Maximum Likelihood Ensemble Filter Applied to Multisensor Systems
|
|
- James Osborne
- 5 years ago
- Views:
Transcription
1 Maximum Likelihood Ensemble Filter Applied to Multisensor Systems Arif R. Albayrak a, Milija Zupanski b and Dusanka Zupanski c abc Colorado State University (CIRA), 137 Campus Delivery Fort Collins, CO 823, USA ABSTRACT Maximum Likelihood Ensemble Filter (MLEF) is an alternative deterministic ensemble based filter technique that optimizes a non-linear cost function along with a Maximum Likelihood approach. In addition to the common use of ensembles for calculating error covariance, the ensembles in MLEF are exploited to efficiently calculate Hessian preconditioning and the gradient of the cost function. This study is divided into two segments. The first part presents a one sensor approach, were MLEF is compared to different filters using Lorenz 63 system. These filters are: Extended KalmanFilter,Ensemble Kalman Filter. The second part develops a multi-sensor system. Here we study a moving particle on an orbit obtained from the same Lorenz system. We analyze the information content of MLEF s ensemble subspace for each sensor and consider the effects of different number of ensembles on the fusion process. In practice, when using ensemble based filtering techniques, a large ensemble size is required to obtain the best results. In this study we show that MLEF can obtain similar results using a smaller ensemble size by utilizing an information matrix, where essential characteristics are captured. This is a vital consideration when working with multi-sensor data fusion systems. Keywords: Ensemble filter, maximum likelihood, information content, data fusion 1. INTRODUCTION Data assimilation in atmospheric applications has been based on the Kalman Filtering theory introduced by Kalman and Bucy 1 in Since then, data assimilation methodologies used in real applications can be seen as efforts to approximate the Kalman filter/smoother theoretical framework. The lack of knowledge of statistical properties of models and observations, together with the computational burden associated with high dimensionality of realistic atmospheric data assimilation problems, have made approximations necessary. A novel approach based on the use of ensemble forecasting in nonlinear Kalman Filtering has been pursued in recent years by different studies (Evensen ; Houtekamer and Mitchell ; Whitaker and Hamill 4 22; Ott 24). Zupanski 6 introduced the MLEF approach as an alternative deterministic ensemble based filter technique. A similar algorithm called Ensemble Transform Kalman Filter (ETKF) was introduced by Bishop. 7 However, MLEF differs from this algorithm by acting on state space instead of sample space. In short, MLEF optimizes a non-linear cost function along with a Maximum Likelihood approach. As in variational and ensemble data assimilation methods, in MLEF the cost function is derived using a Gaussian probability density function framework. Furthermore, like other ensemble data assimilation algorithms, MLEF produces an estimate of the analysis uncertainty. In addition to the common use of ensembles for calculating error covariance, the ensembles in MLEF are exploited to efficiently calculate Hessian preconditioning and the gradient of the cost function. In order to understand the behavior of the MLEF for the multi-sensor problems, we concentrated on two different types of experiments. In the first experiment we established the credibility of MLEF method for highly nonlinear state estimation using a one sensor approach. MLEF, EKF and Ensemble Kalman Filter algorithms were applied to data assimilation with three-dimensional Lorenz equations. We have chosen Lorenz attractor as a simulation model because of its high non-linear chaotic behavior. Our goal in choosing such a system was to Further author information: (Send correspondence to Arif Albayrak) Arif Albayrak: albayrak@cira.colostate.edu, Telephone:
2 emphasize the power of ensemble approaches compared to the classical Kalman Filter techniques. After reviewing the performance of MLEF algorithm, we extended our study of MLEF to a second experiment to analyze multisensor systems with information fusion. Here we study a moving particle on a an orbit that is obtained from the same Lorenz system. We analyze the information content of the MLEF algorithm and introduce a information fusion notion to combine different sensors states and covariance estimates. The rest of this study is organized in 4 sections. In section 2 the algorithms and models used throughout the paper are summarized. Section 3 presents the results for the two experiments. Finally, section 4 summarizes the findings and offers concluding remarks. 2. METHODS This section describes the methods considered during our study. Those methods are The maximum Likelihood Ensemble Filter, Ensemble Kalman filter, Kalman Filter and Extended Kalman Filter. Because KF and EKF are widely used approaches we have cited references for the review of their methodology. However, in the case of Ensemble Kalman Filter we offer a summary of the equations used by this approach The Maximum Likelihood Ensemble Filter The Maximum Likelihood Ensemble Filter (MLEF - Zupanski and Zupanski 8 and 6 ) is an ensemble system for data assimilation and forecasting based on control theory. The MLEF seeks a posterior mode by employing an unconstrained iterative minimization, such as the nonlinear conjugate-gradient and quasi-newton methods. The MLEF performs minimization in a low-dimensional, ensemble-spanned subspace of the state space. Although the MLEF may be used as a full-rank algorithm, in realistic high-dimensional problems the maximum number of degrees of freedom is drastically reduced due to computational limits. This reduction of degrees of freedom allows a superior explicit Hessian preconditioning, otherwise not feasible. As true for other ensemble data assimilation algorithms, the ensembles in the MLEF are used to calculate the flow-dependent error covariance. Assuming Gaussian PDF, the cost function is defined in the familiar form (1), J(x) = 1 2 (x xf ) T P 1 f (x xf )+ 1 2 (y H(x))T R 1 (y H(x)), (1) where R is the observation error covariance, y is the observation vector, H is a nonlinear observation operator, x is the state vector, and P f is the forecast error covariance. In practice, the inverse of the matrix P f is never calculated; instead, a square-root is defined from ensemble forecasts, with the i-th column-vector p f i defined as in Eqs. (2) and (3) P 1 2 f =[p f 1 pf 2...pf N E ], (2) P f i = M(xa + P a i ) M(xa ), (3) where M denotes the prediction model p a i, pf i (i =1...N E) are the analysis and forecast ensemble perturbations, respectively, and N E is the dimension of ensemble subspace. The uncertainties of the forecast are calculated from Equation (2). The uncertainties of the analysis are obtained from the square-root analysis error covariance given in Equation (4) P a i =[pa 1 pa 2...pa N E ], (4) P a i = P 1/2 ( f Iens + Z T Z ) 1 2, () P a i = P 1/2 f U ( I ens + Λ 2) 1 2 U T, (6) where U and Λ are the eigenvectors and eigenvalues of Z T Z, respectively. The column-vectors of Z are (7), z i (x) =R 1 2 H(x + p f i ) R 1 2 H(x), (7) It is important to note that column-vectors z i are defined using information from both data (e.g. H, R) and state (e.g. x, p f i ). This proves to be important for the information content calculations. As shown in Zupanski
3 et al. 9 the matrix Z T Z and its eigenvalues are related to Shannon information theory (Shannon and Weaver 1949) and can be used to calculate the degrees of freedom d s and entropy reduction h (e.g., Rodgers 2) d s = i λ 2 i 1+λ 2 i 2.2. The Kalman Filter and The Ensemble Kalman Filter and h = 1 ln(1 + λ 2 i 2 ), (8) During our study, in addition to the MLEF algorithm we consider Extended Kalman Filter and Ensemble Kalman Filter algorithms for the purpose of comparison. Kalman filter is an estimation technique used for linear models. 11 The theory supporting KF can be found in reference 11. To solve weak non-linear applications the KF algorithm can be modified using Taylor series expansion around the current estimate, thus obtaining an Extended Kalman Filter algorithm (EKF). An explanation of EKF can be found in references 11, and. Ensemble Kalman Filter is the first example of ensemble filtering techniques considered for data assimilation purposes. Bellow, a short summary of Ensemble Kalman Filter is provided in Eqs. (9) through (1) Consider a non-linear discrete time system given by Eqs.(9), and () x(k + 1) =f(ξ i (k, k), u(k), w i (k, k)), (9) y(k) =C(k)x(k) +v(k), () where x denotes the state vector, u model input,v is the measurement error, and w is the model error. They are assumed to be independent Gaussian white noise processes with covariances Σ state,andσ observation. Finally y denotes observation vector. i ξ i (k +1,k)=f(ξ i (k, k), u(k), w i (k, k)), (11) ˆx(k +1,k)= 1 q ξ i (k +1,k), q (12) i=1 [L c (k +1,k)] 1:n,i = 1 q ξ i (k +1,k) ˆx(k +1,k), (13) K c (k+1) = L c (k+1,k)l c (k+1,k) T C(k+1) T ( C(k +1)L c (k +1,k)L c (k +1,k) T C(k +1) T ) +Σ (k+1), (14) ξ i (k +1,k+1)=ξ i (k +1,k)+K c (y(k +1) C(k)ξ i (k +1,k) v i (k +1)), (1) where ξ i is an ensemble of state vectors, 2.3. The Lorenz Model To provide an example of the performance of the MLEF approach, we compare it to Kalman and Extended Kalman Filters using three dimensional Lorenz equations (1963) 12. These equations were originally designed as a approximation to the convective motion associated with a two dimensional cell fluid which is heated from below and cooled from above. The corresponding equations are given in Eqs (16), (17), and (18), dx = α(x Y ), dt (16) dy = rx Y XZ, dt (17) dz = bz + XY, dt (18)
4 z 2 x y z Truth Solution for x Truth Solution for y Truth Solution for z x 1 1 y Figure 1. 3-D Lorenz Attractor. Where α, b and r are the parameters of the system. Equation (19), X t+1 1 ατ ατ Y t+1 = rτ 1 τ X t τ Z t+1 Y t τ 1 bτ Lorenz system can be written in discrete form as in X t Y t Z t, (19) The system was integrated using the 4 th order Runge-Kutta method with time step.1. In the simulations, we generated the true state by running the model for non-dimensional time units, (i.e., steps) with α =, b = 8 3, and 28 which resulted in a butterfly like attractor (Lorenz 1963). Infrequent observations were created every. time units by adding Gaussian noise with mean and variance 4 to each coordinate of the true state x. Hence the observation operator H is linear and equal to the identity matrix, and the observation error covariance matrix R is a diagonal matrix. See Fig. 1 for the illustration of Lorenz system Multi-Sensor Data Fusion In this study we introduce a multi-sensor data fusion application to show the effectiveness of the MLEF method. The consideration of multiple sensors is a complex problem which depends on the types of sensors and their attributes. Because the focus of this study is to show the applicability of MLEF to such problems, we have simplified our application. We considered a two sensor system each separately design with a MLEF tracker for the estimation of the parameters and covariance. It is assumed that while both sensors are of the same type, their observations are collected with different noise components. During this study, state estimates obtained from different sensors are combined in the sense of information fusion. This approach is introduced by Chang et al. 13 using Eqs. (2), and (21), 14 ˆx c = P c ( P 1 i ˆx i + P 1 j ˆx j P 1 pi ˆx pi P 1 pj ˆx pj + Pp 1 ) ˆx p, (2) P c = [ P 1 i + P 1 j P 1 pi P 1 pj + Pp 1 ] 1, (21) where ˆx pj, ˆx pi are previous sensor state estimates, P pi, P pj are previous sensor covariance matrices, and ˆx p is previous composite, and P p is covariance matrix. These composite formulations are based on the information filter concept that is also introduced in Refs. 14. A full oscillation around one of the butterfly wings corresponds to roughly one time unit.
5 RMS MonteCarlo Runs.4.3 KF Kal Ens MLEF.3.2 Error Figure 2. RMS for estimation results (x axis * ). 3. RESULTS The results obtained for the two experiments performed in this study are provided in this section. First, the outcome from the performance comparison of MLEF versus Ensemble Kalman Filter and Extended Kalman Filter is offered for a one sensor case. The simulation was designed using a total of 3 observations created from original ground truth values distorted with normally distributed random noise and a standard deviation of.1. We initiated the state vector and observation covariance to an average background covariance and state vector from the first n number of observations which were chosen randomly between 2 and. In order to compare the results effectively we applied this parameter values to each algorithm in consideration. Construction of ensemble filters require perturbations in order to create the covariance structure; therefore, additional calculations were required for MLEF and Ensemble Kalman Filter. A total of ensemble perturbations were obtained by multiplying the square root observation covariance matrix with a randomly chosen 3 by x, y,z values. Note that x, y, z values can only be in the interval and 1. Case observations were created irregularly from the first 6 time steps. After that we continue to run our simulation without observations. The results for each algorithm were compared for Monte Carlo runs. As a first step we analyze the RMS errors of the estimated states from all the techniques. We focused on the interval that includes observations, the results are shown in Fig. 2. In this figure we can observe that MLEF gave lower RMS error values compared to Ensemble Kalman Filter and EKF. Next we analyzed the behavior of filters in the absent of observations. The results of the simulation for that interval is given in Fig. 3. Here we see that MLEF and Ensemble Kalman Filter techniques offer similar performance when observations are not present. We believe that the optimization and preconditioning algorithms built within MLEF are the reasons for better estimate results. From this results we can also conclude that MLEF is able to obtain similar RMS values to those obtained by Ensemble Kalman Filter using a low number of ensembles. Second, we analyze the outcome from our second experiment, which is the result of the application of MLEF to a multi-sensor case. To apply the information fusion concept, we considered only x, andy coordinates of the Lorenz attractor. The results provided are the average of Monte Carlo runs. In Fig. 4 the location of the observation and the ground truth values can be seen. In Fig., an example of an instant observation during the run with the MLEF estimates of sensor-1 and sensor-2 is shown. In addition to that we included a σ uncertainty ellipse around the estimated state variable for each sensor. In this figure it can be observed that the fused state is in a perfect location including the estimated values, observations, as well as the original ground truth value. Furthermore, we analyzed the RMS errors of each sensor and fused state. As we did earlier we look at the
6 RMS MonteCarlo Runs.2 KF Kal Ens MLEF.2 Error.1.1. Figure 3. RMS for prediction results (x axis * ). 2 1 Attractor Orbit Ground Truth Observations 2D Lorenz Attractor y x Figure 4. 2D Lorenz attractor with observation points.
7 2D Lorenz Attractor Attractor Orbit Ground Truth Observations y x Figure. Uncertainity ellipses. Green ellipse sensor-1, blue ellipse sensor-2 and red ellipse fused state RMS MLEF sensor 1 MLEF sensor 2 Fused 12 RMS error Steps Figure 6. Left: RMS errors for MLEF sensor-1,2 and fused state Right: Run average (x axis * ). Fig. 6 in two separate intervals. In the first interval where observations exist we see that the RMS errors for each case are very close to each other. On the other hand in the no observation region, after the 6th step we begin to see a smaller RMS error for the fused states. 4. DISCUSSION AND CONCLUSION In this study, the Maximum Likelihood Ensemble Filter approach is presented in applications to three-dimensional Lorenz attractor. 12 The filter combines the maximum likelihood approach with the ensemble Kalman filter methodology, to create a new ensemble data assimilation algorithm. Thus, MLEF becomes a powerful estimation prediction algorithm where optimization, preconditioning and ensemble ideas are all combined. In this paper we extend the MLEF application area in order to show the effectiveness of the MLEF algorithm for multi-sensor applications.
8 During our study we perform two different experiments. First the MLEF algorithm was compared to Extended Kalman Filter and Ensemble Kalman filter. From the results obtained from the simulation, we concluded that MLEF offers a better performance than Extended Kalman Filter at all times. When compared against Ensemble Kalman filter while observations exist, the MLEF algorithm obtained better results. And when the observations were missing the MLEF algorithm was as good as the Ensemble Kalman Filter. We believe that the optimization and preconditioning algorithms built within MLEF are the reasons for better estimate results. From this results we can also conclude that MLEF is able to obtain similar RMS values to those obtained by Ensemble Kalman Filter using a lower number of ensembles. In our second experiment, MLEF is considered for a multi-sensor problem where an information fusion process is also included. During these experiment it was shown that fused states have reduced uncertainty areas as expected. In addition to that, while observations were included the RMS error of fused states was similar to those of the sensor with minimum RMS. However in the case of missing observations, the RMS of the fused states was considerably smaller than the RMS of the sensors. We believe that the major contribution of this study is the use of MLEF for a multi-sensor application with the incorporation of an information fusion concept, for the first time. Although the simulation used is a basic one, MLF results were very encouraging, setting the foundation for future uses in more complex systems. There are some issues that need further attention, but have not been address because of the limited scope of this study. These are the definition of the number of ensembles, initiation of ensembles in a non-random manner, and further analysis of information content in subspaces. REFERENCES 1. R. Kalman and R. Bucy, New results in linear prediction and filtering theory, Trans. AMSEJ. Basic Eng. 83-D, pp. 9 8, G. Evensen, Sequential data assimilation with a nonlinear quasi-geostrophic model usin monte-carlo methods to forcast error statistics, J. Geophysics. Res 99, pp , P. Houtekamer and H. Mitchell, Data assimilation using kalman filter technique, Mon. Wea. Rev 126, pp , J. Whitaker and T. Hamill, Ensemble data assimilation without perturbed observations, Mon. Wea. Rev 13, pp , 22.. E. Ott and E. K. M. C. E. K. D. P. J. Y. I. Szunyogh, A.V. Zimin, A local ensemble kalman filter for atmospheric data assimilation, Tellus 6A, pp , M. Zupanski, Maximum likelihood ensemble filter: Theoretical aspects, Mon. Wea. Rev 133, pp , B. Bishop and S. M. J. Etherton, Adaptive sampling with the ensemble transform kalman filter. part 1: Theoretical aspects, Mon. Wea. Rev 129, pp , D. Zupanski and M. Zupanski, Model error estimation employing an ensemble data assimilation approach, Mon. Wea. Rev. 134, pp , D. Zupanski, A. Hou, S. Zhang, M. Zupanski, C. Kummerow, and W. E. Oliu, Information theory and ensemble data assimilation, Submitted to Q. J. Roy. Meteorol. Soc, 27.. M. Verlaan and A. Heemink, Non-linearity in data assimilation applications: A practical method for analysis, Mon. Wea. Rev., A. Gelb, Applied Optimal Estimation, The MIT Press, Cambridge, E. N. Lorenz, Deterministic non-periodic flow, J. Atmos. Sci. 2, pp , K. Chang, R. Saha, and Y.Bar-Shalom, On optimal track-to-track fusion, IEEE Transactions on Aerospace and Electronic Systems 33, pp , October, S. Blackman and R. Popoli, Design and Analysis of Modern Tracking Systems, Artech House, Boston, 1999.
A Note on the Particle Filter with Posterior Gaussian Resampling
Tellus (6), 8A, 46 46 Copyright C Blackwell Munksgaard, 6 Printed in Singapore. All rights reserved TELLUS A Note on the Particle Filter with Posterior Gaussian Resampling By X. XIONG 1,I.M.NAVON 1,2 and
More informationMAXIMUM LIKELIHOOD ENSEMBLE FILTER: THEORETICAL ASPECTS. Milija Zupanski
MAXIMUM LIKELIHOOD ENSEMBLE FILTER: THEORETICAL ASPECTS Milija Zupanski Cooperative Institute for Research in the Atmosphere Colorado State University Foothills Campus Fort Collins, CO 8053-1375 ZupanskiM@cira.colostate.edu
More informationLocal Ensemble Transform Kalman Filter
Local Ensemble Transform Kalman Filter Brian Hunt 11 June 2013 Review of Notation Forecast model: a known function M on a vector space of model states. Truth: an unknown sequence {x n } of model states
More informationApplications of information theory in ensemble data assimilation
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY Q. J. R. Meteorol. Soc. 33: 533 545 (007) Published online in Wiley InterScience (www.interscience.wiley.com).3 Applications of information theory
More informationFour-Dimensional Ensemble Kalman Filtering
Four-Dimensional Ensemble Kalman Filtering B.R. Hunt, E. Kalnay, E.J. Kostelich, E. Ott, D.J. Patil, T. Sauer, I. Szunyogh, J.A. Yorke, A.V. Zimin University of Maryland, College Park, MD 20742, USA Ensemble
More informationLocal Ensemble Transform Kalman Filter: An Efficient Scheme for Assimilating Atmospheric Data
Local Ensemble Transform Kalman Filter: An Efficient Scheme for Assimilating Atmospheric Data John Harlim and Brian R. Hunt Department of Mathematics and Institute for Physical Science and Technology University
More informationAbstract 2. ENSEMBLE KALMAN FILTERS 1. INTRODUCTION
J5.4 4D ENSEMBLE KALMAN FILTERING FOR ASSIMILATION OF ASYNCHRONOUS OBSERVATIONS T. Sauer George Mason University, Fairfax, VA 22030 B.R. Hunt, J.A. Yorke, A.V. Zimin, E. Ott, E.J. Kostelich, I. Szunyogh,
More informationFundamentals of Data Assimila1on
2015 GSI Community Tutorial NCAR Foothills Campus, Boulder, CO August 11-14, 2015 Fundamentals of Data Assimila1on Milija Zupanski Cooperative Institute for Research in the Atmosphere Colorado State University
More informationEnsemble square-root filters
Ensemble square-root filters MICHAEL K. TIPPETT International Research Institute for climate prediction, Palisades, New Yor JEFFREY L. ANDERSON GFDL, Princeton, New Jersy CRAIG H. BISHOP Naval Research
More informationFour-dimensional ensemble Kalman filtering
Tellus (24), 56A, 273 277 Copyright C Blackwell Munksgaard, 24 Printed in UK. All rights reserved TELLUS Four-dimensional ensemble Kalman filtering By B. R. HUNT 1, E. KALNAY 1, E. J. KOSTELICH 2,E.OTT
More informationP 1.86 A COMPARISON OF THE HYBRID ENSEMBLE TRANSFORM KALMAN FILTER (ETKF)- 3DVAR AND THE PURE ENSEMBLE SQUARE ROOT FILTER (EnSRF) ANALYSIS SCHEMES
P 1.86 A COMPARISON OF THE HYBRID ENSEMBLE TRANSFORM KALMAN FILTER (ETKF)- 3DVAR AND THE PURE ENSEMBLE SQUARE ROOT FILTER (EnSRF) ANALYSIS SCHEMES Xuguang Wang*, Thomas M. Hamill, Jeffrey S. Whitaker NOAA/CIRES
More informationA METHOD FOR INITIALIZATION OF ENSEMBLE DATA ASSIMILATION. Submitted January (6 Figures) A manuscript submitted for publication to Tellus
A METHOD FOR INITIALIZATION OF ENSEMBLE DATA ASSIMILATION Milija Zupanski 1, Steven Fletcher 1, I. Michael Navon 2, Bahri Uzunoglu 3, Ross P. Heikes 4, David A. Randall 4, and Todd D. Ringler 4 Submitted
More informationarxiv: v1 [physics.ao-ph] 23 Jan 2009
A Brief Tutorial on the Ensemble Kalman Filter Jan Mandel arxiv:0901.3725v1 [physics.ao-ph] 23 Jan 2009 February 2007, updated January 2009 Abstract The ensemble Kalman filter EnKF) is a recursive filter
More informationFundamentals of Data Assimila1on
014 GSI Community Tutorial NCAR Foothills Campus, Boulder, CO July 14-16, 014 Fundamentals of Data Assimila1on Milija Zupanski Cooperative Institute for Research in the Atmosphere Colorado State University
More informationState and Parameter Estimation in Stochastic Dynamical Models
State and Parameter Estimation in Stochastic Dynamical Models Timothy DelSole George Mason University, Fairfax, Va and Center for Ocean-Land-Atmosphere Studies, Calverton, MD June 21, 2011 1 1 collaboration
More informationKalman Filter and Ensemble Kalman Filter
Kalman Filter and Ensemble Kalman Filter 1 Motivation Ensemble forecasting : Provides flow-dependent estimate of uncertainty of the forecast. Data assimilation : requires information about uncertainty
More informationOptimal Localization for Ensemble Kalman Filter Systems
Journal December of the 2014 Meteorological Society of Japan, Vol. Á. 92, PERIÁÑEZ No. 6, pp. et 585 597, al. 2014 585 DOI:10.2151/jmsj.2014-605 Optimal Localization for Ensemble Kalman Filter Systems
More informationOn the Kalman Filter error covariance collapse into the unstable subspace
Nonlin. Processes Geophys., 18, 243 250, 2011 doi:10.5194/npg-18-243-2011 Author(s) 2011. CC Attribution 3.0 License. Nonlinear Processes in Geophysics On the Kalman Filter error covariance collapse into
More informationA Comparative Study of 4D-VAR and a 4D Ensemble Kalman Filter: Perfect Model Simulations with Lorenz-96
Tellus 000, 000 000 (0000) Printed 20 October 2006 (Tellus LATEX style file v2.2) A Comparative Study of 4D-VAR and a 4D Ensemble Kalman Filter: Perfect Model Simulations with Lorenz-96 Elana J. Fertig
More information4D-Var or Ensemble Kalman Filter? TELLUS A, in press
4D-Var or Ensemble Kalman Filter? Eugenia Kalnay 1 *, Hong Li 1, Takemasa Miyoshi 2, Shu-Chih Yang 1, and Joaquim Ballabrera-Poy 3 1 University of Maryland, College Park, MD, 20742-2425 2 Numerical Prediction
More informationA Spectral Approach to Linear Bayesian Updating
A Spectral Approach to Linear Bayesian Updating Oliver Pajonk 1,2, Bojana V. Rosic 1, Alexander Litvinenko 1, and Hermann G. Matthies 1 1 Institute of Scientific Computing, TU Braunschweig, Germany 2 SPT
More informationJ1.3 GENERATING INITIAL CONDITIONS FOR ENSEMBLE FORECASTS: MONTE-CARLO VS. DYNAMIC METHODS
J1.3 GENERATING INITIAL CONDITIONS FOR ENSEMBLE FORECASTS: MONTE-CARLO VS. DYNAMIC METHODS Thomas M. Hamill 1, Jeffrey S. Whitaker 1, and Chris Snyder 2 1 NOAA-CIRES Climate Diagnostics Center, Boulder,
More informationSimple Doppler Wind Lidar adaptive observation experiments with 3D-Var. and an ensemble Kalman filter in a global primitive equations model
1 2 3 4 Simple Doppler Wind Lidar adaptive observation experiments with 3D-Var and an ensemble Kalman filter in a global primitive equations model 5 6 7 8 9 10 11 12 Junjie Liu and Eugenia Kalnay Dept.
More informationAccelerating the spin-up of Ensemble Kalman Filtering
Accelerating the spin-up of Ensemble Kalman Filtering Eugenia Kalnay * and Shu-Chih Yang University of Maryland Abstract A scheme is proposed to improve the performance of the ensemble-based Kalman Filters
More informationHandling nonlinearity in Ensemble Kalman Filter: Experiments with the three-variable Lorenz model
Handling nonlinearity in Ensemble Kalman Filter: Experiments with the three-variable Lorenz model Shu-Chih Yang 1*, Eugenia Kalnay, and Brian Hunt 1. Department of Atmospheric Sciences, National Central
More informationPractical Aspects of Ensemble-based Kalman Filters
Practical Aspects of Ensemble-based Kalman Filters Lars Nerger Alfred Wegener Institute for Polar and Marine Research Bremerhaven, Germany and Bremen Supercomputing Competence Center BremHLR Bremen, Germany
More informationA Non-Gaussian Ensemble Filter Update for Data Assimilation
4186 M O N T H L Y W E A T H E R R E V I E W VOLUME 138 A Non-Gaussian Ensemble Filter Update for Data Assimilation JEFFREY L. ANDERSON NCAR Data Assimilation Research Section,* Boulder, Colorado (Manuscript
More informationP3.11 A COMPARISON OF AN ENSEMBLE OF POSITIVE/NEGATIVE PAIRS AND A CENTERED SPHERICAL SIMPLEX ENSEMBLE
P3.11 A COMPARISON OF AN ENSEMBLE OF POSITIVE/NEGATIVE PAIRS AND A CENTERED SPHERICAL SIMPLEX ENSEMBLE 1 INTRODUCTION Xuguang Wang* The Pennsylvania State University, University Park, PA Craig H. Bishop
More informationQuarterly Journal of the Royal Meteorological Society !"#$%&'&(&)"&'*'+'*%(,#$,-$#*'."(/'*0'"(#"(1"&)23$)(4#$2#"( 5'$*)6!
!"#$%&'&(&)"&'*'+'*%(,#$,-$#*'."(/'*0'"(#"("&)$)(#$#"( '$*)! "#$%&'()!!"#$%&$'()*+"$,#')+-)%.&)/+(#')0&%&+$+'+#')+&%(! *'&$+,%-./!0)! "! :-(;%/-,(;! '/;!?$@A-//;B!@
More informationEnsemble Kalman Filter
Ensemble Kalman Filter Geir Evensen and Laurent Bertino Hydro Research Centre, Bergen, Norway, Nansen Environmental and Remote Sensing Center, Bergen, Norway The Ensemble Kalman Filter (EnKF) Represents
More informationLocalization in the ensemble Kalman Filter
Department of Meteorology Localization in the ensemble Kalman Filter Ruth Elizabeth Petrie A dissertation submitted in partial fulfilment of the requirement for the degree of MSc. Atmosphere, Ocean and
More informationConvergence of Square Root Ensemble Kalman Filters in the Large Ensemble Limit
Convergence of Square Root Ensemble Kalman Filters in the Large Ensemble Limit Evan Kwiatkowski, Jan Mandel University of Colorado Denver December 11, 2014 OUTLINE 2 Data Assimilation Bayesian Estimation
More informationFundamentals of Data Assimilation
National Center for Atmospheric Research, Boulder, CO USA GSI Data Assimilation Tutorial - June 28-30, 2010 Acknowledgments and References WRFDA Overview (WRF Tutorial Lectures, H. Huang and D. Barker)
More informationData assimilation with and without a model
Data assimilation with and without a model Tim Sauer George Mason University Parameter estimation and UQ U. Pittsburgh Mar. 5, 2017 Partially supported by NSF Most of this work is due to: Tyrus Berry,
More informationAdaptive ensemble Kalman filtering of nonlinear systems
Adaptive ensemble Kalman filtering of nonlinear systems Tyrus Berry George Mason University June 12, 213 : Problem Setup We consider a system of the form: x k+1 = f (x k ) + ω k+1 ω N (, Q) y k+1 = h(x
More informationAddressing the nonlinear problem of low order clustering in deterministic filters by using mean-preserving non-symmetric solutions of the ETKF
Addressing the nonlinear problem of low order clustering in deterministic filters by using mean-preserving non-symmetric solutions of the ETKF Javier Amezcua, Dr. Kayo Ide, Dr. Eugenia Kalnay 1 Outline
More informationGaussian Process Approximations of Stochastic Differential Equations
Gaussian Process Approximations of Stochastic Differential Equations Cédric Archambeau Dan Cawford Manfred Opper John Shawe-Taylor May, 2006 1 Introduction Some of the most complex models routinely run
More informationA Gaussian Resampling Particle Filter
A Gaussian Resampling Particle Filter By X. Xiong 1 and I. M. Navon 1 1 School of Computational Science and Department of Mathematics, Florida State University, Tallahassee, FL 3236, USA 14 July ABSTRACT
More informationDART_LAB Tutorial Section 2: How should observations impact an unobserved state variable? Multivariate assimilation.
DART_LAB Tutorial Section 2: How should observations impact an unobserved state variable? Multivariate assimilation. UCAR 2014 The National Center for Atmospheric Research is sponsored by the National
More informationBred vectors: theory and applications in operational forecasting. Eugenia Kalnay Lecture 3 Alghero, May 2008
Bred vectors: theory and applications in operational forecasting. Eugenia Kalnay Lecture 3 Alghero, May 2008 ca. 1974 Central theorem of chaos (Lorenz, 1960s): a) Unstable systems have finite predictability
More informationA Local Ensemble Kalman Filter for Atmospheric Data Assimilation
Tellus 000, 000 000 (0000) Printed 1 April 2004 (Tellus LATEX style file v2.2) A Local Ensemble Kalman Filter for Atmospheric Data Assimilation By EDWARD OTT 1, BRIAN R. HUNT 2, ISTVAN SZUNYOGH 3, ALEKSEY
More informationA HYBRID ENSEMBLE KALMAN FILTER / 3D-VARIATIONAL ANALYSIS SCHEME
A HYBRID ENSEMBLE KALMAN FILTER / 3D-VARIATIONAL ANALYSIS SCHEME Thomas M. Hamill and Chris Snyder National Center for Atmospheric Research, Boulder, Colorado 1. INTRODUCTION Given the chaotic nature of
More informationThe Ensemble Kalman Filter:
p.1 The Ensemble Kalman Filter: Theoretical formulation and practical implementation Geir Evensen Norsk Hydro Research Centre, Bergen, Norway Based on Evensen 23, Ocean Dynamics, Vol 53, No 4 p.2 The Ensemble
More informationEnsemble Data Assimilation and Uncertainty Quantification
Ensemble Data Assimilation and Uncertainty Quantification Jeff Anderson National Center for Atmospheric Research pg 1 What is Data Assimilation? Observations combined with a Model forecast + to produce
More informationA new unscented Kalman filter with higher order moment-matching
A new unscented Kalman filter with higher order moment-matching KSENIA PONOMAREVA, PARESH DATE AND ZIDONG WANG Department of Mathematical Sciences, Brunel University, Uxbridge, UB8 3PH, UK. Abstract This
More informationSmoothers: Types and Benchmarks
Smoothers: Types and Benchmarks Patrick N. Raanes Oxford University, NERSC 8th International EnKF Workshop May 27, 2013 Chris Farmer, Irene Moroz Laurent Bertino NERSC Geir Evensen Abstract Talk builds
More informationAnalysis sensitivity calculation in an Ensemble Kalman Filter
Analysis sensitivity calculation in an Ensemble Kalman Filter Junjie Liu 1, Eugenia Kalnay 2, Takemasa Miyoshi 2, and Carla Cardinali 3 1 University of California, Berkeley, CA, USA 2 University of Maryland,
More informationImplications of the Form of the Ensemble Transformation in the Ensemble Square Root Filters
1042 M O N T H L Y W E A T H E R R E V I E W VOLUME 136 Implications of the Form of the Ensemble Transformation in the Ensemble Square Root Filters PAVEL SAKOV AND PETER R. OKE CSIRO Marine and Atmospheric
More informationChapter 6: Ensemble Forecasting and Atmospheric Predictability. Introduction
Chapter 6: Ensemble Forecasting and Atmospheric Predictability Introduction Deterministic Chaos (what!?) In 1951 Charney indicated that forecast skill would break down, but he attributed it to model errors
More informationA mechanism for catastrophic filter divergence in data assimilation for sparse observation networks
Manuscript prepared for Nonlin. Processes Geophys. with version 5. of the L A TEX class copernicus.cls. Date: 5 August 23 A mechanism for catastrophic filter divergence in data assimilation for sparse
More informationRelative Merits of 4D-Var and Ensemble Kalman Filter
Relative Merits of 4D-Var and Ensemble Kalman Filter Andrew Lorenc Met Office, Exeter International summer school on Atmospheric and Oceanic Sciences (ISSAOS) "Atmospheric Data Assimilation". August 29
More informationESTIMATING CORRELATIONS FROM A COASTAL OCEAN MODEL FOR LOCALIZING AN ENSEMBLE TRANSFORM KALMAN FILTER
ESTIMATING CORRELATIONS FROM A COASTAL OCEAN MODEL FOR LOCALIZING AN ENSEMBLE TRANSFORM KALMAN FILTER Jonathan Poterjoy National Weather Center Research Experiences for Undergraduates, Norman, Oklahoma
More informationA new structure for error covariance matrices and their adaptive estimation in EnKF assimilation
Quarterly Journal of the Royal Meteorological Society Q. J. R. Meteorol. Soc. 139: 795 804, April 2013 A A new structure for error covariance matrices their adaptive estimation in EnKF assimilation Guocan
More informationA new Hierarchical Bayes approach to ensemble-variational data assimilation
A new Hierarchical Bayes approach to ensemble-variational data assimilation Michael Tsyrulnikov and Alexander Rakitko HydroMetCenter of Russia College Park, 20 Oct 2014 Michael Tsyrulnikov and Alexander
More informationHow 4DVAR can benefit from or contribute to EnKF (a 4DVAR perspective)
How 4DVAR can benefit from or contribute to EnKF (a 4DVAR perspective) Dale Barker WWRP/THORPEX Workshop on 4D-Var and Ensemble Kalman Filter Intercomparisons Sociedad Cientifica Argentina, Buenos Aires,
More informationData assimilation with Lorenz 3-variable model. Prepared by Shu-Chih Yang Modified by Juan Ruiz.
Data assimilation with Lorenz 3-variable model Prepared by Shu-Chih Yang Modified by Juan Ruiz. Governing equations dx dt dy dt dz dt = σ(y x) = rx y xz = xy bz Lorenz, E. N, 1963: Deterministic nonperiodic
More informationGaussian Filtering Strategies for Nonlinear Systems
Gaussian Filtering Strategies for Nonlinear Systems Canonical Nonlinear Filtering Problem ~u m+1 = ~ f (~u m )+~ m+1 ~v m+1 = ~g(~u m+1 )+~ o m+1 I ~ f and ~g are nonlinear & deterministic I Noise/Errors
More informationInitiation of ensemble data assimilation
Initiation of ensemble data assimilation By M. ZUPANSKI 1*, S. J. FLETCHER 1, I. M. NAVON 2, B. UZUNOGLU 3, R. P. HEIKES 4, D. A. RANDALL 4, T. D. RINGLER 4 and D. DAESCU 5, 1 Cooperative Institute for
More informationCarbon flux bias estimation employing Maximum Likelihood Ensemble Filter (MLEF)
Click Here for Full Article JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 112,, doi:10.1029/2006jd008371, 2007 Carbon flux bias estimation employing Maximum Likelihood Ensemble Filter (MLEF) Dusanka Zupanski,
More informationNonlinear Estimation Techniques for Impact Point Prediction of Ballistic Targets
Nonlinear Estimation Techniques for Impact Point Prediction of Ballistic Targets J. Clayton Kerce a, George C. Brown a, and David F. Hardiman b a Georgia Tech Research Institute, Georgia Institute of Technology,
More informationQuarterly Journal of the Royal Meteorological Society
Quarterly Journal of the Royal Meteorological Society Effects of sequential or simultaneous assimilation of observations and localization methods on the performance of the ensemble Kalman filter Journal:
More informationLagrangian Data Assimilation and Manifold Detection for a Point-Vortex Model. David Darmon, AMSC Kayo Ide, AOSC, IPST, CSCAMM, ESSIC
Lagrangian Data Assimilation and Manifold Detection for a Point-Vortex Model David Darmon, AMSC Kayo Ide, AOSC, IPST, CSCAMM, ESSIC Background Data Assimilation Iterative process Forecast Analysis Background
More informationThe Ensemble Kalman Filter:
p.1 The Ensemble Kalman Filter: Theoretical formulation and practical implementation Geir Evensen Norsk Hydro Research Centre, Bergen, Norway Based on Evensen, Ocean Dynamics, Vol 5, No p. The Ensemble
More information(Extended) Kalman Filter
(Extended) Kalman Filter Brian Hunt 7 June 2013 Goals of Data Assimilation (DA) Estimate the state of a system based on both current and all past observations of the system, using a model for the system
More informationBayesian Inverse problem, Data assimilation and Localization
Bayesian Inverse problem, Data assimilation and Localization Xin T Tong National University of Singapore ICIP, Singapore 2018 X.Tong Localization 1 / 37 Content What is Bayesian inverse problem? What is
More informationEnKF-based particle filters
EnKF-based particle filters Jana de Wiljes, Sebastian Reich, Wilhelm Stannat, Walter Acevedo June 20, 2017 Filtering Problem Signal dx t = f (X t )dt + 2CdW t Observations dy t = h(x t )dt + R 1/2 dv t.
More informationData assimilation with and without a model
Data assimilation with and without a model Tyrus Berry George Mason University NJIT Feb. 28, 2017 Postdoc supported by NSF This work is in collaboration with: Tim Sauer, GMU Franz Hamilton, Postdoc, NCSU
More informationA Moment Matching Ensemble Filter for Nonlinear Non-Gaussian Data Assimilation
3964 M O N T H L Y W E A T H E R R E V I E W VOLUME 139 A Moment Matching Ensemble Filter for Nonlinear Non-Gaussian Data Assimilation JING LEI AND PETER BICKEL Department of Statistics, University of
More informationLagrangian Data Assimilation and Its Application to Geophysical Fluid Flows
Lagrangian Data Assimilation and Its Application to Geophysical Fluid Flows Laura Slivinski June, 3 Laura Slivinski (Brown University) Lagrangian Data Assimilation June, 3 / 3 Data Assimilation Setup:
More informationMonthly Weather Review The Hybrid Local Ensemble Transform Kalman Filter
Monthly Weather Review The Hybrid Local Ensemble Transform Kalman Filter --Manuscript Draft-- Manuscript Number: Full Title: Article Type: Corresponding Author: Corresponding Author's Institution: First
More informationR. E. Petrie and R. N. Bannister. Department of Meteorology, Earley Gate, University of Reading, Reading, RG6 6BB, United Kingdom
A method for merging flow-dependent forecast error statistics from an ensemble with static statistics for use in high resolution variational data assimilation R. E. Petrie and R. N. Bannister Department
More informationA Moment Matching Particle Filter for Nonlinear Non-Gaussian. Data Assimilation. and Peter Bickel
Generated using version 3.0 of the official AMS L A TEX template A Moment Matching Particle Filter for Nonlinear Non-Gaussian Data Assimilation Jing Lei and Peter Bickel Department of Statistics, University
More informationRevision of TR-09-25: A Hybrid Variational/Ensemble Filter Approach to Data Assimilation
Revision of TR-9-25: A Hybrid Variational/Ensemble ilter Approach to Data Assimilation Adrian Sandu 1 and Haiyan Cheng 1 Computational Science Laboratory Department of Computer Science Virginia Polytechnic
More informationAnalysis Scheme in the Ensemble Kalman Filter
JUNE 1998 BURGERS ET AL. 1719 Analysis Scheme in the Ensemble Kalman Filter GERRIT BURGERS Royal Netherlands Meteorological Institute, De Bilt, the Netherlands PETER JAN VAN LEEUWEN Institute or Marine
More informationRAO-BLACKWELLISED PARTICLE FILTERS: EXAMPLES OF APPLICATIONS
RAO-BLACKWELLISED PARTICLE FILTERS: EXAMPLES OF APPLICATIONS Frédéric Mustière e-mail: mustiere@site.uottawa.ca Miodrag Bolić e-mail: mbolic@site.uottawa.ca Martin Bouchard e-mail: bouchard@site.uottawa.ca
More informationAn Iterative EnKF for Strongly Nonlinear Systems
1988 M O N T H L Y W E A T H E R R E V I E W VOLUME 140 An Iterative EnKF for Strongly Nonlinear Systems PAVEL SAKOV Nansen Environmental and Remote Sensing Center, Bergen, Norway DEAN S. OLIVER Uni Centre
More informationA Comparison of Error Subspace Kalman Filters
Tellus 000, 000 000 (0000) Printed 4 February 2005 (Tellus LATEX style file v2.2) A Comparison of Error Subspace Kalman Filters By LARS NERGER, WOLFGANG HILLER and JENS SCHRÖTER Alfred Wegener Institute
More informationData Assimilation: Finding the Initial Conditions in Large Dynamical Systems. Eric Kostelich Data Mining Seminar, Feb. 6, 2006
Data Assimilation: Finding the Initial Conditions in Large Dynamical Systems Eric Kostelich Data Mining Seminar, Feb. 6, 2006 kostelich@asu.edu Co-Workers Istvan Szunyogh, Gyorgyi Gyarmati, Ed Ott, Brian
More informationTracking an Accelerated Target with a Nonlinear Constant Heading Model
Tracking an Accelerated Target with a Nonlinear Constant Heading Model Rong Yang, Gee Wah Ng DSO National Laboratories 20 Science Park Drive Singapore 118230 yrong@dsoorgsg ngeewah@dsoorgsg Abstract This
More informationState Estimation for Nonlinear Systems using Restricted Genetic Optimization
State Estimation for Nonlinear Systems using Restricted Genetic Optimization Santiago Garrido, Luis Moreno, and Carlos Balaguer Universidad Carlos III de Madrid, Leganés 28911, Madrid (Spain) Abstract.
More informationDATA ASSIMILATION FOR FLOOD FORECASTING
DATA ASSIMILATION FOR FLOOD FORECASTING Arnold Heemin Delft University of Technology 09/16/14 1 Data assimilation is the incorporation of measurement into a numerical model to improve the model results
More informationCOMPARISON OF THE INFLUENCES OF INITIAL ERRORS AND MODEL PARAMETER ERRORS ON PREDICTABILITY OF NUMERICAL FORECAST
CHINESE JOURNAL OF GEOPHYSICS Vol.51, No.3, 2008, pp: 718 724 COMPARISON OF THE INFLUENCES OF INITIAL ERRORS AND MODEL PARAMETER ERRORS ON PREDICTABILITY OF NUMERICAL FORECAST DING Rui-Qiang, LI Jian-Ping
More informationA Comparison of the EKF, SPKF, and the Bayes Filter for Landmark-Based Localization
A Comparison of the EKF, SPKF, and the Bayes Filter for Landmark-Based Localization and Timothy D. Barfoot CRV 2 Outline Background Objective Experimental Setup Results Discussion Conclusion 2 Outline
More informationA Novel Maneuvering Target Tracking Algorithm for Radar/Infrared Sensors
Chinese Journal of Electronics Vol.19 No.4 Oct. 21 A Novel Maneuvering Target Tracking Algorithm for Radar/Infrared Sensors YIN Jihao 1 CUIBingzhe 2 and WANG Yifei 1 (1.School of Astronautics Beihang University
More informationWeight interpolation for efficient data assimilation with the Local Ensemble Transform Kalman Filter
Weight interpolation for efficient data assimilation with the Local Ensemble Transform Kalman Filter Shu-Chih Yang 1,2, Eugenia Kalnay 1,3, Brian Hunt 1,3 and Neill E. Bowler 4 1 Department of Atmospheric
More informationAdvancing Data AssimilaJon Science for OperaJonal Hydrology: Methodology, ComputaJon, and Algorithms
2014 CAHMDA & HEPEX- DAFOH Workshop September 8-12, 2014 The University of Texas at AusJn Advancing Data AssimilaJon Science for OperaJonal Hydrology: Methodology, ComputaJon, and Algorithms Milija Zupanski
More informationENGR352 Problem Set 02
engr352/engr352p02 September 13, 2018) ENGR352 Problem Set 02 Transfer function of an estimator 1. Using Eq. (1.1.4-27) from the text, find the correct value of r ss (the result given in the text is incorrect).
More informationA Local Ensemble Kalman Filter for Atmospheric Data Assimilation
arxiv:physics/0203058v4 [physics.ao-ph] 30 Jul 2003 A Local Ensemble Kalman Filter for Atmospheric Data Assimilation Edward Ott, 1 Brian R. Hunt, Istvan Szunyogh, Aleksey V. Zimin, Eric J. Kostelich, Matteo
More informationFisher Information Matrix-based Nonlinear System Conversion for State Estimation
Fisher Information Matrix-based Nonlinear System Conversion for State Estimation Ming Lei Christophe Baehr and Pierre Del Moral Abstract In practical target tracing a number of improved measurement conversion
More informationStochastic Collocation Methods for Polynomial Chaos: Analysis and Applications
Stochastic Collocation Methods for Polynomial Chaos: Analysis and Applications Dongbin Xiu Department of Mathematics, Purdue University Support: AFOSR FA955-8-1-353 (Computational Math) SF CAREER DMS-64535
More informationAccepted in Tellus A 2 October, *Correspondence
1 An Adaptive Covariance Inflation Error Correction Algorithm for Ensemble Filters Jeffrey L. Anderson * NCAR Data Assimilation Research Section P.O. Box 3000 Boulder, CO 80307-3000 USA Accepted in Tellus
More informationMultivariate localization methods for ensemble Kalman filtering
doi:10.5194/npg-22-723-2015 Author(s) 2015. CC Attribution 3.0 License. Multivariate localization methods for ensemble Kalman filtering S. Roh 1, M. Jun 1, I. Szunyogh 2, and M. G. Genton 3 1 Department
More informationTracking and Identification of Multiple targets
Tracking and Identification of Multiple targets Samir Hachour, François Delmotte, Eric Lefèvre, David Mercier Laboratoire de Génie Informatique et d'automatique de l'artois, EA 3926 LGI2A first name.last
More informationObservability, a Problem in Data Assimilation
Observability, Data Assimilation with the Extended Kalman Filter 1 Observability, a Problem in Data Assimilation Chris Danforth Department of Applied Mathematics and Scientific Computation, UMD March 10,
More informationData assimilation in the MIKE 11 Flood Forecasting system using Kalman filtering
Water Resources Systems Hydrological Risk, Management and Development (Proceedings of symposium IlS02b held during IUGG2003 al Sapporo. July 2003). IAHS Publ. no. 281. 2003. 75 Data assimilation in the
More informationEnsemble forecasting and flow-dependent estimates of initial uncertainty. Martin Leutbecher
Ensemble forecasting and flow-dependent estimates of initial uncertainty Martin Leutbecher acknowledgements: Roberto Buizza, Lars Isaksen Flow-dependent aspects of data assimilation, ECMWF 11 13 June 2007
More informationAsynchronous data assimilation
Ensemble Kalman Filter, lecture 2 Asynchronous data assimilation Pavel Sakov Nansen Environmental and Remote Sensing Center, Norway This talk has been prepared in the course of evita-enkf project funded
More informationAssimilating Nonlocal Observations using a Local Ensemble Kalman Filter
Tellus 000, 000 000 (0000) Printed 16 February 2007 (Tellus LATEX style file v2.2) Assimilating Nonlocal Observations using a Local Ensemble Kalman Filter Elana J. Fertig 1, Brian R. Hunt 1, Edward Ott
More informationWeight interpolation for efficient data assimilation with the Local Ensemble Transform Kalman Filter
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY Published online 17 December 2008 in Wiley InterScience (www.interscience.wiley.com).353 Weight interpolation for efficient data assimilation with
More informationApproximating Optimal State Estimation
Approximating Optimal State Estimation Brian F. Farrell and Petros J. Ioannou Harvard University Cambridge MA 238, U.S.A farrell@deas.harvard.edu, pji@cc.uoa.gr ABSTRACT Minimizing forecast error requires
More information