Wei Tian
Kailing Shen
Lei Yi1,2
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- 1School of Computer Science and Software, Nanjing University of Information Science and Technology, Nanjing, China
- 2Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China
- 3Shijiazhuang Meteorological Bureau, Shijiazhuang, China
- 4School of Automation, Nanjing University of Information Science and Technology, Nanjing, China
A Corrigendum on
Quantitative precipitation estimation model integrating meteorological and geographical factors at multiple spatial scales
by Tian W, Shen K, Yi L, Zhang L, Feng Y and Chen S (2022). Front. Earth Sci. 10:908869. doi:10.3389/feart.2022.908869
In the original article, there was an error in Figure 9C, page 13. The authors intended to use the root mean square error, relative error, and correlation coefficient as evaluation metrics when discussing the performance of their model and the comparison model at 17 different national weather stations. However, due to an oversight, the performance of the model under the correlation coefficient was not submitted, but instead there was a duplication of the performance of the model under the relative error. The corrected figure appears below.
FIGURE 9
FIGURE 9. MS-FCVNet scores for (A) RMSE, (B) MAE, (C) CC at 17 NWSs.
The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
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Keywords: precipitation estimation, weather radar, deep learning, multi-scale, meteorological factors, geographical factors
Citation: Tian W, Shen K, Yi L, Zhang L, Feng Y and Chen S (2022) Corrigendum: Quantitative precipitation estimation model integrating meteorological and geographical factors at multiple spatial scales. Front. Earth Sci. 10:998491. doi: 10.3389/feart.2022.998491
Received: 20 July 2022; Accepted: 25 July 2022;
Published: 06 September 2022.
Edited and reviewed by:
Yuqing Wang, University of Hawaii at Manoa, United StatesCopyright © 2022 Tian, Shen, Yi, Zhang, Feng and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Wei Tian, dHdAbnVpc3QuZWR1LmNu; Kailing Shen, c2tsaW5nQG51aXN0LmVkdS5jbg==