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correct publications, add resources,etc #103

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82 changes: 34 additions & 48 deletions content/publications/_index.md
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Expand Up @@ -7,15 +7,25 @@ heroBackground: 'images/susan-q-yin-2JIvboGLeho-unsplash.jpg'

## M²LInES research and other relevant publications

If you are interested in understanding how M²LInES is using machine learning to improve climate models, we have developed an educational JupyterBook [Learning Machine Learning for Climate modeling with Lorenz 96](https://m2lines.github.io/L96_demo). This JupyterBook describes the key research themes in M²LInES, through the use of a simple climate model and machine learning algorithms. You can run the notebooks yourself, contribute to the development of the JupyterBook or let us know what you think on GitHub.
M²LInES educational [Jupyter Book](https://m2lines.github.io/L96_demo) is out. Learn about about the interface between machine learning & climate modeling whether you are an ML expert or a climate scientist.

You can also check all our publications on our **[Google Scholar profile](https://scholar.google.com/citations?hl=en&user=iY8RO4QAAAAJ)**


<img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon"> M²LInES funded research

### 2024

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<img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon">
<strong>Dhruv Balwada et al. </strong><br>
<a href="https://doi.org/10.21105/jose.00241" target="_blank"><strong>Learning Machine Learning with Lorenz-96</strong></a><br>
<i>JOSE</i> <strong>DOI</strong>:10.21105/jose.00241
</p>
</div>

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<img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon">
<strong>Pavel Perezhogin, Cheng Zhang, Alistair Adcroft, Carlos Fernandez-Granda, Laure Zanna </strong><br>
<a href="https://doi.org/10.1029/2023MS004104" target="_blank"><strong>A Stable Implementation of a Data-Driven Scale-Aware Mesoscale Parameterization</strong></a><br>
<i>JAMES</i> <strong>DOI</strong>: 10.1029/2023MS004104
<i>JAMES 2024</i> <strong>DOI</strong>: 10.1029/2023MS004104
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<img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon">
<strong>William Gregory, Ronald MacEachern, So Takao, Isobel Lawrence, Carmen Nab, Marc Deisenroth, Michel Tsamados</strong><br>
<a href="https://doi.org/10.21203/rs.3.rs-4209064/v1" target="_blank"><strong>Scalable interpolation of satellite altimetry data with probabilistic machine learning</strong></a><br>
<i>Nature Comms. 2024</i> <strong>DOI</strong>: 10.21203/rs.3.rs-4209064/v1
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<img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon">
<strong>Cheng Zhang, Pavel Perezhogin, Cem Gultekin, Alistair Adcroft, Carlos Fernandez-Granda, Laure Zanna </strong><br>
<a href="https://doi.org/10.1029/2023MS003697" target="_blank"><strong>Implementation and Evaluation of a Machine Learned Mesoscale Eddy Parameterization Into a Numerical Ocean Circulation Model</strong></a><br>
<i>James 2023.</i> <strong>DOI</strong>: 10.1029/2023MS003697
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<img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon">
<strong>Pavel Perezhogin, Cheng Zhang, Alistair Adcroft, Carlos Fernandez-Granda, Laure Zanna </strong><br>
<a href="https://arxiv.org/abs/2311.02517" target="_blank"><strong>Implementation of a data-driven equation-discovery mesoscale parameterization into an ocean model</strong></a><br>
<i>James 2023.</i> <strong>DOI</strong>: 10.48550/arXiv.2311.02517
<i>JAMES 2023.</i> <strong>DOI</strong>: 10.1029/2023MS003697
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<img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon">
<strong>Sara Shamekh and Pierre Gentine</strong><br>
<a href="https://essopenarchive.org/doi/full/10.22541/essoar.168748456.60017486" target="_blank"><strong>Learning Atmospheric Boundary Layer Turbulence</strong></a><br>
<i>JAMES 2023.</i> <strong>DOI</strong>: 10.22541/essoar.168748456.60017486/v1
<i>Authorea Preprint 2023</i> <strong>DOI</strong>: 10.22541/essoar.168748456.60017486/v1
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<img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon">
<strong>Karl Otness, Laure Zanna, Joan Bruna</strong><br>
<a href="https://www.climatechange.ai/papers/iclr2023/60" target="_blank"><strong>Data-driven multiscale modeling of subgrid parameterizations in climate models</strong></a><br>
<i>Preprint accepted at ICLR Workshop on Climate Change AI. 2023.</i> <strong>DOI</strong>: 10.48550/arXiv.2303.17496
<i>ICLR Workshop on Climate Change AI. 2023.</i> <strong>DOI</strong>: 10.48550/arXiv.2303.17496
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<img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon">
<strong>Fabrizio Falasca, Andrew Brettin, Laure Zanna, Stephen M. Griffies, Jianjun Yin, Ming Zhao</strong><br>
<a href="https://arxiv.org/abs/2211.04608" target="_blank"><strong>Exploring the non-stationarity of coastal sea level probability distributions</strong></a><br>
<a href="https://doi.org/10.1017/eds.2023.10" target="_blank"><strong>Exploring the non-stationarity of coastal sea level probability distributions</strong></a><br>
<i>EDS. volume 2 2023.</i> <strong>DOI</strong>: 10.1017/eds.2023.10
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<img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon">
<strong>Pavel Perezhogin, Laure Zanna, Carlos Fernandez-Granda</strong><br>
<a href="https://doi.org/10.1029/2023MS003681" target="_blank"><strong>Generative data-driven approaches for stochastic subgrid parameterizations in an idealized ocean model</strong></a><br>
<i>JAMES. 2023.</i> <strong>DOI</strong>: 10.1029/2023MS003681
<i>JAMES 2023.</i> <strong>DOI</strong>: 10.1029/2023MS003681
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<img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon">
<strong>Pavel Perezhogin, Andrey Glazunov</strong><br>
<a href="https://doi.org/10.1029/2023MS003771" target="_blank"><strong>Subgrid Parameterizations of Ocean Mesoscale Eddies Based on Germano Decomposition</strong></a><br>
<i>JAMES. 2023.</i> <strong>DOI</strong>: 10.1029/2023MS003771
<i>JAMES 2023.</i> <strong>DOI</strong>: 10.1029/2023MS003771
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<strong>Takaya Uchida, Dhruv Balwada, Quentin Jamet, William K. Dewar, Bruno Deremble, <br>
Thierry Penduff, Julien Le Sommer</strong><br>
<a href="https://www.sciencedirect.com/science/article/abs/pii/S1463500323000136?via%3Dihub)" target="_blank"><strong>Cautionary tales from the mesoscale eddy transport tensor</strong></a><br>
<i>ScienceDirect 2023.</i> <strong>DOI</strong>: 10.1016/j.ocemod.2023.102172
<i>Ocean Modelling 2023.</i> <strong>DOI</strong>: 10.1016/j.ocemod.2023.102172
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<img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon">
<strong>Sara Shamekh, Kara D Lamb, Yu Huang, Pierre Gentine</strong><br>
<a href="https://doi.org/10.1073/pnas.2216158120" target="_blank"><strong>Implicit learning of convective organization explains precipitation stochasticity</strong></a><br>
<i>PNAS 2023.</i> <strong>DOI</strong>: 10.1073/pnas.2216158120
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<img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon">
<strong>Lorenzo Zampieri, Gabriele Arduini, Marika Holland, Sarah Keeley, Kristian S. Mogensen, <br>
Matthew D. Shupe, Steffen Tietsche</strong><br>
<a href="https://doi.org/10.1002/essoar.10511269.1" target="_blank"><strong>A Machine Learning Correction Model of the Winter Clear-Sky Temperature Bias over the Arctic Sea Ice in Atmospheric Reanalyses</strong></a><br>
<a href="https://doi.org/10.1175/MWR-D-22-0130.1" target="_blank"><strong>A Machine Learning Correction Model of the Winter Clear-Sky Temperature Bias over the Arctic Sea Ice in Atmospheric Reanalyses</strong></a><br>
<i>AMS Journals, Monthy Weather Review: volume151, issue6</i> <strong>DOI</strong>: 10.1175/MWR-D-22-0130.1
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<img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon">
<strong>Sara Shamekh, Kara D Lamb, Yu Huang, Pierre Gentine</strong><br>
<a href="https://doi.org/10.1002/essoar.10512517.1" target="_blank"><strong>Implicit learning of convective organization explains precipitation stochasticity</strong></a><br>
<i>In review. 2022.</i> <strong>DOI</strong>: 10.1002/essoar.10512517.1
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<img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon">
<strong>Hugo Frezat, Julien Le Sommer, Ronan Fablet, Guillaume Balarac, Redouane Lguensat</strong><br>
<a href="https://doi.org/10.1029/2022MS003124" target="_blank"><strong>A posteriori learning for quasi-geostrophic turbulence parametrization</strong></a><br>
<i>JAMES. 2022.</i> <strong>DOI</strong>: 10.1029/2022MS003124
<i>JAMES 2022.</i> <strong>DOI</strong>: 10.1029/2022MS003124
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---
### Open Storage Network Pod

M²LInES has an Open Storage Network (OSN) Pod!
What can you get out of the Pod as a member of the team:
* A project for a specific bucket (authenticated or public) to work with your team
* Move data (needs LEAP DCT team admin) to the publication bucket (m2lines-pubs) before publishing a paper.
* Ingest publicly available datasets into Analysis Ready Cloud Optimized formats. Start by adding an issue [here](https://github.com/leap-stc/data-management/issues/new?template=new_dataset.yaml) and work with the LEAP DCT on the recipe. More info in the [docs](https://leap-stc.github.io/_preview/206/guides/data_guide.html#ingesting-datasets-into-cloud-storage)
You can find the relevant guide to the pod [here](https://leap-stc.github.io/_preview/206/guides/team_guide.html#)

Non-members can access publicly available data from our team. As with all the OSN Pod, 20% of our space is reserved for public use.

### Learning Machine Learning with Lorenz-96

The M²LInES team is proud to share this **[article](https://doi.org/10.21105/jose.00241)** and **[Jupyter Book](https://github.com/m2lines/L96_demo)** published in the Journal of Open Science Education (JOSE) and led by Dhruv Balwada. Developed by our team, it aims to introduce Machine Learning (ML) methods to climate scientists and also climate modeling to machine learning experts. The book presents a wide range of ML applications for climate modeling, focusing on hybrid AI+Physics methods using the Lorenz-96 model. We hope this book can be used as a pedagogical tool for self-learning, a reference manual, or for teaching modules in an introductory class on ML or hybrid climate modeling.


🚧 Under Development 🚧