[1] Satellite-based precipitation estimates have great potential for a wide range of critical applications, but their error characteristics need to be examined and understood. In this study, six (6) high-resolution, satellite-based precipitation data sets are evaluated over the contiguous United States against a gauge-based product. An error decomposition scheme is devised to separate the errors into three independent components, hit bias, missed precipitation, and false precipitation, to better track the error sources associated with the satellite retrieval processes. Our analysis reveals the following. (1) The three components for each product are all substantial, with large spatial and temporal variations.(2) The amplitude of individual components sometimes is larger than that of the total errors. In such cases, the smaller total errors are resulting from the three components canceling one another. (3) All the products detected strong precipitation (>40 mm/d) well, but with various biases. They tend to overestimate in summer and underestimate in winter, by as much as 50% in either season, and they all miss a significant amount of light precipitation (<10 mm/d), up to 40%. (4) Hit bias and missed precipitation are the two leading error sources. In summer, positive hit bias, up to 50%, dominates the total errors for most products. (5) In winter, missed precipitation over mountainous regions and the northeast, presumably snowfall, poses a common challenge to all the data sets. On the basis of the findings, we recommend that future efforts focus on reducing hit bias, adding snowfall retrievals, and improving methods for combining gauge and satellite data. Strategies for future studies to establish better links between the errors in the end products and the upstream data sources are also proposed.

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what: 50 participants from precipitation research community met to develop a list of research priorities and recommendations in the field of remote sensing of precipitation whEn: 15-17 March 2010 whErE: University of California, Irvine Overview Of recOmmendatiOns (i) Uncertainty of merged products and multisensor observations warrants a great deal of research. Quantification of uncertainties and their propagation into combined products is vital for future development. (ii) Future improvements in satellite-based precipitation retrieval algorithms will rely on more indepth research on error properties in different climate regions, storm regimes, surface conditions, seasons, and altitudes. Given such information, precipitation algorithms for retrieval, downscaling, and data fusion can be optimized for different situations. (iii) Based on the currently available data, global multichannel precipitation estimates with spatial and temporal resolutions of 4 km and 30 min can be considered as the target dataset that can be achieved in the near future. At high resolutions, however, achieving desirable accuracy is the main challenge. Extensive development and validation efforts are required to make such a dataset available to the community for research and applications. (iv) Development of metrics for validation and uncertainty analysis are of great importance. Various metrics with emphasis on different aspects of performance are required so that users can decide which product fits their purposes/applications best. Furthermore, developing diagnostic statistics (shifting and rotation) will help to capture the systematic deficiency inherent in precipitation retrieval algorithms.