Towards a Data-Derived Observation Error Covariance Matrix for Satellite Measurements

The observation error covariance (R) matrix is a key component in the data assimilation (DA) process for retrieval of atmospheric state parameters (ASPs), also impacting the subsequent numerical weather forecast. However, one commonly used type of R matrix depends on instrument noise, which contravenes reality because the retrieved ASPs would depend on the instrument used. Other types of R matrix rely on the observation operator (H), analyzed state ( x a ), background error covariance (B) matrix or the background state ( x b ), and the selected forecast ensemble. All these dependences reduce the representativeness of the R matrix, since the correctness of H needs verification and no true values exist for x a or x b . As such, a better method to correctly specify the R matrix is needed. Through the physical mechanism occurring between incident radiation and particles in the atmosphere, which complies with the phenomena of energy absorption and emission, correlations among bands or channels in a detected atmospheric radiance spectrum occur. This paper thus proposes a data-derived R matrix based on a large number (N) of detected atmospheric radiance spectra constructed from N real-time measurements, where N real-time measurements can be acquired by staring at some observation location of interest during a short amount of time. This data-derived R matrix for satellite radiance observations does not rely on any assumed quantities and is unambiguous. Technically, recording N real-time measurements is achievable by modifying the trigger configuration of data recording from ground.