CISESS Seed Grant: Retrieving Latent Heat from Passive Microwave Satellite Observations

GPM Dual-frequency Precipitation Radar (DPR) Latent Heat (LH) profile for the period Apr 2014 – Oct 2023. Shownis mean LH profile (K h-1) at 500-meter vertical resolution for precipitating DPR pixels, as a function of DPR [

© ] convective volume fraction (VF) over collocated GMI Field of View. VF value of 0.0 indicates fully stratiform field of view. VF value of 1.0 indicates fully convective field of view.

Veljko Petkovic, Associate Research Scientist, UMD/CISESS/ESSIC

This CISESS Seed Grant Project seeks to build a robust link between top-of-the-atmosphere microwave radiance and vertical properties of cloud systems. Specifically, the study attempts to demonstrate the feasibility of retrieving the latent heating profile of the atmospheric column using passive microwave satellite observations. It is intended to back a new retrieval development and improve understanding of variability in cloud morphology at a range of spatial and temporal scales. If successful, the new satellite retrieval will support the fundamental science of NOAA's satellite programs by promoting a) unique cloud system analyses and new insights into cloud processes, b) potential for advances in numerical prediction, and c) a better understanding of energy and water budgets at global and regional scales.

Existing radar- and radiometer-based latent heating products, combined with modeled data and advanced statistical techniques, will capture relevant relationships between passive microwave observations and latent heat distributions. Sensors of specific interest are the GCOM/GOSAT Advanced Microwave Scanning Radiometer (AMSR) series and GPM Dual-frequency Precipitation Radar (DPR). Ultimately, the work is expected to pave a road towards complementing the existing satellite data records with valuable, currently-missing, information significant to Earth's atmosphere's radiative balance and hydrology cycle.

Advancements made in deep learning over the past several years, combining physical process-based research with automated data-driven analysis, now enable efficient identification of the links between cloud patterns and their radiometric properties from passive microwave observations proposed here. Therefore, the present study proposes the use of Quantile Regression Neural Networks and similar models to build links between the fields of microwave radiances observed at the top of the atmosphere and radar-observed latent heat profiles. To achieve this, radar-based latent heat profiles observed during a 9-year period (see Figure above) will be collocated with passive microwave observations. These collocations will support a neural network training designed to link the vertical profile of (radar-based) latent heating and passive microwave brightness temperatures.

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