Characterizing Soil Moisture Sensitivity to IMERG and CHIRPS Rainfall Products in the Global FLDAS Product

The Famine Early Warning Systems Network (FEWS NET) land data assimilation system (FLDAS) is a new, global, and publicly available product that is produced with NASA's Land Information System (LIS). FLDAS integrates remote sensing products and land-surface models to produce hydrological variables to aid in drought monitoring of food insecure regions. Verification of precipitation is challenging in data sparse regions, like sub-Saharan Africa, which introduces a challenge when quantifying potential errors and biases in other modeled hydrological variables, such as soil moisture. This study compares the Integrated Multi-satellite Retrievals for GPM (IMERG) and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) rainfall products to rain gauge data and North American Land Data Assimilation System (NLDAS-2) data over the continental US. False alarm rates (FAR) and biases for low precipitation events and drought are quantified. While the regional biases and FAR over CONUS are not necessarily equivalent to those in Africa, estimation of these precipitation metrics will aid in providing a first-guess of the biases in FLDAS-generated soil moisture. Analyses over CONUS show that there are discrepancies in the rainfall products for light precipitation days and for days with high amounts of rainfall. These differences in rainfall led to modeled soil moisture fields that deviated from NLDAS-2 soil moisture. Agreement between the precipitation products was high for days when no rainfall was reported. This initial attempt to quantify the potential errors and biases in the FLDAS product will allow for more regional based corrections and increased confidence in the drought monitoring product that is being provided to our partners.