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Automatic weather classification at MeteoSwiss

Automatic weather classification at MeteoSwiss. Tanja Weusthoff / Pierre Eckert COSMO GM 05.09.2011.

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Automatic weather classification at MeteoSwiss

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  1. Automatic weather classification at MeteoSwiss Tanja Weusthoff / Pierre Eckert COSMO GM 05.09.2011

  2. “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) W SE L Mean distributions of pressure, precipitation and temperature anomaly for the given weather class and for the time range 1958 – 2001.

  3. New (automatic) weather classifications • The old manual weather classifications are replaced with new automated weather classifications. NEW OLD Alpenwetterstatistik AWS Perret Zala-Klassifikation GWT & CAP/PCACA automated Since January 2011, Calculated back until 01.09.1957 Manual, until 31.12.2010

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

  5. 1. Neue (automatisierte) Wetterlagenklassifikationen COST733@MCH 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.

  6. 1. Neue (automatisierte) Wetterlagenklassifikationen Methods 10 classifications are computed every day, based on two different kind of methods: 1. CAP = Cluster Analysis of Principal Component 2. GWT = GrossWetterTypes 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. • Correlation with „Prototype“ patterns • Attribution to the predefined classes using correlations. • Wind directions, high and low pressure. 3. GWTWS = adapted GWT • Wind at 500 hPa for distinguishing convective / advective

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

  8. 1. Neue (automatisierte) Wetterlagenklassifikationen Database • Classifications computed back using ECMWF reanalyses 01.09.1957-31.08.2002 ERA40 01.09.2002-31.12.2010 ERA interim • 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 • Domain: alpine region 41N - 52N (12pts) 3E - 20E (18pts)

  9. Operational aspects Daily computation at 9 pm Europe, 1° horizontal res. 12 UTC run IFS- Data Conversion to netCDF (CDO) CLIMAP retrieve_dwh cost733class (Version 0.31_07, Mai 2010) Output as ASCII file, 1 File per parameter DWH Users

  10. 3. Auswertungen Trends in CAP9 Winter Year  Increase of cold, dry high pressure situations, mainly in Winter. Year Spring  Decrease of warm, wet northeast situations, mainly in spring.

  11. Verification (for the moment GWTWS) COSMO-7 minus Radar, for each class SE L W „Neighbourhood“ verification

  12. COSMO-MOS (GWTWS) (postponed) Weather classes can be used as potential predictors for the statistical correction of NWP models. • Dust (PM10) concentration (GWT26_MSL) Usefulness of the GWT26_MSL classification for predicting the concentration of dust (PM10). Comparison with neural classification.

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