The last several years have seen the development of a number of new satellite-derived, globally complete, high-resolution precipitation products with a spatial resolution of at least 0.25° and a temporal resolution of at least 3-hourly. These products generally merge geostationary infrared data and polar-orbiting passive microwave data to take advantage of the frequent sampling of the infrared and the superior quality of the microwave. The Program to Evaluate High Resolution Precipitation Products (PEHRPP) was established to evaluate and intercompare these datasets at a variety of spatial and temporal resolutions with the intent of guiding dataset developers and informing the user community regarding the error characteristics of the products. As part of this project, the authors have performed a subdaily intercomparison of five high-resolution datasets [Climate Prediction Center morphing (CMORPH) technique; Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA); Naval Research Laboratory (NRL) blended technique; National Environmental Satellite, Data, and Information Service Hydro-Estimator; and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)] with existing subdaily gauge data over the United States and the Pacific Ocean. Results show that these data are effective at representing high-resolution precipitation, with correlations against 3-hourly gauge data as high as 0.7 for CMORPH, which had the highest correlations with the validation data. Biases are relatively high for most of the datasets over land (apart from the TMPA, which is gauge adjusted) and ocean, with a general tendency to overestimate warm season rainfall over the United States and to underestimate rainfall over the tropical Pacific Ocean. Additionally, all the products studied faithfully resolve the diurnal cycle of precipitation when compared with the validation data.

Global precipitation variations over the satellite era are reviewed using the Global Precipitation Climatology Project (GPCP) monthly, globally complete analyses, which integrate satellite and surface gauge information. Mean planetary values are examined and compared, over ocean, with information from recent satellite programs and related estimates, with generally positive agreements, but with some indication of small underestimates for GPCP over the global ocean. Variations during the satellite era in global precipitation are tied to ENSO events, with small increases during El Ninos, and very noticeable decreases after major volcanic eruptions. No overall significant trend is noted in the global precipitation mean value, unlike that for surface temperature and atmospheric water vapor. However, there is a pattern of positive and negative trends across the planet with increases over tropical oceans and decreases over some middle latitude regions. These observed patterns are a result of a combination of inter-decadal variations and the effect of the global warming during the period. The results reviewed here indicate the value of such analyses as GPCP and the possible improvement in the information as the record lengthens and as new, more sophisticated and more accurate observations are included.

[1] The definition and quantification of uncertainty depend on the error model used. For uncertainties in precipitation measurements, two types of error models have been widely adopted: the additive error model and the multiplicative error model. This leads to incompatible specifications of uncertainties and impedes intercomparison and application. In this letter, we assess the suitability of both models for satellite-based daily precipitation measurements in an effort to clarify the uncertainty representation. Three criteria were employed to evaluate the applicability of either model: (1) better separation of the systematic and random errors; (2) applicability to the large range of variability in daily precipitation; and (3) better predictive skills. It is found that the multiplicative error model is a much better choice under all three criteria. It extracted the systematic errors more cleanly, was more consistent with the large variability of precipitation measurements, and produced superior predictions of the error characteristics. The additive error model had several weaknesses, such as nonconstant variance resulting from systematic errors leaking into random errors, and the lack of prediction capability. Therefore, the multiplicative error model is a better choice.

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