synoptic-climatological evaluation of cost733 circulation classifications: czech contribution radan...
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Synoptic-climatological evaluation
of COST733 circulation classifications: Czech
contribution
Radan HUTH
Monika CAHYNOVÁ
Institute of Atmospheric Physics,
Prague, Czech Republic
huth@ufa.cas.cz
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
a) goodness-of-fit testing
• 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
a) goodness-of-fit testing
• 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
RANKING OF CLASS’S
0 20 40 60 80rank
0
10
20
30
40
50
no
. of
typ
es
Tmax, JJA, domain 00
RANKING OF CLASS’S
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 - -
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 - -
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 - -
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
Kirchhofer 23 24 23 70 16
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
PCAXTR (Esteban) 9 16 - - -
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
Hess & Brezowsky 5 - 3 - -
objective Hess&Brez - - 1 - -
obj. H&B – SLP - - 5 - -
Peczely 13 - - - -
Perret - - 9 - -
Schüepp - - 15 - -
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 - -
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
Kirchhofer 23 24 23 70 16
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
PCAXTR (Esteban) 9 16 - - -
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
Hess & Brezowsky 5 - 3 - -
objective Hess&Brez - - 1 - -
obj. H&B – SLP - - 5 - -
Peczely 13 - - - -
Perret - - 9 - -
Schüepp - - 15 - -
ZAMG - - 24 - -
Tmax, DJF, dom. 07 ~9 ~18 ~27 Σ rank
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
Kirchhofer 15 2 11 28 8
Litynski 3 3 5 11 1
Lund 13 12 26 51 15
Lamb (Jenk.-Coll.) 11 4 4 19 3
neural nets 26 18 25 69 16
P27 (Kruizinga) 8 7 10 25 5-6
PCACA (Rasilla) 5 1 7 13 2
PCAXTR (Esteban) 20 17 - - -
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
Hess & Brezowsky 2 - 2 - -
objective Hess&Brez - - 3 - -
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”
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