基于GNSS-IR的潮位反演及气象强迫下的气压相关性分析

Tide inversion based on GNSS-IR and analysis of barometric correlation under meteorological forcing

  • 摘要: 准确的潮位反演以及潮位响应分析对于海洋工程建设、海岸安全、海洋分析和气候分析十分重要. 近年来,随着GNSS的发展与完善,一种全球导航卫星系统干涉反射测量(Global Navigation Satellite System Interferometry Reflectometry, GNSS-IR)潮位反演技术被提出. 而在GNSS-IR潮位反演这一课题中,基于GNSS-IR潮位反演的气象强迫研究,特别是气压与潮位间的相关性分析是其进一步延伸的研究领域. 与之相关的研究主要关注了风暴潮期间的短时响应特征,而未关注其他情况下的潮位-气压间的响应特征. 因此,本文拟基于GNSS-IR潮位反演技术,利用GNSS连续跟踪站点的4年数据,进行潮位-气压间短时和长时相关性特征分析. 首先,本实验考虑到GNSS-IR反演值存在大量误差和粗差的情况,拟引入抗差估计,进行多模多频GNSS-IR潮位反演融合以提高精度. 结果表明,单信号反演结果剔除高度变化误差后,其均方根误差(root mean squared error, RMSE)为10~16 cm;对单信号反演结果进行多模多频融合后,RMSE降低至7~8 cm,精度提升30%. 接着,对潮位和气压进行交叉小波分析. 结果发现:潮位存在短时气象强迫,在风暴潮期间尤为明显;春、冬季节潮位与气压相关性较高,夏季的相关性不明显;这可能与当地亚热带季风性气候有关. 同时,对于风暴潮等剧烈气象变化情况,潮位-气压响应很快;而在气候影响的气象变化情况下,潮位对气压存在一定程度的响应延迟.

     

    Abstract: Accurate tidal level inversion and tidal response analysis are crucial for marine engineering construction, coastal safety, marine analysis, and climate analysis. In recent years, with the development and improvement of the GNSS, a Global Navigation Satellite System Interferometry Reflectometry (GNSS-IR) tidal level inversion method has been proposed. In the context of GNSS-IR tidal level inversion, research on meteorological forcing based on GNSS-IR, particularly the analysis of the correlation between air pressure and tidal level, has become an extended research area. Related studies have primarily focused on short-term response characteristics during storm surges, while the tidal level-air pressure response under other conditions has not been given much attention. Therefore, this paper aims to analyze the short-term and long-term correlation characteristics between tidal level and air pressure using data during four years from GNSS continuous tracking stations, based on the GNSS-IR tidal level inversion technique. First, considering the presence of significant errors and outliers in the GNSS-IR inversion values, robust estimation is introduced to perform multi-mode and multi-frequency GNSS-IR tidal level inversion fusion to improve accuracy. The results show that after removing height variation errors from the single-signal inversion, the root mean squared error (RMSE) is between 10~16 cm. After performing multi-mode and multi-frequency fusion of the single-signal inversion results, the RMSE reduces to 7~8 cm, improving accuracy by 30%. Next, cross-wavelet analysis of tidal levels and air pressure is conducted. The results reveal that tidal levels are influenced by the short-term meteorological forcing, especially during storm surges. The correlation between tidal level and air pressure is higher in spring and winter, while the correlation is less obvious in summer, which may be related to the local subtropical monsoon climate. Moreover, during extreme meteorological changes like storm surges, the tidal level-air pressure response is rapid. However, under meteorological changes influenced by climate, the tidal level shows a certain degree of response delay to air pressure.

     

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