Radar-Based Bayesian Estimation of Ice Crystal Growth Parameters within a Microphysical Model
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,...
Published in: | Journal of the Atmospheric Sciences |
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Online Access: | http://www.osti.gov/servlets/purl/1830714 https://www.osti.gov/biblio/1830714 https://doi.org/10.1175/jas-d-20-0134.1 |
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ftosti:oai:osti.gov:1830714 2023-07-30T04:01:56+02:00 Radar-Based Bayesian Estimation of Ice Crystal Growth Parameters within a Microphysical Model Schrom, Robert S. van Lier-Walqui, Marcus Kumjian, Matthew R. Harrington, Jerry Y. Jensen, Anders A. Chen, Yao-Sheng 2022-02-02 application/pdf http://www.osti.gov/servlets/purl/1830714 https://www.osti.gov/biblio/1830714 https://doi.org/10.1175/jas-d-20-0134.1 unknown http://www.osti.gov/servlets/purl/1830714 https://www.osti.gov/biblio/1830714 https://doi.org/10.1175/jas-d-20-0134.1 doi:10.1175/jas-d-20-0134.1 58 GEOSCIENCES 2022 ftosti https://doi.org/10.1175/jas-d-20-0134.1 2023-07-11T10:08:19Z 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. Furthermore, 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. Other/Unknown Material Arctic SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) Arctic Journal of the Atmospheric Sciences 78 2 549 569 |
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SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) |
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language |
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58 GEOSCIENCES |
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58 GEOSCIENCES Schrom, Robert S. van Lier-Walqui, Marcus Kumjian, Matthew R. Harrington, Jerry Y. Jensen, Anders A. Chen, Yao-Sheng Radar-Based Bayesian Estimation of Ice Crystal Growth Parameters within a Microphysical Model |
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58 GEOSCIENCES |
description |
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. Furthermore, 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. |
author |
Schrom, Robert S. van Lier-Walqui, Marcus Kumjian, Matthew R. Harrington, Jerry Y. Jensen, Anders A. Chen, Yao-Sheng |
author_facet |
Schrom, Robert S. van Lier-Walqui, Marcus Kumjian, Matthew R. Harrington, Jerry Y. Jensen, Anders A. Chen, Yao-Sheng |
author_sort |
Schrom, Robert S. |
title |
Radar-Based Bayesian Estimation of Ice Crystal Growth Parameters within a Microphysical Model |
title_short |
Radar-Based Bayesian Estimation of Ice Crystal Growth Parameters within a Microphysical Model |
title_full |
Radar-Based Bayesian Estimation of Ice Crystal Growth Parameters within a Microphysical Model |
title_fullStr |
Radar-Based Bayesian Estimation of Ice Crystal Growth Parameters within a Microphysical Model |
title_full_unstemmed |
Radar-Based Bayesian Estimation of Ice Crystal Growth Parameters within a Microphysical Model |
title_sort |
radar-based bayesian estimation of ice crystal growth parameters within a microphysical model |
publishDate |
2022 |
url |
http://www.osti.gov/servlets/purl/1830714 https://www.osti.gov/biblio/1830714 https://doi.org/10.1175/jas-d-20-0134.1 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
http://www.osti.gov/servlets/purl/1830714 https://www.osti.gov/biblio/1830714 https://doi.org/10.1175/jas-d-20-0134.1 doi:10.1175/jas-d-20-0134.1 |
op_doi |
https://doi.org/10.1175/jas-d-20-0134.1 |
container_title |
Journal of the Atmospheric Sciences |
container_volume |
78 |
container_issue |
2 |
container_start_page |
549 |
op_container_end_page |
569 |
_version_ |
1772812680016429056 |