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In this study, we utilised ground-based in situ measurements of the aerosol chemical composition and particle size distribution, along with meteorological data from the European Centre for Medium-Range Weather Forecasts (ECMWF), to predict vertical profiles of aerosol optical properties, including the aerosol scattering coefficient, backscatter coefficient, extinction coefficient, and lidar ratio. The predicted ambient profiles were compared to retrievals by a multi-wavelength Raman lidar during the Ruisdael Land–Atmosphere Interactions Intensive Trace-gas and Aerosol (RITA) campaign in the Netherlands in 2021 for 26 time periods of approximately 1 h each. Predicted and retrieved extensive aerosol properties (scattering, backscatter, and extinction coefficient) were comparable only approximately 35 % of the time, mostly under the condition of well-mixed boundary layers. In this case, ground-based measurements can provide a way to extend extinction profiles to lower altitudes, where they cannot be retrieved, and to verify the lidar-measured profiles. Accurate representation of hygroscopic growth is required for adjusting the dry size distribution to ambient size distribution, and the estimated relative humidity profile may have a substantial influence on the shape of the calculated profiles. On the other hand, the lidar ratio profiles predicted by ground-based data also compared reasonably well to the retrieved lidar profiles (starting at 800 m) for conditions where the predicted and retrieved backscatter profiles differed considerably. The difference in the predicted and retrieved lidar ratio is usually less than ±30 %. Our study thus shows that, for well-mixed boundary layers, a representative lidar ratio can be estimated from ground-based in situ measurements of chemical composition and dry size distribution. This approach offers a method of providing lidar ratios calculated from independent in situ measurements for simple backscatter lidars or at times when Raman lidar profiles cannot be measured (e.g. during the daytime). It only uses data that are routinely available at aerosol measurement stations and is therefore not only useful for further validating lidar measurements but also for bridging the gap between in situ measurements and lidar remote sensing. [ABSTRACT FROM AUTHOR]

A novel atmospheric layer detection approach has been developed based on deep learning techniques for image segmentation. Our proof of concept estimated the layering in the atmosphere, distinguishing between pollution-rich layers closer to the surface and cleaner layers aloft. Knowledge of the spatio-temporal development of atmospheric layers, such as the mixing boundary layer height (MBLH), is important for the dispersion of air pollutants and greenhouse gases, as well as for assessing the performance of numerical weather prediction systems. Existing lidar-based layer detection algorithms typically do not use the full resolution of the available data, require manual feature engineering, often do not enforce temporal consistency of the layers, and lack the ability to be applied in near-real time. To address these limitations, our Deep-Pathfinder algorithm represents the MBLH profile as a mask and directly predicts it from an image with backscatter lidar observations. Deep-Pathfinder was applied to range-corrected signal data from Lufft CHM15k ceilometers at five locations of the operational ceilometer network in the Netherlands. Input samples of 224 × 224 px were extracted, each covering a 45 min observation period. A customised U-Net architecture was developed with a nighttime indicator and MobileNetV2 encoder for fast inference times. The model was pre-trained on 19.4×106 samples of unlabelled data and fine-tuned using 50 d of high-resolution annotations. Qualitative and quantitative results showed competitive performance compared to two reference methods: the Lufft and STRATfinder algorithms, applied to the same dataset. Deep-Pathfinder enhances temporal consistency and provides near-real-time estimates at full spatial and temporal resolution. These properties make our approach valuable for application in operational networks, as near-real-time and high-resolution MBLH detection better meets the requirements of users, such as in aviation, weather forecasting, and air quality monitoring. [ABSTRACT FROM AUTHOR]

In this study, a Mie theory-based model was built to predict the vertical profile of the aerosol optical properties, including the aerosol scattering coefficient, backscatter coefficient, extinction coefficient, and lidar ratio. The model utilizes ground-based in-situ measurements of the aerosol chemical composition and particle size distribution, as well as the meteorological data from the Weather Forecasts (ECMWF) as input values. These are all parameters readily obtained for ACTRIS sites and the aim of this study was to investigate their suitability for generating representative estimates of the lidar ratio, and then further improve the lidar retrievals by utilizing these estimates. The measurements were performed during the Ruisdael land-atmosphere interactions Intensive Trace-gas and Aerosol (RITA) campaign in the Netherlands in 2021. The calculated dry aerosol optical properties were validated against a Nephelometer with good agreements (R2 ≈ 0.9). The predicted ambient vertical profiles of aerosol optical properties were compared to retrievals by a multi-wavelength Raman lidar. Predicted and retrieved backscatter coefficients were usually comparable under conditions of a well-mixed boundary layer. The extinction coefficients and lidar ratios were retrieved by the Raman lidar only at a height above 800 m. The estimated lidar ratio profiles based on in-situ data connected reasonably well to the lidar profiles within the boundary layer, with differences on average ± 30 %. Our study shows that for well-mixed boundary layers, a representative lidar ratio can be estimated based on ground-based in-situ measurements of dry size distribution and chemical composition taking into account the hygroscopic growth and ambient humidity. This allows to extend extinction profiles to lower altitudes, where they cannot be retrieved, or for use with simple elastic backscatter lidar to derive extinction profiles. The proposed method could be further applied to predict aerosol optical depth and also might be beneficial for large-scale or global radiation simulations. [ABSTRACT FROM AUTHOR]

In this study, a Mie theory-based model was built to predict the vertical profile of the aerosol optical properties, including the aerosol scattering coefficient, backscatter coefficient, extinction coefficient, and lidar ratio. The model utilizes ground-based in-situ measurements of the aerosol chemical composition and particle size distribution, as well as the meteorological data from the Weather Forecasts (ECMWF) as input values. These are all parameters readily obtained for ACTRIS sites and the aim of this study was to investigate their suitability for generating representative estimates of the lidar ratio, and then further improve the lidar retrievals by utilizing these estimates. The measurements were performed during the Ruisdael landatmosphere interactions Intensive Trace-gas and Aerosol (RITA) campaign in the Netherlands in 2021. The calculated dry aerosol optical properties were validated against a Nephelometer with good agreements (R2 = 0.9). The predicted ambient vertical profiles of aerosol optical properties were compared to retrievals by a multi-wavelength Raman lidar. Predicted and retrieved backscatter coefficients were usually comparable under conditions of a well-mixed boundary layer. The extinction coefficients and lidar ratios were retrieved by the Raman lidar only at a height above 800 m. The estimated lidar ratio profiles based on in-situ data connected reasonably well to the lidar profiles within the boundary layer, with differences on average ± 30%. Our study shows that for well-mixed boundary layers, a representative lidar ratio can be estimated based on groundbased in-situ measurements of dry size distribution and chemical composition taking into account the hygroscopic growth and ambient humidity. This allows to extend extinction profiles to lower altitudes, where they cannot be retrieved, or for use with simple elastic backscatter lidar to derive extinction profiles. The proposed method could be further applied to predict aerosol optical depth and also might be beneficial for large-scale or global radiation simulations. [ABSTRACT FROM AUTHOR]

LALINET (Latin American Lidar Network) follows its goal to consolidation as a federative lidar network to provide regional coverage over Latin America in providing aerosol and greenhouse gas profiles following QA/QC protocols and promoting the development of researchers and students in atmopheric science field. We show recent results on different approaches for studying the optical properties of the atmosphere regarding aerosols at tropospheric and stratospheric level and greenhouse gas mixing ratio profiles followed by our recent support and validation efforts towards present and future satellite missions.

The mixing layer height (MLH) indicates the change between vertical mixing of air near the surface and less turbulent air above. MLH is important for the dispersion of air pollutants and greenhouse gases, and for assessing the performance of numerical weather prediction systems. Existing lidar-based MLH detection algorithms typically do not use the full resolution of the ceilometer, require manual feature engineering, and often do not enforce temporal consistency of the MLH. To address these limitations, a novel MLH detection approach has been developed based on deep learning techniques for image segmentation. The concept of our Deep-Pathfinder algorithm is to represent the 24-hour MLH profile as a mask and directly predict it from an image with lidar observations. Therefore, range-corrected signal data was obtained from Lufft CHM 15k ceilometers at five locations in the Netherlands that were part of the operational ceilometer network. Input samples of 224×224 pixels were extracted, each covering a 45-minute observation period. A customised U-Net architecture was devel oped with a nighttime indicator and MobileNetV2 encoder for fast inference times. The model was pre-trained on 19.4 million samples of unlabelled data and fine-tuned using 50 days of high-resolution annotations. Qualitative and quantitative results showed competitive performance compared to two benchmark models: the Lufft and STRATfinder algorithms. Existing path optimisation algorithms have good temporal consistency, but can only be evaluated after a full day of ceilometer data has been recorded. Deep-Pathfinder retains the advantages of temporal consistency but can also provide real-time estimates. This makes our approach valuable for operational settings, as real-time MLH detection better meets the requirements of users such as in aviation, weather forecasting and air quality monitoring. [ABSTRACT FROM AUTHOR]

LALINET lidar stations were used to track down aerosols generated over Amazon region and transported over the continent. These data were merged with collocated Aeronet stations in order to help in their identification together with HYSPLIT simulations. The results show potential indication of how aerosol can age in their long transport over regions South and Westward from the source areas by change of their optical properties.

Lidar measurements of cirrus clouds are highly influenced by multiple scattering (MS). We therefore developed an iterative approach to correct elastic backscatter lidar signals for multiple scattering to obtain best estimates of single-scattering cloud optical depth and lidar ratio as well as of the ice crystal effective radius. The approach is based on the exploration of the effect of MS on the molecular backscatter signal returned from above cloud top.

LALINET is expanding regionally to guarantee spatial coverage over South and Central Americas. One of the network goals is to obtain a set of regional representative aerosol optical properties such as particle backscatter, extinction and lidar ratio. Given the North-South extension and influence of distinct airmass circulation patterns it is paramount to distinguish these optical parameters in order to gain better perfomance in radiation transfer models. A set of lidar ratio data is presented.

The Latin American Lidar Network, ALINE a.k.a LALINET is a federation lidar network established in 2008 which became a member of GALION/GAW program in 2013. Currently the network consists of 9 operational stations with the perspective of two more stations to be included. The network today covers more than 18 million Km2 and spans in latitude from -52° to 21° and in longitude from -78° to -47°. It should cover a larger area in the future as planned with the inclusion of more active stations.

For one year, from July 2011 to June 2012, a ground-based raman lidar provided atmospheric observations north of Manaus, Brazil, at an experimental site (2.89° S and 59.97° W) for long-term aerosol and cloud measurements. Upper tropospheric cirrus clouds were observed more frequently than previous reports in tropical regions. The frequency of occurrence was found to be as high as 82% during the wet season and not lower than 55% during the dry season. The diurnal cycle shows a minimum around local noon and maximum during late afternoon, associated with the diurnal cycle precipitation. Optical and geometrical characteristics of these cirrus clouds were derived. The mean values were 14.4 ± 2.0 km (top), 12.7 ± 2.3 km (base), 1.7 ± 1.5 km (thickness), and 0.36 ± 1.20 (cloud optical depth). Cirrus clouds were found at temperatures down to -90 °C and 7% were above the tropopause base. The vertical distribution was not uniform and two cloud types were identified: (1) cloud base > 14 km and optical depth ~0.02, and (2) cloud base < 14 km and optical depth ~0.2. A third type, not previously reported, was identified during the wet season, between 16 and 18 km with optical depth ~0.005. The mean lidar ratio was 20.2 ± 7.0 sr, indicating a mixture of thick plates and long columns. However, the clouds above 14 km have a bimodal distribution during the dry season with a secondary peak at about 40 sr suggesting that thin plates are a major habit. A dependence of the lidar ratio with cloud temperature (altitude) was not found, thus indicating they are well mixed in the vertical. Cirrus clouds classified as subvisible (τ < 0.03) were 40%, whilst 37.7% were thin cirrus (0.03 < τ < 0.3) and 22.3% opaque cirrus (τ > 0.3). Hence, not only does the central Amazon have a high frequency of cirrus clouds, but a large fraction of subvisible cirrus clouds as well. This high frequency of subvisible cirrus clouds may contaminate aerosol optical depth measured by sun-photometers and satellite sensors to an unknown extent. [ABSTRACT FROM AUTHOR]