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: Alessandro Bracci, Luca Baldini, Nicoletta Roberto, Elisa Adirosi, Mario Montopoli, Claudio Scarchilli, Paolo Grigioni, Virginia Ciardini, Vincenzo Levizzani, Federico Porcù
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2021
Subjects:
MRR
DDA
Online Access:https://doi.org/10.3390/rs14010082
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spelling ftmdpi:oai:mdpi.com:/2072-4292/14/1/82/ 2023-08-20T04:01:01+02:00 Quantitative Precipitation Estimation over Antarctica Using Different Ze-SR Relationships Based on Snowfall Classification Combining Ground Observations Alessandro Bracci Luca Baldini Nicoletta Roberto Elisa Adirosi Mario Montopoli Claudio Scarchilli Paolo Grigioni Virginia Ciardini Vincenzo Levizzani Federico Porcù agris 2021-12-24 application/pdf https://doi.org/10.3390/rs14010082 EN eng Multidisciplinary Digital Publishing Institute Atmospheric Remote Sensing https://dx.doi.org/10.3390/rs14010082 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 14; Issue 1; Pages: 82 remote sensing snowfall Antarctica quantitative precipitation estimation Ze-SR relation MRR radar disdrometer DDA snow classification Text 2021 ftmdpi https://doi.org/10.3390/rs14010082 2023-08-01T03:39:35Z 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 ... Text Antarc* Antarctic Antarctica MDPI Open Access Publishing Antarctic The Antarctic Mario Zucchelli ENVELOPE(164.123,164.123,-74.695,-74.695) Remote Sensing 14 1 82
institution Open Polar
collection MDPI Open Access Publishing
op_collection_id ftmdpi
language English
topic remote sensing
snowfall
Antarctica
quantitative precipitation estimation
Ze-SR relation
MRR
radar
disdrometer
DDA
snow classification
spellingShingle remote sensing
snowfall
Antarctica
quantitative precipitation estimation
Ze-SR relation
MRR
radar
disdrometer
DDA
snow classification
Alessandro Bracci
Luca Baldini
Nicoletta Roberto
Elisa Adirosi
Mario Montopoli
Claudio Scarchilli
Paolo Grigioni
Virginia Ciardini
Vincenzo Levizzani
Federico Porcù
Quantitative Precipitation Estimation over Antarctica Using Different Ze-SR Relationships Based on Snowfall Classification Combining Ground Observations
topic_facet remote sensing
snowfall
Antarctica
quantitative precipitation estimation
Ze-SR relation
MRR
radar
disdrometer
DDA
snow classification
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 ...
format Text
author Alessandro Bracci
Luca Baldini
Nicoletta Roberto
Elisa Adirosi
Mario Montopoli
Claudio Scarchilli
Paolo Grigioni
Virginia Ciardini
Vincenzo Levizzani
Federico Porcù
author_facet Alessandro Bracci
Luca Baldini
Nicoletta Roberto
Elisa Adirosi
Mario Montopoli
Claudio Scarchilli
Paolo Grigioni
Virginia Ciardini
Vincenzo Levizzani
Federico Porcù
author_sort Alessandro Bracci
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
publisher Multidisciplinary Digital Publishing Institute
publishDate 2021
url https://doi.org/10.3390/rs14010082
op_coverage agris
long_lat ENVELOPE(164.123,164.123,-74.695,-74.695)
geographic Antarctic
The Antarctic
Mario Zucchelli
geographic_facet Antarctic
The Antarctic
Mario Zucchelli
genre Antarc*
Antarctic
Antarctica
genre_facet Antarc*
Antarctic
Antarctica
op_source Remote Sensing; Volume 14; Issue 1; Pages: 82
op_relation Atmospheric Remote Sensing
https://dx.doi.org/10.3390/rs14010082
op_rights https://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.3390/rs14010082
container_title Remote Sensing
container_volume 14
container_issue 1
container_start_page 82
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