Variational methods of data assimilation and inverse problems for studying the atmosphere, ocean, and environment

Penenko, Vladimir

doi:10.1134/s1995423909040065

Cited by 30 publications

(25 citation statements)

References 15 publications

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“…To work out the problem of data assimilation and functional assessment in a sliding mode in time and to use splitting, the system (18)-(24), as a splitting scheme at each time step, can be solved by a direct algorithm without iterations. This version is implemented by a technique of local adjoint problems in time (PENENKO 2009). Now we turn back to the basic scheme.…”

Section: Sensitivitymentioning

confidence: 99%

“…In scheme (31), the sign and value of @ m are chosen from minimum conditions for the goal functionalŨ k u ð Þ in accordance with a generalized approximation procedure of Newton's type in the space of parameters (PENENKO 1981(PENENKO , 2009). …”

Section: Inverse Problems and Data Assimilationmentioning

confidence: 99%

“…A new version of the 4D-Var method for solving inverse problems by (18)-(24) and for successive data assimilation was proposed by PENENKO (2009). It is based on use of a local adjoint problem technique in time together with the algorithm of UFs calculation in (23) and (24) by using sliding assimilation windows.…”

Section: Inverse Problems and Data Assimilationmentioning

confidence: 99%

“…This is because the discrepancy between the calculated values and measured data can be distributed by the assimilating system through estimations of the model uncertainty function (UF) over the whole time interval of the assimilation window (PENENKO 2009). From the control theory point of view, the UFs are a control introduced into the system to reach a minimum of the observational functional.…”

Section: Introductionmentioning

confidence: 99%

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Direct and Inverse Problems in a Variational Concept of Environmental Modeling

Penenko

Baklanov

Tsvetova

et al. 2011

Pure Appl. Geophys.

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A concept of environmental forecasting based on a variational approach is discussed. The basic idea is to augment the existing technology of modeling by a combination of direct and inverse methods. By this means, the scope of environmental studies can be substantially enlarged. In the concept, mathematical models of processes and observation data subject to some uncertainties are considered. The modeling system is derived from a specially formulated weak-constraint variational principle. A set of algorithms for implementing the concept is presented. These are: algorithms for the solution of direct, adjoint, and inverse problems; adjoint sensitivity algorithms; data assimilation procedures; etc. Methods of quantitative estimations of uncertainty are of particular interest since uncertainty functions play a fundamental role for data assimilation, assessment of model quality, and inverse problem solving. A scenario approach is an essential part of the concept. Some methods of orthogonal decomposition of multi-dimensional phase spaces are used to reconstruct the hydrodynamic background fields from available data and to include climatic data into longterm prognostic scenarios. Subspaces with informative bases are constructed to use in deterministic or stochastic-deterministic scenarios for forecasting air quality and risk assessment. The results of implementing example scenarios for the Siberian regions are presented.

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Section: Sensitivitymentioning

confidence: 99%

Section: Inverse Problems and Data Assimilationmentioning

confidence: 99%

Section: Inverse Problems and Data Assimilationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Direct and Inverse Problems in a Variational Concept of Environmental Modeling

Penenko

Baklanov

Tsvetova

et al. 2011

Pure Appl. Geophys.

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“…Accounting for model error and/or extending the length of the data assimilation window would require generalizing it to weak-constraint 4D-Var (Penenko, 1996(Penenko, , 2009Fisher et al, 2005). However, several difficulties arise, such as the necessity to characterize model error and to significantly extend control space.…”

Section: Variational Approachesmentioning

confidence: 99%

Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

et al. 2015

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Abstract. Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of three-dimensional chemical (including aerosol) concentrations and perform inverse modeling of input variables or model parameters (e.g., emissions). Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. They offer the possibility to assimilate both meteorological and chemical data; however, because CCMM are fairly recent, data assimilation in CCMM has been limited to date. We review here the current status of data assimilation in atmospheric chemistry models with a particular focus on future prospects for data assimilation in CCMM. We first review the methods available for data assimilation in atmospheric models, including variational methods, ensemble Kalman filters, and hybrid methods. Next, we review past applications that have included chemical data assimilation in chemical transport models

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Applying Data Assimilation on the Urban Environment

Khassenova

Kussainova

2019

Communications in Computer and Information Science

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Variational methods of data assimilation and inverse problems for studying the atmosphere, ocean, and environment

Cited by 30 publications

References 15 publications

Direct and Inverse Problems in a Variational Concept of Environmental Modeling

Direct and Inverse Problems in a Variational Concept of Environmental Modeling

Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

Applying Data Assimilation on the Urban Environment

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