A retrieval strategy for interactive ensemble data assimilation

TL;DR

This paper introduces a retrieval strategy using averaging kernels and EOF transformations to improve the assimilation of remotely sensed atmospheric data, reducing bias and error correlations for enhanced data assimilation accuracy.

Contribution

It presents a novel method that leverages averaging kernels and EOF space to transform retrievals into unbiased, uncorrelated observations suitable for data assimilation.

Findings

Transforms retrievals into unbiased, uncorrelated observations

Eliminates smoothing and prior effects in retrievals

Provides a data compression method via EOF representation

Abstract

As an alternative to either directly assimilating radiances or the naive use of retrieved profiles (of temperature, humidity, aerosols, and chemical species), a strategy is described that makes use of the so-called averaging kernel (AK) and other information from the retrieval process. This AK approach has the potential to improve the use of remotely sensed observations of the atmosphere. First, we show how to use the AK and the retrieval noise covariance to transform the retrieved quantities into observations that are unbiased and have uncorrelated errors, and to eliminate both the smoothing inherent in the retrieval process and the effect of the prior. Since the effect of the prior is removed, any prior, including the forecast from the data assimilation cycle can be used. Then we show how to transform this result into EOF space, when a truncated EOF series has been used in the retrieval process. This provides a degree of data compression and eliminates those transformed variables that have very small information content. In both approaches a vertical interpolation from the dynamical model coordinate to the radiative transfer coordinate is required. We define an algorithm using the EOF representation to optimize this vertical interpolation