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In the atmosphere, aerosols can originate from numerous sources, leading to the mixing of different particle types. This paper introduces an approach to the partitioning of aerosol mixtures in terms of backscattering coefficients. The method utilizes data collected from the Mie-Raman-fluorescence lidar, with the primary input information being the aerosol backscattering coefficient, particle depolarization ratio (δ), and fluorescence capacity (GF). The fluorescence capacity is defined as the ratio of the fluorescence backscattering coefficient to the particle backscattering coefficient at the laser wavelength. By solving a system of equations that model these three properties (bF, δ and GF), it is possible to characterize a three-component aerosol mixture. Specifically, the paper assesses the contributions of smoke, urban, and dust aerosols to the overall backscattering coefficient at 532 nm. It is important to note that aerosol properties (δ and GF) may exhibit variations even within a specified aerosol type. To estimate the associated uncertainty, we employ the Monte Carlo technique, which assumes that GF and δ are random values uniformly distributed within predefined intervals. In each Monte Carlo run, a solution is obtained. Rather than relying on a singular solution, an average is computed across the whole set of solutions, and their dispersion serves as a metric for method uncertainty. This methodology was tested using observations conducted at the ATOLL observatory, Laboratoire d'Optique Atmosphérique, University of Lille, France. [ABSTRACT FROM AUTHOR]

The objective of the paper is to validate SolaRes (Solar Resource estimate) in clear-sky conditions, and to examine the aerosol influence on the differences between observation and estimate. SolaRes has the ambition to fulfil both research and industrial applications exploiting downwelling solar radiation at surface level. Consistently with solar resource applications, we show the capacity of SolaRes to reproduce the angular behaviour of the angular field, by validating not only global horizontal irradiance (GHI), but also direct normal irradiance (DNI), diffuse horizontal irradiance DifHI), global and diffuse irradiance in tilted plane (GTI, DifTI), and even the circumsolar contributions. Computations are made with the SMART-G radiative transfer code, taking spectral aerosol optical thickness (AOT) data sets as input, which are delivered by the Aerosol Robotic network (AERONET) and the Copernicus Atmospheric Monitoring Service (CAMS). A mixture of two aerosol models is required to compute aerosol optical properties. Measurements for validation are made at two sites in Northern France. Clear-sky is identified by two methods to show its influence: 1) a method reproducing the AOT variability conditions, and 2) a stricter method eliminating some residual cloud influence but also conditions with largest AOT. SolaRes is validated according to comparison scores found in the literature, with the (relative) root mean square difference (RMSD) in GHI as low as 1%, and the mean bias difference (MBD) which could be 0%. Angular behaviour is reproduced with satisfying scores. The circumsolar contribution improves MBD in DNI and DifHI, by 1% and 4% respectively, as well as RMSD by ~0.5%. MBD in DNI is around -1% and RMSD around 2%, and MBD in DifHI is 2% and RMSD around 9%. RMSD and MBD in both DNI and DifHI are larger than in GHI because they are more sensitive to the aerosol and surface properties. DifTI measured in a vertical plane facing South is reproduced with a RMSD of 8%, similar to DifHI. It is suggested a strong influence of reflection by not only ground surface but also surrounding buildings, increasing the albedo from 0.13 to 0.35. The sensitivity study on the aerosol parameterisation shows that the spectral AOT contains enough information for best quality in DNI retrieval. The choice of aerosol models in the parameterisation has an influence in RMSD smaller than 0.7%. Complementary information on angular scattering and aerosol absorption has a significant influence in GHI by reducing RMSD by ~0.5%, and MBD by ~0.8%. The uncertainty on the data source has a significant influence. The CAMS data set increases the RMSD in DNI by 5%, but has has less influence in GHI, by increasing RMSD by ~1% and MBD by ~0.4%. RMSD in GHI still remains slightly smaller than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Identifying how aerosol particles interact with atmospheric water is critical to understand climate and precipitation. Ice‐nucleating particles (INP) trigger the formation of atmospheric ice crystals at higher temperatures than pure water. They are difficult to characterize because of their scarce occurrence, and variability, in the atmosphere, especially at temperatures above −20°C. It has been demonstrated that at these temperatures, biological aerosol particles can contribute significantly to INP number concentration. This study incorporates a series of offline, size‐segregated measurements of INPs collected at the Puy de Dôme station (PUY, 1,465 m a.s.l.) over a 6 month period from October to May, covering the transitions from autumn, winter, to spring. These measurements show a general trend of decreasing particle number concentrations during the winter months and higher concentration during autumn and spring. INP concentrations measured in the range of −5 and −18°C, had concentrations of 0.001 INP/Lair at the warmest temperatures, and between 0.01 and 0.1 INP/Lair at the coldest temperatures. The majority of INP measured at temperatures warmer than −15°C were heat labile, suggesting a biological or organic origin. The INP variability was compared with collocated aerosol physical and chemical properties, allowing us to associate highest INP concentrations with local and marine origins. Following these comparisons, we use aerosol total number concentration to develop a new parameterization. In addition, this parameterization is specifically optimized for warmer temperature INP measurements, and demonstrated a good performance when tested on independent data sets. Plain Language Summary: Understanding how tiny particles in the air (aerosol particles) interact with water in the atmosphere is crucial for studying climate and precipitation. Ice Nuclei Particles (INP) play a role in forming ice crystals in the atmosphere, especially at higher temperatures. This is important because it's been found that biological aerosol particles can significantly contribute to INP at temperatures above −20°C. The study conducted offline measurements of INPs over 6 months at the Puy de Dôme station, covering the transition from autumn to spring. Results showed a general trend of fewer particles during winter and more during autumn and spring. The INP concentrations were highest at temperatures between −5 and −18°C, with warmer temperatures having 0.001 INP per liter of air and colder temperatures having 0.01 to 0.1 INP per liter of air. Most INPs at temperatures above −15°C were heat‐sensitive, indicating a biological or organic origin. This study linked high INP concentrations to local and marine sources by comparing them with aerosol properties. A new parameterization based on total aerosol number concentration, specifically optimized for warmer temperatures, performed well when tested on independent data sets. Key Points: Size‐segregated airborne ice‐nucleating particles (INP) concentrations were measured at an altitude station in Central France over 6 monthsSeasonal variations are observed with a minimum in winter. The majority of INPs is biological and activated above −18°CWe developed a parameterization for predicting INP concentrations at warm temperatures based on total aerosol concentrations [ABSTRACT FROM AUTHOR]

Ice crystal formation in mixed-phase clouds is initiated by specific aerosol particles, termed ice-nucleating particles (INPs). Only a tiny fraction of all aerosol particles are INPs, providing a challenge for contemporary INP measurement techniques. Models have shown that the presence of INPs in clouds can impact their radiative properties and induce precipitation formation. However, for a qualified implementation of INPs in models, measurement techniques able to accurately detect the temperature-dependent INP concentration are needed. Here we present measurements of INP concentrations in ambient air under conditions relevant to mixed-phase clouds from a total of 10 INP methods over 2 weeks in October 2018 at the Puy de Dôme observatory in central France. A special focus in this intercomparison campaign was placed on having overlapping sampling periods. Although a variety of different measurement principles were used, the majority of the data show INP concentrations within a factor of 5 of one another, demonstrating the suitability of the instruments to derive model-relevant INP data. Lower values of comparability are likely due to instrument-specific features such as aerosol lamina spreading in continuous-flow diffusion chambers, demonstrating the need to account for such phenomena when interpreting INP concentration data from online instruments. Moreover, consistently higher INP concentrations were observed from aerosol filters collected on the rooftop at the Puy de Dôme station without the use of an aerosol inlet. [ABSTRACT FROM AUTHOR]