Improving Tropical Cyclogenesis Prediction Using Satellite All-Sky Radiances Data Assimilation

Improving guidance on pre-formation disturbances is one of the major goals of NOAA’s Hurricane Forecast Improvement Program as well as Section 104 of the Weather Research and Forecasting Innovation Act of 2017. Due to the paucity of in-situ observations over the open ocean, effective assimilation of high-resolution satellite observations is therefore of paramount importance. Using Hurricane Irma (2017) as a case study, we will show that that assimilating upper-tropospheric water vapor channel infrared (IR) brightness temperatures (BTs) from the Meteosat-10 SEVIRI instrument can significantly enhance the accuracy of genesis forecasts, improving predictions by up to 24 hours. Moreover, the assimilation of all-sky radiance data from satellites better captures the storm's rapid intensification in subsequent deterministic forecasts. In an experiment that withholds the assimilation of all-sky IR BTs, the assimilation of conventional observations from the Global Telecommunications System (GTS) leads to the premature genesis of Hurricane Irma by at least 24 h. This premature genesis is shown to result from an overestimation of the spatial coverage of deep convection within the African easterly wave (AEW) from which Irma eventually forms. The gross overestimation of deep convection without all-sky IR BTs is accompanied by higher column saturation fraction, stronger low-level convergence, and the earlier spin up of a low-level meso-β-scale vortex within the AEW that ultimately becomes Hurricane Irma. By adjusting initial moisture and cloud conditions, the assimilation of all-sky IR BTs leads to a more realistic convective evolution in forecasts and a more accurate timing of genesis. Building on these preliminary results, we plan to collaborate with NOAA scientists to optimize the assimilation of satellite all-sky radiances and other observations in the Hurricane Analysis and Forecast System (HAFS), ultimately improving operational tropical cyclogenesis prediction.