Python script development for analyzing Aquarius salinity data in the Southern Ocean
| dc.contributor.advisor | Xie, Hongjie | |
| dc.contributor.author | Mueller, Chase | |
| dc.contributor.committeeMember | Ackley, Stephen | |
| dc.contributor.committeeMember | Gao, Yongli | |
| dc.date.accessioned | 2024-02-12T18:29:58Z | |
| dc.date.available | 2024-02-12T18:29:58Z | |
| dc.date.issued | 2014 | |
| dc.description | This item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID. | |
| dc.description.abstract | With the Aquarius mission having completed its second full year of acquiring global sea surface salinity (SSS) measurements, many corrections were accounted for and biases were removed. However, some biases remain, keeping the mission from achieving its goal of +/- 0.2 psu accuracy for monthly products (150 km pixel size). Uncertainties in the Southern Ocean (among other biases) not only keep the mission from attaining such accuracy globally, but it also forces continued reliance on in situ point data sources. A Python script package is developed to process the Level 2 data for use, allowing users to target specific variables and to prepare ship and buoy data for analysis with the Aquarius data. To test the application of the scripting package, multiple assessments are completed. (1) The relationship between Aquarius brightness temperatures (T b) and the percentage of ice and land cover is analyzed. Exponential and linear increases in Tb are observed with increasing ice and land, respectively. Little to no effect on Tb is found when there is less than 1% ice or land cover. (2) In situ SSS, in situ sea surface temperature (SST), and Aquarius Tb within a Response Surface Model are used to generate an equation to predict SSS using only Tb and SST as inputs. SSS is found strongly relying on SST, nearly removing the need for Aquarius T b . While this does not assist in converting Aquarius Tb into SSS, the use of SST alone proved a significantly more accurate method in predicting SSS over current Aquarius estimations for the Southern Ocean. This is not to say that SST should be used to predict SSS, but rather that the two are highly linked. | |
| dc.description.department | Geosciences | |
| dc.format.extent | 77 pages | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.isbn | 9781321194845 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12588/4803 | |
| dc.language | en | |
| dc.subject | Aquarius | |
| dc.subject | Ocean | |
| dc.subject | Salinity | |
| dc.subject | Southern | |
| dc.subject | Surface | |
| dc.subject | Temperature | |
| dc.subject.classification | Remote sensing | |
| dc.subject.lcsh | Salinity -- Antarctic Ocean | |
| dc.title | Python script development for analyzing Aquarius salinity data in the Southern Ocean | |
| dc.type | Thesis | |
| dc.type.dcmi | Text | |
| dcterms.accessRights | pq_closed | |
| thesis.degree.department | Geosciences | |
| thesis.degree.grantor | University of Texas at San Antonio | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science |
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