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

Identifying, detecting, and localizing extreme weather events is a crucial first step in understanding how they may vary under different climate change scenarios. Pattern recognition tasks such as classification, object detection, and segmentation (i.e., pixel-level classification) have remained challenging problems in the weather and climate sciences. While there exist many empirical heuristics for detecting extreme events, the disparities between the output of these different methods even for a single event are large and often difficult to reconcile. Given the success of deep learning (DL) in tackling similar problems in computer vision, we advocate a DL-based approach. DL, however, works best in the context of supervised learning – when labeled datasets are readily available. Reliable labeled training data for extreme weather and climate events is scarce. We create “ClimateNet” – an open, community-sourced human-expert-labeled curated dataset that captures tropical cyclones (TCs) and atmospheric rivers (ARs) in high-resolution climate model output from a simulation of a recent historical period. We use the curated ClimateNet dataset to train a state-of-the-art DL model for pixel-level identification – i.e., segmentation – of TCs and ARs. We then apply the trained DL model to historical and climate change scenarios simulated by the Community Atmospheric Model (CAM5.1) and show that the DL model accurately segments the data into TCs, ARs, or “the background” at a pixel level. Further, we show how the segmentation results can be used to conduct spatially and temporally precise analytics by quantifying distributions of extreme precipitation conditioned on event types (TC or AR) at regional scales. The key contribution of this work is that it paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data using a curated expert-labeled dataset – ClimateNet. ClimateNet and the DL-based segmentation method provide several unique capabilities: (i) they can be used to calculate a variety of TC and AR statistics at a fine-grained level; (ii) they can be applied to different climate scenarios and different datasets without tuning as they do not rely on threshold conditions; and (iii) the proposed DL method is suitable for rapidly analyzing large amounts of climate model output. While our study has been conducted for two important extreme weather patterns (TCs and ARs) in simulation datasets, we believe that this methodology can be applied to a much broader class of patterns and applied to observational and reanalysis data products via transfer learning.

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Author 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
Publisher UCAR/NCAR - Library
Publication Date 2021-01-08T00:00:00
Digital Object Identifier (DOI) Not Assigned
Alternate Identifier N/A
Resource Version N/A
Topic Category geoscientificInformation
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Metadata Date 2023-08-18T18:31:29.701671
Metadata Record Identifier edu.ucar.opensky::articles:23991
Metadata Language eng; USA
Suggested Citation 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. (2021). ClimateNet: An expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather. UCAR/NCAR - Library. http://n2t.net/ark:/85065/d7pg1w3n. Accessed 16 February 2025.

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