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S. Maksyutov T. Yokota, et al. NIES GOSAT project National Institute for Environmental Studies

Global carbon dioxide and methane column observation by GOSAT (Greenhouse gases observing satellite). S. Maksyutov T. Yokota, et al. NIES GOSAT project National Institute for Environmental Studies e-mail: shamil@nies.go.jp. Carbon Fusion Workshop, Edinburgh, May 9-11, 2006.

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S. Maksyutov T. Yokota, et al. NIES GOSAT project National Institute for Environmental Studies

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  1. Global carbon dioxide and methane column observation by GOSAT (Greenhouse gases observing satellite) S. Maksyutov T. Yokota, et al. NIES GOSAT project National Institute for Environmental Studies e-mail: shamil@nies.go.jp Carbon Fusion Workshop, Edinburgh, May 9-11, 2006

  2. Project overview • GOSAT is a joint project between Ministry of Environment, NIES and JAXA • JAXA supplies observations with TANSO-FTS (radiance spectra, NIR and thermal IR) and TANSO-CAI (cloud aerosol imager) sensors • Cloud aerosol imager group develops operational algorithm for cloud and aerosol retrieval and assimilated aerosol properties fields for GHG gas retrievals. • NIES retrieval group estimate CO2/CH4 column abundance from radiance spectra (operational) • NIES modeling/assimilation group assimilate CO2 concentration fields, inverse model CO2 fluxes

  3. Organization of NIES and MoE National Institute for Environmental Studies Center for Global Environmental Research GOSAT Project Ministry of the Environment Div. Global Environment Sec. Research Management Research Funding Office Chief: Tsukamoto Vice-chef: Yoshikawa NIES GOSAT Project Leader: T. Yokota Sub-leader: S. Maksyutov Sensor Requirement Team Data Analysis Algorithm Team Validation Team Inverse Model Team Oguma Morino Yokota, Higurashi, Aoki, Eguchi, Yoshida, Ota Oschepkov, Bril Oguma, Morino, Inoue (invited) Machida Maksyutov, Peregon, Carouge, Koyama, Valsala, Kadygrov, Nakatsuka

  4. (1) O2A-band (0.76 µm ) (2) CO2 1.6 µmand CH4 1.67 µmband (3) CO2 (H2O) 2.0 µmband Nadir looking of solar scattered light with three bands H2O O2-A CO2 CO2 CO2 CH4 (1) 0.76 µm band(2) 1.6 µm band(3) 2.0µm band

  5. Retrieval under existence of cirrus/aerosol Unknown parameters • Optical thickness • Surface reflectance • Height distribution • Improvement of SNR • Increase of useful data cirrus aerosols

  6. Forward Model • GOSAT forward model is based on HSTAR code • HSTAR (High resolution RSTAR code) calculates spectral radiance of a spectral band by using the line-by-line method (Dn 0.01 cm-1) under the clouds/aerosols existing conditions • RSTAR is a radiative transfer code calculating radiance at a single wavenumber (or within a small band-width). RSTAR was developed by T. Nakajima’s group at CCSR, UTYO. • Multiple scattering, including several types of clouds and aerosols, for any patterns of the observation geometry • RSTAR/HSTAR code development members: T. Nakajima (CCSR), R. Imasu ,M. Sekiguchi , A. Higurashi , H. Chan , T. Kimura

  7. Simulated Spectra with andwithoutCirrus Optical Thickness t= 0.2t= 1.0t= 5.0 2.0 µm band 1.6 µm band 0.76 µm band

  8. Cirrus correction with 2 step procedure from three bands • Step 1: Cirrus optical thickness (t) estimation of cloud height distribution (h) and ground surface albedo (a) from H2O saturated area of the 2.0 µm band and 0.76 µm (O2-A) band • Saturated H2O lines appear in light reflected from cirrus (and absent in light reflected from ground) • Step 2: Simultaneous retrieval of column density of the CO2, cirrus optical thickness (t’), and ground surface albedo (a’) from 1.6 µm band

  9. A stepwise retrieval testfrom three bands • Step 1: Cirrus optical thickness (t) estimation from H2O saturated area of the 2.0 µm band Saturated area：73 points

  10. Simulation simultaneous retrieval of column density of the carbon dioxide, cirrus optical thickness (t), and ground surface albedo (a) • Calculation conditions

  11. Error of the cirrus optical thickness (t=1.0) Table 1. Optical thickness (t) and height (h) estimated by Step #1 Case 1: t=1.0 (Initial: t= 0.5), Cirrus height: (True: 11 km,Initial: 9 km)

  12. Error of the cirrus optical thickness (t=0.2) Table 1a. Optical thickness (t) and height (h) estimated by Step #1 Case 2: t=0.2 (Initial: t= 0.5), Cirrus height: (True: 11 km,Initial: 9 km)

  13. Simulation simultaneous retrieval of column density of the carbon dioxide, cirrus optical thickness (t), and ground surface albedo (a), STEP 2 • Retrieval error of CO2 column from 1.6µm Table 3. Retrieval error [%] of Step #2

  14. Simulation simultaneous retrieval of column density of the carbon dioxide, cirrus optical thickness (t), and ground surface albedo (a), STEP 2 • Retrieval error of conifer surface albedo at 1.6µm t = 1.0 t = 0.2

  15. Aerosol MODIS aerosol retrievals over land and ocean: MOD08_M3- Aerosol Optical Thickness at 1.64m: a, 1.64m = a, l0 (1.64/0)- - calculated with ta,0.55mm, a0.55&0.865mm for Ocean ta,0.47mm, a0.47&0.66mm for Land January, 2002 0.07-0.08 (Ocean) 0.01-0.02 (Land) July, 2002 0.02-0.03 (Land) 0.07-0.08 (Ocean)

  16. Sensitivity analysis of Aerosol Optical Thickness True value 0.2 0.1 0.0 -0.1 -0.2 -0.3 -0.4 The analysis is under progress. Difficult points are the simultaneous determination of three parameters; Optical thickness, Surface reflectance, Vertical distribution. Error of CO2 estimation (%) 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Optical Thickness at 0.55mm

  17. Height Distribution Initial Guess Brightness Pattern Forward Calc. Full Fragmented Clear Cirrus Aerosol Sink/Source Distr. Future Target PBL height Opt.Thick. Surface Reflect. Opt.thick.:2.0µm Height:O2band Analysis Inverse Model Atm. Transp. Analysis Emission Inv. Ocean, Terrest. Surface CO2 data GOSAT Observation Data Data Analysis Flow Near Infrared FTS Data Cloud & Aerosol Sensor

  18. Inverse modeling of regional carbon fluxes - Large data volume challenge - Need for optimization algorithms/assimilation approaches 1990s: (Enting, 1995) Inverse modeling with fixed number, fixed number of observations of base regions 22 or 64 Direct matrix solver gives fluxes and uncertainties 2000-2005: (Rodenbeck, 2003) Grid-based inverse models, too many unknown fluxes, finite number of observations, use iterative algorithms to find optimum fit and spatial correlation of fluxes to reduce degrees of freedom

  19. Inverse modeling of regional carbon fluxes - NIES group research in forward inverse modeling 1. Very high resolution forward tracer transport (0.5 or 0.25 degree globally) 2. Inverse modeling: Iterative optimization solvers with cutoff filter (works faster than SVD, use very large precompiled matrices ) Filtering out noise in posterior surface fluxes, still good fit to observed fluxes

  20. Development status Radiation simulation tests are conducted to take into account uncertainties in aerosols, PBL height, cirrus cloud heightCirrus cloud height and thickness detection algorithm by H2O saturation was developedPossible to retrieve column amounts of CO2 with errors of 0.1% in case of exact absolute radiance observation and 0.7% in case of actual accuracy (predicted) radiance observationsData policy for data distribution will be established within a year.

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