Articles | Volume 14, issue 1
Geosci. Model Dev., 14, 107–124, 2021
https://doi.org/10.5194/gmd-14-107-2021
Geosci. Model Dev., 14, 107–124, 2021
https://doi.org/10.5194/gmd-14-107-2021

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 et al.

Download

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

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
Download
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.