Zenith Total Delay Research Articles

Kinematic ZTD Estimation from Train-Borne Single-Frequency GNSS: Validation and Assimilation

Water vapour is one of the most important parameters utilized for the description of state and evolution of the Earth’s atmosphere. It is the most effective greenhouse gas and shows high variability, both in space and time. Thus, detailed knowledge of its distribution is of immense importance for weather forecasting, and therefore high resolution observations are crucial for accurate precipitation forecasts, especially for the short-term prediction of severe weather. Although not intentionally built for this purpose, Global Navigation Satellite Systems (GNSS) have proven to meet those requirements. The derivation of water vapour content from GNSS observations is based on the fact that electromagnetic signals are delayed when travelling through the atmosphere. The most prominent parameterization of this delay is the Zenith Total Delay (ZTD), which has been studied extensively as a major error term in GNSS positioning. On the other hand, the ZTD has also been proven to provide substantial benefits for atmospheric research and especially Numerical Weather Prediction (NWP) model performance. Based on these facts, the scientific area of GNSS Meteorology has emerged. The present study goes beyond the current status of GNSS Meteorology, showing how reasonable estimates of ZTD can be derived from highly-kinematic, single-frequency (SF) GNSS data. This data was gathered from trains of the Austrian Federal Railways (ÖBB) and processed using the Precise Point Positioning (PPP) technique. The special nature of the observations yields a number of additional challenges, ranging from appropriate pre-processing and parameter settings in PPP to more sophisticated validation and assimilation methodologies . The treatment of the ionosphere for SF-GNSS data represents one of the major challenges of this study. Two test cases (train travels) were processed using different strategies and validated using ZTD calculated from ERA5 reanalysis data. The validation results indicate a good overall agreement between the GNSS-ZTD solutions and ERA5-derived ZTD, although substantial variability between solutions was still observed for specific sections of the test tracks. The bias and standard deviation values ranged between 1 mm and 8 cm, heavily depending on the utilized processing strategy and investigated train route. Finally, initial experiments for the assimilation of GNSS-ZTD estimates into a NWP model were conducted, and the results showed observation acceptance rates of 30–100% largely depending on the test case and processing strategy.

A neural network-based approach for the detection of heavy precipitation using GNSS observations and surface meteorological data

Recent years have witnessed a growing interest in using GNSS observations to detect heavy precipitation. In this study, a neural network-based (NN-based) approach taking seven meteorological variables as input data was developed based on the back propagation (BP) algorithm for detecting heavy precipitation. Apart from the surface meteorological variables of temperature, pressure and relative humidity, the model has also adopted other information such as day-of-year, hour-of-day and GNSS-derived zenith total delay and precipitable water vapor (PWV) as input variables. The feasibility of using these variables for developing the BP-NN-based model was elaborated by conducting the feature analysis of the seven input variables. In addition, the criterion for selecting a proper size of training sample was also briefly investigated by studying the impact and sensibility of the sample lengths in the model. The proposed model was developed using a sample size of an 8-year (2010–2017) period in the summer at a pair of co-located GNSS/weather stations−HKSC-KP in Hong Kong. The use of a long-term data is to “reliably” capture the characteristics of the selected variables. The detection results for the summer months in 2018 and 2019 were then compared against corresponding precipitation records to valid the effectiveness of the newly proposed model. Results of the correct detection and false alarm rates were 94.5 % and 20.8 %, respectively, which were significant improvements compared with the existing models.

GNSS-based water vapor estimation and validation during the MOSAiC expedition

Abstract. Within the transpolar drifting expedition MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate), the Global Navigation Satellite System (GNSS) was used among other techniques to monitor variations in atmospheric water vapor. Based on 15 months of continuously tracked GNSS data including GPS, GLONASS and Galileo, epoch-wise coordinates and hourly zenith total delays (ZTDs) were determined using a kinematic precise point positioning (PPP) approach. The derived ZTD values agree to 1.1 ± 0.2 mm (root mean square (rms) of the differences 10.2 mm) with the numerical weather data of ECMWF's latest reanalysis, ERA5, computed for the derived ship's locations. This level of agreement is also confirmed by comparing the on-board estimates with ZTDs derived for terrestrial GNSS stations in Bremerhaven and Ny-Ålesund and for the radio telescopes observing very long baseline interferometry in Ny-Ålesund. Preliminary estimates of integrated water vapor derived from frequently launched radiosondes are used to assess the GNSS-derived integrated water vapor estimates. The overall difference of 0.08 ± 0.04 kg m−2 (rms of the differences 1.47 kg m−2) demonstrates a good agreement between GNSS and radiosonde data. Finally, the water vapor variations associated with two warm-air intrusion events in April 2020 are assessed.

GPS-ZTD data assimilation and its impact on wintertime haze prediction over North China Plain using WRF 3DVAR and CMAQ modeling system.

Severe haze frequently hits the North China Plain (NCP), especially in winter during recent years. Meteorological factors affect aerosol formation and its optical properties, and accurate meteorological fields are imperative for accurate aerosol simulations. The impacts of Global Positioning System Zenith Total Delay (GPS-ZTD) data assimilation on meteorology and aerosol simulations were evaluated in this study using the WRF-CMAQ (the Weather Research and Forecasting model and Community Multiscale Air Quality) modelling system over the NCP during 01-31 December 2019. After bias correction, GSP-ZTD data were assimilated into the WRF model using the 3DVAR technique. Two sensitivity tests (CTR and ZTD) were conducted. The WRF model had generally acceptable performance for surface and upper air meteorological variables, PM2.5 and visibility. From the aspect of BIAS, STDE, RMSE, and R, the assimilation of ZTD data improved the underestimation of ground relative humidity (RH). The improvement was more pronounced in the first 18 forecast hours. The mean RH BIAS decreased by 8%. Surface pressure was also improved in ZTD. The influence of ZTD data assimilation on ground temperature and wind tended to be neutral. The BIAS of ZTD decreased by 3% after data assimilation while STED or RMSE increased slightly. After ZTD data assimilation, the PM2.5 underestimation decreased by 3.4% over NCP. And station mean BIAS or RMSE of PM2.5 decreased at more than 70% stations. After ZTD data assimilation, the visibility overestimation was reduced by 2.5%. And more than 81% stations over had lower visibility BIAS or RMSE. Station mean PM2.5 mass concentration increased by 1.5% in ZTD. The primary aerosol species increased by approximately 1%, and most secondary aerosol species increased by greater than 2% affected by both aerosol physical and chemical process. Although the improvement of PM2.5 seems marginal from the perspective of regional or temporal average, the contribution of ZTD data assimilation on specific pollution episodes at specific stations can be great. The improvement of PM2.5 troughs was in the range of 1-5 μg/m3, while the overestimation of PM2.5 peaks was reduced by few up to dozens μg/m3. This will contribute to the extreme value prediction during pollution episode.

The impact of GPS and high-resolution radiosonde nudging on the simulation of heavy precipitation during HyMeX IOP6

Abstract. Heavy precipitation is one of the most devastating weather extremes in the western Mediterranean region. Our capacity to prevent negative impacts from such extreme events requires advancements in numerical weather prediction, data assimilation, and new observation techniques. In this paper we investigate the impact of two state-of-the-art data sets with very high resolution, Global Positioning System (GPS)-derived zenith total delays (GPS-ZTD) with a 10 min temporal resolution and radiosondes with ∼ 700 levels, on the representation of convective precipitation in nudging experiments. Specifically, we investigate whether the high temporal resolution, quality, and coverage of GPS-ZTDs can outweigh their lack of vertical information or if radiosonde profiles are more valuable despite their scarce coverage and low temporal resolution (24 to 6 h). The study focuses on the Intensive Observation Period 6 (IOP6) of the Hydrological cycle in the Mediterranean eXperiment (HyMeX; 24 September 2012). This event is selected due to its severity (100 mm/12 h), the availability of observations for nudging and validation, and the large observation impact found in preliminary sensitivity experiments. We systematically compare simulations performed with the Consortium for Small-scale Modeling (COSMO) model assimilating GPS, high- and low-vertical-resolution radiosoundings in model resolutions of 7 km, 2.8 km, and 500 m. The results show that the additional GPS and radiosonde observations cannot compensate for errors in the model dynamics and physics. In this regard the reference COSMO runs have an atmospheric moisture wet bias prior to precipitation onset but a negative bias in rainfall, indicative of deficiencies in the numerics and physics, unable to convert the moisture excess into sufficient precipitation. Nudging GPS and high-resolution soundings corrects atmospheric humidity but even further reduces total precipitation. This case study also demonstrates the potential impact of individual observations in highly unstable environments. We show that assimilating a low-resolution sounding from Nîmes (southern France) while precipitation is taking place induces a 40 % increase in precipitation during the subsequent 3 h. This precipitation increase is brought about by the moistening of the 700 hPa level (7.5 g kg−1) upstream of the main precipitating systems, reducing the entrainment of dry air above the boundary layer. The moist layer was missed by GPS observations and high-resolution soundings alike, pointing to the importance of profile information and timing. However, assimilating GPS was beneficial for simulating the temporal evolution of precipitation. Finally, regarding the scale dependency, no resolution is particularly sensitive to a specific observation type; however, the 2.8 km run has overall better scores, possibly as this is the optimally tuned operational version of COSMO. Future work will aim at a generalization of these conclusions, investigating further cases of the autumn 2012, and the Icosahedral Nonhydrostatic Model (ICON) will be investigated for this case study to assert whether its updates are able to improve the quality of the simulations.

Tropospheric Correction of Sentinel-1 Synthetic Aperture Radar Interferograms Using a High-Resolution Weather Model Validated by GNSS Measurements

Synthetic Aperture Radar Interferometry (InSAR) is a space geodetic technique used for mapping deformations of the Earth’s surface. It has been developed and used increasingly during the last thirty years to measure displacements produced by earthquakes, volcanic activity and other crustal deformations. A limiting factor to this technique is the effect of the troposphere, as spatial and temporal variations in temperature, pressure, and relative humidity introduce significant phase delays in the microwave imagery, thus “masking” surface displacements due to tectonic or other geophysical processes. The use of Numerical Weather Prediction (NWP) models as a tropospheric correction method in InSAR can tackle several of the problems faced with other correction techniques (such as timing, spatial coverage and data availability issues). High-resolution tropospheric modelling is particularly useful in the case of single interferograms, where the removal of the atmospheric phase screen (and especially the highly variable turbulent component) can reveal large-amplitude deformation signals (as in the case of an earthquake). In the western Gulf of Corinth, prominent topography makes the removal of both the stratified and turbulent atmospheric phase screens a challenging task. Here, we investigate the extent to which a high-resolution WRF 1-km re-analysis can produce detailed tropospheric delay maps of the required accuracy by coupling its output (in terms of Zenith Total Delay or ZTD) with the vertical delay component in GNSS measurements. The model is operated with varying physical parameterization in order to identify the best configuration, and validated with GNSS zenithal tropospheric delays, providing a benchmark of real atmospheric conditions. We correct sixteen Sentinel-1A interferograms with differential delay maps at the line-of-sight (LOS) produced by WRF re-analysis. In most cases, corrections lead to a decrease in the phase gradient, with average root-mean-square (RMS) and standard deviation (SD) reductions in the wrapped phase of 6.0% and 19.3%, respectively. Results suggest a high potential of the model to reproduce both the long-wavelength stratified atmospheric signal and the short-wave turbulent atmospheric component which are evident in the interferograms.

Performance Assessment of PPP-AR Positioning and Zenith Total Delay Estimation with Modernized CSRS-PPP

Abstract The precise point positioning (PPP) method has become more popular due to powerful online global navigation satellite system (GNSS) data processing services, such as the Canadian Spatial Reference System-PPP (CSRS-PPP). At the end of 2020, the CSRS-PPP service launched the ambiguity resolution (AR) feature for global positioning system (GPS) satellites. More reliable results are obtained with AR compared to the results with traditional ambiguity-float PPP. In this study, the performance of the modernized CSRS-PPP was comparatively assessed in terms of static positioning and zenith total delay (ZTD) estimation. Data for 1 month in the year 2019 obtained from 47 international GNSS service (IGS) stations were processed before and after modernization of the CSRS-PPP. The processes were conducted for GPS and GPS + GLONASS (GLObalnaya NAvigatsionnaya Sputnikovaya Sistema) satellite combinations. Besides, the results were analyzed in terms of accuracy and convergence time. According to the solutions, the AR feature of the CSRS-PPP improved the accuracy by about 50% in the east component for GPS + GLONASS configuration. The root-mean-square error (RMSE) of the ZTD difference between modernized CSRS-PPP service and IGS final troposphere product is 5.8 mm for the GPS-only case.

Use of GNSS Tropospheric Delay Measurements for the Parameterization and Validation of WRF High-Resolution Re-Analysis over the Western Gulf of Corinth, Greece: The PaTrop Experiment

In the last thirty years, Synthetic Aperture Radar interferometry (InSAR) and the Global Navigation Satellite System (GNSS) have become fundamental space geodetic techniques for mapping surface deformations due to tectonic movements. One major limiting factor to those techniques is the effect of the troposphere, as surface velocities are of the order of a few mm yr−1, and high accuracy (to mm level) is required. The troposphere introduces a path delay in the microwave signal, which, in the case of GNSS Precise Point Positioning (PPP), can nowadays be partly removed with the use of specialized mapping functions. Moreover, tropospheric stratification and short wavelength spatial turbulences produce an additive noise to the low amplitude ground deformations calculated by the (multitemporal) InSAR methodology. InSAR atmospheric phase delay corrections are much more challenging, as opposed to GNSS PPP, due to the single pass geometry and the gridded nature of the acquired data. Thus, the precise knowledge of the tropospheric parameters along the propagation medium is extremely useful for the estimation and correction of the atmospheric phase delay. In this context, the PaTrop experiment aims to maximize the potential of using a high-resolution Limited-Area Model for the calculation and removal of the tropospheric noise from InSAR data, by following a synergistic approach and integrating all the latest advances in the fields of remote sensing meteorology (GNSS and InSAR) and Numerical Weather Forecasting (WRF). In the first phase of the experiment, presented in the current paper, we investigate the extent to which a high-resolution 1 km WRF weather re-analysis can produce detailed tropospheric delay maps of the required accuracy, by coupling its output (in terms of Zenith Total Delay or ZTD) with the vertical delay component in GNSS measurements. The model is initially operated with varying parameterization, with GNSS measurements providing a benchmark of real atmospheric conditions. Subsequently, the final WRF daily re-analysis run covers an extended period of one year, based on the optimum model parameterization scheme demonstrated by the parametric analysis. The two datasets (predicted and observed) are compared and statistically evaluated, in order to investigate the extent to which meteorological parameters that affect ZTD can be simulated accurately by the model under different weather conditions. Results demonstrate a strong correlation between predicted and observed ZTDs at the 19 GNSS stations throughout the year (R ranges from 0.91 to 0.93), with an average mean bias (MB) of –19.2 mm, indicating that the model tends to slightly underestimate the tropospheric ZTD as compared to the GNSS derived values. With respect to the seasonal component, model performance is better during the autumn period (October–December), followed by spring (April–June). Setting the acceptable bias range at ±23 mm (equal to the amplitude of one Sentinel-1 C-band phase cycle when projected to the zenithal distance), it is demonstrated that the model produces satisfactory results, with a percentage of ZTD values within the bias margin ranging from 57% in summer to 63% in autumn.

A global grid model for the correction of the vertical zenith total delay based on a sliding window algorithm

Reanalysis products have been applied to calculate the tropospheric delay for Global Navigation Satellite System (GNSS) positioning purposes widely. It is necessary to obtain high-precision tropospheric delay information from GNSS users with a high-precision tropospheric vertical stratification model because the height of the grids of the atmospheric reanalysis data is inconsistent with that of GNSS users, especially in regions with high terrains. In addition, the variation of the tropospheric delay in the vertical direction is much higher than that in the horizontal direction. The zenith total delay (ZTD) vertical stratification model is also key to the development of real-time and high-precision ZTD models. A new approach, the sliding window algorithm, is proposed to develop a ZTD vertical stratification model. In this work, a ZTD vertical stratification model considering spatiotemporal factors is developed based on the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) data, which is named the GZTD-H model. Radiosonde and International GNSS Service (IGS) data are treated as reference values to evaluate the performance of the GZTD-H model, which is compared to the model GPT2w. The results show that the GZTD-H model realizes the highest performance in ZTD layered vertical interpolation against ZTD layered profiles obtained at radiosonde sites, which achieves an improvement of 10% over the model GPT2w. Compared to model GPT2w, the GZTD-H model attains a spatial interpolation improvement of 8% for the Global Geodetic Observing System (GGOS) Atmosphere gridded ZTD over the surface ZTD calculated from radiosonde profiles. Furthermore, compared to model GPT2w, the model GZTD-H also attains improvements of 11% over the precise ZTD products acquired at IGS sites. In terms of model parameters, the GZTD-H model is greatly reduced and optimized over model GPT2w. Hence, the applicability of this model is enhanced in terms of GNSS atmospheric sounding and precise GNSS positioning.

Evaluation of Weather Research and Forecast (WRF) microphysics schemes in simulating zenith total delay for InSAR atmospheric correction

ABSTRACT Many researches have demonstrated the potential of Interferometric Synthetic Aperture Radar (InSAR) atmospheric correction method based on the Weather Research and Forecast (WRF) model. However, there remain some doubts about its robustness and accuracy over complex topographical areas. For meteorology, microphysics schemes play a significant role on the simulation of atmospheric parameters. But no studies have focused specifically on the impact of the microphysics schemes (MPs) on the atmospheric zenith total delay (ZTD) simulation for InSAR atmospheric correction. Therefore, we test four sophisticated MPs in WRF version 3.9.1 to simulate ZTD during the SAR signal propagation over Haiyuan in China in summer. And the ZTD values from WRF are validated by comparing to those obtained from four global position system (GPS) stations. The results show that all of the MPs can effectively reconstruct the major absolute ZTD, but regarding differential ZTD values, the Morrison double moment (M2M) and WRF Double moment 6 classes Model (WDM6) predict more accurate differential ZTD data. Moreover, WRF-based InSAR atmospheric correction utilizing the M2M obtains the most accurate deformation map results. A proper choice of MP schemes can enhance the WRF model to simulate more precise ZTD and contribute to reach a conclusion on the robustness of WRF-based InSAR atmospheric correction, especially in the complex mountain area in summer.

A priori zenith wet delays in the analysis of VLBI CONT sessions

Tropospheric delay is one of the major sources of error in VLBI (Very Long Baseline Interferometry) analysis. The principal component of this error can be accurately computed through reliable surface pressure data —hydrostatic delay— yet there is also a small but volatile component —wet delay— which is difficult to be modelled a priori. In VLBI analysis, troposphere delay is typically modelled in the theoretical delays using Zenith Hydrostatic Delays (ZHD) and a dry mapping function. Zenith Wet Delay (ZWD) is not modelled but estimated in the analysis process. This work studies inter alia the impact of including external GNSS estimates to model a priori ZWD in VLBI analysis, as well as other models of a priori ZWD.In a first stage, two different sources of GNSS troposphere products are compared to VLBI troposphere estimates in a period of 5 years. The solution with the best agreement to VLBI results is injected in the VLBI analysis as a priori ZWD value and is compared to other options to model a priori ZWD. The dataset used for this empirical analysis consists of the six CONT campaigns.It has been found that modelling a priori ZWD has no significant impact either on baseline length and coordinates repeatabilities. Nevertheless, modelling a priori ZWD can change the magnitude of the estimated coordinates a few millimeters in the up component with respect to the non-modelling approach. In addition, the influence of a priori ZWD on Earth Orientation Parameters (EOP) and troposphere estimates —Zenith Total Delays (ZTD) and gradients—has also been analysed, resulting in a small but significant impact on both geodetic products.

Utilization of WRF 3D Meteorological Data to Calculate Slant Total Delay for InSAR Atmospheric Correction

The atmosphere introduces excess delays into the synthetic aperture radar (SAR) signal trajectory, especially in the troposphere. InSAR atmospheric correction methods include the use of SAR data and external water vapor products. The latter is more effective. However, since the removal of atmospheric effects should use atmospheric delay products in the direction of the line of sight (LOS), it is necessary to convert the zenith total delay to slant delay in the LOS direction. Conventionally, the zenith delay is divided by the cosine of the average incident angles to obtain slant phase delays. But this method could cause large errors because it ignores the atmospheric horizontal gradient change and the small-scale vertical structure. These problems can be solved by using three-dimensional atmospheric data simulated by numerical models, especially in the case of intense weather changes or complex terrain. However, few scholars paid attention to the application into InSAR atmospheric correction, because of the computation complexity and low efficiency. As the requirement for higher accuracy and the introduction of large errors caused by increasing incidence angles, it is significantly imperative to make the utmost of this method. Weather Research Forecast (WRF) model can provide the precipitate water vapor (PWV) and refraction index at different levels in the three dimensions, and then the slant total delay can be obtained for removing the atmospheric effect on the InSAR process. The results demonstrate that using 3D data can obtain more accurate slant total delay and improve the accuracy of surface deformation from InSAR technology.

An Improved Model for Detecting Heavy Precipitation Using GNSS-Derived Zenith Total Delay Measurements

In recent years, precipitable water vapor has been widely used in heavy precipitation prediction, which is obtained from a conversion of the zenith total delay (ZTD) of the GNSS signal. Since the parameter directly estimated for the tropospheric delay from Global Navigation Satellite Systems (GNSS) data processing is the ZTD, this study investigated the feasibility of directly using ZTD to predict heavy precipitation. Based on the finding that, prior to a heavy precipitation events, ZTD was likely to start with a duration of continuous rise followed by a sharp drop, a new heavy precipitation detection model containing seven predictors derived from ZTD was established. The seven predictors reflect not only the ascending and descending trends but also long-term and short-term variations in the ZTD time series. Three criteria, representing different situations for the formation of heavy precipitation, were also constructed using the predictors to detect heavy precipitation. The optimal set of thresholds for the seven predictors for each summer month were determined based on hourly ZTD and precipitation records at a pair of co-located GNSS/weather station−HKSC-KP in Hong Kong over the period 2010−2017. The model was evaluated using the predictions in 2018 and 2019 to compare against the corresponding precipitation records. Results showed that the new model correctly predicted 98.8% of the heavy precipitation events, with a mean lead time of 4.37 h. Compared with the existing models, the new model also reduced the false alarms by 32.3%. Similar results were also obtained from the other three pairs of co-located stations. These results suggest that it is rational and effective to use the new ZTD-based model for improving the performance of heavy precipitation detection.

ASSESSMENT OF ATMOSPHERIC WATER VAPOR REMOTE SENSING USING GPS SIGNALS BY RADIOSONDE AND MODIS SATELLITE IMAGES

This paper is an evaluation of the atmospheric water vapor remote sensing by GPS signals. The integrated water vapor (IWV) is calculated based on the measurement of the tropospheric zenith total delay (ZTD) effects on the microwave signals emitted by GPS satellites. The methodology proposed in this work is based on the combination of the global navigation satellite system (GNSS) observations and navigation data from the international GNSS service (IGS) products with meteorological data, measured at the stations level, to calculate the ZTD delay and estimate the integrated water vapor value. This work was carried out using data records from12 IGS stations distributed in seven countries in the four seasons of the year. The obtained results are compared with the values generated by Radiosonde measurement and MODIS satellite images level 2 (Water Vapor data product). In more than 90% of cases, the difference between the GPS and Radiosonde solutions is less than 3 mm with a monthly RMS less than 1.6 and a correlation of about 95%. The comparison between the GPS and MODIS shows that in more than 65 % of the time, the difference between the two solutions is less than 4 mm with amonthly RMS less than 2.3, and the correlation is about 73%.

Meteorological OSSEs for New Zenith Total Delay Observations: Impact Assessment for the Hydroterra Geosynchronous Satellite on the October 2019 Genoa Event

Along the Mediterranean coastlines, intense and localized rainfall events are responsible for numerous casualties and several million euros of damage every year. Numerical forecasts of such events are rarely skillful, because they lack information in their initial and boundary conditions at the relevant spatio-temporal scales, namely O(km) and O(h). In this context, the tropospheric delay observations (strongly related to the vertically integrated water vapor content) of the future geosynchronous Hydroterra satellite could provide valuable information at a high spatio-temporal resolution. In this work, Observing System Simulation Experiments (OSSEs) are performed to assess the impact of assimilating this new observation in a cloud-resolving meteorological model, at different grid spacing and temporal frequencies, and with respect to other existent observations. It is found that assimilating the Hydroterra observations at 2.5 km spacing every 3 or 6 h has the largest positive impact on the forecast of the event under study. In particular, a better spatial localization and extent of the heavy rainfall area is achieved and a realistic surface wind structure, which is a crucial element in the forecast of such heavy rainfall events, is modeled.

On the Definition of the Strategy to Obtain Absolute InSAR Zenith Total Delay Maps for Meteorological Applications

Atmospheric Phase Screens (APSs) derived from Interferometric Synthetic Aperture Radar (InSAR) observations contain the difference between the tropospheric water vapor induced delay of two acquisition epochs, i.e. the slave and the master (or reference) epochs. Using estimates of the atmospheric state coming from independent sources, for example numerical models and/or Global Navigation Satellite System (GNSS) observations, the APSs can be transformed into absolute maps of Tropospheric Delay (Zenith Total Delay or ZTD), related to the columnar atmospheric water vapor content. In this work, a systematic comparison between various APS and ZTD products aims to determine a convenient strategy to go from APSs to InSAR-derived absolute ZTD maps, highlighting the uncertainties and approximations introduced in the entire processing. The main problem to solve is the evaluation of a sufficiently accurate high resolution master delay map. Different sources of data and two different approaches to derive the master are validated and compared to define the most suitable strategy for meteorological applications. Maps of ZTD obtained by an iterative interpolation of a global atmospheric circulation model values result more suited than those derived from the assimilation of GNSS observations into a NWP model. A time average approach to estimate the master map is more robust than the single epoch approach with respect to the choice of the master epoch. Still the choice of a proper master epoch in the InSAR processing chain as well as that of the maps to be averaged result crucial in the estimate of the master.

The Importance of Atmospheric Corrections on InSAR Surveys Over Turkey: Case Study of Tectonic Deformation of Bodrum-Kos Earthquake

The Bodrum-Kos earthquake which occurred on 20th July 2017 at 1:31 am local time with a magnitude of Mw 6.6 in the Gulf of Gokova in Turkey. The Bodrum-Kos earthquake caused a small radius tsunami in Bodrum coast and its surroundings. Here, the Interferometric Synthetic Aperture Radar (InSAR) method was utilized to reveal tectonic deformation caused by the Bodrum-Kos earthquake. The InSAR technique was increasingly used to detect ground deformation such as landslide, tectonic or volcanic. In this study, the crucial atmospheric corrections which are GACOS and phase-elevation corrections on InSAR data are discussed. GACOS correction is based on high-resolution European Centre for Medium-Range Weather Forecasts weather model at 0.125° and 6-h resolutions, SRTM DEM and ASTER GDEM. GACOS is used to separate stratified and turbulent signals from tropospheric total delays and generate high spatial resolution zenith total delay maps to be used for correcting InSAR measurements and other applications. Phase-elevation linear correction is also used to separate mostly stratified signals from tropospheric total delays due to the topographic elevation changes and 3 arc second topographic map was used in the phase-elevation correction. These atmospheric correction methods were tried both separately and sequentially and standard deviations of each method were presented. The points comparison of the cumulative line of sight displacement maps were made after and before correction. The point on the Karaada (C point) shows subsidence after the earthquake and then recovers again. Time-series results of all points show similar values until the earthquake occurs. Time-series result also shows that the Bodrum-Kos earthquake has a small radius effect over the land area only the north and maybe west island due to the occurrence on the sea and lost its much energy without reaching the land.

Assessing atmospheric moisture effects on heavy precipitation during HyMeX IOP16 using GPS nudging and dynamical downscaling

Abstract. Gaining insight into the interaction between atmospheric moisture and convection is determinant for improving the model representation of heavy precipitation, a weather phenomenon that causes casualties and monetary losses in the western Mediterranean region every year. Given the large variability of atmospheric moisture, an accurate representation of its distribution is expected to reduce the errors related to the representation of moist convective processes. In this study, we use a diagnostic approach to assess the sensitivity of convective precipitation and underlying mechanisms during a heavy precipitation event (Hydrological cycle in the Mediterranean eXperiment Intensive Observation Period; HyMeX IOP16) to variations of the atmospheric moisture spatio-temporal distribution. Sensitivity experiments are carried out by nudging a homogenized data set of the Global Positioning System-derived zenith total delay (GPS-ZTD) with sub-hourly temporal resolution (10 min) in 7 and 2.8 km simulations with the COnsortium for Small-scale MOdeling in CLimate Mode (COSMO-CLM) model over the western Mediterranean region. The analysis shows that (a) large atmospheric moisture amounts (integrated water vapour; IWV ∼ 40 mm) precede heavy precipitation in the affected areas. This occurs 12 h prior to initiation over southern France and 4 h over Sardinia, north-eastern Italy and Corsica, which is our main study area. (b) We found that the moisture is swept from the Atlantic by a westerly large-scale front associated with an upper level low on the one hand and evaporated from the Mediterranean Sea and north Africa on the other. The latter moisture transport occurs in the 1 to 4 km layer. (c) COSMO-CLM overestimated the atmospheric humidity over the study region (Corsica), and this was, to a good extent, corrected by the GPS-ZTD nudging. This reduced maximum precipitation (−49 % for 7 km and −16 % for 2.8 km) drastically, considerably improving the precipitation representation in the 7 km simulation. The convection-permitting simulation (2.8 km) without the GPS-ZTD nudging already did a good job in representing the precipitation amount. (d) The two processes that exerted the largest control on precipitation reduction were the decrease of atmospheric instability over Corsica (convective available potential energy; CAPE −35 %) and the drying of the lower free troposphere bringing additional dry air entrainment. In addition, the 7 km simulation showed a weakening of the represented low-pressure system and the associated cyclonic wind circulation. This ultimately reduced the intensity and number of convective updrafts represented over the island. These results highlight the large impact exerted by moisture corrections on precipitating convection and the chain of processes leading to it across scales.

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.

Assimilation of GPS Zenith Total Delay estimates in RAMS NWP model: Impact studies over central Italy

The purpose of the data assimilation is to use all the available information to determine as accurately as possible the state of the atmosphere. In this context we consider the assimilation of the GPS-ZTD (Global Positioning System – Zenith Total Delay) by a nudging scheme in Regional Atmospheric Modeling System at Institute of Atmospheric Sciences and Climate ([email protected]) at medium horizontal resolution (5 km). The water vapour mixing ratio (qv) given by the [email protected] is perturbed according to the differences between observed and simulated ZTD. To verify the impact of the GPS-ZTD data assimilation on the representation of the atmospheric humidity field, especially at the local scale, a numerical experiment was performed over central Italy area, over a one-month time frame. The studied network consists of 41 geodetic receivers and 3 single frequency receivers, this latter category of receivers has turned up more and more in marketing for the very low cost with respect to high level of performance. For what concerns all devices, processing was carried out in PPP (Precise Point Positioning) using goGPS, an Open Source Program Package for GNSS Positioning. For the application of PPP processing to single-frequency receivers, goSEID method (an alternative implementation of the SEID approach integrated in the goGPS open source software) was applied; the algorithm, starting from the observations of a unique (or more) dual frequency “reference” receiver, is able to generate L2 synthetic observations for any single frequency receiver located in its surroundings (15–20 km).