Articles | Volume 2, issue 1
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 49–62, 2016
https://doi.org/10.5194/ascmo-2-49-2016
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 49–62, 2016
https://doi.org/10.5194/ascmo-2-49-2016

  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.

Data sets

M-PACE HSR Lidar, Atmospheric Radiation Measurement (ARM) Climate Research Facility Data Archive: Oak Ridge, Tennessee, USA E. W. Eloranta https://doi.org/10.5439/1254822

Micro-ARSCL, Atmospheric Radiation Measurement (ARM) Climate Research Facility Data Archive: Oak Ridge, Tennessee, USA M. P. Jensen, E. Luke, and P. Kollias https://doi.org/10.5439/1225811

Active Remotely-Sensed Cloud Locations (ARSCL1CLOTH), Atmospheric Radiation Measurement (ARM) Climate Research Facility Data Archive: Oak Ridge, Tennessee, USA K. Johnson and M. P. Jensen https://doi.org/10.5439/1027282

In-Situ Microphysics from the MPACE IOP, Atmospheric Radiation Measurement (ARM) Climate Research Facility Data Archive ARM Data Archive: Oak Ridge, Tennessee, USA G. McFarquhar and G. Zhang https://doi.org/10.5439/1171943

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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.