京藏高速呼集段道路结冰期CLDAS气温适用性及其在交通风险预警中的应用

doi:10.3969/j.issn.1673-503X.2023.06.012

Abstract

Abstract:

Hourly air temperature data from 26 conventional and traffic meteorological stations along Hoji section (from Huhhot to Jining) of Beijing-Tibet Expressway during 2020-2022 were used to compare and analyze the temperature characteristics of high-resolution land data assimilation system (CLDAS) of China Meteorological Administration using mean error (ME), root mean square error (RMSE), mean absolute error (MAE) and accuracy rate (AR) during road icing period. The results show that CLDAS air temperature along the Hoji section of the G6 expressway is generally negatively biased during the icing period with ME varying from -1.2 to 1.4 ℃ daily. From Huhhot to Jining, CLDAS temperature changes from cold bias to warm bias during 20:00 to the next day 08:00, and the cold bias increases during 08:00-20:00. Elevation, slope, and aspect have impacts on the accuracy of CLDAS temperature with relatively small errors in low-altitude, plain, and sunny slope sections. After bias correction, the ME of CLDAS temperature approaches 0, and the hourly RMSE and MAE decrease by 0.0-0.5 ℃ each month, with AR increasing by 0-14%. The bias-corrected CLDAS temperature can be used for refined spatial-temporal characteristic analysis of air temperature and classification of early warning for icy road.

Key words: Expressway, Transportation meteorology, Air temperature correction

CLC Number:

P468.0⁺21

Xiaohong JIA,Qi LI. Applicability of CLDAS air temperature during road icing period on Hoji section of Beijing-Tibet expressway and its application in traffic risk early warning[J]. Journal of Meteorology and Environment, 2023, 39(6): 96-104.

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地形因子		ME/℃	MAE/℃	r_ME	r_MAE
海拔高度	≤1300 m	-0.22	1.01	0.01	0.55^**
海拔高度	＞1300 m	-0.13	1.40	0.01	0.55^**
坡度	平原	-0.01	1.08	-0.32	0.28
	微斜坡	-0.18	1.13
	缓斜坡	-0.30	1.33
坡向	阳坡	-0.16	1.07	-	-
坡向	阴坡	-0.38	1.62	-	-

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Applicability of CLDAS air temperature during road icing period on Hoji section of Beijing-Tibet expressway and its application in traffic risk early warning

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