Validation and uncertainty analysis of satellite rainfall algorithms

Satellites, while offering excellent spatial coverage, determine precipitat ion indirectly, using algorithms that transform satellite-sensed radiance ( either emitted or scattered) from clouds or raindrops into precipitation. A large uncertainty is associated with satellite precipitation estimates, st emming from unknown variation in space and rime of the physical and statist ical relationships between precipitation and satellite-sensed radiance. To mitigate this, satellite algorithms must be calibrated and verified using s urface precipitation sampled from different climate regimes and seasons. Re cently developed statistical techniques have been used effectively to reduc e spatial sampling error associated with sparsely distributed raingages and thereby improve our understanding of satellite algorithm quality. This pap er provides an example of satellite precipitation validation, including a d escription of the types of satellite data used to estimate precipitation, a s well as the results from a major project (the Global Precipitation Climat ology Project [GPCP]), to estimate global precipitation through a combinati on of satellite and raingage products. In addition, a recently developed pr ocedure to investigate spatial averaging, scaling, and uncertainty analysis will be used to examine the GPCP product. Specifically, uncertainty analys is applied to comparisons between satellite monthly rainfall estimates and rainfall estimates constructed from Pacific atoll-sited raingauge sites wil l be discussed.