AI / ML for hydro-climatology
| Title | Author / Year | Theme | Comment |
|---|---|---|---|
| Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling | Razavi (2021) | Easy steps into DL | Introductory overview |
| A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists | Shen (2018) | First review on DL for hydrological modelling | Literature review |
| HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community | Shen et al. (2018) | First commentary on DL for hydrological modelling | Commentary |
| Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks | Kratzert et al. (2018) | Application of LSTM to rainfall-runoff modelling | Technical paper |
| Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets | Kratzert et al. (2019) | Application of LSTM to a large-sample dataset | Technical paper |
| Deep learning to represent subgrid processes in climate models | Rasp et al. (2018) | Use deep learning to leverage the power of short-term cloud-resolving simulations for climate modeling | Technical paper |
| Could Machine Learning Break the Convection Parameterization Deadlock? | Gentine et al. (2018) | Represention of unresolved moist convection in coarse-scale climate models | Technical paper |
| Can deep learning beat numerical weather prediction? | Schultz et al. (2021) | Review of state-of-the-art machine learning concepts and their applicability to weather data | Commentary |
| From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling | Tsai et al. (2021) | Introduction to differentiable parameter learning | Technical paper |
| Differentiable modelling to unify machine learning and physical models for geosciences | Shen et al. (2023) | Position paper on the potential of differentiable modelling approaches | Commentary |
