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In this study, we propose a pavement temperature forecast model based on stepwise regression\u2014model 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\u20131.6 \u00b0C, 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/rs15163956","type":"journal-article","created":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T10:24:47Z","timestamp":1691663087000},"page":"3956","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Pavement Temperature Forecasts Based on Model Output Statistics: Experiments for Highways in Jiangsu, China"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4741-1179","authenticated-orcid":false,"given":"Shoupeng","family":"Zhu","sequence":"first","affiliation":[{"name":"Key Laboratory of Transportation Meteorology of China Meteorological Administration, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China"}]},{"given":"Yang","family":"Lyu","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters\/Key Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science & Technology, Nanjing 210044, China"}]},{"given":"Hongbin","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Transportation Meteorology of China Meteorological Administration, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China"}]},{"given":"Linyi","family":"Zhou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Transportation Meteorology of China Meteorological Administration, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China"}]},{"given":"Chengying","family":"Zhu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Transportation Meteorology of China Meteorological Administration, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China"}]},{"given":"Fu","family":"Dong","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters\/Key Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science & Technology, Nanjing 210044, China"}]},{"given":"Yi","family":"Fan","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters\/Key Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science & Technology, Nanjing 210044, China"}]},{"given":"Hong","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Transportation Meteorology of China Meteorological Administration, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China"}]},{"given":"Ling","family":"Zhang","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters\/Key Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science & Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6716-9383","authenticated-orcid":false,"given":"Duanyang","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Transportation Meteorology of China Meteorological Administration, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China"}]},{"given":"Ting","family":"Yang","sequence":"additional","affiliation":[{"name":"Henan Climate Center, Zhengzhou 450003, China"}]},{"given":"Dexuan","family":"Kong","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters\/Key Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science & Technology, Nanjing 210044, China"},{"name":"Meteorological Bureau of Qian Xinan Buyei and Miao Autonomous Prefecture, Xingyi 562400, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1007\/s10584-019-02640-1","article-title":"Conspicuous temperature extremes over Southeast Asia: Seasonal variations under 1.5 degrees C and 2 degrees C global warming","volume":"160","author":"Zhu","year":"2020","journal-title":"Clim. 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