Usage of multivariate geostatistics in interpolation processes for meteorological precipitation maps

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• 作者：Ismail Bulent Gundogdu
• 刊名：Theoretical and Applied Climatology
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：127
• 期：1-2
• 页码：81-86
• 全文大小：
• 刊物类别：Earth and Environmental Science
• 刊物主题：Atmospheric Sciences; Climatology; Atmospheric Protection/Air Quality Control/Air Pollution; Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution;
• 出版者：Springer Vienna
• ISSN：1434-4483
• 卷排序：127

文摘

Long-term meteorological data are very important both for the evaluation of meteorological events and for the analysis of their effects on the environment. Prediction maps which are constructed by different interpolation techniques often provide explanatory information. Conventional techniques, such as surface spline fitting, global and local polynomial models, and inverse distance weighting may not be adequate. Multivariate geostatistical methods can be more significant, especially when studying secondary variables, because secondary variables might directly affect the precision of prediction. In this study, the mean annual and mean monthly precipitations from 1984 to 2014 for 268 meteorological stations in Turkey have been used to construct country-wide maps. Besides linear regression, the inverse square distance and ordinary co-Kriging (OCK) have been used and compared to each other. Also elevation, slope, and aspect data for each station have been taken into account as secondary variables, whose use has reduced errors by up to a factor of three. OCK gave the smallest errors (1.002 cm) when aspect was included.