. A global vertically resolved aerosol data set covering more than 10 years of observations at more than 20 measurement sites distributed from 63° N to 52° S and 72° W to 124° E has been achieved within the Raman and polarization lidar network PollyNET. This network consists of portable, remote-controlled multiwavelength-polarization-Raman lidars (Polly) for automated and continuous 24/7 observations of clouds and aerosols. PollyNET is an independent, voluntary, and scientific network. All Polly lidars feature a standardized instrument design with different capabilities ranging from single wavelength to multiwavelength systems, and now apply unified calibration, quality control, and data analysis. The observations are processed in near-real time without manual intervention, and are presented online at http://polly.tropos.de/ . The paper gives an overview of the observations on four continents and two research vessels obtained with eight Polly systems. The specific aerosol types at these locations (mineral dust, smoke, dust-smoke and other dusty mixtures, urban haze, and volcanic ash) are identified by their Angstrom exponent, lidar ratio, and depolarization ratio. The vertical aerosol distribution at the PollyNET locations is discussed on the basis of more than 55 000 automatically retrieved 30 min particle backscatter coefficient profiles at 532 nm as this operating wavelength is available for all Polly lidar systems. A seasonal analysis of measurements at selected sites revealed typical and extraordinary aerosol conditions as well as seasonal differences. These studies show the potential of PollyNET to support the establishment of a global aerosol climatology that covers the entire troposphere.

/pdf/an-overview-of-the-first-decade-of-polly-net-an-emerging-4nli48cy79.pdf

An overview of the first decade of Polly NET : an emerging network of automated Raman-polarization lidars for continuous aerosol profiling

We present a method using Doppler lidar data for identifying the main sources of turbulent mixing within the atmospheric boundary layer. The method identifies the presence of turbulence and then assigns a turbulent source by combining several lidar quantities: attenuated backscatter coefficient, vertical velocity skewness, dissipation rate of turbulent kinetic energy, and vector wind shear. Both buoyancy‐driven and shear‐driven situations are identified, and the method operates in both clear‐sky and cloud‐topped conditions, with some reservations in precipitation. To capture the full seasonal cycle, the classification method was applied to more than 1 year of data from two sites, Hyytiala, Finland, and Julich, Germany. Analysis showed seasonal variation in the diurnal cycle at both sites; a clear diurnal cycle was observed in spring, summer, and autumn seasons, but due to their respective latitudes, a weaker cycle in winter at Julich, and almost non‐existent at Hyytiala. Additionally, there are significant contributions from sources other than convective mixing, with cloud‐driven mixing being observed even within the first 500 m above ground. Also evident is the considerable amount of nocturnal mixing within the lowest 500 m at both sites, especially during the winter. The presence of a low‐level jet was often detected when sources of nocturnal mixing were diagnosed as wind shear. The classification scheme and the climatology extracted from the classification provide insight into the processes responsible for mixing within the atmospheric boundary layer, how variable in space and time these can be, and how they vary with location.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2017JD028169

Atmospheric Boundary Layer Classification With Doppler Lidar

Use of 2D-video disdrometer to derive mean density–size and Ze–SR relations: Four snow cases from the light precipitation validation experiment

Processes in the climate system that can either amplify or dampen the climate response to an external perturbation are referred to as climate feedbacks. Climate sensitivity estimates depend critically on radiative feedbacks associated with water vapor, lapse rate, clouds, snow, and sea ice, and global estimates of these feedbacks differ among general circulation models. By reviewing recent observational, numerical, and theoretical studies, this paper shows that there has been progress since the Third Assessment Report of the Intergovernmental Panel on Climate Change in (i) the understanding of the physical mechanisms involved in these feedbacks, (ii) the interpretation of intermodel differences in global estimates of these feedbacks, and (iii) the development of methodologies of evaluation of these feedbacks ( or of some components) using observations. This suggests that continuing developments in climate feedback research will progressively help make it possible to constrain the GCMs' range of climate feedbacks and climate sensitivity through an ensemble of diagnostics based on physical understanding and observations.

How well do we understand and evaluate climate change feedback processes

Under certain conditions, secondary organic aerosol (SOA) particles can exist in the atmosphere in an amorphous solid or semi-solid state. To determine their relevance to processes such as ice nucleation or chemistry occurring within particles requires knowledge of the temperature and relative humidity (RH) range for SOA to exist in these states. In the Cosmics Leaving Outdoor Droplets (CLOUD) experiment at The European Organisation for Nuclear Research (CERN), we deployed a new in situ optical method to detect the viscous state of α-pinene SOA particles and measured their transition from the amorphous highly viscous state to states of lower viscosity. The method is based on the depolarising properties of laboratory-produced non-spherical SOA particles and their transformation to non-depolarising spherical particles at relative humidities near the deliquescence point. We found that particles formed and grown in the chamber developed an asymmetric shape through coagulation. A transition to a spherical shape was observed as the RH was increased to between 35 % at −10 °C and 80 % at −38 °C, confirming previous calculations of the viscosity-transition conditions. Consequently, α-pinene SOA particles exist in a viscous state over a wide range of ambient conditions, including the cirrus region of the free troposphere. This has implications for the physical, chemical, and ice-nucleation properties of SOA and SOA-coated particles in the atmosphere.

/pdf/observation-of-viscosity-transition-in-a-pinene-secondary-2oteeaebdi.pdf

Observation of viscosity transition in α -pinene secondary organic aerosol

. The Finnish Meteorological Institute, in collaboration with the University of Helsinki, has established a new ground-based remote-sensing network in Finland. The network consists of five topographically, ecologically and climatically different sites distributed from southern to northern Finland. The main goal of the network is to monitor air pollution and boundary layer properties in near real time, with a Doppler lidar and ceilometer at each site. In addition to these operational tasks, two sites are members of the Aerosols, Clouds and Trace gases Research InfraStructure Network (ACTRIS); a Ka band cloud radar at Sodankyla will provide cloud retrievals within CloudNet, and a multi-wavelength Raman lidar, PollyXT (POrtabLe Lidar sYstem eXTended), in Kuopio provides optical and microphysical aerosol properties through EARLINET (the European Aerosol Research Lidar Network). Three C-band weather radars are located in the Helsinki metropolitan area and are deployed for operational and research applications. We performed two inter-comparison campaigns to investigate the Doppler lidar performance, compare the backscatter signal and wind profiles, and to optimize the lidar sensitivity through adjusting the telescope focus length and data-integration time to ensure sufficient signal-to-noise ratio (SNR) in low-aerosol-content environments. In terms of statistical characterization, the wind-profile comparison showed good agreement between different lidars. Initially, there was a discrepancy in the SNR and attenuated backscatter coefficient profiles which arose from an incorrectly reported telescope focus setting from one instrument, together with the need to calibrate. After diagnosing the true telescope focus length, calculating a new attenuated backscatter coefficient profile with the new telescope function and taking into account calibration, the resulting attenuated backscatter profiles all showed good agreement with each other. It was thought that harsh Finnish winters could pose problems, but, due to the built-in heating systems, low ambient temperatures had no, or only a minor, impact on the lidar operation – including scanning-head motion. However, accumulation of snow and ice on the lens has been observed, which can lead to the formation of a water/ice layer thus attenuating the signal inconsistently. Thus, care must be taken to ensure continuous snow removal.

/pdf/observing-wind-aerosol-particles-cloud-and-precipitation-s2jtaonypw.pdf

M. Bauer-Pfundstein

Papers

Observing wind, aerosol particles, cloud and precipitation: Finland's new ground-based remote-sensing network