Temporal and Spatial Characteristics of Meteorological Elements in the Vertical Direction at Airports and Hourly Airport Visibility Prediction by Artificial Intelligence Methods

Jin Ding, Guoping Zhang, Jing Yang (), Shudong Wang (), Bing Xue, Xiangyu Du, Ye Tian, Kuoyin Wang, Ruijiao Jiang and Jinbing Gao
Additional contact information
Jin Ding: Public Meteorological Service Center, China Meteorological Administration, Beijing 100081, China
Guoping Zhang: Public Meteorological Service Center, China Meteorological Administration, Beijing 100081, China
Jing Yang: Public Meteorological Service Center, China Meteorological Administration, Beijing 100081, China
Shudong Wang: Public Meteorological Service Center, China Meteorological Administration, Beijing 100081, China
Bing Xue: Public Meteorological Service Center, China Meteorological Administration, Beijing 100081, China
Xiangyu Du: Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China
Ye Tian: School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
Kuoyin Wang: Public Meteorological Service Center, China Meteorological Administration, Beijing 100081, China
Ruijiao Jiang: Public Meteorological Service Center, China Meteorological Administration, Beijing 100081, China
Jinbing Gao: Public Meteorological Service Center, China Meteorological Administration, Beijing 100081, China

Sustainability, 2022, vol. 14, issue 19, 1-24

Abstract: Based on second-level L-band sounding data, the vertical distribution and variation of meteorological elements at airports in 2010–2020 are investigated. At the same time, the relationships between airport visibility and meteorological elements at different potential heights are also investigated. Then, based on hourly measurements of 26 meteorological elements in 2018–2020, the hourly visibility of airports is predicted by 9 artificial intelligence algorithm models. The analyses show: (1) For the vertical changes in four meteorological elements of the airports, the negative vertical trends of temperature and relative humidity increase clearly from northwestern to southeastern China. The significant negative trend of air pressure in the vertical direction in the eastern China is greater. (2) Within about 2000 geopotential metres (gpm) from the ground, the visibility has a strong correlation with the air pressure, and most of them are negative. Within 400 gpm from the ground, airport visibility is negatively correlated with the relative humidity. At 8:00 a.m., airport visibility is positively correlated with the wind speed within 2000 gpm from the ground at most airports, while at 20:00 p.m., the positive correlation mainly appears within 400 gpm from the ground. (3) The passive aggressive regression-(PAR) and isotonic regression-(IST) based models have the worst effect on airport visibility prediction. The dispersion degree of the visibility simulation results obtained by Huber regression-(HBR) and random sample consensus regression-(RANSAC) based models is relatively consistent with the observations.

Keywords: airport visibility; prediction; artificial intelligence; L-band sounding data (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/19/12213/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/19/12213/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:19:p:12213-:d:925917

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().