Prediction of weather parameters on a very short time scale by an Auto Regressive process for aviation flight planning

Abstract

Forecasting weather parameters such as temperature and pressure with a reasonable degree of accuracy three hours ahead of the scheduled departure of an aircraft helps economic and efficient planning of aircraft operations. However, these two parameters exhibit a high degree of persistency and have nonstationary mean and variance at sub-periods (i.e. at 0000, 0300, 0600,…, 2100UTC). Hence these series have been standardised (to have mean 0 and variance 1) and thereafter seasonal differenced (lag 8) to achieve almost near stationarity. An attempt has been made to fit the standardised and seasonal differenced series of Chennai (a coastal station) and Trichy (an inland station) airport into an Auto Regressive (AR) process. The model coefficients have been estimated based on adaptive filter algorithm which uses the method of convergence by the steepest descent. The models were tested with an independent data set and diagnostic checks were made on the residual error series. An independent estimation of fractal dimension has also been made in this study to conform the number parameters used in the AR processes. The models contemplated in this study are parsimonious and can be used to forecast surface temperature and pressure.