基于机器学习的华北气温多模式集合预报的订正方法

门晓磊; 焦瑞莉; 王鼎; 赵晨光; 刘亚昆; 夏江江; 李昊辰; 严中伟; 孙建华; 王立志

doi:10.3878/j.issn.1006-9585.2018.18049

基于机器学习的华北气温多模式集合预报的订正方法

doi: 10.3878/j.issn.1006-9585.2018.18049

1.
北京信息科技大学信息与通信工程学院, 北京 100101
2.
北京信息科技大学信息管理学院, 北京 100192
3.
中国科学院大气物理研究所东亚区域气候-环境重点实验室, 北京 100029
4.
北京大学数学科学学院, 北京 100871
5.
中国科学院大学, 北京 100049

基金项目:

中国科学院战略性先导科技专项 A-XDA19030403

中国科学院战略性先导科技专项 XDA19040202

详细信息

作者简介:
门晓磊, 男, 1993年出生, 硕士研究生, 主要从事机器学习和深度学习方向的研究。E-mail:1160727887@qq.com

通讯作者:
夏江江, E-mail:xiajj@tea.ac.cn

中图分类号: P457.3
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- 被引次数: 0
出版历程
- 收稿日期: 2018-04-02
- 网络出版日期: 2018-07-04
- 刊出日期: 2019-01-20

A Temperature Correction Method for Multi-model Ensemble Forecast in North China Based on Machine Learning

1.
School of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing 100101
2.
School of Information Management, Beijing Information Science and Technology University, Beijing 100192
3.
Key Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
4.
School of Mathematical Sciences, Peking University, Beijing 100871
5.
University of Chinese Academy of Sciences, Beijing 100049

Funds:

Strategic Priority Research Program of Chinese Academy of Sciences A-XDA19030403

Strategic Priority Research Program of Chinese Academy of Sciences XDA19040202

摘要

摘要: 模式预报的订正是决定局地天气预报结果的一个重要步骤，基于机器学习的后处理模型近年来开始崭露头角。本文发展了基于岭回归（Ridge）、随机森林（Random Forest，RF）和深度学习（Deep Learning，DL）的3种后处理模型，基于中国气象局（CMA）的BABJ模式、欧洲中期天气预报中心（ECMWF）的ECMF模式、日本气象厅（JMA）的RJTD模式和NCEP的KWBC模式这4个数值天气预报模式2014年2月至2016年9月（训练期）近地面2 m气温预报和实况资料确定各模型参数，进而对2016年10月至2017年9月（预报期）华北地区（38°N~43°N，113°E~119°E）的逐日地面2 m气温预报进行了多模式集合预报分析。采用均方根误差对预报效果进行评估，这3种后处理模型的预报效果和4个数值天气预报模式以及通常的多模式集合平均（Ensemble Mean，EMN）的预报效果的对比表明：1）随着预报时长增加，4个数值预报模式及各种后处理模型的均方根误差均呈上升趋势；但区域平均而言，Ridge、RF和DL的预报效果在任何预报时长上都明显优于EMN和单个天气预报模式；特别是前几天的短期预报DL的预报效果更好，中后期预报Ridge的预报效果略好。2）华北地区的东南部均方根误差较小，其余格点上均方根误差较高，从空间分布而言，DL的订正预报效果最好，3种机器学习模型的误差在1.24~1.26℃之间，而EMN的误差达1.69℃。3）夏季各种方法的预报效果都较好，冬季预报效果都较差；但是Ridge、RF和DL的预报效果明显优于EMN，这3种模型预报的平均均方根误差在2.15~2.18℃之间，而EMN的平均均方根误差达2.45℃。
- 地面2 m气温 /
- 多模式集合平均 /
- 岭回归 /
- 随机森林 /
- 深度学习
Abstract: Post-forecast data processing is critical for obtaining reliable local weather forecast. In this study, the authors developed three post-processing models based on ridge regression (Ridge), random forest (RF), and deep learning (DL) methods. The post-processing models were trained by observational and forecast data of daily 2-m above surface air temperature in North China (38°N-43°N, 113°E-119°E) from four numerical weather forecast (NWF) models (BABJ model from China Meteorological Administration, ECMF model from ECMWF, RJTD model from Japan Meteorological Agency, and KWBC model from NCEP, respectively), for the training period from February 2014 to September 2016, and then applied to the forecast period from October 2016 to September 2017. The forecast results of the post-processing models together with those of commonly-used multi-model ensemble mean (EMN) and individual NWF models were evaluated according to the root-mean-square error (RMSE). The main results are as follows:1) For the region as a whole, with the increase in the forecast lead time, all the NWF models, EMN and the post-processing models exhibit increasing RMSEs, but the RMSEs of the three post-processing models are all significantly smaller than those of EMN and individual NWF models; especially, DL is slightly better for the short-term (the first few days) forecast and RF is slightly better for the longer-term prediction. 2) The RMSEs are relatively smaller in the southeastern part of North China, approximately in the range of (38°N-41°N, 115.5°E-119°E) than else where; on average, DL is slightly better, and the RMSEs of the three machine learning models are between 1.24℃ and 1.26℃, while the EMN error is 1.69℃. 3) There are seasonal differences:The results of all the models are relatively good for the summer, but poor in general for the winter. All the three post-processing models perform better than EMN and individual NWF models, with a smallest average RMSE of 2.15℃ for Ridge compared with 2.45℃ for EMN.
- 2-m above surface air temperature /
- Multi-model ensemble mean forecast /
- Ridge regression /
- Random forest /
- Deep learning

HTML全文

图 1 深层神经网络构建的多模式集合温度预报模型

Figure 1. Multi-model ensemble temperature prediction model constructed by deep neural network

下载: 全尺寸图片幻灯片

图 2 4个模式以及4种后处理模型的均方根误差（RMSE）随预报时长的变化

Figure 2. Root-mean-square errors (RMSEs) in four models and four post-processing models as a function of forecast lead time

下载: 全尺寸图片幻灯片

图 3 4种预报订正处理模型的均方根误差空间分布

Figure 3. RMSEs spatial distributions in four post-predictive processing models

下载: 全尺寸图片幻灯片

图 4 4种预报订正处理模型的均方根误差的时间序列（2016年10月至2017年9月）

Figure 4. Time series of RMSEs for four forecast correction processing models (Oct 2016−Sep 2017)

下载: 全尺寸图片幻灯片

图 5 4个数值预报模式的均方根误差的时间序列（2016年10月至2017年9月）

Figure 5. Time series of RMSEs for four numerical weather prediction models (Oct 2016−Sep 2017)

下载: 全尺寸图片幻灯片

表 1 4个模式以及4种后处理模型的均方根误差随预报时长的线性趋势

Table 1. Trends of RMSEs in four models and four postrocessing models with the forecast lead time

℃ d⁻¹
模式或模型名称	线性趋势
BABJ	0.22
ECMF	0.29
RJTD	0.22
KWBC	0.27
EMN	0.20
Ridge	0.23
RF	0.23
DL	0.23

下载: 导出CSV

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