1. VALIDATION OF SCIAMACHY CH 4 SCIENTIFIC PRODUCTS USING GROUND-BASED FTIR MEASUREMENTS B. Dils, M. De Mazière, C. Vigouroux, C. Frankenberg, M. Buchwitz, A. Gloudemans, T. Blumenstock, F. Hase, I. Kramer, E. Mahieu, P. Demoulin, P. Duchatelet, J. Mellqvist, A. Strandberg, K. Petersen, J. Notholt, R. Sussmann and T. Borsdorff

2. Introduction Validation using FTIR measurements commenced in ~2004 Since then improvements on the SCIAMACHY algorithms Also the FTIR comparison dataset has evolved  Hard to inter-compare results from different validation studies  Re-evaluate ‘all’ SCIAMACHY CH 4 algorithms with a ‘standard’ FTIR dataset

3. Timeline for CH 4 validation 2005, Dils et al. ACPD,5: WFMDv0.41 XCH 4, IMAPv0.9 XCH 4, IMLMv5.5 CH 4 Covering 2003 only CO 2 normalized XCH 4 for IMAP and WFMD, total columns for IMLM Channel 6 for IMAP, Channel 8 for WFMD and IMLM Channel 8 affected by Ice layer build-up and decontamination phases Very limited datasets (large gaps in annual coverage) 2006, Dils et al. ACP,6: WFMDv0.5 XCH 4, IMAPv1.0 XCH 4, IMLMv6.3 CH 4 Covering 2003 only WFMD, now also using Channel 6 Solar zenith angle (sza) dependence of WFMD data Inverse seasonality of southern hemisphere IMAP XCH 4 2007, Dils et al. ACVE-3 proceeding, ESA SP-642: WFMDv1.0 XCH 4 2003+2004 Overall improvement of data quality sza issue resolved 2008, Current HYMN validation effort: IMAPv4.9 XCH 4, WFMDv1.0/C XCH 4 2003+2004+2005 Updated CH 4 spectroscopy for IMAPv4.9 WFMD XCH 4 CO 2 normalised data using carbon tracker data in stead of a constant value ( algorithm itself remains the same (v1.0))

4. The contributing FTIR-NDACC network Spatial coordinates of the ground-based FTIR stations. StationLat NLon EAlt (m) NY.ALESUND78.9111.8820 KIRUNA67.8420.41419 HARESTUA60.2210.75580 BREMEN53.118.8527 ZUGSPITZE47.4210.982964 JUNGFRAUJOCH46.557.983580 IZAÑA28.30-16.482367 UFTIR, http://www.nilu.no/uftirhttp://www.nilu.no/uftir Currently a harmonized global FTIR dataset is being developed within the HYMN project! (http://www.knmi.nl/samenw/hymn/)

5. Validation Issues Time of measurement (limited overlap  small dataset) Compared the SCIA data with a 3rd order polynomial fit through the FTIR data or Compared Monthly averages. Used Spatial collocation grid around location of gb station Large grid = Lat ± 2.5° Lon ± 10° Small grid = Lat ± 2.5° Lon ± 5° Altitude of FTIR station vs ‘altitude’ of SCIA data Conversion of total column data to effective mean volume mixing ratios (with ECMWF model data) Assumes constant VMR with altitude!  extra vmr correction using TM4 model data FTIR airmass vs. SCIA airmass (averaged over pixel) gets worse with grid! (two grids allows us to assess the impact) Retrieval parameters, averaging kernels etc. (minor impact)

6. Validation Parameters Bias: Weighted bias of the SCIAMACHY measurements with respect to the FTIR polynomial fit weighted mean [(SCIA-FTIR)/FTIR] the corresponding weighted standard error = 3*std/sqrt(N) Weight = 1/ (error of SCIA data point) 2 Scatter: Weighted standard deviation around the polynomial FTIR fit, shifted with the bias, acting as the mean. R: Correlation coefficient between SCIAMACHY and FTIR weighted monthly means FTIR stations in Europe only, thus limited variability

7. Evolution of CH 4 quality (year 2003 data) CH 4 2003 WFMDv041WFMDv0.5WFMDv1.0WFMDv1.0/C LG Bias-6.95 ± 0.28-3.45 ± 0.05-2.70 ± 0.04-1.17 ± 0.04 LG  scat 8.381.751.401.29 LG R0.370.550.65 LG N9131339582133117084 IMAPv0.9IMAPv1.1IMAPv4.9 LG Bias12.6 ± 0.09-0.87 ± 0.03-1.015 ± 0.026 LG  scat 1.321.121.04 LG R0.330.580.69 LG N45852069536238 IMLMv5.5IMLMv6.3 LG Bias-1.88 ± 0.12-3.00 ± 0.13 LG  scat 2.593.16 LG R0.230.60 LG N62486433

8. Evolution of CH 4 quality Evolution of R and  scat for all validated versions of SCIAMACHY CH 4 algorithms *IMLM is markedly different from IMAP and WFMD, since it does not include a dry air normalisation step (using CO 2 data) and uses a different spectral window: → Needs strict cloud filtering → Less datapoints → More scatter Now IMLM focuses on CO retrievals, IMAP (Frankenberg) now developed at SRON

9. Time series  seasonal variation These plots show the weighted monthly mean and error of the SCIAMACHY data aafo time together with daily mean FTIR data. Months that have less than 10 data points are not shown.

10. Evolution of quality (IMAP,WFMD and IMLM): *Large improvements for all algorithms *Better seasonality for IMAPv1.1 than IMAPv4.9??? (fig A)

11. Current status (IMAPv4.9 and WFMDv1.0/Carbon Tracker) More scatter in WFMD data low values for February IMAP?

12. Current status seasonality is not that well captured Slightly worse for IMAPv4.9?

13. Conclusions Overall, one can state that all SCIAMACHY algorithms have evolved significantly over time. Both the correlation as well as the scatter have improved with each new development. Correlation coefficients of ~0.7 and scatter values of ~1% have been obtained. However several issues still remain. The latest IMAP product (v4.9) seems to do a worse job at capturing the seasonality than v1.1 An in depth validation study, using the latest HYMN harmonised date (http://www.knmi.nl/samenw/hymn) (using a ‘quasi-global’ FTIR dataset), is currently undertaken. This expanded dataset will allow a closer look at station-to-station biases in the SCIAMACHY data.http://www.knmi.nl/samenw/hymn