基于物理信息中立网络的稀疏散射计数据重建海面风场新方法

IF 3.4 2区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES
Ran Bo, Huadong Du, Zeming Zhou, Pinglv Yang, Xiaofeng Zhao, Qian Li, Zengliang Zang
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引用次数: 0

摘要

基于卫星的全球海面风研究对于理解和监测动态海气界面过程至关重要,推动了对可靠评估和预报的需求。实现这些目标需要精确地重建风速和风向,以吸收实时观测数据,但目前用于此类重建的方法在计算上仍然昂贵,并且在求解高度非线性方程时具有挑战性。以太平洋多个区域为例,探讨了不同卫星稀疏散射计观测数据与物理信息神经网络(pinn)融合重建大尺度海面风场的方法,有效整合观测数据,填补数据空白。结果表明,该方法能够以较低的计算成本从稀疏数据中高效地重建全真实风场。此外,我们提出了一种新的损失函数,可以在捕获风场的关键特征的同时保留精细尺度的细节。根据参考数据集(包括浮标测量数据、ERA5和交叉校准多平台(CCMP)风速和风向)进行评估,强调了所提出方法的准确性和鲁棒性。与ERA5和CCMP的再分析数据相比,PINN重建的风场与观测结果吻合较好。这些发现强调了PINN技术作为一种可替代的、计算效率高的海面风重建方法的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Approach for Sea Surface Wind Field Reconstruction Using Sparse Scatterometer Data Based on Physics-Informed Neutral Network

Satellite-based research on global sea surface winds is crucial for understanding and monitoring dynamic air-sea interface processes, driving the need for reliable assessments and forecasts. Achieving these objectives requires accurate reconstruction of wind speeds and wind directions that assimilate real-time observations, but current methods used for such reconstructions remain computationally expensive and challenging to solve highly nonlinear equations. By selecting multiple regions in the Pacific Ocean as case studies, we explore the fusion of sparse scatterometer observations from different satellites with physics-informed neural networks (PINNs) to reconstruct large-scale sea surface wind fields, effectively integrating observations and filling data gaps. The result demonstrates that PINNs can efficiently reconstruct full realistic wind fields from sparse data at a low computation cost. Moreover, we propose a novel loss function that can preserve fine-scale details while capturing the key features of the wind field. Evaluation against reference data sets, including buoy measurements, ERA5, and cross-calibrated multiplatform (CCMP) wind speed and direction, highlights the accuracy and robustness of the proposed method. Compared to the reanalysis data of ERA5 and CCMP, the wind field reconstructed by the PINN closely aligns with the observation. These findings underscore the potential of the PINN technique as an alternative, computationally efficient approach for operational sea surface wind reconstruction.

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来源期刊
Journal of Geophysical Research: Atmospheres
Journal of Geophysical Research: Atmospheres Earth and Planetary Sciences-Geophysics
CiteScore
7.30
自引率
11.40%
发文量
684
期刊介绍: JGR: Atmospheres publishes articles that advance and improve understanding of atmospheric properties and processes, including the interaction of the atmosphere with other components of the Earth system.
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