A Machine Learning Nowcasting Method based on Real-time Reanalysis Data
College of Information Science and Engineering, Ocean University of China（中国海洋大学信息科学与工程学院）；Institute of Urban Meteorology, China Meteorological Administration（中国气象局城市气象研究所）；National Center for Atmospheric Research（国家大气研究中心）；National Meteorological Center, China Meteorological Administration（中国气象局国家气象中心）
Lei Han, Juanzhen Sun, Wei Zhang, Yuanyuan Xiu, Hailei Feng, Yinjing Lin
备注：
22 pages, 11 figures, submitted to Journal of Geophysical Research: Atmospheres
Despite marked progress over the past several decades, convective storm nowcasting remains a challenge because most nowcasting systems are based on linear extrapolation of radar reflectivity without much consideration for other meteorological fields. The variational Doppler radar analysis system (VDRAS) is an advanced convective-scale analysis system capable of providing analysis of 3-D wind, temperature, and humidity by assimilating Doppler radar observations. Although potentially useful, it is still an open question as to how to use these fields to improve nowcasting. In this study, we present results from our first attempt at developing a Support Vector Machine (SVM) Box-based nOWcasting (SBOW) method under the machine learning framework using VDRAS analysis data. The key design points of SBOW are as follows: 1) The study domain is divided into many position-fixed small boxes and the nowcasting problem is transformed into one question, i.e., will a radar echo > 35 dBZ appear in a box in 30 minutes? 2) Box-based temporal and spatial features, which include time trends and surrounding environmental information, are elaborately constructed, and 3) The box-based constructed features are used to first train the SVM classifier, and then the trained classifier is used to make predictions. Compared with complicated and expensive expert systems, the above design of SBOW allows the system to be small, compact, straightforward, and easy to maintain and expand at low cost. The experimental results show that, although no complicated tracking algorithm is used, SBOW can predict the storm movement trend and storm growth with reasonable skill.
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University of Colorado（科罗拉多大学）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；Univ. Savoie Mont Blanc（萨瓦大学）；CNRS（法国国家科学研究中心）；IRD（法国发展研究院）；IFSTTAR（法国交通发展规划与网络研究院）；ISTerre（地球科学研究所）；Linear Accelerator Laboratory（线性加速器实验室）；Université Paris-Sud（巴黎第十一大学）；Inria Saclay–Ile-de-France（法国国家信息与自动化研究所萨克莱-法兰西岛分院）；LRI（信息学实验室）；University of Colorado Boulder（科罗拉多大学博尔德分校）；Cooperative Institute for Research in Environmental Sciences（环境科学合作研究所）；NOAA/OAR/ESRL/Global Systems Division（美国国家海洋和大气管理局海洋与大气研究办公室地球系统研究实验室全球系统部）
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Department of Earth and Environmental Engineering, Columbia University（哥伦比亚大学地球与环境工程系）；Learning the Earth with Artificial Intelligence and Physics (LEAP) Center, Columbia University（人工智能与物理联合地球学习中心（LEAP）, 哥伦比亚大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
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Shanghai Academy of Artificial Intelligence for Science（上海人工智能科学研究院）；Artificial Intelligence Innovation and Incubation Institute（人工智能创新与孵化院）；Fudan University（复旦大学）；FuXi Intelligent Computing Technology Co., Ltd.（FuXi智能计算技术有限公司）
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Artificial Intelligence Innovation and Incubation Institute（人工智能创新与孵化院）；Fudan University（复旦大学）；Shanghai Innovation Institute（上海创新研究院）；Department of Atmospheric and Oceanic Sciences（大气科学与海洋科学系）；Shanghai Academy of Artificial Intelligence for Science（上海人工智能科学研究院）；FuXi Intelligent Computing Technology Co., Ltd.（复西智能计算技术有限公司）
