CCSRUNIV.TOKYOGOSAT データ利用

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Oct. 5, 2008

GOSAT TANSO-FTS TIR (Band 4) data analysis method

Ryoichi Imasu, Naoko Saitoh, and Yosuke Niwa
（Center for Climate System Research (CCSR) , The University of Tokyo）

GOSAT sensors【Satellite】
・Mass, power ： Approx. 1650kg, 3.3kw - EOL・Designed life span ： 5 years
・Orbit ： Altitude 666km , Orbit Inclination 98 deg.
Sun-Synchronous Sub-Recurrent Orbit【TANSO-FTS】
・Spectral range
Band 1 ： 0.75μm～0.78μm (O2-A)
Band 2 ： 1.56μm～1.92μm (CO2 column)
Band 3 ： 1.92μm～2.08μm (CO2 , CH4, cirrus cloud)Band 4 ： 5.5μm～14.3μm (CO2 vertical profile, CH4)
・Spectral resolution ： 0.2cm-1
・SNR ： > 300

CO2 retrieval from spectrum data measured from space

European group led by Dr. Chedin

4A+TIGR+3R, MSU → 3I, N.N; AIRS+AMSU.

HIRS: Chedin et al., 2002a, 2002b, 2003a, 2003b,
AIRS: Crevoisier et al., 2004, Chevallier et al., 2005, Engelen et al., 2004, 2005

andAIRS group led by Dr. Chahine

AMSU, 4D-VAR

Chahine et al., 2008, 2005(VPD), Maddy et al. (2008), Aumann et al., 2005, Engelen et al., 2004

have successfully obtained global maps of CO2 in theupper troposphere from the thermal infrared spectrum.
Next target :Information on vertical structure of CO2,
particularly on CO2 concentration in PBL→ Synergetic usage of GOSAT FTS bands (SWIR, TIR)
is effective ・・・ benefit of GOSAT sensors！

inter-annual variation of CO2 at various height
Air craft obs. (in situ)(Nakazawa et al., 1996)

Calculation of Jacobian
1. Optimization of layer thickness of "retrieval grid"
In order to obtain uniform sensitivity and retrieval error, layer thickness
have to be optimized→ Radiative transfer cal. at "full grid" (finest grid)→ Determining layer thickness based on "Area value" of averaging kernel→ "Linear-mapping" : "retrieval grid" ←→ "full grid"
2. Selecting retrieval channels
In order to reduce the effects of temperature estimation error, spectral channel
that are used for retrieval have to be selected→ Channel selection based on "Shannon information contents"

Optimal estimation retrieval

Error analysis(by Naoko Saitoh)

no T errors(by Naoko Saitoh)with T errors

When T bias of -1 K for forward spectra,±15% CO2 errors below 100 hPa

Only channels with CO2 IC > 0.003
selected±15% CO2 errors decrease to less than
±2% when channel selection based on CO2IC

In order to reduce the retrieval error down to 1 %, "averaging" isnecessary
Assumption:
・Temp. estimation error : random
・CO2 variation : systematic(e.g., Engelen et al., 2002)↓Averaging causes degradation of vertical and temporal structureof CO2 distribution retrieved
Simulation:
How degraded by averaging even if the best performanceof retrieval can be achieved.
↓Convolving original profile with "Averaging Kernel",then averaging spatially and temporally.

CO2 distribution(CO2 trans. model:"NICAM-CO2")GOSAT TIR observation simulator

now emissivity andvegetation data are available

MODIS cloud data
Satellite orbit, obs. modes

cloud top

ice/water

optical depth

cirrus flagby Yosuke Niwa

Meteorological data （ERA40）

Forward models
Radiance in each IFOV
Temperature

TANSO-CAI IFOVTANSO-FTS IFOVWater vapor

GOSAT sim. dataset(Interferogram, spectrum)

General circulation model:
NICAM (Nonhydrostatic ICosahedral grid Model)-based
CO2 transport model (developed by Mr. Yosuke Niwa)
NICAM was originally developed by Prof. Satoh and Dr. TomitaAdvantages• High-resolution– No polar problem• Mass conservation– Tracer advection is consistent withcontinuity– Tracer masses are completely conserved
without a mass fixer• Easier to develop a nest generation inverse
model

Icosahedral grid

Schemes and setting parameters• Horizontal resolution: glevel-5 (dx~240km)• Vertical layer: 54 layers• Advection scheme : Miura2004 scheme (no limiter version)• Cumulus convection scheme : simplified prognostic Arakawa-Schubert• Vertical diffusion scheme : Mellor-Yamada 2 with modification by Smith(1990)

GCM based CO2 transport mode, NICAM-CO2NICAM (Nonhydrostatic ICosahedral Atmospheric Model)
Seasonal variation of surface CO2 concentration

MAM

JJA

NICAM grid(glevel–5:dx~240km)

Surface fluxes

SONDJF• NEP flux derived
from CASA model• Fossil fuel (CDIAC)• Air-sea change(Takahashi et al., 2002)

CO2 concentration (original)

Original(4 times/day)(hPa)

JanEast Asia35N 140E

Amazonian forest

Mid-Africa (Nigeria)

10N
20E
20E 10N

Australia

Day

10S 45W

20S 140E

Dec(Calc. : using NICAM-CO2 by Y. Niwa)

Convolved with ※Averaging kernel
before averaging(hPa)Jan

CO2 concentration (retrieved-averaged)( spatially averaged: 4.5°×4.5°; temporally: 15 days)

East Asia

35N 140E

Mid-Africa (Nigeria)

10N 20E

Day

Amazonian forest

due to a priori
no info. from obs. Australia

Dec

10S 45W

20S 140E※Max. performance without retrieval errors

CO2 concentration (original)(hPa)

East Asia

35N 140E(4 times/day)Amazonian forest

Cloud Fraction in GCM grids (NICAM-CO2 model output）

JanDayDec

10S 45W

CO2 concentration (original)(hPa)

Mid-Africa (Nigeria)

10N
20E
20E 10N(4 times/day)
Australia

Cloud Fraction in GCM grids (NICAM-CO2 model output）

JanDayDec

20S 140E

Cloud Fraction in GCM grids (NICAM-CO2 model output）

Cloud Frac. < 0.1

Amazonian forest

Eeat Asia : . 7 %Mid-Africa : 15 %Amazon : 30 %Australia : 37 %Australia

East Asia
Mid-Africa (Nigeria)

Cloud screening
Band

IFOV

Cloud/aerosol properties

Surf. Press., Cloud top height
FTS 0.76μm
1.6μm
10.5 km CO2
2.0 μmCirrus clouds
5.5-14μm
Cloud top height ,Opt. thickness (8-10, 11-13μm),
Ice/liquid water (11-13μm)
Cirrus clouds (11-13μm)
CAI 380nm
670nm
870nm
1.6μm

Absorbing aerosols
0.5 km Optical thickness
Optical thickness,
Ice/Vegetation
1.5 km Ice/liquid water

Fractional cloud coverage in FTS-IFOV
can be estimated at ～1% resolution

Day/Night
・Slicing method (Z, Frac. cov.)
・Split window method
・Cloud score approach
・Adiabatic lapse rate analysis
based on the retrieve temp.

SummaryRetrieval method
・Temp. data : Meteorological analysis data (sonde: land, MW: ocean)・"Area value of averaging kernel" → layer thickness → uniform sensitivity and error
・"Shannon information contents" → channel selection → reduction temp. error

CO2 distribution feature that will be retrieved by TANSO-FTS (TIR)・What we want to retrieve with TANSO-FTS (TIR) is ・・・・Degrading of fine structure due to spatially and temporally averaging was estimated・Clear sky probability = ～10% or less・Detailed simulations considering cloud coverage have been continued
using GOSAT-Sim (TIR) for source/sink inversion of CO2

Synergetic usage of GOSAT sensor data
・Cloud screening based on CAI, SWIR(2μm）, and TIR
・Estimation of CO2 in the boundary layer (PBL) : "Lower = column – upper"
・Usage of columnar data for retrieving profiles"as an additional constraint" or "scaling after retrieval"