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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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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1774722113213562880 |