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Pavement Temperature Forecasts Based on Model Output Statistics: Experiments for Highways in Jiangsu, China

Authors :
Shoupeng Zhu
Yang Lyu
Hongbin Wang
Linyi Zhou
Chengying Zhu
Fu Dong
Yi Fan
Hong Wu
Ling Zhang
Duanyang Liu
Ting Yang
Dexuan Kong
Source :
Remote Sensing, Vol 15, Iss 16, p 3956 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

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.

Details

Language :
English
ISSN :
20724292
Volume :
15
Issue :
16
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.f705639c629b4da4af71e54b5c2ac748
Document Type :
article
Full Text :
https://doi.org/10.3390/rs15163956