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Python script development for analyzing aquarius salinity data in the Southern Ocean.

机译：Python脚本开发，用于分析南部海洋中的水瓶座盐度数据。

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摘要

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 (Tb) 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. Discrepancies in the relationships between SSS, SST, and T b require.
机译：随着水瓶座飞行任务完成了获取全球海表盐度（SSS）测量值的第二个全年，许多校正已被考虑在内，偏差也得以消除。但是，仍然存在一些偏差，使任务无法实现每月产品（150 km像素大小）的+/- 0.2 psu精度的目标。南大洋的不确定性（除其他偏见外）不仅使飞行任务无法在全球范围内达到这种准确性，而且还迫使人们继续依赖实地点数据源。开发了Python脚本程序包来处理2级数据以供使用，从而允许用户确定特定变量的位置，并准备用于分析Aquarius数据的船舶和浮标数据。为了测试脚本包的应用，需要完成多次评估。 （1）分析了水瓶座亮度温度（Tb）与冰和土地覆盖的百分比之间的关系。随着冰和陆地的增加，Tb呈指数增长和线性增长。当冰或土地覆盖不足1％时，对Tb几乎没有影响。 （2）使用原位SSS，原位海面温度（SST）和响应面模型中的水瓶座Tb来生成仅使用Tb和SST作为输入来预测SSS的方程。发现SSS非常依赖SST，几乎消除了对Aquarius T b的需求。尽管这无助于将水瓶座Tb转换为SSS，但仅靠SST的使用已被证明比目前对南大洋的水瓶座估计值预测SSS的方法更为精确。这并不是说应该使用SST来预测SSS，而是要把两者高度联系起来。需要在SSS，SST和T b之间的关系存在差异。

著录项

  • 作者

    Mueller, Chase.;

  • 作者单位

    The University of Texas at San Antonio.;

  • 授予单位 The University of Texas at San Antonio.;
  • 学科 Remote sensing.
  • 学位 M.S.
  • 年度 2014
  • 页码 77 p.
  • 总页数 77
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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