我国自动与人工蒸发量观测资料的对比分析
Comparative Analysis on Automatic and Manual Evaporation Measurements in China
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摘要: 截止到2005年,全国共有130个台站进行蒸发量自动与人工业务观测。该文利用2005年平行观测月数据,对资料的差异和相关性以及比对系数和影响因子进行了讨论,结果表明:月蒸发量差值不满足正态分布,近80%的数据为自动观测值大于人工观测值;在人工观测值较小时,对应的自动与人工相对差值较大,随着人工观测值的增加,差值有减小趋势;自动与人工观测数据之间存在很好的线性相关关系,相关系数为0.98,通过了0.01的显著性检验;比对系数年平均值在1.0~1.2之间,两大高值中心分别位于广西都安和湖南南县;比对系数月平均值的变化近似于二次型拟合曲线,1月最大,6,7月最小;在定性讨论特征站比对系数影响因子的基础上,进一步查明了影响月比对系数的气象因子有月平均相对湿度和月平均风速。Abstract: Automatic Observation System (AOS) has been set up since 2002 and Manual Observation System (MOS) will be substituted gradually. The results show that data quality will be influenced by the changes of either the measurement instrument or the manner. So it is necessary to conduct AOS-MOS parallel observations to determine the discrepancy and correlation. For evaporation observations the difference is great between the manual and automatic observation. In fact manual evaporation is measured once a day at about 20:00, the difference of the two days measurements is the daily evaporation quantity. However, automatic measurements have been implemented to record continuously water surface height which is then used to calculate the hourly and daily evaporation at a given station. It is wanted by many meteorologists to know how much is the difference and the relationship between the amount of evaporation measured by automatic and manual observations and what is the main causes of the difference.There are 130 stations by 2005 conducting evaporation parallel measurements in China. The monthly or annual value is often used for evaporation analysis. Moreover the contrast coefficient is defined as the ratio between automatic and manual value and often used, too. Based on the monthly, annual value and contrast coefficient, various analyses are carried out on evaporation difference arising from two observation systems. The results are as follows: Monthly difference doesn't satisfy normal distribution pattern. The average monthly evaporation a mount observed by automatic way is 5.74 mm (or 5.51%) more than that by manual way. The standard deviation of the difference is 9.02 mm. In the 1050 evaporation comparison observations, 3.33% of data observed in the two ways is identical, 19.71% of data observed by automatic way is smaller, 76.95% of data observed by automatic way is larger. Namely, about 80% of automatic monthly data is greater than corresponding manual ones. Super sensitive of automatic ultrasonic probe and the measurement time discrepancy may be the reasons. When manual amount is small, its associated difference between automatic and manual is large, especially in the Wanyuan station in Sichuan Province. The monthly evaporation by automatic observation is obviously a linear function of that by manual one with the correlation coefficient passing 0.01 significant level test. The annual contrast coefficient is within the range of 1.0 to 1.2, with two high value regions centered at Douan and Nanxian stations in Guangxi and Hunan provinces, respectively. The change of monthly contrast coefficient is similar to bi-parabolic distribution pattern with highest and lowest value in January and June or July, respectively. Meteorological factors influencing the monthly contrast coefficient include the monthly average relative humidity and monthly average wind speed. There is a trend to merge the different kind of data to compose high quality evaporation dataset which deserves further research in the future.
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图 6 各代表台站比对系数月变化曲线图
(全年观测的台站点划线表示年比对系数; 冬季停止观测的台站点划线表示各月平均比对系数)
Fig. 6 Monthly changing curves of contrast coefficient in typical stations
(dash lines denote annual average contrast coefficient for all-year measured stations and monthly average parallel coeffient for winter-stopped measuring stations, respectively)
表 1 月蒸发量各差值段内的数据量和所占百分比
Table 1 Amount and percentage in every difference range of monthly evaporation
表 2 不同气候带代表台站的比对系数及风速、相对湿度表
Table 2 Contrast coefficient and wind speed, relative humidity of typical stations at different climatic zones
表 3 逐步回归方法筛选因子结果及贡献率
Table 3 Selected factors by stepwise regression and its contributing ratio
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