Difference between revisions of "Machine learning related to GEOS-Chem"

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(Current GEOS-Chem Machine Learning Projects)
Line 72: Line 72:
|University of York  
|University of York  
|Spatial concentration prediction at the ocean surface
|Spatial and temporal concentration prediction from sparse obsevations at the ocean surface
|[mailto:tomas.sherwen@york.ac.uk Tomás Sherwen]
|[mailto:tomas.sherwen@york.ac.uk Tomás Sherwen]
|14th May 2019
|14th May 2019
== Useful resources ==
== Useful resources ==

Revision as of 08:33, 16 May 2019

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
  • Send an email to geos-chem-ml+subscribe [at] g.harvard.edu


To unsubscribe from email list Either
  • Send an email to geos-chem-ml+unsubscribe [at] g.harvard.edu


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

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 obsevations at the ocean surface Tomás Sherwen 14th May 2019

Useful resources

Resource Description
Wikipedia Page Default Wikipedia ML launch page
Google Tensor Flow Google's Machine Learning package
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

Upcoming conferences / workshops

Conference Date
Machine Learning in Earth Systems workshop at NCAS'/RMetS' Atmospheric Science Conference 2019 2-3rd July 2019
Machine Learning for Weather and Climate Modelling 2019 2-5th Sept 2019
AGU Fall 2019 9-13th Dec 2019