Difference between revisions of "Machine learning related to GEOS-Chem"
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− | All users interested in the use of Machine Learning within the GEOS-Chem community are encouraged to subscribe to the | + | All users interested in the use of Machine Learning within the GEOS-Chem community are encouraged to subscribe to the machine learning email list (click on the link in the [[#Contact information|contact information section]] below). |
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− | !bgcolor="#CCCCCC"| | + | !bgcolor="#CCCCCC"|GEOS-Chem Machine Learning email list |
− | |<tt>geos-chem- | + | |<tt>geos-chem-ml [at] g.harvard.edu</tt> |
|-valign="top" | |-valign="top" | ||
!bgcolor="#CCCCCC"|To subscribe to email list | !bgcolor="#CCCCCC"|To subscribe to email list | ||
|Either | |Either | ||
− | *Send an email to <tt>geos-chem- | + | *Send an email to <tt>geos-chem-ml+subscribe [at] g.harvard.edu</tt> |
Or | Or | ||
− | *Go to the [https://groups.google.com/a/g.harvard.edu/forum/#!forum/geos-chem- | + | *Go to the [https://groups.google.com/a/g.harvard.edu/forum/#!forum/geos-chem-ml GEOS-Chem Machine Learning] |
*Click on '''Subscribe to this group''' | *Click on '''Subscribe to this group''' | ||
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!bgcolor="#CCCCCC"|To unsubscribe from email list | !bgcolor="#CCCCCC"|To unsubscribe from email list | ||
|Either | |Either | ||
− | *Send an email to <tt>geos-chem- | + | *Send an email to <tt>geos-chem-ml+unsubscribe [at] g.harvard.edu</tt> |
Or | Or | ||
− | *Go to the [https://groups.google.com/a/g.harvard.edu/forum/#!forum/geos-chem- | + | *Go to the [https://groups.google.com/a/g.harvard.edu/forum/#!forum/geos-chem-ml GEOS-Chem Machine Learning] |
*Click on the '''My Settings''' button | *Click on the '''My Settings''' button | ||
*Click on '''Leave this group''' | *Click on '''Leave this group''' | ||
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− | == GEOS-Chem Machine Publications | + | == GEOS-Chem Machine Learning Publications == |
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|[https://www.earth-syst-sci-data-discuss.net/essd-2019-40/ A machine learning based global sea-surface iodide distribution] | |[https://www.earth-syst-sci-data-discuss.net/essd-2019-40/ A machine learning based global sea-surface iodide distribution] | ||
|14th May 2019 | |14th May 2019 | ||
+ | |- | ||
+ | |[mailto:kyle.w.dawson@nasa.gov Kyle Dawson] <br> [mailto:nmeskhi@ncsu.edu Nicholas Meskhidze] | ||
+ | |[http://bit.ly/catch-dawson Creating Aerosol Types from CHemistry (CATCH)] | ||
+ | |29th May 2019 | ||
+ | |- | ||
+ | |[mailto:mkelp93@gmail.com Makoto Kelp] <br> | ||
+ | |[https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021MS002926 Online machine learning chemical solver in GEOS-Chem] | ||
+ | |19th May 2022 | ||
|} | |} | ||
− | == Current GEOS-Chem Machine Learning Projects | + | == Current GEOS-Chem Machine Learning Projects == |
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|- bgcolor="#cccccc" | |- bgcolor="#cccccc" | ||
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|Duke University | |Duke University | ||
|Ozone metrics predictor | |Ozone metrics predictor | ||
− | |[mailto:psk9@duke.edu Prasad | + | |[mailto:psk9@duke.edu Prasad Kasibhatla] |
|14th May 2019 | |14th May 2019 | ||
+ | |- | ||
+ | |University of York | ||
+ | |Spatial and temporal concentration prediction from sparse observations at the ocean surface | ||
+ | |[mailto:tomas.sherwen@york.ac.uk Tomás Sherwen] | ||
+ | |14th May 2019 | ||
+ | |- | ||
+ | |NASA GMAO | ||
+ | |GEOS-Chem emulator within the GEOS model | ||
+ | |[mailto:christoph.a.keller@nasa.gov Christoph Keller] | ||
+ | |16th May 2019 | ||
+ | |- | ||
+ | |North Carolina State University | ||
+ | |Creating Aerosol Types from CHemistry (CATCH) <br> | ||
+ | A supervised clustering approach to assign aerosol ''types'' to GEOS-Chem output | ||
+ | |[mailto:nmeskhi@ncsu.edu Nicholas Meskhidze] <br> [mailto:kyle.w.dawson@nasa.gov Kyle Dawson] | ||
+ | |29th May 2019 | ||
+ | |- | ||
+ | |Boston University | ||
+ | |Machine Learning Parameterization of Stomatal Resistance | ||
+ | |[mailto:ayhwong@bu.edu Anthony Wong] | ||
+ | |26th June 2019 | ||
|} | |} | ||
− | == Useful resources | + | == Useful resources == |
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|- bgcolor="#cccccc" | |- bgcolor="#cccccc" | ||
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!width="600px"|Description | !width="600px"|Description | ||
|- | |- | ||
− | |[https://en.wikipedia.org/wiki/Machine_learning | + | |[https://en.wikipedia.org/wiki/Machine_learning Wikipedia Page] |
|Default Wikipedia ML launch page | |Default Wikipedia ML launch page | ||
|- | |- | ||
+ | |[https://www.tensorflow.org Google Tensor Flow] | ||
+ | |Google's Machine Learning package | ||
+ | |- | ||
+ | |[https://pytorch.org/ PyTorch] | ||
+ | |Open-source deep learning platform | ||
+ | |- | ||
+ | |[https://scikit-learn.org/stable/ Scikit-Learn] | ||
+ | |Scikit-Learn package for machine learning in Python | ||
+ | |- | ||
+ | |[https://xgboost.readthedocs.io/en/latest/ XGboost] | ||
+ | | Machine learning algorithms implemented under the Gradient Boosting framework in Python | ||
+ | |- | ||
+ | |[https://lightgbm.readthedocs.io/en/latest/ GBMLight] | ||
+ | | Microsofts' distributed high-performance gradient boosting implementation | ||
+ | |- | ||
+ | |[https://rapids.ai/ RAPIDS] | ||
+ | | NVIDIA's software package for GPU-accelerated data analytics and machine learning | ||
+ | |} | ||
+ | |||
+ | == Upcoming conferences / workshops == | ||
+ | {| border=1 cellspacing=0 cellpadding=5 | ||
+ | |- bgcolor="#cccccc" | ||
+ | !width="300px"|Conference | ||
+ | !width="600px"|Date / Location | ||
+ | |- | ||
+ | |[https://www.eventbrite.co.uk/e/workshop-machine-learning-for-environmental-sciences-tickets-60865256621 Machine Learning for Environmental Sciences 2019] | ||
+ | |17-18th June 2019 / Cambridge, UK | ||
+ | |- | ||
+ | |[https://www.rmets.org/event/atmospheric-science-conference-2019 Machine Learning in Earth Systems workshop at NCAS'/RMetS' Atmospheric Science Conference 2019] | ||
+ | |2-3rd July 2019 / Birmingham, UK | ||
+ | |- | ||
+ | |[http://users.ox.ac.uk/~phys0895/mlwc2019/index.html Machine Learning for Weather and Climate Modelling 2019] | ||
+ | |2-5th Sept 2019 / Oxford, UK | ||
+ | |- | ||
+ | |[https://www2.acom.ucar.edu/workshop/fascinate-2019 Frontiers of Atmospheric Science and Chemistry: Integration of Novel Applications and Technological Endeavors (FASCINATE)] | ||
+ | |9-11th Sept 2019 / NCAR Center Green Campus in Boulder, Colorado, USA | ||
+ | |- | ||
+ | |[https://sites.google.com/view/climateinformatics2019/ 9th International Workshop on Climate Informatics] | ||
+ | |October 3-4, 2019 / École Normale Supérieure, Paris, France | ||
+ | |- | ||
+ | |[https://meetings.agu.org/fall-meeting-2019/ AGU Fall 2019] | ||
+ | |9-13th Dec 2019 / San Francisco, CA, USA | ||
|} | |} |
Latest revision as of 19:21, 21 August 2023
All users interested in the use of Machine Learning within the GEOS-Chem community are encouraged to subscribe to the machine learning email list (click on the link in the contact information section below).
Machine Learning in GEOS-Chem support | |
---|---|
GEOS-Chem Machine Learning email list | geos-chem-ml [at] g.harvard.edu |
To subscribe to email list | Either
Or
|
To unsubscribe from email list | Either
Or
|
Contents
GEOS-Chem Machine Learning Publications
Contact Persons | Paper | Date Added |
---|---|---|
Christoph Keller Mat Evans |
Application of random forest regression to the calculation of gas-phase chemistry within the GEOS-Chem chemistry model v10 | 14th May 2019 |
Sam Silva Colette Heald |
A Deep Learning Parameterization for Ozone Dry Deposition Velocities | 14th May 2019 |
Tomas Sherwen Mat Evans |
A machine learning based global sea-surface iodide distribution | 14th May 2019 |
Kyle Dawson Nicholas Meskhidze |
Creating Aerosol Types from CHemistry (CATCH) | 29th May 2019 |
Makoto Kelp |
Online machine learning chemical solver in GEOS-Chem | 19th May 2022 |
Current GEOS-Chem Machine Learning Projects
User Group | Description | Contact Person | Date Added |
---|---|---|---|
University of York | Post processing bias corrector | Peter Ivatt Mat Evans |
14th May 2019 |
Duke University | Ozone metrics predictor | Prasad Kasibhatla | 14th May 2019 |
University of York | Spatial and temporal concentration prediction from sparse observations at the ocean surface | Tomás Sherwen | 14th May 2019 |
NASA GMAO | GEOS-Chem emulator within the GEOS model | Christoph Keller | 16th May 2019 |
North Carolina State University | Creating Aerosol Types from CHemistry (CATCH) A supervised clustering approach to assign aerosol types to GEOS-Chem output |
Nicholas Meskhidze Kyle Dawson |
29th May 2019 |
Boston University | Machine Learning Parameterization of Stomatal Resistance | Anthony Wong | 26th June 2019 |
Useful resources
Resource | Description |
---|---|
Wikipedia Page | Default Wikipedia ML launch page |
Google Tensor Flow | Google's Machine Learning package |
PyTorch | Open-source deep learning platform |
Scikit-Learn | Scikit-Learn package for machine learning in Python |
XGboost | Machine learning algorithms implemented under the Gradient Boosting framework in Python |
GBMLight | Microsofts' distributed high-performance gradient boosting implementation |
RAPIDS | NVIDIA's software package for GPU-accelerated data analytics and machine learning |
Upcoming conferences / workshops
Conference | Date / Location |
---|---|
Machine Learning for Environmental Sciences 2019 | 17-18th June 2019 / Cambridge, UK |
Machine Learning in Earth Systems workshop at NCAS'/RMetS' Atmospheric Science Conference 2019 | 2-3rd July 2019 / Birmingham, UK |
Machine Learning for Weather and Climate Modelling 2019 | 2-5th Sept 2019 / Oxford, UK |
Frontiers of Atmospheric Science and Chemistry: Integration of Novel Applications and Technological Endeavors (FASCINATE) | 9-11th Sept 2019 / NCAR Center Green Campus in Boulder, Colorado, USA |
9th International Workshop on Climate Informatics | October 3-4, 2019 / École Normale Supérieure, Paris, France |
AGU Fall 2019 | 9-13th Dec 2019 / San Francisco, CA, USA |