A New Algorithm for Blending Multiple Satellite Precipitation Estimates With In-situ Precipitation Measurements Over Canada

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Tuesday, 19 January 2010: 4:00 PM
B211 (GWCC)
Achan Lin, EC, Toronto, ON, Canada; and X. L. Wang

This study proposes a new algorithm for blending satellite precipitation estimates with in-situ gauge precipitation measurements over Canada, which is to be used to produce a blended monthly total precipitation data covering entire area of Canada with a 50 km resolution on an oblique stereographic projection (CanGrid). The input satellite data used in this study include SSMI GPROF and SSMI UMORA (from 1987 to present), TVOS (from 1979 to 2002) and AIRS (from 2003 to present). The field of satellite precipitation estimates was adjusted against the field of gauge data based on the moving averages of both satellite field and gauge field before being used on the blending analysis. The blending (merging) technique is a combination of ordinary kriging and statistical interpolation with gauge data as observation field and multiple satellite precipitation estimates as the background field. A preliminary assessment of the performance of this algorithm is presented in this study.