1 Impact study of AMSR-E radiances in NCEP Global Data Assimilation System Masahiro Kazumori (1) Q....
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![Page 1: 1 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.](https://reader030.fdocuments.us/reader030/viewer/2022032704/56649d645503460f94a47113/html5/thumbnails/1.jpg)
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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
22
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
21
)(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