Abstract
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.
Highlights
Global Climate Models (GCMs) may be sufficient for describing large-scale circulations and climate [1], but it is difficult for them to reproduce regional and local circulations and climate
Compare with T2, the statistical indices of RH2, wind speed (WS), and wind direction (WD) are more complex, and the WS is obviously overestimated with the mean bias (MB) of 1.82 m s−1
National Centers for Environmental Prediction (NCEP) FNL operational global analysis data were used as the driving field of the regional climate model Weather Research and Forecasting (WRF) to carry out numerical experiments of dynamic downscaling in the Pearl River Delta region during the five years of 2013–2017
Summary
Global Climate Models (GCMs) may be sufficient for describing large-scale circulations and climate [1], but it is difficult for them to reproduce regional and local circulations and climate. When GCMs focus on the regional climate, they generally exhibit several problems, such as an output with a low spatial resolution, the inability to clearly describe the climate distribution difference in the basin region, and a limited ability to simulate extreme weather events These limitations are further amplified in areas with a complex topography, irregular coastline, and uneven soil cover, where the thermal and dynamic mechanical cycles are greatly affected by the heterogeneity of the surface. GCMs or weather prediction model and reanalysis data) can be employed as the driving field for WRF, making WRF one of the most widely used RCMs. Dynamical downscaling studies using WRF include the assessment of downscaling results, comparing results from different model resolutions, the impact of different physical parameterizations, the effect of initial conditions (e.g., sea surface and soil temperature) and large-scale circulations [9,10]. The impacts of season and weather conditions on WRF dynamical downscaling were investigated
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