Assessment of the Stability of Passive Microwave Brightness Temperatures for NASA Team Sea Ice Concentration Retrievals

Gridded passive microwave brightness temperatures (TB) from special sensor microwave imager and sounder (SSMIS) instruments on three different satellite platforms are compared in different years to investigate the consistency between the sensors over time. The orbits of the three platforms have drif...

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Published in:Remote Sensing
Main Authors: Walter N. Meier, J. Scott Stewart
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2020
Subjects:
Online Access:https://doi.org/10.3390/rs12142197
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spelling ftmdpi:oai:mdpi.com:/2072-4292/12/14/2197/ 2023-08-20T04:00:58+02:00 Assessment of the Stability of Passive Microwave Brightness Temperatures for NASA Team Sea Ice Concentration Retrievals Walter N. Meier J. Scott Stewart agris 2020-07-09 application/pdf https://doi.org/10.3390/rs12142197 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/rs12142197 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 12; Issue 14; Pages: 2197 sea ice passive microwave remote sensing Arctic Antarctic Text 2020 ftmdpi https://doi.org/10.3390/rs12142197 2023-07-31T23:45:10Z Gridded passive microwave brightness temperatures (TB) from special sensor microwave imager and sounder (SSMIS) instruments on three different satellite platforms are compared in different years to investigate the consistency between the sensors over time. The orbits of the three platforms have drifted over their years of operation, resulting in changing relative observing times that could cause biases in TB estimates and near-real-time sea ice concentrations derived from the NASA Team algorithm that are produced at the National Snow and Ice Data Center. Comparisons of TB histograms and concentrations show that there are small mean differences between sensors, but variability within an individual sensor is much greater. There are some indications of small changes due to orbital drift, but these are not consistent across different frequencies. Further, the overall effect of the drift, while not definitive, is small compared to the intra- and interannual variability in individual sensors. These results suggest that, for near-real-time use, the differences in the sensors are not critical. However, for long-term time series, even the small biases should be corrected for. The strong day-to-day, seasonal, and interannual variability in TB distributions indicate that time-varying algorithm coefficients in the NASA team algorithm would lead to improved, more consistent sea ice concentration estimates. Text Antarc* Antarctic Arctic National Snow and Ice Data Center Sea ice MDPI Open Access Publishing Arctic Antarctic Remote Sensing 12 14 2197
institution Open Polar
collection MDPI Open Access Publishing
op_collection_id ftmdpi
language English
topic sea ice
passive microwave
remote sensing
Arctic
Antarctic
spellingShingle sea ice
passive microwave
remote sensing
Arctic
Antarctic
Walter N. Meier
J. Scott Stewart
Assessment of the Stability of Passive Microwave Brightness Temperatures for NASA Team Sea Ice Concentration Retrievals
topic_facet sea ice
passive microwave
remote sensing
Arctic
Antarctic
description Gridded passive microwave brightness temperatures (TB) from special sensor microwave imager and sounder (SSMIS) instruments on three different satellite platforms are compared in different years to investigate the consistency between the sensors over time. The orbits of the three platforms have drifted over their years of operation, resulting in changing relative observing times that could cause biases in TB estimates and near-real-time sea ice concentrations derived from the NASA Team algorithm that are produced at the National Snow and Ice Data Center. Comparisons of TB histograms and concentrations show that there are small mean differences between sensors, but variability within an individual sensor is much greater. There are some indications of small changes due to orbital drift, but these are not consistent across different frequencies. Further, the overall effect of the drift, while not definitive, is small compared to the intra- and interannual variability in individual sensors. These results suggest that, for near-real-time use, the differences in the sensors are not critical. However, for long-term time series, even the small biases should be corrected for. The strong day-to-day, seasonal, and interannual variability in TB distributions indicate that time-varying algorithm coefficients in the NASA team algorithm would lead to improved, more consistent sea ice concentration estimates.
format Text
author Walter N. Meier
J. Scott Stewart
author_facet Walter N. Meier
J. Scott Stewart
author_sort Walter N. Meier
title Assessment of the Stability of Passive Microwave Brightness Temperatures for NASA Team Sea Ice Concentration Retrievals
title_short Assessment of the Stability of Passive Microwave Brightness Temperatures for NASA Team Sea Ice Concentration Retrievals
title_full Assessment of the Stability of Passive Microwave Brightness Temperatures for NASA Team Sea Ice Concentration Retrievals
title_fullStr Assessment of the Stability of Passive Microwave Brightness Temperatures for NASA Team Sea Ice Concentration Retrievals
title_full_unstemmed Assessment of the Stability of Passive Microwave Brightness Temperatures for NASA Team Sea Ice Concentration Retrievals
title_sort assessment of the stability of passive microwave brightness temperatures for nasa team sea ice concentration retrievals
publisher Multidisciplinary Digital Publishing Institute
publishDate 2020
url https://doi.org/10.3390/rs12142197
op_coverage agris
geographic Arctic
Antarctic
geographic_facet Arctic
Antarctic
genre Antarc*
Antarctic
Arctic
National Snow and Ice Data Center
Sea ice
genre_facet Antarc*
Antarctic
Arctic
National Snow and Ice Data Center
Sea ice
op_source Remote Sensing; Volume 12; Issue 14; Pages: 2197
op_relation https://dx.doi.org/10.3390/rs12142197
op_rights https://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.3390/rs12142197
container_title Remote Sensing
container_volume 12
container_issue 14
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