A core concept behind the FIDUCEO FCDRs is the definition of error correlations between different observations so that this information can be used in the propagation of uncertainties to climate data records. In FIDUCEO we consider that errors may be correlated across a set of identified dimensions. On this page, we\u2019ll briefly introduce two of three such dimensions: spatial and temporal. In the next lesson, we\u2019ll move on to examine a third correlation dimension: spectral.<\/p>\r\n\r\n\r\n\r\n
For the spatial and temporal dimensions, the appropriate error correlation dimensions depend on the imaging method of the instrument.<\/p>\r\n\r\n\r\n\r\n
For low Earth orbit (LEO) platforms with across-track scanning<\/strong>, we have concluded that the most useful error correlation dimensions are:<\/p>\r\n\r\n\r\n\r\n For Geostationary Orbit Satellites (GEO) that take images in lines<\/strong>, the error correlation dimensions are slightly different:<\/p>\r\n\r\n\r\n\r\n Effects can have different correlation structures for different dimensions. For example, a calibration performed once per line, with some rolling average between lines, would yield effects that would be fully correlated (systematic) for all elements within a line and would have a triangular-shaped correlation structure from line to line over an averaging window and would be uncorrelated (random) beyond those lines and from orbit to orbit. We\u2019ll discuss correlation structures in more detail in a later recipe, but before we do, let\u2019s briefly introduce the spectral correlation dimension, which we will do on the next page.<\/p>\r\n","protected":false},"excerpt":{"rendered":"\r\n
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