Data processing (3)Cloud and Aerosol Imager (CAI)

1)Nobuyuki Kikuchi, 2)Haruma Ishida, 2)Takashi Nakajima,

3)Satoru Fukuda, 3)Nick Schutgens, 3)Teruyuki Nakajima

1)
2)
3)

National Institute for Environmental Studies
School of Information & Design Engineering, Tokai University
Center for Climate System Research, The University of Tokyo

Cloud Aerosol Imager (CAI) Objective

 Correct the effects of clouds and aerosols on thespectral radiation measurements obtained by GOSATTANSO-FTS

 CAI Atmosphere Products
 Cloud Flag Cloud Properties (Optical Thickness, Cloud ParticleRadius) Aerosol Properties (Optical Thickness, Single
Scattering Albedo, Phase Function, Soot Ratio)

 SPRINTARS (Spectral Radiation-Transport
Model for Aerosol Species) Aerosol Properties on Sun glint regionCAI Atmosphere Products
CAI

Cloud Flag
Cloud Properties

Surface Albedo
Database

Aerosol Properties
From CAI

Aerosol Properties

FTS

Aerosol Parameter
From SPRINTARS

Aerosol Parameter
From Other Satellite

SPRINTARSAssimilation

Ground base
Instruments

Cloud Flag
Objective
Distinct clear sky condition for FTS
observation

Products
Clear Confidence Level

CAI bands in useBand 2 (0.67μm)
Band 3 (0.87μm)
Band 4 (1.6μm)

Clear Confidence Level

Cloud detection schemes

Scene Type

Reflectance at 0.66µm: R(0.66µm)

Land

Reflectance at 0.87µm: R(0.87µm)

Ocean

Reflectance Ratio: R(0.66µm)/R(0.87µm)

Thick Cloud

NDVI

Forest, Ocean

Reflectance Ratio: R(0.87µm)/R(1.64µm)

DesertNDSI

Snow

CloudyClear

NDVI =R(0.87μm) − R(0.66μm)
R(0.87μm) + R(0.66μm)

NDSI =R(0.66μm) − R(1.64μm)
R(0.66μm) + R(1.64μm)

G = 1− (1− F1 ) ⋅ (1− F2 ) ⋅ (1− F3 ) ⋅ ⋅ ⋅ (1− Fn )Lower limit

Upper limit

Algorithm flow chart of Cloud Flag
start
Satellite data

Land/water flag
Data preparation
Meteorological data

Surface albedo

Yes (snow)

Snow area by NDSINo

Tagged as snow possibility

Calculation ofconfidence level for each test
(F1, F2, F3, … Fn)
Calculation ofIntegrated Confidence level (G)end

1. r(0.66 µm) (land)or r(0.87 µm) (water)
2. r(0.87 µm)/r(0.68 µm)
3. NDVI
4. r(0.87 µm)/r(1.64 µm)

A case study over Ocean with MODIS

2006.07.18 0510

Confidence level by CAI

RGB

Confidence level by MODIS

Algorithm for retrieving cloud properties
from CAI

Objective
Estimate contamination of cloud
effects in the FTS-measured signals.

Products
Cloud Optical Thickness
Cloud Effective Particle Radius

CAI bands in useBand2Band40.67µm
1.6µm

Formulation
L (τ c , re ;θ , θ 0, φ ) = L obs (τ c , re ;θ , θ 0, φ )Observed calibrated radianceAg−t (τ c , re ;θ )t (τ c , re ;θ 0 )1 − r (τ c , re )AgWhere,t (τ c , re ;θ 0 ) =r (τ c , re ;θ 0 ) =r (τ c , re ) = 22π

T (τ , r ;θ , θ , φ )cos θdθdφ + e τ∫
∫
π
11π

1002π

100∫ ∫ce

F0 : solar _ irradianceθ 0 : solar _ zenith _ angle
θ : satellite _ zenith _ angle
φ : relative _ azimuth _ angle

R (τ c , re ;θ ', θ 0, φ )cos θ ' dθ ' dφ

10π

Surface
Reflection
correction− / cos θ 00∫ r (τ , r ;θ )cos θdθccos θ 0 F0

R : reflec tance _ of _ atmospheric _ layer
T : transmit tance _ of _ atmospheric _ layere(τ c ,re ) = F (Lvis ,Lswir )
−1

Band2

Band4

Solving by iteration with LUT of L, t, r, r, and AgCloud Optical Thickness from GLI

Data source: ADEOS‐II GLI, Data period: 2003 April, Algorithm: Capcom ver102

Cloud Effective Particle Radius from GLI

Data source: ADEOS‐II GLI, Data period: 2003 April, Algorithm: Capcom ver102

Aerosol Properties
 Objective
 Correct for the effects of aerosols on spectral
measurements of FTS Products Optical thickness, single scattering albedo andphase function of each aerosol type

 Band character band1(=0.38µm) is good for land aerosol retrieval

 GLI’s heritage
 drawing on its experience

GLI result 1 (one day)
380nm
Ispra(Italy)

678nm

τ(GLI)
τ(AERONET)
True color composite image
RGB True color
Apr. 25, 2003
Europe, North Africa, West Asiaτ (550nm)

LETKF-SPRINTARS
Aerosol Ensemble Assimilation
1)

FTS requires Aerosol
optical thickness, even insun-glint area2)

Use model calculations tofill in aerosol opticalthickness for sun-glintarea3)Standard SPRINTARS
model suffers fromoutdated emission
inventories4)

Assimilation of CAIaerosol optical thickness
observations leads toimproved model prediction

SPRINTARS: global aerosol model
LETKF: Local Ensemble Transform
Kalman Filter
CAI: GOSAT Cloud Aerosol Imager

LETKF is applied to SPRINTARS by comparing model prediction toobservations every 3 hours and adjusting the aerosol loads accordingly.

Experiment 1
Real QA lvl 2.0 AERONET Aerosol OpticalThickness

SPRINTARS assimilated
real AERONET 675 nm
Aerosol Optical Thickness.
Results were validated withindependent AERONET
sites & MODIS.
1)
2)

Real test
Hard to define reference truth

July 14, SE Asia

Experiment 2a & b
Simulated GOSAT Aerosol OpticalThickness
Here SPRINTARS assimilated simulated
GOSAT CAI aerosol optical thickness at 675
nm. Results were easily validated with theknown truth (assumed a-priori).
1)2)

Perfect model experimentWell-defined reference truth

Experiment with sulfate, carbon and sea-salt
ensembles. Error is global average.

Experiment with sulfate & carbon ensembles.
Error is 2-week average.