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Artículos de revistas sobre el tema "Ensemble Kalman filter hyper-localized"

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Publicado: 14 de diciembre de 2024
Última modificación: 31 de julio de 2025

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1

Nerger, Lars. "On Serial Observation Processing in Localized Ensemble Kalman Filters." Monthly Weather Review 143, no. 5 (2015): 1554–67. http://dx.doi.org/10.1175/mwr-d-14-00182.1.

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Abstract Ensemble square root filters can either assimilate all observations that are available at a given time at once, or assimilate the observations in batches or one at a time. For large-scale models, the filters are typically applied with a localized analysis step. This study demonstrates that the interaction of serial observation processing and localization can destabilize the analysis process, and it examines under which conditions the instability becomes significant. The instability results from a repeated inconsistent update of the state error covariance matrix that is caused by the l
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2

Huang, Bo, Xuguang Wang, and Craig H. Bishop. "The High-Rank Ensemble Transform Kalman Filter." Monthly Weather Review 147, no. 8 (2019): 3025–43. http://dx.doi.org/10.1175/mwr-d-18-0210.1.

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Abstract The ensemble Kalman filter is typically implemented either by applying the localization on the background error covariance matrix (B-localization) or by inflating the observation error variances (R-localization). A mathematical demonstration suggests that for the same effective localization function, the background error covariance matrix from the B-localization method shows a higher rank than the R-localization method. The B-localization method is realized in the ensemble transform Kalman filter (ETKF) by extending the background ensemble perturbations through modulation (MP-localiza
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3

Bergeron, Jean, Robert Leconte, Mélanie Trudel, and Sepehr Farhoodi. "On the Choice of Metric to Calibrate Time-Invariant Ensemble Kalman Filter Hyper-Parameters for Discharge Data Assimilation and Its Impact on Discharge Forecast Modelling." Hydrology 8, no. 1 (2021): 36. http://dx.doi.org/10.3390/hydrology8010036.

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An important step when using some data assimilation methods, such as the ensemble Kalman filter and its variants, is to calibrate its parameters. Also called hyper-parameters, these include the model and observation errors, which have previously been shown to have a strong impact on the performance of the data assimilation method. Many metrics can be used to calibrate these hyper-parameters but may not all yield the same optimal set of values. The current study investigated the importance of the choice of metric used during the hyper-parameter calibration phase and its impact on discharge fore
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4

Bishop, Craig H., Bo Huang, and Xuguang Wang. "A Nonvariational Consistent Hybrid Ensemble Filter." Monthly Weather Review 143, no. 12 (2015): 5073–90. http://dx.doi.org/10.1175/mwr-d-14-00391.1.

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Abstract A consistent hybrid ensemble filter (CHEF) for using hybrid forecast error covariance matrices that linearly combine aspects of both climatological and flow-dependent matrices within a nonvariational ensemble data assimilation scheme is described. The CHEF accommodates the ensemble data assimilation enhancements of (i) model space ensemble covariance localization for satellite data assimilation and (ii) Hodyss’s method for improving accuracy using ensemble skewness. Like the local ensemble transform Kalman filter (LETKF), the CHEF is computationally scalable because it updates local p
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5

Etherton, Brian J. "Preemptive Forecasts Using an Ensemble Kalman Filter." Monthly Weather Review 135, no. 10 (2007): 3484–95. http://dx.doi.org/10.1175/mwr3480.1.

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Abstract An ensemble Kalman filter (EnKF) estimates the error statistics of a model forecast using an ensemble of model forecasts. One use of an EnKF is data assimilation, resulting in the creation of an increment to the first-guess field at the observation time. Another use of an EnKF is to propagate error statistics of a model forecast forward in time, such as is done for optimizing the location of adaptive observations. Combining these two uses of an ensemble Kalman filter, a “preemptive forecast” can be generated. In a preemptive forecast, the increment to the first-guess field is, using e
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6

Zhou, Haiyan, Liangping Li, and J. Jaime Gómez-Hernández. "Characterizing Curvilinear Features Using the Localized Normal-Score Ensemble Kalman Filter." Abstract and Applied Analysis 2012 (2012): 1–18. http://dx.doi.org/10.1155/2012/805707.

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The localized normal-score ensemble Kalman filter is shown to work for the characterization of non-multi-Gaussian distributed hydraulic conductivities by assimilating state observation data. The influence of type of flow regime, number of observation piezometers, and the prior model structure are evaluated in a synthetic aquifer. Steady-state observation data are not sufficient to identify the conductivity channels. Transient-state data are necessary for a good characterization of the hydraulic conductivity curvilinear patterns. Such characterization is very good with a dense network of observ
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7

Potthast, Roland, Anne Walter, and Andreas Rhodin. "A Localized Adaptive Particle Filter within an Operational NWP Framework." Monthly Weather Review 147, no. 1 (2019): 345–62. http://dx.doi.org/10.1175/mwr-d-18-0028.1.

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Particle filters are well known in statistics. They have a long tradition in the framework of ensemble data assimilation (EDA) as well as Markov chain Monte Carlo (MCMC) methods. A key challenge today is to employ such methods in a high-dimensional environment, since the naïve application of the classical particle filter usually leads to filter divergence or filter collapse when applied within the very high dimension of many practical assimilation problems (known as the curse of dimensionality). The goal of this work is to develop a localized adaptive particle filter (LAPF), which follows clos
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8

Delijani, Ebrahim Biniaz, Mahmoud Reza Pishvaie, and Ramin Bozorgmehry Boozarjomehry. "Subsurface characterization with localized ensemble Kalman filter employing adaptive thresholding." Advances in Water Resources 69 (July 2014): 181–96. http://dx.doi.org/10.1016/j.advwatres.2014.04.011.

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9

Chen, Yan, Weimin Zhang, and Mengbin Zhu. "A localized weighted ensemble Kalman filter for high‐dimensional systems." Quarterly Journal of the Royal Meteorological Society 146, no. 726 (2019): 438–53. http://dx.doi.org/10.1002/qj.3685.

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10

Auligné, Thomas, Benjamin Ménétrier, Andrew C. Lorenc, and Mark Buehner. "Ensemble–Variational Integrated Localized Data Assimilation." Monthly Weather Review 144, no. 10 (2016): 3677–96. http://dx.doi.org/10.1175/mwr-d-15-0252.1.

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Hybrid variational–ensemble data assimilation (hybrid DA) is widely used in research and operational systems, and it is considered the current state of the art for the initialization of numerical weather prediction models. However, hybrid DA requires a separate ensemble DA to estimate the uncertainty in the deterministic variational DA, which can be suboptimal both technically and scientifically. A new framework called the ensemble–variational integrated localized (EVIL) data assimilation addresses this inconvenience by updating the ensemble analyses using information from the variational dete
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11

Poterjoy, Jonathan. "A Localized Particle Filter for High-Dimensional Nonlinear Systems." Monthly Weather Review 144, no. 1 (2015): 59–76. http://dx.doi.org/10.1175/mwr-d-15-0163.1.

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Abstract This paper presents a new data assimilation approach based on the particle filter (PF) that has potential for nonlinear/non-Gaussian applications in geoscience. Particle filters provide a Monte Carlo approximation of a system’s probability density, while making no assumptions regarding the underlying error distribution. The proposed method is similar to the PF in that particles—also referred to as ensemble members—are weighted based on the likelihood of observations in order to approximate posterior probabilities of the system state. The new approach, denoted the local PF, extends the
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12

Poterjoy, Jonathan, and Jeffrey L. Anderson. "Efficient Assimilation of Simulated Observations in a High-Dimensional Geophysical System Using a Localized Particle Filter." Monthly Weather Review 144, no. 5 (2016): 2007–20. http://dx.doi.org/10.1175/mwr-d-15-0322.1.

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This study presents the first application of a localized particle filter (PF) for data assimilation in a high-dimensional geophysical model. Particle filters form Monte Carlo approximations of model probability densities conditioned on observations, while making no assumptions about the underlying error distribution. Unlike standard PFs, the local PF uses a localization function to reduce the influence of distant observations on state variables, which significantly decreases the number of particles required to maintain the filter’s stability. Because the local PF operates effectively using sma
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13

Sommer, Matthias, and Martin Weissmann. "Observation impact in a convective-scale localized ensemble transform Kalman filter." Quarterly Journal of the Royal Meteorological Society 140, no. 685 (2014): 2672–79. http://dx.doi.org/10.1002/qj.2343.

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14

Ruchi, Sangeetika, and Svetlana Dubinkina. "Application of ensemble transform data assimilation methods for parameter estimation in reservoir modeling." Nonlinear Processes in Geophysics 25, no. 4 (2018): 731–46. http://dx.doi.org/10.5194/npg-25-731-2018.

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Abstract. Over the years data assimilation methods have been developed to obtain estimations of uncertain model parameters by taking into account a few observations of a model state. The most reliable Markov chain Monte Carlo (MCMC) methods are computationally expensive. Sequential ensemble methods such as ensemble Kalman filters and particle filters provide a favorable alternative. However, ensemble Kalman filter has an assumption of Gaussianity. Ensemble transform particle filter does not have this assumption and has proven to be highly beneficial for an initial condition estimation and a sm
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15

Bellsky, Thomas, Jesse Berwald, and Lewis Mitchell. "Nonglobal Parameter Estimation Using Local Ensemble Kalman Filtering." Monthly Weather Review 142, no. 6 (2014): 2150–64. http://dx.doi.org/10.1175/mwr-d-13-00200.1.

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Abstract The authors study parameter estimation for nonglobal parameters in a low-dimensional chaotic model using the local ensemble transform Kalman filter (LETKF). By modifying existing techniques for using observational data to estimate global parameters, they present a methodology whereby spatially varying parameters can be estimated using observations only within a localized region of space. Taking a low-dimensional nonlinear chaotic conceptual model for atmospheric dynamics as a numerical test bed, the authors show that this parameter estimation methodology accurately estimates parameter
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16

Zhen, Yicun, and Fuqing Zhang. "A Probabilistic Approach to Adaptive Covariance Localization for Serial Ensemble Square Root Filters." Monthly Weather Review 142, no. 12 (2014): 4499–518. http://dx.doi.org/10.1175/mwr-d-13-00390.1.

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Abstract This study proposes a variational approach to adaptively determine the optimum radius of influence for ensemble covariance localization when uncorrelated observations are assimilated sequentially. The covariance localization is commonly used by various ensemble Kalman filters to limit the impact of covariance sampling errors when the ensemble size is small relative to the dimension of the state. The probabilistic approach is based on the premise of finding an optimum localization radius that minimizes the distance between the Kalman update using the localized sampling covariance versu
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17

Pang, Mijie, Jianbing Jin, Arjo Segers, et al. "Dust storm forecasting through coupling LOTOS-EUROS with localized ensemble Kalman filter." Atmospheric Environment 306 (August 2023): 119831. http://dx.doi.org/10.1016/j.atmosenv.2023.119831.

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18

Li, L., H. Zhou, H. J. Hendricks Franssen, and J. J. Gómez-Hernández. "Groundwater flow inverse modeling in non-MultiGaussian media: performance assessment of the normal-score Ensemble Kalman Filter." Hydrology and Earth System Sciences Discussions 8, no. 4 (2011): 6749–88. http://dx.doi.org/10.5194/hessd-8-6749-2011.

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Abstract. The normal-score ensemble Kalman filter (NS-EnKF) is tested on a synthetic aquifer characterized by the presence of channels with a bimodal distribution of its hydraulic conductivities. Fourteen scenarios are analyzed which differ among them in one or various of the following aspects: the prior random function model, the boundary conditions of the flow problem, the number of piezometers used in the assimilation process, or the use of covariance localization in the implementation of the Kalman filter. The performance of the NS-EnKF is evaluated through the ensemble mean and variance m
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19

Xia, Chuan-An, Bill X. Hu, Juxiu Tong, and Alberto Guadagnini. "Data Assimilation in Density-Dependent Subsurface Flows via Localized Iterative Ensemble Kalman Filter." Water Resources Research 54, no. 9 (2018): 6259–81. http://dx.doi.org/10.1029/2017wr022369.

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20

Chen, Yan, Weimin Zhang, and Pinqiang Wang. "An application of the localized weighted ensemble Kalman filter for ocean data assimilation." Quarterly Journal of the Royal Meteorological Society 146, no. 732 (2020): 3029–47. http://dx.doi.org/10.1002/qj.3824.

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21

Li, L., H. Zhou, H. J. Hendricks Franssen, and J. J. Gómez-Hernández. "Groundwater flow inverse modeling in non-MultiGaussian media: performance assessment of the normal-score Ensemble Kalman Filter." Hydrology and Earth System Sciences 16, no. 2 (2012): 573–90. http://dx.doi.org/10.5194/hess-16-573-2012.

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Abstract. The normal-score ensemble Kalman filter (NS-EnKF) is tested on a synthetic aquifer characterized by the presence of channels with a bimodal distribution of its hydraulic conductivities. This is a clear example of an aquifer that cannot be characterized by a multiGaussian distribution. Fourteen scenarios are analyzed which differ among them in one or various of the following aspects: the prior random function model, the boundary conditions of the flow problem, the number of piezometers used in the assimilation process, or the use of covariance localization in the implementation of the
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22

Nerini, Daniele, Loris Foresti, Daniel Leuenberger, Sylvain Robert, and Urs Germann. "A Reduced-Space Ensemble Kalman Filter Approach for Flow-Dependent Integration of Radar Extrapolation Nowcasts and NWP Precipitation Ensembles." Monthly Weather Review 147, no. 3 (2019): 987–1006. http://dx.doi.org/10.1175/mwr-d-18-0258.1.

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Abstract A Bayesian precipitation nowcasting system based on the ensemble Kalman filter is formulated. Starting from the last available radar observations, the prediction step of the filter consists of a stochastic radar extrapolation technique, while the correction step updates the radar extrapolation nowcast using information from the most recent forecast by the numerical weather prediction model (NWP). The result is a flow-dependent and seamless blending scheme that is based on the spread of the nowcast and NWP ensembles, used as the definition of the forecast error. To simplify the matrix
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23

Yoon, Young-noh, Edward Ott, and Istvan Szunyogh. "On the Propagation of Information and the Use of Localization in Ensemble Kalman Filtering." Journal of the Atmospheric Sciences 67, no. 12 (2010): 3823–34. http://dx.doi.org/10.1175/2010jas3452.1.

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Abstract Several localized versions of the ensemble Kalman filter have been proposed. Although tests applying such schemes have proven them to be extremely promising, a full basic understanding of the rationale and limitations of localization is currently lacking. It is one of the goals of this paper to contribute toward addressing this issue. The second goal is to elucidate the role played by chaotic wave dynamics in the propagation of information and the resulting impact on forecasts. To accomplish these goals, the principal tool used here will be analysis and interpretation of numerical exp
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24

Zhou, Yongbo, Yubao Liu, and Wei Han. "Demonstrating the Potential Impacts of Assimilating FY-4A Visible Radiances on Forecasts of Cloud and Precipitation with a Localized Particle Filter." Monthly Weather Review 151, no. 5 (2023): 1167–88. http://dx.doi.org/10.1175/mwr-d-22-0133.1.

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Abstract The Advanced Geostationary Radiation Imager (AGRI) on board the Fengyun-4A (FY-4A) satellite provides visible radiances that contain critical information on clouds and precipitation. In this study, the impact of assimilating FY-4A/AGRI all-sky visible radiances on the simulation of a convective system was evaluated with an observing system simulation experiment (OSSE) using a localized particle filter (PF). The localized PF was implemented into the Data Assimilation Research Testbed (DART) coupled with the Weather Research and Forecasting (WRF) Model. The results of a 2-day data assim
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25

Yoshida, Takuma, and Eugenia Kalnay. "Correlation-Cutoff Method for Covariance Localization in Strongly Coupled Data Assimilation." Monthly Weather Review 146, no. 9 (2018): 2881–89. http://dx.doi.org/10.1175/mwr-d-17-0365.1.

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Abstract Strongly coupled data assimilation (SCDA), where observations of one component of a coupled model are allowed to directly impact the analysis of other components, sometimes fails to improve the analysis accuracy with an ensemble Kalman filter (EnKF) as compared with weakly coupled data assimilation (WCDA). It is well known that an observation’s area of influence should be localized in EnKFs since the assimilation of distant observations often degrades the analysis because of spurious correlations. This study derives a method to estimate the reduction of the analysis error variance by
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26

Poterjoy, Jonathan, and Fuqing Zhang. "Systematic Comparison of Four-Dimensional Data Assimilation Methods With and Without the Tangent Linear Model Using Hybrid Background Error Covariance: E4DVar versus 4DEnVar." Monthly Weather Review 143, no. 5 (2015): 1601–21. http://dx.doi.org/10.1175/mwr-d-14-00224.1.

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Abstract Two ensemble formulations of the four-dimensional variational (4DVar) data assimilation technique are examined for a low-dimensional dynamical system. The first method, denoted E4DVar, uses tangent linear and adjoint model operators to minimize a cost function in the same manner as the traditional 4DVar data assimilation system. The second method, denoted 4DEnVar, uses an ensemble of nonlinear model trajectories to replace the function of linearized models in 4DVar, thus improving the parallelization of the data assimilation. Background errors for each algorithm are represented using
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27

Steward, Jeffrey L., Jose E. Roman, Alejandro Lamas Daviña, and Altuǧ Aksoy. "Parallel Direct Solution of the Covariance-Localized Ensemble Square Root Kalman Filter Equations with Matrix Functions." Monthly Weather Review 146, no. 9 (2018): 2819–36. http://dx.doi.org/10.1175/mwr-d-18-0022.1.

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Abstract Recently, the serial approach to solving the square root ensemble Kalman filter (ESRF) equations in the presence of covariance localization was found to depend on the order of observations. As shown previously, correctly updating the localized posterior covariance in serial requires additional effort and computational expense. A recent work by Steward et al. details an all-at-once direct method to solve the ESRF equations in parallel. This method uses the eigenvectors and eigenvalues of the forward observation covariance matrix to solve the difficult portion of the ESRF equations. The
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28

Pérez Hortal, Andrés A., Isztar Zawadzki, and M. K. Yau. "A Sequential Non-Gaussian Approach for Precipitation Data Assimilation." Monthly Weather Review 149, no. 4 (2021): 1069–87. http://dx.doi.org/10.1175/mwr-d-20-0086.1.

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AbstractIn two recent studies, the authors presented a new data assimilation (DA) method for precipitation observations that does not require Gaussianity or linearity assumptions. The method, called localized ensemble mosaic assimilation (LEMA), initializes the new ensemble forecast by relaxing the background ensemble (prior) toward a single analysis composed of different column states taken from the ensemble members with the lowest error in the precipitation forecast. However, a limitation of the LEMA method is that relaxing the background ensemble toward that analysis severely reduces the sp
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29

Zhao, Yuxin, Shuo Yang, Di Zhou, Xiong Deng, and Mengbin Zhu. "The Improved Localized Equivalent-Weights Particle Filter with Statistical Observation in an Intermediate Coupled Model." Journal of Marine Science and Engineering 9, no. 11 (2021): 1153. http://dx.doi.org/10.3390/jmse9111153.

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Data assimilation has been widely applied in atmospheric and oceanic forecasting systems and particle filters (PFs) have unique advantages in dealing with nonlinear data assimilation. They have been applied to many scientific fields, but their application in geoscientific systems is limited because of their inefficiency in standard settings systems. To address these issues, this paper further refines the statistical observation and localization scheme which used in the classic localized equivalent-weights particle filter with statistical observation (LEWPF-Sobs). The improved method retains th
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30

Arroyo, Elkin, Deepak Devegowda, Akhil Datta-Gupta, and Jonggeun Choe. "Streamline-Assisted Ensemble Kalman Filter for Rapid and Continuous Reservoir Model Updating." SPE Reservoir Evaluation & Engineering 11, no. 06 (2008): 1046–60. http://dx.doi.org/10.2118/104255-pa.

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Summary The use of the ensemble Kalman filter (EnKF) is a promising approach for data assimilation and assessment of uncertainties during reservoir characterization and performance forecasting. It provides a relatively straightforward approach to incorporating diverse data types, including production and/or time-lapse seismic data. Unlike traditional sensitivity-based history matching methods, the EnKF relies on a cross-covariance matrix computed from an ensemble of reservoir models to relate reservoir properties to production data. For practical field applications, we need to keep the ensembl
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31

Shen, Meng, Yan Chen, Pinqiang Wang, and Weimin Zhang. "Assimilating satellite SST/SSH and in-situ T/S profiles with the Localized Weighted Ensemble Kalman Filter." Acta Oceanologica Sinica 41, no. 2 (2022): 26–40. http://dx.doi.org/10.1007/s13131-021-1903-2.

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32

Tong, Juxiu, Bill X. Hu, and Jinzhong Yang. "Assimilating transient groundwater flow data via a localized ensemble Kalman filter to calibrate a heterogeneous conductivity field." Stochastic Environmental Research and Risk Assessment 26, no. 3 (2011): 467–78. http://dx.doi.org/10.1007/s00477-011-0534-0.

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33

Bishop, Craig H., and Daniel Hodyss. "Adaptive Ensemble Covariance Localization in Ensemble 4D-VAR State Estimation." Monthly Weather Review 139, no. 4 (2011): 1241–55. http://dx.doi.org/10.1175/2010mwr3403.1.

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Abstract An adaptive ensemble covariance localization technique, previously used in “local” forms of the ensemble Kalman filter, is extended to a global ensemble four-dimensional variational data assimilation (4D-VAR) scheme. The purely adaptive part of the localization matrix considered is given by the element-wise square of the correlation matrix of a smoothed ensemble of streamfunction perturbations. It is found that these purely adaptive localization functions have spurious far-field correlations as large as 0.1 with a 128-member ensemble. To attenuate the spurious features of the purely a
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34

Knopfmeier, Kent H., and David J. Stensrud. "Influence of Mesonet Observations on the Accuracy of Surface Analyses Generated by an Ensemble Kalman Filter." Weather and Forecasting 28, no. 3 (2013): 815–41. http://dx.doi.org/10.1175/waf-d-12-00078.1.

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Abstract The expansion of surface mesoscale networks (mesonets) across the United States provides a high-resolution observational dataset for meteorological analysis and prediction. To clarify the impact of mesonet data on the accuracy of surface analyses, 2-m temperature, 2-m dewpoint, and 10-m wind analyses for 2-week periods during the warm and cold seasons produced through an ensemble Kalman filter (EnKF) approach are compared to surface analyses created by the Real-Time Mesoscale Analysis (RTMA). Results show in general a similarity between the EnKF analyses and the RTMA, with the EnKF ex
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35

Majumdar, S. J., S. D. Aberson, C. H. Bishop, R. Buizza, M. S. Peng, and C. A. Reynolds. "A Comparison of Adaptive Observing Guidance for Atlantic Tropical Cyclones." Monthly Weather Review 134, no. 9 (2006): 2354–72. http://dx.doi.org/10.1175/mwr3193.1.

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Abstract Airborne adaptive observations have been collected for more than two decades in the neighborhood of tropical cyclones, to attempt to improve short-range forecasts of cyclone track. However, only simple subjective strategies for adaptive observations have been used, and the utility of objective strategies to improve tropical cyclone forecasts remains unexplored. Two objective techniques that have been used extensively for midlatitude adaptive observing programs, and the current strategy based on the ensemble deep-layer mean (DLM) wind variance, are compared quantitatively using two met
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36

Mu, Longjiang, Xi Liang, Qinghua Yang, Jiping Liu, and Fei Zheng. "Arctic Ice Ocean Prediction System: evaluating sea-ice forecasts during Xuelong's first trans-Arctic Passage in summer 2017." Journal of Glaciology 65, no. 253 (2019): 813–21. http://dx.doi.org/10.1017/jog.2019.55.

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AbstractIn an effort to improve the reliability of Arctic sea-ice predictions, an ensemble-based Arctic Ice Ocean Prediction System (ArcIOPS) has been developed to meet operational demands. The system is based on a regional Arctic configuration of the Massachusetts Institute of Technology general circulation model. A localized error subspace transform ensemble Kalman filter is used to assimilate the weekly merged CryoSat-2 and Soil Moisture and Ocean Salinity sea-ice thickness data together with the daily Advanced Microwave Scanning Radiometer 2 (AMSR2) sea-ice concentration data. The weather
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37

Steiner, Michael, Luca Cantarello, Stephan Henne, and Dominik Brunner. "Flow-dependent observation errors for greenhouse gas inversions in an ensemble Kalman smoother." Atmospheric Chemistry and Physics 24, no. 21 (2024): 12447–63. http://dx.doi.org/10.5194/acp-24-12447-2024.

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Abstract. Atmospheric inverse modeling is the process of estimating emissions from atmospheric observations by minimizing a cost function, which includes a term describing the difference between simulated and observed concentrations. The minimization of this difference is typically limited by uncertainties in the atmospheric transport model rather than by uncertainties in the observations. In this study, we showcase how a temporally varying, flow-dependent atmospheric transport uncertainty can enhance the accuracy of emission estimation through idealized experiments using an ensemble Kalman sm
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38

Sun, Luyu, and Stephen G. Penny. "Lagrangian Data Assimilation of Surface Drifters in a Double-Gyre Ocean Model Using the Local Ensemble Transform Kalman Filter." Monthly Weather Review 147, no. 12 (2019): 4533–51. http://dx.doi.org/10.1175/mwr-d-18-0406.1.

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Abstract The assimilation of position data from Lagrangian observing platforms is underdeveloped in operational applications because of two main challenges: 1) nonlinear growth of model and observation error in the Lagrangian trajectories, and 2) the high dimensionality of realistic models. In this study, we propose a localized Lagrangian data assimilation (LaDA) method that is based on the local ensemble transform Kalman filter (LETKF). The algorithm is tested with an “identical twin” approach in observing system simulation experiments (OSSEs) using a simple double-gyre configuration of the G
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39

Khade, V. M., J. A. Hansen, J. S. Reid, and D. L. Westphal. "Ensemble filter based estimation of spatially distributed parameters in a mesoscale dust model: experiments with simulated and real data." Atmospheric Chemistry and Physics Discussions 12, no. 11 (2012): 28837–89. http://dx.doi.org/10.5194/acpd-12-28837-2012.

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Abstract. The Ensemble Adjustment Kalman Filter (EAKF) is used to estimate the erodibility fraction parameter field in a coupled meteorology and dust aerosol model (Coupled Ocean Atmosphere Mesoscale Prediction System-COAMPS) over the Sahara desert. Erodibility is often employed as the key parameter to map dust source. It is used along with surface winds (or surface wind stress) to calculate dust emissions. Using the Saharan desert as a test bed, a perfect model Observation System Simulation Experiments (OSSEs) with 40 ensemble members, and observations of aerosol optical depth (AOD), the EAKF
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Khade, V. M., J. A. Hansen, J. S. Reid, and D. L. Westphal. "Ensemble filter based estimation of spatially distributed parameters in a mesoscale dust model: experiments with simulated and real data." Atmospheric Chemistry and Physics 13, no. 6 (2013): 3481–500. http://dx.doi.org/10.5194/acp-13-3481-2013.

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Abstract. The ensemble adjustment Kalman filter (EAKF) is used to estimate the erodibility fraction parameter field in a coupled meteorology and dust aerosol model (Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS)) over the Sahara desert. Erodibility is often employed as the key parameter to map dust source. It is used along with surface winds (or surface wind stress) to calculate dust emissions. Using the Saharan desert as a test bed, a perfect model Observation System Simulation Experiments (OSSEs) with 40 ensemble members, and observations of aerosol optical depth (AOD), the EA
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Yu, Y., J. Koller, V. K. Jordanova, S. G. Zaharia, and H. C. Godinez. "Radiation belt data assimilation of a moderate storm event using a magnetic field configuration from the physics-based RAM-SCB model." Annales Geophysicae 32, no. 5 (2014): 473–83. http://dx.doi.org/10.5194/angeo-32-473-2014.

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Abstract. Data assimilation using Kalman filters provides an effective way of understanding both spatial and temporal variations in the outer electron radiation belt. Data assimilation is the combination of in situ observations and physical models, using appropriate error statistics to approximate the uncertainties in both the data and the model. The global magnetic field configuration is one essential element in determining the adiabatic invariants for the phase space density (PSD) data used for the radiation belt data assimilation. The lack of a suitable global magnetic field model with high
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Chen, Zhiqiang, Jiping Liu, Mirong Song, Qinghua Yang, and Shiming Xu. "Impacts of Assimilating Satellite Sea Ice Concentration and Thickness on Arctic Sea Ice Prediction in the NCEP Climate Forecast System." Journal of Climate 30, no. 21 (2017): 8429–46. http://dx.doi.org/10.1175/jcli-d-17-0093.1.

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Here sea ice concentration derived from the Special Sensor Microwave Imager/Sounder and thickness derived from the Soil Moisture and Ocean Salinity and CryoSat-2 satellites are assimilated in the National Centers for Environmental Prediction Climate Forecast System using a localized error subspace transform ensemble Kalman filter (LESTKF). Three ensemble-based hindcasts are conducted to examine impacts of the assimilation on Arctic sea ice prediction, including CTL (without any assimilation), LESTKF-1 (with initial sea ice assimilation only), and LESTKF-E5 (with every 5-day sea ice assimilatio
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Pendergrass, Drew C., Daniel J. Jacob, Hannah Nesser, et al. "CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model." Geoscientific Model Development 16, no. 16 (2023): 4793–810. http://dx.doi.org/10.5194/gmd-16-4793-2023.

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Abstract. We present a versatile, powerful, and user-friendly chemical data assimilation toolkit for simultaneously optimizing emissions and concentrations of chemical species based on atmospheric observations from satellites or suborbital platforms. The CHemistry and Emissions REanalysis Interface with Observations (CHEEREIO) exploits the GEOS-Chem chemical transport model and a localized ensemble transform Kalman filter algorithm (LETKF) to determine the Bayesian optimal (posterior) emissions and/or concentrations of a set of species based on observations and prior information using an easy-
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Aydoğdu, Ali, Alberto Carrassi, Colin T. Guider, Chris K. R. T. Jones, and Pierre Rampal. "Data assimilation using adaptive, non-conservative, moving mesh models." Nonlinear Processes in Geophysics 26, no. 3 (2019): 175–93. http://dx.doi.org/10.5194/npg-26-175-2019.

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Abstract. Numerical models solved on adaptive moving meshes have become increasingly prevalent in recent years. Motivating problems include the study of fluids in a Lagrangian frame and the presence of highly localized structures such as shock waves or interfaces. In the former case, Lagrangian solvers move the nodes of the mesh with the dynamical flow; in the latter, mesh resolution is increased in the proximity of the localized structure. Mesh adaptation can include remeshing, a procedure that adds or removes mesh nodes according to specific rules reflecting constraints in the numerical solv
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Kotsuki, Shunji, Kenta Kurosawa, Shigenori Otsuka, Koji Terasaki, and Takemasa Miyoshi. "Global Precipitation Forecasts by Merging Extrapolation-Based Nowcast and Numerical Weather Prediction with Locally Optimized Weights." Weather and Forecasting 34, no. 3 (2019): 701–14. http://dx.doi.org/10.1175/waf-d-18-0164.1.

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Abstract Over the past few decades, precipitation forecasts by numerical weather prediction (NWP) models have been remarkably improved. Yet, precipitation nowcasting based on spatiotemporal extrapolation tends to provide a better precipitation forecast at shorter lead times with much less computation. Therefore, merging the precipitation forecasts from the NWP and extrapolation systems would be a viable approach to quantitative precipitation forecast (QPF). Although the optimal weights between the NWP and extrapolation systems are usually defined as a global constant, the weights would vary in
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Li, Hongyi, Ting Yang, Lars Nerger, et al. "NAQPMS-PDAF v2.0: a novel hybrid nonlinear data assimilation system for improved simulation of PM2.5 chemical components." Geoscientific Model Development 17, no. 23 (2024): 8495–519. http://dx.doi.org/10.5194/gmd-17-8495-2024.

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Abstract. Identifying PM2.5 chemical components is crucial for formulating emission strategies, estimating radiative forcing, and assessing human health effects. However, accurately describing spatiotemporal variations in PM2.5 chemical components remains a challenge. In our earlier work, we developed an aerosol extinction coefficient data assimilation (DA) system (Nested Air Quality Prediction Model System with the Parallel Data Assimilation Framework (NAQPMS-PDAF) v1.0) that was suboptimal for chemical components. This paper introduces a novel hybrid nonlinear chemical DA system (NAQPMS-PDAF
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Reynolds, C. A., M. S. Peng, S. J. Majumdar, S. D. Aberson, C. H. Bishop, and R. Buizza. "Interpretation of Adaptive Observing Guidance for Atlantic Tropical Cyclones." Monthly Weather Review 135, no. 12 (2007): 4006–29. http://dx.doi.org/10.1175/2007mwr2027.1.

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Abstract Adaptive observing guidance products for Atlantic tropical cyclones are compared using composite techniques that allow one to quantitatively examine differences in the spatial structures of the guidance maps and relate these differences to the constraints and approximations of the respective techniques. The guidance maps are produced using the ensemble transform Kalman filter (ETKF) based on ensembles from the National Centers for Environmental Prediction and the European Centre for Medium-Range Weather Forecasts (ECMWF), and total-energy singular vectors (TESVs) produced by ECMWF and
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Ruckstuhl, Y., and T. Janjić. "Combined State-Parameter Estimation with the LETKF for Convective-Scale Weather Forecasting." Monthly Weather Review 148, no. 4 (2020): 1607–28. http://dx.doi.org/10.1175/mwr-d-19-0233.1.

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Abstract We investigate the feasibility of addressing model error by perturbing and estimating uncertain static model parameters using the localized ensemble transform Kalman filter. In particular we use the augmented state approach, where parameters are updated by observations via their correlation with observed state variables. This online approach offers a flexible, yet consistent way to better fit model variables affected by the chosen parameters to observations, while ensuring feasible model states. We show in a nearly operational convection-permitting configuration that the prediction of
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Zhang, Yong-Fei, Cecilia M. Bitz, Jeffrey L. Anderson, et al. "Insights on Sea Ice Data Assimilation from Perfect Model Observing System Simulation Experiments." Journal of Climate 31, no. 15 (2018): 5911–26. http://dx.doi.org/10.1175/jcli-d-17-0904.1.

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Simulating Arctic sea ice conditions up to the present and predicting them several months in advance has high stakeholder value, yet remains challenging. Advanced data assimilation (DA) methods combine real observations with model forecasts to produce sea ice reanalyses and accurate initial conditions for sea ice prediction. This study introduces a sea ice DA framework for a sea ice model with a parameterization of the ice thickness distribution by resolving multiple thickness categories. Specifically, the Los Alamos Sea Ice Model, version 5 (CICE5), is integrated with the Data Assimilation Re
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Zhao, Fu, Xi Liang, Zhongxiang Tian, Ming Li, Na Liu, and Chengyan Liu. "Southern Ocean Ice Prediction System version 1.0 (SOIPS v1.0): description of the system and evaluation of synoptic-scale sea ice forecasts." Geoscientific Model Development 17, no. 17 (2024): 6867–86. http://dx.doi.org/10.5194/gmd-17-6867-2024.

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Abstract. An operational synoptic-scale sea ice forecasting system for the Southern Ocean, namely the Southern Ocean Ice Prediction System (SOIPS), has been developed to support ship navigation in the Antarctic sea ice zone. Practical application of the SOIPS forecasts had been implemented for the 38th Chinese National Antarctic Research Expedition for the first time. The SOIPS is configured on an Antarctic regional sea ice–ocean–ice shelf coupled model and an ensemble-based localized error subspace transform Kalman filter data assimilation model. Daily near-real-time satellite sea ice concent
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