Virtual Weatherman: A pattern recognition approach to weather prediction

Joo Hyun (Paul) Song

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Why predict weather?

• Our daily activities often depend on weather

• Weather conditions affect transportation safety

• Using only current weather conditions to make plans is undesirable

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Some history…

• Babylon – 650 BC• China – 300 BC

– Weather lores

• 1955– Dawn of numerical

weather prediction– Development of

computers

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Weather lores

• Solar halo or lunar corona is precursor to rain– 60 – 70% accuracy– Movement of moisture

to increasingly lower levels

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More weather lores

• Red sky at night probably means good weather tomorrow– Low moisture level in

air near the horizon

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Modern weather forecasting

• Persistence forecasting– Simplest method of

forecasting weather – Today’s weather carries on to

tomorrow’s weather (works well in steady-state weather conditions)

• Medium range forecasting– Analog technique

• Pattern recognition– Ensemble forecasting

• Uses lots of forecasts produced to reflect the uncertainty in the initial state of the atmosphere

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Neural Networks

• Simply: variable interconnections of simple elements

• Formally: nonlinear function from a set of inputs to a set of outputs controlled by a vector of adjustable parameters

• Nonlinear• Neural networks “learn from

examples and capture subtle functional relationships among the data even if the underlying relationships are unknown or hard to describe”

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Neural Networks (framework)

• Weighted combinations of activation functions– Typically chosen to be

nonlinear sigmoidal functions such as logsig or tansig

• Set of weights that produce the best fit is estimated using gradient descent

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Dataset

• 3 locations– Kuala Lumpur, Malaysia

• Tropical• Small weather fluctuations• Daily data: 10/11/2001 – 11/30/2007

– Seoul, South Korea• Temperate• Mild weather fluctuations with 4 distinct seasons• Daily data: 1/1/1996 – 11/30/2007 (minus year 2000)

– Iowa City, IA• Hell on earth• Meteorologists’ nightmare• Daily data: 4/17/2002 – 11/30/2007

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Dataset (description)

• Weather Underground• Daily weather summary of 22 parameters

– Date– Max/min/mean temperature– Wind speed– Cloud cover– Precipitation– Events– etc

• Hourly data also available

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Setup• MATLAB + Neural Network Toolbox• Input

– 10 features: month, mean temp, mean dew point, mean humidity, mean pressure, precipitation, rain, thunderstorm, snow and fog

– Past 3 days’ data– Previous years’ data for training– This year’s data for testing

• Neural Network– 4 layer network: 30-10-30-4– purelin basis– Resilient backpropagation training function (trainrp)– 1000 iterations

• Output– 4 features: mean temp, mean dew point, mean humidity and mean

pressure

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Results (Kuala Lumpur)Actual

Predicted

RMSE = 2.9049

Mean Temp

Mean Dew Point

Mean Humidity

Mean Atm. Pressure

84 73 77 29.89

83 70 65 29.87

82 70 66 29.88

81 69 63 29.87

80 70 75 29.85

Mean Temp

Mean Dew Point

Mean Humidity

Mean Atm. Pressure

81.2038 73.2786 78.119 29.4727

81.6449 73.2485 77.1193 30.8002

81.8915 71.9013 71.8189 28.6854

80.6329 70.9803 70.7164 29.886

80.4533 69.7421 66.9419 29.5301

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Results (Seoul)Actual

Predicted

RMSE = 5.7240

Mean Temp

Mean Dew Point

Mean Humidity

Mean Atm. Pressure

40 34 80 30.2

32 22 62 30.16

36 20 71 30.13

42 30 68 30.18

32 21 66 30.31

Mean Temp

Mean Dew Point

Mean Humidity

Mean Atm. Pressure

41.8923 36.1952 83.302 29.5045

36.7941 30.674 77.2184 27.183

32.7943 18.6508 65.5613 32.9435

37.674 28.8922 74.1195 30.5462

40.1838 30.4024 69.1113 28.5581

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Results (Hell)Actual

Predicted

RMSE = 6.5957

Results (Iowa City)Mean Temp

Mean Dew Point

Mean Humidity

Mean Atm. Pressure

34 26 81 30.05

28 10 53 30.32

34 20 61 30.05

28 12 54 30.29

26 10 54 30.41

Mean Temp

Mean Dew Point

Mean Humidity

Mean Atm. Pressure

34.0975 27.3607 79.8476 30.9704

33.3135 25.8775 75.6677 30.5564

28.127 13.2836 59.7353 31.1441

35.5852 25.1806 70.7051 29.5297

29.7713 15.283 60.4396 30.4157

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Conclusion

• Neural network weather predictor performs fairly well considering small number of input features.

• There was slight improvement in prediction results if data for the corresponding season was used to train the system.

• Performance may improve with more intelligent combination of inputs (i.e. weather conditions of surrounding regions, etc).

• Comparison to other pattern recognition schemes such as Fuzzy set predictor may be worth investigating.

• Prediction of weather events using logsig/tansig activation functions would be something worthwhile to implement.

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SORRY.NO QUESTIONS, PLEASE.