Short Term Forecasting of Cloud Ceiling Using Artificial Neural Networks

Abstract

Numerical weather prediction systems do not provide cloud information directly, these information can be assessed during post processing only in an indirect manner. However accuracy of these forecasts is not sufficient for operational usage, therefore appropriate prediction of cloud ceiling is a real challenge for the forecasters. Our research focuses on whether short term forecasts of cloud ceiling could be improved using model outputs statistic based artificial neural network method. Our research based on three years of WRF numerical model output which was initiated by GFS forecast as initial boundary and lateral condition. The performance of artificial neural network highly depends on network configuration, therefore choosing the appropriate topology, transfer function and the right learning algorithm is a crucial element. The results of the best artificial neural network configurations have been compared to some old cloud assessments methods.