NoahPy: A differentiable Noah land surface model for simulating permafrost thermo-hydrology
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Le résumé fourni par la source
Abstract. Accurately representing permafrost in Earth System Models is a grand challenge that creates major uncertainty. A promising path forward is to create hybrid models that synergize process-based physics with deep learning, but this is fundamentally hindered by the non-differentiable nature of traditional land surface models (LSMs), which are incompatible with modern AI workflows. To overcome this limitation, we present NoahPy, a fully differentiable LSM developed by reconstructing the Noah LSM’s governing partial differential equations into a process-encapsulated Recurrent Neural Network (RNN). We first demonstrate that NoahPy perfectly replicates the numerical behaviour of the modified Noah LSM, achieving Nash-Sutcliffe Efficiency (NSE) coefficients above 0.99 for both soil temperature and liquid water. We then show that at a permafrost site, the calibrated NoahPy achieves robust simulation performance for for soil temperature (NSE > 0.9) and liquid water (NSE > 0.8). Critically, the differentiable workflow, when combined with the Adam optimizer, is significantly faster, more stable, and yields simulations with lower uncertainty compared to traditional SCE-UA calibration algorithm. NoahPy thus provides a foundational, "glass-box" framework that closes a key technical gap, enabling the development of the next generation of hybrid AI-physics models needed to more reliably predict the future of the cryosphere.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- NoahPy: A differentiable Noah land surface model for simulating permafrost thermo-hydrology
- Date Crossref
- 02/09/2025
- Éditeur
- Copernicus GmbH
- Type
- posted-content
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Nanjing Normal University Key Laboratory of Ministry of Education on Virtual Geographic Environment pays non établi dans la noticeUniversité ou école supérieure
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Northeast Forestry University pays non établi dans la noticeUniversité ou école supérieure
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Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application pays non établi dans la noticeStructure de recherche
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North Information Control Group pays non établi dans la noticeInstitution
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College of Forestry pays non établi dans la noticeUniversité ou école supérieure
Key Laboratory of Ministry of Education on Virtual Geographic Environment — Nanjing Normal University, Northeast Forestry University et Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, avec 2 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.