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成飞飞行空域包含高原、盆地、山区等多种地形,局地气候显著,短时强降水频发。本文使用国家气象信息中心2017-2021年多资料融合逐小时降水数据、国家自动站探空观测数据。统计分析出盆周沿山区为盆地短时强降水高发区;101°E~102°E,31°N~32°N区域为高原短时强降水高发区。利用百分位法得到高原地区强对流指数阈值:CAPE值≥1930.5J/kg;BCAPE值≥1974.7J/kg;抬升指数≥2.6℃;大气可降水量≥86.1mm;K指数≥37.2℃;SI指数≤-0.9℃。盆地地区强对流指数阈值:CAPE值≥2230.6J/kg;BCAPE值≥2264.4J/kg;抬升指数≥1.8℃;大气可降水量≥93.0mm;K指数≥40.8℃;SI指数≤-1.8℃。建立短时强降水不同下垫面强对流指数阈值,为今后短时强降雨客观预报方法,提供新的思路和方向。  相似文献   
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Meteo-hydrological forecasting models are an effective way to generate high-resolution gridded rainfall data for water source research and flood forecast. The quality of rainfall data in terms of both intensity and distribution is very important for establishing a reliable meteo-hydrological forecasting model. To improve the accuracy of rainfall data, the successive correction method is introduced to correct the bias of rainfall, and a meteo-hydrological forecasting model based on WRF and WRF-Hydro is applied for streamflow forecast over the Zhanghe River catchment in China. The performance of WRF rainfall is compared with the China Meteorological Administration Multi-source Precipitation Analysis System (CMPAS), and the simulated streamflow from the model is further studied. It shows that the corrected WRF rainfall is more similar to the CMPAS in both temporal and spatial distribution than the original WRF rainfall. By contrast, the statistical metrics of the corrected WRF rainfall are better. When the corrected WRF rainfall is used to drive the WRF-Hydro model, the simulated streamflow of most events is significantly improved in both hydrographs and volume than that of using the original WRF rainfall. Among the studied events, the largest improvement of the NSE is from -0.68 to 0.67. It proves that correcting the bias of WRF rainfall with the successive correction method can greatly improve the performance of streamflow forecast. In general, the WRF / WRF-Hydro meteo-hydrological forecasting model based on the successive correction method has the potential to provide better streamflow forecast in the Zhanghe River catchment.  相似文献   
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