CoAuthors

Lotfi Aouf (Météo-France, France)

The maximum wave height (Hmax) is an extremely important factor that has a significant impact on the safety of maritime activities. However, the Hmax is much less investigated than significant wave height (SWH) in the wave remote sensing. Nowadays, radar altimeters and CFOSAT provide the SWH operational products but without MaxH. A method of obtaining the MaxH from CFOSAT SWIM Level 2 parameters is presented. The buoys are the most reliable way to observe the MaxH, but the collocations between buoys and CFOSAT tracks are too few to perform the supervised learning training. The ERA5 wave reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) is one of the most accurate datasets. However, the obvious bias and scatter index of MaxH are found from the comparison between ERA5 and buoys located west of France. A machine learning model is firstly built to reduce the error of ERA5 MaxH. Then the corrected ERA5 MaxH is collocated with CFOSAT observations and used for the training target of SWIM MaxH retrieval. The SWIM parameters both from SWIM nadir and boxes, including the SWH, wavelength and wave partition information, are used to obtain the MaxH based on machine learning. The CFOSAT data in 2021 are used to train the MaxH machine learning model while the data in 2020 are used to perform the independent validation. The bias, RMSE and scatter index of CFOSAT MaxH are 0.01m, 0.51m, 16%, while 0.77m, 1.09m, 19% are for the ERA5. Therefore, this study provides a perspective to obtain the MaxH from satellite remote sensing for further applications such as wave forecasts.