Merge pull request #96 from IamShubhamGupto/master

October updates

content/blog/atmosphere.md

@@ -0,0 +1,11 @@
+---
+title: 'Atmosphere'
+draft: false
+featured: true
+weight: 1
+heroHeading: 'Atmosphere'
+heroSubHeading: ''
+heroBackground: '/images/retrosupply-jLwVAUtLOAQ-unsplash.jpeg'
+---
+
+🚧 Under Development 🚧

content/blog/climate.md

@@ -0,0 +1,11 @@
+---
+title: 'Climate Model Development'
+draft: false
+featured: true
+weight: 1
+heroHeading: 'Climate Model Development'
+heroSubHeading: ''
+heroBackground: '/images/retrosupply-jLwVAUtLOAQ-unsplash.jpeg'
+---
+
+🚧 Under Development 🚧

content/research/research1.md → content/blog/climateprocess.md

@@ -1,19 +1,17 @@
 ---
-title: 'Deepening our understanding of key climate processes'
+title: 'Key Climate Processes'
 draft: false
 featured: true
 weight: 1
-heroHeading: 'Deepening our understanding of key climate processes '
+heroHeading: 'Key Climate Processes'
 heroSubHeading: ''
-heroBackground: '/images/32109555763_eb9bb215ef_k.jpg'
+heroBackground: '/images/retrosupply-jLwVAUtLOAQ-unsplash.jpeg'
 ---
 
-
-
 Despite drastic improvements in climate model development, current simulations have difficulty capturing the interactions among different processes in the atmosphere, oceans, and ice and how they affect the Earth’s climate; this can hinder projections of temperature, rainfall, and sea level.
 M²LInES will be focusing on understanding these key climate processes using two types of data:
-1. [Data from high-resolution simulation and observations. ](../research4)
-2. [Data resulting from model errors, also called data assimilation increments.](../research5)
+1. [Data from high-resolution simulation and observations. ](/blog/research4)
+2. [Data resulting from model errors, also called data assimilation increments.](/blog/dataassimilation)
 
 Below is a representation of the physical processes that will be studied by the team:
 

content/blog/coupledphysics.md

@@ -0,0 +1,13 @@
+---
+title: 'Coupled Physics'
+draft: false
+featured: true
+weight: 1
+heroHeading: 'Coupled Physics'
+heroSubHeading: ''
+heroBackground: '/images/retrosupply-jLwVAUtLOAQ-unsplash.jpeg'
+---
+
+[Developing new physics-aware machine learning tools](/blog/research2)
+
+🚧 Under Development 🚧

content/blog/projectgoals.md

@@ -0,0 +1,28 @@
+---
+title: 'Project Goals and Vision'
+draft: false
+featured: true
+weight: 1
+heroHeading: 'Project Goals and Vision'
+heroSubHeading: ''
+heroBackground: '/images/retrosupply-jLwVAUtLOAQ-unsplash.jpeg'
+---
+
+## Project Goal
+
+_We aim to reduce biases at the air-sea-ice interface in existing global climate models for reliable seasonal to multidecadal timescale projections, focusing on fundamental ocean, atmosphere, and sea-ice processes._
+
+Two leading sources of errors contribute to climate model biases: missing processes and numerics. The missing or inadequate representation of multiscale ocean, sea-ice, and atmosphere processes (e.g., clouds, mixing, turbulence), are not resolved by the current generation of climate models due to computational limitations. Another error source arises from the climate models' numerics, which include spatial and temporal discretizations and numerical dissipation. These errors can accumulate or compensate for each other, making improving climate models intricate and requiring a range of approaches.
+
+To tackle these biases and reduce the potential sources of error, **M²LInES’ strategy is to leverage advances in machine learning & "interrogate” the data to**
+
+1. Develop data-informed, interpretable & generalizable subgrid physics models (ocean, ice, atm);
+2. Produce error corrections derived from observational products for climate model components.
+
+By improving model physics, this strategy ensures a more faithful representation of feedbacks and sensitivities under different climates.
+
+### Our vision
+1. _​​Generate new scientific knowledge_ in climate science from innovative use of data and machine learning: e.g., which physics did we overlook that might be important for scale interaction?
+2. _Accelerate end-to-end, from development to delivery, for a new generation of climate models_; this includes learning and testing parameterizations in global frameworks to tackle significant biases in climate models.
+3. _Drive a change of direction in the field by building models and tools centered around data-driven methods_ for the community to advance climate science discovery.
+4. _Enable a new generation of versatile scientists working at the interface of machine learning, climate science & numerical modeling._

content/research/research2.md → content/blog/research2.md

@@ -1,4 +1,3 @@
-
 ---
 title: 'Developing new physics-aware machine learning tools'
 draft: false

content/blog/seaice.md

@@ -0,0 +1,11 @@
+---
+title: 'Sea Ice'
+draft: false
+featured: true
+weight: 1
+heroHeading: 'Sea Ice'
+heroSubHeading: ''
+heroBackground: '/images/retrosupply-jLwVAUtLOAQ-unsplash.jpeg'
+---
+
+🚧 Under Development 🚧

content/jobs/_index.md

@@ -17,5 +17,3 @@ Postdoctoral researcher or more senior scientist for Ocean Surface Boundary Laye
 ### Columbia University (NCAR location)
 
 Associate Research Scientist at the interface between climate modeling and machine learning. The successful candidate will be hired by Columbia University but the main work location will be at NCAR (Boulder, CO). [Apply here](https://apply.interfolio.com/140294)
-
-

content/news/2410Shamekh.md

@@ -0,0 +1,11 @@
+---
+date: 2024-10-01T09:29:16+10:00
+title: "Distilling Machine Learning’s Added Value"
+heroHeading: ''
+heroSubHeading: 'Distilling Machine Learning’s Added Value: Pareto Fronts in Atmospheric Applications'
+heroBackground: ''
+thumbnail:  'images/news/2410Shamekh.png'
+images: ['images/news/2410Shamekh.png']
+link: 'https://doi.org/10.48550/arXiv.2408.02161'
+---
+This [project](https://doi.org/10.48550/arXiv.2408.02161) addresses the challenge of explaining the added value of machine learning in weather and climate models, particularly for complex deep learning models. By constructing a hierarchy of Pareto-optimal models along an error-complexity plane, the researchers, including **Sara Shamekh**, provide insights into model development and performance. Through three applications—cloud cover parameterization, shortwave radiative transfer, and tropical precipitation modeling—it demonstrates how machine learning can uncover nonlinear relationships, improve parameterization, and capture key physical processes. This hierarchical approach aims to improve understanding and trust in machine learning models for atmospheric science.

content/news/2410Yuval.md

@@ -0,0 +1,11 @@
+---
+date: 2024-10-02T09:29:16+10:00
+title: "Neural general circulation models for weather and climate"
+heroHeading: ''
+heroSubHeading: 'Neural general circulation models for weather and climate'
+heroBackground: ''
+thumbnail:  'images/news/2410Yuval.png'
+images: ['images/news/2410Yuval.png']
+link: 'https://doi.org/10.1038/s41586-024-07744-y'
+---
+General circulation models (GCMs) are essential for weather and climate prediction. They use physics-based simulations to model large-scale dynamics and small-scale processes. Recently, machine-learning models have matched or exceeded GCMs in weather forecasting accuracy, but struggled with long-term stability and ensemble forecasts. In this **[Nature paper](https://doi.org/10.1038/s41586-024-07744-y)**, co-led by **Janni Yuval**, a new model, NeuralGCM, integrates machine learning with a differentiable solver for atmospheric dynamics. It performs as well as top machine-learning and physics-based methods for short-term forecasts and can track climate metrics accurately for decades with prescribed sea surface temperature. NeuralGCM offers significant computational savings and demonstrates that deep learning can enhance traditional GCMs in predicting the Earth system. **Griffin Mooers** also contributed to the research.

content/news/Newsletters/_index.md

@@ -10,6 +10,9 @@ tags:
 
  Links to our past newsletters are below.
 ### 2024
+
+* 10/01/2024 - [M²LInES newsletter - October 2024](https://mailchi.mp/d53f7fd6537d/m2lines-oct2024)
+
 * 09/03/2024 - [M²LInES newsletter - September 2024](https://mailchi.mp/bee785c9dfef/m2lines-sept2024)
 
 * 08/05/2024 - [M²LInES newsletter - August 2024](https://mailchi.mp/673f0ae414a1/m2lines-august2024)

content/publications/_index.md

@@ -13,6 +13,30 @@ If you are interested in understanding how M²LInES is using machine learning to
 
 ### 2024
 
+<div style="display: flex; align-items: center;">
+  <div style="width: 100px; height: 100px; overflow: hidden;  margin-right: 10px;">
+    <img src="/images/news/2410Yuval.png" style="width: 100px; height: 100px;">
+  </div>
+  <p>
+    <!-- <img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon"> -->
+    <strong>Dmitrii Kochkov, ... Stephan Hoyer</strong><br>
+    <a href="https://doi.org/10.1038/s41586-024-07744-y" target="_blank"><strong>Neural general circulation models for weather and climate</strong></a><br>
+    <i>Nature 2024</i> <strong>DOI</strong>: 10.1038/s41586-024-07744-y
+  </p>
+</div>
+
+<div style="display: flex; align-items: center;">
+  <div style="width: 100px; height: 100px; overflow: hidden;  margin-right: 10px;">
+    <img src="/images/news/2410Shamekh.png" style="width: 100px; height: 100px;">
+  </div>
+  <p>
+    <img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon">
+    <strong>Tom Beucler, Arthur Grundner, Sara Shamekh, Peter Ukkonen, Matthew Chantry, Ryan Lagerquist </strong><br>
+    <a href="https://doi.org/10.48550/arXiv.2408.02161" target="_blank"><strong>Distilling Machine Learning's Added Value: Pareto Fronts in Atmospheric Applications</strong></a><br>
+    <i>ArXiv 2024</i> <strong>DOI</strong>: 10.48550/arXiv.2408.02161
+  </p>
+</div>
+
 <div style="display: flex; align-items: center;">
   <div style="width: 100px; height: 100px; overflow: hidden;  margin-right: 10px;">
     <img src="/images/news/2409Gregory.png" style="width: 100px; height: 100px;">
@@ -166,7 +190,7 @@ If you are interested in understanding how M²LInES is using machine learning to
   </div>
   <p>
     <img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon">
-    <strong>Tom Beucler,  Michael Pritchard, Janni Yuval, Ankitesh Gupta, Liran Peng, Stephan Rasp, Fiaz Ahmed, Paul O’Gorman, J. David Neelin, Nicholas J. Lutsko, Pierre Gentine</strong><br>
+    <strong>Tom Beucler, ... Pierre Gentine</strong><br>
     <a href="https://doi.org/10.1126/sciadv.adj7250" target="_blank"><strong>Climate-Invariant Machine Learning</strong></a><br>
     <i>Science Advances 2024</i> <strong>DOI</strong>: 10.1126/sciadv.adj7250
   </p>

content/research/_index.md

@@ -5,25 +5,6 @@ heroSubHeading: ''
 heroBackground: '/images/32109555763_eb9bb215ef_k.jpg'
 
 ---
-<center>
-    <img src="research-images/workplan.png" style="width: 50vw; padding-bottom: 30px; padding-top: 0px">
-</center>
 
-## Project Goal
 
-_We aim to reduce biases at the air-sea-ice interface in existing global climate models for reliable seasonal to multidecadal timescale projections, focusing on fundamental ocean, atmosphere, and sea-ice processes._
-
-Two leading sources of errors contribute to climate model biases: missing processes and numerics. The missing or inadequate representation of multiscale ocean, sea-ice, and atmosphere processes (e.g., clouds, mixing, turbulence), are not resolved by the current generation of climate models due to computational limitations. Another error source arises from the climate models' numerics, which include spatial and temporal discretizations and numerical dissipation. These errors can accumulate or compensate for each other, making improving climate models intricate and requiring a range of approaches.
-
-To tackle these biases and reduce the potential sources of error, **M²LInES’ strategy is to leverage advances in machine learning & "interrogate” the data to**
-
-1. Develop data-informed, interpretable & generalizable subgrid physics models (ocean, ice, atm);
-2. Produce error corrections derived from observational products for climate model components.
-
-By improving model physics, this strategy ensures a more faithful representation of feedbacks and sensitivities under different climates.
-
-### Our vision
-1. _​​Generate new scientific knowledge_ in climate science from innovative use of data and machine learning: e.g., which physics did we overlook that might be important for scale interaction?
-2. _Accelerate end-to-end, from development to delivery, for a new generation of climate models_; this includes learning and testing parameterizations in global frameworks to tackle significant biases in climate models.
-3. _Drive a change of direction in the field by building models and tools centered around data-driven methods_ for the community to advance climate science discovery.
-4. _Enable a new generation of versatile scientists working at the interface of machine learning, climate science & numerical modeling._
+&#x200B;

content/team/AbigailBodner.md

@@ -6,7 +6,8 @@ jobtitle: "Affiliate"
 promoted: true
 weight: 27
 Website:  https://abodner.github.io/
-tags: [Ocean, Machine Learning, Climate Model Development, Turbulence]
+position: Turbulence
+tags: [Ocean, Machine Learning, Climate Model Development]
 ---
 
 

content/team/AlexConnolly.md

@@ -6,7 +6,8 @@ jobtitle: "Postdoc"
 promoted: true
 weight: 16
 Website:  http://efmh.berkeley.edu/alexconnolly
-tags: [Atmosphere, Machine Learning, Boundary Layer Turbulence]
+position: Boundary Layer Turbulence
+tags: [Atmosphere, Machine Learning ]
 ---
 
 Columbia University

content/team/ArthurGuillaumin.md

@@ -7,7 +7,8 @@ linkedinurl: ""
 promoted: true
 Website: https://www.qmul.ac.uk/maths/profiles/arthurguillaumin.html
 weight: 13
-tags: [Machine Learning, Statistics]
+position: Statistics
+tags: [Machine Learning]
 ---
 
 Queen Mary University of London

content/team/BrandonReichl.md

@@ -6,7 +6,8 @@ jobtitle: "Lead Scientist"
 promoted: true
 Website: https://breichl.github.io/
 weight: 6
-tags: [Ocean, Climate Model Development, Air-Sea Interactions]
+position: Air-Sea Interactions
+tags: [Ocean, Climate Model Development]
 ---
 
 

content/team/CarlosFerndandezGranda.md

@@ -7,6 +7,7 @@ jobtitle: "PI"
 promoted: true
 Website: https://math.nyu.edu/~cfgranda/
 weight: 4
-tags: [Ocean, Machine Learning, Deep Learning]
+position: Deep Learning
+tags: [Ocean, Machine Learning]
 ---
 NYU, Courant Institute + Center for Data Science

content/team/CemGultekin.md

@@ -6,7 +6,8 @@ jobtitle: "Affiliate"
 promoted: true
 weight: 20
 Website:
-tags: [Machine Learning, Interpretability, Deep Learning]
+position: Interpretability
+tags: [Machine Learning]
 ---
 
 

content/team/DanniDu.md

@@ -0,0 +1,13 @@
+---
+title: "Danni Du"
+draft: false
+image: "/images/team/DanniDu.jpg"
+jobtitle: "Postdoc"
+promoted: true
+weight: 41
+Website: https://danni-du.github.io/
+tags: [Machine Learning, Data Assimilation, Climate Model Development]
+---
+
+
+Princeton University

content/team/DavidKamm.md

@@ -6,7 +6,8 @@ jobtitle: "Affiliate, Graduate Student "
 promoted: true
 weight: 41
 Website:
-tags: [Ocean, Eddy Parameterizations]
+position: Eddy Parameterizations
+tags: [Ocean]
 ---
 
 

content/team/DhruvBalwada.md

@@ -6,7 +6,8 @@ jobtitle: "Lead Scientist"
 promoted: true
 Website:  https://dhruvbalwada.github.io/
 weight: 22
-tags: [Ocean, Machine Learning, Macro-turbulence]
+position: Macro-turbulence
+tags: [Ocean, Machine Learning]
 ---
 
 

content/team/FabrizioFalasca.md

@@ -6,7 +6,8 @@ jobtitle: "Affiliate, Postdoc"
 promoted: true
 weight: 33
 Website:
-tags: [Ocean, Data Mining, Complex Systems, Dynamical Systems]
+position: Dynamical Systems
+tags: [Ocean]
 ---
 
 

content/team/FeiyuLu.md

@@ -6,7 +6,7 @@ jobtitle: "Lead Scientist"
 promoted: true
 Website: https://scholar.princeton.edu/feiyulu
 weight: 6
-tags: [Ocean, Machine Learning, Data Assimilation, Climate Model Development]
+tags: [Ocean, Data Assimilation, Climate Model Development]
 ---
 
 

content/team/GabrielMouttapa.md

@@ -0,0 +1,13 @@
+---
+title: "Gabriel Mouttapa"
+draft: false
+image: "/images/newlogo.png"
+jobtitle: "Affiliate"
+promoted: true
+weight: 41
+position: Tuning
+Website:
+tags: [Ocean, Data Assimilation]
+---
+
+IGE

content/team/GretaMiller.md

@@ -0,0 +1,12 @@
+---
+title: "Greta Miller"
+draft: false
+image: "/images/team/GretaMiller.jpeg"
+jobtitle: "Postdoc"
+promoted: true
+weight: 41
+Website:
+tags: [Atmosphere, Machine Learning]
+---
+
+University of Oxford/NCAR