Multi-GNSS Slant Wet Delay Retrieval Using Multipath Mitigation Maps

Author(s):  
Addisu Hunegnaw ◽  
Yohannes Getachew Ejigu ◽  
Felix Norman Teferle ◽  
Gunnar Elgered

<p>The conventional Global Navigation Satellite System (GNSS) processing is typically contaminated with errors due to atmospheric variabilities, such as those associated with the mesoscale phenomena. These errors are manifested in the parameter estimates, including station coordinates and atmospheric products. To enhance the accuracy of these GNSS products further, a better understanding of the local-scale atmospheric variability is necessary. As part of multi-GNSS processing, station coordinates, carrier phase ambiguities, orbits, zenith total delay (ZTD) and horizontal gradients are the main parameters of interest. Here, ZTD is estimated as the average zenith delay along the line-of-sight to every observed GNSS satellite mapped to the vertical while the horizontal gradients are estimated in NS and EW directions and provide a means to partly account for the azimuthally inhomogeneous atmosphere. However, a better atmospheric description is possible by evaluating the slant path delay (SPD) or slant wet delay (SWD) along GNSS ray paths, which are not resolved by ordinary ZTD and gradient analysis. SWD is expected to provide better information about the inhomogeneous distribution of water vapour that is disregarded when retrieving ZTD and horizontal gradients. Usually, SWD cannot be estimated directly from GNSS processing as the number of unknown parameters exceeds the number of observations. Thus, SWD is generally calculated from ZTD for each satellite and may be dominated by un-modelled atmospheric delays, clock errors, unresolved carrier-phase ambiguities and near-surface multipath scattering.</p><p> </p><p>In this work, we have computed multipath maps by stacking individual post-fit carrier residuals incorporating the signals from four GNSS constellations, i.e. BeiDou, Galileo, Glonass and GPS. We have selected a subset of global International GNSS Service (IGS) stations capable of multi-GNSS observables located in different climatic zones. The multipath effects are reduced by subtracting the stacked multipath maps from the raw post-fit carrier phase residuals. We demonstrate that the multipath stacking technique results in significantly reduced variations in the one-way post-fit carrier phase residuals. This is particularly evident for lower elevation angles, thus, producing a retrieval method for SWD that is less affected by site-specific multipath effects. We show a positive impact on SWD estimation using our multipath maps during increased atmospheric inhomogeneity as induced by severe weather events.</p>

2020 ◽  
Vol 101 (9) ◽  
pp. E1497-E1511 ◽  
Author(s):  
Karthik Balaguru ◽  
Gregory R. Foltz ◽  
L. Ruby Leung ◽  
John Kaplan ◽  
Wenwei Xu ◽  
...  

Abstract Tropical cyclone (TC) rapid intensification (RI) is difficult to predict and poses a formidable threat to coastal populations. A warm upper ocean is well known to favor RI, but the role of ocean salinity is less clear. This study shows a strong inverse relationship between salinity and TC RI in the eastern Caribbean and western tropical Atlantic due to near-surface freshening from the Amazon–Orinoco River system. In this region, rapidly intensifying TCs induce a much stronger surface enthalpy flux compared to more weakly intensifying storms, in part due to a reduction in SST cooling caused by salinity stratification. This reduction has a noticeable positive impact on TCs undergoing RI, but the impact of salinity on more weakly intensifying storms is insignificant. These statistical results are confirmed through experiments with an ocean mixed layer model, which show that the salinity-induced reduction in SST cold wakes increases significantly as the storm’s intensification rate increases. Currently, operational statistical–dynamical RI models do not use salinity as a predictor. Through experiments with a statistical RI prediction scheme, it is found that the inclusion of surface salinity significantly improves the RI detection skill, offering promise for improved operational RI prediction. Satellite surface salinity may be valuable for this purpose, given its global coverage and availability in near–real time.


Author(s):  
Hussein Ahmad Abdulsalam ◽  
Sule Omeiza Bashiru ◽  
Alhaji Modu Isa ◽  
Yunusa Adavi Ojirobe

Gompertz Rayleigh (GomR) distribution was introduced in an earlier study with few statistical properties derived and parameters estimated using only the most common traditional method, Maximum Likelihood Estimation (MLE). This paper aimed at deriving more statistical properties of the GomR distribution, estimating the three unknown parameters via a competitive method, Maximum Product of Spacing (MPS) and evaluating goodness of fit using rainfall data sets from Nigeria, Malaysia and Argentina. Properties of statistical distributions including distribution of smallest and largest order statistics, cumulative or integrated hazard function, odds function, rth non-central moments, moment generating function, mean, variance and entropy measures for GomR distribution were explicitly derived. The fitted data sets reveal the flexibility of GomR distribution over other distributions been compared with. Simulation study was used to evaluate the consistency, accuracy and unbiasedness of the GomR distribution parameter estimates obtained from the method of MPS. The study found that GomR distribution could not provide a better fit for Argentine rainfall data but it was the best distribution for the rainfall data sets from Nigeria and Malaysia in comparison with the distributions; Generalized Weibull Rayleigh (GWR), Exponentiated Weibull Rayleigh (EWR), Type (II) Topp Leone Generalized Inverse Rayleigh (TIITLGIR), Kumarawamy Exponential Inverse Raylrigh (KEIR), Negative Binomial Marshall-Olkin Rayleigh (NBMOR) and Exponentiated Weibull (EW). Furthermore, the estimates from MPSE were consistent as the sample size increases but not as efficient as those from MLE.


Author(s):  
Anindya Chatterjee ◽  
Joseph P. Cusumano

Abstract We present a new observer-based method for parameter estimation for nonlinear oscillatory mechanical systems where the unknown parameters appear linearly (they may each be multiplied by bounded and Lipschitz continuous but otherwise arbitrary, possibly nonlinear, functions of the oscillatory state variables and time). The oscillations in the system may be periodic, quasiperiodic or chaotic. The method is also applicable to systems where the parameters appear nonlinearly, provided a good initial estimate of the parameter is available. The observer requires measurements of displacements. It estimates velocities on a fast time scale, and the unknown parameters on a slow time scale. The fast and slow time scales are governed by a single small parameter ϵ. Using asymptotic methods including the method of averaging, it is shown that the observer’s estimates of the unknown parameters converge like e−ϵt where t is time, provided the system response is such that the coefficient-functions of the unknown parameters are not close to being linearly dependent. It is also shown that the method is robust in that small errors in the model cause small errors in the parameter estimates. A numerical example is provided to demonstrate the effectiveness of the method.


Author(s):  
Fu Zhang ◽  
Ehsan Keikha ◽  
Behrooz Shahsavari ◽  
Roberto Horowitz

This paper presents an online adaptive algorithm to compensate damping and stiffness frequency mismatches in rate integrating Coriolis Vibratory Gyroscopes (CVGs). The proposed adaptive compensator consists of a least square estimator that estimates the damping and frequency mismatches, and an online compensator that corrects the mismatches. In order to improve the adaptive compensator’s convergence rate, we introduce a calibration phase where we identify relations between the unknown parameters (i.e. mismatches, rotation rate and rotation angle). Calibration results show that the unknown parameters lie on a hyperplane. When the gyro is in operation, we project parameters estimated from the least square estimator onto the hyperplane. The projection will reduce the degrees of freedom in parameter estimates, thus guaranteeing persistence of excitation and improving convergence rate. Simulation results show that utilization of the projection method will drastically improve convergence rate of the least square estimator and improve gyro performance.


2009 ◽  
Vol 46 (8) ◽  
pp. 627-636
Author(s):  
Ahmed A. El-Ghazouly ◽  
Mohamed Elhabiby ◽  
Naser El-Sheimy

In carrier-phase measurements, which are the most precise observations for Global Positioning System (GPS) relative positioning, multipath error is still a factor that interferes with achieving the desired accuracy. Various improvements in receiver and antenna technologies, as well as modeling strategies, have resulted in better ways of coping with this error source. However, errors caused by multipath can be as large as 5 cm, which is not an acceptable accuracy, especially in precise surveying applications like deformation monitoring. In this paper, a full assessment of different wavelets techniques that can be used in multipath mitigation is made to evaluate the optimum way of using wavelets to reduce or remove this type of error. Also, a new approach based on the wavelet detrending technique is introduced to remove carrier-phase multipath error in the measurement domain. To mitigate multipath, GPS double-difference observables are fed to an adaptive wavelet analysis procedure based on high- and low-pass filter decomposition with different levels of resolution. Consequently, the observable sequences are corrected; these corrected observables can then be used to reduce the ambiguity search volume during the initial float solution stage. Meanwhile, double-difference observations with multipath mitigation offer an efficient method for obtaining a better baseline solution.


2020 ◽  
Author(s):  
Leila farhadi ◽  
Abedeh Abdolghafoorian

<p>Evapotranspiration (ET) is a key component of terrestrial water cycle that plays an important role in the Earth system. Aaccurate estimation of ET is crucial in various hydrological, meteorological, and agricultural applications. In situ measurements of ET are costly and cannot be readily scaled to regional scales relevant to weather and climate studies. Therefore, there is a need for techniques to make quantitative estimates of ET using land surface state observations that are widely available from remote sensing across a range of spatial scales.</p><p>In this work, A variational data (VDA) assimilation framework is developed to estimate ET by assimilating Soil Moisture Active Passive (SMAP) soil moisture and Geostationary Operational Environmental Satellite (GOES) land surface temperature data into a coupled dual-source energy and water balance model.</p><p>The VDA framework estimates the key parameters of the coupled model, which regulate the partitioning of available energy (i.e., neutral bulk heat transfer coefficient (CH<sub>N</sub>) and evaporative fraction from soil (EF<sub>S</sub>) and canopy (EF<sub>C</sub>)). The uncertainties of the retrieved unknown parameters are estimated through the inverse of Hessian of cost function, obtained using the Lagrangian methodology. Analysis of the second-order information provides a tool to identify the optimum parameter estimates and guides towards a well-posed estimation problem.</p><p>The VDA framework is implemented over an area of 21780 km<sup>2</sup> in the U.S. Southern Great Plains (with computational grid size of 0.05 degree) during a nine-month period. The maps of retrieved evaporation and transpiration are used to study a number of dynamic feedback mechanisms between the land and atmosphere, such as the dependence of evapotranspiration on vegetation and soil moisture.</p>


2010 ◽  
Vol 2010 ◽  
pp. 1-27 ◽  
Author(s):  
Chu-Tong Wang ◽  
Jason S. H. Tsai ◽  
Chia-Wei Chen ◽  
You Lin ◽  
Shu-Mei Guo ◽  
...  

An active fault-tolerant pulse-width-modulated tracker using the nonlinear autoregressive moving average with exogenous inputs model-based state-space self-tuning control is proposed for continuous-time multivariable nonlinear stochastic systems with unknown system parameters, plant noises, measurement noises, and inaccessible system states. Through observer/Kalman filter identification method, a good initial guess of the unknown parameters of the chosen model is obtained so as to reduce the identification process time and enhance the system performances. Besides, by modifying the conventional self-tuning control, a fault-tolerant control scheme is also developed. For the detection of fault occurrence, a quantitative criterion is exploited by comparing the innovation process errors estimated by the Kalman filter estimation algorithm. In addition, the weighting matrix resetting technique is presented by adjusting and resetting the covariance matrix of parameter estimates to improve the parameter estimation for faulty system recovery. The technique can effectively cope with partially abrupt and/or gradual system faults and/or input failures with fault detection.


2006 ◽  
Author(s):  
Xiaoli Liu ◽  
Jingnan Liu ◽  
Zhigang Hong

2000 ◽  
Vol 10 (03) ◽  
pp. 611-620 ◽  
Author(s):  
YU-PING TIAN ◽  
XINGHUO YU

A novel adaptive time-delayed control method is proposed for stabilizing inherent unstable periodic orbits (UPOs) in chaotic systems with unknown parameters. We first explore the inherent properties of chaotic systems and use the system state and time-delayed system state to form an asymptotically stable invariant manifold so that when the system state enters the manifold and stays in it thereafter, the resulting motion enables the stabilization of the desired UPOs. We then use the model following concept to construct an identifier for the estimation of the uncertain system parameters. We shall prove that under the developed scheme, the system parameter estimates will converge to their true values. The effectiveness of the method is confirmed by computer simulations.


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