Evaluation of SM2RAIN-derived and GPM-based Precipitation Products for Hydrological Simulation

Constellation of precipitation measuring satellites comprises of geosynchronous microwave radars, radiometers and infrared sensors. The persevering challenge with the passive microwave constellations remains uncertainties which impacts applications such as hydrological modeling. Recent constellations have aided the development of the next generation of earth and space science missions, customizing measurement parameters to fit changing scientific knowledge. There are two techniques for estimating precipitation using satellite observations: the traditional top down approach and the relatively recent bottom up approach. In a top-down method, the properties of precipitation-sized particles (hydrometeors) are recorded using satellite-borne sensors that operate in several regions of the electromagnetic spectrum, such as visible (VIS), infrared (IR), and microwave (MW). In addition to the top down technique, a new innovative bottom up approach for predicting precipitation using in-situ and satellite-based soil moisture measurements is developed. Despite the fact that land surface models (LSMs) provide a supplementary way of investigating fluxes, their simulations is frequently susceptible to errors due to model parameters, model structure, and forcing data. Precipitation forcing uncertainty is known to have a substantial impact on LSM production. The influence of several satellite-based precipitation products on LSM simulated soil moisture over India is investigated in this study using the Variable Infiltration Capacity model. The precipitation products utilized include the Global Precipitation Measurement mission's (GPM) integrated Multi-satellite Retrievals (IMERG) Late run, the SM2RAIN-Climate Change Initiative (SM2RAIN-CCI), and the SM2RAIN-Advanced SCATerometer (SM2RAIN-ASCAT). The first product is a multi-satellite precipitation product (top-down method), while the next two are surface satellite soil moisture products (bottom up approach). During the monsoon, the IMERG precipitation shows a bias (mm) of 1.3014 and an RMSE (mm) of 1.8024. SM2RAIN-ASCAT has a bias of 0.0206 and an RMSE of 0.9368 during the monsoon, but SM2RAIN-CCI has a bias of -0.3711 and an RMSE of 0.9345 during the monsoon. We benchmark the precipitation products produced from top down and bottom up approaches with reference to India Meteorological Department gauge-based precipitation dataset from the standpoint of the hydrology community in this work. Acknowledgement: The support from Department of Science and Technology INSPIRE project is duly acknowledged.