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Impact study of AMSR-E radiances in NCEP Global Data Assimilation System

Masahiro Kazumori(1)

Q. Liu(2), R. Treadon(1), J. C. Derber(1) , F. Weng(2), S. J. Lord(1)

(1) NOAA/NCEP/EMC(2)NOAA/NESDIS

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Contents

Purpose of this studyDevelopment of Microwave Ocean Emissivity ModelData Assimilation ExperimentResultsConclusions

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Purpose of this study

Image: JAXA/EORC

Investigate the impact of AMSR-E radiance on NCEP global model

AMSR-E (Advanced Microwave Scanning Radiometer for EOS)(Advanced Microwave Scanning Radiometer for EOS) observes the radiance from the Earth with 6 microwave dual-polarized channels.

Frequency [GHz]

Polarization

Physical Observabl

e

6.925 V,H SST

10.65 V,H SSW

18.7 V,H WV

23.8 V,H WV

36.5 V,H SSW

89.0 V,H Rain

These low frequency channels are sensitive to SST and SSW and less sensitive to hydrometeor in the atmosphere.They can be assimilated in the all weather condition.

AMSR-E Sensor

Unit

Aqua

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Development of Microwave Ocean Emissivity Model for AMSR-E

Community Radiative transfer model (CRTM) has two options for Microwave Ocean Emissivity Model

1. FASTEM (Developed by UKMO)2. NESDISEM (Developed by NESDIS)

Necessary to develop a new microwave ocean emissivity model

FASTEM NESDISEM

00z 16 August 2005

Both models have large bias(about 3K) in 10.65GHz (H).

Comparison of TBcal - Tbobs

operational use

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Design of New Microwave Ocean emissivity model

Wind speed dependent model: Fresnel Reflectivity in a calm sea Two-Scale Ocean roughness model

Small Scale correction (Liu1998, Bjerkaas1979)Large Scale correction (Modified Storyn1972)Foam emissivity and foam fraction (Modified Storyn1972,Rose2004)

Coefficients were derived from the fitting to satellite measurements (AMSR-E, SSMI and AMSU-A).

TL and AD models with respect to SSW and SST

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Comparison of (TBcal - Tbobs) [K]AMSR-E 10.65 GHz (H)

FASTEM(operational)

NESDISEM

New model

00z 16 August 2005

Biases are substantially

reduced.

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Comparison of (Tbcal-Tbobs) vs Wind Speed

AMSR-E 10.65 GHz (H)FASTEM NEWMDL

Bias is depend on surface wind speed.

New Model has smaller bias than operational (FASTEM).

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Comparison of FASTEM & NEWMDLin AMSR-E channels

Horizontal-polarization

New model is better in the low frequency (< 20GHz).

Statistic period:1-5 December 2005Bar:

BIAS

Line:STD

Vertical-polarization

FASTEMNew Model

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Data Assimilation ExperimentConfiguration

Analysis: NCEP GSI 3D-Var assimilation systemForecast: NCEP global model (as of May 2006)

00z Initial 180 hour forecastResolution: T382L64 (same as operational, about 50km in horizontal)

Cntl: Same as operational

Test1: Cntl + AMSR-E with FASTEM ( all microwave frequency range)Test2: Cntl + AMSR-E with NEWMDL (<20GHz only) and FASTEM (>=20GHz)

Period: 12 Aug.-11 Sep. 2005AMSR-E 6.925GHz channels(V,H) are not used because their FOV size are too large (43.2x75.4km)

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Data Assimilation ExperimentQuality Control of AMSR-E radiance

data1. Select ocean data and

thin with 160km distance

2. Remove rain and cloud affected data(Criteria are based on CLW)

3. Remove land or ice contaminated data (FOV size is 29.4x51.4km at 10.65GHz)

4. Remove sun glint affected data in the ascending orbit

5. Gross error check(|Tbobs- Tbcal| < Threshold )

Tbcal-Tbobs [K] 10.65 GHz (V) 00z 16 Aug. 2005

TB bias correction term =

FOV dependent + air-mass dependent

0.1% of all data are used for the assimilation.

A few thousand / analysis

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ResultsImpact on Analysis

Test1

Mean difference Test-Cntl

T & Q at 850hPa

T[K]

Q[g/kg]

No systematic bias in temperature and moisture

Period:Aug.12-Sep.11 2005

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ResultsImpact on Analysis

Test2

Mean difference Test-Cntl

T & Q at 850hPa

T[K]

Q[g/kg]

Period:Aug.12-Sep.11 2005

Increase of Temperature (about 0.2K) in the high latitude.

Decrease of moisture (about 0.1g/kg) over ocean.

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ResultsImpact on Forecast (A.C. at 1000hPa

Height)

N.H. Almost Neutral

S.H. Positive (Test1&Test2)

ControlTest1Test2

AMSR-E radiance assimilation is positive for

the S.H.

Period:00z 12 Aug.-00z 11Sep. 2005

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ResultsImpact on Forecast (A.C. at 500hPa

Height)

N.H. Almost Neutral

S.H. Positive (Test1&Test2)

Test2 is slightly better than Test1

ControlTest1Test2

Period:00z 12 Aug.-00z 11Sep. 2005

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ResultsImpact on Forecast (Fits to RAOB

wind)RMSE of 24 and 48 hour Vector Wind forecast are reduced in the S.H.

Test1

Test2

dotted: Test

solid : Cntl

Black:24hr forecast

Red :48hr forecast

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ResultsImpact on Forecast

RMSE Difference

(Test – Cntl)

Test1 Test2

Blue color means improvements

Zonal mean of 5-day Temperature Forecast RMSE against initial

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Case study Hurricane Track Prediction (Katrina 2005)5 samples in the experiment period (00z 25 August – 00z 29 August, 00Z initial forecast)

Best Track(OBS)ControlTest1Test2

Test2 is better than Test1.

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Conclusions(1/2)

A MW Ocean emissivity model was developed for AMSR-E

1. The model is an empirical two scale roughness model, the coefficients were derived from the fitting to the satellite measurements.

2. The model has a better performance for low frequency channels than FASTEM.

Impact study of AMSR-E radiances in NCEP global data assimilation system

1. The new MW ocean emissivity model was used in CRTM for the experiment.

2. Three cycle experiments were conducted.Cntl : same as operationalTest1: Cntl + AMSR-E (with FASTEM) Test2: Cntl + AMSR-E (with New model < 20GHz, with FASTEM >=20GHz)

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Conclusions(2/2)

Impacts on analysisIncrease of Temperature in high latitudes, decease of moisture over ocean at 850hPa.

Impacts on forecast Positive for the S.H. (A.C., RMSE, Fits to RAOB) Neutral for the Tropic and the N.H.

New emissivity model showed better results.

The new emissivity model can extract the information on the ocean surface (SSW, SST) effectively from AMSR-E radiances in the data assimilation system.

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Thank you

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backup

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Microwave Ocean emissivity

In a calm sea, the ocean surface is specular.Reflectivity can be calculated by Fresnel law.

),(1),( pp ( p = h or v )),( p Total Reflectivity

2

h

2h

Fresnel,hsin),(cos

sin),(cos

R

Frequency Zenith angle

2

vv

2vv

Fresnel,vsin),(cos),(

sin),(cos),(

R

sea surface

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Microwave Ocean emissivity

When wind starts blowing, it makes small ripples on the ocean surface.The height variance is

0

2 )( dKKS

)(KS :Ocean roughness spectrum function (Bjerkaas1979)

c

)(2R K

dKKS

Small-scale height variance is

cK:cutoff wave

number

)cos4exp( 22R

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Fresnel,

2kRR pp

Small Scale roughness correction

( p = h or v )

c

)(2R4

2c

KdKKS

k

K

1R k 1c k

K

R

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Microwave Ocean emissivity

Large scale roughness correction A function of wind speed, incidence angle and frequency

)()()cos4exp( 2321

22R

22

Fresnelv,

2

v faaaWkRR s

)()()cos4exp( 2321

22R

22

Fresnelh,

2

h fbbbWkRR s

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)(cc

f

Large Scale roughness correction

Coefficients were obtained from the fitting to the satellite measurements (AMSR-E,SSMI and AMSU-A)

11 ba

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Microwave Ocean emissivity

Foam emissivity

Foam fraction

Total reflectivity

2

freeFoam,

2

coveredFoam, ),()1(),(),( ppp RfRf

231.3610751.7 uf

),(1),( Foam,

2

coveredFoam, ppR

Modified Stogryn[1972] function based on Rose[2004]

FASTEM uses a constant (1.0) for both polarization.

Stogryn[1972]

FASTEM use Monahan(1986)

55.251095.1 uf

:u 10m wind speed

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ResultsImpact on Forecast (Fits to RAOB

wind)For the N.H. and the Tropics, impacts are almost neutral

for Test1 and Test2.

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Zonal mean of RMSE of 500 hPa height forecast against initial.

Difference ( Test – Cntl )

5-day forecast3-day forecast

1-day forecast

Test1: (AMSRE with FASTEM)

Cntl: (W/O AMSR-E)

1-day forecast

3-day forecast

5-day forecast

Negative value indicate improvement

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5-day forecast3-day forecast

1-day forecastZonal mean of RMSE of 500 hPa height forecast against initial.

Difference ( Test – Cntl )

Test2: (AMSRE with NEWMDL)

Cntl: (W/O AMSR-E)

1-day forecast

3-day forecast

5-day forecast

Negative value indicate improvement

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Conclusions

Impact on analysisIn Test1, no systematic bias in mean analysis field

(850hPa temperature, humidity).

In Test2, increase 850hPa temperature (0.2K) in the high latitude. decrease 850hPa humidity (0.1g/kg) over ocean. decrease guess TPW bias

no significant difference mean 6-hour rain (not shown).

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ConclusionsImpact on forecastPositive

• A.C. of 500hPa for S.H., A.C. of 1000hPa N.H. and S.H.• Fits to RAOB of 24, 48 hour vector wind forecast in the S.H.• RMSE of 500hPa height for 3day and 5day forecast • RMSE of temperature from 1000 to 100hPa for 3,5 day forecast

(Test2 has larger improvement than Test1)

• RMSE of 200hPa vector wind (negative for FASTEM case) not shownNeutral

• A.C.500hPa of N.H. (Slightly positive for Test1 case)• Fits to RAOB of 24 and 48 hour vector wind for the Tropics, N.H.

Negative• RMSE of 850hPa vector wind in the Tropics (not shown)

A Case Study of Hurricane Track prediction (Katrina)• Test1(FASTEM) degrade a hurricane track prediction.

Test2(New model) keeps the accuracy

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ResultsImpact on Analysis (Total Precipitable water

[kg/m^2])

Test1 Test2

Bias of total precipitable water in guess field are reduced slightly.

Zonal mean

Bias in guess

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ResultsImpact on Forecast

Zonal mean of 3-day Temperature Forecast RMSE against initial

RMSE Difference

(Test – Cntl)

Test1 Test2

Blue color means improvements