Transcript Document 7460689

Introduction of KMA data
assimilation system with FGAT
Sei-Young Park
KMA/Numerical Weather Prediction Division
Numerical Weather Prediction Division, Korea Meteorological Administration
Numerical Weather Prediction Division
Contents
Introduction of KMA NWP system
KMA data assimilation system
 History and status
(satellite data assimilation & unified 3dvar)
 First Guess at Appropriate Time (FGAT)
On-going & plan
Numerical Weather Prediction Division, Korea Meteorological Administration
Numerical Weather Prediction Division
Operational NWP model
Model
Analysis
Resolution
Lead
time
(Days)
Global
Spectral Model
(GDAPS)
3dVar
T426 (30km, 40
levels, top 0.4hPa)
10
DEC 1, 2005
3dVar
T106 (110km, 30
levels)
8
17 Ensemble
Regional
Model
(RDAPS)
3dOI/
3dVar
30/ 10/ 5km (33
levels)
2/1
Triple Mesh
Typhoon
Model (DBAR)
Bogus
20km (barotropic)
3
Typhoon
Track
0.25°
2
Asian
1°× 1°
10
Global
-
2
Temp, PoP
Wave Model
Statistical
Model
Numerical Weather Prediction Division, Korea Meteorological Administration
Remark
Numerical Weather Prediction Division
Implementation of global forecasting system
Resolution:
horizontal/vertical
Computing
system
Analysis
1997. 2.
T106/L21
CrayC90,VPX22
0/10
2DOI
2001. 3.
T213/L30
(10hPa)
SX-5
3DOI
2003. 12.
T213/L30
(10hPa)
SX-5
2005. 2. 18.
ET426L40 &
ST426L40
(0.4hPa)
2006. 1.~
비고
Change of data
assimilation
3dVar
Cray X1
Test of high
resolution global
model
T106 (110km/L30)
For ensemble
T213 (55km/L30)
Supporting of
digital forecasting
system
ET426
(30km/L40 : 0.4hPa)
Cray X1E
3dVar
Operational
global model
ST426
(30km/L40: 0.4hPa)
Numerical Weather Prediction Division, Korea Meteorological Administration
Numerical Weather Prediction Division
Verification of Global model
(T213L30 for 1998-2005.9 / RMSE of 500hPa )
RMSE of 500hPa Z North Hemisphere
T106(1day)
120
T106(3day)
TOVS 1dVar
100
T106(5day)
ATOVS 1dVar
3doi & T213
T213(1day)
T213(3day)
T213(5day)
SATEM
Direct 3dVar
SATOB
3dVar
60
40
20
0
98. 2
4
6
8
10
12
99. 2
4
6
8
10
12
00. 2
4
6
8
10
12
01. 2
4
6
8
10
12
02. 2
4
6
8
10
12
03. 2
4
6
8
10
12
04. 2
4
6
8
10
12
05. 2
4
6
8
RMSE
80
Numerical Weather Prediction Division, Korea Meteorological Administration
Numerical Weather Prediction Division
Data assimilation system
Year
2001
Main development for data assimilation
- Acquisition of new observation data (SSMI,QuikSCAT,Metar…) and QC
- Construction of 3dvar
2002
2003
- Computation of observation and background error covariance
- Operation of 3dvar
- Development of operator for Non-synoptic observation data
- Direct assimilation of satellite radiance data (ATOVS) with 3dvar
2004
- QuikSCAT seawinds data assimilation (with MRF PBL operator)
- Development of Unified 3dvar (U3VR)
- Development of operator for satellite radiance on U3VR
- Development of operator for directly received satellite data
2005
(AMSU-A from NOAA and AQUA)
- Development of operator for stationary satellite radiance data
- Development of FGAT for global 3dvar
- Development of Unified 3dvar with direct satellite assimilation
Numerical Weather Prediction Division, Korea Meteorological Administration
Numerical Weather Prediction Division
On-going and Plan of data assimilation
Year
Data assimilation
- Application of FGAT at global 3dVar
2006
- Operation of U3dVar for regional model
- Improvement of surface data assimilation
- application of SSM/I, MODIS polar winds and stationary satellite
- Operation of U3dVar for global model
2007
- Improvement of U4dVar on WRF
- Semi-operation of U4dVar
- Operation of U4dVar for regional model
2008
- assimilation of COMS data
- Developing of the operator for U4dVar adiabatic processing
2009
2010
- Operation of U4dVar for global model
- Developing of the operator for U4dVar diabatic processing
- Operation of U4dVar with diabatic processing
Numerical Weather Prediction Division, Korea Meteorological Administration
Numerical Weather Prediction Division
Thank you!
Numerical Weather Prediction Division, Korea Meteorological Administration
Numerical Weather Prediction Division
Direct Assimilation Algorithm for ATOVS
Structures
RTTOV
version
7
ATOVS Radiance
QC, bias correction
& Thinning
1DVAR
1DVAR
Radiance departure
Observation error(Joo & Lee 2002)
J ( )   T  H  D  R 1 H  D 
T
Increment
al 3dVar
R
H xb   yo H xb   yo T
• Observation error
• RTM error and
instrument error
• Square of innovation
• First estimates of
Derber and Wu (1999)
Numerical Weather Prediction Division, Korea Meteorological Administration
 HBH T
• Background error
in radiance space
Numerical Weather Prediction Division
Results
Typhoon Track Forecast Error
Mean
TY Trackerror
Error(ALL)
Typhoon track
forecast
is much reduced.
-TY0406,TY0407,TY0408,TY0410-
3500
G3VR
Distance(km)
3000
DG3V
2500
2000
1500
1000
500
0
0
24
48
72
96
120
144
Forecast Hour
168
192
216
Reduction of error is about 200km at 72 hour forecast.
Mean Diff of TY Track Error(CNTL-EXPR)
G3VR – DG3V
Distance(km)
-TY0406,TY0407,TY0408,TY0410-
500
450
400
350
300
250
200
150
100
50
0
0
24
48
72
96
120
144
Numerical Weather Prediction Division, Korea Meteorological
ForecastAdministration
Hour
168
192
216
Numerical Weather Prediction Division
Results of BIAS correction
One month averaged RMSE of 500hPa
NH 500Z 200412
DG3V
Experiments
BIAS
160
140
120
• DG3V : Direct assimilation + 3dVar
100
80
• BIAS : DG3V + Bias correction is
applied in the stratospheric channels
depending on the latitude
60
40
20
0
0
1
2
3
TR 500Z 200412
DG3V
4
5
6
7
8
9
10
SH 500Z 200412
DG3V
BIAS
BIAS
140
25
120
20
100
80
15
60
10
40
5
20
0
0
0
1
2
3
4
5
6
7
8
9
10
0
1
2
3
4
5
6
7
8
9
10
Bias correction is important to improve forecast skill.
Numerical Weather Prediction Division, Korea Meteorological Administration
Numerical Weather Prediction Division
Minimization of data number for QuikSCAT
(Quality Control)
Numerical Weather Prediction Division, Korea Meteorological Administration
Numerical Weather Prediction Division
Change of basic fields due to QuikSCAT
Numerical Weather Prediction Division, Korea Meteorological Administration
Numerical Weather Prediction Division
Verification of typhoon track
Songa
Meari
Maon
Numerical Weather Prediction Division, Korea Meteorological Administration
Toka
Numerical Weather Prediction Division
Unified 3dVar (U3VR)
 Motivation (2004)
- Common code share for global and regional DA (observations,
preconditioning, background error statistics, minimization
algorithm, observation operator etc)
- Man Power
 2005
- Basic Performance Test for T213L30 and WRF cycling
 2006
- Background error tuning
- Improvement of observation data processing ( Burf format, QC … )
- Introduction of satellite radiance
- Test on operational frame
- Starting 4DVAR on WRF model
Numerical Weather Prediction Division, Korea Meteorological Administration
Numerical Weather Prediction Division
Comparison of R3VR, G3VR and U3VR
R3VR
Control variables (v)
 ,  , Pu , q
G3VR
U3VR
 , Du , ( Ps, T )u , ln q  ,  u , Tu , Psu , RH
Background E (B)
NMC
NMC
NMC and ENS
Adaptive QC
No
No
Yes
FGAT
No
late 2005
Yes
Satellite Radiance
Late 2005
Radiance
End of 2005
Analysis Grid
Grid (LC)
Wave
Grid (LL/LC)
No vertical Transform
Wave to Grid
EOF
Spectral Power or
Recursive Filter
U transformation
B  UU
x'  U pU vU h v
T
EOF
Recursive Filter
IO
Binary
Binary
NETCDF
Code
f90
f77
F90 / MPI
Minimization
QN
QN
QN and CG
Numerical Weather Prediction Division, Korea Meteorological Administration
Numerical Weather Prediction Division
Initial MSLP (00 UTC 18 May 2005)
T426 (ANAL)
SI (CNTL)
3h-Cycled U3dVR (+2 day)
• U3dVar by continuous cycling gives the reasonable initial MSLP pattern.
• SI gives too small scaled MSLP by the topography.
Numerical Weather Prediction Division, Korea Meteorological Administration
Numerical Weather Prediction Division
First Guess at Appropriate Time (FGAT)
• FGAT is a method to make an innovation with first
guess at appropriate time (observation time).
: Normally in 3dvar we’ve considered the observation data
within the time window are observed at the same time with
first guess. → It can make an error!
- introduced by D. Vasiljevic (mid 1980’s)
- ECMWF reanalysis ERA-40
- NCEP GSI
- WRF FGAT
Numerical Weather Prediction Division, Korea Meteorological Administration
Numerical Weather Prediction Division
How to make the first guess at appropriate time
WRF 3DVAR FGAT
-03
+03
–02
X b 3
X b 2
–01
X b 1
00
X b0
+01
X b 1
+02
X b 3
X b 2
GDPS 3DVAR FGAT
-03
–02
–01
00
+01
×
X
X
b 3
b 3
y
X
b
+02
+03
×
X
y
b0
X
X b0 - X b 3

 (t (y)  t (X b 3 ))
180
b
X b 3
X b 3 - X b0
X 
 (t (X b 3 )  t ( y))
180
b0
Numerical Weather Prediction Division, Korea Meteorological Administration
Numerical Weather Prediction Division
Data processing
CDA (Comprehensive Database for Assimilation)
: routine to process the observational data for 3DVAR
 D-value
 CTRL : DVAL = OBS-GUES
 FGAT : DVAL = OBS-(GUES+GSDT×DELT)
 Unify data which are the same position and height

Priority : maximum priority data

Time difference : the nearest data to anal time

Observation error : minimum observation error data

D-value : minimum D-value data

QC : good quality data , etc..
⇒ FGAT : skip this algorithm except simultaneously happened data
Numerical Weather Prediction Division, Korea Meteorological Administration
Numerical Weather Prediction Division
Processing time for 3dvar (20051115-20060109)
CNTL
0:20:23
FGAT
0:21:24
Daily averaged data number (20051229-20060108)
TOTAL
SYNOP
SHIP
BUOY
UPPER
AVIATION
WINDP
SATEM
TOVS
CNTL
63918
14442
18%
1738
355
11225
6864
96
29199
675
FGAT
77694
24229
3316
548
11230
6913
103
31354
799
↑
Numerical Weather Prediction Division, Korea Meteorological Administration
Numerical Weather Prediction Division
TIME DIFFERENCES BETWEEN OBS AND GUESS (SYNOP)
In Time
4000
DATA NUMBER
3500
3000
HEIT
WIND
TEMP
RHMD
2500
2000
1500
1000
500
0
-180 -160 -140 -120 -100
-80
-60
-40
-20
0
20
40
60
80
100
120
140
160
180
DELT (O BS-GUESS)
TIME DIFFERENCES BETWEEN OBS AND GUESS (SHIP)
600
DATA NUMBER
500
HEIT
WIND
TEMP
RHMD
400
300
200
100
0
-180
-160
-140
-120
-100
-80
-60
-40
-20
0
20
40
60
80
100
120
140
160
180
DELT (OBS-GUESS)
TIME DIFFERENCES BETW EEN O BS AND GUESS (BUO Y )
200
DATA NUMBER
180
160
HEIT
WIND
TEMP
RHMD
140
120
100
80
60
40
20
0
Numerical Weather
Prediction
Division,
-180 -160
-140 -120
-100 -80Korea
-60 Meteorological
-40
-20
0 Administration
20
40
60
80
100
120
140
160
180
Numerical
Weather Prediction
Division
NUMERICAL WEATHER
PREDICTION
DIVISION/KMA
DELT (O BS-GUESS)
TIME DIFFERENCES BETW EEN O BS AND GUESS (UPPER)
In Time
7000
6000
HEIT
WIND
TEMP
RHMD
DATA NUMBER
5000
4000
3000
2000
1000
0
-180
-160
-140
-120
-100
-80
-60
-40
-20
0
20
40
60
80
100
120
140
160
180
DELT (O BS-GUESS)
TIME DIFFERENCES BETWEEN OBS AND GUESS (AVIAT)
80
DATA NUMBER
70
HEIT
WIND
TEMP
RHMD
60
50
40
30
20
10
0
-180
-160
-140
-120
-100
-80
-60
-40
-20
0
20
40
60
80
100
120
140
160
180
DELT (OBS-GUESS)
TIME DIFFERENCES BETWEEN OBS AND GUESS (ATOVS)
25
DATA NUMBER
20
RADIA
TEMP
TION
15
10
5
0
Numerical Weather
Prediction
Division,
Korea
Meteorological
Administration
-180
-160
-140
-120
-100
-80
-60
-40
-20
0
20
40
60
80
100
120
140
160
180
Numerical
Weather Prediction
Division
NUMERICAL WEATHER
PREDICTION
DIVISION/KMA
DELT (OBS-GUESS)
IV for ATOVS (2005.10.04.)
CTRL
FGAT
Numerical Weather Prediction Division, Korea Meteorological Administration
Numerical
Weather Prediction
Division
NUMERICAL WEATHER
PREDICTION
DIVISION/KMA
KIROGI 2005-20 (2005.10.13.12.)
Numerical Weather Prediction Division, Korea Meteorological Administration
Numerical
Weather Prediction
Division
NUMERICAL WEATHER
PREDICTION
DIVISION/KMA
RMSE for Analysis field (20051121-20051231)
FGAT gave somewhat positive impact.
Numerical Weather Prediction Division, Korea Meteorological Administration
Numerical Weather Prediction Division
BIAS for Analysis field (20051121-20051231)
Numerical Weather Prediction Division, Korea Meteorological Administration
Numerical Weather Prediction Division
J & DJ
Numerical Weather Prediction Division, Korea Meteorological Administration
Numerical Weather Prediction Division
Numerical Weather Prediction Division, Korea Meteorological Administration
Numerical Weather Prediction Division
독자모델 구축계획
(전지구모델 1) – 기상청-연세대 R&D 모델 체계
Current status of YOURS GSM - 2005
DFS
YOURS
Dry primitive global DFS system with
multiple levels
(Cheong et al. 2003,
Park et al. 2005)
Radiation (Kim et al. 2005, Byun and Hong 2005)
Land surface (Seol and Hong 2005, Hong 2005)
Vertical diffusion and PBL
(Noh et al. 2003, Hong et al. 2005)
Gravity wave drag
(Kim and Arakawa 1995, Chang and Hong 2005)
(Chun and Baik 1998, Jun et al. 2005)
Cumulus parameterization
(Hong and Pan 1998, Byun and Hong 2005)
Shallow convection scheme (Kim et al. 2004)
Explicit cloud scheme
(Hong et al. 2004, Lim 2004, Hong and Byun 2004)
Next-generation KMA GSM
Numerical Weather Prediction Division, Korea Meteorological Administration
Numerical Weather Prediction Division
독자모델 구축계획 (전지구모델 2) – 기상청-연세대 R&D 모델 역학체계
Numerical Weather Prediction Division, Korea Meteorological Administration
Numerical Weather Prediction Division
독자모델 구축계획 (전지구모델 3) – 기상청-연세대 R&D 모델 구축일정
 기상청 슈퍼컴퓨터에 이식 : 3월
 분석 시스템 접목 및 실험 : 4월-6월
 시험 운영 : 7월
 검증 및 보완 : 8-10월
 모델 고정 및 고분해능 전지구 예보시스템과 병행운영: 11월
 해상도는 현 기상청 전지구 예보모델 (T426) 수준
 분석 시스템은 3dVar로 시작  4dVar로 개선
 KMA/YSU GDAPS 공식 발표
Numerical Weather Prediction Division, Korea Meteorological Administration
Numerical Weather Prediction Division
독자모델 구축계획
•
(지역모델)
배경
- 차세대 기상청 지역예보모델 WRF가 근간
- 해외 개발 모델 도입 시에도 독자적인 기술력으로 유지보수가 가능한
수치예보모델로의 전환 필요
- WRF 코드의 분석 해체 작업 필요
•
수행 업무
- WRF 체계의 병렬화 구조 분석
- WRF 입출력 자료 구조 분석
- RSL 기반의 병렬화 구조를 제거한 단일 CPU 용 WRF 코드의 개발
- 단일 CPU 상에서 최적화된 WRF 코드 작성
- 4dVar 접목
 독자기술로서 운영, 개선될 수 있는 차세대 지역예보모델로서
KWRF (Korean WRF) 구축
Numerical Weather Prediction Division, Korea Meteorological Administration
Numerical Weather Prediction Division
물리과정 (전지구 vs. 지역)
Global Model (GDAPS)
Regional Model (RDAPS)
Vertical layers/Top
40/0.4 hPa
33/50 hPa
Numerics
Spectral transform,
Arakawa B-grid
Time integration
semi-implicit
Explicit, split
Radiation scheme
LW-JMA (Sugi et al,1989)
SW-Lacis & Hansen (1974)
Cloud-Radiation
Land-Surface scheme
SiB (Sellers, 1986)
Five-Layer Soil
PBL
Non-Local PBL
Hong and Pan (1996)
Convection
Kuo
New KF/No
Microphysics
Large-scale condensation
Mixed-phase explicit
Numerical Weather Prediction Division, Korea Meteorological Administration
Numerical Weather Prediction Division
향후 계획
1. 전지구 예보모델
- 독자 모델의 구축을 위해
 연세대에서 개발 중인 R&D 모델 을 차세대 기상청 전구 모델로
 R&D 모델의 성능 향상에 주력
 완전 구축 시 까지 T426 체계와 병행 운영
2. 지역 예보모델
- RDAPS 에서 KWRF 체계로의 전환
 10km 해상도
 예보시간 51시간~75시간 시험
 예보시간 연장과 함께 영역확장 시험
 4dVar 시험
 최적 시스템 구축 운영
 이와 함께 WRF 코드의 분석 해체 작업을 통해 보다 현업적이고 독자적인 체계 구현
Numerical Weather Prediction Division, Korea Meteorological Administration
Numerical Weather Prediction Division
3. 확률론적인 예보로의 접근  결정론적 예보의 한계
- 앙상블 예측시스템의 성능 개선
 해상도, 멤버수 증가 후 현업운영 (T106L30  T213L30  T213L40, 32 멤버)
- 앙상블 해석 능력 강화
4. 자료동화의 지속적 개선
- 궁극적으로 4dVar
5. 디지털 예보 지원을 위한 재분석
- 주간 디지털예보 지원을 위한 T426L40의 재분석
- 단기 디지털예보 지원을 위한 10km KWRF의 재분석
Numerical Weather Prediction Division, Korea Meteorological Administration
Numerical Weather Prediction Division