Browsing by Author "Chai, Tianfeng"

Now showing 1 - 6 of 6
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    Autoregressive Models of Background Errors for Chemical Data Assimilation
    Constantinescu, Emil M.; Chai, Tianfeng; Sandu, Adrian; Carmichael, Gregory R. (Department of Computer Science, Virginia Polytechnic Institute & State University, 2006-10-01)
    The task of providing an optimal analysis of the state of the atmosphere requires the development of dynamic data-driven systems that efficiently integrate the observational data and the models. Data assimilation (DA) is the process of adjusting the states or parameters of a model in such a way that its outcome (prediction) is close, in some distance metric, to observed (real) states. It is widely accepted that a key ingredient of successful data assimilation is a realistic estimation of the background error distribution. This paper introduces a new method for estimating the background errors which are modeled using autoregressive processes. The proposed approach is computationally inexpensive and captures the error correlations along the flow lines.
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    Chemical Data Assimilation-An Overview
    Sandu, Adrian; Chai, Tianfeng (MDPI, 2011-08-29)
    Chemical data assimilation is the process by which models use measurements to produce an optimal representation of the chemical composition of the atmosphere. Leveraging advances in algorithms and increases in the available computational power, the integration of numerical predictions and observations has started to play an important role in air quality modeling. This paper gives an overview of several methodologies used in chemical data assimilation. We discuss the Bayesian framework for developing data assimilation systems, the suboptimal and the ensemble Kalman filter approaches, the optimal interpolation (OI), and the three and four dimensional variational methods. Examples of assimilation real observations with CMAQ model are presented.
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    Ensemble-based chemical data assimilation I: An idealized setting
    Constantinescu, Emil M.; Sandu, Adrian; Chai, Tianfeng; Carmichael, Gregory R. (Department of Computer Science, Virginia Polytechnic Institute & State University, 2006-03-01)
    Data assimilation is the process of integrating observational data and model predictions to obtain an optimal representation of the state of the atmosphere. As more chemical observations in the troposphere are becoming available, chemical data assimilation is expected to play an essential role in air quality forecasting, similar to the role it has in numerical weather prediction. Considerable progress has been made recently in the development of variational tools for chemical data assimilation. In this paper we assess the performance of the ensemble Kalman filter (EnKF). Results in an idealized setting show that EnKF is promising for chemical data assimilation.
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    Ensemble-based chemical data assimilation II: Real observations
    Constantinescu, Emil M.; Sandu, Adrian; Chai, Tianfeng; Carmichael, Gregory R. (Department of Computer Science, Virginia Polytechnic Institute & State University, 2006-03-01)
    Data assimilation is the process of integrating observational data and model predictions to obtain an optimal representation of the state of the atmosphere. As more chemical observations in the troposphere are becoming available, chemical data assimilation is expected to play an essential role in air quality forecasting, similar to the role it has in numerical weather prediction. Considerable progress has been made recently in the development of variational tools for chemical data assimilation. In this paper we assess the performance of the ensemble Kalman filter (EnKF) and compare it with a state of the art 4D-Var approach. We analyze different aspects that affect the assimilation process, investigate several ways to avoid filter divergence, and investigate the assimilation of emissions. Results with a real model and real observations show that EnKF is a promising approach for chemical data assimilation. The results also point to several issues on which further research is necessary.
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    Ensemble-based Chemical Data Assimilation III: Filter Localization
    Constantinescu, Emil M.; Sandu, Adrian; Chai, Tianfeng; Carmichael, Gregory R. (Department of Computer Science, Virginia Polytechnic Institute & State University, 2006-03-01)
    Data assimilation is the process of integrating observational data and model predictions to obtain an optimal representation of the state of the atmosphere. As more chemical observations in the troposphere are becoming available, chemical data assimilation is expected to play an essential role in air quality forecasting, similar to the role it has in numerical weather prediction. Considerable progress has been made recently in the development of variational tools for chemical data assimilation. In this paper we implement and assess the performance of a localized ``perturbed observations'' ensemble Kalman filter (LEnKF). We analyze different settings of the ensemble localization, and investigate the joint assimilation of the state, emissions and boundary conditions. Results with a real model and real observations show that LEnKF is a promising approach for chemical data assimilation. The results also point to several issues on which future research is necessary.
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    Evaluating oil and gas contributions to ambient nonmethane hydrocarbon mixing ratios and ozone-related metrics in the Colorado Front Range
    Lyu, Congmeng; Capps, Shannon L.; Kurashima, Kent; Henze, Daven K.; Pierce, Gordon; Hakami, Amir; Zhao, Shunliu; Resler, Jaroslav; Carmichael, Gregory R.; Sandu, Adrian; Russell, Armistead G.; Chai, Tianfeng; Milford, Jana (2021-02-01)
    Recently, oil and natural gas (O&NG) production activities in the Denver-Julesburg Basin have expanded rapidly. Associated nonmethane hydrocarbon (NMHC) emissions contribute to photochemical formation of ground-level ozone and include benzene as well as other hazardous air pollutants. Using positive matrix factorization (PMF) and chemical mass balance (CMB) methods, we estimate how much O&NG activities and other sources contribute to morning NMHC mixing ratios measured from 2013 to mid-2016 at a site in Platteville, CO, in the Denver-Julesburg Basin, and at a contrasting site in downtown Denver. A novel adjoint sensitivity analysis method is then used to estimate corresponding contributions to ozone and ozone-linked mortality in the Denver region. Average 6-9 am NMHC mixing ratios in Platteville were seven times higher than those in Denver in 2013 but four times higher in 2016. CMB estimates that O&NG activities contributed to the Platteville (Denver) site an average of 96% (56%) of NMHC on a carbon basis while PMF indicated 92% (33%). Average vehicle-related contributions of NMHC are estimated as 41% by CMB and 53% by PMF in Denver. Estimates of the fractional contribution to potential ozone and ozone-linked mortality from O&NG activities are smaller while those from vehicles are larger than the NMHC contributions. CMB (PMF) indicate that greater than 78% (40%) of annual average benzene in Denver is attributable to vehicle emissions while greater than 75% (67%) of benzene in Platteville is attributable to O&NG activities.