Reducing Weather Influences on Sea Ice Concentration Retrieval using Spaceborne 89 GHz Passive Microwave Observations
The major challenge of deriving sea ice concentration from the high resolution 89 GHz passive microwave observation is the strong atmospheric attenuation caused by water vapor and liquid water path, and surface variability induced by wind and temperature. In this study, we improve an 89 GHz sea ice...
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2020
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Online Access: | https://dx.doi.org/10.26092/elib/389 https://media.suub.uni-bremen.de/handle/elib/4592 |
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ftdatacite:10.26092/elib/389 2023-05-15T14:58:42+02:00 Reducing Weather Influences on Sea Ice Concentration Retrieval using Spaceborne 89 GHz Passive Microwave Observations Lu, Junshen 2020 https://dx.doi.org/10.26092/elib/389 https://media.suub.uni-bremen.de/handle/elib/4592 en eng Universität Bremen Attribution-NonCommercial-NoDerivs 3.0 Germany http://creativecommons.org/licenses/by-nc-nd/3.0/de/ CC-BY-NC-ND sea ice concentration Arctic atmospheric correction microwave remote sensing radiative transfer model 530 Thesis Other Dissertation thesis 2020 ftdatacite https://doi.org/10.26092/elib/389 2021-11-05T12:55:41Z The major challenge of deriving sea ice concentration from the high resolution 89 GHz passive microwave observation is the strong atmospheric attenuation caused by water vapor and liquid water path, and surface variability induced by wind and temperature. In this study, we improve an 89 GHz sea ice concentration retrieval algorithm called the Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithm, by correcting the observed brightness temperatures for these weather influences before they enter the algorithm. The instrument used is the Advanced Microwave Scanning Radiometer - Earth Observing System (EOS) (AMSR-E) on board NASA’s Aqua satellite. The weather correction is realized by simulating changes induced by weather influences in the top of atmosphere brightness temperatures through a radiative transfer model. Two correction schemes are tested, one utilizing the numerical weather prediction data as input, and the other the retrievals of an optimal estimation method. Two improved versions of the ASI algorithm, ASI2 and ASI3, are developed respectively based on the corrected brightness temperatures and new tie points. For both the influence of the atmosphere on the 89 GHz brightness temperature is successfully reduced. Main results are a better representation of low ice concentration in the marginal ice zone, and a reduction in RMS of 2.3% over high ice compared to Landsat data. Thesis Arctic Sea ice DataCite Metadata Store (German National Library of Science and Technology) Arctic |
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Open Polar |
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DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
English |
topic |
sea ice concentration Arctic atmospheric correction microwave remote sensing radiative transfer model 530 |
spellingShingle |
sea ice concentration Arctic atmospheric correction microwave remote sensing radiative transfer model 530 Lu, Junshen Reducing Weather Influences on Sea Ice Concentration Retrieval using Spaceborne 89 GHz Passive Microwave Observations |
topic_facet |
sea ice concentration Arctic atmospheric correction microwave remote sensing radiative transfer model 530 |
description |
The major challenge of deriving sea ice concentration from the high resolution 89 GHz passive microwave observation is the strong atmospheric attenuation caused by water vapor and liquid water path, and surface variability induced by wind and temperature. In this study, we improve an 89 GHz sea ice concentration retrieval algorithm called the Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithm, by correcting the observed brightness temperatures for these weather influences before they enter the algorithm. The instrument used is the Advanced Microwave Scanning Radiometer - Earth Observing System (EOS) (AMSR-E) on board NASA’s Aqua satellite. The weather correction is realized by simulating changes induced by weather influences in the top of atmosphere brightness temperatures through a radiative transfer model. Two correction schemes are tested, one utilizing the numerical weather prediction data as input, and the other the retrievals of an optimal estimation method. Two improved versions of the ASI algorithm, ASI2 and ASI3, are developed respectively based on the corrected brightness temperatures and new tie points. For both the influence of the atmosphere on the 89 GHz brightness temperature is successfully reduced. Main results are a better representation of low ice concentration in the marginal ice zone, and a reduction in RMS of 2.3% over high ice compared to Landsat data. |
format |
Thesis |
author |
Lu, Junshen |
author_facet |
Lu, Junshen |
author_sort |
Lu, Junshen |
title |
Reducing Weather Influences on Sea Ice Concentration Retrieval using Spaceborne 89 GHz Passive Microwave Observations |
title_short |
Reducing Weather Influences on Sea Ice Concentration Retrieval using Spaceborne 89 GHz Passive Microwave Observations |
title_full |
Reducing Weather Influences on Sea Ice Concentration Retrieval using Spaceborne 89 GHz Passive Microwave Observations |
title_fullStr |
Reducing Weather Influences on Sea Ice Concentration Retrieval using Spaceborne 89 GHz Passive Microwave Observations |
title_full_unstemmed |
Reducing Weather Influences on Sea Ice Concentration Retrieval using Spaceborne 89 GHz Passive Microwave Observations |
title_sort |
reducing weather influences on sea ice concentration retrieval using spaceborne 89 ghz passive microwave observations |
publisher |
Universität Bremen |
publishDate |
2020 |
url |
https://dx.doi.org/10.26092/elib/389 https://media.suub.uni-bremen.de/handle/elib/4592 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_rights |
Attribution-NonCommercial-NoDerivs 3.0 Germany http://creativecommons.org/licenses/by-nc-nd/3.0/de/ |
op_rightsnorm |
CC-BY-NC-ND |
op_doi |
https://doi.org/10.26092/elib/389 |
_version_ |
1766330825919430656 |