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Analog forecasting of extreme-causing weather patterns using deep learning
Open accessAbstract
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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“While the present is the first to use deep neural network to forecast long-lasting extreme heat waves”
From Deep Learning-Based Extreme Heatwave Forecast (2022) · cites this paper as Chattopadhyay et al., 2020
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Metadata
- Authors
- Chattopadhyay, A., Nabizadeh, E., & Hassanzadeh, P.
- Affiliations
- Rice University (US)
- Corresponding author
- 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)
- 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
- 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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