Speaker
Description
Rainfall retrievals from commercial microwave links (CMLs) have become a valuable complement to conventional rainfall sensors, especially in the Global South where weather radar and rain gauge networks remain sparse. However, CML coverage is limited over inaccessible and in rural regions, thereby limiting their applicability for short-term forecasting and flood early warning. Satellites do offer global coverage, but face challenges as accurate retrieval of surface-level precipitation and limited detection of low-intensity rainfall. In this study, we compare CML-derived rainfall estimates with those obtained from the passive microwave radiometer aboard the Global Precipitation Measurement (GPM) Core Observatory across multiple countries with contrasting climates (Netherlands, Sri Lanka, Nigeria). The dual-frequency precipitation radar onboard GPM, providing collocated observations with the onboard radiometer, serves as a consistent reference dataset across all three countries. In addition, over the Netherlands, a high-quality, high-resolution gauge-adjusted radar precipitation dataset is employed as a supplementary reference.
By conducting statistical evaluations at 15-minute temporal resolution, this study provides new insights into the performance of CML-derived rainfall estimates across three distinct regions. The error characteristics of CMLs and satellite radiometers are shown to be complementary. Radiometer-based estimates systematically underestimate high-intensity precipitation, with relative biases ranging between −10% and −50% depending on the study region and the rainfall detection threshold. Conversely, CML retrievals exhibit reduced sensitivity to low-intensity events, with probabilities of detection (POD) between 0.1 (Nigeria) and 0.5 (the Netherlands).