Movement Observation Network Of China Research Articles

Forecasting ionospheric TEC using least squares support vector machine and moth-flame optimization methods in China

The total electron content (TEC) of the ionosphere is an important parameter to describe the ionosphere, and it is a great significance to monitor and predict it accurately. In this paper, a hybrid ionospheric TEC prediction model based on the least squares support vector machine (LSSVM) and the Moth-Flame Optimization (MFO) algorithm is proposed. The parameters of the LSSVM model are optimized by the MFO algorithm. We use observation data of 15 GNSS stations from the Crustal Movement Observation Network of China (CMONOC) to extract ionospheric TEC from 2012 to 2019. The ionospheric TEC is forecasted using solar and geomagnetic activity indices in both the low solar activity year (2019) and the high solar activity year (2015). The results show that the prediction performance of the MFO_LSSVM model is significantly better than that of the IRI model, SVM model, and LSSVM model. Compared with the other three models, there are more stable prediction results in the low and high solar activity years. At the same time, the predicted value of the MFO_LSSVM model has a good correlation with the measured value, and it also has good prediction potential in areas with active geomagnetic activity. The comparison with the Long Short-Term Memory (LSTM) model shows that the MFO_LSSVM model has better performance than the single LSTM model. In conclusion, the MFO_LSSVM model can accurately predict ionospheric TEC in China, and has better accuracy than traditional long-term and short-term models.

Comparison between GPS network analysis with undifferenced and double differenced integer ambiguity resolution: A practical perspective

Global Navigation Satellite System (GNSS) network analysis benefits from precise point positioning with undifferenced (UD) or double differenced (DD) integer ambiguity resolution (IAR). Position time series with UD-IAR and a practical DD-IAR are compared for a mixture network consisting of 57 global and 82 regional stations with 4-year observations. The sparse global stations are more affected by different IAR methods than the regional stations of Crustal Movement Observation Network of China (CMONOC). The most significant differences between UD-IAR and the practical DD-IAR are observed in velocity estimations, with averages of 0.15 and 0.30 mm/yr for the horizontal and vertical components, respectively. UD-IAR can achieve smaller root mean square at 70–83 % of the daily solutions than the practical DD-IAR for global stations. Nevertheless, moderate differences of frame parameters and annual variations are introduced by different IAR methods. The IAR is also proved to be one of the potential origins for Global Positioning System (GPS) draconitic signals and white noise at high frequency.

Assessment of the three representative empirical models for zenith tropospheric delay (ZTD) using the CMONOC data

The precise correction of atmospheric zenith tropospheric delay (ZTD) is significant for the Global Navigation Satellite System (GNSS) performance regarding positioning accuracy and convergence time. In the past decades, many empirical ZTD models based on whether the gridded or scattered ZTD products have been proposed and widely used in the GNSS positioning applications. But there is no comprehensive evaluation of these models for the whole China region, which features complicated topography and climate. In this study, we completely assess the typical empirical models, the IGGtropSH model (gridded, non-meteorology), the SHAtropE model (scattered, non-meteorology), and the GPT3 model (gridded, meteorology) using the Crustal Movement Observation Network of China (CMONOC) network. In general, the results show that the three models share consistent performance with RMSE/bias of 37.45/1.63, 37.13/2.20, and 38.27/1.34 mm for the GPT3, SHAtropE and IGGtropSH model, respectively. However, the models had a distinct performance regarding geographical distribution, elevation, seasonal variations, and daily variation. In the southeastern region of China, RMSE values are around 50 mm, which are much higher than that in the western region, approximately 20 mm. The SHAtropE model exhibits better performance for areas with large variations in elevation. The GPT3 model and the IGGtropSH model are more stable across different months, and the SHAtropE model based on the GNSS data exhibits superior performance across various UTC epochs.

Multiscale Spatiotemporal Variations of GNSS-Derived Precipitable Water Vapor over Yunnan

The geographical location of Yunnan province is at the upstream area of water vapor transportation from the Bay of Bengal and the South China Sea to inland China. Understanding the spatiotemporal variations of water vapor over this region holds significant importance. We utilized the Global Navigation Satellite System (GNSS) data collected from 12 stations situated in Yunnan, which are part of the Crustal Movement Observation Network of China, to retrieve hourly precipitable water vapor (PWV) data from 2011 to 2022. The retrieved PWV data at Station KMIN were evaluated by the nearby radiosonde data, and the results show that the mean bias and RMS of the differences between the two datasets are 0.08 and 1.78 mm, respectively. Average PWV values at these stations are in the range of 11.77 to 33.53 mm, which decrease from the southwest to the north of Yunnan and are negatively correlated with the stations’ heights and latitudes. Differences between average PWV in the wet season and dry season range from 12 to 27 mm. These differences tend to increase as the average PWV increases. The yearly rates of PWV variations, averaging 0.18 mm/year, are all positive for the stations, indicating a year-by-year increase in water vapor. The amplitudes of the PWV annual cycles are 9.75–20.94 mm. The spatial variation of these amplitudes is similar to that of the average PWV over the region. Generally, monthly average PWV values increase from January to July and decrease from July to December, and the growth rate is less than the decline rate. Average diurnal PWV variations show unimodal PWV distributions over the course of the day at the stations except Station YNRL, where bimodal PWV distribution was observed.

Spatiotemporal distribution and impact factors of GNSS-PWV in China based on climate region

Water vapor plays a pivotal role in the intricate processes of energy exchange and climate dynamics. However, a comprehensive understanding of the spatial distribution and temporal variability of water vapor in distinct climatic regions of China is still limited due to the intricate interplay of complex topography and diverse climatic conditions. This study investigates the multiscale variability of water vapor using 10-year 1-hour data of precipitable water vapor (PWV) from 248 observation stations of the Crustal Movement Observation Network of China (CMONOC). Significant variations in PWV distribution among China's climate regions are observed. Average PWV values vary considerably across regions, with the Tropical and Subtropical Monsoon Climate (TSMC) having the highest mean PWV of 32.09 mm, followed by the Midlatitude Monsoon Climate (MMC) with 15.70 mm, the Humid Continental Climate (HCC) with 9.26 mm, and the Plateau Climate (PC) with 7.66 mm. Seasons exhibit summer concentration and winter reduction across regions. Most stations exhibit increasing PWV trends, while a few show decreasing trends. The amplitude of annual PWV variations is higher in TSMC and MMC regions with a range from 8 to 22 mm, compared to HCC and PC regions with a range from 2 to 15 mm. Semiannual variations range from 0 to 6 mm in each region. PWV negatively correlates with latitude, altitude and pressure, particularly in the TSMC. Positive correlations with temperature exist in all regions, with varying strengths. Thermodynamic factors primarily impact PWV, while dynamic factors have secondary influence, equally significant in the PC region.

Assessment of International GNSS Service Global Ionosphere Map products over China region based on measurements from the Crustal Movement Observation Network of China

Assessment of International GNSS Service Global Ionosphere Map products over China region based on measurements from the Crustal Movement Observation Network of China

Constructing a Regional Ionospheric TEC Model in China with Empirical Orthogonal Function and Dense GNSS Observation

Using Global Navigation Satellite Systems (GNSS) observation data for developing a high-precision ionospheric Total Electron Content (TEC) model is one of the essential subjects in ionospheric physics research and the application of satellite navigation correction. In this study, we integrate the Empirical Orthogonal Function (EOF) method with the TEC data provided by the Center for Orbit Determination in Europe (CODE), and observed by the dense GNSS receivers operated by the Crustal Movement Observation Network of China (CMONOC) to construct a regional ionospheric TEC model over China. The EOF analysis of CODE TEC in China from 1998 to 2010 shows that the first-order EOF component accounts for 90.3813% of the total variation of the ionospheric TEC in China. Meanwhile, the average value of CODE TEC is consistent with the spatial and temporal distribution characteristics of the first-order EOF base function, which mainly reflects the latitude and diurnal variations of TEC in China. The first-order coefficient after EOF decomposition shows an obvious 11-year period and semi-annual variations. The maximum amplitude of semi-annual variation mainly appears in March and October, which is closely associated with the variation in geographical longitude, the semi-annual change of the low-latitude electric field, and the ionospheric fountain effect. The second-order coefficient has an evident annual variation, the minimum amplitude mainly occurs in March, August, and September, and the amplitude values in the high solar activity years are more significant than those in the low solar activity years. The third-order coefficient mainly shows the characteristics of annual variation, and the fourth-order coefficient shows the noticeable semi-annual and annual variations. The third and fourth-order coefficients are both modulated by the solar activity index F10.7. The ionospheric TEC model in China, driven by CMONOC real-time GNSS observation data, can better reflect the latitude, local time and seasonal variation characteristics of ionospheric TEC over China. In particular, it can clearly show the spring and autumn asymmetry of ionospheric TEC in the low latitudes. The root mean square error of the absolute error between the model and the actual observation is mainly distributed around 2.45 TECU (1 TECU = 1016 electrons/m2). The values of the TEC model constructed in this study are closer to the actual observed values than those of the CODE TEC in China.

Efficient and accurate TEC modeling and prediction approach with random forest and Bi-LSTM for large-scale region

The total electron content (TEC) is a crucial parameter for ionosphere monitoring, and the development of accurate TEC estimation and prediction models is significant for high-precision positioning. However, establishing a high-precision ionospheric model that can effectively improve prediction accuracy remains a challenging task. We propose a method that combines random forest with a Bi-LSTM neural network to establish a high-precision ionospheric prediction model. The random forest algorithm is used for regression analysis and to estimate the variable importance of input parameters. We use observation data from the Crustal Movement Observation Network of China and International GNSS Services while estimating the relative importance of 14 input variables at different latitudes. The results demonstrate that our proposed model achieves more efficient and accurate predictions, with a correlation coefficient of 0.96, indicating significant improvement in accuracy compared to traditional RNN and LSTM models. Overall, the Bi-LSTM forecast model based on random forest can effectively capture the temporal and spatial variation characteristics of ionospheric TEC in China. This enables the model to provide accurate ionospheric information for positioning users, thereby improving the precision of positioning applications.

Impacts of non-linear ITRF2020 on reference frame alignment

The geometric similarity between two sets of coordinates should be assumed for frame alignment. When aligning Global Navigation Satellite System (GNSS) positions with seasonal variations to the linear Terrestrial Reference Frame (TRF), non-negligible aliasing errors of transformation parameters can appear and bias the station positions. The recent release of the seasonally augmented International Terrestrial Reference Frame 2020 (ITRF2020) necessitates an investigation into the impacts of seasonal terms on the alignment of regional networks. Using four-year of GPS data for Crustal Movement Observation Network of China (CMONOC) stations, we realize five sets of regional reference frames (RFs) aligned to the linear ITRF2020 (REF-LIN), the seasonal ITRF2020 (REF-FRQ), the linear ITRF2020 augmented either with seasonal signals or with full signals from loading models (REF-FIT and REF-NTL), and the combination of seasonal displacements from ITRF2020 and sub-seasonal signals from loading model (REF-CMB). Our analysis revealed that the augmented ITRF2020 had significant impact on the global stations used for frame alignment. Aligning daily solutions to the augmented ITRF2020 instead of the linear ITRF2020 resulted in a noticeable root mean square (RMS) decrease for over 70% of global stations. The REF-CMB method was the most preferred for frame alignment of global networks, with the largest decreased RMS of 0.3 and 1.0 mm in horizontal and vertical components. This indicates that most true signals are preserved in the aligned daily solutions. For regional network, we found only marginal impacts on regional stations in terms of linear velocity, residuals of time series stacking between the augmented solutions and REF-LIN. However, the larger discrepancies are noticed between annual estimates and loading model for the vertical component of REF-CMB and REF-FRQ, compared to RER-LIN solutions.

Improving MODIS Precipitable water vapour in mainland China based on the LSF model

Precipitable Water Vapour (PWV) measurements derived from the Moderate-resolution Imaging Spectroradiometer (MODIS) exhibit considerable promise for global high-resolution PWV monitoring but with limited accuracy. This study proposes a correction model for MODIS PWV, leveraging the least squares method (LS) and fast Fourier transform (FFT). The study used Global Navigation Satellite System (GNSS) PWV products (the accuracy is equivalent to Radiosonde-derived PWV) from 230 stations provided by the Crustal Movement Observation Network of China (CMONOC) for 2016 to 2019, serving as the ground truth for correcting MODIS PWV. Subsequently, the model undergoes an external coincidence test utilising the data from 2020. Initially, a linear or quadratic expression (LS model) was established through the application of the least squares method, effectively capturing the trends between the two PWV datasets. Then the periodic characteristics of residuals from the LS model were analysed by FFT. The outcomes reveal that within regions and seasons abundant in PWV, the quadratic expression is more consistent with the trend. The primary variation period of residuals exhibits variability across regions, with the annual period emerging as the primary fluctuation pattern in the majority of regions. Following this, we constructed the correction model (LSF model) that considered the cycle characteristics of residuals with the model type adopted for the LS model as the main part. After correction by the LSF model, the root-mean-square error (RMSE) of MODIS PWV decreased by 33.1%, 55.8%, 33.0%, and 19.4% in four seasons, respectively. Compared to the LS model, the LSF model showcased relative enhancements of the coefficient of determinations (R2) by 119.8%, 99.6%, 165.9%, and 39.6% in the four seasons, respectively, with an overall further decrease of 35.7% in RMSE. The LSF model demonstrates a superior degree of fitting and yields improved correction effects, thus effectively enhancing the reliability of MODIS PWV.

Effects of different seasonal fitting methods on the spatial distribution characteristics of common mode errors

This study investigated the effects of various seasonal fitting techniques on the spatial distribution of the common mode errors taking the coordinate time series of the continuous GPS reference stations of the Crustal Movement Observation Network of China (CMONOC) as an example. First, the seasonal term of coordinate time series was calculated using constant amplitude harmonic fitting (CAF), continuous wavelet transform (CWT), and smoothing spline fitting (SPF). The seasonal term and linear trend were then removed to obtain the residual time series. Finally, to determine the common mode errors of residual time series, principal component analysis (PCA) was applied. The results indicate that 1) smoothing spline fitting is superior to constant amplitude harmonic fitting and continuous wavelet transform in its ability to fit short-term irregular seasonal signals. In comparison to constant amplitude harmonic fitting, N/E/U has root mean square error (RMSE) values of smoothing spline fitting that are lower by 25%, 20%, and 14.4%, respectively. Smoothing spline fitting also has a higher coefficient of determination than continuous wavelet transform and constant amplitude harmonic fitting. The coefficient of determination in the U direction is larger than that in the N and E directions. 2) Each order PC of the residual series fitted by smoothing spline fitting exhibits apparent spatial aggregation characteristics, with PC1 having a uniform spatial distribution and presenting a largely positive response. Nevertheless, the residual series obtained by constant amplitude harmonic fitting and continuous wavelet transform exhibits scattered spatial response distribution features in each order PC. Compared to N and E, U’s spatial response distribution is distinct. From north to south, the spatial response of PC1 in the U direction progressively diminishes. In addition to being much lower than that in other locations, the Sichuan–Yunnan region’s spatial response value of PC1 and PC3 also exhibits a clear negative reaction. The root mean square error value of the residual series after smoothing spline fitting is the least, and the filtering effect is the best when comparing the spatial filtering effect based on the three fitting methods. We also compared the root mean square error reduction ratio before and after spatial filtering, and the results showed that the root mean square error reduction ratio before and after the residual series obtained by smoothing spline fitting is slightly larger than that obtained by other methods.

Quantifying the effect of non-seasonal non-tidal loadings on background noise properties of GPS vertical displacements in mainland China

Global positioning system (GPS) vertical displacements measuring the elastic response of Earth’s crust to tectonic motion have been used to produce vertical velocity estimates. These accurate estimates require minimal contamination by non-tectonic deformations, mainly including atmospheric-oceanic-hydrologic mass loadings. However, the variability of background noise properties in GPS time-series caused by the removal of these loading deformations remains unclear. In this study, we investigate how the non-seasonal parts of two types of combined non-tidal loading deformations (i.e., atmospheric plus non-tidal oceanic loadings (AO) or AO plus hydrological loadings (AOH)) affect the background noise properties. The study region is focused on the mainland China, and the data are 258 GPS vertical land motion time-series from the Crustal Movement Observation Network of China (CMONOC). Before and after the loading correction, we found that the more satisfactory background noise model is white plus power-law noise. The AO correction achieves significant improvement in white noise estimates and drastically reduces power-law noise content. Meanwhile, the estimated spectral index shows the worse stationarity of background noise properties but can be alleviated by additional hydrological loading correction. Besides, the exploited GPS imaging method reveals the spatial heterogeneity of the background noise amplitude, spectral index, and velocity uncertainty.

A High Precision Tidal Model Based on Multisource Tidal Harmonic Constants for Offshore China

As the accuracy of analytical instruments improve, the accuracy of ocean tide load displacement (OTLD) in geodesy and ocean dynamics is becoming increasingly demanding. The method of integrating through the load Green’s function using the ocean tide model is the main means of OTLD correction. However, although different tidal models have high accuracy in open ocean regions, they are less precise and less practical in the offshore areas of China, making it difficult to correct OTLDs accurately in complicated coastline areas of China. In this work, we evaluate the accuracy of 13 different tidal models in offshore China, and construct a new tidal model, new.chinasea.2022, based on the combination of high-precision tidal harmonic parameters. The OTLD correction efficiency of new.chinasea.2022 and conventional tidal models was assessed by wRMS analysis of GPS coordinate time series from the Crustal Movement Observation Network of China (CMONOC). It turns out that (1) the newly released tidal model performs better than the old model in offshore China, and there are differences in the performance of different models for each tidal constituent in different seas; (2) The OTLD correction of new.chinasea.2022 performed better in the offshore region of China, improving the correction of FES2004 by 1%-12% and osu.chinasea.2010 by 1%-6.5%.

Weakening the Flicker Noise in GPS Vertical Coordinate Time Series Using Hybrid Approaches

Noises in the GPS vertical coordinate time series, mainly including the white and flicker noise, have been proven to impair the accuracy and reliability of GPS products. Various methods were adopted to weaken the white and flicker noises in the GPS time series, such as the complementary ensemble empirical mode decomposition (CEEMD), wavelet denoising (WD), and variational mode decomposition (VMD). However, a single method only works at a limited frequency band of the time series, and the corresponding denoising ability is insufficient, especially for the flicker noise. Hence, in this study, we try to build two combined methods: CEEMD & WD and VMD & WD, to weaken the flicker noise in the GPS positioning time series from the Crustal Movement Observation Network of China. First, we handled the original signal using CEEMD or VMD with the appropriate parameters. Then, the processed signal was further denoised by WD. The results show that the average flicker noise in the time series was reduced from 19.90 mm/year0.25 to 2.8 mm/year0.25. This relates to a reduction of 86% after applying the two methods to process the GPS data, which indicates our solutions outperform CEEMD by 6.84% and VMD by 16.88% in weakening the flicker noise, respectively. Those apparent decreases in the flicker noises for the two combined methods are attributed to the differences in the frequencies between the WD and the other two methods, which were verified by analyzing the power spectrum density (PSD). With the help of WD, CEEMD & WD and VMD & WD can identify more flicker noise hidden in the low-frequency signals obtained by CEEMD and VMD. Finally, we found that the two combined methods have almost identical effects on removing the flicker noise in the time series for 226 GPS stations in China, testified by the Wilcoxon rank sum test.

Evaluation and Analysis of Remotely Sensed Water Vapor from the NASA VIIRS/SNPP Product in Mainland China Using GPS Data

Precipitable water vapor (PWV) is a vitally important factor in atmospheric circulation. PWV is significant for forecasting extreme weather and understanding the dynamics of climate change. Comprehensively evaluating the performance of newly proposed remotely sensed water vapor products is crucial for guaranteeing their suitability for futural PWV applications. In this study, the accuracy of the recently established remotely sensed water vapor product from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite sensor on the Suomi National Polar-orbiting Partnership (SNPP) (VIIRS-PWV) platform within various regions of mainland China was evaluated via the PWV from Global Positioning System (GPS) observations. The GPS-derived PWV from 231 stations of the Crustal Movement Observation Network of China (CMONOC) from 2012 to 2018 was obtained through precise point positioning (PPP) techniques. The results showed that the mean value of the correlation coefficient (CC), the mean bias (MB), and the root-mean-square error (RMSE) between the VIIRS-PWV and the GPS-PWV were 0.92, −1.6 mm, and 4.7 mm, respectively. These values were comparable with the results of the PWV data derived from the Atmospheric Infrared Sounder (AIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) products. This indicates that the VIIRS product could provide PWV data with satisfactory accuracy for large-area scientific applications. Moreover, the MB and RMSE of the differences between the GPS-PWV and VIIRS-PWV showed obvious seasonal variations. The VIIRS-PWV generally performed better in winter (with the MB and RMSE values of 0.1 mm and 2.3 mm) than in summer (with the MB and RMSE values of −4.4 and 7.0 mm). Analysis among different regions revealed that the Central South (CS) region of China attained the largest mean RMSE value of 6.3 mm, and the North West (NW) region attained the smallest mean RMSE value of 3.8 mm. In addition, the southern region of China obtained a mean RMSE value of 5.6 mm, while that for the northern region of China was 3.9 mm. This indicates that the VIIRS-PWV has better accuracy within the northern region of China than within the southern region.

High-Precision ZTD Model of Altitude-Related Correction

Zenith tropospheric delay (ZTD) is one of the main error sources in space geodesy. The existing regional or global models, such as Global Pressure and Temperature 3 (GPT3), Global Tropospheric model, Global Hopfield, and Shanghai Astronomical observatory tropospheric delay model models, have good performance. However, the precision of these models is relatively low in regions with a large height difference, which becomes the focus of this article. A high-precision ZTD model considering the height effect on tropospheric delay is proposed, and China is selected as study area due to its large height difference, which is called the high-precision ZTD model for China (CHZ). The initial ZTD value is calculated on the basis of the GPT3 model, and the periodic terms of ZTD residual between the global navigation satellite system (GNSS) and GPT3 model, such as annual, semiannual, and seasonal periods, are determined by the Lomb–Scargle periodogram method in different subareas of China. The relationship between the ZTD periodic residual term and the height of the GNSS station is further analyzed at different seasons, and linear ZTD periodic residual models are obtained. A total of 164 GNSS stations derived from the Crustal Movement Observation Network of China and 87 radiosonde stations are selected to validate the proposed CHZ model, and hourly ZTD data derived from GNSS are used to establish the CHZ model. Statistical result shows that the averaged root mean square and Bias of the CHZ model are 21.12 and −2.51 mm, respectively, in the whole of China. In addition, the application of CHZ model in precision point positioning (PPP) show that the convergence time is improved by 34%, 15%, and 35%, respectively, in N, E, and U components when compared to GPT3-based PPP.

Gravity yearly transient change around the epicenter of 2022 MS 6.9 Menyuan earthquake, China and seismotectonic implications

The MS 6.9 Menyuan earthquake occurred in the northeastern Tibetan Plateau on 8 January 2022. High-precision absolute gravity observations provide a unique perspective on seismotectonic feature of strong continental earthquakes. An obvious and coincident gravity increase about 5 years before the Menyuan earthquake with the rate up to 3.94 μGal/yr is observed at four absolute gravity stations within 350 km away from the epicenter. These four stations have been carried out repeat gravity observations by the Crustal Movement Observation Network of China (CMONOC) for more than a decade. The observed gravity changes could not be reasonably explained by the land water storage change nor crustal vertical deformation and thus might be related to the processes in the crust. A disc-shaped equivalent source region with a radius of 175 km is then modeled, based on the residual gravity rates. We find that the seismicity increases gradually in the modeled source region from about 2017 to 2021, similar to the pattern of absolute gravity change. Furthermore, the spatiotemporal distribution of epicenters is consistent with the fluids diffusion model, indicating that this could be related to the redistribution of deep fluid mass. With consideration of the near-surface processes, regional crustal structure, and seismicity, multiple lines of evidence consistently refer to the presence of deep fluids migration before the Menyuan earthquake. Our results suggest that high-precision absolute gravimetry can be used to monitor crustal fluid environment and determine mass changes in the source regions of potential large earthquakes.

ROTI Keograms based on CMONOC to characterize the ionospheric irregularities in 2014

Ionospheric irregularities have been studied since ~ 70 years ago. With the development of Global Navigation Satellite system (GNSS), networks of GNSS receivers have been used to obtain the characteristics of the irregularities, including the drift velocity, the structure, and the evolution. In this paper, keograms based on the Crustal Movement Observation Network of China (CMONOC) were used to characterize the irregularities over the area from longitude 85 to 125 °E and latitude 11 to 35 °N in 2014. Keograms were obtained for the rate of TEC index (ROTI) for every 0.5 degree longitude and 30 min universal time pixel. The results showed that the occurrence rate of irregularities in 2014 was high in the equinox months and December, and lowest in June. In equinox months the irregularities often appeared after sunset. In March the irregularities usually had long lifetime of ~ 5–7 h and ~ 5 degrees apparent longitudinal width. The long lifetime usually was accompanied by obvious eastward drift of ~ 100 m/s and large vertical ROTI (vROTI). In September the irregularities had weaker ROTI and shorter lifetime than those in March. The irregularities in the 2 equinox months should be related to the equatorial plasma bubbles (EPBs). In June, they appeared ~ 2–3 h later than those in equinoxes and drifted westward. The summer irregularities had weakest ROTI and their latitude was ~ 30 °N, much higher than those in equinoxes. In December, the irregularities were discrete patches with a longitudinal width of ~ 2 degrees and short lifetime of ~ 2 h. Unlike the equatorial irregularities in equinox months which are part of equatorial plasma bubbles, the solstice irregularities mainly appear to be a local phenomenon.Graphical

On the Improvement of Mass Load Inversion With GNSS Horizontal Deformation: A Synthetic Study in Central China

AbstractWe carry out synthetic experiments of mass load inversion using Global Navigation Satellite System (GNSS) vertical and horizontal displacements in Central China. Using two synthetic mass load models from a checkerboard mass distribution and a more realistic distribution derived from the Gravity Recovery and Climate Experiment (GRACE) observations, the inverted mass changes are determined based on the load theory and then compared with the known mass‐change inputs. We find that the combination of horizontal displacements with the commonly used vertical displacements significantly improves the inversion results when the number of sites is larger than one third of the number of total grids with relatively uniform distribution over the region. Synthetic tests demonstrate that data from the Crustal Movement Observation Network of China, including the total number of GNSS sites, spatial distribution, and precision, are sufficient to infer the annual amplitudes of mass changes in the region with comparable spatial resolution as that of GRACE observations. For the current precision level of the GNSS surface displacements (3.0 mm in the vertical and 1.0 mm in the horizontal components), including horizontal displacements leads to ∼10% improvement in mass inversion, compared to using vertical displacements only. With a higher precision level of 0.50 and 0.17 mm (for vertical and horizontal components, respectively), an improvement of ∼20% can be achieved.

Development of a ZTD Vertical Profile Model Considering the Spatiotemporal Variation of Height Scale Factor with Different Reanalysis Products in China

Tropospheric delay is one of the key factors that may affect high-precision satellite navigation and positioning and water vapor retrieval performance. Its variation in the vertical direction is much greater than that in the horizontal direction. Therefore, the vertical profile model of zenith total delay (ZTD) is important for the spatial interpolation of high-precision ZTD products and the development of ZTD models. However, in China, low spatial and temporal resolutions remain persistent in ZTD vertical profile models and limit their application. In this study, ZTD vertical profile grid models (CZTD-H model: CZTD-HM and CZTD-HE models) were developed by considering the time-varying height scale factor for China and employing ZTD layered profile information with high temporal-spatial resolution calculated using MERRA-2 data and ERA5 data based on the integration method during 2012–2016. The CZTD-H model accuracy was verified using the global navigation satellite system (GNSS) data acquired from the Crustal Movement Observation Network of China (CMONOC) and radiosonde data as reference and was compared with the canonical GPT3 model accuracy. The applicability of CZTD-HM and CZTD-HE models was discussed. The results showed that: (1) CZTD-HM and CZTD-HE models exhibited excellent performance for ZTD layered vertical interpolation in northwestern and southeastern China, respectively. Among ZTD layered profiles from 84 radiosonde stations, the RMSE of ZTD data interpolated using CZTD-HM and CZTD-HE models improved by 12.9/16.23% and 13.8/17.16% compared with GPT3-1 and GPT3-5 models, respectively. (2) The CZTD-H model maintained high performance for the spatial interpolation of GGOS grid ZTD data. Validation with ZTD data from 249 GNSS stations showed that the RMSEs of both CZTD-HM and CZTD-HE models improved by 2.8 mm (19.7%) and 2.6 mm (18.6%) compared with those of the GPT3-1 and GPT3-5 models, respectively. The CZTD-HE model showed excellent performance in summer among all the models. Only the location and day of the year were required for the application of the CZTD-H model, which showed excellent ZTD vertical correction performance in China. With the different performances of the CZTD-HE and CZTD-HM models in China, the ERA5 model can be recommended for practical applications. Therefore, these results can provide a reference for the data source selection of ZTD vertical profile model construction on the basis of high-precision reanalysis data, GNSS real-time high-precision positioning, and GNSS meteorology in China.