Triple-frequency radar retrieval of microphysical properties of snow

<p>An algorithm based on triple-frequency (X, Ka, W) radar measurements that retrieves the size, water content and degree of riming of ice clouds is presented. This study exploits the potential of multi-frequency radar measurements to provide information on bulk snow density that should underp...

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Autores principales:	K. Mroz, A. Battaglia, C. Nguyen, A. Heymsfield, A. Protat, M. Wolde
Formato:	article
Lenguaje:	EN
Publicado:	Copernicus Publications 2021
Materias:	Environmental engineering TA170-171 Earthwork. Foundations TA715-787
Acceso en línea:	https://doaj.org/article/48621ed5b580429ea63b7ff3f1b614c1
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spelling	oai:doaj.org-article:48621ed5b580429ea63b7ff3f1b614c12021-11-17T12:44:33ZTriple-frequency radar retrieval of microphysical properties of snow10.5194/amt-14-7243-20211867-13811867-8548https://doaj.org/article/48621ed5b580429ea63b7ff3f1b614c12021-11-01T00:00:00Zhttps://amt.copernicus.org/articles/14/7243/2021/amt-14-7243-2021.pdfhttps://doaj.org/toc/1867-1381https://doaj.org/toc/1867-8548<p>An algorithm based on triple-frequency (X, Ka, W) radar measurements that retrieves the size, water content and degree of riming of ice clouds is presented. This study exploits the potential of multi-frequency radar measurements to provide information on bulk snow density that should underpin better estimates of the snow characteristic size and content within the radar volume. The algorithm is based on Bayes' rule with riming parameterised by the “fill-in” model. The radar reflectivities are simulated with a range of scattering models corresponding to realistic snowflake shapes. The algorithm is tested on multi-frequency radar data collected during the ESA-funded Radar Snow Experiment For Future Precipitation Mission. During this campaign, in situ microphysical probes were mounted on the same aeroplane as the radars. This nearly perfectly co-located dataset of the remote and in situ measurements gives an opportunity to derive a combined multi-instrument estimate of snow microphysical properties that is used for a rigorous validation of the radar retrieval. Results suggest that the triple-frequency retrieval performs well in estimating ice water content (IWC) and mean mass-weighted diameters obtaining root-mean-square errors of 0.13 and 0.15, respectively, for <span class="inline-formula">log <sub>10</sub>IWC</span> and <span class="inline-formula">log <sub>10</sub><i>D</i><sub>m</sub></span>. The retrieval of the degree of riming is more challenging, and only the algorithm that uses Doppler information obtains results that are highly correlated with the in situ data.</p>K. MrozA. BattagliaA. BattagliaC. NguyenA. HeymsfieldA. ProtatM. WoldeCopernicus PublicationsarticleEnvironmental engineeringTA170-171Earthwork. FoundationsTA715-787ENAtmospheric Measurement Techniques, Vol 14, Pp 7243-7254 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Environmental engineering TA170-171 Earthwork. Foundations TA715-787
spellingShingle	Environmental engineering TA170-171 Earthwork. Foundations TA715-787 K. Mroz A. Battaglia A. Battaglia C. Nguyen A. Heymsfield A. Protat M. Wolde Triple-frequency radar retrieval of microphysical properties of snow
description	<p>An algorithm based on triple-frequency (X, Ka, W) radar measurements that retrieves the size, water content and degree of riming of ice clouds is presented. This study exploits the potential of multi-frequency radar measurements to provide information on bulk snow density that should underpin better estimates of the snow characteristic size and content within the radar volume. The algorithm is based on Bayes' rule with riming parameterised by the “fill-in” model. The radar reflectivities are simulated with a range of scattering models corresponding to realistic snowflake shapes. The algorithm is tested on multi-frequency radar data collected during the ESA-funded Radar Snow Experiment For Future Precipitation Mission. During this campaign, in situ microphysical probes were mounted on the same aeroplane as the radars. This nearly perfectly co-located dataset of the remote and in situ measurements gives an opportunity to derive a combined multi-instrument estimate of snow microphysical properties that is used for a rigorous validation of the radar retrieval. Results suggest that the triple-frequency retrieval performs well in estimating ice water content (IWC) and mean mass-weighted diameters obtaining root-mean-square errors of 0.13 and 0.15, respectively, for <span class="inline-formula">log <sub>10</sub>IWC</span> and <span class="inline-formula">log <sub>10</sub><i>D</i><sub>m</sub></span>. The retrieval of the degree of riming is more challenging, and only the algorithm that uses Doppler information obtains results that are highly correlated with the in situ data.</p>
format	article
author	K. Mroz A. Battaglia A. Battaglia C. Nguyen A. Heymsfield A. Protat M. Wolde
author_facet	K. Mroz A. Battaglia A. Battaglia C. Nguyen A. Heymsfield A. Protat M. Wolde
author_sort	K. Mroz
title	Triple-frequency radar retrieval of microphysical properties of snow
title_short	Triple-frequency radar retrieval of microphysical properties of snow
title_full	Triple-frequency radar retrieval of microphysical properties of snow
title_fullStr	Triple-frequency radar retrieval of microphysical properties of snow
title_full_unstemmed	Triple-frequency radar retrieval of microphysical properties of snow
title_sort	triple-frequency radar retrieval of microphysical properties of snow
publisher	Copernicus Publications
publishDate	2021
url	https://doaj.org/article/48621ed5b580429ea63b7ff3f1b614c1
work_keys_str_mv	AT kmroz triplefrequencyradarretrievalofmicrophysicalpropertiesofsnow AT abattaglia triplefrequencyradarretrievalofmicrophysicalpropertiesofsnow AT abattaglia triplefrequencyradarretrievalofmicrophysicalpropertiesofsnow AT cnguyen triplefrequencyradarretrievalofmicrophysicalpropertiesofsnow AT aheymsfield triplefrequencyradarretrievalofmicrophysicalpropertiesofsnow AT aprotat triplefrequencyradarretrievalofmicrophysicalpropertiesofsnow AT mwolde triplefrequencyradarretrievalofmicrophysicalpropertiesofsnow
_version_	1718425542466732032

Triple-frequency radar retrieval of microphysical properties of snow

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