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Amir Hakami
Researcher at Carleton University
Publications - 44
Citations - 1638
Amir Hakami is an academic researcher from Carleton University. The author has contributed to research in topics: Air quality index & CMAQ. The author has an hindex of 16, co-authored 41 publications receiving 1386 citations. Previous affiliations of Amir Hakami include California Institute of Technology & Georgia Institute of Technology.
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Development of the adjoint of GEOS-Chem
TL;DR: In this paper, the authors present the adjoint of the global chemical transport model GEOS-Chem, focusing on the chemical and thermodynamic relationships between sulfate and ammonium-nitrate aerosols and their gas-phase precursors.
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Nonlinear response of ozone to emissions: source apportionment and sensitivity analysis.
TL;DR: This work explores the nonlinear responses of ozone to emissions of its precursors, nitrogen oxides (NOx) and volatile organic compounds, and introduces a method for applying second-order direct sensitivity method to assess the uncertainty of sensitivity and source apportionment estimates arising from uncertainty in an emissions inventory.
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High-order, direct sensitivity analysis of multidimensional air quality models.
TL;DR: Higher-order sensitivity analysis shows a noticeable improvement in terms of accuracy over the conventional first-order analysis, and is better equipped to address the nonlinear behavior around the peak ozone in NO(x)-rich plumes.
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The adjoint of CMAQ.
Amir Hakami,Daven K. Henze,John H. Seinfeld,Kumaresh Singh,Adrian Sandu,Soontae Kim,Daewon W. Byun,Qinbin Li +7 more
TL;DR: An adjoint model for the internationally used Community Multiscale Air Quality (CMAQ) modeling platform of the U.S. EPA is developed and results show good agreement with brute-force and DDM sensitivities, but the agreement is not perfect for horizontal transport.
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Adjoint inverse modeling of black carbon during the Asian Pacific Regional Aerosol Characterization Experiment
Amir Hakami,Daven K. Henze,John H. Seinfeld,Tianfeng Chai,Youhua Tang,Greg Carmichael,Adrian Sandu +6 more
TL;DR: In this paper, an adjoint model is used for inverse modeling of black carbon during the Asian Pacific Regional Aerosol Characterization Experiment (ACE-Asia), using the four-dimensional variational data assimilation (4D-Var) approach to optimally recover spatially resolved anthropogenic and biomass-burning emissions and initial and boundary conditions.