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## Publications

Publications (62)

The forecast sensitivity to observations (FSO) is embedded into a new optimization framework for improving the observation performance in atmospheric data assimilation. Key ingredients are introduced as follows: the innovation-weight parametrization of the analysis equation, the FSO-based evaluation of the forecast error gradient to parameters, a l...

This article presents a novel approach to couple a deterministic four‐dimensional variational
(4DVAR) assimilation method with the particle filter (PF) ensemble data assimilation system, to produce
a robust approach for dual‐state‐parameter estimation. In our proposed method, the Hybrid Ensemble and
Variational Data Assimilation framework for Envir...

Novel applications of the adjoint-based
sensitivity tools are investigated to obtain a priori
guidance on the forecast impact of modeling correlated observational errors in a four-dimensional variational data assimilation
system (4D-Var DAS)
. A synergistic framework is considered that combines a posteriori estimates to the observation error covari...

This article presents the mathematical framework to evaluate the sensitivity of a forecast error aspect to the input parameters of a weak-constraint four-dimensional variational data assimilation system (w4D-Var DAS), extending the established theory from strong-constraint 4D-Var. Emphasis is placed on the derivation of the equations for evaluating...

This article presents a framework for performing ensemble and hybrid data assimilation in a weak-constraint four-dimensional variational data assimilation system (w4D-Var). A practical approach is considered that relies on an ensemble of w4D-Var systems solved by the incremental algorithm to obtain flow-dependent estimates to the model error statis...

An innovation-weight parametrization is introduced as a practical approach to account for deficiencies in the representation of both background error and observation error covariance in a variational data assimilation system. The adjoint-based evaluation of the forecast error sensitivity provides a computationally efficient diagnosis to observation...

Abstract This chapter presents the mathematical framework to evaluate the sensitivity of a model forecast aspect to the input parameters of a nonlinear four-dimensional variational data assimilation system (4D-Var DAS): observations, prior state (background) estimate, and the error covariance specification. A fundamental relationship is established...

Adjoint techniques are effective tools for the analysis and optimization of the observation performance on reducing the errors in the forecasts produced by atmospheric data assimilation systems (DASs). This chapter provides a detailed exposure of the equations that allow the extension of the adjoint-DAS applications from observation sensitivity and...

This article presents the adjoint-data assimilation system (adjoint-DAS) approach to evaluate the forecast sensitivity with respect to the specification of the observation-error covariance (R-sensitivity) and background-error covariance (B-sensitivity) in a four-dimensional variational (4D-Var) DAS with a single outer-loop iteration. Computationall...

This chapter presents the mathematical framework to evaluate the sensitivity of a model fore-cast aspect to the input parameters of a nonlinear four-dimensional variational data assimilation system (4D-Var DAS): observations, prior state (background) estimate, and the error covariance specification. A fundamental relationship is established between...

An observation sensitivity (OS) method to identify targeted observations is implemented in the context
of four-dimensional variational (4D-Var) data assimilation. This methodology is compared with the wellestablished
adjoint sensitivity (AS) method using a nonlinear Burgers equation as a test model. Automatic
differentiation software is used to imp...

An observation sensitivity (OS) method to identify targeted observations is implemented in the context
of four-dimensional variational (4D-Var) data assimilation. This methodology is compared with the wellestablished
adjoint sensitivity (AS) method using a nonlinear Burgers equation as a test model. Automatic
differentiation software is used to imp...

The task of providing an optimal analysis of the state of the atmosphere requires the development of dynamic data-driven systems (DDDAS) that efficiently integrate the observational data and the models. Data assimilation, the dynamic incorporation of additional data into an executing application, is an essential DDDAS concept with wide applicabilit...

The efficiency of current adjoint-based observations targeting strategies in variational data assimilation is closely determined
by the underlying assumption of a linear propagation of initial condition errors into the model forecasts. A novel targeting
strategy is proposed in the context of four-dimensional variational data assimilation (4D-Var) t...

The development of the adjoint of the forecast model and of the adjoint of the data assimilation system (adjoint-DAS) makes feasible the evaluation of the local sensitivity of a model forecast aspect with respect to a large number of parameters in the DAS. In this study it is shown that, by exploiting sensitivity properties that are intrinsic to th...

The development of the adjoint of the forecast model and of the adjoint of the data assimila- tion system (adjoint-DAS) make feasible the evaluation of the derivative-based forecast sensitiv- ity to DAS input parameters in numerical weather prediction (NWP). The adjoint estimation of the forecast sensitivity to the observation error covariance in t...

An optimal use of the atmospheric data in numerical weather prediction requires an objective assessment of the value added by observations to improve the analyses and forecasts of a specific data assimilation system (DAS). This research brings forward the issue of uncertainties in the assessment of observation values based on deterministic observat...

We describe the global behavior of the dynamics of a particle bouncing down an inclined staircase. For small inclinations all orbits eventually stop (independent of the initial condition). For large enough inclinations all orbits end up accelerating indefinitely (also independent of the initial conditions). There is an interval of inclinations of p...

The role of the second order adjoint in targeting strategies is studied and analyzed. Most targeting strategies use the first
order adjoint to identify regions where additional information is of potential benefit to a data assimilation system. The
first order adjoint posses a restriction on the targeting time for which the linear approximation accu...

A parametric approach to the adjoint estimation of the variation in model functional output due to the assimilation of data is considered as a tool to analyze and develop observation impact measures. The parametric approach is specialized to a linear analysis scheme and it is used to derive various high-order approximation equations. This framework...

The equations of the forecast sensitivity to observations and to the background estimate in a four-dimensional variational data assimilation system (4D-Var DAS) are derived from the first-order optimality condition in unconstrained minimization. Estimation of the impact of uncertainties in the specification of the error statistics is considered by...

Air quality prediction plays an important role in the management of our environment. Computational power and efficiencies have advanced to the point where chemical transport models can predict pollution in an urban air shed with spatial resolution less than a kilometer, and cover the globe with a horizontal resolution of less than 50 km. Predicting...

Strategies to achieve order reduction in four-dimensional variational data assimilation (4DVAR) search for an optimal low-rank state subspace for the analysis update. A common feature of the reduction methods proposed in atmospheric and oceanographic studies is that the identification of the basis functions relies on the model dynamics only, withou...

Air quality prediction plays an important role in the management of our environment. As more atmospheric chemical observations
become available chemical data assimilation is expected to play an essential role in air quality forecasting. In this paper
the current status of air quality forecasting is discussed and illustrated by comparison of predict...

Order reduction strategies aim to alleviate the computational burden of the four-dimensional variational data assimilation by performing the optimization in a low-order control space. The proper orthogonal decomposition (POD) approach to model reduction is used to identify a reduced-order control space for a two-dimensional global shallow water mod...

The practical implementation of 4D-Var data assimilation for atmospheric and oceanographic models is hampered by the large dimensionality of the discrete model initial conditions, typically in the range 106- 10^7. Order reduction strategies aim to alleviate the computational burden of the 4D-Var procedure by formulating the optimal control problem...

The specification of the initial ensemble for ensemble data assimilation is addressed. The presented work examines the impact of ensemble initiation in the Maximum Likelihood Ensemble Filter (MLEF) framework, but is also applicable to other ensemble data assimilation algorithms. Two methods are considered: the first is based on the use of the Karda...

The specification of the initial ensemble for ensemble data assimilation is addressed. The presented work examines the impact of ensemble initiation in the Maximum Likelihood Ensemble Filter (MLEF) framework, but is also applicable to other ensemble data assimilation algorithms. Two methods are considered: the first is based on the use of the Karda...

In this paper, the four-dimensional variational (4D-Var) technique is applied to assimilate aircraft measurements during the Transport and Chemical Evolution over the Pacific (TRACE-P) field experiment into a chemical transport model, Sulfur Transport Eulerian Model, version 2K1 (STEM-2K1). Whether data assimilation would produce better analyzed fi...

Significant advancements have been made in recent years in our ability to measure and model atmospheric chemistry. Not only is our ability to characterize a fixed atmospheric point in space and time expanding, but the spatial coverage is also expanding through growing capabilities to measure atmospheric constituents remotely using sensors mounted a...

To assess the impact of incomplete observations on the 4D-Var data assimilation, twin experiments were carried out with the dynamical core of the new FSU GSM consisting of a T126L14 global spectral model in a MPI parallel environment. Results and qualitative aspects are presented for incomplete data in the spatial dimension and for incomplete data...

The task of providing an optimal analysis of the state of the atmosphere requires the development of dynamic data-driven systems (d
3as) that efficiently integrate the observational data and the models. In this paper we discuss fundamental aspects of nonlinear ensemble data assimilation applied to atmospheric chemical transport models. We formulate...

The task of providing an optimal analysis of the state of the atmosphere requires the development of efficient computational tools that facilitate an efficient integration of observational data into models. In a variational approach the data assimilation problem is posed as a minimization problem, which requires the sensitivity (derivatives) of a c...

In this paper we discuss the algorithmic tools needed for data assimilation for aerosol dynamics. Continuous and discrete adjoints of the aerosol dynamic equation are considered, as well as sensitivity coefficients with respect to the coagulation kernel, the growth rate, and emission and deposition coefficients. Numerical experiments performed in t...

this paper we investigate the design of an adaptive observations system given the configuration of the conventional observational network and assuming that four-dimensional variational data assimilation (4D-Var) is performed. The TESV and ET KF targeting methods were considered in the 4D-Var context by Leutbecher et al (2002) using a perfect model...

The design of adaptive observations strategies must account for the particular properties of the data assimilation method. A new adjoint sensitivity approach to the targeted observations problem is proposed in the context of four-dimensional variational data assimilation (4D-Var). The method is based on a periodic update of the adjoint sensitivity...

The analysis of (TRAnsport and Chemical Evolution over the Pacific) TRACE-P observations using a four-dimensional variational data assimilation technique is discussed. The mathematical theory of adjoint sensitivity analysis applied to three dimensional atmospheric transport and chemistry models is presented. The computational tools developed and us...

The analysis of comprehensive chemical reactions mechanisms, parameter estimation techniques, and variational chemical data assimilation applications require the development of efficient sensitivity methods for chemical kinetics systems. The new release (KPP-1.2) of the kinetic preprocessor (KPP) contains software tools that facilitate direct and a...

The Kinetic PreProcessor KPP was extended to generate the building blocks needed for the direct and adjoint sensitivity analysis of chemical kinetic systems. An overview of the theoretical aspects of sensitivity calculations and a discussion of the KPP software tools is presented in the companion paper.In this work the correctness and efficiency of...

this paper, a comparative analysis of the performance of a hybrid method vs. L-BFGS and HFN optimization methods is presented in the 4D-Var context. Numerical results presented for a two-dimensional shallow-water model show that the performance of the hybrid method is sensitive to the selection of the method parameters such as the length of the L-B...

this paper we investigate the design of an adaptive observations system given the configuration of the conventional observational network and assuming that four-dimensional variational data assimilation (4D-Var) is performed. The TESV and ET KF targeting methods were considered in the 4D-Var context by Leutbecher et al (2002) using a perfect model...

The task of providing an optimal analysis of the state of the atmosphere requires the development of novel computational tools
that facilitate an efficient integration of observational data into models. In this paper we discuss some of the computational
tools developed for the assimilation of chemical data into atmospheric models. We perform a theo...

In variational data assimilation (VDA) for meteorological and/or oceanic models, the assimilated fields are deduced by combining the model and the gradient of a cost functional measuring discrepancy between model solution and observation, via a first-order optimality system. However, existence and uniqueness of the VDA problem along with convergenc...

In four-dimensional variational data assimilation (4D-Var) an optimal estimate of the initial state of a dynamical system is obtained by solving a large-scale unconstrained minimization problem. The gradient of the cost functional may be efficiently computed using the adjoint modeling, at the expense equivalent to a few forward model integrations;...

The design of adaptive observations strategies must account for the particular properties of the data assimilation method. A new adjoint sensitivity approach to the targeted observations problem is proposed in the context of four-dimensional variational data assimilation (4D-Var). The method is based on a periodic update of the adjoint sensitivity...

The spatiotemporal distribution of 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 transport-chemistry model in the context of 4D variational data assimilation. The method is presented in a general framewor...

PAQMSG is an MPI-based, Fortran 90 communication library for the parallelization of air quality models (AQMs) on structured grids. It consists of distribution, gathering and repartitioning routines for different domain decompositions implementing a master–worker strategy. The library is architecture and application independent and includes optimiza...

The 4D variational data assimilation for large scale air quality models is a very intensive computational process. In this paper we analyze a coupled numerical treatment of the transport-chemistry operators in the 4D-var context which may reduce the CPU time and the memory storage requirements of the assimilation process. The integration of the for...

This paper presents an application of adjoint sensitivity calculation to retrieve the initial distribution of the aerosol population from measurements at later times. A general framework is given for the discretization of particle dynamics equation by projection methods. The adjoint of the discrete model is constructed. Adjoint modeling successfull...

The spatial-temporal distribution of the observations plays an essential role in the data assimilation process. Strategies for the adaptive location of the observational system search for locations in space and time where additional observational resources should be deployed in order to minimize the forecast error. In this paper we present an appli...

The assimilation of observational data into comprehensive
transport-chemistry models is a very intensive computational process.
Variational methods (3D-Var, 4D- Var) or Kalman filter based algorithms
may be used in the data assimilation process in order to provide an
optimal analysis of the state of the atmosphere. We present the
theoretical framew...

In variational data assimilation (VDA) for meteorological and/or oceanic models, the assimilated fields are deduced by combining the model and the gradient of a cost functional measuring discrepancy between model solution and observation, via a first order optimality system. However existence and uniqueness of the VDA problem along with convergence...

In the past decade the variational method has been successfully applied in data assimilation problems for atmospheric chemistry models. In 4D-var data assimilation, a minimization algorithm is used to find the set of control variables which minimizes the weighted least squares distance between model predictions and observations over the assimilatio...

Discusses computational challenges in air quality modelling (as viewed by the authors). The focus of the paper will be on Di, the “current” state-of-affairs. Owing to limitation of space the discussion will focus on only a few aspects of air quality modelling: i.e. chemical integration, sensitivity analysis and computational framework, with particu...

The task of providing an optimal analysis of the state of the atmosphere requires the development of dynamic data-driven systems
(DDDAS) that efficiently integrate the observational data and the models. Data assimilation, the dynamic incorporation of
additional data into an executing application, is an essential DDDAS concept with wide applicabilit...

Abstract A comparative analysis of observation targeting methods based on total energy singular vectors (TESVs) and Hessian singular vectors (HSVs) is performed with a finite volume global shallow-water model, along with its first and second order adjoint model. A 4D-Var data assimilation framework is considered that allows for adaptive observation...