Assessing subseasonal forecast skill for use in predicting US coastal inundation risk
<p>Developing predictions of coastal flooding risk on subseasonal timescales (2–6 weeks in advance) is an emerging priority for the National Oceanic and Atmospheric Administration (NOAA). In this study, we assess the ability of two current operational forecast systems, the European Centre for...
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Copernicus Publications
2025-08-01
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| Series: | Ocean Science |
| Online Access: | https://os.copernicus.org/articles/21/1761/2025/os-21-1761-2025.pdf |
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| author | J. R. Albers M. Newman M. A. Balmaseda W. Sweet W. Sweet Y. Wang Y. Wang T. Xu T. Xu |
| author_facet | J. R. Albers M. Newman M. A. Balmaseda W. Sweet W. Sweet Y. Wang Y. Wang T. Xu T. Xu |
| author_sort | J. R. Albers |
| collection | DOAJ |
| description | <p>Developing predictions of coastal flooding risk on subseasonal timescales (2–6 weeks in advance) is an emerging priority for the National Oceanic and Atmospheric Administration (NOAA). In this study, we assess the ability of two current operational forecast systems, the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (IFS) and the Centre National de Recherches Météorologiques climate model (CNRM), to make subseasonal ensemble predictions of the non-tidal residual component of coastal water levels at United States coastal gauge stations for the period 2000–2019. These models were chosen because they assimilate satellite altimetry at forecast initialization and attempt to predict the mean sea level, including a global mean component whose absence in other forecast systems complicates assessment of tide gauge reforecast skill. Both forecast systems have skill that exceeds damped persistence for forecast leads through 2–3 weeks, with IFS skill exceeding damped persistence for leads up to 6 weeks. Post-processing forecasts to include the inverse barometer effect, derived from mean sea level pressure forecasts, improves skill for relatively short forecast leads (1–3 weeks). Accounting for vertical land motion of each gauge primarily improves skill for longer leads (3–6 weeks), especially for the Alaskan and Gulf coasts; sea-level trends contribute to reforecast skill for both model and persistence forecasts, primarily for the East and Gulf coasts. Overall, we find that current forecast systems have sufficiently high levels of deterministic and probabilistic skill to be used in support of operational coastal flood guidance on subseasonal timescales.</p> |
| format | Article |
| id | doaj-art-0176072b33644906a71e282b2ed34154 |
| institution | Kabale University |
| issn | 1812-0784 1812-0792 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Ocean Science |
| spelling | doaj-art-0176072b33644906a71e282b2ed341542025-08-21T09:28:28ZengCopernicus PublicationsOcean Science1812-07841812-07922025-08-01211761178510.5194/os-21-1761-2025Assessing subseasonal forecast skill for use in predicting US coastal inundation riskJ. R. Albers0M. Newman1M. A. Balmaseda2W. Sweet3W. Sweet4Y. Wang5Y. Wang6T. Xu7T. Xu8NOAA Physical Sciences Laboratory, Boulder, CO, USANOAA Physical Sciences Laboratory, Boulder, CO, USAEuropean Centre for Medium-Range Weather Forecasts, Reading, UKNOAA Physical Sciences Laboratory, Boulder, CO, USANOAA National Ocean Service, Silver Spring, MD, USANOAA Physical Sciences Laboratory, Boulder, CO, USACooperative Institute for Research in Environmental Sciences University of Colorado Boulder, Boulder, CO, USANOAA Physical Sciences Laboratory, Boulder, CO, USACooperative Institute for Research in Environmental Sciences University of Colorado Boulder, Boulder, CO, USA<p>Developing predictions of coastal flooding risk on subseasonal timescales (2–6 weeks in advance) is an emerging priority for the National Oceanic and Atmospheric Administration (NOAA). In this study, we assess the ability of two current operational forecast systems, the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (IFS) and the Centre National de Recherches Météorologiques climate model (CNRM), to make subseasonal ensemble predictions of the non-tidal residual component of coastal water levels at United States coastal gauge stations for the period 2000–2019. These models were chosen because they assimilate satellite altimetry at forecast initialization and attempt to predict the mean sea level, including a global mean component whose absence in other forecast systems complicates assessment of tide gauge reforecast skill. Both forecast systems have skill that exceeds damped persistence for forecast leads through 2–3 weeks, with IFS skill exceeding damped persistence for leads up to 6 weeks. Post-processing forecasts to include the inverse barometer effect, derived from mean sea level pressure forecasts, improves skill for relatively short forecast leads (1–3 weeks). Accounting for vertical land motion of each gauge primarily improves skill for longer leads (3–6 weeks), especially for the Alaskan and Gulf coasts; sea-level trends contribute to reforecast skill for both model and persistence forecasts, primarily for the East and Gulf coasts. Overall, we find that current forecast systems have sufficiently high levels of deterministic and probabilistic skill to be used in support of operational coastal flood guidance on subseasonal timescales.</p>https://os.copernicus.org/articles/21/1761/2025/os-21-1761-2025.pdf |
| spellingShingle | J. R. Albers M. Newman M. A. Balmaseda W. Sweet W. Sweet Y. Wang Y. Wang T. Xu T. Xu Assessing subseasonal forecast skill for use in predicting US coastal inundation risk Ocean Science |
| title | Assessing subseasonal forecast skill for use in predicting US coastal inundation risk |
| title_full | Assessing subseasonal forecast skill for use in predicting US coastal inundation risk |
| title_fullStr | Assessing subseasonal forecast skill for use in predicting US coastal inundation risk |
| title_full_unstemmed | Assessing subseasonal forecast skill for use in predicting US coastal inundation risk |
| title_short | Assessing subseasonal forecast skill for use in predicting US coastal inundation risk |
| title_sort | assessing subseasonal forecast skill for use in predicting us coastal inundation risk |
| url | https://os.copernicus.org/articles/21/1761/2025/os-21-1761-2025.pdf |
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