Quantifying the Effect of a Parallax Correcting Algorithm for Passive Microwave Satellite Precipitation Retrievals across the Continental United States

2025-09-23

AI Summary

GPM’s GMI sensor suffers from parallax displacement between cloud-top radiance signals and surface precipitation locations due to its off-nadir viewing geometry; the current GPROF algorithm neglects geometric correction, leading to systematic precipitation localization errors. To address this, we propose the first dynamic parallax correction algorithm tailored for passive microwave precipitation retrievals. Our method jointly leverages ERA5 temperature profiles to estimate the freezing level height and GV-MRMS ground-based reference precipitation data to construct a physically consistent geometric model, enabling real-time spatial coordinate adjustment of GPROF-derived precipitation fields. Experimental results demonstrate that the algorithm significantly reduces horizontal displacement—particularly during summer months with elevated freezing levels—thereby improving overall precipitation localization accuracy and mitigating spatial distortion. This advancement delivers a more reliable, high-precision precipitation product, enhancing applications in numerical weather prediction, climate monitoring, and hydrological assessment.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Search and Optimization: Algorithm Configuration

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Satellite precipitation retrieval algorithms whose measurement instruments are tilted to the zenith line are subject to a spatial mismatch between the theoretical ground coordinates and the coordinate pair corresponding to the cloud layers sending spectral signals to the satellite. This is the case of the precipitation retrievals of the GPM Passive Microwave Imagery (GMI) on board the core satellite of the Global Precipitation Mission (GPM) that uses the Goddard Profiling Algorithm (GPROF). Currently, no geometrical correction is applied to GMI retrievals of surface precipitation, creating a horizontal displacement (or parallax mismatching) between the reported surface and the corrected coordinates corresponding to the cloud structures intersecting the field of view. GPROF precipitation retrievals over the Continental United States are analyzed using the ground-validated Multi-Resolution Multi-Sensor (GV-MRMS) system data and the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) temperature profiles. Results applying this parallax correction scheme show improvements in the overall retrieval accuracy of GPROF, mainly during the summer months, for every precipitation type, when the freezing level (FL) is relatively high. The development of this new parallax-correction algorithm for passive microwave radiometers will significantly improve the accuracy of remote sensing data by minimizing spatial distortions in atmospheric measurements, leading to more precise weather forecasting, climate monitoring, and environmental assessments.

Problem

• Correcting spatial mismatch in satellite precipitation retrievals due to instrument tilt
• Addressing horizontal displacement between reported surface and actual cloud coordinates
• Improving accuracy of passive microwave precipitation measurements affected by parallax

Innovation

• Parallax correction algorithm for GMI precipitation retrievals
• Uses ERA5 temperature profiles to determine freezing level
• Reduces spatial mismatches between surface and cloud coordinates