Gale Warning for Swiss Lakes and Regional Aerodromes based on Ensemble Genetic Programming

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 FDHAFederal Office of Meteorology and Climatology MeteoSwiss

Schweizerische EidgenossenschaftConfédération suisseConfederazione SvizzeraConfederazium svizra

Swiss Confederation

Gale Warning for Swiss Lakes and Regional Aerodromes Lysiane Mayoraz

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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 07.10.2014

www.kweeper.comaviaswiss.xooit.com

Gale Warning for Swiss Lakes and Regional Aerodromes Lysiane Mayoraz

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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 07.10.2014

Gale Warning for Swiss Lakes and Regional Aerodromes Lysiane Mayoraz

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Wind Gust ≥ 25 kt in the next 3 hours?

Method

14th EMS 07.10.2014

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

t0t0-1h t0+1h t0+2h t0+3h

Current timet0+0.5h

Observations Forecasts

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Method

14th EMS 07.10.2014

Development Phase (1X, with historical data)

Observations

COSMO-2 Forecasts

Evolutionary Algorithm

20 Java Methods

Optimal Probability

Threshold q* Verification

Genetic ProgrammingMachine 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

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Method

14th EMS 07.10.2014

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:

+

max

mmofxxs

sin

-

dmomax

-

^

log

+

^

min

-

max

fxx6.12

dmo

6.27

log

+

f00ttt

fxxs

min

6.27/

wshe0.53

min

qfdif^

sin

1.71

*

mmosin

4.16

mmo

Tree Representation

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Method

14th EMS 07.10.2014

Development Phase (1X, with historical data)

Observations

COSMO-2 Forecasts

Evolutionary Algorithm

20 Java Methods

Optimal Probability

Threshold q* Verification

+max

mmo

fxxs

sin-d

mo

max-

^log+

^min-m

ax

fxx

6.12

dmo

6.27

log+f

00

ttt

fxxs

min

6.27

/wshe

0.53

min

qfdif

^sin

1.71

*mmo

sin

4.16

mmo

/ max / ca

pe

6.5

fxxs

* min m

ax -

m̂in 6.

27

/ wshe

0.53

min qf

dif

^ sin1.71

* mmo

sin4.16

mmo

ttt

fxxs

^

min

6.27/

wshe0.53

min

qfdif^

sin

1.71

*

mmosin

4.16

+ max

mmo

fxxs

sin- d

mo

max -

^ log+

^ min - m

ax

fxx

6.12

dmo

6.27

log+f

00

ttt

fxxs

min

6.27

/wshe

0.53

min

qfdif

^ sin

1.71

*mmo

sin

4.16

mmo

Output of evolutionary algorithm 20 java methods forecasting the maximum wind gust in the next hours

Herd of java methods ensemble forecast

Gale Warning for Swiss Lakes and Regional Aerodromes Lysiane Mayoraz

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Method

14th EMS 07.10.2014

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

Gale Warning for Swiss Lakes and Regional Aerodromes Lysiane Mayoraz

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False Alarm Ratio

Hit

Rate

Probability of occurrence in %

Method

14th EMS 07.10.2014

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

Gale Warning for Swiss Lakes and Regional Aerodromes Lysiane Mayoraz

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Method

14th EMS 07.10.2014

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 ktIf P ≥ q*

Verification

For each warning object:

Classminus.evaluate(4.561881696354005, min.evaluate(divide.evaluate(9.9164395430037,

divide.evaluate(max.evaluate(pow.evaluat96354005, in.evaluate(divide.evaluate(9.9164395430037,)

divide.evaluate(max.evaluate(pow.evaluate(1.6373608239449522, fmo), tt40),

min.evaluate(divide.evaluate(9.9164395430037, ddd), f20)))

…

Method 1Method 2Method 3Method 4

every 10 min

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ResultsVariation of Meteorological Threshold Q

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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 = 25 knots Threshold Q = 12 knots

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ResultsComparison with Forecasters Performance

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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

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Conclusions

14th EMS 07.10.2014

• 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

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Thank You!

14th EMS 07.10.2014

Questions?

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