Ground-based Global Navigation Satellite System Research Articles

Assessment and calibration of FY-4A AGRI total precipitable water products based on CMONOC

FengYun 4A (FY-4A) is the first flight unit of the second-generation geostationary meteorological satellite in China (FengYun 4 series, FY-4 series). The Advanced Geostationary Radiation Imager (AGRI) onboard it can provide high spatial resolution precipitable water vapor (PWV) products. This study used Global Navigation Satellite System (GNSS) observations from 230 ground-based GNSS stations within the Crustal Movement Observation Network of China (CMONOC) from March 1, 2019 to February 29, 2020 to obtain PWV, also called total precipitable water (TPW). The Chinese mainland was divided into 13 regions based on climate classification, latitude, and altitude, and GNSS PWV was used to verify the FY-4A AGRI TPW and make calibrations when necessary. The results reveal that the accuracy of FY-4A AGRI TPW shows significant spatial and temporal differences in mainland China. Overall, FY-4A AGRI TPW and GNSS PWV are strongly positively correlated over China, and the correlation coefficients (R) between the two PWV products are highest in autumn (0.78–0.972) while lowest in winter (0.556–0.956). FY-4A AGRI TPW is underestimated in most regions, and the mean bias (MB) values are higher in regions with high PWV content. The root means square error (RMSE) of FY-4A AGRI TPW in all regions are highest in summer (3.34–9.05 mm) and lowest in winter (0.87–3.83 mm). The accuracy of FY-4A AGRI TPW exhibits significant regional and seasonal differences, with higher R, RMSE, and MB in wet seasons and regions, while lower R, RMSE, and MB in dry seasons and regions. This study constructed the calibration model of FY-4A AGRI TPW was based on GNSS PWV in 13 regions. After calibration, the RMSEs can be reduced by 6.6% ~ 32.1%, 5.1% ~ 45%, 5.7% ~ 31.6%, and 7.1% ~ 30.8% in four seasons, respectively, and the MB mainly fluctuates around 0. The accuracy of generated FY-4A AGRI TPW is further improved on the basis of the high resolution.

Conjugate Effect of the 2011 Tohoku Reflected Tsunami‐Driven Gravity Waves in the Ionosphere

AbstractThis study presents the conjugate ionospheric disturbances triggered by the 2011 Tohoku‐Oki reflected tsunami oceanic waves using the ground‐based Global Navigation Satellite System (GNSS) total electron content (TEC) observations. We found that the equatorward and westward propagating nighttime medium‐scale traveling ionospheric disturbances (MSTIDs) occurred over Japan and Australia simultaneously following the tsunami oceanic waves reflected by the Emperor Seamount Chain in the northern hemisphere. The atmospheric gravity waves driven by reflected tsunami oceanic waves are hypothesized to be the source to trigger the conjugate MSTIDs by transporting the polarization electric fields along the field line to the conjugate hemisphere. Moreover, only the southwestward propagating MSTIDs have this conjugate effect, which could be due to the wavefront orientation. The Perkins instability could also be involved in the interhemispheric coupling process. This study provides the first observational evidence that the reflected tsunami can induce conjugate ionospheric disturbances through electrodynamic forcing.

Development and Assessment of an ALLSSA‐Based Atmospheric Weighted Mean Temperature Model With High Time Resolution for GNSS Precipitable Water Retrieval

AbstractThe atmosphere weighted mean temperature, Tm, is an essential parameter for retrieving precipitable water from the ground‐based Global Navigation Satellite Systems (GNSS). The accuracy of high temporal resolution GNSS precipitable water vapor (PWV) estimation requires wideband Tm information and its magnitude. However, existing Tm empirical models use trigonometric functions with only fixed amplitude and low‐frequency for time fitting, which limits real‐time or near real‐time PWV retrieval from GNSS observation. Thus, an improved Tm model for China, LTCm, containing more frequency information of Tm, based on the antileakage least‐squares spectrum analysis by utilizing the ERA5 pressure‐level products during the years 2015–2019, is developed. Both Tm data from ERA5 pressure‐level products and radiosonde stations distributed in China over 2020 are selected as reference values to verify the performance of the LCTm model. The results show that the LCTm model yields significant performance against other models in Tm estimation over China, especially in marine regions and high‐altitude areas. Furthermore, the LCTm model can generally achieve a mean Bias/root mean square (RMS) of −0.33 K/2.06 K in contrast to ERA5 pressure‐level products and 0.03 K/3.47 K in comparison with radiosonde, which corresponds to a 7.2%–13.8% improvement against GPT2w, GTm‐III, and Bevis. Moreover, LCTm has σ pwv and σ pwv /PWV values of 0.27 mm and 1.25% when used to retrieve GNSS‐PWV, respectively. Consequently, the LCTm model that considers high‐frequency information of Tm can obtain more reliable Tm values. Therefore, the LCTm model can be applied to real‐time or near real‐time GNSS PWV retrieval, which is of great significance for GNSS meteorological research.

Assimilation of GNSS PWV with NCAR-RTFDDA to Improve Prediction of a Landfall Typhoon

Precipitable water vapor (PWV) retrieved from ground-based global navigation satellite system (GNSS) stations acquisition signal of a navigation satellite system provides high spatial and temporal resolution atmospheric water vapor. In this paper, an observation-nudging-based real-time four-dimensional data assimilation (RTFDDA) approach was used to assimilate the PWV estimated from GNSS observation into the WRF (Weather Research and Forecasting) modeling system. A landfall typhoon, “Mangkhut”, is chosen to evaluate the impact of GNSS PWV data assimilation on its track, intensity, and precipitation prediction. The results show that RTFDDA can assimilate GNSS PWV data into WRF to improve the water vapor distribution associated with the typhoon. Assimilating the GNSS PWV improved the typhoon track and intensity prediction when and after the typhoon made landfall, correcting a 5–10 hPa overestimation (too deep) of the central pressure of the typhoon at landfall. It also improved the occurrence and the intensity of the major typhoon spiral rainbands.

Land Water Vapor Retrieval for AMSR2 Using a Deep Learning Method

In precipitable water vapor (PWV) retrieval, results with high spatial coverage but low temporal resolution can be achieved through satellite-borne sensors, such as the Advanced Microwave Scanning Radiometer 2 (AMSR2). Conversely, the ground-based global navigation satellite system (GNSS) can provide PWV with high temporal resolution and high precision but low spatial coverage. To combine the advantages of these two technologies, we introduce a backpropagation neural network (BPNN) to realize PWV retrieval from AMSR2 with ground-based GNSS data. We first detect the optimal configuration for the BPNN. Then, based on the results of the retrieval accuracy from different types of orbits, we find that the descending (De) orbit has the highest retrieval accuracy, with a root-mean-square error (RMSE) of 3.25 mm. Afterward, the influence of brightness temperature (Tb) data at different frequencies on PWV retrieval is analyzed. The results of GNSS- PWV verification indicate that the 18 +23-GHz frequency combination has the highest PWV retrieval accuracy, and the mean RMSE of all 82 test stations distributed globally can reach 3.53 mm. We also analyze the influence of differently located stations on retrieval accuracy, and the results show that the accuracy of high-latitude and polar regions is remarkably higher than that of other areas but with a lower relative error. Finally, we use radiosonde data as another external verification method to assess PWV retrieval accuracy. The results reveal that RMSE can reach 3.87 mm. Through a BPNN approach, we have creatively realized PWV retrieval from AMSR2 using ground-based GNSS data on a global scale.

Arctic TEC Mapping Using Integrated LEO-Based GNSS-R and Ground-Based GNSS Observations: A Simulation Study

Ionospheric total electron content (TEC) maps with high spatial and temporal resolutions are essential for depicting the state of the ionosphere and for performing ionospheric delay corrections associated with satellite navigation applications. Low Earth orbit (LEO) CubeSat-based global navigation satellite system (GNSS) reflectometry (GNSS-R) measurements provide a promising opportunity for retrieval of ionospheric TEC over sea ice and calm waters, which offers a potential new data source to fill the gaps of ground-based GNSS networks. However, the GNSS-R slant TEC (sTEC) estimations include contributions from the incident and reflection ray paths, whose ionospheric piercing points (IPPs) can be separated by hundreds of kilometers. This article presents an algorithm that integrates sTEC measurements from the GNSS-R CubeSats and available ground-based GNSS receivers to derive Arctic vertical TEC (vTEC) maps. A simulation study using the model ionosphere constructed from the NeQuick-2 is conducted to assess the performance of the algorithm. Varying levels of temporal resolutions and solar activities, and the number of CubeSats and the number of maximum simultaneously tracked reflection signals by a CubeSat are implemented in the simulation. The results show that the inclusion of coherent GNSS-R measurements improves the accuracy of the vTEC maps under all levels of solar activities. The RMSE improvement percentage is most obvious when the update interval is the shortest. Increasing the number of CubeSats further improves the accuracy. However, no significant improvement in vTEC map accuracy is observed when the number of maximum simultaneously tracked GNSS-R satellites is higher than 4. Quantitative measures and analyses of the algorithm performances are presented in this article.

Sea Surface States Detection in Polar Regions Using Measurements of Ground-Based GNSS Interferometric Reflectometry

This article analyzes the interferometric measurements of ground-based global navigation satellite systems (GNSSs) stations and proposes a novel method for sea surface states detection. The novel technique benefits from a cost-effective data collection from a large number of global GNSS stations. In this study, we extend a traditional GNSS interferometry reflectometry (GNSS-IR) model so that it can be applied to a multilayer surface by considering the surface roughness, total reflectivity, and penetration loss in multilayer situations. Based on this model, the wavelet analysis is used to perform parameterization on the interferometric observations represented by the signal to noise ratio (SNR). An integration factor and power curve are also proposed to characterize the surface state transition. One-year data from an Arctic geodetic GNSS station in the north of Canada are collected for analysis to validate the proposed approach in comparison with the existing methods based on the amplitude and damping factors. The results show that the new method demonstrates good usability and sensitivity to detect surface state transitions, e.g., icing, snowfall, and snow melting. However, the amplitude and damping factor-based methods derived from the single-layer model are only able to detect the pure ice surface and cannot respond to thick snow conditions. Finally, the high-resolution spaceborne images confirm the reliability of this method, exhibiting a great potential for long-term coastal sea surface detection based on the global geodetic GNSS stations and later being expected to be applied to sense cryosphere surface states.

Total Column Water Vapor From INSAT-3D: Assessments With Ground-Based GNSS Receivers and GMI Datasets at Different Temporal Scales

The quality of the total column water vapor (TCWV) data retrieved from the Indian national satellite (INSAT) system series (INSAT-3D) and its variation at different temporal scales have been evaluated. The reference TCWV dataset is obtained from four ground-based global navigation satellite system (GNSS) receivers and over the entire Indian subcontinent from a global precipitation measurement (GPM) microwave imager (GMI) that uses global analysis (GANAL) model data. TCWV comparison of INSAT-3D, GPM-GMI with GNSS show higher correlations for GPM-GMI than INSAT-3D at all temporal scales. Though GMI can reproduce observed TCWV variations better than the INSAT-3D, the large biases of both the data sets indicate errors in the magnitude. Seasonal and monthly comparisons at two locations in the southeast peninsular India region show large correlations and small bias in the northeast and small correlations and large biases in southwest monsoon months. Southeast peninsular India receives a significant amount of rainfall in the northeast monsoon, and large TCWV correlations indicate both the algorithms removing cloud pixels with high accuracy. Therefore, the errors in the TCWV are attributed to the estimated radiances at different spectral channels. The spatiotemporal variations of TCWV correlations and bias of INSAT-3D indicate the need for improving the data quality by validating the estimated radiances at different spectral channels and, in turn, the retrieved TCWV over various regions at different temporal scales.

Evaluation of Shipborne GNSS Precipitable Water Vapor Over Global Oceans From 2014 to 2018

Atmospheric water vapor plays an essential role in climate change and weather forecasting. However, monitoring water vapor with high spatial and temporal resolutions remains a challenge, especially over ocean regions where observations are insufficient. Shipborne global navigation satellite systems (GNSSs) contribute to enriching water vapor measurements over oceans and also can help validate satellite observations. Due to the lack of long-time serial observations, the performance of shipborne GNSS-derived precipitable water vapor (PWV) is inadequately evaluated on the global ocean scale. In this study, an overall assessment of shipborne GNSS PWV over global oceans is performed based on six voyages from 2014 to 2018. In coastal areas, the PWV differences of shipborne GNSS with respect to (w.r.t.) ground-based GNSS and ground-launched radiosonde data are 2.64 and 2.85 mm in the root mean square (rms), respectively. In open oceans, compared to ship-launched radiosonde profiles and satellite measurements, shipborne GNSS PWV shows the rms of differences of 2.54 and 2.53 mm, respectively. In addition, the rms of PWV differences between the whole track of shipborne GNSS PWV and National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2) products is 2.96 mm. The intertechnique validations demonstrate that the accuracy of shipborne GNSS PWV is superior to 3 mm, which meets the requirements of climate research and numerical weather prediction (NWP).

A New Cumulative Anomaly-Based Model for the Detection of Heavy Precipitation Using GNSS-Derived Tropospheric Products

In recent years, tropospheric products obtained from ground-based global navigation satellite system (GNSS) measurements, especially the zenith total delay (ZTD) and precipitable water vapor (PWV) estimates, have advanced their usages in meteorological applications such as the detection of precipitation events. Generally, a cumulative anomaly (CA) time series of any atmospheric variable, which represents the long-term departure of the variable from its “normal” cycle, is widely used for quantitatively estimating the variable’s variations in response to a weather event. In this study, a new cumulative anomaly-based model (NCAM) containing 14 variables, including not only PWV and ZTD values but also their respective six types of derivatives, for detecting heavy precipitation was developed. The 6-h CA time series of the variables were calculated based on the data of hourly precipitation records and time series of ZTD and PWV collected at the co-located HKSC–King’s Park (KP) stations over the eight-year period 2010–2017. The model was evaluated using the 14 variables’ CA time series to detect heavy precipitation events happened in the summer months over the period 2018–2019, and precipitation records in the same period were used as the reference. Results demonstrated that 99.1% of heavy precipitation was correctly detected by the NCAM with a lead time of 2.87 h, and the false alarm ratio (FAR) score resulting from the model was reduced to 22.4%. In addition, two case studies were also conducted to verify the effectiveness of the NCAM. These results all provide a promising direction for the application of using the CA time series of GNSS tropospheric products to the detection of heavy precipitation events.

Polarimetric GNSS-R Sea Level Monitoring Using I/Q Interference Patterns at Different Antenna Configurations and Carrier Frequencies

Coastal sea level variation as an indicator of climate change is extremely important due to its large socioeconomic and environmental impacts. The ground-based global navigation satellite system (GNSS)-reflectometry (GNSS-R) is becoming a reliable alternative for sea surface altimetry. We investigate the impact of antenna polarization and orientation on GNSS-R altimetric performance at different carrier frequencies. A one-year dataset of ground-based observations at the Onsala Space Observatory using a dedicated reflectometry receiver is used. Interferometric patterns produced by the superposition of direct and reflected signals are analyzed using the least-squares harmonic estimation (LS-HE) method to retrieve sea surface height. The results suggest that the observations from global positioning system (GPS) L1 and L2 frequencies provide similar levels of accuracy. However, the overall performance of the height products from the GPS L1 shows slightly better performance due to more observations. The combination of L1 and L2 observations (L12) improves the accuracy up to 25% and 40% compared to the L1 and L2 heights. The impacts of antenna orientation and polarization are also evaluated. A sea-looking left-handed circular polarization (LHCP) antenna shows the best performance compared to both zenith- and sea-looking right-handed circular polarization (RHCP) antennas. The results are presented using different averaging windows ranging from 15 min to 6 h. Based on a 6-h window, the yearly root mean squared errors (RMSEs) between GNSS-R L12 sea surface heights with collocated tide gauge observations are 2.4, 3.1, and 4.1 cm with the correlation of 0.990, 0.982, and 0.969 for LHCP sea-looking, RHCP sea-looking, and RHCP up-looking antennas, respectively.

Systematic Detection of Anomalous Ionospheric Perturbations Above LEOs From GNSS POD Data Including Possible Tsunami Signatures

In this paper we show the capability of Global Navigation Satellite System (GNSS) Precise Orbit Determination (POD) Low Earth Orbiter (LEO) data to detect anomalous ionospheric disturbances in the spectral range of the signals associated to earthquakes and tsunamis, applied to two of these events in Papua New Guinea (PNG) and Solomon Islands during 2016. This is achieved thanks to the new PIES approach <i>( POD-GNSS LEO Detrended Ionospheric Electron Content Significant Deviations)</i>. The significance of such ionospheric signals above the Swarm LEOs is confirmed with different types of independent data: in-situ electron density measurements provided by the Langmuir Probe (LP) on board Swarm LEOs, DORIS and ground-based GNSS co-located measurements, as it is described in the paper. In this way we conclude the possible detection of the tsunami related ionospheric gravity wave in PNG 2016 event, consistent with the most-recent theory which shows that a tsunami (which is localized in space and time) excites a spectrum of gravity waves, some of which have faster horizontal phase speeds than the tsunami. We believe that this work shows as well the feasibility of a future potential monitoring system of ionospheric disturbances, to be made possible by hundreds of cubesats with POD GNSS receivers among other appropriate sensors, and supported for real-time or near real-time confirmation and characterization by thousands of worldwide existing ground GNSS receivers.

Comparison of volcanic explosions in Japan using impulsive ionospheric disturbances

Using the ionospheric total electron content (TEC) data from ground-based Global Navigation Satellite System (GNSS) receivers in Japan, we compared ionospheric responses to five explosive volcanic eruptions 2004–2015 of the Asama, Shin-Moe, Sakurajima, and Kuchinoerabu-jima volcanoes. The TEC records show N-shaped disturbances with a period ~ 80 s propagating outward with the acoustic wave speed in the F region of the ionosphere. The amplitudes of these TEC disturbances are a few percent of the background absolute vertical TEC. We propose to use such relative amplitudes as a new index for the intensity of volcanic explosions.Graphical

High‐Resolution 3‐D Imaging of Daytime Sporadic‐E Over Japan by Using GNSS TEC and Ionosondes

AbstractA novel two‐step three‐dimensional (3‐D) computerized ionospheric tomography (CIT) technique has been developed to image the structure of daytime midlatitude sporadic‐E (Es). The CIT relies on total electron content (TEC) from a dense ground‐based Global Navigation Satellite System (GNSS) receiver network over the Japan area. First, on a coarse grid, the TEC data and a multiplicative algebraic reconstruction technique (MART) are used to reconstruct the F region from a smooth background. Then, on a fine grid and using singular value decomposition (SVD), the residues after deducting the F region contribution to TEC are utilized in reconstructing the E region, extending 80–180 km in altitude. To vertically constrain the E region solution, we introduced a family of subsets of time‐dependent empirical orthogonal functions (EOFs) from a Chapman model function tuned to manually scaled ionosonde observations. We analyzed three event days to validate the results. East‐West (E‐W) aligned frontal structures, spanning several hundred kilometers, migrating northward in the morning and southward in the afternoon, were observed. The new technique effectively tracks the Es‐height variation over time, which had proved difficult to reproduce in earlier tempts at 3‐D Es reconstructions.

Global‐Scale Ionospheric Tomography During the March 17, 2015 Geomagnetic Storm

AbstractThe correct representation of global‐scale electron density is crucial for monitoring and exploring the space weather. This study investigates whether the ground‐based Global Navigation Satellite System (GNSS) tomography can be used to reflect the global spatial and temporal responses of the ionosphere under storm conditions. A global tomography of the ionosphere electron density is constructed based on data from over 2,700 GNSS stations. In comparison to previous techniques, advances are made in spatial and temporal resolution, and in the assessment of results. To demonstrate the capabilities of the approach, the developed method is applied to the March 17, 2015 geomagnetic storm. The tomographic reconstructions show good agreement with electron density observations from worldwide ionosondes, Millstone Hill incoherent scatter radar and in‐situ measurements from satellite missions. Also, the results show that the tomographic technique is capable of reproducing plasma variabilities during geomagnetically disturbed periods including features such as equatorial ionization anomaly enhancements and depletion. Validation results of this brief study period show that the accuracy of our tomography is better than the Neustrelitz Electron Density Model, which is the model used as background, and physics‐based thermosphere‐ionosphere‐electrodynamics general circulation model. The results show that our tomography approach allows us to specify the global electron density from ground to ∼900 km accurately. Given the demonstrated quality, this global electron density reconstruction has potential for improving applications such as assessment of the effects of the electron density on radio signals, GNSS positioning, computation of ray tracing for radio‐signal transmission, and space weather monitoring.

Detecting Seismo‐Ionospheric Anomalies Possibly Associated With the 2019 Ridgecrest (California) Earthquakes by GNSS, CSES, and Swarm Observations

AbstractThe association between earthquakes and ionospheric precursors has been widely reported in previous studies. The Swarm satellite constellation, the China Seismo‐Electromagnetic Satellite (CSES), and the ground‐based global navigation satellite system (GNSS) observations provide us new opportunities to detect ionospheric precursors to earthquakes for a better understanding of the seismic‐ionospheric coupling. In California, two shallow earthquakes with magnitudes of 6.5 and 7.1, the two higher magnitude events of the Ridgecrest earthquakes sequence, occurred on July 4 and July 6, 2019, respectively. This study adopted total electron content (TEC) data from the GNSS, and electron density (Ne), and electron temperature (Te) data from the CSES and Swarm to study the possible ionospheric perturbations before the Ridgecrest earthquakes. A sliding interquartile range method was used to analyze the TEC data, and a spatial differential method and a revisit trajectory comparison were used to analyze the Ne and Te data. While excluding the influences of various factors including solar and geomagnetic activities, planetary waves, tropospheric convection, and medium‐scale traveling ionospheric disturbances, the cross‐validation of the multi‐source data found that anomalies detected on June 20, June 22, and June 26–29, 2019 could be regarded as precursors of the Ridgecrest earthquakes. A spatial distribution analysis showed that the pre‐earthquake anomalies offset the equatorial direction. TEC anomalies were all positively correlated with the Ne anomalies, with most of them were positive. The Ne and Te anomalies were both positively and negatively correlated with mostly negative, while showing a strong linear negative correlation at nighttime.

Nighttime Ionosphere Perturbed by the Annular Solar Eclipse on June 21, 2020

AbstractThis study analyzed the total electron content (TEC) observed by 337 ground‐based Global Navigation Satellite System (GNSS) receivers over China and South Asia, the critical frequency of the ionospheric F2 layer (foF2) and its height (hmF2) recorded by the ionosonde at Puer (22.7°N, 101.05°E, 72% obscuration), Yunnan province, during and after the annular solar eclipse on June 21, 2020. The observations show that the eclipse induced not only a daytime major depression but also nighttime perturbations in the ionosphere. The TEC perturbed intermittently from noon to midnight between 85° and 125°E on the eclipse day. The positive and negative changes of the prereversal enhancement respectively occur in the nearby longitudinal sectors of 100°and 115°E. The TEC perturbations behave as a terminator wave that propagates in a northwestward direction after sunset.

Analysis of the Precipitable Water Vapor Observation in Yunnan–Guizhou Plateau during the Convective Weather System in Summer

The ERA5 reanalysis dataset of the European Center for Medium-Range Weather Forecasts (ECMWF) in the summers from 2015 to 2020 was used to compare and analyze the features of the precipitable water vapor (PWV) observed by six ground-based Global Navigation Satellite System (GNSS) meteorology (GNSS/MET) stations in the Yunnan–Guizhou Plateau. The correlation coefficients of the two datasets ranged between 0.804 and 0.878, the standard deviations ranged between 4.686 and 7.338 mm, and the monthly average deviations ranged between −4.153 and 9.459 mm, which increased with the altitude of the station. Matching the quality-controlled ground precipitation data with the PWV in time and space revealed that most precipitation occurred when the PWV was between 30 and 65 mm and roughly met the normal distribution. We used the vertical integral of divergence of moisture flux (∇p) and S-band Doppler radar networking products combined with the PWV to study the convergence and divergence process and the water vapor delivery conditions during the deep convective weather process from August 24 to 26, 2020, which can be used to analyze the real-time observation capability and continuity of PWV in small-scale and mesoscale weather processes. Furthermore, the 1 h precipitation and the cloud top temperature (ctt) data at the same site were used to demonstrate the effect of PWV on the transit of convective weather systems from different time–space scales.

Study of the Spatiotemporal Characteristics of the Equatorial Ionization Anomaly Using Shipborne Multi-GNSS Data: A Case Analysis (120–150°E, Western Pacific Ocean, 2014–2015)

Ground-based GNSS (Global Navigation Satellite System) reference stations lack the capacity to provide data for ocean regions with sufficient spatial-temporal resolution, limiting the detailed study of the equatorial ionization anomaly (EIA). Thus, this study collected kinematic multi-GNSS data on the ionospheric Total Electron Content (TEC) during two research cruises across the equator in the Western Pacific Ocean in 2014 (31 October–8 November) and 2015 (29 March–6 April). The purpose of the study was to use sufficient spatial–temporal resolution data to conduct a detailed analysis of the diurnal variation of the equatorial ionization anomaly in different seasons. The two-year data collected were used to draw the following conclusions. During the test in 2014, the EIA phenomenon in the Northern and Southern Hemispheres was relatively obvious. The maximum values occurred at local time (LT) 15:00 (~136TECu) and LT22:00 (~107TECu) in the Northern Hemisphere and at LT14:00 (100TECu) and LT22:00 (80TECu) in the Southern Hemisphere. During the test in 2015, the EIA in the Southern Hemisphere reached its maximum level at LT14:00 (~115TECu) and LT20:00 (~85TECu). However, the EIA phenomenon in the Northern Hemisphere was weakened, and a maximum value occurred only at LT 15:00 (~85TECu). The intensity contrast was reversed. The EIA phenomenon manifests a strong hemisphere asymmetry in this region.

Integrated Water Vapor during Rain and Rain-Free Conditions above the Swiss Plateau

Water vapor column density, or vertically-integrated water vapor (IWV), is monitored by ground-based microwave radiometers (MWR) and ground-based receivers of the Global Navigation Satellite System (GNSS). For rain periods, the retrieval of IWV from GNSS Zenith Wet Delay (ZWD) neglects the atmospheric propagation delay of the GNSS signal by rain droplets. Similarly, it is difficult for ground-based dual-frequency single-polarisation microwave radiometers to separate the microwave emission of water vapor and cloud droplets from the rather strong microwave emission of rain. For ground-based microwave radiometry at Bern (Switzerland), we take the approach that IWV during rain is derived from linearly interpolated opacities before and after the rain period. The intermittent rain periods often appear as spikes in the time series of integrated liquid water (ILW) and are indicated by ILW ≥ 0.4 mm. In the present study, we assume that IWV measurements from radiosondes are not affected by rain. We intercompare the climatologies of IWV(rain), IWV(no rain), and IWV(all) obtained by radiosonde, ground-based GNSS atmosphere sounding, ground-based MWR, and ECMWF reanalysis (ERA5) at Payerne and Bern in Switzerland. In all seasons, IWV(rain) is 3.75 to 5.94 mm greater than IWV(no rain). The mean IWV differences between GNSS and radiosonde at Payerne are less than 0.26 mm. The datasets at Payerne show a better agreement than the datasets at Bern. However, the MWR at Bern agrees with the radiosonde at Payerne within 0.41 mm for IWV(rain) and 0.02 mm for IWV(no rain). Using the GNSS and rain gauge measurements at Payerne, we find that IWV(rain) increases with increase of the precipitation rate during summer as well as during winter. IWV(rain) above the Swiss Plateau is quite well estimated by GNSS and MWR though the standard retrievals are limited or hampered during rain periods.