A hybrid model for short-term rainstorm forecasting based on a back-propagation neural network and synoptic diagnosis

被引：1

作者：

Guolu Gao ^{[1
]}

Yang Li ^{[2
]}

Jiaqi Li ^{[3
]}

Xueyun Zhou ^{[1
]}

Ziqin Zhou ^{[4
]}

机构：

[1] Yaan Meteorological Observatory,Sichuan Meteorological Bureau

[2] College of Atmospheric Science,Chengdu University of Information Technology

[3] Leshan Meteorological Observatory,Sichuan Meteorological Bureau

[4] Nanchong Meteorological Observatory,Sichuan Meteorological Bureau

来源：

Atmospheric and Oceanic Science Letters | 2021年 / 14卷 / 05期

基金：

中国国家自然科学基金;

关键词：

暴雨; 短期预测方法; BP神经网络; 复合预测模型;

D O I：

暂无

中图分类号：

P457.6 [降水预报];

学科分类号：

0706 ; 070601 ;

摘要：

暴雨是我国最重要的自然灾害之一.大量的研究表明,暴雨的频率和强度在全球变暖的背景下正在逐年增强.但是如何成功的预测短期暴雨,特别是发生在复杂地形下的暴雨,仍然是一个巨大的挑战.本项研究采用BP神经网络和天气学诊断相结合的方法,探索了一种四川盆地西部复杂地形下的暴雨预报模型.该模型有效改善了喇叭口地形下,受低层偏东风影响的暴雨预报准确性.机器学习与天气学理论的结合,提升了模型的物理基础和预测成功率,同时该方法也为发展具有本地特征的暴雨预报客观工具,提供了一定的参考价值.

引用

页码：15 / 20

页数：6

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