Virtual Weatherman: A pattern recognition approach to weather prediction

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

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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