Improved Estimates of Pentad Precipitation Through the Merging of Independent Precipitation Data Sets

Three independent, quasi-global, gridded data sets of precipitation (a rain gauge-based data set, the satellite-only component of the NASA Integrated Multi-satellitE Retrievals for Global Precipitation Measurement mission (IMERG) Final Run precipitation product, and precipitation estimates derived from NASA Soil Moisture Active Passive (SMAP) soil moisture retrievals), are objectively combined into a single pentad precipitation data set at 36-km resolution using a unique approach based on extended triple collocation. The quality of each of the four data sets is then evaluated against independent observations. When a global land surface model at 36-km resolution is integrated four times, once utilizing the merged precipitation forcing and once with each of the three contributing data sets, the near-surface soil moisture variations produced with the merged forcing validate best against independent satellite-based soil moisture fields. In addition, the merged data set is found to be more consistent, relative to each contributor, with estimates of air temperature variations across the globe. The merged data set thus appears to draw successfully on the complementary strengths of each contributor: the particularly high quality of the rain gauge-based data set in areas of high gauge density, the more uniform accuracy across the globe of the IMERG data, and the moderate accuracy, particularly in semi-arid regions, of the soil moisture retrieval-based data.

Automated summary

This study combined three independent, quasi-global, gridded precipitation data sets with a land surface model, finding that the merged data set performed better in simulating soil moisture and air temperature variations.