EGU25-10119, updated on 15 Mar 2025
https://doi.org/10.5194/egusphere-egu25-10119
EGU General Assembly 2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Pavement Temperature Forecasts Based on Model Output Statistics: Experiments for Highways in Jiangsu, China
Shoupeng Zhu1, Yang Lyu2, Hongbin Wang1, Linyi Zhou1, and Chengying Zhu1
Shoupeng Zhu et al.
  • 1Nanjing Innovation Institute for Atmospheric Sciences, Nanjing, China (zspshoupeng@163.com)
  • 2Nanjing University of Information Science & Technology, Nanjing, China

Forecasts on transportation meteorology, such as pavement temperature, are becoming increasingly important in the face of global warming and frequent disruptions from extreme weather and climate events. In this study, we propose a pavement temperature forecast model based on stepwise regression—model output statistics (SRMOS) at the short-term timescale, using highways in Jiangsu, China, as examples. Experiments demonstrate that the SRMOS model effectively calibrates against the benchmark of the linear regression model based on surface air temperature (LRT). The SRMOS model shows a reduction in mean absolute errors by 0.7–1.6 °C, with larger magnitudes observed for larger biases in the LRT forecasts. Both forecasts exhibit higher accuracy in predicting minimum nighttime temperatures compared to maximum daytime temperatures. Additionally, it overall shows increasing biases from the north to the south, and the SRMOS superiority is greater over the south with larger initial LRT biases. Predictor importance analysis indicates that temperature, moisture, and larger-scale background are basically the key predictors in the SRMOS model for pavement temperature forecasts, of which the air temperature is the most crucial factor in the model’s construction. Although larger-scale circulation backgrounds are generally characterized by relatively low importance, their significance increases with longer lead times. The presented results demonstrate the considerable skill of the SRMOS model in predicting pavement temperatures, highlighting its potential in disaster prevention for extreme transportation meteorology events.

How to cite: Zhu, S., Lyu, Y., Wang, H., Zhou, L., and Zhu, C.: Pavement Temperature Forecasts Based on Model Output Statistics: Experiments for Highways in Jiangsu, China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10119, https://doi.org/10.5194/egusphere-egu25-10119, 2025.