1. Application of Cloudnet data in the validation of SCIAMACHY cloud height products Ping Wang Piet Stammes KNMI, De Bilt, The Netherlands CESAR Science day, 19 June, 2013

2. Overview SCIAMACHY cloud products Cloud retrieval algorithms from O 2 A band Validation data sets Results Summary

3. SCIAMACHY cloud products ESA L2 (v5.02) Cloud top height and Cloud optical thickness are retrieved from SACURA. Cloud optical thickness is only retrieved for thick clouds,  > 5. Cloud fraction is retrieved from PMDs (OCRA). FRESCO (v6) Effective cloud fraction and cloud height are retrieved from FRESCO. The cloud height is close to the middle of the cloud. FRESCO: Fast Retrieval Scheme for Clouds from the Oxygen A band

4. FRESCO cloud algorithms Lambertian cloud model

5. Cloud data sets of SCIAMACHY Data: ESA level-2, version 5.02, FRESCO, version 6 Intercomparison data set - one orbit per month from 2002 to 2012, including 4 full day of data (~160 orbits). Validation data set - SCIAMACHY overpass pixels at Cabauw (2003- 2005) and Lindenberg (2005-2012). (~700 measurements)

6. Distributions of the differences (L2 –FRESCO) Mean difference -0.0225 Standard deviation 0.100 Mean difference 0.609 km Standard deviation 2.16 km Cloud fraction Cloud height

7. Cloudnet data Instruments: Radar : large particles such as rain and drizzle drops, ice particles, and insects Lidar : higher concentrations of smaller particles, such as cloud droplets, aerosol, supercooled liquid layers Microwave radiometer: liquid water path Rain gauge: precipitation Cloudnet cloud top height, cloud base height and target categorization data are selected for one hour, centered at SCIAMACHY overpass time. Illingworth et al., BAMS, 2007. http://www.cloud-net.org

8. Cloudnet categorization product Ice cloud Water cloud height grid time resolution Cabauw: 0.253-11.5km, 90m (126 levels), 15s (240 /hr) Lindenberg: 0.355-15.2km, 30m (495 levels), 30s (120 /hr) Cloud top Cloud base

9. Cloudnet categorization product Ice cloud Water cloud height grid time resolution Cabauw: 0.253-11.5km, 90m (126 levels), 15s (240 /hr) Lindenberg: 0.355-15.2km, 30m (495 levels), 30s (120 /hr) Cloud top Cloud base

10. Cloud top of ESA L2 vs. Cloudnet: single layer clouds All clouds water clouds ice clouds Color scale: ESA L2 cloud fraction

11. All clouds ice clouds water clouds Cloud height of FRESCO vs. Cloudnet cloud top: single layer clouds Color scale: FRESCO effective cloud fraction

12. SCIA cloud heights and Cloudnet cloud boundaries: single layer clouds, ceff>0.1

13. SCIAMACHY and Cloudnet cloud heights: single layer clouds, ceff>0.1

14. SCIAMACHY cloud height vs. Cloudnet cloud top: multi-layer clouds ESA L2 cloud top height FRESCO cloud height

15. SCIAMACHY and Cloudnet cloud heights: Multi-layer clouds

16. Summary (1) SCIAMACHY ESA L2 v5.02 cloud fractions are similar to FRESCO v6 effective cloud fractions. For the selected data from 200210 to 201204, the mean difference is -0.0225 and the standard deviation is 0.10. SCIAMACHY ESA L2 v5.02 cloud top height is higher than FRESCO cloud height. The mean difference of cloud heights is 0.609 km with a standard deviation of 2.16 km for pixels without snow/ice.

17. Summary (2) We compared ESA L2 and FRESCO cloud heights with Cloudnet products for 220 single layer cloud cases in 2003- 2011. For single layer clouds, ESA L2 cloud top height is close to lidar/radar cloud top height for clouds at 3-7 km. FRESCO cloud height is close to lidar/radar cloud middle height for clouds below 5 km.

18. Acknowledgment We would like to thank Henk Klein-Baltink (KMNI) for helpful discussions. We acknowledge the Cloudnet project (European Union contract EVK2-2000-00611) for providing the target classification and cloud boundaries, which was produced by the University of Reading using measurements from Cabauw and Lindenberg.