New automatic weather type classifications at MeteoSwiss

1. New automatic weather type classifications at MeteoSwiss Tanja Weusthoff EMS, 15.09.2011

2. 1. Introduction: New Classifications • The existing manual weather type classifications have been replaced by new (automatic) weather type classifications in January 2011. NEW OLD Alpenwetterstatistik AWS Perret Zala-Klassifikation GWT & CAP/PCACA automated, since Januar 2011, recalculated from 01.09.1957 manual, until 31.12.2010

3. 1. Introduction: Cost733 COST 733: “Harmonisation and Applications of Weather Type Classifications for European regions“ (2005-2010) Provides:  Catalogues of classifications calculated for various European domains cost733cat-1 - original classification of the respective author cost733cat-2.0 - recalculated classifications using cost733class software Software for individual calculations of weather type classifications cost733class Work@MeteoSwiss: Evaluation of catalogue costcat733-1 with regard to the variability of daily precipitation in the Alpine region (Schiemann and Frei, 2010) Selection of suitable weather types for operationalisation in a MeteoSwiss-wide „Beauty Contest“ http://geo21.geo.uni-augsburg.de/cost733wiki

4. 1. Introduction: Methods  Overall 10 classificationsarecalculatedeachday, based on two different methodsusingthe ECMWF 12 UTC analysis (andforecast) data. 2. GWT = GrossWetterTypes 1. CAP = Cluster Analysis of Principal Component • classification with predefined types • correlation with „prototype“ patterns STEP 1: Weather types are derived by means of a principal component analysis and subsequent clustering based on ERA40 data. STEP 2: Actual days are assigned to the derived classes by simple distance measures. CAP9, CAP18 und CAP27 based on MSL GWT10, GWT18 und GWT26 based on (1) MSL and (2) Z500 • classification of each input field by means of the three correlation coefficients and their combination • main wind directions, high and low pressure 3. GWTWS = adapted GWT • differentiation convective / advective by means of wind in 500 hPa • convective: surface pressure for classification into high, low and flat pressure • advective: wind directions using GWT8_Z500 classification GWTWS with 11 classes based on GWT8 for Z500, mean wind in 500 hPa and mean surface pressure

5. 1. Introduction: Data • classifications recalculated using ECMWF reanalysis 01.09.1957-31.08.2002 ERA40 01.09.2002-31.12.2010 ERA interim • daily calculation (since 01.01.2011) using 12 UTC run of the operational ECMWF model IFS; analysis and forecasts up to 10 days are classified • Domain: Alps 41N - 52N (12pts) 3E - 20E (18pts)

6. 2. Evaluations: BSS How good can weather type classifications quantify surface climate variability in the Alps?  consider WTC as a framework that yields a probabilistic forecast (Schiemann und Frei, 2010) Brier Skill Score (BSS) BSS shows an increase with the number of classes  compare only weather type classifications with a similar number of classes use quantiles to define an event (for each grid box); yi = empirical frequency of the event occuring for weather type i; ō = climatological frequency of a event independant of weather type; perfect forecast: BSperf = 0; reference forecast: yi = ō;  simple form of Brier Skill Score:

7. dailyweathertypesfrom 10 automaticandtwomanualclassifications (Schüepp, Perret) griddeddailyprecipitationdataforthe Alps, 25 km resolution, 1971-1999(Frei andSchär, 1998; Frei and Schmidli, 2006 ) griddeddailymeantemperature, project ENSEMBLES (Haylock et al. 2008), 0.5° resolution, interpolated to the same 25 km grid 2. Evaluations: BSS (data basis)

8. 2. Evaluations: BSS (precipitation in the Alps) • CAP27 explains precipitation in the Alps best, • considering only few classes  CAP9 best • rather small differences between the individual classifications 26 classes and more 18 classes 9-11 classes manual

9. 2. Evaluations: BSS (precipitation in the Alps) • Clear differences between the classifications, e.g. : • GWTxx_Z500 better south of the Alps, • GWTxx_MSL better on the alpine ridge and in the north of the Alps, • CAP27 best in the western part of the domain.

10. 2. Evaluations: BSS (temperature in the Alps) • larger differences between individual classifications • CAP27 explains variability of temperature best when the whole year is considered • considering only few classes  CAP9 best 26 classes and more 18 classes 9-11 classes manual

11. 2. Evaluations: BSS (temperature in the Alps) … Schüepp better in individual seasons, especially in summer 26 classes and more 18 classes 9-11 classes manual

12. 2. Evaluations: BSS (temperature in the Alps) • clear differences between the classifications, e.g.: • GWTxx_Z500 better on the Alpine ridge, • GWTxx_MSL and CAP better in the north of the Alps, • Schüepp clearly better in summer.

13. 2. Evaluations: BSS (additional inputparameters)  cost733cat-2.0 SP = mean sea level pressure Z5 = 500hPa geopotential height Y5 = 500hPa geopotential height K5 = thickness between 500 hPa and 850 hPa precipitation temperature 2 input parameters 3 and more input parameters No influence on precipitation results. Large influence on temperature results. 26 classes and more 18 classes 9-11 classes manual

14. 3. Summary • 10 new weather type classifications introduced at MeteoSwiss • all weather types available from 01.09.1957 • Description and a report available on MeteoSwiss Websites • the best suited weather type classification depends on the respective application • already in use for various applications (e.g. verification, climate analyses, …)

15. Thankyoufortheattention ... http://www.meteoschweiz.admin.ch/web/de/services/datenportal/standard_produkte/wetterlagenklassifizierung.html Report Weusthoff, T., 2011: Weather Type Classification at MeteoSwiss – Introduction of new automatic classifications schemes, Arbeitsberichte der MeteoSchweiz, 235, 46 pp.