Seasonal Multifactor Modelling of Weighted-Mean Temperature for Ground-Based GNSS Meteorology in Hunan, China

Li, Li; Wu, Suqin; Wang, Xiaoming; Tian, Ying; Hé, Changyong; Zhang, Kefei

doi:10.1155/2017/3782687

Cited by 12 publications

(11 citation statements)

References 39 publications

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“…where Π is the conversion coefficient to be estimated, and the ρ w , R v , k 2 and k 3 are all constants which can be found in the literature studies [6,15,[21][22][23]. However, the weighted mean temperature (T m ) is a variable that determines the final value of Π, thus plays a decisive role in the GNSS-PWV retrieval.…”

Section: The Determination Of T M 21 Computing T M By Numerical Integrationmentioning

confidence: 99%

“…The Bevis formula (T m = a • T s + b) was first used to calculate T m [6], its coefficients were fitted from the radiosonde data distributed in the North America, but it was found that the relation between T m and T s varies with the locations and seasons, so a Bevis formula with constant coefficients a and b would result in poor accuracy when estimating T m in different locations and seasons. Many scholars have conducted the localization research on the Bevis formula and found that the model coefficients tailored to the specific locations and seasons can better fit the local conditions [15,[23][24][25][26]. Some MMTm models for global GNSS-PWV retrieval have also been developed, such as the GTm-I model, PTm-I model [8] and the TVGG model [27].…”

Section: Introductionmentioning

confidence: 99%

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Enhanced Neural Network Model for Worldwide Estimation of Weighted Mean Temperature

et al. 2021

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Precise modeling of weighted mean temperature (Tm) is critical for realizing real-time conversion from zenith wet delay (ZWD) to precipitation water vapor (PWV) in Global Navigation Satellite System (GNSS) meteorology applications. The empirical Tm models developed by neural network techniques have been proved to have better performances on the global scale; they also have fewer model parameters and are thus easy to operate. This paper aims to further deepen the research of Tm modeling with the neural network, and expand the application scope of Tm models and provide global users with more solutions for the real-time acquisition of Tm. An enhanced neural network Tm model (ENNTm) has been developed with the radiosonde data distributed globally. Compared with other empirical models, the ENNTm has some advanced features in both model design and model performance, Firstly, the data for modeling cover the whole troposphere rather than just near the Earth’s surface; secondly, the ensemble learning was employed to weaken the impact of sample disturbance on model performance and elaborate data preprocessing, including up-sampling and down-sampling, which was adopted to achieve better model performance on the global scale; furthermore, the ENNTm was designed to meet the requirements of three different application conditions by providing three sets of model parameters, i.e., Tm estimating without measured meteorological elements, Tm estimating with only measured temperature and Tm estimating with both measured temperature and water vapor pressure. The validation work is carried out by using the radiosonde data of global distribution, and results show that the ENNTm has better performance compared with other competing models from different perspectives under the same application conditions, the proposed model expanded the application scope of Tm estimation and provided the global users with more choices in the applications of real-time GNSS-PWV retrival.

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Section: The Determination Of T M 21 Computing T M By Numerical Integrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhanced Neural Network Model for Worldwide Estimation of Weighted Mean Temperature

et al. 2021

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“…The accuracies of the other two models were both better than that of the currently used Hong Kong regional models, with the RMSs of 1.91 K and 1.89 K [49]. However, some studies have shown that the accuracies of T m in different seasons are different because the sun radiates differently to the ground in different seasons [47], [50], [51]. Thus, to further improve the accuracy of T m models developed based on RS data from one year or more, four new seasonal models were developed based on RS data from each season during the period 2014 -2016.…”

Section: B Accuracy Of T Mmentioning

confidence: 99%

Precipitable Water Vapor Converted from GNSS-ZTD and ERA5 Datasets for the Monitoring of Tropical Cyclones

Shen

Wan

et al. 2020

IEEE Access

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Tropical cyclones (TCs) are intense rotating storm systems, characterized by strong winds, heavy humidity and storm surges, and usually cause an abnormal increase in regional water vapor during the period of their occurrence. Global navigation satellite system (GNSS) is well-established and initially mainly for positioning, navigation and timing. However, due to their global coverage and 24/7 operability under all weather conditions, GNSS has been widely used in meteorology such as the retrieval of precipitable water vapor (PWV). Since PWV time series obtained from a regional ground GNSS tracking stations network has high temporal and spatial resolutions, in this research, the temporal and spatial variation trends of regional PWVs during several TCs' periods in Hong Kong were investigated, and a new method using high-resolution GNSS-PWV time series to estimate a TC's movement was proposed. Test data were from 15 GNSS tracking stations equipped with meteorological sensors, and three GNSS stations without meteorological sensors but using meteorological data interpolated from ERA5 (fifth-generation reanalysis dataset of the European Centre for Medium-range Weather Forecasting) dataset, and six TC events occurred in 2017 were selected for cases studies. Moreover, for a better accuracy of GNSS-PWV, which is converted from the zenith wet delay of the GNSS signal multiplying a conversion factor that uses a weighted mean temperature (T m), a seasonal multi-factor T m model was established and tested. Results showed that the new T m model outperformed the previous regional T m model used in Hong Kong. In addition, based on the temporal variation trend and spatial distribution of the regional PWV, one can determine if a TC approaches the region. A geometric approach to estimating a TC's movement was developed and its test results agreed well with the truth. These results suggest the potential of using GNSS signals to monitor the movements of TCs for short-time TC forecasting due to their high temporal resolution. INDEX TERMS Tropical cyclone, global navigation satellite system, precipitable water vapor, weighted mean temperature, ERA5. I. INTRODUCTION Over recent years, increasing tropical cyclone (TC) events occurred frequently in the North Pacific, Indian Ocean, Southwest Pacific and North Atlantic, causing considerable damage in nearby cities [1]-[5]. Many countries, provinces and cities have set up meteorological departments, which take the early warning and forecasting of TCs as a key task. Water vapor, as a main component of the atmosphere, is a key factor The associate editor coordinating the review of this manuscript and approving it for publication was Gerardo Di Martino. influencing TCs' development [6], [7]. The monitoring of regional water vapor variation is critical for the forecasting of the movement, intensity and rainfall of TCs [8]-[10]. Traditional techniques for the acquisition of atmospheric water vapor, such as infrared satellite remote sensing, radiosondes (RS) and microwave radiometers, have the disa...

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“…According to the data of 8718 radiosonde stations in the United States (27~60 • N), Bevis et al [5] obtained the unary linear expression of Tm and the surface temperature (Ts): Tm = 0.72Ts + 70.2. This modeling method is simple and practical, and high precision can be obtained in certain areas, so some experts have improved it and established corresponding localized models for different areas [6][7][8]. However, the precision 2 of 13 of this type of model is limited by the scope of the study area, and ensuring that highprecision Tm estimates can be obtained in other areas is difficult.…”

Section: Introductionmentioning

confidence: 99%

A Weighted Mean Temperature Model with Nonlinear Elevation Correction Using China as an Example

2021

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The weighted mean temperature (Tm) is a crucial parameter for determining the tropospheric delay in transforming precipitable water vapor. We used the reanalysis data provided by European Centre for Medium-Range Weather Forecasts (ECMWF) to analyze the distribution characteristics of Tm in the vertical direction in China. To address the problem that the precision of the traditional linear function model is limited in fitting the Tm profile, a scheme using the linear and Fourier functions to fit the Tm profile was constructed. Based on the least squares principle (LSQ) to fit the change in its coefficients over time, a Tm model for China with nonlinear elevation correction (CTm-h) was constructed. The experimental results show that, using ECMWF and radiosonde data to evaluate the precision of the CTm-h model, the RMS is 3.43 K and 4.64 K, respectively. Compared to GPT2w, the precision of the CTm-h model in China is increased by about 26.8%. The CTm-h model provides a significant improvement in the correction effect of Tm in the vertical direction, and the Tm profile calculated by the model is closer to the reference value.

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Seasonal Multifactor Modelling of Weighted-Mean Temperature for Ground-Based GNSS Meteorology in Hunan, China

Cited by 12 publications

References 39 publications

Enhanced Neural Network Model for Worldwide Estimation of Weighted Mean Temperature

Enhanced Neural Network Model for Worldwide Estimation of Weighted Mean Temperature

Precipitable Water Vapor Converted from GNSS-ZTD and ERA5 Datasets for the Monitoring of Tropical Cyclones

A Weighted Mean Temperature Model with Nonlinear Elevation Correction Using China as an Example

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