Optimization of Weather Forecast Data Using Machine Learning Algorithms

Abstract

Numerical weather prediction models exhibit errors while simulating atmospheric processes. To provide alerts for weather hazards, early warning systems are fed with forecast data from these models. The success of such an early warning system requires the minimization of errors that are induced by the forecast models. On the other hand, machine learning techniques have been proposed as an alternate method for nonlinear and dynamic systems due to the fact that this approach includes effective structure and parameter estimation methodologies, and it is powerful when implemented for problems whose resolutions require knowledge that is hard to specify. In this study, the goal is to implement machine learning methods as post-process algorithms on model output. The algorithm will discover the patterns that produce the errors and then lead to improved information for the system. This way, better planning and more efficient decision making are possible. High-resolution forecast data are available from Weather Research and Forecasting Model (WRF) simulations using initial and boundary conditions from the Global Forecasting System (GFS). Using nested domains, the desired downscaling can be achieved. Observations are available from General Aviation Applications 3D S.A.’s automatic weather station network, which has been operational for over 5 years. The network covers the region of Central Macedonia and has more than twenty stations. Ten of them were selected based on the availability of the data and the data quality control checks. Two sets of data are established. The first one is used to train the algorithm and the other to validate the performance of the new forecast.

Keywords

• weather forecast
• machine learning
• forecast error
• artificial neural networks