Machine learning related to GEOS-Chem: Difference between revisions
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|[http://bit.ly/catch-dawson Creating Aerosol Types from CHemistry (CATCH)] | |[http://bit.ly/catch-dawson Creating Aerosol Types from CHemistry (CATCH)] | ||
|29th May 2019 | |29th May 2019 | ||
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|[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 | |||
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== Current GEOS-Chem Machine Learning Projects == | == Current GEOS-Chem Machine Learning Projects == | ||
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
|
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 |