SeiYoung Park - PowerPoint PPT Presentation

Title: SeiYoung Park


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Introduction of KMA data assimilation system with
FGAT
Sei-Young Park
KMA/Numerical Weather Prediction Division
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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

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Operational NWP model
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Implementation of global forecasting system
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Verification of Global model (T213L30 for
1998-2005.9 / RMSE of 500hPa )
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Data assimilation system
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On-going and Plan of data assimilation
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Thank you!
Numerical Weather Prediction Division, Korea
Meteorological Administration
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Direct Assimilation Algorithm for ATOVS
Structures
RTTOV version 7
ATOVS Radiance
QC, bias correction Thinning
1DVAR
1DVAR
Observation error(Joo Lee 2002)
Radiance departure
Incremental 3dVar

• Observation error
• RTM error and
• instrument error

• Square of innovation
• First estimates of
• Derber and Wu (1999)

• Background error
• in radiance space

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Results
Typhoon Track Forecast Error
Typhoon track forecast error is much reduced.
Reduction of error is about 200km at 72 hour
forecast.
G3VR DG3V
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Results of BIAS correction
One month averaged RMSE of 500hPa

• Experiments
• DG3V Direct assimilation 3dVar
• BIAS DG3V Bias correction is applied in the
  stratospheric channels depending on the latitude

Bias correction is important to improve forecast
skill.
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Minimization of data number for QuikSCAT (Quality
Control)
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Change of basic fields due to QuikSCAT
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Verification of typhoon track
Songa Meari Maon
Tokage




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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

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Comparison of R3VR, G3VR and U3VR
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Initial MSLP (00 UTC 18 May 2005)
SI (CNTL)
3h-Cycled U3dVR (2 day)
T426 (ANAL)

• U3dVar by continuous cycling gives the
  reasonable initial MSLP pattern.
• SI gives too small scaled MSLP by the topography.

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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 weve 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 1980s)
• - ECMWF reanalysis ERA-40
• - NCEP GSI
• - WRF FGAT

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How to make the first guess at appropriate time
WRF 3DVAR FGAT
-03 02 01 00 01
02 03
GDPS 3DVAR FGAT
-03 02 01 00 01
02 03


y
y
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Data processing
CDA (Comprehensive Database for Assimilation)
routine to process the observational data for
3DVAR

• D-value
• CTRL DVAL OBS-GUES
• FGAT DVAL OBS-(GUESGSDTDELT)
• 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

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Processing time for 3dvar (20051115-20060109)
CNTL FGAT 02023 02124
Daily averaged data number (20051229-20060108)
18?
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In Time
NUMERICAL WEATHER PREDICTION DIVISION/KMA
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In Time
RADIA TION
NUMERICAL WEATHER PREDICTION DIVISION/KMA
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IV for ATOVS (2005.10.04.)
CTRL
FGAT
NUMERICAL WEATHER PREDICTION DIVISION/KMA
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KIROGI 2005-20 (2005.10.13.12.)
NUMERICAL WEATHER PREDICTION DIVISION/KMA
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RMSE for Analysis field (20051121-20051231)
FGAT gave somewhat positive impact.
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BIAS for Analysis field (20051121-20051231)
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J DJ
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(No Transcript)
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???? ???? (????? 1) ???-??? RD ?? ??
Next-generation KMA GSM
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???? ???? (????? 2) ???-??? RD ?? ????
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???? ???? (????? 3) ???-??? RD ?? ????

• ??? ?????? ?? 3?
• ?? ??? ?? ? ?? 4?-6?
• ?? ?? 7?
• ?? ? ?? 8-10?
• ?? ?? ? ???? ??? ?????? ???? 11?
• ???? ? ??? ??? ???? (T426) ??
• ?? ???? 3dVar? ?? ? 4dVar? ??
• KMA/YSU GDAPS ?? ??

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???? ???? (????)

• ??
• - ??? ??? ?????? WRF? ??
• - ?? ?? ?? ?? ??? ???? ????? ????? ???
• ???????? ?? ??
• - WRF ??? ?? ?? ?? ??
• ?? ??
• - WRF ??? ??? ?? ??
• - WRF ??? ?? ?? ??
• - RSL ??? ??? ??? ??? ?? CPU ? WRF ??? ??
• - ?? CPU ??? ???? WRF ?? ??
• - 4dVar ??
• ? ?????? ??, ??? ? ?? ??? ???????? KWRF (Korean
  WRF) ??

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Table 2. Physical processes in global and
regional models at KMA.
Table 2. Physical processes in global and
regional models at KMA.
???? (??? vs. ??)
 
 
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?? ??
1. ??? ???? - ?? ??? ??? ?? ? ????? ?? ??
RD ?? ? ??? ??? ?? ??? ? RD ??? ?? ???
?? ? ?? ?? ? ?? T426 ??? ?? ?? 2. ?? ????
- RDAPS ?? KWRF ???? ?? ? 10km ???
? ???? 51??75?? ?? ? ???? ??? ?? ???? ??
? 4dVar ?? ? ?? ??? ?? ?? ? ?? ?? WRF
??? ?? ?? ??? ?? ?? ????? ???? ?? ??
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3. ????? ???? ?? ? ???? ??? ?? - ??? ??????
?? ?? ? ???, ??? ?? ? ???? (T106L30 ?
T213L30 ? T213L40, 32 ??) - ??? ?? ?? ??
4. ????? ??? ?? - ????? 4dVar 5. ??? ??
??? ?? ??? - ?? ????? ??? ?? T426L40? ???
- ?? ????? ??? ?? 10km KWRF? ???