Spatially homogeneous and temporally continuous precipitation estimates are also available from the reanalysis systems that use advanced data assimilation techniques to merge estimates from global circulation models with observations. Recently, various “merged” satellite and gauge analyses have been assembled that maximize (and minimize) the relative benefits (and shortcomings) of each data type ( New et al. Satellite products can also suffer from various discontinuities between different platforms and instruments and are not available before the 1970s. However, the accuracy of these products is limited, largely because they are derived from other observables (i.e., cloud-top reflectance or thermal radiance Richards and Arkin 1981 Petty and Krajewski 1996). Satellite-derived precipitation products have been developed that fill some of the data gaps by providing more spatially homogeneous and temporally complete coverage over large areas of ocean and land ( Yilmaz et al. Given the importance of gauge-based precipitation estimates as “ground truth” for other products, significant progress has been made to develop and construct gridded global and regional gauge-based precipitation analyses. In addition, it is well known that gauge measurements are also prone to several sources of systematic and random error ( Sevruk 1985 Kuligowski 1997). However, one of the major shortcomings of gauge measurements is that they do not provide complete areal coverage and are often sparse or nonexistent in remote or politically unstable regions of the world. Rain gauges provide direct and accurate point measurement of precipitation over land and are often the most used and trusted source of information for hydrological studies. To understand the extent of these changes, accurate and timely knowledge of the space–time variability of precipitation is essential. 2006 Trenberth 2011 Wang and Ding 2006 Yao et al. There is growing evidence that the statistical characteristics of precipitation are changing at several places globally ( Brunetti et al. Significantly large biases/errors occur during the winter months, which are likely related to the uncertainty in observations that artificially inflate the existing error in reanalyses and satellite retrievals. Over mountainous regions, 3B42-V7 shows an appreciable improvement over 3B42-V6 and other gauge-based precipitation products. Similar conclusions are drawn for the postmonsoon season, with the exception of 3B42-V7, which underestimates postmonsoon precipitation. Compared to APHRODITE, the gauge-only (CPC-uni) and the satellite-derived precipitation products (GPCP, 3B42-V6, and 3B42-V7) capture the summer monsoon rainfall variability better than CFSR and ERA-Interim. All datasets capture the large-scale characteristics of the seasonal mean precipitation distribution, albeit with pronounced seasonal and/or regional differences. Several verification measures are employed to assess the accuracy of the data. The evaluated precipitation products are the Asian Precipitation–Highly-Resolved Observational Data Integration Towards Evaluation of the Water Resources (APHRODITE), the Climate Prediction Center unified (CPC-uni), the Global Precipitation Climatology Project (GPCP), the Tropical Rainfall Measuring Mission (TRMM) post-real-time research products (3B42-V6 and 3B42-V7), the Climate Forecast System Reanalysis (CFSR), and the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim). This paper evaluates the seasonal (winter, premonsoon, monsoon, and postmonsoon) performance of seven precipitation products from three different sources: gridded station data, satellite-derived data, and reanalyses products over the Indian subcontinent for a period of 10 years (1997/98–2006/07).
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