Assessment of water level, sea-surface temperature, and salinity guidance from three NOAA models of the western North Atlantic.

The Operational Nowcast/Forecast Systems (OFS), presently being developed by the National Ocean Service (NOS) of the National Oceanic and Atmospheric Administration (NOAA), make use of sea-surface height (SSH), sea-surface temperature (SST), and sea-surface salinity (SSS), forecast guidance from the...

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Bibliographic Details
Main Authors: Richardson, Philip H., Yang, Zizang
Format: Text
Language:unknown
Published: U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Ocean Service, Office of Coast Survey, Coast Survey Development Laboratory 2017
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Online Access:https://dx.doi.org/10.7289/v5/tm-nos-cs-39
https://repository.library.noaa.gov/view/noaa/16913
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Summary:The Operational Nowcast/Forecast Systems (OFS), presently being developed by the National Ocean Service (NOS) of the National Oceanic and Atmospheric Administration (NOAA), make use of sea-surface height (SSH), sea-surface temperature (SST), and sea-surface salinity (SSS), forecast guidance from the Global Real-Time Ocean Forecast System (G-RTOFS), and SSH forecast guidance from the Extra-Tropical Storm Surge (ETSS) model and the Extratropical Surge and Tide Operational Forecast System (ESTOFS). The OFS use these forecast guidance to form their open ocean boundary forcings. To support future development of NOS OFS in the eastern U.S. coastal waters, we assessed the performance of the G-RTOFS forecast guidance for SSH, SST, and SSS, as well as the performance of the ETSS and ESTOFS forecast guidance for SSH. Our intention is to gain insight into the model performance from two perspectives: (1) the model performance across a forecast cycle (FC) and (2) the evolution of performance associated with the forecast hour (FH) of the forecast cycle. Accordingly, we developed a FC based method and a FH based method, which are further described in Chapter 2. We applied the FC based method to estimate the bias, standard deviation, and root-mean-squared error of a forecast cycle over a series of cycles. The FH based method was applied to estimate the root-mean-squared error for each given forecast hour of the forecast cycle over a series of cycles.