Added Value of Environmental Variables for Satellite Precipitation Retrieval: A Temporal Coevolution Perspective and a Machine Learning Integration Assessment

Satellite precipitation retrieval is inherently an underdetermined inverse problem where additional physical constraints could substantially enhance accuracy. While previous studies have explored static (pixel-based/spatial-context-based) environmental variables at discrete satellite observation times, their temporal dynamic information remains underutilized. Building on our earlier finding that retrieval errors depend on storm progression (event stage), we propose a new, physically interpretable mechanism for improving retrievals, namely, leveraging environmental variables' temporal dynamics as proxies for event stages. Using IMERG satellite product and GV-MRMS as ground-truth over CONUS (2018–2020), we first demonstrate robust coevolution patterns of environmental variables and satellite errors throughout events, and show that these variables' temporal gradients reliably infer event stages. We then demonstrate that incorporating these variables and their gradients into a machine-learning post-processing framework improves retrieval accuracy. This work inspires and guides more thorough utilization of spatiotemporal atmospheric fields encoding rich physical information within advanced machine-learning frameworks for further algorithm improvement.