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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Language: | English |
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2021
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Online Access: | https://doi.org/10.1175/JAS-D-20-0134.1 |
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ftncar:oai:drupal-site.org:articles_24409 2024-06-23T07:50:36+00:00 Radar-based bayesian estimation of ice crystal growth parameters within a microphysical model Schrom, Robert S. (author) van Lier-Walqui, Marcus (author) Kumjian, Matthew R. (author) Harrington, Jerry Y. (author) Jensen, Anders A. (author) Chen, Yao-Sheng (author) 2021-02 https://doi.org/10.1175/JAS-D-20-0134.1 en eng Journal of the Atmospheric Sciences--0022-4928--1520-0469 articles:24409 ark:/85065/d7cr5xr2 doi:10.1175/JAS-D-20-0134.1 Copyright 2021 American Meteorological Society (AMS). article Text 2021 ftncar https://doi.org/10.1175/JAS-D-20-0134.1 2024-05-27T14:15:41Z 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. DESC0018933 Article in Journal/Newspaper Arctic OpenSky (NCAR/UCAR - National Center for Atmospheric Research/University Corporation for Atmospheric Research) Arctic Journal of the Atmospheric Sciences 78 2 549 569 |
institution |
Open Polar |
collection |
OpenSky (NCAR/UCAR - National Center for Atmospheric Research/University Corporation for Atmospheric Research) |
op_collection_id |
ftncar |
language |
English |
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. 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. DESC0018933 |
author2 |
Schrom, Robert S. (author) van Lier-Walqui, Marcus (author) Kumjian, Matthew R. (author) Harrington, Jerry Y. (author) Jensen, Anders A. (author) Chen, Yao-Sheng (author) |
format |
Article in Journal/Newspaper |
title |
Radar-based bayesian estimation of ice crystal growth parameters within a microphysical model |
spellingShingle |
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 |
2021 |
url |
https://doi.org/10.1175/JAS-D-20-0134.1 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
Journal of the Atmospheric Sciences--0022-4928--1520-0469 articles:24409 ark:/85065/d7cr5xr2 doi:10.1175/JAS-D-20-0134.1 |
op_rights |
Copyright 2021 American Meteorological Society (AMS). |
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_ |
1802641511595114496 |