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Seasonal variations of greenhouse g as column-averaged dry air mole fraction retrieved from SWIR spectra of GOSAT TANSO-FTS.

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Seasonal variations of greenhouse gas column-averaged dry air mole fraction retrieved from SWIR spectra of GOSAT TANSO-FTS

Nawo Eguchi*1, Yukio Yoshida2, Isamu Morino2, Nobuyuki Kikuchi2, Tazu Saeki2, Makoto Inoue2, Osamu Uchino2, Shamil Maksyutov2, Hiroshi Watanabe2 and Tatsuya Yokota2

1: Tohoku University (Now at Kyushu University)

2: National Institute for Environmental Studies


  • Status of SWIR Level 2 current version (Ver01.xx)
  • Seasonal variations of XCO2 and XCH4
    • Comparison with SCIAMACHY(2003-05)
  • Summary
    • Possibility to scientific research use


23 January 2009









(Thermal And Near-infrared Sensor for carbon Observation - Fourier Transform Spectrometer

Alt. 666km

Measurement of reflection

from Surface, clouds and so on

Complex Fourier Inverse


IFOV (観測視野)

FTS 10.5km

SWIR Band2



Greenhouse gases Observing SATellite

Top-Down approach

Synoptic scale – Global

Intra-seasonal, Seasonal, Inter-Annual scales

CO2, CH4, H2O, Clouds, Aerosol

MAP method [e.g., Rodgers, 2000]

Column & Profile : CO2, CH4, H2O




Measurement residual

Difference from a prior



Observed radiance spectra



Optimal concentration



Error covariance of observation










Simulated spectra



Unit matrix for scaling



Prior concentration




Error covariance of prior


Optimal Estimation Method (Rodgers [2000])

SWIR L2 ATBD [2010]

The optimal x is found when an iterated solution Cost function J (x)is a minimum value.

eq. (1)

eq. (2)

  • The columns and profiles of CO2 and CH4 are retrieved by the optimal estimation method based on Rodgers [2002] from the GOSAT TANSO-FTS SWIR (Shortwave InfraRed; 0.76, 1.6, and 2 micron) and TIR (Shortwave InfraRed; 0.76, 1.6, and 2 micron) spectrum data.
    • Optimal solution from eq.(1) eventually required the accurate Sa (a priori error covariance matrix) and its assessment.
  • In the GOSAT retrieval, a priori (Xa) and its covariance matrices (Sa) of CO2 and CH4 are obtained from the simulated data of NIES Transport Model [Maksyutov et al., 2008]. Prior covariance matrix is consisted of variances on the three temporal scales:
  • Synoptic scale variability (SSynoptic) in 2-week using NIES TM to obtain concentrations on global (every grids),
  • Interannual variability (VInteranuual) using observed concentration to obtain variability for a long term (several decade),
  • (3) Seasonal cycle bias (BSeason): to estimate the effects of the errors in the simulated seasonal cycles.





・非線形問題における local minimum への収束の回避

status of swir level2 ver 01 xx
Status of SWIR Level2 Ver.01.xx
  • Improved point from previous version (Ver.00.xx)
    • Cirrus detection method
    • Surface Pressure retrievals by using TANSO-FTS SWIR Band 1 (O2A band)
      • Explicitly-retrieval of equivalent path length which is closely related with aerosol and surface pressure in retrieval field
    • Spectroscopic parameter of CH4 , line-mixing etc…
  • Period of data available to General User (GU)
    • 6 April 2009 to 19 April 2011 (except May 2009)

Comparison Ver. 01.xx with Ver. 00.xx

High and low retrieved values are removed because of improved method treating cirrus and surface pressure (aerosol)

Yoshida et al. (MSJ meeting 2010)


Screening strategy of TANSO-FTS SWIR Level 2 data

To keep a certain quality of retrieved parameter, the filtering and screening of data are conducted before and after the retrieval process, respectively.

  • Before the retrieval process, the level 1B data are filtered out by
  • Level 1B quality flag (spike noise, saturation and so on)
    • approximately 60% NG(Ver01.10), approximately 20% NG(Ver01.20, 30)
  • CAI cloud flag (remove scan which having cloud pixels)
    • approximately 80% NG
  • Totally, 93% (82%) NG before the retrieval process

Table 2: Data number of data passed by L1B quality flag and CAI cloud flag

* With respect to CAI available data number

Ver 01.30 (Ver.01.20 is also same feature)

The L1B quality flag check is weak.

Most of added data are low SNR data.

Clear sky ratio (from MODIS) 16~17 %

Eguchi and Yokota [GRL, 2008]




Screening (After the retrieval process)

Table3:Surviving ratio of retrieved data by screening items(function of land/ocean、clear-sky ratio) 2009 7/24-26 (Shade indicates less than 50%)

Convergence of retrieval process

Spectrum fitting

Evaluation of simultaneous retrieved parameter

Check cloud remain

Sufficiency information of spectrum

Effective screening item is AOD (variety of path length) for land and CAI coherent test for ocean. χ2and2μ scatering material (cirrus) determinations are closely correlated with clear-sky ratio within FOV.


Seasonal characteristic of XCO2

Apr 2009 ~Jun 2011 (GU : Apr 2009 – Apr 2011)

  • White color indicates that the data are removed by screening.
  • The sunglint region is primary measurement area over ocean.

Ave. XCO2 (whole period)

  • The retrieved values at high latitudes are low because the GOSAT measured summer time over there. The CO2 value at summer time is lowest through the year.
  • The Level 1B quality is low at the tropics and Asian monsoon regions where the clouds cover frequently.

Seasonal variation of XCO2 (Monthly mean)

Amplitude (peak-to-peak)


(NIES Transport Model Ver05)

Northern Hemisphere


Southern Hemisphere

Month with the maximum

2009 Apr

2010 Apr

2011 Apr

Max. May / Min. September

Monthly mean STD 3 ppmv

Amplitude 5~10 ppmv

Diff from prior 8 ppmv (~2% low bias)

Only the grids with more than six months of data were taken into consideration.

Interhemispheric Difference (NH-SH)


+0.67 [ppmv/year]

+0.78 [ppmv/year]

+2.4 [ppmv/year]

Regional Characteristic of XCO2

+0.88 [ppmv/year]

+0.98 [ppmv/year]


Seasonal characteristic of XCH4

Apr 2009 ~2011 Jun

(GU : Apr 2009 – Apr 2011)

  • White color indicates that the data are removed by screenings.
  • The sunglint region is primary measurement area over ocean.

Ave. XCH4 (whole period)

  • The seasonal variation in L2 current version is consistent with the previous knowledge.
  • The contrasts of inter-hemispheric and between east and west North America are seen, also the high XCH4 is seen over Asia.

Seasonal variation of XCH4 (Monthly mean)

Amplitude (peak-to-peak)

Non-correction by factor


Higher than a prior (〜1%)



Month of the maximum value

2009 Apr

2010 Apr

2011 Apr

The dip is caused by the seasonal march of observation latitudinal band.


Max : Sep-Nov

Min : Apr- Jul

Amplitude : 20 ppbv

Ocean ???

Only the grids with more than six months of data were taken into consideration.


-5.8 [ppbv/year]

+6.5 [ppbv/year]

+9.6 [ppbv/year]

Regional characteristic of XCH4

Non-correction by factor



Quality check of Level 2 current version (Ver.01.xx)

  • Most of level 1B data (93%) are removed by L1B quality check and CAI cloud flag.
  • There is room for improvement of the screening method of cirrus and aerosol (effective path length), esp. thin cirrus rejection and its effect on retrieved value.

Seasonal Variations of XCO2, XCH4

  • It is found that the seasonal variation on the continental scale is similar to the variation by a prior (NIESTM-05) (phase and amplitude), but the XCH4 seasonal variation (at several regions) is more complex than that of XCO2.
  • XCO2
    • Large differences from a prior are found in the areas of NH where plant activity is high.
  • XCH4
    • Large variances are found over Asia and North America.

Potential to scientific research use

  • Remain negative bias of 〜2% (〜9ppmv) for XCO2, 〜1% (〜20ppbv) for XCH4
  • [Morino et al., AMT, 2011]
    • Improvement of retrieval process
    • Further validation is needed (discussion for seasonal and regional biases).
      • Impacting on flux estimation (Level 4 product) research
  • Seasonal cycle (phase and amplitude) and annual mean (low and middle latitudes) are consistent with the previous knowledge.
    • XCO2: Large differences from prior are located over high activity regions of plant.
    • XCH4: Large variances are located over East Asia.
  • Research of Inter-annual variation requires data accumulation.
    • Rejection of abnormal values near sources and sinks
  • Analysis considering synoptic scales can be done, if the data quality and number meet the level of quality for science.