Impact of seeder-feeder cloud interaction on precipitation formation
a case study based on extensive remote-sensing, in situ and model data
Kevin Ohneiser (Leibniz-Institut für Troposphärenforschung)
Patric Seifert (Leibniz-Institut für Troposphärenforschung)
Willi Schimmel (Leibniz-Institut für Troposphärenforschung)
Fabian Senf (Leibniz-Institut für Troposphärenforschung)
Tom Gaudek (Leibniz-Institut für Troposphärenforschung)
Martin Radenz (Leibniz-Institut für Troposphärenforschung)
Audrey Teisseire (Leibniz-Institut für Troposphärenforschung)
Veronika Ettrichrätz (Leipzig Institute for Meteorology (LIM))
Teresa Vogl (Leipzig Institute for Meteorology (LIM))
Nina Maherndl (TU Delft - Atmospheric Remote Sensing)
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Abstract
A comprehensive approach to study the seeder-feeder mechanism in unprecedented detail from a combined remote-sensing, in situ, and model perspective is shown. This publication aims at investigating the role of the interplay of a seeder-feeder cloud system and its influence on precipitation formation based on a case study from 8 January 2024 observed over the Swiss Plateau in Switzerland.
This case study offers an ideal setup for applying several advanced remote-sensing techniques and retrieval algorithms, including fall streak tracking, radar Doppler peak separation, dual-wavelength radar applications, a liquid detection retrieval, a riming retrieval, and an ice crystals shape retrieval. Results indicate that a large portion of ice mass was rimed, which is attributed to persistent coexistence of falling ice crystals and supercooled water within low-level supercooled liquid water layers. Interaction of seeder and feeder clouds results in a significant precipitation enhancement. This has implications on the water cycle. From the anti-correlation between surface precipitation and liquid water path we estimated that 20 %–40 % of the precipitation stems from the feeder cloud. However, we have to note that the value of 20 %–40 % is strongly dependent on the assumed reproduction rate of liquid water in the feeder cloud. This study aims at giving an overview from a remote-sensing, in situ and model perspective on a seeder-feeder event in an unprecedented detail by exploiting a big set of retrievals applicable to remote-sensing and in situ data. Utilizing different retrievals gives a consistent view on the seeder-feeder case study which is an important basis for future studies. It is demonstrated how improved understanding of seeder-feeder interactions can contribute to enhancing weather forecast models, particularly in regions affected by persistent low-level supercooled stratus clouds.
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