Assimilation of altimeter data in a global third‐generation wave model

Lionello, Piero; Günther, Helmut; Janssen, Peter A. E. M.

doi:10.1029/92jc01055

Cited by 168 publications

(134 citation statements)

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“…Several studies are devoted to the presentation, clarification, and operational use of such type of modules. We refer the reader to Janssen et al (1987); Lionello et al (1992); Lionello et al (1995); Greenslade and Young (2005); Abdalla et al (2005); and Skandrani et al (2004).…”

Section: Introductionmentioning

confidence: 99%

A new methodology for the extension of the impact of data assimilation on ocean wave prediction

et al. 2009

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It is a common fact that the majority of today's wave assimilation platforms have a limited, in time, ability of affecting the final wave prediction, especially that of long-period forecasting systems. This is mainly due to the fact that after "closing" the assimilation window, i.e., the time that the available observations are assimilated into the wave model, the latter continues to run without any external information. Therefore, if a systematic divergence from the observations occurs, only a limited portion of the forecasting period will be improved. A way of dealing with this drawback is proposed in this study: A combination of two different statistical tools-Kolmogorov-Zurbenko and Kalman filters-is employed so as to eliminate any systematic error of (a first run of) the wave model results. Then, the obtained forecasts are used as artificial observations that can be assimilated to a follow-up model simulation inside the forecasting period. The method was successfully applied to an open sea area (Pacific Ocean) for significant wave height forecasts using the wave model WAM and six different buoys as observational stations. The results were encouraging and led to the extension of the assimilation impact to the entire forecasting period as well as to a significant reduction of the forecast bias.

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Section: Introductionmentioning

confidence: 99%

A new methodology for the extension of the impact of data assimilation on ocean wave prediction

et al. 2009

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“…With remotely sensed observations it is often the case that the geophysical variable is not directly observed; its values must be Data assimilation methods used so far in wave modeling are of the sequential insertion type and have been applied mostly to altimeter data. Examples are the assimilation of Seasat altimeter wave heights into the U.K. Meteorological Office global wave model [Francis and Stratton, 1990] and the assimilation of altimeter wave heights into the wave model WAM [Lionello et al, 1992]. Ideally, one would like to combine the algorithm that matches the first guess and observation at the observation point with the assimilation step, which spreads the corrections spatially.…”

Section: Introductionmentioning

confidence: 99%

The effect of assimilating ERS‐1 fast delivery wave data into the North Atlantic WAM model

Dunlap¹,

Olsen²,

Wilson³

et al. 1998

J. Geophys. Res.

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Abstract. The launch of the European ERS satellites has provided a new source of wave information that is particularly suitable for use in improving wave forecasts in the open ocean. We have implemented and tested a simple system for assimilating corrections to model wave fields produced by the WAM model, where the corrections are derived from inverted synthetic aperture radar (SAR) image spectra from ERS-1. Corrections are applied to significant wave height, mean period and direction for wave modes that are detectable in both the model and the SAR data. The system has been tested in a storm situation and in moderate conditions using buoy data and altimeter data, as well as SAR observations for verification. Overall, it is demonstrated that the net effect of assimilating SAR data is beneficial but very small. The small impact is due at least partly to relatively small spatial and temporal coverage of the SAR wave mode data. Locally larger impacts were found in the storm situation in individual cases where SAR observations were collocated with independent buoy observations. IntroductionUp until a few years ago, ocean wave models were run without the use of any wave observations. Model simulations of wave growth, propagation, and decay were obtained using marine winds as input, either as a series of analyses ("hindcast mode") or as forecast winds from an atmospheric model ("forecast mode"). Wave observations were not used for two reasons. First, wave models have been shown to simulate the wave field quite well if they are driven by a consistent highquality wind field [Graber et al., 1994]. Second, so few wave observations were available that they could not be expected to have a significant impact on regional or ocean basin scale wave simulations. With the launch of ERS-1 in July 1991 the spatial and temporal coverage of wave observations increased dramatically, making their use to initialize wave models operationally practical. ERS-1 (and recently ERS-2) wave observations are available in two forms, wave heights from the radar altimeter, and estimates of the two-dimensional (2-D) wave spectrum from the active microwave instrument (AMI) operating in synthetic aperture radar (SAR) mode. The latter offers the opportunity of obtaining real-time wave observations that are the most complete and consistent with the output of a wave model, an estimate of the two-dimensional spectral wave energy.The general purpose of data assimilation is to change a model's estimate of the state of its geophysical variables toward their true state, using information obtained from observations. Both model and data are assumed to contain errors, which must be accounted for in the assimilation procedure. With remotely sensed observations it is often the case that the geophysical variable is not directly observed; its values must be inferred or estimated. The raw SAR mode data from ERS-1•ASA Consulting Ltd., Halifax, Nova Scotia, Canada. . Variational assimilation will probably be the optimal methodology in wave assimilation as well, but it i...

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“…These different characteristics in the two domains, along with the fact that the assimilation analysis does not correct the wind input from the atmospheric model but directly the significant wave height, favors a limited influence of the assimilation scheme in the Mediterranean Sea (see also Lionello et al, 1992).…”

Section: Resultsmentioning

confidence: 99%

“…Previous work on assimilation of satellite data into numerical wave models can be found, among others, in Janssen et al (1987), Lionello et al (1992Lionello et al ( , 1995, Greenslade (2001), Aarnes (2003), Abdalla et al (2005), Skandrani et al (2004), where the theoretical aspects as well as the general benefits of such methods are presented. The main aim of this paper is the investigation of the specific performance of an operational assimilation system in two different wave climate regions: the Mediterranean Sea, a wind sea dominated area with complicated topographic characteristics, and the Northern Indian Ocean (referred from now on as the Indian Ocean), an open swell-dominated sea.…”

Section: Introductionmentioning

confidence: 99%

Assimilation of radar altimeter data in numerical wave models: an impact study in two different wave climate regions

et al. 2007

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Abstract. An operational assimilation system incorporating significant wave height observations in high resolution numerical wave models is studied and evaluated. In particular, altimeter satellite data provided by the European Space Agency (ESA-ENVISAT) are assimilated in the wave model WAM which operates in two different wave climate areas: the Mediterranean Sea and the Indian Ocean. The first is a wind-sea dominated area while in the second, swell is the principal part of the sea state, a fact that seriously affects the performance of the assimilation scheme. A detailed study of the different impact is presented and the resulting forecasts are evaluated against available buoy and satellite observations. The corresponding results show a considerable improvement in wave forecasting for the Indian Ocean while in the Mediterranean Sea the assimilation impact is restricted to isolated areas.

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Assimilation of altimeter data in a global third‐generation wave model

Cited by 168 publications

References 8 publications

A new methodology for the extension of the impact of data assimilation on ocean wave prediction

A new methodology for the extension of the impact of data assimilation on ocean wave prediction

The effect of assimilating ERS‐1 fast delivery wave data into the North Atlantic WAM model

Assimilation of radar altimeter data in numerical wave models: an impact study in two different wave climate regions

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