Merge pull request #82 from IamShubhamGupto/master

april newsletter

Author: johannag126

content/news/2404Adam.md

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+---
+date: 2024-04-01T09:29:16+10:00
+title: "Building Ocean Climate Emulators by Adam"
+heroHeading: ''
+heroSubHeading: 'Building Ocean Climate Emulators by Adam'
+heroBackground: ''
+thumbnail:  'images/news/2404Adam.png'
+images: ['images/news/2404Adam.png']
+link: 'https://arxiv.org/abs/2402.04342'
+---
+
+AI can accelerate climate modeling by generating computationally cheap emulators of existing simulations. The design of climate emulators is an exciting and open challenge in AI+Climate. In this [work](https://arxiv.org/abs/2402.04342), PhD student **Adam Subel** and **Laure Zanna** focus on two fundamental questions to facilitate the creation of ocean emulators: 1) the role of the atmosphere in improving the decadal skill of the emulator and 2) the representation of variables with distinct timescales (e.g., velocity and temperature) in the design of any emulator. This work was accepted in the [ICLR 2024 Workshop: Tackling Climate Change with Machine Learning](https://www.climatechange.ai/events/iclr2024), and selected as a Spotlight Talk! Make sure to catch Adam’s talk in Vienna if you are there, or on our YourTube Channel in May.

content/news/2404Qu.md

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+---
+date: 2024-04-02T09:29:16+10:00
+title: "Joint Parameter and Parameterization Inference by Yongquan"
+heroHeading: ''
+heroSubHeading: 'Joint Parameter and Parameterization Inference with Uncertainty Quantification through Differentiable Programming'
+heroBackground: ''
+thumbnail:  'images/news/2404Qu.png'
+images: ['images/news/2404Qu.png']
+link: 'https://arxiv.org/abs/2403.02215'
+---
+
+In this [article](https://arxiv.org/abs/2403.02215), **Yongquan Qu**, **Mohamed Aziz Bhouri**, and **Pierre Gentine**, tackle the joint inference and uncertainty quantification of poorly known physical parameters and machine learning parametrizations, for sub-grid scale dynamics. Achieved through a Bayesian framework enabled by differentiable programming, this method not only offers accurate parameter estimates, but also enables skillful forecasting, each accompanied by quantified uncertainty. This work is accepted at the ICLR 2024 Workshop on AI4Differential Equations In Science.

content/news/2404Zampieri.md

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+---
+date: 2024-04-03T09:29:16+10:00
+title: "Advances in Machine Learning Techniques for Sea Ice Applications"
+heroHeading: ''
+heroSubHeading: 'Advances in Machine Learning Techniques Can Assist Across a Variety of Stages in Sea Ice Applications'
+heroBackground: ''
+thumbnail:  'images/newlogo.png'
+images: ['images/newlogo.png']
+link: 'https://journals.ametsoc.org/view/journals/bams/105/3/BAMS-D-23-0332.1.xml'
+---
+
+Forty-two international researchers, including Lorenzo Zampieri, were invited to Gwangmyeong (South Korea) to discuss recent advances in sea ice modelling to help guide the development of a state-of-the-art Korean sea ice modelling program under the leadership of the Korean Polar Research Institute (KOPRI). The workshop produced a [report](https://journals.ametsoc.org/view/journals/bams/105/3/BAMS-D-23-0332.1.xml), which highlights 1) the opportunity to use AI techniques in support of various sea ice modelling tasks both for short-term and climate predictions and 2) the need for strengthened collaborations among international groups to connect existing “research bubbles” and facilitate the dissemination of ideas. Also make sure to check M²LInES work on AI+Sea-Ice modeling led by **Lorenzo Zampieri** and **Will Gregory**.

content/publications/_index.md

@@ -283,6 +283,18 @@ If you are interested in understanding how M²LInES is using machine learning to
   </p>
 </div>
 
+<div style="display: flex; align-items: center;">
+  <div style="width: 100px; height: 100px; overflow: hidden;  margin-right: 10px;">
+    <img src="/images/news/0623-Zampieri.jpg" style="width: 100px; height: 100px;">
+  </div>
+  <p>
+    <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"><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
+  </p>
+</div>
+
 <br/>
 
 ### 2022
@@ -367,18 +379,6 @@ If you are interested in understanding how M²LInES is using machine learning to
   </p>
 </div>
 
-<div style="display: flex; align-items: center;">
-  <div style="width: 100px; height: 100px; overflow: hidden;  margin-right: 10px;">
-    <img src="/images/news/0623-Zampieri.jpg" style="width: 100px; height: 100px;">
-  </div>
-  <p>
-    <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"><strong>A machine learning correction model of the clear-sky bias over the Arctic sea ice in atmospheric reanalyses</strong></a><br>
-    <i>J Earth and Space Science Open Archive. 2022 (preprint)</i> <strong>DOI</strong>: 10.1002/essoar.10511269.1
-  </p>
-</div>
-
 <div style="display: flex; align-items: center;">
   <div style="width: 100px; height: 100px; overflow: hidden;  margin-right: 10px;">
     <img src="/images/publications/ogdc_22.png" style="width: 100px; height: 100px;">

content/team/EtienneMeunier.md

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+---
+title: "Etienne Meunier"
+draft: false
+image: "/images/team/EtienneMeunier.jpeg"
+jobtitle: "PostDoc"
+promoted: true
+weight: 39
+Website: https://etienne-meunier.github.io/
+tags: [Ocean, Machine Learning]
+---
+
+
+CNRS-IPSL