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1
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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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 (October 1, 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 midlatitudes. WRF underestimates precipitation on average, but satisfactorily predicts precipitation ≥7.5 mm day−1, the threshold considered to reduce interior Alaska’s fire risk for several days. WRF slightly overestimates wind speed, but captures the temporal mean behavior accurately. WRF predicts the temporal evolution of daily temperature extremes, mean relative humidity, air and dewpoint temperature, and daily accumulated shortwave radiation well. Daily minimum (maximum) temperature and relative humidity are slightly overestimated (underestimated). Fire index trends are suitably predicted. Fire indices derived from daily mean predicted meteorological quantities are more reliable than those based on predicted daily extremes. Indirect evaluation by observed fires suggests that WRF-derived NFDRS indices reflect the variability of fire activity.
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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 (October 15, 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. Using the WRF forecasts, the DHSVM hydrological model was used to predict 24 h snowmelt runoff at the outlet of the Juntanghu watershed. Forecasted results showed a good similarity to the observed data, and the average relative error of maximum runoff simulation was less than 15%. The results demonstrate the potential of using a meso-microscale snowmelt runoff forecasting model for forecasting floods. The model provides a longer forecast period compared with traditional models such as those based on rain gauges or statistical forecasting.
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Havens, Scott, Danny Marks, Katelyn FitzGerald, Matt Masarik, Alejandro N. Flores, Patrick Kormos, and Andrew Hedrick. "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 (May 1, 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 reference stations and are not adequate to resolve the complex interactions between topography and atmospheric flow. In this paper, we evaluate the ability of the Weather Research and Forecasting (WRF) Model to approximate the inputs required for a physics-based snow model, iSnobal, instead of using meteorological measurements, for the Boise River Basin (BRB) in Idaho, United States. An iSnobal simulation using station data from 40 locations in and around the BRB resulted in an average root-mean-square error (RMSE) of 4.5 mm compared with 12 SNOTEL measurements. Applying WRF forcings alone was associated with an RMSE of 10.5 mm, while including a simple bias correction to the WRF outputs of temperature and precipitation reduced the RMSE to 6.5 mm. The results highlight the utility of using WRF outputs as input to snowmelt models, as all required input variables are spatiotemporally complete. This will have important benefits in areas with sparse measurement networks and will aid snowmelt and runoff forecasting in mountainous basins.
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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 (June 23, 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 evaluate the results. Reanalysis data from ERA-5, MERRA-2 and RECON80-17 were also used to perform a comparison of the obtained results with the WRF simulation. The results show that the WRF simulation using ERA-5 presented in this work is slightly different to a mathematical reconstruction using MERRA-2 (RECON80-17), which is widely accepted in Chile for wind resource assessments, presenting a statistical difference of about EMD = 0.8 [m s−1] and RMSE = 0.5. Non-significative differences were found between the WRF simulation and MERRA-2 reanalysis, while ERA-5 with MERRA-2 presented a remarkable statistical difference of about EMD = 1.64 [m s−1] and RMSE = 1.8. In relation to flux comparison, reanalysis and WRF in contrast with in situ observations presented a good performance during the summer season, although a spatial resolution bias was noticed. These results can be used as an input for further research related to WRF simulations in Western Patagonia to provide reliable information on wind energy exploration and extreme climatological phenomena such as heat waves.
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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 (October 7, 2011): 1243–61. http://dx.doi.org/10.1007/s11069-011-9977-3.
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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 (June 30, 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 five physical schemes of WRF for use in the study area. The output WRF’s rainfall was bias corrected by spatially observed rainfall data for 2019 at day resolution. Our results showed the following schemes namely (i) Eta scheme for cloud microphysical parameters; (ii) GD scheme for cumulus cloud parameters, (iii) MYJ scheme for planetary boundary layer parameters; (iv) RRTM for longwave radiation; and (v) New Goddard schemes for shortwave radiation are best combination for being used to predict rainfall in maritime continent. The spatially interpolated observed rainfall with the Inverse Distance Weighting (IDW) was outperformed for calibration process of WRF’s rainfall as shown by statistical indicators used in this study. Further, the findings have contributed to advance knowledge of rainfall forecasting in maritime continent, particularly in providing data to support the development of fire danger rating system for Indonesian peatland.
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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 (March 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 watershed flood forecasting in southern China. The QPF of WRF products has three lead times, including 24, 48 and 72 h, with the grid resolution being 20 km × 20 km. The Liuxihe model is set up with freely downloaded terrain property; the model parameters were previously optimized with rain gauge observed precipitation, and re-optimized with the WRF QPF. Results show that the WRF QPF has bias with the rain gauge precipitation, and a post-processing method is proposed to post-process the WRF QPF products, which improves the flood forecasting capability. With model parameter re-optimization, the model's performance improves also. This suggests that the model parameters be optimized with QPF, not the rain gauge precipitation. With the increasing of lead time, the accuracy of the WRF QPF decreases, as does the flood forecasting capability. Flood forecasting products produced by coupling the Liuxihe model with the WRF QPF provide a good reference for large watershed flood warning due to its long lead time and rational results.
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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 reasonably well, with most biases in the FFDI ranging between –600 and 600. Biases were highest at stations within the metropolitan region. The WRF simulated the geographical gradients in the FFDI across the domain well. The source of errors in the FFDI varied markedly between the different stations, with no one particular variable able to account for the errors at all stations. Overall, this study shows that the WRF is a useful model for simulating fire weather for SWWA, one of the most fire-prone regions in Australia.
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Mandal, Alan, Grzegorz Nykiel, Tomasz Strzyzewski, Adam Kochanski, Weronika Wrońska, Marta Gruszczynska, and Mariusz Figurski. "High-resolution fire danger forecast for Poland based on the Weather Research and Forecasting Model." International Journal of Wildland Fire 31, no. 2 (December 23, 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 were validated using in situ observations from ~70 synoptic stations. Second, we used the correlation method and Eastaugh’s percentile analysis to assess the quality of the FWI index. The data covered the 2019 fire season and were analysed for the whole forest area in Poland. Based on the presented results, it can be concluded that the FWI index (calculated based on the WRF model) has a very high predictive ability of fire risk. However, the results vary by region, distance from human habitats, and size of fire.
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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 (January 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 circulations. The atmospheric model, configured in turbulence-resolving large-eddy-simulation mode, was used to explore the sensitivity of simulated fire characteristics such as perimeter shape, fire intensity, and spread rate to external factors known to influence fires, such as fuel characteristics and wind speed, and to explain how these external parameters affect the overall fire properties. Through the use of theoretical environmental vertical profiles, a suite of experiments using conditions typical of the daytime convective boundary layer was conducted in which these external parameters were varied around a control experiment. Results showed that simulated fires evolved into the expected bowed shape because of fire–atmosphere feedbacks that control airflow in and near fires. The coupled model reproduced expected differences in fire shapes and heading-region fire intensity among grass, shrub, and forest-litter fuel types; reproduced the expected narrow, rapid spread in higher wind speeds; and reproduced the moderate inhibition of fire spread in higher fuel moistures. The effects of fuel load were more complex: higher fuel loads increased the heat flux and fire-plume strength and thus the inferred fire effects but had limited impact on spread rate.
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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 (December 12, 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 and Forecasting (WRF) model. The Goddard microphysics scheme is very suitable for massively parallel computation as there are no interactions among horizontal grid points. Compared to the earlier microphysics schemes, the Goddard scheme incorporates a large number of improvements. Thus, we have optimized the Goddard scheme code. In this paper, we present our results of optimizing the Goddard microphysics scheme on Intel Many Integrated Core Architecture (MIC) hardware. The Intel Xeon Phi coprocessor is the first product based on Intel MIC architecture, and it consists of up to 61 cores connected by a high performance on-die bidirectional interconnect. The Intel MIC is capable of executing a full operating system and entire programs rather than just kernels as the GPU does. The MIC coprocessor supports all important Intel development tools. Thus, the development environment is one familiar to a vast number of CPU developers. Although, getting a maximum performance out of MICs will require using some novel optimization techniques. Those optimization techniques are discussed in this paper. The results show that the optimizations improved performance of Goddard microphysics scheme on Xeon Phi 7120P by a factor of 4.7×. In addition, the optimizations reduced the Goddard microphysics scheme's share of the total WRF processing time from 20.0 to 7.5%. Furthermore, the same optimizations improved performance on Intel Xeon E5-2670 by a factor of 2.8× compared to the original code.
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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 (April 22, 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 boundaries forced by Chinese T213L31 model. (2) The DHSVM hydrological model driven by WRF forecasts was used to predicate 24 h snowmelt runoff at the outlet of Juntanghu watershed. The forecasted result shows a good agreement with the observed data, and the average absolute relative error of maximum runoff simulation result is less than 15%. The result demonstrates the potential of using meso-microscale snowmelt runoff forecasting model for flood forecast. The model can provide a longer forecast period compared to traditional models such as those based on rain gauges, statistical forecast.
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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. WRF simulated the main features of the FFDI distribution and its spatial variation, with an overall positive bias. Errors in average FFDI were caused mostly by errors in the ability of WRF to simulate relative humidity. In contrast, errors in extreme FFDI values were driven mainly by WRF errors in wind speed simulation. However, in both cases the quality of the observed data is difficult to ascertain. WRF run with 50-km grid spacing did not consistently improve upon the reanalysis statistics. Decreasing the grid spacing to 10km led to fire weather that was generally closer to observations than the reanalysis across the full range of evaluation metrics used here. This suggests it is a very useful tool for modelling fire weather over the entire landscape of south-east Australia.
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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 (June 1, 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 interface communication routines was adopted to parallelize the WRFPLUS model. The registry in the WRF model was extended to automatically generate the tangent linear and adjoint codes of the required communication operations. This approach dramatically speeds up the software development cycle of the parallel tangent linear and adjoint codes and leads to improved parallel efficiency. Module interfaces were constructed for coupling tangent linear and adjoint codes of the WRF model with applications such as four-dimensional variational data assimilation, forecast sensitivity to observation, and others.
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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 RUC land-surface (option 3) were compared against observational data from Thai Meteorological Department (TMD). Preliminary analysis indicated WRF simulations with Noah scheme were able to reproduce the most reliable daily mean temperatures over northern Thailand.
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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 (November 13, 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. The simulations were initialized at 0000 UTC with the forecast outputs of the global forecast system (GFS) model. The schemes were evaluated according to the wind field’s representation in the region where the airport is located, the headlands, and the center of the runway. The errors observed are strongly dependent on the occurrence of convection, especially on the intensity and the factors that cause it. During the dry season (November–April), the lowest errors are observed, while the worst performance is appreciable for the rainy period (May–October). Lin and WSM6 schemes reproduce the best behavior of the wind field on the aerodrome.
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De Araújo, Aline Aquino, Denis William Garcia, Juliano Dos Reis Monteiro, Thales Victor Miguel, Bruno De Campos, Vanessa Silveira Barreto Carvalho, and Michelle Simões Reboita. "Avaliação do modelo Weather Research and Forecasting (WRF) na simulação operacional de um evento de frente fria no sudeste do Brasil." Revista Brasileira de Geografia Física 16, no. 2 (April 3, 2023): 805. http://dx.doi.org/10.26848/rbgf.v16.2.p805-817.
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A região sudeste do Brasil é afetada o ano todo por sistemas transientes como as frentes frias. A incursão de sistemas frontais, além de afetar o dia a dia da população, pode causar danos e prejuízos a diversos setores da sociedade. Um modelo que é utilizado operacionalmente para a previsão de tempo no sul de Minas Gerais desde 2014 no Centro de Estudos de Previsão de Tempo e Clima de Minas Gerais (CEPreMG) da Universidade Federal de Itajubá é o Weather Research and Forecast (WRF). Em 2019, em decorrência da disponibilidade de dados do Global Forecast System (GFS) com maior resolução horizontal para a geração das condições iniciais e de fronteira necessárias como entrada ao modelo e do aumento da capacidade computacional disponível para as simulações, foram implementadas alterações no sistema em operação no CEPreMG. Assim, este estudo objetiva avaliar o desempenho do WRF na simulação de um sistema frontal que atuou no sudeste do Brasil em agosto de 2021. De forma geral, o modelo WRF simulou corretamente a posição e tempo de atuação do sistema, mas com algumas diferenças na intensidade. Isso não representa propriamente um problema uma vez que a resolução do WRF e dos dados usados na validação são diferentes. Evaluation of the Weather Research and Forecasting (WRF) model in the operational simulation of a cold weather event in southeastern Brazil A B S T R A C TThe southeastern region of Brazil is affected throughout the year by transient systems such as cold fronts. The incursion of frontal systems, in addition to affecting the daily life of the population, can cause damage and losses to various sectors of the society. A model that has been operationally used for weather forecasting in the southern region of Minas Gerais since 2014 at the Minas Gerais Weather and Climate Forecast Studies Center (CEPreMG) at the Federal University of Itajubá is the Weather Research and Forecast (WRF). In 2019, due to the availability of higher horizontal resolution data from the Global Forecast System (GFS) to generate the initial and boundary conditions necessary as inputs to the model and due to the increase in the computational capacity available, changes were implemented in the operational forecast system at CEPreMG. Thus, this study aims to evaluate the performance of the WRF in the simulation of a frontal system that was registered in the southeastern of Brazil in August 2021. In general, WRF model correctly simulated the position and evolution of the system, but with some differences regarding the intensity. This is not really a problem since the resolution of the WRF and the data used in the validation are different.
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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 (April 6, 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-linear models is a major issue, thus making a sensitivity analysis essential. Consequently, this paper seeks to quantify the performance of the WRF model in the presence of disturbances to the initial conditions (IC), for an arbitrary set of state-space variables (pressure and temperature), simulating a disruption in the inputs of the model. To this aim, we considered three distributions over the error term: a normal standard distribution, a normal distribution, and an exponential distribution. We analyze the sensitivity of the outputs of the WRF model by employing non-parametric and robust statistical techniques, such as kernel distribution estimates, rank tests, and bootstrap. The results show that the WRF model is sensitive in time, space, and vertical levels to changes in the IC. Finally, we demonstrate that the error distribution of the output differs from the error distribution induced over the input data, especially for Gaussian distributions.
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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, objectively identify gust fronts, and graphically display detected gust fronts and similar boundaries in NWP model forecasts. The tool is demonstrated with output from the Weather Research and Forecasting (WRF) model from the operational High-Resolution Rapid Refresh (HRRR) forecasting system and from a WRF ensemble run at the National Center for Atmospheric Research that can provide probabilistic information about model-predicted gust fronts. The tool can identify outflow boundaries in model forecasts of convective events occurring in both simple and complex terrain, both with and without concurrent wildfire activity. With accurate underlying model forecast output, the tool can reliably reveal areas of potential gust front activity and thus provide valuable guidance to incident meteorologists and command personnel.
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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 (November 19, 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 forecasts and analyze the uncertainties caused by physic parameterizations. In this study, we simulated the relevant meteorological parameters using the ensemble scheme of the stochastic physic perturbation tendency (SPPT) based on the weather research forecasting (WRF) model, which is one of the most broadly used NWP models. We selected an area in Foshan, Guangdong Province, in the southeast of China, and calculated the corresponding atmospheric delay. InSAR images were computed through data from the Sentinel-1A satellite and mitigated by the ensemble mean of the WRF-SPPT results. The WRF-SPPT method improves the mitigating effect more than WRF simulation without ensemble forecasting. The atmospherically corrected InSAR phases were used in the stacking process to estimate the linear deformation rate in the experimental area. The root mean square errors (RMSE) of the deformation rate without correction, with WRF-only correction, and with WRF-SPPT correction were calculated, indicating that ensemble forecasting can significantly reduce the atmospheric delay in stacking. In addition, the ensemble forecasting based on a combination of initial uncertainties and stochastic physic perturbation tendencies showed better correction performance compared with the ensemble forecasting generated by a set of perturbed initial conditions without considering the model’s uncertainties.
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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 (April 27, 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 less), the present study seeks to evaluate IB method performance over a range of grid resolutions and aspect ratios. Two classes of IB algorithms, velocity reconstruction and shear stress reconstruction, are tested within the common framework of the Weather Research and Forecasting (WRF) Model. Performance is evaluated in two test cases, one with flat terrain and the other with the topography of Askervein Hill, both under neutrally stratified conditions. WRF-IB results are compared to similarity theory, observations, and native WRF results. Despite sensitivity to the location at which the IB intersects the model grid, the velocity reconstruction IB method shows consistent performance when used with a hybrid RANS/LES surface scheme. The shear stress reconstruction IB method is not sensitive to the grid intersection, but is less consistent and near-surface velocity errors can occur at coarse resolutions. This study represents an initial investigation of IB method variability across grid resolutions in WRF. Future work will focus on improving IB method performance at intermediate to coarse resolutions.
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Thaker, Jayesh, and Robert Höller. "Evaluation of High Resolution WRF Solar." Energies 16, no. 8 (April 18, 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 applications. The aim of this paper is to evaluate the performance of the high resolution WRF solar model and compare the results with the low resolution WRF solar and Global Forecasting System (GFS) models. We investigate the performance of WRF solar for a high-resolution spatial domain of resolution 1 × 1 km and compare the results with a 3 × 3 km domain and GFS. The results show error metrices rMAE {23.14%, 24.51%, 27.75%} and rRMSE {35.69%, 36.04%, 37.32%} for high resolution WRF solar, coarse domain WRF solar and GFS, respectively. This confirms that high resolution WRF solar performs better than coarse domain and in general. WRF solar demonstrates statistically significant improvement over GFS.
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Huang, Xiang-Yu, Qingnong Xiao, Dale M. Barker, Xin Zhang, John Michalakes, Wei Huang, Tom Henderson, et al. "Four-Dimensional Variational Data Assimilation for WRF: Formulation and Preliminary Results." Monthly Weather Review 137, no. 1 (January 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 implicitly evolve the background error covariance and to produce the flow-dependent nature of the analysis increments. Preliminary results from real-data 4D-Var experiments in a quasi-operational setting are presented and the potential of WRF 4D-Var in research and operational applications are demonstrated. A wider distribution of the system to the research community will further develop its capabilities and to encourage testing under different weather conditions and model configurations.
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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 (June 1, 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 treatment. Testing and development work for Polar WRF begins with simulations for ice sheet surface conditions using a Greenland-area domain with 24-km resolution. The winter month December 2002 and the summer month June 2001 are simulated with WRF, version 2.1.1, in a series of 48-h integrations initialized daily at 0000 UTC. The results motivated several improvements to Polar WRF, especially to the Noah land surface model (LSM) and the snowpack treatment. Different physics packages for WRF are evaluated with December 2002 simulations that show variable forecast skill when verified with the automatic weather station observations. The WRF simulation with the combination of the modified Noah LSM, the Mellor–Yamada–Janjić boundary layer parameterization, and the WRF single-moment microphysics produced results that reach or exceed the success standards of a Polar MM5 simulation for December 2002. For summer simulations of June 2001, WRF simulates an improved surface energy balance, and shows forecast skill nearly equal to that of Polar MM5.
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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 (January 21, 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 danger. A drought-fire model known as Peat Fire Vulnerability Index was applied with daily inputs of WRF output at 5km resolution, which were comprised of rainfall, air temperature, and soil moisture. The results showed that calibration reduced rainfall magnitude, and slightly increased the maximum air temperature and soil moisture. The calibration performance was good as shown by a very low percent bias (less than ±5%), and lower error (RMSE=16.5; MAE=9.5). Our analysis showed that drought triggered by El Niño in 2015 had escalated extreme fire danger class by 38% compared to normal year (2018). This has been confirmed by a low variation of proportion of extreme class during July-August 2015. The results suggested that integrating spatial global climate data will benefit to the improved drought-fire model by providing spatial data. The results are expected to be a reference on drought and peat fires mitigation action.
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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 simulated using the WRF model, in Cachoeira do Sul, Rio Grande do Sul, Brazil.
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Lee, Young-Hee, Kwang-Deuk Ahn, Yong Hee Lee, and Hyunmin Eom. "Implementation of tidal parameterization in the Weather Research and Forecasting (WRF) model." Terrestrial, Atmospheric and Oceanic Sciences 31, no. 1 (2020): 33–47. http://dx.doi.org/10.3319/tao.2019.07.03.01.
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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 (December 31, 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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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 (April 2, 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 old USGS and new GLC2015 land-use datasets in the WRF model was performed for July 2018, where the simulated variables included the sensible heat flux (SHF), latent heat flux (LHF), surface skin temperature (Tsk), two-meter air temperature (T2), wind speed (Winds), specific humidity (Q2) and relative humidity (RH). The results indicated that there were significant differences between the two datasets. For example, our statistical verification results found via in situ observations made by the MET (model evaluation tools) illustrated that the bias of T2 decreased by 2.54%, the root mean square error (RMSE) decreased by 1.48%, the bias of Winds decreased by 10.46%, and the RMSE decreased by 6.77% when using the new dataset, and the new parameter values performed a net positive effect on land–atmosphere interactions. These results suggested that the GLC2015 land-use dataset developed in this study was useful in terms of improving the performance of the WRF model in the summer months.
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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 (April 25, 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 predict the event, but without the magnitude representation of the phenomenon. The WRF simulation underestimated the results for the accumulated and area of the snowfall region, which may be linked to overestimations of surface and vertical air temperature and liquid water precipitation. These results were attributed to the choice of WRF Single–moment 6–class (WSM6) microphysics and in the Noah Land Surface Model scheme. Despite these limitations, WRF has proved to be an important tool for predicting the spatial and temporal distribution of snowfall and precipitation in the higher regions of southern Brazil.
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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 (March 3, 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 model analysis and available gridded radar and ground-based precipitation products. Inter-BMPS comparisons of column-integrated mixing ratios and mixing ratio profiles reveal little variability in non-frozen hydrometeor species due to their shared programming heritage, yet their assumptions concerning snow and graupel intercepts, ice supersaturation, snow and graupel density maps, and terminal velocities led to considerable variability in both simulated frozen hydrometeor species and radar reflectivity. WRF-simulated precipitation fields exhibit minor spatiotemporal variability amongst BMPSs, yet their spatial extent is largely conserved. Compared to ground-based precipitation data, WRF simulations demonstrate low-to-moderate (0.217–0.414) threat scores and a rainfall distribution shifted toward higher values. Finally, an analysis of WRF and gridded radar reflectivity data via contoured frequency with altitude diagrams (CFADs) reveals notable variability amongst BMPSs, where better performing schemes favored lower graupel mixing ratios and better underlying aggregation assumptions.
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Bernardet, L., V. Tallapragada, S. Bao, S. Trahan, Y. Kwon, Q. Liu, M. Tong, 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 (June 1, 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’s National Weather Service, but it incorporates contributions from a variety of scientists spread out over several governmental laboratories and academic institutions. This distributed development scenario poses significant challenges: a large number of scientists need to learn how to use the model, operational and research codes need to stay synchronized to avoid divergence, and promising new capabilities need to be tested for operational consideration. This article describes how the Developmental Testbed Center has engaged in the HWRF developmental cycle in the last three years and the services it provides to the community in using and developing HWRF.
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Bao, Jingyi, Fotini Katopodes Chow, and Katherine A. Lundquist. "Large-Eddy Simulation over Complex Terrain Using an Improved Immersed Boundary Method in the Weather Research and Forecasting Model." Monthly Weather Review 146, no. 9 (August 10, 2018): 2781–97. http://dx.doi.org/10.1175/mwr-d-18-0067.1.
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Abstract The Weather Research and Forecasting (WRF) Model is increasingly being used for higher-resolution atmospheric simulations over complex terrain. With increased resolution, resolved terrain slopes become steeper, and the native terrain-following coordinates used in WRF result in numerical errors and instability. The immersed boundary method (IBM) uses a nonconformal grid with the terrain surface represented through interpolated forcing terms. Lundquist et al.’s WRF-IBM implementation eliminates the limitations of WRF’s terrain-following coordinate and was previously validated with a no-slip boundary condition for urban simulations and idealized terrain. This paper describes the implementation of a log-law boundary condition into WRF-IBM to extend its applicability to general atmospheric complex terrain simulations. The implementation of the improved WRF-IBM boundary condition is validated for neutral flow over flat terrain and the complex terrain cases of Askervein Hill, Scotland, and Bolund Hill, Denmark. First, comparisons are made to similarity theory and standard WRF results for the flat terrain case. Then, simulations of flow over the moderately sloped Askervein Hill are used to demonstrate agreement between the IBM and terrain-following WRF results, as well as agreement with observations. Finally, Bolund Hill simulations show that WRF-IBM can handle steep topography (standard WRF fails) and compares well to observations. Overall, the new WRF-IBM boundary condition shows improved performance, though the leeside representation of the flow can be potentially further improved.
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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 (April 1, 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-making process. Second, WRF output was compared directly with output from current operational forecast models. Results indicate that human forecasts showed a small, but measurable, improvement when forecasters had access to the high-resolution WRF output and, in the mean, the WRF output received higher ratings than the operational Eta Model on subjective performance measures related to convective initiation, evolution, and mode. The results suggest that convection-allowing models have the potential to provide a value-added benefit to the traditional guidance package used by severe weather forecasters.
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Koem, Chhuonvuoch, and Sarintip Tantanee. "RAIN DETECTING ACCURACY OF WEATHER RESEARCH FORECASTING (WRF) AND TRMM RAINFALL PRODUCT OVER CAMBODIA." ASEAN Engineering Journal 12, no. 2 (June 1, 2022): 227–34. http://dx.doi.org/10.11113/aej.v12.17504.
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Rainfall is one of the important parameters for evaluating flood hazard risk. Cambodia is a vulnerable country to extreme rainfall where the number of rain gauges over the country is limited. Therefore, the possibilities of applying rainfall products from satellite observation and rainfall forecasting models are crucial for the country. The purpose of this research is to evaluate the detecting accuracy of the rainfall-based Weather Research Forecasting (WRF) model and TRMM rainfall products by comparing with observed rainfall during heavy rainfall for different topography over Cambodia. The categorical statistic is used to calibrate the rainfall from the WRF model with observed rainfall from 23 stations over Cambodia on selected heavy rainfall dates of 15, 17, and 19 September 2019. Cambodia experienced floods along the Tonle Sap River and the Mekong Basin by the triggered heavy rainfall. The results show that the detecting accuracy of days 15, 17, and 19 from TRMM rainfall matched with observed rainfall are 55%, 71%, and 63%, respectively. The average detecting accuracy of mountainous is 65% whereas plains are 63.33%. The average detecting accuracy of coastal and Tonle Sap is 53.66% and 63%, respectively. Moreover, the detecting accuracy of days 15, 17, and 19 forecasts from the WRF model compared with observed rainfall are 41%, 69%, and 63%, respectively. The average detecting accuracy of mountainous, plains, coastal, and Tonle Sap are 52%, 55.66%, 52.33%, and 65.66%, individually. The forecast rainfall from the WRF model and TRMM could detect the rainfall. They are therefore should be used in the areas that lack rainfall stations in Cambodia.
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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 rainfall stations for minimum and medium rainfall events but did not produce good result during maximum rainfall overall. The WSM-6 scheme is found to produce better result compared to WSM-3. The study also found that to acquire accurate precipitation results, it is also required to test some other physics scheme parameterization to enhance the model performance.
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Glotfelty, Timothy, Kiran Alapaty, Jian He, Patrick Hawbecker, Xiaoliang Song, and Guang Zhang. "The Weather Research and Forecasting Model with Aerosol–Cloud Interactions (WRF-ACI): Development, Evaluation, and Initial Application." Monthly Weather Review 147, no. 5 (April 17, 2019): 1491–511. http://dx.doi.org/10.1175/mwr-d-18-0267.1.
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Abstract The Weather Research and Forecasting Model with Aerosol–Cloud Interactions (WRF-ACI) is developed for studying aerosol effects on gridscale and subgrid-scale clouds using common aerosol activation and ice nucleation formulations and double-moment cloud microphysics in a scale-aware subgrid-scale parameterization scheme. Comparisons of both the standard WRF and WRF-ACI models’ results for a summer season against satellite and reanalysis estimates show that the WRF-ACI system improves the simulation of cloud liquid and ice water paths. Correlation coefficients for nearly all evaluated parameters are improved, while other variables show slight degradation. Results indicate a strong cloud lifetime effect from current climatological aerosols increasing domain average cloud liquid water path and reducing domain average precipitation as compared to a simulation with aerosols reduced by 90%. Increased cloud-top heights indicate a thermodynamic invigoration effect, but the impact of thermodynamic invigoration on precipitation is overwhelmed by the cloud lifetime effect. A combination of cloud lifetime and cloud albedo effects increases domain average shortwave cloud forcing by ~3.0 W m−2. Subgrid-scale clouds experience a stronger response to aerosol levels, while gridscale clouds are subject to thermodynamic feedbacks because of the design of the WRF modeling framework. The magnitude of aerosol indirect effects is shown to be sensitive to the choice of autoconversion parameterization used in both the gridscale and subgrid-scale cloud microphysics, but spatial patterns remain qualitatively similar. These results indicate that the WRF-ACI model provides the community with a computationally efficient tool for exploring aerosol–cloud interactions.
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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 (November 7, 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 used for constructing the DL-based wind-velocity simulation model. The experimental area of Northern Taiwan was used for the simulation. Regarding the complex typhoon system, the collected data comprised the typhoon tracks, FNL (Final) Operational Global Analysis Data for the WRF model, typhoon characteristics, and ground weather data. This study included 47 typhoon events that occurred over 2000–2017. Three measures were used to analyze the models for identifying optimal performance levels: Mean absolute error, root mean squared error, and correlation coefficient. This study compared observations with the WRF numerical model and DNN model. The results revealed that (1) simulations by using the WRF-based models were satisfactorily consistent with the observed data and (2) simulations by using the DNN model were considerably consistent with those of the WRF-based model. Consequently, the proposed DNN combined with WRF model can be effectively used in simulations of wind velocity at arbitrary positions of study area.
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Sun, Mingkun, Zhijia Li, Cheng Yao, Zhiyu Liu, Jingfeng Wang, Aizhong Hou, Ke Zhang, Wenbo Huo, and Moyang Liu. "Evaluation of Flood Prediction Capability of the WRF-Hydro Model Based on Multiple Forcing Scenarios." Water 12, no. 3 (March 20, 2020): 874. http://dx.doi.org/10.3390/w12030874.
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The Weather Research and Forecasting (WRF)-Hydro model as a physical-based, fully-distributed, multi-parameterization modeling system easy to couple with numerical weather prediction model, has potential for operational flood forecasting in the small and medium catchments (SMCs). However, this model requires many input forcings, which makes it difficult to use it for the SMCs without adequate observed forcings. The Global Land Data Assimilation System (GLDAS), the WRF outputs and the ideal forcings generated by the WRF-Hydro model can provide all forcings required in the model for these SMCs. In this study, seven forcing scenarios were designed based on the products of GLDAS, WRF and ideal forcings, as well as the observed and merged rainfalls to assess the performance of the WRF-Hydro model for flood simulation. The model was applied to the Chenhe catchment, a typical SMC located in the Midwestern China. The flood prediction capability of the WRF-Hydro model was also compared to that of widely used Xinanjiang model. The results show that the three forcing scenarios, including the GLDAS forcings with observed rainfall, the WRF forcings with observed rainfall and GLDAS forcings with GLDAS-merged rainfall, are optimal input forcings for the WRF-Hydro model. Their mean root mean square errors (RMSE) are 0.18, 0.18 and 0.17 mm/h, respectively. The performance of the WRF-Hydro model driven by these three scenarios is generally comparable to that of the Xinanjiang model (RMSE = 0.17 mm/h).
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Du Duc, Tien, Lars Robert Hole, Duc Tran Anh, Cuong Hoang Duc, and Thuy Nguyen Ba. "Verification of Forecast Weather Surface Variables over Vietnam Using the National Numerical Weather Prediction System." Advances in Meteorology 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/8152413.
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The national numerical weather prediction system of Vietnam is presented and evaluated. The system is based on three main models, namely, the Japanese Global Spectral Model, the US Global Forecast System, and the US Weather Research and Forecasting (WRF) model. The global forecast products have been received at 0.25- and 0.5-degree horizontal resolution, respectively, and the WRF model has been run locally with 16 km horizontal resolution at the National Center for Hydro-Meteorological Forecasting using lateral conditions from GSM and GFS. The model performance is evaluated by comparing model output against observations of precipitation, wind speed, and temperature at 168 weather stations, with daily data from 2010 to 2014. In general, the global models provide more accurate forecasts than the regional models, probably due to the low horizontal resolution in the regional model. Also, the model performance is poorer for stations with altitudes greater than 500 meters above sea level (masl). For tropical cyclone performance validations, the maximum wind surface forecast from global and regional models is also verified against the best track of Joint Typhoon Warning Center. Finally, the model forecast skill during a recent extreme rain event in northeast Vietnam is evaluated.