Constructing a short term heavy rainfall model based on BeiDou/GNSS PWV data and its application

doi:10.13474/j.cnki.11-2246.2024.0917

Abstract

Abstract: The traditional rainfall warning model is only limited to warning whether rainfall events have occurred, ignoring the warning of short-term and imminent heavy rainfall events. Based on the significant increase in precipitable water vapor (PWV) before rainfall occurs, this paper proposes a short-term and imminent heavy rainfall warning model based on BeiDou/GNSS PWV. The model includes three predictive factors: PWV value, PWV change amount and PWV change rate, and introduces the percentile method to determine the optimal threshold for key parameters of the predictive factors. Selecting five GNSS stations in Yichang city, Hubei province for hourly PWV and rainfall data validation in 2022, the statistical results show that the proposed short-term and imminent heavy rainfall warning model can predict 94% of heavy rain events in the next 2～6 hours, with a false alarm rate of only 32.84%.

Key words: heavy rainfall warning, BeiDou/GNSS, PWV, warning model

CLC Number:

P228

DENG Nanshan, LIU Yang, LI Xiaowei, SI Xiaohua, LEI Lei. Constructing a short term heavy rainfall model based on BeiDou/GNSS PWV data and its application[J]. Bulletin of Surveying and Mapping, 2024, 0(9): 96-100.

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