1. EVOLUTION OF SCIAMACHY CH 4 SCIENTIFIC PRODUCT QUALITY & INITIAL LOOK AT THE ‘HYMN FTIR DATASET’ B. Dils, M. De Mazière, C. Vigouroux, R. Sussmann, F. Forster, T. Borsdorff, T. Blumenstock, M. Buchwitz, P. Demoulin, P. Duchatelet, C. Frankenberg, A. Gloudemans, J. Hannigan, F. Hase, N. Jones, J. Klyft, I. Kramer, E. Mahieu, J. Mellqvist, J. Notholt, K. Petersen, A. Strandberg, K. Strong, J. Taylor, S. Wood

2. Part I: EVOLUTION OF SCIAMACHY CH 4 SCIENTIFIC PRODUCT QUALITY

3. Introduction Validation of SCIAMACGY NIR products using FTIR measurements started in ~2004, in the EU Evergreen project Since then continuous improvements on the SCIAMACHY algorithms Also the FTIR comparison dataset has evolved Hard to inter-compare results from different validation studies  Re-evaluate successive SCIAMACHY CH 4 data products (from successive algorithm improvements ) with the same ‘standard’ FTIR dataset

4. Succesive steps in CH 4 validation 2005, Dils et al. ACPD,5 & Sussmann et al. ACP,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 (1630-1670 nm) for IMAP, Channel 8 (2324-2338 nm) 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. & Sussmann 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

5. The contributing NDACC-FTIR 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 CH4 retrieval, using UFTIR (http://www.nilu.no/uftir) strategyhttp://www.nilu.no/uftir

6. Validation Issues Time of measurement (limited overlap) 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) and collocation criterion ‘overlap’ gets worse with grid! (two grids allows us to assess the impact) Retrieval parameters, averaging kernels etc. (minor impact)

7. 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

8. 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

9. 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 data points → More scatter Now IMLM focuses on CO retrievals; CH4 retrievals are done using IMAP (cf. C. Frankenberg now at SRON)

10. Evolution of quality (IMAP,WFMD and IMLM): * Large improvements for all algorithms * Better seasonality for IMAPv1.1 than IMAPv4.9??? (fig A) * FTIR-VMR has not been corrected yet for H 2 O ! (see Ralf)

11. Current status (IMAPv4.9 and WFMDv1.0/Carbon Tracker) seasonality is not that well captured Slightly worse for IMAPv4.9?

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

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 in capturing the seasonality than v1.1 This study was done on a rather limited (spatial coverage) dataset.  No extensive information on bias  A reliable harmonized quasi-global FTIR dataset is a must with respect to validation

14. Part II: INITIAL LOOK AT THE ‘HYMN FTIR DATASET’

15. Introduction Homogenised retrieval strategy developed in HYMN, based on Tikhonov regularisation → dataset called ‘Tikhonov’ hereinafter Dataset was extended with a few non-European stations: Thule (76°N), Toronto (44°N), Wollongong (34°S), Lauder (44°S), Arrival Heights (78°S); the dataset includes 2 tropical stations: Paramaribo (6°N), St Denis/Reunion (21°S) The idea is to get a first feeling of the FTIR Tikhonov data quality by comparing both the UFTIR and the Tikhonov dataset with TM4 model data IMAPv4.9 and WFM-DOASv1.0/C SCIAMACHY data The SCIAMACHY comparisons proceeded using the same method as outlined before For the TM4 data, the spatially collocated pixel was taken, from which the total column was calculated above station altitude. This was then converted (using the same ECMWF pressure data as used in the FTIR conversion) to volume mixing ratios.

16. TM4 comparison examples

17. UFTIRTikhonov

18. TM4 comparison examples UFTIRTikhonov

19. 2004LATTM4- UFTIR TM4- TIK IMAP- UFTIR IMAP- TIK WFMD- UFTIR WFMD- TIK NYA78.92-0.081.27-0.321.91 THU76.53 -0.21 -3.08 KIR67.84-0.301.69-0.132.500.453.04 HAR60.22-1.96 -1.33 -0.75 BRE53.11-1.202.41-1.302.67-0.993.04 GAR47.48 2.73 4.38 4.76 ZUG47.42-3.510.41-1.782.11-1.322.44 JFJ46.55-1.75-0.63-0.740.46-0.520.68 TOR43.66 4.86 5.70 5.87 IZA28.3-1.86-0.28-1.380.19-2.03-0.49 PAR5.81 4.32 6.09 6.73 REU-20.9 1.37 3.02 3.37 WOL-34.4 0.69 2.35 2.13 Bias Large biases in Toronto and Paramaribo: Real or not? Altitude correction method imperfect?

20. Bias as a function of Latitude Large difference between TM4 and SCIA data in Southern hemisphere, and at Thule Still large fluctuations in the station to station biases TM4/SCIA or FTIR errors?

21. Apart from the Zugspitze station, the UFTIR bias looks smoother ZUG,JFJ,IZA Tikhonov bias shift could however be due to badly resolved vertical profiles in the TM4 data Bias as a function of Latitude (Tik and UFTIR overlap)

22. Apart from the Zugspitze station, the UFTIR bias looks smoother Bremen? Increased variability meanly due to Bremen data? Bias as a function of Latitude (Tik and UFTIR overlap)

23. Correlation coefficients (overlapping stations only) IMAPv49-UFTIRIMAPv49-Tikhonov R IMAPv49 drops from 0.69 to 0.24 R WFM-DOASv1.0/C drops from 0.65 to 0.06! It is clear that strong biases at several locations can have a very strong impact on the overall correlation coefficient (Again this could very well be due to the SCIAMACHY data quality)

24. Correlation coefficients TM4-UFTIR TM4-Tikhonov R TM4 drops from 0.62 to 0.32 (or 0.40 taking UFTIR stations only) It is clear that outlier data or strong biases at several locations can have a very strong impact on the overall correlation coefficient (Again this could very well be due to the TM4 data quality, or the FTIR quality criteria) Same stations only

25. Conclusions The comparison exercise as performed here leaves still a lot of questions There is no obvious indication that the Tikhonov regularized dataset has improved station-to-station inter-comparability. However both SCIA and TM4 model data as well as the comparison methodology might be responsible for the issues razed in this overview We have taken the Tikhonov data as is, only eliminating obvious outliers. Data selection criteria might improve quality It is clear that we lack a reliable standard to compare each station’s measurements to. Nevertheless, we would expect that proceeding towards an as uniform as possible retrieval method should improve the station-to-station consistency.

26. Still to be done: to be discussed ! Sensitivity tests on altitude correction Correction of gb FTIR data for relative humidity, i.e., determine dry-air column averaged mixing ratio Add missing stations; Verify data quality of gb FTIR datasets Check error budgets for UFTIR and regularised Tikhonov approaches ? Check AVK (smoothing error): farther away from 1 for actual Tikhonov approach ? Take into account gb FTIR AVK in model comparisons Think about another measure of station-to-station consistency ? ?? Re-do all stations with a common but less constrained retrieval strategy, but spectroscopy as decided at Pasadena (‘FH’ spec’y)

27. Gb FTIR total column averaging kernels at Réunion UFTIRTikhonov 0.6 0.3