🌍 Climate Modeling with Generative AI
🔁 Probabilistic Climate Predictions
In machine learning weather assimilation, scientists use observations to adjust their models’ trajectory and forecasts — like daily temperatures. This traditional method focuses on short-term accuracy. However, for climate models, it’s essential to understand long-term dynamics and distributions, since we cannot replicate the exact future state of a single day.
This work proposes a new strategy to improve data assimilation targeting statistical distributions, looking beyond point forecasts to the broader range of likely states. It also introduces a Bayesian framework for uncertainty-aware parameter inference, enhancing projection reliability.
This approach enables better estimation of extreme events — like heatwaves or cold snaps — and analysis of their joint quantile structures.
📄 Read more:
Li, S., Zheng, T., Farchi, A., Bocquet, M., & Gentine, P. (2025). Projections with generative machine learning: a Lorenz ’96 proof-of-concept. Geophysical Research Letters, 52(12), e2024GL112523.
🛰️ Remote Sensing & Probabilistic Climate Projection
This research enhances climate forecasts by integrating transport processes into Earth System Models (ESMs). I utilize indirect meteorological observations — such as gas flux, vapor, and precipitation — via a deep learning–based data assimilation framework I designed.
Unlike traditional techniques bound by linear/Gaussian assumptions, my multimodal framework leverages generative learning to assimilate both in-situ data (e.g., CPC rain gauges) and satellite remote sensing (e.g., NOAA) under sparse, noisy conditions.
I further developed a probabilistic data assimilation method using density-based modeling for parameter inference and Bayesian uncertainty quantification, which is critical for long-term projections and for estimating the probability of major climate events like flooding and heat extremes.
📄 Read more:
Qu, Y., Juan, N., Li, S., & Gentine, P. (2024). Deep generative data assimilation in multimodal setting. In CVPR Conference on Computer Vision and Pattern Recognition, pp. 449–459.
🌊 Bridging Physics and Environment via AI
This work pioneers a physics-informed ML framework for modeling suspended sediment transport in vegetated flows. It enables data-driven closure of sediment dispersion fluxes, directly linking microscale turbulence to bulk sediment concentration in channel experiments.
Beyond sediment, I extend this approach to model pier scour formation using machine learning–enhanced turbulence physics. By unifying TKE and shear stress budgets, the model links scour depth to the dominant turbulent eddy scale — something previously inaccessible through traditional modeling.
Due to complexity, analytical solutions remain elusive. To overcome this, I apply symbolic regression to derive closed-form expressions linking turbulent structures with geomorphic response — opening new pathways for coupling AI and physical sciences.
📄 Read more:
Li, S., Qu, Y., Zheng, T., & Gentine, P. (2024). Machine‐assisted physical closure for coarse suspended sediments in vegetated turbulent channel flows. Geophysical Research Letters, 51(20), e2024GL110475.