Refractivity Field Research Articles

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.

RadioPropa — A Modular Raytracer for In-Matter Radio Propagation

Experiments for radio detection of UHE particles such as e.g. ARA/ARIANNA or NuMoon require detailed understanding of the propagation of radio waves in the surrounding matter. The index of refraction in e.g. polar ice or lunar rock may have a complex spatial structure that makes detailed simulations of the radio propagation necessary to design the respective experiments and analyse their data. Here, we present RadioPropa as a new modular ray tracing code that solves the eikonal equation with a Runge-Kutta method in arbitrary refractivity fields. RadioPropa is based on the cosmic ray propagation code CRPropa, which has been forked to allow efficient incorporation of the required data structures for ray tracing while retaining its modular design. This allows for the setup of versatile simulation geometries as well as the easy inclusion of additional physical effects such as e.g. partial reflection on boundary layers in the simulations. We discuss the principal design of the code as well as its performance in example applications.

Estimating the Impact of Global Navigation Satellite System Horizontal Delay Gradients in Variational Data Assimilation

We developed operators to assimilate Global Navigation Satellite System (GNSS) Zenith Total Delays (ZTDs) and horizontal delay gradients into a numerical weather model. In this study we experiment with refractivity fields derived from the Global Forecast System (GFS) available with a horizontal resolution of 0.5°. We begin our investigations with simulated observations. In essence, we extract the tropospheric parameters from the GFS analysis, add noise to mimic observation errors and assimilate the simulated observations into the GFS 24h forecast valid at the same time. We consider three scenarios: (1) the assimilation of ZTDs (2) the assimilation of horizontal delay gradients and (3) the assimilation of both ZTDs and horizontal delay gradients. The impact is measured by utilizing the refractivity fields. We find that the assimilation of the horizontal delay gradients in addition to the ZTDs improves the refractivity field around 800 hPa. When we consider a single station there is a clear improvement when horizontal delay gradients are assimilated in addition to the ZTDs because the horizontal delay gradients contain information that is not contained in the ZTDs. On the other hand, when we consider a dense station network there is not a significant improvement when horizontal delay gradients are assimilated in addition to the ZTDs because the horizontal delay gradients do not contain information that is not already contained in the ZTDs. Finally, we replace simulated by real observations, that is, tropospheric parameters from a Precise Point Positioning solution provided with the G-Nut/Tefnut software, in order to show that the GFS 24h forecast is indeed improved when GNSS horizontal delay gradients are assimilated in addition to GNSS ZTDs; for the considered station (Potsdam, Germany) and period (June and July, 2017) we find an improvement in the retrieved refractivity of up to 4%.

On the Statistical Significance of Climatic Trends Estimated From GPS Tropospheric Time Series

AbstractFor more than two decades, Global Positioning System (GPS) tropospheric delays have successfully been exploited to monitor the tropospheric water vapor in near real time and reprocessing mode. Although reprocessed data are considered reliable for climatic research, it is important to address the often present gaps, inhomogeneities, and to use a proper model to describe the stochastic part of the time series so that trustworthy trends are estimated. Having relatively long time series, daily reprocessed tropospheric Zenith Total Delay, precipitable water vapor (PWV), and gradients from the Tide Gauge benchmark monitoring network are used in this work to estimate climatic trends. We use a first‐order autoregressive model AR(1) to describe the residuals after the trend estimation so that a correct trend uncertainty is estimated. Using the same model, we obtain the number of years of PWV data required to estimate statistically significant trends. For comparison, we produce tropospheric parameters at each Tide Gauge station based on ERA‐Interim refractivity fields. We found that 83% of 64 GPS stations show a positive PWV trend below 1 mm/decade independent of the time interval, with approximately half of the trends indicated significant. There is a strong correlation (86%) between the Global Navigation Satellite Systems and ERA‐Interim PWV trends. The trends tend to increase when moving east and south on the European map. The results show a percentage change of PWV of 3–8% per a degree Celsius rise in temperature. The number of years required to detect significant PWV trends varies between 30 and 40 years.

Simulation of liquid water and ice contributions to bending angle profiles in the radio occultation technique

Inversion of radio occultation profiles usually relies on the Abel transform in a spherically symmetric atmosphere. The main contribution to the refractive index comes from gaseous constituents such as pressure, temperature and water vapor. However, the impact of other particles in the troposphere, neglected in the retrieval, can become important during specific weather events causing the commonly applied assumptions to be invalid. In this study, Global Forecast System model is used to simulate the effect of cloud water on bending angle profiles. The information of solid and liquid fractions was assessed from the cloud mixing ratio based on a temperature-dependent separation. The signal was propagated between GPS satellite and low-Earth orbiter through tangent point placed in the vicinity of cloud water. The assumption of a spherically symmetric distribution for cloud refractivity fields is validated based on 2-dimensional simulations with multiple phase screen method. Using one year of forecast data for 2016, the most statistically probable examples were selected and analyzed as well as the uppermost impact of cloud significance was determined in terms of bending angle errors. Typical refractivity values induced by liquid clouds are within 2 ppm and result in single-spike profile structures. Corresponding fractional errors of the bending angle can reach −4% with respect to retrievals with zero clouds contributions. In the presence of unusual weather phenomena, simulations in a horizontally inhomogeneous atmosphere suggest a large dependency on propagation paths that should be considered in the modeling of cloud impact. Bending angles can be affected by −10% fractional error, which inverted with Abel transform corresponds to refractivity error of −2%. The highest fractional difference induced by the ice water is generally below −1% in the bending angle and −0.5% in the refractivity. The most significant cloud content was observed in the first 6 km above the Earth’s surface, whereas ice clouds can affect radio occultation profiles up to the top height of 16 km.

Real-Time Precise Point Positioning Using Tomographic Wet Refractivity Fields

The tropospheric wet delay induced by water vapor is a major error source in precise point positioning (PPP), significantly influencing the convergence time to obtain high-accuracy positioning. Thus, high-quality water vapor information is necessary to support PPP processing. This study presents the use of tomographic wet refractivity (WR) fields in PPP to examine their impacts on the positioning performance. Tests are carried out based on 1-year of 2013 global navigation satellite system (GNSS) observations (30 s sampling rate) from three stations with different altitudes in the Hong Kong GNSS network. Coordinate errors with respect to reference values at a 0.1 m level of convergence is used for the north, east, and up components, whilst an error of 0.2 m is adopted for 3D position convergence. Experimental results demonstrate that, in both static and kinematic modes, the tomography-based PPP approach outperforms empirical tropospheric models in terms of positioning accuracy and convergence time. Compared with the results based on traditional, Saastamoinen, AN (Askne and Nordis), and VMF1 (Vienna Mapping Function 1) models, 23–48% improvements of positioning accuracy, and 5–30% reductions of convergence time are achieved with the application of tomographic WR fields. When using a tomography model, about 35% of the solutions converged within 20 min, whereas only 23%, 25%, 25%, and 30% solutions converged within 20 min for the traditional, Saastamoinen, AN, and VMF1 models, respectively. Our study demonstrates the benefit to real-time PPP processing brought by additional tomographic WR fields as they can significantly improve the PPP solution and reduce the convergence time for the up component.

Detecting Water Vapor Variability during Heavy Precipitation Events in Hong Kong Using the GPS Tomographic Technique

AbstractWater vapor has a strong influence on the evolution of heavy precipitation events due to the huge latent heat associated with the phase change process of water. Accurate monitoring of atmospheric water vapor distribution is thus essential in predicting the severity and life cycle of heavy rain. This paper presents a systematic study on the application of tomographic solutions to investigate water vapor variations during heavy precipitation events. Using global positioning system (GPS) observations, the wet refractivity field was constructed at a temporal resolution of 30 min for three heavy precipitation events occurring in Hong Kong, China, in 2010–14. The zenith wet delay (ZWD) is shown to be a good indicator in observing the water vapor evolution in heavy rain events. The variabilities of water vapor at five altitude layers (<1000, 1000–2000, 2000–3000, 3000–5000, and >5000 m) were examined. It revealed that water vapor above 3000 m has larger fluctuation than that under 3000 m, though it accounts for only 10%–25% of the total amount of water vapor. The relative humidity fields derived from tomographic results revealed moisture variation, accumulation, saturation, and condensation during the heavy rain events. The water vapor variabilities observed by tomography have been validated using European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis and radiosonde data. The results positively demonstrated the potential of using water vapor tomographic technique for detecting and monitoring the evolution of heavy rain events.

Comparison of real‐time refractivity measurements by radar with automatic weather stations, AROME‐WMED and WRF forecast simulations during SOP1 of the HyMeX campaign

Weather radars, originally designed to detect and quantify precipitation, can be used to estimate and map the refractivity at low levels, a proxy for humidity. As highlighted by previous studies, this presents a definite meteorological interest, both for numerical weather prediction and for atmospheric process studies. Recent works have given keys to performing high‐accuracy measurements with operational radar without decreasing the quality of reflectivity and Doppler wind classical measurements, and the retrieval of radar refractivity is now performed in real time with the Météo‐France Application Radar à la Météorologie Infra‐Synoptique (ARAMIS) operational network.Taking advantage of the Hydrological cycle in Mediterranean eXperiment (HyMeX) field campaign (September–November 2012), refractivity measured by a few radars located in southeast France has been compared with in situ measurement by Automatic Weather Stations (AWSs), and correlation between these two independent observations is quite good, in particular giving high quality for the diurnal cycle and during the pre‐convection period measurement by the radar.To go further in the evaluation of the usefulness of such a product, we compared refractivity derived from radar measurements and from two different kinds of model: the numerical prediction model Applications de la Recherche à l'Opérationnel à Méso‐Echelle – Western MEDiterranean (AROME‐WMED) using a 2.5 km resolution grid mesh over southern France and a coarser resolution simulation (54 × 54 km) performed with the Weather Research and Forecasting (WRF) model, which both ran in a forecasting mode during the HyMeX Special Observing Period 1. These two models give access to the variability of the modelling and thus enable us to quantify the uncertainty of the refractivity modelling. The result of this comparison is generally fairly good, with a pattern of refractivity field similar to observations (AWS and radar), although obviously with locally strong differences.We finally illustrate the usefulness of refractivity mapping by radar by investigating a typical meteorological situation of a convective system observed during the HyMeX campaign.

Improving Radar Refractivity Retrieval by Considering the Change in the Refractivity Profile and the Varying Altitudes of Ground Targets

AbstractAccurate radar refractivity retrievals are critical for quantitative applications, such as assimilating refractivity into numerical models or studying boundary layer and convection processes. However, the technique as originally developed makes some simplistic assumptions about the heights of ground targets () and the vertical gradient of refractivity (). In reality, the field of target phases used for refractivity retrieval is noisy because of varying terrain and introduces estimation biases. To obtain a refractivity map at a constant height above terrain, a 2D horizontal refractivity field at the radar height must be computed and corrected for altitude using an average . This is achieved by theoretically clarifying the interpretation of the measured phase considering the varying and the temporal change of . Evolving causes systematic refractivity biases, as it affects the beam trajectory, the associated target range, and the refractivity field sampled between selected targets of different heights. To determine and changes, a twofold approach is proposed: first, can be reasonably inferred based on terrain height; then, a new method of estimation is devised by using the property of the returned powers of a pointlike target at successive antenna elevations. The obtained shows skill based on in situ tower observation. As a result, the data quality of the retrieved refractivity may be improved with the newly added information of and .

Wave propagation simulation of radio occultations based on ECMWF refractivity profiles

AbstractThis paper describes a complete radio occultation simulation environment, including realistic refractivity profiles, wave propagation modeling, instrument modeling, and bending angle retrieval. The wave propagator is used to simulate radio occultation measurements. The radio waves are propagated through a refractivity field which has been calculated with the use of numerical weather prediction models. The numerical weather prediction model used in this paper is a model from the European Centre for Medium‐Range Weather Forecasts (ECMWF). The wave propagator has been used to simulate a number of radio occultations. The output from the wave propagator simulator is used as input to a Full Spectrum Inversion retrieval module which calculates geophysical parameters. These parameters can be compared to the ECMWF atmospheric profiles. The comparison can be used to reveal system errors and get a better understanding of the physics. The wave propagation simulations will in this paper also be compared to real measurements. These radio occultations have been exposed to the same atmospheric conditions as the radio occultations simulated by the wave propagator. This comparison reveals that precise radio occultations can be simulated when the simulations are based on wave propagation and refractivity field inputs from a numerical weather prediction model.

Influence of heterogeneous refractivity on optical wave propagation in coastal environments

Spatial variations of refractivity significantly dictate the characteristics of optical wave propagation through the atmosphere. Consequently, the ability to simulate such propagation is highly dependent upon the accurate characterization of refractivity along the propagation path. Unfortunately, the scarcity of high spatiotemporal resolution observational data has forced many past studies of optical wave propagation to assume horizontally homogeneous (HH) atmospheric conditions. However, the (adverse) impact of such an assumption has not been quantified in the literature. In this paper, we attempt to fill this void by utilizing a mesoscale modeling-based approach to explicitly simulate atmospheric refraction. We then compare the differences of the HH refractivity fields to the mesoscale model-derived refractivity fields by means of a realistic atmospheric event and through ray tracing simulations. In this study, we model a coastal low-level jet, a common coastal atmospheric phenomenon which is associated with heterogeneous thermal and refractivity fields. Observational data from a radiosonde and a radar wind profiler near the northeastern region of the United States are used for model validation. The observed characteristics of low-level jet (e.g., evolution, intensity, location) and associated temperature inversion are found to be reasonably well captured by the mesoscale model. The simulated nighttime refractivity gradient field manifests significant spatial heterogeneity; over land, the refractivity gradient is much stronger and amplified near the ground, whereas it becomes much weaker over the ocean. We quantify the effect of this heterogeneity on optical ray trajectories by simulating a suite of rays and documenting the variability of their altitudes at certain propagation ranges. It is found that the altitude of optical rays may vary tens of meters during a diurnal cycle, and at nighttime the rays may bend downward by more than 150 m at a range of 100 km. We run additional ray tracing simulations using refractivity profiles from a single location and assuming HH refractivity along the propagation path. It is observed that the HH approach yields instantaneous ray bending magnitudes up to 30 % less than the ray bending based on the refractivity simulated by the mesoscale model. At the same time, it is found that the mesoscale model-based refractivity fields may have uncertainty introduced by different factors associated with the model configuration. Of these factors, turbulence parameterization is explored in-depth and found to be responsible for more uncertainty than spatial grid resolution. To be more specific, different turbulence parameterizations are found to produce significantly varying temperature inversion parameters (e.g., height, magnitude), which are critical factors influencing ray trajectories. Collectively, these results highlight the potential advantages and disadvantages of utilizing a mesoscale model to simulate refractivity in coastal areas as opposed to assuming HH refractivity.

Vertical and latitudinal variation of the intertropical convergence zone derived using GPS radio occultation measurements

Using GPS radio occultation refractivity data collected over the period of 2002–2013, we present a new method for identification of the intertropical convergence zone (ITCZ). The ITCZ is identified by estimating the maximum in the monthly meridional refractivity and specific humidity field by applying a Gaussian fit at each longitude. The interannual variability and climatology of the ITCZ is presented from 12years of refractivity data. This new method captures all the general features of ITCZ extent and its variability. We also examine the effects of the ITCZ vertically during different seasons. The ITCZ is observed mostly at eastern Pacific in May month, and it is zonally distributed in the September and October months of each year. The zonal variability is large between lower and higher levels, particularly over the Indian monsoon and western Pacific. The latitudinal difference in the vertical extent between 850hPa and higher levels is larger during the northern hemisphere (NH) summer than NH winter.

Capturing the Signature of Severe Weather Events in Australia Using GPS Measurements

Rapid developments in satellite positioning, navigation, and timing have revolutionized surveying and mapping practice and significantly influenced the way people live and society operates. The advent of new generation global navigation satellite systems (GNSS) has heralded an exciting future for not only the GNSS community, but also many other areas that are critical to our society at large. With the rapid advances in space-based technologies and new dedicated space missions, the availability of large scale and dense contemporary GNSS networks such as regional continuously operating reference station (CORS) networks and the developments of new algorithms and methodologies, the ability of using space geodetic techniques to remotely sense the atmosphere (i.e., the troposphere and ionosphere) has dramatically improved. Real time GNSS-derived atmospheric variables with a high spatio-temporal resolution have become an important new source of measurements for meteorology, particularly for extreme weather events since water vapour (WV), as the most abundant element of greenhouse gas and accounting for $\sim $ 70% of global warming, is under-sampled in current meteorological and climate observing systems. This study investigates the emerging area of GNSS technology for near real-time monitoring and forecasting of severe weather and climate change research. This includes both ground-based global positioning system (GPS)-derived precipitable water vapour (PWV) estimation and four-dimensional (4-D) tomographic modeling for wet refractivity fields. Two severe weather case studies were used to investigate the signature of GPS-derived PWV and wet refractivity derived from the 4-D GPS tomographic model under the influence of severe mesoscale convective systems (MCSs). GPS observations from the Victorian state-wide CORS network, i.e., GPSnet, in Australia were used. Results showed strong spatial and temporal correlations between the variations in the ground-based GPS-derived PWV and the passage of the severe MCS. This indicates that the GPS method can complement conventional meteorological observations for the studying, monitoring, and potentially predicting of severe weather events. The advantage of using the ground-based GPS technique is that it can provide continuous observations for the storm passage with high temporal and spatial resolution. Results from these two case studies also suggest that GPS-derived PWV can resolve the synoptic signature of the dynamics and offer precursors to severe weather, and the tomographic technique has the potential to depict the three-dimensional (3-D) signature of wet refractivity for the convective and stratiform processes evident in MCS events. This research reveals the potential of using GNSS-derived PWV to strengthen numerical weather prediction (NWP) models and forecasts, and the potential for GNSS-derived PWV and wet refractivity fields to enhance early detection and sensing of severe weather.

Tropospheric delay ray tracing applied in VLBI analysis

AbstractTropospheric delay modeling error continues to be one of the largest sources of error in VLBI (very long baseline interferometry) analysis. For standard operational solutions, we use the VMF1 elevation‐dependent mapping functions derived from European Centre for Medium‐Range Weather Forecasts data. These mapping functions assume that tropospheric delay at a site is azimuthally symmetric. As this assumption is not true, we have instead determined the ray trace delay along the signal path through the troposphere for each VLBI quasar observation. We determined the troposphere refractivity fields from the pressure, temperature, specific humidity, and geopotential height fields of the NASA Goddard Space Flight Center Goddard Earth Observing System version 5 numerical weather model. When applied in VLBI analysis, baseline length repeatabilities were improved compared with using the VMF1 mapping function model for 72% of the baselines and site vertical repeatabilities were better for 11 of 13 sites during the 2 week CONT11 observing period in September 2011. When applied to a larger data set (2011–2013), we see a similar improvement in baseline length and also in site position repeatabilities for about two thirds of the stations in each of the site topocentric components.

Atmospheric Duct Estimation Using Radar Sea Clutter Returns by the Adjoint Method with Regularization Technique

Abstract This paper focuses on retrieving the atmospheric duct structure from radar sea clutter returns by the adjoint approach with the regularization technique. The adjoint is derived from the split-step Fourier parabolic equation method, and the regularization term is constructed by the background refractivity field. To ensure successful implementations of the regularization, the L-curve criterion is used to find the optimal regularization parameter. The feasibility of the proposed method is validated by the numerical simulations of different noise-level clutter returns, as well as a real clutter profile measured by the S-Band Space Range Radar located in Wallops Island. In the process of inversions, the refractivity profile is first obtained by genetic algorithm, and then it is used as the background field for the adjoint method. The retrieved results indicate that, with an appropriate regularization parameter, the structure of the background refractivity profile can be improved by the proposed method.

Application of Compressive Sensing to Refractivity Retrieval Using Networked Weather Radars

Radar-derived refractivity from stationary ground targets can be used as a proxy of near-surface moisture field and has the potential to improve the forecast of convection initiation. Refractivity retrieval was originally developed for a single radar and was recently extended for a network of radars by solving a constrained least squares (CLS) minimization. In practice, the number of high-quality ground returns can be often limited, and consequently, the retrieval problem becomes ill-conditioned. In this paper, an emerging technology of compressive sensing (CS) is proposed to estimate the refractivity field using a network of radars. It has been shown that CS can provide an optimal solution for the underdetermined inverse problem under certain conditions and has been applied to different fields such as magnetic resonance imaging, radar imaging, etc. In this paper, a CS framework is developed to solve the inversion. The feasibility of CS for refractivity retrieval using single and multiple radars is demonstrated using simulations, where the model refractivity fields were obtained from the Advanced Regional Prediction System. The root-mean-squared error was introduced to quantify the performance of the retrieval. The performance of CS was assessed statistically and compared to the CLS estimates for various amounts of measurement errors, numbers of radars, and model refractivity fields. Our preliminary results have shown that CS can consistently provide relatively robust and high-quality estimates of the refractivity field.

Tropospheric wet refractivity tomography based on the BeiDou satellite system

This paper presents a novel approach for assessing the precision of the wet refractivity field using BDS (BeiDou navigation satellite system) simulations only, GPS, and BDS+GPS for the Shenzhen and Hongkong GNSS network. The simulations are carried out by adding artificial noise to a real observation dataset. Instead of using the d and s parameters computed from slant wet delay, as in previous studies, we employ the Bias and RMS parameters, computed from the tomography results of total voxels, in order to obtain a more direct and comprehensive evaluation of the precision of the refractivity field determination. The results show that: (1) the precision of tropospheric wet refractivity estimated using BDS alone (only 9 satellites used) is basically comparable to that of GPS; (2) BDS+GPS (as of current operation) may not be able to significantly improve the data’s spatial density for the application of refractivity tomography; and (3) any slight increase in the precision of refractivity tomography, particularly in the lower atmosphere, bears great significance for any applications dependent on the Chinese operational meteorological service.

Tropospheric wet refractivity tomography using multiplicative algebraic reconstruction technique

Algebraic reconstruction techniques (ART) have been successfully used to reconstruct the total electron content (TEC) of the ionosphere and in recent years be tentatively used in tropospheric wet refractivity and water vapor tomography in the ground-based GNSS technology. The previous research on ART used in tropospheric water vapor tomography focused on the convergence and relaxation parameters for various algebraic reconstruction techniques and rarely discussed the impact of Gaussian constraints and initial field on the iteration results. The existing accuracy evaluation parameters calculated from slant wet delay can only evaluate the resultant precision of the voxels penetrated by slant paths and cannot evaluate that of the voxels not penetrated by any slant path. The paper proposes two new statistical parameters Bias and RMS, calculated from wet refractivity of the total voxels, to improve the deficiencies of existing evaluation parameters and then discusses the effect of the Gaussian constraints and initial field on the convergence and tomography results in multiplicative algebraic reconstruction technique (MART) to reconstruct the 4D tropospheric wet refractivity field using simulation method.

The Effect of Phase-Correlated Returns and Spatial Smoothing on the Accuracy of Radar Refractivity Retrievals

Abstract Radar refractivity retrievals have the potential to accurately capture near-surface humidity fields from the phase change of ground clutter returns. In practice, phase changes are very noisy and the required smoothing will diminish large radial phase change gradients, leading to severe underestimates of large refractivity changes (ΔN). To mitigate this, the mean refractivity change over the field (〈ΔN〉field) must be subtracted prior to smoothing. However, both observations and simulations indicate that highly correlated returns (e.g., when single targets straddle neighboring gates) result in underestimates of 〈ΔN〉field when pulse-pair processing is used. This may contribute to reported differences of up to 30 N units between surface observations and retrievals. This effect can be avoided if 〈ΔN〉field is estimated using a linear least squares fit to azimuthally averaged phase changes. Nevertheless, subsequent smoothing of the phase changes will still tend to diminish the all-important spatial perturbations in retrieved refractivity relative to 〈ΔN〉field; an iterative estimation approach may be required. The uncertainty in the target location within the range gate leads to additional phase noise proportional to ΔN, pulse length, and radar frequency. The use of short pulse lengths is recommended, not only to reduce this noise but to increase both the maximum detectable refractivity change and the number of suitable targets. Retrievals of refractivity fields must allow for large ΔN relative to an earlier reference field. This should be achievable for short pulses at S band, but phase noise due to target motion may prevent this at C band, while at X band even the retrieval of ΔN over shorter periods may at times be impossible.

Detecting Severe Weather using GPS Tomography: An Australian Case Study

The dynamics of water vapour (WV) has a strong influence on the formation and lifecycle of severe mesoscale convective storm systems due to the large energy transfers in the hydrological processes. Contrary to its importance WV remains poorly understood and inadequately measured both spatially and temporally, especially in the southern hemisphere where meteorological data are sparse. GPS meteorology is currently one of the most important atmospheric remote sensing instruments for meteorology and climatology due to its high spatial and temporal resolutions. Kalman filtering based GPS tomography is a promising method of reconstructing dynamically changing four dimensional (4D) wet refractivity fields. This method takes advantage of the high density and homogeneity of ground-based GPS Continuously Operating Reference Station (CORS) networks. Ground-based GPS tomography has the potential to utilize the dense groundbased infrastructure in Australia providing wet refractivity field solutions at a high spatial and temporal resolution to improve early detection and prediction of severe weather. This research presents a case study based on the analysis of an extreme convective super cell storm in the Victorian region during March 2010 using GPS tomography and the most advanced state-wide CORS network – GPSnet in Australia. Integrated Precipitable Water Vapour (PWV) estimates derived for the MOBS GPS CORS station confirmed high time resolution as well as sensitivity to incoming severe weather. A wet refractivity index adopted for GPS tomographic wet refractivity profiles shows an excessive increase as a response to supercell thunderstorm formation. Finally a 2D cross section mapping over the lifecycle of this severe weather event concludes a correlation between the highly dynamic spatial and temporal changes of wet refractivity modelled using 4D GPS tomography with precipitation intensities measured using weather radars.