Reconstructing 42 Years (1979–2020) of Great Lakes Surface Temperature through a Deep Learning Approach

Accurate estimates for the lake surface temperature (LST) of the Great Lakes are critical to understanding the regional climate. Dedicated lake models of various complexity have been used to simulate LST but they suffer from noticeable biases and can be computationally expensive. Additionally, the a...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2023-09, Vol.15 (17), p.4253
Main Authors: Kayastha, Miraj B., Liu, Tao, Titze, Daniel, Havens, Timothy C., Huang, Chenfu, Xue, Pengfei
Format: Article
Language:English
Subjects:
Analysis
Bias
Boundary conditions
Climate change
Climate models
Datasets
Deep learning
Great Lakes
Heterogeneity
lake surface temperature
Lakes
Land surface temperature
Liu, Timothy
Long Short-Term Memory
machine learning
Neural networks
Physics
Precipitation
Radiation
Regional Climate Models
Remote sensing
Snow
Spatial discrimination
Spatial heterogeneity
Spatial resolution
spatiotemporal climate change
Training
Unstructured grids (mathematics)
Variables
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Summary:Accurate estimates for the lake surface temperature (LST) of the Great Lakes are critical to understanding the regional climate. Dedicated lake models of various complexity have been used to simulate LST but they suffer from noticeable biases and can be computationally expensive. Additionally, the available historical LST datasets are limited by either short temporal coverage (
ISSN:2072-4292
2072-4292
DOI:10.3390/rs15174253