synoptic-climatological evaluation of cost733 circulation classifications: czech contribution

Synoptic-climatological evaluation

of COST733 circulation classifications: Czech

contribution

Radan HUTH

Monika CAHYNOVÁ

Institute of Atmospheric Physics,

Prague, Czech Republic

[email protected]

http://www.cost.esf.org/

WHAT?

• behaviour of surface climate / weather elements– under a single type

versus – under other types or in all data


HOW?

• several different (complementary) approaches

• similar analyses also done in Augsburg by Christoph Beck & others


HOW?

• goodness-of-fit test: distribution under one type versus distribution under all other types / in all data– 2-sample Kolmogorov-Smirnov test

• explained variance• ratio of std.dev.: within-type / overall long-

term• correlation of time series: real vs.

‘reconstructed’ (mean value of each type)


a) goodness-of-fit testing

• evaluates how well a classif. stratifies surface weather (climate) conditions

• 2-sample Kolmogorov-Smirnov test

• equality of distributions of the climate element under one type against under all the other types

- 1 0 - 5 0 5 1 0 1 5 2 0 2 54 0

4 5

5 0

5 5

6 0

- 1 0 - 5 0 5 1 0 1 5 2 0 2 54 0

4 5

5 0

5 5

6 0

- 1 0 - 5 0 5 1 0 1 5 2 0 2 54 0

4 5

5 0

5 5

6 0

- 1 0 - 5 0 5 1 0 1 5 2 0 2 54 0

4 5

5 0

5 5

6 0

- 1 0 - 5 0 5 1 0 1 5 2 0 2 54 0

4 5

5 0

5 5

6 0

- 1 0 - 5 0 5 1 0 1 5 2 0 2 54 0

4 5

5 0

5 5

6 0

- 1 0 - 5 0 5 1 0 1 5 2 0 2 54 0

4 5

5 0

5 5

6 0

- 1 0 - 5 0 5 1 0 1 5 2 0 2 54 0

4 5

5 0

5 5

6 0

x

- 1 0 - 5 0 5 1 0 1 5 2 0 2 54 0

4 5

5 0

5 5

6 0



• 73 classifications from the v1.2 release of COST733 database

• domains – 00 (whole Europe) – 07 (central Europe)

• winter (DJF) & summer (JJA)• Jan 1958 – Feb 1993• 97 European stations (ECA&D database)• surface climate variables

– maximum temperature– minimum temperature



• at each station• types for which the K-S test rejects the

equality of distributions are counted• the larger the count, the better the

stratification• at each station: methods ranked by the %age

of well separated classes (= rejected K-S tests)

• for each classification: ranks averaged over stations area mean rank final rank of the classification


RANKING OF CLASS’S

0 20 40 60 80rank

0

10

20

30

40

50

no

. of

typ

es

Tmax, DJF, domain 00



0 20 40 60 80rank

0

10

20

30

40

50

no

. of

typ

es

Tmax, JJA, domain 00



0 20 40 60 80rank

0

10

20

30

40

50

no

. of

typ

es

Tmax, DJF, domain 07


Tmax, DJF, dom. 00 ~9 ~18 ~27

Enke & Spekat 6 7 6

Erpicum Z850 20 19 17

Erpicum SLP 22 24 22

Beck (GWT) 8 10 11

Kirchhofer 23 23 23

Litynski 19 9 12

Lund 15 16 15

Lamb (Jenk.-Coll.) 4 2 4

neural nets 18 14 16

P27 (Kruizinga) 1 6 8

PCACA (Rasilla) 13 13 14

PCAXTR (Esteban) 9 12 -

PCAXTRK 12 18 -

Petisco 16 21 18

Sandra 7 5 7

Sandra-S 2 3 5

T-mode PCA 17 15 19

WLKC 24 22 21

Hess & Brezowsky 3 - 2

objective Hess&Brez - - 1

obj. H&B – SLP - - 3

Peczely 11 - -

Perret - - 9

Schüepp - - 13

ZAMG - - 24


Tmax, DJF, dom. 00 ~9 ~18 ~27 Σ

Enke & Spekat 6 7 6 19

Erpicum Z850 20 19 17 56

Erpicum SLP 22 24 22 68

Beck (GWT) 8 10 11 29

Kirchhofer 23 23 23 69

Litynski 19 9 12 40

Lund 15 16 15 46

Lamb (Jenk.-Coll.) 4 2 4 10

neural nets 18 14 16 48

P27 (Kruizinga) 1 6 8 15

PCACA (Rasilla) 13 13 14 40

PCAXTR (Esteban) 9 12 - -

PCAXTRK 12 18 - -

Petisco 16 21 18 55

Sandra 7 5 7 19

Sandra-S 2 3 5 10

T-mode PCA 17 15 19 51

WLKC 24 22 21 67

Hess & Brezowsky 3 - 2 -

objective Hess&Brez - - 1 -

obj. H&B – SLP - - 3 -

Peczely 11 - - -

Perret - - 9 -

Schüepp - - 13 -

ZAMG - - 24 -


Tmax, DJF, dom. 00 ~9 ~18 ~27 Σ rank

Enke & Spekat 6 7 6 19 4-5

Erpicum Z850 20 19 17 56 13

Erpicum SLP 22 24 22 68 15

Beck (GWT) 8 10 11 29 6

Kirchhofer 23 23 23 69 16

Litynski 19 9 12 40 7-8

Lund 15 16 15 46 9

Lamb (Jenk.-Coll.) 4 2 4 10 1-2

neural nets 18 14 16 48 10

P27 (Kruizinga) 1 6 8 15 3

PCACA (Rasilla) 13 13 14 40 7-8

PCAXTR (Esteban) 9 12 - - -

PCAXTRK 12 18 - - -

Petisco 16 21 18 55 12

Sandra 7 5 7 19 4-5

Sandra-S 2 3 5 10 1-2

T-mode PCA 17 15 19 51 11

WLKC 24 22 21 67 14

Hess & Brezowsky 3 - 2 - -

objective Hess&Brez - - 1 - -

obj. H&B – SLP - - 3 - -

Peczely 11 - - - -

Perret - - 9 - -

Schüepp - - 13 - -

ZAMG - - 24 - -



Enke & Spekat 6 7 6 19 4-5

Erpicum Z850 20 19 17 56 13

Erpicum SLP 22 24 22 68 15

Beck (GWT) 8 10 11 29 6


Litynski 19 9 12 40 7-8

Lund 15 16 15 46 9

Lamb (Jenk.-Coll.) 4 2 4 10 1-2

neural nets 18 14 16 48 10

P27 (Kruizinga) 1 6 8 15 3

PCACA (Rasilla) 13 13 14 40 7-8


PCAXTRK 12 18 - - -

Petisco 16 21 18 55 12

Sandra 7 5 7 19 4-5

Sandra-S 2 3 5 10 1-2

T-mode PCA 17 15 19 51 11

WLKC 24 22 21 67 14



obj. H&B – SLP - - 3 - -

Peczely 11 - - - -

Perret - - 9 - -

Schüepp - - 13 - -

ZAMG - - 24 - -

Tmin, DJF, dom. 00 ~9 ~18 ~27 Σ rank

Enke & Spekat 6.5 10 8 24.5 4-5

Erpicum Z850 19 19 18 56 13

Erpicum SLP 22 23 22 67 15

Beck (GWT) 8 8 10 26 6


Litynski 21 4 7 32 7

Lund 14 20 14 48 10

Lamb (Jenk.-Coll.) 4 3 4 11 2

neural nets 18 13 16 47 9

P27 (Kruizinga) 2 6 6 14 3

PCACA (Rasilla) 10 12 13 35 8


PCAXTRK 12 15 - - -

Petisco 11 21 19 51 11

Sandra 6.5 7 11 24.5 4-5

Sandra-S 1 1 2 4 1

T-mode PCA 17 18 17 52 12

WLKC 24 22 20 66 14



obj. H&B – SLP - - 5 - -

Peczely 13 - - - -

Perret - - 9 - -

Schüepp - - 15 - -

ZAMG - - 24 - -



Enke & Spekat 6 7 6 19 4-5

Erpicum Z850 20 19 17 56 13

Erpicum SLP 22 24 22 68 15

Beck (GWT) 8 10 11 29 6


Litynski 19 9 12 40 7-8

Lund 15 16 15 46 9

Lamb (Jenk.-Coll.) 4 2 4 10 1-2

neural nets 18 14 16 48 10

P27 (Kruizinga) 1 6 8 15 3

PCACA (Rasilla) 13 13 14 40 7-8


PCAXTRK 12 18 - - -

Petisco 16 21 18 55 12

Sandra 7 5 7 19 4-5

Sandra-S 2 3 5 10 1-2

T-mode PCA 17 15 19 51 11

WLKC 24 22 21 67 14



obj. H&B – SLP - - 3 - -

Peczely 11 - - - -

Perret - - 9 - -

Schüepp - - 13 - -

ZAMG - - 24 - -

Tmin, DJF, dom. 00 ~9 ~18 ~27 Σ rank

Enke & Spekat 6.5 10 8 24.5 4-5

Erpicum Z850 19 19 18 56 13

Erpicum SLP 22 23 22 67 15

Beck (GWT) 8 8 10 26 6


Litynski 21 4 7 32 7

Lund 14 20 14 48 10


neural nets 18 13 16 47 9

P27 (Kruizinga) 2 6 6 14 3



PCAXTRK 12 15 - - -

Petisco 11 21 19 51 11

Sandra 6.5 7 11 24.5 4-5

Sandra-S 1 1 2 4 1

T-mode PCA 17 18 17 52 12

WLKC 24 22 20 66 14



obj. H&B – SLP - - 5 - -

Peczely 13 - - - -

Perret - - 9 - -

Schüepp - - 15 - -

ZAMG - - 24 - -


Enke & Spekat 16 8 15 39 9

Erpicum Z850 19 11 19 49 12-13

Erpicum SLP 14 13 23 50 14

Beck (GWT) 6 6 13 25 5-6


Litynski 3 3 5 11 1

Lund 13 12 26 51 15


neural nets 26 18 25 69 16

P27 (Kruizinga) 8 7 10 25 5-6



PCAXTRK 9 15 - - -

Petisco 18 10 21 49 12-13

Sandra 4 9 8 21 4

Sandra-S 21 5 1 27 7

T-mode PCA 10 16 20 46 11

WLKC 12 14 18 44 10



obj. H&B – SLP - - 6 - -

Peczely 17 - - - -

Perret - - 16 - -

Schüepp - - 22 - -

ZAMG - - 27 - -

better in large domain

better in small domain


b) other criteria

• selection of classifications: 26– 8 class’s for ~9, ~18, ~27 types– Hess&Brezowsky: GWL (29 types), GWT (10 types)

• domain 07 (central Europe)• separate analysis for Jan, Apr, Jul, Oct• 1961-1998• 21 stations in the Czech Republic• 8 surface climate variables

– temperature min, max, mean– precipitation amount, occurrence– cloudiness, sunshine duration, relative humidity


b) other criteria

• criteria: – explained variance– normalized within-type std.dev.– correlation real vs. reconstructed series

• averaged over stations and variables

D07

0

5

10

15

20

25

30

CK

ME

AN

SC

09

GW

TC

10

LIT

AD

VE

LU

ND

C09

P27C

08

PE

TIS

CO

C09

SA

ND

RA

C09

TP

CA

C09

CK

ME

AN

SC

18

GW

TC

18

LIT

C18

LU

ND

C18

P27C

16

PE

TIS

CO

C18

SA

ND

RA

C18

TP

CA

C18

CK

ME

AN

SC

27

GW

TC

26

LIT

TC

LU

ND

C27

P27C

27

PE

TIS

CO

C27

SA

ND

RA

C27

TP

CA

C27

HB

GW

LH

BG

WT

ran

k

Jan_EV Apr_EV Jul_EV Oct_EV

Jan_WSD Apr_WSD Jul_WSD Oct_WSD

Jan_correl Apr_correl Jul_correl Oct_correl

~9 types ~18 types ~27 types H&B


b) other criteria

• summarizing: ranking by averaged ranks– overall– sensitivity to

• evaluation criterion• season• number of types


Rankings

all

criteria season no. of types

EV STD COR Jan Apr Jul Oct ~9 ~18 ~27

H&B 1 1 2 1 1 1 1 1 1 - 1

Litynski 2 2 5 3 3 4 2 2 8 1 2

GWT 3 3 7 2 2 2 6 6 4-5 2 3

SANDRA 4 4 3-4 4 5 3 4-5 5 4-5 3 4

CKMeans 5 5 3-4 5 4 5 7 4 2 4 6

Petisco 6 8 1 8 7 6 3 3 3 5 5

Lund 7 6 6 7 8 7-8 4-5 8 7 6 7

TPCA 8 7 8 6 6 7-8 8 7 6 7 8

P27 9 9 9 9 9 9 9 9 9 8 9

K-S test,

TX, DJF

1

6

5

3-4

3-4

7

8-9

8-9

2


CONCLUSIONS

• most criteria highly sensitive to the number of types

• to alleviate this: – sort class’s by the approx. no. of types – rank in each group separately

• different criteria may yield different ranking of class. methods

• Hess&Brezowsky is most frequently counted as “best”


synoptic-climatological evaluation of cost733 circulation classifications: czech contribution

Documents

objective hessbrez

hb slp

typea goodness

climate element

typesxa goodness

fit testingevaluates

fit testingat

single type