Greg Hakim

A significant challenge to tropical cyclone storm-scale ensemble data assimilation is that observations tend to make analyses of storms more asymmetric than the background forecasts. Compromised intensity and structure, such as an increase of amplitude across the azimuthal Fourier spectrum, are a routine property of ensemble-based analyses, even with accurate position observations and frequent assimilation. Dynamics in subsequent forecasts evolve these states toward axisymmetry, so it is difficult to distinguish between real and artificial asymmetries for dynamical studies and forecasting. To address this problem, we propose here a novel algorithm using a storm-centered approach.

The new algorithm is designed for use with existing ensemble Kalman filter (EnKFs) with little or no modification, which facilitates adoption and maintenance. The algorithm consists of: (1) an environmental analysis using conventional coordinates, (2) a storm-centered analysis using storm-centered coordinates, and (3) a merged analysis that combines the two fields together at an updated storm location. The algorithm is evaluated for idealized three-dimensional storms in radiative--convective equilibrium and for a field of interacting vortices in a shallow water model, by comparing solutions against a control based on a conventional EnKF scheme. Results show that storm-centered assimilation yields vortices that are more symmetric and exhibit finer inner-core structure than for the conventional EnKF, with errors reduced on average by at least 50%. Fourier spectra of errors exhibit much-reduced assimilation-induced asymmetries compared to the conventional scheme. An assessment of the impact of the merge step on balance is evaluated in forecasts, which reveals comparable height tendency variance in both the storm-centered and conventional EnKF.