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
References
[1]
SCOPE-CM. Version 1.3. SCOPE-CM Report; Geneva, Switzerland, 2009.
[2]
Lattanzio, A.; Schulz, J.; Matthews, J.; Okuyama, A.; Theodore, B.; Bates, J.J.; Knapp, K.R.; Kosaka, Y.; Schüller, L. Land Surface Albedo from Geostationary Satelites: A Multiagency Collaboration within SCOPE-CM. Bull. Amer. Meteorol. Soc 2013, 94, 205–214.
[3]
Ohring, G.; Wielicki, B.A.; Spencer, R.; Emery, B.; Datla, R. Satellite instrument calibration for measuring global climate change: Report of a workshop. Bull. Am. Meteorol. Soc 2005, 86, 1303–1313.
Trenberth, K. E.; Anthes, R. A.; Belward, A.; Brown, O.; Haberman, E.; Karl, T. R.; Running, S.; Ryan, B.; Tanner, M.; Wielicki, B. Challenges of a Sustained Climate Observing System. In Climate Science for Serving Society: Research, Modelling and Prediction Priorities; Hurrell, J.W., Asrar, G., Eds.; Springer: Berlin, Germany, 2013.
[6]
AghaKouchak, A.; Nakhjiri, N. A near real-time satellite-based global drought climate data record. Environ. Res. Lett 2012, 7, 044037.
[7]
Tian, Y.; Peters-Lidard, C.D.; Eylander, J.B. Real-time bias reduction for satellite-based precipitation estimates. J. Hydrometeorol 2010, 11, 1275–1285.
[8]
Liu, Y.Y.; Dorigo, W.A.; Parinussa, R.M.; de Jeu, R.A.M.; Wagner, W.; McCabe, M.F.; Evans, J.P.; van Dijk, A.I.J.M. Trend-preserving blending of passive and active microwave soil moisture retrievals. Remote Sens. Environ 2012, 123, 280–297.
[9]
Comiso, J.C.; Nishio, F. Trends in the Sea Ice Cover Using Enhanced and Compatible AMSR-E, SSM/I, and SMMR Data. J. Geophys. Res. 2008, 113, doi:10.1029/2007JC004257.
[10]
Huld, T.; Müller, R.; Gambardella, A. A new solar radiation database for estimating PV performance in Europe and Africa. Solar Energy 2012, 86, 1803–1815.
[11]
Kothe, S.; Dobler, A.; Beck, A.; Ahrens, B. The radiation budget in a regional climate model. Clim. Dyn 2011, 36, 1023–1036.
[12]
Dobler, A.; Müller, R.; Ahrens, B. Development and evaluation of a simple method to estimate evaportation from satellite data. Meteorol. Z 2011, 20, 615–623.
[13]
WMO Regional Climate Centre on Climate Monitoring for Europe and the Middle East. Available online: www.dwd.de/rcc-cm(accessed on 17 September 2013).
[14]
Posselt, R.; Mueller, R.; Trentmann, J. Spatial and temporal homogeneity of solar surface irradiance across satellite generations. Remote Sens 2011, 3, 1029–1046.
[15]
23-year long CDR of surface solar radiation parameters including solar surface irradiance (SIS) and direct irradiance (SID) for the period 1983–2005. Available online: www.cmsaf.eu (accessed on 17 September 2013).
[16]
Rossow, W.; Due?as, E. The international satellite cloud climatology project (ISCCP) web site: An online resource for research. Bull. Am. Meteorol. Soc 2004, 85, 167–172.
Berrisford, P.; Dee, D.; Fielding, K.; Fuentes, M.; Kallberg, P.; Kobayashi, S.; Uppala, S. The ERA-Interim Archive. In ERA Report Series; ECMWF: Shinfield Park: Reading, UK, 2009; pp. 16–17.
[19]
Brinckmann, S.; Trentmann, J.; Ahrens, B. Homogeneity analysis of the CM SAF solar surface irradiance data set derived from geostationary satellite observations. Remote Sens. 2013. submitted.
[20]
Sanchez-Lorenzo, A.; Wild, M.; Trentmann, J. Validation of the means and temporal stability in the CM SAF high-resolution surface solar radiation product over Europe against a homogenized surface dataset (1983–2005). Remote Sens. Environ 2013, 134, 355–366.
[21]
Alexandersson, H.; Moberg, A. Homogenization of Swedish temperature data. Part I: Homogeneity test for linear trends. Int. J. Climatol 1997, 17, 25–34.
[22]
Wang, X. Accounting for autocorrelation in detecting mean shifts in climate data series using the penalised maximal t or F test. J. Appl. Meteor. Climatol 2008, 47, 2423–2444.
[23]
Wang, X. Penalized maximal F test for detecting undocumented mean shift without trend change. J. Atmos. Oceanic Technol 2008, 25, 368–384.
[24]
Wild, M. Global dimming and brightening: A review. J. Geophys. Res. 2009, 114, doi:10.1029/2008JD011470.
[25]
Cano, D.; Monget, J.M.; Albuisson, M.; Guillard, H.; Regas, N.; Wald, L. A method for the determination of the global solar-radiation from meteorological satellite data. Solar Energy 1986, 37, 31–39.
[26]
Beyer, H.G.; Costanzo, C.; Heinemann, D. Modifications of the heliosat procedure for irradiance estimates from satellite images. Solar Energy 1996, 56, 207–212.
[27]
Mueller, R.; Matsoukas, C.; Gratzki, A.; Behr, H.; Hollmann, R. The CM-SAF operational scheme for the satellite based retrieval of solar surface irradiance—A LUT based eigenvector hybrid approach. Remote Sens. Environ 2009, 113, 1012–1024.
[28]
Mueller, R.; Trentmann, J.; Tr?ger-Chatterjee, C.; Posselt, R.; St?ckli, R. The role of the effective cloud albedo for climate monitoring and analysis. Remote Sens 2011, 3, 2305–2320.
[29]
Posselt, R.; Mueller, R.; St?ckli, R; Trentmann, J. Remote sensing of solar radiation for climate monitoring—The CM-SAF retrieval in international comparison. Remote Sens. of Environ 2012, 188, 186–198.
[30]
Posselt, R.; Mueller, R.; Trentmann, J.; St?ckli, R.; Liniger, M. A surface radiation climatology across two Meteosat satellite generations. J. Geophys. Res 2013, 15. EGU2013-7129.
[31]
Cros, S.; Albuisson, M.; Wald, L. Simulating Meteosat-7 broadband radiances using two visible channels of Meteosat-8. Solar Energy 2006, 80, 361–367.
[32]
Koepke, P.; Hess, M.; Schult, I.; Shettle, E.P. Global Aerosol Dataset. Report N 243;; Max-Plank-Institut für Meteorologie: Hamburg, German, 1997; p. 44.
[33]
Hess, M.; Koepke, P.; Schult, I. Optical Properties of Aerosols and Clouds: The software package OPAC. Bull. Amer. Meteorol. Soc 1998, 79, 831–844.
[34]
Kinne, S.; O’Donnel, D.; Stier, P.; Kloster, S.; Zhang, K.; Schmidt, H.; Rast, S.; Giorgetta, M.; Eck, T.; Stevens, B. A new global aerosol climatology for climate studies. J. Adv. Model. Earth Syst. 2013, doi:10.1002/jame.20035.
[35]
Ohmura, A.; Gilgen, H.; Hegener, H.; Müller, G.; Wild, M.; Dutton, E.G.; Forgan, B.; Fr?hlich, C.; Philipona, R.; Heimo, A.; et al. Baseline Surface Radiation Network (BSRN/WCRP): New precision radiometry for climate research. Bull. Am. Meteorol. Soc 1998, 79, 2115–2136.
[36]
Gilgen, H.; Wild, M.; Ohmura, A. Means and trends of shortwave irradiance at the surface estimated from GEBA. J. Clim 1998, 11, 2042–2061.
[37]
Aguilar, E.; Auer, I.; Brunet, M.; Peterson, T.C.; Wierina, J. Guidelines on Climate Metadata and Homogenization. WMO-TD No. 1186;; World Meteorological Organisation: Geneva, Switzerland, 2003; p. 52.
[38]
Rigollier, C.; Lefevre, M.; Blanc, P.; Wald, L. The operational calibration of images taken in the visible channel of the meteosat series of satellites. J. Atmos. Oceanic Technol 2002, 19, 1285–1293.
[39]
Posselt, R.. MeteoSwiss, Personal Communication,2012.