Title page for ETD etd-02102009-161401


Document Type Master's Dissertation
Author Marx, Hester Gerbrecht
Email estelle.marx@weathersa.co.za
URN etd-02102009-161401
Document Title The use of artificial neural networks to enhance numerical weather prediction model forecasts of temperature and rainfall
Degree MSc
Department Geography, Geo-Informatics and Meteorology
Supervisor
Advisor Name Title
Dr W Tennant Co-Supervisor
Prof C J de W Rautenbach Supervisor
Keywords
  • rainfall
  • forecasts of temperature
  • weather
Date 2008-09-03
Availability unrestricted
Abstract

Statistical post-processing techniques are used to remove systematic biases in modeled data. Models have shortcomings in the physical parameterization of weather events and have the inability to handle sub-grid phenomena successfully. The accuracy of forecasts interpolated to station points is limited by the horizontal resolution of the model. The magnitude of the bias at a station point depends upon geographical location and season. A neural network (NN) is a statistical downscaling method that seeks to model the linear or non-linear relationship between a set of different predictors and the predictand. NNís have a training rule whereby the weights of connections between predictors and the predictand, are adjusted on the basis of the data. NN systems have been developed by using as input, different model variables from the NCEP Ensemble Prediction System (EPS) and Eta model to forecast minimum/maximum temperature and rainfall (Quantitative Precipitation Forecast (QPF) and Probability of Precipitation (PoP)), respectively. Results show some potential for improved NN forecasts over the forecast generated by the Numerical Weather Prediction (NWP) models. The implementation of a NN system can serve as a guidance tool in operational forecasting but with one difficulty that the NWP model has to be frozen, meaning no upgrades or changes on the model.

©University of Pretoria 2008

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