Atmospheric Vapor Content over China and Its Climatological Evaluation Method

Wang Bingzhong; Shen Yanbo

Vol.23, NO.6, 2012

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Article Navigation > Journal of Applied Meteorological Science > 2012 > 23(6): 763-768

Wang Bingzhong, Shen Yanbo. Atmospheric vapor content over China and its climatological evaluation method. J Appl Meteor Sci, 2012, 23(6): 763-768.

Citation:

Wang Bingzhong, Shen Yanbo. Atmospheric vapor content over China and its climatological evaluation method. J Appl Meteor Sci, 2012, 23(6): 763-768.

Wang Bingzhong, Shen Yanbo. Atmospheric vapor content over China and its climatological evaluation method. J Appl Meteor Sci, 2012, 23(6): 763-768.

Citation:

Wang Bingzhong, Shen Yanbo. Atmospheric vapor content over China and its climatological evaluation method. J Appl Meteor Sci, 2012, 23(6): 763-768.

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Atmospheric Vapor Content over China and Its Climatological Evaluation Method

Wang Bingzhong^{1
,
,},
Shen Yanbo²

1.
Chinese Academy of Meteorological Sciences, Beijing 100081
2.
Center for Wind and Solar Energy Resources Assessment, CMA, Beijing 100081

Received Date: 2012-04-13
Rev Recd Date: 2012-10-10
Publish Date: 2012-12-31

Abstract

Abstract

Water vapor content in atmosphere is important for the calculation of surface solar radiation, so it is a necessary parameter. At the same time, water vapor is a kind of climate resources which plays an important role in climatology. In order to evaluate water vapor properly, the integrated water vapor of each station is calculated based on the aerological climate standard data of the whole 124 aerological meteorological stations in China from 1971 to 2000. The distribution of annual value indicates that water vapor content in China increases with latitude except Tibetan Plateau. Using the data from China Surface Climate Standard Monthly Database (1971—2000), the surface vapor pressure is revised by corresponding surface air pressure, and then a consistent or monthly empirical formula which can be used throughout the country is obtained. Root mean square error (RMSE) of fitting value from the empirical formula and observational value is 0.25 cm. The affection of polynomial power on fitting value is discussed in depth and it can be seen that the high power polynomial which makes good fitting correlation doesn't mean the lowest RMSE. The optimal fitting formula of revised surface vapor pressure (x) by surface air pressure and integrated water vapor content (y) is: y=0.185x+0.093. The greatest advantage of this formula is that it can be used all over the country, no matter highland or lowland, in the north or in the south. Therefore, it can be considered much more close to the practical situation.
- water vapor content;
- surface vapor pressure;
- barometer reduction;
- climatological evaluation methods

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References(15)

Figures and Tables

Fig. 1 Distribution of annual integrated atmospheric vapor content over China (unit:cm)

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Fig. 2 The different fitting equations of annual integrated water vapor content for all stations over China in April

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Fig. 3 The consistent fitting equation of annual integrated water vapor content for all stations over China

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Fig. 4 Grade distribution of root mean square error of water vapor content

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Table 1 Integrated water vapor content over part of stations in China (unit:cm)

站名	1月	2月	3月	4月	5月	6月	7月	8月	9月	10月	11月	12月
北京	0.36	0.42	0.64	1.00	1.72	1.72	4.24	3.94	2.37	1.35	0.75	0.46
太原	0.38	0.43	0.62	0.89	1.39	1.39	3.41	3.30	2.12	1.24	0.72	0.47
海拉尔	0.24	0.27	0.36	0.59	0.92	0.92	2.63	2.34	1.36	0.71	0.42	0.30
呼和浩特	0.31	0.33	0.42	0.60	0.95	0.95	2.53	2.41	1.47	0.89	0.53	0.36
锡林浩特	0.26	0.30	0.38	0.53	0.89	0.89	2.44	2.21	1.28	0.73	0.45	0.32
沈阳	0.34	0.38	0.56	0.95	1.57	1.57	4.10	3.65	2.10	1.18	0.68	0.44
长春	0.30	0.33	0.47	0.81	1.33	1.33	3.63	3.22	1.84	1.00	0.57	0.38
嫩江	0.23	0.26	0.37	0.67	1.08	1.08	3.18	2.75	1.60	0.78	0.41	0.28
哈尔滨	0.27	0.31	0.41	0.72	1.23	1.23	3.48	3.04	1.76	0.90	0.50	0.34
上海	1.05	1.20	1.55	2.10	2.84	2.84	5.48	5.08	3.87	2.46	1.65	1.12
南京	0.94	1.07	1.45	2.00	2.71	2.71	5.34	5.14	3.64	2.38	1.51	1.02
杭州	1.17	1.33	1.83	2.40	3.14	3.14	5.47	5.37	4.01	2.65	1.70	1.17
福州	1.89	2.19	2.78	3.44	4.36	4.36	5.51	5.49	4.77	3.56	2.52	1.91
厦门	2.12	2.50	3.01	3.76	4.67	4.67	5.43	5.48	4.92	3.75	2.78	2.13
赣州	1.78	2.13	2.78	3.59	4.51	4.51	5.42	5.45	4.69	3.48	2.28	1.68
南昌	1.42	1.65	2.27	2.99	3.87	3.87	5.69	5.52	4.29	2.93	1.86	1.35
济南	0.51	0.57	0.81	1.21	1.87	1.87	4.48	4.15	2.56	1.55	0.94	0.61
青岛	0.56	0.63	0.87	1.27	1.89	1.89	4.54	4.26	2.58	1.62	1.02	0.66
郑州	0.65	0.77	1.09	1.59	2.20	2.20	4.76	4.50	3.07	1.84	1.12	0.74
武汉	1.18	1.35	1.86	2.51	3.29	3.29	5.68	5.38	3.95	2.67	1.69	1.23
长沙	1.61	1.87	2.45	3.21	4.08	4.08	5.78	5.68	4.53	3.29	2.13	1.61
广州	2.27	2.81	3.52	4.47	5.37	5.37	5.85	5.97	5.35	4.12	2.91	2.25
汕头	2.41	2.83	3.35	4.19	5.13	5.13	5.71	5.85	5.33	4.30	3.23	2.50
东沙岛	3.08	3.32	3.65	4.19	4.87	4.87	5.15	5.40	5.00	4.58	3.76	3.22
南宁	2.35	2.71	3.35	4.32	5.30	5.30	5.95	5.92	5.21	4.24	3.11	2.33
海口	2.86	3.33	3.96	4.86	5.70	5.70	5.86	6.01	5.58	4.83	3.75	2.99
重庆	1.50	1.65	2.08	2.81	3.71	3.71	5.23	4.93	4.32	3.25	2.28	1.68
成都	1.33	1.49	1.91	2.58	3.38	3.38	5.18	4.95	4.13	3.05	2.09	1.50
贵阳	1.34	1.48	1.81	2.43	3.18	3.18	4.08	3.91	3.38	2.68	1.90	1.38
昆明	1.03	1.08	1.24	1.64	2.41	2.41	3.39	3.27	2.93	2.31	1.59	1.13
那曲	0.13	0.14	0.17	0.28	0.52	0.52	1.15	1.13	0.92	0.44	0.21	0.15
拉萨	0.17	0.19	0.26	0.47	0.83	0.83	1.73	1.76	1.44	0.69	0.31	0.20
定日	0.13	0.14	0.19	0.29	0.48	0.48	1.30	1.33	0.95	0.36	0.19	0.14
西安	0.75	0.87	1.22	1.75	2.42	2.42	4.41	4.30	3.23	2.10	1.29	0.85
兰州	0.42	0.46	0.65	0.90	1.35	1.35	2.59	2.57	2.02	1.26	0.69	0.48
格尔木	0.24	0.22	0.28	0.35	0.56	0.56	1.19	1.06	0.75	0.42	0.28	0.26
西宁	0.30	0.33	0.46	0.69	1.11	1.11	2.11	2.03	1.56	0.92	0.51	0.37
玉树	0.22	0.25	0.33	0.50	0.83	0.83	1.54	1.47	1.24	0.70	0.34	0.23
银川	0.39	0.42	0.57	0.74	1.14	1.14	2.67	2.65	1.79	1.04	0.65	0.47
乌鲁木齐	0.46	0.49	0.65	0.86	1.16	1.16	1.98	1.69	1.26	0.94	0.71	0.56
马公	2.24	2.58	3.19	3.60	4.35	4.35	4.92	4.99	4.62	3.61	2.91	2.28
台东	2.84	3.12	3.48	4.14	5.01	5.01	5.29	5.62	5.16	4.72	3.87	3.11
香港	2.21	2.75	3.30	4.08	4.94	4.94	5.32	5.45	4.99	4.04	2.90	2.19

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Table 2 Comparison of absolute error from different fitting equations of water vapor content for all of stations over China in April (unit: cm)

项目	线性式	五次多项式	四次多项式	二次多项式
标准差	0.22	4.75	3.81	0.91
最大值	0.53	27.64	20.80	0.12
最小值	-1.01	-0.07	-0.06	-5.00

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Table 3 The optimal equations from January to December

月份	优选公式	线性式			二次多项式
月份	优选公式	标准差/cm	最大值/cm	最小值/cm	标准差/cm	最大值/cm	最小值/cm
1	y=-0.0003x²+0.221x+0.031	0.13	0.43	-0.50	0.11	0.36	-0.30
2	y=0.192x+0.073	0.09	0.49	-0.90	0.14	0.36	-0.54
3	y=-0.003x²+0.252x-0.107	0.20	0.55	-1.13	0.17	0.44	-0.60
4	y=0.190x+0.021	0.22	0.53	-1.01	0.91	0.12	-5.00
5	y=0.0009x²+0.1655x+0.1373	0.28	0.53	-0.94	0.27	0.52	-1.14
6	y=0.001x²+0.150x+0.367	0.33	0.66	-0.85	0.33	0.74	-1.00
7	y=0.172x+0.428	0.31	0.71	-0.94	1.55	6.05	0.99
8	y=0.0005x²+0.157x+0.4616	0.32	0.66	-0.73	0.32	0.69	-0.73
9	y=0.182x+0.144	0.33	0.59	-0.99	1.44	5.69	0.55
10	y=0.0003x²+0.1907x+0.0448	0.24	0.50	-0.70	0.24	0.50	-0.67
11	y=-0.001x²+0.195x+0.041	0.16	0.38	-0.59	0.15	0.35	-0.50
12	y=0.170x+0.123	0.12	0.33	-0.50	0.17	0.17	-1.03

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