wg4:testing methods for various applications

WG4 Ioannina, 9 -10 May 2008

COST733COST733WG4WG4

CTs vs Teleconnection indices and Precipitation over Spain

María Jesús CasadoMaría Asunción Pastor

Sub. Gral. Climatología y AplicacionesState Meteorological Agency (AEMet)


WG4:Testing methods for various applications

Question: “Which are the best classifications for the selected applications?”

• Evaluation based on the comparison of characteristics of events in the classifications

• Teleconnection indices and Circulation Types (CTs)

• Influence of Circulation Types on Precipitation over SPAIN


COST733 D00-D09

D00 D09

CECGWT LITADVELITTCLUNDLWT2 NNW P27 PCACA PCAXTRKM PCAXTR PETISCO SANDRAS SANDRATPCA07TPCAVWLKC733 HBGWLOGWL PECZELY PERRET SCHUEEPP ZAMG

CECGWT LITADVELITTCLUNDLWT2 NNW P27 PCACA PCAXTRKM PCAXTR PETISCO SANDRAS SANDRATPCA07TPCAVWLKC733


1. Evaluation

• Version 1.1 of the catalogue

• Domains D00 and D09

• Extended winter (DJFM)

We analyze the behaviour of the classifications about the distribution of events, the mean lifetime, the percentage of time spending in events lasting 4 or more days and the number of 1-day events


1. Evaluation

Percentage of time spent in events >=4 days

0

10

2030

40

50

6070

80

90

CE

C

GW

T

LIT

AD

VE

LIT

TC

LUN

D

LWT

2

NN

W

P27

PC

AC

A

PC

AX

TR

KM

PC

AX

TR

PE

TIS

CO

SA

ND

RA

SA

ND

RA

S

TP

CA

V

TP

CA

07

WLK

C73

3

HB

GW

L

OG

WL

PE

CZ

ELY

PE

RR

ET

SC

HU

EE

PP

ZA

MG

Classifications

D00

D09

1-day Events

0

500

1000

1500

2000

2500

3000

3500

4000

CE

C

GW

T

LIT

AD

VE

LIT

TC

LUN

D

LWT

2

NN

W

P27

PC

AC

A

PC

AX

TR

KM

PC

AX

TR

PE

TIS

CO

SA

ND

RA

SA

ND

RA

S

TP

CA

V

TP

CA

07

WLK

C73

3

HB

GW

L

OG

WL

PE

CZ

ELY

PE

RR

ET

SC

HU

EE

PP

ZA

MG

Classifications

Nu

mb

er o

f 1-

day

eve

nts

D00

D09

Mean residence time

0

1

2

3

4

5

6

CE

C

GW

T

LIT

AD

VE

LIT

TC

LUN

D

LWT

2

NN

W

P27

PC

AC

A

PC

AX

TR

KM

PC

AX

TR

PE

TIS

CO

SA

ND

RA

SA

ND

RA

S

TP

CA

V

TP

CA

07

WLK

C73

3

HB

GW

L

OG

WL

PE

CZ

ELY

PE

RR

ET

SC

HU

EE

PP

ZA

MG

Classifications

Day

s D00

D09


SCHUEEPP-D00

0

50

100

150

200

250

300

350

400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

Circulation types

Nu

mb

er o

f d

ays

HBGWL-D00

0

200

400

600

800

1000

1200

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

Circulation types

Nu

mb

er o

f d

ays

TPCA07-D09

0

200

400

600

800

1000

1200

1400

1 2 3 4 5 6 7

Circulation types

Nu

mb

er o

f d

ays

1. Evaluation

NNW-D09

0

200

400

600

800

1000

1200

1400

1600

1800

2000

1 2 3 4 5 6 7 8 9 10 11 12

Circulation types

Nu

mb

er o

f d

ays


For D00

• HBGWL,TPCA07 and SANDRAS exhibit the higher percentages of time spent in events lasting 4 or more days, similar behaviour for the mean residence time

• SCHUEEPP, LITTC and LWT2 exhibit the shorter percentage of time spent in events lasting 4 or more days

• The classifications with the shorter mean residence time have a large proportion of 1-day events

For D09

• PCACA and NNW exhibit the higher percentages of time spent in events lasting 4 or more days, similar behaviour for the mean residence time

• LWT2, WLKC733 and P27 exhibit the shorter percentage of time spent in events lasting 4 or more days

• PCACA and NNW exhibit the most noticeable changes with respect to the results obtained for D00 (both classifications suffer a considerable reduction in the number of CTs in D09)

1. Evaluation


2.- Teleconnection indices/CTs

Which classifications are the best for discriminating NAO phases?

2.1.- Frequency of NAO+ and NAO- of each CT and classification

2.2.- Discrimination of classifications using χ2 statistic

Which classifications are the best for discriminating teleconnection indices?

2.3.- Discrimination of classifications using R2


2.1 NAO+/NAO-

• We analyse the relationship between NAO phases and CTs, using the winter NAO daily index, from the Climate Prediction Center (CPC), after its standardization

• We define NAO+ as the values greater than 1.0, and NAO- as

the values lesser than -1.0

• For each CT we analyse the number of days which are NAO+ and NAO- respectively


HBGWL-D00

0

10

20

30

40

50

60

70

80

90

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

Circulation types

Rel

ativ

e fr

equ

ency

NAO+

NAO-

NNW-D00

0

10

20

30

40

50

60

70

80

90

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Circulation types

Rel

ativ

e fr

equ

ency

NAO+

NAO-

2.1 NAO+/NAO- D00

SANDRA-D00

0

10

20

30

40

50

60

70

80

90

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Circulation types

Rel

ativ

e fr

equ

ency

NAO+

NAO-

OGWL-D00

0

10

20

30

40

50

60

70

80

90

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

Circulation types

Rel

ativ

e fr

equ

ency

NAO+

NAO-


2.1 NAO+/NAO- D00

The largest frequency values are detected for NAO- in:

HBGWL (CT14), 75% of the daysNNW (CT2), 75% of the daysOGWL (CT14,CT15), 65% of the days

The largest frequency values are detected for NAO+ in:

SANDRA (CT15,CT16), 50% of the daysSANDRAS (CT23), 50% of the daysTPCA07 (CT1), >40% of the days


LITTC-D09

0

10

20

30

40

50

60

70

80

90

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

Circulation types

Rel

ativ

e fr

equ

ency

NAO+

NAO-

PCAXTR-D09

0

10

20

30

40

50

60

70

80

90

100

1 2 3 4 5 6 7 8 9 10 11 12

Circulation types

Rel

ativ

e fr

equ

ency

NAO+

NAO-

PCACA-D09

0

10

20

30

40

50

60

70

80

90

100

1 2 3 4

Circulation types

Rel

ativ

e fr

equ

ency

NAO+

NAO-

2.1 NAO+/NAO- D09

GWT-D09

0

10

20

30

40

50

60

70

80

90

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Circulation types

Rel

ativ

e fr

equ

ency

NAO+

NAO-


The largest frequency values are detected for NAO- in:

SANDRA (CT19), 50% of the daysLITTC (CT13), 50% of the daysTPCA07 (CT5), 40% of the days

The largest frequency values are detected for NAO+ in:

SANDRAS (CT16), 40% of the daysPCAXTR (CT9), 40% of the daysPCAXTRKM (CT9), 40% of the days

2.1 NAO+/NAO- D09


2.2 X2 statistics

The X2 statistics:

I

i

teori

teorii NpNpk

1

22 )/)((

pi teor =(ni/N)*(K/N)

ki number of days of NAO+ (NAO-) for each CT and classification

ni total number of days for each CT and classification

K total number of days NAO+ (NAO-)

N total number of days for the period Dec 1957 to Mar 2002 (5456 days)

I number of CTs for each classification

Criteria: the higher values of X2 the best discrimination


2.2 χ2 statistics

NAO+

D00 D09SANDRAS SANDRAS

SANDRA CEC

NNW SANDRA

P27 LITTC

OGWL PCACA

NAO-

D00 D09SANDRAS SANDRAS

CEC SANDRA

OGWL CEC

SANDRA LITTC

NNW PCACA

NAO+

0

200

400

600

800

1000

1200

CEC

GWT

LITADVE

LITTC

LUND

LWT2

NNW

P27

PCACA

PCAXTRKM

PCAXTR

PETISCO

SANDRA

SANDRAS

TPCAV

TPCA07

WLKC733

HBGWL

OGWL

PECZELY

PERRET

SCHUEEPP

ZAMG

Classifications

X2

D00 D09

NAO-

0200400600800

100012001400

CEC

GWT

LITADVE

LITTC

LUND

LWT2

NNW

P27

PCACA

PCAXTRKM

PCAXTR

PETISCO

SANDRA

SANDRAS

TPCAV

TPCA07

WLKC733

HBGWL

OGWL

PECZELY

PERRET

SCHUEEPP

ZAMG

Classifications

X2

D00 D09


2.2 Ranking of classifications for NAO+/NAO- using χ2 statisticsNAO+ NAO-

D00 D09 D00 D09

SANDRASSANDRANNWP27OGWLLITTCTPCA07CECLITADVELUNDHBGWLPERRETPCACAPCAXTRKMLWT2TPCAVGWTPCAXTRPETISCOWLKC733SCHUEEPPZAMGPECZELY

SANDRASCECSANDRALITTCPCACAWLKC733P27PCAXTRZAMGPETISCOPCAXTRKMLWT2TPCAVNNWGWTLUNDLITADVETPCA07

SANDRASCECOGWLSANDRANNWHBGWLPCAXTRKMPCAXTRTPCAVP27PETISCOLITTCLITADVELWT2PERRETGWTLUNDPCACATPCA07WLKC733SCHUEEPPPECZELYZAMG

SANDRASSANDRACECLITTCPCACAP27WLKC733LWT2PETISCOPCAXTRKMGWTTPCA07PCAXTRLUNDTPCAVLITADVENNW


2.3 Teleconnection indices

• Principal Component analysis (PCA) in S-mode followed by an orthogonal rotation (varimax), (Richman, 1986) is applied to the daily winter 500-hPa geopotential height from ERA40 (2.5º x 2.5º).

• Four PCs rotated determined by the Log-Eigenvalue diagram.• Cumulative percentage of variance explained by the four PCs rotated:

62.2%.• Teleconnection indices identified: NAO, SCAN, EA and EU2

• Spatial domain: Euro-Atlantic region: 250N - 700N 450W - 500E (D00: 300N - 760N 370W - 580E)


2.3 R2 Discrimination

• The medians of the four teleconnection indices series for each classification are sorted in ascending order

• The linear trend and the coefficient of determination (R2) are calculated

• R2 value is used for discriminating classifications. The higher R2 values, the best discrimination

LITADVE-D09SCAN

y = 0,0252x - 0,1114

R2 = 0,7784

-0,15

-0,1

-0,05

0

0,05

0,1

0,15

0,2

0,25

1 2 3 4 5 6 7 8 9

Circulation Types

Med

ian

Example



D00

0,75

0,8

0,85

0,9

0,95

1

CE

C

GW

T

LIT

AD

VE

LIT

TC

LUN

D

LWT

2

NN

W

P27

PC

AC

A

PC

AX

TR

KM

PC

AX

TR

PE

TIS

CO

SA

ND

RA

SA

ND

RA

S

TP

CA

V

TP

CA

07

WLK

C73

3

HB

GW

L

OG

WL

PE

CZ

ELY

PE

RR

ET

SC

HU

EE

PP

ZA

MG

Classifications

R2

EU2

EA

SCAN

NAO



D09

0,75

0,8

0,85

0,9

0,95

1

CE

C

GW

T

LIT

AD

VE

LIT

TC

LUN

D

LWT

2

NN

W

P27

PC

AC

A

PC

AX

TR

KM

PC

AX

TR

PE

TIS

CO

SA

ND

RA

SA

ND

RA

S

TP

CA

V

TP

CA

07

WLK

C73

3

HB

GW

L

OG

WL

PE

CZ

ELY

PE

RR

ET

SC

HU

EE

PP

ZA

MG

Classifications

R2

EU2

EA

SCAN

NAO


2.3 Ranking of classifications for teleconnection indices using R2

NAO EA SCAN EU2

D00 D09 D00 D09 D00 D09 D00 D09

SANDRASANDRAGWTGWTLITTCLITTCP27P27NNWNNWWLKC733WLKC733LWT2LWT2PCAXTRPCAXTRSCHUEEPPSCHUEEPPPCAXTRKMPCAXTRKMCECCECOGWLOGWLHBGWLHBGWLPECZELYPECZELYLITADVELITADVETPCAVTPCAVSANDRASSANDRASPERRETPERRETPCACAPCACAZAMGZAMGLUNDLUNDTPCA07TPCA07PETISCOPETISCO

LWT2LWT2CECCECSANDRASSANDRASNNWNNWGWTGWTSANDRASANDRALITTCLITTCP27P27LUNDLUNDLITADVELITADVEWLKC733WLKC733PCAXTRKPCAXTRKMMPETISCOPETISCOTPCAVTPCAVPCACAPCACAPCAXTRPCAXTRTPCA07TPCA07

PCAXTRPCAXTRNNWNNWPERRETPERRETPCACAPCACAGWTGWTZAMGZAMGSCHUEEPPSCHUEEPPSANDRASANDRALITTCLITTCPCAXTRKMPCAXTRKMLWT2LWT2HBGWLHBGWLOGWLOGWLLITADVELITADVECECCECP27P27SANDRASSANDRASTPCAVTPCAVTPCA07TPCA07PETISCOPETISCOLUNDLUNDPECZELYPECZELYWLKC733WLKC733

LUNDLUNDPETISCOPETISCOP27P27LITADVELITADVEPCACAPCACALITTCLITTCSANDRASANDRACECCECLWT2LWT2GWTGWTSANDRASSANDRASTPCAVTPCAVPCAXTRPCAXTRWLKC733WLKC733NNWNNWPCAXTRKMPCAXTRKMTPCA07TPCA07

PERRETPERRETP27P27OGWLOGWLLWT2LWT2GWTGWTLUNDLUNDPCAXTRPCAXTRLITTCLITTCSCHUEEPPSCHUEEPPPCAXTRKMPCAXTRKMHBGWLHBGWLSANDRASANDRAZAMGZAMGPETISCOPETISCOCECCECWLKC733WLKC733PECZELYPECZELYSANDRASSANDRASPCACAPCACALITADVELITADVETPCA07TPCA07NNWNNWTPCAVTPCAV

PETISCOPETISCOGWTGWTLWT2LWT2LITTCLITTCTPCA07TPCA07PCAXTRPCAXTRSANDRASSANDRASP27P27CECCECWLKC733WLKC733SANDRASANDRATPCAVTPCAVPCAXTRKMPCAXTRKMLUNDLUNDPCACAPCACANNWNNWLITADVELITADVE

LITTCLITTCSCHUEEPPSCHUEEPPP27P27LWT2LWT2OGWLOGWLPCAXTRKMPCAXTRKMSANDRASANDRANNWNNWPCAXTRPCAXTRWLKC733WLKC733PETISCOPETISCOCECCECPECZELYPECZELYPERRETPERRETSANDRASSANDRASHBGWLHBGWLGWTGWTLITADVELITADVEZAMGZAMGLUNDLUNDTPCAVTPCAVTPCA07TPCA07PCACAPCACA

P27P27LWT2LWT2CECCECLITADVELITADVEGWTGWTPCAXTRPCAXTRPETISCOPETISCOLUNDLUNDSANDRASSANDRASLITTCLITTCTPCA07TPCA07SANDRASANDRAWLKC733WLKC733NNWNNWPCAXTRKMPCAXTRKMTPCAVTPCAVPCACAPCACA


2.3 Concluding remarks

For D00

• The R2 highest value is shown in EA• LITTC,LWT2,SCHUEEPP and CEC show similar R2 values for

all the teleconnection indices

For D09

• The R2 highest value is shown in EU2• SCAN show small R2 values for a great number of

classifications• LITTC, LWT2 show similar R2 values for all the

teleconnection indices


3. Influence of Circulation Types on Precipitation over Spain

3.1.- Data

3.2.- Precipitation percentage for each CT and classification

3.3.- Discrimination of classifications using the standard deviation of the

precipitation percentage


3.1 Data

• Daily gridded Precipitation data from INM Climatological Data Base

• Temporal domain: extended winter (DJFM) from 1961-1990

• Spatial domain: Spain


203 grid points (50kmx60km)

3.1 Data


3.2 Precipitation Percentage D00

TPCA07

0

10

20

30

1 2 3 4 5 6 7

PCACA

05

10152025

1 2 3 4 5 6 7 8 9 10 11

PETISCO

0

10

20

30

40

1 2 3 4 5 6 7 8 9 10 11 12 13 14

LUND

05

10152025

1 2 3 4 5 6 7 8 9 10


3.2 Precipitation Percentage D09

TPCA07

05

10152025

1 2 3 4 5 6 7

PCAXTRKM

0

10

20

30

40

1 2 3 4 5 6 7 8 9 10 11 12

PCACA

0

20

40

60

1 2 3 4

TPCAV

05

10152025

1 2 3 4 5 6 7 8 9


The red box is limited by : 1st quartile, median and 3rd quartile

3.2 Precipitation Percentage

classifications

1. GWT

2. LITADVE

3. LITTC

4. LUND

5. LWT2

6. NNW

7. P27

8. PCACA

9. PCAXTRKM

10. PCAXTR

11. PETISCO

12. SANDRAS

13. SANDRA

14. TPCA07

15. TPCAV

16. WLKC733

17. HBGWL

18. OGWL

19. PECZELY

20. PERRET

21. SCHUEEPP

22. ZAMG


3.2 Precipitation Percentage

The red box is limited by : 1st quartile, median and 3rd quartile

classifications

1. GWT

2. LITADVE

3. LITTC

4. LUND

5. LWT2

6. NNW

7. P27

8. PCACA

9. PCAXTRKM

10. PCAXTR

11. PETISCO

12. SANDRAS

13. SANDRA

14. TPCA07

15. TPCAV

16. WLKC733


3.2 Precipitation Percentage (Box-plots)

For D00

• The classifications with larger interquantile range are: LUND, PCACA and PECZELY

• The maximum appears in PETISCO followed by TPCA07• The classifications with larger medians are:

TPCA07,LITADVE and LUND

For D09

• The classifications with larger interquantile range are: PCACA, TPCAV and TPCA07

• The maximum appears in PCACA followed by PCAXTR and PCAXTRKM

• The classifications with larger medians are: PCACA, TPCA07 and LITADVE


3.3 Standard Deviation of Precip.Percentage

The Standard Deviation of Precipitation percentage:

2

1

)()1(

1)( j

N

iij xx

NjSTD

j grid-point

xij precipitation percentage at gridpoint j for a CT i and classification

mean of the precipitation percentage at gridpoint j for a given classification

N number of CTs for each classification

Criteria: the higher values of STD the best discrimination

This way, we have spatial patterns of the ‘performance’ of classifications

N

xx

N

iij

j

1


3.3 STD of precipitation percentage DOO


3.3 STD of precipitation percentage DO9


3.3 STD of precipitation percentage (mean value)

STD Precipitation Percentage

0

24

6

8

1012

14

CE

C

GW

T

LIT

AD

VE

LIT

TC

LUN

D

LWT

2

NN

W

P27

PC

AC

A

PC

AX

TR

KM

PC

AX

TR

PE

TIS

CO

SA

ND

RA

SA

ND

RA

S

TP

CA

V

TP

CA

07

WLK

C73

3

HB

GW

L

OG

WL

PE

CZ

ELY

PE

RR

ET

SC

HU

EE

PP

ZA

MG

Classifications

D00

D09


3.3 Ranking of classifications using STD (Prec.Perc.)

STD

D00 D09

TPCA07LUNDPCACAPETISCOPECZELYTPCAVLITADVEPCAXTRKMPCAXTRSANDRAGWTNNWSANDRASCECLWT2HBGWLWLKC733PERRETZAMGLITTCOGWLP27SCHUEEPP

PCACATPCA07TPCAVPCAXTRKMPCAXTRLUNDNNWLITADVEGWTSANDRALITTCPETISCOLWT2SANDRASP27CECWLKC733


3.-Concluding remarks Percentage of precipitation

For D00

• Highest percentages for: PETISCO, PCACA,TPCA07 and PCAXTR (15-40%)

• Smallest percentages for: P27,OGWL, LITTC, PERRET and SCHUEEPP (<10%)

For D09

• Highest percentages for: PCACA, PCAXTRKM, LITADVE, LUND, NNW, PECZELY and PETISCO (15-50%)

• Smallest percentages for: OGWL and LITTC (<10%)


3.-Concluding remarks

STD of precipitation percentage

For D00

• TPCA07 and LUND capture the three main regions of precipitation over Iberian Peninsula: Atlantic region, Cantabrian region and Mediterranean coast. In a lesser extent, PETISCO, PECZELY, TPCAV and LITADVE

For D09

• PCACA is to a large extent the best classification in capturing the three above-mentionned regions of precipitation in Iberian Peninsula.


MANY THANKS FOR YOUR ATTENTION

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