This study evaluates two approaches for retrieving aerosol properties from ceilometer observations, using aerosol optical depth (AOD) from AERONET and synergistic aerosol profiles obtained by combining the AERONET sun–sky photometer and ceilometer measurements through the GRASPpac algorithm. The two re- trieval techniques considered for the ceilometer retrievals are the traditional Klett–Fernald backward inversion and a forward iterative method including an independent calibration procedure. Observations collected at three European stations (Granada, Spain; Payerne, Switzerland; and Lindenberg, Germany) during 2019–2020 are an- alyzed to assess the performance of both approaches under a wide range of aerosol conditions. The results show that the forward iterative approach generally provides better agreement with the reference datasets than the Klett– Fernald backward approach. Under high aerosol load conditions, particularly during coarse-mode-dominated events, the forward retrieval reduces AOD uncertainties by ∼ 50 % and achieves root-mean-square errors com- parable to those reported in previous validation studies. Vertical comparisons against GRASPpac profiles indicate that the forward method maintains consistent accuracy throughout the troposphere, whereas the backward ap- proach exhibits altitude-dependent biases, especially within dust layers. Additional evaluations using COBALD balloon-borne backscatter measurements confirm that the forward retrieval reproduces observed aerosol struc- tures within 10 %–30 % deviation. These results demonstrate the significant performance gains achieved by oper- ational ceilometer networks when applying forward retrievals with independent calibration made under nighttime cloud-free conditions with negligible aerosol loading at the reference altitude. 

This study presents a hybrid methodology to obtain high temporal resolution calibration constants for water vapour Raman lidar measurements, and posteriorly retrieve high-accuracy water vapour mixing ratio profiles. The hybrid method combines correlative measurements of collocated precipitable water vapour and Numerical Weather Prediction data to reconstruct the profile within the incomplete overlap region. The hybrid methodology is applied to the Raman lidar system, which operated at the EARLINET/ACTRIS station of the University of Granada, Spain, for the period 2009–2022. The system has been continuously updated to meet EARLINET/ACTRIS requirements for aerosol measurements, but the hybrid method has allowed tracking the impact of these changes on calibration constants for water vapour retrievals, and consequently to exploit water vapour mixing ratio profiles that were previously unavailable. The hybrid method was optimised for the Granada station by selecting Global Navigation Satellite System precipitable water vapour data as the most appropriate due to its better agreement with collocated and simultaneous radiosonde data (coefficient of determination of 0.95). Furthermore, the ERA5 reanalysis model was selected as the most appropriate because of its better temporal and spatial resolution and its accuracy when evaluated against radiosonde data. The advantages of the hybrid methodology were evaluated in comparison to traditional calibration methods such as those based on radiosondes or precipitable water vapour data assuming a constant water vapour mixing ratio in the incomplete overlap region. 

The intention of the geophysical modelling of rain is to provide a better explanation for the effect of rainfall to the microwave backscattering and thus to interpret radar measurements. However, in the model, physical characteristic of raindrops should be estimated primarily and accurately by considering observation system, measurements and suitable rain rate retrieval algorithms to calculate backscattering coefficients from rainfall. In this study, a geophysical microwave backscattering model of rain type precipitation over sea surface is constructed by using Particle Swarm Optimization (PSO) algorithm in the multilayered Vector Radiative Transfer (VRT) model to estimate vertical profile of rain by using GPM DPR data. Rain column is partition into sublayers and for each sublayer, physical properties of raindrops such as drop radius, water volume fraction or layer thickness are estimated by using PSO to provide the best fit with measurements by searching within certain limits defined by rain rate. Backscattering coefficients from entire rain is provided by the solution of VRT equations via Matrix Doubling Method to consider multilayer effect. Results show that, vertical profile of rain parameters can be estimated accurately for moderate /high rain rates (up to 11–12 mm/h) by using presented model.

Upper troposphere (UT) humidity records are crucial for climate studies. To maximize temporal representativeness and enhance the lidar signal, pseudo-monthly averaging—limited to nighttime measurement—is applied, yielding water vapor mixing ratio (WVMR) profiles up to 16 km. This study evaluates 11 years (2013–2023) of WVMR profiles from a UV Raman lidar (Li1200) at Réunion Island, comparing them with MLS-Aura satellite retrievals, ERA5 reanalysis data, and GRUAN-processed M10 radiosondes. The results reveal a systematic dry shift in MLS of up to 30% above 12 km, particularly during the wet season. The lidar exhibits a slight downward shift in WVMR, approximately 5% lower than ERA5 throughout the UT, with the largest deviations occurring above 14 km and greater variability during the wet season. Calibration-related challenges during the dry season result in lidar WVMR profiles that are up to 10% drier than ERA5. Additionally, comparisons with GRUAN-processed radiosondes show a substantial dry shift relative to the lidar, exceeding 30% above 12 km. We investigate the effect of GNSS-based lidar calibration by applying an alternative calibration method, which produces higher WVMR values. This reveals a dry shift in ERA5 relative to the lidar, increasing with altitude in the UT up to 25%. These measurements contribute to the global effort to monitor and validate tropical and subtropical upper tropospheric humidity.