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<p>Fundamental and thematic climate data records derived from
satellite observations provide unique information for climate
monitoring and research.
</p>
<p>Since any satellite operates over a limited period of time only,
creating a climate data record requires the combination of
space-born measurements from a series of several (often
similar) satellites.
</p>
<p>Simple combination of measurements from several sensors,
however, will produce an inconsistent climate data record
because the behaviour of historical satellites in space was often
different from their behaviour during pre-launch calibration in
the laboratory. More scientific value can be derived from
considering the series of historical and present satellites as a
whole.
</p>
<p>Here we consider harmonisation as a process that obtains new
calibration coefficients and a revised calibration model for each
sensor by comparing the output of each satellite to
radiometrically more accurate sensors using appropriate match-ups, such as simultaneous nadir overpasses.
</p>
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<p>When we perform a comparison of two sensors using match-
ups, we must take into account the fact that those sensors are
not observing exactly the same Earth radiance. This is in part
due to uncertainties in the collocation process itself, but also
due to differences in the spectral response functions of the two
instruments, even when nominally observing the same spectral
band.
</p>
<p>We do not aim to correct for spectral response function
differences, but to reconcile the calibration of different sensors
given their estimated spectral response function differences.
</p>
<p>Here we present the concept of a framework that establishes
calibration coefficients for several sensors simultaneously and
rigorously with respect to their uncertainty and error
covariance.
</p>
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<p>We present the harmonisation and its mathematical
formulation as a large-structured inverse problem. Solving this
problem is a challenge because it involves some hundred
million of match-ups and has significant error correlation in the
measured data.
</p>
<p>We sketch different approaches to solve the harmonisation
problem and present our first attempt to recalibrate AVHRR
radiance from a series of nine NOAA and MetOp satellites. </p>
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</div></div><div><br></div><i>Presented at the EUMETSAT Meteorological Satellite Conference, Rome, October 2017.</i><br