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Synoptic-climatological evaluation of COST733 circulation classifications: Czech contribution Radan HUTH Monika CAHYNOVÁ Institute of Atmospheric Physics,

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Presentation on theme: "Synoptic-climatological evaluation of COST733 circulation classifications: Czech contribution Radan HUTH Monika CAHYNOVÁ Institute of Atmospheric Physics,"— Presentation transcript:

1 Synoptic-climatological evaluation of COST733 circulation classifications: Czech contribution Radan HUTH Monika CAHYNOVÁ Institute of Atmospheric Physics, Prague, Czech Republic

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 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

9

10

11 Tmax, DJF, dom. 00~9~18~27 Enke & Spekat676 Erpicum Z Erpicum SLP Beck (GWT)81011 Kirchhofer23 Litynski19912 Lund Lamb (Jenk.-Coll.)424 neural nets P27 (Kruizinga)168 PCACA (Rasilla)13 14 PCAXTR (Esteban)912- PCAXTRK1218- Petisco Sandra757 Sandra-S235 T-mode PCA WLKC 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 Z Erpicum SLP Beck (GWT) Kirchhofer23 69 Litynski Lund Lamb (Jenk.-Coll.)42410 neural nets P27 (Kruizinga)16815 PCACA (Rasilla) PCAXTR (Esteban)912-- PCAXTRK Petisco Sandra75719 Sandra-S23510 T-mode PCA WLKC 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 & Spekat Erpicum Z Erpicum SLP Beck (GWT) Kirchhofer Litynski Lund Lamb (Jenk.-Coll.) neural nets P27 (Kruizinga) PCACA (Rasilla) PCAXTR (Esteban) PCAXTRK Petisco Sandra Sandra-S T-mode PCA WLKC Hess & Brezowsky3-2-- objective Hess&Brez--1-- obj. H&B – SLP--3-- Peczely Perret--9-- Schüepp ZAMG--24--

14 Tmax, DJF, dom. 00~9~18~27Σrank Enke & Spekat Erpicum Z Erpicum SLP Beck (GWT) Kirchhofer Litynski Lund Lamb (Jenk.-Coll.) neural nets P27 (Kruizinga) PCACA (Rasilla) PCAXTR (Esteban) PCAXTRK Petisco Sandra Sandra-S T-mode PCA WLKC Hess & Brezowsky3-2-- objective Hess&Brez--1-- obj. H&B – SLP--3-- Peczely Perret--9-- Schüepp ZAMG--24--

15 Tmax, DJF, dom. 00~9~18~27Σrank Enke & Spekat Erpicum Z Erpicum SLP Beck (GWT) Kirchhofer Litynski Lund Lamb (Jenk.-Coll.) neural nets P27 (Kruizinga) PCACA (Rasilla) PCAXTR (Esteban) PCAXTRK Petisco Sandra Sandra-S T-mode PCA WLKC Hess & Brezowsky3-2-- objective Hess&Brez--1-- obj. H&B – SLP--3-- Peczely Perret--9-- Schüepp ZAMG Tmin, DJF, dom. 00~9~18~27Σrank Enke & Spekat Erpicum Z Erpicum SLP Beck (GWT) Kirchhofer Litynski Lund Lamb (Jenk.-Coll.) neural nets P27 (Kruizinga) PCACA (Rasilla) PCAXTR (Esteban) PCAXTRK Petisco Sandra Sandra-S11241 T-mode PCA WLKC Hess & Brezowsky5-3-- objective Hess&Brez--1-- obj. H&B – SLP--5-- Peczely Perret--9-- Schüepp ZAMG--24--

16 Tmax, DJF, dom. 00~9~18~27Σrank Enke & Spekat Erpicum Z Erpicum SLP Beck (GWT) Kirchhofer Litynski Lund Lamb (Jenk.-Coll.) neural nets P27 (Kruizinga) PCACA (Rasilla) PCAXTR (Esteban) PCAXTRK Petisco Sandra Sandra-S T-mode PCA WLKC Hess & Brezowsky3-2-- objective Hess&Brez--1-- obj. H&B – SLP--3-- Peczely Perret--9-- Schüepp ZAMG Tmin, DJF, dom. 00~9~18~27Σrank Enke & Spekat Erpicum Z Erpicum SLP Beck (GWT) Kirchhofer Litynski Lund Lamb (Jenk.-Coll.) neural nets P27 (Kruizinga) PCACA (Rasilla) PCAXTR (Esteban) PCAXTRK Petisco Sandra Sandra-S11241 T-mode PCA WLKC Hess & Brezowsky5-3-- objective Hess&Brez--1-- obj. H&B – SLP--5-- Peczely Perret--9-- Schüepp ZAMG Tmax, DJF, dom. 07~9~18~27Σrank Enke & Spekat Erpicum Z Erpicum SLP Beck (GWT) Kirchhofer Litynski Lund Lamb (Jenk.-Coll.) neural nets P27 (Kruizinga) PCACA (Rasilla) PCAXTR (Esteban) PCAXTRK Petisco Sandra Sandra-S T-mode PCA WLKC Hess & Brezowsky2-2-- objective Hess&Brez--3-- obj. H&B – SLP--6-- Peczely Perret Schüepp ZAMG 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 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&B Litynski GWT SANDRA CKMeans Petisco Lund TPCA P K-S test, TX, DJF

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


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