{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-test-case-for-application-of-convolutional","title":"A test case for application of convolutional neural networks to spatio-temporal climate data: Re-identifying clustered weather patterns","arxiv_id":"1811.04817","date":"2018-11-12","proceeding":null,"authors":["Ashesh Chattopadhyay","Pedram Hassanzadeh","Saba Pasha"],"abstract":"Convolutional neural networks (CNNs) can potentially provide powerful tools\nfor classifying and identifying patterns in climate and environmental data.\nHowever, because of the inherent complexities of such data, which are often\nspatio-temporal, chaotic, and non-stationary, the CNN algorithms must be\ndesigned/evaluated for each specific dataset and application. Yet to start,\nCNN, a supervised technique, requires a large labeled dataset. Labeling demands\n(human) expert time, which combined with the limited number of relevant\nexamples in this area, can discourage using CNNs for new problems. To address\nthese challenges, here we (1) Propose an effective auto-labeling strategy based\non using an unsupervised clustering algorithm and evaluating the performance of\nCNNs in re-identifying these clusters; (2) Use this approach to label thousands\nof daily large-scale weather patterns over North America in the outputs of a\nfully-coupled climate model and show the capabilities of CNNs in re-identifying\nthe 4 clustered regimes. The deep CNN trained with $1000$ samples or more per\ncluster has an accuracy of $90\\%$ or better. Accuracy scales monotonically but\nnonlinearly with the size of the training set, e.g. reaching $94\\%$ with $3000$\ntraining samples per cluster. Effects of architecture and hyperparameters on\nthe performance of CNNs are examined and discussed.","url_abs":"http://arxiv.org/abs/1811.04817v1","url_pdf":"http://arxiv.org/pdf/1811.04817v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-test-case-for-application-of-convolutional","repo_url":"https://github.com/ashesh6810/DLC-toolbox","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1811.04817","atlas_url":"https://app.syntology.ai/?focus=1811.04817","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}