基于 CNN-LSTM 模型的 CFOSAT 离中天线 SWIM SWH 产品校准

IF 2.9 3区 地球科学 Q2 ASTRONOMY & ASTROPHYSICS
Rui Zhang, Jinpeng Qi, Qiushuang Yan, Chenqing Fan, Yuchao Yang, Jie Zhang, Yong Wan
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引用次数: 0

摘要

高精度观测显波高度(SWH)对海洋研究至关重要。中法海洋卫星(CFOSAT)上的表面波调查与监测(SWIM)提供的海洋波谱可用于计算离中线 SWH 参数,但原位 SWH 值存在一定偏差。为了提高从偏底角 6°、8°、10° 波谱和整个组合波谱计算 SWH 偏差的精度,本文建立了一个结合卷积神经网络(CNN)和长短期记忆网络(LSTM)的时空混合模型。实验结果表明,校正后的 SWIM 离底 SWH 的精度显著提高,最佳校正结果为 10°,均方根误差(RMSE)为 0.267 m,与 ERA5 值的相关系数(R)为 0.979。我们对提出的模式在不同波高、极端海况和风浪区域下的性能进行了全面研究和分析。同时,利用浮标和高度计对修正后的 SWH 均方根误差小于 0.5 米进行了进一步评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Calibration of CFOSAT Off-Nadir SWIM SWH Product Based on CNN-LSTM Model

Calibration of CFOSAT Off-Nadir SWIM SWH Product Based on CNN-LSTM Model

High-precision observation of significant wave height (SWH) is crucial for marine research. The Surface Waves Investigation and Monitoring (SWIM) aboard the China France Oceanography Satellite (CFOSAT) provides the ocean wave spectrum that allows for the calculation of the off-nadir SWH parameters, but there exists a certain bias with the in-situ SWH values. To improve the accuracy of the SWH calculation bias from the off-nadir 6°, 8°, 10° wave spectra and the whole combined spectrum, this paper establishes a spatio-temporal hybrid model that combines convolutional neural network (CNN) and long short-term memory network (LSTM). Additionally, to further correct bias exhibited under high sea state, we introduce a bias correction module based on deep neural network (DNN) to adjust the SWIM off-nadir SWH greater than 4 m. The experimental results demonstrate a significant enhancement in the accuracy of corrected SWIM off-nadir SWH, and the best calibration result is 10° with 0.267 m root mean square error (RMSE), and 0.979 correlation coefficient (R) compared with the ERA5 value. We conducted a comprehensive study and analysis on the performance of the proposed model under different wave heights, extreme sea states, and wind and swell regions. Meanwhile, the buoy and altimeters are leveraged to render further evaluation the RMSE of the corrected SWH is less than 0.5 m.

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来源期刊
Earth and Space Science
Earth and Space Science Earth and Planetary Sciences-General Earth and Planetary Sciences
CiteScore
5.50
自引率
3.20%
发文量
285
审稿时长
19 weeks
期刊介绍: Marking AGU’s second new open access journal in the last 12 months, Earth and Space Science is the only journal that reflects the expansive range of science represented by AGU’s 62,000 members, including all of the Earth, planetary, and space sciences, and related fields in environmental science, geoengineering, space engineering, and biogeochemistry.
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