Applications of Dynamic Land Surface Information for Passive Microwave Precipitation Retrieval

Area of Science:

• Earth Science
• Atmospheric Science
• Remote Sensing

Background:

• Accurate precipitation retrieval over land is challenging for passive microwave sensors due to warm surface interference.
• The Global Precipitation Measurement Mission (GPM) aims for accurate global precipitation data but faces difficulties over land.
• The Goddard Profiling Algorithm (GPROF) often overestimates low-intensity precipitation over land surfaces.

Purpose of the Study:

• To improve physically based passive microwave precipitation retrieval over global land surfaces.
• To address the overestimation of low precipitation rates by the GPROF algorithm.
• To reduce reliance on static or ancillary data in precipitation retrieval algorithms.

Main Methods:

• Enhancing the GPROF algorithm with dynamic, retrieved surface information from a GPM-derived optimal estimation scheme.
• Replacing static GPROF inputs (emissivity, water vapor, snow cover) with dynamically retrieved parameters.
• Analyzing retrieval sensitivities to dynamic surface conditions.

Main Results:

• The enhanced algorithm significantly decreases the probability of false precipitation detection by 50%.
• Incorporating retrieved surface parameters reduces the algorithm's dependence on ancillary datasets.
• Improvements lead to more physically consistent precipitation retrievals over land.

Conclusions:

• Dynamic surface information is critical for accurate passive microwave precipitation retrieval over land.
• The enhanced GPROF algorithm demonstrates improved performance and physical consistency.
• This approach advances the capabilities of the Global Precipitation Measurement Mission (GPM).