Evaluating Satellite Sounding Temperature Observations for Cold Air Aloft Detection

Cold Air Aloft (CAA) can impact commercial flights when cold air descends below 12,192 m (40,000 ft) and temperatures drop dramatically. A CAA event is identified when air temperature falls below −65 °C, which decreases fuel efficiency and poses a safety hazard. This manuscript assesses the performa...

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Published in:Atmosphere
Main Authors: Rebekah Esmaili, Nadia Smith, Mark Schoeberl, Chris Barnet
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2020
Subjects:
Online Access:https://doi.org/10.3390/atmos11121360
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spelling ftmdpi:oai:mdpi.com:/2073-4433/11/12/1360/ 2023-08-20T04:04:40+02:00 Evaluating Satellite Sounding Temperature Observations for Cold Air Aloft Detection Rebekah Esmaili Nadia Smith Mark Schoeberl Chris Barnet agris 2020-12-15 application/pdf https://doi.org/10.3390/atmos11121360 EN eng Multidisciplinary Digital Publishing Institute Meteorology https://dx.doi.org/10.3390/atmos11121360 https://creativecommons.org/licenses/by/4.0/ Atmosphere; Volume 11; Issue 12; Pages: 1360 satellite soundings cold air aloft arctic weather natural hazards aviation weather Text 2020 ftmdpi https://doi.org/10.3390/atmos11121360 2023-08-01T00:40:08Z Cold Air Aloft (CAA) can impact commercial flights when cold air descends below 12,192 m (40,000 ft) and temperatures drop dramatically. A CAA event is identified when air temperature falls below −65 °C, which decreases fuel efficiency and poses a safety hazard. This manuscript assesses the performance of the National Oceanic and Atmospheric Administration Unique Combined Atmospheric Processing System (NUCAPS) in detecting CAA events using sounders on polar-orbiting satellites. We compare NUCAPS air temperature profiles with those from Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) for January–March 2018. Of 1311 collocated profiles, 236 detected CAA. Our results showed that NUCAPS correctly detects CAA in 48.1% of profiles, while 17.2% are false positives and 34.7% are false negatives. To identify the reason for these detection states, we used a logistic regression trained on NUCAPS diagnostic parameters. We found that cloud cover can impact the skill even at higher vertical levels. This work indicates that a CAA-specific quality flag is feasible and may be useful to help forecasters to diagnose NUCAPS in real-time. Furthermore, the inclusion of an additional sounder data source (e.g., NOAA-20) may increase CAA forecast accuracy. Cloud scenes change rapidly, so additional observations provide more opportunities for correct detection. Text Arctic MDPI Open Access Publishing Arctic Atmosphere 11 12 1360
institution Open Polar
collection MDPI Open Access Publishing
op_collection_id ftmdpi
language English
topic satellite soundings
cold air aloft
arctic weather
natural hazards
aviation weather
spellingShingle satellite soundings
cold air aloft
arctic weather
natural hazards
aviation weather
Rebekah Esmaili
Nadia Smith
Mark Schoeberl
Chris Barnet
Evaluating Satellite Sounding Temperature Observations for Cold Air Aloft Detection
topic_facet satellite soundings
cold air aloft
arctic weather
natural hazards
aviation weather
description Cold Air Aloft (CAA) can impact commercial flights when cold air descends below 12,192 m (40,000 ft) and temperatures drop dramatically. A CAA event is identified when air temperature falls below −65 °C, which decreases fuel efficiency and poses a safety hazard. This manuscript assesses the performance of the National Oceanic and Atmospheric Administration Unique Combined Atmospheric Processing System (NUCAPS) in detecting CAA events using sounders on polar-orbiting satellites. We compare NUCAPS air temperature profiles with those from Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) for January–March 2018. Of 1311 collocated profiles, 236 detected CAA. Our results showed that NUCAPS correctly detects CAA in 48.1% of profiles, while 17.2% are false positives and 34.7% are false negatives. To identify the reason for these detection states, we used a logistic regression trained on NUCAPS diagnostic parameters. We found that cloud cover can impact the skill even at higher vertical levels. This work indicates that a CAA-specific quality flag is feasible and may be useful to help forecasters to diagnose NUCAPS in real-time. Furthermore, the inclusion of an additional sounder data source (e.g., NOAA-20) may increase CAA forecast accuracy. Cloud scenes change rapidly, so additional observations provide more opportunities for correct detection.
format Text
author Rebekah Esmaili
Nadia Smith
Mark Schoeberl
Chris Barnet
author_facet Rebekah Esmaili
Nadia Smith
Mark Schoeberl
Chris Barnet
author_sort Rebekah Esmaili
title Evaluating Satellite Sounding Temperature Observations for Cold Air Aloft Detection
title_short Evaluating Satellite Sounding Temperature Observations for Cold Air Aloft Detection
title_full Evaluating Satellite Sounding Temperature Observations for Cold Air Aloft Detection
title_fullStr Evaluating Satellite Sounding Temperature Observations for Cold Air Aloft Detection
title_full_unstemmed Evaluating Satellite Sounding Temperature Observations for Cold Air Aloft Detection
title_sort evaluating satellite sounding temperature observations for cold air aloft detection
publisher Multidisciplinary Digital Publishing Institute
publishDate 2020
url https://doi.org/10.3390/atmos11121360
op_coverage agris
geographic Arctic
geographic_facet Arctic
genre Arctic
genre_facet Arctic
op_source Atmosphere; Volume 11; Issue 12; Pages: 1360
op_relation Meteorology
https://dx.doi.org/10.3390/atmos11121360
op_rights https://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.3390/atmos11121360
container_title Atmosphere
container_volume 11
container_issue 12
container_start_page 1360
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