Long-term climatological SM2RAIN datasets for rainfall spatiotemporal analysis

A long-term rainfall dataset with high spatial and temporal resolution is an indispensable resource for climatological studies. This information is crucial for water resource management. Among available rainfall products, SM2RAIN datasets estimate rainfall from satellite soil moisture observation through the so-called “bottom-up” approach. Previous research has indicated the high performance of rainfall estimation of SM2RAIN products over different parts of the globe. SM2RAIN-CCI and SM2RAIN-ASCAT are two rainfall products that estimate rainfall at 0.25° and 0.1° spatial and daily temporal resolution for the period of 1998-2015 and 2007-2020 on a global scale, respectively. The goal of this study is to design the long-term climatological datasets with 0.25° spatial and monthly temporal resolution for the period from 1998 to 2020 by merging these two SM2RAIN products for spatiotemporal investigation of rainfall over the United States of America as a case study. Moreover, the spatiotemporal analysis results of the resulting product are compared with other rainfall products based on ground observations and reanalysis, such as the Global Precipitation Climatology Project (GPCP) and ERA5. The results show a good agreement of the developed SM2RAIN-based monthly rainfall dataset with respect to GPCP and ERA5 and pave the way to build a global scale dataset based on satellite soil moisture data through SM2RAIN.