Adequacy of Using a 1/3-Degree Special Sensor Microwave Imager Dataset to Estimate Climate-Scale Rainfall

Until recently, monthly rainfall products using the National Oceanic and Atmospheric Administration National Environmental Satellite, Data, and Information Service Office of Research and Applications Special Sensor Microwave Imager (SSM/I) rainfall algorithm have been generated on a global 2.5° × 2.5° grid. The rainfall estimates are based on a subsampled set of the full-resolution SSM/I data, with a resulting spatial density of about one-third of what is possible at SSM/I’s highest spatial resolution. The reduction in the spatial resolution was introduced in 1992 as a compromise dictated by data processing capabilities. Currently, daily SSM/I data processing at full resolution has been established and is being operated in parallel with the subsampled set. Reprocessing of the entire SSM/I time series based on the full-resolution data is plausible but requires the reprocessing of over 10 yr of retrospective data. Because the Global Precipitation Climatology Project is considering the generation of a daily 1° × 1° rainfall product, it is important that the effects of using the reduced spatial resolution be reexamined. In this study, error due to using the reduced-resolution versus the full-resolution SSM/I data in the gridded products at 2.5° and 1° grid sizes is examined. The estimates are based on statistics from radar-derived rain data and from SSM/I data taken over the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) radar site. SSM/I data at full resolution were assumed to provide rain estimates with 12.5-km spacing. Subsampling with spacings of 25, 37.5 (which corresponds to the present situation of ⅓° latitude–longitude spatial resolution), and 50 km were considered. For the instantaneous 2.5° × 2.5° product, the error due to subsampling, expressed as a percentage of the gridbox mean, was estimated using radar-derived data and was 6%, 10%, and 15% at these successively poorer sampling densities. For monthly averaged products on a 2.5° × 2.5° grid, it was substantially lower: 3%, 4%, and 7%, respectively. Subsampling errors for monthly averages on a 1° × 1° grid were 8%, 16%, and 23%, respectively. Estimates based on SSM/I data at full resolution gave errors that were somewhat larger than those from radar-based estimates. It was concluded that a rain product of monthly averages on a 1° × 1° grid must use the full-resolution SSM/I data. More work is needed to determine how applicable these estimates are to other areas of the globe with substantially different rain statistics.