Methods of sensitivity theory and inverse modeling for estimation of source parameters

Penenko, Vladimir; Baklanov, Alexander; Tsvetova, Elena

doi:10.1016/s0167-739x(02)00031-6

Cited by 32 publications

(24 citation statements)

References 16 publications

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“…Monte Carlo simulation is computationally expensive for complex models so alternative techniques are sought to address this need. Variational methods using the model adjoint show promise for this purpose as they allow the tracking of influences backwards through the model (Penenko et al, 2002). However, although the technique is used in the meteorological and oceanography domain (Moore 1991;Rabier et al, 1996), very little work has been done in the physical infrastructure environmental/water or hydrological/floods areas, although it has potential, e.g.…”

Section: Use the Adjoint Methods In Hydrologymentioning

confidence: 99%

“…Effectively, it provides a local linearized inverse model, that can (1) show the sensitivity of some useful property, possibly an objective function as used in optimization, of the model's output to variations in inputs or parameters and (2) provide gradient information for use in optimizing model fit (White et al, 2003;Belanger and Vincent, 2005) or backward-in-time model runs (Neupauer and Wilson, 1999;Penenko et al, 2002). It is an alternative to the forward calculation of partial derivatives, and is particularly useful and computationally efficient for spatially distributed models.…”

Section: Use the Adjoint Methods In Hydrologymentioning

confidence: 99%

See 1 more Smart Citation

Propagation of uncertainty from observing systems and NWP into hydrological models: COST‐731 Working Group 2

Zappa

Beven

Bruen³

et al. 2010

Atmospheric Science Letters

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The COST-731 action is focused on uncertainty propagation in hydrometeorological forecasting chains. Goals and activities of the action Working Group 2 are presented. Five foci for discussion and research have been identified: (1) understand uncertainties, (2) exploring, designing and comparing methodologies for the use of uncertainty in hydrological models, (3) providing feedback on sensitivity to data and forecast providers, (4) transferring methodologies among the different communities involved and (5) setting up test-beds and perform proof-of-concepts. Current examples of different perspectives on uncertainty propagation are presented.

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Section: Use the Adjoint Methods In Hydrologymentioning

confidence: 99%

Section: Use the Adjoint Methods In Hydrologymentioning

confidence: 99%

Propagation of uncertainty from observing systems and NWP into hydrological models: COST‐731 Working Group 2

Zappa

Beven

Bruen³

et al. 2010

Atmospheric Science Letters

View full text Add to dashboard Cite

show abstract

“…The (ensemble) filtering approach requires the augmentation of the state variables with the parameters (Ruiz et al, 2013). 4D-Var easily lends itself to data assimilation since the parameter variables can often be accounted for in the cost function (Penenko et al, 2002;Elbern et al, 2007;Bocquet, 2012;Penenko et al, 2012). However, it is often required to derive new adjoint operators corresponding to the gradient of the cost function with respect to these parameters if the driving mechanisms are not external forcings.…”

Section: From State Estimation To Physical Parameter Estimationmentioning

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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“…Then, to represent the measurement operator, a matrix H of elements h i,j =(r i , b j ) is calculated and inverted: λ=H −1 µ. A construction of this finite dimensional theory is given by Penenko et al (2002). The global source of CO 2 has been investigated this way by Bousquet et al (2000); Rödenbeck et al (2003).…”

Section: The Theoretical Backgroundmentioning

confidence: 99%

Rebuilding sources of linear tracers after atmospheric concentration measurements

Issartel

2003

Atmos. Chem. Phys.

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Abstract. The identification of widespread sources of passive tracers out of atmospheric concentration measurements has become an important challenge of modern meteorology. The paper proposes some mathematical tracks to address the reconstruction of the complex space-time geometry of the sources of linear tracers. The methods are based upon the use of retroplumes. The inverse problem is addressed in a deterministic non statistical frame. The information obtained by local measurements is spread by introducing the concept of illumination. The constraint that the source is non negative is also addressed. The experimental source ETEX1 is rebuilt in order to evaluate an impulse response of the algorithms.

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Methods of sensitivity theory and inverse modeling for estimation of source parameters

Cited by 32 publications

References 16 publications

Propagation of uncertainty from observing systems and NWP into hydrological models: COST‐731 Working Group 2

Propagation of uncertainty from observing systems and NWP into hydrological models: COST‐731 Working Group 2

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

Rebuilding sources of linear tracers after atmospheric concentration measurements

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