Variability and climatology of precipitable water vapor from 12-year GPS observations in Taiwan

Because of global warming, global sea levels have risen, the frequency of drought in Taiwan is much more frequent in winter and spring, and rainfall tends to concentrate in summer. The probability of disaster-type weather has also increased significantly. Estimating precipitable water vapor (PWV) through GPS signals, related studies and analyses of weather conditions, and the effective use of meteorological forecasts have been valued by many meteorological research organizations and officials. In this study, PWV data from 2006 to 2017 and rainfall data were used for long-term harmonic analysis. PWV data calculated by ECMWF (ECMWF-PWV) and PWV data calculated by GPS (GPS-PWV) were subjected to regression analysis to verify the reliability of the GPS-PWV data. The research results show that GPS-PWV and ECMWF-PWV have extremely high correlations; however, the climatic characteristics of some regions and the high spatial resolution of GPS-PWV are able to accurately calculate the high topographic relief of small areas. It is judged that the GPS-PWV is more accurate than the ECMWF-PWV. It is worth noting that the PWV trend of the regions during the 6-year-before period has not changed very much, but the rainfall trend has changed obviously. Except for the eastern region, most of the regions show a decreasing trend year by year. More long-term observations are still needed to prove whether this phenomenon relates to global warming. Long-term rainfall analysis showed that the topography blocked water vapor to the western, southern, and mountainous regions, making them distinctly wet or dry. The harmonic curve showed great consistency with the peaks of PWV and rainfall. However, in the northern and eastern parts of the windward side, the time when maximum rainfall occurred each year may be one month later than the time when the maximum PWV value occurred each year. The reason for this difference is likely to be a decrease in the number of autumn typhoons, resulting in a nearly one-month difference in PWV peaks and rainfall peaks. Finally, we analyzed the linear trend of GPS-PWV and temperature for all regions in Taiwan, and found that annual increasing rate of GPS-PWV and temperature of all regions are within 0.4–0.5 mm/year and 0.04–0.11 C°/year, respectively.     

Retrieval of the change of precipitable water vapor with zenith tropospheric delay in the Chinese mainland

As a preliminary step for assessing the impact of global positioning system (GPS) refractive delay data in numerical weather prediction (NWP) models, the GPS zenith tropospheric delays (ZTD) are analyzed from 28 permanent GPS sites in the Chinese mainland. The objectives are to estimate the GPS ZTD and their variability in this area. The differences between radiosonde precipitable water vapor (PWV) and GPS PWV have a standard deviation of 4 mm in delay, a bias of 0.24 mm in delay, and a correlation coefficient of 0.94. The correlation between GPS ZTD and radiosonde PWV amounts to 0.89, indicating that the variety of tropospheric zenith delay can reflect the change of precipitable water vapor. The good agreement also guarantees that the information provided by GPS will benefit the NWP models. The time series of GPS ZTD, which were derived continuously from 2002 to 2004, are used to analyze the change of precipitable water vapor in Chinese mainland. It shows that the general trend of GPS ZTD is diminishing from the south-east coastland to the north-west inland, which is in accordance with the distribution of Chinese annual amount of rainfall. The temporal distribution of GPS ZTD in the Chinese mainland is that the GPS ZTD reaches maximum in summer, and it reaches minimum in winter. The long term differences between the observational data sources require further study before GPS derived data become useful for climate studies.     

GPS sensing of precipitable water vapour during the March 2010 Melbourne storm

The March 2010 Melbourne storm is used as a case study to examine the potential of using Global Positioning System (GPS) observations for studying the precipitable water vapour (PWV) field. The Victorian statewide GPS infrastructure network, i.e. GPSnet, was used in this study. GPSnet is currently the only statewide and densest GPS infrastructure network in Australia, which provides an excellent opportunity to examine the distribution of water vapour as the severe weather system passed over the state. Data from 15 GPSnet stations were processed over a one-week period, i.e. a few days prior to and after the storm passage, during which the course of the storm extended from the west to the southeast corner of the state. In addition, data from two radiosonde sites of the Australian Bureau of Meteorology Upper Air Network were used to compare and validate the GPS derived PWV measurements. The findings demonstrate that there is strong spatial and temporal correlation between variations of the ground-based GPS-PWV estimates and the passage of the storm over the state. This is encouraging as the ground-based GPS water vapour sensing technique can be considered as a supplemental meteorological sensor in studying severe weather events. The advantage of using ground-based GPS-PWV technique is that it is capable of providing continuous observation of the storm passage with high temporal resolution. The spatial resolution of the distribution of water vapour is dependent on the geographical location and density of the GPS stations.     

Analysis of precipitable water vapor from GPS measurements in Chengdu region: Distribution and evolution characteristics in autumn

The rainfall process of Chengdu region in autumn has obvious regional features. Especially, the night-time rain rate of this region in this season is very high in China. Studying the spatial distribution and temporal variation of regional atmospheric precipitable water vapor (PWV) is important for our understanding of water vapor related processes, such as rainfall, evaporation, convective activity, among others in this area. Since GPS detection technology has the unique characteristics, such as all-weather, high accuracy, high spatial and temporal resolution as well as low cost, tracking and monitoring techniques on water vapor has achieved rapid developments in recent years. With GPS–PWV data at 30-min interval gathered from six GPS observational stations in Chengdu region in two autumns (September 2007–December 2007 and September 2008–December 2008), it is revealed that negative correlations exist between seasonally averaged value of GPS–PWV as well as its variation amplitude and local terrain altitude. The variation of PWV in the upper atmosphere of this region results from the water vapor variation from surface to 850 hPa. With the help of Fast Fourier Transform (FFT), it is found that the autumn PWV in Chengdu region has a multi-scale feature, which includes a seasonal cycle, 22.5 days period (quasi-tri-weekly oscillation). The variation of the GPS–PWV is related to periodical change in the transmitting of the water vapor caused by zonal and meridional wind strengths’ change and to the East Asian monsoon system. According to seasonal variation characteristics, we concluded that the middle October is the critical turning point in PWV content. On a shorter time scale, the relationship between autumn PWV and ground meteorological elements was obtained using the composite analysis approach.     

Displacement monitoring performance of relative positioning and Precise Point Positioning (PPP) methods using simulation apparatus

Besides the classical geodetic methods, GPS (Global Positioning System) based positioning methods are widely used for monitoring crustal, structural, ground etc., deformations in recent years. Currently, two main GPS positioning methods are used: Relative and Precise Point Positioning (PPP) methods. It is crucial to know which amount of displacement can be detected with these two methods in order to inform their usability according to the types of deformation. Therefore, this study conducted to investigate horizontal and vertical displacement monitoring performance and capability of determining the direction of displacements of both methods using a developed displacement simulator apparatus. For this purpose, 20 simulated displacement tests were handled. Besides the 24?h data sets, 12?h, 8?h, 4?h and 2?h subsets were considered to examine the influence of short time spans. Each data sets were processed using GAMIT/GLOBK and GIPSY/OASIS scientific software for relative and PPP applications respectively and derived displacements were compared to the simulated (true) displacements. Then statistical significance test was applied. Results of the experiment show that using 24?h data sets, relative method can determine up to 6.0?mm horizontal displacement and 12.3?mm vertical displacement, while PPP method can detect 8.1?mm and 19.2?mm displacements in horizontal and vertical directions respectively. Minimum detected displacements are found to grow larger as time spans are shortened.     

Exploration and analysis of the factors influencing GNSS PWV for nowcasting applications

Precipitable water vapor (PWV) can be assimilated into a numerical weather model (NWM) to improve the prediction accuracy of numerical weather prediction. In this study, taking GNSS data for the Beijing Fangshan station (BJFS) as an example, based on the method of Pearson correlation coefficient combined with quantitative analysis, GNSS datasets are used to study the relationships between GNSS-derived PWV (GNSS PWV__Met) and its influencing factors, including the internal influencing factors zenith troposphere delay (ZTD), zenith hydrostatic delay (ZHD), zenith wet delay (ZWD), and surface temperature (T_s), and the external influencing factor haze (mainly PM_2.5). Firstly, based on the strong correlation between PWV__Met and ZTD hourly sequences from the International GNSS Service Network’s BJFS station for DOYS 182–212, 2015, the results of experiment prove that the reliability of GNSS ZTD is used to forecast PWV_Met in short-term forecasting. Secondly, based on hourly data of BJFS in 2016, the correlation between PWV__Met and ZTD, ZWD, ZHD, pressure (P) and T_s is analyzed, and then, with the rate of ZTD variation as the main factor, ZTD variation as auxiliary factor, the prediction success rate is 88.24% from hourly data of precipitation event for DOYs 183–213 in Beijing. The experiment indicates that ZTD can help forecast short-term precipitation. Thirdly, based on data from three hazy periods with relatively stable weather conditions, no heavy rainfall, and relatively continuous data in the past three years, the correlation between GNSS PWV_Met/ZTD and PM_2.5 hourly series is analyzed. The results of the experiments suggests that GNSS ZTD should be considered to assist in haze monitoring. So in the absence of radiosonde stations and meteorological elements, ZTDs on retrieval of GNSS stations have more application value in short-term forecast.     

A comparative and numerical study of effects of gravity waves in small miss-distance and miss-time GPS radio occultation temperature profiles

The Global Positioning System (GPS) Radio Occultation (RO) technique has global coverage and is capable of generating high vertical resolution temperature profiles of the upper troposphere and lower stratosphere with sub-Kelvin accuracy and long-term stability, regardless of weather conditions. In this work, we take advantage of the anomalously high density of occultation events at the eastern side of the highest Andes Mountains during the initial mission months of COSMIC (Constellation Observing System for Meteorology, Ionosphere and Climate). This region is well-known for its high wave activity. We choose to study two pairs of GPS RO, both containing two occultations that occurred close in time and space. One pair shows significant differences between both temperature profiles. Numerical simulations with a mesoscale model were performed, in order to understand this discrepancy. It is attributed to the presence of a horizontal inhomogeneous structure caused by gravity waves.     

Estimation of precipitable water vapour from GPS measurements in Argentina: Validation and qualitative analysis of results

This paper presents PWV estimates from GPS data computed at four continuously operated GPS stations in Argentina established at Buenos Aires, Córdoba, Rosario and Salta over a 1 year period (2006–2007). The objective is to analyze the behaviour of the GPS PWV estimation using mean tropospheric temperature (_Tm$T_m$) values from the Bevis model, Sapucci model and obtained by a numerical integration of variables provided by the operational analysis of the National Centre of Environmental Prediction (NCEP). The results are validated using PWV values from nearest radio soundings. Moreover, a comparison between PWV values determined from microwave sensors deployed on the NOAA-18 satellite and PWV from GPS observations is also presented.     

Comparison of satellite orbit ephemerides for use in GPS meteorology

This paper discusses GPS (Global Position System) meteorology. The research presented is based on a comparison of values of precipitable water vapour PWV, based on GPS measurements using final and predicted ephemerides of satellite orbits. We analysed recent year’s improvement in predicting ephemerides. We compared the data outputs from a radiosonde using GPS receiver measurements directly from the meteorological station from which the radiosondes were launched. The results indicate a high quality of the predicted ephemerides. This finding makes predicted ephemerides highly usable for near real-time estimations of PWV. To use PWV in meteorological forecast applications, this high speed of PWV values supply is necessary.     

Influence of different data fusion methods on the accuracy of three-dimensional displacements field

Combining multi-source data can improve the accuracy and the spatial resolution of the three-dimensional (3-D) displacements field. How to effectively integrate multi-source data to obtain high-precision and high spatial resolution 3-D displacements field is worthy of further study. The stochastic model and fusion model of integrating multi-source data affect the accuracy of data fusion. In this paper, based on the least squares method, the effects of different stochastic models and data fusion models on the 3-D displacements field’s accuracy are studied. The optimal method for estimating large-scale 3-D displacements field from integrated InSAR, leveling and GPS measurements is obtained. Then we realize the integrating InSAR, leveling and GPS measurements to obtain the high-precision 3-D displacements velocity field in Tianjin (China) from 2016 to 2018. The results are validated with GPS measurements at 6 independent stations, with the root mean squares (RMS) residuals of the discrepancies being 2.39 mm/yr, 2.54 mm/yr and 2.83 mm/yr in eastern, northern and vertical directions, respectively. By comparing different stochastic models, the 3-D displacements field obtained from multi-source data is optimized by the variance component estimation-least squares method, which is better than weighted least squares (WLS) method. By comparing different data fusion models, the accuracy of the horizontal displacements velocity is better than that of interpolated GPS results. The horizontal displacements component has a great influence on the vertical displacements velocity accuracy in the process of acquiring the 3-D displacements velocity by integrating InSAR, GPS and leveling measurements. This study provides a reference method for integrating multi-source data to obtain 3-D displacements field. This method effectively utilizes the advantages of GPS, InSAR and leveling measurements, and extends the limitations of single technical in describing surface-time scale applications. The 3-D displacements information with a large spatial scale and high spatial resolution provide a reliable data basis for studying the crustal movement and its dynamic mechanism.     

Sea level trend over Malaysian seas from multi-mission satellite altimetry and vertical land motion corrected tidal data

Rise in sea levels is one of the disastrous effects of climate change. A relatively small increase in sea level could affect natural coastal systems. In a study of long-term changes in sea level and measurements of postglacial rebound, monitoring vertical land motion (VLM) is of crucial interest. This study presents an approach to estimate precise sea level trends based on a combination of multi-sensor techniques in the Malaysian region over 19?years. In this study, satellite altimeters (SALT) were used to derive absolute sea levels (ASLs). Tide gauge (TG) stations along the coast of Malaysia were utilised to derive the rate of relative sea levels using sea level changes and VLMs. To obtain ASL at TGs, VLM at these stations were computed using Global Positioning System (GPS), Persistent Scatterer Interferometric Synthetic Aperture Radar (PS InSAR), and SALT minus TG. The computed VLMs mostly show similarities in signs rather than magnitude. The findings from the multi-sensor techniques showed that regional sea level trends ranged from 2.65?±?0.86?mm/yr to 6.03?±?0.79?mm/yr for chosen sub-areas, with an overall mean of 4.47?±?0.71?mm/yr and overall subsidence. This information is expected to be valuable for a wide variety of climatic applications and for studying environmental issues related to flooding and global warming in Malaysia.     

Time-series analysis of GPS measurements for long-span bridge movements using wavelet and model prediction techniques

This study aims at assessing the safety behavior of the Incheon long-span bridge using high rate (10?Hz) geodetic monitoring global positioning system (GPS). The time series of wavelet spectrum analysis is utilized to assess the dynamic behavior of the bridge. The coefficients and model errors of the time series autoregressive-moving average (ARMA) model are used to evaluate the movement performances of the bridge. The results show that: (i) the accuracy of GPS measurements to extract the dynamic behavior of the bridge is 97.27% when compared with the design results. (ii) the behavior of the bridge is within the safety limits of the bridge design with minimum observed changes for the historical GPS measurements in time and frequency domains, the mean deflection of bridge deck is 8.26?mm and frequency changes of bridge is 0.004?Hz compared with the design results. (iii) the time series analysis of the wavelet spectrum and ARMA model coefficients can be used to detect the significant frequency changes and study the rigidity of the bridge performance, respectively; and the both methods are found to be suitable techniques to estimate the performance changes of the GPS measurements in the time and frequency domains during the monitoring time period.     

A multi-spectral spatial convolution approach of rainfall forecasting using weather satellite imagery

Flood forecasting has long been a major topic of hydrologic research. Recent events and studies indicate that the success of flood forecasting in Taiwan depends heavily on the accuracy of real-time rainfall forecasting. In this study, we demonstrate a multi-spectral spatial convolution approach for real-time rainfall forecasting using geostationary weather satellite images. The approach incorporates cloud-top temperatures of three infrared channels in a spatial convolution context. It not only characterizes the input–output relationship between cloud-top temperature and rainfall at the ground level, but also is more consistent with physical and remote sensing principles than single-pixel matches. Point rainfall measurements at raingauge sites are up-scaled to pixel-average-rainfall by block kriging, then related to multi-spectral cloud-top temperatures derived from Geostationary Meteorological Satellite images by spatial convolution. The kernel function of the multispectral spatial convolution equation is solved by the least squares method. Through a cross-validation procedure, we demonstrate that the proposed approach is capable of achieving high accuracy for 1- to 3-h-lead pixel-average-rainfall forecasting.     

High temporal resolution global PWV dataset of 2005–2016 by using a neural network approach to determine the mean temperature of the atmosphere

Atmospheric water vapour plays an important role in phenomena related to the global hydrologic cycle and climate change. However, the rapid temporal–spatial variation in global tropospheric water vapour has not been well investigated due to a lack of long-term, high-temporal-resolution precipitable water vapour (PWV). Accordingly, this study generates an hourly PWV dataset for 272 ground-based International Global Navigation Satellite System (GNSS) Service (IGS) stations over the period of 2005–2016 using the zenith troposphere delay (ZTD) derived from global-scale GNSS observation. The root mean square (RMS) of the hourly ZTD obtained from the IGS tropospheric product is approximately 4 mm. A fifth-generation reanalysis dataset of the European Centre for Medium-range Weather Forecasting (ECMWF ERA5) is used to obtain hourly surface temperature (T) and pressure (P), which are first validated with GNSS synoptic station data and radiosonde data, respectively. Then, T and P are used to calculate the water vapour-weighted atmospheric mean temperature (Tm) and zenith hydrostatic delay (ZHD), respectively. T and P at the GNSS stations are obtained via an interpolation in the horizontal and vertical directions using the grid-based ERA5 reanalysis dataset. Here, Tm is calculated using a neural network model, whereas ZHD is obtained using an empirical Saastamoinen model. The RMS values of T and P at the collocated 693 radiosonde stations are 1.6 K and 3.1 hPa, respectively. Therefore, the theoretical error of PWV caused by the errors in ZTD, T and P is on the order of approximately 2.1 mm. A practical comparison experiment is performed using 97 collocated radiosonde stations and 23 GNSS stations equipped with meteorological sensors. The RMS and bias of the hourly PWV dataset are 2.87/?0.16 and 2.45/0.55 mm, respectively, when compared with radiosonde and GNSS stations equipped with meteorological sensors. Additionally, preliminary analysis of the hourly PWV dataset during the EI Niño event of 2014–2016 further indicates the capability of monitoring the daily changes in atmospheric water vapour. This finding is interesting and significant for further climate research.     

Analysis of precipitable water vapour characteristics from GNSS measurements during the snow season in Liaoning Province,China

Global Navigation Satellite System (GNSS) remote sensing precipitable water vapour (PWV) data from November 2015 to March 2019 were combined with snowfall observation data and used to analyse PWV characteristics in Liaoning Province during the snow season (from November to March the following year) and their relationship with snowfall. The potential of using GNSS for PWV measurements was demonstrated using sounding data with a correlation coefficient higher than 0.9 and a mean bias error lower than 0.5 mm. According to the GNSS PWV data gathered at 30-min intervals from 68 GNSS stations in Liaoning during the snow season, the monthly PWV average was highest in November and lowest in January. Negative correlations were found between PWV and altitude. Most of the water vapour was concentrated in the low layer of the atmosphere, and the contribution of this vapour to the PWV was higher during the snow season than in summer. A total of 43 snow cases were identified using the snowfall records from 53 GNSS stations, and the characteristics of PWV during these snowfalls were analysed. An increase in PWV was observed before snowfall events. Moreover, the influence of synoptic systems and air mass origins on PWV was analysed based on National Centers for Environmental Prediction (NCEP) reanalysis data and the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The results show that the water vapour condition was better when the synoptic systems or air masses came from areas south of Liaoning.     

Obtaining more accurate electron density profiles from bending angle with GPS occultation data: FORMOSAT-3/COSMIC constellation

Since 1995, with the first GPS occultation mission on board Low Earth Orbiter (LEO) GPS/MET, inversion techniques were being applied to GPS occultation data to retrieve accurate worldwide distributed refractivity profiles, i.e. electron density profiles in the case of Ionosphere. Important points to guarantee the accuracy is to take into account horizontal gradients and topside electron content above the LEO orbit. This allows improving the accuracy from 20% to 50%, depending on the conditions, latitude and epoch regarding to Solar cycle as reported in previous works.     

DORIS and GPS monitoring of the Gavdos calibration site in Crete

Due to its specific geographical location as well as its geodetic equipment (DORIS, GNSS, microwave transponder and tide gauges), the Gavdos station in Crete, Greece is one of the very few sites around the world used for satellite altimetry calibration. To investigate the quality of the Gavdos geodetic coordinates and velocities, we analyzed and compared here DORIS and GPS-derived results obtained during several years of observations. The DORIS solution is the latest ignwd11 solution at IGN, expressed in ITRF2008, while the GPS solution was obtained using the GAMIT software package. Current results show that 1–2 mm/yr agreement can be obtained for 3-D velocity, showing a good agreement with current geophysical models. In particular, the agreement obtained for the vertical velocity is around 0.3–0.4 mm/yr, depending on the terrestrial reference frame. As a by-product of these geodetic GPS and DORIS results, Zenith Tropospheric Delays (ZTDs) estimations were also compared in 2010 between these two techniques, and compared to ECMWF values, showing a 6.6 mm agreement in dispersion without any significant difference between GPS and DORIS (with a 97.6% correlation), but with a 13–14 mm agreement in dispersion when comparing to ECMWF model (with only about 90% correlation for both techniques). These tropospheric delay estimations could also provide an external calibration of the tropospheric correction used for the geophysical data of satellite altimetry missions.     

The impact of microwave absorber and radome geometries on GNSS measurements of station coordinates and atmospheric water vapour

We have used microwave absorbing material in different geometries around ground-based Global Navigation Satellite System (GNSS) antennas in order to mitigate multipath effects on the estimates of station coordinates and atmospheric water vapour. The influence of a hemispheric radome – of the same type as in the Swedish GPS network SWEPOS – was also investigated. Two GNSS stations at the Onsala Space Observatory were used forming a 12 m baseline. GPS data from October 2008 to November 2009 were analyzed by the GIPSY/OASIS II software using the Precise Point Positioning (PPP) processing strategy for five different elevation cutoff angles from 5° to 25°. We found that the use of the absorbing material decreases the offset in the estimated vertical component of the baseline from ∼27 mm to ∼4 mm when the elevation cutoff angle varies from 5° to 20°. The horizontal components are much less affected. The corresponding offset in the estimates of the atmospheric Integrated Water Vapour (IWV) decreases from ∼1.6 kg/m² to ∼0.3 kg/m². Changes less than 5 mm in the offsets in the vertical component of the baseline are seen for all five elevation cutoff angle solutions when the antenna was covered by a hemispheric radome. Using the radome affects the IWV estimates less than 0.4 kg/m² for all different solutions. IWV comparisons between a Water Vapour Radiometer (WVR) and the GPS data give consistent results.     

A comprehensive study of the 2016 Mw 6.0 Italy earthquake based on high-rate (10 Hz) GPS data

The 2016 M_w 6.0 Italy earthquake is successfully recorded by the near-field 10?Hz GPS and 200?Hz Strong Motion (SM) stations, providing valuable data for this study. A comprehensive study of this earthquake is carried out based on GPS data, which contains coseismic deformations analysis, noise analysis, seismic wave picking, and magnitude determination. The noise of most GPS-derived displacement waveforms can be described as a combination of white noise, flicker noise, and random walk noise after the earthquake occurrence, and the spectral indices vary significantly for most stations, implying that the seismic signals have affected the noise characteristic of GPS-derived displacement waveforms. S-transform is employed to assess the GPS capability to detect the seismic arrival time. The SM station AMT and the GPS station AMAT are in good agreement in seismic wave picking, and the difference is only 1.2?s in the north component, suggesting that the outcome of seismic wave picking using GPS data is reliable. Then, a classic empirical formula is employed to determine the moment magnitude. A robust moment magnitude (Mw 5.90) can be estimated by the nine GPS stations with about 23.9?s. If four GPS stations near the epicenter is chosen to determine the magnitude, it only take 13.0?s to retrieve a reliable preliminary (Mw 5.82) magnitude, which is 5.4?s ahead of nine stations. In addition, Cross Wavelet Transform (XWT) is adopted to measuring the correlation and phase relationship between GPS and SM records. The result of XWT analysis indicates 10?Hz GPS is capable of capturing reliable and accurate coseismic dynamic deformations, as evidenced by the XWT-based semblance being close to 1 between GPS and SM records. The above results confirm the capability of 10?Hz GPS to capture coseismic dynamic deformations, detect seismic arrival time, and determine earthquake magnitude. Moreover, rapid magnitude determination based on 10?Hz GPS data can be regarded as an important supplement to Earthquake Early Warning (EEW).