An Improved Method for Rainfall Forecast Based on GNSS-PWV

Abstract

Global navigation satellite systems (GNSS) has been applied to the sounding of precipitable water vapor (PWV) due to its high accuracy and high spatiotemporal resolutions. PWV obtained from GNSS (GNSS-PWV) can be used to investigate extreme weather phenomena, such as the formation mechanism and prediction of rainfalls. In the study, a new, improved model for rainfall forecasting was developed based on GNSS data and rainfall data for the 9-year period from 2010 to 2018 at 66 stations located in the USA. The new model included three prediction factors—PWV value, PWV increase, maximum hourly PWV increase. The two key tasks involved for the development of the model were the determination of the thresholds for each prediction factor and the selection of the optimal strategy for using the three prediction factors together. For determining the thresholds, both critical success index (CSI) and true skill statistic (TSS) were tested, and results showed that TSS outperformed CSI for all rainfall events tested. Then, various strategies by combining the three prediction factors together were also tested, and results indicated that the best forecast result was from the case that any two of the prediction factors were over their own thresholds. Finally, the new model was evaluated using the GNSS data for the 2-year period from 2019 to 2020 at the above mentioned 66 stations, and the probability of detection (POD) and false-alarms rate (FAR) were adopted to measure the model performances. Over the 66 stations, the POD values ranged from 73% to 97% with the mean of 87%, and the FARs ranged from 26% to 77% with the mean of 53%. Moreover, it was also found that both POD and FAR values were related to the region of the station; e.g., the results at the stations that are located in humid regions were better than the ones located in dry regions. All these results suggest the feasibility and good performance of using GNSS-PWV for forecasting rainfall.

Analysis of the Temporal and Spatial Characteristics of PWV and Rainfall with the Typhoon Movement: A Case Study of ‘Meihua’ in 2022

“…From Figure 3, we can see tha there is a strong correlation between rainfall and PWV in coastal areas; the PWV peak occurred about 2~6 h earlier compared with that of the rainfall. This was consistent with Chen's research [5] and the research of S M R [7].…”

“…GNSS water-vapordetection technology has the characteristics of a high resolution, low cost, continuous operation and high precision. The inverted PWV by GNSS is also used for analyzing the PWV's correlation with rainfall to predict extreme weather and improve the temporalspatial resolution of PWV inverted using the radiosonde measurements [4][5][6][7]. He et al investigated the usability of real-time PWV retrieved from GNSS for the characterization typhoons by analyzing the correlation between PWV and atmospheric parameters, including pressure, temperature, precipitation, wind speed, and so on [8].…”

Evaluation of Water Vapor-Weighted Mean Temperature Models in GNSS Station ACOR

“…The needed atmospheric parameters for this study, pressure, and temperature, were obtained from a meteorological station called Coruña-Dique, which belongs to the meteorological station network Meteogalicia, managed by the Xunta of Galicia. According to the limit distance between the GNSS site and a meteorological station of 5 kilometers, as claimed by Li et al 2022, the two sites could be considered co-located [3]. The data of the meteorological station were downloaded from the Meteogalicia website (https://www.meteogalicia.gal/, accessed on 1 February 2023).…”

Machine Learning-Based Estimation of Hourly GNSS Precipitable Water Vapour

“…Radiosondes are highly accurate standard sensors in meteorology that measure a riety of meteorological parameters, including temperature ( = ±0.5° ), pressure ( [±1, ±2] ℎ ), relative humidity ( = ±5 %), and geopotential height [54][55][56]. Figu depicts the distribution of radiosonde stations on the Austrian territory used in this stu To train and test the regional model, we take into account the period of January 20 June 2022.…”

“…In this equation, ∆p (hpa) and g (m/s 2 ) represent the pressure difference between adjacent layers and gravity acceleration, respectively. q i (kg) refers to specific humidity and can be obtained as below [54]: q = 0.622 × P w P − 0.378 P w (16) here P w (hpa) is the water vapour pressure and can be computed as follows [95]:…”