A Regional NWP Tropospheric Delay Inversion Method Based on a General Regression Neural Network Model

Abstract

Tropospheric delay is a major error source that affects the initialization and re-initialization speed of the Global Navigation Satellite System’s (GNSS) medium-/long-range baseline in Network Real-Time Kinematic (NRTK) positioning. Fusing the meteorological data from the Numerical Weather Prediction (NWP) model to estimate the zenith tropospheric delay (ZTD) is one of the current research hotspots. However, research has shown that the ZTD derived from NWP models is still not accurate enough for high-precision GNSS positioning applications without the estimation of the residual tropospheric delay. To date, General Regression Neural Network (GRNN) has been applied in many fields. It has a high learning speed and simple structure, and can approximate any function with arbitrary precision. In this study, we developed a regional NWP tropospheric delay inversion method based on a GRNN model to improve the accuracy of the tropospheric delay derived from the NWP model. The accuracy of the tropospheric delays derived from reanalysis data of the European Center for Medium-Range Weather Forecasts (ECMWF) and the US National Centers for Environmental Prediction (NCEP) was assessed through comparisons with the results of the International GPS Service (IGS). The variation characteristics of the residual of the ZTD inverted by NWP data were analyzed considering the factors of temperature, humidity, latitude, and season. To evaluate the performance of this new method, the National Center Atmospheric Research (NCAR) troposphere data of 650 stations in Japan in 2005 were collected as a reference to compare the accuracy of the ZTD before and after using the new method. The experimental results showed that the GRNN model has obvious advantages in fitting the NWP ZTD residual. The mean residual and the root mean square deviation (RMSD) of the ZTD inverted using the method of this study were 9.5 mm and 12.7 mm, respectively, showing reductions of 20.8% and 19.1%, respectively, as compared to the standard NWP model. For long-range baseline (155 km and 207 km), the corrected NWP-constrained RTK showed a reduction of over 43% in the initialization time compared with the standard RTK, and showed a reduction of over 24% in the initialization time compared with the standard NWP-constrained RTK.

Demand forecasting model for time-series pharmaceutical data using shallow and deep neural network model

“…The popular SNN models used for time-series data forecasting are “Radial Basis Function(RBF) Neural Network (RBF_NN) [ 40 – 45 ]Generalized Regression Neural Network (GR_NN) and Probabilistic Neural Network (P_NN)” [ 41 , 46 – 51 ]. These models are used to build demand forecasting models for pharmaceutical products.…”

Accuracy Evaluation and Analysis of GNSS Tropospheric Delay Inversion from Meteorological Reanalysis Data

“…Compared to the ZTD model, which uses meteorological parameters only in each station (e.g., Saastamoinen [8]), the integral method is more accurate for inverting tropospheric delay, and uses all meteorological parameters above the station [35]. In fact, the reanalysis data provided limited meteorological parameters, because the data above and below the pressure levels were not considered.…”

Geoid undulation prediction using ANNs (RBFNN and GRNN), multiple linear regression (MLR), and interpolation methods: A comparative study

“…It needs only one-way learning. Due to the simplicity of the network structure and ease of implementation, this functional approach has been used in different geodetic applications (Ziggah et al, 2017;Cakir & Konakoglu, 2019;Li et al, 2020). The GRNN consists of four layers: the input layer, the pattern layer, the summation layer, and the output layer (Specht, 1993).…”