CloudSat-Based Assessment of GPM Microwave Imager Snowfall Observation Capabilities
The sensitivity of Global Precipitation Measurement (GPM) Microwave Imager (GMI) high-frequency channels to snowfall at higher latitudes (around 60°N/S) is investigated using coincident CloudSat observations. The 166 GHz channel is highlighted throughout the study due to its ice scattering sensitivi...
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ftdoajarticles:oai:doaj.org/article:ab74b7b57ea44977ba9721b63f90e2d7 2023-05-15T18:18:28+02:00 CloudSat-Based Assessment of GPM Microwave Imager Snowfall Observation Capabilities Giulia Panegrossi Jean-François Rysman Daniele Casella Anna Cinzia Marra Paolo Sanò Mark S. Kulie 2017-12-01T00:00:00Z https://doi.org/10.3390/rs9121263 https://doaj.org/article/ab74b7b57ea44977ba9721b63f90e2d7 EN eng MDPI AG https://www.mdpi.com/2072-4292/9/12/1263 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs9121263 https://doaj.org/article/ab74b7b57ea44977ba9721b63f90e2d7 Remote Sensing, Vol 9, Iss 12, p 1263 (2017) snowfall detection GPM CloudSat CPR CALIPSO high latitudes passive microwave remote sensing of precipitation Science Q article 2017 ftdoajarticles https://doi.org/10.3390/rs9121263 2022-12-31T12:50:03Z The sensitivity of Global Precipitation Measurement (GPM) Microwave Imager (GMI) high-frequency channels to snowfall at higher latitudes (around 60°N/S) is investigated using coincident CloudSat observations. The 166 GHz channel is highlighted throughout the study due to its ice scattering sensitivity and polarization information. The analysis of three case studies evidences the important combined role of total precipitable water (TPW), supercooled cloud water, and background surface composition on the brightness temperature (TB) behavior for different snow-producing clouds. A regression tree statistical analysis applied to the entire GMI-CloudSat snowfall dataset indicates which variables influence the 166 GHz polarization difference (166 ∆TB) and its relation to snowfall. Critical thresholds of various parameters (sea ice concentration (SIC), TPW, ice water path (IWP)) are established for optimal snowfall detection capabilities. The 166 ∆TB can identify snowfall events over land and sea when critical thresholds are exceeded (TPW > 3.6 kg·m−2, IWP > 0.24 kg·m−2 over land, and SIC > 57%, TPW > 5.1 kg·m−2 over sea). The complex combined 166 ∆TB-TB relationship at higher latitudes and the impact of supercooled water vertical distribution are also investigated. The findings presented in this study can be exploited to improve passive microwave snowfall detection algorithms. Article in Journal/Newspaper Sea ice Directory of Open Access Journals: DOAJ Articles Remote Sensing 9 12 1263 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
snowfall detection GPM CloudSat CPR CALIPSO high latitudes passive microwave remote sensing of precipitation Science Q |
spellingShingle |
snowfall detection GPM CloudSat CPR CALIPSO high latitudes passive microwave remote sensing of precipitation Science Q Giulia Panegrossi Jean-François Rysman Daniele Casella Anna Cinzia Marra Paolo Sanò Mark S. Kulie CloudSat-Based Assessment of GPM Microwave Imager Snowfall Observation Capabilities |
topic_facet |
snowfall detection GPM CloudSat CPR CALIPSO high latitudes passive microwave remote sensing of precipitation Science Q |
description |
The sensitivity of Global Precipitation Measurement (GPM) Microwave Imager (GMI) high-frequency channels to snowfall at higher latitudes (around 60°N/S) is investigated using coincident CloudSat observations. The 166 GHz channel is highlighted throughout the study due to its ice scattering sensitivity and polarization information. The analysis of three case studies evidences the important combined role of total precipitable water (TPW), supercooled cloud water, and background surface composition on the brightness temperature (TB) behavior for different snow-producing clouds. A regression tree statistical analysis applied to the entire GMI-CloudSat snowfall dataset indicates which variables influence the 166 GHz polarization difference (166 ∆TB) and its relation to snowfall. Critical thresholds of various parameters (sea ice concentration (SIC), TPW, ice water path (IWP)) are established for optimal snowfall detection capabilities. The 166 ∆TB can identify snowfall events over land and sea when critical thresholds are exceeded (TPW > 3.6 kg·m−2, IWP > 0.24 kg·m−2 over land, and SIC > 57%, TPW > 5.1 kg·m−2 over sea). The complex combined 166 ∆TB-TB relationship at higher latitudes and the impact of supercooled water vertical distribution are also investigated. The findings presented in this study can be exploited to improve passive microwave snowfall detection algorithms. |
format |
Article in Journal/Newspaper |
author |
Giulia Panegrossi Jean-François Rysman Daniele Casella Anna Cinzia Marra Paolo Sanò Mark S. Kulie |
author_facet |
Giulia Panegrossi Jean-François Rysman Daniele Casella Anna Cinzia Marra Paolo Sanò Mark S. Kulie |
author_sort |
Giulia Panegrossi |
title |
CloudSat-Based Assessment of GPM Microwave Imager Snowfall Observation Capabilities |
title_short |
CloudSat-Based Assessment of GPM Microwave Imager Snowfall Observation Capabilities |
title_full |
CloudSat-Based Assessment of GPM Microwave Imager Snowfall Observation Capabilities |
title_fullStr |
CloudSat-Based Assessment of GPM Microwave Imager Snowfall Observation Capabilities |
title_full_unstemmed |
CloudSat-Based Assessment of GPM Microwave Imager Snowfall Observation Capabilities |
title_sort |
cloudsat-based assessment of gpm microwave imager snowfall observation capabilities |
publisher |
MDPI AG |
publishDate |
2017 |
url |
https://doi.org/10.3390/rs9121263 https://doaj.org/article/ab74b7b57ea44977ba9721b63f90e2d7 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
Remote Sensing, Vol 9, Iss 12, p 1263 (2017) |
op_relation |
https://www.mdpi.com/2072-4292/9/12/1263 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs9121263 https://doaj.org/article/ab74b7b57ea44977ba9721b63f90e2d7 |
op_doi |
https://doi.org/10.3390/rs9121263 |
container_title |
Remote Sensing |
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9 |
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12 |
container_start_page |
1263 |
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1766195060496400384 |