Adjoint-Free Variational Data Assimilation into a Regional Wave Model
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...
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ftdtic:ADA623048 2023-05-15T15:54:34+02:00 Adjoint-Free Variational Data Assimilation into a Regional Wave Model Panteleev, Gleb Yaremchuk, Max Rogers, W E NAVAL RESEARCH LAB STENNIS DETACHMENT STENNIS SPACE CENTER MS 2015-07 text/html http://www.dtic.mil/docs/citations/ADA623048 http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA623048 en eng http://www.dtic.mil/docs/citations/ADA623048 Approved for public release; distribution is unlimited. DTIC Atmospheric Physics Meteorology Physical and Dynamic Oceanography Active & Passive Radar Detection & Equipment *OCEAN CURRENTS *OCEAN MODELS ALGORITHMS ALTIMETERS ASSIMILATION BUOYS CHUKCHI SEA COVARIANCE FEASIBILITY STUDIES HIGH FREQUENCY KALMAN FILTERING METEOROLOGICAL SATELLITES MOORING RADAR TRACKING SPATIAL DISTRIBUTION SPECTRA TELEMETERING DATA WAVE PROPAGATION WAVES WEATHER FORECASTING HFRS(HIGH-FREQUENCY RADARS) SWH(SIGNIFICANT WAVE HEIGHT) WAM(WAVE MODEL) DATA ASSIMILATION PE0602435N Text 2015 ftdtic 2016-02-24T19:04:38Z 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 WAMin the forecast mode, whereas skill improvement provided by HFRs is shown to persist up to 9 h. Spaceborne 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. Published in the Journal of Atmospheric and Oceanic Technology, v32 p1386-1399, July 2015. Text Chukchi Chukchi Sea Defense Technical Information Center: DTIC Technical Reports database Chukchi Sea |
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Open Polar |
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Defense Technical Information Center: DTIC Technical Reports database |
op_collection_id |
ftdtic |
language |
English |
topic |
Atmospheric Physics Meteorology Physical and Dynamic Oceanography Active & Passive Radar Detection & Equipment *OCEAN CURRENTS *OCEAN MODELS ALGORITHMS ALTIMETERS ASSIMILATION BUOYS CHUKCHI SEA COVARIANCE FEASIBILITY STUDIES HIGH FREQUENCY KALMAN FILTERING METEOROLOGICAL SATELLITES MOORING RADAR TRACKING SPATIAL DISTRIBUTION SPECTRA TELEMETERING DATA WAVE PROPAGATION WAVES WEATHER FORECASTING HFRS(HIGH-FREQUENCY RADARS) SWH(SIGNIFICANT WAVE HEIGHT) WAM(WAVE MODEL) DATA ASSIMILATION PE0602435N |
spellingShingle |
Atmospheric Physics Meteorology Physical and Dynamic Oceanography Active & Passive Radar Detection & Equipment *OCEAN CURRENTS *OCEAN MODELS ALGORITHMS ALTIMETERS ASSIMILATION BUOYS CHUKCHI SEA COVARIANCE FEASIBILITY STUDIES HIGH FREQUENCY KALMAN FILTERING METEOROLOGICAL SATELLITES MOORING RADAR TRACKING SPATIAL DISTRIBUTION SPECTRA TELEMETERING DATA WAVE PROPAGATION WAVES WEATHER FORECASTING HFRS(HIGH-FREQUENCY RADARS) SWH(SIGNIFICANT WAVE HEIGHT) WAM(WAVE MODEL) DATA ASSIMILATION PE0602435N Panteleev, Gleb Yaremchuk, Max Rogers, W E Adjoint-Free Variational Data Assimilation into a Regional Wave Model |
topic_facet |
Atmospheric Physics Meteorology Physical and Dynamic Oceanography Active & Passive Radar Detection & Equipment *OCEAN CURRENTS *OCEAN MODELS ALGORITHMS ALTIMETERS ASSIMILATION BUOYS CHUKCHI SEA COVARIANCE FEASIBILITY STUDIES HIGH FREQUENCY KALMAN FILTERING METEOROLOGICAL SATELLITES MOORING RADAR TRACKING SPATIAL DISTRIBUTION SPECTRA TELEMETERING DATA WAVE PROPAGATION WAVES WEATHER FORECASTING HFRS(HIGH-FREQUENCY RADARS) SWH(SIGNIFICANT WAVE HEIGHT) WAM(WAVE MODEL) DATA ASSIMILATION PE0602435N |
description |
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 WAMin the forecast mode, whereas skill improvement provided by HFRs is shown to persist up to 9 h. Spaceborne 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. Published in the Journal of Atmospheric and Oceanic Technology, v32 p1386-1399, July 2015. |
author2 |
NAVAL RESEARCH LAB STENNIS DETACHMENT STENNIS SPACE CENTER MS |
format |
Text |
author |
Panteleev, Gleb Yaremchuk, Max Rogers, W E |
author_facet |
Panteleev, Gleb Yaremchuk, Max Rogers, W E |
author_sort |
Panteleev, Gleb |
title |
Adjoint-Free Variational Data Assimilation into a Regional Wave Model |
title_short |
Adjoint-Free Variational Data Assimilation into a Regional Wave Model |
title_full |
Adjoint-Free Variational Data Assimilation into a Regional Wave Model |
title_fullStr |
Adjoint-Free Variational Data Assimilation into a Regional Wave Model |
title_full_unstemmed |
Adjoint-Free Variational Data Assimilation into a Regional Wave Model |
title_sort |
adjoint-free variational data assimilation into a regional wave model |
publishDate |
2015 |
url |
http://www.dtic.mil/docs/citations/ADA623048 http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA623048 |
geographic |
Chukchi Sea |
geographic_facet |
Chukchi Sea |
genre |
Chukchi Chukchi Sea |
genre_facet |
Chukchi Chukchi Sea |
op_source |
DTIC |
op_relation |
http://www.dtic.mil/docs/citations/ADA623048 |
op_rights |
Approved for public release; distribution is unlimited. |
_version_ |
1766389807926214656 |