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

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 simulta-neous 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 ampli-tudes 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 re-covering the truth model parameter values also has sensitivity to the ensemble size and smoothing forecast length, each of which are explored.

Automated summary

In this study, a one-step-ahead ensemble Kalman smoother (EnKS) is introduced for parameter estimation. It is found that the EnKS has the potential to provide new constraints on surface-exchange coefficients of momentum and enthalpy through observing system simulation experiments (OSSEs). The results show that the EnKS can accurately constrain these parameters, but its success varies throughout the tropical cyclone life cycle and is influenced by the ensemble size and smoothing forecast length.