Operational climate monitoring from space: the EUMETSAT satellite application facility on climate monitoring (CM-SAF)

Schulz, Jörg B.; Albert, P.; Behr, H.; Caprion, D.; Deneke, Hartwig; Dewitte, Steven; Dürr, Bruno; Fuchs, P.; Gratzki, A.; Hechler, P.; Hollmann, Rainer; Johnston, Sheldon; Karlsson, Karl‐Göran; Manninen, Terhikki; Müller, Richard; Reuter, M.; Riihelä, Aku; Roebeling, Rob; Selbach, N.; Tetzlaff, Anke; Thomas, Werner; Werscheck, M.; Wolters, E.; Zelenka, A.

doi:10.5194/acpd-8-8517-2008

Cited by 52 publications

(40 citation statements)

References 57 publications

(63 reference statements)

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“…In this study we use data derived from measurements of the Advanced Very High Resolution Radiometer sensor (AVHRR) on board NOAA‐15, which is available at a horizontal grid spacing of 0.25° × 0.25° (Karlsson et al, ; ). We have accessed the data from the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF, Schulz et al, ).…”

Section: Methods and Datamentioning

confidence: 99%

Clouds in Convection‐Resolving Climate Simulations Over Europe

et al. 2019

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Although crucial for the Earth's climate, clouds are poorly represented in current climate models, which operate at too coarse grid resolutions and rely on convection parameterizations. Thanks to advances in high‐performance computing, it is becoming feasible to perform high‐resolution climate simulations with explicitly resolved deep convection. The added value of such convection‐resolving simulations for the representation of precipitation has already been demonstrated in a number of studies, but assessments about clouds are still rare. In the present study, we analyze the representation of clouds in decade‐long convection‐resolving climate simulations (2.2‐km horizontal grid spacing) over a computational domain with 1,536 × 1,536 × 60 grid points covering Europe and compare it against coarser‐resolution convection‐parameterizing simulations (12‐km horizontal spacing). The simulations have been performed with a version of the COSMO model that runs entirely on graphics processing units. The European Centre for Medium‐Range Weather Forecasts Re‐Analysis‐Interim reanalysis‐driven present climate simulations (1999–2008) show that biases in mean summertime cloudiness and top‐of‐the‐atmosphere radiation budget are reduced when convection is resolved instead of parameterized. Especially, the typically underestimated midtropospheric cloud layer is enhanced, thanks to stronger vertical exchange. Future climate simulations (2079–2088) conducted using pseudo global warming experiments for a Representative Concentration Pathway 8.5 scenario show a predominating reduction in low‐level and midlevel cloud cover fraction and an increase in cloud top height, implying positive cloud‐amount and cloud‐height feedbacks. These positive feedbacks are only partly compensated by the negative cloud‐thickness feedback. Although the simulations exhibit substantial differences in terms of clouds in the present climate, the simulated cloud feedbacks are similar between the 2.2‐ and 12‐km models.

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Section: Methods and Datamentioning

confidence: 99%

Clouds in Convection‐Resolving Climate Simulations Over Europe

et al. 2019

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“…Following Hakuba et al [], we make extensive use of the high‐resolution (0.03°) SSR data record (1983–2005) derived from the MVIRI (Visible and Infrared Imager) instruments on board the Meteosat First Generation satellites (Meteosats 2 to 7) by the CM SAF (http://www.cmsaf.eu) [ Schmetz et al , ; Schulz et al , ]. Compared to ground‐based measurements (BSRN [ Posselt et al , ], GEBA [ Sanchez‐Lorenzo et al , ]) and the Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled data product [ Kato et al , ], the CM SAF data set slightly overestimates SSR by around 6–8 W m −2 .…”

Section: Methodsmentioning

confidence: 99%

Spatial representativeness of ground-based solar radiation measurements-Extension to the full Meteosat disk

Hakuba

Folini

Sánchez-Lorenzo

et al. 2014

J. Geophys. Res. Atmos.

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The spatial representativeness of a point measurement of surface solar radiation (SSR) of its larger-scale surrounding, e.g., collocated grid cell, is a potential source of uncertainty in the validation of climate models and satellite products. Here we expand our previous study over Europe to the entire Meteosat disk, covering additional climate zones in Africa, the Middle east, and South America between −70• to 70• East and −70• to 70• North. Using a high-resolution (0.03 [2001][2002][2003][2004][2005], we quantify the spatial subgrid variability in grids of 1 • and 3• resolution and the spatial representativeness of 887 surface sites with respect to site-centered surroundings of variable size. In the multiannual mean the subgrid variability is the largest in some mountainous and coastal regions but varies seasonally due to changes in the Intertropical Convergence Zone location. The absolute mean representation errors at the surface sites with respect to surroundings of 1 • and 3• are on average 1-2% (3 W m −2 ) and 2-3% (4 W m −2 ), respectively. The majority of sites are found to be representative within the in situ measurement accuracy. We show that their site-specific representativeness can be reliably approximated by the subgrid variability in a fixed grid (1 • ). The subgrid variability in turn is only moderately reduced when computed from coarser grid data, typically the only data available in areas not covered by the 0.03• resolved Meteosat disk. Together, this paves the way to a fully global assessment of site-specific spatial representativeness.

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“…However, the full understanding of the mechanisms and the contributions of both phenomena to the observed changes are still uncertain due to, for example, the lack of long‐term SSR data, especially over ocean, remote land areas (e.g., Africa and Siberia), and areas characterized by complex orography (e.g., the Alpine region) (Sanchez‐Lorenzo et al, ; Wild, , ). Here data from satellite observations (e.g., those provided by the EUMETSAT Satellite Application Facility on Climate Monitoring‐CM SAF) provide valuable additional information (Schulz et al, ), as they deliver an unique spatial coverage since the 1980s for both land and oceans (Hinkelmann et al, ; Karlsson et al, ; Raschke et al, ; Zhang et al, ).…”

Section: Introductionmentioning

confidence: 99%

Trends and Variability of Surface Solar Radiation in Europe Based On Surface‐ and Satellite‐Based Data Records

et al. 2018

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The incoming solar radiation is the essential climate variable that determines the Earth's energy cycle and climate. As long‐term high‐quality surface measurements of solar radiation are rare, satellite data are used to derive more information on its spatial pattern and its temporal variability. Recently, the EUMETSAT Satellite Application on Climate Monitoring (CM SAF) has published two satellite‐based climate data records: Surface Solar Radiation Data Set‐Heliosat, Edition 2 (SARAH‐2), and Clouds and Radiation Data Set based on AVHRR (advanced very high resolution radiometer) Satellite Measurements, Edition 2 (CLARA‐A2). Both data records provide estimates of surface solar radiation. In this study, these new climate data records are compared to surface measurements in Europe during the period 1983–2015. SARAH‐2 and CLARA‐A2 show a high accuracy compared to ground‐based observations (mean absolute deviations of 6.9 and 7.3 W/m2, respectively) highlighting a good agreement considering the temporal behavior and the spatial distribution. The results show an overall brightening period since the 1980s onward (comprised between 1.9 and 2.4 W/m2/decade), with substantial decadal and spatial variability. The strongest brightening is found in eastern Europe in spring. An exception is found for northern and southern Europe, where the trends shown by the station data are not completely reproduced by satellite data, especially in summer in southern Europe. We conclude that the major part of the observed trends in surface solar radiation in Europe is caused by changes in clouds and that remaining differences between the satellite‐ and the station‐based data might be connected to changes in the direct aerosol effect and in snow cover.

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Operational climate monitoring from space: the EUMETSAT satellite application facility on climate monitoring (CM-SAF)

Cited by 52 publications

References 57 publications

Clouds in Convection‐Resolving Climate Simulations Over Europe

Clouds in Convection‐Resolving Climate Simulations Over Europe

Spatial representativeness of ground-based solar radiation measurements-Extension to the full Meteosat disk

Trends and Variability of Surface Solar Radiation in Europe Based On Surface‐ and Satellite‐Based Data Records

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