Preprints
https://doi.org/10.5194/egusphere-2024-2081
https://doi.org/10.5194/egusphere-2024-2081
12 Aug 2024
 | 12 Aug 2024

Advances in Land Surface Model-based Forecasting: A Comparison of LSTM, Gradient Boosting, and Feedforward Neural Networks as Prognostic State Emulators in a Case Study with ECLand

Marieke Wesselkamp, Matthew Chantry, Ewan Pinnington, Margarita Choulga, Souhail Boussetta, Maria Kalweit, Joschka Bödecker, Carsten F. Dormann, Florian Pappenberger, and Gianpaolo Balsamo

Abstract. Most useful weather prediction for the public is near the surface. The processes that are most relevant for near-surface weather prediction are also those that are most interactive and exhibit positive feedback or have key role in energy partitioning. Land surface models (LSMs) consider these processes together with surface heterogeneity and forecast water, carbon and energy fluxes, and coupled with an atmospheric model provide boundary and initial conditions. This numerical parametrization of atmospheric boundaries being computationally expensive, statistical surrogate models are increasingly used to accelerated progress in experimental research. We evaluated the efficiency of three surrogate models in speeding up experimental research by simulating land surface processes, which are integral to forecasting water, carbon, and energy fluxes in coupled atmospheric models. Specifically, we compared the performance of a Long-Short Term Memory (LSTM) encoder-decoder network, extreme gradient boosting, and a feed-forward neural network within a physics-informed multi-objective framework. This framework emulates key states of the ECMWF's Integrated Forecasting System (IFS) land surface scheme, ECLand, across continental and global scales. Our findings indicate that while all models on average demonstrate high accuracy over the forecast period, the LSTM network excels in continental long-range predictions when carefully tuned, the XGB scores consistently high across tasks and the MLP provides an excellent implementation-time-accuracy trade-off. The runtime reduction achieved by the emulators in comparison to the full numerical models are significant, offering a faster, yet reliable alternative for conducting numerical experiments on land surfaces.

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Journal article(s) based on this preprint

19 Feb 2025
Advances in land surface forecasting: a comparison of LSTM, gradient boosting, and feed-forward neural networks as prognostic state emulators in a case study with ecLand
Marieke Wesselkamp, Matthew Chantry, Ewan Pinnington, Margarita Choulga, Souhail Boussetta, Maria Kalweit, Joschka Bödecker, Carsten F. Dormann, Florian Pappenberger, and Gianpaolo Balsamo
Geosci. Model Dev., 18, 921–937, https://doi.org/10.5194/gmd-18-921-2025,https://doi.org/10.5194/gmd-18-921-2025, 2025
Short summary
Marieke Wesselkamp, Matthew Chantry, Ewan Pinnington, Margarita Choulga, Souhail Boussetta, Maria Kalweit, Joschka Bödecker, Carsten F. Dormann, Florian Pappenberger, and Gianpaolo Balsamo

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on egusphere-2024-2081', Astrid Kerkweg, 06 Sep 2024
    • AC1: 'Reply on CEC1', Marieke Wesselkamp, 06 Oct 2024
  • RC1: 'Comment on egusphere-2024-2081', Simon O'Meara, 12 Sep 2024
    • AC2: 'Reply on RC1', Marieke Wesselkamp, 14 Oct 2024
  • RC2: 'Comment on egusphere-2024-2081', Anonymous Referee #2, 28 Sep 2024
    • AC3: 'Reply on RC2', Marieke Wesselkamp, 14 Oct 2024

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on egusphere-2024-2081', Astrid Kerkweg, 06 Sep 2024
    • AC1: 'Reply on CEC1', Marieke Wesselkamp, 06 Oct 2024
  • RC1: 'Comment on egusphere-2024-2081', Simon O'Meara, 12 Sep 2024
    • AC2: 'Reply on RC1', Marieke Wesselkamp, 14 Oct 2024
  • RC2: 'Comment on egusphere-2024-2081', Anonymous Referee #2, 28 Sep 2024
    • AC3: 'Reply on RC2', Marieke Wesselkamp, 14 Oct 2024

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Marieke Wesselkamp on behalf of the Authors (28 Oct 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to minor revisions (review by editor) (20 Nov 2024) by David Topping
AR by Marieke Wesselkamp on behalf of the Authors (21 Nov 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (11 Dec 2024) by David Topping
AR by Marieke Wesselkamp on behalf of the Authors (16 Dec 2024)  Author's response 

Post-review adjustments

AA: Author's adjustment | EA: Editor approval
AA by Marieke Wesselkamp on behalf of the Authors (07 Feb 2025)   Author's adjustment   Manuscript
EA: Adjustments approved (13 Feb 2025) by David Topping

Journal article(s) based on this preprint

19 Feb 2025
Advances in land surface forecasting: a comparison of LSTM, gradient boosting, and feed-forward neural networks as prognostic state emulators in a case study with ecLand
Marieke Wesselkamp, Matthew Chantry, Ewan Pinnington, Margarita Choulga, Souhail Boussetta, Maria Kalweit, Joschka Bödecker, Carsten F. Dormann, Florian Pappenberger, and Gianpaolo Balsamo
Geosci. Model Dev., 18, 921–937, https://doi.org/10.5194/gmd-18-921-2025,https://doi.org/10.5194/gmd-18-921-2025, 2025
Short summary
Marieke Wesselkamp, Matthew Chantry, Ewan Pinnington, Margarita Choulga, Souhail Boussetta, Maria Kalweit, Joschka Bödecker, Carsten F. Dormann, Florian Pappenberger, and Gianpaolo Balsamo
Marieke Wesselkamp, Matthew Chantry, Ewan Pinnington, Margarita Choulga, Souhail Boussetta, Maria Kalweit, Joschka Bödecker, Carsten F. Dormann, Florian Pappenberger, and Gianpaolo Balsamo

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Short summary
We compared spatio-temporal forecast performances of three popular machine learning approaches that learned processes of water and energy exchange on the earth surface from a large physical model. The forecasting models were developed with reanalysis data and simulations on a European scale and transferred to the Globe. We found that all approaches deliver highly accurate predictions until long time horizons, implying their usefulness to advance land surface forecasting when data is available.
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