seasonal predictability in east asian region

Seasonal Predictability Seasonal Predictability in East Asian Region in East Asian Region

TTargeted argeted TTraining raining AActivity:ctivity:

SSeasonal easonal PPredictability in redictability in TTropical ropical RRegions: egions: RResearch and esearch and AApplicationspplications

『『 East Asian East Asian GroupGroup 』』

Juhyun Park (Republic of Juhyun Park (Republic of Korea)Korea)

Yanju Liu (China), qiaoping Yanju Liu (China), qiaoping

Li (China)Li (China) N. Jyothi (India), A. N. Jyothi (India), A. P. Dimri (India)P. Dimri (India)

Table of ContentsTable of Contents

3. Deterministic Forecast skill in DMME3. Deterministic Forecast skill in DMME

5. Conclusion5. Conclusion

1. Introduction1. Introduction

2. Climatological map of DEMETER 7 models 2. Climatological map of DEMETER 7 models

4. Probabilistic Forecast skill in DMME4. Probabilistic Forecast skill in DMME

PeriodPeriod 1980 yr ~ 2001 yr (Summer mean/Winter mean)1980 yr ~ 2001 yr (Summer mean/Winter mean) Region Region

Lon. : 40E~160E , Lat. : 20S~60NLon. : 40E~160E , Lat. : 20S~60N

Variable Variable Precipitation , 2m TemperaturePrecipitation , 2m Temperature

1. Introduction1. Introduction

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850 hPa wind climatology (JJA)850 hPa wind climatology (JJA)

2. Climatological structure of DEMETER models2. Climatological structure of DEMETER models

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2m Temperature climatology (JJA)2m Temperature climatology (JJA)


Precipitation climatology (JJA)Precipitation climatology (JJA)


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Correlation between observation and MME of Precipitation Correlation between observation and MME of Precipitation

observation and MME of Precipitation over Northeast Chinaobservation and MME of Precipitation over Northeast China

observation and MME of Precipitation over Mid-lower observation and MME of Precipitation over Mid-lower Yangtze River basinsYangtze River basins


Effect of MME Effect of MME : Mean Bias reduction : Mean Bias reduction

CMAP climatology – ECMW model climatologyCMAP climatology – ECMW model climatology

CMAP climatology – Multi-model climatology CMAP climatology – Multi-model climatology

3. Deterministic Forecast skill in MME3. Deterministic Forecast skill in MME

Correlation skill between Observation and Multi-Model Correlation skill between Observation and Multi-Model

MSLP MSLP JJAJJA

DJFDJF

PRCP PRCP JJAJJA

DJFDJF

TA2M TA2M JJAJJA

DJFDJF


Indian ocean index Indian ocean index JJAJJA

DJFDJF

The Indian Ocean The Indian Ocean RegionRegion

Lon. : 40 E ~ 110 ELon. : 40 E ~ 110 E

Lat. : 15 S ~ 10 NLat. : 15 S ~ 10 N

Var. : SST anomalyVar. : SST anomaly

Black line : Black line : Observation indexObservation index

Red line : the index in Red line : the index in DMMEDMME


East Asia Summer Monsoon index East Asia Summer Monsoon index

The East Asia RegionThe East Asia Region

Black line : Observation Black line : Observation indexindex

Green line : the index in Green line : the index in DMMEDMME

)500:(

):(sin

45sin

)125,20(25.0

)125,40(50.0

)125,60(25.0

heightlgeopotentahPaZZZZ

latitudeZZ

ENZ

ENZ

ENZEASM

s

s

s

s

Correlation between EASM Index and Precipitation Correlation between EASM Index and Precipitation

Observation(left) MME(right)


Precipitation in strong monsoon year(1997)Precipitation in strong monsoon year(1997)


Precipitation in weak monsoon year(1998)Precipitation in weak monsoon year(1998)


First Step : Climatological Probability Distribution Function

Climatological PDF

0 Xc-Xc

Second Step : Probability Forecast for particular time

- Normalizing all the forecast value )1,0(~),( NxZ

• In the 3 category case, -Xc and Xc make the below area separate 1/3 value each.

Ensemble PDF of particular time

BN NN AN

Below normal (BN)

Near normal (NN)

Above normal (AN)

Non-parametric approach

Parametric approach

dotstotal

dotsred

N

nP AAN

),(1

XcAP mGP

/ For Above normal case /

m : Ensemble mean value for particular year


TS2M PRCP

1982 winter mean

1985 winter mean

Reliability diagramReliability diagram: graphically represent the performance of probability forecasts of

dichotomous events for each category

The plot of observed relative frequency as a function of forecast probability :

The 1:1 diagonal perfect reliability line : A summary of the frequency of use of each forecast value


qqf )(

qagainstqfofvaluethe )(

Black, dashed line : Each Black, dashed line : Each modelmodel

Red, solid line : DMMERed, solid line : DMME

Above Normal Category ( Global Above Normal Category ( Global Region )Region )

Precipitation / JJAPrecipitation / JJA


Brier Score (B)Brier Score (B)

Brier skill score (BSS)Brier skill score (BSS)

n : the number of realizations of the forecasts over

which the validation is preformed

For each realization i ,

pi : forecast probability of the occurrence of the event

vi : a value equal to 1 or 0

depending on the event occurred / not.

refB

BBSS 1

Bref : a reference forecast

(taken to be the low-skill climatological forecasts)

BSS = 1 : a perfect forecast system

BSS = 0 (negative) : performs like (poorer than) the reference system

n

iii vp

nB

1

2)(1


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Cr

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orophorpfroV tt

,),min(

)1()()1()(),min(

Economic valueEconomic value

Observation (real event)

Yes No

Forecast(Action)

YesHit (h)

Cost (C)False (f)Cost (C)

NoMiss (m)Loss (L)

Correct reject

0

),(min, oL

CE

L

CoE

EE

EEV

mcliperf

perfimlc

fcstimcl

V = 1 : a perfect forecast system

V = 0 : performs like the reference system

* pt : threshold probability


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Above Normal Category Above Normal Category


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The Multi-model shows the better predictability than the single model following this study.

But, the forecast skill is different about the variables and the target region. This is the same results as in the deterministic forecast.

Probability forecasts show more information for users about future climate than deterministic forecast. Because this contains the uncertainty in the forecast problem.

5. Conclusion5. Conclusion

Thank you !! ^o^Thank you !! ^o^

seasonal predictability in east asian region

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