Incorporating TPC observed parameters and QuikSCAT surface wind observations into hurricane initialization using 4D-VAR approaches

This study aims to develop an objective hurricane initialization scheme which incorporates not only forecast model constraints but also observed features such as the initial intensity and size. It is based on the four-dimensional variational (4D-Var) bogus data assimilation (BDA) scheme originally proposed by Zou and Xiao (1999). The 4D-Var BDA consists of two steps: (i) specifying a bogus sea level pressure (SLP) field based on parameters observed by the Tropical Prediction Center (TPC) and (ii) assimilating the bogus SLP field under a forecast model constraint to adjust all model variables. This research focuses on improving the specification of the bogus SLP indicated in the first step. Numerical experiments are carried out for Hurricane Bonnie (1998) and Hurricane Gordon (2000) to test the sensitivity of hurricane track and intensity forecasts to specification of initial vortex. Major results are listed below: (1) A linear regression model is developed for determining the size of initial vortex based on the TPC observed radius of 34kt. (2) A method is proposed to derive a radial profile of SLP from QuikSCAT surface winds. This profile is shown to be more realistic than ideal profiles derived from Fujita's and Holland's formulae. (3) It is found that it takes about 1 h for hurricane prediction model to develop a conceptually correct hurricane structure, featuring a dominant role of hydrostatic balance at the initial time and a dynamic adjustment in less than 30 minutes. (4) Numerical experiments suggest that track prediction is less sensitive to the specification of initial vortex structure than intensity forecast. (5) Hurricane initialization using QuikSCAT-derived initial vortex produced a reasonably good forecast for hurricane landfall, with a position error of 25 km and a 4-h delay at landfalling. (6) Numerical experiments using the linear regression model for the size specification considerably outperforms all the other formulations tested in terms of the intensity prediction for both Hurricanes. For examples, the maximum track error is less than 110 km during the entire three-day forecasts for both hurricanes. The simulated Hurricane Gordon using the linear regression model made a nearly perfect landfall, with no position error and only 1-h error in landfalling time. (7) Diagnosis of model output indicates that the initial vortex specified by the linear regression model produces larger surface fluxes of sensible heat, latent heat and moisture, as well as stronger downward angular momentum transport than all the other schemes do. These enhanced energy supplies offset the energy lost caused by friction and gravity wave propagation, allowing for the model to maintain a strong and realistic hurricane during the entire forward model integration.