Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part I: Design of the Assimilation System

Voshtani, Sina; Ménard, Richard; Walker, Thomas; Hakami, Amir

doi:10.3390/rs14020371

Cited by 6 publications

(14 citation statements)

References 89 publications

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“…Since methane is characterized by almost linear chemistry, a modified and even less expensive version of parametric Kalman filtering, in which only the error variance evolution is computed explicitly while the correlation lengths are kept fixed over the assimilation window, is derived. The details of the development of the assimilation, including its algorithm, formulation, and verifications, will be explained in Chapter 5 (Voshtani et al 2022a).…”

Section: Potential Use Of Parametric Filtering For Methane Assimilationmentioning

confidence: 99%

“…This thesis in Chapters 5 and 6 presents the development and results of the PvKF assimilation system using GOSAT methane and the hemispheric CMAQ model (Voshtani et al 2022a(Voshtani et al , 2022b. It is shown that the PvKF algorithm, besides its simplicity, is welladapted for the assimilation of long-lived species without loss of variance.…”

Section: Paramteric Varaince Kalman Filter (Pvkf)mentioning

confidence: 99%

“…Previous studies assumed that the initial (or boundary) concentrations provided by data assimilation are perfectly known; hence, potential uncertainties from the state cannot be taken into account for performing an inversion (Basu et al 2013;Deng et al 2014). On the other hand, obtaining the state and its error statistics using conventional data assimilation approaches, such as 4D-Var and EnKF assimilations, is a task with high computational cost (Skachko et al 2014;Skachko et al 2016;Pannekoucke et al 2021;Voshtani et al 2022a). We have used a newly developed assimilation system known as parametric variance Kalman filter (PvKF) that provides a computationally efficient way of estimating methane state along with its error variance (Voshtani et al 2022a;2022b).…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, obtaining the state and its error statistics using conventional data assimilation approaches, such as 4D-Var and EnKF assimilations, is a task with high computational cost (Skachko et al 2014;Skachko et al 2016;Pannekoucke et al 2021;Voshtani et al 2022a). We have used a newly developed assimilation system known as parametric variance Kalman filter (PvKF) that provides a computationally efficient way of estimating methane state along with its error variance (Voshtani et al 2022a;2022b).…”

Section: Introductionmentioning

confidence: 99%

“…In this framework, not only can we change the first guess concentrations to the cost function, but also explicitly include the estimate of error covariances of concentrations for performing an inversion. PvKF formulation allows for dynamically propagating those errors while not relying on a perfect model transport assumption (Voshtani et al 2022a). This enables our modified inversion formulation to account for model spatial correlation structures during the inversion, which is typically missed in methane inversions studies since only diagonal observational error 188 covariances are considered Bousserez et al 2016;Bousserez and Henze 2018;Turner et al 2019;Maasakkers et al 2021;Stanevich et al 2021;Yu et al 2021;Zhang et al 2021).…”

Section: Introductionmentioning

confidence: 99%

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Atmospheric Methane Data Assimilation in the CMAQ Air Quality Model

Voshtani¹

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Atmospheric methane is a potent greenhouse gas (GHG) and the second-largest contributor to anthropogenic climate forcing. After stabilizing in the early 2000s, the global methane concentration has sharply risen since 2007, mainly due to human-related activities. Curbing the rise of methane concentrations entails identifying and reducing methane emissions, which may otherwise significantly impact climate and air quality. Due to their near-continuous global coverage, satellite observations of methane are often combined with chemical transport models (CTMs) to improve model concentrations and emissions estimates.Previous methane studies are still faced with significant gaps and challenges such that considerable discrepancies among their results have been reported consistently. On the estimation side, most studies assumed that the model is perfect and characterization of uncertainties is already optimal. Obtaining information on methane uncertainties using conventional approaches requires extensive computational resources compared to model integration. Furthermore, there is a lack of independent and objective evaluation of those estimated uncertainties.The first thesis objective is to develop a novel cost-efficient data assimilation framework capable of estimating error statistics using a CTM. This method is referred to as parametric variance Kalman filter (PvKF), which relies on continuous formulation of error covariance propagation without making the perfect model assumption. We test the validity of our assumptions and the performance of the PvKF assimilation using simulated GOSAT observations. iii Our next goal is to conduct near-optimal assimilation to represent the true methane field. Cross-validation offers an objective manner to characterize the success of the method. We extend that method to the satellite observations and multiple covariance parameter estimations. Using estimated error statistics and GOSAT observations, we found that the quality of the analysis substantially depends on the optimality of those error covariances.Lastly, we evaluate the use of PvKF assimilation in a source inversion context in comparison with a traditional 4D-Var inversion. Using Observing System Simulation Experiments (OSSEs), we verify the ability of our new inversion framework to recover a distribution of known emissions. Our results indicate that both the analysis field and its error covariance exert a tangible influence in lowering the bias and variance of the recovered emissions. iv PrefaceThis thesis includes the following copyrighted articles. The published manuscripts have been reproduced in Chapter 5 and Chapter 6 of this thesis with the permission of the co-authors and the publisher.

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Section: Potential Use Of Parametric Filtering For Methane Assimilationmentioning

confidence: 99%

Section: Paramteric Varaince Kalman Filter (Pvkf)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Atmospheric Methane Data Assimilation in the CMAQ Air Quality Model

Voshtani¹

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Boundary Conditions for the Parametric Kalman Filter Forecast

Sabathier,

Pannekoucke,

Maget

et al. 2023

J Adv Model Earth Syst

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This paper is a contribution to the exploration of the parametric Kalman filter (PKF), which is an approximation of the Kalman filter, where the error covariances are approximated by a covariance model. Here we focus on the covariance model parameterized from the variance and the anisotropy of the local correlations, and whose parameters dynamics provides a proxy for the full error‐covariance dynamics. For this covariance model, we aim to provide the boundary condition to specify in the prediction of PKF for bounded domains, focusing on Dirichlet and Neumann conditions when they are prescribed for the physical dynamics. An ensemble validation is proposed for the transport equation and for the heterogeneous diffusion equation over a bounded 1D domain. This ensemble validation requires to specify the auto‐correlation time‐scale needed to populate boundary perturbation that leads to prescribed uncertainty characteristics. The numerical simulations show that the PKF is able to reproduce the uncertainty diagnosed from the ensemble of forecast appropriately perturbed on the boundaries, which show the ability of the PKF to handle boundaries in the prediction of the uncertainties. It results that Dirichlet condition on the physical dynamics implies Dirichlet condition on the variance and on the anisotropy.

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Use of Assimilation Analysis in 4D-Var Source Inversion: Observing System Simulation Experiments (OSSEs) with GOSAT Methane and Hemispheric CMAQ

et al. 2023

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We previously introduced the parametric variance Kalman filter (PvKF) assimilation as a cost-efficient system to estimate the dynamics of methane analysis concentrations. As an extension of our development, this study demonstrates the linking of PvKF to a 4D-Var inversion aiming to improve on methane emissions estimation in comparison with the traditional 4D-Var. Using the proposed assimilation–inversion framework, we revisit fundamental assumptions of the perfect and already optimal model state that is typically made in the 4D-Var inversion algorithm. In addition, the new system objectively accounts for error correlations and the evolution of analysis error variances, which are non-trivial or computationally prohibitive to maintain otherwise. We perform observing system simulation experiments (OSSEs) aiming to isolate and explore various effects of the assimilation analysis on the source inversion. The effect of the initial field of analysis, forecast of analysis error covariance, and model error is examined through modified 4D-Var cost functions, while different types of perturbations of the prior emissions are considered. Our results show that using PvKF optimal analysis instead of the model forecast to initialize the inversion improves posterior emissions estimate (~35% reduction in the normalized mean bias, NMB) across the domain. The propagation of analysis error variance using the PvKF formulation also tends to retain the effect of background correlation structures within the observation space and, thus, results in a more reliable estimate of the posterior emissions in most cases (~50% reduction in the normalized mean error, NME). Our sectoral analysis of four main emission categories indicates how the additional information of assimilation analysis enhances the constraints of each emissions sector. Lastly, we found that adding the PvKF optimal analysis field to the cost function benefits the 4D-Var inversion by reducing its computational time (~65%), while including only the error covariance in the cost function has a negligible impact on the inversion time (10–20% reduction).

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Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part I: Design of the Assimilation System

Cited by 6 publications

References 89 publications

Atmospheric Methane Data Assimilation in the CMAQ Air Quality Model

Atmospheric Methane Data Assimilation in the CMAQ Air Quality Model

Boundary Conditions for the Parametric Kalman Filter Forecast

Use of Assimilation Analysis in 4D-Var Source Inversion: Observing System Simulation Experiments (OSSEs) with GOSAT Methane and Hemispheric CMAQ

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