Articles | Volume 2, issue 1
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 49–62, 2016
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 49–62, 2016

  10 Jun 2016

10 Jun 2016

A path towards uncertainty assignment in an operational cloud-phase algorithm from ARM vertically pointing active sensors

Laura D. Riihimaki et al.

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Cited articles

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Bühl, J., Ansmann, A., Seifert, P., Baars, H., and Engelmann, R.: Toward a quantitative characterization of heterogeneous ice formation with lidar/radar: Comparison of CALIPSO/CloudSat with ground-based observations, Geophys. Res. Lett., 40, 4404–4408,, 2013.
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Choi, Y. S., Lindzen, R. S., Ho, C. H., and Kim, J.: Space observations of cold-cloud phase change, P. Natl. Acad. Sci. USA, 107, 11211–11216,, 2010.
Short summary
Between atmospheric temperatures of 0 and −38 °C, clouds contain ice crystals, super-cooled liquid droplets, or a mixture of both, impacting how they influence the atmospheric energy budget and challenging our ability to simulate climate change. Better cloud-phase measurements are needed to improve simulations. We demonstrate how a Bayesian method to identify cloud phase can improve on currently used methods by including information from multiple measurements and probability estimates.