首页|Relationships between Terrain Features and Forecasting Errors of Surface Wind Speeds in a Mesoscale Numerical Weather Prediction Model

Relationships between Terrain Features and Forecasting Errors of Surface Wind Speeds in a Mesoscale Numerical Weather Prediction Model

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Numerical weather prediction(NWP)models have always presented large forecasting errors of surface wind speeds over regions with complex terrain.In this study,surface wind forecasts from an operational NWP model,the SMS-WARR(Shanghai Meteorological Service-WRF ADAS Rapid Refresh System),are analyzed to quantitatively reveal the relationships between the forecasted surface wind speed errors and terrain features,with the intent of providing clues to better apply the NWP model to complex terrain regions.The terrain features are described by three parameters:the standard deviation of the model grid-scale orography,terrain height error of the model,and slope angle.The results show that the forecast bias has a unimodal distribution with a change in the standard deviation of orography.The minimum ME(the mean value of bias)is 1.2 m s-1 when the standard deviation is between 60 and 70 m.A positive correlation exists between bias and terrain height error,with the ME increasing by 10%-30%for every 200 m increase in terrain height error.The ME decreases by 65.6%when slope angle increases from(0.5°-1.5°)to larger than 3.5° for uphill winds but increases by 35.4%when the absolute value of slope angle increases from(0.5°-1.5°)to(2.5°-3.5°)for downhill winds.Several sensitivity experiments are carried out with a model output statistical(MOS)calibration model for surface wind speeds and ME(RMSE)has been reduced by 90%(30%)by introducing terrain parameters,demonstrating the value of this study.

surface wind speedterrain featureserror analysisMOS calibration model

Wenbo XUE、Hui YU、Shengming TANG、Wei HUANG

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Chinese Academy of Meteorological Sciences,Beijing 100081,China

University of Chinese Academy of Sciences,Beijing 101408,China

Shanghai Typhoon Institute/CMA,Shanghai 200030,China

Key Laboratory of Numerical Modeling for Tropical Cyclone/CMA,Shanghai 200030,China

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国家自然科学基金

U2142206

2024

大气科学进展(英文版)
中国科学院大气物理研究所

大气科学进展(英文版)

CSTPCD
影响因子:0.741
ISSN:0256-1530
年,卷(期):2024.41(6)