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2024-03-29T15:51:25Z
User contributions
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https://wiki.seas.harvard.edu/geos-chem/index.php?title=Machine_learning_related_to_GEOS-Chem&diff=43409
Machine learning related to GEOS-Chem
2019-06-25T21:33:30Z
<p>Ayhwong: </p>
<hr />
<div>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).<br />
<br />
{| border=1 cellspacing=0 cellpadding=5<br />
|-valign="top"<br />
!width="300px" bgcolor="#CCCCCC"|Machine Learning in GEOS-Chem support<br />
|width="600px"|<br />
*[http://www.york.ac.uk/chemistry/staff/academic/d-g/evansm/ Mat Evans]<br />
*[https://sciences.gsfc.nasa.gov/sed/bio/christoph.a.keller Christoph Keller]<br />
<br />
|-valign="top"<br />
!bgcolor="#CCCCCC"|GEOS-Chem Machine Learning email list<br />
|<tt>geos-chem-ml [at] g.harvard.edu</tt><br />
<br />
|-valign="top"<br />
!bgcolor="#CCCCCC"|To subscribe to email list<br />
|Either<br />
*Send an email to <tt>geos-chem-ml+subscribe [at] g.harvard.edu</tt><br />
Or<br />
*Go to the [https://groups.google.com/a/g.harvard.edu/forum/#!forum/geos-chem-ml GEOS-Chem Machine Learning]<br />
*Click on '''Subscribe to this group'''<br />
<br />
|-valign="top"<br />
!bgcolor="#CCCCCC"|To unsubscribe from email list<br />
|Either<br />
*Send an email to <tt>geos-chem-ml+unsubscribe [at] g.harvard.edu</tt><br />
Or<br />
*Go to the [https://groups.google.com/a/g.harvard.edu/forum/#!forum/geos-chem-ml GEOS-Chem Machine Learning]<br />
*Click on the '''My Settings''' button<br />
*Click on '''Leave this group'''<br />
<br />
|}<br />
<br />
<br />
== GEOS-Chem Machine Learning Publications ==<br />
{| border=1 cellspacing=0 cellpadding=5<br />
|- bgcolor="#cccccc"<br />
!width="150px"|Contact Persons<br />
!width="600px"|Paper<br />
!width="100px"|Date Added<br />
|-<br />
|[mailto:christoph.a.keller@nasa.gov Christoph Keller] <br> [mailto:mat.evans@york.ac.uk Mat Evans]<br />
|[https://www.geosci-model-dev.net/12/1209/2019/gmd-12-1209-2019.html Application of random forest regression to the calculation of gas-phase chemistry within the GEOS-Chem chemistry model v10]<br />
|14th May 2019<br />
|-<br />
|[mailto:samsilva@mit.edu Sam Silva] <br> [mailto:heald@mit.edu Colette Heald]<br />
|[https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2018GL081049 A Deep Learning Parameterization for Ozone Dry Deposition Velocities]<br />
|14th May 2019<br />
|-<br />
|[mailto:tomas.sherwen@york.ac.uk Tomas Sherwen] <br> [mailto:mat.evans@york.ac.uk Mat Evans]<br />
|[https://www.earth-syst-sci-data-discuss.net/essd-2019-40/ A machine learning based global sea-surface iodide distribution]<br />
|14th May 2019<br />
|-<br />
|[mailto:kyle.w.dawson@nasa.gov Kyle Dawson] <br> [mailto:nmeskhi@ncsu.edu Nicholas Meskhidze]<br />
|[http://bit.ly/catch-dawson Creating Aerosol Types from CHemistry (CATCH)]<br />
|29th May 2019<br />
|}<br />
<br />
<br />
== Current GEOS-Chem Machine Learning Projects ==<br />
{| border=1 cellspacing=0 cellpadding=5<br />
|- bgcolor="#cccccc"<br />
!width="200px"|User Group <br />
!width="600px"|Description <br />
!width="150px"|Contact Person<br />
!width="100px"|Date Added<br />
|-<br />
|University of York <br />
|Post processing bias corrector <br />
|[mailto:pi517@york.ac.uk Peter Ivatt]<br>[mailto:mat.evans@york.ac.uk Mat Evans]<br />
|14th May 2019<br />
|-<br />
|Duke University<br />
|Ozone metrics predictor<br />
|[mailto:psk9@duke.edu Prasad Kasibhatla]<br />
|14th May 2019<br />
|-<br />
|University of York <br />
|Spatial and temporal concentration prediction from sparse observations at the ocean surface<br />
|[mailto:tomas.sherwen@york.ac.uk Tomás Sherwen]<br />
|14th May 2019<br />
|-<br />
|NASA GMAO<br />
|GEOS-Chem emulator within the GEOS model<br />
|[mailto:christoph.a.keller@nasa.gov Christoph Keller]<br />
|16th May 2019<br />
|-<br />
|North Carolina State University<br />
|Creating Aerosol Types from CHemistry (CATCH) <br><br />
A supervised clustering approach to assign aerosol ''types'' to GEOS-Chem output<br />
|[mailto:nmeskhi@ncsu.edu Nicholas Meskhidze] <br> [mailto:kyle.w.dawson@nasa.gov Kyle Dawson]<br />
|29th May 2019<br />
|-<br />
|Boston University<br />
|Machine Learning Parameterization of Stomatal Resistance<br />
|[mailto:ayhwong@bu.edu Anthony Wong] <br />
|26th June 2019<br />
|}<br />
<br />
== Useful resources ==<br />
{| border=1 cellspacing=0 cellpadding=5<br />
|- bgcolor="#cccccc"<br />
!width="300px"|Resource<br />
!width="600px"|Description <br />
|-<br />
|[https://en.wikipedia.org/wiki/Machine_learning Wikipedia Page]<br />
|Default Wikipedia ML launch page<br />
|-<br />
|[https://www.tensorflow.org Google Tensor Flow]<br />
|Google's Machine Learning package<br />
|-<br />
|[https://pytorch.org/ PyTorch]<br />
|Open-source deep learning platform<br />
|-<br />
|[https://scikit-learn.org/stable/ Scikit-Learn]<br />
|Scikit-Learn package for machine learning in Python<br />
|-<br />
|[https://xgboost.readthedocs.io/en/latest/ XGboost]<br />
| Machine learning algorithms implemented under the Gradient Boosting framework in Python<br />
|-<br />
|[https://lightgbm.readthedocs.io/en/latest/ GBMLight]<br />
| Microsofts' distributed high-performance gradient boosting implementation<br />
|-<br />
|[https://rapids.ai/ RAPIDS]<br />
| NVIDIA's software package for GPU-accelerated data analytics and machine learning<br />
|}<br />
<br />
== Upcoming conferences / workshops ==<br />
{| border=1 cellspacing=0 cellpadding=5<br />
|- bgcolor="#cccccc"<br />
!width="300px"|Conference<br />
!width="600px"|Date<br />
|-<br />
|[https://www.eventbrite.co.uk/e/workshop-machine-learning-for-environmental-sciences-tickets-60865256621 Machine Learning for Environmental Sciences 2019]<br />
|17-18th June 2019<br />
|-<br />
|[https://www.rmets.org/event/atmospheric-science-conference-2019 Machine Learning in Earth Systems workshop at NCAS'/RMetS' Atmospheric Science Conference 2019]<br />
|2-3rd July 2019<br />
|-<br />
|[http://users.ox.ac.uk/~phys0895/mlwc2019/index.html Machine Learning for Weather and Climate Modelling 2019]<br />
|2-5th Sept 2019<br />
|-<br />
|[https://www2.acom.ucar.edu/workshop/fascinate-2019 Frontiers of Atmospheric Science and Chemistry: Integration of Novel Applications and Technological Endeavors (FASCINATE)]<br />
|9-11th Sept 2019<br />
|-<br />
|[https://meetings.agu.org/fall-meeting-2019/ AGU Fall 2019]<br />
|9-13th Dec 2019<br />
|}</div>
Ayhwong