A Path Towards Operational Uncertainty of Cloud Phase Identification Algorithms

Cloud phase state is a key piece of information in characterizing the impact of clouds on radiation and dynamics. Identifying cloud phase is also the first step towards deriving further information about hydrometeor mass, concentration, and size in remote sensing retrievals. While a variety of phase identification algorithms exist, they don’t have quantitative estimates of their uncertainty when run operationally. The difficulty of assigning uncertainties to remote sensing retrievals stems both from a lack of objective “truth” of hydrometeor properties, and insufficient independent information available to fully constrain the properties of cloud particles.

Two promising directions for improving and identifying uncertainties in cloud phase characterization are inclusion of additional information available in cloud radar Doppler spectra and using statistical techniques to quantify the value of the information available from multiple sensors at a given time. We will report on progress incorporating multiple remote sensing observations from the ARM Climate Research Facility into a Bayesian net framework to characterize the rigor of identifying cloud phase given different sets of information, including higher order moments of the doppler spectra than are often used. Comparisons to available in situ data will be used to evaluate the results, identify further in situ measurements needed to characterize the problem, and to discuss the implications of the uncertainty in phase state identification for microphysical retrievals.