利用CMA-MESO和SWC-WARMS高分辨率模式2023年5—9月小时降水预报产品,四川自动站小时降水资料及CLDAS(CMA Land Data Assimilation System)三源融合小时降水资料,采用“点对点”和“点对面”检验方法,对两家高分辨率模式小时降水产品在...利用CMA-MESO和SWC-WARMS高分辨率模式2023年5—9月小时降水预报产品,四川自动站小时降水资料及CLDAS(CMA Land Data Assimilation System)三源融合小时降水资料,采用“点对点”和“点对面”检验方法,对两家高分辨率模式小时降水产品在四川地区的预报性能进行评估。主要结论如下:①小时降水10 mm以下和50 mm以上的极端降水,CMA-MESO的预报参考性显著优于SWC-WARMS;小时降水10~30 mm,5—6月以SWC-WARMS表现更优,7—9月则以CMA-MESO表现更佳。②空间偏差特征分析表明,两家模式的小时降水平均绝对误差空间分布整体较为一致,在盆周山区、川西高原及凉山州北部误差较大,模式误差和实况降水强度、海拔高度呈一定正相关,且SWC-WARMS的空间误差更大。③时间偏差特征分析表明,CMA-MESO的短时强降水站点频次峰值时间偏差为1~2 h,而SWC-WARMS达3~4 h。④为优化TS评分,针对CMA-MESO和SWC-WARMS模式,在空间上,预报时可分别考虑邻域半径9 km和12 km内出现对应量级降水的可能性;在时间上,对于5~30 mm小时降水,预报时可考虑预报时刻前后1 h和2 h发生的可能性,其他量级可考虑前后1 h。展开更多
High spatiotemporal resolution infrared radiances from FY-4A/AGRI(Advanced Geostationary Radiation Imager)can provide crucial information for rapidly developing severe convective weather.This study established a symme...High spatiotemporal resolution infrared radiances from FY-4A/AGRI(Advanced Geostationary Radiation Imager)can provide crucial information for rapidly developing severe convective weather.This study established a symmetric observation error model that differentiates between land and sea for FY-4A/AGRI all-sky assimilation,developed an all-sky assimilation scheme for FY-4A/AGRI based on hydrometeor control variables,and investigated the impacts of all-sky FY-4A/AGRI water vapor channels at different altitudes and rapid-update assimilation at different frequencies on the assimilation and forecasting of a severe convective weather event.Results show that simultaneous assimilation of two water vapor channels can enhance precipitation forecasts compared to single-channel assimilation,which is mainly attributable to a more accurate analysis of water vapor and hydrometeor information.Experiments with different assimilation frequencies demonstrate that the hourly assimilation frequency,compared to other frequencies,incorporates the high-frequency information from AGRI while reducing the impact of spurious oscillations caused by excessively high-frequency assimilation.This hourly assimilation frequency reduces the incoordination among thermal,dynamical,and water vapor conditions caused by excessively fast or slow assimilation frequencies,thus improving the forecast accuracy compared to other frequencies.展开更多
使用2021年GRAPES(Global/Regional Assimilation and Prediction System)模式数据和FY-4A卫星数据分析酒泉地区云量时空特征,采用时间自适应方法、动态变参数方法以及随机森林方法建立云量预测模型。结果表明:酒泉及周边地区总云量日...使用2021年GRAPES(Global/Regional Assimilation and Prediction System)模式数据和FY-4A卫星数据分析酒泉地区云量时空特征,采用时间自适应方法、动态变参数方法以及随机森林方法建立云量预测模型。结果表明:酒泉及周边地区总云量日变化幅度不大,季节变化特征明显,春、夏季多,秋、冬季较少,北部云量较少、南部云量多。不同格点的云量受到不同因素的影响,使用动态变参数方法,即根据预报因子和云量相关性在不同格点上动态选取预报因子构建随机森林模型,云量预测准确率为0.55~0.80。采用时间自适应方法使随机森林模型能够更新换代,云量预测准确性在0.55左右,数据量不足导致随机森林模型预测云量的准确率下降。展开更多
基金supported by the National Key R&D Program of China(Grant No.2022YFC3080500)the National Natural Science Foundation of China(Grant Nos.U2142208,42475158,and 42105149)the High-Performance Computing Center of Nanjing University of Information Science&Technology for supporting this work。
文摘High spatiotemporal resolution infrared radiances from FY-4A/AGRI(Advanced Geostationary Radiation Imager)can provide crucial information for rapidly developing severe convective weather.This study established a symmetric observation error model that differentiates between land and sea for FY-4A/AGRI all-sky assimilation,developed an all-sky assimilation scheme for FY-4A/AGRI based on hydrometeor control variables,and investigated the impacts of all-sky FY-4A/AGRI water vapor channels at different altitudes and rapid-update assimilation at different frequencies on the assimilation and forecasting of a severe convective weather event.Results show that simultaneous assimilation of two water vapor channels can enhance precipitation forecasts compared to single-channel assimilation,which is mainly attributable to a more accurate analysis of water vapor and hydrometeor information.Experiments with different assimilation frequencies demonstrate that the hourly assimilation frequency,compared to other frequencies,incorporates the high-frequency information from AGRI while reducing the impact of spurious oscillations caused by excessively high-frequency assimilation.This hourly assimilation frequency reduces the incoordination among thermal,dynamical,and water vapor conditions caused by excessively fast or slow assimilation frequencies,thus improving the forecast accuracy compared to other frequencies.
文摘使用2021年GRAPES(Global/Regional Assimilation and Prediction System)模式数据和FY-4A卫星数据分析酒泉地区云量时空特征,采用时间自适应方法、动态变参数方法以及随机森林方法建立云量预测模型。结果表明:酒泉及周边地区总云量日变化幅度不大,季节变化特征明显,春、夏季多,秋、冬季较少,北部云量较少、南部云量多。不同格点的云量受到不同因素的影响,使用动态变参数方法,即根据预报因子和云量相关性在不同格点上动态选取预报因子构建随机森林模型,云量预测准确率为0.55~0.80。采用时间自适应方法使随机森林模型能够更新换代,云量预测准确性在0.55左右,数据量不足导致随机森林模型预测云量的准确率下降。
