Transparent Ohmic Contacts on p‐GaN Using an Indium Tin Oxide Overlayer

Data assimilation and adaptive forecasting of water levels in the river Severn catchment, United Kingdom

“…In this paper, we develop further an earlier approach to forecasting system design for a single component rainfall‐flow model [ Young , 2002a, 2002b] so that it can be used with a complete, multicomponent, quasi‐distributed model of the river Severn catchment in the United Kingdom. An early version of this system has been outlined by Romanowicz et al [2004b].…”

“…At any level measurement location, it is assumed that the rainfall and water level measurements above the base level, r ( t ) and y ( t ), are sampled uniformly in time at a sampling interval of Δ t time units (here hours) and that these discrete time, sampled measurements are denoted by r k and y k . It has been shown [ Young , 1993, 2001, 2002a, 2002b, 2003; Young and Beven , 1994; Young and Tomlin , 2000; Romanowicz et al , 2004b] that the nonlinearities arising from the relationship between measured rainfall r k and transformed (effective) rainfall, denoted here by u k , can be approximated using gauged flow or water levels as a surrogate measure of the antecedent wetness or soil water storage in the catchment. In particular, the scalar function describing the nonlinearity between the rainfall and the soil moisture surrogate y k is initially identified nonparametrically using SDP estimation.…”

“…The flow forecasting procedures described in the paper are incorporated within a two‐step data assimilation (DA) procedure based on data‐based mechanistic (DBM) models [e.g., Young , 2002a, 2002b], formulated within a stochastic state space setting for the purposes of recursive state estimation and forecasting. In the first step, available observations of rainfall and river levels at different locations along the river are sequentially assimilated into the forecasting algorithm, based on the statistically identified and estimated DBM dynamic models, in order to derive the multistep‐ahead forecasts.…”

“…In relation to his classification, the methods developed in the present paper utilize both recursive parameter and state updating. However, unlike approaches such as the EKF algorithm used by Refsgaard, where the updating is carried out within a single, nonlinear, state space setting, with the parameters considered as adjoined state variables, our state and parameter estimation procedures are carried out separately but concurrently, employing coordinated recursive estimation algorithms, as used by Young [2002a, 2002b]. This avoids the well known deficiencies of the EKF (such as problems with covariance estimation and convergence for multidimensional, nonlinear models) and yields more statistically efficient estimates of the model parameters.…”

“…The methodology used in this study is presented in the next section. It exploits the top‐down, data‐based mechanistic (DBM) approach to the stochastic‐dynamic modeling of environmental processes, concentrating on the identification and estimation of those physically interpretable, “dominant modes” of dynamic behavior that are most important for flood prediction [e.g., Young , 2002a, 2002b]. In particular, hydrological processes active in the catchment are modeled using the SDP method of estimating the location and nature of significant nonlinearities in the system (here the effective rainfall input nonlinearity), together with a stochastic transfer function (STF) method for characterizing both the linear effective rainfall–water level behavior and the water level routing processes.…”