In order to evaluate the prediction performances of the climate prediction system developed by the China Meteorological Administration(CMA-CPSv3)over the Tibetan Plateau region,the precipitation and temperature predic...In order to evaluate the prediction performances of the climate prediction system developed by the China Meteorological Administration(CMA-CPSv3)over the Tibetan Plateau region,the precipitation and temperature predicted by CMA-CPSv3 at different lead time was evaluated for the period of 2001-2023,by comparing with observations—the monthly precipitation of the CMAP and 2 m temperature data of the NCEP/DOE reanalysis data.The main conclusions were as follows:1)the forecast skill of the model is very sensitive to the initial conditions and value,and the model forecast capability decreases rapidly as the lead time is extended.2)CPSv3 performed well in capturing the spatial distribution of precipitation over Xizang in January,August,October,November and December at 0-month lead,and the forecast skill is relatively poor in March and April.CPSv3 has better performance in the east-central part of the land in January,in the central and western part of the region in March,in the whole region in April,in the northern part of Nagchu,northern part of Chamdo and central part of Shigatse in July,in the eastern part of Chamdo,northern part of Nagchu and northern part of Ali in August,in the whole region in September,and in the cities of Shigatse and Shannan in November.3)At each lead time,the forecast skill for 2-m temperatures was higher than that for precipitation.For 2-m temperature,CPSv3 performed best in January,May,July,August,September,October,and December,while performing relatively poor in March,April,and November.Specifically,the prediction skill is higher in January and December for most of the regions,for Shigatse and Ali in May,for southern Shannan in July,and for northern Nagchu and southern Shannan in August,and there is some prediction skill in March,April and October,while CPSv3 performed poorly in November.展开更多
在新疆天山大地形背景下,实现了中国气象局研发的高分辨率气候业务预测系统CMA-CPSv3(China Meteorological Administration-Climate Prediction System version 3)在天山北坡经济带的本地化应用,分别评估控制预报、传统集合平均预报以...在新疆天山大地形背景下,实现了中国气象局研发的高分辨率气候业务预测系统CMA-CPSv3(China Meteorological Administration-Climate Prediction System version 3)在天山北坡经济带的本地化应用,分别评估控制预报、传统集合平均预报以及改进后的最优概率阈值集合方法(deterministic ensemble forecast using a probabilistic threshold,DEFPT)对该区域次季节-季节降水的预测水平。评估结果表明:基于CMA-CPSv3预测系统的DEFPT方法可以提升天山北坡次季节-季节尺度1~5 mm阈值降水落区以及持续性的预测效果,优于传统集合平均预报和控制预报。从2016年7月29日—8月2日、2017年6月7—12日以及2020年7月8—12日时段发生在天山北坡的降水事件个例分析结果看,不论从降水落区、降水异常还是降水持续性,DEFPT集合预报在天山北坡西部和南部均有更好的效果,但在天山北坡东部和北部预测能力相对略低,这与该区域水汽的预报偏差增大有关。展开更多
文摘In order to evaluate the prediction performances of the climate prediction system developed by the China Meteorological Administration(CMA-CPSv3)over the Tibetan Plateau region,the precipitation and temperature predicted by CMA-CPSv3 at different lead time was evaluated for the period of 2001-2023,by comparing with observations—the monthly precipitation of the CMAP and 2 m temperature data of the NCEP/DOE reanalysis data.The main conclusions were as follows:1)the forecast skill of the model is very sensitive to the initial conditions and value,and the model forecast capability decreases rapidly as the lead time is extended.2)CPSv3 performed well in capturing the spatial distribution of precipitation over Xizang in January,August,October,November and December at 0-month lead,and the forecast skill is relatively poor in March and April.CPSv3 has better performance in the east-central part of the land in January,in the central and western part of the region in March,in the whole region in April,in the northern part of Nagchu,northern part of Chamdo and central part of Shigatse in July,in the eastern part of Chamdo,northern part of Nagchu and northern part of Ali in August,in the whole region in September,and in the cities of Shigatse and Shannan in November.3)At each lead time,the forecast skill for 2-m temperatures was higher than that for precipitation.For 2-m temperature,CPSv3 performed best in January,May,July,August,September,October,and December,while performing relatively poor in March,April,and November.Specifically,the prediction skill is higher in January and December for most of the regions,for Shigatse and Ali in May,for southern Shannan in July,and for northern Nagchu and southern Shannan in August,and there is some prediction skill in March,April and October,while CPSv3 performed poorly in November.
文摘在新疆天山大地形背景下,实现了中国气象局研发的高分辨率气候业务预测系统CMA-CPSv3(China Meteorological Administration-Climate Prediction System version 3)在天山北坡经济带的本地化应用,分别评估控制预报、传统集合平均预报以及改进后的最优概率阈值集合方法(deterministic ensemble forecast using a probabilistic threshold,DEFPT)对该区域次季节-季节降水的预测水平。评估结果表明:基于CMA-CPSv3预测系统的DEFPT方法可以提升天山北坡次季节-季节尺度1~5 mm阈值降水落区以及持续性的预测效果,优于传统集合平均预报和控制预报。从2016年7月29日—8月2日、2017年6月7—12日以及2020年7月8—12日时段发生在天山北坡的降水事件个例分析结果看,不论从降水落区、降水异常还是降水持续性,DEFPT集合预报在天山北坡西部和南部均有更好的效果,但在天山北坡东部和北部预测能力相对略低,这与该区域水汽的预报偏差增大有关。
