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21 pages, 972 KiB

Open AccessArticle

Error Correlations in High-Resolution Infrared Radiation Sounder (HIRS) Radiances

by Gerrit Holl, Jonathan P. D. Mittaz and Christopher J. Merchant

Remote Sens. 2019, 11(11), 1337; https://doi.org/10.3390/rs11111337 - 3 Jun 2019

Cited by 2 | Viewed by 3695

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 accurate characterisation of uncertainties and inter-channel error correlations, which has so far been lacking. Here, we calculate error correlation [...] Read more.

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 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 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 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. Full article

(This article belongs to the Special Issue Assessment of Quality and Usability of Climate Data Records)

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25 pages, 12221 KiB

Open AccessArticle

Uncertainty Characterization and Propagation in the Community Long-Term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS)

by Nadia Smith and Christopher D. Barnet

Remote Sens. 2019, 11(10), 1227; https://doi.org/10.3390/rs11101227 - 23 May 2019

Cited by 51 | Viewed by 4901

Abstract

The Community Long-term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS) retrieves multiple Essential Climate Variables (ECV) about the vertical atmosphere from hyperspectral infrared measurements made by the Atmospheric InfraRed Sounder (AIRS, 2002–present) and its successor, the Cross-track Infrared Sounder (CrIS, 2011–present). CLIMCAPS ECVs [...] Read more.

The Community Long-term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS) retrieves multiple Essential Climate Variables (ECV) about the vertical atmosphere from hyperspectral infrared measurements made by the Atmospheric InfraRed Sounder (AIRS, 2002–present) and its successor, the Cross-track Infrared Sounder (CrIS, 2011–present). CLIMCAPS ECVs are profiles of temperature and water vapor, column amounts of greenhouse gases (CO₂, CH₄), ozone (O₃) and precursor gases (CO, SO₂) as well as cloud properties. AIRS (and CrIS) spectral measurements are highly correlated signals of many atmospheric state variables. CLIMCAPS inverts an AIRS (and CrIS) measurement into a set of discrete ECVs by employing a sequential Bayesian approach in which scene-dependent uncertainty is rigorously propagated. This not only linearizes the inversion problem but explicitly accounts for spectral interference from other state variables so that the correlation among ECVs (and their uncertainty) may be minimized. Here, we outline the CLIMCAPS retrieval methodology with specific focus given to its sequential scene-dependent uncertainty propagation system. We conclude by demonstrating continuity in two CLIMCAPS ECVs across AIRS and CrIS so that a long-term data record may be generated to study the feedback cycles characterizing our climate system. Full article

(This article belongs to the Special Issue Assessment of Quality and Usability of Climate Data Records)

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32 pages, 6885 KiB

Open AccessArticle

Recalibration of over 35 Years of Infrared and Water Vapor Channel Radiances of the JMA Geostationary Satellites

by Tasuku Tabata, Viju O. John, Rob A. Roebeling, Tim Hewison and Jörg Schulz

Remote Sens. 2019, 11(10), 1189; https://doi.org/10.3390/rs11101189 - 18 May 2019

Cited by 10 | Viewed by 4112 | Correction

Abstract

Infrared sounding measurements of the Infrared Atmospheric Sounding Interferometer (IASI), Atmospheric Infrared Sounder (AIRS), and High-resolution Infrared Radiation Sounder/2 (HIRS/2) instruments are used to recalibrate infrared (IR; ~11 µm) channels and water vapor (WV; ~6 µm) channels of the Visible and Infrared Spin [...] Read more.

Infrared sounding measurements of the Infrared Atmospheric Sounding Interferometer (IASI), Atmospheric Infrared Sounder (AIRS), and High-resolution Infrared Radiation Sounder/2 (HIRS/2) instruments are used to recalibrate 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 onboard the historical geostationary satellites of the Japan Meteorological Agency (JMA). The recalibration was performed using a common recalibration method developed by European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), which can be applied to the historical geostationary satellites to produce Fundamental Climate Data Records (FCDR). Pseudo geostationary imager radiances were computed from the infrared sounding measurements and regressed against the radiances from the geostationary satellites. Recalibration factors were computed from these pseudo imager radiance pairs. This paper presents and evaluates the result of recalibration of longtime-series of IR (1978–2016) and WV (1995–2016) measurements from JMA’s historical geostationary satellites. For the IR data of the earlier satellites (Geostationary Metrological Satellite (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 variations range from 3 K for the earlier satellites to <0.4 K for the recent satellites (GMS-5, Geostationary Operational Environmental Satellite-9 (GOES-9), Multi-functional Transport Satellite-1R (MTSAT-1R) and MTSAT-2). For the WV data of GOES-9, 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 operational Spectral Response Function (SRF) of the WV channel of GMS-5. The method used in this study is based on the principles developed within Global Space-based Inter-calibration System (GSICS). Moreover, presented results contribute to the Inter-calibration of imager observations from time-series of geostationary satellites (IOGEO) project under the umbrella of the World Meteorological Organization (WMO) initiative Sustained and Coordinated Processing of Environmental Satellite data for Climate Monitoring (SCOPE-CM). Full article

(This article belongs to the Special Issue Assessment of Quality and Usability of Climate Data Records)

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28 pages, 4069 KiB

Open AccessArticle

Towards a Traceable Climate Service: Assessment of Quality and Usability of Essential Climate Variables

by Yijian Zeng, Zhongbo Su, Iakovos Barmpadimos, Adriaan Perrels, Paul Poli, K. Folkert Boersma, Anna Frey, Xiaogang Ma, Karianne de Bruin, Hasse Goosen, Viju O. John, Rob Roebeling, Jörg Schulz and Wim Timmermans

Remote Sens. 2019, 11(10), 1186; https://doi.org/10.3390/rs11101186 - 18 May 2019

Cited by 26 | Viewed by 6953

Abstract

Climate services are becoming the backbone to translate climate knowledge, data & information into climate-informed decision-making at all levels, from public administrations to business operators. It is essential to assess the technical and scientific quality of the provided climate data and information products, [...] Read more.

Climate services are becoming the backbone to translate climate knowledge, data & information into climate-informed decision-making at all levels, from public administrations to business operators. It is essential to assess the technical and scientific quality of the provided climate data and information products, including their value to users, to establish the relation of trust between providers of climate data and information and various downstream users. The climate data and information products (i.e., from satellite, in-situ and reanalysis) shall be fully traceable, adequately documented and uncertainty quantified and can provide sufficient guidance for users to address their specific needs and feedbacks. This paper discusses details on how to apply the quality assurance framework to deliver timely assessments of the quality and usability of Essential Climate Variable (ECV) products. It identifies an overarching structure for the quality assessment of single product ECVs (i.e., consists of only one single variable), multi-product ECVs (i.e., more than one single parameter), thematic products (i.e., water, energy and carbon cycles), as well as the usability assessment. To support a traceable climate service, other than rigorously evaluating the technical and scientific quality of ECV products, which represent the upstream of climate services, how the uncertainty propagates into the resulting benefit (utility) for the users of the climate service needs to be detailed. Full article

(This article belongs to the Special Issue Assessment of Quality and Usability of Climate Data Records)

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21 pages, 5658 KiB

Open AccessArticle

On the Methods for Recalibrating Geostationary Longwave Channels Using Polar Orbiting Infrared Sounders

by Viju O. John, Tasuku Tabata, Frank Rüthrich, Rob Roebeling, Tim Hewison, Reto Stöckli and Jörg Schulz

Remote Sens. 2019, 11(10), 1171; https://doi.org/10.3390/rs11101171 - 16 May 2019

Cited by 13 | Viewed by 4128

Abstract

This study presents a common recalibration method that has been applied to geostationary imagers’ infrared (IR) and water vapour (WV) channel measurements, referred to as the multi-sensor infrared channel calibration (MSICC) method. The method relies on data of the Infrared Atmospheric Sounding Interferometer [...] Read more.

This study presents a common recalibration method that has been applied to geostationary imagers’ infrared (IR) and water vapour (WV) channel measurements, referred to as the multi-sensor infrared channel calibration (MSICC) method. The method relies on data of the Infrared Atmospheric Sounding Interferometer (IASI), Atmospheric Infrared Sounder (AIRS), and High-Resolution Infrared Radiation Sounder (HIRS/2) on polar orbiting satellites. The geostationary imagers considered here are VISSR/JAMI/IMAGER on JMA’s GMS/MTSAT series and MVIRI/SEVIRI on EUMETSAT’s METEOSAT series. IASI hyperspectral measurements are used to determine spectral band adjustment factors (SBAF) that account for spectral differences between the geostationary and polar orbiting satellite measurements. A new approach to handle the spectral gaps of AIRS measurements using IASI spectra is developed and demonstrated. Our method of recalibration can be directly applied to the lowest level of geostationary measurements available, i.e., digital counts, to obtain recalibrated radiances. These radiances are compared against GSICS-corrected radiances and are validated against SEVIRI radiances, both during overlapping periods. Significant reduction in biases have been observed for both IR and WV channels, 4% and 10%, respectively compared to the operational radiances. Full article

(This article belongs to the Special Issue Assessment of Quality and Usability of Climate Data Records)

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30 pages, 9435 KiB

Open AccessArticle

Climate Data Records from Meteosat First Generation Part III: Recalibration and Uncertainty Tracing of the Visible Channel on Meteosat-2–7 Using Reconstructed, Spectrally Changing Response Functions

by Frank Rüthrich, Viju O. John, Rob A. Roebeling, Ralf Quast, Yves Govaerts, Emma R. Woolliams and Jörg Schulz

Remote Sens. 2019, 11(10), 1165; https://doi.org/10.3390/rs11101165 - 15 May 2019

Cited by 6 | Viewed by 5087

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Assessment of Quality and Usability of Climate Data Records)

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21 pages, 1021 KiB

Open AccessEditor’s ChoiceArticle

Cloud Detection with Historical Geostationary Satellite Sensors for Climate Applications

by Reto Stöckli, Jędrzej S. Bojanowski, Viju O. John, Anke Duguay-Tetzlaff, Quentin Bourgeois, Jörg Schulz and Rainer Hollmann

Remote Sens. 2019, 11(9), 1052; https://doi.org/10.3390/rs11091052 - 3 May 2019

Cited by 20 | Viewed by 6314

Abstract

Can we build stable Climate Data Records (CDRs) spanning several satellite generations? This study outlines how the ClOud Fractional Cover dataset from METeosat First and Second Generation (COMET) of the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) was created for the [...] Read more.

Can we build stable Climate Data Records (CDRs) spanning several satellite generations? This study outlines how the ClOud Fractional Cover dataset from METeosat First and Second Generation (COMET) of the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) was created for the 25-year period 1991–2015. Modern multi-spectral cloud detection algorithms cannot be used for historical Geostationary (GEO) sensors due to their limited spectral resolution. We document the innovation needed to create a retrieval algorithm from scratch to provide the required accuracy and stability over several decades. It builds on inter-calibrated radiances now available for historical GEO sensors. It uses spatio-temporal information and a robust clear-sky retrieval. The real strength of GEO observations—the diurnal cycle of reflectance and brightness temperature—is fully exploited instead of just accounting for single “imagery”. The commonly-used naive Bayesian classifier is extended with covariance information of cloud state and variability. The resulting cloud fractional cover CDR has a bias of 1% Mean Bias Error (MBE), a precision of 7% bias-corrected Root-Mean-Squared-Error (bcRMSE) for monthly means, and a decadal stability of 1%. Our experience can serve as motivation for CDR developers to explore novel concepts to exploit historical sensor data. Full article

(This article belongs to the Special Issue Assessment of Quality and Usability of Climate Data Records)

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22 pages, 3408 KiB

Open AccessArticle

A Novel Framework to Harmonise Satellite Data Series for Climate Applications

by Ralf Giering, Ralf Quast, Jonathan P. D. Mittaz, Samuel E. Hunt, Peter M. Harris, Emma R. Woolliams and Christopher J. Merchant

Remote Sens. 2019, 11(9), 1002; https://doi.org/10.3390/rs11091002 - 27 Apr 2019

Cited by 13 | Viewed by 5146

Abstract

Fundamental and thematic climate data records derived from satellite observations provide unique information for climate monitoring and research. Since any satellite only operates over a relatively short period of time, creating a climate data record also requires the combination of space-borne measurements from [...] Read more.

Fundamental and thematic climate data records derived from satellite observations provide unique information for climate monitoring and research. Since any satellite only operates over a relatively short period of time, creating a climate data record also requires the combination of space-borne measurements from a series of several (often similar) satellite sensors. Simply combining calibrated measurements from several sensors can, however, 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 and 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 matchups, such as simultaneous nadir overpasses and which reconciles the calibration of different sensors given their estimated spectral response function differences. 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 that is extremely challenging when some hundreds of millions of matchups 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 computation of first and second-order partial derivatives using Algorithmic Differentiation. Finally, we present re-calibrated radiances from a series of nine Advanced Very High Resolution Radiometer sensors showing that the new time series has smaller matchup differences compared to the unharmonised case while being consistent with uncertainty statistics. Full article

(This article belongs to the Special Issue Assessment of Quality and Usability of Climate Data Records)

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21 pages, 2670 KiB

Open AccessArticle

Ten Priority Science Gaps in Assessing Climate Data Record Quality

by Joanne Nightingale, Jonathan P.D. Mittaz, Sarah Douglas, Dick Dee, James Ryder, Michael Taylor, Christopher Old, Catherine Dieval, Celine Fouron, Guillaume Duveau and Christopher Merchant

Remote Sens. 2019, 11(8), 986; https://doi.org/10.3390/rs11080986 - 25 Apr 2019

Cited by 19 | Viewed by 6938

Abstract

Decision makers need accessible robust evidence to introduce new policies to mitigate and adapt to climate change. There is an increasing amount of environmental information available to policy makers concerning observations and trends relating to the climate. However, this data is hosted across [...] Read more.

Decision makers need accessible robust evidence to introduce new policies to mitigate and adapt to climate change. There is an increasing amount of environmental information available to policy makers concerning observations and trends relating to the climate. However, this data is hosted across a multitude of websites often with inconsistent metadata and sparse information relating to the quality, accuracy and validity of the data. Subsequently, the task of comparing datasets to decide which is the most appropriate for a certain purpose is very complex and often infeasible. In support of the European Union’s Copernicus Climate Change Service (C3S) mission to provide authoritative information about the past, present and future climate in Europe and the rest of the world, each dataset to be provided through this service must undergo an evaluation of its climate relevance and scientific quality to help with data comparisons. This paper presents the framework for Evaluation and Quality Control (EQC) of climate data products derived from satellite and in situ observations to be catalogued within the C3S Climate Data Store (CDS). The EQC framework will be implemented by C3S as part of their operational quality assurance programme. It builds on past and present international investment in Quality Assurance for Earth Observation initiatives, extensive user requirements gathering exercises, as well as a broad evaluation of over 250 data products and a more in-depth evaluation of a selection of 24 individual data products derived from satellite and in situ observations across the land, ocean and atmosphere Essential Climate Variable (ECV) domains. A prototype Content Management System (CMS) to facilitate the process of collating, evaluating and presenting the quality aspects and status of each data product to data users is also described. The development of the EQC framework has highlighted cross-domain as well as ECV specific science knowledge gaps in relation to addressing the quality of climate data sets derived from satellite and in situ observations. We discuss 10 common priority science knowledge gaps that will require further research investment to ensure all quality aspects of climate data sets can be ascertained and provide users with the range of information necessary to confidently select relevant products for their specific application. Full article

(This article belongs to the Special Issue Assessment of Quality and Usability of Climate Data Records)

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15 pages, 2924 KiB

Open AccessArticle

Intercomparisons of Long-Term Atmospheric Temperature and Humidity Profile Retrievals

by Jessica L. Matthews and Lei Shi

Remote Sens. 2019, 11(7), 853; https://doi.org/10.3390/rs11070853 - 9 Apr 2019

Cited by 3 | Viewed by 4499

Abstract

This study builds upon a framework to develop a climate data record of temperature and humidity profiles from high-resolution infrared radiation sounder (HIRS) clear-sky measurements. The resultant time series is a unique, long-term dataset (1978–2017). To validate this long-term dataset, evaluation of the [...] Read more.

This study builds upon a framework to develop a climate data record of temperature and humidity profiles from high-resolution infrared radiation sounder (HIRS) clear-sky measurements. The resultant time series is a unique, long-term dataset (1978–2017). To validate this long-term dataset, evaluation of the stability of the intersatellite time series is coupled with intercomparisons with independent observation platforms as available in more recent years. Eleven pairs of satellites carrying the HIRS instrument with time periods that overlap are examined. Correlation coefficients were calculated for the retrieval of each atmospheric pressure level and for each satellite pair. More than 90% of the cases examining both temperature and humidity have correlation coefficients greater than 0.7. Very high correlation is demonstrated at the surface and two meter levels for both temperature (>0.99) and specific humidity (>0.93). For the period of 2006–2017, intercomparisons are performed with four independent observations platforms: radiosonde (RS92), constellation observing system for meteorology ionosphere and climate (COSMIC), global climate observing system (GCOS) reference upper-air network (GRUAN), and infrared atmospheric sounding interferometer (IASI). Very close matching of surface and two meter temperatures over a wide domain of values is depicted in all presented intercomparisons: intersatellite matches of HIRS retrievals, HIRS vs. GRUAN, and HIRS vs. IASI. Full article

(This article belongs to the Special Issue Assessment of Quality and Usability of Climate Data Records)

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23 pages, 2826 KiB

Open AccessArticle

An Uncertainty Quantified Fundamental Climate Data Record for Microwave Humidity Sounders

by Imke Hans, Martin Burgdorf, Stefan A. Buehler, Marc Prange, Theresa Lang and Viju O. John

Remote Sens. 2019, 11(5), 548; https://doi.org/10.3390/rs11050548 - 6 Mar 2019

Cited by 14 | Viewed by 5540

Abstract

To date, there is no long-term, stable, and uncertainty-quantified dataset of upper tropospheric humidity (UTH) that can be used for climate research. As intermediate step towards the overall goal of constructing such a climate data record (CDR) of UTH, we produced a new [...] Read more.

To date, there is no long-term, stable, and uncertainty-quantified dataset of upper tropospheric humidity (UTH) that can be used for climate research. As intermediate step towards the overall goal of constructing such a climate data record (CDR) of UTH, we produced a new fundamental climate data record (FCDR) on the level of brightness temperature for microwave humidity sounders that will serve as basis for the CDR of UTH. Based on metrological principles, we constructed and implemented the measurement equation and the uncertainty propagation in the processing chain for the microwave humidity sounders. We reprocessed the level 1b data to obtain newly calibrated uncertainty quantified level 1c data in brightness temperature. Three aspects set apart this FCDR from previous attempts: (1) the data come in a ready-to-use NetCDF format; (2) the dataset provides extensive uncertainty information taking into account the different correlation behaviour of the underlying errors; and (3) inter-satellite biases have been understood and reduced by an improved calibration. Providing a detailed uncertainty budget on these data, this new FCDR provides valuable information for a climate scientist and also for the construction of the CDR. Full article

(This article belongs to the Special Issue Assessment of Quality and Usability of Climate Data Records)

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39 pages, 5503 KiB

Open AccessArticle

Climate Data Records from Meteosat First Generation Part II: Retrieval of the In-Flight Visible Spectral Response

by Ralf Quast, Ralf Giering, Yves Govaerts, Frank Rüthrich and Rob Roebeling

Remote Sens. 2019, 11(5), 480; https://doi.org/10.3390/rs11050480 - 26 Feb 2019

Cited by 9 | Viewed by 6267

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 key to developing Climate Data Records from the Meteosat Visible and Infrared Imager on board the Meteosat First Generation [...] Read more.

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 key 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 metrologically sound method for retrieving the VIS spectral response from matchups of pseudo-invariant calibration site (PICS) pixels with datasets of simulated top-of-atmosphere spectral radiance used as reference. Calibration sites include bright desert, open ocean and deep convective cloud targets. The absolute instrument spectral response function is decomposed into generalised Bernstein basis polynomials and a degradation function that is based on plain physical considerations and able to represent typical chromatic ageing characteristics. Retrieval uncertainties are specified in terms of an error covariance matrix, which is projected from model parameter space into the spectral response function domain and range. The retrieval method considers target type-specific biases due to errors in, e.g., the selection of PICS target pixels and the spectral radiance simulation explicitly. It has been tested with artificial and well-comprehended observational data from the Spinning Enhanced Visible and Infrared Imager on-board Meteosat Second Generation and has retrieved meaningful results for all MFG satellites apart from Meteosat-1, which was not available for analysis. Full article

(This article belongs to the Special Issue Assessment of Quality and Usability of Climate Data Records)

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17 pages, 2447 KiB

Open AccessArticle

Radiance Uncertainty Characterisation to Facilitate Climate Data Record Creation

by Christopher J. Merchant, Gerrit Holl, Jonathan P. D. Mittaz and Emma R. Woolliams

Remote Sens. 2019, 11(5), 474; https://doi.org/10.3390/rs11050474 - 26 Feb 2019

Cited by 16 | Viewed by 5133

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Assessment of Quality and Usability of Climate Data Records)

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20 pages, 4310 KiB

Open AccessArticle

Evaluation of CLARA-A2 and ISCCP-H Cloud Cover Climate Data Records over Europe with ECA&D Ground-Based Measurements

by Vasileios Tzallas, Nikos Hatzianastassiou, Nikos Benas, Jan Fokke Meirink, Christos Matsoukas, Paul Stackhouse, Jr. and Ilias Vardavas

Remote Sens. 2019, 11(2), 212; https://doi.org/10.3390/rs11020212 - 21 Jan 2019

Cited by 22 | Viewed by 5584

Abstract

Clouds are of high importance for the climate system but they still remain one of its principal uncertainties. Remote sensing techniques applied to satellite observations have assisted tremendously in the creation of long-term and homogeneous data records; however, satellite data sets need to [...] Read more.

Clouds are of high importance for the climate system but they still remain one of its principal uncertainties. Remote sensing techniques applied to satellite observations have assisted tremendously in the creation of long-term and homogeneous data records; however, satellite data sets need to be validated and compared with other data records, especially ground measurements. In the present study, the spatiotemporal distribution and variability of Total Cloud Cover (TCC) from the Satellite Application Facility on Climate Monitoring (CM SAF) Cloud, Albedo And Surface Radiation dataset from AVHRR data—edition 2 (CLARA-A2) and the International Satellite Cloud Climatology Project H-series (ISCCP-H) is analyzed over Europe. The CLARA-A2 data record has been created using measurements of the Advanced Very High Resolution Radiometer (AVHRR) instrument onboard the polar orbiting NOAA and the EUMETSAT MetOp satellites, whereas the ISCCP-H data were produced by a combination of measurements from geostationary meteorological satellites and the AVHRR instrument on the polar orbiting satellites. An intercomparison of the two data records is performed over their common period, 1984 to 2012. In addition, a comparison of the two satellite data records is made against TCC observations at 22 meteorological stations in Europe, from the European Climate Assessment & Dataset (ECA&D). The results indicate generally larger ISCCP-H TCC with respect to the corresponding CLARA-A2 data, in particular in the Mediterranean. Compared to ECA&D data, both satellite datasets reveal a reasonable performance, with overall mean TCC biases of 2.1 and 5.2% for CLARA-A2 and ISCCP-H, respectively. This, along with the higher correlation coefficients between CLARA-A2 and ECA&D TCC, indicates the better performance of CLARA-A2 TCC data. Full article

(This article belongs to the Special Issue Assessment of Quality and Usability of Climate Data Records)

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16 pages, 518 KiB

Open AccessArticle

Climate Data Records from Meteosat First Generation Part I: Simulation of Accurate Top-of-Atmosphere Spectral Radiance over Pseudo-Invariant Calibration Sites for the Retrieval of the In-Flight Visible Spectral Response

by Yves M. Govaerts, Frank Rüthrich, Viju O. John and Ralf Quast

Remote Sens. 2018, 10(12), 1959; https://doi.org/10.3390/rs10121959 - 5 Dec 2018

Cited by 10 | Viewed by 5035

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 harmonized and accurate a Fundamental Climate Data Record is a prerequisite to such generation. Meteosat Visible [...] Read more.

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 harmonized and accurate a Fundamental Climate Data Record is a prerequisite to such generation. Meteosat Visible and Infrared Imager radiometers suffer from inaccurate pre-launch spectral function characterization and spectral ageing constitutes a serious limitation to achieve such prerequisite. A new method was developed for the retrieval of the pre-launch instrument spectral function and 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 characterized pre-launch. On average, the accuracy of these simulated spectral radiances is estimated to be about ±2%. Full article

(This article belongs to the Special Issue Assessment of Quality and Usability of Climate Data Records)

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23 pages, 2350 KiB

Open AccessArticle

Performance Assessment of the COMET Cloud Fractional Cover Climatology across Meteosat Generations

by Jędrzej S. Bojanowski, Reto Stöckli, Anke Duguay-Tetzlaff, Stephan Finkensieper and Rainer Hollmann

Remote Sens. 2018, 10(5), 804; https://doi.org/10.3390/rs10050804 - 22 May 2018

Cited by 12 | Viewed by 5953

Abstract

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)

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3 pages, 165 KiB

Open AccessCorrection

Correction: Tabata, T., et al. Recalibration of over 35 Years of Infrared and Water Vapor Channel Radiances of the JMA Geostationary Satellites. Remote Sens. 2019, 11, 1189

by Tasuku Tabata, Viju O. John, Rob A. Roebeling, Tim Hewison and Jörg Schulz

Remote Sens. 2020, 12(5), 861; https://doi.org/10.3390/rs12050861 - 7 Mar 2020

Viewed by 2196

Abstract

The authors wish to make the following corrections to this paper [...] Full article

(This article belongs to the Special Issue Assessment of Quality and Usability of Climate Data Records)

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