A unified neural background-error covariance model for midlatitude and tropical atmospheric data assimilation

TL;DR

This paper introduces a neural-network autoencoder approach to estimate background-error covariances in atmospheric data assimilation, effectively capturing flow-dependent balances in both tropical and midlatitude regions, improving analysis quality.

Contribution

It presents a novel neural-network autoencoder method to estimate background-error covariances in a reduced latent space, enabling flow-dependent and balanced data assimilation across different atmospheric regimes.

Findings

Latent space assimilation preserves physical balances in analysis increments.

Increments are flow-dependent and localized, consistent with physical dynamics.

Forecasts from analyses show realistic weather evolution, including tropical Kelvin waves.

Abstract

Estimating background-error covariances remains a core challenge in variational data assimilation (DA). Operational systems typically approximate these covariances by transformations that separate geostrophically balanced components from unbalanced inertio-gravity modes - an approach well-suited for the midlatitudes but less applicable in the tropics, where different physical balances prevail. This study estimates background-error covariances in a reduced-dimension latent space learned by a neural-network autoencoder (AE). The AE was trained using 40 years of ERA5 reanalysis data, enabling it to capture flow-dependent atmospheric balances from a diverse set of weather states. We demonstrate that performing DA in the latent space yields analysis increments that preserve multivariate horizontal and vertical physical balances in both tropical and midlatitude atmosphere. Assimilating a single 500 hPa geopotential height observation in the midlatitudes produces increments consistent with geostrophic and thermal wind balance, while assimilating a total column water vapor observation with a positive departure in the nearly-saturated tropical atmosphere generates an increment resembling the tropical response to (latent) heat-induced perturbations. The resulting increments are localized and flow-dependent, and shaped by orography and land-sea contrasts. Forecasts initialized from these analyses exhibit realistic weather evolution, including the excitation of an eastward-propagating Kelvin wave in the tropics. Finally, we explore the transition from using synthetic ensembles and a climatology-based background error covariance matrix to an operational ensemble of data assimilations. Despite significant compression-induced variance loss in some variables, latent-space assimilation produces balanced, flow-dependent increments - highlighting its potential for ensemble-based latent-space 4D-Var.