Academic literature on the topic 'Weather Research and Forecasting WRF Model'

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

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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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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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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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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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Dissertations / Theses on the topic "Weather Research and Forecasting WRF Model"

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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,
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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 pa
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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 st
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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 th
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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%), cont
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Books on the topic "Weather Research and Forecasting WRF Model"

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Korea (South). Kisangchʻŏng. Yeboguk. Dijitʻŏl Yebo Kaebalkwa., ред. MOS kion yebo model kaebal mit hyŏnŏp unyŏng chʻegye. Kisangchʻŏng Yeboguk Dijitʻŏl Yebo Kaebalkwa, 2006.

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Saitō, Kazuo. Documentation of the Meteorological Research Institute Numerical Prediction Division unified nonhydrostatic model. 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. Atmospheric Sciences Division, Air Force Geophysics Laboratory, 1985.

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Book chapters on the topic "Weather Research and Forecasting WRF Model"

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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. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3338-5_21.

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Chisale, Sylvester William, Han Soo Lee, and Manuel Soto Calvo. "Exploring Weather Research and Forecasting (WRF) Model Parameterizations for Enhanced Solar and Wind Energy Resources Predictions in Malawi." In Future Directions in Energy Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-62042-3_1.

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Castagna, Jessica, and Giuseppe Mendicino. "Scalability of Saharan Dust Outbreak Modelling with the Advanced Weather Research and Forecasting Model Coupled with Chemistry (WRF-Chem)." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-81244-6_16.

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Enhakhoirunnisa, Rizhqa, Rachmy Fitriani, Muhammad Rais Abdillah, et al. "Performance Analysis of Weather Research and Forecasting Chemistry (WRF-Chem) Model in DKI Jakarta Area (Case Study: July 7–9, 2021)." In Springer Proceedings in Physics. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0740-9_2.

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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. 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. 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. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58124-4_2.

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Sunil Babu, Penugonda, M. K. Singhal, and R. P. Saini. "Wind Resource Assessment Using Weather Research Forecasting (WRF) Tool Over a Complex Terrain: A Case Study on Southern Region of Andhra Pradesh, India." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-6616-5_37.

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Zhang, Zhenyu, Patrick Laux, Jussi Baade, Hassane Moutahir, and Harald Kunstmann. "Regional Land–Atmosphere Interactions in Southern Africa: Potential Impact and Sensitivity of Forest and Plantation Change." In Sustainability of Southern African Ecosystems under Global Change. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-10948-5_10.

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AbstractSouthern Africa is experiencing increasing land transformation and natural vegetation losses. Deforestation is one type of this land degradation where there are indigenous forests present, and afforestation of other nature ecosystems with timber plantations. This study performs regional coupled land–atmosphere model simulations using the Weather Research and Forecast (WRF) model with a resolution of 12 km, to assess the impact of forest and plantation cover change on regional climate in southern Africa. Three WRF simulations were designed for different land covers: (i) MODIS-derived la
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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. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65421-4_11.

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Conference papers on the topic "Weather Research and Forecasting WRF Model"

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Wichitrnithed, Chayanon Namo, Woo-Sun Yang, Yun Helen He, et al. "Optimizing the Weather Research and Forecasting Model with OpenMP Offload and Codee." In SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE, 2024. https://doi.org/10.1109/scw63240.2024.00243.

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Zhou, Yifan, Qingyang Li, Haisong Zhou, and Gaojun Liu. "Research on Large-Area Power Grid Load Forecasting Model under Major Weather Events." In 2024 5th International Conference on Information Science, Parallel and Distributed Systems (ISPDS). IEEE, 2024. http://dx.doi.org/10.1109/ispds62779.2024.10667587.

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Yuan, Yishan, Shrey Sthapit, Eiko Kubo, Yuichi Sano, Tomoo Tomoda, and Tomomi Yagi. "Wind-Resilience of a Cable-Stayed Bridge in the Coastal Region of Mombasa, Kenya." In IABSE Symposium, Tokyo 2025: Environmentally Friendly Technologies and Structures: Focusing on Sustainable Approaches. International Association for Bridge and Structural Engineering (IABSE), 2025. https://doi.org/10.2749/tokyo.2025.0886.

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<p>Resilience to wind is a critical issue in the design of long‐span bridges. In this study, the wind resistant design of a cable stayed bridge planned in Mombasa, Kenya is described, focusing on both static wind load design and dynamic verification. For static wind load design, the appropriate design wind velocity was studied. Given the absence of standardized wind observation data, an approach utilizing Weather Research and Forecasting (WRF) analysis was adopted to obtain an estimated design wind velocity. This estimated velocity was then compared with AASHTO (or ASCE) wind map to esta
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Aldren, Cameron, Nilay Shah, and Adam Hawkes. "Optimisation Under Uncertain Meteorology: Stochastic Modelling of Hydrogen Export Systems." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.191427.

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Deriving accurate cost projections associated with producing hydrogen within the context of an energy-export paradigm is a challenging feat due to non-deterministic nature of weather systems. Many research efforts employ deterministic models to estimate costs, which could be biased by the innate ability of these models to �see the future�. To this end we present the findings of a multistage stochastic model of hydrogen production for energy export (using liquid hydrogen or ammonia as energy vectors), the findings of which are compared to that of a deterministic programme. Our modelling found t
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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 gre
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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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Najian, Maede, and Navid Goudarzi. "Evaluating Critical Weather Parameters Using Machine Learning Models." In ASME Power Applied R&D 2023. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/power2023-108893.

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Abstract Wind speed and temperature forecasting accurately for an urban area are two critical elements to mitigate cost and energy in engineering calculations. Recently, researchers have employed a combination of mesoscale Weather Research and Forecasting (WRF) and microscale models of Computational Fluid Dynamics (CFD) to simulate wind flow in urban areas. Employing machine learning algorithms is also introduced as another promising alternative/complementary tool. In this work, a WRF model is developed to calculate wind speed at 10m above the ground in two different residential regions with d
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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. 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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Reports on the topic "Weather Research and Forecasting WRF Model"

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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), 2015. http://dx.doi.org/10.2172/1172166.

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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.), 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 t
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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. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada575869.

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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). Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ada618215.

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Passner, Jeffrey E. Using the Advanced Research Version of the Weather Research and Forecasting Model (WRF-ARW) to Forecast Turbulence at Small Scales. Defense Technical Information Center, 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). Defense Technical Information Center, 2016. http://dx.doi.org/10.21236/ad1016607.

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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.), 2021. http://dx.doi.org/10.21079/11681/42782.

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Abstract:
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 immedi
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LeGrand, Sandra, Theodore Letcher, Gregory Okin, et al. 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.), 2023. http://dx.doi.org/10.21079/11681/47116.

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Abstract:
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 immedi
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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.), 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 conve
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Alter, Ross, Michelle Swearingen, and Mihan McKenna. The influence of mesoscale atmospheric convection on local infrasound propagation. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48157.

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Infrasound—that is, acoustic waves with frequencies below the threshold of human hearing—has historically been used to detect and locate distant explosive events over global ranges (≥1,000 km). Simulations over these ranges have traditionally relied on large-scale, synoptic meteorological information. However, infrasound propagation over shorter, local ranges (0–100 km) may be affected by smaller, mesoscale meteorological features. To identify the effects of these mesoscale meteorological features on local infrasound propagation, simulations were conducted using the Weather Research and Foreca
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