Evaluation and Applications of a New Satellite-based Surface Solar Radiation Data Set for Climate Analysis

1. Evaluation and applications of a new satellite-based surface solar radiation data set for climate analysis Jörg Trentmann1, Richard Müller1, Christine Träger-Chatterjee1, Rebekka Posselt2, Reto Stöckli2 Satellite Application Facility on Climate Monitoring (CM SAF)1Deutscher Wetterdienst, 2MeteoSwiss

2. Motivation • Surface Solar Irradiance highly relevant: • Climate Monitoring and Climate Analysis • Solar Energy • Available data sets (ISCCP, GEWEX, ERA-Interim) agree well on the mean • Differ substantially in the temporalevolution • Coarse spatial resolution

3. The CM SAF approach • Retrieve surface solar irradiance (SIS) from the geostationary Meteosat satellites (1983 – today) • Apply a well-established method: • Heliosat (Cano et al.,1986, Hammer et al., 2003) • Provide the data at high temporal and spatial resolution • Free and easy accessible • Validate the data with BSRN surface station data • Evaluate the data with alternative data sets

4. The Heliosat method • Fundamental Assumptions • The clear-sky surface radiation can be accurately calculated (information on the water vapor and aerosol is required) • For each satellite pixel and time slot the minimum reflectance of each months represents clear-sky conditions (i.e., effect of Rayleigh scattering + surface albedo on the reflectance) • The cloud optical depth is related to the cloud-reflected solar radiation (= brightness of the visible satellite channel) • The degradation of sensor sensitivity can be monitored with bright cloud targets (maximum reflectance)

5. The Heliosat method • Surface irradiance (global radiation) is retrieved for each satellite pixel / timeslot (30 min) between 1983 and 2005. • Average and interpolate to hourly / daily / monthly means on a 0.03o-lon-lat-grid. • Data is freely available in CF-netcdf-format: www.cmsaf.eu • Additionally, the direct surface irradiance and the cloud index are provided. • Scripts to visualize and analyse the data using open-source software (cdo, R) are provided.

6. Validation • Monthly means surface irradiance at 14 BSRN stations

7. Validation

8. Validation Temporal Stability • Hovmöller Diagram • Temporal evolution of the bias to BSRN surface stations • Apply standard techniques to detect change points (ongoing project, University Frankfurt)

9. Evaluation • CM SAF data about 2-3 Wm-2 higher than alternative data sets • Temporal evolution of CM SAF consistent with alternative data sets, exception 1991 (Pinatubo??)

10. Germany Amazon Mediterranean West Africa Tanzania Applications Close-up Views

11. Topography GEWEX Applications Surface Irradiance, Mean July, CM SAF • No validation with DWD surface network available. • Excellent correspondence with topographic features. • GEWEX SRB data misses details due to coarse resolution.

12. Germany Amazon Mediterranean West Africa Tanzania Applications Close-up Views

13. Applications Mean Surface Radiation, Tanzania • No surface network available • Correspondence of surface irradiance with topographic features. Cooperation with Jörg Bendix, University Marburg

14. Applications Surface Radiation at Mt. Kilimanjaro • Substantially reduced surface radiation around Kilimanjaro due to enhanced cloud cover

15. Applications Time Series of Surface Radiation 37.5° E, 3.1° S 37.6° E, 3.1° S

16. Applications Linear Trend in Surface Radiation(W/m2/dec) • There are significant trends in the surface radiation at Mt. Kilimanjaro between 1983 and 2005 • Strong increase of surface radiation (more than 15 W/m2/dec) over the summit (> 3000 m) • Decrease along the northern and southern slopes

17. Summary • Heliosat Method is applied to Meteosat Satellites to derive solar surface irradiance from 1983 to 2005 • Validation with BSRN surface measurements shows high quality of the CM SAF satellite-derived data set. • Temporal (hourly) and spatial (0.03o) resolution of the data set is unique. • Spatial resolution allows detailed local studies:Strong contrast in temporal trends of surface irradiance at the summit (increase) and the foothills (decrease) of Mt. Kilimanjaro, Tanzania. • The Data Set is freely available in CF-netcdf-format at www.cmsaf.eu, software tools are also provided • Processing of the global AVHRR satellite data (1982 – 2009) ongoing (see Poster XY 220, F. Kaspar et al.)

19. Extra Slides

20. The Heliosat method Reflectivity, 12 UTC, 2 Sept 2008 Min. Reflectivity, Rmin, 12 UTC, Sept 2008

21. The Heliosat method Max. reflectance, Rmax:95 % percentile of counts during one month in the reference region Reflectivity, 12 UTC, 2 Sept 2008 Temporal evolution of Rmax

22. The Heliosat method The definition of the Cloud Index n: Cloud Index, 11 UTC, 1 July 2005

23. The Heliosat method • The cloud index, n, is related to the clear-sky index, k. • The clear-sky index, k, is the ratio between the all-sky surface irradiance, G, and the clear-sky surface irradiance, Gclear clear sky index cloud index

24. The cloud index, n, is related to the clear-sky index, k: • The clear-sky index, k, is the ratio between the all-sky surface irradiance, G, and the clear-sky surface irradiance, Gclear: k = 1  n G = k * Gclear • Gclear can be calculated by radiation transfer calculations using the fast and accurate clear-sky model gnu-MAGIC (Mesoscale Atmospheric Global Irradiance Code, Mueller et al., 2009, http://sourceforge.net/projects/gnu-magic/) The Heliosat method

25. Validation • Monthly means SIS from 14 BSRN stations • Measures: Bias, Variance, Correlation Coefficient of Anomalies, Fraction of months with bias above 15 Wm-2