QpefBD: A Benchmark Dataset Applied to Machine Learning for Minute-Scale Quantitative Precipitation Estimation and Forecasting-Reference-Cited by-同舟云学术

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QpefBD: A Benchmark Dataset Applied to Machine Learning for Minute-Scale Quantitative Precipitation Estimation and Forecasting

Published:2022-02 Issue:1 Volume:36 Page:93-106
ISSN:2095-6037
Container-title:Journal of Meteorological Research
language:en
Short-container-title:J Meteorol Res

Author:

Xiong Anyuan,Liu Na,Liu Yujia,Zhi Shulin,Wu Linlin,Xin Yongjian,Shi Yan,Zhan Yunjian

Publisher

Springer Science and Business Media LLC

Subject

General Materials Science

Link

https://link.springer.com/content/pdf/10.1007/s13351-022-1140-4.pdf

Reference43 articles.

1. Blumberg, W. G., K. T. Halbert, T. A. Supinie, et al., 2017: SHARPpy: An open-source sounding analysis toolkit for the atmospheric sciences. Bull. Amer. Meteor. Soc., 98, 1625–1636, doi: https://doi.org/10.1175/BAMS-D-15-00309.1.

2. Chen, X. C., K. Zhao, and M. Xue, 2014: Spatial and temporal characteristics of warm season convection over Pearl River Delta region, China, based on 3 years of operational radar data. J. Geophys. Res. Atmos., 119, 12,447–12,465, doi: https://doi.org/10.1002/2014JD021965.

3. Dixon, M., and G. Wiener, 1993: TITAN: Thunderstorm identification, tracking, analysis, and nowcasting—A radar-based methodology. J. Atmos. Oceanic Technol., 10, 785–797, doi: https://doi.org/10.1175/1520-0426(1993)010<0785:TTITAA>2.0.CO;2.

4. Doswell, C. A. III, H. E. Brooks, and R. A. Maddox, 1996: Flash flood forecasting: An ingredients-based methodology. Wea. Forecasting, 11, 560–581, doi: https://doi.org/10.1175/1520-0434(1996)011<0560:FFFAIB>2.0.CO;2.

5. Foresti, L., I. V. Sideris, D. Nerini, et al., 2019: Using a 10-year radar archive for nowcasting precipitation growth and decay: A probabilistic machine learning approach. Wea. Forecasting, 34, 1547–1569, doi: https://doi.org/10.1175/WAF-D-18-0206.1.

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