Results 1  10
of
46
OBSTACLES TO HIGHDIMENSIONAL PARTICLE FILTERING
"... Particle filters are ensemblebased assimilation schemes that, unlike the ensemble Kalman filter, employ a fully nonlinear and nonGaussian analysis step to compute the probability distribution function (pdf) of a system’s state conditioned on a set of observations. Evidence is provided that the ens ..."
Abstract

Cited by 94 (5 self)
 Add to MetaCart
Particle filters are ensemblebased assimilation schemes that, unlike the ensemble Kalman filter, employ a fully nonlinear and nonGaussian analysis step to compute the probability distribution function (pdf) of a system’s state conditioned on a set of observations. Evidence is provided that the ensemble size required for a successful particle filter scales exponentially with the problem size. For the simple example in which each component of the state vector is independent, Gaussian and of unit variance, and the observations are of each state component separately with independent, Gaussian errors, simulations indicate that the required ensemble size scales exponentially with the state dimension. In this example, the particle filter requires at least 1011 members when applied to a 200dimensional state. Asymptotic results, following the work of Bengtsson, Bickel and collaborators, are provided for two cases: one in which each prior state component is independent and identically distributed, and one in which both the prior pdf and the observation errors are Gaussian. The asymptotic theory reveals that, in both cases, the required ensemble size scales exponentially with the variance of the observation loglikelihood, rather than with the state dimension per se. 2
Treatment of Input Uncertainty in Hydrologic Modeling: Doing Hydrology Backwards with MarkovChain Monte Carlo Simulation
, 2008
"... There is increasing consensus in the hydrologic literature that an appropriate framework for streamflow forecasting and simulation should include explicit recognition of forcing, parameter and model structural error. This paper presents a novel Markov Chain Monte Carlo (MCMC) sampler, entitled Di ..."
Abstract

Cited by 30 (4 self)
 Add to MetaCart
There is increasing consensus in the hydrologic literature that an appropriate framework for streamflow forecasting and simulation should include explicit recognition of forcing, parameter and model structural error. This paper presents a novel Markov Chain Monte Carlo (MCMC) sampler, entitled DiffeRential Evolution Adaptive Metropolis (DREAM), that is especially designed to efficiently estimate the posterior probability density function of hydrologic model parameters in complex, highdimensional sampling problems. This MCMC scheme adaptively updates the scale and orientation of the proposal distribution during sampling, and maintains detailed balance and ergodicity. It is then demonstrated how DREAM can be used to analyze forcing data error during watershed model calibration using a 5parameter rainfall runoff model with streamflow data from two different catchments. Explicit treatment of precipitation error during hydrologic model calibration not only results in prediction uncertainty bounds that are more appropriate, but also significantly alters the posterior distribution
Graphical Models for Statistical Inference and Data Assimilation
, 2006
"... In data assimilation for a system which evolves in time, one combines past and current observations with a model of the dynamics of the system, in order to improve the simulation of the system as well as any future predictions about it. From a statistical point of view, this process can be regarded ..."
Abstract

Cited by 5 (1 self)
 Add to MetaCart
In data assimilation for a system which evolves in time, one combines past and current observations with a model of the dynamics of the system, in order to improve the simulation of the system as well as any future predictions about it. From a statistical point of view, this process can be regarded as estimating many random variables, which are related both spatially and temporally: given observations of some of these variables, typically corresponding to times past, we require estimates of several others, typically corresponding to future times. Graphical models have emerged as an effective formalism for assisting in these types of inference tasks, particularly for large numbers of random variables. Graphical models provide a means of representing dependency structure among the variables, and can provide both intuition and efficiency in estimation and other inference computations. We provide an overview and introduction to graphical models, and describe how they can be used to represent statistical dependency and how the resulting structure can be used to organize computation. The relation between statistical
RealTime Data Assimilation for Operational Ensemble Streamflow Forecasting
 J. Hydrometeorol
"... Operational flood forecasting requires that accurate estimates of the uncertainty associated with modelgenerated streamflow forecasts be provided along with the probable flow levels. This paper demonstrates a stochastic ensemble implementation of the Sacramento model used routinely by the National ..."
Abstract

Cited by 5 (1 self)
 Add to MetaCart
Operational flood forecasting requires that accurate estimates of the uncertainty associated with modelgenerated streamflow forecasts be provided along with the probable flow levels. This paper demonstrates a stochastic ensemble implementation of the Sacramento model used routinely by the National Weather Service for deterministic streamflow forecasting. The approach, the simultaneous optimization and data assimilation method (SODA), uses an ensemble Kalman filter (EnKF) for recursive state estimation allowing for treatment of streamflow data error, model structural error, and parameter uncertainty, while enabling implementation of the Sacramento model without major modification to its current structural form. Model parameters are estimated in batch using the shuffled complex evolution metropolis stochasticensemble optimization approach (SCEMUA). The SODA approach was implemented using parallel computing to handle the increased computational requirements. Studies using data from the Leaf River, Mississippi, indicate that forecast performance improvements on the order of 30 % to 50 % can be realized even with a suboptimal implementation of the filter. Further, the SODA parameter estimates appear to be less biased, which may increase the prospects for finding useful regionalization relationships. 1. Introduction and
Toward reduction of model uncertainty: integration of Bayesian model averaging and data assimilation
, 2012
"... model averaging and data assimilation ..."
Hydrologic data assimilation with a hillslopescale resolving model and Lband radar observations: Synthetic experiments with the ensemble Kalman filter
 Water Resources Research
"... Hydrologic data assimilation with a hillslopescaleresolving model and L band radar observations: Synthetic experiments with the ensemble Kalman filter ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
Hydrologic data assimilation with a hillslopescaleresolving model and L band radar observations: Synthetic experiments with the ensemble Kalman filter
Quantifying uncertainty in urban flooding analysis considering
, 2011
"... hydroclimatic projection and urban development effects ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
hydroclimatic projection and urban development effects
Article Sensitivity of Subjective Decisions in the GLUE Methodology for Quantifying the Uncertainty in the Flood Inundation Map for Seymour Reach in Indiana, USA
, 2014
"... www.mdpi.com/journal/water ..."
(Show Context)