L. Mayoraz (1), J. Ambühl (1), R. Voisard (2), C. Voisard (1), M. Züger (2), H. Romang (1) (1) MeteoSwiss, Zurich, Switzerland, (2) University of Zurich, Switzerland Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss Schweizerische Eidgenossenschaft Confédération suisse Confederazione Svizzera Confederazium svizra Swiss Confederation

Goal of Project GenWarn Development of a semi-automatic short-term warning system for gale on Swiss lakes and regional aerodromes, sending warning proposals to forecasters, based on genetic programming. 14th EMS Gale Warning for Swiss Lakes and Regional Aerodromes Lysiane Mayoraz 2

Context / Current Situation Strong gusts (≥ 25 kt) = potential danger to aviation and maritime safety  Gale warnings In Switzerland, gale warnings are issued for more than 50 lakes and aerodromes but not automated →First wind gust frequently missed: Low hit rates! Benefit of GenWarn System:  supports the forecasters in their ongoing weather surveillance and alerts them by proposing potential gale warnings 14th EMS Gale Warning for Swiss Lakes and Regional Aerodromes Lysiane Mayoraz 3

Wind Gust ≥ 25 kt in the next 3 hours? Method 14th EMS Gale Warning for Swiss Lakes and Regional Aerodromes Lysiane Mayoraz 4 Development Phase (1X, with historical data) Observations COSMO-2 Forecasts Evolutionary Algorithm 20 Java Methods Optimal Probability Threshold q* Verification Predictor list (from a 2-year data set): -Observations from several observation stations -Forecasts from the COSMO-2 model t0t0 t 0-1h t 0+1h t 0+2h t 0+3h Current time t 0+0.5h Observations Forecasts

Method 14th EMS Gale Warning for Swiss Lakes and Regional Aerodromes Lysiane Mayoraz 5 Development Phase (1X, with historical data) Observations COSMO-2 Forecasts Evolutionary Algorithm 20 Java Methods Optimal Probability Threshold q* Verification Genetic Programming Machine learning technique inspired by the evolution theory of species used for optimization problems. 1)Creation of a random population of computer programs from the predictor list. (= gen. 0) 2)Evaluation of the programs. Fitness function = Hit Rate * (1 - False Alarm Ratio) * 100 3)Selection of the best programs and application of crossing and mutation processes on the selection (= gen. 1) 4)Repetition of steps 2 to 3 until the maximum number of generations is reached. 20 X

Method 14th EMS Gale Warning for Swiss Lakes and Regional Aerodromes Lysiane Mayoraz 6 Development Phase (1X, with historical data) Observations COSMO-2 Forecasts Evolutionary Algorithm 20 Java Methods Optimal Probability Threshold q* Verification Output of evolutionary algorithm 20 java methods forecasting the maximum wind gust in the next hours plus.evaluate (max.evaluate (mmo, fxxs), sine.evaluate (minus.evaluate (dmo, max.evaluate (minus.evaluate (pow.evaluate (min.evaluate (qfdif, sine.evaluate (1.71)), sine.evaluate (multiply.evaluate (4.16, mmo))), pow.evaluate (min.evaluate (6.27, divide.evaluate (0.52, wshe)), log.evaluate (plus.evaluate (pow.evaluate (min.evaluate (6.27, minus.evaluate (dmo, max.evaluate (fxxs, 6.12))), log.evaluate (plus.evaluate(f00, ttt))), fxxs)))), mmo)))) Example of Java Method: Tree Representation

Method 14th EMS Gale Warning for Swiss Lakes and Regional Aerodromes Lysiane Mayoraz 7 Development Phase (1X, with historical data) Observations COSMO-2 Forecasts Evolutionary Algorithm 20 Java Methods Optimal Probability Threshold q* Verification Output of evolutionary algorithm 20 java methods forecasting the maximum wind gust in the next hours Herd of java methods  ensemble forecast

Method 14th EMS Gale Warning for Swiss Lakes and Regional Aerodromes Lysiane Mayoraz 8 Development Phase (1X, with historical data) Observations COSMO-2 Forecasts Evolutionary Algorithm 20 Java Methods Optimal Probability Threshold q* Verification Verification: “ROC-Curve” False Alarm Ratio Hit Rate Probability of occurrence in % -Event-based -On a 2-year independent data set

False Alarm Ratio Hit Rate Probability of occurrence in % Method 14th EMS Gale Warning for Swiss Lakes and Regional Aerodromes Lysiane Mayoraz 9 Development Phase (1X, with historical data) Observations COSMO-2 Forecasts Evolutionary Algorithm 20 Java Methods Optimal Probability Threshold q* Verification Verification: “ROC-Curve” -Event-based -On a 2-year independent data set Fitness Function = HR*(1-FAR) q*: optimal probability threshold q*: probability of occurrence above which an alarm proposal is sent

Method 14th EMS Gale Warning for Swiss Lakes and Regional Aerodromes Lysiane Mayoraz 10 Development Phase (1X, with historical data) Operational Routine Observations COSMO-2 Forecasts Evolutionary Algorithm 20 Java Methods Optimal Probability Threshold q* Observations COSMO-2 Forecasts Alarm Proposal Probability P that wind gust ≥ 25 kt If P ≥ q* Verification For each warning object: Class minus.evaluate( , min.evaluate(divide.evaluate( , divide.evaluate(max.evaluate(pow.evaluat , in.evaluate(divide.evaluate( ,) divide.evaluate(max.evaluate(pow.evaluate( , fmo), tt40), min.evaluate(divide.evaluate( , ddd), f20))) … Method 1 Method 2 Method 3 Method 4 every 10 min

Results Variation of Meteorological Threshold Q 14th EMS Gale Warning for Swiss Lakes and Regional Aerodromes Lysiane Mayoraz 11 Maximum Fitness: -If Q = 25 kt : ~ 30 -If Q = 12 kt : ~ 45  A clear performance limit is reached at this point. (HR ~95%, FAR ~70%)  Performance of system is higher for Q = 12 kt  Storm events stronger than 25 kt are too rare for the system to detect them correctly (tendency of detecting too many events) Verification on the 2-year data set Threshold Q = 25 knots Threshold Q = 12 knots

Results Comparison with Forecasters Performance 14th EMS Gale Warning for Swiss Lakes and Regional Aerodromes Lysiane Mayoraz 12  Overall increase in HR induced by GenWarn System  Contribution of GenWarn System variable, object-dependent  Role of forecaster: decrease the FAR Typical ROC Curve GenWarn Vs. Forecasters Performance Forecaster Experience GenWarn System Typical Performance Forecasters Performance per Warning Object

Conclusions 14th EMS Gale Warning for Swiss Lakes and Regional Aerodromes Lysiane Mayoraz 13 GenWarn gale warning system based on genetic programming shows so far the performance : Hit Rate ~95%, FAR ~70% General increase of hit rate when using the GenWarn System compared to the actual forecasters performance  best solution: mix machine & forecaster to lower the FAR Outlook: – Try with additional predictors: radar data, INCA- forecasts (nowcasting product) – Operationalization, in situ tests

Thank You! 14th EMS Gale Warning for Swiss Lakes and Regional Aerodromes Lysiane Mayoraz 14 Questions? © Sebastien Marti/Scoopmobile