Learning Machine Learning with Lorenz-96

Balwada, Dhruv; Abernathey, Ryan; Acharya, Shantanu; Adcroft, Alistair; Brener, Judith; Balaji, V; Bhouri, Mohamed Aziz; Bruna, Joan; Bushuk, Mitch; Chapman, Will; Connolly, Alex; Deshayes, Julie; Fernandez-Granda, Carlos; Gentine, Pierre; Gorbunova, Anastasiia; Gregory, Will; Guillaumin, Arthur; Gupta, Shubham; Holland, Marika; Johnsson, J Emmanuel; Sommer, Julien Le; Li, Ziwei; Loose, Nora; Lu, Feiyu; O’Gorman, Paul; Perezhogin, Pavel; Reichl, Brandon; Ross, Andrew; Sane, Aakash; Shamekh, Sara; Verma, Tarun; Yuval, Janni; Zampieri, Lorenzo; Zhang, Cheng; Zanna, Laure

doi:10.21105/jose.00241

i

Learning Machine Learning with Lorenz-96

2024
By Balwada, Dhruv ; Abernathey, Ryan ; Acharya, Shantanu ; ...

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Journal Title:

Journal of Open Source Education
Personal Author:

Balwada, Dhruv ; Abernathey, Ryan ; Acharya, Shantanu ; Adcroft, Alistair ; Brener, Judith ; Balaji, V ; Bhouri, Mohamed Aziz ; Bruna, Joan ; Bushuk, Mitch ; Chapman, Will ; Connolly, Alex ; Deshayes, Julie ; Fernandez-Granda, Carlos ; Gentine, Pierre ; Gorbunova, Anastasiia ; Gregory, Will ; Guillaumin, Arthur ; Gupta, Shubham ; Holland, Marika ; Johnsson, J Emmanuel ; Sommer, Julien Le ; Li, Ziwei ; Loose, Nora ; Lu, Feiyu ; O’Gorman, Paul ; Perezhogin, Pavel ; Reichl, Brandon ; Ross, Andrew ; Sane, Aakash ; Shamekh, Sara ; Verma, Tarun ; Yuval, Janni ; Zampieri, Lorenzo ; Zhang, Cheng ; Zanna, Laure
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; GFDL (Geophysical Fluid Dynamics Laboratory)
Description:

Machine learning (ML) is a rapidly growing field that is starting to touch all aspects of our lives, and science is not immune to this. In fact, recent work in the field of scientific ML, i.e. combining ML and with conventional scientific problems, is leading to new breakthroughs in notoriously hard problems, which might have seemed too distant till a few years ago. One such age-old problem is that of turbulence closures in fluid flows. This closure or parameterization problem is particularly relevant for environmental fluids, which span a large range of scales from the size of the planet down to millimeters, and remains a big challenge in the way of improving forecasts of weather and projections of climate.
Source:

Journal of Open Source Education, 7(82), 241
DOI:

https://doi.org/10.21105/jose.00241
ISSN:

2577-3569
Format:

PDF
Publisher:

The Open Journal
Document Type:

Journal Article
License:

https://creativecommons.org/licenses/by/4.0/
Rights Information:

CC BY
Compliance:

Submitted
Main Document Checksum:

urn:sha-512:7b0a464d7f4c2c3873b64335c288dfd73be4489a882a25ca5fa22a6b1b0a7ddbbbc3eb1c2a6792c450ca70f85c6affa99210f4d3dacedc0d9454216236c0f3af
Download URL:

https://repository.library.noaa.gov/view/noaa/71067/noaa_71067_DS1.pdf
File Type:

[PDF - 233.89 KB ]

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