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 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

Or

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

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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


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
Machine Learning for Environmental Sciences 2019 17-18th June 2019
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
Frontiers of Atmospheric Science and Chemistry: Integration of Novel Applications and Technological Endeavors (FASCINATE) 9-11th Sept 2019
AGU Fall 2019 9-13th Dec 2019