Enhanced Large-Scale Validation of Satellite-Based Land Rainfall Products
Fan Chen, Wade T. Crow, Luca Ciabatta, Paolo Filippucci, Giulia Panegrossi, Anna Cinzia Marra, Silvia Puca, Christian Massari
Abstract
Abstract Satellite-based precipitation estimates (SPEs) are generally validated using ground-based rain gauge or radar observations. However, in poorly instrumented regions, uncertainty in these references can lead to biased assessments of SPE accuracy. As a result, at regional or continental scales, an objective basis to evaluate SPEs is currently lacking. Here, we evaluate the potential for large-scale, spatially continuous evaluation of SPEs over land via the application of collocation-based techniques [i.e., triple collocation (TC) and quadruple collocation (QC) analyses]. Our collocation approach leverages the Soil Moisture to Rain (SM2RAIN) rainfall product, derived from the time series analysis of satellite-based soil moisture retrievals, in combination with independent rainfall datasets acquired from ground observations and climate reanalysis to validate four years of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) H23 daily rainfall product. Large-scale maps of the H23 correlation metric are generated using both TC and QC analyses. Results demonstrate that the SM2RAIN product is a uniquely valuable independent product for collocation analyses, because other available large-scale rainfall datasets are often based on overlapping data sources and algorithms. In particular, the availability of SM2RAIN facilitates the large-scale evaluation of SPE products like H23—even in areas that lack adequate ground-based observations to apply traditional validation approaches.