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# Introduction of KMA statistic model and ensemble system - PowerPoint PPT Presentation

Introduction of KMA statistic model and ensemble system. Korea Meteorological Administration Numerical Weather Prediction Division Joo-Hyung Son. PPM (Perfect Prognostic Method) Daily Max/Min and midnight temperature Probability of Precipitation MOS (Model Output Statistics)

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Introduction of KMA statistic model and ensemble system

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## Introduction of KMA statistic model and ensemble system

Numerical Weather Prediction Division

Joo-Hyung Son

PPM (Perfect Prognostic Method)

Daily Max/Min and midnight temperature

Probability of Precipitation

MOS (Model Output Statistics)

Digital Forecast

KF(Kalman Filtering)/DLM(Dynamic Linear Model)

Daily Max/Min Temperature

3 hourly temperature

Daily Max/Min Temperature of 10 days

### Statistical models

Statistical models

Max/Min Temp

PoP

PPM

PPM

RDAPS

KF

Max/Min Temp

KF

DLM

3hr Temp

RDLM

GDAPS

Max/Min Temp

DLM

GDLM

Predictant

00 UTC : +1(00UTC, Max/Min)

12UTC : +1(Max), +2(00UTC, Min)

Forecast regions

70 sites in Korea

Model development

May 1, 1988 – Feb 28, 1992 (4 years)

Regional reanalysis of JMA

Climate data of 70 weather sites

### PPM for Max/Min Temp

PPM Model structure for Max/Min Temp

Forecast equation

Temp (t) = A + B*obs(0) + {Ci*model predictori(t)}

A, B, Ci (i=1,2,…,n): fixed coefficients

predictor

predictor

1000, 850, 700,500,400,300hPa

Wind speed, direction, Temperature

Dewpoint temp,

Height et al. from RDAPS

Observation, climate

Forecast eqs for

each season, sights

predictant

Max/Min and 00LST temperature

of 70 sights

Predictant

PPM Predictors

• select a group of predictors which explain predictant(temperature) well from 44 predictors <method: forward-backward selection>

• the number of the predictors of each seasonal and regional forecast equations are ranged from 5 to 10

• OBS: observation, CLMT: climate, PCWT: virtual prediction, VOR: vorticity, TAD: temperature advection, KYID: KY index

PPM Model structure for PoP

Forecast equation

Temp (t) = A + B*obs(0) + {Ci*modeli(t)}

A, B, Ci (i=1,2,…,n): fixed coefficients

predictor

predictor

1000, 850, 700,500,400,300hPa

Wind speed, direction, Temperature

Dewpoint temp,

Height et al. from RDAPS

Observation, climate

Forecast eqs for

Each region according

to warm and cold season

predictant

PoP of 18 regions

Predictant

PPM Predictors

• PoP

• the number of sites observed precipitation in the region

• Total number of sites in the region

• 18 regions :

• 24 region by cluster analysis

• (Moon(1990))

• + forecast experiment

• the forecast equations are developed according to the warm(April-September) and cold(October-March) season and each regions.

• 18 regions for forecast of PoP

Predictant

00 UTC : +1(Min/Max), +2(Min)

12 UTC : +1(Max), +2(Min/Max)

Forecast regions

40 in Korea,

32 in North Korea, China, Japan

### KF for Max/Min Temp

Kalman Filter algorithm

### KF for Max/Min Temp

vt~N(0,Vt): observation noise

wt~N(0,Wt) : process noise

Gt = 1

V0 = 2

4/365 0 0

W0= 0 1/365 0

0 0 1/365

1

Ft = RDAPS

Latest Obs temp

DLM(Dynamic Linear Model)

• DLM

• Improved Kalman Filter algorithm

• Weights(regression coefficient) are modified according to the prior condition with time.

DLM

DLM(Dynamic Linear Model)

vt~N(0,Vt)

wt~N(0,Wt)

• Use the updating algorithm to estimate Wt with time

• Find appropriate Wt increasing discount factor(0<delta<1) from 0.01 to 1 with interval 0.01

• the discount factor is selected when RMSE between observation and forecast is the lowest

RDLM(Regional DLM)

3hourly forecast up to 48hr

RDAPS

38 sites

GDLM(Global DLM)

Max/Min temp for 10 days

GDAPS

38 sites

DLM(Dynamic Linear Model)

Ensemble Prediction System

### KMA Ensemble Prediction System

Breeding

Pert. run

AnalysisD+Perturbation

normalization

Control run

AnalysisD

AnalysisD+1

Schematic diagram

• The global spectral model T106L30 with the slightly different initial conditions run 17 times.

• Both perturbed analysis and control analysis are projected to 24hours with the model, and departures from the control analysis at +24hours are scaled down to the norm of initial perturbations

Breeding + Rotation

AnalysisD+Perturbation

Pert. run

normalization

Control run

AnalysisD+1

AnalysisD

Rotation

Rotation

Schematic diagram

D+1 day

D day

D day + 12hr

D+1 day +12hr

• 17members could be similar each other because they are generated from the identical model, so this is to make different perturbation among the members manually.

• In the new system, the factor rotation was added every alternative step.

2005. 6. 11

old(cray-before)

NEW (cray-frot)

2005. 6. 11

old(cray-before)

NEW (cray-frot)

• spaghetti

• stamp map

• categorical PoP

• probability of Surface Max Wind

• time series of probability

Spaghetti

5520m

5640m

### Stamp map

• display the global model, mean and standard deviation and spaghetti as well as each member.

12-hour precipitation > given thresholds

: 1, 5, 10mm for winter season

: 1, 10, 50mm for other seasons

The probability

These probability maps are used for the early warning guidance of severe weather.

### Categorical PoP

Surface maximum wind > 10m/s, 14m/s

The probability

These probability maps are used for the early warning guidance of severe weather.

Precipitation

Sfc Max Wind

### Time series of Probability

• Precipitation

12hr accumul >=1mm

12hr accumul >= 10mm

12hr accumul >= 50mm

• Surface Max Wind

sfc wind >= 10m/s

sfc wind >= 14m/s

• Principle cities

Seoul, Daegu, Daejeon

Busan et al.

Largest value

Upper quartile

Median

Lower quartile

Smallest value

Interpretation

of boxplots

Image

of PDF

Time series of primary cities

### EPSgram

Ensemble Plumes

Time series of 8-day forecast at cities

The dispersion of members with forecast evolution

Variable : Pmsl, 500H, 850 T

Hwangsa (yellow sand) trajectory

Typhoon Strike Probability Map by EPS

Thank you

Factor analysis

• Factor analysis

• Factor analysis is a statistical technique to explain the most of the variability among a number of observable random variables in terms of a smaller number of unobservable random variables called factors

• Factor rotation

• Factor rotation is to find a parameterization in which each variable has only a small number of large loadings. That is, each variable is affected by a small number of factors, preferably only one. This can often make it easier to interpret what the factors represent.