Inferring Parameters in a Complex Land Surface Model by Combining Data Assimilation and Machine Learning

Inferring Parameters in a Complex Land Surface Model by Combining Data Assimilation and Machine Learning

Abstract Complex Land Surface Models (LSMs) rely on a plethora of parameters. These parameters and the associated process formulations are often poorly constrained, which hampers reliable predictions of ecosystem dynamics and climate feedbacks. Robust and uncertainty‐aware parameter estimation with...

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Bibliographic Details
Main Authors: L. T. Keetz, K. Aalstad, R. A. Fisher, C. Poppe Terán, B. Naz, N. Pirk, Y. A. Yilmaz, O. Skarpaas
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2025-06-01
Series:Journal of Advances in Modeling Earth Systems
Subjects:
machine learning
data assimilation
Bayesian inference
land surface model
dynamic vegetation model
parameter estimation
Online Access:https://doi.org/10.1029/2024MS004542
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https://doi.org/10.1029/2024MS004542

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