Characteristics of Road Surface Temperature in Beijing Winter and its statistic forecasting models Based on the RMAPS Product

Abstract

Abstract An analysis of the inter-diurnal variation of road surface temperature in the different weather conditions in Beijing winter is done, based on the data of the road stations from 2012 to 2017 and the outcomes from the numerical forecasting model, Rapid-refresh Multi-scale Analysis and Prediction System (RMAPS), which are constructed in this paper. First, the correlation coefficients between road surface temperature and the meteorological factors output by the RMPAS model are investigated. We used the stepwise regression model methods to build three kinds of types of statistical models for hourly road surface temperature in 24 h in winter. Then the best forecasting models are chosen to build for forecasting road minimum temperature in winter from A1027 road station selected. The results show that there exists a significant diurnal variation for the road surface temperature, suggesting that the road surface temperature is obviously different under the different kind of weather conditions. The road surface temperature is correlated with air temperature, atmospheric radiation, and sunshine duration. Compared to the type of statistical model with the only one factor for the road temperature of previous day, the type of regression model with meteorological elements of remarkable correlation inserted performs better in terms of the road surface temperature forecast accuracy by more than 25%, and the prediction error decreases by 1 °C.