A combination method of optimization of the back-ground value and optimization of the initial item is proposed. The sequences of the unbiased exponential distribution are simulated and predicted through the optimizati...A combination method of optimization of the back-ground value and optimization of the initial item is proposed. The sequences of the unbiased exponential distribution are simulated and predicted through the optimization of the background value in grey differential equations. The principle of the new information priority in the grey system theory and the rationality of the initial item in the original GM（1,1） model are ful y expressed through the improvement of the initial item in the proposed time response function. A numerical example is employed to il ustrate that the proposed method is able to simulate and predict sequences of raw data with the unbiased exponential distribution and has better simulation performance and prediction precision than the original GM（1,1） model relatively.展开更多
基于高分辨率快速更新无缝隙融合集成预报RISE系统(Rapid-refresh Integrated Seamless Ensemble system),采用华北3 km分辨率快速循环更新的中尺度数值模式CMA-BJ、欧洲中心0.125°分辨率全球数值模式ECMWF、常规自动站和冬奥赛道...基于高分辨率快速更新无缝隙融合集成预报RISE系统(Rapid-refresh Integrated Seamless Ensemble system),采用华北3 km分辨率快速循环更新的中尺度数值模式CMA-BJ、欧洲中心0.125°分辨率全球数值模式ECMWF、常规自动站和冬奥赛道加密自动站逐时观测资料,以北京冬奥会复杂山地为研究区域,对比分析了不同模式背景场对100 m网格分辨率的地面2 m温度和10 m风场融合分析场和1~24 h逐小时间隔预报准确性的影响。对比试验结果表明:(1)采用区域模式和全球模式的预报数据作为RISE系统背景场,均可有效形成复杂山地百米级精细化融合产品,但不同模式背景场对不同气象要素分析和预报性能的影响存在明显差异;(2)对于温度分析场,以CAM-BJ和ECMWF模式的预报数据为背景场的RISE温度分析场空间分布基本一致,分析平均绝对误差(MAE)均小于0.2℃;(3)对于风场分析场,采用高分辨率区域模式比粗分辨率全球模式更能提升RISE高精度风场融合产品精细化水平;(4)对于温度预报,以ECMWF模式的预报数据为背景场的RISE格点融合预报性能显著优于CMA-BJ模式的预报数据为背景场,冬奥高山站和所有站平均预报MAE分别减小10.5%和7.0%;(5)对于风场预报,以CAM-BJ和ECMWF模式的预报数据为背景场的RISE冬奥高山站临近1~6 h风速预报MAE分别为1.42 m s^(-1)和1.30 m s^(-1),7~24 h预报MAE则分别为1.52 m s^(-1)和1.54 m s^(-1),而RISE区域内所有站1~24 h平均MAE分别为1.38 m s^(-1)和1.24 m s^(-1)。研究成果有助于深入理解模式背景场在百米级融合预报中的作用,对提升复杂地形下天气预报准确性有重要的科学意义和业务应用价值。展开更多
基金supported by the Key Project of National Social Science Foundation(12AZD111)the National Project for Education Science Planning(EFA110351)+2 种基金the Humanities and Social Science Foundation of Ministry of Education of China(12YJCZH207)the Key Project for Jiangsu Province Social Science Foundation(12DDA011)the Jiangsu College of Humanities and Social Sciences outside Campus Research Base:Chinese Development of Strategic Research Base for Internet of Things
文摘A combination method of optimization of the back-ground value and optimization of the initial item is proposed. The sequences of the unbiased exponential distribution are simulated and predicted through the optimization of the background value in grey differential equations. The principle of the new information priority in the grey system theory and the rationality of the initial item in the original GM（1,1） model are ful y expressed through the improvement of the initial item in the proposed time response function. A numerical example is employed to il ustrate that the proposed method is able to simulate and predict sequences of raw data with the unbiased exponential distribution and has better simulation performance and prediction precision than the original GM（1,1） model relatively.
文摘基于高分辨率快速更新无缝隙融合集成预报RISE系统(Rapid-refresh Integrated Seamless Ensemble system),采用华北3 km分辨率快速循环更新的中尺度数值模式CMA-BJ、欧洲中心0.125°分辨率全球数值模式ECMWF、常规自动站和冬奥赛道加密自动站逐时观测资料,以北京冬奥会复杂山地为研究区域,对比分析了不同模式背景场对100 m网格分辨率的地面2 m温度和10 m风场融合分析场和1~24 h逐小时间隔预报准确性的影响。对比试验结果表明:(1)采用区域模式和全球模式的预报数据作为RISE系统背景场,均可有效形成复杂山地百米级精细化融合产品,但不同模式背景场对不同气象要素分析和预报性能的影响存在明显差异;(2)对于温度分析场,以CAM-BJ和ECMWF模式的预报数据为背景场的RISE温度分析场空间分布基本一致,分析平均绝对误差(MAE)均小于0.2℃;(3)对于风场分析场,采用高分辨率区域模式比粗分辨率全球模式更能提升RISE高精度风场融合产品精细化水平;(4)对于温度预报,以ECMWF模式的预报数据为背景场的RISE格点融合预报性能显著优于CMA-BJ模式的预报数据为背景场,冬奥高山站和所有站平均预报MAE分别减小10.5%和7.0%;(5)对于风场预报,以CAM-BJ和ECMWF模式的预报数据为背景场的RISE冬奥高山站临近1~6 h风速预报MAE分别为1.42 m s^(-1)和1.30 m s^(-1),7~24 h预报MAE则分别为1.52 m s^(-1)和1.54 m s^(-1),而RISE区域内所有站1~24 h平均MAE分别为1.38 m s^(-1)和1.24 m s^(-1)。研究成果有助于深入理解模式背景场在百米级融合预报中的作用,对提升复杂地形下天气预报准确性有重要的科学意义和业务应用价值。
