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Providing uncertainties in satellite datasets used for Earth observation can be a daunting prospect because of the many processing stages and input data required to convert raw detector counts to calibrated radiances. The Sea and Land Surface Temperature Radiometer (SLSTR) was designed to provide measurements of the Earth's surface for operational and climate applications. In this paper the authors describe the traceability chain and derivation of uncertainty estimates for the thermal infrared channel radiometry. Starting from the instrument model, the contributing input quantities are identified to build up an uncertainty effects tree. The characterisation of each input effect is described, and uncertainty estimates provided which are used to derive the combined uncertainties as a function of scene temperature. The SLSTR Level-1 data products provide uncertainty estimates for fully random effects (noise) and systematic effects that can be mapped for each image pixel, examples of which are shown. [ABSTRACT FROM AUTHOR]

This paper presents a new Fundamental Climate Data Record (FCDR) for the visible (VIS) channel of the Meteosat Visible and Infrared Imager (MVIRI), with pixel-level metrologically traceable uncertainties and error covariance estimates. MVIRI has flown onboard Meteosat First Generation (MFG) satellites between 1982 and 2017. It has served the weather forecasting community with measurements of "visible", "infra-red" and "water vapour" radiance in near real-time. The precision of the pre-launch sensor spectral response function (SRF) characterisation, particularly of the visible band of this sensor type, improved considerably with time, resulting in higher quality radiances towards the end of the MFG program. Despite these improvements, the correction of the degradation of this sensor has remained a challenging task and previous studies have found the SRF degradation to be faster in the blue than in the near-infrared part of the spectrum. With these limitations, the dataset cannot be immediately applied in climate science. In order to provide a data record that is suited for climate studies, the Horizon 2020 project "FIDelity and Uncertainty in Climate-data records from Earth Observation" (FIDUCEO) conducted (1) a thorough metrological uncertainty analysis for each instrument, and (2) a recalibration using enhanced input data such as reconstructed SRFs. In this paper, we present the metrological analysis, the recalibration results and the resulting consolidated FCDR. In the course of this study we were able to trace-back the remaining uncertainties in the calibrated MVIRI reflectances to underlying effects that have distinct physical root-causes and spatial/temporal correlation patterns. SEVIRI and SCIAMACHY reflectances have been used for a validation of the harmonised dataset. The resulting new FCDR is publicly available for climate studies and for the production of climate data records (CDRs) spanning about 35 years. [ABSTRACT FROM AUTHOR]

The uncertainty in a climate data records (CDRs) derived from Earth observations in part derives from the propagated uncertainty in the radiance record (the fundamental climate data record, FCDR) from which the geophysical estimates in the CDR are derived. A common barrier to providing uncertainty-quantified CDRs is the inaccessibility to CDR creators of appropriate radiance uncertainty information in the FCDR. Here, we propose radiance uncertainty information designed directly to facilitate estimation of propagated uncertainty in derived CDRs at full resolution and in gridded products. Errors in Earth observations are typically highly structured and complex, and the uncertainty information we propose is of intermediate complexity, sufficient to capture the main variability in propagated uncertainty in a CDR, while avoiding unfeasible complexity or data volume. The uncertainty and error correlation characteristics of uncertainty are quantified for three classes of error with different propagation properties: independent, structured and common radiance errors. The meaning, mathematical derivations, practical evaluation and example applications of this set of uncertainty information are presented. [ABSTRACT FROM AUTHOR]