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Journal articles on the topic 'Weather Research and Forecasting WRF Model'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Liang, Xin-Zhong, Min Xu, Xing Yuan, et al. "Regional Climate–Weather Research and Forecasting Model." Bulletin of the American Meteorological Society 93, no. 9 (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, C

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2

Powers, Jordan G., Joseph B. Klemp, William C. Skamarock, et al. "The Weather Research and Forecasting Model: Overview, System Efforts, and Future Directions." Bulletin of the American Meteorological Society 98, no. 8 (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 b

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3

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 a

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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 (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

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Sung, Kwangjae. "The Local Unscented Transform Kalman Filter for the Weather Research and Forecasting Model." Atmosphere 14, no. 7 (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 nonl

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6

Huang, Jian, Wu Wang, Yuzhu Wang, et al. "Performance Evaluation and Optimization of the Weather Research and Forecasting (WRF) Model Based on Kunpeng 920." Applied Sciences 13, no. 17 (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 Divisi

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Huang, Cheng-Liang, Yuan-Kang Wu, Chin-Cheng Tsai, Jing-Shan Hong, and Yuan-Yao Li. "Revolutionizing Solar Power Forecasts by Correcting the Outputs of the WRF-SOLAR Model." Energies 17, no. 1 (2023): 88. http://dx.doi.org/10.3390/en17010088.

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Climate change poses a significant threat to humanity. Achieving net-zero emissions is a key goal in many countries. Among various energy resources, solar power generation is one of the prominent renewable energy sources. Previous studies have demonstrated that post-processing techniques such as bias correction can enhance the accuracy of solar power forecasting based on numerical weather prediction (NWP) models. To improve the post-processing technique, this study proposes a new day-ahead forecasting framework that integrates weather research and forecasting solar (WRF-Solar) irradiances and

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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 (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,

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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 (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 i

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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 (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

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Liu, Yebing, Luoyang Wang, Yihan Lou, Tangao Hu, Jiaxi Wu, and Huiyan Xu. "Enhancing Urban Flood Forecasting: Integrating Weather Forecasts and Hydrological Models." Water 16, no. 14 (2024): 2004. http://dx.doi.org/10.3390/w16142004.

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Precipitation data in urban hydrological models are derived from an ideal stormwater model, which has some uncertainties and limited prediction times. Therefore, to reliably forecast urban flooding, prolong prediction time periods, and better support associated research in urban flood forecasting, a combination of weather forecasts and urban hydrology is necessary. By applying comprehensive cloud microphysical schemes in the Weather Research and Forecasting (WRF) model to the predecessor torrential rainfall associated with Typhoon Khanun (2017), this study evaluated different configurations of

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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

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Zhao, Q., Z. Liu, B. Ye, Y. Qin, Z. Wei, and S. Fang. "A snowmelt runoff forecasting model coupling WRF and DHSVM." Hydrology and Earth System Sciences 13, no. 10 (2009): 1897–906. http://dx.doi.org/10.5194/hess-13-1897-2009.

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Abstract. This study linked the Weather Research and Forecasting (WRF) modelling system and the Distributed Hydrology Soil Vegetation Model (DHSVM) to forecast snowmelt runoff. The study area was the 800 km2 Juntanghu watershed of the northern slopes of Tianshan Mountain Range. This paper investigated snowmelt runoff forecasting models suitable for meso-microscale application. In this study, a limited-region 24-h Numeric Weather Forecasting System was formulated using the new generation atmospheric model system WRF with the initial fields and lateral boundaries forced by Chinese T213L31 model.

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Mölders, Nicole. "Suitability of the Weather Research and Forecasting (WRF) Model to Predict the June 2005 Fire Weather for Interior Alaska." Weather and Forecasting 23, no. 5 (2008): 953–73. http://dx.doi.org/10.1175/2008waf2007062.1.

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Abstract Standard indices used in the National Fire Danger Rating System (NFDRS) and Fosberg fire-weather indices are calculated from Weather Research and Forecasting (WRF) model simulations and observations in interior Alaska for June 2005. Evaluation shows that WRF is well suited for fire-weather prediction in a boreal forest environment at all forecast leads and on an ensemble average. Errors in meteorological quantities and fire indices marginally depend on forecast lead. WRF’s precipitation performance for interior Alaska is comparable to that of other mesoscale models applied to midlatit

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Chaskos, Dimitrios C., Christos J. Lolis, Vassiliki Kotroni, Nikolaos Hatzianastassiou, and Aristides Bartzokas. "Impact of Weather Types on Weather Research and Forecasting Model Skill for Temperature and Precipitation Forecasting in Northwest Greece." Atmosphere 15, no. 12 (2024): 1516. https://doi.org/10.3390/atmos15121516.

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The accuracy of the Weather Research and Forecasting (WRF) model’s predictions for air temperature and precipitation in northwestern Greece varies under different weather conditions. However, there is a lack of understanding regarding how well the model performs for specific Weather Types (WTs), especially in regions with a complex topography like NW Greece. This study evaluates the WRF model’s ability to predict 2 m air temperature and precipitation for 10 objectively defined WTs. Forecasts are validated against observations from the station network of the National Observatory of Athens, focu

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Li, Ji, Yangbo Chen, Huanyu Wang, Jianming Qin, Jie Li, and Sen Chiao. "Extending flood forecasting lead time in a large watershed by coupling WRF QPF with a distributed hydrological model." Hydrology and Earth System Sciences 21, no. 2 (2017): 1279–94. http://dx.doi.org/10.5194/hess-21-1279-2017.

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Abstract. Long lead time flood forecasting is very important for large watershed flood mitigation as it provides more time for flood warning and emergency responses. The latest numerical weather forecast model could provide 1–15-day quantitative precipitation forecasting products in grid format, and by coupling this product with a distributed hydrological model could produce long lead time watershed flood forecasting products. This paper studied the feasibility of coupling the Liuxihe model with the Weather Research and Forecasting quantitative precipitation forecast (WRF QPF) for large waters

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Havens, Scott, Danny Marks, Katelyn FitzGerald, et al. "Approximating Input Data to a Snowmelt Model Using Weather Research and Forecasting Model Outputs in Lieu of Meteorological Measurements." Journal of Hydrometeorology 20, no. 5 (2019): 847–62. http://dx.doi.org/10.1175/jhm-d-18-0146.1.

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Abstract Forecasting the timing and magnitude of snowmelt and runoff is critical to managing mountain water resources. Warming temperatures are increasing the rain–snow transition elevation and are limiting the forecasting skill of statistical models relating historical snow water equivalent to streamflow. While physically based methods are available, they require accurate estimations of the spatial and temporal distribution of meteorological variables in complex terrain. Across many mountainous areas, measurements of precipitation and other meteorological variables are limited to a few refere

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Sanusi, Alfi Rizky, Muh Taufik, and I Putu Santikayasa. "The Use of Weather Research and Forecasting Model to Predict Rainfall in Tropical Peatland: 1. Model Parameterization." Agromet 35, no. 1 (2021): 49–59. http://dx.doi.org/10.29244/j.agromet.35.1.49-59.

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Rainfall dynamics play a vital role in tropical peatland by providing sufficient water to keep peat moist throughout the year. Therefore, information of rainfall data either historical or forecasting data has risen in recent decades especially for an alert system of fire. Here the Weather and Research Forecasting (WRF) model may act as a tool to provide forecasting weather data. This study aims to do parameterization on WRF parameters for peatland in Sumatra, and to perform bias correction on the WRF’s rainfall output with observed data. We performed stepwise calibration to choose the best fiv

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Vásquez Anacona, Hugo, Cristian Mattar, Nicolás G. Alonso-de-Linaje, Héctor H. Sepúlveda, and Jessica Crisóstomo. "Wind Simulations over Western Patagonia Using the Weather Research and Forecasting model and Reanalysis." Atmosphere 14, no. 7 (2023): 1062. http://dx.doi.org/10.3390/atmos14071062.

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The Chilean Western Patagonia has the highest wind potential resources in South America. Its complex terrain deserves a special attention for wind modeling and assessments. In this work, we have performed a comprehensive meso-scale climate simulation on Weather Research and Forecasting (WRF) in order to provide new insights into the wind climatology in Western Patagonia. Simulations were carried out from 1989 to 2020, and we considered a previous sensitivity analysis for their configuration. In situ data from a wind mast, meteorological information and data from eddy flux stations were used to

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Kala, Jatin, Alyce Sala Tenna, Daniel Rudloff, Julia Andrys, Ole Rieke, and Thomas J. Lyons. "Evaluation of the Weather Research and Forecasting model in simulating fire weather for the south-west of Western Australia." International Journal of Wildland Fire 29, no. 9 (2020): 779. http://dx.doi.org/10.1071/wf19111.

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The Weather Research and Forecasting (WRF) model was used to simulate fire weather for the south-west of Western Australia (SWWA) over multiple decades at a 5-km resolution using lateral boundary conditions from the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA)-Interim reanalysis. Simulations were compared with observations at Australian Bureau of Meteorology meteorological stations and the McArthur Forest Fire Danger Index (FFDI) was used to quantify fire weather. Results showed that, overall, the WRF reproduced the annual cumulative FFDI at most stations reason

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Mandal, Alan, Grzegorz Nykiel, Tomasz Strzyzewski, et al. "High-resolution fire danger forecast for Poland based on the Weather Research and Forecasting Model." International Journal of Wildland Fire 31, no. 2 (2021): 149–62. http://dx.doi.org/10.1071/wf21106.

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Due to climate change and associated longer and more frequent droughts, the risk of forest fires increases. To address this, the Institute of Meteorology and Water Management implemented a system for forecasting fire weather in Poland. The Fire Weather Index (FWI) system, developed in Canada, has been adapted to work with meteorological fields derived from the high-resolution (2.5 km) Weather Research and Forecasting (WRF) model. Forecasts are made with 24- and 48-h lead times. The purpose of this work is to present the validation of the implemented system. First, the results of the WRF model

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ЗОЛОТОВ, С. Ю., та А. С. ЛОГИНОВ. "МОДЕЛИРОВАНИЕ ОПАСНЫХ МЕТЕОРОЛОГИЧЕСКИХ ЯВЛЕНИЙ НА ТЕРРИТОРИИ ЗАПАДНОЙ СИБИРИ С ПОМОЩЬЮ МОДЕЛИ АТМОСФЕРЫ WRF". Optika atmosfery i okeana 37, № 10(429) (2024): 861–67. https://doi.org/10.15372/aoo20241008.

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В настоящее время на территории Западной Сибири происходит увеличение частоты и интенсивности опасных погодных явлений. Модель атмосферы Weather Research and Forecasting (WRF) позволяет исследовать такие явления, в частности для предупреждения о возможности их возникновения. В данной статье определен набор схем параметризаций WRF, предоставляющих полноценную возможность изучать причины возникновения опасных метеорологических явлений и их развитие. Подробно рассмотрены две погодные ситуации на территории Западной Сибири 29-30.04.2019 г. и 25-26.12.2020 г., сопровождавшиеся такими опасными явлен

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Zhao, Q., Z. Liu, M. Li, Z. Wei, and S. Fang. "The snowmelt runoff forecasting model of coupling WRF and DHSVM." Hydrology and Earth System Sciences Discussions 6, no. 2 (2009): 3335–57. http://dx.doi.org/10.5194/hessd-6-3335-2009.

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Abstract. This study used the Weather Research and Forecasting (WRF) modeling system and the Distributed Hydrology-Soil-Vegetation Model (DHSVM) to forecast the snowmelt runoff in the 800 km2 Juntanghu watershed of the northern slope of Tianshan Mountains from 29 February–6 March 2008. This paper made an exploration for snowmelt runoff forecasting model combing closely practical application in meso-microscale. It included: (1) A limited-region 24-h Numeric Weather Forecasting System was established by using the new generation atmospheric model system WRF with the initial fields and lateral bou

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Mielikainen, J., B. Huang, and A. H. L. Huang. "Intel Xeon Phi accelerated Weather Research and Forecasting (WRF) Goddard microphysics scheme." Geoscientific Model Development Discussions 7, no. 6 (2014): 8941–73. http://dx.doi.org/10.5194/gmdd-7-8941-2014.

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Abstract. 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 a

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Rama Rao, Y. V., Lyndon Alves, Bhaleka Seulall, Ziona Mitchell, Kelvin Samaroo, and Garvin Cummings. "Evaluation of the weather research and forecasting (WRF) model over Guyana." Natural Hazards 61, no. 3 (2011): 1243–61. http://dx.doi.org/10.1007/s11069-011-9977-3.

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Hinestroza-Ramirez, Jhon E., Juan David Rengifo-Castro, Olga Lucia Quintero, Andrés Yarce Botero, and Angela Maria Rendon-Perez. "Non-Parametric and Robust Sensitivity Analysis of the Weather Research and Forecast (WRF) Model in the Tropical Andes Region." Atmosphere 14, no. 4 (2023): 686. http://dx.doi.org/10.3390/atmos14040686.

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With the aim of understanding the impact of air pollution on human health and ecosystems in the tropical Andes region (TAR), we aim to couple the Weather Research and Forecasting Model (WRF) with the chemical transport models (CTM) Long-Term Ozone Simulation and European Operational Smog (LOTOS–EUROS), at high and regional resolutions, with and without assimilation. The factors set for WRF, are based on the optimized estimates of climate and weather in cities and urban heat islands in the TAR region. It is well known in the weather research and forecasting field, that the uncertainty of non-li

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Thaker, Jayesh, and Robert Höller. "Evaluation of High Resolution WRF Solar." Energies 16, no. 8 (2023): 3518. http://dx.doi.org/10.3390/en16083518.

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The amount of solar irradiation that reaches Earth’s surface is a key quantity of solar energy research and is difficult to predict, because it is directly affected by the changing constituents of the atmosphere. The numerical weather prediction (NWP) model performs computational simulations of the evolution of the entire atmosphere to forecast the future state of the atmosphere based on the current state. The Weather Research and Forecasting (WRF) model is a mesoscale NWP. WRF solar is an augmented feature of WRF, which has been improved and configured specifically for solar energy applicatio

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Clarke, Hamish, Jason P. Evans, and Andrew J. Pitman. "Fire weather simulation skill by the Weather Research and Forecasting (WRF) model over south-east Australia from 1985 to 2009." International Journal of Wildland Fire 22, no. 6 (2013): 739. http://dx.doi.org/10.1071/wf12048.

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The fire weather of south-east Australia from 1985 to 2009 has been simulated using the Weather Research and Forecasting (WRF) model. The US National Oceanic and Atmospheric Administration Centers for Environmental Prediction and National Center for Atmospheric Research reanalysis supplied the lateral boundary conditions and initial conditions. The model simulated climate and the reanalysis were evaluated against station-based observations of the McArthur Forest Fire Danger Index (FFDI) using probability density function skill scores, annual cumulative FFDI and days per year with FFDI above 50

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Coll-Hidalgo, Patricia, Albenis Pérez-Alarcón, and Pedro Manuel González-Jardines. "Evaluation of Microphysics Schemes in the WRF-ARW Model for Numerical Wind Forecast in José Martí International Airport." Environmental Sciences Proceedings 4, no. 1 (2020): 31. http://dx.doi.org/10.3390/ecas2020-08121.

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A sensitivity study was developed with Lin, Morrison 2-moment, weather research and forecasting (WRF) single-moment 5-class (WSM5), and WRF single-moment 6-class (WSM6) microphysics schemes available in the weather research and forecasting-advanced research WRF (WRF-ARW) for the numerical forecast of the wind field at José Martí International Airport, in Cuba. The selection of these schemes was based on their use in numerical weather forecast systems operating in Cuba. As case studies, five storms associated with synoptic patterns that cause dangerous conditions at this aerodrome were selected

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Zhang, Xin, Xiang-Yu Huang, and Ning Pan. "Development of the Upgraded Tangent Linear and Adjoint of the Weather Research and Forecasting (WRF) Model." Journal of Atmospheric and Oceanic Technology 30, no. 6 (2013): 1180–88. http://dx.doi.org/10.1175/jtech-d-12-00213.1.

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Abstract The authors propose a new technique for parallelizations of tangent linear and adjoint codes, which were applied in the redevelopment for the Weather Research and Forecasting (WRF) model with its Advanced Research WRF dynamic core using the automatic differentiation engine. The tangent linear and adjoint codes of the WRF model (WRFPLUS) now have the following improvements: A complete check interface ensures that developers write accurate tangent linear and adjoint codes with ease and efficiency. A new technique based on the nature of duality that existed among message passing interfac

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Ghizoni, Mariana Medeiros, Silvana Maldaner, Greice Scherer Ritter, and Alcimoni Nelci Comin. "Previsão do campo de vento empregando o modelo WRF para a análise do potencial eólico." Ciência e Natura 40 (March 12, 2019): 237. http://dx.doi.org/10.5902/2179460x35526.

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The WRF (Weather Research and Forecast) is a numerical modeling applied to the atmosphere. This model was developed for weather forecasting and investigation of atmospheric mesoscale phenomena. The WRF is a public domain and free distribution, with different applications ranging from the field of meteorology to engineering, and can be applied in situations of idealized atmosphere or real atmosphere. Presently, the wind field simulated by this model has been used as real data. Thus, the WRF model started to be employed in wind analysis for wind power generation. In this work, the wind field was

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Huang, Xiang-Yu, Qingnong Xiao, Dale M. Barker, et al. "Four-Dimensional Variational Data Assimilation for WRF: Formulation and Preliminary Results." Monthly Weather Review 137, no. 1 (2009): 299–314. http://dx.doi.org/10.1175/2008mwr2577.1.

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Abstract The Weather Research and Forecasting (WRF) model–based variational data assimilation system (WRF-Var) has been extended from three- to four-dimensional variational data assimilation (WRF 4D-Var) to meet the increasing demand for improving initial model states in multiscale numerical simulations and forecasts. The initial goals of this development include operational applications and support to the research community. The formulation of WRF 4D-Var is described in this paper. WRF 4D-Var uses the WRF model as a constraint to impose a dynamic balance on the assimilation. It is shown to im

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Saesong, Theerapan, Pakpoom Ratjiranukool, and Sujittra Ratjiranukool. "Evaluation of Temperature Simulation over Northern Thailand from Regional Climate Model Coupled with Land Surface Model." Applied Mechanics and Materials 866 (June 2017): 108–11. http://dx.doi.org/10.4028/www.scientific.net/amm.866.108.

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Numerical Weather Model called The Weather Research and Forecasting model, WRF, developed by National Center for Atmospheric Research (NCAR) is adapted to be regional climate model. The model is run to perform the daily mean air surface temperatures over northern Thailand in 2010. Boundery dataset provided by National Centers for Environmental Prediction, NCEP FNL, (Final) Operational Global Analysis data which are on 10 x 10. The simulated temperatures by WRF with four land surface options, i.e., no land surface scheme (option 0), thermal diffusion (option 1), Noah land-surface (option 2) and

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Coen, Janice L., Marques Cameron, John Michalakes, Edward G. Patton, Philip J. Riggan, and Kara M. Yedinak. "WRF-Fire: Coupled Weather–Wildland Fire Modeling with the Weather Research and Forecasting Model." Journal of Applied Meteorology and Climatology 52, no. 1 (2013): 16–38. http://dx.doi.org/10.1175/jamc-d-12-023.1.

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AbstractA wildland fire-behavior module, named WRF-Fire, was integrated into the Weather Research and Forecasting (WRF) public domain numerical weather prediction model. The fire module is a surface fire-behavior model that is two-way coupled with the atmospheric model. Near-surface winds from the atmospheric model are interpolated to a finer fire grid and are used, with fuel properties and local terrain gradients, to determine the fire’s spread rate and direction. Fuel consumption releases sensible and latent heat fluxes into the atmospheric model’s lowest layers, driving boundary layer circu

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Dou, Fangjia, Xiaolei Lv, and Huiming Chai. "Mitigating Atmospheric Effects in InSAR Stacking Based on Ensemble Forecasting with a Numerical Weather Prediction Model." Remote Sensing 13, no. 22 (2021): 4670. http://dx.doi.org/10.3390/rs13224670.

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The interferometric synthetic aperture radar (InSAR) technique is widely utilized to measure ground-surface displacement. One of the main limitations of the measurements is the atmospheric phase delay effects. For satellites with shorter wavelengths, the atmospheric delay mainly consists of the tropospheric delay influenced by temperature, pressure, and water vapor. Tropospheric delay can be calculated using numerical weather prediction (NWP) model at the same moment as synthetic aperture radar (SAR) acquisition. Scientific researchers mainly use ensemble forecasting to produce better forecast

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Bresch, James F., Jordan G. Powers, Craig S. Schwartz, Ryan A. Sobash, and Janice L. Coen. "Objective identification of thunderstorm gust fronts in numerical weather prediction models for fire weather forecasting." International Journal of Wildland Fire 30, no. 7 (2021): 513. http://dx.doi.org/10.1071/wf20059.

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Abrupt changes in wind direction and speed can dramatically impact wildfire development and spread, endangering firefighters. A frequent cause of such wind shifts is outflow from thunderstorms and organised convective systems; thus, their identification and prediction present critical challenges for fire weather forecasters. Here, we develop a methodology and implement it in a software tool that can identify and depict convective outflow boundaries in high-resolution numerical weather prediction (NWP) models to provide guidance for fire weather forecasting. The tool can process model output, o

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Hines, Keith M., and David H. Bromwich. "Development and Testing of Polar Weather Research and Forecasting (WRF) Model. Part I: Greenland Ice Sheet Meteorology*." Monthly Weather Review 136, no. 6 (2008): 1971–89. http://dx.doi.org/10.1175/2007mwr2112.1.

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Abstract A polar-optimized version of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) was developed to fill climate and synoptic needs of the polar science community and to achieve an improved regional performance. To continue the goal of enhanced polar mesoscale modeling, polar optimization should now be applied toward the state-of-the-art Weather Research and Forecasting (WRF) Model. Evaluations and optimizations are especially needed for the boundary layer parameterization, cloud physics, snow surface physics, and sea ice tre

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Arthur, Robert S., Katherine A. Lundquist, David J. Wiersema, Jingyi Bao, and Fotini K. Chow. "Evaluating Implementations of the Immersed Boundary Method in the Weather Research and Forecasting Model." Monthly Weather Review 148, no. 5 (2020): 2087–109. http://dx.doi.org/10.1175/mwr-d-19-0219.1.

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Abstract The terrain-following coordinate system used by many atmospheric models can cause numerical instabilities due to discretization errors as resolved terrain slopes increase and the grid becomes highly skewed. The immersed boundary (IB) method, which does not require the grid to conform to the terrain, has been shown to alleviate these errors, and has been used successfully for high-resolution atmospheric simulations over steep terrain, including vertical building surfaces. Since many previous applications of IB methods to atmospheric models have used very fine grid resolution (5 m or le

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Guo, Hongwu, Yongjie Ma, Zufeng Li, Qingzhi Zhao, and Yuan Zhai. "The Evaluation of Rainfall Forecasting in a Global Navigation Satellite System-Assisted Numerical Weather Prediction Model." Atmosphere 15, no. 8 (2024): 992. http://dx.doi.org/10.3390/atmos15080992.

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Accurate water vapor information is crucial for improving the quality of numerical weather forecasting. Previous studies have incorporated tropospheric water vapor data obtained from a global navigation satellite system (GNSS) into numerical weather models to enhance the accuracy and reliability of rainfall forecasts. However, research on evaluating forecast accuracy for different rainfall levels and the development of corresponding forecasting platforms is lacking. This study develops and establishes a rainfall forecasting platform supported by the GNSS-assisted weather research and forecasti

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Lisnawati, Muh Taufik, Bambang Dwi Dasanto, and Ardhasena Sopaheluwakan. "Fire Danger on Jambi Peatland Indonesia based on Weather Research and Forecasting Model." Agromet 36, no. 1 (2022): 1–10. http://dx.doi.org/10.29244/j.agromet.36.1.1-10.

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Monitoring drought related to peat fire danger is becoming essentials due to the adverse impacts of peat fires. However, the current monitoring is mostly based on station data and has not yet covered all parts of peatlands. This research was carried out to initiate a spatial monitoring for peat fire, particularly in Jambi province. Our approach was simple by integrating Weather Research Forecasting (WRF) output with a drought-fire model. This research aims to: (i) calibrate rainfall, air temperature and soil moisture data from WRF output; and (ii) analyze temporal drought related to fire dange

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Nicholls, Stephen D., Steven G. Decker, Wei-Kuo Tao, Stephen E. Lang, Jainn J. Shi, and Karen I. Mohr. "Influence of bulk microphysics schemes upon Weather Research and Forecasting (WRF) version 3.6.1 nor'easter simulations." Geoscientific Model Development 10, no. 2 (2017): 1033–49. http://dx.doi.org/10.5194/gmd-10-1033-2017.

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Abstract. This study evaluated the impact of five single- or double-moment bulk microphysics schemes (BMPSs) on Weather Research and Forecasting model (WRF) simulations of seven intense wintertime cyclones impacting the mid-Atlantic United States; 5-day long WRF simulations were initialized roughly 24 h prior to the onset of coastal cyclogenesis off the North Carolina coastline. In all, 35 model simulations (five BMPSs and seven cases) were run and their associated microphysics-related storm properties (hydrometer mixing ratios, precipitation, and radar reflectivity) were evaluated against mod

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Mollmann Junior, Ricardo Antonio, Rita de Cássia Marquês Alves, Gabriel Bonow Münchow, Osvaldo Luiz Leal de Moraes, and Caroline Azzolini Pontel. "Weather Reasearch and Forecasting Model Simulation of a Snowfall Event in Southern Brazil." Revista Brasileira de Geografia Física 14, no. 2 (2021): 1194. http://dx.doi.org/10.26848/rbgf.v14.2.p1194-1205.

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This study evaluates the reliability of the Weather Research and Forecasting (WRF) to simulate a snowfall event in the south of Brazil. The event in August 2013 was considered one of the most intense in recent years in the region with the highest topographic elevations between the states of Rio Grande do Sul (RS) and Santa Catarina (SC). The Snowfall in the mountain region of RS and SC was associated with the configuration involving a polar anticyclone and the intensification of an extratropical cyclone over the Atlantic Ocean. The WRF simulation results demonstrated the model's viability to p

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Li, Huoqing, Hailiang Zhang, Ali Mamtimin, Shuiyong Fan, and Chenxiang Ju. "A New Land-Use Dataset for the Weather Research and Forecasting (WRF) Model." Atmosphere 11, no. 4 (2020): 350. http://dx.doi.org/10.3390/atmos11040350.

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The USGS (United States Geological Survey) land-use data used in the Weather Research and Forecasting (WRF) model have become obsolete as they are unable to accurately represent actual underlying surface features. Therefore, this study developed a new multi-satellite remote-sensing land-use dataset based on the latest GLC2015 (Global Land Cover, 2015) land-use data, which had 300 m spatial resolution. The new data were used to update the default USGS land-use dataset. Based on observational data from national meteorological observing stations in Xinjiang, northwest China, a comparison of the o

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Kain, John S., S. J. Weiss, J. J. Levit, M. E. Baldwin, and D. R. Bright. "Examination of Convection-Allowing Configurations of the WRF Model for the Prediction of Severe Convective Weather: The SPC/NSSL Spring Program 2004." Weather and Forecasting 21, no. 2 (2006): 167–81. http://dx.doi.org/10.1175/waf906.1.

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Abstract Convection-allowing configurations of the Weather Research and Forecast (WRF) model were evaluated during the 2004 Storm Prediction Center–National Severe Storms Laboratory Spring Program in a simulated severe weather forecasting environment. The utility of the WRF forecasts was assessed in two different ways. First, WRF output was used in the preparation of daily experimental human forecasts for severe weather. These forecasts were compared with corresponding predictions made without access to WRF data to provide a measure of the impact of the experimental data on the human decision-

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Shiferaw, Andualem, Tsegaye Tadesse, Clinton Rowe, and Robert Oglesby. "Weather Research and Forecasting Model (WRF) Sensitivity to Choice of Parameterization Options over Ethiopia." Atmosphere 15, no. 8 (2024): 974. http://dx.doi.org/10.3390/atmos15080974.

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Downscaling seasonal climate forecasts using regional climate models (RCMs) became an emerging area during the last decade owing to RCMs’ more comprehensive representation of the important physical processes at a finer resolution. However, it is crucial to test RCMs for the most appropriate model setup for a particular purpose over a given region through numerical experiments. Thus, this sensitivity study was aimed at identifying an optimum configuration in the Weather, Research, and Forecasting (WRF) model over Ethiopia. A total of 35 WRF simulations with different combinations of parameteriz

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Maharjan, Ashish, and Ashish Shakya. "Enhancement of WRF Model Using CUDA." Interdisciplinary Journal of Innovation in Nepalese Academia 1, no. 1 (2022): 16–22. http://dx.doi.org/10.3126/idjina.v1i1.51963.

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The main aim of this paper is to implement and run WRF (Weather Research and Forecasting) model on Graphical Processing Unit (GPU) with the help of NVidia’s CUDA (Compute Unified Device Architecture) in a normal machine. Without GPU, the model needs high-end systems to be executed smoothly. For this, CUDA code is executed for a particular microphysics module to create an object file which is then added to the WRF model. Later the object file is executed on the GPU with the help of CUDA.

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Bernardet, L., V. Tallapragada, S. Bao, et al. "Community Support and Transition of Research to Operations for the Hurricane Weather Research and Forecasting Model." Bulletin of the American Meteorological Society 96, no. 6 (2015): 953–60. http://dx.doi.org/10.1175/bams-d-13-00093.1.

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Abstract The Hurricane Weather Research and Forecasting Model (HWRF) is an operational model used to provide numerical guidance in support of tropical cyclone forecasting at the National Hurricane Center. HWRF is a complex multicomponent system, consisting of the Weather Research and Forecasting (WRF) atmospheric model coupled to the Princeton Ocean Model for Tropical Cyclones (POM-TC), a sophisticated initialization package including a data assimilation system and a set of postprocessing and vortex tracking tools. HWRF’s development is centralized at the Environmental Modeling Center of NOAA’

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Wei, Chih-Chiang. "Study on Wind Simulations Using Deep Learning Techniques during Typhoons: A Case Study of Northern Taiwan." Atmosphere 10, no. 11 (2019): 684. http://dx.doi.org/10.3390/atmos10110684.

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A scheme for wind-speed simulation during typhoons in Taiwan is highly desirable, considering the effects of the powerful winds accompanying the severe typhoons. The developed combination of deep learning (DL) algorithms with a weather-forecasting numerical model can be used to determine wind speed in a rapid simulation process. Here, the Weather Research and Forecasting (WRF) numerical model was employed as the numerical simulation-based model for precomputing solutions to determine the wind velocity at arbitrary positions where the wind cannot be measured. The deep neural network (DNN) was u

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Zaidi, Syeda Maria, and Jacqueline Isabella Anak Gisen. "Evaluation of Weather Research and Forecasting (WRF) Microphysics single moment class-3 and class-6 in Precipitation Forecast." MATEC Web of Conferences 150 (2018): 03007. http://dx.doi.org/10.1051/matecconf/201815003007.

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In this study, the performance of two different Microphysics Scheme options in Weather Research and Forecasting (WRF) model were evaluated for the estimating the precipitation forecast. The schemes WRF single moment class-3 (WSM-3) and single moment class-6 (WSM-6) were employed to produce the minimum, medium and maximum precipitation for the selected events over the Kuantan River Basin (KRB). The obtained simulated results were compared with the observed data from eight different rainfall gauging stations. The results comparison indicate that WRF model provides better forecasting at some rain

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Pratama, Alvin, Ade A. Oktaviana, Prawira Y. Kombara, and Muhammad Isnaenda Ikhsan. "The performance of the weather research & forecasting model (WRF) using ensemble method to predict weather parameters." E3S Web of Conferences 604 (2025): 04002. https://doi.org/10.1051/e3sconf/202560404002.

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The performance of the WRF model is evaluated using different physical options for the Riau Province. There are 12 members, and the ensemble mean method is used to evaluate the temperature, humidity, wind direction, and wind speed. The primary purpose of this study is to determine the appropriate parameterization for the study area. In this study, two nested domains have been used for performance analysis, with the resolution of the coarser domain set at 9 km and the inner domain set at 3 km. The model was run for five days in 2019 during a forest fire episode in Riau Province. The analysis wa

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