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Awesome-AI-for-Atmosphere-and-Ocean

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Awesome paper about AI for atmosphere science and oceanography

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# **Awesome AI for Atmosphere and Ocean**
A collection of research papers on AI for Atmospheric Science and Oceanography. 
If you find some ignored papers, please open issues or pull requests. I'll be appreciated it.
More paper will be updated soon.

## **Contents**
  - [**Overview**](##overview)
  - [**Prediction**](##prediction)
    - [**Weather Prediction**](#weather-prediction)
    - [**Extreme Events**](#extreme-events)
    - [**Climate**](#climate)
    - [**Others**](#others)
  - [**Discovery**](#discovery)
    - [**Theory Discovery**](#theory-discovery)
    - [**Phenomenon Identification**](#phenomenon-identification)
    - [**Data Complement**](#data-complement)
  - [**Modeling**](#modeling)
    - [**Core**](#core)
    - [**Downscalling**](#downscalling)
    - [**Correction**](#correction)
    - [**Parameterization**](#parameterization)
  - [**Benchmarks**](#benchmarks)
  - [**Others**](#others)

## **Overview**
-  [**2023 | EGUsphere**] Machine Learning for numerical weather and climate modelling: a review [[paper](https://egusphere.copernicus.org/preprints/2023/egusphere-2023-350/)]
-  [**2023 | ArXiv**] The rise of data-driven weather forecasting [[paper](https://arxiv.org/abs/2307.10128)]
-  [**2022 | Deep Sea Research Part I**] Deep blue AI: A new bridge from data to knowledge for the ocean science [[paper](https://doi.org/10.1016/j.dsr.2022.103886)]
-  [**2022 | Ocean-Land-Atmosphere Research**] Recent developments in artificial intelligence in oceanography [[paper](https://downloads.spj.sciencemag.org/olar/2022/9870950.pdf)]
-  [**2022 | Environment Research Letters**] Machine learning applications for weather and climate need greater focus on extremes [[paper](https://iopscience.iop.org/article/10.1088/1748-9326/ac9d4e/meta)]
-  [**2021 | Remote Sensing**] Artificial Intelligence Revolutionises Weather Forecast, Climate Monitoring and Decadal Prediction [[paper](https://www.mdpi.com/2072-4292/13/16/3209)]
-  [**2021 | Atmosphere**] Survey on the Application of Deep Learning in Extreme Weather Prediction [[paper](https://www.mdpi.com/2073-4433/12/6/661)]
-  [**2021 | Big Data Research**] Deep Learning-Based Weather Prediction: A Survey [[paper](https://doi.org/10.1016/j.bdr.2020.100178)]
-  [**2021 | Multimedia Systems**] Application of machine learning in ocean data [[paper](https://link.springer.com/article/10.1007/s00530-020-00733-x)]
-  [**2021 | Environmental Research Letters**] Bridging observations, theory and numerical simulation of the ocean using machine learning [[paper](https://iopscience.iop.org/article/10.1088/1748-9326/ac0eb0)]
-  [**2021 | Phil. Trans. R. Soc. A**] Climbing down Charney’s ladder: machine learning and the post-Dennard era of computational climate science [[paper](https://doi.org/10.1098/rsta.2020.0085)]
-  [**2021 | Phil. Trans. R. Soc. A**] Can deep learning beat numerical weather prediction? [[paper](https://doi.org/10.1098/rsta.2020.0097)]
-  [**2021 | Phil. Trans. R. Soc. A**] Physics-informed machine learning: case studies for weather and climate modelling [[paper](https://doi.org/10.1098/rsta.2020.0093)]
-  [**2020 | National Science Review**] Deep-learning-based information mining from ocean remote-sensing imagery [[paper](https://academic.oup.com/nsr/article/7/10/1584/5809984)]
-  [**2019 | Visual Informatics**] A survey on visual analysis of ocean data [[paper](https://doi.org/10.1016/j.visinf.2019.08.001)]
-  [**2018 | Acta Numerica**] Challenges and design choices for global weather and climate models based on machine learning [[paper](https://gmd.copernicus.org/articles/11/3999/2018/)]
-  [**2015 | Nature**] The quiet revolution of numerical weather prediction [[paper](https://www.nature.com/articles/nature14956)]
-  [**2015 | Science Perspectives**] Weather Forecasting with Ensemble Methods [[paper](https://www.science.org/doi/10.1126/science.1115255)]

## **Prediction**
### **Weather Prediction**
-  [**2023 | ArXiv**] AI-GOMS: Large AI-Driven Global Ocean Modeling System [[paper](https://www.researchgate.net/publication/372961184_AI-GOMS_Large_AI-Driven_Global_Ocean_Modeling_System)]
-  [**2023 | Environmental Data Science**] AtmoDist: Self-supervised representation learning for atmospheric dynamics [[paper](https://www.cambridge.org/core/journals/environmental-data-science/article/atmodist-selfsupervised-representation-learning-for-atmospheric-dynamics/877CE83199C921B57C7966AF412C4272)]
-  [**2023 | Nature**] Accurate medium-range global weather forecasting with 3D neural networks [[paper](https://www.nature.com/articles/s41586-023-06185-3)][[code](https://github.com/198808xc/Pangu-Weather)]
-  [**2023 | Nature | Precipitation**] Skilful nowcasting of extreme precipitation with NowcastNet [[paper](https://www.nature.com/articles/s41586-023-06184-4)][[code](https://codeocean.com/capsule/3935105/tree/v1)]
-  [**2023 | PNAS | Precipitation**] Implicit learning of convective organization explains precipitation stochasticity [[paper](https://www.pnas.org/doi/10.1073/pnas.2216158120)]
-  [**2023 | Nature Machine Intelligence | Precipitation**] Interpretable weather forecasting for worldwide stations with a unified deep model [[paper](https://www.nature.com/articles/s42256-023-00667-9)][[code](https://github.com/thuml/Corrformer)]
-  [**2023 | ArXiv**] FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead [[paper](https://arxiv.org/abs/2304.02948)]
-  [**2023 | Nature | Extreme Precipitation**] Skilful nowcasting of extreme precipitation with NowcastNet [[paper](https://www.nature.com/articles/s41586-023-06184-4)]
-  [**2023 | Ocean Engineering | Wave**] Instantaneous prediction of irregular ocean surface wave based on deep learning [[paper](https://doi.org/10.1016/j.oceaneng.2022.113218)]
-  [**2022 | ArXiv**] GraphCast: Learning skillful medium-range global weather forecasting [[paper](https://arxiv.org/abs/2212.12794)][[code](https://github.com/deepmind/graphcast)]
-  [**2022 | ArXiv**] FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators [[paper](https://arxiv.org/abs/2202.11214)][[code](https://github.com/NVlabs/FourCastNet)]
-  [**2022 | IJAEOG | Ocean Temperature**] Prediction of long lead monthly three-dimensional ocean temperature using time series gridded Argo data and a deep learning method [[paper](https://doi.org/10.1016/j.jag.2022.102971)]
-  [**2022 | Remote Sensing | SST**] A Hybrid Deep Learning Model for the Bias Correction of SST Numerical Forecast Products Using Satellite Data [[paper](https://doi.org/10.3390/rs14061339)]
-  [**2022 | EarthArVix | SSH Anomalies**] Synthesizing Sea Surface Temperature and Satellite Altimetry Observations Using Deep Learning Improves the Accuracy and Resolution of Gridded Sea Surface Height Anomalies [[paper](https://doi.org/10.31223/X50Q0N)]
-  [**2021 | Ocean Engineering | Wave**] Ocean wave energy forecasting using optimised deep learning neural networks [[paper](https://doi.org/10.1016/j.oceaneng.2020.108372)]
-  [**2020 | Journal of Marine Science and Engineering | Multi Variables**] Prediction of Ocean Weather Based on Denoising AutoEncoder and Convolutional LSTM [[paper](https://doi.org/10.3390/jmse8100805)]
### **Extreme Events**
-  [**2021 | Nature Communications | Tsunami**] Machine learning-based tsunami inundation prediction derived from offshore observations [[paper](https://www.nature.com/articles/s41467-022-33253-5)]
-  [**2020 | Pure and Applied Geophysics | Tsunami**] Comparison of Machine Learning Approaches for Tsunami Forecasting from Sparse Observations [[paper](https://link.springer.com/article/10.1007/s00024-021-02841-9)]
-  [**2020 | Nature Communications | Sea Ice**] Seasonal Arctic sea ice forecasting with probabilistic deep learning [[paper]([https://doi.org/10.1126/sciadv.aba1482](https://doi.org/10.1038/s41467-021-25257-4))]
-  [**2020 | Science Advance | Tropical Waves**] Purely satellite data–driven deep learning forecast of complicated tropical instability waves [[paper](https://doi.org/10.1126/sciadv.aba1482)]
-  [**2019 | PNAS | General**] Using machine learning to predict extreme events in complex systems [[paper](https://www.pnas.org/doi/full/10.1073/pnas.191728511)]
-  [**2018 | Geophysics Research Letters | Typhoon**] A Deep Learning Algorithm of Neural Network for the Parameterization of Typhoon-Ocean Feedback in Typhoon Forecast Models [[paper](https://doi.org/10.1002/2018GL077004)]
### **Climate**
-  [**2022 | Geocarto International | Sea Level**] A deep-learning model for national scale modelling and mapping of sea level rise in Malaysia: the past, present, and future [[paper](https://doi.org/10.1080/10106049.2021.1958015)]
-  [**2021 | Measurement | Sea Level**] Monitoring of Caspian Sea-level changes using deep learning-based 3D reconstruction of GRACE signal [[paper](https://doi.org/10.1016/j.measurement.2021.109004)]
-  [**2021 | Nature Scientific Reports | Sea Level**] Predicting regional coastal sea level changes with machine learning [[paper](https://doi.org/10.1016/j.oceaneng.2020.108372)]
-  [**2021 | Geophysical Research Letters | India Ocean Dipole**] Forecasting the Indian Ocean Dipole With Deep Learning Techniques [[paper](https://doi.org/10.1029/2021GL094407)]
-  [**2020 | Nature Scientific Report | India Ocean Dipole**] A machine learning based prediction system for the Indian Ocean Dipole [[paper](https://www.nature.com/articles/s41598-019-57162-8)]
### **Others**

## **Discovery**
### **Theory Discovery**
-  [**2023 | ArXiv**] Discovering Causal Relations and Equations from Data [[paper](https://arxiv.org/abs/2305.13341)]
-  [**2023 | ArXiv**] Data-Driven Equation Discovery of a Cloud Cover Parameterization [[paper](https://arxiv.org/abs/2304.08063)]
-  [**2023 | ArXiv**] Learning Closed-form Equations for Subgrid-scale Closures from High-fidelity Data: Promises and Challenges [[paper](https://arxiv.org/abs/2306.05014)]
-  [**2022 | Nature Communications**] A deep-learning estimate of the decadal trends in the Southern Ocean carbon storage [[paper](https://www.nature.com/articles/s41467-022-31560-5)]
-  [**2022 | Transportation Research Procedia**] Analysis of spatio-temporal changes in Arctic Ocean ecosystem using machine learning and its impact on marine transportation system [[paper](https://doi.org/10.1016/j.trpro.2022.06.346)]
-  [**2021 | Nature Communications**] Deep learning to infer eddy heat fluxes from sea surface height patterns of mesoscale turbulence [[paper](https://www.nature.com/articles/s41467-020-20779-9)]
-  [**2021 | Nature Communications**] A shift in the ocean circulation has warmed the subpolar North Atlantic Ocean since 2016 [[paper](https://www.nature.com/articles/s43247-021-00120-y)]
-  [**2021 | Geophysics Review Letters**] Classifying Oceanographic Structures in the Amundsen Sea, Antarctica [[paper](https://doi.org/10.1029/2020GL089412)]
-  [**2021 | JAMES**] Revealing the Impact of Global Heating on North Atlantic Circulation Using Transparent Machine Learning [[paper](https://doi.org/10.1029/2021MS002496)]
-  [**2020 | JGR Oceans**] Water Mass and Biogeochemical Variability in the Kerguelen Sector of the Southern Ocean: A Machine Learning Approach for a Mixing Hot Spot [[paper](https://doi.org/10.1029/2019JC015877)]
-  [**2020 | Geophysics Review Letters**] Data-Driven Equation Discovery of Ocean Mesoscale Closures [[paper](https://doi.org/10.1029/2020GL088376)]
-  [**2019 | Nature Scientific Report**] Estimating global ocean heat content from tidal magnetic satellite observations [[paper](https://www.nature.com/articles/s41598-019-44397-8)]
-  [**2017 | JAMES**] Coherent heat patterns revealed by unsupervised classification of Argo temperature profiles in the North Atlantic Ocean [[paper](https://doi.org/10.1016/j.pocean.2016.12.008)]

### **Phenomenon Identification**
-  [**2022 | IEEE**] A Deep Learning Method for Ocean Front Extraction in Remote Sensing Imagery [[paper](https://doi.org/10.1109/LGRS.2021.3081179)]
-  [**2019 | JGR Oceans**] El Niño Detection Via Unsupervised Clustering of Argo Temperature Profiles [[paper](https://doi.org/10.1029/2019JC015947)]
-  [**2006 | International Journal of Remote Sensing**] Identification of eddies from sea surface temperature maps with neural networks [[paper](https://doi.org/10.1080/01431160500462170)]
### **Data Complement**
-  [**2023 | Science Bulletin**] Super-resolution reconstruction of a 3 arc-second global DEM dataset [[paper](https://www.researchgate.net/publication/365644013_Super-resolution_reconstruction_of_a_3_arc-second_global_DEM_dataset)]
-  [**2020 | Remote Sensing**] A Deep Learning Network to Retrieve Ocean Hydrographic Profiles from Combined Satellite and In Situ Measurements [[paper](https://doi.org/10.3390/rs12193151)]
-  [**2020 | JAMES**] A Deep Learning Approach to Spatiotemporal Sea Surface Height Interpolation and Estimation of Deep Currents in Geostrophic Ocean Turbulence [[paper](https://doi.org/10.1029/2019MS001965)]
-  [**2019 | Atmos. Chem. Phys.**] On the distinctiveness of observed oceanic raindrop distributions [[paper](https://acp.copernicus.org/articles/19/6969/2019/)]

## **Modeling**
### **Core**
-  [**2023 | ArXiv**] AI-GOMS: Large AI-Driven Global Ocean Modeling System [[paper](https://www.researchgate.net/publication/372961184_AI-GOMS_Large_AI-Driven_Global_Ocean_Modeling_System)]
-  [**2022 | Academic Dissertation**] Scientific Machine Learning for Dynamical Systems: Theory and Applications to Fluid Flow and Ocean Ecosystem Modeling [[paper](http://mseas.mit.edu/publications/PDF/gupta_phd_thesis_2022_09abs.pdf)]
-  [**2022 | Marine Science and Engineering**] Improving Ocean Forecasting Using Deep Learning and Numerical Model Integration [[paper](https://www.mdpi.com/2077-1312/10/4/450)]
-  [**2022 | Earth and Space Science**] Improving Numerical Model Predicted Float Trajectories by Deep Learning [[paper](https://doi.org/10.1029/2022EA002362)]
-  [**2022 | Journal of Comupational Physics**] Using Machine Learning at scale in numerical simulations with SmartSim: An application to ocean climate modeling [[paper](https://doi.org/10.1016/j.jocs.2022.101707)]
-  [**2020 | ArVix**] Machine-Learned Preconditioners for Linear Solvers in Geophysical Fluid Flows [[paper](https://doi.org/10.48550/arXiv.2010.02866)]
-  [**2019 | ArXiv**] Learning Generalized Quasi-Geostrophic Models Using Deep Neural Numerical Models [[paper](https://arxiv.org/abs/1911.08856)]
-  [**2019 | J. Stat. Mech.: Theory Exp**] Deep learning for physical processes: incorporating prior scientific knowledge [[paper](https://iopscience.iop.org/article/10.1088/1742-5468/ab3195/meta)]

### **Downscalling**
-  [**2022 | ASCMO**] Deep learning for statistical downscaling of sea states [[paper](https://ascmo.copernicus.org/articles/8/83/2022/)]
-  [**2022 | JGR Solid Earth**] Applying a Deep Learning Algorithm to Tsunami Inundation Database of Megathrust Earthquakes [[paper](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020JB019690)]
-  [**2021 | JAMES**] A Comparison of Data-Driven Approaches to Build Low-Dimensional Ocean Models [[paper](https://doi.org/10.1029/2021MS002537)]

### **Correction**
-  [**2022 | JAMES**] Correcting Coarse-Grid Weather and Climate Models by Machine Learning From Global Storm-Resolving Simulations [[paper](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021MS002794)]

### **Parameterization**
-  [**2019 | JAMES**] Applications of Deep Learning to Ocean Data Inference and Subgrid Parameterization [[paper](https://doi.org/10.1029/2018MS001472)]
-  [**2019 | JAMES**] Prognostic Validation of a Neural Network Unified Physics Parameterization [[paper](https://doi.org/10.1029/2018MS001472)]
-  [**2018 | Geophysical Research Letters**] Prognostic Validation of a Neural Network Unified Physics Parameterization [[paper](https://doi.org/10.1029/2018GL078510)]
-  [**2018 | PNAS**] Deep learning to represent subgrid processes in climate models [[paper](https://doi.org/10.1073/pnas.181028611)]

#### **Cloud and Precipitation**
-  [**2021 | JAMES**] Machine Learning the Warm Rain Process [[paper](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020MS002268)]
-  [**2020 | JAMES**] A Moist Physics Parameterization Based on Deep Learning [[paper](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020MS002076)]
-  [**2018 | Geophysical Research Letters**] Could Machine Learning Break the Convection Parameterization Deadlock? [[paper](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2018GL078202)]
#### **Mesoeddy**
-  [**2023 | ArXiv**] Implementation and Evaluation of a Machine Learned Mesoscale Eddy Parameterization into a Numerical Ocean Circulation Model [[paper](https://arxiv.org/abs/2303.00962)]
#### **Turbulence**
-  [**2021 | Deep Learning for the Earth Sciences**] Deep Learning of Unresolved Turbulent Ocean Processes in Climate Models [[paper](https://onlinelibrary.wiley.com/doi/abs/10.1002/9781119646181.ch20)]
-  [**2019 | PNAS**] Deep learning in turbulent convection networks [[paper](https://www.pnas.org/doi/10.1073/pnas.1900358116)]
#### **Boundary Layer**
-  [**2023 | JAMES**] Deep Learning Parameterization of the Tropical Cyclone Boundary Layer [[paper](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2022MS003034)]
#### **Radiative Transfer**
-  [**2022 | JAMES**] Exploring pathways to more accurate machine learning emulation of atmospheric radiative transfer [[paper](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021MS002875)]
#### **Others**
-  [**2022 | NSR**] Physics-informed deep-learning parameterization of ocean vertical mixing improves climate simulations [[paper](https://doi.org/10.1093/nsr/nwac044)]

## **Benchmarks**
-  [**2023 | JAMES**] Benchmarking of Machine Learning Ocean Subgrid Parameterizations in an Idealized Model [[paper](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2022MS003258)]
-  [**2023 | ArXiv**] The rise of data-driven weather forecasting [[paper](https://arxiv.org/abs/2307.10128)]
-  [**2020 | JAMES**] WeatherBench: a benchmark data set for data‐driven weather forecasting [[paper](https://doi.org/10.1029/2020MS002203)]

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