Potential for new constraints on tropical cyclone surface-exchange coefficients through simultaneous ensemble-based state and parameter estimation

Robert G. Nystrom, Steven J. Greybush, Xingchao Chen, Fuqing Zhang

Research output: Contribution to journalArticlepeer-review

Abstract

The tropical cyclone (TC) surface-exchange coefficients of enthalpy (Ck) and momentum (Cd) at high wind speeds have been notoriously challenging to estimate. This difficulty arises from many factors, including the difficulties in collecting observations within the turbulent TC boundary layer, and the complex coupled physical interactions between the TC boundary layer and ocean surface, which are challenging to accurately model. Motivated by recent studies highlighting the limited practical predictability of TC intensity as a result of uncertainty in the physical representation of the air sea fluxes of momentum and enthalpy at high wind speeds, we investigate the potential to estimate the surface enthalpy and momentum exchange coefficients through ensemble data assimilation. Significant ensemble correlations between tangential wind, radial wind, and simulated infrared brightness temperatures with parameters controlling the enthalpy and momentum exchange coefficients suggest potential to use all-sky satellite and/or airborne radial velocity observations to estimate these unknown parameters. Using a series of observing system simulation experiments (OSSEs), simulated infrared brightness temperature observations, and a known truth, we demonstrate some potential for simultaneous state and parameter estimation with an ensemble-based data assimilation system to converge toward the correct known parameter values. In all OSSEs with either one or multiple unknown parameters, the initial parameter errors are reduced through simultaneous model state and parameter estimation. However, challenges still exist in converging to the precise true parameter values, as state errors during rapid intensification can project onto the parameter estimates.

Original languageEnglish (US)
Pages (from-to)2213-2230
Number of pages18
JournalMonthly Weather Review
Volume149
Issue number7
DOIs
StatePublished - Jul 2021

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

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