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 isin situradiosonde humidity measurements, finding, in this case, bigger differences due to additional sources of error. Uncertainty analysis shows that the temperature model is the most significant error source below 4–5 km. For higher altitudes, the noise of the Raman signals may become the main contribution. We show that the resulting uncertainty (commonly <15% below 5 km) is compatible with the statistical analysis of the model and comparable with the ones obtained using other instruments for temperature profiling. We show that this method permits nocturnal lidar-based RH profiling with uncertainty estimation without the need for measured atmospheric profiles.