Modeling Emissions from Concentrated Sources into Large-Scale Models: Theory and apriori Testing

This paper presents a general procedure to incorporate the effects emissions from localized sources, such as aircraft or ship engines, into chemical transport models (CTM). In this procedure, the species concentrations in each grid box of a CTM are split into plume or small-scale concentrations and background concentrations, respectively, and the corresponding conservation equations are derived. The plume concentrations can be interpreted as subgrid contributions for the CTM grid-box averaged concentrations. The chemical reactions occurring inside the plume are parameterized by introducing suitable “effective” reaction rates rather than modifying the emission indices of the species inside the plume. Various methods for implementation into large-scale models are discussed that differ by the accuracy of the description of plume process. The mathematical consistency of the method is verified on simple idealized setting consisting of a reactive plume in homogeneous turbulence.

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Development of a Reduced Complexity Plant Canopy Physics Surrogate Model for use in Chemical Transport Models: A Case Study with GEOS-Chem v12.3.0

10.5194/gmd-2019-334 ◽

2020 ◽

Author(s):

Sam J. Silva ◽

Colette L. Heald ◽

Alex B. Guenther

Keyword(s):

Surrogate Model ◽

Large Scale ◽

Surface Ozone ◽

Chemical Transport ◽

Spatiotemporal Variability ◽

Transport Models ◽

Chemical Transport Models ◽

Leaf Level ◽

Computationally Intensive ◽

Canopy Model

Abstract. Biosphere-atmosphere interactions strongly influence the chemical composition of the atmosphere. Simulating these interactions at a detailed process-based level has traditionally been computationally intensive and resource prohibitive, commonly due to complexities in calculating radiation and light at the leaf level within plant canopies. Here we describe a surrogate canopy physics model based on the MEGAN3 detailed canopy model parameterized using a statistical learning technique. This surrogate canopy model is designed specifically to rapidly calculate leaf-level temperature and photosynthetically active radiative (PAR) for use in large-scale chemical transport models (CTMs). Our surrogate model can reproduce the dominant spatiotemporal variability of the more detailed MEGAN3 canopy model to within 10 % across the globe. Implementation of this surrogate model into the GEOS-Chem CTM leads to small local changes in ozone dry deposition velocities of less than 5 %, and larger local changes in isoprene emissions of up to ∼40 %, though annual global isoprene emissions remain largely consistent (within 5 %). These changes to surface-atmosphere exchange lead to modest changes in surface ozone concentrations of ± 1 ppbv. The use of this surrogate canopy model drives emissions of isoprene and concentrations of surface ozone closer to observationally constrained values, without any noticeable impact on computational demand. Additionally, this surrogate model allows for the further development and implementation of leaf-level emission factors in the calculation of biogenic emissions in the GEOS-Chem CTM. Though not the focus of this work, this ultimately enables a complete implementation of the MEGAN3 emissions framework within GEOS-Chem, which produces 570 Tg yr−1 of isoprene in 2012.

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Development of a reduced-complexity plant canopy physics surrogate model for use in chemical transport models: a case study with GEOS-Chem v12.3.0

Geoscientific Model Development ◽

10.5194/gmd-13-2569-2020 ◽

2020 ◽

Vol 13 (6) ◽

pp. 2569-2585

Author(s):

Sam J. Silva ◽

Colette L. Heald ◽

Alex B. Guenther

Keyword(s):

Surrogate Model ◽

Large Scale ◽

Surface Ozone ◽

Chemical Transport ◽

Spatiotemporal Variability ◽

Computationally Efficient ◽

Transport Models ◽

Chemical Transport Models ◽

Leaf Level ◽

Canopy Model

Abstract. Biosphere–atmosphere interactions strongly influence the chemical composition of the atmosphere. Simulating these interactions at a detailed process-based level has traditionally been computationally intensive and resource prohibitive, commonly due to complexities in calculating radiation and light at the leaf level within plant canopies. Here we describe a surrogate canopy physics model based on the MEGAN3 detailed canopy model parameterized using a statistical learning technique. This surrogate canopy model is specifically designed to rapidly calculate leaf-level temperature and photosynthetically active radiative (PAR) for use in large-scale chemical transport models (CTMs). Our surrogate model can reproduce the dominant spatiotemporal variability of the more detailed MEGAN3 canopy model to within 10 % across the globe. Implementation of this surrogate model into the GEOS-Chem CTM leads to small local changes in ozone dry deposition velocities of less than 5 % and larger local changes in isoprene emissions of up to ∼40 %, though annual global isoprene emissions remain largely consistent (within 5 %). These changes to surface–atmosphere exchange lead to small changes in surface ozone concentrations of ±1 ppbv, modestly reducing the northern hemispheric ozone bias, which is common to many CTMs, here from 8 to 7 ppbv. The use of this computationally efficient surrogate canopy model drives emissions of isoprene and concentrations of surface ozone closer to observationally constrained values. Additionally, this surrogate model allows for the further development and implementation of leaf-level emission factors in the calculation of biogenic emissions in the GEOS-Chem CTM. Though not the focus of this work, this ultimately enables a complete implementation of the MEGAN3 emissions framework within GEOS-Chem, which produces 570 Tg yr−1 of isoprene for 2012.

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Air quality forecasting system based on chemical transport models

Hydrometeorological research and forecasting ◽

10.37162/2618-9631-2019-4-203-218 ◽

2019 ◽

Vol 4 ◽

pp. 203-218

Author(s):

I.N. Kusnetsova ◽

◽

I.U. Shalygina ◽

M.I. Nahaev ◽

U.V. Tkacheva ◽

...

Keyword(s):

Air Quality ◽

Chemical Transport ◽

Transport Models ◽

Chemical Transport Models ◽

Forecasting System ◽

Air Quality Forecasting

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Machine learning based bias correction for numerical chemical transport models

Atmospheric Environment ◽

10.1016/j.atmosenv.2020.118022 ◽

2021 ◽

Vol 248 ◽

pp. 118022

Author(s):

Min Xu ◽

Jianbing Jin ◽

Guoqiang Wang ◽

Arjo Segers ◽

Tuo Deng ◽

...

Keyword(s):

Machine Learning ◽

Bias Correction ◽

Chemical Transport ◽

Transport Models ◽

Chemical Transport Models

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Modeling an Enclosed, Turbulent Reacting Methane Jet With the Premixed Conditional Moment Closure Method

Volume 1B: Combustion, Fuels and Emissions ◽

10.1115/gt2013-95092 ◽

2013 ◽

Cited By ~ 4

Author(s):

Scott Martin ◽

Aleksandar Jemcov ◽

Björn de Ruijter

Keyword(s):

Turbulent Combustion ◽

Large Scale ◽

Reaction Rates ◽

Moment Closure ◽

Scalar Dissipation ◽

Small Scale ◽

Conditional Moment ◽

Progress Variable ◽

Conditional Moment Closure ◽

Scale Turbulence

Here the premixed Conditional Moment Closure (CMC) method is used to model the recent PIV and Raman turbulent, enclosed reacting methane jet data from DLR Stuttgart [1]. The experimental data has a rectangular test section at atmospheric pressure and temperature with a single inlet jet. A jet velocity of 90 m/s is used with an adiabatic flame temperature of 2,064 K. Contours of major species, temperature and velocities along with velocity rms values are provided. The conditional moment closure model has been shown to provide the capability to model turbulent, premixed methane flames with detailed chemistry and reasonable runtimes [2]. The simplified CMC model used here falls into the class of table lookup turbulent combustion models where the chemical kinetics are solved offline over a range of conditions and stored in a table that is accessed by the CFD code. Most table lookup models are based on the laminar 1-D flamelet equations, which assume the small scale turbulence does not affect the reaction rates, only the large scale turbulence has an effect on the reaction rates. The CMC model is derived from first principles to account for the effects of small scale turbulence on the reaction rates, as well as the effects of the large scale mixing, making it more versatile than other models. This is accomplished by conditioning the scalars with the reaction progress variable. By conditioning the scalars and accounting for the small scale mixing, the effects of turbulent fluctuations of the temperature on the reaction rates are more accurately modeled. The scalar dissipation is used to account for the effects of the small scale mixing on the reaction rates. The original premixed CMC model used a constant value of scalar dissipation, here the scalar dissipation is conditioned by the reaction progress variable. The steady RANS 3-D version of the open source CFD code OpenFOAM is used. Velocity, temperature and species are compared to the experimental data. Once validated, this CFD turbulent combustion model will have great utility for designing lean premixed gas turbine combustors.

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Skill-Testing Chemical Transport Models across Contrasting Atmospheric Mixing States Using Radon-222

10.20944/preprints201811.0607.v1 ◽

2018 ◽

Author(s):

Scott D. Chambers ◽

Elise-Andree Guérette ◽

Khalia Monk ◽

Alan D. Griffiths ◽

Yang Zhang ◽

...

Keyword(s):

Air Quality ◽

Diurnal Cycle ◽

Transport Model ◽

Chemical Transport ◽

Chemical Transport Model ◽

Transport Models ◽

Potential Health ◽

Meteorological Model ◽

Model Skill ◽

Chemical Transport Models

We propose a new technique to prepare statistically-robust benchmarking data for evaluating chemical transport model meteorology and air quality parameters within the urban boundary layer. The approach employs atmospheric class-typing, using nocturnal radon measurements to assign atmospheric mixing classes, and can be applied temporally (across the diurnal cycle), or spatially (to create angular distributions of pollutants as a top-down constraint on emissions inventories). In this study only a short (<1-month) campaign is used, but grouping of the relative mixing classes based on nocturnal mean radon concentrations can be adjusted according to dataset length (i.e., number of days per category), or desired range of within-class variability. Calculating hourly distributions of observed and simulated values across diurnal composites of each class-type helps to: (i) bridge the gap between scales of simulation and observation, (ii) represent the variability associated with spatial and temporal heterogeneity of sources and meteorology without being confused by it, and (iii) provide an objective way to group results over whole diurnal cycles that separates ‘natural complicating factors’ (synoptic non-stationarity, rainfall, mesoscale motions, extreme stability, etc.) from problems related to parameterizations, or between-model differences. We demonstrate the utility of this technique using output from a suite of seven contemporary regional forecast and chemical transport models. Meteorological model skill varied across the diurnal cycle for all models, with an additional dependence on the atmospheric mixing class that varied between models. From an air quality perspective, model skill regarding the duration and magnitude of morning and evening “rush hour” pollution events varied strongly as a function of mixing class. Model skill was typically the lowest when public exposure would have been the highest, which has important implications for assessing potential health risks in new and rapidly evolving urban regions, and also for prioritizing the areas of model improvement for future applications.

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The STRatospheric Estimation Algorithm from Mainz (STREAM): estimating stratospheric NO<sub>2</sub> from nadir-viewing satellites by weighted convolution

Atmospheric Measurement Techniques ◽

10.5194/amt-9-2753-2016 ◽

2016 ◽

Vol 9 (7) ◽

pp. 2753-2779 ◽

Cited By ~ 13

Author(s):

Steffen Beirle ◽

Christoph Hörmann ◽

Patrick Jöckel ◽

Song Liu ◽

Marloes Penning de Vries ◽

...

Keyword(s):

Input Data ◽

Polar Vortex ◽

Chemical Transport ◽

Estimation Algorithm ◽

Satellite Observations ◽

Satellite Measurements ◽

Transport Models ◽

Verification Algorithm ◽

Chemical Transport Models ◽

Total Column

Abstract. The STRatospheric Estimation Algorithm from Mainz (STREAM) determines stratospheric columns of NO2 which are needed for the retrieval of tropospheric columns from satellite observations. It is based on the total column measurements over clean, remote regions as well as over clouded scenes where the tropospheric column is effectively shielded. The contribution of individual satellite measurements to the stratospheric estimate is controlled by various weighting factors. STREAM is a flexible and robust algorithm and does not require input from chemical transport models. It was developed as a verification algorithm for the upcoming satellite instrument TROPOMI, as a complement to the operational stratospheric correction based on data assimilation. STREAM was successfully applied to the UV/vis satellite instruments GOME 1/2, SCIAMACHY, and OMI. It overcomes some of the artifacts of previous algorithms, as it is capable of reproducing gradients of stratospheric NO2, e.g., related to the polar vortex, and reduces interpolation errors over continents. Based on synthetic input data, the uncertainty of STREAM was quantified as about 0.1–0.2 × 1015 molecules cm−2, in accordance with the typical deviations between stratospheric estimates from different algorithms compared in this study.

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