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北京地区汛期降水时空分布的统计降尺度研究

Statistical Downscaling Research on Spatio-Temporal Distributions of Summer Precipitation Across the Beijing Region
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摘要 降水的时空降尺度方案一直以来是智能网格预报业务的难点,通过对比多种时间和空间的降尺度方法,凝练出适用于北京地区的最优方案。空间降尺度方面,对比反距离权重法、普通克里金法、最近邻法、双线性插值法、三维普通克里金法等5种方法的空间分布特征表明,双线性插值法在北京地区的应用效果最好,误差最小且ETS评分最高;时间降尺度方面,采用基于区域数值模式(睿图、CMA-MESO)产品的逐时分配和平均分配两种方案,其中睿图逐时分配、CMA-MESO逐时分配和平均分配在RMSE和MAE的误差表现差距不大,但睿图逐时分配在ETS上的效果最显著,且在强降水时段表现也较优,说明从预报准确率角度采用睿图逐时分配的优势更为明显。双线性插值和睿图逐时分配作为北京地区客观降水预报方法的时空降尺度方案,能够支撑智能网格业务提供精细化的预报产品,其成果可为相关业务研究提供借鉴。 The spatio-temporal statistical downscaling of precipitation has always been a difficult research point in intelligent grid forecasting.By comparing several spatio-temporal downscaling methods,we obtained the optimal scheme suitable for the Beijing Region in this study.In terms of spatial statistical downscaling,spatial distribution characteristics of five methods are compared,namely inverse distance weighting,ordinary Kriging,Nearest,Bilinear,3-D ordinary Kriging method,and the results show that Bilinear interpolation method has the best application effect in Beijing Region,with the smallest error and highest ETS.For temporal downscaling,two kinds of allocation schemes based on regional numerical model are compared,including hourly allocation schemes(RMAPS and CMA-MESO)and average allocation.The results show that there are no significant differences in RMSE and MAE for the three methods.The hourly allocation by RMAPS is more preponderant than by observation in ETS score,and it performs also better in heavy rainfall cases,which means the allocation by RMAPS has better advantages from the perspective of forecast accuracy.The schemes of bilinear interpolation and RMAPS hourly allocation are taken as the spatio-temporal downscaling schemes in the objective forecasting technique of Beijing Meteorological Observatory,and can support intelligent grid forecasting for providing refined forecast products.The results can provide some references for forecasting and associated researches.
作者 郝翠 于波 戴翼 智协飞 张迎新 HAO Cui;YU Bo;DAI Yi;ZHI Xiefei;ZHANG Yingxin(Beijing Weather Forecasting Center,Beijing 100097;Key Laboratory of Meteorological Disasters,Ministry of Education(KLME)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD),Nanjing University of Information Science and Technology,Nanjing 210044)
出处 《气象》 CSCD 北大核心 2023年第7期843-854,共12页 Meteorological Monthly
基金 北京市科技计划项目(Z201100008220005、Z201100005820002) 国家重点研发计划(2018YFC1507305) 北京市自然科学基金项目(8224090、8222079)共同资助。
关键词 降水 时空降尺度 插值方案 分布特征 网格预报 precipitation spatio-temporal downscaling interpolation scheme distribution characteristic grid forecast
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