Runway temperature prediction, a case study for Oslo Airport, Norway

Abstract

In cold regions, slippery conditions can occur on runways causing safety risks for air traffic. Knowledge about the development of surface conditions is of great importance to prevent slippery conditions for traffic. Airports operating in winter conditions have a procedure to regularly check and describe the surface condition in a report called SNOWTAM. An integrated runway information system (IRIS) was developed to provide information about the surface condition on runways to winter maintenance personnel at the airports in Norway. IRIS currently runs based on SNOWTAM data, measured weather and pavement surface temperatures and gives a description of the current surface condition. Ideally, decision making for winter maintenance is based on accurate predictions of the surface condition a couple of hours ahead of time for which both weather forecasts and reliable surface temperature predictions are needed. In this study, a physical model to predict the runway surface temperature is built based on existing models developed for roads. A method is proposed to include the effect of aircraft on the surface temperature. The prediction performance of this model has been evaluated for an entire winter season on a runway at Oslo Airport in Norway. The presented model is stable and can accurately predict the surface temperature during most of the winter season. In “now-casting mode” where the surface temperature was predicted three hours ahead of time, the average error is 0.25°C and the RMSE is 1.65°C. During the beginning of the winter season, the prediction is best with a RMSE of 1.40°C. The proposed method to calculate the sensible heat flux due to aircraft has a positive effect on the performance of the model. Frequent observations of the surface conditions (SNOWTAM data), measured surface and subsurface temperatures, accurate weather data and air traffic data help to improve the accuracy of the runway temperature prediction model.