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

Abstract

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.

摘要

当前数值天气预报模式在复杂地形区域的地面风速预报中存在较大误差. 本研究以上海快速更新同化数值预报系统为代表, 分析了模式近地面风速预报误差与地形特征之间的关联性. 研究引入了三个描述地形特征的参数: 网格尺度模式地形标准差、 模式地形高度误差和坡度. 研究结果表明, 预报误差随地形标准差的增大呈单峰变化, 误差平均值(ME)的最小值1.2 m s⁻¹出现在标准差在60–70 m之间; 预报误差与模式地形高度误差呈正相关, 地形高度误差每增加200 m, ME增加10%–30%. 上坡时, 当坡度从(0.5°–1.5°)增大到大于3.5°时, ME减小65.6%; 下坡时, 当坡度绝对值从(0.5°–1.5°)增大到(2.5°–3.5°)时, ME增大35.4%. 在上述分析的基础上建立了模式近地面风速的统计订正模型, 评估表明引入地形参数使ME(RMSE)降低了90%(30%), 显著地提高了模式近地面风速的预报准确率.