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• W2993646166 abstract "【Objective】 With the aim to provide some clues in acquiring meteorological information for those ungauged or large-scale regions,this study took the Loess Plateau as the target area,which involved two types of meteorological variables-temperature and precipitation-to search proper methods for rasterization of meteorological data.【Method】 Here,multiple linear regression(MLR) and trend surface analysis(TSA) methods were employed to build regression relationships between the measured meteorological data and macro geographic factors(latitude,longitude and elevation).Then these relationships were combined with Digital Elevation Model(DEM) and Inverse Distance Weighting interpolation(IDW) in rasterizing monthly mean temperature and precipitation data derived from 127 meteorological stations for the period of 1971-2000 and data from 38 meteorological stations were taken to test the result.【Result】 It was found that,30-year monthly mean temperature and the MAE values were all above 1.0 ℃ under the scenario of applying direct interpolation,while the MAE values of multiple linear regression method ranged from 0.485 to 0.776 ℃ and trend surface analysis method from 0.242 to 0.509 ℃.So it can be seen that the latter two methods incorporated geographical factors much better than direct interpolation and trend surface analysis method performed better than multiple linear regression method.However,there was no big difference in precipitation between the results of the three methods.Among the three geographic factors,elevation was the most effective factor in predicting the spatial distribution of temperature over the Loess Plateau and latitude was the most influential factor affecting precipitation.【Conclusion】 In general,those methods like multiple linear regression and trend surface analysis methods involving macro geographical factors have great potential in improving the precision of temperature rasterization,particularly the trend surface analysis method.But for precipitation rasterization,there are so many uncertainties waiting to be explored further." @default.
• W2993646166 created "2019-12-13" @default.
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• W2993646166 date "2010-01-01" @default.
• W2993646166 modified "2023-09-23" @default.
• W2993646166 title "Studying the methods for rasterizing meteorological variables in the Loess Plateau" @default.
• W2993646166 hasPublicationYear "2010" @default.
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