development of operational data assimilation system for...
TRANSCRIPT
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Development of Operational Data
Assimilation System for Convective
Scale Model at KMA
SeiYoung Park, Eunhee Lee, Yoonjeong Hwang, Mee-Ja Kim,
Eun-Hee Kim, Hyeyoung Kim, Hee-Jung Kang, Dayoung Choi,
Min-Jong Song, Ho-Yong Lee, Minyou Kim, and Yong Hee Lee
7th WMO DA Symposium
(’17.9.10)
Numerical Modeling Center
Korea Meteorological Administration
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Global Medium-range Prediction (GDAPS / Global EPS)
• Deterministic: UM 17km L70 / T+288hrs (00/12UTC), T+87hrs (06/18UTC) / Hybrid-4DVAR
• Ensemble: UM 32km L70 / T+288hrs (00/12UTC) / 49 Members / Perturb. : ETKF, RP, SKEB2
Short-range Prediction (E-Asia) (RDAPS)
• UM 12km L70 / T+87hrs (6 hourly) / 4DVAR /
Deterministic
(Very) Short-range Prediction
• Deterministic : UM 1.5km L70 /
(LDAPS)T+36hrs (6 hourly) / 3DVAR (3 hourly)
(VDAPS)T+12hrs (1 hourly) / 3DVAR (1 hourly)
• Ensemble : UM 3km L70 / T+72hrs (LENS)
Seasonal Prediction System (Glosea5)
• GloSea5 / 60km L85 / 60, 220days
• Atmosphere(UM)+Ocean(NEMO)
KMA Operational NWP system
[’17.6.]
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LDAPS Local Data Assimilation & Prediction System
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LDAPS
❖ Launch : May 2012 ▪ Forecast length (cycle): 36 hours (8 times/day)
▪ DA system: 3DVAR (FGAT, IAU)
❖ Recent Upgrade : June 2016
▪ UM : v8.5 v10.1
▪ Dynamic core : New Dynamics ENDGame* ENDGame : Even Newer Dynamics for General Atmospheric Modelling of the Environment
▪ Extended domain : 744 x 928 1,188 x 1,148 (variable grid)
(622x810)
LDAPS.2012
LDAPS.2016
- to keep the consistency of the
synoptic scale with the global
model
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Impact of Extended domain
❖ HGT 500hPa 100km wave filter (2015.07.12.00UTC)
LDAPS.2016LDAPS.2016
Decreasing of the error from boundary condition
- to keep the consistency of the synoptic scale with the
global model
GDAPS
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Observation data
• Sonde(temp, pilot, windprofiler), Surface(synop, ship, buoy, metar), Aircraft(amdar), Radar(radial velocity), ScatWind(ASCAT)( Global: (+) ATOVS, AIRS, IASI, COMSCSR, GPSRO, CrIS, ATMS, Satwind, (-)Radar )
• 3 hour-cycling DA: lack of available observation, needs of satellite DA
LDAPS.2012 LDAPS.2016 LDAPS.2012 LDAPS.2016
Scatwind : 155 580 (x 3.8)
Surface : 2696 5160 (x 1.9) Sonde : 50 101 (x 2)
Aircarft : 238 2407 (x 10)
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Initialization (3DVar, FGAT, IAU, LHN)
❖ Incremental 3DVar (3 km resolution)
• 3DVar (with FGAT) + IAU for all observations,
except Latent Heat Nudging for radar-derived surface rain rate
T0-90m T0+90m
Observation Processing System
Moisture OPS
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Initialization (SURFace data assimilation)
ᅳ Rainfall amount • in-situ obs• Model (Old)• Model (New)
Factor Method GDAPS (global) LDAPS
Soil
Moisture
(Old) Nudging scheme
(New) Extended Kalman Filter (‘16.6~) MetOp-A, B / ASCAT
(4 times/day)
Downscale from GDAPS(1 time/day)
SnowIMS fractional snow cover
snow amountIMS
(4 km, 1 time/day)Background
SST
Sea Ice
Convert the resolution
of obs. to modelOSTIA
(5 km, 1 time/day)
OSTIA(Not used sea ice) (1 time/day)
GDAPS LDAPS
Time series of Soil moisture (%) & Rainfall amount in July 2015 at Cheongju Agriculture Observatory
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Simulation of Typhoon in Old and New system
LDAPS.2016LDAPS.2012
Best Track
PPI0 composite (KMA+JMA)
COMS IR
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Verification of 500 hPa Height
0h 6h 12h 18h 24h 30h 36h
ldps 9,61 10,42 10,22 10,78 11,62 12,92 13,2
xldps 8,19 8,55 8,8 9,1 9,32 9,76 10,33
6,00
8,00
10,00
12,00
14,00
RM
SE
HGT 500hPa (Observation)
6 12 18 24 30 36
ldps 3,19 4,1 5,52 6,18 7,44 8,18
xldps 2,60 3,35 4,52 5,17 6,35 7,29
0,00
3,00
6,00
9,00
12,00
RM
SE
HGT 500hPa (Analysis)
July 2016
LDAPS.2012LDAPS.2016
LDAPS.2012LDAPS.2016
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VDAPS Very-short-range Data Assimilation & Prediction System
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❖ Purpose
- supporting the forecaster’s very-short range
forecast (hourly forecast)
- supporting “Olympic and Paralympic winter
games Pyeongchang 2018”
❖ Model
- UM vn10.1 (ENDGame)
❖ Area, resolution
- grid number : 804 (E-W) X 1000 (S-N)
- resolution : 1.5 ~ 4 km (Variable grid),
DA 3 km, 70 levels
❖ Forecast length (cycle)
- 12 hours (hourly)
❖ DA system: 3DVAR (FGAT, IAU)
- surface, sonde, windprofiler, aircraft
- radar (radial velocity, LHN), MAPLE*
- visibility assimilation
VDAPS (June 2017)
* MAPLE: McGill Algorithm for Prediction nowcasting by Lagrangian Extrapolation
http://190.1.20.52/personal/aroma37/vdps_domain_new.png
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MAPLE 1 hr forecast rain
▪ Early cycle system to support the forecasters
- observation time window : -30 ~ +10 minutes( ∴ Only limited observation data can be used! )
⇒ Output should be made within 20 minutes every hour on the hour for forecasters!
Cycle system of VDAPS
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Radar assimilation
LHN with default parameters LHN with optimal parametersNO LHN
Radar reflectivity
A
B
▪Suppress gravity wave generations▪ Improvements in rainfall (A, B)
▪Strong gravity wave generation
AWS
▪ Spatial average of rain rate : 5(km)x5(km)
▪ Nudging coefficient = 0.5 0.1
▪ α = 0.5 0.3
▪ ε = 0.5 0.3
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Visibility DA
visibility
aerosol
saturation vapor
mixing ratio
vapor mixng ratio
temperature
pressure
3DVAR
❖visibility operator
Clark et al. (2008)
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No DA of visibility DA of visibility
Observation (#238 stations)
Visibility DA
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▪ Making the raw observation data file within 10 minutes on the hour
▪ Impact of the Sonde data- simulated the strong rainfall cell
No Sonde With Sonde
Rawin Sonde DA
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Forecast impact for rainfall event
2017. 6. 13. 06 UTC + F03
AWS (1hr accum.)
VDAPSLDAPS
2017. 6. 13. 09 UTC
http://190.1.20.52/personal/minyou/VDAPSCases/20170613/aws_rain_2017061309_acc01h_vdps.png
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Ongoing works for LDAPS
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❖More than 100 stations over Korean peninsular
DA of Ground-based GNSS
going to be used in next LDAPS version
❖ Impact of G-GNSS ZTD (zenith total delay) DA• Period: July 2016 / Data: 40 stations in Korea
❖ Quality Control
▪ Normality test (Anderson-Darling test : p-value > 0.05)
▪ Bias correction (static bias correction: 1 month mean of O-B)
▪ Height difference between station and model surface (if diff >300 m, reject)
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Typhoon Track in 2016
Typhoon (Bogussing)
LDAPS
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❖ TC Bogus method isn’t applied in LDAPS.
Try to test bogussing module with the enlarged domain.
Typhoon (Bogussing)NEPARTAK (1601)
▪ TC bogussing for high resolution model stronger than w/o bogus
▪ Typhoon simulation of high resolution model Anal: similar with OBS
Fcst: usually simulated the TC stronger than OBS
Min. Sea Level Pressure Max. Wind Speed
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Land Surface DA
❖ SMC update time tests- Whenever the SMC was updated from global model every 06 UTC, there were peaks.
Tests for (EXP1) 6 hourly updates & (EXP2) using background
(EXP1) smaller picks & (EXP2) drifting
Forecast performances were similar.
(EXP1) (EXP2)
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Land Surface DA
Soil
Moisture
Contents(1st level)
Difference : EKF - Downscaling
WetDry
Regional EKFDownscaling (modified) from global model
06UTC 1 July 2016
❖ Downscaling : Rain band is shifted to western region.
❖ Regional EKF : no rain area is larger than downscaling exp.
1 hour
rainfall (6hr fcst)
❖ Implementation of regional EKF- Observation: screen T/Q (no satellite data)
- Preliminary results (6 days cycles)
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Summary
❖ Convective scale models in KMA
▪ LDAPS (May 2012~) : Short range forecasting (36 hr fcst), 1.5 km, 3DVar
- New version (2016) : ENDGame & domain extension better results
▪ VDAPS (June 2017~) : Very-short range forecasting (12 hr fcst), hourly cycle
- short obs time window, try to add more data like visibility & radar
❖ Ongoing works for LDAPS
▪ DA of Ground-GNSS ZTD with 40 stations in Korea
▪ TC bogussing & Land Surface DA : tested for extended domain LDAPS
▪ Background Error Covariance of UKV (not shown)
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Plan
❖ Convective Scale Model (’17.10)
▪ Extending the domain
▪ DA: G-GNSS, AMSU-B, Background Error Covariance, …
❖ Data Assimilation (’17~’18)
▪ 3DVar (hybrid) 4DVar (collaboration with MO)
▪ Update of Background Error Covariance
▪ Observation data
- Conventional DA : drifting Rawin Sonde data, Windprofiler QC, Aircraft QC
- Satellite DA : AMSU-B, IASI, GNSS-RO, [VDAPS] G-GNSS, AMV, …
- Radar DA : [VDAPS] radial velocity QC, reflectivity
❖ Development of Limited Area Model for KMA new Global system, KIM
(KIAPS model) (’18~)