Improving the Assimilation of T-TREC-Retrieved Wind Fields with Iterative Smoothing Constraints During Typhoon Linfa
2025
Huimin Bian | Haiyan Fei | Yuqing Mao | Cong Li | Aiqing Shu | Jiajun Chen
Enhancing radar data assimilation at cloud-resolving scales is essential for advancing typhoon analysis and forecasting. This study focuses on Typhoon Linfa, the 10th Pacific Typhoon of 2015, and proposes T-TREC-IS (Typhoon Circulation Tracking Radar Echo by Correlations with Iterative Smoothing), an enhanced version of the T-TREC algorithm. The enhancement incorporates an iterative smoothing constraint into the T-TREC algorithm, which improves the continuity of the retrieved wind field and mitigates the effects of velocity aliasing in radar data, thereby increasing the operational feasibility of the method. Building on this improvement, we evaluate the effectiveness of assimilating the T-TREC-IS-retrieved wind field for analyzing and forecasting Typhoon Linfa. The results demonstrate that the iterative smoothing constraint effectively filters out velocity de-aliasing errors during radar data quality control, enhances wind field intensity near the typhoon core, and retrieves the typhoon circulation more accurately. The refined wind field exhibits improved consistency and continuity, resulting in superior performance in subsequent assimilation analyses and forecasts.
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