1 / 28

Jae Kim 1 , S. M. Kim 1 , and Mike Newchurch 2 Pusan National University, Korea

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

temima
Download Presentation

Jae Kim 1 , S. M. Kim 1 , and Mike Newchurch 2 Pusan National University, Korea

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. The analyses and intercomparison of satellite-derived HCHO measurements with statistical approaches Jae Kim1, S. M. Kim1, and Mike Newchurch2 Pusan National University, Korea University of Alabama in Huntsville, USA AURA Science Workshop, 14- 18 September, Leiden, Netherland

  2. Motivation • 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. .

  3. Data

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

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

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

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

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

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

  10. HCHO Trend analyses

  11. Validation of satellite HCHO • 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 .

  12. Statistical tools for validation • 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

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

  14. Data • EOF and SVD analyses of GOME, SCIAMACHY, and OMI HCHO measurements in conjunction with MOPITT CO. Data periods

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

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

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

  18. Amazon GOME HCHO SCIAMACHY HCHO OMI HCHO MOPITT CO

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

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

  21. HCHO-CO SVD analysis

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

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

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

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

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

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

  28. Conclusions • 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. • SVD analyses shows GOME HCHO-MOPITT CO, SCIAMACHY HCHO – MOPITTCO shows consistent spatial and temporal coherence • 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. • 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. • EOF and SVD analyses can be another useful method for satellite data validation.

More Related