Development of an atmospheric chemistry model coupled to the PALM model system 6.0: implementation and first applications

Abstract. In this article we describe the implementation of an online-coupled gas-phase chemistry model in the turbulence-resolving PALM model system 6.0 (formerly an abbreviation for Parallelized Large-eddy Simulation Model and now an independent name). The new chemistry model is implemented in the PALM model as part of the PALM-4U (PALM for urban applications) components, which are designed for application of the PALM model in the urban environment (Maronga et al., 2020). The latest version of the Kinetic PreProcessor (KPP, 2.2.3) has been utilized for the numerical integration of gas-phase chemical reactions. A number of tropospheric gas-phase chemistry mechanisms of different complexity have been implemented ranging from the photostationary state (PHSTAT) to mechanisms with a strongly simplified volatile organic compound (VOC) chemistry (e.g. the SMOG mechanism from KPP) and the Carbon Bond Mechanism 4 (CBM4; Gery et al., 1989), which includes a more comprehensive, but still simplified VOC chemistry. Further mechanisms can also be easily added by the user. In this work, we provide a detailed description of the chemistry model, its structure and input requirements along with its various features and limitations. A case study is presented to demonstrate the application of the new chemistry model in the urban environment. The computation domain of the case study comprises part of Berlin, Germany. Emissions are considered using street-type-dependent emission factors from traffic sources. Three chemical mechanisms of varying complexity and one no-reaction (passive) case have been applied, and results are compared with observations from two permanent air quality stations in Berlin that fall within the computation domain. Even though the feedback of the model's aerosol concentrations on meteorology is not yet considered in the current version of the model, the results show the importance of online photochemistry and dispersion of air pollutants in the urban boundary layer for high spatial and temporal resolutions. The simulated NOx and O3 species show reasonable agreement with observations. The agreement is better during midday and poorest during the evening transition hours and at night. The CBM4 and SMOG mechanisms show better agreement with observations than the steady-state PHSTAT mechanism.

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Development of an atmospheric chemistry model coupled to the PALM model system 6.0: Implementation and first applications

10.5194/gmd-2020-286 ◽

2020 ◽

Cited By ~ 1

Author(s):

Basit Khan ◽

Sabine Banzhaf ◽

Edward C. Chan ◽

Renate Forkel ◽

Farah Kanani-Sühring ◽

...

Keyword(s):

Urban Environment ◽

Gas Phase ◽

Atmospheric Chemistry ◽

Horizontal Resolution ◽

Model System ◽

Gas Phase Chemistry ◽

Photostationary State ◽

Emission Mode ◽

Phase Chemistry

Abstract. In this article we describe the implementation of an online-coupled gas-phase chemistry model in the turbulence resolving PALM model system 6.0. The new chemistry model is part of the PALM-4U components (read: PALM for you; PALM for urban applications) which are designed for application of PALM model in the urban environment (Maronga et al., 2020). The latest version of the Kinetic PreProcessor (KPP, 2.2.3), has been utilised for the numerical integration of gas-phase chemical reactions. A number of tropospheric gas-phase chemistry mechanisms of different complexity have been implemented ranging from the photostationary state to more complex mechanisms such as CBM4, which includes major pollutants namely O3, NO, NO2, CO, a simplified VOC chemistry and a small number of products. Further mechanisms can also be easily added by the user. In this work, we provide a detailed description of the chemistry model, its structure along with its various features, input requirements, its application and limitations. A case study is presented to demonstrate the application of the new chemistry model in the urban environment. The computation domain of the case study is comprised of part of Berlin, Germany, covering an area of 6.71 × 6.71 km with a horizontal resolution of 10 m. We used "PARAMETERIZED" emission mode of the chemistry model that only considers emissions from traffic sources. Three chemical mechanisms of varying complexity and one no-reaction (passive) case have been applied and results are compared with observations from two permanent air quality stations in Berlin that fall within the computation domain. The results show importance of online photochemistry and dispersion of air pollutants in the urban boundary layer. The simulated NOx and O3 species show reasonable agreement with observations. The agreement is better during midday and poorest during the evening transition hours and at night. CBM4 and SMOG mechanisms show better agreement with observations than the steady state PHSTAT mechanism.

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Application of random forest regression to the calculation of gas-phase chemistry within the GEOS-Chem chemistry model v10

Geoscientific Model Development ◽

10.5194/gmd-12-1209-2019 ◽

2019 ◽

Vol 12 (3) ◽

pp. 1209-1225 ◽

Cited By ~ 15

Author(s):

Christoph A. Keller ◽

Mat J. Evans

Keyword(s):

Machine Learning ◽

Random Forest ◽

Gas Phase ◽

Atmospheric Chemistry ◽

Random Forest Regression ◽

Data Set ◽

Gas Phase Chemistry ◽

Chemical Conditions ◽

Phase Chemistry ◽

The Impact

Abstract. Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models are numerically intense, and previous attempts to reduce the numerical cost of chemistry solvers have not delivered transformative change. We show here the potential of a machine learning (in this case random forest regression) replacement for the gas-phase chemistry in atmospheric chemistry transport models. Our training data consist of 1 month (July 2013) of output of chemical conditions together with the model physical state, produced from the GEOS-Chem chemistry model v10. From this data set we train random forest regression models to predict the concentration of each transported species after the integrator, based on the physical and chemical conditions before the integrator. The choice of prediction type has a strong impact on the skill of the regression model. We find best results from predicting the change in concentration for long-lived species and the absolute concentration for short-lived species. We also find improvements from a simple implementation of chemical families (NOx = NO + NO2). We then implement the trained random forest predictors back into GEOS-Chem to replace the numerical integrator. The machine-learning-driven GEOS-Chem model compares well to the standard simulation. For ozone (O3), errors from using the random forests (compared to the reference simulation) grow slowly and after 5 days the normalized mean bias (NMB), root mean square error (RMSE) and R2 are 4.2 %, 35 % and 0.9, respectively; after 30 days the errors increase to 13 %, 67 % and 0.75, respectively. The biases become largest in remote areas such as the tropical Pacific where errors in the chemistry can accumulate with little balancing influence from emissions or deposition. Over polluted regions the model error is less than 10 % and has significant fidelity in following the time series of the full model. Modelled NOx shows similar features, with the most significant errors occurring in remote locations far from recent emissions. For other species such as inorganic bromine species and short-lived nitrogen species, errors become large, with NMB, RMSE and R2 reaching >2100 % >400 % and <0.1, respectively. This proof-of-concept implementation takes 1.8 times more time than the direct integration of the differential equations, but optimization and software engineering should allow substantial increases in speed. We discuss potential improvements in the implementation, some of its advantages from both a software and hardware perspective, its limitations, and its applicability to operational air quality activities.

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Application of random forest regression to the calculation of gas-phase chemistry within the GEOS-Chem chemistry model v10

10.5194/gmd-2018-229 ◽

2018 ◽

Cited By ~ 1

Author(s):

Christoph A. Keller ◽

Mat J. Evans

Keyword(s):

Machine Learning ◽

Random Forest ◽

Gas Phase ◽

Atmospheric Chemistry ◽

Random Forest Regression ◽

Data Set ◽

Gas Phase Chemistry ◽

Chemical Conditions ◽

Phase Chemistry ◽

The Impact

Abstract. Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models are numerically intense, and previous attempts to reduce the numerical cost of chemistry solvers have not delivered transformative change. We show here the potential of a machine learning (in this case random forest regression) replacement for the gas-phase chemistry in atmospheric chemistry models. Our training data consists of one month (July 2013) of output of chemical conditions together with the model physical state, produced from the GEOS-Chem chemistry model (v10). From this data set we train random forest regression models to predict the concentration of each transported species after the integrator, based on the physical and chemical conditions before the integrator. The choice of prediction type has a strong impact on the skill of the regression model. We find best results from predicting the change in concentration for long-lived species and the absolute concentration for short-lived species. We also find improvements from a simple implementation of chemical families (NOx = NO + NO2). We then implement the trained random forest predictors back into GEOS-Chem to replace the numerical integrator. The machine learning driven GEOS-Chem model compares well to the standard simulation. For O3, error from using the random forests grow slowly and after 5 days the normalised mean bias (NMB), root mean square error (RMSE) and R2 are 4.2 %, 35 %, 0.9 respectively; after 30 days the errors increase to 13 %, 67 %, 0.75. The biases become largest in remote areas such as the tropical Pacific where errors in the chemistry can accumulate with little balancing influence from emissions or deposition. Over polluted regions the model error is less than 10 % and has significant fidelity in following the time series of the full model. Modelled NOx shows similar features, with the most significant errors occurring in remote locations far from recent emissions. For other species such as inorganic bromine species and short lived nitrogen species errors become large, with NMB, RMSE and R2 reaching >2100 % >400 %, <0.1 respectively. This proof-of-concept implementation is 85 % slower than the direct integration of the differential equations but optimisation and software engineering would allow substantial increases in speed. We discuss potential improvements in the implementation, some of its advantages from both a software and hardware perspective, its limitations and its applicability to operational air quality activities.

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ASIS v1.0: an adaptative solver for the simulation of atmospheric chemistry

10.5194/gmd-2016-281 ◽

2016 ◽

Author(s):

Daniel Cariolle ◽

Philippe Moinat ◽

Hubert Teyssèdre ◽

Luc Giraud ◽

Béatrice Josse ◽

...

Keyword(s):

Gas Phase ◽

Atmospheric Chemistry ◽

Implicit Scheme ◽

Gas Phase Chemistry ◽

Time Stepping ◽

Implicit Schemes ◽

Implicit Solvers ◽

One Step ◽

Phase Chemistry ◽

Ordinary Differential Equation Systems

Abstract. This article reports on the development and tests of the Adaptative Semi-Implicit Scheme (ASIS) solver for the simulation of atmospheric chemistry. To solve the Ordinary Differential Equation systems associated with the time evolution of the species concentrations, ASIS adopts a one step linearized implicit scheme with specific treatments of the Jacobian of the chemical fluxes. It conserves mass and has a time stepping module to control the accuracy of the numerical solution. In idealized box model simulations ASIS gives results similar to the higher order implicit schemes derived from the Rosenbrock's and Gear's methods. When implemented in the MOCAGE CTM and the LMD Mars GCM the ASIS solver performs well and reveals weaknesses and limitations of the original semi-implicit solvers used by these two models. ASIS can be easily adapted to various chemical schemes and further developments are foreseen to increase its computational efficiency, and to include the computation of the concentrations of the species in aqueous phase in addition to gas phase chemistry.

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Simulation of the diurnal variations of the oxygen isotope anomaly (Δ<sup>17</sup>O) of reactive atmospheric species

Atmospheric Chemistry and Physics ◽

10.5194/acp-11-3653-2011 ◽

2011 ◽

Vol 11 (8) ◽

pp. 3653-3671 ◽

Cited By ~ 41

Author(s):

S. Morin ◽

R. Sander ◽

J. Savarino

Keyword(s):

Gas Phase ◽

Atmospheric Chemistry ◽

Isotopic Signature ◽

Diurnal Variations ◽

Box Model ◽

Computational Costs ◽

Atmospheric Species ◽

Gas Phase Chemistry ◽

Atmospheric Nitrate ◽

Phase Chemistry

Abstract. The isotope anomaly (Δ17O) of secondary atmospheric species such as nitrate (NO3−) or hydrogen peroxide (H2O2) has potential to provide useful constrains on their formation pathways. Indeed, the Δ17O of their precursors (NOx, HOx etc.) differs and depends on their interactions with ozone, which is the main source of non-zero Δ17O in the atmosphere. Interpreting variations of Δ17O in secondary species requires an in-depth understanding of the Δ17O of their precursors taking into account non-linear chemical regimes operating under various environmental settings. This article reviews and illustrates a series of basic concepts relevant to the propagation of the Δ17O of ozone to other reactive or secondary atmospheric species within a photochemical box model. We present results from numerical simulations carried out using the atmospheric chemistry box model CAABA/MECCA to explicitly compute the diurnal variations of the isotope anomaly of short-lived species such as NOx and HOx. Using a simplified but realistic tropospheric gas-phase chemistry mechanism, Δ17O was propagated from ozone to other species (NO, NO2, OH, HO2, RO2, NO3, N2O5, HONO, HNO3, HNO4, H2O2) according to the mass-balance equations, through the implementation of various sets of hypotheses pertaining to the transfer of Δ17O during chemical reactions. The model results confirm that diurnal variations in Δ17O of NOx predicted by the photochemical steady-state relationship during the day match those from the explicit treatment, but not at night. Indeed, the Δ17O of NOx is "frozen" at night as soon as the photolytical lifetime of NOx drops below ca. 10 min. We introduce and quantify the diurnally-integrated isotopic signature (DIIS) of sources of atmospheric nitrate and H2O2, which is of particular relevance to larger-scale simulations of Δ17O where high computational costs cannot be afforded.

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Matching precursor kinetics to afford a more robust CVD chemistry: a case study of the C chemistry for silicon carbide using SiF4 as Si precursor

Journal of Materials Chemistry C ◽

10.1039/c7tc00138j ◽

2017 ◽

Vol 5 (23) ◽

pp. 5818-5823 ◽

Cited By ~ 4

Author(s):

Pontus Stenberg ◽

Örjan Danielsson ◽

Edvin Erdtman ◽

Pitsiri Sukkaew ◽

Lars Ojamäe ◽

...

Keyword(s):

Silicon Carbide ◽

Gas Phase ◽

Gas Phase Chemistry ◽

Gas Phase Kinetics ◽

Homoepitaxial Growth ◽

Kinetics Modeling ◽

Phase Chemistry

We show by a combination of experiments and gas phase kinetics modeling that the combinations of precursors with the most well-matched gas phase chemistry kinetics gives the largest area of homoepitaxial growth of SiC.

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Description and evaluation of the Multiscale Online Nonhydrostatic AtmospheRe CHemistry model (NMMB-MONARCH) version 1.0: gas-phase chemistry at global scale

Geoscientific Model Development ◽

10.5194/gmd-10-609-2017 ◽

2017 ◽

Vol 10 (2) ◽

pp. 609-638 ◽

Cited By ~ 11

Author(s):

Alba Badia ◽

Oriol Jorba ◽

Apostolos Voulgarakis ◽

Donald Dabdub ◽

Carlos Pérez García-Pando ◽

...

Keyword(s):

Gas Phase ◽

Southern Hemisphere ◽

Biomass Burning ◽

Atmospheric Chemistry ◽

State Of The Art ◽

Seasonal Trends ◽

Gas Phase Chemistry ◽

Cycle Simulation ◽

Extensive Evaluation ◽

Phase Chemistry

Abstract. This paper presents a comprehensive description and benchmark evaluation of the tropospheric gas-phase chemistry component of the Multiscale Online Nonhydrostatic AtmospheRe CHemistry model (NMMB-MONARCH), formerly known as NMMB/BSC-CTM, that can be run on both regional and global domains. Here, we provide an extensive evaluation of a global annual cycle simulation using a variety of background surface stations (EMEP, WDCGG and CASTNET), ozonesondes (WOUDC, CMD and SHADOZ), aircraft data (MOZAIC and several campaigns), and satellite observations (SCIAMACHY and MOPITT). We also include an extensive discussion of our results in comparison to other state-of-the-art models. We note that in this study, we omitted aerosol processes and some natural emissions (lightning and volcano emissions). The model shows a realistic oxidative capacity across the globe. The seasonal cycle for CO is fairly well represented at different locations (correlations around 0.3–0.7 in surface concentrations), although concentrations are underestimated in spring and winter in the Northern Hemisphere, and are overestimated throughout the year at 800 and 500 hPa in the Southern Hemisphere. Nitrogen species are well represented in almost all locations, particularly NO2 in Europe (root mean square error – RMSE – below 5 ppb). The modeled vertical distributions of NOx and HNO3 are in excellent agreement with the observed values and the spatial and seasonal trends of tropospheric NO2 columns correspond well to observations from SCIAMACHY, capturing the highly polluted areas and the biomass burning cycle throughout the year. Over Asia, the model underestimates NOx from March to August, probably due to an underestimation of NOx emissions in the region. Overall, the comparison of the modeled CO and NO2 with MOPITT and SCIAMACHY observations emphasizes the need for more accurate emission rates from anthropogenic and biomass burning sources (i.e., specification of temporal variability). The resulting ozone (O3) burden (348 Tg) lies within the range of other state-of-the-art global atmospheric chemistry models. The model generally captures the spatial and seasonal trends of background surface O3 and its vertical distribution. However, the model tends to overestimate O3 throughout the troposphere in several stations. This may be attributed to an overestimation of CO concentration over the Southern Hemisphere leading to an excessive production of O3 or to the lack of specific chemistry (e.g., halogen chemistry, aerosol chemistry). Overall, O3 correlations range between 0.6 and 0.8 for daily mean values. The overall performance of the NMMB-MONARCH is comparable to that of other state-of-the-art global chemistry models.

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