| 03 Aug 2026
1D-VAR system for a ground-based microwave radiometer: application of an inter-channel observation-error covariance matrix
Abstract. This study presents the specification of an inter-channel observation-error covariance matrix (R) and evaluates its effects on One-Dimensional Variational (1D-VAR) retrievals from a ground-based microwave radiometer (MWR) using RTTOV-gb as the observation operator. In the 1D-VAR, the accurate characterization of the background error covariance matrix and R is crucial, as they determine the relative weighting of background and observational uncertainties and thus directly control retrieval performance. However, realistic specification of R remains challenging due to the complexity of error sources, including instrument noise, forward-model error, and representativeness error. Consequently, many previous studies have adopted a simplified diagonal approximation. In this study, a correlated R is specified and compared with its diagonal configuration to quantify the effects of inter-channel error correlations on 1D-VAR performance. Radiosonde validation shows root mean square error (RMSE) reductions of 1.02 % for temperature and 4.52 % for humidity relative to the diagonal configuration below 1000 m. Using the correlated configuration, the 1D-VAR retrieval outperforms the Numerical Weather Prediction (NWP) model used as the background in the lowest 1000 m, achieving RMSE reductions of up to 23 % for temperature and 12 % for humidity, indicating an improved representation of near-surface variability during the intensive observation periods. The correlated configuration improves convergence efficiency, with 5.7 % of successful retrievals converging in a single iteration (compared to 0.1 % for the diagonal configuration), reducing the total processing time by approximately 2.6 %. In addition to enhanced computational efficiency, the correlated R maintains comparable retrieval accuracy in the lower troposphere. Overall, explicitly accounting for inter-channel observation-error correlations enhances convergence efficiency and provides modest improvements in retrieval accuracy below 1000 m, highlighting the importance of realistic observation-error covariance specification for lower-tropospheric thermodynamic profiling.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Measurement Techniques.
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