The challenge of identifying dust events in a highly polluted Eastern Mediterranean region.

Dust pollution largely impacts our environment, health and well-being. However, there is no agreement on how dust-contaminated days are identified to study exposures, as methods differ across disciplines. Different quantitative thresholds, which rely on ground measurements, are generally used to define these events. In this study, we used ground-based lidar measurements to detect dust layers. The dataset was then compared to methods that are widely used to define the presence of dust on the ground. Our results show that dust layers extend to a height of up to 10 km and a depth of up to 6.3 km. We show that at least 50 % of days that include dust components according to the lidar were not included by any of the methods that we investigated. As a result, these days are not considered in many health-related studies and climate models. Many dust events exhibit a high anthropogenic component and can be misinterpreted: (Ångström exponent>1.2), high-altitude (on average above 1.7 km) and relatively shallow (average depth 1.4 km) dust layers, and low PM10 on the ground. Mixed pollution (0.8 < Ångström exponent < 1.2) accounts for 45 % of these events. The most accurate dust-detection method considered the aerosol optical depth and Ångström exponent parameters, and provided 60 % of the dust days as determined by lidar. It does not seem to be possible to differentiate between anthropogenic and dust events because most measurements contained dust, resulting in further biased estimations. Our results indicate that there is a need to change our perception of what constitutes a dust day, when studying the impact of dust exposure. We suggest that in arid and semiarid, and in particular Eastern Mediterranean climates, where dust is a frequent and strong meteorological component, a greater number of days need to be included in the analyses or critically evaluated.

Cloud micro- and macrophysical properties from ground-based remote sensing during the MOSAiC drift experiment.

In the framework of the Multidisciplinary drifting Observatory for the Study of Arctic Climate Polarstern expedition, the Leibniz Institute for Tropospheric Research, Leipzig, Germany, operated the shipborne OCEANET-Atmosphere facility for cloud and aerosol observations throughout the whole year. OCEANET-Atmosphere comprises, amongst others, a multiwavelength Raman lidar, a microwave radiometer, and an optical disdrometer. A cloud radar was operated aboard Polarstern by the US Atmospheric Radiation Measurement program. These measurements were processed by applying the so-called Cloudnet methodology to derive cloud properties. To gain a comprehensive view of the clouds, lidar and cloud radar capabilities for low- and high-altitude observations were combined. Cloudnet offers a variety of products with a spatiotemporal resolution of 30 s and 30 m, such as the target classification, and liquid and ice microphysical properties. Additionally, a lidar-based low-level stratus retrieval was applied for cloud detection below the lowest range gate of the cloud radar. Based on the presented dataset, e.g., studies on cloud formation processes and their radiative impact, and model evaluation studies can be conducted.

Cloud Aerosol Transport System (CATS) 1064 nm Calibration and Validation.

The Cloud-Aerosol Transport System (CATS) lidar on board the International Space Station (ISS) operated from 10 February 2015 to 30 October 2017 providing range-resolved vertical backscatter profiles of Earth's atmosphere at 1064 and 532 nm. The CATS instrument design and ISS orbit lead to a higher 1064 nm signal-to-noise ratio than previous space-based lidars, allowing for direct atmospheric calibration of the 1064 nm signals. Nighttime CATS Version 3-00 data were calibrated by scaling the measured data to a model of the expected atmospheric backscatter between 22 and 26 km above mean sea level (AMSL). The CATS atmospheric model is constructed using molecular backscatter profiles derived from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) re-analysis data and aerosol scattering ratios measured by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The nighttime normalization altitude region was chosen to simultaneously minimize aerosol loading and variability within the CATS data frame, which extends from 28 km to -2 km AMSL. Daytime CATS Version 3-00 data were calibrated through comparisons with nighttime measurements of the layer integrated attenuated total backscatter (iATB) from strongly scattering, rapidly attenuating opaque cirrus clouds. The CATS nighttime 1064 nm attenuated total backscatter (ATB) uncertainties for clouds and aerosols are primarily related to the uncertainties in the CATS nighttime calibration technique, which are estimated to be ~9%. Median CATS V3-00 1064 nm ATB relative uncertainty at night within cloud and aerosol layers is 7%, slightly lower than these calibration uncertainty estimates. CATS median daytime 1064 nm ATB relative uncertainty is 21% in cloud and aerosol layers, similar to the estimated 16-18% uncertainty in the CATS daytime cirrus cloud calibration transfer technique. Coincident daytime comparisons between CATS and the Cloud Physics Lidar (CPL) during the CATS-CALIPSO Airborne Validation Experiment (CCAVE) project show good agreement in mean ATB profiles for clear-air regions. Eight nighttime comparisons between CATS and the PollyXT ground based lidars also show good agreement in clear-air regions between 3-12 km, with CATS having a mean ATB of 19.7 % lower than PollyXT. Agreement between the two instruments (~7%) is even better within an aerosol layer. Six-month comparisons of nighttime ATB values between CATS and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) also show that iATB comparisons of opaque cirrus clouds agree to within 19%. Overall, CATS has demonstrated that direct calibration of the 1064 nm channel is possible from a space based lidar using the atmospheric normalization technique.