1. An Integrated Approach to the Remote Sensing of Methane with SCIAMACHY, IASI and AATSR Georgina Miles, Richard Siddans, Alison Waterfall, Brian Kerridge Rutherford Appleton Laboratory, UK

2. Contents Introduction AATSR cloud retrievals AATSR cloud information derived for SCIAMACHY IMAP-DOAS CH 4 retrievals (Frankenberg et al) Simulations of sensitivity of IMAP-DOAS approach to cloud within the scene New retrievals of CH 4 from IASI Prospects for use of limb information Prospects for joint AATSR/IASI/SCIA retrievals

3. Introduction CH 4 important greenhouse gas which is challenging to observe from space –Variations are small (few %) Detection of variations in the lower troposphere particularly important for determination of source / sinks –Important to push information content to obtain profile in troposphere –Vital to characterise sensitivity of observations near surface Here we aim to improve upon the demonstrated capability of SCIAMACHY to observe column averaged mixing ratio via a synergistic approach Seek to use –Imager (AATSR) data to characterise cloud in the SCIA scene identify scenes containing significant cloud characterise vertical sensitivity of scenes when cloud present –IASI data to provide additional profile information within the troposphere –MIPAS limb to reduce uncertainty caused by stratospheric variability

4. TIR/NIR synergy Assumes 0 (solid) or 10K (dashed) air/surface temperature contrast SCIA 1.6 micron averaging kernels for total column retrieval For surface albedo 0.16 (typical vegetated land)  IASI has profile information but SCIA has better sensitivity near surface over land IASI averaging kernels using 1240-1290 cm -1 (as in RAL retrieval)

5. The ATSR scan geometry (courtesy ESA). Dual-viewing visible & IR radiometers –1km spatial resolution over 512km Swath –Channels: 0.55,0.67,0.87,1.6,3.7,10.8,12 µm –Spatial resolution 1km AATSR

6. Cloud properties retrieved using the Oxford-RAL scheme (ORAC) Uses optimal estimation to fit spectrally consistent model of cloud to radiances in all channels Products are cloud optical thickness, effective radius, phase, height, ice+liquid water path – gives ~2000 observations per SCIAMACHY scene AATSR-Cloud retrievals vis/near-ir composite Cloud top pressure (hPa) Cloud optical thickness (550nm) ATSR-2 + AATSR data processed from 1995-2009 Data available from BADC Contact C.Poulsen@stfc.ac.uk

7. SCIAMACHY CH 4 State-of-the-art CH 4 retrievals performed from SCIAMACHY by SRON (Frankenberg) and Bremen (Schneising/Buchwitz), Based on deriving CO 2 and CH 4 column-averaged vmr from 1.6μm -> xCO2, xCH4 Direct results for xCO 2 and xCH 4 rather poor (mainly due to cloud) However xCH 4 much improved when scaled by ratio of model:observed CO 2 Currently working with Frankenberg CH 4 data to investigate use of AATSR cloud to improve cloud screening and better characterise scene

8. Directly Retrieved SCIA xCH 4 ECMWF GEMS reanalysis xCH 4 SCIAMACHY CH 4 July ‘07 xCH4 / ppmv

9. CO 2 corrected SCIA xCH 4 ECMWF GEMS xCH 4 SCIAMACHY CH 4 July ‘07 xCH4 / ppmv

10. AATSR Cloud Fraction Cloud fraction only for scenes which pass recommended quality control –includes cloud screening based on apparent CO2 column Number of scenes reduced as only half of SCIA swath covered by AATSR Cases of high cloud fraction have little or no sensivity to boundary layer

11. AATSR Cloud Mid/high level cloud fraction Shows fraction of scene occupied by cloud higher than 650 hPa. Sensitivity to mid troposphere compromised in particularly important regions, NB Monsoon.  AATSR valuable for identifying scenes affected by cloud  Partially cloudy scenes may be better exploited by using knowledge of cloud from AATSR to characterise the vertical sensitivity...

12. Retrieval simulations to test SCIA sensitivity to cloud Test scene containing ice cloud at 10km liquid cloud at 3km Vary optical thickness (COT) of both layers Liquid cloud Ice cloud Typical land reflectance 3km 10km Reflance at 1.6 microns

13. Retrieval Simulations Generate simulated measurements using full line-by-line (RFM) + Multiple scattering codes (DISORT), including detailed treatment of clouds Fit column CO2 and CH4 following IMAP- DOAS approach (i.e. assume no clouds present but use CO2 to correct CH4)  0.5 COT too high for uncorrected CH 4  CO2 correction works well up to Ice COT = 1

14. Cloud Fraction  Optically thick ice cloud has significant (3%) impact on CH4 column at fraction 0.2  Larger fractions of cloud with optical depth 5 or less can be tolerated

15. Vertical Sensitivity  Cloud amount strongly modifies column sensitivity to CH4 profile changes High cloud changes response to stratospheric perturbations Thick cloud below thin ice increases sensitivity between the cloud layers  Some of this sensitivity can be characterised using AATSR cloud Multilayer effects can be flagged but not well retrieved using only AATSR so will want to identify single layer SCIA scenes in first instance - - - Varying Ice COT

16. IASI CH4 Retrievals at RAL OE retrieval of CH 4 from 1240-1290cm-1 range (following Razavi et al, NB avoiding line-mixing issues) Joint retrieval of CH 4, N 2 O, H 2 O and HDO –N 2 O useful as provides control variable analogous to role of CO 2 in NIR –Spectra have comparable info content for N 2 O and CH 4 but N 2 O results should be considerably less variable (<1%) Uses RTTOV as fast FM –Feasible to perform large scale processing (though currently optimising based on analysing few days) –New RTTOV coefficients generated specifically for this purpose based on Oxford RFM and latest Hitran spectroscopy and treating HDO as a separate species. –Scheme also being applied for O 3 and SO 2 retrievals –Use of 3.7 band for CH 4 to be investigated shortly

17. Column Averaged CH 4 IASI – 17 th July 2007 GEMS

18. NB 0.1 ppmv subtracted from RAL line xN2O retrieved simultaneously from same band using same prior constraint as CH 4 gives value of 0.309ppmv +/- 0.003 (1%) Zonal mean column averaged CH 4

19. 19 Need to characterise Stratosphere Tropospheric column averaged vmr from ground-based FTS closer to surface values and less variable than total column average vmr.  Synergy with limb observations beneficial to capture stratospheric variations Within NCEO will be using Oxford-MIPAS retrievals as prior for RAL IASI (and IASI+SCIA) Stratospheric CH 4 FTS CH4 retrievals from Lauder, New Zealand

20. Conclusions & Outlook Use of SCIA CH 4 retrievals should benefit from co-located AATSR observations. A dedicated AATSR product for SCIA scene will be developed to enable –cloud free scenes to be better identified –vertical sensitivity in partially cloudy scenes, dominated by single-layer cloud, to be better characterised AATSR scheme to be developed further in ECV project –Will enable better detection / retrieval of thin cloud over land + treatment of multi-layer scenes RAL scheme for IASI starting to deliver valuable column data –Will seek to combine with SCIA (L2) measurement using the averaging kernel derived for SCIA using AATSR cloud info. –Inclusion of Oxford-MIPAS retrievals of prior also planned in next months Depends on results of recent ESA MIPAS Cloud Study (Spang et al) to ensure MIPAS retrievals cloud-free. The resulting improved SCIA + IASI datasets are planned to be exploited within the UK National Centre for Earth Observation –Assimilation / inverse modelling at Univ Edinburgh and Leeds

21. Acknowledgements UK NERC National Centre for Earth Observation for funding work. A. Gloudeman (SRON) & C. Frankenberg (JPL) for access to SCIAMACHY methane data ESA & Eumetsat for L1 data ECMWF for access to GEMS/MACC data Univ Oxford for use of RFM

22. Supplementary slides

23. Total Column N 2 O Retrieve N2O simultaneously Should have a more uniform distribution, so useful for showing up problems with retrieval

24. Mean CH4 residuals (for latitude range: 20-30N over sea) Spectral residuals improved by retrieving HDO scaling factor: Nb. Other strong features are believed to be due to CH4 line mixing. Mean residual after retrieving a scaling factor Original retrievals (unadjusted HDO ratio)

25. Factor which scales directly fitted CH 4 Values > 1 compensate for obscuration by cloud Values <1 are over highly reflecting desert surfaces and correct for apparently enhanced columns caused by multiple scattering by dust / surface

26. 26 Methane Latitude-Height Cross-Section Volume Mixing Ratio Altitude (km ) ~10% of column <200hPa where variability is high

27. Fraction of CALIPSO scenes which contain multiple cloud layers

28. Non Linear retrieval simulations of multi layer clouds using the ORAC cloud model. Quality control applied to is cost <10 Identifies many multi-layer cloud conditions but not all – some heights still radiative average of 2 layers Multi-layer performance of ORAC

29. Future of synergistic retrievals Better schemes for future satellites: - to re-write Sentinel 5 Precursor (2015) Sentinel 5 (2020) Both will have: smaller pixel size than SCIA (~10km) have global coverage in 1 day (2000km swath) have entire swath covered by high resolution imagers (VIIRS for S5P, METIMAGE + multi-view-polarisation aerosol imager(? brian can clarify names) for S5) also measure CH4 in stronger 2.3 micron band 2.3 micron band should in principle be better than 1.6 but does not allow the CO2 ratio technique - any cloud in the scene has to be properly modelled to do a good retrieval - this would be based on using imager data in conjunction with O2 A-band and possibly also CO2 bands (e.g. at 1.6) S5P does not have 1.6 micron band so has to rely on 2.3 entirely for CH4 - so techniques we're developing here will be vital for this instrument.