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KMA

Numerical weather forecast and Digital forecast at KMA

Young-Youn ParkNumerical Prediction Center/

Korea Meteorological Administration, currently visiting ECMWF

With contributions from

SangWon Joo, SunOk Mun, Young-Kyoung Seo

11th MOS workshop 12-16 Nov 2007

KMA

Outline

█ Overview of KMA’s NWP Systems █ Medium-range forecast

Recent Changes in KMA’s EPSTIGGE : Multi-model test

█ Digital Forecast System


KMA

Temp, POP(statistical

model)

GWAM(wave model,

1.25º, 10days)

RTSM(1/12º,48hrs)

GDLM( statistical

model,10days)

Data Acquisition & Q.C

EPS

(T213L40, 16members,10days,00/12)

KWAM(1/12º, 66hrs)

KLAPS(15/5km, 18hrs)

(Downscale,5km/33L, 1day)

RDAPS

RWAM(0.25º,66hrs)

On-Going Project

Storm-Scale Model

1~5Km grid modelRadar assimilationIntensify model physics

GDAPS(3dVar, FGAT

T426/L40, 10days)

RDAPS(Downscale,

FDDA, 30km/33L,66hrs)

RDAPS(3dVar,1day10km/33L)

Typhoon Model(30km, 72hrs)

Digital ForecastMIN/MAX T

PoP3Hr TRH

Wave HeightWind S/DPrep. Typ.Sky Cond.Rain/Snow

KWRF(3dVar

10km/30L,60hrs)

NWP systemTIGGE

Digital ForecastTMNTMXPoPPTYSKY


KMA GBEPS 1.1.1~ GBEPS 1.2.1

GBEPS 2.1.1~ GBEPS 2.3.1 GBEPS 3.3.1

Operation period 2001.3.1 ~ 2003.11.1 ~ From 2006.7.Data assimilation 2dOI → 3dOI 3dOI → 3dVar 3dVar

Model GDAPS T106L21 GDAPS T106L30 GDAPS T213L40Vertical

resolution 21 levels 30 levels 40 levels

Perturbation method Breeding Breeding +

Factor RotationBreeding +

Factor Rotation

Target area (BV) Global Northern Hemisphere

Northern Hemisphere

Run per days 1 (12UTC) 1 (12UTC) 2 (00, 12UTC)Lead time 10 days 8 days 10 daysEnsemble members 16 (16 members + 1 control) (16+1)*2

members

EPS (Ensemble Prediction System)


KMA

Recent Changes█ Increasing resolution and ensemble size

T106L30M16 → T213L40M32 once a day (12Z) → twice a day (00Z, 12Z)

█ Statistical Post-Processing : Bias Correction Decaying averaging bias estimate ( from Bo cui )

█ EPS does not produce enough spreadBred vectors seem to have similarity

Introduce factor rotationIntroduce factor rotationGrowth rate of perturbation is small

Test stochastic perturbationTest stochastic perturbation


KMA

Factor Rotation

█ What is the Factor analysis?Use relationship among variables Find the structure of the relationship and Tie to similar variables Factors

█ How to produce perturbation with factors?RRotateotate the factors obtained from factor analysis With keeping orthogonal feature among factors

⇒⇒ New perturbationsNew perturbations


KMA

Breeding+Factor Rotation

AnalysisAnalysisDD

AnalysisD+PerturbationPert. run

Control run

normalization

AnalysisAnalysisD+1D+1

Breeding

D day D day + 12hr D+1 day D+1 day +12hr

AnalysisAnalysisDD

AnalysisD+Perturbation

Pert. ru

n

Control run Rotation

normalization

AnalysisAnalysisD+1D+1 Rotation

Breeding + Rotation


KMA

Bias Corr. & Stochastic Pert.Bias Correction█ Motivation: strong +bias over east Asia █ Method: Decaying averaging bias estimation (Bo Cui)

Stochastic Perturbation█ Estimate error of model physics from background

error covariance used in 3dVar.random (stochastic) forcing << model error

█ Apply stochastic perturbation to tendency every hour


KMA

Impact of Factor rotation

RMSE OBVRMSE OBVRMSE RBVRMSE RBV

Spread RBVSpread RBVSpread OBVSpread OBV

IC: 2004 July 13 12UTC

PoP : July 16 12UTC (+3d)After factor rotation,probability of precipitation successfully reproduces the heavy rainfall as observed.

Operation(+3d) Factor rotation(+3d)

OBS : 24h rain


KMA

Impact of Resolution & Membership

T106L30T106L30

T213L40T213L40

RMSERMSE M16M16RMSE M32RMSE M32

Spread M32Spread M32SpreadSpread M16M16


KMA

Impact of Bias Correction

RMSE of Z500 SUMMER 2006

10

20

30

40

50

60

70

1 2 3 4 5 6 7

Lead time (Day)

RM

SE (m

)

before(NH)after(NH)before(EA)after(EA)

RMSE of Z500 WINTER 2006

10

20

30

40

50

60

70

80

90

1 2 3 4 5 6 7

Lead Time (Day)

RM

SE (m

)

편자보정 전(NH)

편자보정 후(NH)

편자보정 전(EA)

편자보정 후(EA)


KMA

TIGGE: Multi-model testMany Questions/Choices█ Which analysis to use█ How to combine ?

Use all the centers/members or only good centers/members?Simple or weighted combine How to determine the weight ?Using calibration? How to calibrate?

Multi-model ensemble test█ Combine test period: 8 Jun – 1 Sep (84 cases)█ Centres: ECMWF, UKMO, JMA, CMA█ Method:

Simply combine all the members With/without bias-correction (use previous 30 days)Calculate bias for each centres, cf/pf separately


KMA

15JUL

2007

16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14AUG

2007

5760

5764

5768

5772

5776

5780

()

ecmwf ukmo ncep jma bmrc cmaz500hPa mean of an area n.hem int 12h

Which an ? (Area mean of an)

15JUL

2007

16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14AUG

2007

289.2

289.4

289.6

289.8

290

290.2

290.4

290.6

290.8

()

ecmwf ukmo ncep jma bmrc cmat850hPa mean of an area tropics int 12h

15JUL

2007

16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14AUG

2007

5444

5448

5452

5456

5460

5464

5468(

)

ecmwf ukmo ncep jma bmrc cmaz500hPa mean of an area s.hem int 12h


KMA

Raw/Cal data ? (RMSE/RPSS : Z500 NH)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15fc-step (d)

0

10

20

30

40

50

60

RM

S (m

)

ecmwf ukmo cma jma eu eu_bccases 20070608-20070901_N84, area n.hemz at 500hPa 12 UTC (ecmwf_as_an) RMSE of Ens. Mean

EC/UK(BC)

EC/UK

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15fc-step (d)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Ran

ked

Prob

abilit

y Sk

ill Sc

ore

ecmwf ukmo cma jma eu eu_bc10 categories, cases 20070608-20070901_N84, area n.hemz at 500hPa (ecmwf_as_an)

EC/UK(BC)

EC/UK


KMA

Raw/Cal data ? (RMSE/RPSS : Z500 TR)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15fc-step (d)

0

2

4

6

8

10

12

14

16

RM

S (m

)

ecmwf ukmo cma jma eu eu_bccases 20070608-20070901_N84, area tropicsz at 500hPa 12 UTC (ecmwf_as_an) RMSE of Ens. Mean

EC/UK(BC)

EC/UK

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15fc-step (d)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Ran

ked

Prob

abilit

y Sk

ill Sc

ore

ecmwf ukmo cma jma eu eu_bc10 categories, cases 20070608-20070901_N84, area tropicsz at 500hPa (ecmwf_as_an)

EC/UK(BC)

EC/UK


KMA

Which centers ? (RPSS – Z500 NH/TR)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15fc-step (d)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Ran

ked

Prob

abilit

y Sk

ill Sc

ore

ecmwf eujc eujc_b10 categories, caz at 500hPa (ecm

c euj euj_bc eu eu_bc e_bcses 20070608-20070901_N84, area n.hemwf_as_an)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15fc-step (d)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Ran

ked

Prob

abilit

y Sk

ill Sc

ore

ecmwf eujc eujc_bc euj euj_bc eu eu_bc e_bc10 categories, cases 20070608-20070901_N84, area tropicsz at 500hPa (ecmwf_as_an)

EC/UK/JMA/CMA(BC)

EC(BC)

EC/UK(BC)

EC/UK/JMA(BC)


KMA

Sensitivity to parameters ?

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15fc-step (d)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Ran

ked

Prob

abilit

y Sk

ill Sc

ore

ecmwf eujc eujc_bc euj euj_bc eu eu_bc e_bc10 categories, cases 20070608-20070901_N84, area n.hemz at 500hPa (ecmwf_as_an)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15fc-step (d)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Ran

ked

Prob

abilit

y Sk

ill Sc

ore

ecmwf eujc eujc_bc euj euj_bc eu eu_bc e_bc10 categories, cases 20070608-20070901_N84, area n.hemt at 850hPa (ecmwf_as_an)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15fc-step (d)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Ran

ked

Prob

abilit

y Sk

ill Sc

ore

ecmwf eujc eujc_bc euj euj_bc eu eu_bc e_bc10 categories, cases 20070608-20070901_N84, area tropicsz at 500hPa (ecmwf_as_an)

EC/UK/JMA/CMA(BC)

EC(BC)

EC/UK(BC)

EC/UK/JMA(BC)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15fc-step (d)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1R

anke

d Pr

obab

ility

Skill

Scor

e

ecmwf eujc eujc_bc euj euj_bc eu eu_bc e_bc10 categories, cases 20070608-20070901_N84, area tropicst at 850hPa (ecmwf_as_an)

EC/UK/JMA/CMA(BC)EC(BC)

EC/UK(BC)

EC/UK/JMA(BC)


KMA

9JUN

12151821242730 3JUL

6 9 12151821242730 2AUG

5 8 11141720232629 1

1.2

1.35

1.5

1.65

1.8

1.95

2.1

2.25

2.4

2.55

rmse

_em

(K)

ecmwf ukmo eu eu_bct850hPa 12UTC step: 72h rmse_em area: n.hem

Daily RMSE – t850 +3d/+9d NH

9JUN

12151821242730 3JUL

6 9 12151821242730 2AUG

5 8 11141720232629 1

2.4

2.6

2.8

3

3.2

3.4

3.6rm

se_e

m(K

)

ecmwf ukmo eu eu_bct850hPa 12UTC step: 216h rmse_em area: n.hem


KMA

Summary of Preliminary Result █ More gains on probabilistic scores than deterministic scores █ AS, NA, NH: small gains, distinctive gains over tropics (where

ecmwf shows under-dispersion)█ Difference btn parameters and areas█ Bc better in early period, but nbc better in the later period of

forecast█ Larger benefit of multi-model over strong bias area█ Decision should be made on the bases of areas, parameters

of interest█ Lower level parameter (T2m, precipitation) ?█ Advanced calibration ? █ Optimum weight for each centers?


KMA

Digital Forecast : Motivation

Quantitative

Detailed

Applicable

VariousContents

█ Strong demand for detailed, quantitative meteorological services

█ Public does not want weather at station, but at their house

█ The use of meteorological data is too difficultPrivate sector requires easy to handle data to reproduce new application information

Nwp output is used by only very limited area

one of important barrier to develop application information


KMA

Change

Current Forecast

Goal of Digital ForecastGoal of Digital Forecast

When, Where, How Much, Computer Friendly form

QuantitativeDetailed

Applicable

VariousContents

Digital Forecast


KMA

Conceptual Structure

NWP

GDAPS/RDAPS

GWAM/ReWAM

Observation

Remote Sens.

Synoptic Obs.

AWS

Interpretationof NWP

MOS

PPM

Obs analysis

5km grid obs

Forecaster’s Editing (GEM)

Temporal Editing

Spatial Editing

Digital Digital ForecastForecast

DBDB

Verification/Intranet/InternetService

Spatial analysis

Spatial analysis

Graphical Editing

Sat: Sky Cover

Radar QPE

WEM


KMA

Status of Digital ForecastDigital Forecast range Short range Medium range

NWP Model RDAPS GDAPS

Forecast length/interval 48hours/3hours 10.5day/12hours

Resolution 5km 30km

Frequency (per day) 8 2

Forecast parameter 12 5

Grid/element transform module 11 5

Statistical analysis module (MOS) MOS 8(/9) developing

Spatial analysis module (2d-OI) apply, improvement not apply

Graphical editing module (GEM) semi-operation, improvement

Web service module (WEM) semi-operation, improvement

Verification system operation, improvement


KMA

Interpretation of Model Output : shortDFS

• Combine NWP Model and statistical methods• Generate 5 km-grid forecast, Predict every 3 hour up to 48 hours

MOS modelsat 103 stations

MOS

PPM

T, WIND, HUMIDITY PRESSURE, RAIN,

RDAPS:Forecast 66hourResolution : 30km/3hour

ReWAMForecast 66hourResolution : 0.25deg/3hourly

Sea surface wind, Wave height,

3-hour/Max/Min Temp., Probability of Precip., RH,

Wind, single-station eq.

Sky cover, Precip. TypeSnow-depth

Generalized-operator eq.

Precip.

Effective wave height


KMA

█ Need tool that mange and produce MOS eq. efficiently according to the changes of NWP models

█ Using this tool, make the indicator for MOS modelo Speed up for developing and improving MOS eq.o Standardization of MOS modelo Total system of development/verification/management of MOS

efficiency, accuracy, convenience

MOS Tool


KMA

1. Sampling Module

2. Statistics Module

3. Display/GUI Module

MOS Tool


KMA

Forecaster’s Editing (GEM)Weather Station

Regional Met. Office

KMA Headquarter

Time Series Editing Spatial Editing Issue Forecast

Objective Analysis

http://190.1.20.44/moni_gem/fcstday.php


KMA

Lua Editor

Lua Script filesSmart Tool manager

Lua Wrapper

SmartToolInterface Library

GEM3.0

Lua

GEM Editing Window Apply Smart Tool

GEM


KMA

Spatial analysis module (2d-OI)

Observation Grid point

First Guess (Y0f)

ffj

oj

m

j

j YXXhY 01

0 )( +−=∑=

guessFirstincrementweight +×=∑ )(

X

X

X

X

increment

0YfjX

ojX

Weight determine the reliability of analysis (Y0)

2 dimensional-Optimal Interpolation

( X ) ( Y0 )

determine weight that minimize (Y0-X0 )


KMA

h1h2...hm

σ01σ02...σ0m

σ11 , σ12 , .... , σ1mσ21 , σ22 , .... , .. . .. . .. . . σm1 , ......…. ,σmm

-1

=

Weight co-variance of obs to obs co-variance of obs to grid (fixed) (alter)

Weight (h) : 2dOI P

igjjiij

P

ijj

H μλλμμ =+∑=

)( 07

1 b

oσσ

λ =

i,j : station g: digital grid μij

P : BE covariance μijo: OE covariance

μigP : BE covariance between grid & station

assume OE covariance ~ 0

(Forecaster editing ~ true)

P

igj

P

ij

N

jH μμ =∑

=)(

1

Weight (h): DFS 2dOI

Weight in 2d-OI


KMA

21 braP

ij +=μ

r : distance a : error ratio (fixed, 1 )

b : error cov wrt r (fixed, 1/1600 )

Lorentz’s type function fitting

0.0

0.2

0.4

0.6

0.8

1.0

0 50 100 150 200 250 300

a

R(km)

Rd=100Rd=50

Rd=112 Rd=200

a=0.9a=0.8

a=0.5

a=1.0

Rd=112

)bR(1af(R) 2+

=

2dR

1 b =

OLD NEW

Use BE cov between stations

jjii

ijij

σσ

σρ =

(75х75)

ijσ : covariance

• Construct full matrix

- isotropic ⇒ anisotropic- homogeneous⇒ inhomogeneous

ρ11 , ρ12 , .... , ρ1mρ21 , ρ22 , .... , .. . .. . .. . .

ρm1 , ......…. , ρmm

=Pijμ

Correlation between obs stations


KMA

21 braP

ig +=μ

r : distance a : error ratio (fixed, 1 )

b : error cov wrt r (fixed, 1/1600 )

OLD NEW

1.Use BEC between stations2.Use BEC between station and AWS

3. Use grid GIS (PRISM :weight wrt distance, altitude, slope, ,)

assume: correlation of spatial error ~ correlation of geographic

information

PRISM0.6

0.05

0.2

0.15

0.1

0.4

0.1

0.4

∑

∑=

=

=K

kgk

K

kPkigkP

ig

1

1

ω

ρωμ

Pikρ

Pgkω

: BEC of neighboring station : weight of neighboring stations

Correlation between grid and station


KMA OLD - OBS NEW - OBS

RMSE: 2.82 RMSE: 2.24

Temperature ( 06UTC 5 OCT 2007)

Impact: ERROR


KMA

WEB SERVICE MODULE (WEM)

IMAGE-GRAPHIC TIME SERIES

WORDED/ VOICED GRID-POINT


KMA

Accuracy : Short range DFS

Verification items FCST MOS NWP IMPROVEMENT % (NWP vs.FCST) Remarks

RMSE TMP(C) 2.1 2.0 2.72.93.011.70.872.90.440.7418.0

RMSE TMAX(C) 2.4 2.422.217.220.011.11.53.42.274.05

RMSE TMIN(C) 2.4 2.4RMSE PPTN(mm) 10.5 -

ACC PPTN IDF 0.86 -RMSE WS (m/s) 2.8 -

RF WD (deg) 0.45 -ACC2 SKY 0.77 - Comparison with PPM

RMSE RH (%) 14.0 12.4 22.2 MOS since March 2006

DEC 2005 - SEP 2006


KMA

Distribution of RMSE

WINTER SPRING SUMMER

Mountainous area large error

Summer better than winter


KMA

Accuracy: Medium range DFS

Birer Skill Score(Warm Season)

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

+12h +36h +60h +84h +108h +132h +156h +180h +204h +228h +252hForecast time

BSS

103STN_MOS 76STN_MOS 41STN_MOS103STN_GDP 76STN_GDP 41STN_GDP

Birer Skill Score(Cold Season)

-0.2

-0.1

0

0.1

0.2

0.3

0.4

+12h +36h +60h +84h +108h +132h +156h +180h +204h +228h +252hForecast time

BSS

103STN_MOS 76STN_MOS 41STN_MOS103STN_GDP 76STN_GDP 41STN_GDP

BSS (Warm Season) BSS (Cold Season)

GDAPS

Medium MOSMedium MOS

GDAPS


KMA

Skill

Short term (’07-’09) Medium term(’10-’12) Long term (’13-’15)-Smart tool-MOS improvement-Editing module imp.

-5km KWRF -linkage with specialweather system-short, medium rangeunified system

-unified NWP system-dynamic time-spaceediting-seamless WID

Time

High Quality Digital Forecast

70%

80%

90%

100%

Rain or notTmaxTmin

95 %

69 %

62 %

97 %

84 %82 %

96 %

77 %

73 %

2009 2012

Extended public service-quantitative and detail forecast

-support to disaster prevension

Service for life benefit-life index, fire index

-Service of 3hr forecast

Service of industry-management of water source

-management of energy and harvest

deve

lopm

ent

Appl

icatio

n pl

anPlan : Digital Forecast

KMA

Thank You~