{"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/forecasting-tropical-cyclones-with-cascaded","title":"Forecasting Tropical Cyclones with Cascaded Diffusion Models","arxiv_id":"2310.01690","date":"2023-10-02","proceeding":null,"authors":["Pritthijit Nath","Pancham Shukla","Shuai Wang","César Quilodrán-Casas"],"abstract":"As tropical cyclones become more intense due to climate change, the rise of Al-based modelling provides a more affordable and accessible approach compared to traditional methods based on mathematical models. This work leverages generative diffusion models to forecast cyclone trajectories and precipitation patterns by integrating satellite imaging, remote sensing, and atmospheric data. It employs a cascaded approach that incorporates three main tasks: forecasting, super-resolution, and precipitation modelling. The training dataset includes 51 cyclones from six major tropical cyclone basins from January 2019 - March 2023. Experiments demonstrate that the final forecasts from the cascaded models show accurate predictions up to a 36-hour rollout, with excellent Structural Similarity (SSIM) and Peak-Singal-To-Noise Ratio (PSNR) values exceeding 0.5 and 20 dB, respectively, for all three tasks. The 36-hour forecasts can be produced in as little as 30 mins on a single Nvidia A30/RTX 2080 Ti. This work also highlights the promising efficiency of Al methods such as diffusion models for high-performance needs in weather forecasting, such as tropical cyclone forecasting, while remaining computationally affordable, making them ideal for highly vulnerable regions with critical forecasting needs and financial limitations. Code accessible at https://github.com/nathzi1505/forecast-diffmodels.","url_abs":"https://arxiv.org/abs/2310.01690v7","url_pdf":"https://arxiv.org/pdf/2310.01690v7.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":"forecasting-tropical-cyclones-with-cascaded","repo_url":"https://github.com/nathzi1505/forecast-diffmodels","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"ssim","task_name":"SSIM"},{"task_slug":"super-resolution","task_name":"Super-Resolution"},{"task_slug":"weather-forecasting","task_name":"Weather Forecasting"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2310.01690","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.01690"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/nathzi1505/forecast-diffmodels","reach":null}],"summary":{"ran":5},"by_repo_kind":{"official":{"samples":5,"ran":5,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":5,"samples":[{"code_sha256_prefix":"9d4e003e7f4b2362","entry":"DoubleConv","repo":"nathzi1505/forecast-diffmodels","repo_kind":"official","path":"imagen/modules.py","file_url":"https://github.com/nathzi1505/forecast-diffmodels/blob/HEAD/imagen/modules.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"9d4e003e7f4b2362"}},{"code_sha256_prefix":"cd4fd7e641f85bf1","entry":"Down","repo":"nathzi1505/forecast-diffmodels","repo_kind":"official","path":"imagen/modules.py","file_url":"https://github.com/nathzi1505/forecast-diffmodels/blob/HEAD/imagen/modules.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"cd4fd7e641f85bf1"}},{"code_sha256_prefix":"9e6b6e12b1ec4d34","entry":"SelfAttention","repo":"nathzi1505/forecast-diffmodels","repo_kind":"official","path":"imagen/modules.py","file_url":"https://github.com/nathzi1505/forecast-diffmodels/blob/HEAD/imagen/modules.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"9e6b6e12b1ec4d34"}},{"code_sha256_prefix":"46767937e4b4f09c","entry":"UNet_Conditional_VAE","repo":"nathzi1505/forecast-diffmodels","repo_kind":"official","path":"imagen/modules.py","file_url":"https://github.com/nathzi1505/forecast-diffmodels/blob/HEAD/imagen/modules.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"46767937e4b4f09c"}},{"code_sha256_prefix":"646365212220d06e","entry":"Up","repo":"nathzi1505/forecast-diffmodels","repo_kind":"official","path":"imagen/modules.py","file_url":"https://github.com/nathzi1505/forecast-diffmodels/blob/HEAD/imagen/modules.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"646365212220d06e"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}