Nowcasting and Very Short-range Forecasts of the Convective System: The Korean Perspective
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Nowcasting and Very Short-range Forecasts
of the Convective System: The Korean
Perspective
Nowcasting and Very Short-range Forecasts
of the Convective System: The Korean
Perspective
Dong-Eon Chang, Y. H. Lee, J.-C. Ha, H. C. Lee, Y.-H Kim
Forecast Research Lab
National Institute of Meteorological Research
Dong-Eon Chang, Y. H. Lee, J.-C. Ha, H. C. Lee, Y.-H Kim
Forecast Research Lab
National Institute of Meteorological Research
Pre-CAS TECO, 16-17 Nov 2009, Incheon, KoreaPre-CAS TECO, 16-17 Nov 2009, Incheon, Korea
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BackgroundBackground
Isolated thunderstorm
Cloud clusterConvection band
Squall line
Convection band
Cloud cluster
Squall line
Isolated thunderstorm
Not defined
Total
No. of events 31 53 8 13 8 113
Ratio(%) 27.4 46.9 7.1 11.5 7.1 100
Heavy rainfall events (2000-2006)
Lee and Kim (2007)
4 Typical heavy rainfall types
• In Korea 45 % of Casualties by natural disaster is caused by Heavy rainfall events (NEMA, 2006)
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Forecast SkillForecast Skill
Forecast Length
Extrapolation
NWP
Fore
cast
Skill
Be
st
3-8 h
Explicit model
By J. Wilson (NCAR)
Position, Intensity
Initiation, growth
Extrapolation
○ ⅹ
NWP ⅹ ○
Nowcasting : 0~2hrNowcasting : 0~2hr
Very short-range forecast : Very short-range forecast : ~12h~12h
By WMO Tech Note No. 1024 By WMO Tech Note No. 1024
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Forecast Skill – Forecast Skill – KMA ApproachKMA Approach
Forecast Length
Fore
cast
Skill
Be
st
3-8 h
KMA Operational Model - KWRF, UM
KLAPS (Korea Local Analysis and Prediction System)
MAPLE (McGill Algorithm for Precipitation nowcasting by Lagrangian Extrapolation)
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MAPLEMAPLE
[ Variational echo tracking ]
[ Lagrangian persistence ]
[ Advection scheme ]
[ Scale dependence ]
[ Predictability of PDF ]
Rainfall QPF algorithm Variational Echo Tracking Semi-Lagrangian Advection
Scale dependence of predictability wavelet filtering Life time for each scale
Probabilistic nowcast Conditional ranked probability
score
MAPLE AlgorithmsMAPLE Algorithms
• Collaborative work with McGill University (2007-2009)
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KMA Operational Radar NetworkKMA Operational Radar Network
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MAPLE - VerificationsMAPLE - Verifications
False AlarmMissed LocationMissed EventFar
( 20km< )
OverestimateHitUnderestimateClose
(<=20km)
Too Much
(More than 10%)
Approx. Correct
(within 10% diff.)
Too Little
(Less than 10%)
False AlarmMissed LocationMissed EventFar
( 20km< )
OverestimateHitUnderestimateClose
(<=20km)
Too Much
(More than 10%)
Approx. Correct
(within 10% diff.)
Too Little
(Less than 10%)
Mean Forecast Rain Rate
Displacementof forecastrain pattern
False AlarmMissed LocationMissed EventFar
( 20km< )
OverestimateHitUnderestimateClose
(<=20km)
Too Much
(More than 10%)
Approx. Correct
(within 10% diff.)
Too Little
(Less than 10%)
False AlarmMissed LocationMissed EventFar
( 20km< )
OverestimateHitUnderestimateClose
(<=20km)
Too Much
(More than 10%)
Approx. Correct
(within 10% diff.)
Too Little
(Less than 10%)
Mean Forecast Rain Rate
Displacementof forecastrain pattern Radar OBS MAPLE
0100 KST 23 May, 2008 (~6hr fcst)
• High level of forecast skill has been shown up to about 2hr 30min according to the verification of 2008 summertime.
• There are overestimation or underestimation due to the missing of initiation and dissipation process. But more likely overestimate.
Hit
overestimates
underestimates
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KLAPSKLAPS
LSM(Soil)
lm1,lm2
LC3’(Cloud-Driven)
lcp,lty,lwc,lil,lct,lmd,lmt,lco,lrp,lst,(lwm),lhe,liw,lmr,lf1
lps,lcv,lso,lw3, lwm,vrc
L1S(Accu.)
l1svrc
LC3(3D Cloud) lps,lcb,lcv
lso,vrc,lvd,pin,lm2,lga
lc3(3D cld)
LSX(Surface)
lsx(sfc.anal)
lso,lgb,lwm
LT1(3D Temp.)
lga,snd,pin
lt1(temp./height)
tmg
LH3(Humidity)
lga,snd,lvd
lh3(rel.humidity)
lq3,lh4
LW3(3D Wind)
lso,cdw,pin,snd,lga
(pig),lwm,lw3pig,prg,sag
lwm (wind.anal
)
LW3(3D Wind)
lso,cdw,pin,snd,lga
(pig),lwm,lw3pig,prg,sag
lwm (wind.anal
)
LSX(Surface)
lsx(sfc.anal)
lso,lgb,lwm
LT1(3D Temp.)
lga,snd,pin
lt1(temp./height)
tmg
LC3(3D Cloud) lps,lcb,lcv
lso,vrc,lvd,pin,lm2,lga
lc3(3D cld)
LH3(Humidity)
lga,snd,lvd
lh3(rel.humidity)
lq3,lh4
LC3’(Cloud-Driven)
lcp,lty,lwc,lil,lct,lmd,lmt,lco,lrp,lst,(lwm),lhe,liw,lmr,lf1
lps,lcv,lso,lw3, lwm,vrc
L1S(Accu.)
l1svrc
LSM(Soil)
lm1,lm2
LSM(Soil)
LC3’(Cloud-Driven)
L1S(Accu.)
LC3(3D Cloud)
LSX(Surface)
LT1(3D Temp.)
LH3(Humidity)
LW3(3D Wind)
WRF modelWRF modelWeather Research & Forecasting modelWeather Research & Forecasting model
Analysis
Prediction
• Horizontal resolution : 5km, Forecast length : ~12h
KLAPS : Korea Local Analysis and Prediction System
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KLAPS Data IngestKLAPS Data Ingest
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KLAPS Data Ingest : LightningKLAPS Data Ingest : Lightning
CTL
LGT
If Lightning(grid) ±30min-> cloud base = LCL-> fill the cloud cover 0.9-> cloud ω * 2
CTL
LGT
If Lightning(grid) ±30min-> cloud base = LCL-> fill the cloud cover 0.9-> cloud ω * 2
Lightning NetworkLightning Network
IMPACT (IMProved Accuracy from Combined Technology) - Sensor : IMPACT ESP, LDAR II - Method : MDF + TOA and TOA, Detect CG and CC - Period : Since March 2001
IMPACT (IMProved Accuracy from Combined Technology) - Sensor : IMPACT ESP, LDAR II - Method : MDF + TOA and TOA, Detect CG and CC - Period : Since March 2001
Build deep convective cloudBuild deep convective cloud
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KLAPS Data Ingest : Radar KLAPS Data Ingest : Radar reflectivityreflectivity
uf_to_nc.exe
Remapping
(remap_polar_netcdf.exe)
Mosaic
mosaic_radar.x
……
Raw data (Polar coordinate)
Composite site (nearest site)
……
Remapping to Cartesian grid (each site)
Raw data (UF)
Polar netcdf file
3-D LAPS GRID(vxx)
2-D LAPS GRID(vrc)
3-D LAPS GRID(vrz)
Elev 0.0° Elev 7.03°
uf_to_nc.exe
Remapping
(remap_polar_netcdf.exe)
Mosaic
mosaic_radar.x
…………
Raw data (Polar coordinate)
Composite site (nearest site)
……
Remapping to Cartesian grid (each site)
Raw data (UF)
Polar netcdf file
3-D LAPS GRID(vxx)
2-D LAPS GRID(vrc)
3-D LAPS GRID(vrz)
Elev 0.0° Elev 7.03°
uf_to_nc.exe
Remapping
(remap_polar_netcdf.exe)
Mosaic
mosaic_radar.x
……
Raw data (Polar coordinate)
Composite site (nearest site)
……
Remapping to Cartesian grid (each site)
Raw data (UF)
Polar netcdf file
3-D LAPS GRID(vxx)
2-D LAPS GRID(vrc)
3-D LAPS GRID(vrz)
Elev 0.0° Elev 7.03°
uf_to_nc.exe
Remapping
(remap_polar_netcdf.exe)
Mosaic
mosaic_radar.x
…………
Raw data (Polar coordinate)
Composite site (nearest site)
……
Remapping to Cartesian grid (each site)
Raw data (UF)
Polar netcdf file
3-D LAPS GRID(vxx)
2-D LAPS GRID(vrc)
3-D LAPS GRID(vrz)
Elev 0.0° Elev 7.03°
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Operational FeaturesOperational Features
• Forecasts(~12h) guidance ready by 42 min from initial time
• 3D analysis is produced within 10 min each hour
3D Analysis (every 3D Analysis (every hour)hour)
Forecasts (every 3 Forecasts (every 3 hour)hour)
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Diabatic InitializationDiabatic Initialization• Diabatic initialization is unique technique of the KLAPS for the
improvement of precipitation forecast in the early integration time.
• Variational adjustment process is applied to produce dynamically balanced wind fields
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Effect of Diabatic InitializationEffect of Diabatic Initialization
Verification scoreVerification score (3 months (3 months average)average)
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Recent ImprovementRecent Improvement
Optimization of initialization- Seeking optimal cloud updraft- Tuning of radar reflectivity threshold
Ingest of VAD wind Adapting WDM microphysics scheme
Wind Profiler
VAD
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Optimization of Cloud updraft Optimization of Cloud updraft velocityvelocity
- W to height ratio Cu types (0.5)
- W to height ratio Sc types (0.05)
- W for St (0.01)
0.45.0 1 x
50.005.0 2 x
05.001.0 3 x
Wmax = depth * / dx for CuWmax = depth * / dx for Sc W = for St
1x2x3x
i
iETS Fitness 50,,2,1 i
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Genetic AlgorithmGenetic Algorithm
Start
Initialization
Fitness Evaluation
Selection
Crossover
Mutation
Fitness Evaluation
Terminal condition
End
NO
YES
The Genetic Algorithm (GA) is a global optimization approach based on the Darwinian principles of natural selection.
This method, developed from the concept of Holland [1975], aims to efficiently seek the extrema of complex function .
The PIKAIA seeks to maximize a function f(X)
in a bounded n-dimensional space,
),,,( 21 nxxx 0.1,0.0kx Each generation has 20 chromosomes. The
crossover probability is set to 0.85, implying that 85% of the chromosomes in a generation are allowed to crossover in an average sense. The maximum and minimum mutation probability is set to 0.05 and 0.005, respectively.
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Parameter estimationParameter estimation
animation
• GA shows quick convergence. The parameter X1 converged within 5~6th generation.
• Optimal value X1 = 3.95 X2 = 0.22 X3 = 0.035
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Optimization ResultsOptimization Results
CTRL Optimized Exp
AWS
RADAR
6h rainfall
50~80 mm50~80 mm
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Performance - examplesPerformance - examples
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Performance - examplesPerformance - examples
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KLAPS vs Regional ModelKLAPS vs Regional Model
• Precipitation verification score (ETS) for Jun – Aug 2009
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Summary and ConclusionSummary and Conclusion
MAPLE with KMA operational radar observation provided useful guidance up to 2~3hr.
Diabatic initialization of KLAPS showed promising results in the very short-range precipitation forecasts, and optimization of some parameters using GA was quite successful and efficient.
In the future, blending of MAPLE and KLAPS precipitation forecast will be tested.
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Thank youThank you
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■ ETS ■ BIAS
Default :10.3 :
0.310.2 :0.210.1 :0.11 :
xxxx
Sensitivity to parameter X1Sensitivity to parameter X1
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Verification ScoresVerification Scores
ETSETS BIASBIAS
thresholdthreshold::
1mm/1mm/3hr3hr
threshold:threshold:10mm/3hr10mm/3hr
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WSM vs WDMWSM vs WDM
A CASE A CASE (INIT: 2008. 6. 18. (INIT: 2008. 6. 18. 00UTC)00UTC)
Verification (Jun-Aug, Verification (Jun-Aug, 2008)2008)threshold: threshold:
1mm/3hr1mm/3hr
threshold: threshold: 10mm/3hr10mm/3hr
F03HF03H F06HF06H F09HF09H
AWSAWS
WSWSMM
WDWDMM