@article{fdi:010092772,
  title = {{S}ea surface temperature forecasting using foundational models : a novel approach assessed in the {C}aribbean {S}ea},
  author = {{U}sta, {D}. {F}. {B}. and {R}odríguez-{L}ópez, {L}. and {P}arra, {R}. {R}. {T}. and {B}ourrel, {L}uc},
  editor = {},
  language = {{ENG}},
  abstract = {{S}ea surface temperature ({SST}) plays a pivotal role in air-sea interactions, with implications for climate, weather, and marine ecosystems, particularly in regions like the {C}aribbean {S}ea, where upwelling and dynamic oceanographic processes significantly influence biodiversity and fisheries. {T}his study evaluates the performance of foundational models, {C}hronos and {L}ag-{L}lama, in forecasting {SST} using 22 years (2002-2023) of high-resolution satellite-derived and in situ data. {T}he {C}hronos model, leveraging zero-shot learning and tokenization methods, consistently outperformed {L}ag-{L}lama across all forecast horizons, demonstrating lower errors and greater stability, especially in regions of moderate {SST} variability. {T}he {C}hronos model's ability to forecast extreme upwelling events is assessed, and a description of such events is presented for two regions in the southern {C}aribbean upwelling system. {T}he {C}hronos forecast resembles {SST} variability in upwelling regions for forecast horizons of up to 7 days, providing reliable short-term predictions. {B}eyond this, the model exhibits increased bias and error, particularly in regions with strong {SST} gradients and high variability associated with coastal upwelling processes. {T}he findings highlight the advantages of foundational models, including reduced computational demands and adaptability across diverse tasks, while also underscoring their limitations in regions with complex physical oceanographic phenomena. {T}his study establishes a benchmark for {SST} forecasting using foundational models and emphasizes the need for hybrid approaches integrating physical principles to improve accuracy in dynamic and ecologically critical regions.},
  keywords = {{L}ag-{L}lama model ; {C}hronos model ; extreme upwelling events ; southern ; {C}aribbean upwelling system ; {CARAIBE}},
  booktitle = {},
  journal = {{R}emote {S}ensing},
  volume = {17},
  numero = {3},
  pages = {517 [25 p.]},
  year = {2025},
  DOI = {10.3390/rs17030517},
  URL = {https://www.documentation.ird.fr/hor/fdi:010092772},
}