The Spatial and Temporal Variations of Weighted Mean Atmospheric Temperature and Its Models in China

Gong Shaoqi

Vol.24, NO.3, 2013

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Gong Shaoqi. The spatial and temporal variations of weighted mean atmospheric temperature and its models in China. J Appl Meteor Sci, 2013, 24(3): 332-341.

Citation:

Gong Shaoqi. The spatial and temporal variations of weighted mean atmospheric temperature and its models in China. J Appl Meteor Sci, 2013, 24(3): 332-341.

Gong Shaoqi. The spatial and temporal variations of weighted mean atmospheric temperature and its models in China. J Appl Meteor Sci, 2013, 24(3): 332-341.

Citation:

Gong Shaoqi. The spatial and temporal variations of weighted mean atmospheric temperature and its models in China. J Appl Meteor Sci, 2013, 24(3): 332-341.

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The Spatial and Temporal Variations of Weighted Mean Atmospheric Temperature and Its Models in China

Gong Shaoqi^{1,2
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1.
School of Remote Sensing, Nanjing University of Information Science & Technology, Nanjing 210044
2.
Key Lab of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing 210046

Received Date: 2012-08-25
Rev Recd Date: 2013-03-18
Publish Date: 2013-06-30

Abstract

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 (T_m) is a key parameter in retrieval of atmospheric precipitable water vapor from ground-based GPS measurements, and the precision of P_W retrieved by ground-based GPS is proportional to the accuracy of estimated T_m. Using radiosonde data of 123 stations in China from 2008 to 2011, the relationship of T_m is analyzed with its affecting factors, latitude, altitude, surface temperature (T_s), partial pressure of water vapor (e) and atmospheric pressure (P₀). Results show that T_m 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 T_m is discovered. The spatial variation of T_m displays the distinct latitudinal and climatic features and its spatial heterogeneity is evidently different in different regions, so T_m in different latitudinal zone is dissimilarly affected by seasonal climate change. For the temporal variation, T_m displays the prominent inter-annual variation and its diurnal variation is accord with the quadratic function. Given that, the regression models of T_m based on single factor T_s multi-factor T_s, 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 T_m would achieve an uncertainty of 2% for precipitable water vaper retrieved from GPS measurements, and these models are suitable to estimate T_m for the retrieval from ground-based GPS measurements in China.
- weighted mean atmospheric temperature;
- ground-based GPS;
- precipitable water vaper;
- spatial and temporal variations

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References(41)

Figures and Tables

Fig. 1 Map of climatology divisions and radiosonde stations in China

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图 2 T_m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 T_m to latitude (a) and altitude (b)

(x-axis for Julian day, 1 January 2008 for 1, 31 December 2011 for 1435)

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Fig. 3 Scatter plots of T_m to T_s (a), e (b), P₀ (c), respectively

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Fig. 4 Spatial interpolation maps of T_m mean (a) and its standard deviation (b)(unit:K)

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图 5 2008年1月1日—2011年12月31日T_m时间序列图

(a) 典型站点 (57127) T_m日变化, (b) 典型站点 (57127) T_m年均日变化, (c) 全国所有站点T_m年均日变化

Fig. 5 Time series chart of T_m from 1 Jan 2008 to 31 Dec 2011

(a)T_m of the representative radiosonde station No.57127, (b) annual mean diurnal variation of T_m of the station No.57127 during four years, (c) annual mean diurnal variation of T_m in all the stations during four years

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Table 1 Regression model of T_m single factor

分类	模型	模拟			预测		Bevis模型预测
分类	模型	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	T_m=105.45+0.594T_s	0.840^*	174351	3.248	17900	3.393	17900	3.708
高原高山气候	T_m=144.09+0.452T_s	0.765^*	34343	2.843	3491	2.679	3491	4.045
温带大陆性气候	T_m=140.63+0.467T_s	0.849^*	37251	2.755	3819	2.650	3819	4.476
温带季风气候	T_m=86.87+0.658T_s	0.871^*	37899	3.368	3946	3.568	3946	3.882
亚热带季风气候	T_m=90.10+0.650T_s	0.799^*	57718	2.734	5891	2.945	5891	2.985
热带季风气候	T_m=123.65+0.540T_s	0.604^*	7143	2.165	753	2.154	753	2.719
冬季	T_m=105.14+0.599T_s	0.778^*	43738	3.527	6663	3.754	6663	4.289
春季	T_m=84.14+0.666T_s	0.778^*	44620	3.151	11237	3.031	111237	3.348
夏季	T_m=39.81+0.815T_s	0.733^*	44220	2.709
秋季	T_m=90.71+0.648T_s	0.822^*	41773	2.879
注：*表示达到0.01显著性水平。

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Table 2 Median parameters of variables for normalization

分类	T_m/K	T_s/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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Table 3 Regression model of T_m multi-factors

分类	模型	模拟			预测
分类	模型	R²	样本量	均方根误差/K	样本量	均方根误差/K
全国	T_m=0.291+0.477T_s+0.00925lne+0.215P0	0.879^*	174351	2.937	17900	3.136
高原高山气候	T_m=0.602+0.472T_s+0.00215lne-0.067P0	0.880^*	34343	2.507	3491	2.416
温带大陆性气候	T_m=0.238+0.496T_s+0.00405lne+0.267P0	0.867^*	37251	2.673	3819	2.558
温带季风气候	T_m=0.480+0.450T_s+0.01220lne+0.071P0	0.893^*	37899	3.118	3946	3.266
亚热带季风气候	T_m=0.648+0.497T_s+0.00797lne-0.143P0	0.827^*	57718	2.578	5891	2.726
热带季风气候	T_m=-0.299+0.609T_s+0.00640lne+0.692P0	0.680^*	7143	1.947	753	2.296
冬季	T_m=0.591+0.446T_s+0.00878lne-0.036P0	0.810^*	43738	3.526	6663	3.708
春季	T_m=0.138+0.606T_s+0.00786lne+0.259P0	0.816^*	44620	2.903	111237	2.669
夏季	T_m=-0.064+0.695T_s+0.01094lne+0.406P0	0.686^*	44220	2.142
秋季	T_m=0.249+0.584T_s+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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