Using Model Data to Build On Accuracy of Ground Data Based on Kriging, Interpolation Techniques for Bias Correction

The estimation, with precision, of spatial patterns in rainfall from different independent sources is a challenge because of the extreme variability of rainfall in space and time. Quantification and understanding of the uncertainties associated with the spatial analysis of climate data are thus essential for efficient forecasting using interpolation techniques to estimate the spatial distribution of the precipitation. Given this variability in time and space, ground-based gauge networks may not provide a robust basis for interpolation, and the reliability of remote sensing products, although improving, is still imperfect. The technique we proposed in this paper is based on combination different kinds of measurements to correct the bias. We used model ERAI data domain 1 and 3 and model CCSM4 “The Community Climate System Model� data domain 1 and 3 to correct the ground station data (ground data corrects model). Kriging method is used for bias correction. The statistical properties of the data are used to analysis the data, simulation before the bias correction to estimation bias. And the bias has been re-evaluated after correction. The simulation result shown kriging interpolation can be used for site data interpolation for better prediction.. Daily data (ERAI “EUNIS Research and Analysis Initiative�, CCMS4) for the five years (2001 to 2005) are used.