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Application of the shipborne remote sensing supersite OCEANET for profiling of Arctic aerosols and clouds during Polarstern cruise PS106
2020, Griesche, Hannes J., Seifer, Patric, Ansmann, Albert, Baars, Holger, Velasco, Carola Barrientos, Bühl, Johannes, Engelmann, Ronny, Radenz, Martin, Zhenping, Yin, Macke, Andreas
From 25 May to 21 July 2017, the research vessel Polarstern performed the cruise PS106 to the high Arctic in the region north and northeast of Svalbard. The mobile remote-sensing platform OCEANET was deployed aboard Polarstern. Within a single container, OCEANET houses state-of-the-art remote-sensing equipment, including a multiwavelength Raman polarization lidar PollyXT and a 14-channel microwave radiometer HATPRO (Humidity And Temperature PROfiler). For the cruise PS106, the measurements were supplemented by a motion-stabilized 35 GHz cloud radar Mira-35. This paper describes the treatment of technical challenges which were immanent during the deployment of OCEANET in the high Arctic. This includes the description of the motion stabilization of the cloud radar Mira-35 to ensure vertical-pointing observations aboard the moving Polarstern as well as the applied correction of the vessels heave rate to provide valid Doppler velocities. The correction ensured a leveling accuracy of ±0.5◦ during transits through the ice and an ice floe camp. The applied heave correction reduced the signal induced by the vertical movement of the cloud radar in the PSD of the Doppler velocity by a factor of 15. Low-level clouds, in addition, frequently prevented a continuous analysis of cloud conditions from synergies of lidar and radar within Cloudnet, because the technically determined lowest detection height of Mira-35 was 165 m above sea level. To overcome this obstacle, an approach for identification of the cloud presence solely based on data from the near-field receiver of PollyXT at heights from 50 m and 165 m above sea level is presented. We found low-level stratus clouds, which were below the lowest detection range of most automatic ground-based remote-sensing instruments during 25 % of the observation time. We present case studies of aerosol and cloud studies to introduce the capabilities of the data set. In addition, new approaches for ice crystal effective radius and eddy dissipation rates from cloud radar measurements and the retrieval of aerosol optical and microphysical properties from the observations of PollyXT are introduced. © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License.
Significant continental source of ice-nucleating particles at the tip of Chile's southernmost Patagonia region
2022, Gong, Xianda, Radenz, Martin, Wex, Heike, Seifert, Patric, Ataei, Farnoush, Henning, Silvia, Baars, Holger, Barja, Boris, Ansmann, Albert, Stratmann, Frank
The sources and abundance of ice-nucleating particles (INPs) that initiate cloud ice formation remain understudied, especially in the Southern Hemisphere. In this study, we present INP measurements taken close to Punta Arenas, Chile, at the southernmost tip of South America from May 2019 to March 2020, during the Dynamics, Aerosol, Cloud, And Precipitation Observations in the Pristine Environment of the Southern Ocean (DACAPO-PESO) campaign. The highest ice nucleation temperature was observed at −3◦C, and from this temperature down to ∼ −10◦C, a sharp increase of INP number concentration (NINP) was observed. Heating of the samples revealed that roughly 90 % and 80 % of INPs are proteinaceous-based biogenic particles at > −10 and −15◦C, respectively. The NINP at Punta Arenas is much higher than that in the Southern Ocean, but it is comparable with an agricultural area in Argentina and forestry environment in the US. Ice active surface site density (ns) is much higher than that for marine aerosol in the Southern Ocean, but comparable to English fertile soil dust. Parameterization based on particle number concentration in the size range larger than 500 nm (N>500 nm) from the global average (DeMott et al., 2010) overestimates the measured INP, but the parameterization representing biological particles from a forestry environment (Tobo et al., 2013) yields NINP comparable to this study. No clear seasonal variation of NINP was observed. High precipitation is one of the most important meteorological parameters to enhance the NINP in both cold and warm seasons. A comparison of data from in situ and lidar measurements showed good agreement for concentrations of large aerosol particles (> 500 nm) when assuming continental conditions for retrieval of the lidar data, suggesting that these particles were well mixed within the planetary boundary layer (PBL). This corroborates the continental origin of these particles, consistent with the results from our INP source analysis. Overall, we suggest that a high NINP of biogenic INPs originated from terrestrial sources and were added to the marine air masses during the overflow of a maximum of roughly 150 km of land before arriving at the measurement station.
Retrieval of ice-nucleating particle concentrations from lidar observations and comparison with UAV in situ measurements
2019, Marinou, Eleni, Tesche, Matthias, Nenes, Athanasios, Ansmann, Albert, Schrod, Jann, Mamali, Dimitra, Tsekeri, Alexandra, Pikridas, Michael, Baars, Holger, Engelmann, Ronny, Voudouri, Kalliopi-Artemis, Solomos, Stavros, Sciare, Jean, Groß, Silke, Ewald, Florian, Amiridis, Vassilis
Aerosols that are efficient ice-nucleating particles (INPs) are crucial for the formation of cloud ice via heterogeneous nucleation in the atmosphere. The distribution of INPs on a large spatial scale and as a function of height determines their impact on clouds and climate. However, in situ measurements of INPs provide sparse coverage over space and time. A promising approach to address this gap is to retrieve INP concentration profiles by combining particle concentration profiles derived by lidar measurements with INP efficiency parameterizations for different freezing mechanisms (immersion freezing, deposition nucleation). Here, we assess the feasibility of this new method for both ground-based and spaceborne lidar measurements, using in situ observations collected with unmanned aerial vehicles (UAVs) and subsequently analyzed with the FRIDGE (FRankfurt Ice nucleation Deposition freezinG Experiment) INP counter from an experimental campaign at Cyprus in April 2016. Analyzing five case studies we calculated the cloud-relevant particle number concentrations using lidar measurements (n250,dry with an uncertainty of 20 % to 40 % and Sdry with an uncertainty of 30 % to 50 %), and we assessed the suitability of the different INP parameterizations with respect to the temperature range and the type of particles considered. Specifically, our analysis suggests that our calculations using the parameterization of Ullrich et al. (2017) (applicable for the temperature range −50 to −33 ∘C) agree within 1 order of magnitude with the in situ observations of nINP; thus, the parameterization of Ullrich et al. (2017) can efficiently address the deposition nucleation pathway in dust-dominated environments. Additionally, our calculations using the combination of the parameterizations of DeMott et al. (2015, 2010) (applicable for the temperature range −35 to −9 ∘C) agree within 2 orders of magnitude with the in situ observations of INP concentrations (nINP) and can thus efficiently address the immersion/condensation pathway of dust and nondust particles. The same conclusion is derived from the compilation of the parameterizations of DeMott et al. (2015) for dust and Ullrich et al. (2017) for soot.
Optical and geometrical properties of cirrus clouds in Amazonia derived from 1 year of ground-based lidar measurements
2017, Gouveia, Diego A., Barja, Boris, Barbosa, Henrique M. J., Seifert, Patric, Baars, Holger, Pauliquevis, Theotonio, Artaxo, Paulo
Cirrus clouds cover a large fraction of tropical latitudes and play an important role in Earth's radiation budget. Their optical properties, altitude, vertical and horizontal coverage control their radiative forcing, and hence detailed cirrus measurements at different geographical locations are of utmost importance. Studies reporting cirrus properties over tropical rain forests like the Amazon, however, are scarce. Studies with satellite profilers do not give information on the diurnal cycle, and the satellite imagers do not report on the cloud vertical structure. At the same time, ground-based lidar studies are restricted to a few case studies. In this paper, we derive the first comprehensive statistics of optical and geometrical properties of upper-tropospheric cirrus clouds in Amazonia. We used 1 year (July 2011 to June 2012) of ground-based lidar atmospheric observations north of Manaus, Brazil. This dataset was processed by an automatic cloud detection and optical properties retrieval algorithm. Upper-tropospheric cirrus clouds were observed more frequently than reported previously for tropical regions. The frequency of occurrence was found to be as high as 88 % during the wet season and not lower than 50 % during the dry season. The diurnal cycle shows a minimum around local noon and maximum during late afternoon, associated with the diurnal cycle of precipitation. The mean values of cirrus cloud top and base heights, cloud thickness, and cloud optical depth were 14.3 ± 1.9 (SD) km, 12.9 ± 2.2 km, 1.4 ± 1.1 km, and 0.25 ± 0.46, respectively. Cirrus clouds were found at temperatures down to ĝ'90 °C. Frequently cirrus were observed within the tropical tropopause layer (TTL), which are likely associated to slow mesoscale uplifting or to the remnants of overshooting convection. The vertical distribution was not uniform, and thin and subvisible cirrus occurred more frequently closer to the tropopause. The mean lidar ratio was 23.3 ± 8.0 sr. However, for subvisible cirrus clouds a bimodal distribution with a secondary peak at about 44 sr was found suggesting a mixed composition. A dependence of the lidar ratio with cloud temperature (altitude) was not found, indicating that the clouds are vertically well mixed. The frequency of occurrence of cirrus clouds classified as subvisible (τ < 0. 03) were 41.6 %, whilst 37.8 % were thin cirrus (0. 03;lt; τ < 0. 3) and 20.5 % opaque cirrus (τ > 0. 3). Hence, in central Amazonia not only a high frequency of cirrus clouds occurs, but also a large fraction of subvisible cirrus clouds. This high frequency of subvisible cirrus clouds may contaminate aerosol optical depth measured by sun photometers and satellite sensors to an unknown extent.