Cloud liquid water and precipitation: novel remote-sensing techniques for identifying cloud liquid water in mixed-phase clouds, detecting riming, and classifying rain

Abstract:

Deep mixed-phase clouds pose an observational challenge to ground-based remote sensing. Since both the amount and vertical distribution of cloud liquid water strongly influence cloud radiative effects, cloud precipitation formation, and thus cloud lifetime, improved estimates of these properties are needed to better represent deep mixed-phase clouds in atmospheric models.

The first part of this overview talk will focus on a novel approach for thermodynamic phase classification of ground-based remote-sensing observations using cloud radar Doppler spectra. Deep learning techniques enable the detection of liquid water beyond full lidar signal attenuation in multi-layer or thick mixed-phase cloud situations. The presence of supercooled liquid in mixed-phase clouds is a prerequisite for riming as precipitation formation mechanism. Absolute values of mean radar Doppler velocity (MDV) can, in some situations, be used to retrieve rime mass fraction, which is a quantity needed for observation-model comparison studies. However, this method cannot be applied when strong vertical air motion, e.g. induced by orographic waves, greatly influences MDV values. A new method for riming detection based on linking cloud radar Doppler spectra signatures to the presence of riming without relying on absolute values of MDV is introduced. Finally, a new machine learning method using solely micro-rain radar (MRR) observables to distinguish between stratiform and convective precipitation is presented.

The illustrated methods are applied to data obtained in the project DACAPO-PESO (Dynamics, Aerosol, Cloud And Precipitation Observations in the Pristine Environment of the Southern Ocean) which is being conducted in Punta Arenas (53°S, 71°W), Chile from Nov 2018 – Fall 2020.This field experiment fills an observational gap in the pristine atmosphere of the Southern Ocean, for which to date hardly any combined observations of lidar, cloud radar and microwave radiometer are available and where climate models struggle to represent the high supercooled cloud fraction properly. During that field campaign, the Leipzig Aerosol and Cloud Remote Observations System (LACROS) of Leibniz Institute for Tropospheric Research (TROPOS) is deployed and was enhanced by a 94 GHz cloud Doppler radar from the University of Leipzig for ten months.