Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part II: Results Using Optimal Error Statistics

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

doi:10.3390/rs14020375

Cited by 4 publications

(9 citation statements)

References 65 publications

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“…Since cross-validation is a promising technique for maintaining that goal, this research extends cross-validation application to GOSAT satellite observations and multiple covariance parameters estimation. The details of the assumptions and the modifications, along with 48 the evaluation of the assimilation analysis against independent sources of observations, are demonstrated in Chapter 6 (Voshtani et al 2022b).…”

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%

“…The same process computes the lateral boundary while it is assumed to be timeinvariant. Following Voshtani et al (2022b), we also exclude a buffer zone below 5° N to minimize the influence of the boundary conditions on domain concentrations for a month of assimilation/inversion.…”

Section: Chemical Transport Model and Methane Emissionsmentioning

confidence: 99%

“…The idea of evolving only error variance with an advection scheme emerged in Kalman filtering by Cohn (1993), and the first practical atmospheric implementation was demonstrated by and Menard and Changs (2000). The design and implementation of the PvKF assimilation with the CMAQ model using GOSAT methane 195 observations have been detailed in Voshtani et al (2022a;2022b). One main objective of developing the scheme was to reduce the assimilation cost while avoiding the challenges in the previous assimilation approaches, such as EnKF and 4D-Var.…”

Section: Pvkf Assimilationmentioning

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%

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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: Chemical Transport Model and Methane Emissionsmentioning

confidence: 99%

Section: Pvkf Assimilationmentioning

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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Methods for Quantifying Source‐Specific Air Pollution Exposure to Serve Epidemiology, Risk Assessment, and Environmental Justice

Shan,

Casey,

Shearston

et al. 2024

GeoHealth

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Identifying sources of air pollution exposure is crucial for addressing their health impacts and associated inequities. Researchers have developed modeling approaches to resolve source‐specific exposure for application in exposure assessments, epidemiology, risk assessments, and environmental justice. We explore six source‐specific air pollution exposure assessment approaches: Photochemical Grid Models (PGMs), Data‐Driven Statistical Models, Dispersion Models, Reduced Complexity chemical transport Models (RCMs), Receptor Models, and Proximity Exposure Estimation Models. These models have been applied to estimate exposure from sources such as on‐road vehicles, power plants, industrial sources, and wildfires. We categorize these models based on their approaches for assessing emissions and atmospheric processes (e.g., statistical or first principles), their exposure units (direct physical measures or indirect measures/scaled indices), and their temporal and spatial scales. While most of the studies we discuss are from the United States, the methodologies and models are applicable to other countries and regions. We recommend identifying the key physical processes that determine exposure from a given source and using a model that sufficiently accounts for these processes. For instance, PGMs use first principles parameterizations of atmospheric processes and provide source impacts exposure variability in concentration units, although approaches within PGMs for source attribution introduce uncertainties relative to the base model and are difficult to evaluate. Evaluation is important but difficult—since source‐specific exposure is difficult to observe, the most direct evaluation methods involve comparisons with alternative models.

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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 II: Results Using Optimal Error Statistics

Cited by 4 publications

References 65 publications

Atmospheric Methane Data Assimilation in the CMAQ Air Quality Model

Atmospheric Methane Data Assimilation in the CMAQ Air Quality Model

Methods for Quantifying Source‐Specific Air Pollution Exposure to Serve Epidemiology, Risk Assessment, and Environmental Justice

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

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