Three-component seismometers deployed on the sea bed have been used for many years. These so-called ocean bottom seismometers (OBS) are used to register both compressional and shear waves. The recorded seismic information is used to enhance our understanding of areas of interest located offshore, like hydrocarbon reservoirs, mid oceanic ridges, and continental margins. A lot of progress has been made on OBS applications. However, there are still some distinctive problems mainly related to clock drift, orientation, and coupling. We use and test \textit{OCloC}, a new method which seeks to improve the ability to detect and correct clock errors of OBSs. The method was successfully developed using an extensive dataset from a seismic network, which included many land- and ocean bottom-seismometers. One of the objectives is to understand under which conditions OCloC can be used when the dataset is relatively small. To do this, data from a network deployed in the Red Sea coastal area of the Kingdom of Saudi Arabia is used. This network includes two ocean bottom seismometers which are complemented by several land-stations. We collected all used data during five campaigns, both offshore and onshore. To analyse the data quality we designed and applied an extensive data quality control, where after the data is prepared for Ocloc through pre-processing. Both workflows are presented in a roadmap. This roadmap is made applicable for ocean bottom seismometers by including an orientation estimation, for which we used a technique based on Rayleigh wave polarization. Our quality control shows that the data from both ocean bottom seismometers show significant attenuation, primarily for short periods, which is possibly caused by a poor coupling to the loose sediments on the sea bed. The noise source illumination turns out to be highly anisotropic, with a high microseism noise source concentration in the Red Sea. This results in a poor signal to noise ratio for the interferometric surface wave responses. As a consequence, we find that the limited station deployment time and data quality interferes with the ability to obtain an accurate clock error. Nevertheless, the technique to estimate the OBS orientation turns out to be successful.