classifications of circulation patterns from the cost733 database: an assessment of synoptic-...
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Classifications of circulation patterns
from the COST733 database: An assessment of synoptic-
climatological applicability by two-sample Kolmogorov-Smirnov test
Radan HUTH, Monika CAHYNOVÁ
Institute of Atmospheric Physics,
Prague, Czech Republic
COST733 database (collection)
• COST733 Action – “Harmonization and Applications of Weather Types Classifications for European Regions”
• (very) large number of classifications produced
• on unified data– SLP at 12 UTC – ERA40 (Sep 1957 – Aug 2002)– ~9, ~18, ~27 types wherever possible – 12 European domains
COST733 database (collection)
• version 2.0 of the database– released this spring
• 18 methods for each domain– threshold-based: GWT (Beck), Litynski, Lamb (Jenkinson-
Collison), P27 (Kruizinga), WLK– leader algorithm: Lund, Kirchhofer, Erpicum– PCA-based: T-mode PCA– optimization algorithms: CKMEANS, PCACA (k-means),
Petisco, PCAXTRKMS, SANDRA, SANDRA-S, NNW (SOMs), PCAXTR
– pseudo-random: random centroids
• plus 7 subjective and objectivized classifications not attributable to any domain– ignored today
COST733 database (collection)
• different attributes of classifications– number of types (9 x 18 x 27)– sequencing (no vs. 4-day sequences)– seasonal vs. year-round definition– variable: all based on SLP, several additional
variables used
GOAL
• assess the synoptic-climatological applicability of classifications
• i.e., how well they stratify surface weather (climate) conditions
• demonstrate effect of– sequencing– seasonal vs. annual definition– adding more variables
• 500 hPa height • 500 hPa vorticity• 850/500 hPa thickness
– number of types
Classifications examined
• 11 methods – 30 classifications available for each of them– differing in
• sequencing (no x 4 days)• additional variables (Z500, THICK850/500, VOR500, all
together)• number of types (9, 18, 27)
• 5 methods– additional 6 classifications available– differing in
• seasonality of definition (year-round x seasonal)
TOOL
• 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
TOOL
• 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, the better the synoptic-climatological applicability
ANALYSIS
• preliminary results• maximum temperature
(minimum temperature – very similar results)(precipitation – different)
• domain 07 (central Europe)• 39 stations from ECA&D database• winter (DJF)• Jan 1961 – Dec 2000
RANKING OF CLASS’S
• at all stations individually: – for each classification: number of rejected K-S
counted– classifications ranked by the %age of rejected
K-S tests (= well separated classes)– higher %age better lower rank
• for each classification: ranks averaged over stations
• area mean rank ranking of the classification
Result 1: comparison of methods
• area mean ranks averaged over 30 realizations of each method
• result: order of the method, independent of any attribute (no. of types, sequencing, variable)
Result 1: comparison of methods
1 cluster analysis of PCs 7PCA-extreme scores reassigned by k-means
2SANDRA (optimized k-means)
8obliquely rotated T-mode PCA
3 C-k-means 9PCA-extreme scores reassigned by Eucl. distance
4 random centroids 10 Erpicum
5 Lund correlation-based 11orthogonally rotated T-mode PCA
6 Kruizinga
NOTE: not all methods participated in the race!
Result 2: sensitivity to the number of types
• all pairs of classifications– differing in no. of types
• 9 vs. 18
• 18 vs. 27
– with all other attributes equal
• difference in rank is calculated
• histogram of differences• t-test: equality of the
difference to zero-320 -240 -160 -80 0 80 160 240
0
0.04
0.08
0.12
0.16Effect o f no. o f types: 9 vs. 18
-106 ± 17
-320 -280 -240 -200 -160 -120 -80 -40 0 40 80 120
0
0.04
0.08
0.12
0.16Effect o f no. o f types: 18 vs. 27
Result 2: sensitivity to the number of types
• all pairs of classifications– differing in no. of types
• 9 vs. 18
• 18 vs. 27
– with all other attributes equal
• difference in rank is calculated
• histogram of differences• t-test: equality of the
difference to zero
-55 ± 12
-280 -240 -200 -160 -120 -80 -40 0 40 80 120 160 200
0
0.04
0.08
0.12
0.16Effect o f sequencing
Result 3: effect of sequencing
• all pairs of classifications– differing in sequencing (no
vs. 4-days)– with all other attributes equal
• difference in rank is calculated
• histogram of differences• t-test: equality of the
difference to zero
-30 ± 11
-200 -160 -120 -80 -40 0 40 80 120
0
0.04
0.08
0.12
0.16
0.2Effect o f seasonal defin ition
Result 4: effect of seasonality
• all pairs of classifications– differing in the seasonality in
their definition– with all other attributes equal
• difference in rank is calculated
• histogram of differences• t-test: equality of the
difference to zero
-44 ± 24
Result 5: effect of additional variables
-200 -160 -120 -80 -40 0 40 80 120 160 200 240 280 320
0
0.04
0.08
0.12
Effect o f add ing 500 hPa he ight & 850/500 hP a th ickness &
500 hPa vortic ity
-120 -80 -40 0 40 80 120 160 200 240 280
0
0.04
0.08
0.12
0.16Effect o f Z5 addition
+42 ± 24+68 ± 18
-160 -120 -80 -40 0 40 80 120 160 200 240 280
0
0.04
0.08
0.12
0.16Effect o f adding 500 hPa vortic ity
-160 -120 -80 -40 0 40 80 120 160 200 240 280
0
0.04
0.08
0.12
0.16Effect o f adding 850/500 hPa th ickness
Result 5: effect of additional variables
+61 ± 19+41 ± 18
CONCLUSIONS
• various kinds of cluster analysis perform well• fewer types better performance• sequencing adds value: surface temperature is
better described by types of 4-day sequences than types of instantaneous fields
• seasonal definition better than annual, but:– systematic difference in the number of types (7 vs. 9)
• additional variables bring no benefit; in fact they worsen the synoptic-climatological applicability
OUTLOOK
• analysis to extend to– all domains– more variables (Tmin, Precip)
• more comparisons will be possible results may be more general
• several other criteria as well• other datasets (gridded: ENSEMBLES,
reanalyses)