Errors and Their Scale Effect for Spatialization of Air Temperature Data

Abstract:

Spatialization of attribute data is a way to output grid data products from vector data. It is beneficial to integrated analysis of geosciences data from various sources and in different formats. However it is also a process companied with errors, and the errors are closely related to density of data sources, spatializing models and resolution of grid cells. In this paper, seven levels of density of meteorological stations, five spatializing models and nineteen levels of resolutions of grid cells were used to analyze the relationships between the errors for spatialization of air temperature and these factors.

It was found that reduction of density of meteorological stations led to increasing of errors. Of the five models, Adjusted IDW, Regression and ANUSPIN had higher accuracy than IDW and Kriging. And the accuracy generally decreases with increasing of size of grid cells. Of the three factors affecting accuracy of spatialization, the models had the greatest impact on the accuracy, the resolution of grid cells second and the density of meteorological stations the lowest.

Section:
Time: Tuesday, October 30, 2012 - 11:00 to 11:30

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