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71
Ensemble Kalman Filter Assimilation of Doppler Radar Data with a Compressible Nonhydrostatic Model: OSS Experiments
, 2004
"... A Doppler radar data assimilation system is developed based on ensemble Kalman filter (EnKF) method and tested with simulated radar data from a supercell storm. As a first implementation, we assume the forward models are perfect and radar data are sampled at the analysis grid points. A general pur ..."
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Cited by 127 (78 self)
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A Doppler radar data assimilation system is developed based on ensemble Kalman filter (EnKF) method and tested with simulated radar data from a supercell storm. As a first implementation, we assume the forward models are perfect and radar data are sampled at the analysis grid points. A general purpose nonhydrostatic compressible model is used with the inclusion of complex multiclass ice microphysics. New aspects compared to previous studies include the demonstration of the ability of EnKF method in retrieving multiple microphysical species associated with a multiclass ice microphysics scheme, and in accurately retrieving the wind and thermodynamic variables. Also new are the inclusion of reflectivity observations and the determination of the relative role of radial velocity and reflectivity data as well as their spatial coverage in recovering the full flow and cloud fields. In general, the system is able to reestablish the model storm extremely well after a number of assimilation cycles, and best results are obtained when both radial velocity and reflectivity data, including reflectivity information outside precipitation regions, are used. Significant positive impact of the reflectivity assimilation
Model Error Estimation Employing an Ensemble Data Assimilation Approach
, 2006
"... A methodology for model error estimation is proposed and examined in this study. It provides estimates of the dynamical model state, the bias, and the empirical parameters by combining three approaches: 1) ensemble data assimilation, 2) state augmentation, and 3) parameter and model bias estimation. ..."
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Cited by 40 (16 self)
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A methodology for model error estimation is proposed and examined in this study. It provides estimates of the dynamical model state, the bias, and the empirical parameters by combining three approaches: 1) ensemble data assimilation, 2) state augmentation, and 3) parameter and model bias estimation. Uncertainties of these estimates are also determined, in terms of the analysis and forecast error covariances, employing the same methodology. The model error estimation approach is evaluated in application to Korteweg–de Vries–Burgers (KdVB) numerical model within the framework of maximum likelihood ensemble filter (MLEF). Experimental results indicate improved filter performance due to model error estimation. The innovation statistics also indicate that the estimated uncertainties are reliable. On the other hand, neglecting model errors—either in the form of an incorrect model parameter, or a model bias—has detrimental effects on data assimilation, in some cases resulting in filter divergence. Although the method is examined in a simplified model framework, the results are encouraging. It
2004: Meteorological Research Needs for Improved Air Quality Forecasting
 Report of the 11th Prospectus Development Team of the U.S. Weather Research
"... *This is an abridged version of the final report of PDT11. The complete version can be found at ..."
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Cited by 27 (2 self)
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*This is an abridged version of the final report of PDT11. The complete version can be found at
LaxHopf based incorporation of internal boundary conditions into HamiltonJacobi equation. Part I: Theory
 IEEE TRANS. AUTOM. CONTROL
, 2010
"... This article proposes a new approach for computing a semiexplicit form of the solution to a class of Hamilton–Jacobi (HJ) partial differential equations (PDEs), using control techniques based on viability theory. We characterize the epigraph of the value function solving the HJ PDE as a capture bas ..."
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Cited by 26 (4 self)
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This article proposes a new approach for computing a semiexplicit form of the solution to a class of Hamilton–Jacobi (HJ) partial differential equations (PDEs), using control techniques based on viability theory. We characterize the epigraph of the value function solving the HJ PDE as a capture basin of a target through an auxiliary dynamical system, called “characteristic system”. The properties of capture basins enable us to define components as building blocks of the solution to the HJ PDE in the Barron/JensenFrankowska sense. These components can encode initial conditions, boundary conditions, and internal “boundary” conditions, which are the topic of this article. A generalized LaxHopf formula is derived, and enables us to formulate the necessary and sufficient conditions for a mixed initial and boundary conditions problem with multiple internal boundary conditions to be well posed. We illustrate the capabilities of the method with a data assimilation problem for reconstruction of highway traffic flow using Lagrangian measurements generated from Next Generation Simulation (NGSIM) traffic data.
Data Assimilation for a Coupled Ocean–Atmosphere Model. Part II: Parameter Estimation
 MONTHLY WEATHER REVIEW
, 2008
"... The parameter estimation problem for the coupled ocean–atmosphere system in the tropical Pacific Ocean is investigated using an advanced sequential estimator [i.e., the extended Kalman filter (EKF)]. The intermediate coupled model (ICM) used in this paper consists of a prognostic upperocean model a ..."
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Cited by 22 (7 self)
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The parameter estimation problem for the coupled ocean–atmosphere system in the tropical Pacific Ocean is investigated using an advanced sequential estimator [i.e., the extended Kalman filter (EKF)]. The intermediate coupled model (ICM) used in this paper consists of a prognostic upperocean model and a diagnostic atmospheric model. Model errors arise from the uncertainty in atmospheric wind stress. First, the state and parameters are estimated in an identicaltwin framework, based on incomplete and inaccurate observations of the model state. Two parameters are estimated by including them into an augmented state vector. Modelgenerated oceanic datasets are assimilated to produce a timecontinuous, dynamically consistent description of the model’s El Niño–Southern Oscillation (ENSO). State estimation without correcting erroneous parameter values still permits recovering the true state to a certain extent, depending on the quality and accuracy of the observations and the size of the discrepancy in the parameters. Estimating both state and parameter values simultaneously, though, produces much better results. Next, real sea surface temperatures observations from the tropical Pacific are assimilated for a 30yr period (1975–2004). Estimating both the state and parameters by the EKF method helps to track the observations better, even when the ICM is not capable of simulating all the details of the observed state. Furthermore, unobserved ocean
Assimilation of Standard and Targeted Observations within the Unstable Subspace of the ObservationAnalysisForecast Cycle System
 J. Atmos. Sci
"... In this paper it is shown that the flowdependent instabilities that develop within an observation–analysis– forecast (OAF) cycle and that are responsible for the background error can be exploited in a very simple way to assimilate observations. The basic idea is that, in order to minimize the analy ..."
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Cited by 16 (9 self)
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In this paper it is shown that the flowdependent instabilities that develop within an observation–analysis– forecast (OAF) cycle and that are responsible for the background error can be exploited in a very simple way to assimilate observations. The basic idea is that, in order to minimize the analysis and forecast errors, the analysis increment must be confined to the unstable subspace of the OAF cycle solution. The analysis solution here formally coincides with that of the classical threedimensional variational solution with the background error covariance matrix estimated in the unstable subspace. The unstable directions of the OAF system solution are obtained by breeding initially random perturbations of the analysis but letting the perturbed trajectories undergo the same process as the control solution, including assimilation of all the available observations. The unstable vectors are then used both to target observations and for the assimilation design. The approach is demonstrated in an idealized environment using a simple model, simulated standard observations over land with a single adaptive observation over the ocean. In the application a simplified form is adopted of the analysis solution and a single unstable vector at each analysis time whose amplitude is determined by means of the adaptive observation. The remarkable reduction of the analysis and forecast error obtained by
An Ensemble Kalman Filter with a 1D Marine Ecosystem Model
 J. MARINE SYST
, 1999
"... The Ensemble Kalman Filter (EnKF) has been examined in a data assimilation experiment with a onedimensional four component ecosystem model. The model is an extension of the zerodimensional model developed by Evans and Parslow (1985). The purpose of this paper is to examine the possibilities of ..."
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Cited by 16 (4 self)
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The Ensemble Kalman Filter (EnKF) has been examined in a data assimilation experiment with a onedimensional four component ecosystem model. The model is an extension of the zerodimensional model developed by Evans and Parslow (1985). The purpose of this paper is to examine the possibilities of using data assimilation methods for state estimation in biological models which differs from the more traditional parameter estimation studies. It has been shown that the EnKF captures the nonlinear error evolution in time very well and is capable both of tracking the reference solution and to provide realistic error estimates for the estimated state. This is an indication that the methodology might be suitable for future operational data assimilation systems using more complex threedimensional models.
Impact of Parameter Estimation on the Performance of the FSU Global Spectral Model Using Its FullPhysics Adjoint
 Mon. Wea. Rev
, 1999
"... The fullphysics adjoint of the Florida State University Global Spectral Model at resolution T42L12 is applied to carry out parameter estimation using an initialized analysis dataset. The three parameters, that is, the biharmonic horizontal diffusion coefficient, the ratio of the transfer coeffici ..."
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Cited by 15 (4 self)
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The fullphysics adjoint of the Florida State University Global Spectral Model at resolution T42L12 is applied to carry out parameter estimation using an initialized analysis dataset. The three parameters, that is, the biharmonic horizontal diffusion coefficient, the ratio of the transfer coefficient of moisture to the transfer coefficient of sensible heat, and the Asselin filter coefficient, as well as the initial condition, are optimally recovered from the dataset using adjoint parameter estimation.
EnsembleBased Simultaneous State and Parameter Estimation in a TwoDimensional SeaBreeze Model
, 2005
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An adjoint sensitivity method for the adaptive location of the observations in air quality modeling
 J. Atmos. Sci
, 2003
"... The spatiotemporal distribution of the observations plays an essential role in the data assimilation process. An adjoint sensitivity method is applied to the problem of adaptive location of the observational system for a nonlinear transportchemistry model in the context of 4D variational data assi ..."
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Cited by 14 (2 self)
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The spatiotemporal distribution of the observations plays an essential role in the data assimilation process. An adjoint sensitivity method is applied to the problem of adaptive location of the observational system for a nonlinear transportchemistry model in the context of 4D variational data assimilation. The method is presented in a general framework and it is shown that in addition to the initial state of the model, sensitivity with respect to emission and deposition rates and certain types of boundary values may be obtained at a minimal additional cost. The adjoint modeling is used to evaluate the influence function and to identify the domain of influence associated with the observations. These essential tools are further used to develop a novel algorithm for targeting observations that takes into account the interaction among observations at different instants in time and spatial locations. Issues related with the case of multiple observations are addressed and it is shown that using the adjoint modeling an efficient implementation may be achieved. Computational and practical aspects are discussed and our analysis indicate that it is feasible to implement the proposed method for comprehensive air quality models. Numerical experiments performed with a two dimensional test model show promising results. 1 1