Due to their importance for Earth’s climate, the formation of clouds is extensively studied, and especially their formation inside the atmospheric boundary layer (ABL). Radiosonde is one of the most used tools for atmospheric research and studying the ABL in particular, since it is a simple and direct means of measuring a variety of variables. This, however, come at the account of the data not being temporally or laterally focused. Remote sensing methods, such as the light detection and ranging (LiDAR) technique, do not share the radiosonde shortcomings, but on the other hand, produce data that is interpretable. Despite these limitations, using data from both types of systems may provide additional insight. In this work, simultaneous measurements of radiosondes and ceilometer data acquired during a week at the end of November are comparatively analyzed and temporally adjusted. A transformation of the radiosonde’s temperature and humidity data into simulated optical backscatter signal is implemented using a condensation model which includes an initial rate limiting step which may be crucial in activating cloud condensation nuclei. Comparing these transformed signals to the ceilometer’s measured signals allows studying condensation processes and deducing the size of the smallest effective cloud condensation nucleus.