Analog Forecasting of Extreme‐Causing Weather Patterns Using Deep Learning
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 nov...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Geophysical Union (AGU)
2020-02-01
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| Series: | Journal of Advances in Modeling Earth Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2019MS001958 |
| _version_ | 1818158956330090496 |
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| author | Ashesh Chattopadhyay Ebrahim Nabizadeh Pedram Hassanzadeh |
| author_facet | Ashesh Chattopadhyay Ebrahim Nabizadeh Pedram Hassanzadeh |
| author_sort | Ashesh Chattopadhyay |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-11T15:38:19Z |
| format | Article |
| id | doaj.art-d4eeaa857a9c4084b2f3395359f82eff |
| institution | Directory Open Access Journal |
| issn | 1942-2466 |
| language | English |
| last_indexed | 2024-12-11T15:38:19Z |
| publishDate | 2020-02-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Journal of Advances in Modeling Earth Systems |
| spelling | doaj.art-d4eeaa857a9c4084b2f3395359f82eff2022-12-22T00:59:52ZengAmerican Geophysical Union (AGU)Journal of Advances in Modeling Earth Systems1942-24662020-02-01122n/an/a10.1029/2019MS001958Analog Forecasting of Extreme‐Causing Weather Patterns Using Deep LearningAshesh Chattopadhyay0Ebrahim Nabizadeh1Pedram Hassanzadeh2Department of Mechanical Engineering Rice University Houston TX USADepartment of Mechanical Engineering Rice University Houston TX USADepartment of Mechanical Engineering Rice University Houston TX USAAbstract 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.https://doi.org/10.1029/2019MS001958extreme weather eventsdeep learninganalog forecastingweather predictiondata‐driven modeling |
| spellingShingle | Ashesh Chattopadhyay Ebrahim Nabizadeh Pedram Hassanzadeh Analog Forecasting of Extreme‐Causing Weather Patterns Using Deep Learning Journal of Advances in Modeling Earth Systems extreme weather events deep learning analog forecasting weather prediction data‐driven modeling |
| title | Analog Forecasting of Extreme‐Causing Weather Patterns Using Deep Learning |
| title_full | Analog Forecasting of Extreme‐Causing Weather Patterns Using Deep Learning |
| title_fullStr | Analog Forecasting of Extreme‐Causing Weather Patterns Using Deep Learning |
| title_full_unstemmed | Analog Forecasting of Extreme‐Causing Weather Patterns Using Deep Learning |
| title_short | Analog Forecasting of Extreme‐Causing Weather Patterns Using Deep Learning |
| title_sort | analog forecasting of extreme causing weather patterns using deep learning |
| topic | extreme weather events deep learning analog forecasting weather prediction data‐driven modeling |
| url | https://doi.org/10.1029/2019MS001958 |
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