{"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/precipitation-prediction-using-an-ensemble-of","title":"Precipitation Prediction Using an Ensemble of Lightweight Learners","arxiv_id":"2401.09424","date":"2023-11-30","proceeding":null,"authors":["Xinzhe Li","Sun Rui","Yiming Niu","Yao Liu"],"abstract":"Precipitation prediction plays a crucial role in modern agriculture and industry. However, it poses significant challenges due to the diverse patterns and dynamics in time and space, as well as the scarcity of high precipitation events. To address this challenge, we propose an ensemble learning framework that leverages multiple learners to capture the diverse patterns of precipitation distribution. Specifically, the framework consists of a precipitation predictor with multiple lightweight heads (learners) and a controller that combines the outputs from these heads. The learners and the controller are separately optimized with a proposed 3-stage training scheme. By utilizing provided satellite images, the proposed approach can effectively model the intricate rainfall patterns, especially for high precipitation events. It achieved 1st place on the core test as well as the nowcasting leaderboards of the Weather4Cast 2023 competition. For detailed implementation, please refer to our GitHub repository at: https://github.com/lxz1217/weather4cast-2023-lxz.","url_abs":"https://arxiv.org/abs/2401.09424v1","url_pdf":"https://arxiv.org/pdf/2401.09424v1.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":"precipitation-prediction-using-an-ensemble-of","repo_url":"https://github.com/lxz1217/weather4cast-2023-lxz","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"ensemble-learning","task_name":"Ensemble Learning"},{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2401.09424","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.09424"}},"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":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/lxz1217/weather4cast-2023-lxz","reach":null}],"summary":{"ran_draft_wrong":2,"ran_honours":1,"unverified":2},"by_repo_kind":{"official":{"samples":5,"ran":3,"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":"16742794473930b0","entry":"crop_slice","repo":"lxz1217/weather4cast-2023-lxz","repo_kind":"official","path":"models/models_MoE.py","file_url":"https://github.com/lxz1217/weather4cast-2023-lxz/blob/HEAD/models/models_MoE.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"16742794473930b0"}},{"code_sha256_prefix":"ddeef0d06ed0d696","entry":"load_model","repo":"lxz1217/weather4cast-2023-lxz","repo_kind":"official","path":"train_stage1.py","file_url":"https://github.com/lxz1217/weather4cast-2023-lxz/blob/HEAD/train_stage1.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"ddeef0d06ed0d696"}},{"code_sha256_prefix":"2a32cce617ea05d5","entry":"read_samples_ids","repo":"lxz1217/weather4cast-2023-lxz","repo_kind":"official","path":"utils/data_utils.py","file_url":"https://github.com/lxz1217/weather4cast-2023-lxz/blob/HEAD/utils/data_utils.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"2a32cce617ea05d5"}},{"code_sha256_prefix":"86ad848edec4fa11","entry":"generate_and_cache_sequences","repo":"lxz1217/weather4cast-2023-lxz","repo_kind":"official","path":"utils/data_utils.py","file_url":"https://github.com/lxz1217/weather4cast-2023-lxz/blob/HEAD/utils/data_utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"86ad848edec4fa11"}},{"code_sha256_prefix":"ffccc99d2cd01eb0","entry":"load_sample_ids","repo":"lxz1217/weather4cast-2023-lxz","repo_kind":"official","path":"utils/data_utils.py","file_url":"https://github.com/lxz1217/weather4cast-2023-lxz/blob/HEAD/utils/data_utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"ffccc99d2cd01eb0"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}