Development and Application of a Novel Snow Peak Sighting Forecast System over Chengdu

• Published in: Atmosphere Vol. 14; no. 7; p. 1181
• Main Authors: Lu, Chengwei, Chen, Ting, Yang, Xinyue, Tan, Qinwen, Kang, Xue, Zhang, Tianyue, Zhou, Zihang, Yang, Fumo, Chen, Xi, Wang, Yuancheng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-07-2023
• Subjects: Air quality; Air quality forecasting; Biological surveys; Chengdu; Cloud cover; Emissions; Environmental aspects; Environmental monitoring; Generalized linear models; Humidity; Mathematical models; Megacities; Mixing ratio; Monte Carlo method; Monte Carlo simulation; Mountains; Numerical models; Numerical weather forecasting; Occlusion; Outdoor air quality; Particulate matter; Remote sensing; Snow; snow peak sighting forecast; Social media; Social networks; Solar radiation; Statistical methods; threshold method; VOCs; Volatile organic compounds; Water mixing; Weather; WRF-CMAQ; China; Tibetan Plateau
• ISSN: 2073-4433; 2073-4433
• DOI: 10.3390/atmos14071181

As air quality has improved rapidly in recent years, the public has become more interested in whether a famous snow peak, Yaomei Feng on the Tibetan Plateau, can be seen from Chengdu, a megacity located on the western plain of the Sichuan Basin, east of the plateau. Therefore, a threshold-method-based forecasting system for snow peak sighting was developed in this study. Variables from numerical models, including cloud–water mixing ratio, cloud cover over snow peak, water mixing ratio, PM2.5 concentration, and ground solar radiation, were used in the snow peak sighting forecast system. Terrain occlusion rate of each model grid was calculated. Monte Carlo simulations were applied for threshold determination. A WRF-CMAQ hindcast was conducted for 2020, owing to insufficient observation data, hindcast results on the snow peak sighting were compared with posts collected from social media. Estimations showed that the snow peak sighting forecast system performed well in reflecting the monthly trend of snow peak sightings, and the hindcast results matched the daily observations, especially from May to August. Accuracy of the snow peak sighting forecast model was 78.9%, recall value was 57.1%, and precision was 24.4%.