The usage of the ATOVS data in the Korea
Meteorological Administration (KMA)
Sang-Won Joo
Korea Meteorological Administration
History of the satellite sounding assimilation in KMA
• Feb. 1999 : TOVS data assimilation in the Global model (1DVAR)
• Nov. 2001 : AOTVS(HIRS+AMSU-A) assimilation in the Global Model (1DVAR)
Numerical Weather Prediction Division
Introduction
1.
1DVAR in KMA
- Background error implies geographical variation
- Observation error is calculated from the innovation and background
error
•
Evaluation of effect on the model performance
- Evaluation of the time averaged fields
- Typhoon track forecast error
Numerical Weather Prediction Division
Inhomogeneous background error
Background Error Correlation (N.H.)
Distribution of Background Error
25
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
80
NH
TR
SH
20
Control Variables
40
15
10
20
5
0
-6
-4
-2
0
2
4
Background Error Correlation (Tropics)
TTB-BTB [K]
6
8
5
25
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
20
15
15
5
5
15
Control Variables
20
25
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
20
10
10
25
25
10
5
10
15
20
Background
Error Correlation
(S.H)
Control Variables
Control Variables
-8
Control Variables
Frequency
60
5
10
15
20
25
Control Variables
Numerical Weather Prediction Division
Methodology
Error variance changes but correlation is fixed
Damping area is assigned
eb      15enh  (25   )etr ,15N    25 N
eb      15esh  (25   )etr ,15S    25S
e
esh
90S
eb  
etr
20S
Eq.
eb  
20N
enh
90N
Error covariance becomes
Cv1 , v2  j  e j v1 Rv1 , v2 e j v2 
Inverse matrix of error covariance becomes
C 1 v1 , v 2  j  e j
1
v1 R 1 v1 , v2 e j 1 v2 
Numerical Weather Prediction Division
Observation error
Statistical Method for observation error
Assumption
1.
Tangent linear approximation
2.
No correlation between background error and RTM error
3.
Biases are well removed
Derivation
H xb   H xt   Hx  H t xt   H  xt   Hx
1st assumption
H xb   yo  H t xt   H  ( xt )  Hx   yt  y   H  xt   Hx  y
H xb   yo H xb   yo T
RTM error
 F  H xxT HT  yyT
2nd assumption
 F  E  H xxT HT  H x H T   y
2
R  H xb   y o H xb   y o 
T
 HBH T
2
3rd assumption
Numerical Weather Prediction Division
Meaning of the resulting equation
R
H xb   yo H xb   yo T
• Observation error
• Square of innovation
• RTM error and
instrument error
• First estimates of
Derber and Wu (1999)
 HBH T
• Background error in
radiance space
• Innovation is the sum of observation error and background error if there is
no correlation
• The resulting equation says the above statement in radiance space.
Numerical Weather Prediction Division
Feedback of observation error
OBSERVATION
1DVAR
ANALYSIS
R
INNOVATION
MODEL
BACKGROUND
B
Benefits of our method
• Relationship exists between observation error and NWP analysis through B
• Improvement of background error can readily affect the observation error
• The error ratio (eigenvlaue) is changed automatically
Numerical Weather Prediction Division
Description of the Global Model
MODEL
ANALYSIS
Basic Equation
Primitive Equation
Resolution
Triangular truncation of 213 in horizontal and 30 levels sigma-p hybrid
coordinate from surface to 10hPa
Numerical Scheme
Semi-implicit time integration, spherical harmonics for horizontal
representation and finite difference in the vertical
Radiation
Lacis and Hansen (1974) for short-wave and water vapor, carbon
dioxide and ozone for long-wave
Convective
Parameterization
Kuo type(1974)
Large Scale Condensation
Kanamitsu et. al. (1983)
Shallow Convection
Tiedke(1985)
Gravity Wave Drag
Iwasaki et. al. (1989)
PBL scheme
2 Layer method from Yamada and Meller (1982)
Land Surface Processes
SiB
Method
3 Dimensional Multivariate Optimum Interpolation
Resolution
0.5625 degrees
Update Cycle
6 hourly
Numerical Weather Prediction Division
Observation (ATOVS TBB Data=OTB)
CHANNEL
CHANNEL
CHANNEL
CHANNEL
CHANNEL
HIRS1
HIRS6
HIRS12
AMSU5
HIRS10
HIRS2
HIRS7
HIRS13
AMSU6
HIRS11
HIRS3
HIRS8
HIRS14
AMSU7
HIRS12
HIRS4
HIRS10
HIRS15
HIRS8
AMSU13
HIRS5
HIRS11
AMSU4
HIRS9
AMSU14
Background (Profile=BPR)
Variable
Level
Source
Element
Temperature [K]
Surface – 10 hPa
6 hour forecast from GDAPS
1-16
Temperature [K]
10 hPa – 0.4 hPa
NESDIS retrieval
17-20
Specific Humidity [g/g]
Surface – 300 hPa
6 hour forecast from GDAPS
21-27
Skin Temperature
Surface
NOAA weekly SST analysis
28
Pressure [hPa]
Sea Level
6 hour forecast from GDAPS
29
U [m/s]
Surface
6 hour forecast from GDAPS
30
V [m/s]
Surface
6 hour forecast from GDAPS
31
CTP [hPa]
NESDIS retrieval
32
Cloudness
NESDIS retrieval
33
Numerical Weather Prediction Division
Others
•
Quality control(Eyre, 1992)
•
Forward operator: RTM(RTTOV version 6) + Vertical interpolation
•
Minimization algorithm: BFGS method (quasi-Newtonian algorithm)
•
Dimension reduction to the TOVS BUFR format
•
Optimum interpolation interface (Lorence 1986, Eyre 1993)
•
Bias correction: Scan angle and air mass bias correction (Joo and Okamoto, 2000)
Numerical Weather Prediction Division
Flowchart
PREFIX:
Background(B),
Observation(O),
Analysis(A)
Departure(D)
SURFIX:
Profile(PR)
Brightness Temperature(TB)
BFGS
Physical
Space
BPR
1st
no
MINIMIZATION
Background
Error
-
yes
DPR
APR
APR
ADJOINT
J&
J
RTM
DTB_B
OTB
ATB
-
BIAS C.
DTB
BIAS C.
Observation
Error
Radiance Space
OTB_B
Numerical Weather Prediction Division
Flow chart of the 1DVAR with NWP analysis
B
Background Error
R
B
1
D
V
A
R
1DVAR
background
Tv
24 and 48 hour
Forecasts for 1
Month
Bias
Observation error
3D O.I.
Global Model
Bias
O-B for 1
Month
ATOVS data
Synoptic Obs.
6 hour forecast
10 day forecast
FEP
Diagnostics
Numerical Weather Prediction Division
Analysis verification (September 2001)
ANALYSIS VERIFICATION of 500hPa GPH(Northern Hemisphere)
1DVA
120
OPER
100
RMSE [m]
80
ANALYSIS VERIFICATION of 500hPa GPH(Tropics)
60
1DVA
18
OPER
40
16
20
14
0
24
48
72
96
120
144
168
192
216
240
FORECAST HOUR
RMSE [m]
12
0
ANALYSIS VERIFICATION of 500hPa GPH(Southern Hemisphere)
1DVA
160
10
8
6
OPER
4
140
2
120
0
RMSE [m]
0
100
24
48
72
96
120
144
168
192
FORECAST HOUR
80
60
40
20
0
0
24
48
72
96
120
144
FORECAST HOUR
168
192
216
240
Numerical Weather Prediction Division
216
240
Observation verification(Sep. 2001)
OBERVATION VERIFICATION of 500hPa GPH(Northern Hemisphere)
OBERVATION VERIFICATION of 500hPa GPH(ASIA)
1DVA
80
OPER
1DVA
120
70
OPER
100
60
RMSE [m]
RMSE [m]
80
50
40
30
60
40
20
20
10
0
0
0
24
48
72
96
120
144
168
192
216
0
240
1DVA
OPER
48
72
96
120
144
168
192
216
240
FORECASTofHOUR
OBERVATION VERIFICATION
500hPa GPH(Southern Hemisphere)
FORECAST HOURof 500hPa GPH(TROPICS)
OBERVATION VERIFICATION
20
24
1DVA
120
OPER
18
100
16
80
RMSE [m]
RMSE [m]
14
12
10
8
60
40
6
4
20
2
0
0
0
24
48
72
96
120
144
FORECAST HOUR
168
192
216
240
0
24
48
72
96
120
144
168
192
216
240
FORECAST
HOUR
Numerical
Weather
Prediction Division
Averaged typhoon track forecast error
(TY0111-TY0123)
AVERAGED TYPHOON TRACK ERROR
TRACK ERROR[km]
600
500
OPER
1DVA
400
300
200
100
0
12
24
36
48
60
72
FORECAST HOUR
Numerical Weather Prediction Division
Summary
•
The 1DVAR is developed in KMA to assimilate the ATOVS data
•
The statistics shows positive effect mostly and also in ASIA
•
Typhoon track is well predicted with the 1DVAR and it is mainly caused by
the better specification of the Pacific High
•
The 1DVAR is in operation from 1 November, 2001
Numerical Weather Prediction Division
Future Plans
•
Improvement of the bias correction scheme
•
Utilization of the ATOVS data over the land
•
Improvement of cloud detection scheme
•
Implementation of the 1DVAR in the regional model
Numerical Weather Prediction Division
Verification with RAOB
RMSD of Tv Error of BG, Anal and D from RAOB (NH)
RMSD of Tv Error of BG, Anal and D from RAOB (TR)
RMSD of Tv Error of BG, Anal and D from RAOB (NH)
RMSD of Tv Error of BG, Anal and D from RAOB (TR)
30 10
30 10
70 30
100 70
bg
anal
100 70
300 100
300 100
500 300
500 300
700 500
700 500
1000 700
0
1000 700
1
2
3
1
2
4
3
4
30 10
70 30
100 70
300 100
300 100
500 300
500 300
700 500
1
2
0
1
3
4
Mean Virtual Temperaure [K]
2
3
5
4
Mean Virtual Temperaure [K]
• Poor performance near surface and
tropopause
bg
anal
70 30
100 70
bg
anal
70 30
100 70
1000 700
RMSD of Tv Error
BG, Anal
and D [K]
from RAOB (SH)
MeanofVirtual
Temperaure
30 10
bg
anal
700 500
1000 700
0
RMSD of Tv Error
BG, Anal
and D [K]
from RAOB (SH)
MeanofVirtual
Temperaure
0
Layer [hPa - hPa]
Layer [hPa - hPa]
30 10
70 30
bg
anal
Layer [hPa - hPa]
Layer [hPa - hPa]
Layer [hPa - hPa]
Layer [hPa - hPa]
30 10
70 30
bg
anal
• Large improvement in the S.H.
100 70
300 100
• We need more improvement in the
N.H. and Tropics.
300 100
500 300
500 300
700 500
700 500
1000 700
1
2
3
1000 700
4
5
6
Mean Virtual Temperaure [K]
1
2
3
4
Mean Virtual Temperaure [K]
5
6
Numerical Weather Prediction Division
5
ATOVS Information
CNTL (NB)
8
True Value(T)
Wrong
Information
2
Right
Information
T-B
B.G.(B)
Anal(A)
Obs(O)
(TTB-BTB) [K]
1
0
-8
0
O-B
(T-B) X (O-B) > 0
-8
(OTB-BTB) [K]
• Observation should be in the same
direction as RAOB from background
• The 2nd and 4th quadrants data
mislead the analysis.
• There are many data in the 2nd
quadrant.
Numerical Weather Prediction Division
8