1. Jae Kim 1, S. M. Kim 1, and Mike Newchurch 2 1. Pusan National University, Korea 2. University of Alabama in Huntsville, USA The analyses and intercomparison of satellite-derived HCHO measurements with statistical approaches AURA Science Workshop, 14- 18 September, Leiden, Netherland

2. Global climate change is currently the biggest issue. Palmer et al., 2003; 2006 Because global temperature has been increased, isoprene from biogenic activity must be increased.  Expect to see an increasing tendency in HCHO trend. Motivation.

3. Data Satellite HCHO dataPeriod GOMEApril 1996 - June 2003 SCIAMACHYJan 2003-Dec 2007 OMIOct 2004-Dec 2008

4. HCHO trend on tropical rain forests Amazon Africa rain forest Western Pacific

5. GOME over Pacific with all time GOME over Pacific till Year 2001 6.9%/year 1.2%/year

6. GOME P2 SCIAMACHY OMI Africa rainforest 0.7%/year 0.5%/year 7.5%/year

7. GOME P2 SCIAMACHY OMI Amazon -2.3%/year 1.7%/year 7.9%/year

8. GOME P2 SCIAMACHY OMI Western Pacific -1.0%/year 1.0%/year 10.1%/year

9. GOME P2 SCIAMACHY OMI Central Pacific 1.2%/year 0.0%/year 7.0%/year

10. /yearPeriod Amazon [75W-50W, 10S-5N] Africa [15W-25E, 5S-10N] Western Pacific [90E-150E, 10S-10N] Central Pacific [180W-160W, 10S-10N] GOME P2 Apr96- Dec01 -2.3%0.7%-1.0%1.2% SCIAMACHY Jan03- Dec07 1.7%0.5%1.0 0.0% OMI Oct04- Dec08 7.9%7.5%10.1%7.0% HCHO Trend analyses

11. Satellite data have an intrinsic problem, ill-posed problem, that comes from the fact that a number of various physical parameters can have a similar effect on measured radiance. Most of the previous evaluations of satellite performance have relied on point-by-point comparisons with limited spatial and temporal coverage of in-situ measurements The levels of agreement from these comparisons vary according to location and season, so there is not a clear superior method for various satellite tropospheric gas products. Difficulty in satellite measurement validation comes from large uncertainties, especially HCHO vertical columns, whose error typically range from 40-105% [Palmer, et al., 2006; Kurosu, et al., 2008].  Inter-comparison between satellites HCHO measurements are challenging Validation of satellite HCHO.

12. Our approach is to validate the satellite measurements by analyzing spatial and temporal coherence between individual satellite products and a known source data set  MOPITT CO A promising statistical tools for identifying these coupled relationships with spatial-temporal patterns are  individual parameters is Empirical Orthogonal Function (EOF)  combinations of two parameters, Singular Value Decomposition (SVD)  Power Spectrum analyses for cycle of the data sets Statistical tools for validation

13. Tropical areas with biomass burning and biogenic activity in rain forests South America Africa Western Pacific

14. EOF and SVD analyses of GOME, SCIAMACHY, and OMI HCHO measurements in conjunction with MOPITT CO. Data periods Data SensorPeriod GOMEApril 1996 - June 2003 SCIAMACHYJan 2003-Dec 2007 OMIOct 2004-Dec 2008 MOPITT COMarch 2000-Dec 2008

15. EOF Mode1 GOME P1 HCHO SCIAMACHY HCHO OMI HCHO MOPITT CO sudden increasing tendency January September Red: + Blue: -

16. Africa GOME HCHO SCIAMACHY HCHO OMI HCHO MOPITT CO Power Spectrum analysis

17. GOME P1 HCHO SCIAMACHY HCHO OMI HCHO MOPITT CO May September

18. GOME HCHO SCIAMACHY HCHO OMI HCHO MOPITT CO Amazon

19. GOME P1 HCHO SCIAMACHY HCHO OMI HCHO MOPITT CO March October

20. GOME HCHO SCIAMACHY HCHO OMI HCHO MOPITT CO Central Pacific western Pacific

21. HCHO-CO SVD analysis dataPeriod SCIAMACHY HCHOJan 2003-Dec 2007 OMI HCHOOct 2004-Dec 2008 MOPITT COMarch 2000-Dec 2008

22. SVD 1 st mode of MOPITT CO and SCIAMACHY HCHO AugustFebruary

23. SVD 1 st mode of MOPITT CO and OMI HCHO August January

24. SVD 1 st mode of MOPITT CO and SCIAMACHY HCHO September February

25. SVD 1 st mode of MOPITT CO and OMI HCHO September January

26. SVD 1 st mode of MOPITT CO and SCIAMACHY HCHO

27. SVD 1 st mode of MOPITT CO and OMI HCHO

28. 1. EOF analyses shows spatial and temporal distribution of GOME, SCIAMACHY HCHO, MOPITT CO match each other. However, OMI HCHO shows different spatial and temporal pattern compared with others. 2. SVD analyses shows GOME HCHO-MOPITT CO, SCIAMACHY HCHO – MOPITTCO shows consistent spatial and temporal coherence 3. However, OMI HCHO – MOPITT CO shows relatively low correlation.  Relationship between GOME (SCIAMACHY) HCHO and CO shows that biomass burning is most likely the major source of HCHO over Africa and South America.  However, relationship between OMI HCHO and CO suggests biomass burning is not as significant source as of HCHO. 4. GOME and SCIAMACHY HCHO trend is marginal, but OMI HCHO trend is as high as 10%  climate change can not explain the big increase. It could be due to OMI instrument or calibration error. 5. EOF and SVD analyses can be another useful method for satellite data validation. Conclusions