with different Planetary Boundary Layer parameterization (PBLs) schemes and investigate the impact of each scheme on the quality of forecast; and 3) the investigation of the capability of WRF model to successfully simulate evolution of polar lows. The impact on the quality of forecast was investigated. The results of the study, obtained by numerical modeling of polar mesoscale low over the Barents Sea. One polar low, near Spitsbergen, from 24 of March to 26 of March 2014 were targeted. The results of numerical experiments showed that each of Planetary Boundary Layer parameterization scheme isn't successful for simulation of polar low.
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Polezhayeva, Antonina, and Antonina Polezhayeva. "NUMERICAL MODELING OF POLAR LOWS OVER THE BARENTS SEA: IMPACT OF WRF PARAMETRIZATIONS ON THE QUALITY OF FORECAST." In Managing risks to coastal regions and communities in a changing world. Academus Publishing, 2017. http://dx.doi.org/10.21610/conferencearticle_58b43155456a5.

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Polar lows are generally characterized by severe weather in the form of strong winds, showers and occasionally heavy snow, which have sometimes resulted in the loss of life, especially at sea. Numerical simulations with mesoscale atmospheric models is a good alternative to investigate polar low phenomenon, because they produce temporally and spatially regular-spaced fields of atmospheric variables with high resolution. To describe the evolution of atmospheric processes the Advanced Weather Research and Forecasting (WRF-ARW) model was used. The principal objectives of this study were 1) the understanding of mesoscale WRF model and adapting the model for the Barents Sea region; 2) to conduct numerical experiments using WRF model with different Planetary Boundary Layer parameterization (PBLs) schemes and investigate the impact of each scheme on the quality of forecast; and 3) the investigation of the capability of WRF model to successfully simulate evolution of polar lows. The impact on the quality of forecast was investigated. The results of the study, obtained by numerical modeling of polar mesoscale low over the Barents Sea. One polar low, near Spitsbergen, from 24 of March to 26 of March 2014 were targeted. The results of numerical experiments showed that each of Planetary Boundary Layer parameterization scheme isn't successful for simulation of polar low.
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Gamarro, Harold, Jorge E. Gonzalez, and Luis E. Ortiz. "Urban WRF-Solar Validation and Potential for Power Forecast in New York City." In ASME 2018 12th International Conference on Energy Sustainability collocated with the ASME 2018 Power Conference and the ASME 2018 Nuclear Forum. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/es2018-7130.

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Recent developments in the Weather Research and Forecasting (WRF) Model have made it possible to accurately approximate solar power through the implementation of WRF-Solar. This study couples the WRF-Solar module with a multilayer urban canopy and building energy model in New York City (NYC) to create a unified WRF forecasting model called uWRF-Solar. Hourly time resolution forecasts are validated against ground station data collected at eight different sites. The validation is carried out independently for two different sky conditions: clear and cloudy. Results indicate that the uWRF-Solar model can forecast solar irradiance considerably well for the global horizontal irradiance (GHI) with an R squared value of 0.93 for clear sky conditions and 0.76 for cloudy sky conditions. Results are further used to directly forecast solar power production in the NYC region, where a power evaluation is done at a city scale. The outputs show a gradient of power generation produced by the potential available solar energy on the entire uWRF-Solar grid. In total, for the month of July 2016, NYC had a city PV potential of 233 kW/day/m2 and 7.25 MWh/month/m2.
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Reports on the topic "Weather Research And Forecasting Model (WRF)"

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Iacono, Michael J. Enhancing Cloud Radiative Processes and Radiation Efficiency in the Advanced Research Weather Research and Forecasting (WRF) Model. Office of Scientific and Technical Information (OSTI), March 2015. http://dx.doi.org/10.2172/1172166.

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Pattantyus, Andre, Jr Dumais, and Robert. Investigating Lateral Boundary Forcing of Weather Research and Forecasting (WRF) Model Forecasts for Artillery Mission Support. Fort Belvoir, VA: Defense Technical Information Center, January 2013. http://dx.doi.org/10.21236/ada575869.

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LeGrand, Sandra, Christopher Polashenski, Theodore Letcher, Glenn Creighton, Steven Peckham, and Jeffrey Cetola. The AFWA dust emission scheme for the GOCART aerosol model in WRF-Chem v3.8.1. Engineer Research and Development Center (U.S.), August 2021. http://dx.doi.org/10.21079/11681/41560.

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Airborne particles of mineral dust play a key role in Earth’s climate system and affect human activities around the globe. The numerical weather modeling community has undertaken considerable efforts to accurately forecast these dust emissions. Here, for the first time in the literature, we thoroughly describe and document the Air Force Weather Agency (AFWA) dust emission scheme for the Georgia Institute of Technology–Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) aerosol model within the Weather Research and Forecasting model with chemistry (WRF-Chem) and compare it to the other dust emission schemes available in WRF-Chem. The AFWA dust emission scheme addresses some shortcomings experienced by the earlier GOCART-WRF scheme. Improved model physics are designed to better handle emission of fine dust particles by representing saltation bombardment. WRF-Chem model performance with the AFWA scheme is evaluated against observations of dust emission in southwest Asia and compared to emissions predicted by the other schemes built into the WRF-Chem GOCART model. Results highlight the relative strengths of the available schemes, indicate the reasons for disagreement, and demonstrate the need for improved soil source data.
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Smith, Jeffrey A., Theresa A. Foley, John W. Raby, and Brian Reen. Investigating Surface Bias Errors in the Weather Research and Forecasting (WRF) Model using a Geographic Information System (GIS). Fort Belvoir, VA: Defense Technical Information Center, February 2015. http://dx.doi.org/10.21236/ada618215.

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5

Passner, Jeffrey E. Using the Advanced Research Version of the Weather Research and Forecasting Model (WRF-ARW) to Forecast Turbulence at Small Scales. Fort Belvoir, VA: Defense Technical Information Center, September 2008. http://dx.doi.org/10.21236/ada487156.

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Cogan, James L. Change in Weather Research and Forecasting (WRF) Model Accuracy with Age of Input Data from the Global Forecast System (GFS). Fort Belvoir, VA: Defense Technical Information Center, September 2016. http://dx.doi.org/10.21236/ad1016607.

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Gallagher, Alex, Sandra LeGrand, Taylor Hodgdon, and Theodore Letcher. Simulating environmental conditions for Southwest United States convective dust storms using the Weather Research and Forecasting Model v4.1. Engineer Research and Development Center (U.S.), August 2022. http://dx.doi.org/10.21079/11681/44963.

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Dust aerosols can pose a significant detriment to public health, transportation, and tactical operations through reductions in air quality and visibility. Thus, accurate model forecasts of dust emission and transport are essential to decision makers. While a large number of studies have advanced the understanding and predictability of dust storms, the majority of existing literature considers dust production and forcing conditions of the underlying meteorology independently of each other. Our study works to-wards filling this research gap by inventorying dust-event case studies forced by convective activity in the Desert Southwest United States, simulating select representative case studies using several configurations of the Weather Research and Forecasting (WRF) model, testing the sensitivity of forecasts to essential model parameters, and assessing overall forecast skill using variables essential to dust production and transport. We found our control configuration captured the initiation, evolution, and storm structure of a variety of convective features admirably well. Peak wind speeds were well represented, but we found that simulated events arrived up to 2 hours earlier or later than observed. Our results show that convective storms are highly sensitive to initialization time and initial conditions that can preemptively dry the atmosphere and suppress the growth of convective storms.
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Michaels, Michelle, Theodore Letcher, Sandra LeGrand, Nicholas Webb, and Justin Putnam. Implementation of an albedo-based drag partition into the WRF-Chem v4.1 AFWA dust emission module. Engineer Research and Development Center (U.S.), January 2021. http://dx.doi.org/10.21079/11681/42782.

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Employing numerical prediction models can be a powerful tool for forecasting air quality and visibility hazards related to dust events. However, these numerical models are sensitive to surface conditions. Roughness features (e.g., rocks, vegetation, furrows, etc.) that shelter or attenuate wind flow over the soil surface affect the magnitude and spatial distribution of dust emission. To aide in simulating the emission phase of dust transport, we used a previously published albedo-based drag partition parameterization to better represent the component of wind friction speed affecting the immediate soil sur-face. This report serves as a guide for integrating this parameterization into the Weather Research and Forecasting with Chemistry (WRF-Chem) model. We include the procedure for preprocessing the required input data, as well as the code modifications for the Air Force Weather Agency (AFWA) dust emission module. In addition, we provide an example demonstration of output data from a simulation of a dust event that occurred in the Southwestern United States, which incorporates use of the drag partition.
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LeGrand, Sandra, Theodore Letcher, Gregory Okin, Nicholas Webb, Alex Gallagher, Saroj Dhital, Taylor Hodgdon, Nancy Ziegler, and Michelle Michaels. Application of a satellite-retrieved sheltering parameterization (v1.0) for dust event simulation with WRF-Chem v4.1. Engineer Research and Development Center (U.S.), May 2023. http://dx.doi.org/10.21079/11681/47116.

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Employing numerical prediction models can be a powerful tool for forecasting air quality and visibility hazards related to dust events. However, these numerical models are sensitive to surface conditions. Roughness features (e.g., rocks, vegetation, furrows, etc.) that shelter or attenuate wind flow over the soil surface affect the magnitude and spatial distribution of dust emission. To aide in simulating the emission phase of dust transport, we used a previously published albedo-based drag partition parameterization to better represent the component of wind friction speed affecting the immediate soil sur-face. This report serves as a guide for integrating this parameterization into the Weather Research and Forecasting with Chemistry (WRF-Chem) model. We include the procedure for preprocessing the required input data, as well as the code modifications for the Air Force Weather Agency (AFWA) dust emission module. In addition, we provide an example demonstration of output data from a simulation of a dust event that occurred in the Southwestern United States, which incorporates use of the drag partition.
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Sauter, Barbara. Weather Research and Forecasting (WRF) Results Over New Mexico. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada443014.

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