Document Detail


Observing and studying extreme low pressure events with altimetry.
MedLine Citation:
PMID:  22573955     Owner:  NLM     Status:  PubMed-not-MEDLINE    
Abstract/OtherAbstract:
The ability of altimetry to detect extreme low pressure events and the relationship between sea level pressure and sea level anomalies during extra-tropical depressions have been investigated. Specific altimeter treatments have been developed for tropical cyclones and applied to obtain a relevant along-track sea surface height (SSH) signal: the case of tropical cyclone Isabel is presented here. The S- and C-band measurements are used because they are less impacted by rain than the Ku-band, and new sea state bias (SSB) and wet troposphere corrections are proposed. More accurate strong altimeter wind speeds are computed thanks to the Young algorithm. Ocean signals not related to atmospheric pressure can be removed with accuracy, even within a Near Real Time context, by removing the maps of sea level anomaly (SLA) provided by SSALTO/Duacs. In the case of Extra-Tropical Depressions, the classical altimeter processing can be used. Ocean signal not related to atmospheric pressure is along-track filtered. The sea level pressure (SLP)-SLA relationship is investigated for the North Atlantic, North Pacific and Indian oceans; three regression models are proposed allowing restoring an altimeter SLP with a mean error of 5 hPa if compared to ECMWF or buoys SLP. The analysis of barotropic simulation outputs points out the regional variability of the SLP/Model Sea Level relationship and the wind effects.
Authors:
Loren Carrère; Françoise Mertz; Joel Dorandeu; Yves Quilfen; Jerome Patoux
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Publication Detail:
Type:  Journal Article     Date:  2009-02-26
Journal Detail:
Title:  Sensors (Basel, Switzerland)     Volume:  9     ISSN:  1424-8220     ISO Abbreviation:  Sensors (Basel)     Publication Date:  2009  
Date Detail:
Created Date:  2012-05-10     Completed Date:  2012-09-10     Revised Date:  2013-05-29    
Medline Journal Info:
Nlm Unique ID:  101204366     Medline TA:  Sensors (Basel)     Country:  Switzerland    
Other Details:
Languages:  eng     Pagination:  1306-29     Citation Subset:  -    
Affiliation:
CLS, 8-10 rue Hermès, 31520 Ramonville St Agne, France; E-mails : fmertz@cls.fr (F.M.); jdorandeu@cls.fr (J.D.).
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