Publication Abstracts

Schrom et al. 2020, submitted

Schrom, R.S., M. van Lier-Walqui, M.R. Kumjian, J.Y. Harrington, A.A. Jensen, and Y.-S. Chen, 2020: Radar-based Bayesian estimation of ice crystal growth parameters within a microphysical model. submitted.

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. Other model and observational limitations that may impact our ability to estimate the microphysical parameters are discussed.

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BibTeX Citation

@unpublished{sc04700w,
  author={Schrom, R. S. and van Lier-Walqui, M. and Kumjian, M. R. and Harrington, J. Y. and Jensen, A. A. and Chen, Y.-S.},
  title={Radar-based Bayesian estimation of ice crystal growth parameters within a microphysical model},
  year={2020},
  note={Manuscript submitted for publication}
}

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RIS Citation

TY  - UNPB
ID  - sc04700w
AU  - Schrom, R. S.
AU  - van Lier-Walqui, M.
AU  - Kumjian, M. R.
AU  - Harrington, J. Y.
AU  - Jensen, A. A.
AU  - Chen, Y.-S.
PY  - 2020
TI  - Radar-based Bayesian estimation of ice crystal growth parameters within a microphysical model
ER  -

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