Ensemble-based data assimilation of significant wave height from Sofar Spotters and satellite altimeters with a global operational wave model

Houghton, Isabel A.; Penny, Stephen G.; Hegermiller, Christie; Cesaretti, Moriah; Teicheira, Camille; Smit, Pieter B.

doi:10.1016/j.ocemod.2023.102200

i

Ensemble-based data assimilation of significant wave height from Sofar Spotters and satellite altimeters with a global operational wave model

2023
By Houghton, Isabel A. ; Penny, Stephen G. ; Hegermiller, Christie ; ...

Details

Journal Title:

Ocean Modelling
Personal Author:

Houghton, Isabel A. ; Penny, Stephen G. ; Hegermiller, Christie ; Cesaretti, Moriah ; Teicheira, Camille ; Smit, Pieter B.
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research)
Description:

An ensemble-based method for wave data assimilation is implemented using significant wave height observations from the globally distributed network of Sofar Spotter buoys and satellite altimeters. The Local Ensemble Transform Kalman Filter (LETKF) method generates skillful analysis fields resulting in reduced forecast errors out to 2.5 days when used as initial conditions in a cycled wave data assimilation system. The LETKF method provides more physically realistic model state updates that better reflect the underlying sea state dynamics and uncertainty compared to methods such as optimal interpolation. Skill assessment far from any included observations and inspection of specific storm events highlight the advantages of LETKF over an optimal interpolation method for data assimilation. This advancement has immediate value in improving predictions of the sea state and, more broadly, enabling future coupled data assimilation and utilization of global surface observations across domains (atmosphere-wave-ocean).
Keywords:

Atmospheric Science Computer Science (miscellaneous) Geotechnical Engineering And Engineering Geology Oceanography Geotechnical Engineering And Engineering Geology Geotechnical Engineering And Engineering Geology
Source:

Ocean Modelling, 183, 102200
DOI:

https://doi.org/10.1016/j.ocemod.2023.102200
ISSN:

1463-5003
Format:

PDF
Publisher:

Elsevier BV
Document Type:

Journal Article
Funding:

Grant no. NA22OAR4590510
License:

https://creativecommons.org/licenses/by-nc-nd/4.0/
Rights Information:

CC BY-NC-ND
Compliance:

Library
Main Document Checksum:

urn:sha-512:f4ad8dc50853ffb42bb31c500cb5d6659eefd946bc4b9ef24679240b5de3453176c7324a15737986f6a7aa260c55c2e6bca4d18b7351a9bfaf3b7831a76ed29d
Download URL:

https://repository.library.noaa.gov/view/noaa/59518/noaa_59518_DS1.pdf
File Type:

[PDF - 1.85 MB ]

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