Correction of real-time satellite precipitation with multi-sensor satellite observations of land surface variables

Precipitation is an important hydro-meteorological variable, and is a primary driver of the water cycle. In large parts of the world, real-time ground-based observations of precipitation are sparse and satellite-derived precipitation products are the only information source.We used changes in satellite-derived soil moisture (SM) and land surface temperature (LST) to reduce uncertainties in the real-time TRMM Multi-satellite Precipitation Analysis product (TMPA-RT). The Variable Infiltration Capacity (VIC) model was used to model the response of LST and SM on precipitation, and a particle filter was used to update TMPA-RT. Observations from AMSR-E (LPRM and LSMEM), ASCAT, SMOS and LST from AMSR-E were assimilated to correct TMPA-RT over the continental United States.Assimilation of satellite-based SM observations alone reduced the false detection of precipitation (by 85.4%) and the uncertainty in the retrieved rainfall volumes (5%). However, a higher number of observed rainfall events were not detected after assimilation (34%), compared to the original TMPA-RT (46%). Noise in the retrieved SM changes resulted in a relatively low potential to reduce uncertainties. Assimilation of LST observations alone increased the rainfall detection rate (by 51%), and annual precipitation totals were closer to ground-based precipitation observations. Combined assimilation of both satellite SM and LST, did not significantly reduce the uncertainties compared to the original TMPA-RT, because of the influence of satellite SM over LST. However, in central United States improvements were found after combined assimilation of SM and LST observations. This study shows the potential for reducing the uncertainties in TMPA-RT estimates over sparsely gauged areas