Preprints
https://doi.org/10.5194/amt-2021-60
https://doi.org/10.5194/amt-2021-60

  07 Jun 2021

07 Jun 2021

Review status: this preprint is currently under review for the journal AMT.

Evaluating cloud liquid detection using cloud radar Doppler spectra in a pre-trained artificial neural network against Cloudnet liquid detection

Heike Kalesse-Los1,2, Willi Schimmel1, Edward Luke3, and Patric Seifert2 Heike Kalesse-Los et al.
  • 1Institute for Meteorology, Universität Leipzig, Leipzig, Germany
  • 2Leibniz Institute for Tropospheric Research, Leipzig, Germany
  • 3Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, New York, USA

Abstract. Detection of liquid-containing cloud layers in thick mixed-phase clouds or multi-layer cloud situations from ground-based
remote sensing instruments still pose observational challenges yet improvements are crucial since the existence of multi-layer
liquid layers in mixed-phase cloud situations influences cloud radiative effects, cloud life time, and precipitation formation
processes. Hydrometeor target classifications such as Cloudnet that require a lidar signal for the classification of liquid are
limited to the maximum height of lidar signal penetration and thus often lead to underestimations of liquid-containing cloud
layers. Here we evaluate the Cloudnet liquid detection against the approach of Luke et al. (2010) which extracts morphological
features in cloud-penetrating cloud radar Doppler spectra measurements in a artificial neural network (ANN) approach to
classify liquid beyond full lidar signal attenuation based on the simulation of the two lidar parameters particle backscatter
coefficient and particle depolarization ratio. We show that the ANN of Luke et al. (2010) which was trained in Arctic conditions
can successfully be applied to observations in the mid-latitudes obtained during the seven-week long ACCEPT field experiment
in Cabauw, the Netherlands, 2014. In a sensitivity study covering the whole duration of the ACCEPT campaign, different liquid-detection
thresholds for ANN-predicted lidar variables are applied and evaluated against the Cloudnet target classification.
Independent validation of the liquid mask from the standard Cloudnet target classification against the ANN-based technique
is realized by comparisons to observations of microwave radiometer liquid water path, ceilometer liquid-layer base altitude,
and radiosonde relative humidity. Four conclusions were drawn from the investigation: First, it was found that the threshold
selection criteria of liquid-related lidar backscatter and depolarization alone control the liquid detection considerably. Second,
nevertheless, all threshold values used in the ANN-framework were found to outperform the Cloudnet target classification for
deep or multi-layer cloud situations where the lidar signal is fully attenuated within low liquid layers and the cloud reflectivity
in higher cloud layers was sufficiently high to be detectable by the cloud radar. Third, in convective situations for which
lidar data is available and for which the imprint of cloud microphysics on the radar Doppler spectrum is decreased, Cloudnet
outperforms the ANN retrieval. Fourth, in high-level clouds both approaches (Cloudnet and the ANN technique), are limited.

Heike Kalesse-Los 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 amt-2021-60', Anonymous Referee #1, 03 Jul 2021
  • RC2: 'Comment on amt-2021-60', Anonymous Referee #2, 02 Aug 2021

Heike Kalesse-Los et al.

Heike Kalesse-Los et al.

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Short summary
It is important to detect the vertical distribution of cloud droplets and ice in mixed-phase clouds. Here, an artificial neural network previously developed for Arctic clouds is applied to a mid-latitudinal cloud radar data set. The performance of this technique is contrasted to the Cloudnet target classification. For thick/multi-layer clouds, the machine-learning technique is better at detecting liquid, but in convective clouds where lidar data is available, Cloudnet performs better.