【摘要】 提出一种基于先进机器学习算法的纯客观、实时天气预报后处理方法。该方法使用历史数值天气预报结果和实况观测值,训练出一个人工神经网络模型,再将该模型应用于每日实时发布的数值天气预报结果中,得到台站级别的天气要素预报结果。于张家口市和北京市分别建立该模型,并使用2005—2020年的观测数据和模式数据进行训练、验证和调优。结果表明,该模型预报的5个常规气象要素的预报误差普遍优于一元线性回归、多元线性回归以及数值天气预报模式的原始输出值,尤其对3天以内的天气预报具有明显优势。基于该模型发展的全自动实时后处理系统已于2020年11月1日开始每日自动化地输出预报结果,并服务于2022年北京冬奥会的气象保障工作。更多还原

【Abstract】 A new objective post-processing system with cutting-edge machine learning algorithm for numerical weather prediction is presented. The core of the system, a matrix of artificial neural network trained by using historical in-situ observations and model results, can be applied on the latest numerical weather prediction results and produce real-time forecast for specific stations. The authors investigate the performance of post-processing system for two stations, Zhangjiakou and Beijing, for the period 2005-2020. It is shown that the forecasts produced by the new system are significantly more accurate than those produced by raw model forecasts, single-variable linear regression, and multi-variable linear regression, especially in terms of 3-day forecast. The authors developed all the core and auxiliary code by serving as Zhangjiakou and Beijing post-processing systems, which are routinely deployed since Nov. 1, 2020, to facilitate the weather service for the Olympic Winter Games at Beijing in 2022.更多还原