GNSS PPP with different troposphere models during severe weather conditions

Tunalı, Engin; Ozludemir, M. T.

doi:10.1007/s10291-019-0874-7

Cited by 12 publications

(6 citation statements)

References 38 publications

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“…Another method is to correct wet delay directly using external water vapor information [7][8][9]. For this method, the positioning accuracy highly depends on the accuracy of external water vapor information used.…”

Section: Introductionmentioning

confidence: 99%

Augmenting GNSS PPP Accuracy in South China Using Water Vapor Correction Data from WRF Assimilation Results

Gong

Liu

Chan

et al. 2021

Lecture Notes in Electrical Engineering

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Wet delay in Global Navigation Satellite System (GNSS), mainly caused by water vapor in the atmosphere, is difficult to be accurately modeled using empirical wet delay models as water vapor is highly variable in both space and time. In this paper we propose correcting the GNSS wet delay using water vapor data from Weather Research and Forecasting (WRF) model's assimilation results. We conduct six consecutive 24-h WRF forecasts to model the three-dimension (3D) distribution of water vapor in the South China region 20° N -33° N and 108° E -123° E from 0 h UTC April 06, 2020 to 0 h UTC April 11, 2020. GNSS Precipitable Water Vapor (PWV) from 27 stations of the Crustal Movement Observation Network of China (CMONOC) and meteorological profiles from 22 radiosonde stations are assimilated into WRF model to improve the water vapor modeling performance of WRF. Totally, four WRF schemes are adopted, i.e. WRF scheme 0: WRF without water vapor data assimilation; WRF scheme 1: WRF with GNSS PWV assimilation only; WRF scheme 2: WRF with radiosonde profiles assimilation only; WRF scheme 3: WRF with both GNSS PWV and radiosonde profiles assimilation.The water vapor data from the four WRF schemes are used to augment Precise Point Positioning (PPP) by correcting GNSS wet delay at seven International GNSS Service (IGS) stations distributed in South China. The static PPP results show that using the water vapor correction data from different WRF schemes can

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Section: Introductionmentioning

confidence: 99%

Augmenting GNSS PPP Accuracy in South China Using Water Vapor Correction Data from WRF Assimilation Results

Gong

Liu

Chan

et al. 2021

Lecture Notes in Electrical Engineering

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“…A tropical cyclone is a rapidly rotating storm system accompanied by complicated weather, such as powerful winds, heavy rainstorms, and magnificent thunderstorms (Marks 2015). Calori et al (2016), Chen et al (2017) and Tunalı and Özlüdemir (2019) have demonstrated that tropospheric delays in GPS signals varied significantly under high-dynamic tropospheric condition. Wilgan et al (2017) and Zheng et al (2018) suggested that GPS positioning performance was degraded by unpredictable tropospheric variation.…”

Section: Introductionmentioning

confidence: 99%

Tropical cyclone-induced periodical positioning disturbances during the 2017 Hato in the Hong Kong region

Liu

2021

GPS Solut

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The tropospheric delay is an important error source in the Global Positioning System (GPS) positioning and navigation applications. Although most of the tropospheric delays can be removed in the double-differencing (DD) positioning mode, their remaining residuals can still contaminate the positioning accuracy and become unpredictable when tropospheric condition encounters severe variations such as during a tropical cyclone (TC) event. We investigated the positioning performance of five baselines with lengths ranging from 7.8 km to 49.9 km during the 2017 TC Hato. The results showed that the TC Hato brought a significant disturbance to the GPS baseline positioning results, particularly in the vertical (up) component. The TC Hato started to affect Hong Kong and the root mean squares (RMS) of GPS positioning errors increased dramatically from about 30 mm to 140 mm.when it was at a distance of 400-600 km from Hong Kong on August 22, 2017. We found that the vertical positioning errors on that day have the major periods: 2.7 h, 3.0 h, 3.4 h, 4.0 h, and 4.8 h.Examining the wet and hydrostatic parts of the tropospheric delays via the continuous wavelet spectral analysis, we found that the periodical variation of the positioning results on August 22 was caused by the periodical variation of the precipitable water vapor (PWV). The variation of differenced PWV between two baseline stations had consistent periods of 2-5 h. Besides, the periods of differenced PWV time series are in good agreement with the spiral rainband in the TC. This finding suggests that the TC Hato induce periodical PWV variations at two GPS stations of baseline, which resulted in GPS positioning errors of the same periods.

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“…In addition, floods and droughts have been caused by extreme weather frequently. Severe weather, such as strong regional convection, and large-scale climate anomalies have brought on heavy economic losses and casualties in recent years [4][5][6]. With increasing requirements for high-precision GNSS positioning, and in-depth research in fields such as climate change and extreme weather generation mechanisms, the demand for real-time and high-spatiotemporal-resolution tropospheric parameters is increasing.…”

Section: Introductionmentioning

confidence: 99%

A New Approach for the Development of Grid Models Calculating Tropospheric Key Parameters over China

et al. 2021

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Pressure, water vapor pressure, temperature, and weighted mean temperature (Tm) are tropospheric parameters that play an important role in high-precision global navigation satellite system navigation (GNSS). As accurate tropospheric parameters are obligatory in GNSS navigation and GNSS water vapor detection, high-precision modeling of tropospheric parameters has gained widespread attention in recent years. A new approach is introduced to develop an empirical tropospheric delay model named the China Tropospheric (CTrop) model, providing meteorological parameters based on the sliding window algorithm. The radiosonde data in 2017 are treated as reference values to validate the performance of the CTrop model, which is compared to the canonical Global Pressure and Temperature 3 (GPT3) model. The accuracy of the CTrop model in regards to pressure, water vapor pressure, temperature, and weighted mean temperature are 5.51 hPa, 2.60 hPa, 3.09 K, and 3.35 K, respectively, achieving an improvement of 6%, 9%, 10%, and 13%, respectively, when compared to the GPT3 model. Moreover, three different resolutions of the CTrop model based on the sliding window algorithm are also developed to reduce the amount of gridded data provided to the users, as well as to speed up the troposphere delay computation process, for which users can access model parameters of different resolutions for their requirements. With better accuracy of estimating the tropospheric parameters than that of the GPT3 model, the CTrop model is recommended to improve the performance of GNSS positioning and navigation.

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GNSS PPP with different troposphere models during severe weather conditions

Cited by 12 publications

References 38 publications

Augmenting GNSS PPP Accuracy in South China Using Water Vapor Correction Data from WRF Assimilation Results

Augmenting GNSS PPP Accuracy in South China Using Water Vapor Correction Data from WRF Assimilation Results

Tropical cyclone-induced periodical positioning disturbances during the 2017 Hato in the Hong Kong region

A New Approach for the Development of Grid Models Calculating Tropospheric Key Parameters over China

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