Cited by 224Meteorological Phenomena and SimulationsClimate variability and modelsHydrological Forecasting Using AI

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Analog forecasting of extreme-causing weather patterns using deep learning

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Chattopadhyay, Ashesh, Nabizadeh, Ebrahim, Hassanzadeh, Pedram·2020·Journal of Advances in Modeling Earth Systems

DOI 10.1029/2019ms001958PMC PMC7375135

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Abstract

Numerical weather prediction models require ever‐growing computing time and resources but, still, have sometimes difficulties with predicting weather extremes. We introduce a data‐driven framework that is based on analog forecasting (prediction using past similar patterns) and employs a novel deep learning pattern‐recognition technique (capsule neural networks, CapsNets) and an impact‐based autolabeling strategy. Using data from a large‐ensemble fully coupled Earth system model, CapsNets are trained on midtropospheric large‐scale circulation patterns (Z500) labeled 0–4 depending on the existence and geographical region of surface temperature extremes over North America several days ahead. The trained networks predict the occurrence/region of cold or heat waves, only using Z500, with accuracies (recalls) of 69–45% (77–48%) or 62–41% (73–47%) 1–5 days ahead. Using both surface temperature and Z500, accuracies (recalls) with CapsNets increase to 80% (88%). In both cases, CapsNets outperform simpler techniques such as convolutional neural networks and logistic regression, and their accuracy is least affected as the size of the training set is reduced. The results show the promises of multivariate data‐driven frameworks for accurate and fast extreme weather predictions, which can potentially augment numerical weather prediction efforts in providing early warnings.

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Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
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LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
An introduction to ROC analysis
Fawcett, T. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.10.010 · Cited by 21,879
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839
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Deep learning and process understanding for data-driven Earth system science
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Authors: Chattopadhyay, A., Nabizadeh, E., & Hassanzadeh, P.
Affiliations: Rice University (US)
Corresponding author: Hassanzadeh, Pedram
ORCID: Chattopadhyay, Ashesh
Nabizadeh, Ebrahim
Hassanzadeh, Pedram
Venue: Journal of Advances in Modeling Earth Systems
Publisher: American Geophysical Union (AGU)
Published: 2020-02
Volume: 12
Issue: 2
Pages: e2019MS001958-e2019MS001958
ISSN: 1942-2466 (ISSN-L)
DOI: 10.1029/2019ms001958
PMC: PMC7375135
OpenAlex: W2965580012
Cited by: 224
References: 73
Citations by year: 2026: 21 · 2025: 30 · 2024: 42 · 2023: 34 · 2022: 35 · 2021: 46 · 2020: 16
FWCI: 10.73
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor, 22 d)
Locations: Journal of Advances in Modeling Earth Systems (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
EarthArXiv (California Digital Library) (submittedVersion)
arXiv (Cornell University) (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
EarthArXiv (OSF Preprints) (submittedVersion)
Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
arXiv (Cornell University)
Funders: Gulf Research Program
National Aeronautics and Space Administration (80NSSC17K0266)
Topics: Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
Keywords: analog forecasting, extreme weather prediction, cold waves, heat waves, capsule networks, Earth system model ensembles, early warning

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Analog forecasting of extreme-causing weather patterns using deep learning

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