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Title: Radar-Based Bayesian Estimation of Ice Crystal Growth Parameters within a Microphysical Model

Journal Article · · Journal of the Atmospheric Sciences
 [1];  [2];  [3];  [3];  [4];  [5]
  1. NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States); Universities Space Research Association, Columbia, MD (United States)
  2. Columbia Univ., New York, NY (United States); NASA Goddard Inst. for Space Studies (GISS), New York, NY (United States)
  3. Pennsylvania State Univ., University Park, PA (United States)
  4. National Center for Atmospheric Research (NCAR), Boulder, CO (United States)
  5. Univ. of Colorado, Boulder, CO (United States); NOAA Chemical Sciences Lab., Boulder, CO (United States)
+ Show Author Affiliations

AbstractThe 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.

Research Organization:
Pennsylvania State Univ., University Park, PA (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Data Center
Sponsoring Organization:
USDOE
Grant/Contract Number:
SC0018933
OSTI ID:
1830714
Alternate ID(s):
OSTI ID: 1841759
Journal Information:
Journal of the Atmospheric Sciences, Vol. 78, Issue 2; ISSN 0022-4928
Publisher:
American Meteorological SocietyCopyright Statement
Country of Publication:
United States
Language:
English

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