Targeting of observations for accidental atmospheric release monitoring

Abida, Rachid; Bocquet, Marc

doi:10.1016/j.atmosenv.2009.09.029

Cited by 46 publications

(33 citation statements)

References 25 publications

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“…Targeting has been little investigated in atmospheric chemistry, but recent studies have demonstrated its potential, especially in an accidental context (Abida and Bocquet, 2009). It would certainly be interesting to use a coupled chemicalmeteorological targeting system, since targeting of concentration observations could also require meteorological observations at the same location for a proper assimilation of chemical concentrations into a CCMM.…”

Section: Optimal Monitoring Network Designmentioning

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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Section: Optimal Monitoring Network Designmentioning

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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“…From these methods, S. Kirkpatrick proposed some improvements [12] by changing the definition of the energy, used in the algorithm to optimise position in system presenting an inner local order. Other applications of this algorithm already exist for example in nuclear incident network design since a few years [13,14]. A brief description of the main points of Simulated Annealing is presented in appendix A.…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of Measurement Network for Neutronic Activity Field Reconstruction by Data Assimilation

Bouriquet

Argaud

Thual³

2012

JPES

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Using data assimilation framework, to merge information from model and measurement, an optimal reconstruction of the neutronic activity field can be determined for a nuclear reactor core. In this paper, we focus on solving the inverse problem of determining an optimal repartition of the measuring instruments within the core, to get the best possible results from the data assimilation reconstruction procedure. The position optimisation is realised using Simulated Annealing algorithm, based on the Metropolis-Hastings one. Moreover, in order to address the optimisation computing challenge, algebraic improvements of data assimilation have been developed and are presented here.

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“…Only few studies focus on optimal design for the pure purpose of source characterization. As an example Abida and Bocquet [7] presented a sequential reconstruction technique of dispersed plume by coupling inverse modelling to observation targeting strategy. This technique has been used to determine the optimal sensors' positions that improve the source term.…”

Section: Introductionmentioning

confidence: 99%

Optimization of sensor networks for the estimation of atmospheric pollutants sources

Kouichi

Turbelin

Ngae

et al. 2016

WIT Transactions on Ecology and the Environment

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This study describes a process to design a sensor network. This network could include: wireless mobile sensors deployed by first responders in hazardous material operations, stationary sensors used to protect an area against accidental, or intentional, contaminations or stationary air quality monitoring stations. The objective of the network is the estimation (localization -quantification) of releases sources. The design of such a network has an important issue in determining the optimal placement of sensors. This paper presents the first application of the renormalized data assimilation method to address this issue. It is associated with a classical optimization algorithm (simulate annealing) to solve the combinatory optimization problem consisting of finding the optimal configuration of sensors among a set of potential positions. Three scenarios, corresponding with three different cost functions, are proposed. The first one consists of optimizing the design of a network deployed in emergency situations. Experimental data from a wind tunnel experiment are used. The objective is to characterize the source to minimize error in measurement forecasts. The second one is to optimize the design of the same network but in a situation where the source can be anywhere in the domain. To that end, an entropic criterion is used. The last one consists of optimizing the design of a stationary network. The objective is to characterize the source with varying meteorological conditions (experimental meteorological data are used).

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Targeting of observations for accidental atmospheric release monitoring

Cited by 46 publications

References 25 publications

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

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

Optimal Design of Measurement Network for Neutronic Activity Field Reconstruction by Data Assimilation

Optimization of sensor networks for the estimation of atmospheric pollutants sources

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