Transcript
Page 1: AOD Assimilation with CMA 3-D Var Scheme

AOD Assimilation with CMA 3-D Var Scheme

Sunling Gong

Page 2: AOD Assimilation with CMA 3-D Var Scheme

Why AOD Data Assimilation?

Page 3: AOD Assimilation with CMA 3-D Var Scheme

PM Predictions at US and Europe

PM10 (1997-2004)

EMEP-Observation [ g m-3 ]

0 20 40 60 80 100

GE

M-A

Q-S

imu

lati

on

[ g

m-3

]

0

20

40

60

80

100Spring (26.30; 73.32; 0.38)%Summer (5.25; 94.36; 0.39)%Autumn (35.84; 64.16; 0.0)%Winter (63.69; 35.71; 0.60)%Y=0.2069X+7.0222; r = 0.3064

PM2.5 (1998-2004)

EMEP-Observation [ g m-3 ]

0 10 20 30 40 50

GE

M-A

Q-S

imu

lati

on

[

g m

-3 ]

0

10

20

30

40

50Spring (9.69; 82.70; 7.61)%Summer (0.68; 94.54; 4.78)%Autumn (16.61; 81.73; 1.66)%Winter (47.01; 51.87; 1.12)%Y=0.2052X+7.2826; r = 0.2544

Gong et al. submitted

US

Europe

Page 4: AOD Assimilation with CMA 3-D Var Scheme

PM 10 Predictions in China

PM10 (2004)

China-Observation [ g m-3 ]

0 100 200 300 400 500

GE

M-A

Q-S

imu

lati

on

[

g m

-3 ]

0

100

200

300

400

500Spring (100.0; 0.0; 0.0)%Summer (100.0;0.0;0.0)%Autumn (100.0; 0.0;0.0)%Winter (100.0; 0.0;0.0)%Y=0.0484X+11.4929; r = 0.3899

Page 5: AOD Assimilation with CMA 3-D Var Scheme

AOD Comparisons

AOD (1995-2004)

AERONET-Observation

0.0 0.5 1.0 1.5 2.0 2.5

GE

M-A

Q-S

imu

lati

on

0.0

0.5

1.0

1.5

2.0

2.5Spring (2.09; 67.14; 30.77)%Summer (1.94; 65.44; 32.62)%Autumn (5.60; 63.55; 30.85)%Winter (3.17; 63.03; 33.80)%Y=0.3473X+0.1903; r = 0.5787

Gong et al. submitted

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Year 1 Annual Correlation (R) Values

Moran et al. 10th CMAS Conference

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Application of near-real-time (NRT) data:

(1) Provide more realistic initial conditions;(2) Emission corrections.

Page 8: AOD Assimilation with CMA 3-D Var Scheme

AOD Data Assimilation System

AODAS

Page 9: AOD Assimilation with CMA 3-D Var Scheme

3D-Var is to minimize objective function J(x):

))(())(()()(2

1)( 1T1T

oobb xHxHxxxxxJ yOyB

3D-Var Method

where x is the analysis field of AOD, xb the background field of AOD provided by model, B the background error covariance matrix, y0 the observation of AOD and O the observation error covariance matrix. H is the observation operator matrix that transfers the variables from model space to observational space.

Page 10: AOD Assimilation with CMA 3-D Var Scheme

Obs. Model

DAS for Dust

First Guess

3D-Var

analysis-field

Forecast result

Satellite: IDDISurface: Visibility

Satellite: FY-2C

Surface: Visibility

CMA Dust Assimilation System

CUACE/Dust

Page 11: AOD Assimilation with CMA 3-D Var Scheme

With DASWith DASNo DASNo DAS

CUACE/Dust : WMO SDS-WAS

Niu et al 2008

2006 Spring Forecasts: threat Score (TS) increased from 0.22 to 0.31, a 41% enhancement.

2006 Spring Forecasts: threat Score (TS) increased from 0.22 to 0.31, a 41% enhancement.

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GEM-MACH AOD Assimilation Scheme

GEM-MACH AOD12 size bins of 5 aerosol types

3-D Var

MODIS or others AOD

Assimilated AOD

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NRT AOD from GOES Satellite

Page 14: AOD Assimilation with CMA 3-D Var Scheme

0 010E 30E 50E 70E 90E 110E 130E 150E 170E 170W 150W 130W 110W 90W 70W 50W 30W

90S

80S

70S

60S

50S

40S

30S

20S

10S

0

10N

20N

30N

40N

50N

60N

70N

80N

90N

Undef

< 0.4

0.4 - 0.8

0.8 - 1.2

1.2 - 1.6

1.6 - 2

2 - 2.4

2.4 - 2.8

2.8 - 3.2

3.2 - 3.6

3.6 - 4

> 4

MeteoInfo: Meteological Data Information System

A Daily AOD from MODIS

Deep Blue” AOD productover bright land surface

0.55 μm bandfrom both Terra and Aqua.

M O D - TerraM YD - Aqua

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AOD Assimilation for GEM-MACH Globe

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Future Work

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GEM-MACH Global Futures

• Implement into GEM-MACH Global and late in to GEM-MACH regional;

• Evaluate the results;

• Compare the assimilation results with MODIS and GOES AOD;

• More ......

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Thanks!


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