Atmospheric weighted mean temperature modeling for Japan
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摘要: 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(Tm)模型. 鉴于此，采用2009—2016年全球大地测量观测系统(GGOS) Atmosphere Tm和ERA-Interim 2 m Ts格网数据新建立一种考虑Tm残差季节性变化和周日变化的适合日本区域的Tm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 Tm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 Tm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Tm值，为研究此区域大气水汽和极端天气提供重要依据.
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关键词:
- 日本区域 /
- 全球卫星导航系统(GNSS)水汽 /
- 全球大地测量观测系统(GGOS) Atmosphere Tm /
- 探空资料 /
- JQTm模型
Abstract: 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 Tm model in this area. In view of this article adopts the 2009 to 2016 Global Geodetic Observing System (GGOS) weighted average temperature (Tm) with the ERA-Interim 2 m Ts grid data, a new Tm model (JQTm model) suitable for the Japanese region that takes into account the seasonal variation of the Tm residual and the daily cycle variation is established. In addition, using the data of 13 sounding stations and 110 GGOS Atmosphere Tm 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 Tm 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 Tm values for the Japanese region and provide an important basis for studying atmospheric water vapor and extreme weather in this region. -
表 1 日本区域4种模型对比GGOS Atmosphere Tm格网数据的精度
K 模型 bias RMSE 最大值 最小值 平均值 最大值 最小值 平均值 Bevis+GPT2w-1 8.61 –6.60 1.04 10.07 1.78 4.21 GPT2w-1 6.25 –5.64 –0.19 7.71 1.33 3.25 GPT2w-5 6.23 –5.63 –0.20 7.80 1.74 3.45 JQTm 2.15 –2.35 0.15 3.70 0.98 1.92 
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表 2 4种模型对比2015年探空站数据的精度
K 模型 bias RMSE 最大值 最小值 平均值 最大值 最小值 平均值 Bevis 4.71 –4.29 0.16 5.31 0.95 2.86 GPT2w-1 6.28 –6.81 –0.83 7.21 0.87 2.99 GPT2w-5 6.44 –6.72 –0.70 7.42 0.64 3.04 JQTm 2.72 –3.82 –0.66 4.28 0.89 2.14
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