Machine learning related to GEOS-Chem

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	Mat Evans Christoph Keller
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 Go to the GEOS-Chem Machine Learning Click on Subscribe to this group
To unsubscribe from email list	Either Send an email to geos-chem-ml+unsubscribe [at] g.harvard.edu Or Go to the GEOS-Chem Machine Learning Click on the My Settings button Click on Leave this group

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