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Remote Sensing of Essential Climate Variables and Their Applications
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Remote Sensing of Essential Climate Variables and Their Applications

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 June 2018) | Viewed by 106976

Special Issue Editors

Dr. Jeffrey L. Privette

E-Mail Website
Guest Editor
NOAA/NESDIS National Centers for Environmental Information, 151 Patton Ave., Asheville, NC 28801, USA
Interests: remote sensing; satellites; climate data records; climate services; data management
Dr. Rainer Hollmann

E-Mail Website
Guest Editor
Satellite based Climate Monitoring / KU43, Deutscher Wetterdienst, 63067 Offenbach, Germany
Interests: climatology; meteorology; remote sensing; satellite based climate data records
Prof. Dr. Byung-Ju Sohn

E-Mail Website
Guest Editor
School of Earth and Environmental Science, Seoul National University, NS80, Seoul 151-747, Korea
Interests: water vapor-cloud-radiation budget; water vapor transport studies using remote sensing; tropical and midlatitude precipitation studies

Special Issue Information

Dear Colleagues,

In the 1990s, when continuous satellite observations of the Earth began extending over decades in length, researchers began merging data from successive missions into seamless time-series homogeneous records. Their goal was to provide consistently-processed records of sufficient length and quality to detect climate trends—typically small but persistent signals superimposed by weather systems and various natural oscillations. The resulting records contributed to many important scientific discoveries, including the warming characteristics of Earth’s atmosphere and ocean surfaces, the acceleration in Arctic sea ice loss, and the impact of distant aerosol sources in hurricane genesis. These pioneering efforts ultimately led to a new and now internationally-recognized class of satellite products—Climate Data Records (CDRs; National Research Council, 2004)—as well as a formal framework and taxonomy—Essential Climate Variables (ECVs; Bojinski et al. 2014). Over time, ECVs and CDRs have become increasingly common, accurate and useful in a wide range of applications. Although originally developed primarily for climate research, CDRs now are also used for commercial applications the reinsurance, energy and agriculture sectors, among others, as well as derivative service products such as climate indicators and assessments. Their development and provision has also been a focus of dedicated programs, including NOAA’s CDR program, the ESA Climate Change Initiative (Hollmann et al., 2013), and EUMETSAT’s Satellite Application Facility networks climate activities. Recently, the Committee for Earth Observation Systems (CEOS) and Coordination Group for Meteorological Satellites (CGMS) Working Group on Climate demonstrated with the first comprehensive inventory of CDRs—including more than 400 unique entries provided by 10 space agencies and organizations—the strong uptake in the community. In this Special Issue of Remote Sensing, we call for papers describing all aspects of CDR development, generation, validation, application and resulting societal benefits. We also seek papers on broader CDR and ECV guidelines, standards and frameworks such as requirements development, metadata, application of metrological standards, documentation and production practices, assessment tools and inventories.  Our goal is to provide the most comprehensive compendium of CDR-related articles yet compiled. Although we recognize the societal value of all satellite records, we request that contributors adhere to the NRC working definition of a CDR, i.e., a time series of measurements of sufficient length, consistency, and continuity to determine climate variability and change. This mostly requires compilations stemming from multiple satellites, however in special cases where a reprocessed record from a single mission meets that definition, associated papers are welcomed.

Dr. Jeffrey L. Privette
Dr. Rainer Hollmann
Dr. Byung-Ju Sohn
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 submissions that pass pre-check are 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 2700 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.

Keywords

  • Essential Climate Variable
  • Climate Data Record
  • Satellite
  • Remote Sensing
  • Homogeneous
  • Harmonize
  • Intercalibrate

Published Papers (18 papers)

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Research

28 pages, 4660 KiB  
Article
The GEWEX Water Vapor Assessment: Overview and Introduction to Results and Recommendations
by Marc Schröder, Maarit Lockhoff, Lei Shi, Thomas August, Ralf Bennartz, Helene Brogniez, Xavier Calbet, Frank Fell, John Forsythe, Antonia Gambacorta, Shu-peng Ho, E. Robert Kursinski, Anthony Reale, Tim Trent and Qiong Yang
Remote Sens. 2019, 11(3), 251; https://doi.org/10.3390/rs11030251 - 26 Jan 2019
Cited by 26 | Viewed by 5379
Abstract
To date, a large variety of water vapour data records from satellite and reanalysis are available. It is key to understand the quality and uncertainty of these data records in order to fully exploit these records and to avoid data being employed incorrectly [...] Read more.
To date, a large variety of water vapour data records from satellite and reanalysis are available. It is key to understand the quality and uncertainty of these data records in order to fully exploit these records and to avoid data being employed incorrectly or misinterpreted. Therefore, it is important to inform users on accuracy and limitations of these data records based on consistent inter-comparisons carried out in the framework of international assessments. Addressing this challenge is the major objective of the Global Water and Energy Exchanges (GEWEX) water vapor assessment (G-VAP) which was initiated by the GEWEX Data and Assessments Panel (GDAP). Here, an overview of G-VAP objectives and an introduction to the results from G-VAP’s first phase are given. After this overview, a summary of available data records on water vapour and closely related variables and a short introduction to the utilized methods are presented. The results from inter-comparisons, homogeneity testing and inter-comparison of trend estimates, achieved within G-VAP’s first phase are summarized. The conclusions on future research directions for the wider community and for G-VAP’s next phase are outlined and recommendations have been formulated. For instance, a key recommendation is the need for recalibration and improved inter-calibration of radiance data records and subsequent reprocessing in order to increase stability and to provide uncertainty estimates. This need became evident from a general disagreement in trend estimates (e.g., trends in TCWV ranging from −1.51 ± 0.17 kg/m2/decade to 1.22 ± 0.16 kg/m2/decade) and the presence of break points on global and regional scale. It will be a future activity of G-VAP to reassess the stability of updated or new data records and to assess consistency, i.e., the closeness of data records given their uncertainty estimates. Full article
(This article belongs to the Special Issue Remote Sensing of Essential Climate Variables and Their Applications)
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15 pages, 4552 KiB  
Article
Long-Term Arctic Snow/Ice Interface Temperature from Special Sensor for Microwave Imager Measurements
by Sang-Moo Lee, Byung-Ju Sohn and Christian D. Kummerow
Remote Sens. 2018, 10(11), 1795; https://doi.org/10.3390/rs10111795 - 12 Nov 2018
Cited by 10 | Viewed by 3953
Abstract
The Arctic sea ice region is the most visible area experiencing global warming-induced climate change. However, long-term measurements of climate-related variables have been limited to a small number of variables such as the sea ice concentration, extent, and area. In this study, we [...] Read more.
The Arctic sea ice region is the most visible area experiencing global warming-induced climate change. However, long-term measurements of climate-related variables have been limited to a small number of variables such as the sea ice concentration, extent, and area. In this study, we attempt to produce a long-term temperature record for the Arctic sea ice region using Special Sensor for Microwave Imager (SSM/I) Fundamental Climate Data Record (FCDR) data. For that, we developed an algorithm to retrieve the wintertime snow/ice interface temperature (SIIT) over the Arctic Ocean by counting the effect of the snow/ice volume scattering and ice surface roughness on the apparent emissivity (the total effect is referred to as the correction factor). A regression equation was devised to predict the correction factor from SSM/I brightness temperatures (TBs) only and then applied to SSM/I 19.4 GHz TB to estimate the SIIT. The obtained temperatures were validated against collocated Cold Regions Research and Engineering Laboratory (CRREL) ice mass balance (IMB) drifting buoy-measured temperatures at zero ice depth. It is shown that the SSM/I retrievals are in good agreement with the drifting buoy measurements, with a correlation coefficient of 0.95, bias of 0.1 K, and root-mean-square error of 1.48 K on a daily time scale. By applying the algorithm to 24-year (1988–2011) SSM/I FCDR data, we were able to produce the winter-time temperature at the sea ice surface for the 24-year period. Full article
(This article belongs to the Special Issue Remote Sensing of Essential Climate Variables and Their Applications)
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18 pages, 3167 KiB  
Article
Best Practices in Crafting the Calibrated, Enhanced-Resolution Passive-Microwave EASE-Grid 2.0 Brightness Temperature Earth System Data Record
by Mary J. Brodzik, David G. Long and Molly A. Hardman
Remote Sens. 2018, 10(11), 1793; https://doi.org/10.3390/rs10111793 - 12 Nov 2018
Cited by 13 | Viewed by 3758
Abstract
Since the late 1970s, satellite passive-microwave brightness temperatures have been a mainstay in remote sensing of the cryosphere. Polar snow and ice-covered ocean and land surfaces are especially sensitive to climate change and are observed to fluctuate on interannual to decadal timescales. In [...] Read more.
Since the late 1970s, satellite passive-microwave brightness temperatures have been a mainstay in remote sensing of the cryosphere. Polar snow and ice-covered ocean and land surfaces are especially sensitive to climate change and are observed to fluctuate on interannual to decadal timescales. In regions of limited sunlight and cloudy conditions, microwave measurements are particularly valuable for monitoring snow- and ice-covered ocean and land surfaces, due to microwave sensitivity to phase changes of water. Historically available at relatively low resolutions (25 km) compared to optical techniques (less than 1 km), passive-microwave sensors have provided short-timescale, large-area spatial coverage, and high temporal repeat observations for monitoring hemispheric-wide changes. However, historically available gridded passive microwave products have fallen short of modern requirements for climate data records, notably by using inconsistently-calibrated input data, including only limited periods of sensor overlaps, employing image-reconstruction methods that tuned for reduced noise rather than enhanced resolution, and using projection and grid definitions that were not easily interpreted by geolocation software. Using a recently completed Fundamental Climate Data Record of the swath format passive-microwave record that incorporated new, cross-sensor calibrations, we have produced an improved, gridded data record. Defined on the EASE-Grid 2.0 map projections and derived with numerically efficient image-reconstruction techniques, the Calibrated, Enhanced-Resolution Brightness Temperature (CETB) Earth System Data Record (ESDR) increases spatial resolution up to 3.125 km for the highest frequency channels, and satisfies modern Climate Data Record (CDR) requirements as defined by the National Research Council. We describe the best practices and development approaches that we used to ensure algorithmic integrity and to define and satisfy metadata, content and structural requirements for this high-quality, reliable, consistently gridded microwave radiometer climate data record. Full article
(This article belongs to the Special Issue Remote Sensing of Essential Climate Variables and Their Applications)
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18 pages, 4678 KiB  
Article
The AMSU-Based Hydrological Bundle Climate Data Record—Description and Comparison with Other Data Sets
by Ralph R. Ferraro, Brian R. Nelson, Tom Smith and Olivier P. Prat
Remote Sens. 2018, 10(10), 1640; https://doi.org/10.3390/rs10101640 - 16 Oct 2018
Cited by 7 | Viewed by 4637
Abstract
Passive microwave measurements have been available on satellites back to the 1970s, first flown on research satellites developed by the National Aeronautics and Space Administration (NASA). Since then, several other sensors have been flown to retrieve hydrological products for both operational weather applications [...] Read more.
Passive microwave measurements have been available on satellites back to the 1970s, first flown on research satellites developed by the National Aeronautics and Space Administration (NASA). Since then, several other sensors have been flown to retrieve hydrological products for both operational weather applications (e.g., the Special Sensor Microwave/Imager—SSM/I; the Advanced Microwave Sounding Unit—AMSU) and climate applications (e.g., the Advanced Microwave Scanning Radiometer—AMSR; the Tropical Rainfall Measurement Mission Microwave Imager—TMI; the Global Precipitation Mission Microwave Imager—GMI). Here, the focus is on measurements from the AMSU-A, AMSU-B, and Microwave Humidity Sounder (MHS). These sensors have been in operation since 1998, with the launch of NOAA-15, and are also on board NOAA-16, -17, -18, -19, and the MetOp-A and -B satellites. A data set called the “Hydrological Bundle” is a climate data record (CDR) that utilizes brightness temperatures from fundamental CDRs (FCDRs) to generate thematic CDRs (TCDRs). The TCDRs include total precipitable water (TPW), cloud liquid water (CLW), sea-ice concentration (SIC), land surface temperature (LST), land surface emissivity (LSE) for 23, 31, 50 GHz, rain rate (RR), snow cover (SC), ice water path (IWP), and snow water equivalent (SWE). The TCDRs are shown to be in general good agreement with similar products from other sources, such as the Global Precipitation Climatology Project (GPCP) and the Modern-Era Retrospective Analysis for Research and Applications (MERRA-2). Due to the careful intercalibration of the FCDRs, little bias is found among the different TCDRs produced from individual NOAA and MetOp satellites, except for normal diurnal cycle differences. Full article
(This article belongs to the Special Issue Remote Sensing of Essential Climate Variables and Their Applications)
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27 pages, 4768 KiB  
Article
Inter-Comparison and Evaluation of the Four Longest Satellite-Derived Cloud Climate Data Records: CLARA-A2, ESA Cloud CCI V3, ISCCP-HGM, and PATMOS-x
by Karl-Göran Karlsson and Abhay Devasthale
Remote Sens. 2018, 10(10), 1567; https://doi.org/10.3390/rs10101567 - 01 Oct 2018
Cited by 36 | Viewed by 5321
Abstract
Results from four global cloud climate data records (ISCCP-HGM, ESA Cloud CCI V3, CLARA-A2 and PATMOS-x) have been inter-compared in global time series plots, in global maps and in zonal region plots covering the period in common, 1984–2009. The investigated cloud parameters were [...] Read more.
Results from four global cloud climate data records (ISCCP-HGM, ESA Cloud CCI V3, CLARA-A2 and PATMOS-x) have been inter-compared in global time series plots, in global maps and in zonal region plots covering the period in common, 1984–2009. The investigated cloud parameters were total cloud fraction and cloud top pressure. Averaged seasonal cycles of cloud cover, as observed by the CALIPSO-CALIOP sensor over the 2007–2015 period, were also used as an additional independent and high-quality reference for the study of global cloud cover. All CDRs show good agreement on global cloud amounts (~65%) and also a weak negative trend (0.5–1.9% per decade) over the period of investigation. Deviations between the CDRs are seen especially over the southern mid-latitude region and over the poles. Particularly good results are shown by PATMOS-x and by ESA Cloud CCI V3 when compared to the CALIPSO-CALIOP reference. Results for cloud top pressure show large differences (~60 hPa) between ISCCP-HGM and the other CDRs for the global mean. The two CDR groups show also opposite signs in the trend over the period. Full article
(This article belongs to the Special Issue Remote Sensing of Essential Climate Variables and Their Applications)
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21 pages, 6587 KiB  
Article
Temporal Means and Variability of Arctic Sea Ice Melt and Freeze Season Climate Indicators Using a Satellite Climate Data Record
by Ge Peng, Michael Steele, Angela C. Bliss, Walter N. Meier and Suzanne Dickinson
Remote Sens. 2018, 10(9), 1328; https://doi.org/10.3390/rs10091328 - 21 Aug 2018
Cited by 39 | Viewed by 7890
Abstract
Information on the timing of Arctic snow and ice melt onset, sea ice opening, retreat, advance, and closing, can be beneficial to a variety of stakeholders. Sea ice modelers can use information on the evolution of the ice cover through the rest of [...] Read more.
Information on the timing of Arctic snow and ice melt onset, sea ice opening, retreat, advance, and closing, can be beneficial to a variety of stakeholders. Sea ice modelers can use information on the evolution of the ice cover through the rest of the summer to improve their seasonal sea ice forecasts. The length of the open water season (as derived from retreat/advance dates) is important for human activities and for wildlife. Long-term averages and variability of these dates as climate indicators are beneficial to business strategic planning and climate monitoring. In this study, basic characteristics of temporal means and variability of Arctic sea ice climate indicators derived from a satellite-based climate data record from March 1979 to February 2017 melt and freeze seasons are described. Our results show that, over the Arctic region, anomalies of snow and ice melt onset, ice opening and retreat dates are getting earlier in the year at a rate of more than 5 days per decade, while that of ice advance and closing dates are getting later at a rate of more than 5 days per decade. These significant trends resulted in significant upward trends for anomalies of inner and outer ice-free periods at a rate of nearly 12 days per decade. Small but significant downward trends of seasonal ice loss and gain period anomalies were also observed at a rate of −1.48 and −0.53 days per decade, respectively. Our analyses also demonstrated that the means of these indicators and their trends are sensitive to valid data masks and regional averaging methods. Full article
(This article belongs to the Special Issue Remote Sensing of Essential Climate Variables and Their Applications)
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13 pages, 3948 KiB  
Communication
HIRS Outgoing Longwave Radiation—Daily Climate Data Record: Application toward Identifying Tropical Subseasonal Variability
by Carl J. Schreck III, Hai-Tien Lee and Kenneth R. Knapp
Remote Sens. 2018, 10(9), 1325; https://doi.org/10.3390/rs10091325 - 21 Aug 2018
Cited by 28 | Viewed by 6140
Abstract
This study describes the development of a new globally gridded climate data record (CDR) for daily outgoing longwave radiation (OLR) using the High-Resolution Infrared Radiation Sounder (HIRS) sensor. The new product, hereafter referred to as HIRS OLR, has several differences and advantages over [...] Read more.
This study describes the development of a new globally gridded climate data record (CDR) for daily outgoing longwave radiation (OLR) using the High-Resolution Infrared Radiation Sounder (HIRS) sensor. The new product, hereafter referred to as HIRS OLR, has several differences and advantages over the widely-used daily OLR dataset derived from the Advanced Very High-Resolution Radiometer (AVHRR) sensor on the same NOAA Polar Operational Environmental Satellites (POES), hereafter AVHRR OLR. As a CDR, HIRS OLR has been intersatellite-calibrated to provide the most homogeneous record possible. AVHRR OLR only used the daytime and nighttime overpasses from a single satellite at a time, which creates some challenges for resolving the large diurnal cycle of OLR. HIRS OLR leverages all available overpasses and then calibrates geostationary estimates of OLR to represent that cycle more faithfully. HIRS also has more spectral channels, including those for measuring water vapor, which provides a more accurate measure of OLR. This difference is particularly relevant for large-scale convective systems such as the El Niño–Southern Oscillation and the Madden–Julian Oscillation, whereby the HIRS OLR can better identify the subtropical variability between the tropical convection and the extratropical teleconnections. Full article
(This article belongs to the Special Issue Remote Sensing of Essential Climate Variables and Their Applications)
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20 pages, 5419 KiB  
Article
Contribution of Land Surface Temperature (TCI) to Vegetation Health Index: A Comparative Study Using Clear Sky and All-Weather Climate Data Records
by Virgílio A. Bento, Isabel F. Trigo, Célia M. Gouveia and Carlos C. DaCamara
Remote Sens. 2018, 10(9), 1324; https://doi.org/10.3390/rs10091324 - 21 Aug 2018
Cited by 35 | Viewed by 6390
Abstract
The Vegetation Health Index (VHI) is widely used for monitoring drought using satellite data. VHI depends on vegetation state and thermal stress, respectively assessed via (i) the Vegetation Condition Index (VCI) that usually relies on information from the visible and near infra-red parts [...] Read more.
The Vegetation Health Index (VHI) is widely used for monitoring drought using satellite data. VHI depends on vegetation state and thermal stress, respectively assessed via (i) the Vegetation Condition Index (VCI) that usually relies on information from the visible and near infra-red parts of the spectrum (in the form of Normalized Difference Vegetation Index, NDVI); and (ii) the Thermal Condition Index (TCI), based on top of atmosphere thermal infrared (TIR) brightness temperature or on TIR-derived Land Surface Temperature (LST). VHI is then estimated as a weighted average of VCI and TCI. However, the optimum weights of the two components are usually not known and VHI is usually estimated attributing a weight of 0.5 to both. Using a previously developed methodology for the Euro-Mediterranean region, we show that the multi-scalar drought index (SPEI) may be used to obtain optimal weights for VCI and TCI over the area covered by Meteosat satellites that includes Africa, Europe, and part of South America. The procedure is applied using clear-sky Meteosat Climate Data Records (CDRs) and all-sky LST derived by combining satellite and reanalysis data. Results obtained present a coherent spatial distribution of VCI and TCI weights when estimated using clear- and all-sky LST. This study paves the way for the development of a future VHI near-real time operational product for drought monitoring based on information from Meteosat satellites. Full article
(This article belongs to the Special Issue Remote Sensing of Essential Climate Variables and Their Applications)
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12 pages, 1787 KiB  
Article
Fundamental Climate Data Records of Microwave Brightness Temperatures
by Wesley Berg, Rachael Kroodsma, Christian D. Kummerow and Darren S. McKague
Remote Sens. 2018, 10(8), 1306; https://doi.org/10.3390/rs10081306 - 19 Aug 2018
Cited by 28 | Viewed by 4770
Abstract
An intercalibrated Fundamental Climate Data Record (FCDR) of brightness temperatures (Tb) has been developed using data from a total of 14 research and operational conical-scanning microwave imagers. This dataset provides a consistent 30+ year data record of global observations that is well suited [...] Read more.
An intercalibrated Fundamental Climate Data Record (FCDR) of brightness temperatures (Tb) has been developed using data from a total of 14 research and operational conical-scanning microwave imagers. This dataset provides a consistent 30+ year data record of global observations that is well suited for retrieving estimates of precipitation, total precipitable water, cloud liquid water, ocean surface wind speed, sea ice extent and concentration, snow cover, soil moisture, and land surface emissivity. An initial FCDR was developed for a series of ten Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) instruments on board the Defense Meteorological Satellite Program spacecraft. An updated version of this dataset, including additional NASA and Japanese sensors, has been developed as part of the Global Precipitation Measurement (GPM) mission. The FCDR development efforts involved quality control of the original data, geolocation corrections, calibration corrections to account for cross-track and time-dependent calibration errors, and intercalibration to ensure consistency with the calibration reference. Both the initial SSMI(S) and subsequent GPM Level 1C FCDR datasets are documented, updated in near real-time, and publicly distributed. Full article
(This article belongs to the Special Issue Remote Sensing of Essential Climate Variables and Their Applications)
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31 pages, 7932 KiB  
Article
Land Surface Albedo Derived on a Ten Daily Basis from Meteosat Second Generation Observations: The NRT and Climate Data Record Collections from the EUMETSAT LSA SAF
by Dominique Carrer, Suman Moparthy, Gabriel Lellouch, Xavier Ceamanos, Florian Pinault, Sandra C. Freitas and Isabel F. Trigo
Remote Sens. 2018, 10(8), 1262; https://doi.org/10.3390/rs10081262 - 10 Aug 2018
Cited by 23 | Viewed by 5371
Abstract
Land surface albedo determines the splitting of downwelling solar radiation into components which are either reflected back to the atmosphere or absorbed by the surface. Land surface albedo is an important variable for the climate community, and therefore was defined by the Global [...] Read more.
Land surface albedo determines the splitting of downwelling solar radiation into components which are either reflected back to the atmosphere or absorbed by the surface. Land surface albedo is an important variable for the climate community, and therefore was defined by the Global Climate Observing System (GCOS) as an Essential Climate Variable (ECV). Within the scope of the Satellite Application Facility for Land Surface Analysis (LSA SAF) of EUMETSAT (European Organization for the Exploitation of Meteorological Satellites), a near-real time (NRT) daily albedo product was developed in the last decade from observations provided by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on board the geostationary satellites of the Meteosat Second Generation (MSG) series. In this study we present a new collection of albedo satellite products based on the same satellite data. The MSG Ten-day Albedo (MTAL) product incorporates MSG observations over 31 days with a frequency of NRT production of 10 days. The MTAL collection is more dedicated to climate analysis studies compared to the daily albedo that was initially designed for the weather prediction community. For this reason, a homogeneous reprocessing of MTAL was done in 2018 to generate a climate data record (CDR). The resulting product is called MTAL-R and has been made available to the community in addition to the NRT version of the MTAL product which has been available for several years. The retrieval algorithm behind the MTAL products comprises three distinct modules: One for atmospheric correction, one for daily inversion of a semi-empirical model of the bidirectional reflectance distribution function, and one for monthly composition, that also determines surface albedo values. In this study the MTAL-R CDR is compared to ground surface measurements and concomitant albedo products collected by sensors on-board polar-orbiting satellites (SPOT-VGT and MODIS). We show that MTAL-R meets the quality requirements if MODIS or SPOT-VGT are considered as reference. This work leads to 14 years of production of geostationary land surface albedo products with a guaranteed continuity in the LSA SAF for the future years with the forthcoming third generation of European geostationary satellites. Full article
(This article belongs to the Special Issue Remote Sensing of Essential Climate Variables and Their Applications)
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21 pages, 3393 KiB  
Article
Quality Assurance Framework Development Based on Six New ECV Data Products to Enhance User Confidence for Climate Applications
by Joanne Nightingale, Klaas Folkert Boersma, Jan-Peter Muller, Steven Compernolle, Jean-Christopher Lambert, Simon Blessing, Ralf Giering, Nadine Gobron, Isabelle De Smedt, Pierre Coheur, Maya George, Jörg Schulz and Alexander Wood
Remote Sens. 2018, 10(8), 1254; https://doi.org/10.3390/rs10081254 - 09 Aug 2018
Cited by 21 | Viewed by 6604
Abstract
Data from Earth observation (EO) satellites are increasingly used to monitor the environment, understand variability and change, inform evaluations of climate model forecasts, and manage natural resources. Policymakers are progressively relying on the information derived from these datasets to make decisions on mitigating [...] Read more.
Data from Earth observation (EO) satellites are increasingly used to monitor the environment, understand variability and change, inform evaluations of climate model forecasts, and manage natural resources. Policymakers are progressively relying on the information derived from these datasets to make decisions on mitigating and adapting to climate change. These decisions should be evidence based, which requires confidence in derived products, as well as the reference measurements used to calibrate, validate, or inform product development. In support of the European Union’s Earth Observation Programmes Copernicus Climate Change Service (C3S), the Quality Assurance for Essential Climate Variables (QA4ECV) project fulfilled a gap in the delivery of climate quality satellite-derived datasets, by prototyping a generic system for the implementation and evaluation of quality assurance (QA) measures for satellite-derived ECV climate data record products. The project demonstrated the QA system on six new long-term, climate quality ECV data records for surface albedo, leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), nitrogen dioxide (NO2), formaldehyde (HCHO), and carbon monoxide (CO). The provision of standardised QA information provides data users with evidence-based confidence in the products and enables judgement on the fitness-for-purpose of various ECV data products and their specific applications. Full article
(This article belongs to the Special Issue Remote Sensing of Essential Climate Variables and Their Applications)
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20 pages, 11469 KiB  
Article
A Long-Term Fine-Resolution Record of AVHRR Surface Temperatures for the Laurentian Great Lakes
by Charles H. White, Andrew K. Heidinger, Steven A. Ackerman and Peter B. McIntyre
Remote Sens. 2018, 10(8), 1210; https://doi.org/10.3390/rs10081210 - 02 Aug 2018
Cited by 6 | Viewed by 3604
Abstract
Inland waters are warming at highly variable rates that often differ from regional air temperature trends. This variable warming is partially attributable to an individual lake’s geographical and morphological characteristics. In very large lakes, significant intralake variability in long-term warming trends has also [...] Read more.
Inland waters are warming at highly variable rates that often differ from regional air temperature trends. This variable warming is partially attributable to an individual lake’s geographical and morphological characteristics. In very large lakes, significant intralake variability in long-term warming trends has also been observed. In light of this intralake and interlake heterogeneity of lake surface water temperature (LSWT) and LSWT trends, we revisit the 1.1 km Advanced Very High Resolution Radiometer (AVHRR) record for the Laurentian Great Lakes. In this work, we have assembled a long-term (1986–2016) and high-spatial-resolution (0.018°) daily LSWT dataset using AVHRR record. Subtracting an empirically-determined mean diurnal cycle mitigates the effects of varying observation times. Adjustments in the georegistration of the images are made to reduce the impact of AVHRR navigational errors on the earlier platforms. Both the original daily composites, and a gap-filled product using locally weighted interpolation methods will be made available to support fine-scale physical and environmental research in the region. Full article
(This article belongs to the Special Issue Remote Sensing of Essential Climate Variables and Their Applications)
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15 pages, 4586 KiB  
Article
Assessing the Pattern Differences between Satellite-Observed Upper Tropospheric Humidity and Total Column Water Vapor during Major El Niño Events
by Lei Shi, Carl J. Schreck III and Marc Schröder
Remote Sens. 2018, 10(8), 1188; https://doi.org/10.3390/rs10081188 - 28 Jul 2018
Cited by 9 | Viewed by 5159
Abstract
As part of the activities for the Global Energy and Water Exchanges (GEWEX) water vapor assessment project, the consistency of satellite-observed upper tropospheric humidity (UTH) and total column water vapor (TCWV) is examined. The examination is focused on their respective patterns during major [...] Read more.
As part of the activities for the Global Energy and Water Exchanges (GEWEX) water vapor assessment project, the consistency of satellite-observed upper tropospheric humidity (UTH) and total column water vapor (TCWV) is examined. The examination is focused on their respective patterns during major El Niño events. The analysis shows that the two datasets, consisting of one measurement of vertically averaged relative humidity in the upper troposphere and one of absolute water vapor integrated over the atmospheric vertical column with dominant contribution from the lower troposphere, are consistent over the equatorial central–eastern Pacific, both showing increases of water vapor during major El Niño events as expected. However, the magnitude of drying in the TCWV field over the western Pacific is much weaker than that of moistening over the central–eastern Pacific, while the UTH field exhibits equivalent magnitude of drying and moistening. Furthermore, the drying in the UTH field covers larger areas in the tropics. The difference in their patterns results in an opposite phase in the time series during a major El Niño event when a tropical average is taken. Both UTH and TCWV are closely correlated with major climate indices. However, they have significantly different lag correlations with the Niño 3.4 index in both the sign (positive or negative) and lag time over tropical oceans. Full article
(This article belongs to the Special Issue Remote Sensing of Essential Climate Variables and Their Applications)
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26 pages, 24855 KiB  
Article
East Africa Rainfall Trends and Variability 1983–2015 Using Three Long-Term Satellite Products
by Elsa Cattani, Andrés Merino, José A. Guijarro and Vincenzo Levizzani
Remote Sens. 2018, 10(6), 931; https://doi.org/10.3390/rs10060931 - 13 Jun 2018
Cited by 71 | Viewed by 10317
Abstract
Daily time series from the Climate Prediction Center (CPC) Africa Rainfall Climatology version 2.0 (ARC2), Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) and Tropical Applications of Meteorology using SATellite (TAMSAT) African Rainfall Climatology And Time series version 2 (TARCAT) high-resolution long-term satellite [...] Read more.
Daily time series from the Climate Prediction Center (CPC) Africa Rainfall Climatology version 2.0 (ARC2), Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) and Tropical Applications of Meteorology using SATellite (TAMSAT) African Rainfall Climatology And Time series version 2 (TARCAT) high-resolution long-term satellite rainfall products are exploited to study the spatial and temporal variability of East Africa (EA, 5S–20N, 28–52E) rainfall between 1983 and 2015. Time series of selected rainfall indices from the joint CCl/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices are computed at yearly and seasonal scales. Rainfall climatology and spatial patterns of variability are extracted via the analysis of the total rainfall amount (PRCPTOT), the simple daily intensity (SDII), the number of precipitating days (R1), the number of consecutive dry and wet days (CDD and CWD), and the number of very heavy precipitating days (R20). Our results show that the spatial patterns of such trends depend on the selected rainfall product, as much as on the geographic areas characterized by statistically significant trends for a specific rainfall index. Nevertheless, indications of rainfall trends were extracted especially at the seasonal scale. Increasing trends were identified for the October–November–December PRCPTOT, R1, and SDII indices over eastern EA, with the exception of Kenya. In March–April–May, rainfall is decreasing over a large part of EA, as demonstrated by negative trends of PRCPTOT, R1, CWD, and R20, even if a complete convergence of all satellite products is not achieved. Full article
(This article belongs to the Special Issue Remote Sensing of Essential Climate Variables and Their Applications)
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37 pages, 32727 KiB  
Article
Intercomparison and Validation of SAR-Based Ice Velocity Measurement Techniques within the Greenland Ice Sheet CCI Project
by John Peter Merryman Boncori, Morten Langer Andersen, Jørgen Dall, Anders Kusk, Martijn Kamstra, Signe Bech Andersen, Noa Bechor, Suzanne Bevan, Christian Bignami, Noel Gourmelen, Ian Joughin, Hyung-Sup Jung, Adrian Luckman, Jeremie Mouginot, Julia Neelmeijer, Eric Rignot, Kilian Scharrer, Thomas Nagler, Bernd Scheuchl and Tazio Strozzi
Remote Sens. 2018, 10(6), 929; https://doi.org/10.3390/rs10060929 - 12 Jun 2018
Cited by 18 | Viewed by 7869
Abstract
Ice velocity is one of the products associated with the Ice Sheets Essential Climate Variable. This paper describes the intercomparison and validation of ice-velocity measurements carried out by several international research groups within the European Space Agency Greenland Ice Sheet Climate Change Initiative [...] Read more.
Ice velocity is one of the products associated with the Ice Sheets Essential Climate Variable. This paper describes the intercomparison and validation of ice-velocity measurements carried out by several international research groups within the European Space Agency Greenland Ice Sheet Climate Change Initiative project, based on space-borne Synthetic Aperture Radar (SAR) data. The goal of this activity was to survey the best SAR-based measurement and error characterization approaches currently in practice. To this end, four experiments were carried out, related to different processing techniques and scenarios, namely differential SAR interferometry, multi aperture SAR interferometry and offset-tracking of incoherent as well as of partially-coherent data. For each task, participants were provided with common datasets covering areas located on the Greenland ice-sheet margin and asked to provide mean velocity maps, quality characterization and a description of processing algorithms and parameters. The results were then intercompared and validated against GPS data, revealing in several cases significant differences in terms of coverage and accuracy. The algorithmic steps and parameters influencing the coverage, accuracy and spatial resolution of the measurements are discussed in detail for each technique, as well as the consistency between quality parameters and validation results. This allows several recommendations to be formulated, in particular concerning procedures which can reduce the impact of analyst decisions, and which are often found to be the cause of sub-optimal algorithm performance. Full article
(This article belongs to the Special Issue Remote Sensing of Essential Climate Variables and Their Applications)
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24 pages, 5084 KiB  
Article
A Multisensor Approach to Global Retrievals of Land Surface Albedo
by Aku Riihelä, Terhikki Manninen, Jeffrey Key, Qingsong Sun, Melanie Sütterlin, Alessio Lattanzio and Crystal Schaaf
Remote Sens. 2018, 10(6), 848; https://doi.org/10.3390/rs10060848 - 29 May 2018
Cited by 3 | Viewed by 4835
Abstract
Satellite-based retrievals offer the most cost-effective way to comprehensively map the surface albedo of the Earth, a key variable for understanding the dynamics of radiative energy interactions in the atmosphere-surface system. Surface albedo retrievals have commonly been designed separately for each different spaceborne [...] Read more.
Satellite-based retrievals offer the most cost-effective way to comprehensively map the surface albedo of the Earth, a key variable for understanding the dynamics of radiative energy interactions in the atmosphere-surface system. Surface albedo retrievals have commonly been designed separately for each different spaceborne optical imager. Here, we introduce a novel type of processing framework that combines the data from two polar-orbiting optical imager families, the Advanced Very High-Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS). The goal of the paper is to demonstrate that multisensor albedo retrievals can provide a significant reduction in the sampling time required for a robust and comprehensive surface albedo retrieval, without a major degradation in retrieval accuracy, as compared to state-of-the-art single-sensor retrievals. We evaluated the multisensor retrievals against reference in situ albedo measurements and compare them with existing datasets. The results show that global land surface albedo retrievals with a sampling period of 10 days can offer near-complete spatial coverage, with a retrieval bias mostly comparable to existing single sensor datasets, except for bright surfaces (deserts and snow) where the retrieval framework shows degraded performance because of atmospheric correction design compromises. A level difference is found between the single sensor datasets and the demonstrator developed here, pointing towards a need for further work in the atmospheric correction, particularly over bright surfaces, and inter-sensor radiance homogenization. The introduced framework is expandable to include other sensors in the future. Full article
(This article belongs to the Special Issue Remote Sensing of Essential Climate Variables and Their Applications)
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17 pages, 85073 KiB  
Article
A Multilayer Surface Temperature, Surface Albedo, and Water Vapor Product of Greenland from MODIS
by Dorothy K. Hall, Richard I. Cullather, Nicolo E. DiGirolamo, Josefino C. Comiso, Brooke C. Medley and Sophie M. Nowicki
Remote Sens. 2018, 10(4), 555; https://doi.org/10.3390/rs10040555 - 04 Apr 2018
Cited by 28 | Viewed by 8494
Abstract
A multilayer, daily ice surface temperature (IST)–albedo–water vapor product of Greenland, extending from March 2000 through December 2016, has been developed using standard MODerate-resolution Imaging Spectroradiometer (MODIS) data products from the Terra satellite. To meet the needs of the ice sheet modeling community, [...] Read more.
A multilayer, daily ice surface temperature (IST)–albedo–water vapor product of Greenland, extending from March 2000 through December 2016, has been developed using standard MODerate-resolution Imaging Spectroradiometer (MODIS) data products from the Terra satellite. To meet the needs of the ice sheet modeling community, this new Earth Science Data Record (ESDR) is provided in a polar stereographic projection in NetCDF format, and includes the existing standard MODIS Collection 6.1 IST and derived melt maps, and Collection 6 snow albedo and water vapor maps, along with ancillary data, and is provided at a spatial resolution of ~0.78 km. This ESDR enables relationships between IST, surface melt, albedo, and water vapor to be evaluated easily. We show examples of the components of the ESDR and describe some uses of the ESDR such as for comparison with skin temperature, albedo, and water vapor output from Modern Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2). Additionally, we show validation of the MODIS IST using in situ and aircraft data, and validation of MERRA-2 skin temperature maps using MODIS IST and in situ data. The ESDR has been assigned a DOI and will be available through the National Snow and Ice Data Center by the summer of 2018. Full article
(This article belongs to the Special Issue Remote Sensing of Essential Climate Variables and Their Applications)
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17 pages, 7705 KiB  
Article
Comparison of Wind Speeds from Spaceborne Microwave Radiometers with In Situ Observations and ECMWF Data over the Global Ocean
by Lei Zhang, Hanqing Shi, Zhenzhan Wang, Hong Yu, Xiaobin Yin and Qixiang Liao
Remote Sens. 2018, 10(3), 425; https://doi.org/10.3390/rs10030425 - 09 Mar 2018
Cited by 13 | Viewed by 4924
Abstract
This study compares wind speeds derived from five satellite microwave radiometers with those directly observed by buoy-mounted anemometers and the global analyses produced by the European Center for Medium-Range Weather Forecasts (ECMWF) model. Buoy comparisons yield wind speed root mean square errors of [...] Read more.
This study compares wind speeds derived from five satellite microwave radiometers with those directly observed by buoy-mounted anemometers and the global analyses produced by the European Center for Medium-Range Weather Forecasts (ECMWF) model. Buoy comparisons yield wind speed root mean square errors of 0.82 m/s for WindSat, 1.45 m/s for SSMIS F16, 1.39 m/s for SSMIS F17, 1.43 m/s for AMSR-E, and 1.45 m/s for AMSR2. The overall mean bias for each satellite is typically <0.25 m/s when averaged over all selected buoys for a given study time. The satellite wind speeds are underestimated with respect to the buoy observations at a band of the tropical Pacific Ocean from −8°S to 4°N. The mean buoy–satellite difference as a function of year is always <0.4 m/s, except for SSMIS F16. The selected satellite wind speeds show an obvious seasonal characteristic at high latitudes. In comparison with the ECMWF data, some obviously positive differences exist at high southern latitudes in January and at high northern latitudes in July. Full article
(This article belongs to the Special Issue Remote Sensing of Essential Climate Variables and Their Applications)
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