{"url":"/dataset/noaa-atmospheric-temperature-dataset","name":"NOAA Atmospheric Temperature Dataset","full_name":null,"description_markdown":"This dataset contains meteorological observations (temperature) at the\r\nland-based weather stations located in the United States, collected from the Online Climate\r\nData Directory of the National Oceanic and Atmospheric Administration (NOAA). The weather\r\nstations are sampled from the Western and Southeastern states that have actively measured\r\nmeteorological observations during 2015. The 1-year sequential data of hourly temperature\r\nrecords are divided into small sequences of 24 hours. For training, validation, and test a sequential\r\n8-2-2 (months) split is used.","description_withheld":null,"homepage":"https://www.ncdc.noaa.gov/sotc/global/201513","introduced_date":"2020-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/physics-aware-difference-graph-networks-for","title":"Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics","first_author":"Sungyong Seo*","url":null},"license":null,"modalities":[],"tasks":[{"name":"Weather Forecasting","url":"/task/weather-forecasting","datasets_with_task":"/datasets/task/weather-forecasting"}],"languages":[],"variants":["NOAA Atmospheric Temperature Dataset"],"data_loaders":[],"num_papers_in_archive":7,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/weather-forecasting-on-noaa-atmospheric","task":"Weather Forecasting","dataset_variant":"NOAA Atmospheric Temperature Dataset","rows":5,"metrics":["MAE (t+1)","MAE (t+10)"],"first_row_in_archive_order":{"model":"NADE","paper":"/paper/climate-modeling-with-neural-advection","metrics":{"MAE (t+1)":"0.2571 ± 0.0064","MAE (t+10)":"1.3492 ± 0.0988"},"code_links":[{"title":"hwangyong753/NADE","url":"https://github.com/hwangyong753/NADE"},{"title":"jeongwhanchoi/NADE","url":"https://github.com/jeongwhanchoi/NADE"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/climate-modeling-with-neural-advection","title":"Climate modeling with neural advection–diffusion equation","date":"2023-01-31","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/graph-neural-controlled-differential","title":"Graph Neural Controlled Differential Equations for Traffic Forecasting","date":"2021-12-07","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/climate-modeling-with-neural-diffusion","title":"Climate Modeling with Neural Diffusion Equations","date":"2021-11-11","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/adaptive-graph-convolutional-recurrent","title":"Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting","date":"2020-07-06","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":5,"samples_unverified":2,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/neural-dynamics-on-complex-networks","title":"Neural Dynamics on Complex Networks","date":"2019-08-18","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":10,"samples_ran":5,"samples_unverified":5,"pointer_only_for_licence":1,"papers_with_no_sample_that_ran":1,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}