The first WetNet precipitation intercomparison project (PIP‐1): Interpretation of results

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Abstract
The first WetNet Precipitation Intercomparison Project (PIP‐1) was designed as part of the activity of WetNet's Precipitation Working Group, and intended to advance the science of global rainfall monitoring primarily through evaluations of existing passive microwave algorithms, both in relation to each other and also against conventional (rain gauge) data sets. In PIP‐1, intercomparisons of global rainfall estimates for August, September, October and November 1987 have been undertaken for 15 algorithms based on passive microwave DMSP‐SSM/I image data, one based on passive microwave NOAA‐MSU vertical profile data, one infrared image‐based algorithm, one combined passive‐based microwave/infrared imager algorithm, and one numerical weather prediction model, plus rainfall observations from continental rain gauges and from Pacific Atoll rain gauges. This paper begins by summarising the objectives of PIP‐1, the problems facing it, and its resulting intercomparison strategy. It then describes, exemplifies, and discusses results obtained by both qualitative and quantitative means for global, continental, and oceanic regions, before setting out some conclusions and recommendations for further studies. No single method of global rainfall estimation was found to be always better than all others, but several methods were each generally better over some major regions of the world. In view of the relatively short history of development of rainfall algorithms based on passive microwave imagery, and the relatively infrequent data presently available to support such types of approach, some passive microwave image‐based algorithms are already remarkably successful for estimating monthly global rainfall, and will benefit from on‐going developments in both related science and technology. Further global intercomparison projects should be designed to build on the many lessons learned by participants in PIP‐1. At the same time, more effort must be directed towards the acquisition of independent, in situ data sets for better calibration and validation of the satellite‐based results.