In this paper, the basic Wiener filter structure and adaptive algorithm module are used to optimize the parameter adjustment and data noise processing in the adaptive filter algorithm. Based on the LMS criterion, the algorithm is further refined by quantization error and affine projection optimization, which improves the accuracy and speed of vortex and circulation data analysis. The optimized algorithm reduces noise and covariance error, and achieves excellent performance in filtering evaluation (SRTAE: \(1.623\times10^{-2}\,\text{m}\) and \(1.162\times10^{-4}\,\text{m/s}\)). The results show that the spatio-temporal coupling effect between vortex and circulation can be found through numerical modeling and spatio-temporal analysis. This study provides a valuable reference for promoting the application of computational mathematics in the field of climate monitoring.
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