Multimodel Superensemble Forecasts of Surface TMultimodel Superensemble Forecasts of Surface Temperature in the Northern Hemisphere emperature in the Northern Hemisphere

Xiefei Zhi, Yongqing Bai, Chunze Lin, Xiefei Zhi, Yongqing Bai, Chunze Lin, Haixia Qi, Wen ChenHaixia Qi, Wen Chen

Nanjing University of Information Science & TechnologyNanjing University of Information Science & TechnologyNanjing, China, 210044Nanjing, China, 210044

Monterey, CAMonterey, CA

Sep 2009Sep 2009

OutlineOutline

IntroductionIntroductionData and methodsData and methodsError evaluation Error evaluation Multimodel superensemble forecastMultimodel superensemble forecastImproved superensemble forecastImproved superensemble forecastSummarySummary

IntroductionIntroduction

Krishnamurti, T. N.Krishnamurti, T. N. et al (1999) in et al (1999) in Science,Science,Krishnamurti, T. N.Krishnamurti, T. N. et al (2000) in et al (2000) in J. ClimateJ. ClimateKrishnamurti, T. N.Krishnamurti, T. N. et al (2001) in Mon. Wea. Rev. et al (2001) in Mon. Wea. Rev.

PPostulated ostulated multimodel superensemble forecastmultimodel superensemble forecast method method for weather and seasonal climatefor weather and seasonal climate and compared the forecast skill and compared the forecast skill of the multimodel forecasts with that of the individual models, the of the multimodel forecasts with that of the individual models, the ensembleensemble mean, and individually bias-removed ensembmean, and individually bias-removed ensemblle mean.e mean.

IntroductionIntroduction

The multimodelThe multimodel superensemblesuperensemble forecasts forecasts outperformoutperform all all the individual models.the individual models.

The skillThe skill of the superensemble-based rain rates is of the superensemble-based rain rates is higherhigher than (a) individual model’s skills, (b) skill of the ensemble than (a) individual model’s skills, (b) skill of the ensemble mean, and (c) skill of the ensemble mean of individually mean, and (c) skill of the ensemble mean of individually biasbias--removedremoved models. models.

The work includesThe work includes

Model 1Model 1

Model 2Model 2

Model 3Model 3

Model nModel n

Error EvaluationError Evaluationof the of the

Ensemble MeanEnsemble Mean ForecastsForecasts

Model 1Model 1

Model 2Model 2

Model 3Model 3

Model nModel n

Ensemble MeanEnsemble Mean

SuperensembleSuperensembleForecastingForecasting

EvaluatingEvaluatingthe the

Forecast SkillForecast Skill of of

SuperensembleSuperensembleForecastsForecasts

New Idea

Data and MethodsData and MethodsDataData

1) Ensemble forecasts of the temperature at 2m from ECMWF, JMA, NCEP and UKMO provided by TIGGE archives.

Period: 1 June 2007 to 31 August 2007

Area: 10°-80°N ， 0°-357.5°,with a resolution of 1.25°×1.25°

Forecast: 24h-168h with a time interval of 24hrs

2) NCEP/NCAR Reanalyses are used as “observational data”

Period: 1 June 2007 to 7 September 2007

Area: same as that of dataset 1) with a resolution of 2.5°×2.5°

MethodsMethods

Superensemble:Superensemble:

Bias-removed Bias-removed

Ensemble Mean:Ensemble Mean:

Ensemble Mean:Ensemble Mean:

Root Mean Square Error:Root Mean Square Error:

2

1

( ) (1 .5)N train

t t

t

G S O

12 2

1

1[ ( ) ]

N

n nn

RMSerror F On

1

1( ) (1.3)

n

i ii

E F F On

1

1( ) (1.2)

n

mn ii

E Fn

MethodsMethods

Creation of a superensemble forecast at a given grid point:

.

The weights ai are computed at each grid point by minimizing

the function G in (1.5)

,1

[ ] (1.1)n

t i i t ii

S O a F F

2

1

( ) (1 .5)N train

t t

t

G S O

Error EvaluationError Evaluation

Forecast errors of ensemble mean forecasts of each model

1. 5

2

2. 5

3

3. 5

24 48 72 96 120 144 168Forecast(hrs)

RMSE

UKMONCEPECMWFJ MA

Error EvaluationError Evaluation

Mean RMSEs of the surface temperature in China, USA and Mean RMSEs of the surface temperature in China, USA and Europe for (a) ECMWF, (b)JMA, (c)NCEP, and (d)UKMO (UnitEurope for (a) ECMWF, (b)JMA, (c)NCEP, and (d)UKMO (Unit ：℃：℃ ). ).

0

0. 5

1

1. 5

2

2. 5

3

3. 5

4

4. 5

5

Chi na USA Europe

RMSE

24h48h72h96h120h144h168h

0

0. 5

1

1. 5

2

2. 5

3

3. 5

4

4. 5

5

Chi na USA Europe

RMSE

24h48h72h96h120h144h168h

0

0. 5

1

1. 5

2

2. 5

3

3. 5

4

4. 5

5

Chi na USA Europe

RMSE

24h48h72h96h120h144h168h

0

0. 5

1

1. 5

2

2. 5

3

3. 5

4

4. 5

5

Chi na USA Europe

RMSE

24h48h72h96h120h144h168h

aa

cc

bb

dd

Error EvaluationError Evaluation

Geographical distribution of tGeographical distribution of t

he RMSEs of the 24h forecasthe RMSEs of the 24h forecast

ECMWFECMWF JMAJMA

NCEP UKMOUKMO

Comparisons among the four models

Multimodel superensemble forecast Multimodel superensemble forecast

Mean RMSEs of the surface temperature forecast Mean RMSEs of the surface temperature forecast

with fixed training period (a) 24h, (b)48h, (c)72h, with fixed training period (a) 24h, (b)48h, (c)72h,

(d)96h(e)120h, (f)144h and (g)168h(d)96h(e)120h, (f)144h and (g)168h0

0. 5

1

1. 5

2

2. 5

3

3. 5

8 10 12 14 16 18 20 22 24 26 28 30

ECMWF

J MA

NCEP

UKMO

EMN

LRSUP

NNSUP

whywhy??How to How to deal with itdeal with it

Improved superensemble forecast Improved superensemble forecast with with running training periodsrunning training periods

RMSEs of the improved surface RMSEs of the improved surface temperature forecast for (a) 24h, (b)48h, temperature forecast for (a) 24h, (b)48h, (c)72h, (d)96h, (e)120h, (f)144h and (g)1(c)72h, (d)96h, (e)120h, (f)144h and (g)168h (Unit68h (Unit：℃：℃ ). ). 0

0. 5

1

1. 5

2

2. 5

3

8 10 12 14 16 18 20 22 24 26 28 30

EMN

LRSUP

NNSUP

R- LRSUP

R- NNSUP

Improved superensemble forecast Improved superensemble forecast with running training periodswith running training periods

0

0.5

1

1.5

2

2.5

3

24h 48h 72h 96h 120h 144h 168h

Time (Hours)Time (Hours)

ECMWF

JMA

NCEP

UKMO

EMN

LRSUP

NNSUP

R-LRSUP

R-NNSUP

Rm

s er

ror(

)℃

Mean RMSEs of the 24-168h surface teMean RMSEs of the 24-168h surface temperature forecastmperature forecast

0

10

20

30

40

50

24h 48h 72h 96h 120h 144h 168h

Time (Hours)

% I

mpr

ovem

ent

EMN

LRSUP

NNSUP

R-LRSUP

R-NNSUP

Percentage improvement of the EMN, Percentage improvement of the EMN, LRSUP, NNSUP, R-LRSUP, R-NNSUP LRSUP, NNSUP, R-LRSUP, R-NNSUP over the best modelover the best model

Improved superensemble forecast Improved superensemble forecast with running training periodswith running training periods

Geographical distribution of the RMSEs for 24hGeographical distribution of the RMSEs for 24h 、、 120h forecast from the be120h forecast from the best model, EMN, R-LRSUP and R-NNSUPst model, EMN, R-LRSUP and R-NNSUP

24h24h 120120hh

Optimal length of the training period Optimal length of the training period

0

1

2

3

4

20 25 30 35 40 45 50 55 60 65 70

24h 48h 72h 96h 120h 144h 168h

0

0. 5

1

1. 5

2

2. 5

3

20 25 30 35 40 45 50 55 60 65 70l ength of the t rai ni ng per i od(Days)

RMSE

(K)

Ave: 10°-80°N; 0°-357.5°E

0

0. 5

1

1. 5

2

2. 5

20 25 30 35 40 45 50 55 60 65 70l engt h of t he t r ai ni ng per i od( Days)

RMSE

(K)

Ave: 10°-30°N; 0°-357.5°E

00. 5

11. 5

22. 5

33. 5

4

20 25 30 35 40 45 50 55 60 65 70l engt h of t he t r ai ni ng per i od( Days)

RMSE

(K)

Ave: 30°-60°N; 0°-357.5°E

00. 5

11. 5

22. 5

33. 5

4

20 25 30 35 40 45 50 55 60 65 70l engt h of t he t r ai ni ng per i od( Days)

RMSE

(K)

Ave: 60°-80°N; 0°-357.5°E

The mean RMSEs of the surface temperature forecasts veThe mean RMSEs of the surface temperature forecasts versus the length of the running training period rsus the length of the running training period

SummarySummary

The superensemble with fixed training period gives a good imprThe superensemble with fixed training period gives a good improvement of 24h-72h temperature forecast with RMSEs reductioovement of 24h-72h temperature forecast with RMSEs reduction n over the best single model forecast and the multimodel ensemover the best single model forecast and the multimodel ensemble meanble mean..

The superensemble forecast using running training period furthThe superensemble forecast using running training period further improves 96h-168h temperature forecasts.er improves 96h-168h temperature forecasts.

The optimal training length is different for different forecast tiThe optimal training length is different for different forecast timeme..

Wen ChenWen ChenMonterey, CAMonterey, CA

Sep 2009Sep 2009