1. Eidgenössisches Departement des Innern EDI Bundesamt für Meteorologie und Klimatologie MeteoSchweiz Automatic weather classification at MeteoSwiss Tanja Weusthoff / Pierre Eckert COSMO GM 05.09.2011

2. 2 Automatic weather classifications| COSMO GM 2011 Tanja Weusthoff / Pierre Eckert “A weather situation represents the state of the atmosphere over a certain region and at a certain time. The weather situation determines the local weather elements of the day.” (to a certain extent, personal note) Mean distributions of pressure, precipitation and temperature anomaly for the given weather class and for the time range 1958 – 2001. W SEL

3. 3 Automatic weather classifications| COSMO GM 2011 Tanja Weusthoff / Pierre Eckert New (automatic) weather classifications  The old manual weather classifications are replaced with new automated weather classifications. OLD NEW Alpenwetterstatistik AWS Perret Zala-Klassifikation Manual, until 31.12.2010 GWT & CAP/PCACA automated Since January 2011, Calculated back until 01.09.1957

4. 4 Automatic weather classifications| COSMO GM 2011 Tanja Weusthoff / Pierre Eckert “Harmonisation and Applications of Weather Type Classifications for European regions“ (2005-2010) Among others 1.Catalogue of computed classifications cost733cat-1  original classifications of the various authors cost733cat-2.0  classifications recalculated with the cost733class software 2.Software for computation of own classifications cost733class (still under development) http://geo21.geo.uni-augsburg.de/cost733wiki

5. 5 Automatic weather classifications| COSMO GM 2011 Tanja Weusthoff / Pierre Eckert Classes chosen at MCH: GWT (10, 18, 26 classes) Weather classes are predefined according to fixed rules and threshold (Quasi-objective). Explains precipitations rather well, except GWT10. Explains also other parameters (SLP, 2mT) not badly in the alpine region. PCACAC (9, 18, 27 classes)  neu: CAP Weather classes are derived following a optimisation procedure. Explains precipitation fluctuations best in the alpine region. Already good with 9 classes, except in summer. Explains also other parameters (SLP, 2mT) not badly in the alpine region PCACAC18: In summer, 2/3 of the days come from 4 classes. 1. Neue (automatisierte) Wetterlagenklassifikationen COST733@MCH

6. 6 Automatic weather classifications| COSMO GM 2011 Tanja Weusthoff / Pierre Eckert 10 classifications are computed every day, based on two different kind of methods: 1. CAP = Cluster Analysis of Principal Component STEP 1: Derivation of weather classes by using principal component analysis and clustering on ERA40-data. STEP 2: Attribution of other days to these predefined classes. 1. Neue (automatisierte) Wetterlagenklassifikationen 2. GWT = GrossWetterTypes 3. GWTWS = adapted GWT Correlation with „Prototype“ patterns Attribution to the predefined classes using correlations. Wind directions, high and low pressure. Wind at 500 hPa for distinguishing convective / advective Methods

7. 7 Automatic weather classifications| COSMO GM 2011 Tanja Weusthoff / Pierre Eckert 1. CAP = Cluster Analysis of Principal Component 1. Neue (automatisierte) Wetterlagenklassifikationen 2. GWT = GrossWetterTypes 3. GWTWS = adapted GWT GWT10, GWT18 and GWT26 based on (1) MSLP and (2) Z500 GWTWS with 11 classes based on GWT8 for Z500, mean wind at 500 hPa and mean MSLP CAP9, CAP18 and CAP27 based on MSLP 10 classifications are computed every day, based on two different kind of methods Methods

8. 8 Automatic weather classifications| COSMO GM 2011 Tanja Weusthoff / Pierre Eckert For daily computation (since 01.01.2011), use of the operational IFS 12z run from ECMWF; Analysis and forecasts out to 10 days are classified Classifications computed back using ECMWF reanalyses 01.09.1957-31.08.2002 ERA40 01.09.2002-31.12.2010 ERA interim Domain: alpine region 41N - 52N (12pts) 3E - 20E (18pts) 1. Neue (automatisierte) Wetterlagenklassifikationen Database

9. 9 Automatic weather classifications| COSMO GM 2011 Tanja Weusthoff / Pierre Eckert Operational aspects IFS- Data Conversion to netCDF (CDO) cost733class (Version 0.31_07, Mai 2010) Output as ASCII file, 1 File per parameter DWH Europe, 1° horizontal res. 12 UTC run Daily computation at 9 pm CLIMAP retrieve_dwh Users

10. 10 Automatic weather classifications| COSMO GM 2011 Tanja Weusthoff / Pierre Eckert 3. Auswertungen Trends in CAP9 Year Winter SpringYear  Increase of cold, dry high pressure situations, mainly in Winter.  Decrease of warm, wet northeast situations, mainly in spring.

11. 11 Automatic weather classifications| COSMO GM 2011 Tanja Weusthoff / Pierre Eckert Bader et al., 2011: „Milde und kalte Bergwinter“ Climate analysis (GWT26_Z500) Correlation between four selected classes and the winter Untersuchung des Zusammenhangs zwischen (je vier) ausgesuchten Warm- und Kaltluft – Wetterlagen und der hochalpinen Wintertemperatur.  Milde oder kalte Bergwinter werden zu einem grossen Teil durch herantransportierte milde oder kalte Luftmassen verursacht.

12. 12 Automatic weather classifications| COSMO GM 2011 Tanja Weusthoff / Pierre Eckert W Verification (for the moment GWTWS) SEL COSMO-7 minus Radar, for each class „Neighbourhood“ verification

13. 13 Automatic weather classifications| COSMO GM 2011 Tanja Weusthoff / Pierre Eckert COSMO-MOS (GWTWS) (postponed) Weather classes can be used as potential predictors for the statistical correction of NWP models. Usefulness of the GWT26_MSL classification for predicting the concentration of dust (PM10). Comparison with neural classification. Dust (PM10) concentration (GWT26_MSL)