4.7 Article

Impact of Correction Target Selection on Long-Term Spectral Nudging in Luzon Strait and Its Adjacent Regions

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MDPI
DOI: 10.3390/jmse11112164

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spectral nudging; long-term simulation; correction target; multi-scale energy analysis

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This study discussed the impact of selecting correction targets on long-term spectral nudging, focusing on the Luzon Strait and its adjacent areas. The results showed that using real-time targets improved consistency with observations and better simulated meso- and small-scale characteristics. Adjusting large-scale potential energy was found to be significant in improving overall simulation ability.
Previous studies have pointed out that spectral nudging is still insufficient in improving the long-term simulation ability of numerical models. In response to this problem, this study started with the Luzon Strait and its adjacent areas and discussed the influence of the selection of correction targets on its long-term spectral nudging. We established two sets of numerical experiments with the same parameter configuration except for the correction target: one was the monthly climatological target, and the other was the monthly real-time. The results showed that, compared with the climatology, the real-time target improved the consistency with the observations in large-scale variability on the premise of ensuring the correction of the climatological bias of the model. Further verification of the real-time scheme better simulated the meso- and small-scale characteristics, especially more accurately reproducing the position, intensity, and movement trend of eddies when the Kuroshio intrusion event occurred. Multi-scale energy analysis revealed the significance of adjusting large-scale potential energy to improve the overall simulation ability. The premise is that the correction target needs to fully contain these effective large-scale signals and non-stationary features, and then introduce them into the numerical integration of the regional model through appropriate band-pass filter parameter settings, driving a more reasonable large-scale background state thereby.

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