COMPoSE: Charakterisierung der Verteilung der Eis- und Flüssigphase in Mischphasenwolken
Zusammenfassung der Projektergebnisse
In order to characterize phase partitioning in mixed-phase clouds, extensive ground-based remote-sensing data sets obtained in different geographical regions (Finland, North Slope of Alaska, the Netherlands) were analysed. Particular focus was on certain case studies on a) a wintertime snow fall system with riming conditions, b) a low-level stratiform Arctic mixed-phase cloud, and c) midlatitudinal midlevel clouds (partly with multiple supercooled liquid layers). In these studies, different aspects increasing our understanding of mixed-phase clouds were considered from observational and modelling points of view. In study a) it was shown that the fingerprinting of the microphysical growth process riming is more obvious on cloud radar Doppler spectra than on cloud radar moments (like reflectivity and mean Doppler velocity) and that in cases of wind shear, the evolution of hydrometeor populations should be observed along slanted paths (fall streaks) instead of along vertical profiles. It was shown that while 1D microphysical bin model results were capable to reproduce the observed radar variables of the rimed snow mode well, the ground-based observations during the BAECC experiment in Finland were not sufficient to give restrictions on the most fitting riming parameterization for that particular case which highlights the need of also performing in-situ cloud measurements. In b), the focus was on determining the main factors driving rapid changes in cloud phase partitioning in a low-level stratiform Arctic mixed-phase cloud based on observational and modelling perspectives via which cloud-scale vs. large-scale processes were analysed. It was found that besides the advection of air masses with different moisture and aerosol contents, changes in thermodynamic cloud-surface coupling also played a big role. The latter has an effect on the residence time of ice particles in the sub-cloud region, and thus their sublimation and subsequently the potential recirculation of ice nuclei into the cloud. The work suggests that such detailed process-studies are most fruitful if they consider cloud dynamical and thermodynamical aspects to study aerosol-cloud interactions. In addition, it showed that lidar-radar instrument synergies and the use of higher radar moments - in this case skewness - help to discriminate multiple hydrometeor populations in the same radar volume. Besides detailed mixed-phase cloud case study analyses aimed at microphysical process understanding, the project also focused on the development of different algorithms to help disentangling the contribution of (supercooled) liquid and ice to cloud radar returns. The algorithm developments are ongoing and will further be refined by me and my newly-formed research group at LIM with close collaborations at TROPOS. In particular, we developed a semi-supervised cloud radar Doppler peak-finding method, its performance compared to other (published) methods is being tested. Moreover, an artificial neural network (ANN) developed by Ed Luke (BNL) which uses cloud radar Doppler spectra features to predict lidar backscatter coefficient and depolarization to detect cloud liquid was employed. The ANN was previously trained on Arctic mixed-phase clouds but was found to also perform well for midlatitudinal mid-level mixed-phase clouds with multiple (thick) liquid layers and can thus help to improve the Cloudnet target classification mask for certain conditions. A publication on this topic is ongoing work. The great potential of the synergistic measurements of radar-lidar to detect SCL will be further explored by a PhD within the frame of an ESF proposal. Furthermore, funding within the DFG SPP-PROM aiming at cloud phase characterization and microphysical fingerprinting with the additional use of cloud polarimetric data is applied for.
Projektbezogene Publikationen (Auswahl)
- 2016: “Understanding rapid changes in phase partitioning between cloud liquid and ice in stratiform mixed-phase clouds: An Arctic Case Study”, Mon. Wea. Rev., 144
Kalesse, H., G. de Boer, A. Solomon, M.Oue, M. Ahlgrimm, D. Zhang, M. Shupe, E. Luke, A. Protat
(Siehe online unter https://doi.org/10.1175/MWR-D-16-0155.1) - 2016: „First observations of triple-frequency radar Doppler spectra in snowfall: Interpretation and applications“, Geophys. Res. Lett., 43
Kneifel, S., P. Kollias, A. Battaglia, J. Leinonen, M. Maahn, H. Kalesse, F. Tridon
(Siehe online unter https://dx.doi.org/10.1002/2015/GL067618) - 2016a: „Fingerprints of a riming event on cloud radar Doppler spectra: Observations and Modeling“, Atm. Chem. and Phys.,16
Kalesse, H., W. Szyrmer, S. Kneifel, P. Kollias, and E. Luke
(Siehe online unter https://doi.org/10.5194/acp-16-2997-2016) - 2017: Direct Estimation of the global distribution of vertical velocity within cirrus clouds. Nature Scientific Reports, Article number: 6840 (2017)
Barahona, D., A. Molod, A., and H. Kalesse
(Siehe online unter https://doi.org/10.1038/s41598-017-07038-6) - 2017: Remote Sensing, AMS Meteorological Monograph, Ch. 10, 2017
Bühl, J., S. Alexander, S. Crewell, A. Heymsfield, H. Kalesse, A. Khain, M. Maahn, K. Van Tricht, and M. Wendisch
(Siehe online unter https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0015.1) - 2017: Secondary Ice Production: Current State of the Science and Recommendations for the Future, AMS Meteorological Monograph, Ch. 7, 2017
P.R. Field, R.P. Lawson, P.R.A Brown, G. Lloyd, C. Westbrook, D. Moisseev, A. Miltenberger, A. Nenes, A. Blyth, T. Choularton, P. Connolly, J. Bühl, J. Croisier, Z. Cui, C. Dearden, P. deMott, A. Flossmann, A. Heymsfield, Y. Huang, H. Kalesse, Z.A., Kanji, A. Korolev, A. Kirchgaessner, S. Lasher-Trapp, T. Leisner, G. McFarquhar, V. Phillips, J. Stith, and S. Sullivan
(Siehe online unter https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0014.1)
