{"id":1323,"date":"2015-07-15T13:40:58","date_gmt":"2015-07-15T12:40:58","guid":{"rendered":"https:\/\/research.reading.ac.uk\/fiduceo\/?p=1323"},"modified":"2019-07-29T13:42:50","modified_gmt":"2019-07-29T12:42:50","slug":"why-worry-about-all-sources-of-errors","status":"publish","type":"post","link":"https:\/\/research.reading.ac.uk\/fiduceo\/why-worry-about-all-sources-of-errors\/","title":{"rendered":"Why worry about all sources of errors?"},"content":{"rendered":"by Chris Merchant<\/a>\r\n\r\n\r\n\r\nThere are many effects that act as sources of errors in a climate data record. No measured value is perfect, whether taken in the laboratory or inferred from radiances measured in low Earth orbit.\r\n\r\n\r\n\r\nThe most obvious characteristic to establish about a source of error is the magnitude of its effect. The (standard) uncertainty is a measure of the typical size of errors, and is generally what is represented by “error bars” on a plot.\r\n\r\n\r\n\r\nLess obvious is the need to know whether the error is correlated between different measured values. This becomes very important for climate data records: looking at climate change, highly correlated errors are the ones we need to worry about.\r\n\r\n\r\n\r\nTo illustrate this, consider a climate data record (CDR) for sea surface temperature (SST). Full resolution satellite data typically measures an instantaneous SST across a pixel of about 1 km. For a typical case, we might have three categories of effect, causing uncertainties of different size:\r\n\r\n\r\n