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

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

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)

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

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)

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)

KMA Operational Radar NetworkKMA Operational Radar Network

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

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

KLAPS Data IngestKLAPS Data Ingest

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

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°

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)

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

Effect of Diabatic InitializationEffect of Diabatic Initialization

Verification scoreVerification score (3 months (3 months average)average)

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

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

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.

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

Optimization ResultsOptimization Results

CTRL Optimized Exp

AWS

RADAR

6h rainfall

50~80 mm50~80 mm

Performance - examplesPerformance - examples

Performance - examplesPerformance - examples

KLAPS vs Regional ModelKLAPS vs Regional Model

• Precipitation verification score (ETS) for Jun – Aug 2009

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.

Thank youThank you

■ ETS ■ BIAS

Default :10.3 :

0.310.2 :0.210.1 :0.11 :

xxxx

Sensitivity to parameter X1Sensitivity to parameter X1

Verification ScoresVerification Scores

ETSETS BIASBIAS

thresholdthreshold::

1mm/1mm/3hr3hr

threshold:threshold:10mm/3hr10mm/3hr

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