改进的GNSS-PWV三因子阈值降雨预报方法

董传凯; 余法承; 张卫星; 方礼喆; 魏康丽; 楼益栋; 欧书圆

doi:10.13203/j.whugis20220798

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改进的GNSS-PWV三因子阈值降雨预报方法

大地测量学与测量工程 | 更新时间：2026-11-05

- 改进的GNSS-PWV三因子阈值降雨预报方法
- An Improved Rainfall Forecasting Method with GNSS-PWV Three Factor Threshold
- 武汉大学学报（信息科学版） 2025年50卷第5期页码：866-873
- 作者机构：
  
  1.武汉大学数学与统计学院,湖北武汉，430072
  2.武汉大学卫星导航定位技术研究中心,湖北武汉，430079
  3.武汉大学国家网络安全学院,湖北武汉，430072
- 作者简介：
  
  董传凯,硕士,研究方向为GNSS气象学。chuankaid@whu.edu.cn
  张卫星,博士,副教授。zhangweixing89@whu.edu.cn
- 基金信息：
  
  国家自然科学基金(42174027);中央高校基本科研业务费专项资金(2042022kf1198);青年托举人才工程(YESS20200110);武汉市知识创新专项——曙光计划(2022010801020129)
- DOI：10.13203/j.whugis20220798
  中图分类号： P228
- 收稿：2023-02-20，
  
  纸质出版：2025-05-05
- 稿件说明：
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董传凯,余法承,张卫星，等.改进的GNSS-PWV三因子阈值降雨预报方法[J].武汉大学学报（信息科学版）,2025,50(5):866-873.

DONG Chuankai,YU Facheng,ZHANG Weixing,et al.An Improved Rainfall Forecasting Method with GNSS-PWV Three Factor Threshold[J].Geomatics and Information Science of Wuhan University,2025,50(5):866-873.
董传凯,余法承,张卫星，等.改进的GNSS-PWV三因子阈值降雨预报方法[J].武汉大学学报（信息科学版）,2025,50(5):866-873. DOI：10.13203/j.whugis20220798

DONG Chuankai,YU Facheng,ZHANG Weixing,et al.An Improved Rainfall Forecasting Method with GNSS-PWV Three Factor Threshold[J].Geomatics and Information Science of Wuhan University,2025,50(5):866-873. DOI：10.13203/j.whugis20220798

摘要

大气可降水量（precipitable water vapor

PWV）在研究大气水汽含量与降雨之间关系的研究中发挥着越来越重要的作用。基于全球导航卫星系统(global navigation satellite system

GNSS)-PWV的三因子（PWV、PWV变化量和PWV变化率）阈值的降雨预报方法已经在一些场景中取得了不错的效果，但该方法目前仍存在一些问题，包括部分场景下无法有效反映PWV的变化，预测因子的阈值确定不够合理。对传统方法进行了改进，以PWV为主要预测因子、PWV增量和PWV增率为辅助预测因子，并采用定量选取月阈值的方法进行降雨预报。基于SuomiNet网的实验结果显示，所提的改进方法能够取得92%以上的平均正确率和63%左右的平均误报率，与传统三因子算法相比，改进方法的降雨预报正确率更高，误报率更低，且命中率处于相同水平。可见，改进方法能够充分利用PWV的季节特征和实时信息捕捉PWV和降雨之间的关系，更有效地预测降雨。

Abstract

Objectives

Precipitable water vapor (PWV) plays an increasingly significant role in the quantitative study of the potential meteorological factors that cause rainfall. The PWV-based three-factor (PWV

PWV change and rate of PWV change) threshold method for the rain forecast has been established

empirically proving its effectiveness in some scenarios. However

an apparent issue is that not fully using real-time information restricts performance. We propose an improved monthly threshold method to tackle this problem.

Methods

The basic idea is to refine the predictors. The three-factor monthly threshold method based on PWV

PWV increment and rate of PWV increment is used to realize the short-term rainfall forecast within 6 h

in which PWV is used as the main predictor

and PWV increment and PWV increase rate are used as secondary factors to assist in the prediction. We offer a quantitative standard for picking the threshold based on maximizing critical success index (CSI).The specific prediction steps are as follows: (1) The time series of the three predictors are extracted from the raw data using a 6 hours sliding window. Within each window

the PWV increment and the rate of PWV increment are computed. The window is then shifted by 30 minutes

and the process is repeated

resulting in sequences for both the PWV increment and the rate of PWV increment. (2) The monthly threshold is determined using data from 2015 to 2018

with the optimal PWV threshold for each month established based on the CSI. Once the threshold is determined

the CSI maximum principle is applied to simultaneously select the optimal thresholds for both the PWV increment and the rate of PWV increment. (3) The optimal thresholds

determined by the 2019 data

are subsequently applied to predict rainfall over the next 6 hours. Rainfall is predicted if the PWV exceeds the threshold at any given time. In cases where the PWV does not exceed the threshold

but both the PWV increment and the rate of PWV increment surpass their respective thresholds

rainfall is also predicted. If neither condition is met

no rainfall is predicted. The predicted rainfall is then compared with the observed rainfall to calculate key performance metrics

including the correct rate (CR)

false alarm rate (FAR)

and CSI. Additionally

it is argued that

unlike the probability of detection (POD)

the use of CR provides a more reasonable evaluation of the proportion of correctly predicted rainfall events.

Results

We apply our method to 11 different stations of SuomiNet. The CR of the improved method is above 89%

while the FAR is controlled below 73%. Among them the CR of the three stations P031

P047 and CN00 is about 95%

and the FAR is not higher than 65%. In general

the proposed method can predict more than 92% of rainfall on average

and the average FAR of each forecast is about 63%. Compared with the traditional three-factor algorithm

the average CR of the improved three-factor algorithm is increased by nearly 6%

the average FAR is reduced by more than 4%

and the average POD is at the same level.

Conclusions

Using PWV increment and the rate of PWV increment

the new predictor can better reflect the characteristics of the rising phase of PWV and keep updating synchronously with PWV. Compared with the traditional method

our algorithm can predict rainfall more effectively and has higher applicability.

关键词

Keywords

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