Recent Developments in assimilation of ATOVS
at JMA
Kozo Okamoto,
Yoshiaki Takeuchi, Yukihiro Kaido, Masahiro Kazumori
NWP Division, Forecast Dept, Japan Meteorological Agency
1.Introduction
2.1DVar preprocessor
3.Simple test for 3DVar radiance assimilation
4.Cycle experiments
5.Conclusion and plan
Recent Change in the JMA NWP system
• Mar. 2001 : Replace the supercomputer (768GFlops, 640GByte, 80node)
GSM T213L30 => T213L40 (model top : 10=>0.4 hPa)
• Sep. 2001 : Global 3DVar system started in operational data assimilation
system
• Mar. 2002 : Meso 4DVar system is going to start in operational data
assimilation system (H.Res.: 10km, Assimilation window: 3h)
Use of ATOVS in the JMA assimilation system
Present Status
Retrieval Use
Plan
TBB Use
1DVar as preprocessor
NESDIS/MSC
T,Q retrievals
NESDIS 120km BUFR TBB
・conversion
・QC
・select region
・QC
・Channel Selection
・Obs Error Assignment
・Bias Correction
dZ( -1000hPa)
3DVar
Bias Corrected TBB
Tskin
3DVar
ATOVS 1DVar as Pre-processor (1)
Quality Control (QC)
•
•
•
•
•
•
•
Geographical check : reject data over the coast, lake and river ..
Edge scan check:
reject data with outer edge swath
Gross check :
reject data for TBB >400K or <100K
Rogue check-1:
reject data including some channels with |dTBB|>a*Ostd
Minimize check:
reject data not converged within 12 iterations
Jend check:
reject data with Jend>8*used channel number
Rogue check-2:
tighter Rogue check-1
2000
1800
1600
1400
1200
1000
800
600
400
200
0
pass data number for each QC : 00Z 20Dec2001
NOAA15
NOAA16
all
geographic
edge
scan
gross
rogue1 minimize
Jend
rogue2
ATOVS 1DVar as Pre-processor (2)
Bias Correction
•
The TBB bias for each channel j can be described by
3
BIAS j  a j 0   a ji ( yi  yi ) 
i 1
a j 4 (TPW  TPW )  a j 5 (TS  TS )  a j 6
1
cos 
– y: background TBB (TBbg) of AMSU-5,7,10
– TPW: background total column precipitable water
–  : satellite scan angle, Ts:skin temperature
– overbar represents spatial and temporal mean
•
•
The regression coefficients aji are updated every day using previous 2 weeks
data and calculated for NH/Trop/SH and each analysis time.
The bias-correction is not applied to HIRS11,12,AMSU13,14 because of large
systematic errors in the JMA forecast model
ATOVS 1DVar as
Pre-processor (3)
AMSU-A
The channels to be used and observation errors
for each observation condition : Clear/Cloudy
and Sea/Ice/Land
– Clear Sea : HIRS1-8, HIRS10-16,
AMSU5-14
– Land : only HIRS1-3 and AMSU 8-14 are
used.
• Observation errors used in 3DVar are
multiplied by 1.5.
• At the moment,
– Cloud detection is based on NESDIS flag
– Ice detection based on SST<1K
and the classification is corrected as sea
when TBob - TBbg <-50 for AMSU1
HIRS
Channel Selection and Observation Errors
ch
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
HIRS1
HIRS2
HIRS3
HIRS4
HIRS5
HIRS6
HIRS7
HIRS8
HIRS9
HIRS10
HIRS11
HIRS12
HIRS13
HIRS14
HIRS15
HIRS16
HIRS17
HIRS18
HIRS19
HIRS20
MSU1
MSU2
MSU3
MSU4
SSU1
SSU2
SSU3
AMSU1
AMSU2
AMSU3
AMSU4
AMSU5
AMSU6
AMSU7
AMSU8
AMSU9
AMSU10
AMSU11
AMSU12
AMSU13
AMSU14
AMSU15
Clear
Sea
1.40
0.35
0.30
0.20
0.30
0.40
0.60
1.10
Cloudy Clear Cloudy
Sea Sea Ice Sea Ice
1.40
1.40
1.40
0.35
0.35
0.35
0.30
0.30
0.30
0.20
0.30
0.80
1.20
2.20
0.80
1.10
1.50
0.50
0.35
0.30
0.80
0.80
1.10
1.50
0.50
0.35
0.30
0.80
Clear
Land
1.40
0.35
0.30
0.20
Cloudy
Land
1.40
0.35
0.30
1.50
0.30
0.22
0.25
0.60
0.30
0.22
0.25
0.60
0.60
0.22
0.25
0.60
0.60
0.22
0.25
0.60
0.22
0.25
0.60
0.22
0.25
0.60
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.50
1.60
2.50
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.50
1.60
2.50
0.80
0.80
0.40
0.40
0.40
0.40
0.40
0.50
1.60
2.50
0.80
0.80
0.40
0.40
0.40
0.40
0.40
0.50
1.60
2.50
0.40
0.40
0.40
0.40
0.50
1.60
2.50
0.40
0.40
0.40
0.40
0.50
1.60
2.50
Surface type and TBob-TBbg
• Due to mis-classimication of surface type, TBbg is quite different from TBob.
– The mis-classification of the coast accounts for 95% of data with TBobTBbg >50K
– The mis-classification of the sea ice accounts for 98% of data with TBobTBbg <-50K
Distribution of data with large TBob-TBbg for AMSU A1 (10 Oct - 11 Nov 2001)
JMA 3DVar
• Incremental method
– Outer loop : T213L40
– Inner loop : T106L40
• Background error covariance is calculated by using the NMC method
– Horizontal homogeneous
• Observation operator for radiance data
– RTTOV6 ADJ and TL model
Evolution of Cost function J and Gradient of J with iteration
Retrievals Assimilation
All
All
Radiance
Z
Others
|gradJ|
• The minimization
is continued for
100 iterations
• Case of 12Z on
18th Dec. 2001
Cost J
Radiance Assimilation
Other
Analysis Increment for 1ch-1point observation
•
•
Only one HIRS4 observation with TBB departure of +10*Observation error
STD is assimilated at the point of 35N,137E
Analysis Increments are large in the stratosphere because of the large
background error covariance and wide spread RT sensitivity.
Z[m]
T[K]
Cross Section
along observation
longitude(137E)
0.4
10
0.4
10
100
100
300
500
700
300
500
700
0.4
10
Q[g/kg]
0.4
10
100
100
300
500
700
300
500
700
U[m/s]
Analysis Increment for 1ch-1point observation
T[K]
Z[m]
At the 35th level of
JMA eta level
(around 10hPa)
Q[g/kg]
U[m/s]
ATOVS Radiance Assimilation Impacts on NWP
-Parallel Assimilation Experiments (Jul 2001)• TEST : 1DVar preprocessor + 3DVar Radiance Assimilation
• CNTL: 3DVar Retrieval Assimilation
• Data Configurations
– TEST : ATOVS TBB from 120km BUFR
• note: All HIRS and AMSU-14 radiances from NOAA15 are not
used due to instrumental problems
– CNTL: ATOVS NESDIS retrievals (BUFR + SATEM)
• System
– 6hourly intermittent data assimilation
– forecast model : T106L40 (model top 0.4hPa) global spectral model,
216h forecasts for 12Z initial
– analysis model : 3DVar Incremental method
• 1 month run
Bias
RMSE and Bias of
Analysis/Guess verified
against radiosonde
N.H.
• Temperature on the
standard pressure levels
from 1000 to 10 hPa
• Case of 30th Jul 2001
Trp.
Test Anal
Cntl Anal
Test Gues
Cntl Gues
S.H.
RMSE
Bias
RMSE and Bias of
Analysis/Guess verified
against radiosonde
N.H.
• Wind Speed on the
standard pressure levels
from 1000 to 10 hPa
• Case of 30th Jul 2001
Trp.
Test Anal
Cntl Anal
Test Gues
Cntl Gues
S.H.
RMSE
Forecast Errors verified against radiosonde for 500hPa Z
RMSE
• Improvements especially in
the S.H.
N.H.
• But in the N.H. and Tropics,
the improvements diminish
beyond day 5 of the forecast.
Test
Cntl
Trp.
S.H.
Bias
Forecast Errors verified against radiosonde for
250hPa Wind Speed
RMSE
• Nearly Neutral Impact on
forecast
Test
Cntl
N.H.
Trp.
S.H.
Bias
Averaged Zonal Mean for
Forecast Error at day 5 and Analysis difference
•
•
•
Average during 13th - 29th Jul 2001
Large systematic forecast errors around 10 hPa and above 3hPa, especially in the
S.H. are obvious.The value is positive around 10hPa while negative above 3hPa.
Averaged analysis difference is also obvious. Unfortunately Test fits radiosonde
worse than Cntl for the 10hPa temperature.
Averaged Zonal Mean Forecast
error (Fcst - Init ) at day 5 for
temperature from 850 to 1 hPa
Averaged Zonal Mean Analysis
difference between Test and Cntl for
temperature from 850 to 0.4 hPa
1hPa
10
10
10hPa
3
1hPa
10
100
100
-3
-10
90S
90N
90S
90N
Conclusion and Plan
• JMA global 3DVar started operationally since Sep. 2001. At the moment
NESDIS and MSC thickness retrievals are assimilated.
• The direct radiance assimilation system is being developed. QC, channel
selection and bias correction are performed in the 1DVar pre-processing
system.
• Parallel assimilation experiments have been run. Some improvements for
analyses and forecasts are given but are not found beyond day 5 of the
forecast.
• The problem can be attributed to QC, observation error assignment and
data selection ( thinning ). Besides forecast systematic error in the
stratosphere probably have something to do with it.
• We have other plans to
– assimilate AMSU-B radiance
– improve QC
– use level 1B data
AMSU-B Assimilation : initial results
•
•
Accuracy of AMSU-B 1DVar products verified against radiosonde
observations for specific humidity below 100 hPa
Studying the impact of AMSU-B radiance on analysis and forecast
Bias
N.H.
AMSU-B
retrieval
First Guess
Trp.
S.H.
RMSE
Improve QC (1)
• Detect clear/thin cloud/thick cloud/rain using only observation information
(not guess)
• The system is based on AAPP.
• Cloud detection
J = ( y-m )T C-1 ( y-m )
– y: TBob of HIRS1-4, 13-15, AMSU4-5 for thin cloud detection
AMSU1-3 for thick cloud detection
– m:average clear TBB , C: clear TBB covariance
• designate as cloudy when J>J0
STD of clear TBob-TBbg over land
Histogram of TBob-TBbg for HIRS8 over sea 10
NESDIS STD
TEST STD
8
Clear
Thin cloudy
6
Thick Cloudy
4
2
0
1
TBob-TBbg
3
5
7
9 11 13 15 17 19
HIRS CH
Improve QC (2)
• Rain detection : Scattering Index SI = TBcal(A15) - TBob(A15)
– TBcal(A15) is calculated based on a statistical regression approach
with predictors of AMSU1-3
• designate as rainy when SI > 10.
– The threshold 10 is determined based on collocated TRMM TMI and
PR rain
TBob-TBbg STD of each HIRS and AMSU channel for clear/cloudy/rain over sea
NOAA16
10Oct2001 - 31Jan2002, over Sea
25
clear
thin cloudy
thick cloudy
rain
20
15
10
AMSU-14
AMSU-12
AMSU-10
AMSU-8
AMSU-6
AMSU-4
HIRS-19
HIRS-17
HIRS-15
HIRS-13
HIRS-11
HIRS-9
HIRS-7
HIRS-5
HIRS-3
0
AMSU-2
5
HIRS-1
STD of TBob-TBbg
30