缩小尺度
均方误差
环境科学
期限(时间)
比例(比率)
时间分辨率
图像分辨率
空气质量指数
数据同化
插值(计算机图形学)
地球观测
计算机科学
遥感
气象学
数据挖掘
统计
人工智能
数学
地理
地图学
图像(数学)
航空航天工程
卫星
工程类
物理
降水
量子力学
作者
Yi Xiao,Yuan Wang,Qiangqiang Yuan,Jiang He,Liangpei Zhang
标识
DOI:10.1016/j.scitotenv.2022.157747
摘要
Generating a long-term high-spatiotemporal resolution global PM2.5 dataset is of great significance for environmental management to mitigate the air pollution concerns worldwide. However, the current long-term (2003-2020) global reanalysis dataset Copernicus Atmosphere Monitoring Service (CAMS) reanalysis has drawbacks in fine-scale research due to its coarse spatiotemporal resolution (0.75°, 3-h). Hence, this paper developed a deep learning-based framework (DeepCAMS) to downscale CAMS PM2.5 product on the spatiotemporal dimension for resolution enhancement. The nonlinear statistical downscaling from low-resolution (LR) to high-resolution (HR) data can be learned from the high quality (0.25°, hourly) but short-term (2018-2020) Goddard Earth Observing System composition forecast (GEOS-CF) system PM2.5 product. Compared to the conventional spatiotemporal interpolation methods, simulation validations on GEOS-CF demonstrate that DeepCAMS is capable of producing accurate temporal variations with an improvement of Root-Mean-Squared Error (RMSE) of 0.84 (4.46 to 5.30) ug/m3 and spatial details with an improvement of Mean Absolute Error (MAE) of 0.16 (0.34 to 0.50) ug/m3. The real validations on CAMS reflect convincing spatial consistency and temporal continuity at both regional and global scales. Furthermore, the proposed dataset is validated with OpenAQ air quality data from 2017 to 2019, and the in-situ validations illustrate that the DeepCAMS maintains the consistent precision (R: 0.597) as the original CAMS (R: 0.593) while tripling the spatiotemporal resolution. The proposed dataset will be available at https://doi.org/10.5281/zenodo.6381600.
科研通智能强力驱动
Strongly Powered by AbleSci AI