DATA ASSIMILATION AND INITIALIZATION OF HURRICANE PREDICTION MODELS.

Abstract

This paper investigates the problem of initializing operational hurricane models with several types of real data. Imbalances in real data generate inertia-gravity waves with periods that vary strongly in different regions of the hurricane domain. The energy of these waves is removed by propagation out of the domain, by horizontal diffusion process, and by the truncation errors associated with the Matsuno time-differencing scheme. Several initialization schemes are tested with a symmetric hurricane model. Random and bias errors superimposed on perfect data produce imbalances that lend to significant errors in short-range forecasts. A general dynamic initialization scheme that is suitable for diabatic, viscous models yields very promising results.