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Influence of Local Water Vapor Analysis Uncertainty on Ensemble Forecasts of Tropical Cyclogenesis Initialized via All-Sky Infrared and Microwave Radiance-based Data Assimilation

Presentation Date
Tuesday, January 30, 2024 at 4:45pm - Tuesday, January 30, 2024 at 5:00pm
The Baltimore Convention Center - 342



Tropical cyclone formation is known to require abundant water vapor in the lower to middle troposphere. In this study, we assess the impacts of water vapor analysis uncertainty on the predictability of the formation of Hurricane Irma (2017). To this end, we reduce the magnitude of the incipient disturbance’s water vapor perturbations obtained from an ensemble-based data assimilation system that constrained moisture by assimilating all-sky infrared and microwave radiances. Five-day convection-permitting ensemble forecasts are initialized two days prior to genesis using each set of modified analysis perturbations. Growth of convective differences and intensity uncertainty are evaluated for each ensemble forecast. We observe that initial moisture uncertainty within the nascent disturbance explains more than half of the intensity uncertainty at every lead time compared to that of an ensemble containing perturbations to all variables. Although each ensemble reveals a similar pathway to genesis, uncertainty in genesis timing varies substantially across ensembles since moister members exhibit earlier spin-up of the low-level vortex. These differences in genesis timing are traced back to the first six to twelve hours of integration. By the end of that period, ensembles with greater initial moisture uncertainty have larger intensity uncertainty. The rapid growth of intensity uncertainty during the early hours of integration results from earlier development of mesoscale convective system dislocation and larger meso-β- to meso-α-scale errors when initial moisture uncertainty is greater. Ultimately, this study underscores the importance of targeting the incipient disturbance with high spatio-temporal water vapor observations for ingestion into data assimilation systems.

Funding Program Area(s)