A New High-Resolution Satellite-Derived Precipitation Dataset for Climate Studies

Abstract

Abstract The importance of analyzing climate at high spatiotemporal resolution has been emphasized in the recent Intergovernmental Panel on Climate Change (IPCC) report. Several high-resolution analyses of global precipitation have recently been created to meet this need by combining high-quality passive microwave estimates with frequently sampled geosynchronous infrared estimates. A new daily 0.25° analysis has been developed at the Cooperative Institute for Climate Studies (CICS), called the CICS High-Resolution Optimally Interpolated Microwave Precipitation from Satellites (CHOMPS), which is based only on passive microwave satellite estimates. The analysis was developed using all available sensors and the most up-to-date common retrieval scheme. An important advantage of CHOMPS is, therefore, its consistency. The microwave estimates from the different sensors at hourly time scales are combined using optimum interpolation (OI), using estimates of the noise and spatial correlation scales and standardized analysis weights. This technique reduces the random errors while still capturing the tails of the distribution of precipitation and provides estimates of output error based on the noise-to-signal ratio of the inputs. Hourly values are combined to produce a daily average and error estimate. Evaluation of CHOMPS against surface-based observational data, such as the stage IV radar and the Tropical Atmosphere Ocean (TAO)/Triangle Trans-Ocean Buoy Network (TRITON) gauges, indicates that CHOMPS performs well, especially when compared to other high-resolution products. This analysis, therefore, highlights OI as a feasible methodology for the creation of a high-resolution precipitation product. The analysis begins in 1998 and has thus far been continued through 2007, making it a useful dataset to examine intraseasonal variability. When the period of the data becomes long enough, it will prove to be a useful dataset to study longer modes of climate variability.