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 * email@example.com
Contents • 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
種子島宇宙センター 23 January 2009 Interferogram(干渉光) Solar CO2 Sun CH4 H2O フーリエ変換分光器 TANSO-FTS (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 Transform IFOV (観測視野) FTS 10.5km SWIR Band2 Cirrus Aerosol 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 JAXA/NIES/MOE (宇宙航空研究開発機構・環境研・環境省)
Measurement residual Difference from a prior y : Observed radiance spectra x : Optimal concentration S : Error covariance of observation ε l : Factor F ( x ) : Simulated spectra D : Unit matrix for scaling x : Prior concentration a : S Error covariance of prior a Optimal Estimation Method (Rodgers ) SWIR L2 ATBD  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  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. 先験値情報を設定する意味 もっともらしい値から計算を開始するため。 また、Saによる先験値への拘束は ・観測ノイズに起因する解の発散の抑制 ・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 • 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] Finally 2～3%
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) prior (NIES Transport Model Ver05) Northern Hemisphere SCIAMACHY 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 Land Higher than a prior (〜1%) delay Ocean Month of the maximum value 2009 Apr 2010 Apr 2011 Apr The dip is caused by the seasonal march of observation latitudinal band. Land 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
Summary 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.