日本区域加权平均温度建模

Atmospheric weighted mean temperature modeling for Japan

由于日本区域易受自然灾害频发、水汽特征变化复杂、探空站点分布稀疏的问题,进而制约了高精度水汽的获取,因此缺少此区域的高精度加权平均温度(T_(m))模型.鉴于此,采用2009—2016年全球大地测量观测系统(GGOS) Atmosphere T_(m)和ERA-Interim 2 m T_(s)格网数据新建立一种考虑T_(m)残差季节性变化和周日变化的适合日本区域的T_(m)模型(JQTm模型).同时,利用2017年日本区域13个探空站和110个GGOS Atmosphere T_(m)格网数据,对新建立的JQTm模型在日本区域的精度进行评估.研究发现:与GGOS Atmosphere T_(m)格网数据对比,JQTm模型的偏差(bias)和均方根误差(RMSE)分别为0.15 K和1.92 K,RMSE分别比GPT2w-1模型、GPT2w-5模型提升41.16%(1.33 K)、44.41%(1.53 K);与探空资料对比,JQTm模型的bias和RMSE分别为–0.66 K和2.14 K,RMSE分别比GPT2w-1模型、GPT2w-5模型提升28.43%(0.85 K)、29.61%(0.90 K).JQTm模型能够为日本区域提供高精度的T_(m)值,为研究此区域大气水汽和极端天气提供重要依据.

Due to the frequent occurrence of natural disasters,complex changes in water vapor characteristics,and sparse sounding stations in Japan,it restricts the acquisition of high-precision water vapor,and lacks a high-precision T_(m) model in this area.In view of this article adopts the 2009 to 2016 Global Geodetic Observing System(GGOS) weighted average temperature(T_(m)) with the ERA-Interim 2 m T_(s) grid data,a new T_(m) model(JQTm model) suitable for the Japanese region that takes into account the seasonal variation of the T_(m) residual and the daily cycle variation is established.In addition,using the data of 13 sounding stations and 110 GGOS Atmosphere T_(m) grids in Japan in 2017,the accuracy of the newly established JQTm model in this paper is evaluated in Japan.The study found that compared with the GGOS Atmosphere T_(m) grid data,the bias and root mean square error(RMSE) of the JQTm model are 0.15 K and 1.92 K,respectively.The RMSE is 41.16%(1.33 K) and 44.41%(1.53 K) higher than the GPT2w-1 and GPT2w-5 models,respectively.Compared with the sounding data,the bias and RMSE of the JQTm model are –0.66 K and 2.14 K,respectively.The RMSE is 28.43%(0.85 K) and 29.61%(0.90 K) higher than the Bevis model,GPT2w-1 model,and GPT2w-5 model,respectively.The JQTm model can provide high-precision T_(m) values for the Japanese region and provide an important basis for studying atmospheric water vapor and extreme weather in this region.