EGUsphere EGUsphere EGUsphere EGUsphere Copernicus Publications Göttingen, Germany 10.5194/egusphere-2026-3545 1D-VAR system for a ground-based microwave radiometer: application of an inter-channel observation-error covariance matrix Song Yunyoung 1 Ahn Myoung-Hwan https://orcid.org/0000-0002-2044-5336 1 Lee Su Jeong https://orcid.org/0000-0002-5052-9039 1 Cimini Domenico https://orcid.org/0000-0002-5962-223X 2 3 Song Chang-Keun 4 5 6 Department of Climate and Energy Systems Engineering, Ewha Womans University, Seoul, 03760, Republic of Korea National Research Council of Italy, Institute of Methodologies for Environmental Analysis, Potenza, 85050, Italy Center of Excellence CETEMPS, University of L’Aquila, L’Aquila, 67100, Italy Department of Civil, Urban, Earth, and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, South Korea Graduate School of Carbon Neutrality, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, South Korea Research & Management Center for Particulate Matters at the Southeast Region of Korea, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, South Korea 03 08 2026 2026 1 23 Copyright: © 2026 Yunyoung Song et al. 2026 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://egusphere.copernicus.org/preprints/2026/egusphere-2026-3545/ The full text article is available as a PDF file from https://egusphere.copernicus.org/preprints/2026/egusphere-2026-3545/egusphere-2026-3545.pdf

This study presents the specification of an inter-channel observation-error covariance matrix (<strong>R</strong>) 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 <strong>R</strong> is crucial, as they determine the relative weighting of background and observational uncertainties and thus directly control retrieval performance. However, realistic specification of <strong>R</strong> 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 <strong>R</strong> 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 <strong>R</strong> 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.

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