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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]

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]