{"url":"/dataset/mlo-cn2","name":"MLO-Cn2","full_name":"Mauna Loa Seeing Study","description_markdown":"The Mauna Loa Seeing Study was performed by the EOL/Integrated Surface\r\nFlux System team, capturing surface meteorology and flux products at the Mauna Loa Observatory in Hawaii.\r\n\r\nThe MLO-Cn2 dataset enables researchers to evaluate physics-inspired models for optical turbulence ($C_n^2$), as well as develop new models from sonic anemometer data.\r\n\r\n\r\n## Summary\r\n\r\nSonic anemometers were deployed during Summer 2006 at the Mauna Loa Observatory, Hawaii to quantify the atmospheric seeing quality, to help NCAR/HAO plan current and future telescope observations. This study was intended to replicate measurements made previously for planning the Advanced Technology Solar Telescope (ATST). Deployment and operations for this study were done by HAO staff, and ISFF provided equipment and some data management support.\r\n\r\n## Details\r\n\r\nIdentifiers\r\n * Local archive identifier: 160.007\r\n * doi:10.26023/CQR2-TQJ9-AH10\r\n   https://doi.org/10.26023/CQR2-TQJ9-AH10\r\n\r\nRelated projects\r\n * MLO_CN2: Mauna Loa Seeing Study (Cn-squared study)\r\n   https://data.eol.ucar.edu/project/MLO_CN2\r\n\r\nAdditional information\r\n * Frequency: 5 minute\r\n * Language: English\r\n\r\nCategories\r\n * Surface\r\n\r\nPlatforms\r\n * Flux Tower\r\n * NCAR/EOL Integrated Surface Flux System - ISFS\r\n\r\nInstruments\r\n * Eddy Correlation Devices\r\n * Sonic Anemometer\r\n * Surface Flux\r\n * Surface Meteorology\r\n\r\nRelated links\r\n * homepage: MLO_CN2 project main web page\r\n   http://www.eol.ucar.edu/isf/projects/MLO_CN2/\r\n * info: Integrated Surface Flux System\r\n   https://www.eol.ucar.edu/observing_facilities/isfs\r\n * homepage: MLO_CN2 Project Homepage\r\n   https://www.eol.ucar.edu/field_projects/mlocn2\r\n\r\nTemporal coverage\r\n * Begin datetime: 2006-06-09 00:00:00 \r\n * End datetime: 2006-08-08 23:59:59 \r\n\r\nSpatial coverage\r\n * Maximum (North) Latitude: 19.53\r\n * Minimum (South) Latitude: 19.53\r\n * Minimum (West) Longitude: -155.57\r\n * Maximum (East) Longitude: -155.57\r\n\r\nPrimary contact information\r\n * pointOfContact: EOL Data Support <datahelp@eol.ucar.edu>\r\n   https://data.eol.ucar.edu/contact/show/1\r\n\r\nAdditional contact information\r\n * author: NSF NCAR/EOL ISFS Team\r\n   https://data.eol.ucar.edu/contact/show/3140\r\n\r\nAlternate metadata formats\r\n * DataCite: https://data.eol.ucar.edu/dataset/160.007?format=datacite\r\n * ISO/TC 211: https://data.eol.ucar.edu/dataset/160.007?format=isotc211","description_withheld":null,"homepage":"https://data.eol.ucar.edu/dataset/160.007","introduced_date":"2023-05-05","introduced_date_note":null,"introduced_by":{"paper":"/paper/effective-benchmarks-for-optical-turbulence","title":"Effective Benchmarks for Optical Turbulence Modeling","first_author":"Christopher Jellen","url":null},"license":{"name":"Creative Commons Attribution 4.0 International license","url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"modalities":[{"name":"Time series","url":"/datasets/modality/time-series"}],"tasks":[{"name":"Time Series Forecasting","url":"/task/time-series-forecasting","datasets_with_task":"/datasets/task/time-series-forecasting"},{"name":"Time Series Regression","url":"/task/time-series-regression","datasets_with_task":"/datasets/task/time-series-regression"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["MLO-Cn2"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/time-series-forecasting-on-mlo-cn2","task":"Time Series Forecasting","dataset_variant":"MLO-Cn2","rows":7,"metrics":["RMSE"],"first_row_in_archive_order":{"model":"GBRT","paper":"/paper/effective-benchmarks-for-optical-turbulence","metrics":{"RMSE":"0.428"},"code_links":[{"title":"cdjellen/otbench","url":"https://github.com/cdjellen/otbench"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/time-series-regression-on-mlo-cn2","task":"Time Series Regression","dataset_variant":"MLO-Cn2","rows":5,"metrics":["RMSE"],"first_row_in_archive_order":{"model":"GBRT","paper":"/paper/effective-benchmarks-for-optical-turbulence","metrics":{"RMSE":"0.212"},"code_links":[{"title":"cdjellen/otbench","url":"https://github.com/cdjellen/otbench"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/effective-benchmarks-for-optical-turbulence","title":"Effective Benchmarks for Optical Turbulence Modeling","date":"2024-01-07","rows_on_this_dataset":12,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"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."}