11 citations as recorded by crossref.

1. Using Machine Learning for Climate Modelling: Application of Neural Networks to a Slow-Fast Chaotic Dynamical System as a Case Study S. Soldatenko & Y. Angudovich https://doi.org/10.3390/cli12110189
2. Joint Arctic Sea Ice Forecasting Based on Graph-Structured Spatial Modeling and Temporal Transformers B. Liu et al. https://doi.org/10.3390/jmse14050495
3. Identifying drivers and dynamics of phytoplankton in the Black Sea: application of a neural emulator P. Smith et al. https://doi.org/10.3389/fmars.2026.1760162
4. Prediction of monthly Arctic sea ice concentration using physics-constrained U2-Net M. Liu et al. https://doi.org/10.1016/j.engappai.2026.115334
5. The challenge of land in a neural network ocean model R. Furner et al. https://doi.org/10.1017/eds.2024.49
6. sciCUN: A deep learning model for daily sea surface current fields inference—A case study of the Gulf of Riga A. Barzandeh et al. https://doi.org/10.1016/j.ocemod.2026.102693
7. Daily-scale spatiotemporal prediction of thin sea ice thickness during the early freezing season based on EOF-Trans J. Liu et al. https://doi.org/10.3389/fmars.2026.1657592
8. Snow effects on altimeter waveforms over sea ice in the Weddell Sea — Part I: Radar waveform decomposition L. Zhou et al. https://doi.org/10.1016/j.rse.2025.115112
9. Four-dimensional variational data assimilation with a sea-ice thickness emulator C. Durand et al. https://doi.org/10.5194/tc-19-5613-2025
10. SeaGraph: A spatiotemporal onboard wave forecasting method G. Xia et al. https://doi.org/10.1016/j.apor.2026.105062
11. MET-AICE v1.0: an operational data-driven sea ice prediction system for the European Arctic C. Palerme et al. https://doi.org/10.5194/gmd-18-9751-2025