Virtual Weatherman: A pattern recognition approach to weather prediction

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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
• Todays weather carries on to tomorrows 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 years 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
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Results (Seoul)
Actual
Predicted
RMSE 5.7240
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Results (Hell)
Results (Iowa City)
Actual
Predicted
RMSE 6.5957
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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.