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Retrieval of Methane Distributions from IASI

Retrieval of Methane Distributions from IASI. Waterfall, R. Siddans, B. Kerridge, G. Miles, B. Latter Rutherford Appleton Laboratory Acknowledgements: NCEO Atmospheric Composition Theme. Why measure methane with IASI?. Methane:

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Retrieval of Methane Distributions from IASI

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  1. Retrieval of Methane Distributions from IASI Waterfall, R. Siddans, B. Kerridge, G. Miles, B. Latter Rutherford Appleton Laboratory Acknowledgements: NCEO Atmospheric Composition Theme

  2. Why measure methane with IASI? Methane: • Important for its role in atmospheric chemistry and as a greenhouse gas • Concentrations are increasing with time (but not consistently) • Uncertainties in the global methane budget Satellite measurements available (tropospheric methane): • mid-IR (IASI, TES, AIRS) • near-IR (GOSAT, SCIAMACHY) Advantages/disadvantages of IASI • Global day/night coverage • Different sensitivity • Long time series of planned instruments (monitoring of trends)

  3. IASI CH4 retrieval scheme

  4. Vertical sensitivity T Simulated averaging kernels Example real averaging kernels • Retrieve methane on a fixed pressure grid (~0,6,12,16,20... km) • Principal sensitivity is in mid to upper troposphere • Limited sensitivity at the surface (dependent on air-surface temperature contrast) Averaging kernels: x = vmr Averaging kernels: x = ln(vmr) 1km retrieval grid, 6km correlation length Latitude

  5. Expected methane precision (fractional error) Fractional error Retrieval error Retrieved/apriori error Latitude Latitude • Apriori error values: 10% in troposphere, higher in stratosphere • Significant improvement over apriori in mid and upper tropospheric layers

  6. 23rd August 2009 Day Version 1 of methane data • 4 continuous months of data (August – November 2009), April 2009, August 2008 • Profile retrieval • => Column averaged mixing ratios Xch4 averaging kernel XCH4 /ppmv Night • ‘least cloudy’ out of every 4 pixels • Nb. retrieval very sensitive to cloud

  7. September 2009 Day • Monthly mean column averaged mixing ratios • gridded 1x1 degree bins • Nb. Apriori data is a constant profile => N-S gradient comes completely from IASI Xch4 (ppmv) Night

  8. DAY • Possible reasons for day/night difference: • Difference in sensitivity • Problem with apriori • CH4 apriori has a low bias, inconsistent with apriori error • Different cloud sensitivity NIGHT

  9. NCEO Model + satellite comparisons: Aug 2009 Aug 2009, bias: -0.0296 IASI (night) GEOSCHEM (U. Edinburgh) TOMCAT (U. Leeds) Xch4 (ppmv) :….. GOSAT (U. Leicester)

  10. Monthly mean data, 1x1°bins November 2009 August 2009 April 2009 IASI (night time only data) Xch4 (ppmv) GOSAT GOSAT data produced by R. Parker, U. Leicester, see poster by Byckling et al.

  11. Monthly mean data on retrieval levels August 2009 DAY ppmv ppmv Column averaged mixing ratio 178 hPa 422 hPa NIGHT

  12. Are these distributions reasonable? 300 hPa IASI night: 422 hPa MACC Reanalysis data 500 hPa ppmv August 2009

  13. Cloud effects on the retrieval data • Real retrievals show a tail of high methane retrievals believed to be due to cloud • Simulations based on AVHRR-3 data indicate that the apparent methane is expected to increase with increasing cloud top height and optical depth • Multiple scattering simulations based on more complicated (realistic) cloud distribution also show higher methane values due to increasing cloud amount • Will be hard to filter out as even small amounts of cloud can cause slightly increased radiances. • Retrieval of cloud top height/cloud fraction may improve retrievals in many cases.

  14. Cloud height / km IASI cloud sensitivity - AVHRR/3 ORAC CH4 enhancement Estimated Relative error on CH4 column LUT of retrieval simulations of error due to cloud on IASI CH4 column averaged vmr Cloud Altitude / km Cloud optical depth

  15. Summary and Future Plans • A scheme for retrieving methane profiles from IASI has been developed at RAL. • Version 1 global distributions of methane are now available for: August-November 2009, April 2009, August 2008 • Agreement with many features seen in models / GOSAT • Current version has some issues related to cloud contamination and certain land types • Simulations show cloud can introduce a high bias to the IASI data • Further simulations are planned to assess the impact of other important error sources • Future versions will include improved handling of clouds and improved apriori data

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