{"id":1331,"date":"2015-06-08T13:45:32","date_gmt":"2015-06-08T12:45:32","guid":{"rendered":"https:\/\/research.reading.ac.uk\/fiduceo\/?p=1331"},"modified":"2019-07-29T13:47:31","modified_gmt":"2019-07-29T12:47:31","slug":"the-origins-of-the-fiduceo-project","status":"publish","type":"post","link":"https:\/\/research.reading.ac.uk\/fiduceo\/the-origins-of-the-fiduceo-project\/","title":{"rendered":"The Origins of the FIDUCEO Project"},"content":{"rendered":"by <a href=\"http:\/\/www.met.reading.ac.uk\/users\/users\/2103\">Jonathan Mittaz<\/a>\r\n\r\n<!-- \/wp:post-content --><!-- wp:paragraph -->\r\n\r\nWhy did we decide to propose FIDUCEO?\u00a0 The basic idea behind the project arose from discussions over what was needed to improve the broad band infrared data used to produce certain climate data records (CDRs).\u00a0\u00a0 Over the past few years it had become apparent (at least to us) that the radiances provided from many sensors could be significantly in error when compared to the accuracies required for climate change studies.\u00a0 So we decided to try and fix some of the well-known sensors and do it in a way which would also make the data as trustworthy as possible.\r\n\r\n<!-- \/wp:paragraph --><!-- wp:paragraph -->\r\n\r\nIt also turns out that many of the sensors which we knew had problems were among the most used and\/or most long lived available and are crucial for climate change studies.\u00a0 The sensors we were thinking of included the Advanced Very High Resolution Radiometer (AVHRR), microwave sensors to look at the critical upper tropospheric humidity and the High resolution Infrared Radiation Sounder (HIRS), all of which have versions dating back more than 30 years.\u00a0 It is also true that some of the more modern sensors have also shown significant biases relative to top-of-atmosphere reference sensors.\u00a0 \u00a0So it may be that a different approach to the problem of accurate instrument calibration is needed \u2013 which is where FIDUCEO comes in.\r\n\r\n<!-- \/wp:paragraph --><!-- wp:paragraph -->\r\n\r\nPerhaps the first question to ask is why does the operational calibration for so many sensors introduce biases and errors?\u00a0 There are a number of reasons for this.\u00a0 Sometimes there were issues with the design of the sensor.\u00a0 It should be remembered that many of the early operational sensors were designed with weather forecasting in mind and so what was important were things like \u2018where are the clouds?\u2019 rather than providing very high levels of accuracy.\u00a0 As an example, the design specification for the original AVHRR sensors was (in the IR) an accuracy of 1K only, a long way from &lt;0.1 (or 0.05)K requirement from the climate community.\u00a0\u00a0 So problems such as stray light issues (caused by a very open design) or the presence of strong thermal gradients across calibration targets limiting their usability were not really an issue for the original designers.\u00a0 Also, often problems were caused by limited and\/or poor pre-launch characterization either related to lack of facilities and\/or time pressures or simply not understanding that the orbital conditions can be significantly different from that of the test environment.\u00a0 But from an operational point of view instruments such as the AVHRR still met their design specification so there was no strong requirement to push the calibration to higher levels of accuracy.\r\n\r\n<!-- \/wp:paragraph --><!-- wp:paragraph -->\r\n\r\nThe presence of such biases in the radiance record make it very difficult (if not impossible) to produce CDRs at the level of accuracy needed for climate studies.\u00a0 It turns out that all is not lost, however, in that for many sensors the problems listed above can be removed or at least ameliorated by understanding that often there is a good stable sensor \u201cunder the hood\u201d which was being hindered by a poor (or non-existent) calibration system. \u00a0The reason for this is simple as at the core of many sensors is a detector which is (mostly) kept at a fixed operating temperature so its characteristics should be quite stable.\u00a0 Then the problem devolves to whether or not the problems with the calibration system (if present) can be modelled in such a way as to improve the accuracy of the data.\u00a0 It turns out that in certain circumstances you can as long as the problem is approached in a physically based manner.\u00a0 \u00a0\u00a0By physically based we mean approaching the instrument from a physicists point of view and try and incorporate physically based models as much as possible rather than use ad-hoc algorithms.\u00a0 Previous work by members of the FIDUCEO team had already shown an order of magnitude improvement in accuracy for the AVHRRs flown on the MetOp series of satellites so aspects of the approach had already been validated.\u00a0 The FIDUCEO plan is to broaden the scope of such work and provide a framework for satellite recalibration that could be applied to a range of sensors.\r\n\r\n<!-- \/wp:paragraph --><!-- wp:paragraph -->\r\n\r\nOf course just improving the calibration of a series of sensors does not provide all the information needed.\u00a0 From our point of view representative uncertainties are just as important as accurate radiances.\u00a0 This is why we brought in the concepts of Metrology (the science of measurement) which is defined as \u201cthe science of measurement, embracing both experimental and theoretical determinations at any level of uncertainty in any field of science and technology\u201d. \u00a0From our point of view perhaps the most important part of Metrology is the concept of traceability defined as &#8220;the property of a measurement result whereby the result can be related to a reference through a documented unbroken chain of calibrations, each contributing to the measurement uncertainty&#8221;.\u00a0 This statement is actually more complex than it may seem because in order to get sensible estimates of the uncertainties you first have to remove known sources of bias and error from the data.\u00a0 \u00a0It also forces you to think about every single process however small, and also think about any assumptions that have been made.\u00a0 The issue of understanding assumptions is critical to getting a good Fundamental Climate Data Record (FCDR).\u00a0 One of the major issues with the AVHRR, for example, was an assumption that the calibration parameters derived from pre-launch testing would be applicable for the in-orbit conditions even though the satellite thermal environments were completely different between the two and this difference lead to large (up to 0.5K) biases in the data.\r\n\r\n<!-- \/wp:paragraph --><!-- wp:paragraph -->\r\n\r\nFinally\u00a0we realized that we couldn\u2019t just leave it at creating new FCDRs with traceable uncertainties &#8211; most people who study climate change look at geophysical variables and\/or essential climate variables such as surface temperature, not raw satellite radiances.\u00a0\u00a0 So we also included climate data record generation as part of FIDUCEO with the idea of both using the data with proper uncertainties developed for the FIDUCEO FCDRs as well as applying the same metrological rigor to the CDR generation process itself.\u00a0 Then, by having metrological sound CDRs which will have traceable uncertainties traced back to a known reference and\/or physical processes we (and the rest of the climate change community) will be in a position to know to what extent the data can be trusted and the limits to which the data can be pushed in detecting the sometimes small changes introduced by climate change.","protected":false},"excerpt":{"rendered":"<p>The origins of the FIDUCEO project which aims to provide the most accurate climate data records to date &#8211; with 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