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Development of a Satellite SAR Image Spectra and Altimeter Wave Height Data Assimilation System for ERS-1

MPG-Autoren

Hasselmann,  Klaus
MPI for Meteorology, Max Planck Society;

Hasselmann,  Susanne
MPI for Meteorology, Max Planck Society;

Bauer,  Eva
MPI for Meteorology, Max Planck Society;

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019-Report.pdf
(Verlagsversion), 6MB

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Zitation

Hasselmann, K., Hasselmann, S., Bauer, E., Brüning, C., Lehner, S., Graber, H., et al. (1988). Development of a Satellite SAR Image Spectra and Altimeter Wave Height Data Assimilation System for ERS-1. Report / Max-Planck-Institute for Meteorology, 019.


Zitierlink: http://hdl.handle.net/21.11116/0000-0001-2999-5
Zusammenfassung
A study on the applicability of ERS—l wind and wave data for wave models is carried out using the WAM third generation wave model and SEASAT altimeter, scatterometer and SAR data. A series of global wave hindcasts is made for the surface stress and surface Wind fields derived by Atlas et al. (1987) by assimilation of scatterometer data for the full 96—day SEASAT and also for two wind field analyses for shorter periods produced by Anderson et al. (1987) (by assimilation with the higher resolution ECMWF T63 model) and by Woiceshyn et al. (1987) (by sub— jective analysis methods). It is found that wave models respond very sensi— tively to inconsistencies in wind field analyses and therefore provide a valuable data validation tool. Comparisons between SEASAT SAR image spectra and theoretical SAR spectra derived from the hindcast wave spectra by Monte Carlo simulations yielded good eyerall agreement for 32 cases representing a wide variety of wave conditions. It is concluded that SAR wave imaging is sufficiently Well under— stood to apply SAR image spectra with confidence for wave studies if supported by realistic wave models and theoretical computations of the strongly non— linear mapping of the wave spectrum into the SAR image spectrum. A new, closed nonlinear integral expression for this spectral mapping relation is derived which avoids the inherent statistical errors of Monte Carlo compu— tations and may prove to be more efficient numerically. A theoretical frameWOrk is developed for the assimilation of arbitrary wave data in models in which the wind and wave fields are modified simultaneouely in accordance with the constraints imposed by the wave model. As a first step, a simplified wave data assimilation exercise is carried out in which only the wave field is modified.