Suqin Wu

A new dynamic approach for statistical optimization of GNSS radio occultation bending angles for optimal climate monitoring utility

[1] Global Navigation Satellite System (GNSS)-based radio occultation (RO) is a satellite remote sensing technique providing accurate profiles of the Earth’s atmosphere for weather and climate applications. Above about 30km altitude, however, statistical optimization is a critical process for initializing the RO bending angles in order to optimize the climate monitoring utility of the retrieved atmospheric profiles. Here we introduce an advanced dynamic statistical optimization algorithm, which uses bending angles from multiple days of European Centre for Medium-range Weather Forecasts (ECMWF) short-range forecast and analysis fields, together with averaged-observed bending angles, to obtain background profiles and associated error covariance matrices with geographically varying background uncertainty estimates on a daily updated basis. The new algorithm is evaluated against the existing Wegener Center Occultation Processing System version 5.4 (OPSv5.4) algorithm, using several days of simulated MetOp and observed CHAMP and COSMIC data, for January and July conditions. We find the following for the new method’s performance compared to OPSv5.4: 1.) it significantly reduces random errors (standard deviations), down to about half their size, and leaves less or about equal residual systematic errors (biases) in the optimized bending angles; 2.) the dynamic (daily) estimate of the background error correlation matrix alone already improves the optimized bending angles; 3.) the subsequently retrievedrefractivityprofilesandatmospheric(temperature)profilesbenefit by improvederror characteristics,especiallyabove about 30km. Based on theseencouraging results, we work to employ similar dynamic error covariance estimation also for the observed bending angles and to apply the method to full months and subsequently to entire climate data records.

Water vapor‐weighted mean temperature and its impact on the determination of precipitable water vapor and its linear trend

Water vapor-weighted mean temperature, T-m, is a vital parameter for retrieving precipitable water vapor (PWV) from the zenith wet delay (ZWD) of Global Navigation Satellite Systems (GNSS) signal propagation. In this study, the T-m at 368 GNSS stations for 2000-2012 were calculated using three methods: (1) temperature and humidity profiles from ERA-Interim, (2) the Bevis T-m-T-s relationship, and (3) the Global Pressure and Temperature 2 wet model. T-m derived from the first method was used as a reference to assess the errors of the other two methods. Comparisons show that the relative errors of the T-m derived from these two methods are in the range of 1-3% across more than 95% of all the stations. The PWVs were calculated using the aforementioned three types of T-m and the GNSS-derived ZWD at 107 stations. Again, the PWVs calculated using T-m from the first method were used as the reference of the other two PWVs. The root-mean-square errors of these two PWVs are both in the range of 0.1-0.7mm. The second method is recommended in real-time applications, since its performance is slightly better than the third method. In addition, the linear trends of the PWV time series from the first method were also used as the reference to evaluate the trends from the other two methods. Results show that 13% and 23% of the PWV trends from the respective second and third methods have a relative error of larger than 10%. For climate change studies, the first method, if available, is always recommended.

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 ~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.

Overview of RFID-Based Indoor Positioning Technology.

Traditional GPS location technology cannot work in indoor environment. In order to sum up the positioning theory of RFID positioning method and find an indoor location algorithm suitable for an indoor environment, this paper reviews the composition of RFID indoor positioning system and the location algorithms of RFID indoor positioning system. And more comprehensive study and a systematic summary are carried out. The paper provides an important basis for the selection of RFID location algorithm and positioning system under different conditions.Radio frequency identification (RFID) technology was originally invented for military uses. From 1980s, commercial RFID products started to be available and they were mainly applied in areas of supply chains, transport, manufacturing, personnel access, animal tagging, toll collection etc. Nowadays, RFID has been recognised as an emerging technology for ubiquitous positioning (UP), especially in an indoor environment. The development and implementation of RFID-based positioning technology are very fast, whilst according to the literature, little comprehensive review and convinced assessment for the latest RFID technology have been conducted, and some of the main features of the latest RFID technology have rarely or unclearly been presented in the literature, for example, the longest reading range of RFID systems, the smallest tag size and overall commercial application fields. This paper provides an overview of state-of-the-art RFID technology, particularly for the purpose of indoor positioning. It includes a review of historical and current development of RFID technology and its applications, an evaluation of up-to-date RFID-based positioning techniques and their performance as well as a prediction of future trends of RFID-based indoor positioning techniques. This paper can be a valuable guidance and solution for researchers and other end users to better understand RFID and critical factors considered on system requirements, hardware selection and positioning performance for various applications.

A new method for improving Wi-Fi-based indoor positioning accuracy

Wi-Fi- and smartphone-based positioning technologies are playing a more and more important role in location-based service industries due to the rapid development of the smartphone market. However, the low positioning accuracy of these technologies is still an issue for indoor positioning. To address this problem, a new method for improving the indoor positioning accuracy was developed. The new method initially used the nearest neighbour (NN) algorithm of the fingerprinting method to identify the initial position estimate of the smartphone user. Then two distance correction values in two roughly perpendicular directions were calculated by the path loss model based on the two signal strength indicator values observed. The systematic error from the path loss model were eliminated by differencing two model-derived distances from the same access point. The new method was tested and the results compared and assessed against that of the commercial Ekahau RTLS system and the NN algorithm. The preliminary results showed that the positioning accuracy has been improved consistently after the new method was applied and the root mean square accuracy improved to 3.3 m from 3.8 m compared with the NN algorithm.

Adaptive Node Parameterization for Dynamic Determination of Boundaries and Nodes of GNSS Tomographic Models

Water vapor is one of the primary greenhouse gases and significantly impacts the atmosphere. Water vapor is the most active meteorological element and varies rapidly in both the spatial and temporal domains. As a promising means, Global Navigation Satellite Systems (GNSS) tomography has been used to construct the 3D distribution of water vapor in high resolutions. Currently, in the most commonly used node parameterization approaches, the region for the 3D modeling has a preset fixed regular shape for all tomographic epochs. As a result, too many unknown parameters need to be estimated and thus to degrade the performance of the tomographic solution. In this study, an innovative node parameterization approach using a combination of three meshing techniques to dynamically adjust both the boundary of the tomographic region and the position of nodes at each tomographic epoch is proposed. The three meshing techniques were boundary extraction, Delaunay triangulation, and force-displacement algorithm. The performance of the tomographic model resulting from the new approach was tested using one month GNSS data in May 2015 from the Hong Kong GNSS network and was compared against that of the conventional node parameterization approach. The reference for the validation of the accuracy of the test results were the radiosonde measurements from Kings Park Meteorological Station (HKKP) in Hong Kong. Results showed that in terms of root-mean-square error the accuracy of the new approach significantly improved in comparison to the traditional approach.

Seasonal Multifactor Modelling of Weighted-Mean Temperature for Ground-Based GNSS Meteorology in Hunan, China

In this study, radiosonde observations during the period of 2012-2013 from three stations in the Hunan region, China, were used to establish regional models (RTMs) that are a fitting function of multiple meteorological factors (, , and ). One-factor, two-factor, and three-factor RTMs were assessed by comparing their against the radiosonde-derived (as the truth) during the period of 2013-2014. Statistical results showed that the bias and RMS of the one-factor RTM, in comparison to the BTM result, were reduced by 88% and 28%, respectively. The two-factor and three-factor RTMs showed similar accuracy and both outperformed the one-factor RTM, with an improvement of 7% in RMS. The bias and RMS of all the four seasonal two-factor RTMs were smaller than the yearly two-factor RTM, with the improvements of 3%, 10%, 2%, and 3% in RMS. The improvement of the conversion factors in mean bias and RMS resulting from the seasonal two-factor RTM is 92% and 31%. The bias and RMS of the PWV resulting from the seasonal two-factor RTM are improved by 37% and 12%, respectively. Therefore, the seasonal two-factor RTMs are recommended for the research and applications of GNSS meteorology in the Hunan region, China.

An enhanced singular spectrum analysis method for constructing nonsecular model of GPS site movement

Many GPS time series contain offsets, sometimes nonsecular trends, and seasonal signals withtime-varying amplitudes due to several different types of geophysical phenomena. Therefore, the use ofnonsecular models to depict the real geophysical movement of GPS sites is better than a linear model. Inthis study, an enhanced singular spectrum analysis (SSA) method for fitting GPS time series and predictingits coordinates is proposed. Simulation results show that the root-mean-square (RMS) of differencesbetween the reconstructed and simulated signals is 1.7 mm; the RMS of the differences between thepredicted coordinates and simulated signal is about 3 mm for the first half 1.5 years of testing period anddecreases to 10 mm for the last half 1.5 years. Fitting results for three GPS time series are obtained usingmaximum likelihood estimation (MLE), which is used to fit the time series with a piecewise linear trend plusan annual/semiannual components, SSA, and state space model (SSM). Both SSA and SSM perform similarlyand better than the MLE in extracting the nonsecular trend and annual/semiannual components from theGPS time series. The prediction results from SSA have higher coefficients with raw time series and lowerpower of annual/semiannual in their residuals than that from MLE for two case studies. The differencesbetween the linear trend estimated by Plate Boundary Observatory and SSA nonsecular model for 16 GPStime series are all larger than 2 mm in up direction, which is not negligible for a high-accuracy terrestrialreference frame construction.

A new approach for GNSS tomography from a few GNSS stations

13 The determination of the distribution of water vapor in the atmosphere plays an important role in 14 the atmospheric monitoring. Global Navigation Satellite Systems (GNSS) tomography can be 15 used to construct 3D distribution of water vapor over the field covered by a GNSS network with 16 high temporal and spatial resolutions. In current tomographic approaches, a pre-set fixed 17 rectangular field that roughly covers the area of the distribution of the GNSS signals on the top 18 plane of the tomographic field is commonly used for all tomographic epochs. Due to too many 19 unknown parameters needing to be estimated, the accuracy of the tomographic solution degrades. 20 Another issue of these approaches is their unsuitability for GNSS networks with a few stations as 21 the shape of the field covered by the GNSS signals is in fact roughly an upside-down cone rather 22 than the rectangular cube as the pre-set. In this study, a new approach for determination of 23 tomographic fields fitting the real distribution of GNSS signals on different tomographic planes 24 at different tomographic epochs and also for discretization of the tomographic fields based on the 25 perimeter of the tomographic boundary on the plane and meshing techniques is proposed. The 26 new approach was tested using three stations from the Hong Kong GNSS network and validated 27 by comparing the tomographic results against radiosonde data from Kings Park Meteorological 28 Station (HKKP) during the one month period of May, 2015. Results indicated that the new 29 approach is feasible for a three-station GNSS network tomography. This is significant due to the 30 fact that the conventional approaches cannot even solve a few stations network tomography. 31

A new algorithm for improving the tracking and positioning of cell of origin

Wi-Fi and smartphone based positioning technologies are playing a more and more important role for tracking and positioning due to the rapid development of the smartphone market. However, the low positioning accuracy of these technologies is still an issue for indoor positioning. This research is part of an Australian Research Council (ARC) project required by a large global shopping mall company located in Australia. It aims to develop an effective customer tracking approach for acquiring shopping behavior of customers and providing better services. Currently, the log data provided by the company only records one Wi-Fi connection at a time for each smartphone user, which makes most of the conventional tracking and positioning methods inapplicable. Only the cell of origin (CoO) method can be selected for the customer tracking and positioning. The designated shopping mall floor was initially partitioned using Voronoi diagram based on the distribution of the access point (AP) and received signal strength indicator (RSSI) values. However, the cells created by the Voronoi diagram did not reflect the real indoor complexity of the surrounding environment. In order to solve this problem, a new algorithm called the common hand over point determination (CHOPD) algorithm was developed, in which the statistical RSSI values from the real Wi-Fi network log data are used for the spatial and temporal position calculation of the handover point (HOP). The boundary of the cell can be determined once the HOP position is calculated. The RSSI values are detected by the Wi-Fi network from the smartphone carried by each customer walking on the shopping mall floor as they passed through two adjacent APs. The new algorithm was tested in a large shopping-mall-like space and 100 test sampling records were used for the HOP calculation. The results from the new algorithm were assessed and were found to be within 9cm difference from the true HOP location.