Wind Climate Estimating using WRF model Output: Model Sensitivities
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This paper describes a dynamical downscaling simulation over China using the nested model system,which consists of the modified Weather Research and Forecasting Model(WRF)nested with the NCAR Community Atmosphere Model(CAM).Results show that dynamical downscaling is of great value in improving the model simulation of regional climatic characteristics.WRF simulates regional detailed temperature features better than CAM.With the spatial correlation coefficient between the observation and the simulation increasing from 0.54 for CAM to 0.79 for WRF,the improvement in precipitation simulation is more perceptible with WRF.Furthermore,the WRF simulation corrects the spatial bias of the precipitation in the CAM simulation.
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The aim of this research is to estimate wind speed for northern Thailand by using a high resolution regional climate model called Weather Research Forecasting (WRF) version 3.7. The model was forced by the Community Climate System Model version 3 (CCSM3) which is a coupled climate model for simulating global climate system. The period of study was 5 years interval (2000-2004). The spatial wind speed distribution was analyzed seasonally by using the Grid Analysis and Display system (GrADS). The 5-year averaged wind speed simulations were compared to 13 station observation data sets provided by the Thai Meteorological Department (TMD). The statistical analysis presented Root mean square error (RMSE) and correlation.
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The aim of this paper is to evaluate the quality of high-resolution weather forecasts from the Weather Research and Forecasting (WRF) numerical weather prediction model. The lateral and boundary conditions were obtained from the numerical output of the Consortium for Small-scale Modeling (COSMO) model at 7 kmhorizontal resolution. The WRF model was run for January and July 2013 at two horizontal resolutions(3 and 1 km). The numerical forecasts of the WRF model were evaluated using different statistical scores for 2 m temperature and 10 m wind speed. Results showed a tendency of the WRF model to overestimate the values of the analyzed parameters in comparison to observations.
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Evaluation of Flood Prediction Capability of the WRF-Hydro Model Based on Multiple Forcing Scenarios
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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In this study, National Centers for Environmental Prediction (NCEP) Final (FNL) operational global analysis data and meteorological observation data from 2013 to 2017 were used to evaluate the impact of seasonal changes and different circulation classifications on the dynamical downscaling simulation results of Weather Research and Forecasting (WRF) in the Pearl River Delta (PRD) region. The results show that the dynamical downscaling method can accurately simulate the time variation characteristics of the near-surface meteorological field and the hit rates of a 2-m temperature, 2-m relative humidity, 10-m wind speed, and 10-m wind direction are 92.66%, 93.98%, 26.78%, and 76.78%, respectively. The WRF model slightly underestimates the temperature and relative humidity, and overestimates the wind speed and precipitation. For precipitation, the WRF model can better simulate the variation characteristics of light rain and heavy rain, with the probability of detection are 0.59 and 0.69, respectively. For seasonal factors, the WRF model can conduct a perfect simulation in autumn and winter, followed by spring, while summer is vulnerable to extreme weather, so the result of the simulation is relatively poor. The circulation type is an important parameter of downscaling assessment. When the PRD is controlled by high pressure, the simulated results of WRF are good, and when the PRD is affected by low pressure or extreme weather, the simulation results are relatively poor.
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The Weather Research and Forecast (WRF) model was run over South-east Australia from 1985 through 2008. The model used the following physics schemes: WRF Single Moment 5-class microphysics scheme; Rapid Radiative Transfer Model longwave radiation scheme; Dudhia shortwave radiation scheme; Monin-Obukhov surface layer similarity; Noah land-surface scheme; Yonsei University boundary layer scheme and Kain-Fritsch cumulus physics scheme. The model simulation uses boundary conditions from the NCEP/NCAR reanalysis with an outer 50km resolution nest and an inner 10km resolution nest. Both nests used 30 vertical levels. In order to assess WRF's potential for use in investigating the current and future climate, the simulation is evaluated against gridded surface temperature and precipitation observations created as part of the Australian Water Availability Project (AWAP). The WRF simulation is found to reproduce the climate of the south-east Australian coast reasonably well. WRF was able to improve on the climate produced by the NCEP/NCAR reanalysis (NNRP) which provided the boundary conditions for the WRF simulation. Investigation of the time series of precipitation anomalies show that WRF is able to capture the recent drought in the SE Australia. While the overall time series captured the drought well, the spatial patterns associated with the anomalies produced by WRF differed from those found in the AWAP dataset. Further work will investigate the reasons for these spatial differences as well as WRF's performance at shorter time scales.
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In this study, we present some results of the Weather Research and Forecast (WRF) modelling system with different land-surface schemes. These results are from a WRF 48 hours forecast in the autumn season (22 and 23 November 2004). The purpose of the study is to evaluate the effects of the different land-surface models available in the WRF modelling system on the predicted 2-meter temperature over Portugal.
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