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Virtual Weatherman: A pattern recognition approach to weather prediction

Virtual Weatherman: A pattern recognition approach to weather prediction. Joo Hyun (Paul) Song. 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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Virtual Weatherman: A pattern recognition approach to weather prediction

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  1. Virtual Weatherman: A pattern recognition approach to weather prediction Joo Hyun (Paul) Song

  2. 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 55:145 PR Final Project

  3. Some history… • Babylon – 650 BC • China – 300 BC • Weather lores • 1955 • Dawn of numerical weather prediction • Development of computers 55:145 PR Final Project

  4. Weather lores • Solar halo or lunar corona is precursor to rain • 60 – 70% accuracy • Movement of moisture to increasingly lower levels 55:145 PR Final Project

  5. More weather lores • Red sky at night probably means good weather tomorrow • Low moisture level in air near the horizon 55:145 PR Final Project

  6. 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 55:145 PR Final Project

  7. 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” 55:145 PR Final Project

  8. 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 55:145 PR Final Project

  9. 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 55:145 PR Final Project

  10. 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 55:145 PR Final Project

  11. 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 55:145 PR Final Project

  12. Results (Kuala Lumpur) Actual Predicted RMSE = 2.9049 55:145 PR Final Project

  13. 55:145 PR Final Project

  14. Results (Seoul) Actual Predicted RMSE = 5.7240 55:145 PR Final Project

  15. Results (Iowa City) Results (Hell) Actual Predicted RMSE = 6.5957 55:145 PR Final Project

  16. 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. 55:145 PR Final Project

  17. SORRY.NO QUESTIONS, PLEASE. 55:145 PR Final Project

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