Overview of the SLOPE I and II campaigns: aerosol properties retrieved with lidar and sun–sky photometer measurements

Hocke

Nanía

et al. 2023

Preprint

Abstract. The south-central interior of Andalusia experiences intricate precipitation patterns as a result of its semi-arid Mediterranean climate and the impact of Saharan dust and human-made pollutants. The primary aim of this study is to monitor the inter-relations between various factors, such as aerosols, clouds, and meteorological variables, and precipitation systems in Granada using ground-based remote sensing and in situ instruments including microwave radiometer, ceilometer, cloud radar, nephelometer, and weather station. The objective is to identify potential properties of precipitation in the region and in that way improve precipitation forecasting. Over an 11-year period, we detected rain events using a physical retrieval method that employed microwave radiometer measurements. A composite analysis was applied to them to construct a climatology of the temporal evolution of precipitation. It was found that convective rain is the dominant precipitation type in Granada, accounting for 68 % of the rain events. The height of the cloud base is mainly distributed at an altitude of 2 to 7 km. Integrated water vapor (IWV) and integrated cloud liquid water (ILW) increase rapidly before the onset of rain. Aerosol scattering at surface level and hence the aerosol concentration is reduced during rain, and the predominant mean size distribution of aerosol particles before, during, and after rain is almost the same. A meteorological environment favorable for virga formation is observed in Granada. The surface weather station detected rainfall later than the microwave radiometer, indicating virga according to ceilometer and cloud radar data. We used rain-day events identified by weather station data to determine precipitation intensity classes and found that light rain is the main precipitation intensity class in Granada, accounting for 72 % of the rain-day events. This can be a result of the high tropospheric temperature induced by the Andalusian climate and the reduction of cloud droplet size by the high availability of aerosol particles in the urban atmosphere. This study provides evidence that aerosols, clouds, and meteorological variables have a combined impact on precipitation which can be considered for water resource management and improving rain forecasting accuracy.

Section: Study Areamentioning

confidence: 99%

Inter-relations of precipitation, aerosols, and clouds over Andalusia, Southern Spain revealed by the AGORA Observatory

Hocke

Nanía

et al. 2023

Preprint

“…For pursuing an even deeper synergy of lidar and sun-photometer, Generalized Aerosol Retrieval from Radiometer and Lidar Combined data (GARRLiC) was created by modification of AERONET algorithms to adapt them for inclusion of lidar data (Lopatin et al, 2013). As a part of the extensive Generalized Retrieval of Atmosphere and Surface Properties (GRASP), it can get the properties profiles separately for fine and coarse mode particles and has been applied for the characterization of atmospheric aerosols (Lopatin et al, 2013;Tsekeri et al, 2017;Bovchaliuk et al, 2016) and evaluated by air-borne in situ measurements (Benavent-Oltra et al, 2021;Benavent-Oltra et al, 2017). Although GARRLiC was able to quantitatively retrieve aerosol components (Li et al, 2019a), it still stayed on the columnar level and can not get the flexible volume proportion of different components vertically.…”

Section: Fine Mode Aerosol Properties From Garrlicmentioning

confidence: 99%

“…𝑍 𝑚𝑎𝑥 and 𝑍 𝑚𝑖𝑛 represents the upper and lower height limit, respectively. Besides, for the accuracy of GARRLiC, the cases with the relative residual larger than 15 % in the inversion process have been eliminated according to Benavent-Oltra et al (2021). Consequently, there were 133 retrievals remaining in February of 2021.…”

Section: The Input Data Of Garrlicmentioning

confidence: 99%

Algorithm for vertical distribution of boundary layer aerosol components in remote sensing data

Yang

et al. 2022

Preprint

Abstract. The vertical distribution of atmospheric aerosol components is vital to the estimation of radiation forcing and the catalysis of atmospheric photochemical processes. Based on the synergy of ground-based lidar and sun-photometer, this paper developed a new algorithm to get the vertical mass concentration profiles of fine aerosol components for the first time. The sky radiance at multiple scatter angles, the total optical depth (TOD) at 440, 675, 870, and 1020 nm, and the lidar signals at 532 nm and 1064 nm were applied to retrieve the aerosol properties. Besides, the internal mixing model and normalized volume size distribution model were constructed, according to the absorption and water-solubility of aerosol components, to separate the profiles of black carbon (BC), water-insoluble organic matter (WIOM), water-soluble organic matter (WSOM), ammonium nitrate-like (AN), and fine aerosol water content (AW). The results showed a reasonable vertical distribution of aerosol components compared with in situ observations and reanalysis data. The estimated and observed BC concentration matched well with a correlation coefficient up to 0.91, while there was an evident overestimation of OM (NMB=0.98). And the retrieved AN concentrations were closer to the simulated results (the correlation coefficient of 0.85), especially in the polluted condition. The correlations of BC and OM were weaker relatively, with a correlation coefficient of about 0.5. Besides, the uncertainties caused by input parameters (i.e. RH, volume concentration, and extinction coefficients) were assessed by Monte Carlo method. AN and AW had smaller uncertainties at higher RH. In this paper, the algorithm was also applied to the remote sensing measurements of Beijing and two typical cases were presented. Under the clean condition with low RH, there were comparable AN and WIOM but peaking at different altitudes. While in the polluted case, AN was dominant and the maximum mass concentration occurred near the surface. We expected the algorithm can provide a new idea for lidar inversion and promote the development of aerosol components profiles.

“…To successfully solve the inverse problem, lidar needs to be working at least at three wavelengths. More often, to restore the vertical structure of the microphysical parameters of aerosol, lidar measurements are to be taken together with other measurements, such as direct parallel ground-based [28] or aircraft [29,30] measurements of aerosol concentrations. Lidar measurements are also often supplemented by photometric measurements [25,[31][32][33].…”

Section: Introductionmentioning

confidence: 99%

A Regional Aerosol Model for the Middle Urals Based on CALIPSO Measurements

Nagovitsyna,

Dzholumbetov,

Karasev

et al. 2023

Atmosphere

The present work aims to develop a regional Middle Urals Aerosol model (MUrA model) based on the joint analysis of long-term ground-based photometric measurements of the Aerosol Robotic NETwork (AERONET) and the results of lidar measurements of the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellite relying on information on the air trajectories at different altitudes calculated using the HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory model) software package. The MUrA model contains parameters of normalized volume size distributions (NVSDs) characterizing the tropospheric aerosol subtypes detected by the CALIPSO satellite. When comparing the MUrA model with the global CALIPSO Aerosol Model (CAMel), we found significant differences in NVSDs for elevated smoke and clean continental aerosol types. NVSDs for dust and polluted continental/smoke aerosol types in the global and regional models differ much less. The total volumes of aerosol particles along the atmospheric column reconstructed from satellite measurements of the attenuation coefficient at a wavelength of 532 nm based on the regional MUrA model and global CAMel are compared with the AERONET inversion data. The mean bias error for the regional model is 0.016 μm3/μm2, and 0.043 μm3/μm2 for the global model.