伊朗德黑兰大学Alireza A. Ardalan团队最新的研究以波罗的海为例，报道了基于具有高预测能力的TOPEX/Poseidon，Jason1，Jason2和 Jason3卫星测高观测的潮汐模型。相关论文发表在2024年6月5日出版的《大地测量与地球动力学》杂志上。

据研究人员介绍，本研究旨在优化利用TOPEX/Poseidon、Jason1、Jason2和Jason3测高任务的长期海平面数据进行潮汐模拟。

研究过程中生成一个时间序列的沿航迹观测值，并应用一种先进的方法为每个地点生成具有特定潮汐成分的潮汐模型。潮汐建模方法遵循一个迭代过程：将海面高度(SSH)观测划分为分析/训练和预测/验证部分，并最终确定在每个时间序列位置提供最佳预测的潮汐成分集。该项研究的重点是沿着波罗的海上空的高程轨迹开发出1256个时间序列，每个时间序列都有自己的一套潮汐成分。在预测/验证部分，将开发的潮汐模型与SSH观测数据进行验证，平均绝对误差(MAE)范围在0.0334m和 0.1349m之间，平均MAE为0.089m。

研究人员对FES2014和EOT20全球潮汐模型进行了相同的验证过程，结果表明，他们的潮汐模型BT23(波罗的海潮汐2023的简称)优于这两个模型，平均MAE分别提高了0.0417m和0.0346m。除了详细介绍时间序列的发展和潮汐建模过程的发展细节外，研究人员还提供了1256沿迹时间序列及其相关的潮汐模型作为补充资料。研究结果强调了卫星测高界应利用这些资源进行进一步的研究和应用。

附：英文原文

Title: Tidal modeling based on satellite altimetry observations of TOPEX/Poseidon, Jason1, Jason2, and Jason3 with high prediction capability: A case study of the Baltic Sea

Author: Alireza A. Ardalan, Asiyeh Hashemifaraz

Issue&Volume: 2024/06/05

Abstract: This research aims to optimize the utilization of long-term sea level data from the TOPEX/Poseidon, Jason1, Jason2, and Jason3 altimetry missions for tidal modeling. We generate a time series of along-track observations and apply a developed method to produce tidal models with specific tidal constituents for each location. Our tidal modeling methodology follows an iterative process: partitioning sea surface height (SSH) observations into analysis/training and prediction/validation parts and ultimately identifying the set of tidal constituents that provide the best predictions at each time series location. The study focuses on developing 1256 time series along the altimetry tracks over the Baltic Sea, each with its own set of tidal constituents. Verification of the developed tidal models against the SSH observations within the prediction/validation part reveals mean absolute error (MAE) values ranging from 0.0334 m to 0.1349 m, with an average MAE of 0.089 m. The same validation process is conducted on the FES2014 and EOT20 global tidal models, demonstrating that our tidal model, referred to as BT23 (short for Baltic Tide 2023), outperforms both models with an average MAE improvement of 0.0417 m and 0.0346 m, respectively. In addition to providing details on the development of the time series and the tidal modeling procedure, we offer the 1256 along-track time series and their associated tidal models as supplementary materials. We encourage the satellite altimetry community to utilize these resources for further research and applications.

DOI: 10.1016/j.geog.2024.04.010

Source: https://www.sciencedirect.com/science/article/pii/S1674984724000454