{"id":25653030,"url":"https://github.com/SarwanShah/Precipitation-Nowcasting-Using-Deep-Learning-2024","last_synced_at":"2026-06-30T07:30:17.625Z","repository":{"id":276705418,"uuid":"930030973","full_name":"SarwanShah/ASU_2024_Precipitation-Nowcasting-Using-Deep-Learning","owner":"SarwanShah","description":"Using deep learning methodologies to address the problem of precipitation nowcasting.","archived":false,"fork":false,"pushed_at":"2025-02-10T00:05:22.000Z","size":0,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-10T00:25:21.906Z","etag":null,"topics":["computer-vision","deep-learning","gru","lstm","machine-learning","meteorology","precipitation-nowcasting","remote-sensing"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"cc0-1.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/SarwanShah.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-02-09T23:53:09.000Z","updated_at":"2025-02-10T00:19:41.000Z","dependencies_parsed_at":"2025-02-10T00:37:04.946Z","dependency_job_id":null,"html_url":"https://github.com/SarwanShah/ASU_2024_Precipitation-Nowcasting-Using-Deep-Learning","commit_stats":null,"previous_names":["sarwanshah/asu_2024_precipitation-nowcasting-using-deep-learning"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SarwanShah%2FASU_2024_Precipitation-Nowcasting-Using-Deep-Learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SarwanShah%2FASU_2024_Precipitation-Nowcasting-Using-Deep-Learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SarwanShah%2FASU_2024_Precipitation-Nowcasting-Using-Deep-Learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SarwanShah%2FASU_2024_Precipitation-Nowcasting-Using-Deep-Learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SarwanShah","download_url":"https://codeload.github.com/SarwanShah/ASU_2024_Precipitation-Nowcasting-Using-Deep-Learning/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":240364706,"owners_count":19789807,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["computer-vision","deep-learning","gru","lstm","machine-learning","meteorology","precipitation-nowcasting","remote-sensing"],"created_at":"2025-02-23T19:19:26.471Z","updated_at":"2026-06-30T07:30:17.571Z","avatar_url":"https://github.com/SarwanShah.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Abstract\n\nPrecipitation nowcasting for short-term\nstorm forecasting (0–6 hours) is essential for timely\nsevere weather warnings. Traditional methods such as\nnumerical weather prediction (NWP) and radar extrapolation, often lack accuracy at short scales and are\ncomputationally intensive. Recent deep learning models,\nsuch as ConvLSTM and TrajGRU have offered promising\nadvances by capturing complex spatiotemporal dynamics.\nThis paper aims to evaluate these models on satellite data,\naddressing the limitation posed radar’s limited global\ncoverage, while focusing on the region of Sindh, Pakistan\n— a region with minimal meteorological infrastructure.\nThus, by contributing towards the improvement of global\nnowcasting capabilities this work addresses critical forecasting needs heightened by climate change.\n\n**REPORT**: [Final_Report.pdf](Paper/EEE598_Final_Paper.pdf)  \n\n**Sample Test Result: Target (Left) vs Prediction (Right)**\n\n\u003cimg src=\"Sample%20Test%20Result%20Gifs/1_ConvLSTM_2hr.gif\" alt=\"Sample Test Result GIF\" width=\"800\"\u003e\n\n\n# Poster\n![Poster Presentation](Poster%20Presentation/poster.png)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FSarwanShah%2FPrecipitation-Nowcasting-Using-Deep-Learning-2024","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FSarwanShah%2FPrecipitation-Nowcasting-Using-Deep-Learning-2024","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FSarwanShah%2FPrecipitation-Nowcasting-Using-Deep-Learning-2024/lists"}