🌍 Abstract:
Surface-level weather is what matters most to the public—but it's also where feedback loops and complex interactions dominate. Land Surface Models (LSMs) capture these dynamics. Coupled with atmospheric models, they help forecast water, carbon, and energy fluxes. This study explores machine learning emulators as fast, accurate alternatives for ecLand, the ECMWF’s land surface scheme.
⚡ Bullet points summary:
Three machine learning models—LSTM, XGB, and MLP—were evaluated as statistical emulators for ECLand to enable faster experimentation in land surface forecasting.
All models showed strong accuracy, but LSTM excelled in long-range continental forecasts, XGB was robust across tasks, and MLP balanced accuracy and ease of use.
Emulators offered significant runtime savings over traditional numerical models, boosting potential for quicker simulations and integration into data assimilation pipelines.
Model strengths varied by scale and variable: XGB led in European soil water predictions, MLP scored highest in global accuracy, and LSTM improved snow cover forecasts on a continental scale.
The study provides a clear comparison of model trade-offs, helping guide the choice of emulator based on accuracy needs, compute budget, and geographic focus.
đź’ˇ Big Idea:
Machine learning emulators can dramatically speed up land surface forecasting without compromising accuracy—empowering faster, more adaptable weather research and operations.
đź“š Citation:
Wesselkamp, M., et al. 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, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-2081, 2024.
