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79
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 130 (79 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 (15 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 30 (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.
Adjoint inverse modeling of black carbon during the Asian Pacific Regional Aerosol Characterization Experiment.
 Journal of Geophysical Research – Atmospheres,
, 2005
"... [1] An adjoint model is used for inverse modeling of black carbon during the Asian Pacific Regional Aerosol Characterization Experiment (ACEAsia). We use the fourdimensional variational data assimilation (4DVar) approach to optimally recover spatially resolved anthropogenic and biomassburning em ..."
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Cited by 26 (13 self)
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[1] An adjoint model is used for inverse modeling of black carbon during the Asian Pacific Regional Aerosol Characterization Experiment (ACEAsia). We use the fourdimensional variational data assimilation (4DVar) approach to optimally recover spatially resolved anthropogenic and biomassburning emissions and initial and boundary conditions of black carbon. Boundary conditions and biomassburning emissions are assigned daily scaling factors. Anthropogenic emissions are scaled by a combination of daily and monthly scaling factors. Simulation results are compared to various observations of black carbon concentrations during the campaign. Measurements at five islands and on board the research vessel Ronald H. Brown are used for inverse modeling. Different levels of constraints are examined for inversion, and a case with 62% reduction in the total square errors is chosen. The assimilated results are compared with the observations on board the Twin Otter aircraft that were not used for assimilation. Among the scaled variables, anthropogenic emissions are the most significant, followed by the boundary conditions. The domainwide emissions inventory does not change significantly as a result of the assimilation, but sizable changes occur on the subregional level. Most noticeably, anthropogenic emissions over southeastern China are reduced while those in northeast China and Japan are increased.
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 21 (6 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
EnsembleBased Simultaneous State and Parameter Estimation in a TwoDimensional SeaBreeze Model
, 2005
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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 17 (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.
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 15 (8 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
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 (3 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.