edu.ucar.opensky::articles:42701 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-24T18:04:15.615433 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.net/ark:/85065/d7794913 https://doi.org/10.17603/ds2-3qzm-0464 https Related Dataset #1 Interviews with National Weather Service (NWS) Forecasters related to coastal fog and new artificial intelligence/machine learning (AI/ML) guidance: Interview materials for the “AI/ML” version https://doi.org/10.17603/ds2-8mgd-2j44 https Related Dataset #2 Interviews with NWS Forecasters related to severe weather and new artificial intelligence/machine learning (AI/ML) guidance predicting severe hail and storm mode: Interview materials for “AI/ML” version https://doi.org/10.17603/ds2-nfgd-3g25 https Related Dataset #3 Interviews with NWS Forecasters related to severe weather and new artificial intelligence/machine learning (AI/ML) guidance predicting severe hail and storm mode: Interview materials for “No AI/ML” version https://doi.org/10.48550/arXiv.2202.11214 https Related Preprint #1 FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators https://doi.org/10.48550/arXiv.2212.12794 https Related Preprint #2 GraphCast: Learning skillful medium-range global weather forecasting National Weather Service (NWS) forecasters’ perceptions of AI/ML and its use in operational forecasting 2024-11-01T00:00:00Z publication https://n2t.net/ark:/85065/d7794913 https://doi.org/10.1175/BAMS-D-24-0044.1 Wirz, Christopher D. author Demuth, Julie L. author Cains, Mariana author White, M. author Radford, J. author Bostrom, A. author UCAR/NCAR - Library publisher Artificial intelligence and machine learning (AI/ML) have attracted a great deal of attention from the atmospheric science community. The explosion of attention on AI/ML development carries implications for the operational community, prompting questions about how novel AI/ML advancements will translate from research into operations. However, the field lacks empirical evidence on how National Weather Service (NWS) forecasters, as key intended users, perceive AI/ML and its use in operational forecasting. This study addresses this crucial gap through structured interviews conducted with 29 NWS forecasters from October 2021 through July 2023 in which we explored their perceptions of AI/ML in forecasting. We found that forecasters generally prefer the term &#8220;machine learning&#8221; over &#8220;artificial intelligence&#8221; and that labeling a product as being AI/ML did not hurt perceptions of the products and made some forecasters more excited about the product. Forecasters also had a wide range of familiarity with AI/ML, and overall, they were (tentatively) open to the use of AI/ML in forecasting. We also provide examples of specific areas related to AI/ML that forecasters are excited or hopeful about and that they are concerned or worried about. One concern that was raised in several ways was that AI/ML could replace forecasters or remove them from the forecasting process. However, forecasters expressed a widespread and deep commitment to the best possible forecasts and services to uphold the agency mission using whatever tools or products that are available to assist them. Last, we note how forecasters&#8217; perceptions evolved over the course of the study. UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication <span style="font-family:Arial;font-size:10pt;font-style:normal;font-weight:normal;" data-sheets-root="1">Copyright 2024 American Meteorological Society (AMS).</span> None eng geoscientificInformation