Ground-based Global Positioning System Research Articles

Impact of incorporating Spire CubeSat GPS observations in a global GPS network solution

Commonly, Global Positioning System (GPS)-based precise orbit determination (POD) of a low Earth orbiting (LEO) satellite is conducted by introducing fixed GPS orbits and clock corrections that were derived in a previous independent network solution using ground-based GPS receivers only. There have been a number of studies showing that the integration of LEO GPS observations can be advantageous in global network solutions, particularly when it comes to estimating geodetic parameters, such as the Earth’s center-of-mass. There has been an increase in GPS data available from LEO CubeSats over the past few years, such as from the Spire satellites, which are equipped with dual-frequency GPS receivers. The goal of the present study is to determine how GPS observations collected by specific Spire satellites affect global network solutions when combined with GPS observations from ground stations of the International GNSS Service. To obtain a combined GPS-LEO solution, GPS observations collected from receivers of selected Spire satellites and additional scientific LEOs are used to obtain combined GPS-LEO solutions by performing one joint least-squares adjustment process. Using this method, LEO satellite orbit parameters are determined along with GPS orbit parameters and geodetic parameters, namely GNSS station coordinates, Earth rotation parameters (ERPs), and the Earth’s center-of-mass. The influence of the Spire LEOs is assessed by analyzing different solutions that use GPS observations from different Spire and LEO satellites. Besides comparing the different global network solutions with one another, a comparison to a solution using only terrestrial GPS data is performed as well. As part of the quality assessment of the GPS-LEO solution, quality characteristics are analyzed, which are the geodetic parameters, orbit overlaps at arc boundaries, and formal errors determined for the estimated parameters. The results show that the estimation of the Earth’s center-of-mass coordinates benefits from including GPS-LEO observations. The solution is more improved by integrating GPS-LEO observations from scientific LEOs than from GPS-Spire-included solutions. The analysis reveals that GPS orbits are improved using the LEO-integrated approach, while orbit misclosures of LEO and Spire trajectories currently indicate a decrease in LEO orbit precision due to LEO orbit modeling deficiencies.

Improved Ionospheric Total Electron Content Maps over China Using Spatial Gridding Approach

Improved Ionospheric Total Electron Content Maps over China Using Spatial Gridding Approach

Ionospheric TEC variation over Svalbard archipelago, Norway and assessment of Bilinear interpolated GIM model

The Svalbard archipelago region is a group of islands in the Arctic Ocean, located between the North Pole and the northern coastal region of Norway at high latitudes. The current study focuses on the spatiotemporal distribution of total electron content (TEC) over the Svalbard archipelago region with the ground-based global positioning system (GPS) tracking stations during the years 2020–2022. The spatial ionospheric TEC characteristics from GPS observables and their correlation with the Bilinear interpolated global ionospheric map (GIM) and the latest edition of the international reference ionosphere (IRI- 2020) model are investigated. The results showed that both GPS-TEC and GIM-TEC have minimum ionospheric TEC during the winter season while they have maximum TEC occurrence in the summer and spring seasons with a higher difference of magnitude. We suspect such discrepancy could be due to expected uncertainties in the estimation of GIM-TEC interpolation over the region. The correlation coefficients between GPS-TEC versus GIM-TEC and GPS-TEC versus IRI-TEC showed that they are correlated with an almost equivalent correlation coefficient of approximately 0.76 which is reasonably acceptable but relatively lower than other middle latitude regions. This poor correlation happened because of the diurnal variation in the polar cap region during the summer season which is minimal. Finally, a Multitaper spectral analysis is employed on the frequency spectra of the time-varying periodograms of the four oscillation components. The outcome of the spectral analysis confirms that the first principal component (PC) of GPS-TEC is well correlated with the respective PC of GIM and IRI models. Although the higher order PCs of GIM models remain well-correlated with GPS-TEC, the subdued correlation between IRI model outputs and GPS-TEC suggests further improvements in the IRI model representation with respect to the high latitude characteristics.

A comparison of Jason-2 plasmasphere electron content measurements with ground-based measurements

Abstract. Previous studies utilizing the Global Positioning System (GPS) receivers aboard Jason satellites have performed measurements of plasmasphere electron content (PEC) by determining the total electron content (TEC) above these satellites, which are at altitudes of about 1340 km. This study uses similar methods to determine PEC for the Jason-2 receiver for 24 July 2011. These PEC values are compared to previous determinations of PEC from a chain of ground-based GPS receivers in Africa using the SCORPION method, with a nominal ionosphere–plasmasphere boundary at 1000 km. The Jason-2 PECs with elevations greater than 60∘ were converted to equivalent vertical PEC and compared to SCORPION vertical PEC determinations. In addition, slant (off-vertical) PECs from Jason-2 were compared to a small set of nearly co-aligned ground-based slant PECs. The latter comparison avoids any conversion of Jason-2 slant PEC to equivalent vertical PEC, and it can be considered a more representative comparison. The mean difference between the vertical PEC (ground-based minus Jason-2 measurements) values is 0.82 ± 0.28 TEC units (1 TEC unit=1016 electrons m−2). Similarly, the mean difference between slant PEC values is 0.168 ± 0.924 TEC units. The Jason-2 slant PEC comparison method may provide a reliable determination for the plasmasphere baseline value for the ground-based receivers, especially if the ground stations are confined to only midlatitude or low-latitude regions, which can be affected by a non-negligible PEC baseline.

Characterisations of Europe's integrated water vapour and assessments of atmospheric reanalyses using more than 2 decades of ground-based GPS

Abstract. The ground-based Global Positioning System (GPS) has been used extensively to retrieve integrated water vapour (IWV) and has been adopted as a unique tool for the assessments of atmospheric reanalyses. In this study, we investigated the multi-temporal-scale variabilities and trends of IWV over Europe by using IWV time series from 108 GPS stations for more than 2 decades (1994–2018). We then adopted the GPS IWV as a reference to assess six commonly used atmospheric reanalyses, namely the Climate Forecast System Reanalysis (CFSR); ERA5; ERA-Interim; the Japanese 55-year Reanalysis (JRA-55); the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2); and NCEP-DOE AMIP-II Reanalysis (NCEP-2). The GPS results show that the peaks of the diurnal harmonics are within 15:00–21:00 in local solar time at 90 % of the stations. The diurnal amplitudes are 0–1.2 kg m−2 (0 %–8 % of the daily mean IWV), and they are found to be related to seasons and locations with different mechanisms, such as solar heating, land–sea breeze, and orographic circulation. However, mismatches in the diurnal cycle of ERA5 IWV between 09:00 and 10:00 UTC as well as between 21:00 and 22:00 UTC were found and evaluated for the first time, and they can be attributed to the edge effect in each ERA5 assimilation cycle. The average ERA5 IWV shifts are −0.08 and 0.19 kg m−2 at the two epochs, and they were found to be more significant in summer and in the Alps and in Eastern and central Europe in some cases. Nevertheless, ERA5 outperforms the other reanalyses in reproducing diurnal IWV anomalies at all the 1-, 3-, and 6-hourly temporal resolutions. ERA5 is also superior to the others in modelling the annual cycle and linear trend of IWV. For instance, the IWV trend differences between ERA5 and GPS are quite small, with a mean value and a standard deviation of 0.01 % per decade and 0.97 % per decade, respectively. However, due to significant discrepancies with respect to GPS, CFSR and NCEP-2 are not recommended for the analysis of IWV trends over southern Europe and the whole of Europe, respectively.

An enhanced integrated water vapour dataset from more than 10 000 global ground-based GPS stations in 2020

Abstract. We developed a high-quality global integrated water vapour (IWV) dataset from 12 552 ground-based global positioning system (GPS) stations in 2020. It consists of 5 min GPS IWV estimates with a total number of 1 093 591 492 data points. The completeness rates of the IWV estimates are higher than 95 % at 7253 (58 %) stations. The dataset is an enhanced version of the existing operational GPS IWV dataset provided by the Nevada Geodetic Laboratory (NGL). The enhancement is reached by employing accurate meteorological information from the fifth generation of European ReAnalysis (ERA5) for the GPS IWV retrieval with a significantly higher spatiotemporal resolution. A dedicated data screening algorithm is also implemented. The GPS IWV dataset has a good agreement with in situ radiosonde observations at 182 collocated stations worldwide. The IWV biases are within ±3.0 kg m−2 with a mean absolute bias (MAB) value of 0.69 kg m−2. The standard deviations (SD) of IWV differences are no larger than 3.4 kg m−2. In addition, the enhanced IWV product shows substantial improvements compared to NGL's operational version, and it is thus recommended for high-accuracy applications, such as research of extreme weather events and diurnal variations of IWV and intercomparisons with other IWV retrieval techniques. Taking the radiosonde-derived IWV as reference, the MAB and SD of IWV differences are reduced by 19.5 % and 6.2 % on average, respectively. The number of unrealistic negative GPS IWV estimates is also substantially reduced by 92.4 % owing to the accurate zenith hydrostatic delay (ZHD) derived by ERA5. The dataset is available at https://doi.org/10.5281/zenodo.6973528 (Yuan et al., 2022).

On the Impact of GPS Multipath Correction Maps and Post-Fit Residuals on Slant Wet Delays for Tracking Severe Weather Events

Climate change has increased the frequency and intensity of weather events with heavy precipitation, making communities worldwide more vulnerable to flash flooding. As a result, accurate fore- and nowcasting of impending excessive rainfall is crucial for warning and mitigating these hydro-meteorological hazards. The measurement of integrated water vapour along slant paths is made possible by ground-based global positioning system (GPS) receiver networks, delivering three-dimensional (3D) water vapour distributions at low cost and in real-time. As a result, these data are an invaluable supplementary source of knowledge for monitoring storm events and determining their paths. However, it is generally known that multipath effects at GPS stations have an influence on incoming signals, particularly at low elevations. Although estimates of zenith total delay and horizontal linear gradients make up the majority of the GPS products for meteorology to date, these products are not sufficient for understanding the full 3D distribution of water vapour above a station. Direct utilization of slant delays can address this lack of azimuthal information, although, at low elevations it is more prone to multipath (MP) errors. This study uses the convective storm event that happened on 27 July 2017 over Bulgaria, Greece, and Turkey, which caused flash floods and severe damage, to examine the effects of multipath-corrected slant wet delay (SWD) estimations on monitoring severe weather events. First, we reconstructed the one-way SWD by adding GPS post-fit phase residuals, describing the anisotropic component of the SWD. Because MP errors in the GPS phase observables can considerably impact SWD from individual satellites, we used an averaging technique to build station-specific MP correction maps by stacking the post-fit phase residuals acquired from a precise point positioning (PPP) processing strategy. The stacking was created by spatially organizing the residuals into congruent cells with an optimal resolution in terms of the elevation and azimuth at the local horizon.This enables approximately equal numbers of post-fit residuals to be distributed across each congruent cell. Finally, using these MP correction maps, the one-way SWD was improved for use in the weather event analysis. We found that the anisotropic component of the one-way SWD accounts for up to 20% of the overall SWD estimates. For a station that is strongly influenced by site-specific multipath error, the anisotropic component of SWD can reach up to 4.3 mm in equivalent precipitable water vapour. The result also showed that the spatio-temporal changes in the SWD as measured by GPS closely reflected the moisture field estimated from a numerical weather prediction model (ERA5 reanalysis) associated with this weather event.

Correlation Analysis between Precipitation and Precipitable Water Vapor over China Based on 1999–2015 Ground-Based GPS Observations

Abstract Correlation analysis between precipitable water vapor (PWV) and precipitation over China was conducted by combining high-quality PWV data based on 1999–2015 ground-based global positioning system (GPS) observations with the measurements at matched meteorological stations in the same period. The mean correlation coefficient at all the stations is approximately 0.73, indicating that there is a significant positive correlation between PWV content and precipitation measurements, and the comparison of correlation among different climate types suggests that the distribution characteristics of the correlation coefficients are distinctively related to different climate types. There is also some positive correlation between PWV and precipitation long-term trends, with the correlation coefficients of monthly anomalies ranging generally from 0.2 to 0.6. Furthermore, the intensity of both PWV and precipitation extremes shows a long-term upward trend overall, with the most-intense events showing more significant increases. The extreme precipitation–temperature scaling rate of changes can reach above Clausius–Clapeyron (CC) scaling, whereas that of the extreme PWV-temperature is sub-CC overall, with regional differences in the specific scaling values. The correlation analysis in this work is of great significance for long-term climate analysis and extreme weather understanding, which provides a valuable reference for better utilizing the advantages of PWV data to carry out the studies above. Significance Statement Atmospheric water vapor is crucial to the climate system, especially in the context of global warming, and accurate knowledge of the correlation between precipitable water vapor (PWV) and precipitation is of great significance for long-term climate analysis and extreme precipitation weather forecasting. We take full advantage of the long-term homogeneity of ground-based GPS to conduct long-term correlation analysis between GPS-derived PWV and precipitation over China. Results show a significant positive correlation between them, and the degree of correlation is related to different climate types. The correlation of monthly anomalies is also positive, and, over the long-term, both water vapor and precipitation extremes have been increasing in intensity, with more significant increases occurring in the most-intense events. Extreme precipitation might increase beyond thermodynamic expectations, whereas PWV increases below expectations.

Assessment of machine learning techniques for prediction of integrated water vapor using meteorological data

Weather and Climatological studies are very important in assessing atmospheric conditions like storms and cyclones. Integrated water vapor (IWV) is an important greenhouse gas in the atmosphere responsible for the Earth's radiative balance. Global Positioning System (GPS) observations have been used for monitoring the IWV variability. The IWV estimations are carried out using ground-based GPS observations at Hyderabad (17.4°N, 78.46°E), India using GAMIT software. GAMIT is GPS analysis software developed by MIT, USA. It takes input as GPS observation data containing pseudo ranges, navigation data containing ephemeris, clock errors, g-files with orbital information, and meteorological data like pressure, temperature, and relative humidity to calculate IWV. However, estimating IWV for forecasting applications is impossible with a GPS system. This paper introduces a methodology to predict IWV during normal days and severe cyclonic events using machine learning (ML) techniques. Rational quadratic Gaussian process regression (RQ-GPR) and neural network (NN) algorithms are considered for identifying suitable ML prediction algorithms over tropical conditions. Meteorological surface data like Pressure, Temperature, and relative humidity are given as input to the machine learning models. The IWV values computed from GPS are compared with the model's predicted values. RQ-GPR model is showing good accuracy with the IWV values computed from GPS against the NN model. The correlation coefficient (ρ) achieved for RQ-GPR is 0.93, and 0.86 is obtained for the NN model.
 The RMSE (Root Mean Square Error) of the predicted IWV value with RQ-GPR is better than the NN model. We have obtained mean square error (MSE) and mean absolute error (MAE) as 18.146 kg/m2 and 3.0762 kg/m2 for RQ-GPR and 27.509 kg/m2 and 3.9102 kg/m2 for the NN model which is showing RQ-GPR is a suitable model for forecasting applications. The HUDHUD cyclonic event that occurred in October 2014 is considered for testing the proposed ML algorithms. RQ-GPR model has better results in the Prediction of IWV than the NN model. The RMSE value obtained is 2.837 kg/m2 for RQ-GPR and 3.327 kg/m2 obtained from the NN model. The results indicate that the RQ-GPR model has more accuracy than the other IWV prediction models. The prediction results are helpful for meteorology, weather, and climatology studies and useful to improve the accuracy of the regional numerical weather prediction models.

Retrospect and prospect of ionospheric weather observed by FORMOSAT-3/COSMIC and FORMOSAT-7/COSMIC-2

FORMOSAT-3/COSMIC (F3/C) constellation of six micro-satellites was launched into the circular low-earth orbit at 800 km altitude with a 72-degree inclination angle on 15 April 2006, uniformly monitoring the ionosphere by the GPS (Global Positioning System) Radio Occultation (RO). Each F3/C satellite is equipped with a TIP (Tiny Ionospheric Photometer) observing 135.6 nm emissions and a TBB (Tri-Band Beacon) for conducting ionospheric tomography. More than 2000 RO profiles per day for the first time allows us globally studying three-dimensional ionospheric electron density structures and formation mechanisms of the equatorial ionization anomaly, middle-latitude trough, Weddell/Okhotsk Sea anomaly, etc. In addition, several new findings, such as plasma caves, plasma depletion bays, etc., have been reported. F3/C electron density profiles together with ground-based GPS total electron contents can be used to monitor, nowcast, and forecast ionospheric space weather. The S4 index of GPS signal scintillations recorded by F3/C is useful for ionospheric irregularities monitoring as well as for positioning, navigation, and communication applications. F3/C was officially decommissioned on 1 May 2020 and replaced by FORMOSAT-7/COSMIC-2 (F7/C2). F7/C2 constellation of six small satellites was launched into the circular low-Earth orbit at 550 km altitude with a 24-degree inclination angle on 25 June 2019. F7/C2 carries an advanced TGRS (Tri Gnss (global navigation satellite system) Radio occultation System) instrument, which tracks more than 4000 RO profiles per day. Each F7/C2 satellite also has a RFB (Radio Reference Beacon) on board for ionospheric tomography and an IVM (Ion Velocity Meter) for measuring ion temperature, velocity, and density. F7/C2 TGRS, IVM, and RFB shall continue to expand the F3/C success in the ionospheric space weather forecasting.

Assessment of ZTD Derived from COSMIC Occultation Data with ECWMF, Radiosondes, and GNSS.

Global Navigation Satellite System (GNSS) signals generate slant tropospheric delays when they pass through the atmosphere, which is recognized as the main source of error in many spatial geodetic applications. The zenith tropospheric delay (ZTD) derived from radio occultation data is of great significance to atmospheric research and meteorology and needs to be assessed in the use of precision positioning. Based on the atmPrf, sonPrf, and echPrf data from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) Data Analysis and Archiving Center (CDAAC) from 1 January to 31 December 2008 and 2012, we obtained the ZTDs of the radio occultation data (occZTD) and the corresponding radiosonde (sonZTD) and ECWMF data (echZTD). The ZTDs derived from ground-based global positioning system (GPS) observations from the International GNSS Service (IGS) were corrected to the lowest tangent point height of the matched radio occultation profile by the barometric height formula (gnsZTD). The statistical results show that the absolute values of the bias between occZTD and echZTD, sonZTD, or gnsZTD are less than 5 mm, and the standard deviations are approximately 20 mm or less, indicating that occZTD had significant accuracy in the GNSS positioning model even when the local spherical symmetry assumption error was introduced when the Abel inversion algorithm was used to obtain the refractive index profile of atmPrf. The effects of the horizontal/vertical matching resolution and the variation in the station height/latitude on the biases of occZTD and gnsZTD were analyzed. The results can be used to quantify the performance of radio occultation data for tropospheric delay error correction in dynamic high-precision positioning.

Application of Classical Kalman filtering technique in assimilation of multiple data types to NeQuick model

This study attempts to improve the estimation of ionospheric electron density profiles over Korea and adjacent areas by employing the classical Kalman filtering technique to assimilate Total Electron Content (TEC) data from various sources into the NeQuick model. Successive corrections method was applied to spread the effect of TEC data assimilation at a given location to others that lacked TEC observations. In order to reveal that the assimilation results emulate the complex ionospheric changes during geomagnetic storms, the selected study days included both quiet (Kp ≤ 3) and disturbed geomagnetic conditions in the year 2015. The results showed that assimilation of TEC data derived from ground-based Global Positioning System (GPS) receivers could improve the root mean squared error (RMSE) associated with the NeQuick model estimation of ionospheric parameters by ≥56%. The improvement of RMSE achieved by assimilating TEC data measured using ionosondes was ~50%. The assimilation of TEC observations made by the COSMIC radio occultation technique yielded results that depicted RMSE improvement of >10%. The assimilation of TEC data measured by GPS receiver onboard Low Earth Orbiting satellites yielded results that revealed deterioration of RMSE. This outcome might be due to either the fact that the receivers are on moving platforms, and these dynamics might not have been accounted for during TEC computation or the limitation of the assimilation process. Validation of our assimilation results with global ionosphere TEC data maps as processed at the Center for Orbit Determination in Europe (CODE) revealed that both depicted similar TEC changes, showing response to a geomagnetic storm.

The First Validation of Sentinel-3 OLCI Integrated Water Vapor Products Using Reference GPS Data in Mainland China

The integrated water vapor (IWV) products collected from June 1, 2019 to May 31, 2020 from the ocean and land color instrument (OLCI) sensor, onboard the Sentinel-3 satellites, are evaluated against reference water vapor data estimated from ground-based 214 global positioning system (GPS) stations in the Mainland China. This is the first time to thoroughly evaluate the quality of Sentinel-3 OLCI IWV products by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> GPS-measured IWV data from such a large spatial coverage as China. The validation results show that, under cloud-free conditions, the OLCI IWV measurements agree very well with the ground-based GPS water vapor data, with a root-mean-square error (RMSE) of 3.03 mm for Sentinel-3A satellite and 3.13 mm for Sentinel-3B satellite. The dependence of OLCI IWV on various parameters was also analyzed. Analysis showed that the accuracy of inland OLCI IWV products was superior to that in coastal areas and that OLCI tended to overestimate IWV value in lower elevation and underestimate IWV value in higher elevation. The accuracy of OLCI IWV measurements increased as IWV decreased. Solar zenith angle analysis showed that the OLCI IWV product had a higher accuracy at a larger solar zenith angle. In spring and winter, the OLCI IWV observations had higher accuracy than those in summer and autumn. OLCI IWV tended to underestimate IWV value in most land cover types. Except for the polar climatic zone, the Sentinel-3 OLCI IWV products tended to overestimate IWV value. The validation results against previous studies were also discussed in this work.

Tropospheric Refractivity Profile Estimation by GNSS Measurement at China Big-Triangle Points

Atmospheric radio refractivity has an obvious influence on the signal transmission path and communication group delay effect. The uncertainty of water vapor distribution is the main reason for the large error of tropospheric refractive index modeling. According to the distribution and characteristics of water vapor pressure, temperature, and pressure, which are the basic components of the refractive index, a method for retrieving atmospheric refractivity profile based on GNSS (Global Navigation Satellite System) and meteorological sensor measurement is introduced and investigated in this study. The variation of the correlation between zenith wet delay and water vapor pressure is investigated and analyzed in detail. The partial pressure profiles of water vapor are retrieved with relevance vector machine method based on tropospheric zenith wet delay calculated by single ground-based GPS (Global Positioning System) receiver. The atmospheric temperature and pressure is calculated with the least square method, which is used to fit the coefficients of the polynomial model based on a large number of historical meteorological radiosonde data of local stations. By combining the water vapor pressure profile retrieving from single ground-based GPS and temperature and pressure profile from reference model, the refractivity profile can be obtained, which is compared to radiosonde measurements. The comparison results show that results of the proposed method are consistent with the results of radiosonde. By using over ten years’ (through 2008 to 2017) historical radiosonde meteorological data of different months at China Big-Triangle Points, i.e., Qingdao, Sanya, Kashi, and Jiamusi radiosonde stations, tropospheric radio refractivity profiles are retrieved and modeled. The comparison results present that the accuracies of refractivity profile of the proposed method at Qingdao, Sanya, Kashi, and Jiamusi are about 5.48, 5.63, 3.58, and 3.78 N-unit, respectively, and the annual average relative RMSE of refractivity at these stations are about 1.66, 1.53, 1.49, and 1.23%, respectively.

Investigating Ionospheric Perturbations Following the 2020 Beirut Explosion Event

AbstractWe assess the detection of the August 4, 2020 chemical explosion in Beirut, Lebanon (33°N, 35°E). We use ionospheric total electron content (TEC) observations from a regional network of ground‐based Global Positioning System (GPS) receivers to study the ionospheric variability and wave perturbations generated by the Beirut explosion. Our analysis reveals that the induced wave structures arrived in the ionosphere 10 ± 2 min after the explosion, which is a strong indication of acoustic gravity wave activity. These wave structures are characterized with ΔTEC of 0.06 TECU moving at ∼750 m/s in south‐east direction away from the explosion epicenter. The continuous wavelet analysis we applied shows a dominant wave periodicity of 1.5–2.5 min (6.78–11 mHz) in the ionosphere. Furthermore, we use measurements from ERA‐interim analysis to establish the ambient neutral atmospheric conditions before the Beirut explosion event. Finally, the observed ionospheric wave perturbations appear to be in good agreement with the expected arrival of acoustic gravity waves generated following the explosion.

Estimating precipitable water vapour using the global positioning system and radio occultation over Ethiopian regions

ABSTRACT The Global Positioning System (GPS) is a space-based radio positioning system, which is capable of providing continuous position, velocity, and time information to users anywhere on, or near, the surface of the Earth. The main objective of this work was to estimate the integrated precipitable water vapour (IPWV) using ground GPS and Low Earth Orbit (LEO) Radio Occultation (RO) to study spatial-temporal variability. For LEO-GPS RO, we used Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) datasets. We estimated the daily and monthly mean of IPWV using six selected ground-based GPS stations for the years 2012 to 2016. The main perspective for selecting the years 2012 to 2016 is that, continuous data were available during these periods at all Ethiopian GPS stations. We studied temporal, seasonal, diurnal, and vertical variations of precipitable water vapour using GPS observables extracted from the precise geodetic GAMIT-GLOBK software package. Finally, we determined the cross-correlation of our GPS-derived IPWV values with those of the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-40 Interim reanalysis and of the second generation National Oceanic and Atmospheric Administration (NOAA) model ensemble Forecast System Reforecast (GEFS/R) for validation and static comparison. There are higher values of the IPWV range from 30 to 37.5 millimetres (mm) in Gambela and Southern Regions of Ethiopia. Some parts of Tigray, Amhara, and Oromia regions had low IPWV ranges from 8.62 to 15.27 mm. The correlation coefficient between GPS-derived IPWV with ECMWF and GEFS/R exceeds 90%. We conclude that there are highly temporal, seasonal, diurnal, and vertical variations of precipitable water vapour in the study area.

Feasibility of ERA5 integrated water vapor trends for climate change analysis in continental Europe: An evaluation with GPS (1994–2019) by considering statistical significance

Although the statistical significances for the trends of integrated water vapor (IWV) are essential for a correct interpretation of climate change signals, obtaining accurate IWV trend estimates with realistic uncertainties remains a challenge. This study evaluates the feasibility of the IWV trends derived from the newly released fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5) for climate change analysis in continental Europe. This is achieved by comparing the trends derived from in-situ ground-based Global Positioning System (GPS)’s daily IWV series from 1994 to 2019 at 109 stations. The realistic uncertainties and statistical significances of the IWV trends are evaluated with the time series analysis on their noise characteristics and proper noise models. Results show that autoregressive moving average ARMA(1,1) noise model is preferred rather than the commonly assumed white noise (WN) or first-order autoregressive AR(1) noise for about 68% of the ERA5 and GPS IWV series. An improper noise model would misevaluate the trend uncertainty of an IWV time series, compared with its specific preferred noise model. For example, ARMA(1,1) may misevaluate the standard deviations of their trend estimates (0.1–0.3 kg m−2 decade−1) by 10%. Nevertheless, ARMA(1,1) is recommended as the default noise model for the ERA5 and GPS IWV series. However, the preferred noise model for each ERA5 minus GPS (E-G) IWV series should be specifically determined, because the AR(1)-related models can result in an underestimation on its trend uncertainty by 90%. In contrast, power-law (PL) model can lead to an overestimation by up to nine times. The E-G IWV trends are within −0.2–0.4 kg m−2 decade−1, indicating that the ERA5 is a potential data source of IWV trends for climate change analysis in continental Europe. The ERA5 and GPS IWV trends are consistent in their magnitudes and geographical patterns, lower in Northwest Europe (0–0.4 kg m−2 decade−1) but higher around the Mediterranean Sea (0.6–1.4 kg m−2 decade−1).

Water vapor retrieval from MERSI NIR channels of Fengyun-3B satellite using ground-based GPS data

An ensemble-based empirical regression algorithm is for the first time developed to retrieve total column water vapor from the Medium Resolution Spectral Imager (MERSI) near-infrared (NIR) channels onboard the Fengyun-3B (FY-3B) satellite. This retrieval method uses precipitable water vapor (PWV) data estimated from ground-based Global Positioning System (GPS) data to build a regression model in which the reflectance ratio observed from MERSI NIR absorption channels and the corresponding GPS PWV data are the parameters. The MERSI Level 1b data, specifically the three water vapor absorption channels centered at 905 nm, 940 nm, and 980 nm are used to retrieve water vapor. PWV data observed from 256 ground-based GPS stations located in the western North America in 2016 are used as reference data for model development. Then, validation is performed with data obtained during 2017–2019 from both the western North America and Australia to assess the performance of the proposed algorithm. The results indicate that the new PWV results agree very well with ground-based PWV reference data. The mean absolute percentage error (MAPE) for ensemble median PWV is 16.72% ~ 36.74% in western North America and is 15.47% ~ 32.31% in Australia. The RMSE is 4.635 mm ~ 8.156 mm in western North America and is 5.383 mm ~ 8.900 mm in Australia. The weighted mean value using three-channel ratio transmittance has the best retrieval accuracy, with RMSE of 4.635 mm in western North America and 5.383 mm in Australia. This new PWV algorithm can retrieve PWV from FY-3B data with a higher accuracy for different regions. Different from conventional algorithms, no pre-observed information of atmospheric parameters is required in this model.

Comparison of seasonal and longitudinal variation of daytime MSTID activity using GPS observation and GAIA simulations

The total electron content (TEC) data derived from the GAIA (Ground-to-topside model of Atmosphere Ionosphere for Aeronomy) is used to study the seasonal and longitudinal variation of occurrence of medium-scale traveling ionospheric disturbances (MSTIDs) during daytime (09:00–15:00 LT) for the year 2011 at eight locations in northern and southern hemispheres, and the results are compared with ground-based Global Positioning System (GPS)-TEC. To derive TEC variations caused by MSTIDs from the GAIA (GPS) data, we obtained detrended TEC by subtracting 2-h (1-h) running average from the TEC, and calculated standard deviation of the detrended TEC in 2 h (1 h). MSTID activity was defined as a ratio of the standard deviation to the averaged TEC. Both GAIA simulation and GPS observations data show that daytime MSTID activities in the northern and southern hemisphere (NH and SH) are higher in winter than in other seasons. From the GAIA simulation, the amplitude of the meridional wind variations, which could be representative of gravity waves (GWs), shows two peaks in winter and summer. The winter peak in the amplitude of the meridional wind variations coincides with the winter peak of the daytime MSTIDs, indicating that the high GW activity is responsible for the high MSTID activity. On the other hand, the MSTID activity does not increase in summer. This is because the GWs in the thermosphere propagate poleward in summer, and equatorward in winter, and the equatorward-propagating GWs cause large plasma density perturbations compared to the poleward-propagating GWs. Longitudinal variation of daytime MSTID activity in winter is seen in both hemispheres. The MSTID activity during winter in the NH is higher over Japan than USA, and the MSTID activity during winter in the SH is the highest in South America. In a nutshell, GAIA can successfully reproduce the seasonal and longitudinal variation of the daytime MSTIDs. This study confirms that GWs cause the daytime MSTIDs in GAIA and amplitude and propagation direction of the GWs control the noted seasonal variation. GW activities in the middle and lower atmosphere cause the longitudinal variation.

Variability of GPS-derived ionospheric TEC over Nigeria during a year of low solar activity

An investigation of the diurnal and seasonal variability of ionospheric total electron content (TEC) over Nigeria is carried out in this study using the global positioning system (GPS) observable. Nigeria coordinates fall within the trough of the equatorial ionisation anomaly region of the African sector. The TEC data used were obtained from the ground-based GPS receiver stations of the Nigerian GNSS network of stations (NIGNET). The stations with their respective geomagnetic latitudes are: Abuja (–1.64°), Yola (–1.32°), Zaria (–0.13°), Kebbi (0.72°). The results of the diurnal analysis of the relative variability index (VD) revealed higher nighttime values than daytime values. The diurnal variation of VD also showed two conspicuous peaks: post-midnight and post-sunset. The diurnal–seasonal variation does not reveal any consistent pattern (no particular season leads the others throughout). On average, considering all the seasons together, maximum TEC variability occurred in Zaria (62%) and minimum in Yola (54%). Seasonally, maximum VD was recorded during the March equinox and the lowest was recorded during the December equinox.