Physics-Informed Machine Learning for Prediction of Sea Ice Dynamics Derived from Spaceborne Passive Microwave Data
Author:
Affiliation:
1. Lehigh University,Department of Computer Science & Engineering, Department of Civil & Environmental Engineering,Bethlehem,PA,USA
Funder
NSF
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10548015/10548012/10549268.pdf?arnumber=10549268
Reference27 articles.
1. Observed Arctic sea-ice loss directly follows anthropogenic CO 2 emission
2. Arctic sea ice thickness, volume, and multiyear ice coverage: losses and coupled variability (1958–2018)
3. Arctic sea ice circulation and drift speed: Decadal trends and ocean currents
4. A climatology of thermodynamic vs. dynamic Arctic wintertime sea ice thickness effects during the CryoSat-2 era
5. Verification of a new NOAA/NSIDC passive microwave sea-ice concentration climate record
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