Challenges in Unifying Physically Based and Machine Learning Simulations Through Differentiable Modeling: A Land Surface Case Study
Abstract Differentiable geoscientific modeling has shown promise for leveraging machine learning (ML) to unify physically based and data‐based modeling. Here, we critically analyze this promise in the context of large‐scale parameter optimization with the Noah‐MP land model as an example. The differ...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-02-01
|
| Series: | Geophysical Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2024GL112893 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850136281470730240 |
|---|---|
| author | Shahryar K. Ahmad Sujay V. Kumar Clara Draper Rolf H. Reichle |
| author_facet | Shahryar K. Ahmad Sujay V. Kumar Clara Draper Rolf H. Reichle |
| author_sort | Shahryar K. Ahmad |
| collection | DOAJ |
| description | Abstract Differentiable geoscientific modeling has shown promise for leveraging machine learning (ML) to unify physically based and data‐based modeling. Here, we critically analyze this promise in the context of large‐scale parameter optimization with the Noah‐MP land model as an example. The differentiable parameter learning framework is used to calibrate Noah‐MP soil and vegetation parameters such that the simulated surface soil moisture better matches satellite observations over the contiguous US. We found that the optimized parameters only marginally improved soil moisture (average RMSE = 0.092 m3 m−3) upon uncalibrated Noah‐MP (RMSE = 0.10 m3 m−3). Scaling and bias correction factors, often used in ML approaches for enhancing generalizability, were found to limit the transferability of the optimized physical parameters to the land model. The global objective function further compromises the algorithm's ability to simultaneously capture contrasting moisture regimes. Addressing these challenges is necessary to advance ML‐based calibration frameworks to better learn and represent the constraints of the physical model. |
| format | Article |
| id | doaj-art-cc8a87a5e6fc4d90a36eaddfa381c7a4 |
| institution | OA Journals |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-cc8a87a5e6fc4d90a36eaddfa381c7a42025-08-20T02:31:10ZengWileyGeophysical Research Letters0094-82761944-80072025-02-01524n/an/a10.1029/2024GL112893Challenges in Unifying Physically Based and Machine Learning Simulations Through Differentiable Modeling: A Land Surface Case StudyShahryar K. Ahmad0Sujay V. Kumar1Clara Draper2Rolf H. Reichle3Hydrological Sciences Lab NASA Goddard Space Flight Center (GSFC) Greenbelt MD USAHydrological Sciences Lab NASA Goddard Space Flight Center (GSFC) Greenbelt MD USANOAA Physical Sciences Laboratory (PSL) Boulder CO USAGlobal Modeling and Assimilation Office (GMAO) NASA Goddard Space Flight Center (GSFC) Greenbelt MD USAAbstract Differentiable geoscientific modeling has shown promise for leveraging machine learning (ML) to unify physically based and data‐based modeling. Here, we critically analyze this promise in the context of large‐scale parameter optimization with the Noah‐MP land model as an example. The differentiable parameter learning framework is used to calibrate Noah‐MP soil and vegetation parameters such that the simulated surface soil moisture better matches satellite observations over the contiguous US. We found that the optimized parameters only marginally improved soil moisture (average RMSE = 0.092 m3 m−3) upon uncalibrated Noah‐MP (RMSE = 0.10 m3 m−3). Scaling and bias correction factors, often used in ML approaches for enhancing generalizability, were found to limit the transferability of the optimized physical parameters to the land model. The global objective function further compromises the algorithm's ability to simultaneously capture contrasting moisture regimes. Addressing these challenges is necessary to advance ML‐based calibration frameworks to better learn and represent the constraints of the physical model.https://doi.org/10.1029/2024GL112893machine learningland surface modelcalibrationsoil moisturegenetic algorithmtransferability |
| spellingShingle | Shahryar K. Ahmad Sujay V. Kumar Clara Draper Rolf H. Reichle Challenges in Unifying Physically Based and Machine Learning Simulations Through Differentiable Modeling: A Land Surface Case Study Geophysical Research Letters machine learning land surface model calibration soil moisture genetic algorithm transferability |
| title | Challenges in Unifying Physically Based and Machine Learning Simulations Through Differentiable Modeling: A Land Surface Case Study |
| title_full | Challenges in Unifying Physically Based and Machine Learning Simulations Through Differentiable Modeling: A Land Surface Case Study |
| title_fullStr | Challenges in Unifying Physically Based and Machine Learning Simulations Through Differentiable Modeling: A Land Surface Case Study |
| title_full_unstemmed | Challenges in Unifying Physically Based and Machine Learning Simulations Through Differentiable Modeling: A Land Surface Case Study |
| title_short | Challenges in Unifying Physically Based and Machine Learning Simulations Through Differentiable Modeling: A Land Surface Case Study |
| title_sort | challenges in unifying physically based and machine learning simulations through differentiable modeling a land surface case study |
| topic | machine learning land surface model calibration soil moisture genetic algorithm transferability |
| url | https://doi.org/10.1029/2024GL112893 |
| work_keys_str_mv | AT shahryarkahmad challengesinunifyingphysicallybasedandmachinelearningsimulationsthroughdifferentiablemodelingalandsurfacecasestudy AT sujayvkumar challengesinunifyingphysicallybasedandmachinelearningsimulationsthroughdifferentiablemodelingalandsurfacecasestudy AT claradraper challengesinunifyingphysicallybasedandmachinelearningsimulationsthroughdifferentiablemodelingalandsurfacecasestudy AT rolfhreichle challengesinunifyingphysicallybasedandmachinelearningsimulationsthroughdifferentiablemodelingalandsurfacecasestudy |