Adaptive AOD Forecast Model Based on GNSS-Derived PWV and Meteorological Parameters

Zhao, Qingzhi; Yang, Pengfei; Yao, Wanqiang; Yao, Yibin

doi:10.1109/tgrs.2021.3099155

Cited by 5 publications

(3 citation statements)

References 48 publications

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“…If k ij w is the same as that in the ART formula expressed in (5), then ( 8) is for the SIRT method. Similarly, if k ij w is the same as that expressed in (6), then the equation is for ASIRT [23].…”

Section: B Classical Iterative Methods For Tomographic Inversionmentioning

confidence: 99%

A Two-Step Projected Iterative Algorithm for Tropospheric Water Vapor Tomography

Liu

Zhang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The tropospheric tomography is an ill-posed inversion problem due to the sparsity of Global Navigation Satellite Systems (GNSS) stations and the limitation on the projection angles of GNSS signals, which in turn affects the stability and robustness of the tomographic solution. To address this, a new tomographic algorithm, named two-step projected iterative algorithm (TSPIA), is proposed. The wet refractivity (WR) field was constructed in two steps: first, an iterative preprocessing for the initial input values was performed, then its resultant solution was input into the projected iterative method, in which a hypothesis convex set was constructed to constrain the reconstruction based on the classical algebraic iterative reconstruction (AIR) methods. In addition, a two-dimensional normalized cumulative periodogram (2D-NCP) termination criterion was investigated since the traditional criteria for judging the convergence of iterations use pre-fixed empirical thresholds, which may lead to excessive iterations and need complicated work. The TSPIA was tested using GNSS data in Hong Kong over a wet period and a dry period. Statistical results showed that, compared to the classical AIR methods, the accuracy of the reconstructed WR field of the TSPIA were improved by about 10% and 15% when radiosonde and ECMWF data were used as the reference, respectively. Moreover, experiments for the proposed 2D-NCP criterion demonstrated noticeable computational efficiency. These results suggest that the new approaches proposed in this study can improve the performance of the iterative methods for GNSS tropospheric tomography.

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Section: B Classical Iterative Methods For Tomographic Inversionmentioning

confidence: 99%

A Two-Step Projected Iterative Algorithm for Tropospheric Water Vapor Tomography

Liu

Zhang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…This dataset covers almost 2700 global stations for both RS and pilot balloon observations dating from 1960 to present and can be downloaded for free online (ftp://ftp.ncdc.noaa.gov/pub/data/igra/, accessed on 1 July 2021) [35]. RS data can provide the vertical profiles of meteorological parameters, including temperature, geopotential, and water vapor pressure, usually two or four times a day [36]. In this paper, RS data with the temporal resolution of two times (UTC 00:00 and 12:00) daily are selected from 87 RS stations in China over the period between 2011 to 2020.…”

Section: Data Descriptionmentioning

confidence: 99%

Comprehensive Analysis and Validation of the Atmospheric Weighted Mean Temperature Models in China

Zhao

et al. 2022

Remote Sensing

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Atmospheric weighted mean temperature (Tm) is a key parameter used by the Global Navigation Satellite System (GNSS) for calculating precipitable water vapor (PWV). Some empirical Tm models using meteorological or non-meteorological parameters have been proposed to calculate PWV, but their accuracy and reliability cannot be guaranteed in some regions. To validate and determine the optimal Tm model for PWV retrieval in China, this paper analyzes and evaluates some typical Tm models, namely, the Linear, Global Pressure and Temperature 3 (GPT3), the Tm model for China (CTm), the Global Weighted Mean Temperature-H (GTm-H) and the Global Tropospheric (GTrop) models. The Tm values of these models are first obtained at corresponding radiosonde (RS) stations in China over the period of 2011 to 2020. The corresponding Tm values of 87 RS stations in China are also calculated using the layered meteorological data and regarded as the reference. Comparison results show that the accuracy of these five Tm models in China has an obvious geographical distribution and decreases along with increasing altitude and latitude, respectively. The average root mean square (RMS) and Bias for the Linear, GPT3, CTm, GTm-H and GTrop models are 4.2/3.7/3.4/3.6/3.3 K and 0.7/−1.0/0.7/−0.1/0.3 K, respectively. Among these models, Linear and GPT3 models have lower accuracy in high-altitude regions, whereas CTm, GTm-H and GTrop models show better accuracy and stability throughout the whole China. These models generally have higher accuracy in regions with low latitude and lower accuracy in regions with middle and high latitudes. In addition, Linear and GPT3 models have poor accuracy in general, whereas GTm-H and CTm models are obviously less accurate and stable than GTrop model in regions with high latitude. These models show different accuracies across the four geographical regions of China, with GTrop model demonstrating the relatively better accuracy and stability. Therefore, the GTrop model is recommended to obtain Tm for calculating PWV in China.

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“…In addition, there has been some progress in the application of GNSS to meteorology. Many scholars have applied GNSS PWV to typhoon monitoring [13], drought index improvement [14], drought monitoring [15,16], air quality monitoring [17][18][19], and short-term rainfall early warning [3,[20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

A Rainfall Forecast Model Based on GNSS Tropospheric Parameters and BP-NN Algorithm

Zhang

et al. 2022

Atmosphere

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The occurrence of rainfall is the result of a combination of various meteorological factors. Traditional rainfall early warning models solely use Global Navigation Satellite System (GNSS)-derived Zenith Total Delay (ZTD) or Precipitable Water Vapor (PWV) to forecast rainfall, resulting in a low true detected rate. While non-linear rainfall early warning models based on the Back-Propagation Neural Network (BP-NN) algorithm consider the influences of various meteorological factors, the forecasts often exhibit a high false rate. To further improve the prediction of rainfall, a short-term rainfall early warning model based on the GNSS and BP-NN algorithms is proposed in this study. The method uses the traditional rainfall forecasting model and utilizes the BP-NN algorithm to combine various meteorological factors for rainfall early warning. The results of GNSS and BP-NN together improve the precision of rainfall early warning. Observation data from eight GNSS stations, the fifth-generation reanalysis of European Centre for Medium-Range Weather Forecast (ECMWF ERA5), and temperature, pressure, and rainfall data from corresponding meteorological stations in Ningbo, China were utilized to verify the rainfall early warning model proposed in this study. The results show that the proposed model can complement the advantages of the traditional linear and non-linear rainfall early warning methods. The model can maintain a high True Detected Rate (TDR) of rainfall early warning while simultaneously reducing the False Forecasted Rate (FFR). The average TDR of the eight GNSS stations is 100% and the FFR is 20.75%, which are both better than those of existing traditional linear and non-linear rainfall early warning models.

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Adaptive AOD Forecast Model Based on GNSS-Derived PWV and Meteorological Parameters

Cited by 5 publications

References 48 publications

A Two-Step Projected Iterative Algorithm for Tropospheric Water Vapor Tomography

A Two-Step Projected Iterative Algorithm for Tropospheric Water Vapor Tomography

Comprehensive Analysis and Validation of the Atmospheric Weighted Mean Temperature Models in China

A Rainfall Forecast Model Based on GNSS Tropospheric Parameters and BP-NN Algorithm

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