Preprints
https://doi.org/10.5194/acp-2021-382
https://doi.org/10.5194/acp-2021-382

  01 Jul 2021

01 Jul 2021

Review status: a revised version of this preprint is currently under review for the journal ACP.

Improving the Representation of Aggregation in a Two-moment Microphysical Scheme with Statistics of Multi-frequency Doppler Radar Observations

Markus Karrer1, Axel Seifert2, Davide Ori1, and Stefan Kneifel1 Markus Karrer et al.
  • 1Institute for Geophysics and Meteorology, University of Cologne, Cologne, Germany
  • 2Deutscher Wetterdienst, Offenbach, Germany

Abstract. The simulation of aggregation of ice particles is critical for precipitation prediction, but still a major challenge. Its simulation requires assumptions about numerous parameters, many of which are either not well known or difficult to represent accurately in bulk microphysics schemes. However, knowing the sensitivity of aggregation to various simplified assumptions can help to identify critical parameters. By comparison with suitable observations, these critical parameters can even be constrained. We investigate the sensitivity of the model variables, and the modeled multi-frequency and Doppler radar observables to different parameters in a two-moment microphysics scheme. Therefore, we revise hydrometeor parameters by using a recently published dataset of particle properties, modify the formulations of the aggregation process (which allows using an area-based differential sedimentation kernel) and update other ice microphysical parameters in the scheme such as the sticking efficiency Estick and the shape of the size distribution. Overall, particle properties, definition of the aggregation kernel, and size distribution width prove to be most important, while Estick and the cloud ice habit have less influence. Finally, we run multi-week simulations with the most promising parameter combinations. The statistical comparison between real and synthetic observables shows a reduction in the velocity and snow particle size. With this study, we show a possible way to revise processes in microphysical schemes by using statistics of detailed cloud radar observations.

Markus Karrer et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2021-382', Anonymous Referee #1, 28 Jul 2021
    • AC1: 'Reply on RC1', Markus Karrer, 14 Sep 2021
  • RC2: 'Comment on acp-2021-382', Anonymous Referee #2, 23 Aug 2021
    • AC2: 'Reply on RC2', Markus Karrer, 14 Sep 2021

Markus Karrer et al.

Markus Karrer et al.

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Short summary
Modeling of precipitation is of great relevance e.g. for damage mitigating caused by extreme weather. A key component in accurate modeling precipitation is aggregation, i.e., sticking together of snowflakes. Simulating aggregation is difficult due to multiple, not-well known parameters. Knowing how these parameters affect aggregation can help its simulation. Therefore, we put new parameters in the model and select a combination of parameters with which the model can simulate observations better.
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