Review of snow water equivalent retrieval methods using spaceborne passive microwave radiometry

ABSTRACT

The physical properties of a snowpack strongly influence the emissions from the substratum, making snow property retrievals feasible by means of the surface brightness temperature observed by passive microwave sensors. Depending on the spatial resolution observed, time series records of daily snow coverage and critical snowpack properties such as snow depth (SD) and snow water equivalent (SWE) could be helpful in applications ranging from modelling snow variations for water resources management in a catchment to global climatologic studies. However, the challenge of including spaceborne SWE products in operational hydrological and hydroclimate modelling applications is very demanding with limited uptake by these systems, mostly attributed to insufficient SWE estimation accuracy. The root causes of this challenge include the coarse spatial resolution of passive microwave (PM) observations that observe highly aggregated snowpack properties at the spaceborne scale, and inadequacies during the retrieval process caused by uncertainties with the forward emission modelling of snow and challenges to find robust parameterizations of the models. While the spatial resolution problem is largely in the realm of engineering design and constrained by physical restrictions, a better understanding of developed and adopted retrieval methodologies can provide the clarity needed to enhance our knowledge in this field. In this paper, we review snow depth and SWE retrieval methods using PM observations, taking only dry snow retrieval processes into consideration. Snow properties using PM observations can be modelled by purely empirical relations based on underlying physical processes, and SD and SWE can be estimated by regression-based approaches. Snow property retrievals have been refined gradually throughout four decades use of PM observations in tandem with better understanding of physical processes, inclusion of better snowpack parameterizations, improved uncertainty analysis frameworks, and applying better inversion algorithms. Studying available methods, we conclude that snowpack parameterization is key to accurate retrieval. By improving retrieval algorithm architectures to better capture dynamic snowpack evolution processes, SWE estimates are likely to improve. We conclude that this challenge can be addressed by coupling emission models and land surface models or integrating weather-driven snowpack evolution into emission models and performing inversion in a Bayesian framework.     

Adaptive stabilization of a class of nonlinear systems using high-gain feedback

This note presents a high-gain feedback stabilizing control algorithm in which the high-gain parameter is adapted on-line. The algorithm is developed for a class of nonlinear systems which can be viewed as the nonlinear counterpart of uniform rank systems. The system can be unknown except for a number of vital pieces of information. For single-input single-output linear systems such information is usually required in the traditional adaptive control literature.     

Comparison of bolted end plate and T-stub connections sensitivity to bolt diameter on cyclic behavior

Considering the construction limitations, proper and correct design of connections is vital and of great importance. At the construction stage, it is probable to use a connection bolt with a diameter less than the design assumptions. It can occur due to an imperfection in construction or even to changes in the function of the building. The unforeseen changes can increase the structural load, and consequently the moment and shear force demand values. Therefore, the present paper is aimed to examine the sensitivity of the bolted connections to the diameter of bolts using a numerical method. The results show that bolted T-stub connections are more sensitive to bolt diameter than end plate connections. Hence, the bolted end plate connections are recommended where the imperfection in construction or changes in function of the building is probable. Moreover, in construction of bolted connections, if changing the number of bolts regarding the constant total cross sectional area is considered, it is recommended to use the symmetric arrangement of bolts on each beam flange. In design of connections for a particular frame according to AISC, the moment capacity of T-stub connection is higher than that of end plate connection. However, the total energy absorptions of these connection types are approximately equal.     

An improvement to the low gain design for discrete‐time linear systems in the presence of actuator saturation nonlinearity

A low‐gain design for linear discrete‐time systems subject to input saturation was recently developed to solve both semi‐global stabilization and semi‐global output regulation problems. This paper proposes an improvement to the low‐gain design and determines controllers with the new design that achieve semi‐global output regulation. The improvement is reflected in better utilization of available control capacity and consequently better closed‐loop performance. Copyright © 2000 John Wiley & Sons, Ltd.     

On lp-stabilization of strictly unstable discrete-time linear systems with saturating actuators

For a continuous-time linear system with saturating actuators, it is known that, irrespective of the locations of the open-loop poles, both global and semi-global finite gain L_p-stabilization are achievable, by nonlinear and linear feedback, respectively, and the L_p gain can also be made arbitrarily small. In this paper we show that, these results do not hold for discrete-time systems. © 1998 John Wiley & Sons, Ltd.     

Output regulation for linear discrete-time systems subject to input saturation

The purpose of this paper is to examine the problem of controlling a linear discrete-time system subject to input saturation in order to have its output track (or reject) a family of reference (or disturbance) signals produced by some external generator. It is shown that a semi-global framework, rather than a global framework, for this problem is a natural one. Within this framework, a set of solvability conditions are given and feedback laws which solve the problem are constructed. The theory developed in this paper parallels the one we developed earlier for the continuous-time system. © 1997 by John Wiley & Sons, Ltd.     

Chaos control of a bounded 4D chaotic system

This paper is concerned with the problem of optimal and adaptive control for controlling chaos in a novel bounded four-dimensional (4D) chaotic system. This system can display hyperchaos, chaos, quasiperiodic and periodic behaviors, and may have a unique equilibrium, three equilibria and five equilibria for the different system parameters. An optimal control law is designed for the novel bounded chaotic system, based on the Pontryagin minimum principle. Furthermore, we propose Lyapunov stability conditions to control the new bounded 4D chaotic system with unknown parameters by a feedback control approach. Numerical simulations are presented to show the effectiveness of the proposed chaos control scheme.     

Performance assessment of decision-making units using an adaptive neural network algorithm: one period case

This study proposes a nonparametric method based on adaptive neural network (ANN) technique for measuring efficiency of decision making units (DMUs) in one period case as a complementary tool for the ANN-based efficiency methods in the previous studies. In previous studies, there are needed to have large volume of data, and so the proposed method in this study is more applicable because it can be used for the cases which have no historical data. In fact, a limitative weakness of the ANN-based efficiency methods about applying them for these cases is removed. So, it can be a competitive method to the other common tools for measuring efficiency. By noting the importance of flexible manufacturing system, this study presents a decision-making model for optimization of operators’ allocation in cellular manufacturing system by computer simulation. The methodology is illustrated through its application on a previously reported dataset. It was found out that ANN provides more robust results and identifies more efficient units than the conventional methods since better performance patterns are explored.     

Predictive control of drying process using an adaptive neuro-fuzzy and partial least squares approach

In this paper, adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and partial least squares (PLS) approaches are applied to predictive control of a drying process. In the proposed approaches, the PLS analysis is used to pre-process actual data and to provide the necessary background to apply ANN and ANFIS approaches. A reasonable section of this study is assigned to the modeling with the aim at predicting the granule particle size and executing by ANFIS and ANN. ANN holds the promise of being capable of producing non-linear models, being able to work under noise conditions, and being fault tolerant to the loss of neurons or connections. Also, the ANFIS approach combines the advantages of fuzzy system and artificial neural network to design architecture and is capable of dealing with both limitation and complexity in the data set. The efficiencies of ANFIS and ANN approaches in prediction are compared and the superior approach is selected. Finally, by deploying the preferred approach, several scenarios are presented to be used in predictive control of spray drying as an accurate, fast running, and inexpensive tool. This is the first study that presents a flexible intelligent approach for predictive control of drying process by ANN, ANFIS, and PLS. The approach of this study may be easily applied to other production process.