Bridging the Microwave Data Gap; Using Bayesian Deep Learning to “See” the Unseen (Papers Track)
Pedro Ortiz (Naval Postgraduate School); Eleanor Casas (Naval Postgraduate School); Marko Orescanin (Naval Postgraduate School); Scott Powell (Naval Postgraduate School)
Having microwave data with the spatial and temporal resolution of infrared data would provide a large positive impact on many climate and weather applications. We demonstrate that Bayesian deep learning is a promising technique for both creating and improving synthetic microwave data from infrared data. We report 0.7% mean absolute percentage error for 183+/-3 GHz microwave brightness temperature and uncertainty metrics and find that more training data is needed to achieve improved performance at 166 GHz, 37 GHz, and 23 GHz. Analysis of the spatial distribution of uncertainty reveals that additional cloud data will provide the greatest increase in skill, which will potentially allow for generation of many secondary products derived from microwave data in the future.