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Adjoint-Free Variational Data Assimilation into a Regional Wave Model

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  • 1 University of Alaska Fairbanks, Fairbanks, Alaska, and National Research Tomsk Polytechnic University, Tomsk, Russia
  • 2 Naval Research Laboratory, Stennis Space Center, Mississippi
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Abstract

A variational data assimilation algorithm is developed for the ocean wave prediction model [Wave Model (WAM)]. The algorithm employs the adjoint-free technique and was tested in a series of data assimilation experiments with synthetic observations in the Chukchi Sea region from various platforms. The types of considered observations are directional spectra estimated from point measurements by stationary buoys, significant wave height (SWH) observations by coastal high-frequency radars (HFRs) within a geographic sector, and SWH from satellite altimeter along a geographic track. Numerical experiments demonstrate computational feasibility and robustness of the adjoint-free variational algorithm with the regional configuration of WAM. The largest improvement of the model forecast skill is provided by assimilating HFR data (the most numerous among the considered types). Assimilating observations of the wave spectrum from a moored platform provides only moderate improvement of the skill, which disappears after 3 h of running WAM in the forecast mode, whereas skill improvement provided by HFRs is shown to persist up to 9 h. Space-borne observations, being the least numerous, do not have a significant impact on the forecast skill but appear to have a noticeable effect when assimilated in combination with other types of data. In particular, when spectral data from a single mooring are used, the satellite data are found to be the most beneficial as a supplemental data type, suggesting the importance of spatial coverage of the domain by observations.

Corresponding author address: Max Yaremchuk, Ocean Prediction, Naval Research Laboratory, Bldg. 1009, Stennis Space Center, MS 39529. E-mail: max.yaremchuk@nrlssc.navy.mil

Abstract

A variational data assimilation algorithm is developed for the ocean wave prediction model [Wave Model (WAM)]. The algorithm employs the adjoint-free technique and was tested in a series of data assimilation experiments with synthetic observations in the Chukchi Sea region from various platforms. The types of considered observations are directional spectra estimated from point measurements by stationary buoys, significant wave height (SWH) observations by coastal high-frequency radars (HFRs) within a geographic sector, and SWH from satellite altimeter along a geographic track. Numerical experiments demonstrate computational feasibility and robustness of the adjoint-free variational algorithm with the regional configuration of WAM. The largest improvement of the model forecast skill is provided by assimilating HFR data (the most numerous among the considered types). Assimilating observations of the wave spectrum from a moored platform provides only moderate improvement of the skill, which disappears after 3 h of running WAM in the forecast mode, whereas skill improvement provided by HFRs is shown to persist up to 9 h. Space-borne observations, being the least numerous, do not have a significant impact on the forecast skill but appear to have a noticeable effect when assimilated in combination with other types of data. In particular, when spectral data from a single mooring are used, the satellite data are found to be the most beneficial as a supplemental data type, suggesting the importance of spatial coverage of the domain by observations.

Corresponding author address: Max Yaremchuk, Ocean Prediction, Naval Research Laboratory, Bldg. 1009, Stennis Space Center, MS 39529. E-mail: max.yaremchuk@nrlssc.navy.mil
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