From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling

The behaviors and skills of models in many geosciences (e.g., hydrology and ecosystem sciences) strongly depend on spatially-varying parameters that need calibration. A well-calibrated model can reasonably propagate information from observations to unobserved variables via model physics, but traditi...

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Detalles Bibliográficos
Autores principales: Tsai, Wen-Ping, Feng, Dapeng, Pan, Ming, Beck, Hylke, Lawson, Kathryn, Yang, Yuan, Liu, Jiangtao, Shen, Chaopeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8514470/
https://www.ncbi.nlm.nih.gov/pubmed/34645796
http://dx.doi.org/10.1038/s41467-021-26107-z

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8514470/
https://www.ncbi.nlm.nih.gov/pubmed/34645796
http://dx.doi.org/10.1038/s41467-021-26107-z

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