Data Assimilation and Predictability Studies for Improving Tropical Cyclone Intensity Forecasts
MARYLAND UNIV COLLEGE PARK DEPT OF ATMOSPHERIC AND OCEANIC SCIENCE
Pagination or Media Count:
This project aims to understand and improve the forecast of Tropical Cyclone TC lifecycle evolution and intensity, focusing on both large-scale environment and mesoscale phenomena in the TC system, which are major components responsible for intensity change. Two major challenges in TC intensity forecasting are the general lack of observations in the vicinity of TCs and the adaptive representation of the forecast error covariance. This project attempts to address both challenges for improving TC intensity forecasting. Intensive T-PARC THORPEX1 Pacific Asian Regional Campaign observations and other available observations will be assimilated with the LETKF Local Ensemble Transform Kalman Filter into the CFES Coupled ocean-atmosphere general circulation model For the Earth Simulator and the WRF Weather Research and Forecasting mesoscale model to study 1 the characteristics and role of coupled ocean-atmosphere covariance, 2 the impact of each observation assessed by an efficient ensemble sensitivity analysis method, 3 a better way to assimilate observations in the vicinity of the TC center and potential usefulness of Lagrangian data assimilation LaDA, 4 several new data assimilation techniques to improve the performance of LETKF, and 5 the predictability of TC intensity due to the uncertainty of initial conditions.