基于观测和再分析数据的LSTM深度神经网络沿海风速预报应用研究

王国松; 王喜冬; 侯敏; 齐义泉; 宋军; 刘克修; 吴新荣; 白志鹏

doi:10.3969/j.issn.0253-4193.2020.01.008

基于观测和再分析数据的LSTM深度神经网络沿海风速预报应用研究

doi: 10.3969/j.issn.0253-4193.2020.01.008

王国松^{1, 2,},
王喜冬¹,
侯敏^3, ,,
齐义泉¹,
宋军⁴,
刘克修²,
吴新荣²,
白志鹏⁵

1.
河海大学海洋学院，江苏南京 210098
2.
国家海洋信息中心，天津 300171
3.
天津市滨海新区气象局，天津 300457
4.
大连海洋大学海洋科技与环境学院应用海洋研究所，辽宁大连 116023
5.
61741部队，北京 100094

基金项目: 国家重点研发计划（2016YFC1401903）；国家自然科学基金（41776004）；河海大学中央高校基本科研业务费（2016B12514）。

详细信息

作者简介:
王国松（1987—），男，天津市人，助研，主要从事数值模拟与资料处理研究。E-mail：xifengbishu2110@163.com

通讯作者:
侯敏，工程师，主要从事海洋气象预报业务。E-mail：ande0604@163.com

中图分类号: P732.4
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- 文章访问数: 2216
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- 被引次数: 0
出版历程
- 收稿日期: 2019-01-15
- 修回日期: 2019-06-22
- 网络出版日期: 2021-04-21
- 刊出日期: 2020-01-25

Research on application of LSTM deep neural network on historical observation data and reanalysis data for sea surface wind speed forecasting

Wang Guosong^{1, 2
,},
Wang Xidong¹,
Hou Min^{3
, ,},
Qi Yiquan¹,
Song Jun⁴,
Liu Kexiu²,
Wu Xinrong²,
Bai Zhipeng⁵

1.
College of Oceanography, Hohai University, Nanjing 210098, China
2.
National Marine Data and Information Service, Tianjin 300171, China
3.
Tianjin Binhai New Area Meteorology Administration, Tianjin 300457, China
4.
Operational Oceanographic Institution, School of Marine Science and Environment Engineering, Dalian Ocean University, Dalian 116023, China
5.
61741 Troops, Beijing 100094, China

摘要

摘要: 基于海洋气象历史观测资料和再分析数据等，利用LSTM深度神经网络方法，开展在有监督学习情况下的海面风场短时预报应用研究。以中国近海5个代表站为研究区域，通过气象台站观测数据和ERA-Interim 6 h再分析数据构建数据集。选取21个变量作为预报因子，分别构建两个LSTM深度神经网络框架(OBS_LSTM和ALL_LSTM)。经与2017年WRF模式6 h预报结果对比分析，得出如下结论：构建的两个LSTM风速预报模型可以大幅降低风速预报误差，RMSE分别降低了41.3%和38.8%，MAE平均降低了43.0%和40.0%；风速误差统计和极端大风分析发现，LSTM模型能够抓住地形、短时大风和台风等敏感信息，对于大风过程预报结果明显优于WRF模式；两种LSTM模型对比发现，ALL_LSTM模型风速预报误差最小，具有很好的稳定性和鲁棒性，OBS_LSTM模型应用范围更广泛。
- 深度学习 /
- LSTM /
- 海面风速 /
- 短时预报 /
- WRF模式
Abstract: Based on historical meteorological observation data and reanalysis data, the application of LSTM (longs short-term memory) deep neural network in short-term forecasting of sea surface wind speed under supervised learning was studied. Five representative meteorological stations in the offshore were taken as research areas. Twenty-one variables, which has been quality control and preprocessing, were selected as the prediction factors, and two LSTM deep neural network frameworks (OBS_LSTM and ALL_LSTM) were constructed. The 6 h wind speed forecast of WRF model over two-nesting domains in 2017 was included to validate the real performance of the proposed model. The result indicate that, the LSTM wind speed forecast models could significantly reduce the forecast error, RMSE was reduced by 41.3% and 38.8%, and MAE was reduced by 43.0% and 40.0%, respectively; wind speed error statistics and strong wind events comparison shows that, LSTM model can grasp sensitive information such as topography and typhoon, and superior to WRF model. The ALL_LSTM model has the smallest prediction error, good stability and robustness, and OBS_LSTM model has a wider range of application.
- deep neural network /
- LSTM /
- sea surface wind speed /
- short-term forecasting /
- WRF model

HTML全文

图 1 气象观测台站分布（蓝点）与WRF模式嵌套区域

黑红点线为2017年影响汕头和西沙两站的台风最佳路径

Fig. 1 Locations of meteorological observation stations (blue points) and nested domain of WRF model

Black dot line is the best track of typhoon affecting Shantou and Xisha stations in 2017

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图 2 预处理后测试集主要变量序列分布

Fig. 2 Time series of main variable of the test set after preprocessing

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图 3 西沙站与观测风速相关性最高的9个变量热力图

Fig. 3 The heat map of highest correlation between 9 variables and observed wind speed at Xisha Station

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图 4 ALL_LSTM预报模型结构图

Fig. 4 ALL_LSTM prediction model structure diagram

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图 5 ALL_LSTM，OBS_LSTM和WRF模式日平均风速预报结果与台站观测资料对比

Fig. 5 Comparison of daily mean wind speed forecast results of ALL_LSTM, OBS_LSTM and WRF models with observational data

下载: 全尺寸图片幻灯片

表 1 中国近海5个气象台站基本信息

Tab. 1 Basic information of the five meteorological stations in China offshore

站号	站名	缩写	站类	纬度	经度	观测场海拔高度/m
54662	大连	DL	基准站	38°54'N	121°38'E	91.5
54857	青岛	QD	基本站	36°04'N	120°20'E	76.0
58265	吕泗	LS	基准站	32°04'N	121°36'E	5.5
59316	汕头	ST	基准站	23°24'N	116°41'E	2.9
59981	西沙	XS	基准站	16°50'N	112°20'E	4.7

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表 2 WRF模式设置

Tab. 2 Specification of the WRF model

区域与选项	具体设置
区域与分辨率	三重嵌套，Lambert conformal投影，中心点（42°N，115°E）
区域与分辨率	D01:水平分辨率 30 km	D02:水平分辨率 10 km	D03:水平分辨率 3.3 km	D04:水平分辨率 3.3 km	D05:水平分辨率 3.3 km
垂直分辨率	44η层
输出时间间隔	6 h
边界层方案	Lin et al. scheme方案
积云方案	Kain-Fritsch方案
微物理方案	WSM 5-class 方案
辐射方案	长短波辐射方案:RRTMG方案
陆面过程	Noah陆面模式
背景误差	CV5
热启动	是

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表 3 验证集风速误差统计

Tab. 3 Compare the performance of 6 h wind speed on validation sets

站位	误差统计	ALL_LSTM 模型	OBS_LSTM 模型	WRF 模型
大连	均方根误差/m·s⁻¹	1.44	1.51	1.87
	绝对误差/m·s⁻¹	1.09	1.17	1.43
	相关系数	0.55	0.48	0.70
青岛	均方根误差/m·s⁻¹	1.47	1.53	3.00
	绝对误差/m·s⁻¹	1.10	1.15	2.33
	相关系数	0.60	0.53	0.11
吕泗	均方根误差/m·s⁻¹	1.15	1.21	1.17
	绝对误差/m·s⁻¹	0.90	0.94	0.93
	相关系数	0.64	0.58	0.73
汕头	均方根误差/m·s⁻¹	0.74	0.77	1.13
	绝对误差/m·s⁻¹	0.58	0.61	0.85
	相关系数	0.55	0.50	0.51
西沙	均方根误差/m·s⁻¹	1.16	1.20	2.99
	绝对误差/m·s⁻¹	0.88	0.92	2.44
	相关系数	0.73	0.71	0.70
各地平均	均方根误差/m·s⁻¹	1.19	1.24	2.03
	绝对误差/m·s⁻¹	0.91	0.96	1.60
	相关系数	0.61	0.56	0.55
注：最小均方根误差、最小绝对误差和最大相关系数分别用粗体表示。

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