E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Special Issue "Assessment of Quality and Usability of Climate Data Records"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmosphere Remote Sensing".

Deadline for manuscript submissions: 20 November 2018

Special Issue Editors

Guest Editor
Dr. Zhongbo Su

Department of Water Resources in Faculty of Geo-Information and Earth Observation, University of Twente, 7522 NB Enschede, The Netherlands
Website | E-Mail
Fax: +31 (0) 53 4874 336
Interests: remote sensing and numerical modeling of land surface processes and interactions with the atmosphere; Earth observation of water cycle and applications in climate; ecosystem and water resources studies; monitoring food security and water-related disasters
Guest Editor
Dr. Yijian Zeng

Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente Drienerlolaan 5, 7500 AE Enschede, the Netherlands
Website | E-Mail
Interests: land–atmosphere interaction via hydrologic processes and how this interaction affects the climate system; generation of consistent climate data record using multi-source of geo-datasets; physical mechanisms of land surface models; application of data assimilation
Guest Editor
Dr. R.A. Roebeling

European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), Darmstadt, Germany
Website | E-Mail
Interests: climate service and product expert at EUMETSAT, climate research, generation of climate data records, atmospheric radiative transfer, cloud physics, boundary layer meteorology and multi-sensor remote sensing

Special Issue Information

Dear Colleagues,

In its 2004 report, the National Research Council of the U.S. National Academy of Science recommended the development of Climate Data Records (CDRs) from satellites, wherein the CDR was defined as a time series of measurements of sufficient length, consistency, and continuity to determine climate variability and change, accounting for systematic errors and noise in the measurements. For satellite-based CDR, these can be further defined as fundamental CDRs (FCDRs), which are calibrated and quality-controlled sensor data designed to allow the generation of consistent products for climate monitoring, and thematic CDRs (TCDRs), which denotes a long-term data record of rigorously validated and quality-controlled geophysical variables derived from FCDRs.

Applying the nomenclature that a satellite record meets the definition of a CDR implies that the products should be fully traceable, adequately documented and uncertainty quantified, and can provide sufficient guidance for users to address their specific needs and feedbacks, when it is used for climate services. As such, the evaluation of the complete chain from CDRs to climate services need considerations not only from the scientific quality perspective but also the usability one.

Potential Topics

  • Development, generation and production of FCDRs (e.g. inter-satellite calibrations, homogenizations, uncertainty analysis, trend detection);
  • Development, generation and production of TCDRs (e.g. retrieval algorithms, validation approaches, uncertainty characterization and propagation, climate/environmental change monitoring);
  • Technical and scientific quality of CDRs (e.g. traceability of CDR products in terms of its production chain, and the associated validation chain, uncertainty propagation);
  • Climate information and knowledge derived from CDRs for climate services (e.g. serving public sectors including water management, agriculture and forestry, tourism, insurance, transport, energy, health, infrastructure, disaster risk reduction, coastal areas etc.);
  • Usability assessment of the climate information and knowledge applied for climate services (e.g. how the uncertainty of CDR products is propagated into the decision making for the public sectors).

Prof. Dr. Zhongbo Su
Dr. Yijian Zeng
Dr. R.A. Roebeling
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

View options order results:
result details:
Displaying articles 1-1
Export citation of selected articles as:


Open AccessArticle Performance Assessment of the COMET Cloud Fractional Cover Climatology across Meteosat Generations
Remote Sens. 2018, 10(5), 804; https://doi.org/10.3390/rs10050804
Received: 1 May 2018 / Revised: 18 May 2018 / Accepted: 20 May 2018 / Published: 22 May 2018
PDF Full-text (2350 KB) | HTML Full-text | XML Full-text
The CM SAF Cloud Fractional Cover dataset from Meteosat First and Second Generation (COMET, https://doi.org/10.5676/EUM_SAF_CM/CFC_METEOSAT/V001) covering 1991–2015 has been recently released by the EUMETSAT Satellite Application Facility for Climate Monitoring (CM SAF). COMET is derived from the MVIRI and SEVIRI imagers aboard geostationary
[...] Read more.
The CM SAF Cloud Fractional Cover dataset from Meteosat First and Second Generation (COMET, https://doi.org/10.5676/EUM_SAF_CM/CFC_METEOSAT/V001) covering 1991–2015 has been recently released by the EUMETSAT Satellite Application Facility for Climate Monitoring (CM SAF). COMET is derived from the MVIRI and SEVIRI imagers aboard geostationary Meteosat satellites and features a Cloud Fractional Cover (CFC) climatology in high temporal (1 h) and spatial (0.05° × 0.05°) resolution. The CM SAF long-term cloud fraction climatology is a unique long-term dataset that resolves the diurnal cycle of cloudiness. The cloud detection algorithm optimally exploits the limited information from only two channels (broad band visible and thermal infrared) acquired by older geostationary sensors. The underlying algorithm employs a cyclic generation of clear sky background fields, uses continuous cloud scores and runs a naïve Bayesian cloud fraction estimation using concurrent information on cloud state and variability. The algorithm depends on well-characterized infrared radiances (IR) and visible reflectances (VIS) from the Meteosat Fundamental Climate Data Record (FCDR) provided by EUMETSAT. The evaluation of both Level-2 (instantaneous) and Level-3 (daily and monthly means) cloud fractional cover (CFC) has been performed using two reference datasets: ground-based cloud observations (SYNOP) and retrievals from an active satellite instrument (CALIPSO/CALIOP). Intercomparisons have employed concurrent state-of-the-art satellite-based datasets derived from geostationary and polar orbiting passive visible and infrared imaging sensors (MODIS, CLARA-A2, CLAAS-2, PATMOS-x and CC4CL-AVHRR). Averaged over all reference SYNOP sites on the monthly time scale, COMET CFC reveals (for 0–100% CFC) a mean bias of −0.14%, a root mean square error of 7.04% and a trend in bias of −0.94% per decade. The COMET shortcomings include larger negative bias during the Northern Hemispheric winter, lower precision for high sun zenith angles and high viewing angles, as well as an inhomogeneity around 1995/1996. Yet, we conclude that the COMET CFC corresponds well to the corresponding SYNOP measurements, and it is thus useful to extend in both space and time century-long ground-based climate observations. Full article
(This article belongs to the Special Issue Assessment of Quality and Usability of Climate Data Records)

Graphical abstract

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Recalibrated IR/WV Channel Radiance Time-Series of JMA Geo-Stationary Satellites
Authors: Tabata 1, V.O. John 2, R.A. Roebeling 2, T. Hewison 2 and J. Schulz 1
1. Japan Meteorological Agency (JMA),Tokyo, Japan
2. European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), Darmstadt, Germany
Abstract: Infrared sounding measurements from the Infrared Atmospheric Sounding Interferometer (IASI), Atmospheric Infrared Sounder (AIRS), and High-resolution Infrared Radiation Sounder/2 (HIRS/2) instruments are used to re-calibrate infrared (IR; ~11 µm) channels and water vapor (WV; ~6 µm) channels of the Visible and Infrared Spin Scan Radiometer (VISSR), Japanese Advanced Meteorological Imager (JAMI), and IMAGER, instruments on-board the historical geostationary satellites from Japan Meteorological Agency (JMA). The re-calibration was performed using a common re-calibration method developed by EUMETSAT, which can be applied to the historical geostationary satellites. Pseudo imager radiances were computed from the infrared sounding measurements and regressed against the radiances from the geostationary satellites. Re-calibration factors were computed from these pseudo imager radiance pairs. This paper presents and evaluates the result of re-calibration of longtime-series of IR (1978-2016) and WV (1995-2016) measurements from JMA’s historical geostationary satellites. For the IR data from the old JMA satellites (GMS to GMS-4) significant seasonal variations in radiometric biases were observed. This suggests that the sensors on GMS to GMS-4 were strongly affected by seasonal variations in solar illumination. The amplitudes of these seasonal variation ranges from 3 K for the earlier satellites to <0.5 K for the recent satellites (GMS-5, MTSAT-1R and MTSAT-2). For the WV data from the recent satellites, MTSAT-1R and MTSAT-2, no seasonal variations in radiometric biases were observed. However, for GMS-5, the amplitude of seasonal variation in bias was about 0.5 K. Overall, the magnitude of the biases for GMS-5, MTSAT-1R and MTSAT-2 were smaller than 0.3 K. Finally, our analysis confirms the existence of errors due to atmospheric absorption contamination in the original Spectral Response Function (SRF) of the WV channel of GMS-5.

Type: Article
Title: Harmonisation of Satellite Sensors
Authors: Ralf Giering 1,, Ralf Quast 1, Samuel Hunt 2, Peter Harris2, Jonathan Mittaz 2,3, Emma Woolliams2
Affiliation: 1. FastOpt GmbH, Germany
2. National Physical Laboratory, UK
3. University of Reading, UK
* Correspondence: ralf.giering@fastopt.de; Tel.: +49-40-48096347
Abstract: Fundamental and thematic climate data records derived from satellite observations provide unique information for climate monitoring and research. Since every satellite operates for only a limited period of time, creating a climate data record requires the combination of space-borne measurements from a series of several (often similar) satellite sensors. However, the simple combination of calibrated measurements from several sensors can produce an inconsistent climate data record. This is particularly true of older, historic sensors whose behaviour 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 for revised sensor calibration models by comparing calibrated measurements over appropriate satellite-to-satellite match-ups, such as simultaneous nadir overpasses. However, when we perform a comparison of two sensors, we must consider that those sensors are not observing exactly the same Earth radiance. This partly due to differences in exact location and time tolerated by the match-up process itself, but also due to differences in the spectral response functions of the two instruments, even when nominally observing the same spectral band. To derive a harmonised dataset, 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 and their uncertainty and error covariance for an arbitrary number of sensors in a metrologically-rigorous manner. We describe harmonisation and its mathematical formulation as an inverse problem. Solving this problem is challenging when some hundreds of millions of match-ups are involved and the errors of fundamental sensor measurements are correlated. We solve the harmonisation problem as marginalised errors in variables regression. The algorithm involves the computation of first- and second-order partial derivatives, and the corresponding computer source code is generated by automatic differentiation. Finally, we present recalibrated AVHRR radiances from a series of 10 sensors. It is shown that the new time series have much less matchup differences while remaining consistent with uncertainty statistics.
Keywords: harmonisation; re-calibration; climate data records; automatic differentiation; orthogonal distance regression; marginalised errors in variables regression

Type: Article
Title: Climate Data Records from Meteosat First Generation: Simulation of Accurate Top-Of-Atmosphere Spectral Radiance over Pseudo-Invariant Calibration Sites for the Retrieval of the in-Flight MVIRI/VIS Spectral Response
Authors: Yves Govaerts 1*, Frank Rüthrich 2, Viju John 2 and Ralf Quast 3
Affiliation: 1. Rayference; yves.govaerts@rayference.eu
2. Eumetsat; frank.ruethrich@eumetsat.int
3. FastOpt; quast@fastopt.de
* Correspondence: yves.govaerts@rayference.eu
Abstract: Meteosat First Generation satellites have acquired more than 30 years of observations that could potentially be used for the generation of a Climate Data Record. The availability of a consistent and accurate Fundamental Climate Data Record is a prerequisite to this generation. Meteosat Visible and Infrared Imager radiometers suffer from inaccurate pre-launch spectral response characterisation. They are also subject to spectral ageing, which constitutes a serious limitation to achieving this prerequisite. Within the FIDUCEO project, a new method has been developed for the original sensor spectral response retrieval and the characterisation of its ageing. This recovery method relies on accurately simulated top-of-atmosphere spectral radiances matching observed digital count values. This paper describes how these spectral radiances are simulated over pseudo-invariant targets such as open ocean, deep convective clouds, and bright desert surface. The radiative properties of these targets are described with a limited number of parameters of known uncertainty. Typically, a single top-of-atmosphere radiance spectrum can be simulated with an estimated uncertainty of about 5%. The independent evaluation of the simulated radiance accuracy is also addressed in this paper. It includes two aspects: the comparison with narrow-band well-calibrated radiometers and a spectral consistency analysis using SEVIRI/HRVIS band on-board Meteosat Second Generation which was accurately characterised pre-launch. On average, the accuracy of these simulated spectral radiances is estimated to be about ±2%.
Keywords: climate data records; calibration; sensor spectral response function; radiative transfer modelling; Meteosat Visible and Infrared Imager (MVIRI)

Type: Article
Title: An Uncertainty Quantified Fundamental Climate Data Record for Microwave Humidity Sounders
Authors: Imke Hans 1, Martin Burgdorf 1 and Stefan A. Buehler 1
Affiliation: University of Hamburg, Hamburg, Germany
*   Correspondence: Imke.Hans@uni-hamburg.de
Abstract: To date, there is no long-term stable uncertainty-quantified data set of upper tropospheric humidity (UTH) that can be used for climate research. As an intermediate step towards the overall goal of constructing such a Climate Data Record (CDR) of UTH, we produce a new Fundamental Climate Data Record (FCDR) on the level of brightness temperature for microwave humidity sounders that will serve as a basis for the CDR of UTH. Based on metrological principles, we construct and implement the measurement equation, the uncertainty propagation, and the processing chain for the microwave humidity sounders. We reprocess the level 1b data to obtain newly calibrated uncertainty-quantified level 1c data in brightness temperature. Three aspects set this FCDR apart from previous attempts: 1) the data come in a ready-to-use NetCDF format, 2) the data set provides extensive uncertainty information considering the different correlation behaviour of the underlying errors, 3) inter-satellite biases are understood and reduced by harmonisation. While the last aspect of harmonisation is still being improved, the new data format as well as the uncertainty propagation is already accomplished. Providing a detailed uncertainty budget on these data, this new FCDR provides valuable information for climate scientists and also for the construction of the CDR.
Keywords: climate data records; microwave humidity sounders; uncertainty propagation; instrument degradation, harmonisation

Type: Article
Title: Radiance Uncertainty Characterisation to Facilitate Climate Data Record Creation
Authors: Christopher J. Merchant1, Gerrit Holl2, Jonathan Mittaz2, Emma Woolliams3,*
Affiliation: 1 National Centre for Earth Observation and Department of Meteorology, University of Reading, UK; c.j.merchant@reading.ac.uk
2   Department of Meteorology, University of Reading, UK; g.holl@reading.ac.uk & j.mittaz@reading.ac.uk
3   National Physical Laboratory, Teddington, UK; e.woolliams@npl.ac.uk
Abstract: The uncertainty in a Climate Data Record derived from Earth observations derives in part 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 the 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.
Keywords: climate data record; fundamental climate data record; essential climate variable; Earth observation; remote sensing; metrology; uncertainty; error budget; error propagation; radiance

Title: Climate Data Records from Meteosat First Generation: Retrieval of the in-Flight VIS Spectral Response
Authors: Ralf Quast 1,*, Ralf Giering 1, Yves Govaerts 2, Frank Rüthrich 3 and Rob Roebeling 3
Affiliation: 1 FastOpt GmbH, Lerchenstraße 28a, 22767 Hamburg, Germany; info@fastopt.de
2   Rayference, Brussels, Belgium
3   EUMETSAT, Eumetsat Allee 1, 64295 Darmstadt, Germany
Abstract: How can the in-flight spectral response functions of a series of decades-old broad-band radiometers in Space be retrieved post-flight? This question is the clue to developing Climate Data Records from the Meteosat Visible and Infrared Imager on board the Meteosat First Generation (MFG) of geostationary satellites, which acquired Earth radiance images in the visible (VIS) broad-band from 1977 to 2017. This article presents a new metrological sound method for retrieving the VIS spectral response from matchups of pseudo-invariant calibration site (PICS) pixels with a reference database of simulated top-of-atmosphere spectral radiance. Calibration sites include bright desert, open ocean, and deep convective cloud targets. The absolute instrument spectral response function is specified in terms of a decomposition into Bernstein basis polynomials and a degradation function that is based on simple physical considerations and able to represent typical chromatic ageing characteristics. Retrieval uncertainties are specified in terms of an error covariance matrix, which is projected into the spectral response function domain and range. The retrieval method explicitly considers possible PICS selection biases and target type-specific errors in the spectral radiance reference database. It was tested with artificial and observational data and retrieved meaningful results for all MFG satellites.
Keywords: climate data records; instrument spectral response function; instrument degradation; Meteosat Visible and Infrared Imager (MVIRI); algorithmic differentiation; uncertainty propagation

Type: Article
Title: Recalibration of Infrared and Water Vapour Channels on Geostationary Satellites
Viju O. John1, R. A. Roebeling1, T. Tabata2, T. Hewison1, and J. Schulz1
Affiliation: 1 EUMETSAT, Eumetsat Allee 1, 64295 Darmstadt, Germany
2 JMA, 1-3-4 Otemachi, Chiyoda-ku, Tokyo 100-8122, Japan
Abstract: Geostationary meteorological satellites have been observing the Earth for more than 40 years to support weather nowcasting and forecasting and other environmental applications. Due to their long observation period, good temporal sampling, and spatial coverage, these observations could be of tremendous value for climate studies (e.g., on cloud properties and their impact on the global radiation budget). They are also shown to be valuable for assimilating into numerical weather prediction models used for generating reanalysis products. The historical geostationary satellites and imagers were mainly built for weather applications. Climate applications require high-accuracy satellite observations or at least a quantification and correction of effects due to changes in the characteristics of satellites and sensors that appear during their operational lifetime. This article presents a common re-calibration approach that was applied to the imagers’ infrared and water vapour channels (at ~11 µm and ~6 µm, respectively) on both JMA and EUMETSAT geostationary satellites (i.e., VISSR/JAMI/IMAGER on the GMS/MTSAT series and MVIRI/SEVIRI on the METEOSAT series). Data from the Infrared Atmospheric Sounding Interferometer (IASI), Atmospheric Infrared Sounder (AIRS), and High-Resolution Infrared Radiation Sounder (HIRS/2) were used as references for recalibration. The referencing was performed at the HIRS/2 channel’s spectral response, which shows the best fit to the instruments in geostationary orbit. To keep the time series of reference measurements the same, AIRS and IASI spectra were convoluted using the HIRS/2 spectral response, which revealed very small uncertainties compared to directly using hyper-spectral sounder spectra convoluted with the spectral response from the geostationary instruments. Collocated geostationary counts and the reference radiances were then regressed to derive re-calibration coefficients for each geostationary instrument.
Keywords: fundamental climate data record; essential climate variable; Earth observation; remote sensing; recalibration; radiance.

Type: Article
Title: Error Covariances in High-Resolution Infrared Radiation Sounder (HIRS) Radiances
Authors: Gerrit Holl1, Jonathan P. D. Mittaz1, Christopher J. Merchant1
Affiliation: 1. Department of Meteorology, University of Reading, UK; g.holl@reading.ac.uk & j.mittaz@reading.ac.uk
2. EUMETSAT, Eumetsat Allee 1, 64295 Darmstadt, Germany
Abstract: 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 the accurate characterisation of uncertainties and inter-channel error covariances, which has so far been lacking. Here, we calculate error correlation matrices by accumulating count anomalies for sequential sets of calibration measurements and then correlating anomalies between channels (for a fixed position) or positions (for a fixed channel).
The inter-channel error covariance is usually assumed to be diagonal, but we show that large error covariances, both positive and negative, exist between channels and between views that are close in time. We show that correlated error exists for all HIRS, and that the degree of correlation varies markedly on both short and long timescales. Error correlations in excess of 0.5 are not unusual.
Correlations between calibration observations taken sequentially in time arise from periodic error affecting both calibration and Earth counts. A Fourier spectral analysis shows that for some HIRS instruments, this instrumental effect dominates at some or all spatial frequencies. These findings are significant for the application of HIRS data in data assimilation and retrieval of geophysical parameters, such as atmospheric temperature and humidity profiles.
Keywords: climate data record; fundamental climate data record; essential climate variable; Earth observation; remote sensing; metrology; uncertainty; error budget; error propagation; radiance

Type: Article
Title: A Fundamental Climate Data Record for the High-Resolution Infrared Radiation Sounder
Gerrit Holl1, Jonathan P. D. Mittaz1, Christopher J. Merchant1, Viju O. John2
1. Department of Meteorology, University of Reading, UK; g.holl@reading.ac.uk & j.mittaz@reading.ac.uk
2. EUMETSAT, Eumetsat Allee 1, 64295 Darmstadt, Germany
Abstract: We present a new Fundamental Climate Data Record (FCDR) for the High-resolution Infrared Radiation Sounder (HIRS), with pixel-level metrologically traceable uncertainties and error covariance estimates. HIRS has flown on 16 polar-orbiting satellites between 1978 and the present day. It is a 20-channel radiometer with one visible channel and 19 infrared channels, the latter covering a range from 3.7 to 15 ţm. As HIRS was designed for weather forecasting, it cannot be immediately applied in climate science. Rather, usage for climate measurements requires (1) a thorough metrological uncertainty analysis for each instrument, and (2) harmonisation to bring all instruments to a common reference. In this paper, we present a detailed metrological analysis and the resulting harmonised FCDR. The analysis techniques and FCDR are developed as part of the Horizon 2020 project, Fidelity and Uncertainty in Climate-data records from Earth Observation (FIDUCEO). We identified 17 distinct physical effects that cause an error in calibrated HIRS radiances. Where possible, we corrected those errors. Starting with the measurement equation, which relates the calibrated radiances to measurands and calibration parameters, we explored the magnitude and correlation structures of some of those effects. The correlation structure affects how errors propagate upon the calculation of averages and other statistics, so this information is critically important for climate applications. Error correlations are present at all timescales, from between neighbouring soundings to the lifetime of a sensor. We also show significant correlations between channels, with correlation coefficients either positive or negative and in some cases exceeding 0.5. The result is a new publicly available FCDR designed for appropriately produced Climate Data Records (CDRs) spanning nearly 40 years.
Keywords: climate data record; fundamental climate data record; essential climate variable; Earth observation; remote sensing; metrology; uncertainty; error budget; error propagation; radiance

Type: Article
Title: An Improved Approach to the Harmonisation of Fundamental Climate Data Records
Authors:  Samuel E. Hunt 1,, Jonathan P.D. Mittaz 1,2, Peter M. Harris 1, Ralf Quast 3, Ralf Giering 3, Emma R. Woolliams 1 and Christopher J. Merchant 2
Affiliation: 1. National Physical Laboratory, Hampton Road, Teddington TW11 0LW, UK; peter.harris@npl.co.uk (P.M.H.); emma.woolliams@npl.co.uk (E.R.W.)
2. Department of Meteorology, University of Reading, Reading RG6 6AL, UK; j.mittaz@reading.ac.uk (J.P.D.M.); c.j.merchant@reading.ac.uk (C.J.M.)
3. FastOpt GmbH, Lerchenstraße 28a, 22767 Hamburg, Germany; ralf.quast@fastopt.de (R.Q.); ralf.giering@fastopt.de (R.G.)* Correspondence: sam.hunt@npl.co.uk; Tel.: +44-20-8943-8710Abstract:
Fundamental climate data records provide continuous, long-term sensor observations of the Earth system, typically by combining the data from a series of similar sensors from consecutive satellite missions. Such records could be extremely valuable for the observation of climatic trends, but in general many of the sensors available to form such series were not designed to operate at the level of accuracy or stability required for this use. To make such FCDRs useable for climate study, a retrospective recalibration (or harmonisation) of all the series’ sensors is therefore required. To perform this recalibration, we use the well-established approach of using sensor-to-sensor match-up data between all the sensors in the series and between sensors in the series with a high-quality, well-characterised reference sensor. The problem then becomes a large non-linear regression problem, solving together for new calibration parameters in the measurement equation of each sensor. The problem is not optimally tractable by existing solvers due to the make-up of the match-up datasets, which have a high data volume (potentially >108 total match-ups) and typically complex error correlation structure. We describe a new mathematical framework and a developed processing chain for a new implementation to solve such problems.
Keywords: FCDRs; harmonisation; metrology
Funding: This work was supported by FIDUCEO, which has received funding from the European 17 Union’s Horizon 2020 Programme for Research and Innovation under Grant Agreement no. 638822.

Type: Article
Title: A Fundamental Climate Data Record for the AVHRR
Jonathan P. D. Mittaz1, Michael Taylor1, Marine Desmons2, Christopher J. Merchant1,3
Affiliation: 1. Department of Meteorology, University of Reading, UK
2. Koninklijk Nederlands Meteorologisch Instituut, De Bilt, Nederlands
3. National Centre for Earth Observation, University of Reading, UK
Abstract: We present a Fundamental Climate Data Record (FCDR) of top of atmosphere brightness temperatures and uncertainties from the infrared channels of the Advanced Very High Resolution Radiometer (AVHRR), generated metrologically from a radiance measurement equation as part of the Horizon 2020 project, Fidelity and Uncertainty in Climate-data records from Earth Observation (FIDUCEO). Data from AVHRR sensors flying on-board NOAA and MetOp polar-orbiting Earth observation satellites was recalibrated and harmonised to produce a multi-decadal data record suitable for the retrieval of essential climate variables used in climate studies. To accomplish this, 12 physical sources of uncertainty in the radiance model were identified and characterised. Major sources of error such as instrumental noise and instrument thermal state were modelled and corrected for. Any remaining uncertainties were then propagated using sensitivity coefficients and typed as being either independent, structured, or common via their correlation and covariance properties. We report on the global statistics of the public release of this version of the FCDR and compare against previous AVHRR datasets.
Keywords: AVHRR; infrared; climate data record; Earth observation; remote sensing; metrology

Type: Article
Title: Instrumental Noise Uncertainty in the Infrared Channels of the AVHRR
Authors:  Marine Desmons 1, Jonathan P. D. Mittaz 2, Michael Taylor 2, Christopher J. Merchant 2,3
Affiliation: 1. Koninklijk Nederlands Meteorologisch Instituut, De Bilt, Nederlands
2. Department of Meteorology, University of Reading, UK
3. National Centre for Earth Observation, University of Reading, UK
Abstract: The Advanced Very High Resolution Radiometer (AVHRR) measures infrared radiance at the top of the atmosphere and provides a very long data record for use in climate applications. AVHRR infrared data are used as a principal input to optimal estimation algorithms that retrieve important atmospheric (cloud properties, aerosol optical depth) and surface parameters (sea surface temperature, surface reflectance). Therefore, associated uncertainties are required to ensure that there can be confidence in any inferences made using these radiances. One very important component of the radiance uncertainty is instrumental noise, and we describe a dynamic filter that is incorporated into the processing algorithm to remove significant outliers from the telemetry. By monitoring the mean PRT temperature, gain, noise of the space, internal calibration target (ICT) view counts, and noise equivalent error (NEDT) on the scale of an individual orbit and also over the lifetime of different AVHRR versions flying on the NOAA and MetOp satellites, we find different noise characteristics that can be traced back to different physical drivers. In terms of individual AVHRR sensors, we show that TIROS-N is very noisy and has different regimes within an orbit and that the AVHRR/1 and AVHRR/2 models have higher noise levels than the AVHRR/3 sensors, whose sun shield has helped to stabilize the temporal evolution of its thermal noise. We also show that the 3.7 mm channel is much noisier than the 11 and 12 mm channels. The derived noise is then included as part of the new AVHRR fundamental climate data record developed under the FIDUCEO project.
Keywords: AVHRR; climate data record; infrared channel; noise filtering.

Type: Article
Title: A Fundamental Climate Data Record for the Meteosat Visible and Infrared Imager (MVIRI)
Authors: Frank Rüthrich 1, Ralf Quast 2, Yves Govaerts 3, Viju O. John 1, Rob Roebeling 1, Emma Wooliams 4 and Joerg Schulz 1
Affiliation: 1. EUMETSAT, Eumetsat Allee 1, 64295 Darmstadt, Germany; frank.ruethrich@eumetsat.int
2. FastOpt, Hamburg, Germany
3. Rayference, Brussels, Belgium
4. National Physical Laboratory, London, UK
Abstract: We present a new Fundamental Climate Data Record (FCDR) for the Meteosat Visible and Infrared Imager (MVIRI), with pixel-level metrologically traceable uncertainties and error covariance estimates. MVIRI has flown on-board 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” radiances in near real-time. The precision of the pre-launch sensor spectral response function (SRF) characterization, particularly of the visible band of this sensor type, improved considerably with time, resulting in higher-quality radiances nearing the end of the MFG program. Despite those amendments, 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 those 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 nine effects that have distinct physical root-causes and spatial/temporal correlation patterns. SEVIRI and SCIAMACHY reflectances were used for a validation of the recalibrated dataset. The resulting new FCDR is publicly available for climate studies and for the production of Climate Data Records (CDRs) spanning about 35 years.
Keywords: climate data record; fundamental climate data record; essential climate variable; Earth observation; remote sensing; metrology; uncertainty; error budget; error propagation; radiance

















Back to Top