Spatio-temporal dynamic model and parallelized ensemble Kalman filter for precipitation data

Sánchez, Lissette Jiménez; Infante, Saba; Griffin, Victor; Rey, Demetrio

doi:10.1214/15-bjps297

Cited by 8 publications

(5 citation statements)

References 29 publications

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“…The data is available in http : //agrometeorologia .inia.gob.ve/. Missing data was estimated by generating independent random variables with the algorithm of Gibbs and sampling from a truncated normal distribution ( see [37] for more details). The EnKF algorithm was programmed in parallel using the POSIX (Pthread) library in the ANSI C programming environment, in an Intel CPU Core i7 3.6GHz with 16GB RAM running 64Bit Debian Linux.…”

Section: Resultsmentioning

confidence: 99%

Polynomial Chaos based on the parallelized ensemble Kalman filter to estimate precipitation states

Sánchez

Infante²,

Marcano³

et al. 2015

Stat., optim. inf. comput.

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This article develops a methodology combining methods of numerical analysis and stochastic differential equations with computational algorithms to treat problems which have complex nonlinear dynamics in high dimensions. A method to estimate parameters and states of a dynamic system is proposed inspired by the parallelized ensemble Kalman filter (PEnKF) and the polynomial chaos theory of Wiener-Askey. The main advantage of the proposal is in providing a precise efficient algorithm with low computational cost. For the analysed data, the methods provide good predictions, spatially and temporally, for the unknown precipitation states for the first 24 hours. Two goodness of fit measures provide confidence in the quality of the model predictions. The performance of the parallel algorithm, measured by the acceleration and efficiency factors, shows an increase of 7% in speed with respect to the sequential version and is most efficient for P = 2 threads.

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Section: Resultsmentioning

confidence: 99%

Polynomial Chaos based on the parallelized ensemble Kalman filter to estimate precipitation states

Sánchez

Infante²,

Marcano³

et al. 2015

Stat., optim. inf. comput.

Self Cite

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“…Application of the tEnKF to the identification of conductivities Multivariate spatiotemporal random fields have been used in a variety of geophysical applications. For example, Bodas-Salcedo et al (2003) combined spatiotemporal random fields with the Kalman filter method to predict solar radiation in the earth-atmosphere system; Suciu (2014) used a diffusion model to predict solutes transport in groundwater under uncertainty about spatiotemporal evolution of velocity fields; a similar approach was used by Suciu et al (2016) to model reactive transport; Sanchez et al (2016) developed a spatiotemporal dynamic model based on the classical EnKF for Bayesian inference of rainfall; and Liang et al (2016) used a stochastic groundwater flow model to analyze the effect of uncertainty in recharge and transmissivity on the spatiotemporal variations of groundwater level in an unconfined aquifer. Finally, Moslehi and de Barros (2017) investigated the impact of uncertainty in spatial variability of soil hydraulic conductivity on several environmental performance metrics that are relevant for environmental risk assessments, such as species concentrations and arrival times, using a stochastic advection-dispersion model to represent the spatiotemporal evolution of the concentration field.…”

Section: Numerical Implementationmentioning

confidence: 99%

Hydraulic conductivities identification via Ensemble Kalman Filtering with transformed data considering the risk of systematic bias

Vázquez-Guillén¹,

Auvinet

2017

GeofInt

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In subsurface hydrology, Ensemble Kalman Filtering (EnKF) has been coupled with groundwater flow and transport models to solve the inverse problem. Several extensions of the EnKF have been proposed to improve its performance when dealing with non-multi-Gaussian random field models of the hydraulic conductivity. One such variant is the EnKF with transformed data (tEnKF), which uses Gaussian anamorphosis within a conditioning step. Although this transformation has been used in the past to identify hydraulic conductivities, previous studies have ignored the risk of introducing a systematic bias in the spatiotemporal evolution of the hydraulic head field during the forecast steps that the update steps may not correct over time. This paper proposes that in order to evaluate the performance of tEnKFs, applications in synthetically generated random porous media should take into account this risk by incorporating prior knowledge with a multi-Gaussian conductivity correlation structure, and by adopting a reference field with asymmetric correlation structure. As an example of this application, hydraulic conductivities using the tEnKF were identified by solving a one-dimensional, single phase flow problem in a continuous random porous medium. Common concepts in Geostatistics are used to explain the hypothesis underlying both EnKF and tEnKF and to establish a clear link between the tEnKF and the stochastic simulation of conditional random fields.

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“…There is an extensive literature, see: [29], [15], among others. The structures of that models are widely used, in many applications such as: [12], [21], [37], [11], [13], [33], [44], [16], [47], [46], [45], [22], [16], [1], [24], [27], [26], [42], [43], among others.…”

Section: Introductionmentioning

confidence: 99%

“…The estimation of parameters and states through the transition density of the equation ( 1) in closed form are not evaluated, then techniques to approximate the solution are required. There are a variety of methods in the literature, those include maximum likelihood estimation; Monte Carlo techniques by Markov Chains (MCMC); and Monte Carlo Sequential (MCS) algorithms, such as: the Kalman filter (KF), extended Kalman filter (EKF), particle filter (PF), unscented particle filter (UPF) (see [29], [30], [28], [39], [8], [5], [42], [43], among others).…”

Section: Introductionmentioning

confidence: 99%

Solution of a model for pricing options with hedging strategy through Nonlinear Filters

Sanchez,

Sanchez P,

Sanchez A

et al. 2023

Stat., optim. inf. comput.

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A methodology is presented to estimate the solution states for a non-linear price problem, a model for pricing options with a hedging strategy in the F$\ddot{o}$llmer-Schweizer sense is defined. The problem is to determine the price of a contingent claim, that is a contract, that pays of an amount at time $t$ in a incomplete market, that is not possible to replicate a payoff by a controlled portfolio of the basic securities. Two algorithms are presented to estimate the solution of the presented problem, the nested sequential Monte Carlo (NSMC) and space-time particle filter (STPF) are defined from sequences of probability distributions. The methodology is validated to use real data from option Asian, the states in real-time are estimated, that is proposed on the basis of the a price model. The efficiency of the forecasts of the model is compared, reproducing accuracy in the estimates. Finally, one goodness-of-fit measure to validate the performance of the model are used, obtaining insignificant estimation error.

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Spatio-temporal dynamic model and parallelized ensemble Kalman filter for precipitation data

Cited by 8 publications

References 29 publications

Polynomial Chaos based on the parallelized ensemble Kalman filter to estimate precipitation states

Polynomial Chaos based on the parallelized ensemble Kalman filter to estimate precipitation states

Hydraulic conductivities identification via Ensemble Kalman Filtering with transformed data considering the risk of systematic bias

Solution of a model for pricing options with hedging strategy through Nonlinear Filters

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