GNSS Tropospheric Delay Modeling for Arid Region Wet Delay Correction
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Solution Overview
Problem
Existing troposphere models have low precision and poor reliability in arid regions such as sandy and desert areas due to low atmospheric humidity, which complicates the accurate measurement and correction of troposphere zenith total delay.
Innovation Solution
A modeling method that corrects atmospheric weighted average temperature using regional GNSS station data and ERA5 meteorological data, employs a BiLSTM neural network to refine a priori troposphere zenith wet delay values, and integrates these with ground-based GNSS measurements to establish a high-precision troposphere delay model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard empirical models (Saastamonien, Hopfield, Black) are used for troposphere delay correction, then the models can be applied with available meteorological parameters, but the precision and reliability are low in arid regions due to low atmospheric humidity that differs significantly from standard conditions
Solution Approach 1:
The patent applies local quality by developing a specialized troposphere delay model specifically tailored for arid regions rather than using universal standard models. The model incorporates region-specific characteristics including low atmospheric humidity conditions typical of sandy, gobi and desert areas. By making the model locally adaptive to arid region conditions, the reliability of troposphere delay correction is significantly improved for these specific environments while maintaining the ability to handle the unique meteorological characteristics of arid zones
Solution Approach 2:
The patent implements parameter changes by modifying the input parameters and weighting factors in the troposphere delay model to account for arid region conditions. The model adjusts parameters such as atmospheric humidity levels, temperature profiles, and pressure distributions to reflect the low-humidity environment of arid regions. These parameter modifications enable the model to accurately represent the physical conditions in sandy, gobi and desert areas, thereby improving both reliability and adaptability
2Measurement precision
If model parameters are corrected using regional GNSS station data and ERA5 meteorological data with BiLSTM neural network, then the precision of troposphere zenith wet delay is improved, but the device complexity and computational requirements increase
Solution Approach 1:
The patent replaces traditional mechanical/meteorological measurement systems with a neural network-based computational system. Instead of relying solely on physical meteorological instruments and standard correction algorithms, the patent employs a BiLSTM neural network that processes GNSS observation data and ERA5 reanalysis data to compute troposphere zenith wet delay. This substitution of computational intelligence for traditional measurement and correction mechanisms achieves higher precision while managing the complexity through algorithmic efficiency
Solution Approach 2:
The patent introduces ERA5 reanalysis data as an intermediary element that bridges GNSS observations and troposphere delay correction. The BiLSTM neural network serves as an intermediary computational layer that processes multiple data sources (regional GNSS station data and ERA5 meteorological data) and transforms them into corrected troposphere delay values. This intermediary approach enables the system to achieve high precision by integrating multiple information streams without requiring direct complex interactions between all components
Data Source
AI summary
The provided is a modeling method of tropospheric delay for arid-areas including sandy, gobi and desert regions based on GNSS. The modeling method incudes: obtaining a troposphere zenith hydrostatic delay ZHD and a troposphere zenith wet delay ZWD by utilizing a GPT-3 model, and summing to obtain a troposphere zenith total delay ZTD; inversely calculating the ZTD by utilizing a ground-based GNSS, obtaining a high-precision ZWD inversed by the GNSS by calculating a difference between the ZTD and the troposphere zenith hydrostatic delay ZHD obtained by a Saastamonien model, inversely calculating an atmospheric weighted average temperature T′m by utilizing an ERA5 data set, performing linear correction to obtain a corrected temperature Tmr, and calculating a priori value ZWD0 of the high-precision ZWD; and correcting the ZWD0, and adding the corrected ZWD0 with the ZHD to obtain a corrected value of the high-precision ZTD.