Polarimetry Influenced by CCN aNd INP in Cyprus and Chile (PICNICC)

Joint project between
Leibniz-Institute for Tropospheric Research (TROPOS) and University of Leipzig

Leibniz-Institute for Tropospheric Research (TROPOS): Audrey Teisseire (PhD student) and Patric Seifert (PI)
University of Leipzig: Teresa Vogl (PhD student), Johannes Quaas (PI) and Heike Kalesse (PI)

PICNICC’s goal is to improve our understanding of aerosol effects on microphysical growth processes in mixed-phase clouds. Based on a combination of radar polarimetry, dual-frequency Doppler radar and lidar observations from a clean, pristine site in Punta Arenas (Chile, 53°S,71°W) and an aerosol-burden site in Limassol (Cyprus, 35°N,33°E ), the contrasts in the microphysical fingerprints of mixed-phase clouds, specifically of pristine ice formation, riming and aggregation, are studied. The investigations are supported by high-resolved numerical weather simulations using the ICON-NWP model with 2-moment microphysics, which are used to evaluate the impact of aerosol variations on cloud microphysics over the two field sites. Polarimetric, Doppler-capable radar forward operators, such as PAMTRA or CR-SIM, will be used to transfer the simulation results into observational space.

Our main hypothesis is, that the availability of ice nucleating particles (INP) drives the process by which ice grows in clouds in the temperature range between 0°C and -40°C, where liquid water can be present. When high concentrations of INPs are available, we expect clouds to glaciate at higher temperatures (Seifert et al., 2010; Kanitz et al., 2011), leading to aggregation being the dominant growth process. Opposed to this, if there is a scarcity in INP, liquid water layers will be more persistent. As a result, riming will occur more frequently than in high-INP conditions. Fig. 1 summarizes the proposed hypothesis in a schematic.

Fig. 1 Schematic of the PICNICC hypothesis: Large concentrations in INP (mainly dust) lead to a higher ice fraction in clouds, thus promoting growth by aggregation. When INP concentrations are low, more supercooled liquid water will be present in the clouds, and riming will occur more frequently

Current status
The DACAPO-PESO field experiment (short for Dynamics, Aerosol, Cloud and Precipitation Observations in the Pristine Environment of the Southern Ocean) has started in December 2018 (Check out our blog! https://dacapo.tropos.de/). The campaign is a collaborative effort between TROPOS, University of Leipzig and the University of Magallanes, Punta Arenas. The installed instrumentation is the Leipzig Aerosol and Cloud Remote Observations System (LACROS) of TROPOS (Bühl et al., 2013), which comprises a 35-GHz cloud radar Mira-35, a multiwavelength-Raman-polarization lidar Polly-XT, Doppler lidar, microwave radiometer, disdrometer and radiation observations. A W-band radar from University of Leipzig was employed at the site, in addition, until September 25, 2019, meaning that almost 9 months of dual-wavelength observations are available. The operation of LACROS in Punta Arenas will be continued until April 2020.

Fig. 2 Picture of the DACAPO-PESO site in Punta Arenas taken in August 2019. Photo credit: Martin Radenz

Contribution of University Leipzig
● Cloud radar Doppler spectra

The detection of riming and aggregation processes is the first step required to analyze the differences in ice growth mechanisms between the two sites of Punta Arenas and Limassol. Cloud radar Doppler spectra contain useful information about the hydrometeor populations which are present in the radar observation volume. We want to take advantage of this connection to detect dominant microphysical ice growth processes. Especially riming can be evident from the cloud radar Doppler spectra: Supercooled liquid water droplets have fall velocities close to zero, while rimed particles fall at a considerable speed, which is significantly higher than e.g. for fluffy aggregates. Fig. 3 shows an example for observed riming fingerprints in the radar reflecitivity (left panel) and the cloud radar Doppler spectra. The reflectivity time-height plot shows signatures of high-reflectivity “streaks” in higher cloud regions coinciding with the time of strongest precipitation. A range spectrogram plot at 14:22 UTC (top right panel) reveals a layer of supercooled liquid water (SLW) present (i.e. a mode with fall velocity around 0 m/s) between 2500 and 4000 m altitude. Below the SLW layer, the reflectivity increases. An example Doppler spectrum from 2800 m altitude reveals that two modes of ice particles are present in the same volume as the supercooled cloud droplets.

Fig. 3 Example for riming signature evident in cloud radar Doppler spectra. The top left panel shows the radar reflectivity measured by the W-band radar on February 23, 2019 in Punta Arenas. In the top right panel, a range spectrogram at 14:22 UTC is plotted. The bottom panel shows Doppler spectra measured by the W-band (blue) and the Ka-band (red) at approximately the same time and range. Both spectra exhibit a narrow liquid water peak at around 0 m/s Doppler velocity.

● Cloud-resolving ICON modeling

The ICON model with the two-moments microphysics scheme, which was introduced by Seifert & Beheng (2006), is utilized to attribute observed differences in ice microphysical processes between the two sites. To make a comparison between modeled and measured clouds feasible, a forward operator is required. We are using the Passive and Active Microwave Transfer (PAMTRA) forward model (Mech et al., 2020; https://github.com/igmk/pamtra) to convert model output of one grid-box (the “meteogram” output for the location of Punta Arenas) to radar variables.

Fig. 4 Left panel: Measured reflectivity (Ka-band) on June 11, 2019 in Punta Arenas. Right panel: Forward-simulated reflectivity at Ka-band using the PAMTRA forward model. The ICON model was run at 10 km horizontal grid resolution for this case.

Contribution of TROPOS
● SLDR-mode scanning cloud radar

Main instrument for the contribution of TROPOS to PICNICC is the 35-GHz cloud radar Mira-35 which was operated SLDR mode in both campaigns. This mode is implemented based on the setup of a conventional LDR-mode cloud radar, with the receiving plane rotated by 45 degrees with respect to the plane of transmission. In comparison to the standard linear depolarization ratio, slanted linear depolarization ratio is considerably less susceptible to variations in the orientation of hydrometeors, enabling the determination of the hydrometeor shape and orientation from range-height-indicator (RHI) scans (Myagkov et al., 2016). During both field campaigns, Mira-35 performed RHI scans from 90° to 30° elevation once per hour (at minute 30). During the remaining time, vertical-stare observations were performed to enable the characterization of the overall evolution of the atmosphere.

● Cloud microphysical fingerprinting from polarimetric parameters in SLDR mode

The combination of the gradient of both SLDR and cross correlation coefficient for elevations from 30° to 90° permits us to differentiate the main ice particle shapes, as is illustrated in Figure 5 below. Considering in addition the fall velocity of the particles yields to a refinement in the differentiation between spherically shaped aggregates and graupel.
Fig. 5: Illustration of the relationship between SLDR and cross correlation coefficient (ρcx) and antenna elevation angle for different ice particle shapes (column, aggregate, graupel, dendrite).

From the hourly SLDR-mode RHI scans of MIRA-35, profiles of the shape and orientation of particles can be derived for detected cloud layers. Figure 6 represents a specific case of mixed-phase cloud observed in Punta Arenas on 4th September 2019. For example, for the given positive gradient in both SLDR and cross correlation coefficient with decreasing elevation (increasing distance) informs us about the presence of dendritic hydrometeors.

Fig. 6: Observation of a mixed-phase cloud during an SLDR-mode RHI scan in Punta Arenas on 4 September 2019. (a) SLDR and (b) cross correlation coefficient.

From the long-term datasets of SLDR-mode RHI scans from Limassol and Punta Arenas, statistics of the vertical gradient of particle shape and orientation in the observed cloud layers will be studied and put into context with the overall atmospheric conditions as well as the aerosol load. Focus of the analysis will be put on the relationship of the microphysical evolution (i.e., the vertical gradient of shape and orientation) on aerosol conditions, with specific interest being put on distinguishing aggregation and graupel growth processes.

Bühl, J., Seifert, P, Wandinger, U., Baars, H., Kanitz, T., Schmidt, J., Myagkov, A., Engelmann, R., Skupin, A., Heese, B., Klepel, A., Althausen, D., Ansmann, A. (2013), LACROS: the Leipzig Aerosol and Cloud Remote Observations System, Proc. SPIE 8890, Remote Sensing of Clouds and the Atmosphere XVIII; and Optics in Atmospheric Propagation and Adaptive Systems XVI, 889002, https://doi.org/10.1117/12.2030911

Kanitz, T., Seifert, P., Ansmann, A., Engelmann, R., Althausen, D., Casiccia, C., and Rohwer, E. G. ( 2011), Contrasting the impact of aerosols at northern and southern midlatitudes on heterogeneous ice formation, Geophys. Res. Lett., 38, L17802, doi:10.1029/2011GL048532.

Mech, M., Maahn, M., Kneifel, S., Ori, D., Orlandi, E., Kollias, P., Schemann, V, Crewell, S. (2020), PAMTRA 1.0: A Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere, submitted.

Myagkov, A., Seifert, P., Bauer-Pfundstein, M., and Wandinger, U. (2016): Cloud radar with hybrid mode towards estimation of shape and orientation of ice crystals, Atmos. Meas. Tech., 9, 469–489, https://doi.org/10.5194/amt-9-469-2016.

Seifert, P., Ansmann, A., Mattis, I., Wandinger, U., Tesche, M., Engelmann, R., Müller, D., Pérez, C., and Haustein, K. ( 2010), Saharan dust and heterogeneous ice formation: Eleven years of cloud observations at a central European EARLINET site, J. Geophys. Res., 115, D20201, doi:10.1029/2009JD013222.

Seifert, A. & Beheng, K. D. (2006), A two-moment cloud microphysics parameterization for mixed-phase clouds. Part 1: Model description Meteorol. Atmos. Phys., 92, 45-66, 10.1007/s00703-005-0112-4