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17
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
Practical and Theoretical Aspects of Adjoint Parameter Estimation and Identifiability in . . .
, 1997
"... The present paper has two aims. One is to survey briefly the state of the art of parameter estimation in meteorology and oceanography in view of applications of 4D variational data assimilation techniques to inverse parameter estimation problems, which bear promise of serious positive impact on imp ..."
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Cited by 73 (6 self)
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The present paper has two aims. One is to survey briefly the state of the art of parameter estimation in meteorology and oceanography in view of applications of 4D variational data assimilation techniques to inverse parameter estimation problems, which bear promise of serious positive impact on improving model prediction. The other aim is to present crucial aspects of identifiability and stability essential for validating results of optimal parameter estimation and which have not been addressed so far in either the meteorological or the oceanographic literature. As noted by Yeh (1986, Water Resour. Res. 22, 95108) in the context of ground water flow parameter estimation the inverse or parameter estimation problem is often illposed and beset by instability and nonuniqueness, particularly if one seeks parameters distributed in spacetime domain. This approach will allow one to assess and rigorously validate results of parameter estimation, i.e. do they indeed represent a real identification of physical model parameters or just compensate model errors? A brief survey of other approaches for solving the problem of optimal parameter estimation in meteorology and oceanography is finally presented. 1997 Elsevier Science B.V.
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.
Parameter Estimation In Dynamical Models
, 1998
"... This paper will give a general introduction to the parameter estimation problem for dynamical models. The basic formulation and methodology in a parameter estimation problem will be discussed and some rather simple examples will be presented. It will be shown that even for linear dynamics the parame ..."
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Cited by 12 (1 self)
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This paper will give a general introduction to the parameter estimation problem for dynamical models. The basic formulation and methodology in a parameter estimation problem will be discussed and some rather simple examples will be presented. It will be shown that even for linear dynamics the parameter estimation problem becomes nonlinear and may become extremely difficult to solve. Also, to have a well posed problem with a unique solution, care must be taken when a parameter estimation problem is formulated. The discussion leads into a conclusion that it is possible to estimate poorly known parameters in a model, at least for simple dynamical models, but care must be taken to have a consistent solution. The rule is that all paramenters which will be estimated should be added in a penalty function as weak constraints measuring their distance from a first guess in some norm. Some previous works where data assimilation methods have been used to improve estimates of poorly known model p...
Data Assimilation for Numerical Weather Prediction: A Review
"... Abstract During the last 20 years data assimilation has gradually reached a mature center stage position at both Numerical Weather Prediction centers as well as being at the center of activities at many federal research institutes as well as at many universities. The research encompasses now activit ..."
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Cited by 11 (1 self)
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Abstract During the last 20 years data assimilation has gradually reached a mature center stage position at both Numerical Weather Prediction centers as well as being at the center of activities at many federal research institutes as well as at many universities. The research encompasses now activities which involve, beside meteorologists and oceanographers at operational centers or federal research facilities, many in the applied and computational mathematical research communities. Data assimilation or 4D VAR extends now also to other geosciences fields such as hydrology and geology and results in the publication of an ever increasing number of books and monographs related to the topic. In this short survey article we provide a brief introduction providing some historical perspective and background, a survey of data assimilation prior to 4D VAR and basic concepts of data assimilation. I first proceed to outline the early 4D VAR stages (1980–1990) and addresses in a succinct manner the period of the 1990s that saw the major developments and the flourishing of all aspects of 4D VAR both at operational centers and at research Universities and Federal Laboratories. Computational aspects of 4D Var data assimilation addressing computational burdens as well as ways to alleviate them are briefly outlined. Brief interludes are provided for each period surveyed allowing the reader to have a better perspective A brief survey of different topics related to state of the art 4D Var today is then presented and we conclude with what we perceive to be main directions of research and the future of data assimilation and some open problems. We will strive to use the unified notation of Ide et al. (J Meteor Soc Japan 75:181–189,
A Note on Consistency and Adjointness for Numerical Schemes
, 1995
"... This work deals with the consistency of finite difference approximations. We investigate the relation between the consistency of a numerical scheme and the consistency of its adjoint. We exhibit examples of numerical schemes which are consistent with a (direct) equation and whose adjoint is not cons ..."
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Cited by 7 (0 self)
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This work deals with the consistency of finite difference approximations. We investigate the relation between the consistency of a numerical scheme and the consistency of its adjoint. We exhibit examples of numerical schemes which are consistent with a (direct) equation and whose adjoint is not consistent with the adjoint equation. This undesirable feature appears in the application of the adjoint state technique which requires an adjointness relation to be satisfied. Therefore, the numerical scheme for the adjoint equation is determined by the choice of the numerical scheme on the direct equation. We conclude that in general consistency is not conserved by adjointness. Key Words. Finite Differences, Adjoint Schemes, Consistency AMS(MOS) subject classifications. 65M06, 65M12 1 Introduction The equivalence theorem (cf [1, 2]) is the fundamental tool to derive convergent finite difference approximations of linear partial differential equations. It states that if a scheme is consistent...
Linearized inversion: a significant step beyond prestack migration, Geophys
 J. Int
, 1989
"... One of the advantages of seismic inversion methods in petroleum exploration is the potential quantitative evaluation of the distributed parameters (propagation velocity, acoustic impedance) which characterize subsurface formations. Such methods are particularly attractive for detecting stratigraphi ..."
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Cited by 2 (0 self)
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One of the advantages of seismic inversion methods in petroleum exploration is the potential quantitative evaluation of the distributed parameters (propagation velocity, acoustic impedance) which characterize subsurface formations. Such methods are particularly attractive for detecting stratigraphic traps, which can be recognized by a lateral variation in these parameters. Consequently, they can yield a substantial improvement over conventional prestack migrations which only provide images of heterogeneities. Among inversion methods, the linearized inversion is arousing great interest because of simplifications it brings to computing. The disadvantage of this approach stems from the difficulty in finding a socalled reference medium, sufficiently close to the actual unknown medium as to justify the linearization. The first part of this work aims at providing a better understanding of the 2D linearized forward problem and attempts to answer the following question: how close must the reference medium be to the exact medium for the linearization to be justified? The second part of this work examines the 2D linearized inverse problem and analyses how errors resulting from the linearization can influence the solution of the problem. Numerical experiments show the effectiveness of the linearized inversion. More specifically it allows a quantitative identification of the heterogeneities, as well as a nonlinear inversion does, when the reference medium accurately approximates the velocity of the actual medium. With a cruder reference medium the quantitative identification of the heterogeneities becomes usually less accurate, but at least the linearized inversion yields a substantially better image than the prestack migration. Key words: reflection seismology, seismic interpretation, seismic inversion, seismic migration, seismic modelling, wave equation 1
LIST OF TABLES............................................ x
, 1994
"... LIST OF FIGURES.......................................... vii ..."
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Sensitivity analysis and parameter estimation for distributed
, 2009
"... hydrological modeling: potential of variational methods ..."
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