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Figure: Case study of August 11, 2018. Convective/stratiform split of the raining system observed by GPM-core satellite (orbit: 025293). From left to right: (a) PMW-retrieved (GPROF) – a current operational benchmark; (b) Dual-frequency Precipitation Radar-derived product – the truth; (c) Bayesian model prediction ResNetV2; (d) Entropy for the Bayesian model prediction – uncertainty map.

Using Bayesian Deep Learning to Improve Precipitation Retrievals

ESSIC/CISESS Scientist Veljko Petković co-authored a study on the application of new and emerging field of BDL concepts to mitigate problems associated with the accuracy of precipitation retrievals from satellite-borne passive microwave (PMW) radiometers, which was published in IEEE Geoscience and Remote Sensing Letters.

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