Next Article in Journal
Detection and Monitoring of Surface Motions in Active Open Pit Iron Mine in the Amazon Region, Using Persistent Scatterer Interferometry with TerraSAR-X Satellite Data
Previous Article in Journal
Synthesis of Transportation Applications of Mobile LIDAR
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2013, 5(9), 4693-4718;

A Satellite-Based Surface Radiation Climatology Derived by Combining Climate Data Records and Near-Real-Time Data

Institute for Atmospheric and Environmental Sciences, Goethe University Frankfurt, Altenhöferallee 1, D-60437 Frankfurt am Main, Germany
Deutscher Wetterdienst, Frankfurter Str. 135, D-60387 Offenbach, Germany
Current Address: Deutscher Wetterdienst, Frankfurter Str. 135, D-60387 Offenbach, Germany
Author to whom correspondence should be addressed.
Received: 22 July 2013 / Revised: 11 September 2013 / Accepted: 12 September 2013 / Published: 18 September 2013
Full-Text   |   PDF [1138 KB, uploaded 19 June 2014]


This study presents a method for adjusting long-term climate data records (CDRs) for the integrated use with near-real-time data using the example of surface incoming solar irradiance (SIS). Recently, a 23-year long (1983–2005) continuous SIS CDR has been generated based on the visible channel (0.45–1 μm) of the MVIRI radiometers onboard the geostationary Meteosat First Generation Platform. The CDR is available from the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF). Here, it is assessed whether a homogeneous extension of the SIS CDR to the present is possible with operationally generated surface radiation data provided by CM SAF using the SEVIRI and GERB instruments onboard the Meteosat Second Generation satellites. Three extended CM SAF SIS CDR versions consisting of MVIRI-derived SIS (1983–2005) and three different SIS products derived from the SEVIRI and GERB instruments onboard the MSG satellites (2006 onwards) were tested. A procedure to detect shift inhomogeneities in the extended data record (1983–present) was applied that combines the Standard Normal Homogeneity Test (SNHT) and a penalized maximal T-test with visual inspection. Shift detection was done by comparing the SIS time series with the ground stations mean, in accordance with statistical significance. Several stations of the Baseline Surface Radiation Network (BSRN) and about 50 stations of the Global Energy Balance Archive (GEBA) over Europe were used as the ground-based reference. The analysis indicates several breaks in the data record between 1987 and 1994 probably due to artefacts in the raw data and instrument failures. After 2005 the MVIRI radiometer was replaced by the narrow-band SEVIRI and the broadband GERB radiometers and a new retrieval algorithm was applied. This induces significant challenges for the homogenisation across the satellite generations. Homogenisation is performed by applying a mean-shift correction depending on the shift size of any segment between two break points to the last segment (2006–present). Corrections are applied to the most significant breaks that can be related to satellite changes. This study focuses on the European region, but the methods can be generalized to other regions. To account for seasonal dependence of the mean-shifts the correction was performed independently for each calendar month. In comparison to the ground-based reference the homogenised data record shows an improvement over the original data record in terms of anomaly correlation and bias. In general the method can also be applied for the adjustment of satellite datasets addressing other variables to bridge the gap between CDRs and near-real-time data. View Full-Text
Keywords: solar surface irradiance; homogeneity; adjustment solar surface irradiance; homogeneity; adjustment
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Share & Cite This Article

MDPI and ACS Style

Krähenmann, S.; Obregon, A.; Müller, R.; Trentmann, J.; Ahrens, B. A Satellite-Based Surface Radiation Climatology Derived by Combining Climate Data Records and Near-Real-Time Data. Remote Sens. 2013, 5, 4693-4718.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top