摘要
随着人工智能技术的快速发展,深度学习的技术应用已日渐成熟,并逐步在各个领域投入实际业务使用。提升短临降水预报的精确度是当前天气预报领域最为艰巨的任务,传统预报方式已无法应对当前急剧变化的天气状况。基于深度学习的神经网络模型能够充分弥补传统预报方式的缺陷,它利用复杂的网络来学习输入和输出数据之间复杂的非线性关系,能够有效处理天气数据中的复杂模式。该文详细介绍几种实用性较强的模型方法,阐述在短临降水预报方面的应用情况,对深度学习在气象领域的发展有重要的借鉴意义。
With the rapid development of artificial intelligence technology,the technical application of deep learning has become increasingly mature,and has gradually been put into actual business use in various fields.Improving the accuracy of short-term and imminent precipitation forecasts is the most arduous task in the current field of weather forecasting.Traditional forecasting methods are no longer able to cope with the current rapidly changing weather conditions.The neural network model based on deep learning can fully make up for the shortcomings of traditional forecasting methods.It uses complex networks to learn complex nonlinear relationships between input and output data,and can effectively process complex patterns in weather data.This paper introduces in detail several practical model methods and expounds their application in short-term and imminent precipitation prediction,which is of important reference significance for the development of deep learning in the meteorological field.
出处
《科技创新与应用》
2025年第4期164-167,172,共5页
Technology Innovation and Application
关键词
深度学习
短临降水预报
神经网络
ConvLSTM
数据集
deep learning
short-term and imminent precipitation forecast
neural network
ConvLSTM
dataset
