The height of the peak electron density (hmF2) and the critical frequency of the F2 layer (foF2) are very important in the research of ionospheric electrodynamics and high frequency (HF) wireless communication. In the article, we validated the hmF2/foF2 model values of the latest version of the International Reference Ionosphere (IRI-2016) with observations from three ionosonde stations which belong to low, middle, and high latitudes (i.e., Sanya, Beijing and Mohe) over China during a high solar activity year (2014, F10.7 = 145.9 sfu) and a low solar activity year (2016, F10.7 = 88.7 sfu). Among them, foF2 model values can be obtained through the International Radio Consulting Committee (CCIR) model or the International Union of Radio Science (URSI) model, both of which have the “F-peak storm model” on or ‘off’ options; hmF2 model values can be obtained through Bilitza-Sheikh-Eyfrig (BSE-1979), Altadill-Magdaleno-Torta-Blanch (AMTB-2013), or SHUbin (SHU-2015) model. The IRI-2016 hmF2/foF2 model values were evaluated by root mean square (RMS) values and mean absolute relative error (MARE). The results show that for the foF2 parameter, the performance of IRI-2016 can be improved by choosing “F-peak storm model” on option in geomagnetic-disturbed days. Whether in high or low solar activity years, for foF2, the IRI-2016 options of CCIR have better prediction ability than IRI-2016 options of URSI in low and high latitudes over China, and the IRI-2016 options of URSI have better prediction ability than IRI-2016 options of URSI in middle latitudes. For hmF2, the IRI-2016 option of SHU-2015 has better prediction ability than the IRI-2016 options of AMTB-2013 and BSE-1949 in high latitudes over China, the IRI-2016 options of SHU-2015 and BSE-1979 have better prediction ability than IRI-2016 options of AMTB-2013 in mid and low latitudes over China.

The total electron content (TEC) of ionosphere at low latitudes is significantly influenced by solar-geomagnetic activity and seasonal variations. Traditional ionospheric models often struggle to accurately forecast TEC in low latitudes, which limits the improvement of positioning accuracy for single-frequency GNSS (Global Navigation Satellite System) receivers. This study focuses on the Sichuan and Yunnan areas of China that locate on the northern crest of the equatorial ionization anomaly, utilizing data from 48 stations of the Chinese GNSS network. It employs a Convolutional Long Short-Term Memory network with multi-channel characteristics, combined with the Whale Optimization Algorithm, to construct a WOA-CNN-LSTM model for predicting TEC variations. The results demonstrate that the WOA-CNN-LSTM model outperforms Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU), Bidirectional Long Short-Term Memory (BiLSTM), and Recurrent Neural Network (RNN) models in spatial morphology. In 2015 (a year with geomagnetic storms), the root mean square error (RMSE) values for all four seasons are at or below 1.96TECu, with mean absolute error (MAE) values at or below 1.42TECu, and Pearson correlation coefficients at or above 0.98. In 2019 (a calm year), the RMSE values are all below 0.74TECu, MAE values are all below 0.54TECu, and Pearson correlation coefficients remain at or above 0.95. In terms of temporal variation, the RMSE prediction results for the four observation stations are all at or below 2.75TECu for each of the four seasons in 2015, improving to 1.56TECu in 2019. Therefore, this model significantly enhances ionospheric prediction accuracy in low-latitude regions, benefiting navigation positioning, space environment forecasting, and disaster early warning systems.

The global ionosphere map (GIM) is not capable of serving precise positioning and navigation for single frequency receivers in Australia due to sparse International GNSS Service (IGS) stations located in the vast land. This study proposes an approach to represent Australian total electron content (TEC) using the spherical cap harmonic analysis (SCHA) and artificial neural network (ANN). The new Australian TEC maps are released with an interval of 15 min for longitude and latitude in 0.5° × 0.5°. The validation results show that the Australian Ionospheric Maps (AIMs) well represent the hourly and seasonally ionospheric electrodynamic features over the Australian continent; the accuracy of the AIMs improves remarkably compared to the GIM and the model built only by the SCHA. The residual of the AIM is inversely proportional to the level of solar radiation. During the equinoxes and solstices in a solar minimum year, the residuals are 2.16, 1.57, 1.68, and 1.98 total electron content units (TECUs, 1 TECU = 10¹⁶ electron/m²), respectively. Furthermore, the AIM has a strong capability in capturing the adequate electrodynamic evolutions of the traveling ionospheric disturbances under severe geomagnetic storms. The results demonstrate that the ANN-aided SCHA method is an effective approach for mapping and investigating the TEC maps over Australia.

Weighted average temperature (Tm) plays a crucial role in global navigation satellite system (GNSS) precipitable water vapor (PWV) retrieval. Aiming at the poor applicability of the existing Tm models in Europe, in the article, we used observations from 48 radiosonde stations over Europe from 2014 to 2020 to establish a weighted average temperature model in Europe (ETm) by the nonlinear least squares estimation method. The ETm model takes into account factors such as ground temperature, water vapor pressure, latitude, and their annual variation, semiannual variation and diurnal variation. Taking the Tm obtained from the radiosonde data by the integration method in 2021 as the reference value, the accuracy of the ETm model was evaluated and compared with the commonly used Bevis model, ETmPoly model, and GPT2w model. The results of the 48 modeled stations showed that the mean bias and root mean square (RMS) values of the ETm model were 0.06 and 2.85 K, respectively, which were 21.7%, 11.5%, and 31.8% higher than the Bevis, ETmPoly, and GPT2w-1 (1° × 1° resolution) models, respectively. In addition, the radiosonde data of 12 non-modeling stations over Europe in 2021 were selected to participate in the model accuracy validation. The mean bias and RMS values of the ETm model were –0.07 and 2.87 K, respectively. Compared with the Bevis, ETmPoly, and GPT2w-1 models, the accuracy (in terms of RMS values) increased by 20.5%, 10.6%, and 35.2%, respectively. Finally, to further verify the superiority of the ETm model, the ETm model, and other Tm models were applied to the GNSS PWV calculation. The ETm model had mean RMS_PWV and RMS_PWV/PWV values of 0.17 mm and 1.03%, respectively, which were less than other Tm models. Therefore, the ETm model has essential applications in GNSS PWV over Europe.

Geocenter is the center of the mass of the Earth system including the solid Earth, ocean, and atmosphere. The time-varying characteristics of geocenter motion (GCM) reflect the redistribution of the Earth’s mass and the interaction between solid Earth and mass loading. Multi-channel singular spectrum analysis (MSSA) was introduced to analyze the GCM products determined from satellite laser ranging data released by the Center for Space Research through January 1993 to February 2017 for extracting the periods and the long-term trend of GCM. The results show that the GCM has obvious seasonal characteristics of the annual, semiannual, quasi-0.6-year, and quasi-1.5-year in the X, Y, and Z directions, the annual characteristics make great domination, and its amplitudes are 1.7, 2.8, and 4.4 mm, respectively. It also shows long-period terms of 6.09 years as well as the non-linear trends of 0.05, 0.04, and –0.10 mm/yr in the three directions, respectively. To obtain real-time GCM parameters, the MSSA method combining a linear model (LM) and autoregressive moving average model (ARMA) was applied to predict GCM for 2 years into the future. The precision of predictions made using the proposed model was evaluated by the root mean squared error (RMSE). The results show that the proposed method can effectively predict GCM parameters, and the prediction precision in the three directions is 1.53, 1.08, and 3.46 mm, respectively.

Atmospheric weighted mean temperature (Tm) is a crucial parameter for retrieving atmospheric precipitation using the Global Navigation Satellite System (GNSS). It plays a significant role in GNSS meteorology research. Although existing empirical models can quickly obtain Tm values for the Yunnan–Guizhou Plateau, their accuracy is generally low due to the region’s complex environmental and climatic conditions. To address this issue, this study proposes an enhanced empirical Tm model tailored for the Yunnan–Guizhou Plateau. This new model incorporates surface temperature (Ts) data and employs the least squares method to determine model coefficients, thereby improving the accuracy of the Tm empirical model. The research utilizes observational data from 16 radiosonde stations in the Yunnan–Guizhou Plateau from 2010 to 2018. By integrating Ts into the Hourly Global Pressure and Temperature (HGPT2) model, we establish the enhanced empirical Tm model, referred to as YGTm. We evaluate the accuracy of the YGTm model using Tm values obtained from the 2019 radiosonde station measurements as a reference. A comparative analysis is conducted against the Bevis model, the HGPT2 model, and the regional linear model LTm. The results indicate that at the modeling stations, the proposed enhanced model improves Tm prediction accuracy by 24.9%, 16.1%, and 22.4% compared to the Bevis, HGPT2, and LTm models, respectively. At non-modeling stations, the accuracy improvements are 26.2%, 17.1% and 24.4%, respectively. Furthermore, the theoretical root mean square error and relative error from using the YGTm model for GNSS water vapor retrieval are 0.27 mm and 0.93%, respectively, both of which outperform the comparative models.