A Global Evaluation of IMERG Precipitation Occurrence Using SMAP Detected Soil Moisture Change

Abstract

Abstract A globally consistent ground validation method for remotely sensed precipitation products is crucial for building confidence in these products. This study develops a new methodology to validate the IMERG precipitation products through the use of SMAP soil moisture changes as a proxy for precipitation occurrence. Using a standard 2 × 2 contingency table method, preliminary results provide confidence in SMAP’s ability to be utilized as a validation tool for IMERG as results are comparable to previous validation studies. However, the method allows for an overestimate of false alarm frequency due to light precipitation events that can evaporate before the subsequent SMAP overpass and changes in overpass-to-overpass SMAP soil moisture that are within the range of SMAP uncertainty. To counter these issues, a 3 × 3 contingency table is used to reduce noise and extract more signal from the detection method. Through the use of this novel approach, the validation method produces a global mean POD of 0.64 and global mean FAR of 0.40, the first global-scale ground validation skill scores for the IMERG products. Advancing the method to validate precipitation quantity and the development of a real-time validation for the IMERG Early product are the crucial next developments. Significance Statement We wanted to see if there was a method in which remotely sensed precipitation observations could be validated at a near-global scale for land areas. Scientific literature is filled with studies that validate various precipitation datasets over local-to-regional scales, with very few extending beyond that domain. This study provides a robust first attempt at validating a global precipitation product at a global scale using changes in remotely sensed soil moisture as an independent proxy for precipitation presence/absence. While the method demonstrates that there is skill in using soil moisture as a tool to validate precipitation at the global scale, we find that there are still instances of a systemic bias for arid climate regimes. This method lays the groundwork for future studies to provide a comprehensive global validation in a globally consistent manner.