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The AVHRR aerosol optical depth (AOD) is inverted from measured reflectances in the red band using a statistical correlation of surface reflectance with mid-infrared channel reflectances and a modelling climatology of the aerosol type. For such a sensor not specifically designed for AOD retrieval, propagating uncertainties is crucial because the sensitivity of the retrieved AOD to the measured signal varies largely with retrieval conditions (AOD itself, surface brightness, aerosol optical properties/aerosol type, observing geometry). In order to quantify the different contributions to the AOD uncertainties, we have undertaken a thorough analysis of the retrieval operator and its sensitivities to the used input and auxiliary variables. Uncertainties are then propagated from measured reflectances to geophysical retrieved AOD datasets at the super-pixel level and further to gridded daily and monthly products. The propagation uses uncertainty correlations of separate uncertainty contributions from the FIDUCEO easyFCDR level1b products (common fully correlated, independent random, and structured parts) and estimated uncertainty correlation structures of other major effects in the retrieval (surface brightness, aerosol type ensemble, cloud mask). The pixel-level uncertainties are statistically validated against true error estimates versus AERONET ground-based AOD measurements. It is shown that a 10-year time record over Europe compares well to a merged multi-satellite record and that pixel-level uncertainties provide a meaningful representation of error distributions. The study demonstrates the benefits of new recipes for uncertainty characterization from the Horizon-2020 project FIDUCEO (“Fidelity and uncertainty in climate data records from Earth Observations”) and extends them further with recent additions developed within the ESA Climate Change Initiative.

The altimetry data record (1993-2022) has enabled the global mean sea level (GMSL) rise to be quantified. Not only is the sea level rising, but it is accelerating (Dieng et al., 2017; Nerem et al., 2018; Ablain et al. 2019) and understanding this acceleration is necessary both to support society’s response to climate change and to inform climate research on the sea level budget, greenhouse gas forcing and the Earth energy imbalance. Here we describe work on quantifying the uncertainty on the sea level climate data record, and identifying the uncertainty needed to provide new answers to the climate research questions. Work by Ablain et al. (2019) and updated in Guérou et al. (2022) at a global scale, has quantified GMSL rise as +3.3 mm/yr (90% confidence uncertainties 0.3 mm/yr) over 1993-2021. Its acceleration has been quantified at 0.12 ± 0.05 mm/yr². At local scales, (Prandi et al. 2021), the sea level is rising almost everywhere over the globe, at rates ranging between 0 and 6 mm/yr, with uncertainties ranging from 0.8 to 1.2 mm/yr depending on the location. The local sea level accelerations are ranging between -1 mm/yr² and +1 mm/yr² with uncertainties between 0.057 and 0.12 mm/yr² (Prandi et al., 2021). Here we present more recent work refining these uncertainty estimates through a metrological approach that propagates uncertainties, and error covariance structures, through the full processing chain of the altimeter measurement from the raw waveform to GMSL, using methods described in Mittaz et al. (2019). We review the assumptions in the derivation of the retracking model from the radar equation, consider error correlation structures in the corrections, and provide fresh consideration of the error covariance matrix, building on the earlier research. The work was developed in the ASELSU project funded by ESA. The project has also reviewed the climate science research questions to understand the need for improved altimeter observations. There are three major science questions that require accuracies greater than those currently achieved. These questions are the closure of the sea level budget, the detection and attribution of the signal in sea level that is forced by greenhouse gas emissions (GHG) and the estimate of the Earth energy imbalance (EEI). Dieng et al. 2017: https://doi.org/10.1002/joc.4996. Nerem et al. 2018: https://doi.org/10.1073/pnas.1717312115 Ablain et al 2019: https://doi.org/10.5194/essd-2019-10 Guérou et al. 2022: https://doi.org/10.5194/egusphere-2022-330 Prandi et al. 2021: https://doi.org/10.1038/s41597-020-00786-7 Mittaz et al. 2019: https://doi.org/10.1088/1681-7575/ab1705

The GHRSST task team on Coral Heat Stress Task Team updated the report to include other uses of SST regarding coral reef science and management beyond the quantification of heat stress. To requirements for stability, geo-location accuracy, SST accuracy (including high SSTs), SST uncertainty, and timeliness, the update adds requirements for diurnal variation, increased spatial resolution, preservation of oceanographic features such as upwelling, and a measure of effective spatial resolution. Find more about this task team:https://www.ghrsst.org/about-ghrsst/task-teams/task-team-on-coral-heat-stress-user-sst-requirements/

At GHRSST Science Team Meeting in 2019, the Science Team asked the Coral Heat Stress User Requirements Task Team to make recommendations on satellite SST requirements for use by coral scientists, users and coral reef managers. In 2020 the Task Team reported its findings to the Science Team at GHRSST 21st Meeting and published the present report on Coral Heat Stress User SST Requirements. Resources: About the GHRSST task team on Coral Heat Stress user SST requirements: https://www.ghrsst.org/about-ghrsst/task-teams/coral-heat-stress-user-requirements-task-team/ Proceedings of the GHRSST Science Team Meeting 2019: https://zenodo.org/record/4700315 Proceedings of the GHRSST Science Team Meeting 2020: https://zenodo.org/record/4626734, The GHRSST Project Office is funded by the European Union (European Commission, EUMETSAT, Copernicus). Web: http://www.ghrsst.org Follow us on Twitter: https://twitter.com/GHRSST

Poster presented at the 21st. GHRSST Science meeting (XXI), June 1 - 4, 2020.

Sea surface temperature (SST) is observed by a constellation of sensors, and SST retrievals\ud are commonly combined into gridded SST analyses and climate data records (CDRs). Differential\ud biases between SSTs from different sensors cause errors in such products, including feature artefacts.\ud We introduce a new method for reducing differential biases across the SST constellation, by reconciling\ud the brightness temperature (BT) calibration and SST retrieval parameters between sensors. We use the\ud Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature\ud Radiometer (SLSTR) as reference sensors, and the Advanced Very High Resolution Radiometer\ud (AVHRR) of the MetOp-A mission to bridge the gap between these references. Observations across a\ud range of AVHRR zenith angles are matched with dual-view three-channel skin SST retrievals from\ud the AATSR and SLSTR. These skin SSTs act as the harmonization reference for AVHRR retrievals\ud by optimal estimation (OE). Parameters for the harmonized AVHRR OE are iteratively determined,\ud including BT bias corrections and observation error covariance matrices as functions of water-vapor\ud path. The OE SSTs obtained from AVHRR are shown to be closely consistent with the reference sensor\ud SSTs. Independent validation against drifting buoy SSTs shows that the AVHRR OE retrieval is stable\ud across the reference-sensor gap. We discuss that this method is suitable to improve consistency across\ud the whole constellation of SST sensors. The approach will help stabilize and reduce errors in future\ud SST CDRs, as well as having application to other domains of remote sensing.

The High-resolution Infrared Radiation Sounder (HIRS) has been flown on 17 polar-orbiting satellites between the late 1970s and the present day. HIRS applications require accurate characterisation of uncertainties and inter-channel error correlations, which has so far been lacking. Here, we calculate error correlation matrices by accumulating count deviations for sequential sets of\ud calibration measurements, and then correlating deviations between channels (for a fixed view) or views (for a fixed channel). The inter-channel error covariance is usually assumed to be diagonal, but we show that large error correlations, both positive and negative, exist between channels and between views close in time. We show that correlated error exists for all HIRS and that the degree\ud of correlation varies markedly on both short and long timescales. Error correlations in excess of 0.5\ud are not unusual. Correlations between calibration observations taken sequentially in time arise from\ud periodic error affecting both calibration and Earth counts. A Fourier spectral analysis shows that,\ud for some HIRS instruments, this instrumental effect dominates at some or all spatial frequencies.\ud These findings are significant for application of HIRS data in various applications, and related information will be made available as part of an upcoming Fundamental Climate Data Record covering all HIRS channels and satellites.

Presentation given at the 18th GHRSST science meeting (XVIII), Qingdao, China, June 5 -9, 2017

Fundamental and thematic climate data records derived from satellite observations provide unique information for climate monitoring and research. 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. 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. 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. 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. 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. 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. 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. 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. Presented at the EUMETSAT Meteorological Satellite Conference, Rome, October 2017.

Presentation given at the 17th GHRSST science meeting (XVII), Washington DC, USA, June 6 - 10, 2016.

Presentation given at the 17th GHRSST science meeting (XVII), Washington DC, USA, June 6 - 10, 2016.

Presentation given at the 16th GHRSST science meeting (XVI), ESA/ESTEC, The Netherlands, July 20 - 24, 2015.

Presentation given at the 16th GHRSST science meeting (XVI), ESA/ESTEC, The Netherlands, July 20 - 24, 2015.

The GHRSST Climate Data Assessment Framework (CDAF) provides authoritative, comparable information about GHRSST datasets that allows users to make their own judgement on the suitability of SST datasets for use as Climate Data Records (CDRs) in their applications. The CDAF is described in the present report, produced by the GHRSST CDR-Technical Advisory Group. The CDAF is split into four main sections: Basic information Assessment information Review of information Approval and publication of information The CDAF Task Team (TT) provides a community accessible tool which facilitates the production of the CDAF metrics (section 2 above) so helping producers provide the requisite information to the CDAF. The TT provides an easy-to-use support tool based on open source resources. The tool supports users of SST datasets, enabling them to understand the suitability of GHRSST datasets for use as CDRs. The datasets are required to be in GDS2.0 format and available for a minimum 10 year time period. The tool and product summaries will be hosted in a web resource. The users, after downloading the tool, will use their own resources to run Python notebook. Resources: About the CLIMATE DATA ASSESSMENT FRAMEWORK task team: https://www.ghrsst.org/about-ghrsst/task-teams/task-team-on-climate-data-assessment-framework/ About the Climate Data Records-Technical Advisory Group: https://www.ghrsst.org/about-ghrsst/technical-advisory-groups-tags/, The GHRSST Project Office is funded by the European Union (European Commission, EUMETSAT, Copernicus). Web: http://www.ghrsst.org Follow us on Twitter: https://twitter.com/GHRSST

The WMAP Q, V, and W band radial profiles of temperature deviation of the cosmic microwave background (CMB) were constructed for a sample of 31 randomly selected nearby clusters of galaxies in directions of Galactic latitude $|b| >$ 30$^o$. The profiles were compared in detail with the expected CMB Sunyaev-Zel'dovich effect (SZE) caused by these clusters, with the hot gas properties of each cluster inferred observationally by applying gas temperatures as measured by ASCA to isothermal $\beta$ models of the ROSAT X-ray surface brightness profiles, and with the WMAP point spread function fully taken into consideration. After co-adding the 31 cluster field, it appears that WMAP detected the SZE in all three bands. Quantitatively, however, the observed SZE only accounts for about 1/4 of the expected decrement. The discrepancy represents too much unexplained extra flux: in the W band, the detected SZE corresponds on average to 5.6 times less X-ray gas mass within a 10 arcmin radius than the mass value given by the ROSAT $\beta$ model. We examined critically how the X-ray prediction of the SZE may depend on our uncertainties in the density and temperature of the hot intracluster plasma, and emission by cluster radio sources. Although our comparison between the detected and expected SZE levels is subject to a margin of error, the fact remains that the average observed SZE depth and profile are consistent with those of the primary CMB anisotropy, i.e. in principle the average WMAP temperature decrement among the 31 rich clusters is too shallow to accomodate any extra effect like the SZE. A unique aspect of this SZE investigation is that because all the data being analyzed are in the public domain, our work is readily open to the scrutiny of others., Comment: ApJ v648, p176 (2006). This is the final version to appear on Sept. 1st

This is the first meeting specifically devoted to the topic of cluster soft excess and related phenomena. It calls together a group of some 40 scientists, mostly experts in the field, to present their findings on the observational aspects of emission from clusters, signature of the WHIM in the cluster, intergalactic, and local environment, theoretical modeling of the WHIM in cosmological hydrodynamic simulations, theory and observations of cluster cosmic rays, magnetic fields, radio data and the use of the Sunyaev-Zeldovich effect as a means of constraining the important parameters. We are particularly grateful to F.J. Lockman and S.L. Snowden for presenting their latest picture of the cold gas distribution in the interstellar medium, as this affects our ability to model extragalactic EUV and soft X-ray data via Galactic absorption by HI and HeI. Papers given on future hardware concepts to further the spatial and spectral diagnosis of diffuse soft X-ray emission in general are also included., Proceedings of conference held at the University if Alabama, Huntsville, December 2002

The magnification of distant sources by mass clumps at lower ($z \leq 1$) redshifts is calculated analytically. The clumps are initially assumed to be galaxy group isothermal spheres with properties inferred from an extensive survey. The average effect, which includes strong lensing, is exactly counteracted by the beam divergence in between clumps (more precisely, the average reciprocal magnification cancels the inverse Dyer-Roeder demagnification). This conclusion is in fact independent of the matter density function within each clump, and remains valid for arbitrary densities of matter and dark energy. When tested against the CMB, a rather large lensing induced {\it dispersion} in the angular size of the primary acoustic peaks of the TT power spectrum is inconsistent with WMAP observations. The situation is unchanged by the use of NFW profiles for the density distribution of groups. Finally, our formulae are applied to an ensemble of NFW mass clumps or isothermal spheres having the parameters of galaxy {\it clusters}. The acoustic peak size dispersion remains unobservably large, and is also excluded by WMAP. For galaxy groups, two possible ways of reconciling with the data are proposed, both exploiting maximally the uncertainties in our knowledge of group properties. The same escape routes are not available in the case of clusters, however, because their properties are well understood. Here we have a more robust conclusion: neither of the widely accepted models are good description of clusters, or important elements of physics responsible for shaping zero curvature space are missing from the standard cosmological model. When all the effects are accrued, it is difficult to understand how WMAP could reveal no evidence whatsoever of lensing by groups and clusters., ApJ v628, pp. 583-593 (August 1, 2005)

The Virgo and A1795 clusters of galaxies were re-observed by EUVE with {\it in situ} background measurements by pointing at small offsets. Earlier, a similar re-observational strategy applied to the cluster A2199 revealed that the background radial profile was consistent with a flat distribution, and therefore the original method of extracting cluster EUV signals by the subtraction of an aymptotically determined background was valid. It is shown here that the same conclusions hold for the current sample. A model of the background was obtained from its known properties and the {\it in situ} measurements, and the subtracted cluster fluxes remain in agreement with those reported in our discovery papers. They are also consistent with results from the most conservative procedure of direct point-to-point subtraction of the {\it in situ} background and proper error propagation, which still preserves the existence of the EUV excess and its rising radial trend. We present evidence which argues against the soft excess as due to peculiarities in the line-of-sight Galactic absorption. The data appear to favor a thermal origin of the emission., 17 pages, 4 figures, ApJL in press

Several authors (S. Bowyer, T. Berghoeffer, and E. Korpela, hereinafter abbreviated as BBK) recently announced that the luminous extended EUV radiation from the clusters Abell 1795 and Abell 2199, which represents the large scale presence of a new and very soft emission component, is an illusion in the EUVE Deep Survey (DS) detector image. Specifically BBK found that the radial profile of photon background surface brightness, for concentric annuli centered at a detector position which has been used to observe cluster targets, shows an intrinsic `hump' of excess at small radii which resembles the detection of extended cluster EUV. We accordingly profiled background data, but found no evidence of significant central excess. However, to avoid argument concerning possible variability in the background pattern, we performed a clincher test which demonstrates that a cluster's EUV profile is invariant with respect to photon background. The test involves re-observation of A2199 and A1795 when the photon background was respectively three and two times higher than before, and using a different part of the detector. The radial profiles of both clusters, which have entirely different shapes, were accurately reproduced. In this way the BBK scenario is quantitatively excluded, with the inevitable conclusion that the detected signals are genuinely celestial., 9 pages, 7 figures

Since all of the five clusters of galaxies observed by the Extreme Ultraviolet Explorer deep survey telescope are found to possess a diffuse EUV emitting component which is unrelated to the virial gas at X-ray temperatures, the question concerning the nature of this new component has been a subject of controversy. Here we present results of an EUV and soft X-ray spatial analysis of the rich clusters Abell 2199 and 1795. The EUV emission does not resemble the X-ray morphology of clusters: at the cluster core the EUV contours are organized, but at larger radii they are anisotropic, and therefore need not have originated from a hydrostatically stable medium. The ratio of EUV to soft X-ray intensity rises with respect to cluster radius, to reach values ~10 times higher than that expected from the virial gas. The strong EUV excess which exists in the absence of soft X-ray excess poses formidable problems to the non-thermal (inverse-Compton) scenario, but may readily be explained as due to emission lines present only in the EUV range. In particular, warm gas produced by shock heating could account for such lines without proliferation of bolometric luminosities and mass budgets., Comment: 13 pages, 7 figures (4 in color), minor typos corrected; ApJL accepted

We report the detection of both soft and hard excess X-ray emission in the cluster of galaxies A 2199, based upon spatially resolved spectroscopy with data from the BeppoSAX, EUVE and ROSAT missions. The excess emission is visible at radii larger than 300 kpc and increases in strength relative to the isothermal component. The total 0.1-100 keV luminosity of this component is 15 % of the cluster luminosity, but it dominates the cluster luminosity at high and low energies. We argue that the most plausible interpretation of the excess emission is an inverse Compton interaction between the cosmic microwave background and relativistic electrons in the cluster. The observed spatial distribution of the non-thermal component implies that there is a large halo of cosmic ray electrons between 0.5-1.5 Mpc surrounding the cluster core. The prominent existence of this component has cosmological implications, as it is significantly changing our picture of a clusters's particle acceleration history, dynamics between the thermal and relativistic media, and total mass budgets., Comment: Accepted for publication in Astrophysical Journal, Letters

The question of how to derive and present uncertainty information in climate data records (CDRs) has received sustained attention within the European Space Agency Climate Change Initiative (CCI), a programme to generate CDRs addressing a range of essential climate variables (ECVs) from satellite data. Here, we review the nature, mathematics, practicalities, and communication of uncertainty information in CDRs from Earth observations. This review paper argues that CDRs derived from satellite-based Earth observation (EO) should include rigorous uncertainty information to support the application of the data in contexts such as policy, climate modelling, and numerical weather prediction reanalysis. Uncertainty, error, and quality are distinct concepts, and the case is made that CDR products should follow international metrological norms for presenting quantified uncertainty. As a baseline for good practice, total standard uncertainty should be quantified per datum in a CDR, meaning that uncertainty estimates should clearly discriminate more and less certain data. In this case, flags for data quality should not duplicate uncertainty information, but instead describe complementary information (such as the confidence in the uncertainty estimate provided or indicators of conditions violating the retrieval assumptions). The paper discusses the many sources of error in CDRs, noting that different errors may be correlated across a wide range of timescales and space scales. Error effects that contribute negligibly to the total uncertainty in a single-satellite measurement can be the dominant sources of uncertainty in a CDR on the large space scales and long timescales that are highly relevant for some climate applications. For this reason, identifying and characterizing the relevant sources of uncertainty for CDRs is particularly challenging. The characterization of uncertainty caused by a given error effect involves assessing the magnitude of the effect, the shape of the error distribution, and the propagation of the uncertainty to the geophysical variable in the CDR accounting for its error correlation properties. Uncertainty estimates can and should be validated as part of CDR validation when possible. These principles are quite general, but the approach to providing uncertainty information appropriate to different ECVs is varied, as confirmed by a brief review across different ECVs in the CCI. User requirements for uncertainty information can conflict with each other, and a variety of solutions and compromises are possible. The concept of an ensemble CDR as a simple means of communicating rigorous uncertainty information to users is discussed. Our review concludes by providing eight concrete recommendations for good practice in providing and communicating uncertainty in EO-based climate data records.

Presentation on the comparison of errors-in-variables and marginalised errors-in-variables harmonisation methods given by R. Quast during the FIDUCEO science meeting at NPL in January 2019. This presentation supplements the journal article: Giering, R.; Quast, R.; Mittaz, J.P.D.; Hunt, S.E.; Harris, P.M.; Woolliams, E.R.; Merchant, C.J. A Novel Framework to Harmonise Satellite Data Series for Climate Applications. Remote Sens. 2019, 11, 1002. doi:10.3390/rs11091002., {"references":["Giering, R.; Quast, R.; Mittaz, J.P.D.; Hunt, S.E.; Harris, P.M.; Woolliams, E.R.; Merchant, C.J. A Novel Framework to Harmonise Satellite Data Series for Climate Applications. Remote Sens. 2019, 11, 1002"]}