Synergy of Raman lidar and modeled temperature for relative humidity profiling: assessment and uncertainty analysis

Relative humidity (RH) profiling using Raman lidars requires simultaneous range-resolved temperature and pressure data that are not always available. We propose and assess a method based on the use of a locally retrieved atmospheric model to estimate the temperature and pressure profiles. This model relies on the data from daily radiosonde launches at Barcelona during a five-year-long period (2015-2019). We have computed the range-resolved error of the model compared with the radiosonde ``true'' temperature profiles. Then, we have calculated the induced uncertainty in the recalculated RH profiles, finding that the standard deviation at 5 km is