A One-Step-Ahead Ensemble Kalman Smoothing Approach Toward Estimating the Tropical Cyclone Surface-Exchange Coefficients

Abstract

Abstract In this study, a one-step-ahead ensemble Kalman smoother (EnKS) is introduced for the purposes of parameter estimation. The potential for this system to provide new constraints on the surface-exchange coefficients of momentum (Cd) and enthalpy (Ck) is then explored using a series of observing system simulation experiments (OSSEs). The surface-exchange coefficients to be estimated within the data assimilation system are highly uncertain, especially at high wind speeds, and are well known to be important model parameters influencing the intensity and structure of tropical cyclones in numerical simulations. One major benefit of the developed one-step-ahead EnKS is that it allows for simultaneous updates of the rapidly evolving model state variables found in tropical cyclones using a short assimilation window and a long smoother window for the parameter updates, granting sufficient time for sensitivity to the parameters to develop. Overall, OSSEs demonstrate potential for this approach to accurately constrain parameters controlling the amplitudes of Cd and Ck, but the degree of success in recovering the truth model parameters varies throughout the tropical cyclone life cycle. During the rapid intensification phase, rapidly growing errors in the model state project onto the parameter updates and result in an overcorrection of the parameters. After the rapid intensification phase, however, the parameters are correctly adjusted back toward the truth values. Last, the relative success of parameter estimation in recovering the truth model parameter values also has sensitivity to the ensemble size and smoothing forecast length, each of which are explored. Significance Statement Large uncertainty in the surface-exchange coefficients of momentum and heat/moisture exists for hurricane conditions. This is a problem because the numerical weather model predictions of hurricane intensity and storm structure are sensitive to the surface-exchange coefficient values used. In this study we use data assimilation, or the relationships estimated between the surface-exchange coefficients and forecasted observations, to constrain uncertainty in the model’s surface-exchange coefficient values. More specifically, an approach to limit both the rapidly growing errors associated with the hurricane itself and the hurricane’s accumulated response to the surface-exchange coefficient values is presented. Overall, this approach has potential to accurately estimate the surface-exchange coefficients, but the success depends on the number of forecast realizations used and how rapidly the hurricane is changing.