Retrieving microphysical properties of concurrent pristine ice and snow using polarimetric radar observations
<p>Ice and mixed-phase clouds play a key role in our climate system because of their strong controls on global precipitation and radiation budget. Their microphysical properties have been characterized commonly by polarimetric radar measurements. However, there remains a lack of robust estimat...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2021-10-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | https://amt.copernicus.org/articles/14/6885/2021/amt-14-6885-2021.pdf |
| _version_ | 1819194774736863232 |
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| author | N. J. Kedzuf J. C. Chiu V. Chandrasekar S. Biswas S. S. Joshil Y. Lu Y. Lu P. J. van Leeuwen P. J. van Leeuwen C. Westbrook Y. Blanchard S. O'Shea |
| author_facet | N. J. Kedzuf J. C. Chiu V. Chandrasekar S. Biswas S. S. Joshil Y. Lu Y. Lu P. J. van Leeuwen P. J. van Leeuwen C. Westbrook Y. Blanchard S. O'Shea |
| author_sort | N. J. Kedzuf |
| collection | DOAJ |
| description | <p>Ice and mixed-phase clouds play a key role in our climate
system because of their strong controls on global precipitation and
radiation budget. Their microphysical properties have been characterized
commonly by polarimetric radar measurements. However, there remains a lack
of robust estimates of microphysical properties of concurrent pristine ice
and aggregates because larger snow aggregates often dominate the radar
signal and mask contributions of smaller pristine ice crystals. This paper
presents a new method that separates the scattering signals of pristine ice
embedded in snow aggregates in scanning polarimetric radar observations and
retrieves their respective abundances and sizes for the first time. This
method, dubbed ENCORE-ice, is built on an iterative stochastic ensemble
retrieval framework. It provides the number concentration, ice water content,
and effective mean diameter of pristine ice and snow aggregates with
uncertainty estimates. Evaluations against synthetic observations show that
the overall retrieval biases in the combined total microphysical properties
are within 5 % and that the errors with respect to the truth are well
within the retrieval uncertainty. The partitioning between pristine ice and
snow aggregates also agrees well with the truth. Additional evaluations
against in situ cloud probe measurements from a recent campaign for a
stratiform cloud system are promising. Our median retrievals have a bias of
98 % in the total ice number concentration and 44 % in the total ice water
content. This performance is generally better than the retrieval from
empirical relationships. The ability to separate signals of different ice
species and to provide their quantitative microphysical properties will open up
many research opportunities, such as secondary ice production studies and
model evaluations for ice microphysical processes.</p> |
| first_indexed | 2024-12-23T02:02:13Z |
| format | Article |
| id | doaj.art-865365616bb8495a871ff34c9b6c31be |
| institution | Directory Open Access Journal |
| issn | 1867-1381 1867-8548 |
| language | English |
| last_indexed | 2024-12-23T02:02:13Z |
| publishDate | 2021-10-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Measurement Techniques |
| spelling | doaj.art-865365616bb8495a871ff34c9b6c31be2022-12-21T18:03:58ZengCopernicus PublicationsAtmospheric Measurement Techniques1867-13811867-85482021-10-01146885690410.5194/amt-14-6885-2021Retrieving microphysical properties of concurrent pristine ice and snow using polarimetric radar observationsN. J. Kedzuf0J. C. Chiu1V. Chandrasekar2S. Biswas3S. S. Joshil4Y. Lu5Y. Lu6P. J. van Leeuwen7P. J. van Leeuwen8C. Westbrook9Y. Blanchard10S. O'Shea11Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80523, USADepartment of Atmospheric Science, Colorado State University, Fort Collins, CO 80523, USADepartment of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523, USADepartment of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523, USADepartment of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523, USADepartment of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, PA 16802, USACenter for Advanced Data Assimilation and Predictability Techniques, The Pennsylvania State University, University Park, PA 16802, USADepartment of Atmospheric Science, Colorado State University, Fort Collins, CO 80523, USADepartment of Meteorology, University of Reading, Reading, RG6 6BB, UKDepartment of Meteorology, University of Reading, Reading, RG6 6BB, UKESCER Centre, Department of Earth and Atmospheric Sciences, University of Québec at Montréal, Montréal, Quebec, H3C 3P8, CanadaDepartment of Earth and Environmental Sciences, University of Manchester, Manchester, M13 9PL, UK<p>Ice and mixed-phase clouds play a key role in our climate system because of their strong controls on global precipitation and radiation budget. Their microphysical properties have been characterized commonly by polarimetric radar measurements. However, there remains a lack of robust estimates of microphysical properties of concurrent pristine ice and aggregates because larger snow aggregates often dominate the radar signal and mask contributions of smaller pristine ice crystals. This paper presents a new method that separates the scattering signals of pristine ice embedded in snow aggregates in scanning polarimetric radar observations and retrieves their respective abundances and sizes for the first time. This method, dubbed ENCORE-ice, is built on an iterative stochastic ensemble retrieval framework. It provides the number concentration, ice water content, and effective mean diameter of pristine ice and snow aggregates with uncertainty estimates. Evaluations against synthetic observations show that the overall retrieval biases in the combined total microphysical properties are within 5 % and that the errors with respect to the truth are well within the retrieval uncertainty. The partitioning between pristine ice and snow aggregates also agrees well with the truth. Additional evaluations against in situ cloud probe measurements from a recent campaign for a stratiform cloud system are promising. Our median retrievals have a bias of 98 % in the total ice number concentration and 44 % in the total ice water content. This performance is generally better than the retrieval from empirical relationships. The ability to separate signals of different ice species and to provide their quantitative microphysical properties will open up many research opportunities, such as secondary ice production studies and model evaluations for ice microphysical processes.</p>https://amt.copernicus.org/articles/14/6885/2021/amt-14-6885-2021.pdf |
| spellingShingle | N. J. Kedzuf J. C. Chiu V. Chandrasekar S. Biswas S. S. Joshil Y. Lu Y. Lu P. J. van Leeuwen P. J. van Leeuwen C. Westbrook Y. Blanchard S. O'Shea Retrieving microphysical properties of concurrent pristine ice and snow using polarimetric radar observations Atmospheric Measurement Techniques |
| title | Retrieving microphysical properties of concurrent pristine ice and snow using polarimetric radar observations |
| title_full | Retrieving microphysical properties of concurrent pristine ice and snow using polarimetric radar observations |
| title_fullStr | Retrieving microphysical properties of concurrent pristine ice and snow using polarimetric radar observations |
| title_full_unstemmed | Retrieving microphysical properties of concurrent pristine ice and snow using polarimetric radar observations |
| title_short | Retrieving microphysical properties of concurrent pristine ice and snow using polarimetric radar observations |
| title_sort | retrieving microphysical properties of concurrent pristine ice and snow using polarimetric radar observations |
| url | https://amt.copernicus.org/articles/14/6885/2021/amt-14-6885-2021.pdf |
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