automatic weather classification at meteoswiss

Eidgenössisches Departement des Innern EDIBundesamt für Meteorologie und Klimatologie MeteoSchweiz

Automatic weather classification at MeteoSwiss

Tanja Weusthoff / Pierre EckertCOSMO GM 05.09.2011

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“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 SE L

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New (automatic) weather classifications The old manual weather classifications are replaced with new

automated weather classifications.

OLDNEW

Alpenwetterstatistik AWS

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010 GWT & CAP/PCACA

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957

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• “Harmonisation and Applications of Weather Type Classifications for European regions“ (2005-2010)

• Among others

1. Catalogue of computed classificationscost733cat-1 original classifications of the various authors

cost733cat-2.0 classifications recalculated with the cost733class software

2. Software for computation of own classificationscost733class (still under development)

http://geo21.geo.uni-augsburg.de/cost733wiki

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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

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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

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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

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• 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 region41N - 52N (12pts)

3E - 20E (18pts)

1. Neue (automatisierte) Wetterlagenklassifikationen

Database

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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

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3. Auswertungen

Trends in CAP9Year Winter

SpringYear

Increase of cold, dry high pressure situations, mainly in Winter.

Decrease of warm, wet northeast situations, mainly in spring.

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W

• Verification (for the moment GWTWS)

SE L

COSMO-7 minus Radar, for each class

„Neighbourhood“ verification

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• 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)