Radar-based Bayesian estimation of ice crystal growth parameters within a microphysical model

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  • 1 NASA Goddard Space Flight Center, Greenbelt, and Universities Space Research Association, Columbia, MD
  • 2 Center for Climate Systems Research, Columbia University and NASA Goddard Institute for Space Studies, New York, NY
  • 3 The Pennsylvania State University Department of Atmospheric Science, University Park, PA
  • 4 National Center for Atmospheric Research, Boulder, Colorado, USA
  • 5 Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, NOAA Chemical Sciences Laboratory, Boulder, CO
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Abstract

The potential for polarimetric Doppler radar measurements to improve predictions of ice microphysical processes within an idealized model-observational framework is examined. In an effort to more rigorously constrain ice growth processes (e.g., vapor deposition) with observations of natural clouds, a novel framework is developed to compare simulated and observed radar measurements, coupling a bulk adaptive-habit model of vapor growth to a polarimetric radar forward model. Bayesian inference on key microphysical model parameters is then used, via a Markov chain Monte Carlo sampler, to estimate the probability distribution of the model parameters. The statistical formalism of this method allows for robust estimates of the optimal parameter values, along with (non-Gaussian) estimates of their uncertainty. To demonstrate this framework, observations from Department of Energy radars in the Arctic during a case of pristine ice precipitation are used to constrain vapor deposition parameters in the adaptive habit model. The resulting parameter probability distributions provide physically plausible changes in ice particle density and aspect ratio during growth. A lack of direct constraint on the number concentration produces a range of possible mean particle sizes, with the mean size inversely correlated to number concentration. Consistency is found between the estimated inherent growth ratio and independent laboratory measurements, increasing confidence in the parameter PDFs and demonstrating the effectiveness of the radar measurements in constraining the parameters. The combined Doppler and polarimetric observations produce the highest-confidence estimates of the parameter PDFs, with the Doppler measurements providing a stronger constraint for this case.

Corresponding author: Robert S. Schrom, robert.s.schrom@nasa.gov

NCAR is supported by the National Science Foundation

Abstract

The potential for polarimetric Doppler radar measurements to improve predictions of ice microphysical processes within an idealized model-observational framework is examined. In an effort to more rigorously constrain ice growth processes (e.g., vapor deposition) with observations of natural clouds, a novel framework is developed to compare simulated and observed radar measurements, coupling a bulk adaptive-habit model of vapor growth to a polarimetric radar forward model. Bayesian inference on key microphysical model parameters is then used, via a Markov chain Monte Carlo sampler, to estimate the probability distribution of the model parameters. The statistical formalism of this method allows for robust estimates of the optimal parameter values, along with (non-Gaussian) estimates of their uncertainty. To demonstrate this framework, observations from Department of Energy radars in the Arctic during a case of pristine ice precipitation are used to constrain vapor deposition parameters in the adaptive habit model. The resulting parameter probability distributions provide physically plausible changes in ice particle density and aspect ratio during growth. A lack of direct constraint on the number concentration produces a range of possible mean particle sizes, with the mean size inversely correlated to number concentration. Consistency is found between the estimated inherent growth ratio and independent laboratory measurements, increasing confidence in the parameter PDFs and demonstrating the effectiveness of the radar measurements in constraining the parameters. The combined Doppler and polarimetric observations produce the highest-confidence estimates of the parameter PDFs, with the Doppler measurements providing a stronger constraint for this case.

Corresponding author: Robert S. Schrom, robert.s.schrom@nasa.gov

NCAR is supported by the National Science Foundation

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