https://scholars.lib.ntu.edu.tw/handle/123456789/629264
標題: | Extracting 3D Radar Features to Improve Quantitative Precipitation Estimation in Complex Terrain Based on Deep Learning Neural Networks | 作者: | Cheng, YY Chang, CT Chen, BF HUNG-CHI KUO Lee, CS |
關鍵字: | Radars; Radar observations; Weather radar signal processing; Deep learning; Neural networks; Precipitation; RAINFALL ESTIMATION; SIZE DISTRIBUTION; PARAMETERIZATION; REFLECTIVITY | 公開日期: | 二月-2023 | 出版社: | AMER METEOROLOGICAL SOC | 卷: | 38 | 期: | 2 | 起(迄)頁: | 273 | 來源出版物: | WEATHER AND FORECASTING | 摘要: | This paper proposes a new quantitative precipitation estimation (QPE) technique to provide accurate rainfall estimates in complex terrain, where conventional QPE has limitations. The operational radar QPE in Taiwan is mainly based on the simplified relationship between radar reflectivity Z and rain rate R [R(Z) relation] and only utilizes the single-point lowest available echo to estimate rain rates, leading to low accuracy in complex terrain. Here, we conduct QPE using deep learning that extracts features from 3D radar reflectivities to address the above issues. Convolutional neural networks (CNN) are used to analyze contoured frequency by altitude diagrams (CFADs) to generate the QPE. CNN models are trained on existing rain gauges in northern and eastern Taiwan with the 3-yr data during 2015–17 and validated and tested using 2018 data. The weights of heavy rains ($10 mm h21) are increased in the model loss calculation to handle the unbal-anced rainfall data and improve accuracy. Results show that the CNN outperforms the R(Z) relation based on the 2018 rain gauge data. Furthermore, this research proposes methods to conduct 2D gridded QPE at every pixel by blending estimates from various trained CNN models. Verification based on independent rain gauges shows that the CNN QPE solves the un-derestimation of the R(Z) relation in mountainous areas. Case studies are presented to visualize the results, showing that the CNN QPE generates better small-scale rainfall features and more accurate precipitation information. This deep learning QPE technique may be helpful for the disaster prevention of small-scale flash floods in complex terrain. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/629264 | ISSN: | 0882-8156 | DOI: | 10.1175/WAF-D-22-0034.1 |
顯示於: | 大氣科學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。