Data-driven localization mappings in filtering the monsoon-Hadley multicloud convective flows

TL;DR

This study introduces data-driven localization mappings using supervised learning to enhance satellite data assimilation in a complex multicloud convective model, improving analysis accuracy especially with small ensembles.

Contribution

It presents a novel approach of using supervised learning for localization in ensemble Kalman filtering applied to a nonlinear multicloud model, addressing model errors and small ensemble challenges.

Findings

Localization maps improve analysis accuracy with small ensembles.

Localization methods perform well even with model errors.

Supervised learning enhances correlation estimates in data assimilation.

Abstract

This paper demonstrates the efficacy of data-driven localization mappings for assimilating satellite-like observations in a dynamical system of intermediate complexity. In particular, a sparse network of synthetic brightness temperature measurements is simulated using an idealized radiative transfer model and assimilated to the monsoon-Hadley multicloud model, a nonlinear stochastic model containing several thousands of model coordinates. A serial ensemble Kalman filter is implemented in which the empirical correlation statistics are improved using localization maps obtained from a supervised learning algorithm. The impact of the localization mappings is assessed in perfect model observing system simulation experiments (OSSEs) as well as in the presence of model errors resulting from the misspecification of key convective closure parameters. In perfect model OSSEs, the localization mappings that use adjacent correlations to improve the correlation estimated from small ensemble sizes produce robust accurate analysis estimates. In the presence of model error, the filter skills of the localization maps trained on perfect and imperfect model data are comparable.