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AGU 2008 Highlight

AGU 2008 Highlight. Le Kuai Lunch seminar 12/30/2008. Retrieval Algorithm of CO2 Profile from Thermal Infrared Spectra of GOSAT/TANSO-FTS N. Saitoh, R. Imasu, Y. Ota, and Y. Niwa Center for Climate System Research, Univ. Tokyo (CCSR), National Institute for Environmental Studies (NIES).

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AGU 2008 Highlight

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  1. AGU 2008 Highlight Le Kuai Lunch seminar 12/30/2008

  2. Retrieval Algorithm of CO2 Profile from Thermal Infrared Spectra of GOSAT/TANSO-FTSN. Saitoh, R. Imasu, Y. Ota, and Y. NiwaCenter for Climate System Research, Univ. Tokyo (CCSR), National Institute for Environmental Studies (NIES) • The Greenhouse gases Observing Satellite (GOSAT) • Algorithm to retrieve CO2 vertical profiles • 700-800 cm-1 (15 micron band) • NIR Sensor for Carbon Observation(TANSO)-Fourier Transform Spectrometer (FTS) • Accuracy and precision of CO2 concentrations

  3. GOSAT/TANSO/FTS • Sun-synchronous sub-recurrent orbital satellite • National Institute of Environmental Studies (NIES) • Japan Aerospace Exploration Agency (JAXA) • CO2 and CH4 • 5 years, launched on Jan. 21st, 2009 • Two sensors: TANSO-FTS and TANSO-Cloud and Aerosol Imager • TANSO-FTS: 0.75-0.78; 1.56-1.72; 1.92-2.08; 5.5-14.3 micron (spectral resolution:~0.2 cm-1) • SNR: ~300 at 280K

  4. Retrieval simulations • NICAM (Nonhydrostatic Icosahedral Atmospheric Model • Horizontal resolution: ~240 km, 54 layers from surface to 40 km • 5 measurement locations randomly selected for each 15x15 degree • Methods: Non-linear MAP retrieval with linear mapping (Rogers, 2000)

  5. Results • Dependence on a priori information: [case1] NICAM CO2 15x15 grid average (best a priori) [case2] 370 ppmv fixed in vertical (better a priori) [case3] 380 ppmv fixed in vertical (not good a priori) • Channel selection 1) Above 500 hPa, 100-channel 2) Stratosphere: additional 10 channels above 55 hPa 3) Lower troposphere: 50 channels below 800 hPa • Seasonal and latitudinal dependences of CO2 retrieval • Effect of uncertainties in the estimations of T, H2O and O3

  6. Conclusion • When off-diagonal elements were introduced to an a priori covariance matrix, the differences between true and retrieved CO2 values were within +/- 1% above 800 hPa. • The differences were the smallest at 600-200 hPa for every season and latitude. • Separately selecting 100,10 and 50 channels on the basis of CO2 information content for all 110 layers, above 55, and below 80 hPa, provided retrieval results equivalent to those using all 1000 channels in the CO2 15 micron band • Additional channels that focused on the two regions above 55 hPa and below 800 hPa were required for CO2 retrieval in the stratophsere and the lower troposphere, respectively

  7. XCO2 Retrieval Based on Correlation Between Weak(Strong) CO2 and O2 channelsIgor Polonsky and Denis O’BrienDept. of Atmospheric Science, Colorado State University • Reducing the processing time by using a simplified algorithm based on the observed correlation between the weak (strong) CO2 and O2 A bands. the air mass the apparent optical path difference AOPD the extinction coefficient

  8. Strong correlation between the AOPDs of O2 and CO2 • The line of correlation is insensitive to the profiles of scattering material, temperature and water vapor, and also to the surface pressure • XCO2 can be estimated by interpolating between the lines

  9. Two simulation strategies Strategy 1 • Atmosphere-underlying surface system using profiles extracted from ECMWF profile database • 60 level system • Water cloud, optical properties Strategy 2 • The joint CloudSat-Calipso data for reconstruction of cloud-aerosol layer system • 125 layers • The reflection properties of the land determined by the polarized BRDF derived from POLDER data

  10. Conclusion • Demonstrated simple algorithms should provide a better initial guess for the full-physics than CO2 climatology. • Provide with a first estimate of cloud water and cloud ice. • The OCO science team will assimilate the global product to test the consistency of OCO XCO2 with all other sources of CO2 data.

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