1. Synoptic-climatological evaluation of COST733 circulation classifications: Czech contribution Radan HUTH Monika CAHYNOVÁ Institute of Atmospheric Physics, Prague, Czech Republic huth@ufa.cas.cz

2. WHAT? behaviour of surface climate / weather elements –under a single type versus –under other types or in all data

3. HOW? several different (complementary) approaches similar analyses also done in Augsburg by Christoph Beck & others

4. 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)

5. 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 x

6. 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

7. 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

8. RANKING OF CLASSS

11. Tmax, DJF, dom. 00~9~18~27 Enke & Spekat676 Erpicum Z850201917 Erpicum SLP222422 Beck (GWT)81011 Kirchhofer23 Litynski19912 Lund151615 Lamb (Jenk.-Coll.)424 neural nets181416 P27 (Kruizinga)168 PCACA (Rasilla)13 14 PCAXTR (Esteban)912- PCAXTRK1218- Petisco162118 Sandra757 Sandra-S235 T-mode PCA171519 WLKC242221 Hess & Brezowsky3-2 objective Hess&Brez--1 obj. H&B – SLP--3 Peczely11-- Perret--9 Schüepp--13 ZAMG--24

12. Tmax, DJF, dom. 00~9~18~27Σ Enke & Spekat67619 Erpicum Z85020191756 Erpicum SLP22242268 Beck (GWT)8101129 Kirchhofer23 69 Litynski1991240 Lund15161546 Lamb (Jenk.-Coll.)42410 neural nets18141648 P27 (Kruizinga)16815 PCACA (Rasilla)13 1440 PCAXTR (Esteban)912-- PCAXTRK1218-- Petisco16211855 Sandra75719 Sandra-S23510 T-mode PCA17151951 WLKC24222167 Hess & Brezowsky3-2- objective Hess&Brez--1- obj. H&B – SLP--3- Peczely11--- Perret--9- Schüepp--13- ZAMG--24-

13. Tmax, DJF, dom. 00~9~18~27Σrank Enke & Spekat676194-5 Erpicum Z8502019175613 Erpicum SLP2224226815 Beck (GWT)81011296 Kirchhofer23 6916 Litynski19912407-8 Lund151615469 Lamb (Jenk.-Coll.)424101-2 neural nets1814164810 P27 (Kruizinga)168153 PCACA (Rasilla)13 14407-8 PCAXTR (Esteban)912--- PCAXTRK1218--- Petisco1621185512 Sandra757194-5 Sandra-S235101-2 T-mode PCA1715195111 WLKC2422216714 Hess & Brezowsky3-2-- objective Hess&Brez--1-- obj. H&B – SLP--3-- Peczely11---- Perret--9-- Schüepp--13-- ZAMG--24--

14. Tmax, DJF, dom. 00~9~18~27Σrank Enke & Spekat676194-5 Erpicum Z8502019175613 Erpicum SLP2224226815 Beck (GWT)81011296 Kirchhofer23 6916 Litynski19912407-8 Lund151615469 Lamb (Jenk.-Coll.)424101-2 neural nets1814164810 P27 (Kruizinga)168153 PCACA (Rasilla)13 14407-8 PCAXTR (Esteban)912--- PCAXTRK1218--- Petisco1621185512 Sandra757194-5 Sandra-S235101-2 T-mode PCA1715195111 WLKC2422216714 Hess & Brezowsky3-2-- objective Hess&Brez--1-- obj. H&B – SLP--3-- Peczely11---- Perret--9-- Schüepp--13-- ZAMG--24--

15. Tmax, DJF, dom. 00~9~18~27Σrank Enke & Spekat676194-5 Erpicum Z8502019175613 Erpicum SLP2224226815 Beck (GWT)81011296 Kirchhofer23 6916 Litynski19912407-8 Lund151615469 Lamb (Jenk.-Coll.)424101-2 neural nets1814164810 P27 (Kruizinga)168153 PCACA (Rasilla)13 14407-8 PCAXTR (Esteban)912--- PCAXTRK1218--- Petisco1621185512 Sandra757194-5 Sandra-S235101-2 T-mode PCA1715195111 WLKC2422216714 Hess & Brezowsky3-2-- objective Hess&Brez--1-- obj. H&B – SLP--3-- Peczely11---- Perret--9-- Schüepp--13-- ZAMG--24-- Tmin, DJF, dom. 00~9~18~27Σrank Enke & Spekat6.510824.54-5 Erpicum Z85019 185613 Erpicum SLP2223226715 Beck (GWT)8810266 Kirchhofer2324237016 Litynski2147327 Lund1420144810 Lamb (Jenk.-Coll.)434112 neural nets181316479 P27 (Kruizinga)266143 PCACA (Rasilla)101213358 PCAXTR (Esteban)916--- PCAXTRK1215--- Petisco1121195111 Sandra6.571124.54-5 Sandra-S11241 T-mode PCA1718175212 WLKC2422206614 Hess & Brezowsky5-3-- objective Hess&Brez--1-- obj. H&B – SLP--5-- Peczely13---- Perret--9-- Schüepp--15-- ZAMG--24--

16. Tmax, DJF, dom. 00~9~18~27Σrank Enke & Spekat676194-5 Erpicum Z8502019175613 Erpicum SLP2224226815 Beck (GWT)81011296 Kirchhofer23 6916 Litynski19912407-8 Lund151615469 Lamb (Jenk.-Coll.)424101-2 neural nets1814164810 P27 (Kruizinga)168153 PCACA (Rasilla)13 14407-8 PCAXTR (Esteban)912--- PCAXTRK1218--- Petisco1621185512 Sandra757194-5 Sandra-S235101-2 T-mode PCA1715195111 WLKC2422216714 Hess & Brezowsky3-2-- objective Hess&Brez--1-- obj. H&B – SLP--3-- Peczely11---- Perret--9-- Schüepp--13-- ZAMG--24-- Tmin, DJF, dom. 00~9~18~27Σrank Enke & Spekat6.510824.54-5 Erpicum Z85019 185613 Erpicum SLP2223226715 Beck (GWT)8810266 Kirchhofer2324237016 Litynski2147327 Lund1420144810 Lamb (Jenk.-Coll.)434112 neural nets181316479 P27 (Kruizinga)266143 PCACA (Rasilla)101213358 PCAXTR (Esteban)916--- PCAXTRK1215--- Petisco1121195111 Sandra6.571124.54-5 Sandra-S11241 T-mode PCA1718175212 WLKC2422206614 Hess & Brezowsky5-3-- objective Hess&Brez--1-- obj. H&B – SLP--5-- Peczely13---- Perret--9-- Schüepp--15-- ZAMG--24-- Tmax, DJF, dom. 07~9~18~27Σrank Enke & Spekat16815399 Erpicum Z8501911194912-13 Erpicum SLP1413235014 Beck (GWT)6613255-6 Kirchhofer15211288 Litynski335111 Lund1312265115 Lamb (Jenk.-Coll.)1144193 neural nets2618256916 P27 (Kruizinga)8710255-6 PCACA (Rasilla)517132 PCAXTR (Esteban)2017--- PCAXTRK915--- Petisco1810214912-13 Sandra498214 Sandra-S2151277 T-mode PCA1016204611 WLKC1214184410 Hess & Brezowsky2-2-- objective Hess&Brez--3-- obj. H&B – SLP--6-- Peczely17---- Perret--16-- Schüepp--22-- ZAMG--27-- better in large domainbetter in small domain

17. b) other criteria selection of classifications: 26 –8 classs 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

18. b) other criteria criteria: –explained variance –normalized within-type std.dev. –correlation real vs. reconstructed series averaged over stations and variables ~9 types~18 types~27 typesH&B

19. b) other criteria summarizing: ranking by averaged ranks –overall –sensitivity to evaluation criterion season number of types

20. Rankings all criteriaseasonno. of types EVSTDCORJanAprJulOct~9~18~27 H&B112111111-1 Litynski22533422812 GWT337222664-523 SANDRA443-44534-55 34 CKMeans553-454574246 Petisco68187633355 Lund766787-84-58767 TPCA878667-887678 P2799999999989 K-S test, TX, DJF 1 6 5 3-4 7 8-9 2

21. CONCLUSIONS most criteria highly sensitive to the number of types to alleviate this: –sort classs 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