A benchmark to test generalization capabilities of deep learning methods to classify severe convective storms in a changing climate
Molina, M., Gagne, D. J., Prein, A.. (2021). A benchmark to test generalization capabilities of deep learning methods to classify severe convective storms in a changing climate. Earth and Space Science, doi:https://doi.org/10.1029/2020EA001490
| Title | A benchmark to test generalization capabilities of deep learning methods to classify severe convective storms in a changing climate |
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| Author(s) | Maria Molina, David John Gagne, Andreas Prein |
| Abstract | This is a test case study assessing the ability of deep learning methods to generalize to a future climate (end of 21st century) when trained to classify thunderstorms in model output representative of the present-day climate. A convolutional neural network (CNN) was trained to classify strongly rotating thunderstorms from a current climate created using the Weather Research and Forecasting model at high-resolution, then evaluated against thunderstorms from a future climate and found to perform with skill and comparatively in both climates. Despite training with labels derived from a threshold value of a severe thunderstorm diagnostic (updraft helicity), which was not used as an input attribute, the CNN learned physical characteristics of organized convection and environments that are not captured by the diagnostic heuristic. Physical features were not prescribed but rather learned from the data, such as the importance of dry air at mid-levels for intense thunderstorm development when low-level moisture is present (i.e., convective available potential energy). Explanation techniques also revealed that thunderstorms classified as strongly rotating are associated with learned rotation signatures. Results show that the creation of synthetic data with ground truth is a viable alternative to human-labeled data and that a CNN is able to generalize a target using learned features that would be difficult to encode due to spatial complexity. Most importantly, results from this study show that deep learning is capable of generalizing to future climate extremes and can exhibit out-of-sample robustness with hyperparameter tuning in certain applications. |
| Publication Title | Earth and Space Science |
| Publication Date | Sep 1, 2021 |
| Publisher's Version of Record | https://dx.doi.org/https://doi.org/10.1029/2020EA001490 |
| OpenSky Citable URL | https://n2t.org/ark:/85065/d73b63m3 |
| OpenSky Listing | View on OpenSky |
| CISL Affiliations | TDD, AIML |