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The Spatial and Temporal Variations of Weighted Mean Atmospheric Temperature and Its Models in China
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摘要: 大气加权平均温度Tm是地基GPS水汽遥感的关键参数，决定了水汽反演的精度。利用2008—2011年全国123个探空站点资料，分析了Tm与其影响要素纬度、海拔、地面气温、水汽压及大气压之间的关系，结果表明：Tm与纬度和海拔随季节变化呈周期性负相关，与地面温度和水汽压的自然对数呈正相关，与大气压呈负相关；Tm的空间变化具有纬度地带性和明显的气候分布特征，其变异程度在空间分布上有显著差别，不同地理位置的Tm受季节性影响程度不一，Tm也具有明显的年际周期性变化，年内Tm的每日变化符合二次函数分布规律。按照全国、气候分区和季节分区方法，分别建立了Tm单因子和多因子回归模型，并利用2012年1—5月数据对所建模型进行验证，Tm的估算误差能满足GPS水汽遥感2%的精度，模型普遍适用于我国地基GPS水汽遥感中Tm的估算。Abstract: Atmospheric water vapor plays an important role in the high-energy thermodynamics of the atmosphere and the generation of storm systems. Water vapor remote sensing can provide a detailed primary parameter within meteorological prediction and climate models. Ground-based GPS can obtain continuously precipitable water vapor with high spatial and temporal resolution since regional GPS networks are established widely all over the globe. Weighted mean atmospheric temperature (Tm) is a key parameter in retrieval of atmospheric precipitable water vapor from ground-based GPS measurements, and the precision of PW retrieved by ground-based GPS is proportional to the accuracy of estimated Tm. Using radiosonde data of 123 stations in China from 2008 to 2011, the relationship of Tm is analyzed with its affecting factors, latitude, altitude, surface temperature (Ts), partial pressure of water vapor (e) and atmospheric pressure (P0). Results show that Tm has a negative periodic correlativity with latitude and altitude as season changes, it has a good positive correlativity with surface temperature and partial pressure of water vapor transformed by natural logarithm, and it also has a negative one with atmospheric pressure. Furthermore, the spatial and temporal variations of Tm is discovered. The spatial variation of Tm displays the distinct latitudinal and climatic features and its spatial heterogeneity is evidently different in different regions, so Tm in different latitudinal zone is dissimilarly affected by seasonal climate change. For the temporal variation, Tm displays the prominent inter-annual variation and its diurnal variation is accord with the quadratic function. Given that, the regression models of Tm based on single factor Ts multi-factor Ts, e and P0 are deduced respectively corresponding to all areas in China, climatology divisions and seasonal divisions, and then these models are validated by radiosonde data from January to May in 2012. The results show that estimated Tm would achieve an uncertainty of 2% for precipitable water vaper retrieved from GPS measurements, and these models are suitable to estimate Tm for the retrieval from ground-based GPS measurements in China.
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图 1 我国气候分区与探空站点分布图
Fig. 1 Map of climatology divisions and radiosonde stations in China
图 2 Tm与纬度 (a) 和海拔 (b) 的相关系数时间序列图
(横坐标的时间为儒略日，2008年1月1日为1，2011年12月31日为1435)
Fig. 2 Relative coefficient time series chart of Tm to latitude (a) and altitude (b)
(x-axis for Julian day, 1 January 2008 for 1, 31 December 2011 for 1435)
图 3 Tm与近地层气温Ts (a)、水汽压e (b) 和大气压P0 (c) 的散点图
Fig. 3 Scatter plots of Tm to Ts (a), e (b), P0 (c), respectively
图 4 Tm平均值 (a) 和标准差 (b) 的空间插值 (单位:K)
Fig. 4 Spatial interpolation maps of Tm mean (a) and its standard deviation (b)(unit:K)
图 5 2008年1月1日—2011年12月31日Tm时间序列图
(a) 典型站点 (57127) Tm日变化, (b) 典型站点 (57127) Tm年均日变化, (c) 全国所有站点Tm年均日变化
Fig. 5 Time series chart of Tm from 1 Jan 2008 to 31 Dec 2011
(a)Tm of the representative radiosonde station No.57127, (b) annual mean diurnal variation of Tm of the station No.57127 during four years, (c) annual mean diurnal variation of Tm in all the stations during four years
表 1 Tm单因子回归建模
Table 1 Regression model of Tm single factor
分类 模型 模拟 预测 Bevis模型预测 R2 样本量 均方根误差/K 样本量 均方根误差/K 样本量 均方根误差/K 全国 Tm=105.45+0.594Ts 0.840* 174351 3.248 17900 3.393 17900 3.708 高原高山气候 Tm=144.09+0.452Ts 0.765* 34343 2.843 3491 2.679 3491 4.045 温带大陆性气候 Tm=140.63+0.467Ts 0.849* 37251 2.755 3819 2.650 3819 4.476 温带季风气候 Tm=86.87+0.658Ts 0.871* 37899 3.368 3946 3.568 3946 3.882 亚热带季风气候 Tm=90.10+0.650Ts 0.799* 57718 2.734 5891 2.945 5891 2.985 热带季风气候 Tm=123.65+0.540Ts 0.604* 7143 2.165 753 2.154 753 2.719 冬季 Tm=105.14+0.599Ts 0.778* 43738 3.527 6663 3.754 6663 4.289 春季 Tm=84.14+0.666Ts 0.778* 44620 3.151 11237 3.031 111237 3.348 夏季 Tm=39.81+0.815Ts 0.733* 44220 2.709 秋季 Tm=90.71+0.648Ts 0.822* 41773 2.879 注：*表示达到0.01显著性水平。 
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表 2 因子标准化过程所取的中值
Table 2 Median parameters of variables for normalization
分类 Tm/K Ts/K e/hPa P0/hPa 全国 275.34 286.70 8.87 1015.20 高原高山气候 269.63 279.65 4.61 1016.75 温带大陆性气候 272.04 282.10 4.24 1016.65 温带季风气候 273.18 283.68 7.61 1016.45 亚热带季风气候 279.44 291.40 15.84 1014.50 热带季风气候 282.27 294.65 20.46 1010.40 冬季 266.94 271.70 2.92 1026.65 春季 274.32 286.45 7.40 1014.15 夏季 281.71 296.95 18.55 1004.90 秋季 275.83 286.60 9.86 1017.85
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表 3 Tm多因子回归建模
Table 3 Regression model of Tm multi-factors
分类 模型 模拟 预测 R2 样本量 均方根误差/K 样本量 均方根误差/K 全国 Tm=0.291+0.477Ts+0.00925lne+0.215P0 0.879* 174351 2.937 17900 3.136 高原高山气候 Tm=0.602+0.472Ts+0.00215lne-0.067P0 0.880* 34343 2.507 3491 2.416 温带大陆性气候 Tm=0.238+0.496Ts+0.00405lne+0.267P0 0.867* 37251 2.673 3819 2.558 温带季风气候 Tm=0.480+0.450Ts+0.01220lne+0.071P0 0.893* 37899 3.118 3946 3.266 亚热带季风气候 Tm=0.648+0.497Ts+0.00797lne-0.143P0 0.827* 57718 2.578 5891 2.726 热带季风气候 Tm=-0.299+0.609Ts+0.00640lne+0.692P0 0.680* 7143 1.947 753 2.296 冬季 Tm=0.591+0.446Ts+0.00878lne-0.036P0 0.810* 43738 3.526 6663 3.708 春季 Tm=0.138+0.606Ts+0.00786lne+0.259P0 0.816* 44620 2.903 111237 2.669 夏季 Tm=-0.064+0.695Ts+0.01094lne+0.406P0 0.686* 44220 2.142 秋季 Tm=0.249+0.584Ts+0.00649lne+0.169P0 0.844* 41773 2.685 注：*表示达到0.01显著性水平。
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