Wednesday, 15 January 2020: 10:30 AM
Microwave imagery at frequencies of 22, 37, and 89 GHz from low-earth orbit is routinely used to monitor hurricane structure revealing deep convection, ice, and liquid water structure that is typically hidden by operational geostationary imagery at infrared and visible wavelengths. Such information has been vital to better understanding and monitoring convective structure, intensity, and eyewall replacement cycles. While microwave imagery provides important insight into tropical cyclones, information from polar-orbiting satellites is delayed, each imager has different scanning patterns and resolutions, and passes often miss critical portions of the storm that need to be captured to provide forecasters and models useful information. Because of these challenges, the utility of polar-orbiting data is limited in tropical cyclone operations. This is particularly true for tropical cyclone guidance applications (e.g., statistical–dynamical intensify forecast model such as the Statistical Hurricane Intensity Prediction Scheme) and numerical weather prediction. In the absence of a microwave imager in geostationary orbit, the high-quality data from the next generation of geostationary satellites infrared imagers (e.g., GOES-16/17, Himawari-8, GEO-KOMPSAT-2A) create an opportunity to use the information as features in machine-learning techniques. This work demonstrates the potential to create tropical cyclone centric synthetic 89-GHz microwave imagery from the Level 1b and 2 products through using a random forest algorithm and extensions into learning methods. Routinely available synthetic microwave data have the potential to be impactful as additional guidance for forecasters and leveraged in existing operational microwave-based tropical cyclone products.
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