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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: closed (31 March 2019)

Special Issue Editors

Guest Editor
Prof. Dr. Zhongbo Su

University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), The Netherlands
Website | E-Mail
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
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 semimonthly 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 (7 papers)

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Research

Open AccessArticle Intercomparisons of Long-Term Atmospheric Temperature and Humidity Profile Retrievals
Remote Sens. 2019, 11(7), 853; https://doi.org/10.3390/rs11070853
Received: 12 February 2019 / Revised: 25 March 2019 / Accepted: 3 April 2019 / Published: 9 April 2019
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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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Open AccessArticle An Uncertainty Quantified Fundamental Climate Data Record for Microwave Humidity Sounders
Remote Sens. 2019, 11(5), 548; https://doi.org/10.3390/rs11050548
Received: 29 January 2019 / Revised: 23 February 2019 / Accepted: 26 February 2019 / Published: 6 March 2019
Cited by 1 | PDF Full-text (2826 KB) | HTML Full-text | XML Full-text
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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Open AccessArticle Climate Data Records from Meteosat First Generation Part II: Retrieval of the In-Flight Visible Spectral Response
Remote Sens. 2019, 11(5), 480; https://doi.org/10.3390/rs11050480
Received: 15 January 2019 / Revised: 8 February 2019 / Accepted: 12 February 2019 / Published: 26 February 2019
Cited by 1 | PDF Full-text (5503 KB) | HTML Full-text | XML Full-text
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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Open AccessArticle Radiance Uncertainty Characterisation to Facilitate Climate Data Record Creation
Remote Sens. 2019, 11(5), 474; https://doi.org/10.3390/rs11050474
Received: 12 February 2019 / Accepted: 16 February 2019 / Published: 26 February 2019
Cited by 1 | PDF Full-text (2447 KB) | HTML Full-text | XML Full-text
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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Open AccessArticle Evaluation of CLARA-A2 and ISCCP-H Cloud Cover Climate Data Records over Europe with ECA&D Ground-Based Measurements
Remote Sens. 2019, 11(2), 212; https://doi.org/10.3390/rs11020212
Received: 9 November 2018 / Revised: 10 January 2019 / Accepted: 16 January 2019 / Published: 21 January 2019
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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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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
Remote Sens. 2018, 10(12), 1959; https://doi.org/10.3390/rs10121959
Received: 5 November 2018 / Revised: 29 November 2018 / Accepted: 3 December 2018 / Published: 5 December 2018
Cited by 2 | PDF Full-text (518 KB) | HTML Full-text | XML Full-text
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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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
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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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