Capturing the Signature of Severe Weather Events in ... - IEEE Xplore

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 8, NO. 4, APRIL 2015

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Capturing the Signature of Severe Weather Events in Australia Using GPS Measurements Kefei Zhang, Toby Manning, Suqin Wu, Witold Rohm, D. Silcock, and Suelynn Choy

Abstract—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 ∼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 Manuscript received September 27, 2014; revised February 03, 2015; accepted February 11, 2015. Date of publication April 29, 2015; date of current version May 26, 2015. This work was supported in part by the Natural Disaster Resilience Grants Scheme (NDRG) of Victoria, in part by the Australian Space Research Program (ASRP), and in part by the Australian Research Council (ARC) Linkage (LP0883288) projects funded by the Australian Federal Government. K. Zhang is with the Satellite Positioning for Atmosphere, Climate and Environment (SPACE) Research Centre, RMIT University, Melbourne, VIC3001, Australia and also with the School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China (e-mail: [email protected]). T. Manning, S. Wu, D. Silcock, and S. Choy are with the SPACE Research Centre, RMIT University, Melbourne VIC3001, Australia. W. Rohm was with the SPACE Research Centre, RMIT University, Melbourne, VIC3001, Australia. He is now with the Institute of Geodesy and Geoinformatics, WUELS, Wroclaw 51-141, Poland. Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JSTARS.2015.2406313

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. Index Terms—Global positioning system (GPS), precipitable water vapour (PWV), severe weather, tomography, wet refractivity.

I. I NTRODUCTION

T

HE DYNAMICS of water vapour (WV) has a strong influence on the formation, lifecycle, and dissipation of severe mesoscale convective systems (MCSs) events due to the large energy transfer in the hydrological processes in the troposphere, hence it is significant to measure WV in response to severe weather detection. WV is also a key variable for long-term climate study [1], [17]. However, due to the limitation on both the number of conventional sounding stations and the observing times, WV in the atmosphere remains inadequately measured [38]. This is particularly evident in Australia, and more broadly, in the southern hemisphere in which large areas of land remain unoccupied. For regional WV modeling, sparse observation data mean low resolution of the model. The spatial and temporal resolutions are two imperative parameters for depiction of hydrological hazards using highly dynamic WV modeling [18], [37]. Furthermore, the amount of WV contained in the troposphere also has significant implications for determining the strength and severity of a severe weather event [8], [19]. In order to obtain sufficient WV measurements, global positioning system (GPS) has extensively been used as a robust observational system for measuring the integrated amount of WV in the troposphere due to its high accuracy and allweather operability. It is currently regarded as one of the most important atmospheric remote sensing instruments for weather forecasting and climatology, due to the rapid development and deployment of regional GPS continuously operating reference station (CORS) networks, the development of space-borne GPS technologies and the continuous operability of the systems involved [3]–[5], [13], [16], [26], [32], [34], [36], [38]. In addition, unlike conventional meteorological observing methods, the tropospheric WVs can be continuously obtained from existing ground-based GPS CORS stations at almost no further costs.

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For a four-dimensional (4-D) modeling of regional wet refractivity fields, tomography is a promising method for reconstructing the dynamic changes of wet refractivity fields [7]. Concept studies have identified the potential benefit for tropospheric tomography including [2], [10]–[12], [15], [21], [26], [29], [35]. The dense and well-distributed ground-based GPS CORS networks in Australia can be used to provide wet refractivity fields at a high spatial and temporal resolution. These GPS-derived wet refractivity solutions are a critical input for the tomographic modeling and have the potential to improve early detection and prediction of severe weather and precipitation systems [7]. Currently, longstanding joint collaboration between the RMIT SPACE Research Centre and the Australian Bureau of Meteorology has provided a research platform for the implementation of space-borne GPS meteorological information into the current numerical weather prediction (NWP) model for Australia [14]. This study aims to incorporate precipitable water vapour (PWV) derived from ground-based GPS CORS networks within Australia and use a tomographic model to investigate the signature of regional wet refractivity distribution during the influence of a severe weather event. Two severe weather case studies are presented for analyses of the spatial and temporal dynamics of PWV in one-dimension (1-D) and wet refractivity in three-dimension (3-D) using a discretized model of the atmosphere and the direct correlation with cumulative rainfall and the precipitation intensities of a convective super cell thunderstorm. The structure of the paper is outlined below. Detailed description of GPS signal refraction, the principles and functional models of GPS-derived wet refractivity tomography are given in Section II. The model setup and fundamentals of severe MCS and methods of deriving humidity sensitivity from GPS measurements during these events including a new average wet refractivity index (RIwet ) is presented in Section III. Investigations into GPS-derived PWV estimates for two case studies are presented with supporting results detecting a strong signature of WV distribution prior to, during and after the severe weather events in Section IV. A case study investigating 4-D wet refractivity distribution during severe weather using a Kalman filter-based tomographic technique is presented in Section V and conclusion are given in Section VI. II. F UNDAMENTALS OF G LOBAL NAVIGATION S ATELLITE S YSTEMS (GNSS) M ETEOROLOGY The concept of GPS meteorology was first presented in [4]. The GPS signals are delayed and bent due to the refractive index (n) of the ionosphere and troposphere. The ionospheric effect is dispersive and it can be eliminated for all practical purposes using an ionosphere free (IF) linear combination [9]. This leaves the tropospheric path delay, which can be separated into two components: the hydrostatic and wet delays. The hydrostatic delay can be obtained from a standard meteorological model at a high accuracy, while the wet delay needs to be estimated from GPS measurements due to the dynamic nature of the troposphere. From the wet delay, the integrated WV and wet refractivity over a GPS station can be derived. If a sufficient

number of discrete tropospheric delays can be obtained from a regional GPS CORS network, the tomographic technique can be used to model the 3-D spatial distribution of wet refractivity over the region. A 4-D tomographic model is a time series of such a 3-D tomographic modeling result, which can be used to investigate regional wet refractivity distribution in both spatial and temporal domains. The integration of refractivity N along the tropospheric slant path delay (SPD) between satellite x and receiver a can be expressed as [33] x Δρxa =

(n − 1) ds = 10−6 ·

a

x Nds = SHD + SWD (1) a

where Δρxa SPD from station a to satellite x (m); ds integral increment along the SPD (m); and SHD and SWD slant hydrostatic delay and wet delay (m). It is noted that the bending of the GPS signal is neglected in (1). Refractivity N can be expressed by the sum of Ndry and Nwet , which are functions of other atmospheric variables [27] Pd T Pw Pw + 375 463 2 = 71.295 T T

Ndry = 77.689

(2)

Nwet

(3)

where Pd partial pressure of dry air (hPa); T atmospheric temperature (K); and Pw partial pressure of WV (hPa). In GPS data processing, the commonly used observation model is an IF linear combination of the dual-frequency observations for eliminating the ionospheric effect term. In order to decrease the number of unknown parameters to be estimated, all the slant tropospheric delays from the same station but different satellites are mapped into the zenith direction using a mapping function, e.g., the Neill mapping function [9], [22]. The tropospheric delay to be estimated is the zenith total delay (ZTD) and the PWV derived from this ZTD is consequently for the zenith direction. In addition, using a standard tropospheric model, e.g., the Saastamoinen hydrostatic equation (Saastamoinen, 1972), and the surface meteorological data such as pressure and temperature measured at nearby synoptic weather stations along with the geographic latitude (φ) and geoid height (H) of the GPS station, the hydrostatic or dry delay at the zenith [i.e., the zenith hydrostatic delay (ZHD)] can be calculated at a high accuracy. Alternative tropospheric models may be used for this step, which take advantage of different parameters. As a result, the remaining delay to be estimated is regarded as the zenith ) and a wet delay (ZWD). Based on the ZWD (i.e., ΔZWD a conversion factor Π, PWV can be obtained by PWV = ZWD · Π.

(4)

There are several software packages available for estimating the GPS-derived ZWD. In this study, the Bernese GPS processing software V5.2 [9] was used, and the maximum number of baselines strategy and double-difference (DD) approach

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for phase observations were adopted; the DD phase residuals Δ∇Φx,y a,b were obtained from the DD observation model; the Neill mapping function [22] m(elxa ) was used to map the STD to the ZTD; the Saastamoinen model [28] with ground meteorological observations for the GPS stations as its input were adopted to calculate the ZHD for the dry delay correction in the observation equations. After the GPS-derived ZWD estimates for all baselines were obtained, the reconstruction of a tomographic model for the wet delays over the area can be performed. The input for the tomographic model is all the baselines’ DD SWD (i.e., Δ∇SWDx,y a,b ), which can be constructed by [18], [23], [33] x,y x,y Δ∇SWDx,y a,b = Δ∇SWDa,b + Δ∇Φa,b

(5)

x,y where Δ∇Φx,y a,b is the DD phase residual; and Δ∇SWDa,b is the isotropic DD SWD constructed by the mapping function and the GPS-derived ZWDs at the two stations of the baseline   ZWD · m (elx ) − ΔZWD · m (elx ) Δ∇SWDx,y b a a a,b = Δb  (6)  y ZWD · m (ely ) · m (el ) − Δ − ΔZWD a a b b

where ΔZWD GPS-derived ZWD estimate at station a; a m(el) Neill wet mapping function; and elevation angle from station a to satellite x. (elxa ) It should be noted that the use of DD results like (5) and (6) takes advantage of elimination of the clock biases of the satellite and receiver [35]. The final DD SWD (i.e., Δ∇SWDx,y a,b ) was used as the integral observations of the tomographic modeling for reconstructing the wet refractivity fields. The tomographic inversion process involves the estimation of the scalar field of wet refractivity values within a finite curvilinear grid from multiple integrated values passing through the media at different positions and orientations [2], [6], [7], [12], [14], [23], [25], [31], [33]. In this study, the 4-D wet refractivity fields were processed using the Atmospheric Water Vapour Tomography Software 2 (AWATOS 2). This software uses Kalman filtering for the forward modeling due to its significant advantages for estimating the evolution of dynamically changing parameters. Using a trilinear parameterized field algorithm, the DD SWD observations were expressed in AWATOS 2 as a weighted-sum of the grid nodes [24]. The weighted-sum of the Nwet at the voxel corners was used to solve the integral   si+1 x Nwet,i dsi xa . (7) ΔSWD a = 10−6 i

si

In the tomographic observation system solved for the wet refractivity field, additional constraints were also used in the form of pseudo observations [19], [24]. III. T OMOGRAPHIC M ODEL S ETUP A. Discretization of the Troposphere The GPSnet CORS network covering the extent of Victoria was used for the two case studies. The area under investigation ranged from 141◦ to 148◦ E longitude and from −35.8◦

Fig. 1. (a) Horizontal location (A) of a profile along with the GPSnet horizontal distribution and base ellipsoidal voxel model. (b) Vertical location (A) of the profile along with the vertical distribution of GPSnet and vertical layers.

to −38.6◦ S latitude [Fig. 1(a)] with a height domain of 0– 15 000 m [Fig. 1(b)]. The discretization of this domain for the lower atmosphere was defined with a finite curvilinear voxel grid using latitude and longitude boundaries referenced to the WGS84 ellipsoid. A 10◦ boundary buffer was attached to the outside of this model domain to ensure that all rays were within the model and pass through the top boundary. The horizontal resolution was set to 0.5◦ [Fig. 1(a)], which is approximately the overall diameter size of the super cell thunderstorm and is slightly denser than the GPSnet inter-station distance of 70 km [19]. This was the suggested voxel size following the preconditions of GPS-derived wet refractivity fields using a tomographic modeling strategy from [2]. To comprehensively depict the dynamics of the individual convection and stratiform domains of a severe supercell storm, a much finer horizontal resolution would be needed. However, this discretization is limited by the resolution of GPSnet, where an increase in the number of unknowns in the observation equation system does not add additional observations and ultimately, in the presence of horizontal constraints a smoothing effect was generated as concluded in [20].

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TABLE I PARAMETER S ETTINGS FOR THE I NITIAL S TATE OF THE K ALMAN F ILTER

B. Model Setup The tropospheric delays from GPSnet (Fig. 1) were obtained using the Bernese V5.2 and adopting the maximum number of observations baseline strategy and the DD approach. The hourly ZTD estimates, DD phase residuals, interpolated meteorological data from the NWP model called ACCESS-R, along with the satellite and receiver coordinates were all used to reconstruct the DD SWD observations expressed in (5). These were the primary input for the tomographic model/inversion during each update step of the Kalman filter. The tri-linear voxel parameterization method represented by (7) was based on a long-term GPS tomographic study conducted by [23]. An exponential wet refractivity field was used to initialize the state vector of the Kalman filter. This field was modeled on average wet refractivity profiles from radio sounding balloons during a 7-day period. The initial state setting of the Kalman filter is shown in Table I. The horizontal and vertical correlation lengths of the prediction model were set through statistical optimization analysis of model resolution [20]. The Kalman filter implements a random walk process on the refractivity parameter with the prediction process noise modeled as Gaussian. The data sampling interval was set to 30 s, which coincided with the epoch rate of the GPS data processing and output for DD residuals, and the update step size of the Kalman filter was set to 5 min. C. Evaluation Methods Several methods were used to evaluate the accuracies of the GPS-derived PWV and wet refractivity solutions and also to identify the signature of these GPS observations in response to convective storm and precipitation intensities. First, radiosonde-derived PWV for the co-located GPS stations was computed for the validation of the result accuracy of the two case studies. Cumulative rainfall was used to identify correlations between the two data sets as presented in Section IV. Second, the radiosonde-derived wet refractivity results at the co-located profile points (A), see Fig. 1(a) and (b), were used to validate the GPS tomographic modeling result. It provided 15 comparison points along the vertical profile from 0 to 15 000 m altitude. This profile extracted from the 4-D wet refractivity fields were also used to identify the signature of convection and regions of precipitation and inflow common to severe MCS. Vertical and horizontal wet refractivity gradients were used to depict the 4-D distribution of wet refractivity in comparison to radar image intensities. The differences between radar images and the two-dimensional (2-D) tomographic cross

Fig. 2. 2-D profile of a severe convective supercell thunderstorm: radar reflectivity boundary presented as thick black lines, and the approximate convective and stratiform precipitation regions identified within the red and blue boxes respectively (based on Houze, 2004).

sections could be shown as: 1) the wavelength of the signal and 2) the signal path. The radar pulse sent toward the MCS (5 GHz frequency) is partially reflected by the rain drops, snow, and ice crystals contained in all MCSs. GPS signals (1.5 GHz frequency), however, are refracted by the WV suspended in the air or that are moving upward/ downward in air parcels and are afterward recorded in GPS receivers as the phase delay. Therefore, the 2-D tomographic gradients are capable of sensing the increase of humidity before an MCS, as well as a decrease during heavy rain and in the stratiform region. Both the convective and stratiform regions are identified in Fig. 2. Horizontal 2-D cross sections at three altitude layers, i.e., 578, 1613, and 5780 m, were extracted for analyses of the horizontal gradients in comparison to the path, domain, and intensity of the severe weather phenomena. Gradient magnitudes denoting the dynamic changes in the wet refractivity field were calculated according to the following [19]   ∂Nwet ∂Nwet ∂Nwet i+ k+ p . (8) ∇2DNwet = ∂x ∂h ∂t IV. PWV FOR S EVERE W EATHER S ENSING Two case studies were selected to investigate a common signature of GPS-derived PWV in response to the formation and lifecycle of severe MCS depicted using cumulative rainfall data. They are discussed in Sections IV-A and IV-B. Both case studies were validated with the Melbourne International Airport Radiosonde station, which is located 6 km horizontally and 51 m vertically away from the GPS station [20]. A. Case Study 1: March 2010 Super Cell Storm Event The focus of this case study is for a series of supercell MCS that passed through Victoria, Australia, March 6–8, 2010. This event brought heavy rainfall, lightning, flash flooding, strong winds, and large hailstones. At the storm’s peak strength, a 400-km band of heavy rain and large hail extended across the state reaching Melbourne at approximately 14:40 AEDT (5:40 UTC). The synoptic weather station network revealed that the total rainfall on the day was the highest in historical record for March in Victoria. At approximately 14:40 (5:40 UTC) Melbourne experienced 19 mm within 18 min and 26 mm within 60 min. In addition, wind speeds in excess of 100 km/h

ZHANG et al.: CAPTURING THE SIGNATURE OF SEVERE WEATHER EVENTS IN AUSTRALIA

Fig. 3. Time series of GPS-derived PWV (mm) compared to the cumulative rainfall (mm) over the 7-day case study period (AEDT).

and hailstones of about 2–5 cm were recorded throughout Melbourne [8], [19]. The large increase in PWV, from 20 to 47 mm, from day-ofyear (DOY) 63 to 64 corresponds to the steady increase in warm moist air attributed to a prefrontal air mass. Prefrontal warm air holds more WV than the cold postfrontal air. Thus, Fig. 3 presents this clear signature of increasing PWV to 47 mm representing the warm prefront air and the immediate atmospheric procession of a sharp drop back to approximately 23 mm, characterizing the arrival of cold dry postfront air mass. A high increase in PWV is then evident 8–10 h prior to the extreme precipitation storm anomaly reaching Melbourne at 5:40 (UTC) on DOY 65. The sharp drop in PWV coincides with the passing of intense precipitation. This correlation between PWV and precipitation intensity repeats on DOY 66 with a sharp rise 3 h prior to, and then a dramatic drop after the precipitation anomaly. Comparison of the GPS-derived PWV estimates with radiosonde observations concluded an overall RMS error of 2–3 mm for greater Melbourne, as presented in [8] and [19].

B. Case Study 2: January 2011 Severe Storm and Precipitation Event The severe storm event for this case study is the one that passed over Victoria, January 9–15, 2011 and led to a monthly rainfall three times that of the long-term average, which made the wettest January on record. The state average rainfall for this period was 118 mm compared to the long-term average of 39 mm. The storm precipitation intensity resulted in widespread and flash flooding across northern, western, and central Victoria. Fig. 4 shows the GPS-derived PWV estimates with a 1-h resolution. The cumulative rainfall series indicates a correlated time scale of the precipitation intensities over the greater Melbourne region during the severe weather event. The initial increase in warm, moist air during the unstable conditions of severe weather was evident from 8 January as the dynamics of the severe convection forced WV up through the atmospheric layers. This generated higher WV content in the vertical column. Consequently, PWV increased from an average of 17 mm during a stable atmosphere to a maximum of 62 mm during the severe storm and precipitation processes. As evident in the figure, a large increase in PWV occurred at 00:00 (UTC) on the 8th approximately 12-h prior to the initial developments of

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Fig. 4. Time series of GPS-derived PWV (mm) from the KEPK station compared to radiosonde-derived PWV from the MELB station and the cumulative rainfall (mm) during this severe precipitation case study period January 1–15, 2011.

the extreme precipitation system. The storm passed over the Melbourne region from 10 to 15 January, with the maximum intensity occurring during 13–14 January. A sharp decrease in GPS-derived PWV occurred from the 14th with values of less than 20 mm by 6 A . M . (UTC). This coincided with the final and the most intense precipitation front passing over Melbourne. This trend (from GPS-derived PWV) is consistent with radiosonde-derived PWV, which were computed every 12 h. The sharp drop in PWV coincided with the final precipitation system with the time series dropping back to stable values. Both GPS- and radiosonde-derived PWV revealed similar results throughout the 15-day period. The storm period (8–15 January) had minimal effect on the accuracy of GPS-derived PWV, with an RMS error of 1.90 mm. The above two case studies have indicated the capability to use a state-wide GNSS CORS network to map the passage of WV in the pre- and post-frontal air mass of severe weather events with a high temporal resolution. Based on this, it can be concluded that GPS-derived PWV can resolve the synoptic signature for the dynamics and precursors to severe weather, and also has the potential to strengthen NWP models and forecasts in view of severe weather. The signature of PWV when influenced by rain intensity was also supported by similar severe weather studies conducted by [5]–[7], [29] using GPS-derived ZTDs. However, the limitation of this 1-D solution is that the mesoscale storm processes cannot be fully resolved due to the integral observation geometry of GPS-derived PWV. To monitor the 4-D dynamics and mesoscale processes of severe weather, tomographic modeling was investigated in this study. V. 4-D T OMOGRAPHY In this section, case study 1 is presented for tomographic analysis of wet refractivity to identify the 4-D signature of the convective and stratiform elements for severe MCS events. As discussed previously, it covered the 7-day period of March 3– 10, 2010. This time interval allowed an in-depth analysis of the finite atmosphere presented in Fig. 1 for three periods—before, during, and after the severe supercell convective storm system. The scope of the analysis is: 1) to identify the physical dynamics and signature of wet refractivity during the lifecycle of this severe weather event; 2) to identify mechanisms of convection and precipitation regions in the wet refractivity and gradient

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Fig. 5. Top graph presents the time series of tomographic profile solution for wet refractivity every 10 min during March 3–8, 2010 (UTC). The bottom graph is for the evolution of the refractivity in which the blue line is cumulative rainfall (mm) from the MOBS synoptic station.

Fig. 6. Time series from 22:00 on 5 March to 06:00 on 6 March, 2010 (UTC) presents the lifecycle of the extreme supercell thunderstorm passing over greater Melbourne. Tomographic domain represented with black dots corresponding to tomographic grid corners, red dots representing tomographic profile grid points used to construct the tomographic 2-D cross section, weather radar image intensity and red circles representing the interpolation domain for the weather radar image.

analysis; and 3) to quantify the accuracy of the GPS-derived wet refractivity field during the influence of severe weather. For measuring atmospheric instability, Sharma et al. [30] used GPS RO-derived refractivity to derive a refractivity index

Fig. 7. Time series from 01:00 to 06:00 on 6 March, 2010 (UTC) presents 2-D tomographic cross section against weather radar image intensity (represented as a black line connecting the interpolation points).

(RI) to measure atmospheric instability. The advantage of using the RI is its direct correlation with refractivity without the need for retrieving temperature, pressure, and water vapor. The average RI is the mean of the values from height layers between above the planetary boundary layer (PBL) (∼2000 m) to the tropopause. In this study, we adopted the RI and applied it to the wet refractivity profile determined by a GPS tomographic model to produce a wet RI (RIwet ) value. This optimizes the

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Fig. 8. Horizontal 2-D gradient distribution of wet refractivity for height layers at 578, 1614, and 5780 m and three times at 6:00, 7:00, and 8:00 on DOY 65, 2010 (UTC) extracted from the tomographic solution. The Melbourne radar image intensity is overlaid for identification of relative path and intensity of the convective storm system.

sensitivity to moisture content as it excludes the dominating dry component of refractivity. The RI index is primarily used as an integral measure to identify early mechanisms of lift due to warm, moist air convection during the formation and lifecycle of severe weather through excessive and rapid increase and decrease of the index. The RIwet is defined as   k RIwet = Nwet,l /n (9) l

where l and k represent the first and last Nwet terms relating to the initial term above the PBL (∼2000 m) and the last term at the top of the tomographic model (15 000 m), respectively; and n is the number of all the terms. As the discretization of the voxel model was fixed, n was set to 11 for this research. In Fig. 5, the top graph is the time series of the tomographic wet refractivity profile solution at the voxel profile point (A) [see Fig. 1(a) and (b)] over the 7-day study period, and the bottom graph is the time series of RIwet over the same period. A RIwet value of 12.73 ppm was adopted as a standard value for a stable atmosphere, based on the results of observations during February–May 2010 from the MELB radiosonde station [20]. The cumulative rainfall is referenced to the right y-axis with a supercell storm occurring on DOY 65 depicted by a significant spike, where ∼40 mm rainfall was recorded in less than 2 h. The RIwet index presents an approximately linear rise from DOY 63 to 64.3, coinciding with the large amounts of warm, moist air and the process of strong lift or convection in the unstable prefrontal atmosphere. This index reached 28 ppm, an increase in the standard RIwet by a factor of 2.2, followed by a cold dry postfrontal air triggering a sharp drop in the RIwet . There was then a sharp rise until the heavy rainfall, hail, and flash flooding reached Melbourne. The evolution of the

tomographic profile extracted from the model presents a similar signature to the RIwet . However, the highly dynamic distribution of wet refractivity in the vertical direction was detected. A distinctive signature of convective updraft due to warm, moist air and atmospheric instability were revealed as large increases in wet refractivity rise up the vertical profile. Periods of cool dry air were also detected—represented by sharp drops in wet refractivity down through the vertical layers caused by cold air mass advection associated with the stratiform region (Fig. 2). This occurred during the mature and dissipating phase of the storm lifecycle due to heavy precipitation. A 2-D vertical cross section was extracted from the tomographic solution to investigate the pattern of the spatial and temporal dynamics of wet refractivity during an MCS system. The path and precipitation intensity of the March 2010 supercell storm is represented using a color scale of the weather radar intensity at hourly intervals in Fig. 6. This figure shows the lifecycle and direction of this supercell MCS from formation to dissipation. The grid points depict the tomographic voxel resolution and the red circles represent the area of interpolation for the radar image intensity. The diagonal tomographic grid profiles were selected to reconstruct a 2-D profile for comparisons with the radar intensity. Fig. 7 shows the time evolving tomographic solution gradient along the 2-D cross-section profile with the interpolated radar image intensity mapped to the right hand y-axis. The processing is for the 6-h session from 01:00 on 6 March (local time 10:00 on 6 March AEDT) to 06:00 on 6 March UTC (local time 15:00 on 6 March AEDT) at hourly intervals. As the convective system matured the precipitation domain increased in size and intensity. Areas of intense precipitation represented by regions bound within the solid black line present

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a distinct drop in wet refractivity over all layers. This is especially evident in the mid troposphere. Furthermore, regions in front of the storm path indicate a large increase in wet refractivity gradients up through each layer. This dynamic trend at the front of the storm depicts the convection process of warm, moist air in the violent updraft of the gust front and formation of the mature cell as defined in Fig. 2. To examine the horizontal signature of wet refractivity using the 4-D tomographic solution, the horizontal gradients were determined at hourly intervals for the passing of the severe MCS event. As stated previously, these gradient changes were extracted from the tomographic solution for three height layers: 578, 1613, and 5780 m altitudes and at three times: 6:00, 7:00, and 8:00 on DOY 65, 2010 (UTC). These results are shown in Fig. 8. The weather radar image intensity is superimposed over the top of each subfigure to depict the path, location, and precipitation intensities of the storm. Significant increases in the gradients are visible at the front of the supercell storm structure through each layer. These represent significant convective updraft, which is identified at each time interval. In contrast, significant decreases in the gradients are also pronounced, where cool dry air formed downdraft and inflow jets through the back of the storm indicating the larger stratiform regions. VI. C ONCLUSION This research indicates that ground-based GPS is a highly effective and robust observation technique for detection of the dynamics of WV during formation and lifecycle of severe weather. The tropospheric WV estimated from a GNSS CORS network can be used to reconstruct a 4-D tomographic model for regional wet refractivity field. These results are significant for meteorology, especially for Australia and the southern hemisphere where other atmospheric sensors are spatially and temporally sparse. The GPS-derived WV results from GPSnet, validated with co-located radiosonde-derived PWV, reveal a high accuracy and reliability, and also indicate high sensitivity to the formation and precursors of severe weather events at a synoptic scale. The two case studies presented in this paper assessed the dynamics and sensitivity of PWV during severe weather. Both cases showed significant increases in PWV approximately 48 h prior to the storm front and precipitation extremes. These are associated with the synoptic scale precursors of warm, moist air creating unstable atmospheric conditions with a cold front. The 4-D wet refractivity tomographic modeling results suggest that a state-wide tomographic solution can be used to identify the signature of convection in the vertical layers at the front of a storm and also the gradients of rear inflow jets in the stratiform region. Compared with the co-located radiosonde-derived wet refractivity under the influence of severe weather, the tomographic models achieved an accuracy/(RMS) of 8.58 ppm. These findings suggest that ground-based GPS-derived PWV and tomographic modeling for wet refractivity fields have the potential to enhance the ability of early detection and forecasting of severe weather when assimilated into a NWP model. Future research will be focusing on real-time capabilities and optimization of algorithms for assimilating multiple types of

observations from various sources into the wet refractivity or WV tomographic model. ACKNOWLEDGMENTS The authors would like to thank the Geodesy and Geodynamics Laboratory, ETH Zurich, Switzerland, for providing the AWATOS 2 software package, the Australian Bureau of Meteorology for providing the synoptic weather station and radiosonde data, and the Department of Sustainability and Environment for providing the GPSnet data. R EFERENCES [1] Z. Bai, “Near-real-time GPS sensing of atmospheric water vapour,” Ph.D. dissertation, Queensland Univ. of Technol., Brisbane, Queensland, 2004. [2] M. Bender and A. Raabe, “Preconditions to ground-based GPS water vapour tomography,” Ann. Geophys., vol. 25, no. 8, pp. 1727–1734, 2007. [3] M. Bender et al., “Validation of GPS slant delays using water vapour radiometers and weather models,” Meteorologishe Zeitschrift, vol. 17, no. 6, pp. 807–812, 2008. [4] M. Bevis et al., “GPS meteorology: Remote sensing of atmospheric water vapor using the global positioning system,” J. Geophys., vol. 97, no. 15, pp. 787–801, 1992. [5] K. Boniface et al., “Impact of high-resolution data assimilation of GPS zenith delay on Mediterranean heavy rainfall forecasting,” Ann. Geophys., vol. 27, pp. 2739–2753, 2009. [6] C. Champollion et al., “GPS water vapour tomography: Preliminary results from the ESCOMPTE field experiment,” Atmos. Res., vol. 74, pp. 253–274, 2005. [7] B. Chen and Z. Liu, “Voxel-optimized regional water vapor tomography and comparison with radiosonde and numerical weather model,” J. Geod., vol. 88, no. 7, pp. 691–703, 2014. [8] S. Choy, K. Zhang, C. Wang, Y. Li, and Y. Kuleshov, “Remote sensing of the earth’s lower atmosphere during severe weather events using GPS technology: A study in Victoria, Australia,” in Proc. ION GNSS, Portland, OR, USA, Sep. 20–23, 2011, pp. 559–571. [9] R. Dach and P. Walser, “Bernese GNSS software version 5.2,” Astronomical Inst., Univ. of Bern, Bern, 2013. [10] A. Flores, “Atmospheric tomography using satellite radio signals,” Ph.D. dissertation, Inst. d’Estudis Espacials de Catalunya, Barcelona, Spain, 1999. [11] A. Flores, G. Ruffini, and G. Rius, “4-D tropospheric tomography using GPS slant wet delays,” Ann. Geophys., vol. 18, pp. 223–234, 2000. [12] A. Flores, J. de Arellano, L. Gradinarsky, and A. Rius, “Tomography of the lower troposphere using a small dense network of GPS receivers,” IEEE Trans. Geosci. Remote Sens., vol. 39, no. 2, pp. 439–447, Feb. 2001. [13] L. Gradinarsky, J. Johansson, H. Bouma, H Scherneck, and G. Elgered, “Climate monitoring using GPS,” Phys. Chem. Earth, vol. 27, pp. 335– 340, 2002. [14] L. Gradinarsky and P. Jarlemark, “Ground-based GPS tomography of water vapour: Analysis of simulated and real data,” J. Meteorol. Soc. Japan, vol. 82, pp. 551–560, 2004. [15] L. Kruse, “Spatial and temporal distribution of atmospheric water vapor using space geodetic techniques,” in GeArbeiten in der Schweiz, vol. 62. Zurich, Switzerland: ETH Hönggerburg, Swiss Geodetic Commission, 2001. [16] J. Le Marshall et al., “The beneficial impact of radio occultation observations on Australian region forecasts,” Aust. Meteorol. Oceanogr. J., vol. 60, pp. 121–125, 2010. [17] Z. Liu, M. S. Wong, J. Nichol, and P. W. Chan, “A multi-sensor study of water vapour from radiosonde, MODIS and AERONET: A case study of Hong Kong,” Int. J. Climatol., vol. 33, pp. 109–120, 2013. [18] S. Lutz, “High-resolution GPS tomography in view of hydrological hazard assessment,” Ph.D. dissertation, ETH Zurich, Zürich, Switzerland, 2008. [19] T. Manning, K. Zhang, W. Rohm, S. Choy, and F. Hurter, “Detecting severe weather using GPS tomography: An Australian case study,” J. Global Positioning Syst., vol. 11, no. 1, pp. 58–70, 2012. [20] T. Manning, “Sensing the dynamics of severe weather using 4-D GPS tomography in the Australian region,” Ph.D. dissertation, Math. and Geospatial Sci., RMIT Univ., Melbourne, Australia, 2013.

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[21] A. MacDonald, Y. Xie, and R. Ware, “Diagnosis of three-dimensional water vapor using a GPS network,” Mon. Weather Rev., vol. 130, pp. 386– 397, 2002. [22] A. Neill, “Global mapping functions for the atmospheric delay at radio wavelengths,” J. Geophys. Res., vol. 101, pp. 3227–3246, 1996. [23] D. Perler, “Water vapour tomography using global navigation satellite systems,” Ph.D. dissertation, No. 20012, ETH Zurich, Zürich, Switzerland, 2011. [24] D. Perler, A. Geiger, and F. Hurter, “4-D GPS water vapor tomography: New parameterized approaches,” J. Geod., vol. 85, pp. 539–550, 2011, doi: 10.1007/s00190-011-0454-2. [25] W. Rohm, K. Zhang, and J. Bosy, “Limited constraint, robust Kalman filtering for GNSS troposphere tomography,” Atmos. Meas. Tech., vol. 7, no. 5, pp. 1475–1486, 2014. [26] C. Rocken et al., “GPS/STORM: GPS sensing of atmospheric water vapor for meteorology,” J. Atmos. Oceanic Technol., vol. 12, pp. 468–478, 1995. [27] J. Rueger, “Refractive index formulae for electronic distance measurement with radio and millimetre waves,” Sch. of Surv. Spatial Inf. Syst., Univ. of New South Wales, Sydney, Australia, Unisurv Rep. 68, 2002. [28] J. Saastamoinen, “Atmospheric correction for troposphere and stratosphere in radio ranging of satellites,” in The Use of Artificial Satellites for Geodesy, vol. 15, S. W. Henriksen, A. Macini, and B. H. Chovitz, Eds. Washington DC, USA: American Geophysical Union, 1972, pp. 247–251. [29] H. Seko, M. Kunii, Y. Shoji, and K. Saito, “Improvement of rainfall forcast by assimilation of ground-based GPS data and radio occultation data,” Sci. Online Lett. Atmos., vol. 6, pp. 81–84, 2010. [30] N. Sharma, D. Jagadheesha, P. Joshi, and P. Pal, “Atmospheric stability estimation using radio occultation data over India and surrounding region,” Indian J. Radio Space Phys., vol. 38, pp. 317–325, 2009. [31] S. Song, W. Zhu, J. Ding, and J. Peng, “3-D water-vapor tomography with Shanghai GPS network to improve forecasted moisture field,” Chin. Sci. Bull., vol. 51, no. 5, pp. 607–614, 2006. [32] P. Tregoning, R. Boers, and D. O’Brien, “Accuracy of absolute precipitable water vapor estimates from GPS observations,” J. Geophys. Res., vol. 103, no. 28, pp. 701–719, 1998. [33] M. Troller, A. Geiger, E. Brockmann, and H. Kahle, “Determination of the spatial and temporal variation of tropospheric water vapour using CGPS networks,” Geophys. J. Int., vol. 167, no. 2, pp. 509–520, 2006. [34] C. Wang et al., “Investigation into the atmospheric parameters retrieved from ROPP and CDAAC using GPS radio occultation measurements over the Australian area,” Aust. J. Earth Sci., vol. 61, no. 6, pp. 785–792, 2014. [35] R. Ware, C. Alber, C. Rocken, and F. Solheim, “Sensing integrated water vapor along GPS ray paths,” Geophys. Res. Lett., vol. 24, pp. 417–420, 1997. [36] K. Yu et al., “An overview of GNSS remote sensing,” EURASIP J. Adv. Signal Process., vol. 2014, p. 134, 2014. [37] Y. Yuan et al., “Real time retrieval of atmospheric water vapour from GPS precise point positioning,” J. Geophys. Res., vol. 119, pp. 10044–10057, 2014, doi: 10.1002/2014JD021486. [38] K. Zhang, E. Fu, D. Silcock, Y. Wang, and Y. Kuleshov, “An investigation of atmospheric temperature profiles in the Australian region using collocated GPS radio occultation and radiosonde data,” Atmos. Meas. Tech., vol. 4, no. 10, pp. 2087–2092, 2011.

Kefei Zhang received the Ph.D. degree in geodesy from Curtin University, Bentley, WA, Australia, in 1998. He is the Founder and Director of the RMIT SPACE Research Centre. He is a co-inventor of eight patents and has authored over 300 publications in these fields and attracted in excess of 20 million dollars in funding, since 1990. His research was featured in the Australian Technology Network (ATN) of Universities “50 solutions that count” and in the showcase of the “Partners for a Better Future— Australia and China: Science and Technology Week” at the Shanghai World Expo 2010. He is currently a Program Leader of the newly established Australian Cooperative Research Centre for Space Environment Management (CRC-SEM). His research interests include primarily involved in algorithm development and innovative applications of GNSS technologies for highaccuracy positioning, atmospheric studies (e.g., radio occultation, space weather, climate change, and weather), space situational awareness (e.g., space debris tracking, surveillance, and collision warning), and people mobility and object tracking.

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Prof. Zhang led an international research consortium and won a prestigious, multi-million-dollar Australia Space Research Program project in satellite positioning, space tracking, and atmospheric studies for climate and space weather, as the Chief Scientist, in 2010. He and his team have received numerous awards in research and technology innovation including the 2012 Excellence in Innovation for Australia (EIA) trial and RMIT University Research Excellence awards. Toby Manning received the Ph.D. degree in GPS meteorology from RMIT University, Melbourne, Australia, in 2014. He is currently a VET Teacher in Surveying with the School of Vocational Engineering and a Postdoctorate Researcher for the SPACE Research Centre, RMIT University. His teaching responsibilities include land and engineering surveying, geodesy, and satellite positioning. His research interests include sensing the dynamics of wet refractivity using GPS tomographic modeling as well as severe weather applications, which involves both ground-based and space-born GPS observation platforms. Suqin Wu received the Ph.D. degree in regional atmospheric error modeling for high accuracy, real-time kinematic positioning (RTK) from RMIT University, Melbourne, Australia, in 2009. She is currently a Research Fellow with the RMIT SPACE Research Centre. Her research interests include GPS precise positioning and GPS meteorology, GPS radio occultation, atmospheric error modeling for high accuracy GPS applications, and precise orbit determination and prediction for space debris objects. Witold Rohm received the Bachelor degree in geography from Wroclaw University, Wrocław, Poland, the Master degree in geodesy and Ph.D. degree in satellite geodesy from WUELS, Wrocław, Poland. He is an Assistant Professor with the Wroclaw University of Environmental and Life Sciences (WUELS). He spent 2.5 years with the RMIT SPACE Research Centre working closely with staff and Ph.D. students on GNSS tomography for near-real-time weather and severe weather modeling and prediction research. He has a strong research track record in geodesy, meteorology, and tomography. His work at WUELS led to the successful design and development of a near-real-time GNSS tomography model for the Polish region. Dr. Rohm is currently a Co-Chair of International Association of Geodesy Working Group IAG-WG4.3.2 “Inter-comparison and cross validation of tomography models” and GNSS4SWEC Cost project WG.2/WG.1 Tomography research area subgroup. D. Silcock is a Licensed Surveyor, and Senior Lecturer of Geospatial Science with the RMIT University (School of Mathematical and Geospatial Sciences), Melbourne, Australia. He joined RMIT in 2000 after 10 years with the University of South Australia. He has also provided geospatial expertise in investigations for the Australian Transport Safety Bureau (ATSB). His research interests include high precision engineering surveying, GNSS and plate tectonics, geodetic applications, and heritage investigations. Suelynn Choy received the Ph.D. degree in GPS precise point positioning (PPP) from RMIT University, Melbourne, Australia, in 2009. Since 2009, she has been an Academic Staff with the School of Mathematical and Geospatial Sciences, RMIT University. Her teaching responsibilities include land surveying, geodesy, and satellite positioning and navigation systems. Her research interests include multi-GNSS precise positioning as well as utilization of GNSS satellites in disaster management and atmospheric remote sensing. Dr. Choy is the Co-Chair of the International Association of Geodesy (IAG) Working Group 4.5.2 on PPP and Network RTK under Sub-Commission 4.5: High Precision GNSS Algorithms and Applications. She is also the Co-Chair of the FIG (International Federation of Surveyors) Working Group 5.4 on GNSS under Commission 5: Positioning and Measurement.