An Integrated Approach to the Remote Sensing of Methane with SCIAMACHY, IASI and AATSR

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 CH4 retrievals (Frankenberg et al) Simulations of sensitivity of IMAP-DOAS approach to cloud within the scene New retrievals of CH4 from IASI Prospects for use of limb information Prospects for joint AATSR/IASI/SCIA retrievals

3. Introduction CH4 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 • IASI averaging kernels using 1240-1290 cm-1 (as in RAL retrieval) • For surface albedo 0.16 (typical vegetated land) • IASI has profile information but SCIA has better sensitivity near surface over land

5. 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 The ATSR scan geometry (courtesy ESA).

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 Cloud opticalthickness (550nm) vis/near-ircomposite Cloud top pressure (hPa) AATSR-Cloud retrievals • ATSR-2 + AATSR data processed from 1995-2009 • Data available from BADC • Contact C.Poulsen@stfc.ac.uk

7. State-of-the-art CH4 retrievals performed from SCIAMACHY by SRON (Frankenberg) and Bremen (Schneising/Buchwitz), Based on deriving CO2 and CH4 column-averaged vmr from 1.6μm -> xCO2, xCH4 Direct results for xCO2 and xCH4 rather poor (mainly due to cloud) However xCH4 much improved when scaled by ratio of model:observed CO2 Currently working with Frankenberg CH4 data to investigate use of AATSR cloud to improve cloud screening and better characterise scene SCIAMACHY CH4

8. SCIAMACHY CH4 July ‘07 Directly Retrieved SCIA xCH4 xCH4 / ppmv ECMWF GEMS reanalysis xCH4

9. SCIAMACHY CH4 July ‘07 CO2 corrected SCIA xCH4 xCH4 / ppmv ECMWF GEMS xCH4

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 CloudMid/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 Ice cloud 10km Liquid cloud Reflance at 1.6 microns 3km Typical land reflectance • Test scene containing • ice cloud at 10km • liquid cloud at 3km • Vary optical thickness (COT) of both layers

13. 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) Retrieval Simulations • 0.5 COT too high for uncorrected CH4 • 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 - - - Varying Ice COT • 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

16. IASI CH4 Retrievals at RAL • OE retrieval of CH4 from 1240-1290cm-1 range (following Razavi et al, NB avoiding line-mixing issues) • Joint retrieval of CH4, N2O, H2O and HDO • N2O useful as provides control variable analogous to role of CO2 in NIR • Spectra have comparable info content for N2O and CH4 but N2O 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 O3 and SO2 retrievals • Use of 3.7 band for CH4 to be investigated shortly

17. Column Averaged CH4 IASI – 17th July 2007 GEMS

18. Zonal mean column averaged CH4 • NB 0.1 ppmv subtracted from RAL line • xN2O retrieved simultaneously from same band using same prior constraint as CH4 gives value of 0.309ppmv +/- 0.003 (1%)

19. Need to characteriseStratosphere FTS CH4 retrievals from Lauder, New Zealand Stratospheric CH4 • 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) 19

20. Conclusions & Outlook Use of SCIA CH4 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 N2O 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) Original retrievals (unadjusted HDO ratio) Mean residual after retrieving a scaling factor Spectral residuals improved by retrieving HDO scaling factor: Nb. Other strong features are believed to be due to CH4 line mixing.

25. Factor which scales directly fitted CH4 • 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. Methane Latitude-Height Cross-Section Volume Mixing Ratio Altitude (km) ~10% of column <200hPa where variability is high 26

27. Fraction of CALIPSO scenes which contain multiple cloud layers

28. Multi-layer performance of ORAC 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

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.