A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Improving the accuracy and spatial resolution of precipitable water vapor dataset using a neural network-based downscaling method
https://orcid.org/0000-0002-5609-0880
https://orcid.org/0000-0002-9820-6773
Abstract Precipitable water vapor (PWV) is a crucial variable in water and energy transfers between the surface and atmosphere, and it is sensitive to climate and environmental changes. Among various PWV monitoring techniques, the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5)-derived PWV, with a spatial resolution of 0.25 × 0.25°, has excellent spatiotemporal continuity. However, its accuracy has relatively large uncertainties, and its spatial resolution is inadequate for small regions such as the Tibetan Plateau (TP). Therefore, the aim of this study was to propose machine learning-based modification and downscaling methods to improve the accuracy and spatial resolution of ERA5-derived PWV. First, a modification model based on a back propagation neural network (BPNN) was proposed to improve the accuracy of ERA5-derived PWV. The results showed that the root mean square error (RMSE) of ERA5-derived PWV decreased from 2.83 mm to 2.24 mm in China, i.e., an improvement of 20.8%. In the TP region, the RMSE decreased from 2.92 mm to 1.96 mm, and the improvement was 32.9%. Subsequently, a BPNN-based downscaling model was established using the modified ERA5-derived PWV to generate PWV with a 6-hourly, 0.1° × 0.1° spatiotemporal resolution in the TP region. Compared with global navigation satellite system-derived PWV, the RMSE of the generated PWV was 2.13 mm. The spatial distribution of BPNN-derived PWV based on the downscaling method exhibited suitable stability in the TP region, indicating that the proposed method could significantly improve the accuracy and spatial resolution of ERA5-derived PWV in the TP region.
Highlights A BPNN-based modification model was proposed to improve accuracy of ERA5-PWV. A BPNN-based downscaling model was established to improve spatial resolution of PWV. The RMS improvements of ERA5-PWV were 20.8% and 32.9% in China and TP, respectively. The spatial resolution of PWV improved 2.5-fold using the proposed downscaling model.
Improving the accuracy and spatial resolution of precipitable water vapor dataset using a neural network-based downscaling method
https://orcid.org/0000-0002-5609-0880
https://orcid.org/0000-0002-9820-6773
Abstract Precipitable water vapor (PWV) is a crucial variable in water and energy transfers between the surface and atmosphere, and it is sensitive to climate and environmental changes. Among various PWV monitoring techniques, the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5)-derived PWV, with a spatial resolution of 0.25 × 0.25°, has excellent spatiotemporal continuity. However, its accuracy has relatively large uncertainties, and its spatial resolution is inadequate for small regions such as the Tibetan Plateau (TP). Therefore, the aim of this study was to propose machine learning-based modification and downscaling methods to improve the accuracy and spatial resolution of ERA5-derived PWV. First, a modification model based on a back propagation neural network (BPNN) was proposed to improve the accuracy of ERA5-derived PWV. The results showed that the root mean square error (RMSE) of ERA5-derived PWV decreased from 2.83 mm to 2.24 mm in China, i.e., an improvement of 20.8%. In the TP region, the RMSE decreased from 2.92 mm to 1.96 mm, and the improvement was 32.9%. Subsequently, a BPNN-based downscaling model was established using the modified ERA5-derived PWV to generate PWV with a 6-hourly, 0.1° × 0.1° spatiotemporal resolution in the TP region. Compared with global navigation satellite system-derived PWV, the RMSE of the generated PWV was 2.13 mm. The spatial distribution of BPNN-derived PWV based on the downscaling method exhibited suitable stability in the TP region, indicating that the proposed method could significantly improve the accuracy and spatial resolution of ERA5-derived PWV in the TP region.
Highlights A BPNN-based modification model was proposed to improve accuracy of ERA5-PWV. A BPNN-based downscaling model was established to improve spatial resolution of PWV. The RMS improvements of ERA5-PWV were 20.8% and 32.9% in China and TP, respectively. The spatial resolution of PWV improved 2.5-fold using the proposed downscaling model.
Improving the accuracy and spatial resolution of precipitable water vapor dataset using a neural network-based downscaling method
https://orcid.org/0000-0002-5609-0880
https://orcid.org/0000-0002-9820-6773
Abstract Precipitable water vapor (PWV) is a crucial variable in water and energy transfers between the surface and atmosphere, and it is sensitive to climate and environmental changes. Among various PWV monitoring techniques, the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5)-derived PWV, with a spatial resolution of 0.25 × 0.25°, has excellent spatiotemporal continuity. However, its accuracy has relatively large uncertainties, and its spatial resolution is inadequate for small regions such as the Tibetan Plateau (TP). Therefore, the aim of this study was to propose machine learning-based modification and downscaling methods to improve the accuracy and spatial resolution of ERA5-derived PWV. First, a modification model based on a back propagation neural network (BPNN) was proposed to improve the accuracy of ERA5-derived PWV. The results showed that the root mean square error (RMSE) of ERA5-derived PWV decreased from 2.83 mm to 2.24 mm in China, i.e., an improvement of 20.8%. In the TP region, the RMSE decreased from 2.92 mm to 1.96 mm, and the improvement was 32.9%. Subsequently, a BPNN-based downscaling model was established using the modified ERA5-derived PWV to generate PWV with a 6-hourly, 0.1° × 0.1° spatiotemporal resolution in the TP region. Compared with global navigation satellite system-derived PWV, the RMSE of the generated PWV was 2.13 mm. The spatial distribution of BPNN-derived PWV based on the downscaling method exhibited suitable stability in the TP region, indicating that the proposed method could significantly improve the accuracy and spatial resolution of ERA5-derived PWV in the TP region.
Highlights A BPNN-based modification model was proposed to improve accuracy of ERA5-PWV. A BPNN-based downscaling model was established to improve spatial resolution of PWV. The RMS improvements of ERA5-PWV were 20.8% and 32.9% in China and TP, respectively. The spatial resolution of PWV improved 2.5-fold using the proposed downscaling model.
Improving the accuracy and spatial resolution of precipitable water vapor dataset using a neural network-based downscaling method
Ma, Xiongwei (author) / Yao, Yibin (author) / Zhang, Bao (author) / Yang, Mengjia (author) / Liu, Hang (author)
Atmospheric Environment ; 269
2021-11-11
Article (Journal)
Electronic Resource
English
Precipitable Water Vapor Retrieval Using Neural Network from Infrared Hyperspectral Soundings
| British Library Online Contents | 2012
Precipitable water vapor fusion based on a generalized regression neural network
| Online Contents | 2021
Satellite-Based Retrieval of Precipitable Water Vapor Over Land by Using a Neural Network Approach
| Online Contents | 2011
Evaluation of precipitable water vapor variation for east mediterranean using GNSS
| Online Contents | 2020