Utilizing Quantified Uncertainty in Synthetic Microwave Brightness Temperatures to Reveal Hidden Tropical Cyclone Structures

Passive microwave (PMW) satellite data provides numerous benefits for forecasting tropical cyclone (TC) track and intensity when available, but due to the low-earth orbits of PMW sensors, several days may pass before a PMW swath fully captures a TC center, which is insufficient for operational needs. This study uses Bayesian deep learning to "fill in the gaps" of PMW data and explore the potential for deriving a high spatio-temporal "full-disk" synthetic PMW brightness temperature (TB) product and its uncertainty from geostationary infrared observations. Current results indicate that synthetic product uncertainty may be particularly beneficial for TC forecasters, and can be used to enhance the visibility of shallow low-level convection, environmental moisture, and nascent TC eyes that are thinly obscured.