Satellite-Derived Precipitation Verification Activities Within the International Precipitation Working Group (IPWG)

In recent years, the requirements for climate modeling, data assimilation, nowcasting, and hydrological applications have necessitated the need for daily and sub-daily precipitation analyses and their associated accuracy. The development of blended satellite (typically blends of low Earth orbiting passive microwave radiometric (PMW) and geostationary-based imagers, but not exclusively) has also proceeded to the point where a thorough analysis of their performance across space and time scales, seasons, etc. is required. In early 2004, the International Precipitation Working Group (IPWG) began a satellite precipitation algorithm validation/intercomparison project over three domains (continental United States, Australia, and northern Europe) covered by quality-controlled surface networks. Its aim is to provide information to users on the daily-scale performance metrics (bias, RMSE, skill score, etc) relative to ground networks, and give algorithm developers a better understanding of the strengths and weaknesses of different algorithmic approaches and satellite data blends. A secondary aim is to investigate when and where satellite rainfall estimates generally perform better or worse than short-term rainfall predictions from NWP models.

To expand this to other rainfall regimes, the IPWG has initiated the Pilot Evaluation of High Resolution Precipitation Products (PEHRPP). PEHRPP is an effort that will bring together scientists who develop and product High Resolution Precipitation Products (HRPP), those who provide the basic data (principally observations from earth orbiting satellites), and those who have a need for high resolution precipitation fields to conduct their research. The principal goal of PEHRPP is to characterize as clearly as possible the errors in various high resolution precipitation products (HRPP) on many spatial and temporal scales, over varying surfaces and climatic regimes. Furthermore, errors of and differences between HRPP are meaningful in that they can be systematically related to precipitation characteristics and/or algorithm methodology, therby potentially improving HRPP's by combining products or methods based on the observed errors and differences. In this presentation we will provide an overview of the vaidation strategies and summarize validation results to date.