Slant Wet Delay Research Articles

Tomographic Reconstruction of Atmospheric Water Vapor Profiles Using Multi-GNSS Observations

Continuously operating reference stations (CORS) provide augmentation services for the highly accurate, cm-level GNSS positioning needs of land surveyors, agriculture, and even autonomous vehicles. These stations have accurate coordinates, thus they can be used to estimate the signal delay caused by the neutral atmosphere including the atmospheric water vapor. The estimated zenith wet delay (ZWD) is in a close correlation with the integrated water vapor in the atmospheric column. Since a ground station tracks several satellites at every epoch, one could also estimate the slant tropospheric delays, which can provide information on the spatial distribution of the atmospheric water vapor, too. This paper introduces a near real-time multi-GNSS processing approach to estimate slant wet tropospheric delays and a coupled tomographic reconstruction technique to estimate the 3D wet refractivity model that can be assimilated in numerical weather models. The estimated zenith tropospheric delays (ZTDs) and tropospheric gradients are used to restore the slant wet delays (SWD) affecting the observed satellite-receiver range. The SWDs are used as input for a tomographic reconstruction algorithm providing the wet refractivities in a pre-defined voxel model. The derived refractivity profiles have been validated with radiosonde observations. The results show that our GNSS tomography approach could reconstruct the refractivities with the uncertainty of 10 ppm below 3 km of altitude and of 0.3 ppm at the altitude of 10 km in terms of standard deviation.

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.

An Improved Tropospheric Tomographic Model Based on Artificial Neural Network

Global navigation satellite systems (GNSS) tropospheric tomography can be used to build a three-dimensional water vapor field. In traditional tomography, the signals crossing from the four sides of the tomographic region are not utilized. To make the best use of these valuable side-crossing signals, an improved tomographic model based on back propagation artificial neural network (BP-ANN) is proposed. In the new tomographic model, the inside part of the slant wet delay (SWD) of the side-crossing signal is divided into two sections: the isotropic and anisotropic components. The former is estimated by the zenith wet delay multiplied by the mapping function multiplied by an isotropic scale factor using a BP-ANN model, and the latter is estimated by horizontal gradients of the SWD multiplied by an anisotropic scale factor using an empirical model. The new tomographic model is experimentally evaluated using the HK CORS network measurements for the period of 21 days from 1 to 21 August 2019. Statistical results show that the root mean square error (RMSE) of slant water vapor reconstructed from the improved model is reduced to 1.35 from 2.85 mm of the traditional model. Compared with the traditional/height factor models, the percentages of the reduction in the RMSE of the tomographic result derived from the new model are 16%/9% and 22%/16%, respectively, using radiosonde and ERA5 data as references. These results suggest a good performance of the new model for GNSS tropospheric tomography.

GPS-Derived Slant Water Vapor for Cloud Monitoring in Singapore

This paper presents a GPS-derived slant water vapor technique for cloud monitoring in Singapore. The normalized slant wet delay (SWD) and slant water vapor (SWV) are introduced. The suitability of the normalized SWV over SWV for cloud monitoring is demonstrated, as it is not very sensitive to the satellite elevation angle. For better illustration and representation of the spatial distribution of the normalized SWV, the skyplot is discretized into different cells based on the azimuth and elevation angles to produce the spatial plot. The spatial plots are analyzed for cloud monitoring and compared alongside the sky images. The results show that the spatial plots of normalized SWV are generally consistent with the cloud formation observed in the sky images, hence demonstrating their usefulness for cloud monitoring. The probability distribution of the normalized SWV associated with cloudy and clear sky conditions is also analyzed, which shows that the mean values of normalized SWV associated with the former are higher. Finally, the time series of the normalized SWV is explored in relation to the solar irradiance. It is shown that the time series and spatial plots of normalized SWV are also consistent with the ratio of clear sky to measured irradiance.

Assessment of the Accuracy of the Saastamoinen Model and VMF1/VMF3 Mapping Functions with Respect to Ray-Tracing from Radiosonde Data in the Framework of GNSS Meteorology

In this paper, we assess, in the framework of Global Navigation Satellite System (GNSS) meteorology, the accuracy of GNSS propagation delays corresponding to the Saastamoinen zenith hydrostatic delay (ZHD) model and the Vienna Mapping function VMF1/VMF3 (hydrostatic and wet), with reference to radiosonde ray-tracing delays over a three-year period on 28 globally distributed sites. The results show that the Saastamoinen ZHD estimates have a mean root mean square (RMS) error of 1.7 mm with respect to the radiosonde. We also detected some seasonal signatures in these Saastamoinen ZHD estimates. This indicates that the Saastamoinen model, based on the hydrostatic assumption and the ground pressure, is insufficient to capture the full variability of the ZHD estimates over time with the accuracy needed for GNSS meteorology. Furthermore, we found that VMF3 slant hydrostatic delay (SHD) estimates outperform the corresponding VMF1 SHD estimates (equivalent SHD RMS error of 4.8 mm for VMF3 versus 7.1 mm for VMF1 at 5° elevation angle), with respect to the radiosonde SHD estimates. Unexpectedly, the situation is opposite for the VMF3 slant wet delay (SWD) estimates compared to VMF1 SWD estimates (equivalent SWD RMS error of 11.4 mm for VMF3 versus 7.0 mm for VMF1 at 5° elevation angle). Our general conclusion is that the joint approach using ZHD models and mapping functions must be revisited, at least in the framework of GNSS meteorology.

Improved performance of ERA5 in global tropospheric delay retrieval

Reanalysis products have played an important role in space geodetic tropospheric delay retrieval and modeling in the past two decades. As the release of the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA5), with improved temporal-spatial resolutions compared to its predecessor, ECMWF Re-Analysis Interim (ERAI), the performance of ERA5 in tropospheric delay retrieval was comprehensively investigated in this study. Hourly tropospheric delays in zenith and 5° elevation directions at 312 International GNSS Service (IGS) stations covering the year of 2018 from ERA5 and ERAI were ray-traced. Taking IGS Zenith Total Delay (ZTD) as reference, the reanalysis-derived ZTDs were evaluated in annual, seasonal and diurnal scales, and superior performances of ERA5 ZTD to ERAI ZTD in all scales were revealed, with bias, Root Mean Square (RMS) and standard deviation of 1.6, 11.0 and 10.1 mm for ERA5, and of 3.1, 13.8 and 12.6 mm for ERAI, respectively. Due to the absence of reliable Slant Path Delay (SPD) references, the SPDs as well as the mapping factors derived from ERA5 and ERAI were directly compared and converted to equivalent station height errors at these stations. Obvious differences were also found for SPDs and mapping factors, especially for the wet component, with slant wet delay and wet mapping factor difference RMSs of 51.9 mm and 146.1 × 10−3, respectively, corresponding to equivalent station height RMSs of about 10.4 mm. Tropospheric delays and models (e.g., mapping functions) derived from ERA5, with improved performance and temporal resolution (e.g., to support potential tropospheric parameter diurnal variation study), therefore can be expected for space geodetic applications.

Rapid troposphere tomography using adaptive simultaneous iterative reconstruction technique

A near real-time troposphere tomography system (WATS-NRT) based on Global Positioning System (GPS) slant wet delays (SWD) was developed to reconstruct three-dimensional wet refractivity fields over a wide area. The NCEP Global Forecast System (GFS) short-range forecast products (GFS-FC) were taken as the tomography background to tackle the ill-condition issue, and the Adaptive Simultaneous Iterative Reconstruction Technique (ASIRT) was used for parameter estimation to avoid an inversion of a large sparse matrix. Experiments were set up for a 7-day period covering a severe storm event to evaluate the performance of the tomography system over an area with about 5000 tomographic nodes. Statistics of the computation time illustrate that WATS-NRT is efficient enough for providing 30 min interval tomography products in the studied area. Wet refractivity time series from the tomography product experience dramatic rises before the advent of heavy rain, which is not presented in the GFS-FC products. Comparisons also show general better agreements with the nearby radiosonde profiles in tomography products than GFS-FC, especially at the lower troposphere. In addition, average error RMS of SWD derived from tomography products is about 38 mm at an elevation of 15° and 9 mm in the zenith direction, compared to more than 70 and 16 mm for SWD from both GFS-FC and the reanalysis product ERA5, and compared to about 44 and 9 mm for ZWDINT method. These results suggest that WATS-NRT can be promising in some applications, such as monitoring extreme weather events, providing tropospheric delay estimates in geodetic data analysis and developing new mapping functions.

Application of the GPS reflected signals in tomographic reconstruction of the wet refractivity in Italy

GPS troposphere tomography is one of the most powerful techniques used to extract 3D model of the water vapor and wet refractivity using the observations from local and regional GPS networks. In this technique each unknown parameter is related to the one 3D elements called voxels. This approach is a mixed-determined inverse problem because propagated signals do not pass through some of the voxels. As consequence, the design matrix has rank deficiency and unique solution cannot be reached. To this end, additional constraints are usually applied. In this research as an innovation, we utilized reflected signals from an air-borne reflectometry mission over Italy as additional constraint. According to the obtained results from four distinct parts in DOYs 158 & 160 of 2012, reflected signals help to remove or reduce the rank deficiency of our tomographic models. Due to the lack of radiosonde and radio occultation profiles, to validate the tomographic model results, GPS slant wet delay observations of singular GPS stations within the desired study areas are used as a measure for validation. Depending on the number of reflected and direct signals that we used in our reconstruction area, the range of bias in the developed models change from 1.6 to 6.9 mm. Moreover, the range of RMSE is 30–40 mm. The accuracy and precision of reconstructed images is adequate as far as the mean, minimum and maximum values of the slant wet delays in the test areas of this study are taken into account.

A Tropospheric Tomography Method with a Novel Height Factor Model Including Two Parts: Isotropic and Anisotropic Height Factors

Global Navigation Satellite System (GNSS) tomography has developed into an efficient tool for sensing the high spatiotemporal variability of atmospheric water vapor. The integration of GNSS top signals and side rays for tropospheric tomography systems using a novel height factor model (HFM) is proposed and discussed in this paper. Within the HFM, the sectional slant wet delay (SWD) of inside signals (the part of the side signal inside the tomography area), which is considered a key factor for modeling side rays, is separated into isotropic and anisotropic components. Correspondingly, two height factors are defined to calculate the isotropic and anisotropic part of tropospheric delays in the HFM. In addition, the dynamic tomography top boundary is first analyzed and determined based on 30-year radiosonde data to reasonably divide signals into top and side rays. Four special experimental schemes based on different tomography regions of Hong Kong are performed to assess the proposed HFM method, the results of which show increases of 33.42% in the mean utilization of rays, as well as decreases of 0.46 g/m3 in the average root mean square error (RMSE), compared to the traditional approach, revealing the improvement of tomography solutions when the side signals are included in the modeling. Furthermore, compared with the existing correction model for modeling side rays, the water vapor profiles retrieved from the proposed improved model are closer to the radiosonde data, which highlights the advantages of the proposed HFM for optimizing the GNSS tomography model.

Modeling the Slant Wet Delays From One GPS Receiver as a Series Expansion With Respect to Time and Space: Theory and an Example of Application for the Tahiti Island

Traditionally, the modeling of the water vapor contents of the atmosphere is done through the estimation of precipitable water (PW)—the integrated value of the mass of water vapor over a vertical column expressed in millimeter equivalent height. This modeling method is justified by the fact that, to a high degree of approximation, the atmosphere can be seen as the stacking of horizontal layers, including water vapor, over distances larger than the height of the tropopause. Nevertheless, the cycle of the water vapor, the most prevalent of the greenhouse gases in the atmosphere, has a highly turbulent regime both in time and in space that the variations in PW cannot fully embrace. In this article, we explore the modeling method, as a series expansion in time and space, of the slant wet delays (SWDs) from one GPS receiver, as an extension of the usual modeling in zenith wet delays (ZWDs) and then PW values. In the first part, we assess, from a metrological point of view, the derivation of the SWDs computed from GPS carrier phase measurements, in the case of a very humid location, the tropical island of Tahiti, for a typical sample over the wet and dry seasons. In the second part, we introduce the series expansion of the SWDs, as seen from one GPS receiver, in terms of trigonometric functions of time and spherical harmonics of elevation and azimuth. This allows us to infer time and space correlations for the SWDs that are unreachable through the modeling of ZWD values alones. In a third part, to show that our approach also includes the zenith case, we make a comparison between the modeled SWDs in the zenith direction with wind velocities from a ground weather station and radiosonde soundings (RSs). The three main conclusions from our data case are: first, the SWDs are correlated in time by about four days, and in space, with an angular correlation distance of about 20°, both for the dry and wet seasons; second, the postfit residuals are almost uncorrelated with the SWDs from a temporal and spatial point of view, but with a diurnal component; third, there is a weak correlation between the SWDs and wind velocity, the pattern depends on the season.

Estimation of tropospheric wet refractivity using tomography method and artificial neural networks in Iranian case study

Using the observations from local and regional GPS networks, the estimation of slant wet delays (SWDs) is possible for each line of sight between satellite and receiver. The observations of SWD are used to model horizontal and vertical variations of the wet refractivity in the atmosphere above the study area. This work is done using the tomography method. In tomography, the horizontal variations of tropospheric wet refractivity are modeled with the polynomial in degree and rank of 2 with latitude and longitude as variables. Also, altitude variations are modeled in the form of discrete layers with constant heights. The main innovation is to estimate the tropospheric parameters for each line of sight by the artificial neural networks (ANNs). The SWD obtained from GPS observations for the different signals at each station is compared with the SWD generated by the ANNs (SWDGPS–SWDANNs). The square of the difference between these two values is introduced as the cost function in the ANNs. To evaluate, we used observations from October 27 to 31, 2011. The availability of GPS and radiosonde data is the main reason for choosing this timeframe. The correlation coefficient, root mean square error (RMSE), and relative error allow for evaluation of the proposed model. The results were also compared with the results of the voxel-based troposphere tomography method. For a more detailed evaluation, four test stations are selected and ANN zenith wet delays (ZWDANN) are compared with the ZWDGPS. Observations of test stations are not used in the modeling step. The correlation coefficient in the testing step for TomoANN and Tomovoxel is 0.9006 and 0.8863, respectively. The mean RMSE at 5 days for TomoANN and Tomovoxel is calculated as 0.63 and 0.71 mm/km, respectively. Also, the average relative error at the four test stations for TomoANN is 15.37% and for Tomovoxel it is 19.69%. The results demonstrate the better capability of the proposed method in the modeling of the tropospheric wet refractivity in the region of Iran.

Precise Point Positioning on the Reliable Detection of Tropospheric Model Errors.

Precise point positioning (PPP) is one of the well-known applications of Global Navigation Satellite System (GNSS) and provides precise positioning solutions using accurate satellite orbit and clock products. The tropospheric delay due to the neutral atmosphere for microwave signals is one of the main sources of measurement error in PPP. As one component of this delay, the hydrostatic delay is usually compensated by using an empirical correction model. However, how to eliminate the effects of the wet delay during a weather event is a challenge because current troposphere models are not capable of considering the complex atmosphere around the receiver during situations such as typhoons, storms, heavy rainfall, et cetera. Thus, how positioning results can be improved if the residual wet delays are taken into account needs to be investigated . In this contribution, a real-time procedure of recursive detection, identification and adaptation (DIA) is applied to detect the model errors which have the same effects on both phase and code observables; e.g., the model error caused by the tropospheric delay. Once the model errors are identified, additional parameters are added to the functional model to account for the measurement residuals. This approach is evaluated with Global Positioning System (GPS) data during two rainfall events in Darwin, Australia, proving the usefulness of compensated residual slant wet delay for positioning results. Comparisons with the standard approach show that the precision of the up component is improved significantly during the periods of the weather events; for the two case studies, and improvements of root mean squared error (RMS) resulted, and the precision of the horizontal component obtained by the proposed approach is also improved more than compared to the standard approach. The results also show that the identified model errors are concentrated at the beginning of both heavy rainfall processes when the front causes significant spatial and temporal gradients of the integrated water vapor above the receiver.

Observing geometry effects on a Global Navigation Satellite System (GNSS)-based water vapor tomography solved by least squares and by compressive sensing

Abstract. In this work, the effect of the observing geometry on the tomographic reconstruction quality of both a regularized least squares (LSQ) approach and a compressive sensing (CS) approach for water vapor tomography is compared based on synthetic Global Navigation Satellite System (GNSS) slant wet delay (SWD) estimates. In this context, the term “observing geometry” mainly refers to the number of GNSS sites situated within a specific study area subdivided into a certain number of volumetric pixels (voxels) and to the number of signal directions available at each GNSS site. The novelties of this research are (1) the comparison of the observing geometry's effects on the tomographic reconstruction accuracy when using LSQ or CS for the solution of the tomographic system and (2) the investigation of the effect of the signal directions' variability on the tomographic reconstruction. The tomographic reconstruction is performed based on synthetic SWD data sets generated, for many samples of various observing geometry settings, based on wet refractivity information from the Weather Research and Forecasting (WRF) model. The validation of the achieved results focuses on a comparison of the refractivity estimates with the input WRF refractivities. The results show that the recommendation of Champollion et al. (2004) to discretize the analyzed study area into voxels with horizontal sizes comparable to the mean GNSS intersite distance represents a good rule of thumb for both LSQ- and CS-based tomography solutions. In addition, this research shows that CS needs a variety of at least 15 signal directions per site in order to estimate the refractivity field more accurately and more precisely than LSQ. Therefore, the use of CS is particularly recommended for water vapor tomography applications for which a high number of multi-GNSS SWD estimates are available.

Assimilation of GNSS tomography products into the Weather Research and Forecasting model using radio occultation data assimilation operator

Abstract. From Global Navigation Satellite Systems (GNSS) signals, accurate and high-frequency atmospheric parameters can be determined in all-weather conditions. GNSS tomography is a technique that takes advantage of these parameters, especially of slant troposphere observations between GNSS receivers and satellites, traces these signals through a 3-D grid of voxels, and estimates by an inversion process the refractivity of the water vapour content within each voxel. In the last years, the GNSS tomography development focused on numerical methods to stabilize the solution, which has been achieved to a great extent. Currently, we are facing new challenges and possibilities in the application of GNSS tomography in numerical weather forecasting, the main research objective of this paper. In the first instance, refractivity fields were estimated using two different GNSS tomography models (TUW, WUELS), which cover the area of central Europe during the period of 29 May–14 June 2013, when heavy-precipitation events were observed. For both models, slant wet delays (SWDs) were calculated based on estimates of zenith total delay (ZTD) and horizontal gradients, provided for 88 GNSS sites by Geodetic Observatory Pecny (GOP). In total, three sets of SWD observations were tested (set0 without compensation for hydrostatic anisotropic effects, set1 with compensation of this effect, set2 cleaned by wet delays outside the inner voxel model), in order to assess the impact of different factors on the tomographic solution. The GNSS tomography outputs have been assimilated into the nested (12 and 36 km horizontal resolution) Weather Research and Forecasting (WRF) model, using its three-dimensional variational data assimilation (WRFDA 3D-Var) system, in particular, its radio occultation observation operator (GPSREF). As only total refractivity is assimilated in GPSREF, it was calculated as the sum of the hydrostatic part derived from the ALADIN-CZ model and the wet part from the GNSS tomography. We compared the results of the GNSS tomography data assimilation to the radiosonde (RS) observations. The validation shows the improvement in the weather forecasting of relative humidity (bias, standard deviation) and temperature (standard deviation) during heavy-precipitation events. Future improvements to the assimilation method are also discussed.

On the Study of Influences of Different Factors on the Rapid Tropospheric Tomography

A rapid tropospheric tomography system was developed by using algebraicreconstruction technique. Influences of different factors on the tomographic results, including theground meteorological data, the multi-Global Navigation Satellite System (GNSS) observations, theground station distribution and the tomographic horizontal resolution, were systematicallyinvestigated. In order to exclude the impacts from discrepancies of water vapor informationbetween input observations and references on the tomographic results, the latest reanalysisproducts, ERA5, which were taken as references for result evaluations, were used to simulate slantwet delay (SWD) observations at GNSS stations. Besides, the slant delays derived from GNSSprocessing were also used to evaluate the reliability of simulated observations. Tomography resultsshow that the input both SWD and ground meteorological data could improve the tomographicresults where SWD mainly improve the results at middle layers (500 to 5000m, namely 2 to 16 layer)and ground meteorological data could improve the humidity fields at bottom layers further (0 to500m, namely 0 to 2 layer). Compared to the usage of Global Positioning System (GPS) only SWD,the inclusion of multi-GNSS SWD does not significantly improve the tomographic results at alllayers due to the almost unchanged dispersion of puncture points of GNSS signals. However,increases in the ground GNSS stations can benefit the tomography, with improvements of morethan 10% at bottom and middle layers. Higher tomographic horizontal resolution can furtherslightly improve the tomographic results (about 3-6% from 0.5 to 0.25 degrees), which, however,will also increase the computational burden at the same time.

PENCITRAAN DISTRIBUSI VERTIKAL UAP AIR DI KAWASAN BANDUNG MENGGUNAKAN TEKNIK GPS TOMOGRAFI 1D

Global Positioning System (GPS) tidak hanya berguna untuk keperluan navigasi dan penentuan posisi. GPS juga dapat dimanfaatkan untuk keperluan meteorologi seperti pemantauan kondisi atmosfer Bumi. Sinyal GPS yang ditransmisikan dari satelit ke receiver mengalami perlambatan dan pembelokan akibat adanya partikel-partikel pada lapisan troposfer. Salah satu partikel yang memiliki peranan penting terhadap berlangsungnya siklus hidrologi di Bumi adalah uap air. Uap air tersebut bersifat sangat dinamis baik secara spasial maupun temporal. Perbedaan kandungan uap air pada setiap tempat dan ketinggian menyebabkan besarnya refraktivitas pada lapisan troposfer juga bervariasi. Pemantauan uap air dengan ketelitian tinggi masih menjadi tantangan di Indonesia. Informasi yang akurat mengenai uap air tentunya bermanfaat dalam melakukan prediksi cuaca lokal maupun mempelajari iklim global. Salah satu produk dari pengolahan data GPS adalah informasi mengenai estimasi besarnya bias troposfer pada arah zenith atau disebut sebagai Zenith Tropospheric Delay (ZTD). Besarnya hasil estimasi tersebut kemudian dapat dipisahkan menjadi komponen kering atau Zenith Hydrostatic Delay (ZHD) dan uap air atau Zenith Wet Delay (ZWD). Untuk mengetahui besarnya kandungan uap air pada setiap lintasan sinyal, maka ZWD harus dikonversi menjadi Slant Wet Delay (SWD) dengan mengalikan ZWD dan mapping function. Nilai SWD kemudian digunakan untuk melakukan rekonstruksi distribusi vertikal uap air pada troposfer dengan teknik tomografi. Pada penelitian ini digunakan data pengamatan GPS dari stasiun GPS kontinu (CGPS) ITB selama satu hari. Hasil yang didapatkan menyatakan bahwa Multiplicative Algebraic Reconstruction Technique (MART) berhasil diterapkan untuk menyelesaikan persamaan linear dalam menentukan refraktivitas komponen basah dengan GPS tomografi 1D. Penelitian lebih lanjut dengan memanfaatkan jaringan stasiun CGPS dan pengembangan metode numerik dapat memberikan hasil yang lebih optimal dan akurat.

Comparisons between the WRF data assimilation and the GNSS tomography technique in retrieving 3-D wet refractivity fields in Hong Kong

Abstract. Water vapor plays an important role in various scales of weather processes. However, there are limited means to accurately describe its three-dimensional (3-D) dynamical changes. The data assimilation technique and the Global Navigation Satellite System (GNSS) tomography technique are two of the limited means. Here, we conduct an interesting comparison between the GNSS tomography technique and the Weather Research and Forecasting Data Assimilation (WRFDA) model (a representative of the data assimilation models) in retrieving wet refractivity (WR) in the Hong Kong area during a wet period and a dry period. The GNSS tomography technique is used to retrieve WR from the GNSS slant wet delays. The WRFDA is used to assimilate the zenith tropospheric delay to improve the background data. The radiosonde data are used to validate the WR derived from the GNSS tomography, the WRFDA output, and the background data. The root mean square (rms) of the WR derived from the tomography results, the WRFDA output, and the background data are 6.50, 4.31, and 4.15 mm km−1 in the wet period. The rms becomes 7.02, 7.26, and 6.35 mm km−1 in the dry period. The lower accuracy in the dry period is mainly due to the sharp variation of WR in the vertical direction. The results also show that assimilating GNSS ZTD into the WRFDA only slightly improves the accuracy of the WR and that the WRFDA WR is better than the tomographic WR in most cases. However, in a special experimental period when the water vapor is highly concentrated in the lower troposphere, the tomographic WR outperforms the WRFDA WR in the lower troposphere. When we assimilate the tomographic WR in the lower troposphere into the WRFDA, the retrieved WR is improved.

Influence of station density and multi-constellation GNSS observations on troposphere tomography

Abstract. Troposphere tomography, using multi-constellation observations from global navigation satellite systems (GNSSs), has become a novel approach for the three-dimensional (3-D) reconstruction of water vapour fields. An analysis of the integration of four GNSSs (BeiDou, GPS, GLONASS, and Galileo) observations is presented to investigate the impact of station density and single- and multi-constellation GNSS observations on troposphere tomography. Additionally, the optimal horizontal resolution of the research area is determined in Hong Kong considering both the number of voxels divided, and the coverage rate of discretized voxels penetrated by satellite signals. The results show that densification of the GNSS network plays a more important role than using multi-constellation GNSS observations in improving the retrieval of 3-D atmospheric water vapour profiles. The root mean square of slant wet delay (SWD) residuals derived from the single-GNSS observations decreased by 16 % when the data from the other four stations are added. Furthermore, additional experiments have been carried out to analyse the contributions of different combined GNSS data to the reconstructed results, and the comparisons show some interesting results: (1) the number of iterations used in determining the weighting matrices of different equations in tomography modelling can be decreased when considering multi-constellation GNSS observations and (2) the reconstructed quality of 3-D atmospheric water vapour using multi-constellation GNSS data can be improved by about 11 % when compared to the SWD estimated with precise point positioning, but this was not as high as expected.

Hybrid Regularized GPS Tropospheric Sensing Using 3-D Ray Tracing Technique

Tropospheric water vapor plays an important role in the atmospheric processes. It is a major uncertainty parameter in numerical weather models due to the large and fast variation in spatiotemporal scales. Because of its variations, modeling the spatiotemporal distribution of water vapor is a complex problem. The 4-D Global Navigation Satellite Systems tropospheric tomography is a powerful technique to compute the accurate spatiotemporal distribution of water vapor. Tropospheric tomography usually is an ill-conditioned inverse problem. Therefore, using the regularization methods is inevitable. The precision of a tomography technique depends on the accuracy of the GPS-derived tropospheric slant wet delay, the ray tracing technique, and the regularization method. In this letter, the 3-D ray tracing technique based on Eikonal equations and hybrid regularization frameworks is used to increase the precision of the 4-D tropospheric tomography technique. After solving the tropospheric tomography problem, the modeled water vapor is validated using radiosonde observations and meteorological data obtained from synoptic stations. Using hybrid regularization techniques reduces the root mean square of the results of common regularization methods by an average of 0.39 g/m3.

Radar Path Delay Effects in Volcanic Gas Plumes: The Case of Láscar Volcano, Northern Chile

Modern volcano monitoring commonly involves Interferometric Synthetic Aperture Radar (InSAR) measurements to identify ground motions caused by volcanic activity. However, InSAR is largely affected by changes in atmospheric refractivity, in particular by changes which can be attributed to the distribution of water (H2O) vapor in the atmospheric column. Gas emissions from continuously degassing volcanoes contain abundant water vapor and thus produce variations in the atmospheric water vapor content above and downwind of the volcano, which are notably well captured by short-wavelength X-band SAR systems. These variations may in turn cause differential phase errors in volcano deformation estimates due to excess radar path delay effects within the volcanic gas plume. Inversely, if these radar path delay effects are better understood, they may be even used for monitoring degassing activity, by means of the precipitable water vapor (PWV) content in the plume at the time of SAR acquisitions, which may provide essential information on gas plume dispersion and the state of volcanic and hydrothermal activity. In this work we investigate the radar path delays that were generated by water vapor contained in the volcanic gas plume of the persistently degassing Láscar volcano, which is located in the dry Atacama Desert of Northern Chile. We estimate water vapor contents based on sulfur dioxide (SO2) emission measurements from a scanning UV spectrometer (Mini-DOAS) station installed at Láscar volcano, which were scaled by H2O/SO2 molar mixing ratios obtained during a multi-component Gas Analyzer System (Multi-GAS) survey on the crater rim of the volcano. To calculate the water vapor content in the downwind portion of the plume, where an increase of water vapor is expected, we further applied a correction involving estimation of potential evaporation rates of water droplets governed by turbulent mixing of the condensed volcanic plume with the dry atmosphere. Based on these estimates we obtain daily average PWV contents inside the volcanic gas plume of 0.2–2.5 mm equivalent water column, which translates to a slant wet delay (SWD) in DInSAR data of 1.6–20 mm. We used these estimates in combination with our high resolution TerraSAR-X DInSAR observations at Láscar volcano, in order to demonstrate the occurrence of repeated atmospheric delay patterns that were generated by volcanic gas emissions. We show that gas plume related refractivity changes are significant and detectable in DInSAR measurements. Implications are two-fold: X-band satellite radar observations also contain information on the degassing state of a volcano, while deformation signals need to be interpreted with care, which has relevance for volcano observations at Láscar and for other sites worldwide.