Donat Perler, MeteoSwiss Joint work with Oliver Marchand, MeteoSwiss

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Presentation transcript:

Donat Perler, MeteoSwiss Joint work with Oliver Marchand, MeteoSwiss Automatic weather interpretation using modern classification algorithms Donat Perler, MeteoSwiss Joint work with Oliver Marchand, MeteoSwiss

Overview: The aLMo Model Model features: Non-hydrostatic Prognostic variables (u, v, w, T, p´, qv, qc, qi) aLMo7 (today) aLMo2 (planned) 2 times per day, +72h 8 times per day, +18h 358 x 325 x 45 grid points 480 x 350 x 45 grid points 7km horizontal grid scale 2km horizontal grid scale Donat.Perler@MeteoSwiss.ch COSMO Meeting, Date, Zürich

Result: Output of aLMo and its interpretation 2000 plots per simulation Donat.Perler@MeteoSwiss.ch COSMO Meeting, Date, Zürich

Operational Weather Forecasting Donat.Perler@MeteoSwiss.ch COSMO Meeting, Date, Zürich ?Thunderstorm risk?  ?Thunderstorm alert?

Automatic Weather Interpretation at DWD Pseudo Code for DWD Expert System: […] if ((STABILITY_INDEX < -6) AND (P_CLOUD_BAS-P_CLOUD_TOP > 400hPa) AND (T_CLOUD_TOP < -45C) AND (PERCIP_CONV > 2.0mm/h)) then report(`Thunderstorm´) end Donat.Perler@MeteoSwiss.ch COSMO Meeting, Date, Zürich

Output of the System used at DWD Thunder- storm Donat.Perler@MeteoSwiss.ch COSMO Meeting, Date, Zürich

Main Drawbacks of DWD`s Method Rules are manually chosen Parameters/threshold values set by hand No measure for the certainty of classification given Questions How can the parameters be tuned more exact? Do all rules have the same importance? Are there other important rules? How does one find these rules? Donat.Perler@MeteoSwiss.ch COSMO Meeting, Date, Zürich

Machine Learning use! Training set Actual data Neural Networks Boosting SVM train! Donat.Perler@MeteoSwiss.ch COSMO Meeting, Date, Zürich Output Classification Problem

Select appropriate model features Steps in this work Select appropriate model features Principal Component Analysis Use DWD experience from Expert System Compare and evaluate different supervised machine learning algorithms Boosting Support Vector Machines Bayesian- and Neural Networks Introduce the best algorithm to the aLMo for operational use Donat.Perler@MeteoSwiss.ch COSMO Meeting, Date, Zürich

Thank you for your attention! We very much welcome discussions. Donat.Perler@MeteoSwiss.ch COSMO Meeting, Date, Zürich