Year: 2022
Type: article
Abstract: In this study, ground observation data were selected from January 2016 to January 2020. First, six machine learning methods were used to predict visibility. We verified the accuracy of the method with and without principal components analysis (PCA) by combining actual examples with the European Centre for Medium-Range Weather Forecast (ECMWF) data and National Centers for Environmental Prediction (NECP) data. The results show that PCA can improve visibility prediction. Neural networks have high accuracy in machine learning algorithms. The initial visibility data plays an important role in the visibility forecast and can effectively improve forecast accuracy.
Source: Atmosphere
Authors: Yu Zhang, Yangjun Wang, Yingqian Zhu, Lizhi Yang, Lin Ge, Chun Luo
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,094
OpenAlex ID: W4285732704