Analysis of SAR Wave Mode Imagette Taken Under Extreme Wind and Wave Conditions
Antonio Reppucci(1) , Johannes Schulz-Stellenfleth(1) , Susanne Lehner(1) , and Thomas Koenig(1)
German Aerospace Center,
Munchener Str. 20,
Due to relatively small amount of in situ data available for the open oceans, particularly during extreme events, remote sensing techniques take an important role in the retrieval of geophysical information under such conditions. Up to now the only remote sensing system capable of providing two dimensional sea state information on a global and continuous scale is the Synthetic Aperture Radar (SAR). Is well known that ocean waves play an important role in the dynamics of extreme events like tropical or extratropical cyclones by conditioning the air/sea fluxes of momentum, heat and moisture.
In this study data set of ERS-2 SAR images “imagette” is used to study extreme wind and wave conditions in hurricane cases.
SAR images of 10 by 5 km size are acquired in Wave Mode every 200 km along the orbit over all oceans providing about 1200 measurements daily. The data set has been reprocessed as single look complex images using the DLR BSAR processor.
A homogeneity test is applied to the imagettes to detect data takes which are affected by atmospheric features, e.g., rain cells. The imagettes are then calibrated using I/Q channel standard deviation, as imagettes acquired in high wind conditions are particularly affected by power loss.
The retrieved radar cross section is used in combination with collocated ERS-2 Scatterometer data to derive wind speed and direction. Ocean wave parameters are computed for this dataset using the empirical CWAVE approach (Schulz-Stellenfleth et al., this issue).
The work focuses on some illustrative case studies, e.g., Hurricanes Floyd and Gert in the North Atlantic in September 1999. Comparisons of the SAR retrieved parameters with parametric Holland type cyclone models are performed. Observed deviations between SAR measurements and model parameters are discussed. A strategy to apply the method on a statistical basis to improve parametric cyclone models is presented.