联合GNSS PWV和气象参数的短临定性降雨预报改进模型

doi:10.11947/j.AGCS.2024.20230064.

测绘学报 ›› 2024, Vol. 53 ›› Issue (10): 1981-1992.doi: 10.11947/j.AGCS.2024.20230064.

• 大地测量学与导航 • 上一篇

联合GNSS PWV和气象参数的短临定性降雨预报改进模型

熊朝晖¹^,(), 郑敦勇¹(), 姚宜斌², 何畅勇³, 龙四春¹, 卢世德⁴, 周健⁴, 赖咸根⁴

^1.湖南科技大学地球科学与空间信息工程学院，湖南　湘潭　411201
^2.武汉大学测绘学院，湖北　武汉　430072
^3.湖南科技大学地理空间信息技术国家地方联合工程实验室，湖南　湘潭　411201
^4.中建五局土木工程有限公司，湖南　长沙　410021

收稿日期:2023-03-08 发布日期:2024-11-26
通讯作者: 郑敦勇 E-mail:zhxiong@hnust.edu.cn;zdymath@hust.edu.cn
作者简介:熊朝晖（1995—），男，博士，主要从事GNSS地学应用、灾害天气预警等相关研究。E-mail：zhxiong@hnust.edu.cn
基金资助:
国家自然科学基金(41704004);湖南省自然科学基金(2022JJ30245);湖南省教育厅优秀青年项目(23B0470);湖南省自然资源厅项目(20240105CH);湖南省科技创新计划资助(2021RC4037)

An improved model for short-term qualitative rainfall prediction combined with GNSS PWV and meteorological parameters

Zhaohui XIONG¹^,(), Dunyong ZHENG¹(), Yibin YAO², Changyong HE³, Sichun LONG¹, Shide LU⁴, Jian ZHOU⁴, Xiangen LAI⁴

^1.College of Earth Science and Space Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
^2.School of Geodesy and Geomatics, Wuhan University, Wuhan 430072, China
^3.National-local Joint Engineering Laboratory of Geo-spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China
^4.China Construction Fifth Engineering Bureau Civil Engineering Co., Ltd., Changsha 410021, China

Received:2023-03-08 Published:2024-11-26
Contact: Dunyong ZHENG E-mail:zhxiong@hnust.edu.cn;zdymath@hust.edu.cn
About author:XIONG Zhaohui (1995—), male, PhD, majors in GNSS geoscience application and disaster weather warning. E-mail: zhxiong@hnust.edu.cn
Supported by:
The National Natural Science Foundation of China(41704004);The Science and Technology Innovation Program of Hunan Province(2022JJ30245);Excellent Youth Project of Hunan Department of Education(23B0470);Hunan Provincial Natural Resources Department Project(20240105CH);The Science and Technology Innovation Program of Hunan Province(2021RC4037)

摘要/Abstract

摘要：

伴随GNSS数据处理技术的进步及其衍生水汽产品精度的提高，GNSS大气可降水量（PWV）所蕴含的丰富水汽信息被逐渐应用于降雨预报。然而，目前联合GNSS PWV和气象参数的短临降雨预报模型在挖掘参数信息方面仍存在局限性。因此，本文提出了一种基于随机森林算法（RF），联合PWV、温度、气压和相对湿度等参数的距平和变化率的改进模型。通过对湖北、湖南和江西3省的定性降雨预报结果进行分析，发现本文模型能准确地捕捉到PWV和气象参数对降雨的影响。与只使用PWV和气象参数的反向传播神经网络算法（BPNN）的模型相比，本文模型的预报正确率为89.57%，其提升幅度为2.29%；错报率为15.06%，降低了2.76%。在暴雨频发期间，本文模型的预报性能表现出更明显的提升，相较于基于BPNN算法的预报模型，本文模型的预报正确率提高了5.57%，而错报率则降低了2.37%。进一步研究发现，当起报时刻分别设为时刻t和时刻t-1，以预报t+1时刻的降雨状态时，选择时刻t-1作为起报时刻会导致预报正确率有所提高，但同时错报率也会相应上升。

关键词: GNSS PWV, 定性降雨预报, 距平信息

Abstract:

With the progress of GNSS data processing technology and the improvement of the accuracy of its derived water vapor products, rich information of water vapor contained in GNSS PWV (precipitable water vapor) has been gradually applied to precipitation forecast. Due to the limited ability of the current short-range rainfall forecasting model, which combines GNSS PWV and meteorological parameters in mining parameter information, this paper proposes an improved model based on RF algorithm and the use of the variations and anomalies of PWV, temperature, pressure and relative humidity as model inputs. Applying the new model to Hubei, Hunan and Jiangxi provinces, the model performance is assessed and compared with the BPNN algorithm. The results show that, compared with the BPNN algorithm, the rainfall forecast correct rate of the new method rises from 87.28% to 89.57% while the false rate is reduced from 17.82% to 15.06%. Thereby, the new model can better capture the influence of PWV and meteorological parameters on rainfall. During periods of frequent severe rains, the new model performs even better with an increase of correct rate by 5.57% and a decrease of false rate by 2.37%. Further studies reveal that setting the forecast starting time at the current epoch t and the previous epoch t-1 to make a forecast at time t+1, the correct rate is increased slightly, but the false rate is also increased marginally.

Key words: GNSS PWV, qualitative rainfall forecast, anomaly departure

中图分类号:

P258

熊朝晖, 郑敦勇, 姚宜斌, 何畅勇, 龙四春, 卢世德, 周健, 赖咸根. 联合GNSS PWV和气象参数的短临定性降雨预报改进模型[J]. 测绘学报, 2024, 53(10): 1981-1992.

Zhaohui XIONG, Dunyong ZHENG, Yibin YAO, Changyong HE, Sichun LONG, Shide LU, Jian ZHOU, Xiangen LAI. An improved model for short-term qualitative rainfall prediction combined with GNSS PWV and meteorological parameters[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(10): 1981-1992.

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模型	决策树数量	模型	决策树数量
RF_A_t	75	RF_B_t-1	80
RF_B_t	75	RF_C_t-1	95
RF_C_t	95	RF_D_t-1	85
RF_D_t	85

模型	Ts	正确率/(%)	错报率/(%)	漏报率/(%)
BPNN_A_t	0.73	87.3	17.8	12.7
BPNN_B_t	0.75	88.3	16.5	11.7
BPNN_C_t	0.75	88.4	16.3	11.6
BPNN_D_t	0.76	88.5	16.1	11.5
BPNN_B_t-1	0.75	88.4	16.8	11.6
BPNN_C_t-1	0.75	88.5	16.6	11.5
BPNN_D_t-1	0.76	88.7	16.3	11.3
RF_A_t	0.77	89.0	15.5	11.1
RF_B_t	0.77	89.3	15.4	10.7
RF_C_t	0.77	89.3	15.2	10.7
RF_D_t	0.77	89.6	15.1	10.4
RF_B_t-1	0.77	89.6	16.0	10.4
RF_C_t-1	0.77	89.6	15.8	10.4
RF_D_t-1	0.77	89.8	15.7	10.2

模型	Ts变化	正确率变化/(%)	漏报率变化/(%)	错报率变化/(%)
RF_A_t	0.03	1.7	-2.3	-1.7
RF_B_t	0.03	2.0	-2.4	-2.0
RF_C_t	0.04	2.0	-2.6	-2.0
RF_D_t	0.04	2.3	-2.8	-2.3
RF_B_t-1	0.03	2.3	-1.8	-2.3
RF_C_t-1	0.03	2.3	-2.0	-2.3
RF_D_t-1	0.04	2.5	-2.1	-2.5

模型	Ts评分	正确率/(%)	错报率/(%)	漏报率/(%)
BPNN_A_t	0.66	78.0	18.4	22.0
RF_A_t	0.70	81.8	17.1	18.2
RF_D_t	0.72	83.6	15.6	16.5

模型	Ts评分	正确率/(%)	错报率/(%)	漏报率/(%)
BPNN_A_t	0.70	83.4	18.5	16.7
RF_A_t	0.73	84.7	16.6	15.3
RF_D_t	0.74	85.3	15.5	14.7

联合GNSS PWV和气象参数的短临定性降雨预报改进模型

An improved model for short-term qualitative rainfall prediction combined with GNSS PWV and meteorological parameters

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摘要/Abstract

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图/表 16

参考文献 30

相关文章 15

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序号	参数方案	起报时刻	预报时刻	算法	代号
1	A	t	t+1	BPNN	BPNN_A_t
2	B	t	t+1	BPNN	BPNN_B_t
3	C	t	t+1	BPNN	BPNN_C_t
4	D	t	t+1	BPNN	BPNN_D_t
5	B	t-1	t+1	BPNN	BPNN_B_t-1
6	C	t-1	t+1	BPNN	BPNN_C_t-1
7	D	t-1	t+1	BPNN	BPNN_D_t-1
8	A	t	t+1	RF	RF_A_t
9	B	t	t+1	RF	RF_B_t
10	C	t	t+1	RF	RF_C_t
11	D	t	t+1	RF	RF_D_t
12	B	t-1	t+1	RF	RF_B_t-1
13	C	t-1	t+1	RF	RF_C_t-1
14	D	t-1	t+1	RF	RF_D_t-1

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