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Abstract Straits are strategically and economically vital due to their role as maritime choke points, controlling access to regions and resources. This is particularly pertinent in the Arctic, where navigability along critical shipping routes relies on access through straits that are frequently ice impacted. With the retreat of Arctic sea ice under anthropogenic climate change, scenarios using CMIP6 projections have the potential to provide valuable insights into future maritime accessibility regimes. However, typical climate model spatial resolutions limit the capacity to represent Arctic straits accurately. This study introduces a novel approach, the sea Ice Concentration Enhancement Generative Adversarial Network (ICE‐GAN), to enhance the spatial resolution of sea ice concentration (SIC) in Vilkitsky Strait, a passage along the Northern Sea Route (NSR). By employing the ICE‐GAN model, the spatial resolution is functionally increased from to . The approach is prototyped using ERA5 Reanalysis training data to predict ice cover for 2021 and 2022. The results indicate that the ICE‐GAN method outperforms, across multiple metrics, standard interpolation techniques such as Nearest Neighbor Interpolation and Bilinear Interpolation, both used in maritime accessibility models, as well as the super‐resolution convolutional neural network, the best practice method for super‐resolution in SIC. Importantly, the approach is robust to the non‐stationarity of the sea ice record. Moreover, by incorporating a physics‐informed approach into the ICE‐GAN, the model is able to further improve the accurate representation of sea ice cover in the studied Strait.
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Reconstruction of Continuous High-Resolution Sea Surface Temperature Data Using Time-Aware Implicit Neural Representation
Accurate climate data at fine spatial resolution are essential for scientific research and the development and planning of crucial social systems, such as energy and agriculture. Among them, sea surface temperature plays a critical role as the associated El Niño–Southern Oscillation (ENSO) is considered a significant signal of the global interannual climate system. In this paper, we propose an implicit neural representation-based interpolation method with temporal information (T_INRI) to reconstruct climate data of high spatial resolution, with sea surface temperature as the research object. Traditional deep learning models for generating high-resolution climate data are only applicable to fixed-resolution enhancement scales. In contrast, the proposed T_INRI method is not limited to the enhancement scale provided during the training process and its results indicate that it can enhance low-resolution input by arbitrary scale. Additionally, we discuss the impact of temporal information on the generation of high-resolution climate data, specifically, the influence of the month from which the low-resolution sea surface temperature data are obtained. Our experimental results indicate that T_INRI is advantageous over traditional interpolation methods under different enhancement scales, and the temporal information can improve T_INRI performance for a different calendar month. We also examined the potential capability of T_INRI in recovering missing grid value. These results demonstrate that the proposed T_INRI is a promising method for generating high-resolution climate data and has significant implications for climate research and related applications.
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- Award ID(s):
- 2239175
- PAR ID:
- 10511531
- Publisher / Repository:
- Remote Sensing
- Date Published:
- Journal Name:
- Remote Sensing
- Volume:
- 15
- Issue:
- 24
- ISSN:
- 2072-4292
- Page Range / eLocation ID:
- 5646
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/rs15245646
