Articles | Volume 21, issue 22
https://doi.org/10.5194/acp-21-17133-2021
https://doi.org/10.5194/acp-21-17133-2021
Research article
 | 
25 Nov 2021
Research article |  | 25 Nov 2021

Improving the representation of aggregation in a two-moment microphysical scheme with statistics of multi-frequency Doppler radar observations

Markus Karrer, Axel Seifert, Davide Ori, and Stefan Kneifel

Related authors

snowScatt 1.0: consistent model of microphysical and scattering properties of rimed and unrimed snowflakes based on the self-similar Rayleigh–Gans approximation
Davide Ori, Leonie von Terzi, Markus Karrer, and Stefan Kneifel
Geosci. Model Dev., 14, 1511–1531, https://doi.org/10.5194/gmd-14-1511-2021,https://doi.org/10.5194/gmd-14-1511-2021, 2021
Short summary
Linking rain into ice microphysics across the melting layer in stratiform rain: a closure study
Kamil Mróz, Alessandro Battaglia, Stefan Kneifel, Leonie von Terzi, Markus Karrer, and Davide Ori
Atmos. Meas. Tech., 14, 511–529, https://doi.org/10.5194/amt-14-511-2021,https://doi.org/10.5194/amt-14-511-2021, 2021
Short summary

Related subject area

Subject: Clouds and Precipitation | Research Activity: Atmospheric Modelling | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
Evaluation of aerosol–cloud interactions in E3SM using a Lagrangian framework
Matthew W. Christensen, Po-Lun Ma, Peng Wu, Adam C. Varble, Johannes Mülmenstädt, and Jerome D. Fast
Atmos. Chem. Phys., 23, 2789–2812, https://doi.org/10.5194/acp-23-2789-2023,https://doi.org/10.5194/acp-23-2789-2023, 2023
Short summary
Impact of formulations of the homogeneous nucleation rate on ice nucleation events in cirrus
Peter Spichtinger, Patrik Marschalik, and Manuel Baumgartner
Atmos. Chem. Phys., 23, 2035–2060, https://doi.org/10.5194/acp-23-2035-2023,https://doi.org/10.5194/acp-23-2035-2023, 2023
Short summary
Temperature and cloud condensation nuclei (CCN) sensitivity of orographic precipitation enhanced by a mixed-phase seeder–feeder mechanism: a case study for the 2015 Cumbria flood
Julia Thomas, Andrew Barrett, and Corinna Hoose
Atmos. Chem. Phys., 23, 1987–2002, https://doi.org/10.5194/acp-23-1987-2023,https://doi.org/10.5194/acp-23-1987-2023, 2023
Short summary
Aerosol–precipitation elevation dependence over the central Himalayas using cloud-resolving WRF-Chem numerical modeling
Pramod Adhikari and John F. Mejia
Atmos. Chem. Phys., 23, 1019–1042, https://doi.org/10.5194/acp-23-1019-2023,https://doi.org/10.5194/acp-23-1019-2023, 2023
Short summary
Machine learning of cloud types in satellite observations and climate models
Peter Kuma, Frida A.-M. Bender, Alex Schuddeboom, Adrian J. McDonald, and Øyvind Seland
Atmos. Chem. Phys., 23, 523–549, https://doi.org/10.5194/acp-23-523-2023,https://doi.org/10.5194/acp-23-523-2023, 2023
Short summary

Cited articles

Andrić, J., Kumjian, M. R., Zrnić, D. S., Straka, J. M., and Melnikov, V. M.: Polarimetric signatures above the melting layer in winter storms: An observational and modeling study, J. Appl. Meteorol. Clim., 52, 682–700, https://doi.org/10.1175/JAMC-D-12-028.1, 2013. a
Barrett, A. I., Westbrook, C. D., Nicol, J. C., and Stein, T. H. M.: Rapid ice aggregation process revealed through triple-wavelength Doppler spectrum radar analysis, Atmos. Chem. Phys., 19, 5753–5769, https://doi.org/10.5194/acp-19-5753-2019, 2019. a, b, c, d, e, f, g
Barthazy, E. and Schefold, R.: Fall velocity of snowflakes of different riming degree and crystal types, Atmos. Res., 82, 391–398, https://doi.org/10.1016/j.atmosres.2005.12.009, 2006. a
Battaglia, A., Westbrook, C. D., Kneifel, S., Kollias, P., Humpage, N., Löhnert, U., Tyynelä, J., and Petty, G. W.: G band atmospheric radars: new frontiers in cloud physics, Atmos. Meas. Tech., 7, 1527–1546, https://doi.org/10.5194/amt-7-1527-2014, 2014. a
Battaglia, A., Tanelli, S., Tridon, F., Kneifel, S., Leinonen, J., and Kollias, P.: Triple-Frequency Radar Retrievals, Adv. Glob. Change Res., 67, 211–229, https://doi.org/10.1007/978-3-030-24568-9_13, 2020. a
Download
Short summary
Modeling precipitation is of great relevance, e.g., for mitigating damage caused by extreme weather. A key component in accurate precipitation modeling is aggregation, i.e., sticking together of snowflakes. Simulating aggregation is difficult due to multiple parameters that are not well-known. Knowing how these parameters affect aggregation can help its simulation. We put new parameters in the model and select a combination of parameters with which the model can simulate observations better.
Altmetrics
Final-revised paper
Preprint