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Related Experiment Videos
Analog Forecasting of Extreme-Causing Weather Patterns Using Deep Learning.
Ashesh Chattopadhyay1, Ebrahim Nabizadeh1, Pedram Hassanzadeh1,2
1Department of Mechanical Engineering Rice University Houston TX USA.
Journal of Advances in Modeling Earth Systems
|July 28, 2020
Summary
This study introduces a data-driven framework using capsule neural networks (CapsNets) for predicting extreme weather events like heat and cold waves. This approach shows promise for faster, more accurate early warnings to augment traditional weather prediction.
Area of Science:
- Meteorology
- Artificial Intelligence
- Climate Science
Background:
- Numerical weather prediction (NWP) models face challenges in predicting extreme weather events despite increasing computational demands.
- Existing methods often struggle with the accuracy and timeliness required for effective early warnings of severe weather phenomena.
Purpose of the Study:
- To develop and evaluate a novel data-driven framework for predicting extreme weather events, specifically heat and cold waves.
- To leverage capsule neural networks (CapsNets) for enhanced pattern recognition in meteorological data.
- To assess the performance of this framework in comparison to traditional methods and its potential to improve early warning systems.
Main Methods:
- A data-driven analog forecasting framework was developed, incorporating capsule neural networks (CapsNets) for pattern recognition.
- CapsNets were trained on mid-tropospheric large-scale circulation patterns (Z500) from a large-ensemble Earth system model.
- An impact-based autolabeling strategy was used to categorize circulation patterns based on the occurrence and location of surface temperature extremes over North America.
Main Results:
- CapsNets accurately predicted the occurrence and region of cold or heat waves using only Z500 data, achieving accuracies of 69-45% (recalls of 77-48%) 1-5 days ahead.
- Incorporating both surface temperature and Z500 data improved prediction accuracies to over 80% (88% recall).
- CapsNets outperformed simpler methods like convolutional neural networks and logistic regression, demonstrating robustness even with reduced training data.
Conclusions:
- The developed multivariate data-driven framework shows significant promise for accurate and rapid extreme weather prediction.
- Capsule neural networks offer a powerful tool for identifying complex patterns associated with weather extremes.
- This approach can potentially augment numerical weather prediction, enhancing early warning capabilities for severe weather events.