Details:

Journal Title:
Remote Sensing
Personal Author:
Xue, Pengfei ; Wagh, Aditya ; Ma, Gangfeng ; Wang, Yilin ; Yang, Yongchao ; ... More +
Xue, Pengfei ; Wagh, Aditya ; Ma, Gangfeng ; Wang, Yilin ; Yang, Yongchao ; Liu, Tao ; Huang, Chenfu Less -
NOAA Program & Office:
OAR (Oceanic and Atmospheric Research)
Description:
The Laurentian Great Lakes, one of the world’s largest surface freshwater systems, pose a modeling challenge in seasonal forecast and climate projection. While physics-based hydrodynamic modeling is a fundamental approach, improving the forecast accuracy remains critical. In recent years, machine learning (ML) has quickly emerged in geoscience applications, but its application to the Great Lakes hydrodynamic prediction is still in its early stages. This work is the first one to explore a deep learning approach to predicting spatiotemporal distributions of the lake surface temperature (LST) in the Great Lakes. Our study shows that the Long Short-Term Memory (LSTM) neural network, trained with the limited data from hypothetical monitoring networks, can provide consistent and robust performance. The LSTM prediction captured the LST spatiotemporal variabilities across the five Great Lakes well, suggesting an effective and efficient way for monitoring network design in assisting the ML-based forecast. Furthermore, we employed an explainable artificial intelligence (XAI) technique named SHapley Additive exPlanations (SHAP) to uncover how the features impact the LSTM prediction. Our XAI analysis shows air temperature is the most influential feature for predicting LST in the trained LSTM. The relatively large bias in the LSTM prediction during the spring and fall was associated with substantial heterogeneity of air temperature during the two seasons. In contrast, the physics-based hydrodynamic model performed better in spring and fall yet exhibited relatively large biases during the summer stratification period. Finally, we developed a statistical integration of the hydrodynamic modeling and deep learning results based on the Best Linear Unbiased Estimator (BLUE). The integration further enhanced prediction accuracy, suggesting its potential for next-generation Great Lakes forecast systems.
Keywords:
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General Earth and Planetary Sciences
Source:
Remote Sensing, 14(11), 2640
DOI:
https://doi.org/10.3390/rs14112640
ISSN:
2072-4292
Format:
PDF
Publisher:
MDPI AG
Document Type:
Journal Article
Funding:
Grant no. NA17OAR4320152
License:
https://creativecommons.org/licenses/by/4.0/
Rights Information:
CC BY
Compliance:
Library
Main Document Checksum:
[+]
urn:sha256:c862ccee40e79a6a80f87cc3a7efe5bdb81ee4adcc1eec2894bb6d4a08a2de62
Download URL:
https://repository.library.noaa.gov/view/noaa/58795/noaa_58795_DS1.pdf
File Type:

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