Articles | Volume 18, issue 4
https://doi.org/10.5194/gmd-18-921-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-921-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
19 Feb 2025
Development and technical paper |
| 19 Feb 2025
| 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
CORRESPONDING AUTHOR
Department of Biometry, University of Freiburg, Freiburg, Germany
Matthew Chantry
European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
Ewan Pinnington
European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
Margarita Choulga
European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
Souhail Boussetta
European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
Maria Kalweit
Department of Computer Science, University of Freiburg, Freiburg, Germany
Joschka Bödecker
Department of Computer Science, University of Freiburg, Freiburg, Germany
BrainLinks-BrainTools, University of Freiburg, Freiburg, Germany
Carsten F. Dormann
Department of Biometry, University of Freiburg, Freiburg, Germany
Florian Pappenberger
European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
Gianpaolo Balsamo
European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
World Meteorological Organization, Geneva, Switzerland
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Cited
11 citations as recorded by crossref.
- Predicting thermal trends in smart buildings using machine learning: a case study C. Mejía Rodriguez et al. https://doi.org/10.69821/REMUVAC.v3i1.299
- Temporal pattern-aware temperature forecasting using CatBoost: A hybrid machine learning approach M. Bhih et al. https://doi.org/10.1016/j.rineng.2026.110212
- Benchmarking Model Complexity for Short-Term Surrogate Forecasting of High-Resolution Urban WRF Outputs During an Extreme Heatwave in Chongqing Y. Liu et al. https://doi.org/10.3390/land15091726
- Enhancing mid- to long-term runoff simulation in human-impacted basins through coupled SWAT-LUT and LSTM modelin W. Zhou et al. https://doi.org/10.1016/j.ejrh.2025.103050
- Learning to melt: Emulating Greenland surface melt from a polar RCM with machine learning E. Schlager et al. https://doi.org/10.5194/tc-20-3313-2026
- A taxonomy-based benchmark of parametric and non-parametric machine learning models for data-driven precipitation prediction in Morocco A. EL Fengour & S. El Motaki https://doi.org/10.1007/s00704-026-06174-2
- A thermodynamics-integrated physics-guided neural network for soil temperature forecasting S. Wang & J. Zhu https://doi.org/10.1038/s41598-026-50274-y
- Digital innovations in historical climatology: Classifying weather and climatic extremes and their impacts on societies using machine learning on written documents M. Kahle & R. Glaser https://doi.org/10.37040/geografie.2025.014
- Attention-driven deep learning models for multivariate time series forecasting of reservoir water levels G. Alipour Modab https://doi.org/10.2166/wst.2025.182
- Real-time hydrogen monitoring via Pd/g-C3N4 functionalized HEMT sensor with IoT connectivity and machine learning forecasting V. Pandey et al. https://doi.org/10.1016/j.snb.2025.139217
- Energy Optimization of Motor-Driven Systems Using Variable Frequency Control, Soft Starters, and Machine Learning Forecasting H. Ahmed et al. https://doi.org/10.3390/en18195135
11 citations as recorded by crossref.
- Predicting thermal trends in smart buildings using machine learning: a case study C. Mejía Rodriguez et al. https://doi.org/10.69821/REMUVAC.v3i1.299
- Temporal pattern-aware temperature forecasting using CatBoost: A hybrid machine learning approach M. Bhih et al. https://doi.org/10.1016/j.rineng.2026.110212
- Benchmarking Model Complexity for Short-Term Surrogate Forecasting of High-Resolution Urban WRF Outputs During an Extreme Heatwave in Chongqing Y. Liu et al. https://doi.org/10.3390/land15091726
- Enhancing mid- to long-term runoff simulation in human-impacted basins through coupled SWAT-LUT and LSTM modelin W. Zhou et al. https://doi.org/10.1016/j.ejrh.2025.103050
- Learning to melt: Emulating Greenland surface melt from a polar RCM with machine learning E. Schlager et al. https://doi.org/10.5194/tc-20-3313-2026
- A taxonomy-based benchmark of parametric and non-parametric machine learning models for data-driven precipitation prediction in Morocco A. EL Fengour & S. El Motaki https://doi.org/10.1007/s00704-026-06174-2
- A thermodynamics-integrated physics-guided neural network for soil temperature forecasting S. Wang & J. Zhu https://doi.org/10.1038/s41598-026-50274-y
- Digital innovations in historical climatology: Classifying weather and climatic extremes and their impacts on societies using machine learning on written documents M. Kahle & R. Glaser https://doi.org/10.37040/geografie.2025.014
- Attention-driven deep learning models for multivariate time series forecasting of reservoir water levels G. Alipour Modab https://doi.org/10.2166/wst.2025.182
- Real-time hydrogen monitoring via Pd/g-C3N4 functionalized HEMT sensor with IoT connectivity and machine learning forecasting V. Pandey et al. https://doi.org/10.1016/j.snb.2025.139217
- Energy Optimization of Motor-Driven Systems Using Variable Frequency Control, Soft Starters, and Machine Learning Forecasting H. Ahmed et al. https://doi.org/10.3390/en18195135
Saved (final revised paper)
Latest update: 26 Sep 2026
Short summary
We compared spatiotemporal forecasts of three machine learning models that learned water and energy
states on the land surface from a physical model scheme. 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 approximations of the physical dynamic at long time horizons, implying their usefulness to advance land surface forecasting with synthetic data.
states on the land surface from a physical model scheme. 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 approximations of the physical dynamic at long time horizons, implying their usefulness to advance land surface forecasting with synthetic data.
We compared spatiotemporal forecasts of three machine learning models that learned water and...
