Abstract
Abstract. A new method for the retrieval of ice crystal number concentration (ICNC) from combined active remote-sensing measurements of Raman lidar, cloud radar and radar wind profiler is presented. We exploit – for the first time – measurements of terminal fall velocity together with the radar reflectivity factor and/or the lidar-derived particle extinction coefficient in clouds for retrieving the number concentration of pristine ice particles with presumed particle shapes. A lookup table approach for the retrieval of the properties of the particle size distribution from observed parameters is presented. Analysis of methodological uncertainties and error propagation is performed, which shows that a retrieval of ice particle number concentration based on terminal fall velocity is possible within 1 order of magnitude. Comparison between a retrieval of the number concentration based on terminal fall velocity on the one hand and lidar and cloud radar on the other shows agreement within the uncertainties of the retrieval.
Highlights
IntroductionClouds and precipitation are major components of Earth’s climate system. The complex aerosol–clouddynamics interaction currently poses major challenges for the numerical modeling of climate and weather phenomena because the majority of rain formation on Earth happens through the ice phase (Mülmenstädt et al, 2015)
Aerosols, clouds and precipitation are major components of Earth’s climate system
The process of heterogeneous ice nucleation in clouds is of particular importance because it constitutes the link between aerosol conditions – including ice-nucleating particle concentration (INPC) – and precipitation formation (Ansmann et al, 2019)
Summary
Clouds and precipitation are major components of Earth’s climate system. The complex aerosol–clouddynamics interaction currently poses major challenges for the numerical modeling of climate and weather phenomena because the majority of rain formation on Earth happens through the ice phase (Mülmenstädt et al, 2015). Cazenave et al (2019), Delanoë and Hogan (2010), and Sourdeval et al (2018) employ a forward-iteration method in order to obtain an estimation of N from combined observations of spaceborne lidar and cloud radar. Only the terminal fall velocity vt leaves traces about particle size, but actual observations of vt are difficult to obtain Such observations have been made in laboratories (Fukuta and Takahashi, 1999) and, recently, Bühl et al (2015) and Radenz et al (2018) showed that a combination of the CR and radar wind profiler (RWP; Steinhagen et al, 1998; Lehmann and Teschke, 2001) can be used in order to derive vt of ice particles. The combined measurements of CR and RWP were used to derive a dataset of a particle ensemble mean vt with an error margin of about 0.1 m s−1 (Radenz et al, 2018). (It is worth noting here that in the context of this work we use “uncertainty” to describe retrieval and methodological uncertainties and “errors” for measurement errors.) This unique dataset is used here for the first time to test the retrieval method and give examples
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