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Advanced Thunderstorm Prediction and Quantification System Using Self-Learning Engines

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tamarr
10 diapositives · 261 Views · Sep 14, 2014

This study focuses on the development of a self-learning warning system for thunderstorms, utilizing advanced algorithms such as the Kalman Filter and Artificial Neural Networks. The system aims to quantify thunderstorm characteristics, including Convection Strength (CS) and tornado potential. A 3-step warning model is proposed for accurate forecasting of severe weather events. Collaboration between meteorological institutions in Poland contributes essential data for analysis, offering end-user-oriented solutions for timely and effective warning messages.

#advanced thunderstorm prediction and quantification system using self-learning engines #advanced thunderstorm prediction and quantification system using self-learning engines ppt #powerpoint #presentation
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Advanced Thunderstorm Prediction and Quantification System Using Self-Learning Engines
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  1. ConcerningThunderstorm(Potential) prediction Jan PARFINIEWICZ , Inst. of Meteorology and Water Management,MOLC 01-673 Warszawa, ul. Podleśna 61, Poland

  2. Essential:Self-learning EngineThunderstorms QuantificationEnd-User oriented Warning System

  3. Self-learning Engines:1) Kalman Filter (KF)2) Artificial Neural Network some generalisation to KF:after each signal an automatic renewal of the multi-regression set ofparametersKF++’s: basic statistics allows on dispersion control (clasical physics)

  4. Thunderstorms Quantificationat 1st:SYNOP WW code => Convection Strength (CS) [0-7] • CSClouds ------------------------------------- • 0 No - • 1 light Cu • 2 moderate Cu med. • 3 quite strong Cb cal. • 4 Thunder possib. Cb cap. • 5 Thunder • 6 Hail possibility • 7 Hail ------------------------------------- to be done: • Tornado’sSuCell’s data WW2CS / ! 0 1 2 3 4 5 6 7 8 9 +2,1,1,1,0,0,0,0,0,0, !0 +1,0,0,5,1,2,2,6,7,7, !1 +0,0,0,0,0,4,3,3,0,5, !2 +0,0,0,0,0,0,0,0,0,5, !3 +1,0,0,0,0,0,1,0,0,0, !4 +0,0,0,0,0,0,0,0,0,0, !5 +2,1,1,1,1,1,0,1,0,1, !6 +0,0,0,0,0,0,0,0,0,0, !7 +4,4,3,3,3,3,3,3,3,3, !8 +6,5,5,5,5,6,7,6,5,7/ !9 at 2end:

  5. Tempora mutantur at nos mutamur in illissThe System has been learning & I was learning togetherRecognizing SuperCell as a mobile Power Station [W/m2]- TORNADOgeneratorscaling strength of Tornado :1 – 5where 1 is Chojnice,PL , episode: 20120714:14.30’-15.30’[W/m2] ~ [MJ/10’*10km^2]

  6. Chojnice,PL , episode: 20120714:14.30’-15.30

  7. End-User oriented Warning System End-User = Casualties Essential: EMPATHY to CASUALTIES categories: 1) Presentation system (W. Łazarewicz) 2) Precise Warning Message System 3) End-User Entity including Verification

  8. Presentation: http://awiacja.imgw.pl1)Observed storms: all moderate severe … time scale time scale Severe Tornado

  9. Presentation: http://awiacja.imgw.plcategories: 1) Observed storms 2) Possible danger zone Pb+ = 1 3) Storm Potential 4) Strength of convection model option COSMO: 07km/14km Observed storms 2) Possible danger zone Pb+ = 1

  10. CONCLUSIONS Include Tornado’s scaling into prediction Continue search for potential predictors Dewelop 3-step Warnig System :12h, 3h, 1/2h