Study Of Algorithms For Ocean Wave Retrieval Using ENVISAT Advanced Synthetic Aperture Radar Wave Mode Data

Over the ocean, the SAR and ASAR instruments onboard the ESA's ERS and ENVISAT satellite are operated in wave mode whenever no other operation is requested. In wave mode, SAR collects data globally with high spatial resolution to form small images of 10 km x 5 km size every 100 km along the satellite's orbit. To retrieve ocean wave parameters from ASAR or SAR wave mode data with high quality is essential important. It can benefit the numerical wave model forecast and hindcast, observations and forecast for extreme ocean weather, as well as the global wave climate analysis. Assimilation of SAR wave mode observations into the numerical wave model has been carried out operationally in the European Center for Medium-Range Weather Forecast (ECMWF).The main research demonstrated in the thesis focus on ocean wave information retrieval from SAR and ASAR wave mode data, including validation and intercomparison for two schemes, i.e. non-linear Partition Rescaling and Shift Algorithm (PARSA) and quasi-linear WVW algorithm for retrieving two-dimensional ocean wave spectrum and integral wave parameters.The PARSA algorithm needs the SAR look cross spectra and first guess spectra taken from numerical wave model as the input. It is an extension of improvement for the MPI scheme.Validation results indicate that the PARSA algorithm can yield the full two-dimensional ocean wave spectrum. The retrieved integral wave parameters have good agreement with buoy measurements, the ECMWF reanalysis wave model and the DWD forecast wave model results.While it seems that the PARSA algorithm has a strong dependence with the first guess information and therefore makes the SAR measurements not independent. The quasi-linear algorithm WVW has the advantage without using the prior information from numerical wave model. However, the retrieved spectra are limited to the SAR cut-off wavenumber domain. The validation of integral wave parameters derived from WVW spectra shows that there is a significant underestimation for this algorithm. And the trends increase along with sea state.Further it seems that the artificial effect of SAR imaging of ocean waves is not resolved in the inversion. Another important issue found in the validation is that around 25% ASAR wave mode cross spectra cannot be converted successfully by using this algorithm.Based on the empirical model CWAVE_ERS developed for reprocessed ERS-2 SAR wave mode data, the CWAVE_ENV model is proposed in this thesis and implemented in the ASAR wave mode data. Using the same three months ASAR wave mode data and its collocated dataset, the empirical algorithm is validated. Validation results show that a good agreement is achieved by comparing the retrieved significant wave height results to buoy measurements using 1270 data pairs with bias of 0.05 m, RMSE of 0.72 m and scatter index of 24%. This gives the confidence that CWAVE_ENV algorithm can be used for ASAR wave mode data to retrieve integral wave parameters without using any first prior information and its retrieval accuracy is comparable to non-linear inversion schemes and the measurements of Radar Altimeter.Four case studies, i.e.,Storms in the North Pacific and North Atlantic, cross sea in the Southern Pacific and extreme swell in the Indian Ocean, are analyzed by using SAR/ASAR wave mode data. The research interesting is addressed to evaluate different ocean wave retrieval algorithm in complex and extreme sea state.It is concluded that even in the extreme sea state, the CWAVE_ENV algorithm still can provide reliable sea state measurements, which can benefit the observation and forecast for the extreme sea state.