Quantitative precipitation estimation over antarctica using different Ze-SR relationships based on snowfall classification combining ground observations

Snow plays a crucial role in the hydrological cycle and energy budget of the Earth, and remote sensing instruments with the necessary spatial coverage, resolution, and temporal sampling are essential for snowfall monitoring. Among such instruments, ground-radars have scanning capability and a resolu...

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Published in:Remote Sensing
Main Authors: Bracci A., Baldini L., Roberto N., Adirosi E., Montopoli M., Scarchilli C., Grigioni P., Ciardini V., Levizzani V., Porcu F.
Other Authors: Bracci, A., Baldini, L., Roberto, N., Adirosi, E., Montopoli, M., Scarchilli, C., Grigioni, P., Ciardini, V., Levizzani, V., Porcu, F.
Format: Article in Journal/Newspaper
Language:English
Published: 2022
Subjects:
Online Access:https://hdl.handle.net/20.500.12079/72267
https://doi.org/10.3390/rs14010082
https://www.mdpi.com/2072-4292/14/1/82
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spelling ftenea:oai:iris.enea.it:20.500.12079/72267 2024-09-15T17:45:19+00:00 Quantitative precipitation estimation over antarctica using different Ze-SR relationships based on snowfall classification combining ground observations Bracci A. Baldini L. Roberto N. Adirosi E. Montopoli M. Scarchilli C. Grigioni P. Ciardini V. Levizzani V. Porcu F. Bracci, A. Baldini, L. Roberto, N. Adirosi, E. Montopoli, M. Scarchilli, C. Grigioni, P. Ciardini, V. Levizzani, V. Porcu, F. 2022 https://hdl.handle.net/20.500.12079/72267 https://doi.org/10.3390/rs14010082 https://www.mdpi.com/2072-4292/14/1/82 eng eng volume:14 issue:1 numberofpages:26 journal:REMOTE SENSING https://hdl.handle.net/20.500.12079/72267 doi:10.3390/rs14010082 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85121747039 https://www.mdpi.com/2072-4292/14/1/82 info:eu-repo/semantics/openAccess info:eu-repo/semantics/article 2022 ftenea https://doi.org/20.500.12079/7226710.3390/rs14010082 2024-07-29T23:40:04Z Snow plays a crucial role in the hydrological cycle and energy budget of the Earth, and remote sensing instruments with the necessary spatial coverage, resolution, and temporal sampling are essential for snowfall monitoring. Among such instruments, ground-radars have scanning capability and a resolution that make it possible to obtain a 3D structure of precipitating systems or vertical profiles when used in profiling mode. Radars from space have a lower spatial resolution, but they provide a global view. However, radar-based quantitative estimates of solid precipitation are still a challenge due to the variability of the microphysical, geometrical, and electrical features of snow particles. Estimations of snowfall rate are usually accomplished using empirical, long-term relationships between the equivalent radar reflectivity factor (Ze) and the liquid-equivalent snowfall rate (SR). Nevertheless, very few relationships take advantage of the direct estimation of the microphysical characteristics of snowflakes. In this work, we used a K-band vertically pointing radar collocated with a laser disdrometer to develop Ze-SR relationships as a function of snow classification. The two instruments were located at the Italian Antarctic Station Mario Zucchelli. The K-band radar probes the low-level atmospheric layers, recording power spectra at 32 vertical range gates. It was set at a high vertical resolution (35 m), with the first trusted range gate at a height of only 100 m. The disdrometer was able to provide information on the particle size distribution just below the trusted radar gate. Snow particles were classified into six categories (aggregate, dendrite aggregate, plate aggregate, pristine, dendrite pristine, plate pristine). The method was applied to the snowfall events of the Antarctic summer seasons of 2018–2019 and 2019–2020, with a total of 23,566 min of precipitation, 15.3% of which was recognized as showing aggregate features, 33.3% dendrite aggregate, 7.3% plates aggregate, 12.5% pristine, 24% dendrite ... Article in Journal/Newspaper Antarc* Antarctic Antarctica ENEA-IRIS Open Archive (Agenzia nazionale per le nuove tecnologie, l'energia e lo sviluppo economico sostenibile) Remote Sensing 14 1 82
institution Open Polar
collection ENEA-IRIS Open Archive (Agenzia nazionale per le nuove tecnologie, l'energia e lo sviluppo economico sostenibile)
op_collection_id ftenea
language English
description Snow plays a crucial role in the hydrological cycle and energy budget of the Earth, and remote sensing instruments with the necessary spatial coverage, resolution, and temporal sampling are essential for snowfall monitoring. Among such instruments, ground-radars have scanning capability and a resolution that make it possible to obtain a 3D structure of precipitating systems or vertical profiles when used in profiling mode. Radars from space have a lower spatial resolution, but they provide a global view. However, radar-based quantitative estimates of solid precipitation are still a challenge due to the variability of the microphysical, geometrical, and electrical features of snow particles. Estimations of snowfall rate are usually accomplished using empirical, long-term relationships between the equivalent radar reflectivity factor (Ze) and the liquid-equivalent snowfall rate (SR). Nevertheless, very few relationships take advantage of the direct estimation of the microphysical characteristics of snowflakes. In this work, we used a K-band vertically pointing radar collocated with a laser disdrometer to develop Ze-SR relationships as a function of snow classification. The two instruments were located at the Italian Antarctic Station Mario Zucchelli. The K-band radar probes the low-level atmospheric layers, recording power spectra at 32 vertical range gates. It was set at a high vertical resolution (35 m), with the first trusted range gate at a height of only 100 m. The disdrometer was able to provide information on the particle size distribution just below the trusted radar gate. Snow particles were classified into six categories (aggregate, dendrite aggregate, plate aggregate, pristine, dendrite pristine, plate pristine). The method was applied to the snowfall events of the Antarctic summer seasons of 2018–2019 and 2019–2020, with a total of 23,566 min of precipitation, 15.3% of which was recognized as showing aggregate features, 33.3% dendrite aggregate, 7.3% plates aggregate, 12.5% pristine, 24% dendrite ...
author2 Bracci, A.
Baldini, L.
Roberto, N.
Adirosi, E.
Montopoli, M.
Scarchilli, C.
Grigioni, P.
Ciardini, V.
Levizzani, V.
Porcu, F.
format Article in Journal/Newspaper
author Bracci A.
Baldini L.
Roberto N.
Adirosi E.
Montopoli M.
Scarchilli C.
Grigioni P.
Ciardini V.
Levizzani V.
Porcu F.
spellingShingle Bracci A.
Baldini L.
Roberto N.
Adirosi E.
Montopoli M.
Scarchilli C.
Grigioni P.
Ciardini V.
Levizzani V.
Porcu F.
Quantitative precipitation estimation over antarctica using different Ze-SR relationships based on snowfall classification combining ground observations
author_facet Bracci A.
Baldini L.
Roberto N.
Adirosi E.
Montopoli M.
Scarchilli C.
Grigioni P.
Ciardini V.
Levizzani V.
Porcu F.
author_sort Bracci A.
title Quantitative precipitation estimation over antarctica using different Ze-SR relationships based on snowfall classification combining ground observations
title_short Quantitative precipitation estimation over antarctica using different Ze-SR relationships based on snowfall classification combining ground observations
title_full Quantitative precipitation estimation over antarctica using different Ze-SR relationships based on snowfall classification combining ground observations
title_fullStr Quantitative precipitation estimation over antarctica using different Ze-SR relationships based on snowfall classification combining ground observations
title_full_unstemmed Quantitative precipitation estimation over antarctica using different Ze-SR relationships based on snowfall classification combining ground observations
title_sort quantitative precipitation estimation over antarctica using different ze-sr relationships based on snowfall classification combining ground observations
publishDate 2022
url https://hdl.handle.net/20.500.12079/72267
https://doi.org/10.3390/rs14010082
https://www.mdpi.com/2072-4292/14/1/82
genre Antarc*
Antarctic
Antarctica
genre_facet Antarc*
Antarctic
Antarctica
op_relation volume:14
issue:1
numberofpages:26
journal:REMOTE SENSING
https://hdl.handle.net/20.500.12079/72267
doi:10.3390/rs14010082
info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85121747039
https://www.mdpi.com/2072-4292/14/1/82
op_rights info:eu-repo/semantics/openAccess
op_doi https://doi.org/20.500.12079/7226710.3390/rs14010082
container_title Remote Sensing
container_volume 14
container_issue 1
container_start_page 82
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