摘要
本文通过对2017年11月至2018年3月最高、最低温度格点预报及实况数据,按"邻近距离最短优先"原则,读取西宁市38个测站智能网格预报产品,检验同时段内的实况值,检验结果表明:1)西宁市国家站最高、最低温度格点预报准确率明显高于区域站,预报准确率总体呈现上升趋势,区域站最高温度格点预报准确率上升较明显;2)国家站温度格点预报准确率西宁最高、湟源最低。区域站最高、最低温度格点平均预报准确率为49.2%、47.5%,其中长宁站最高,为83.5%、84.4%;3)大通站最高、最低温度格点预报平均误差最小,为-0.1℃、0.1℃。区域站预报值均偏低,偏低程度最低温度小于最高温度。大华、黑泉水库站温度预报平均误差最高,为-6℃左右;4)长宁站最高、最低温度格点预报值参考度高,湟水河林场、城中、景阳镇次之,大华、朝阳收费站、黑泉水库预报值参考度较差。
Base on the maximum and minimum temperature grid forecast and live data from November 2017 to March 2018,this paper reads the intelligent grid forecast products of 38 stations in Xining according to the principle of "shortest priority in proximity",and test the live values in the CIMISS in the same time.The test results show that:1)The forecast accuracy of the maximum and minimum temperature grids in the national station of Xining City is significantly higher than that of the regional stations,the accuracy of the forecasting is generally on the rise,and the accuracy of the maximum temperature grid forecasting of the regional stations is more obvious;2)The accuracy of the national station temperature grid forecast is the highest in Xining and the lowest in Huangyuan.The grids average forecast accuracy of the maximum and minimum temperature of the regional stations is 49.2% and 47.5%,of which Changning Station is the highest with 83.5% and 84.4%;3) The average error of the maximum and minimum temperature grids in Datong Station is the smallest,-0.1℃,0.1℃.The forecast values of the regional stations are all low,and the minimum temperature forecast of low degree is lower than the maximum temperature’s.The average temperature error of the two stations in Dahua and Heiquan Reservoir is the highest,about-6℃;4) The reference value of the maximum and minimum temperature grid forecast values of Changning Station is high,Huangshuihe Forest Farm,Chengzhong and Jingyang Towns are second,and the reference vaule of Dahua,Chaoyang Toll Station and Heiquan Reservoir are poor.
作者
沈洁
朱宝文
SHEN Jie;ZHU Bao-wen(Observatory of Xining City in Qinghai Province,Xining Qinghai 810003,China;Qinghai Meteorological Cadre Training Institute,Xining Qinghai 810001,China)
出处
《青海农林科技》
2020年第2期54-59,93,共7页
Science and Technology of Qinghai Agriculture and Forestry
基金
青海省气象局2018年预报员专项“智能网格预报在西宁的应用与检验”。
关键词
智能网格
最高、最低温度
检验
Intelligent grid
Maximum and minimum temperature
Inspection