scholarly journals Evaluation of the Possibility of Using the Predicted Tropospheric Delays in Real Time Gnss Positioning

2014 ◽  
Vol 49 (4) ◽  
pp. 179-189 ◽  
Author(s):  
J. Z. Kalita ◽  
Z. Rzepecka ◽  
G. Krzan
Keyword(s):  
Real Time ◽  
Satellite System ◽  
Weather Data ◽  
Observation System ◽  
Tropospheric Delay ◽  
Systematic Bias ◽  
Atmospheric Conditions ◽  
Predicted Values ◽  
Delay Component ◽  
Many Sources

ABSTRACT Among many sources of errors that influence Global Navigation Satellite System (GNSS) observations, tropospheric delay is one of the most significant. It causes nonrefractive systematic bias in the observations on the level of several meters, depending on the atmospheric conditions. Tropospheric delay modelling plays an important role in precise positioning. The current models use numerical weather data for precise estimation of the parameters that are provided as a part of the Global Geodetic Observation System (GGOS). The purpose of this paper is to analyze the tropospheric data provided by the GGOS Atmosphere Service conducted by the Vienna University of Technology. There are predicted and final delay data available at the Service. In real time tasks, only the predicted values can be used. Thus it is very useful to study accuracy of the forecast delays. Comparison of data sets based on predicted and real weather models allows for conclusions concerning possibility of using the former for real time positioning applications. The predicted values of the dry tropospheric delay component, both zenith and mapped, can be safely used in real time PPP applications, but on the other hand, while using the wet predicted values, one should be very careful.

scholarly journals Real-Time Precise Point Positioning Using Orbit and Clock Corrections as Quasi-Observations for Improved Detection of Faults

2018 ◽  
Vol 71 (4) ◽  
pp. 769-787 ◽  
Author(s):  
Ahmed El-Mowafy
Keyword(s):  
Real Time ◽  
Precise Point Positioning ◽  
Exclusion Process ◽  
Satellite Orbit ◽  
Satellite System ◽  
Navigation Satellite ◽  
System Service ◽  
Point Positioning ◽  
Predicted Values ◽  
Global Navigation Satellite

Real-time Precise Point Positioning (PPP) relies on the use of accurate satellite orbit and clock corrections. If these corrections contain large errors or faults, either from the system or by meaconing, they will adversely affect positioning. Therefore, such faults have to be detected and excluded. In traditional PPP, measurements that have faulty corrections are typically excluded as they are merged together. In this contribution, a new PPP model that encompasses the orbit and clock corrections as quasi-observations is presented such that they undergo the fault detection and exclusion process separate from the observations. This enables the use of measurements that have faulty corrections along with predicted values of these corrections in place of the excluded ones. Moreover, the proposed approach allows for inclusion of the complete stochastic information of the corrections. To facilitate modelling of the orbit and clock corrections as quasi-observations, International Global Navigation Satellite System Service (IGS) real-time corrections were characterised over a six-month period. The proposed method is validated and its benefits are demonstrated at two sites using three days of data.

scholarly journals Establishment of a Real-Time Local Tropospheric Fusion Model

2019 ◽  
Vol 11 (11) ◽  
pp. 1321 ◽  
Author(s):  
Yibin Yao ◽  
Xingyu Xu ◽  
Chaoqian Xu ◽  
Wenjie Peng ◽  
Yangyang Wan
Keyword(s):  
Real Time ◽  
Meteorological Data ◽  
Rapid Development ◽  
Water Vapor Pressure ◽  
Auxiliary Information ◽  
Satellite System ◽  
Tropospheric Delay ◽  
Navigation Systems ◽  
Delay Model ◽  
Fusion Model

The tropospheric delay is one major error source affecting the precise positioning provided by the global navigation satellite system (GNSS). This error occurs because the GNSS signals are refracted while travelling through the troposphere layer. Nowadays, various types of model can produce the tropospheric delay. Among them, the globally distributed GNSS permanent stations can resolve the tropospheric delay with the highest accuracy and the best continuity. Meteorological models, such as the Saastamoinen model, provide formulae to calculate temperature, pressure, water vapor pressure and subsequently the tropospheric delay. Some grid-based empirical tropospheric delay models directly provide tropospheric parameters at a global scale and in real time without any auxiliary information. However, the spatial resolution of the GNSS tropospheric delay is not sufficient, and the accuracy of the meteorological and empirical models is relatively poor. With the rapid development of satellite navigation systems around the globe, the demand for real-time high-precision GNSS positioning services has been growing dramatically, requiring real-time and high-accuracy troposphere models as a critical prerequisite. Therefore, this paper proposes a multi-source real-time local tropospheric delay model that uses polynomial fitting of ground-based GNSS observations, meteorological data, and empirical GPT2w models. The results show that the accuracy in the zenith tropospheric delay (ZTD) of the proposed tropospheric delay model has been verified with a RMS (root mean square) of 1.48 cm in active troposphere conditions, and 1.45 cm in stable troposphere conditions, which is significantly better than the conventional tropospheric GPT2w and Saastamoinen models.

IWV observations from a network of ground-based GNSS receivers during EUREC4A

2021 ◽  
Author(s):  
Olivier Bock ◽  
Pierre Bosser ◽  
Cyrille Flamant ◽  
Erik Doerflinger ◽  
Friedhelm Jansen ◽  
...  
Keyword(s):  
Large Scale ◽  
Microwave Radiometer ◽  
Heavy Rain ◽  
Satellite System ◽  
Atmospheric Environment ◽  
Tropospheric Delay ◽  
Systematic Bias ◽  
Data Set ◽  
Radiosonde Station ◽  
Dry Bias

<p>IWV data were retrieved from a network of nearly fifty Global Navigation Satellite System (GNSS) stations distributed over the Caribbean arc for the period 1 January-29 February 2020 encompassing the EUREC4A field campaign. Two of the stations had been installed at the Barbados Cloud Observatory (BCO) during fall 2019 in the framework of the project and are still running. All other stations are permanent stations operated routinely from various geodetic and geophysical organisations in the region. High spatial and temporal Integrated Water Vapour (IWV) observations will be used to investigate the atmospheric environment during the life cycle of convection and its feedback on the large-scale circulation and energy budget.</p><p>This paper describes the ground-based GNSS data processing details and assesses the quality of the GNSS IWV retrievals as well as the IWV estimates from radiosoundings, microwave radiometer measurements and ERA5 reanalysis.</p><p>The GNSS results from five different processing streams run by IGN and ENSTA-B/IPGP are first intercompared. Four of the streams were run operationally, among one was in near-real time, and one was run after the campaign in a reprocessing mode. The uncertainties associated with each of the data sets, including the zenith tropospheric delay to IWV conversion methods and auxiliary data, are quantified and discussed. The IWV estimates from the reprocessed data set are compared to the Vaisala RS41 radiosonde measurements operated from the BCO and to the measurements from the operational radiosonde station at Grantley Adams international airport (GAIA). A significant dry bias is found in the GAIA humidity observations with respect to the BCO sondes (-2.9 kg/m2) and the GNSS results (-1.2 kg/m2). A systematic bias between the BCO sondes and GNSS is also observed (1.7 kg/m2) where the Vaisala RS41 measurements are moister than the GNSS retrievals. The HATPRO IWV estimates agree with the BCO soundings after an instrumental update on 27 January, while they exhibit a dry bias compared to GNSS and BCO sondes before that date. ERA5 IWV estimates are overall close to the GAIA observations, probably due to the assimilation of these observations in the reanalysis. However, during several events where strong peaks in IWV occurred, ERA5 is shown to significantly underestimate the IWV peaks. Two successive peaks are observed on 22 January and 23/24 January which were associated with heavy rain and deep moist layers extending from the surface up to altitudes of 3.5 and 5 km, respectively. ERA5 significantly underestimates the moisture content in the upper part of these layers. The origins of the various moisture biases are currently being investigated.</p>

scholarly journals A New Method for Estimating Tropospheric Zenith Wet-Component Delay of GNSS Signals from Surface Meteorology Data

2020 ◽  
Vol 12 (21) ◽  
pp. 3497
Author(s):  
Pengfei Xia ◽  
Jingchao Xia ◽  
Shirong Ye ◽  
Caijun Xu
Keyword(s):  
Weather Forecasting ◽  
Integral Formula ◽  
Meteorological Data ◽  
Weighted Average ◽  
Satellite System ◽  
Specific Humidity ◽  
Radiosonde Data ◽  
Weather Data ◽  
Tropospheric Delay ◽  
Radiosonde Observation

A new concept is proposed for estimating the zenith wet delay (ZWD) and atmospheric weighted average temperature by inputting the temperature, total pressure, and specific humidity from surface weather data. In addition, a new ZWD integral method is described for highly accurate calculation of the ZWD from radiosonde observation. To evaluate the advantages of the new discrete integral formula, we utilized the 8-year radiosonde profiles of 85 stations in China from 2010 to 2017 to validate the accuracy of the radiosonde-derived ZWD. The results showed that the mean accuracy of the ZWD derived from radiosonde data was 4.28 mm. Next, the new ZWD model was assessed using two sets of reference values derived from radiosonde data and GNSS precise point positioning in China. The results confirm that the new development improved the accuracy of the estimation of the tropospheric wet delay from the surface meteorological data. The performance of this new model can be seen as an important step toward accurately correcting the tropospheric delay in Global Navigation Satellite System (GNSS) real-time navigation and positioning. It can also be used in GNSS meteorology for weather forecasting and climate research.

scholarly journals Residuals of Tropospheric Delays from GNSS Data and Ray-Tracing as a Potential Indicator of Rain and Clouds

2018 ◽  
Vol 10 (12) ◽  
pp. 1917 ◽  
Author(s):  
Paweł Hordyniec ◽  
Jan Kapłon ◽  
Witold Rohm ◽  
Maciej Kryza
Keyword(s):  
Ray Tracing ◽  
Functional Model ◽  
Wrf Model ◽  
Heavy Precipitation ◽  
Satellite System ◽  
Tropospheric Delay ◽  
Atmospheric Conditions ◽  
Weather Events ◽  
The Impact ◽  
Tropospheric Delays

The Global Navigation Satellite System (GNSS) is commonly recognized by its all-weather capability. However, observations depend on atmospheric conditions which requires the induced tropospheric delay to be estimated as an unknown parameter. In the following study, we investigate the impact of intense weather events on GNSS estimates. GNSS slant total delays (STD) in Precise Point Positioning technique (PPP) strategy were calculated for stations in southwest Poland in a 56 days period covering several heavy precipitation cases. The corresponding delays retrieved from Weather Research and Forecasting (WRF) model by a ray-tracing technique considered only gaseous parts of the atmosphere. The discrepancies are correlated with rain rates and cloud type products from remote sensing platforms. Positive correlation is found as well as GNSS estimates tend to be systematically larger than modeled delays. Mean differences mapped to the zenith direction are showed to vary between 10 mm and 30 mm. The magnitude of discrepancies follows the intensity of phenomena, especially for severe weather events. Results suggest that effects induced by commonly neglected liquid and solid water terms in the troposphere modeling should be considered in precise GNSS applications for the atmosphere monitoring. The state-of-art functional model applied in GNSS processing strategies shows certain deficits. Estimated tropospheric delays with gradients and post-fit residuals could be replaced by a loosely constrained solution without loss of quality.

scholarly journals REAL-TIME TROPOSPHERIC PRODUCT ESTABLISHMENT AND ACCURACY ASSESSMENT IN CHINA

2018 ◽  
Vol XLII-3 ◽  
pp. 167-171
Author(s):  
M. Chen ◽  
J. Guo ◽  
J. Wu ◽  
W. Song ◽  
D. Zhang
Keyword(s):  
Real Time ◽  
Accuracy Assessment ◽  
Small Region ◽  
Correction Method ◽  
Satellite System ◽  
Convergence Speed ◽  
Tropospheric Delay ◽  
The North ◽  
Global Navigation Satellite ◽  
Gnss Processing

Tropospheric delay has always been an important issue in Global Navigation Satellite System (GNSS) processing. Empirical tropospheric delay models are difficult to simulate complex and volatile atmospheric environments, resulting in poor accuracy of the empirical model and difficulty in meeting precise positioning demand. In recent years, some scholars proposed to establish real-time tropospheric product by using real-time or near-real-time GNSS observations in a small region, and achieved some good results. This paper uses real-time observing data of 210 Chinese national GNSS reference stations to estimate the tropospheric delay, and establishes ZWD grid model in the country wide. In order to analyze the influence of tropospheric grid product on wide-area real-time PPP, this paper compares the method of taking ZWD grid product as a constraint with the model correction method. The results show that the ZWD grid product estimated based on the national reference stations can improve PPP accuracy and convergence speed. The accuracy in the north (N), east (E) and up (U) direction increase by 31.8 %,15.6 % and 38.3 %, respectively. As with the convergence speed, the accuracy of U direction experiences the most improvement.

scholarly journals Development of an Online Tool for Tracking Soil Nitrogen to Improve the Environmental Performance of Maize Production

2021 ◽  
Vol 13 (10) ◽  
pp. 5649
Author(s):  
Giovani Preza-Fontes ◽  
Junming Wang ◽  
Muhammad Umar ◽  
Meilan Qi ◽  
Kamaljit Banger ◽  
...  
Keyword(s):  
Real Time ◽  
N Uptake ◽  
Growing Season ◽  
Weather Data ◽  
N Availability ◽  
Soil N ◽  
N Losses ◽  
Online Tool ◽  
Support Tool ◽  
Soil N Availability

Freshwater nitrogen (N) pollution is a significant sustainability concern in agriculture. In the U.S. Midwest, large precipitation events during winter and spring are a major driver of N losses. Uncertainty about the fate of applied N early in the growing season can prompt farmers to make additional N applications, increasing the risk of environmental N losses. New tools are needed to provide real-time estimates of soil inorganic N status for corn (Zea mays L.) production, especially considering projected increases in precipitation and N losses due to climate change. In this study, we describe the initial stages of developing an online tool for tracking soil N, which included, (i) implementing a network of field trials to monitor changes in soil N concentration during the winter and early growing season, (ii) calibrating and validating a process-based model for soil and crop N cycling, and (iii) developing a user-friendly and publicly available online decision support tool that could potentially assist N fertilizer management. The online tool can estimate real-time soil N availability by simulating corn growth, crop N uptake, soil organic matter mineralization, and N losses from assimilated soil data (from USDA gSSURGO soil database), hourly weather data (from National Weather Service Real-Time Mesoscale Analysis), and user-entered crop management information that is readily available for farmers. The assimilated data have a resolution of 2.5 km. Given limitations in prediction accuracy, however, we acknowledge that further work is needed to improve model performance, which is also critical for enabling adoption by potential users, such as agricultural producers, fertilizer industry, and researchers. We discuss the strengths and limitations of attempting to provide rapid and cost-effective estimates of soil N availability to support in-season N management decisions, specifically related to the need for supplemental N application. If barriers to adoption are overcome to facilitate broader use by farmers, such tools could balance the need for ensuring sufficient soil N supply while decreasing the risk of N losses, and helping increase N use efficiency, reduce pollution, and increase profits.

scholarly journals Cloud Radar Observations of Diurnal and Seasonal Cloudiness over Reunion Island

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 868
Author(s):  
Jonathan Durand ◽  
Edouard Lees ◽  
Olivier Bousquet ◽  
Julien Delanoë ◽  
François Bonnardot
Keyword(s):  
Sea Breeze ◽  
Satellite System ◽  
Research Infrastructure ◽  
Weather Data ◽  
Reunion Island ◽  
Wet Season ◽  
Trade Winds ◽  
Cloud Radar ◽  
Réunion Island ◽  
Global Navigation Satellite

In November 2016, a 95 GHz cloud radar was permanently deployed in Reunion Island to investigate the vertical distribution of tropical clouds and monitor the temporal variability of cloudiness in the frame of the pan-European research infrastructure Aerosol, Clouds and Trace gases Research InfraStructure (ACTRIS). In the present study, reflectivity observations collected during the two first years of operation (2016–2018) of this vertically pointing cloud radar are relied upon to investigate the diurnal and seasonal cycle of cloudiness in the northern part of this island. During the wet season (December–March), cloudiness is particularly pronounced between 1–3 km above sea level (with a frequency of cloud occurrence of 45% between 12:00–19:00 LST) and 8–12 km (with a frequency of cloud occurrence of 15% between 14:00–19:00 LST). During the dry season (June–September), this bimodal vertical mode is no longer observed and the vertical cloud extension is essentially limited to a height of 3 km due to both the drop-in humidity resulting from the northward migration of the ITCZ and the capping effect of the trade winds inversion. The frequency of cloud occurrence is at its maximum between 13:00–18:00 LST, with a probability of 35% at 15 LST near an altitude of 2 km. The analysis of global navigation satellite system (GNSS)-derived weather data also shows that the diurnal cycle of low- (1–3 km) and mid-to-high level (5–10 km) clouds is strongly correlated with the diurnal evolution of tropospheric humidity, suggesting that additional moisture is advected towards the island by the sea breeze regime. The detailed analysis of cloudiness observations collected during the four seasons sampled in 2017 and 2018 also shows substantial differences between the two years, possibly associated with a strong positive Indian Ocean Southern Dipole (IOSD) event extending throughout the year 2017.

scholarly journals Developing a Simulated Online Model That Integrates GNSS, Accelerometer and Weather Data to Detect Parturition Events in Grazing Sheep: A Machine Learning Approach

Animals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 303
Author(s):  
Eloise S. Fogarty ◽  
David L. Swain ◽  
Greg M. Cronin ◽  
Luis E. Moraes ◽  
Derek W. Bailey ◽  
...  
Keyword(s):  
Machine Learning ◽  
Field Trials ◽  
Satellite System ◽  
False Positives ◽  
Sensor Data ◽  
Weather Data ◽  
Integrated Sensor ◽  
Detection Model ◽  
Independent Field ◽  
Grazing Sheep

In the current study, a simulated online parturition detection model is developed and reported. Using a machine learning (ML)-based approach, the model incorporates data from Global Navigation Satellite System (GNSS) tracking collars, accelerometer ear tags and local weather data, with the aim of detecting parturition events in pasture-based sheep. The specific objectives were two-fold: (i) determine which sensor systems and features provide the most useful information for lambing detection; (ii) evaluate how these data might be integrated using ML classification to alert to a parturition event as it occurs. Two independent field trials were conducted during the 2017 and 2018 lambing seasons in New Zealand, with the data from each used for ML training and independent validation, respectively. Based on objective (i), four features were identified as exerting the greatest importance for lambing detection: mean distance to peers (MDP), MDP compared to the flock mean (MDP.Mean), closest peer (CP) and posture change (PC). Using these four features, the final ML was able to detect 27% and 55% of lambing events within ±3 h of birth with no prior false positives. If the model sensitivity was manipulated such that earlier false positives were permissible, this detection increased to 91% and 82% depending on the requirement for a single alert, or two consecutive alerts occurring. To identify the potential causes of model failure, the data of three animals were investigated further. Lambing detection appeared to rely on increased social isolation behaviour in addition to increased PC behaviour. The results of the study support the use of integrated sensor data for ML-based detection of parturition events in grazing sheep. This is the first known application of ML classification for the detection of lambing in pasture-based sheep. Application of this knowledge could have significant impacts on the ability to remotely monitor animals in commercial situations, with a logical extension of the information for remote monitoring of animal welfare.

scholarly journals Methodology for the Correction of the Spatial Orientation Angles of the Unmanned Aerial Vehicle Using Real Time GNSS, a Shoreline Image and an Electronic Navigational Chart

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2810
Author(s):  
Krzysztof Naus ◽  
Piotr Szymak ◽  
Paweł Piskur ◽  
Maciej Niedziela ◽  
Aleksander Nowak
Keyword(s):  
Real Time ◽  
Spatial Orientation ◽  
Orientation Angle ◽  
Satellite System ◽  
Angle Measurement ◽  
Angle Correction ◽  
Lidar Measurements ◽  
Aerial Vehicle ◽  
Marine Applications ◽  
Global Navigation Satellite

Undoubtedly, Low-Altitude Unmanned Aerial Vehicles (UAVs) are becoming more common in marine applications. Equipped with a Global Navigation Satellite System (GNSS) Real-Time Kinematic (RTK) receiver for highly accurate positioning, they perform camera and Light Detection and Ranging (LiDAR) measurements. Unfortunately, these measurements may still be subject to large errors-mainly due to the inaccuracy of measurement of the optical axis of the camera or LiDAR sensor. Usually, UAVs use a small and light Inertial Navigation System (INS) with an angle measurement error of up to 0.5∘ (RMSE). The methodology for spatial orientation angle correction presented in the article allows the reduction of this error even to the level of 0.01∘ (RMSE). It can be successfully used in coastal and port waters. To determine the corrections, only the Electronic Navigational Chart (ENC) and an image of the coastline are needed.

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