ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather

Prabhat,; Kashinath, Karthik; Mudigonda, Mayur; Kim, Sol; Kapp-Schwoerer, Lukas; Graubner, Andre; Karaismailoglu, Ege; von Kleist, Leo; Kurth, Thorsten; Greiner, Annette; Mahesh, Ankur; Yang, Kevin; Lewis, Colby; Chen, Jiayi; Lou, Andrew; Chandran, Sathyavat; Toms, Ben; Chapman, Will; Dagon, Katherine; Shields, Christine A.; O'Brien, Travis; Wehner, Michael; Collins, William

doi:https://doi.org/10.5194/gmd-14-107-2021

Articles | Volume 14, issue 1

https://doi.org/10.5194/gmd-14-107-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-14-107-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 14, issue 1

Model description paper

|

08 Jan 2021

Model description paper |

| 08 Jan 2021

ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather

Prabhat, Karthik Kashinath, Mayur Mudigonda, Sol Kim, Lukas Kapp-Schwoerer, Andre Graubner, Ege Karaismailoglu, Leo von Kleist, Thorsten Kurth, Annette Greiner, Ankur Mahesh, Kevin Yang, Colby Lewis, Jiayi Chen, Andrew Lou, Sathyavat Chandran, Ben Toms, Will Chapman, Katherine Dagon, Christine A. Shields, Travis O'Brien, Michael Wehner, and William Collins

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Final revised paper (published on 08 Jan 2021)
Preprint (discussion started on 09 Apr 2020)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review of "ClimateNet: an expert-labelled open dataset and Deep Learning architecture for enabling high-precision analyses of extreme weather"', Anonymous Referee #1, 24 Apr 2020
- AC1: 'Response to anonymous reviewer 1', Karthik Kashinath, 04 Jun 2020
RC2: 'Comments for "ClimateNet" manuscript', Imme Ebert-Uphoff, 15 May 2020
- AC2: 'Response to reviewer 2 (Imme Ebert-Uphoff)', Karthik Kashinath, 04 Jun 2020

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Karthik Kashinath on behalf of the Authors (13 Jun 2020) Author's response Manuscript

ED: Publish as is (05 Jul 2020) by David Topping

AR by Karthik Kashinath on behalf of the Authors (15 Jul 2020) Manuscript

Short summary

Detecting extreme weather events is a crucial step in understanding how they change due to climate change. Deep learning (DL) is remarkable at pattern recognition; however, it works best only when labeled datasets are available. We create ClimateNet – an expert-labeled curated dataset – to train a DL model for detecting weather events and predicting changes in extreme precipitation. This work paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data.