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Abstract. Statistics on the occurrence of clear skies, ice clouds, and mixed-phase clouds over Concordia Station, in the Antarctic Plateau, are provided for multiple timescales and analyzed in relation to simultaneous meteorological parameters measured at the surface. Results are obtained by applying a machine learning cloud identification and classification (CIC) code to 4 years of measurements between 2012–2015 of downwelling high-spectral-resolution radiances, measured by the Radiation Explorer in the Far Infrared – Prototype for Applications and Development (REFIR-PAD) spectroradiometer. The CIC algorithm is optimized for Antarctic sky conditions and results in a total hit rate of almost 0.98, where 1.0 is a perfect score, for the identification of the clear-sky, ice cloud, and mixed-phase cloud classes. Scene truth is provided by lidar measurements that are concurrent with REFIR-PAD. The CIC approach demonstrates the key role of far-infrared spectral measurements for clear–cloud discrimination and for cloud phase classification. Mean annual occurrences are 72.3 %, 24.9 %, and 2.7 % for clear sky, ice clouds, and mixed-phase clouds, respectively, with an inter-annual variability of a few percent. The seasonal occurrence of clear sky shows a minimum in winter (66.8 %) and maxima (75 %–76 %) during intermediate seasons. In winter the mean surface temperature is about 9 ∘C colder in clear conditions than when ice clouds are present. Mixed-phase clouds are observed only in the warm season; in summer they amount to more than one-third of total observed clouds. Their occurrence is correlated with warmer surface temperatures. In the austral summer, the mean surface air temperature is about 5 ∘C warmer when clouds are present than in clear-sky conditions. This difference is larger during the night than in daylight hours, likely due to increased solar warming. Monthly mean results are compared to cloud occurrence and fraction derived from gridded (Level 3) satellite products from both passive and active sensors. The differences observed among the considered products and the CIC results are analyzed in terms of footprint sizes and sensors' sensitivities to cloud optical and geometrical features. The comparison highlights the ability of the CIC–REFIR-PAD synergy to identify multiple cloud conditions and study their variability at different timescales.

Abstract. Statistics on the occurrence of clear skies, ice and mixed-phase clouds over the Concordia station, in the Antarctic Plateau, are provided for multiple time scales and analysed in relation to simultaneous meteorological parameters measured at the surface. Results are obtained by applying a machine learning cloud identification and classification code (named CIC) to 4 years of measurements between 2012–2105 of down-welling high spectral resolution radiances, measured by the Radiation Explorer in the Far Infrared-Prototype for Applications and Development (REFIR-PAD) spectroradiometer. The CIC algorithm is optimized for Antarctic sky conditions (clear sky, ice clouds, and mixed-phase clouds) and results in a total hit rate of almost 0.98, where 1.0 is a perfect score. Scene truth is provided by LiDAR measurements that are concurrent with REFIR-PAD. The CIC approach demonstrates the key role of far infrared spectral measurements for clear/cloud discrimination and for cloud phase classification. Mean annual occurrences are 72.3 %, 24.9 % and 2.7 % for clear sky, ice and mixed-phase clouds respectively, with an inter-annual variability of a few percent. The seasonal occurrence of clear sky shows a minimum in winter (66.8 %) and maxima (75–76 %) during intermediate seasons. In winter the mean surface temperature is about 9 °C colder in clear conditions than when ice clouds are present. Mixed-phase clouds are observed only in the warm season; in summer they amount to more than one third of total observed clouds. Their occurrence is correlated with warmer surface temperatures. In the austral summer, the mean surface air temperature is about 5 °C warmer when clouds are present than in clear sky conditions. This difference is larger during the night than in daylight hours, likely due to increased solar warming. A comparison of monthly mean results with cloud occurrence/fraction derived from gridded (Level-3) satellite products, from both passive and active sensors, emphasizes the difficulty of adequately inferring cloud/clear-sky properties in the Antarctic region and highlights the ability of the CIC/REFIR-PAD synergy to identify multiple cloud conditions and study their variability at different time scales.

Abstract. Selected case studies of precipitating ice clouds at Dome C (Antarctic Plateau) were used to test a new approach for the estimation of ice cloud reflectivity at 24 GHz (12.37 mm wavelength) using ground-based far infrared spectral measurements from the REFIR-PAD Fourier transform spectroradiometer and backscattering/depolarization lidar profiles. The resulting reflectivity was evaluated with the direct reflectivity measurements provided by a co-located micro rain radar (MRR) operating at 24 GHz, that was able to detect falling crystals with large particle size, typically above 600 µm. To obtain the 24 GHz reflectivity, we used the particle effective diameter and the cloud optical depth retrieved from the far infrared spectral radiances provided by REFIR-PAD and the tropospheric co-located backscattering lidar to calculate the modal radius and the intercept of the particle size distribution. These parameters spanned in the wide ranges between 570–2400 µm and 10−2–104 cm−5, respectively. The retrieved effective sizes and optical depths mostly varied in the ranges 70–250 µm and 0.1–5, respectively. From these parameters, the theoretical reflectivity at 24 GHz was obtained by integrating the size distribution over different cross sections for various habit crystals provided by Eriksson et al. (2018) databases. From the comparison with the radar reflectivity measurements, we found that the hexagonal column-like habits, the columnar crystal aggregates, and the 5/6 branches bullet rosettes showed the best agreement with the MRR observations. The dispersion coefficient of the crystal particle size distribution was assumed in the range 0–2 according to the temperature dependence found in previous studies. The retrieved values of the intercept and slope were found in good agreement with these studies. The presence of the inferred habits was confirmed by the crystal images taken by the ICE-CAMERA, operating in proximity of REFIR-PAD and the MRR. In particular, the occurrence of hexagonal column-like ice crystals was confirmed by the presence of 22∘ solar halos, detected by the HALO-CAMERA. The average crystal lengths obtained from the retrieved size distribution were also compared to those estimated from the ICE-CAMERA images. The agreement between the two results confirmed that the retrieved parameters of the particle size distributions correctly reproduced the observations.

<p>As global warming continues to be one of the greatest threats to Earth environment, the detection and monitoring of natural and anthropogenic emissions of greenhouse gases holds a critical role as the first step of any danger reduction policy. New generation spaceborne hyperspectral instruments cover large portions of the Earth while maintaining a high enough spectral and spatial resolution to investigate the contribution of single molecular species and accurately localize their emission source. The Matched Filter method is used to search enhanced concentrations of methane in the atmospheric column. PRISMA, ASI’s newest hyperspectral sensor, data are analysed. Both strong and weak CH<sub>4</sub> emissions, in multiple scenarios, are investigated. It is demonstrated that PRISMA data allow also the identification of methane non-punctual sources when the land gas emission is very high. An estimated flux in the order of 4000 kg/h is found for a case study considering a landfill in India.</p>