IRMerg: Enhancing Global Infrared Precipitation Estimates with Land Surface Variables and Contributing Factors Analysis Using Explainable Machine Learning

The Integrated Multi-satellite Retrievals for GPM (IMERG) is a global satellite-based precipitation dataset that provides near real-time precipitation estimates by combining multiple satellite measurements. IMERG integrates microwave (MW) observations from low-orbit satellites with precipitation estimates inferred from the brightness temperature of geostationary infrared (IR) imagery. MW measurements provide accurate precipitation estimates due to their direct interaction with precipitation particles, while IR measurements offer broader spatial and temporal coverage by inferring precipitation from cloud-top brightness temperatures. Together, these complementary techniques balance precision and coverage to improve global precipitation monitoring. However, IR-based precipitation estimates are inherently less reliable due to the weak direct correlation between brightness temperature and precipitation. Conversely, MW-derived estimates are more accurate but spatially constrained by the limited footprint of low-orbit satellites. To investigate the contributing factors in IR precipitation error calibration, we leveraged ERA5 Land, a high-resolution reanalysis dataset that includes surface variables across nine domains, such as temperature, soil moisture, radiation, and vegetation indices. These variables offer a comprehensive lens for understanding the impact of the land surface on precipitation dynamics. We employed the XGBoost machine learning model to predict the errors in IR precipitation estimates relative to MW-derived benchmarks. Additionally, SHapley Additive exPlanations (SHAP) values were used to interpret the model’s predictions, uncovering how individual input features contribute to error correction.

Our findings indicate that the explainable machine learning model can correct the infrared (IR) precipitation estimates to resemble microwave (MW) products, achieving notable improvements across statistical metrics. In the preliminary analysis of 165 countries and territories, the XGBoost model’s calibration improved the RMSE in all validation datasets, with a median reduction of 19.89% and an average reduction of 22.5%. Similarly, the correlation coefficient improved, with a median increase of 18.43% and an average increase of 54.49%. Moreover, the spatial and temporal distributions of the variables' SHAP values show various patterns. The clustered spatial distribution may represent the local climate attributes in specific geographic regions, providing insights into how regional environmental factors influence precipitation estimates. Meanwhile, the temporal distribution may imply seasonal variation, which can help identify patterns in precipitation dynamics and refine IR-based calibration by accounting for temporal variability in precipitation processes. This study provides a robust framework for leveraging land surface variables to refine IR-based precipitation products. By integrating reanalysis data with machine learning models, we present a scalable solution for improving precipitation monitoring in data-sparse regions, particularly where MW observations are unavailable.