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Estimating uncertainties in bio-optical products derived from satellite ocean color imagery using an ensemble approach

机译:使用集成方法估算源自卫星海洋彩色图像的生物光学产品的不确定性

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We propose a methodology to quantify errors and produce uncertainty maps for satellite-derived ocean color bio-optical products using ensemble simulations. Ensemble techniques have been used by the environmental numerical modeling community to propagate initialization, forcing, and algorithm error sources through-out the full simulation process, but similar approaches have not yet been applied to satellite optical properties. Uncertainties in retrievals of bio-optical properties from satellite ocean color imagery are related to a variety of factors, including sensor calibration, atmospheric correction, and the bio-optical inversion algorithms. Errors propagate, amplify, and intertwine along the processing path, so it is important to understand how the errors cascade through each step of the analysis, to assess their impact and identify the main factors contributing to the uncertainties in the final products. Also, we are interested in producing short-term forecasts of the bio-optical property distributions, by coupling the satellite imagery with physical circulation models. So, in addition to the uncertainties in the satellite-derived bio-optical properties due to the above-mentioned factors, the uncertainties in the model currents used to advect the bio-optical properties add another layer of complexity to the problem. We outline these processes and present preliminary results for this approach.
机译:我们提出了一种方法来量化误差,并使用集成模拟为卫星衍生的海洋颜色生物光学产品生成不确定性图。环境数值建模社区已使用集合技术在整个模拟过程中传播初始化,强迫和算法错误源,但尚未将类似方法应用于卫星光学特性。从卫星海洋彩色图像中检索生物光学特性的不确定性与多种因素有关,包括传感器校准,大气校正和生物光学反演算法。错误会沿着处理路径传播,放大和交织,因此,重要的是要了解错误如何在分析的每个步骤中级联,评估其影响并确定导致最终产品不确定性的主要因素。此外,我们有兴趣通过将卫星图像与物理环流模型耦合来生成生物光学特性分布的短期预测。因此,除了由于上述因素导致的源自卫星的生物光学特性的不确定性之外,用于平移生物光学特性的模型电流的不确定性也为该问题增加了另一层复杂性。我们概述了这些过程,并介绍了此方法的初步结果。

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