Predicting mesoscale variability of the North Atlantic using a physically motivated scheme for assimilating altimeter and Argo observations
A computationally efficient scheme is described for assimilating sea level measured by altimeters and vertical profiles of temperature and salinity measured by Argo floats. The scheme is based on a transformation of temperature, salinity, and sea level into a set of physically meaningful variables f...
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Online Access: | https://doi.org/10.1175/2008MWR2625.1 http://hdl.handle.net/10222/27495 |
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ftdalhouse:oai:DalSpace.library.dal.ca:10222/27495 2023-05-15T17:28:22+02:00 Predicting mesoscale variability of the North Atlantic using a physically motivated scheme for assimilating altimeter and Argo observations Liu, Yimin Thompson, K. R. 2013-06-19T18:06:23Z https://doi.org/10.1175/2008MWR2625.1 http://hdl.handle.net/10222/27495 unknown American Meteorological Society Monthly Weather Review Liu, Yimin, and K. R. Thompson. 2009. "Predicting mesoscale variability of the North Atlantic using a physically motivated scheme for assimilating altimeter and Argo observations." Monthly Weather Review 137(7): 2223-37. doi:10.1175/2008MWR2625.1 0027-0644 http://dx.doi.org/10.1175/2008MWR2625.1 http://hdl.handle.net/10222/27495 137 7 2223 2009 American Meteorological Society Altimeters Climatology Ocean temperature Oceanographic regions Oceanographic techniques Sea level article 2013 ftdalhouse https://doi.org/10.1175/2008MWR2625.1 2021-12-29T18:08:45Z A computationally efficient scheme is described for assimilating sea level measured by altimeters and vertical profiles of temperature and salinity measured by Argo floats. The scheme is based on a transformation of temperature, salinity, and sea level into a set of physically meaningful variables for which it is easier to specify spatial covariance functions. The scheme also allows for sequential correction of temperature and salinity biases and online estimation of background error covariance parameters. Two North Atlantic applications, both focused on predicting mesoscale variability, are used to assess the effectiveness of the scheme. In the first application the background is a monthly temperature and salinity climatology and skill is assessed by how well the scheme recovers Argo profiles that were not assimilated. In the second application the backgrounds are short-term forecasts made by an eddy-permitting model of the North Atlantic. Skill is assessed by the quality of forecasts with lead times of 1-60 days. Both applications show that the scheme has useful skill. Article in Journal/Newspaper North Atlantic Dalhousie University: DalSpace Institutional Repository Monthly Weather Review 137 7 2223 2237 |
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Open Polar |
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Dalhousie University: DalSpace Institutional Repository |
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ftdalhouse |
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topic |
Altimeters Climatology Ocean temperature Oceanographic regions Oceanographic techniques Sea level |
spellingShingle |
Altimeters Climatology Ocean temperature Oceanographic regions Oceanographic techniques Sea level Liu, Yimin Thompson, K. R. Predicting mesoscale variability of the North Atlantic using a physically motivated scheme for assimilating altimeter and Argo observations |
topic_facet |
Altimeters Climatology Ocean temperature Oceanographic regions Oceanographic techniques Sea level |
description |
A computationally efficient scheme is described for assimilating sea level measured by altimeters and vertical profiles of temperature and salinity measured by Argo floats. The scheme is based on a transformation of temperature, salinity, and sea level into a set of physically meaningful variables for which it is easier to specify spatial covariance functions. The scheme also allows for sequential correction of temperature and salinity biases and online estimation of background error covariance parameters. Two North Atlantic applications, both focused on predicting mesoscale variability, are used to assess the effectiveness of the scheme. In the first application the background is a monthly temperature and salinity climatology and skill is assessed by how well the scheme recovers Argo profiles that were not assimilated. In the second application the backgrounds are short-term forecasts made by an eddy-permitting model of the North Atlantic. Skill is assessed by the quality of forecasts with lead times of 1-60 days. Both applications show that the scheme has useful skill. |
format |
Article in Journal/Newspaper |
author |
Liu, Yimin Thompson, K. R. |
author_facet |
Liu, Yimin Thompson, K. R. |
author_sort |
Liu, Yimin |
title |
Predicting mesoscale variability of the North Atlantic using a physically motivated scheme for assimilating altimeter and Argo observations |
title_short |
Predicting mesoscale variability of the North Atlantic using a physically motivated scheme for assimilating altimeter and Argo observations |
title_full |
Predicting mesoscale variability of the North Atlantic using a physically motivated scheme for assimilating altimeter and Argo observations |
title_fullStr |
Predicting mesoscale variability of the North Atlantic using a physically motivated scheme for assimilating altimeter and Argo observations |
title_full_unstemmed |
Predicting mesoscale variability of the North Atlantic using a physically motivated scheme for assimilating altimeter and Argo observations |
title_sort |
predicting mesoscale variability of the north atlantic using a physically motivated scheme for assimilating altimeter and argo observations |
publisher |
American Meteorological Society |
publishDate |
2013 |
url |
https://doi.org/10.1175/2008MWR2625.1 http://hdl.handle.net/10222/27495 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_relation |
Monthly Weather Review Liu, Yimin, and K. R. Thompson. 2009. "Predicting mesoscale variability of the North Atlantic using a physically motivated scheme for assimilating altimeter and Argo observations." Monthly Weather Review 137(7): 2223-37. doi:10.1175/2008MWR2625.1 0027-0644 http://dx.doi.org/10.1175/2008MWR2625.1 http://hdl.handle.net/10222/27495 137 7 2223 |
op_rights |
2009 American Meteorological Society |
op_doi |
https://doi.org/10.1175/2008MWR2625.1 |
container_title |
Monthly Weather Review |
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137 |
container_issue |
7 |
container_start_page |
2223 |
op_container_end_page |
2237 |
_version_ |
1766120988971368448 |