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Journal articles on the topic 'Iterative ensemble smoother'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Raanes, Patrick Nima, Andreas Størksen Stordal, and Geir Evensen. "Revising the stochastic iterative ensemble smoother." Nonlinear Processes in Geophysics 26, no. 3 (2019): 325–38. http://dx.doi.org/10.5194/npg-26-325-2019.

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Abstract. Ensemble randomized maximum likelihood (EnRML) is an iterative (stochastic) ensemble smoother, used for large and nonlinear inverse problems, such as history matching and data assimilation. Its current formulation is overly complicated and has issues with computational costs, noise, and covariance localization, even causing some practitioners to omit crucial prior information. This paper resolves these difficulties and streamlines the algorithm without changing its output. These simplifications are achieved through the careful treatment of the linearizations and subspaces. For exampl

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Grudzien, Colin, and Marc Bocquet. "A fast, single-iteration ensemble Kalman smoother for sequential data assimilation." Geoscientific Model Development 15, no. 20 (2022): 7641–81. http://dx.doi.org/10.5194/gmd-15-7641-2022.

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Abstract. Ensemble variational methods form the basis of the state of the art for nonlinear, scalable data assimilation, yet current designs may not be cost-effective for real-time, short-range forecast systems. We propose a novel estimator in this formalism that is designed for applications in which forecast error dynamics is weakly nonlinear, such as synoptic-scale meteorology. Our method combines the 3D sequential filter analysis and retrospective reanalysis of the classic ensemble Kalman smoother with an iterative ensemble simulation of 4D smoothers. To rigorously derive and contextualize

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Bocquet, M., and P. Sakov. "An iterative ensemble Kalman smoother." Quarterly Journal of the Royal Meteorological Society 140, no. 682 (2013): 1521–35. http://dx.doi.org/10.1002/qj.2236.

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Bocquet, M. "Localization and the iterative ensemble Kalman smoother." Quarterly Journal of the Royal Meteorological Society 142, no. 695 (2016): 1075–89. http://dx.doi.org/10.1002/qj.2711.

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Jiang, Simin, Ruicheng Zhang, Jinbing Liu, Xuemin Xia, Xianwen Li, and Maohui Zheng. "Simultaneous Estimation of a Contaminant Source and Hydraulic Conductivity Field by Combining an Iterative Ensemble Smoother and Sequential Gaussian Simulation." Water 14, no. 5 (2022): 757. http://dx.doi.org/10.3390/w14050757.

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Joint estimation of groundwater contaminant source characteristics and hydraulic conductivity is of great significance for contaminant transport models in heterogeneous subsurface media. As for accurate characterization of hydraulic conductivities, both geostatistical modeling and groundwater inverse modeling are alternative approaches. In this study, an iterative ensemble smoother and sequential gaussian simulation (SGSIM) in geostatistics modeling were combined to realize the simultaneous inversion of contaminant sources and hydraulic conductivities, by using directly measured hydraulic cond

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Chen, Yan, and Dean S. Oliver. "Ensemble Randomized Maximum Likelihood Method as an Iterative Ensemble Smoother." Mathematical Geosciences 44, no. 1 (2011): 1–26. http://dx.doi.org/10.1007/s11004-011-9376-z.

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Gineste, Michael, and Jo Eidsvik. "Batch seismic inversion using the iterative ensemble Kalman smoother." Computational Geosciences 25, no. 3 (2021): 1105–21. http://dx.doi.org/10.1007/s10596-021-10043-4.

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AbstractAn ensemble-based method for seismic inversion to estimate elastic attributes is considered, namely the iterative ensemble Kalman smoother. The main focus of this work is the challenge associated with ensemble-based inversion of seismic waveform data. The amount of seismic data is large and, depending on ensemble size, it cannot be processed in a single batch. Instead a solution strategy of partitioning the data recordings in time windows and processing these sequentially is suggested. This work demonstrates how this partitioning can be done adaptively, with a focus on reliable and eff

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Nezhadali, Mohammad, Tuhin Bhakta, Kristian Fossum, and Trond Mannseth. "Iterative multilevel assimilation of inverted seismic data." Computational Geosciences 26, no. 2 (2022): 241–62. http://dx.doi.org/10.1007/s10596-021-10125-3.

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AbstractIn ensemble-based data assimilation (DA), the ensemble size is usually limited to around one hundred. Straightforward application of ensemble-based DA can therefore result in significant Monte Carlo errors, often manifesting themselves as severe underestimation of parameter uncertainties. Localization is the conventional remedy for this problem. Assimilation of large amounts of simultaneous data enhances the negative effects of Monte Carlo errors. Use of lower-fidelity models reduces the computational cost per ensemble member and therefore renders the possibility to reduce Monte Carlo

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Bocquet, M., and P. Sakov. "Joint state and parameter estimation with an iterative ensemble Kalman smoother." Nonlinear Processes in Geophysics 20, no. 5 (2013): 803–18. http://dx.doi.org/10.5194/npg-20-803-2013.

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Abstract. Both ensemble filtering and variational data assimilation methods have proven useful in the joint estimation of state variables and parameters of geophysical models. Yet, their respective benefits and drawbacks in this task are distinct. An ensemble variational method, known as the iterative ensemble Kalman smoother (IEnKS) has recently been introduced. It is based on an adjoint model-free variational, but flow-dependent, scheme. As such, the IEnKS is a candidate tool for joint state and parameter estimation that may inherit the benefits from both the ensemble filtering and variation

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Zhang, Ruicheng, Nianqing Zhou, Xuemin Xia, Guoxian Zhao, and Simin Jiang. "Joint Estimation of Hydraulic and Biochemical Parameters for Reactive Transport Modelling with a Modified ILUES Algorithm." Water 12, no. 8 (2020): 2161. http://dx.doi.org/10.3390/w12082161.

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Multicomponent reactive transport modeling is a powerful tool for the comprehensive analysis of coupled hydraulic and biochemical processes. The performance of the simulation model depends on the accuracy of related model parameters whose values are usually difficult to determine from direct measurements. In this situation, estimates of these uncertain parameters can be obtained by solving inverse problems. In this study, an efficient data assimilation method, the iterative local updating ensemble smoother (ILUES), is employed for the joint estimation of hydraulic parameters, biochemical param

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11

Carpenter, Chris. "Iterative Ensemble Kalman Smoother Applied to History-Matching Coalbed Methane Wells." Journal of Petroleum Technology 74, no. 04 (2022): 88–90. http://dx.doi.org/10.2118/0422-0088-jpt.

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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTEC 208291, “Introducing Iterative Ensemble Kalman Smoother to History-Matching Horizontal Coalbed Methane Wells,” by Rubakumar Sankararaj, SPE, and Sylvain Ducroux, SPE, Resoptima, and Dan Kuznetsov, SPE, Arrow Energy, et al. The paper has not been peer reviewed. This study presents the iterative ensemble Kalman smoother applied to a low-permeability coalbed methane (CBM) field in Australia. All wells in the studied reservoir are completed with an artificial lift system and permanent downhole gauges

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12

Chen, Yan, and Dean S. Oliver. "History Matching of the Norne Full-Field Model With an Iterative Ensemble Smoother." SPE Reservoir Evaluation & Engineering 17, no. 02 (2014): 244–56. http://dx.doi.org/10.2118/164902-pa.

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Summary Although ensemble-based data-assimilation methods such as the ensemble Kalman filter (EnKF) and the ensemble smoother have been extensively used for the history matching of synthetic models, the number of applications of ensemble-based methods for history matching of field cases is extremely limited. In most of the published field cases in which the ensemble-based methods were used, the number of wells and the types of data to be matched were relatively small. As a result, it may not be clear to practitioners how a real history-matching study would be accomplished with ensemble-based m

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13

Liu, Mingliang, and Dario Grana. "Time-lapse seismic history matching with an iterative ensemble smoother and deep convolutional autoencoder." GEOPHYSICS 85, no. 1 (2019): M15—M31. http://dx.doi.org/10.1190/geo2019-0019.1.

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We have developed a time-lapse seismic history matching framework to assimilate production data and time-lapse seismic data for the prediction of static reservoir models. An iterative data assimilation method, the ensemble smoother with multiple data assimilation is adopted to iteratively update an ensemble of reservoir models until their predicted observations match the actual production and seismic measurements and to quantify the model uncertainty of the posterior reservoir models. To address computational and numerical challenges when applying ensemble-based optimization methods on large s

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Liu, Mingliang, and Dario Grana. "Stochastic nonlinear inversion of seismic data for the estimation of petroelastic properties using the ensemble smoother and data reparameterization." GEOPHYSICS 83, no. 3 (2018): M25—M39. http://dx.doi.org/10.1190/geo2017-0713.1.

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We have developed a new stochastic nonlinear inversion method for seismic reservoir characterization studies to jointly estimate elastic and petrophysical properties and to quantify their uncertainty. Our method aims to estimate multiple reservoir realizations of the entire set of reservoir properties, including seismic velocities, density, porosity, mineralogy, and saturation, by iteratively updating the initial ensemble of models based on the mismatch between their seismic response and the measured seismic data. The initial models are generated using geostatistical methods and the geophysica

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Zhang, Yanhui, Dean S. Oliver, Yan Chen, and Hans J. Skaug. "Data Assimilation by Use of the Iterative Ensemble Smoother for 2D Facies Models." SPE Journal 20, no. 01 (2014): 169–85. http://dx.doi.org/10.2118/170248-pa.

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Summary The ensemble Kalman filter (EnKF) and related ensemble-based smoothers are well-suited to history match reservoir models that are multivariate Gaussian. Estimating categorical variables such as facies types is much more difficult with the EnKF, especially when the variables have complex transitional dependencies. In a previous study, the EnKF was used for updating third-order Markov-chain models in one dimension with an efficient post-processing step to ensure that the posterior samples are constrained by the prior. The efficiency of the post-processing step depended on the use of an o

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Fillion, Anthony, Marc Bocquet, Serge Gratton, Selime Gürol, and Pavel Sakov. "An Iterative Ensemble Kalman Smoother in Presence of Additive Model Error." SIAM/ASA Journal on Uncertainty Quantification 8, no. 1 (2020): 198–228. http://dx.doi.org/10.1137/19m1244147.

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Chang, Haibin, Qinzhuo Liao, and Dongxiao Zhang. "Surrogate model based iterative ensemble smoother for subsurface flow data assimilation." Advances in Water Resources 100 (February 2017): 96–108. http://dx.doi.org/10.1016/j.advwatres.2016.12.001.

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Ju, Lei, Jiangjiang Zhang, Long Meng, Laosheng Wu, and Lingzao Zeng. "An adaptive Gaussian process-based iterative ensemble smoother for data assimilation." Advances in Water Resources 115 (May 2018): 125–35. http://dx.doi.org/10.1016/j.advwatres.2018.03.010.

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Bocquet, M., and P. Sakov. "Combining inflation-free and iterative ensemble Kalman filters for strongly nonlinear systems." Nonlinear Processes in Geophysics 19, no. 3 (2012): 383–99. http://dx.doi.org/10.5194/npg-19-383-2012.

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Abstract. The finite-size ensemble Kalman filter (EnKF-N) is an ensemble Kalman filter (EnKF) which, in perfect model condition, does not require inflation because it partially accounts for the ensemble sampling errors. For the Lorenz '63 and '95 toy-models, it was so far shown to perform as well or better than the EnKF with an optimally tuned inflation. The iterative ensemble Kalman filter (IEnKF) is an EnKF which was shown to perform much better than the EnKF in strongly nonlinear conditions, such as with the Lorenz '63 and '95 models, at the cost of iteratively updating the trajectories of

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Gineste, Michael, Jo Eidsvik, and York Zheng. "Ensemble-based seismic inversion for a stratified medium." GEOPHYSICS 85, no. 1 (2020): R29—R39. http://dx.doi.org/10.1190/geo2019-0017.1.

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Seismic waveform inversion is a nontrivial optimization task, which is often complicated by the nonlinear relationship between the elastic attributes of interest and the large amount of data obtained in seismic experiments. Quantifying the solution uncertainty can be even more challenging, and it requires considering the problem in a probabilistic setting. Consequently, the seismic inverse problem is placed in a Bayesian framework, using a sequential filtering approach to invert for the elastic parameters. The method uses an iterative ensemble smoother to estimate the subsurface parameters, an

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21

Wilson, Adam. "History Matching the Norne Full-Field Model Using an Iterative Ensemble Smoother." Journal of Petroleum Technology 66, no. 04 (2014): 119–21. http://dx.doi.org/10.2118/0414-0119-jpt.

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22

Ma, Xiang, and Linfeng Bi. "A robust adaptive iterative ensemble smoother scheme for practical history matching applications." Computational Geosciences 23, no. 3 (2019): 415–42. http://dx.doi.org/10.1007/s10596-018-9786-9.

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23

Martin, Nick, and Jeremy White. "Flow Regime-Dependent, Discharge Uncertainty Envelope for Uncertainty Analysis with Ensemble Methods." Water 15, no. 6 (2023): 1133. http://dx.doi.org/10.3390/w15061133.

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A discharge uncertainty envelope is presented that provides an observation error model for data assimilation (DA) using discharge observations derived from measurement of stage using a rating curve. It uniquely represents the rating curve representation error, which is due to scale and process incompatibility between the rating curve hydrodynamic model and “true” discharge, within the observation error model. Ensemble methods, specifically, the iterative ensemble smoother (IES) algorithms in PEST++, provide the DA framework for this observation error model. The purpose of the uncertainty envel

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24

Fillion, Anthony, Marc Bocquet, and Serge Gratton. "Quasi-static ensemble variational data assimilation: a theoretical and numerical study with the iterative ensemble Kalman smoother." Nonlinear Processes in Geophysics 25, no. 2 (2018): 315–34. http://dx.doi.org/10.5194/npg-25-315-2018.

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Abstract. The analysis in nonlinear variational data assimilation is the solution of a non-quadratic minimization. Thus, the analysis efficiency relies on its ability to locate a global minimum of the cost function. If this minimization uses a Gauss–Newton (GN) method, it is critical for the starting point to be in the attraction basin of a global minimum. Otherwise the method may converge to a local extremum, which degrades the analysis. With chaotic models, the number of local extrema often increases with the temporal extent of the data assimilation window, making the former condition harder

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Zhang, Yin, Zhaoqi Fan, Daoyong Yang, Heng Li, and Shirish Patil. "Simultaneous Estimation of Relative Permeability and Capillary Pressure for PUNQ-S3 Model With a Damped Iterative-Ensemble-Kalman-Filter Technique." SPE Journal 22, no. 03 (2017): 971–84. http://dx.doi.org/10.2118/177846-pa.

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Summary A damped iterative-ensemble-Kalman-filter (IEnKF) algorithm has been proposed to estimate relative permeability and capillary pressure curves simultaneously for the PUNQ-S3 model, while its performance has been compared with that of the CMOST module, iterative-ensemble-smoother (IES) algorithm, and traditional ensemble-Kalman-filter (EnKF) technique. The power-law model is used to represent the relative permeability and capillary pressure curves, while three-phase relative permeability for oil phase is determined by use of the modified Stone II model. By assimilating the observed produ

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Bailey, R. T., and D. Baù. "Estimating geostatistical parameters and spatially-variable hydraulic conductivity within a catchment system using an ensemble smoother." Hydrology and Earth System Sciences 16, no. 2 (2012): 287–304. http://dx.doi.org/10.5194/hess-16-287-2012.

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Abstract. Groundwater flow models are important tools in assessing baseline conditions and investigating management alternatives in groundwater systems. The usefulness of these models, however, is often hindered by insufficient knowledge regarding the magnitude and spatial distribution of the spatially-distributed parameters, such as hydraulic conductivity (K), that govern the response of these models. Proposed parameter estimation methods frequently are demonstrated using simplified aquifer representations, when in reality the groundwater regime in a given watershed is influenced by strongly-

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Bailey, R. T., and D. Baù. "Estimating geostatistical parameters and spatially-variable hydraulic conductivity within a catchment system using an ensemble smoother." Hydrology and Earth System Sciences Discussions 8, no. 5 (2011): 9587–635. http://dx.doi.org/10.5194/hessd-8-9587-2011.

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Abstract. Groundwater flow models are important tools in assessing baseline conditions and investigating management alternatives in groundwater systems. The usefulness of these models, however, is often hindered by insufficient knowledge regarding the magnitude and spatial distribution of the spatially-distributed parameters, such as hydraulic conductivity (K), that govern the response of these models. Proposed parameter estimation methods frequently are demonstrated using simplified aquifer representations, when in reality the groundwater regime in a given watershed is influenced by strongly-

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Kim, Sungil, Baehyun Min, Kyungbook Lee, and Hoonyoung Jeong. "Integration of an Iterative Update of Sparse Geologic Dictionaries with ES-MDA for History Matching of Channelized Reservoirs." Geofluids 2018 (July 29, 2018): 1–21. http://dx.doi.org/10.1155/2018/1532868.

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This study couples an iterative sparse coding in a transformed space with an ensemble smoother with multiple data assimilation (ES-MDA) for providing a set of geologically plausible models that preserve the non-Gaussian distribution of lithofacies in a channelized reservoir. Discrete cosine transform (DCT) of sand-shale facies is followed by the repetition of K-singular value decomposition (K-SVD) in order to construct sparse geologic dictionaries that archive geologic features of the channelized reservoir such as pattern and continuity. Integration of ES-MDA, DCT, and K-SVD is conducted in a

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Luo, Xiaodong, Andreas S. Stordal, Rolf J. Lorentzen, and Geir Nævdal. "Iterative Ensemble Smoother as an Approximate Solution to a Regularized Minimum-Average-Cost Problem: Theory and Applications." SPE Journal 20, no. 05 (2015): 962–82. http://dx.doi.org/10.2118/176023-pa.

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Summary The focus of this work is on an alternative implementation of the iterative-ensemble smoother (iES). We show that iteration formulae similar to those used by Chen and Oliver (2013) and Emerick and Reynolds (2012) can be derived by adopting a regularized Levenberg-Marquardt (RLM) algorithm (Jin 2010) to approximately solve a minimum-average-cost (MAC) problem. This not only leads to an alternative theoretical tool in understanding and analyzing the behavior of the aforementioned iES, but also provides insights and guidelines for further developments of the smoothing algorithms. For illu

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Li, Liangping, Larry Stetler, Zhendan Cao, and Arden Davis. "An iterative normal-score ensemble smoother for dealing with non-Gaussianity in data assimilation." Journal of Hydrology 567 (December 2018): 759–66. http://dx.doi.org/10.1016/j.jhydrol.2018.01.038.

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Cao, Zhendan, Liangping Li, and Kang Chen. "Bridging iterative Ensemble Smoother and multiple-point geostatistics for better flow and transport modeling." Journal of Hydrology 565 (October 2018): 411–21. http://dx.doi.org/10.1016/j.jhydrol.2018.08.023.

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Amezcua, Javier, Haonan Ren, and Peter Jan van Leeuwen. "Using the (Iterative) Ensemble Kalman Smoother to Estimate the Time Correlation in Model Error." Tellus A: Dynamic Meteorology and Oceanography 75, no. 1 (2023): 108–28. http://dx.doi.org/10.16993/tellusa.55.

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Luo, Xiaodong, William C. Cruz, Xin-Lei Zhang, and Heng Xiao. "Hyper-parameter optimization for improving the performance of localization in an iterative ensemble smoother." Geoenergy Science and Engineering 231 (December 2023): 212404. http://dx.doi.org/10.1016/j.geoen.2023.212404.

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Eloundou, Fernand Baguket, Haojin Zhao, Bibi S. Naz, and Harrie-Jan Hendricks-Franssen. "Bridging Water─Carbon Flux Uncertainties in eCLM: Constraining Ecosystem Dynamics using Ensemble Smoother." ARPHA Conference Abstracts 8 (May 28, 2025): e149296. https://doi.org/10.3897/aca.8.e149296.

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The water and carbon cycles are tightly coupled via plant processes such as photosynthesis and transpiration, which are sensitive to changing climate conditions. Land surface models (LSMs) like the European Community Land Model (eCLM), based on CLM5.0, simulate these coupled dynamics but often face challenges due to uncertain parameterizations, particularly for soil and vegetation processes. In its biogeochemical configuration, eCLM includes hundreds of parameters related to stomatal regulation, root distribution, phenology, and soil respiration, all of which introduce substantial model uncert

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Wang, Yudou, Gaoming Li, and Albert C. Reynolds. "Estimation of Depths of Fluid Contacts and Relative Permeability Curves by History Matching Using Iterative Ensemble-Kalman Smoothers." SPE Journal 15, no. 02 (2009): 509–25. http://dx.doi.org/10.2118/119056-pa.

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Summary With the ensemble Kalman filter (EnKF) or smoother (EnKS), it is easy to adjust a wide variety of model parameters by assimilation of dynamic data. We focus first on the case where realizations and estimates of the depths of the initial fluid contacts, as well as grid- block rock-property fields, are generated by matching production data with the EnKS. Then we add the parameters defining power law relative permeability curves to the set of parameters estimated by assimilating production data with EnKS. The efficiency of EnKF and EnKS arises because data are assimilated sequentially in

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Coulon, Cécile, Jeremy T. White, Alexandre Pryet, Laura Gatel, and Jean-Michel Lemieux. "An ensemble-based approach for pumping optimization in an island aquifer considering parameter, observation and climate uncertainty." Hydrology and Earth System Sciences 28, no. 1 (2024): 303–19. http://dx.doi.org/10.5194/hess-28-303-2024.

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Abstract. In coastal zones, a major objective of groundwater management is often to determine sustainable pumping rates which avoid well salinization. Understanding how model and climate uncertainties affect optimal management solutions is essential for providing groundwater managers with information about salinization risk and is facilitated by the use of optimization under uncertainty (OUU) methods. However, guidelines are missing for the widespread implementation of OUU in real-world coastal aquifers and for the incorporation of climate uncertainty into OUU approaches. An ensemble-based OUU

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Ba, Yuming, Lijian Jiang, and Na Ou. "VARIABLE-SEPARATION BASED ITERATIVE ENSEMBLE SMOOTHER FOR BAYESIAN INVERSE PROBLEMS IN ANOMALOUS DIFFUSION REACTION MODELS." International Journal for Uncertainty Quantification 9, no. 3 (2019): 245–73. http://dx.doi.org/10.1615/int.j.uncertaintyquantification.2019028759.

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Chang, Haibin, and Dongxiao Zhang. "History Matching of Stimulated Reservoir Volume of Shale-Gas Reservoirs Using an Iterative Ensemble Smoother." SPE Journal 23, no. 02 (2017): 346–66. http://dx.doi.org/10.2118/189436-pa.

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Summary Economic production from shale-gas reservoirs typically relies on the drilling of horizontal wells and hydraulic fracturing in multiple stages. In addition to the creation of hydraulic fractures, hydraulic-fracturing treatment can also reopen existing natural fractures, which can create a complex-fracture network. The area that is covered by the fracture network is usually termed the stimulated reservoir volume (SRV), and the spatial extent and properties of the SRV are crucial for shale-gas-production behavior. In this work, we propose a method for history matching of the SRV of shale

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Hayley, Kevin, Alexis Valenza, Emma White, Bruce Hutchison, and Jens Schumacher. "Application of the Iterative Ensemble Smoother Method and Cloud Computing: A Groundwater Modeling Case Study." Water 11, no. 8 (2019): 1649. http://dx.doi.org/10.3390/w11081649.

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Numerical groundwater modelling to support mining decisions is often challenging and time consuming. Simulation of open pit mining for model calibration or prediction requires models that include unsaturated flow, large magnitude hydraulic gradients and often require transient simulations with time varying material properties and boundary conditions. This combination of factors typically results in models with long simulation times and/or some level of numerical instability. In modelling practice, long run times and instability can result in reduced effort for predictive uncertainty analysis,

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Chen, Yan, and Dean S. Oliver. "Levenberg–Marquardt forms of the iterative ensemble smoother for efficient history matching and uncertainty quantification." Computational Geosciences 17, no. 4 (2013): 689–703. http://dx.doi.org/10.1007/s10596-013-9351-5.

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Smith, K. W., and D. J. McGillicuddy. "Dynamical interpolation of surface ocean chlorophyll fields via data assimilation with an iterative ensemble smoother." Journal of Marine Systems 85, no. 3-4 (2011): 96–105. http://dx.doi.org/10.1016/j.jmarsys.2010.12.007.

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Pirk, Norbert, Kristoffer Aalstad, Sebastian Westermann, et al. "Inferring surface energy fluxes using drone data assimilation in large eddy simulations." Atmospheric Measurement Techniques 15, no. 24 (2022): 7293–314. http://dx.doi.org/10.5194/amt-15-7293-2022.

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Abstract. Spatially representative estimates of surface energy exchange from field measurements are required for improving and validating Earth system models and satellite remote sensing algorithms. The scarcity of flux measurements can limit understanding of ecohydrological responses to climate warming, especially in remote regions with limited infrastructure. Direct field measurements often apply the eddy covariance method on stationary towers, but recently, drone-based measurements of temperature, humidity, and wind speed have been suggested as a viable alternative to quantify the turbulent

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Defforge, Cécile L., Bertrand Carissimo, Marc Bocquet, Raphaël Bresson, and Patrick Armand. "Improving Numerical Dispersion Modelling in Built Environments with Data Assimilation Using the Iterative Ensemble Kalman Smoother." Boundary-Layer Meteorology 179, no. 2 (2021): 209–40. http://dx.doi.org/10.1007/s10546-020-00588-9.

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AbstractAir-pollution modelling at the local scale requires accurate meteorological inputs such as from the velocity field. These meteorological fields are generally simulated with microscale models (here Code_Saturne), which are forced with boundary conditions provided by larger scale models or observations. Local atmospheric simulations are very sensitive to the boundary conditions, whose accurate estimation is difficult but crucial. When observations of the wind speed and turbulence or pollutant concentration are available inside the domain, they provide supplementary information via data a

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Delottier, H., R. Therrien, N. L. Young, and D. Paradis. "A hybrid approach for integrated surface and subsurface hydrologic simulation of baseflow with Iterative Ensemble Smoother." Journal of Hydrology 606 (March 2022): 127406. http://dx.doi.org/10.1016/j.jhydrol.2021.127406.

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Luo, Xiaodong, and Paulo Ranazzi. "Better stopping through cross validation in an iterative ensemble smoother: A perspective from supervised machine learning." Geoenergy Science and Engineering 243 (December 2024): 213298. http://dx.doi.org/10.1016/j.geoen.2024.213298.

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Xia, Xuemin, Xiang Li, Yue Sun, and Guoqiang Cheng. "Joint Estimation of Adsorptive Contaminant Source and Hydraulic Conductivity Using an Iterative Local Updating Ensemble Smoother with Geometric Inflation Selection." Sustainability 15, no. 2 (2023): 1211. http://dx.doi.org/10.3390/su15021211.

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The joint estimation of groundwater contaminant source characteristics and hydraulic conductivity is of great significance for reactive contaminant transport models in heterogeneous subsurface media. The accurate determination of the sorption parameters of such contaminants is also a key prerequisite for estimating the parameters of the groundwater system. In this study, to investigate the impact of the sorption parameter field on the accuracy of hydraulic conductivity and source characteristics estimation, numerical experiments were conducted in a synthetic aquifer considering the contaminant

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Defforge, Cécile L., B. Carissimo, M. Bocquet, R. Bresson, and P. Armand. "Improving CFD atmospheric simulations at local scale for wind resource assessment using the iterative ensemble Kalman smoother." Journal of Wind Engineering and Industrial Aerodynamics 189 (June 2019): 243–57. http://dx.doi.org/10.1016/j.jweia.2019.03.030.

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White, Jeremy T. "A model-independent iterative ensemble smoother for efficient history-matching and uncertainty quantification in very high dimensions." Environmental Modelling & Software 109 (November 2018): 191–201. http://dx.doi.org/10.1016/j.envsoft.2018.06.009.

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Zhang, Jiangjiang, Guang Lin, Weixuan Li, Laosheng Wu, and Lingzao Zeng. "An Iterative Local Updating Ensemble Smoother for Estimation and Uncertainty Assessment of Hydrologic Model Parameters With Multimodal Distributions." Water Resources Research 54, no. 3 (2018): 1716–33. http://dx.doi.org/10.1002/2017wr020906.

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García-Pintado, Javier, and André Paul. "Evaluation of iterative Kalman smoother schemes for multi-decadal past climate analysis with comprehensive Earth system models." Geoscientific Model Development 11, no. 12 (2018): 5051–84. http://dx.doi.org/10.5194/gmd-11-5051-2018.

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Abstract. Paleoclimate reconstruction based on assimilation of proxy observations requires specification of the control variables and their background statistics. As opposed to numerical weather prediction (NWP), which is mostly an initial condition problem, the main source of error growth in deterministic Earth system models (ESMs) regarding the model low-frequency response comes from errors in other inputs: parameters for the small-scale physics, as well as forcing and boundary conditions. Also, comprehensive ESMs are non-linear and only a few ensemble members can be run in current high-perf

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