scholarly journals Inline Coupling of Simple and Complex Chemistry Modules within the Global Weather Forecast model FIM (FIM-Chem v1)

2021 ◽  
Author(s):  
Li Zhang ◽  
Georg Grell ◽  
Stuart McKeen ◽  
Ravan Ahmadov ◽  
Karl Froyd ◽  
...  
Keyword(s):  
Gas Phase ◽  
Transport Model ◽  
Model Performance ◽  
Chemical Species ◽  
Weather Forecast ◽  
Forecast Model ◽  
Gas Phase Chemistry ◽  
Western Africa ◽  
Phase Chemistry ◽  
The Impact

Abstract. The global Flow-following finite-volume Icosahedral Model (FIM), which was developed in the Global Systems Laboratory of NOAA/ESRL, has been coupled inline with aerosol and gas-phase chemistry schemes of different complexity using the chemistry and aerosol packages from WRF-Chem v3.7, named as FIM-Chem v1. The three chemistry schemes include 1) the simple aerosol modules from the Goddard Chemistry Aerosol Radiation and Transport model that includes only simplified sulfur chemistry, bulk aerosols, and sectional dust and sea salt modules (GOCART); 2) the photochemical gas-phase mechanism RACM coupled to GOCART to determine the impact of more realistic gas-phase chemistry on the GOCART aerosols simulations (RACM_ GOCART); and 3) a further sophistication within the aerosol modules by replacing GOCART with a modal aerosol scheme that includes secondary organic aerosols (SOA) based on the VBS approach (RACM_SOA_VBS). FIM-Chem is able to simulate aerosol, gas-phase chemical species and SOA at various spatial resolutions with different levels of complexity and quantify the impact of aerosol on numerical weather predictions (NWP). We compare the results of RACM_ GOCART and GOCART schemes which uses the default climatological model fields for OH, H2O2, and NO3. We find significant reductions of sulfate that are on the order of 40 % to 80 % over the eastern US and are up to 40 % near the Beijing region over China when using the RACM_GOCART scheme. We also evaluate the model performance by comparing with the Atmospheric Tomography Mission (ATom-1) aircraft measurements in 2016 summer. FIM-Chem shows good performance in capturing the aerosol and gas-phase tracers. The model predicted vertical profiles of biomass burning plumes and dust plumes off the western Africa are also reproduced reasonably well.

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

2019 ◽  
Vol 12 (3) ◽  
pp. 1209-1225 ◽  
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.

scholarly journals Gas-phase chemistry in the online multiscale NMMB/BSC Chemical Transport Model: Description and evaluation at global scale

2016 ◽  
Author(s):  
Alba Badia ◽  
Oriol Jorba ◽  
Apostolos Voulgarakis ◽  
Donald Dabdub ◽  
Carlos Pérez García-Pando ◽  
...  
Keyword(s):  
Vertical Distribution ◽  
Gas Phase ◽  
Biomass Burning ◽  
Transport Model ◽  
State Of The Art ◽  
Chemical Transport ◽  
Global Scale ◽  
Chemical Transport Model ◽  
Gas Phase Chemistry ◽  
Phase Chemistry

Abstract. This paper presents a comprehensive description and benchmark evaluation of the tropospheric gas-phase chemistry component of the NMMB/BSC Chemical Transport Model (NMMB/BSC-CTM), an online chemical weather prediction system conceived for both the regional and the global scale. 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. 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 (RMSE below 9 μg m−3). The modeled vertical distribution 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 modelled 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 is attributed to an overestimation of CO concentration over the southern hemisphere leading to an excessive production of O3. Overall, O3 correlations range between 0.6 to 0.8 for daily mean values. The overall performance of the NMMB/BSC-CTM is comparable to that of other state-of-the-art global chemical transport models.

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

2018 ◽  
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.

Gas Phase Chemistry Study During Deposition of a-Si:H and μc-Si:H Films by HWCVD using Quadrupole Mass Spectrometry

2002 ◽  
Vol 715 ◽  
Author(s):  
Samadhan B. Patil ◽  
Alka A. Kumbhar ◽  
R. O. Dusane
Keyword(s):  
Mass Spectrometry ◽  
Gas Phase ◽  
Chemical Species ◽  
Quadrupole Mass ◽  
Film Properties ◽  
Quadrupole Mass Spectrometry ◽  
Gas Phase Chemistry ◽  
Hydrogen Dilution ◽  
Phase Chemistry

AbstractAmorphous and microcrystalline silicon films were deposited by HWCVD under different deposition conditions and the gas phase chemistry was studied by in situ Quadrupole Mass Spectrometry. Attempt is made to correlate the properties of the films with the gas phase chemistry during deposition. Interestingly, unlike in PECVD, partial pressure of H2 is higher than any other species during deposition of a-Si:H as well as μc-Si:H. Effect of hydrogen dilution on film properties and on concentration of various chemical species in the gas phase is studied. For low hydrogen dilution [H2]/ [SiH4] from 0 to 1 (where [SiH4] is 10 sccm), all films deposited are amorphous with photoconductivity gain of ∼ 106. During deposition of these amorphous films SiH2 was dominant in gas phase next to [H2]. Interestingly [Si]/[SiH2] ratio increases from 0.4 to 0.5 as dilution increased from 0 to 1, and further to more than 1 for higher hydrogen dilution leading to [Si] dominance. At hydrogen dilution ratio 20, consequently films deposited were microcrystalline.

scholarly journals The impact of multiphase reactions of NO<sub>2</sub> with aromatics: a modelling approach

2002 ◽  
Vol 2 (3) ◽  
pp. 215-226 ◽  
Author(s):  
N. Lahoutifard ◽  
M. Ammann ◽  
L. Gutzwiller ◽  
B. Ervens ◽  
Ch. George
Keyword(s):  
Gas Phase ◽  
Aromatic Compounds ◽  
Urban Environments ◽  
Liquid Droplets ◽  
Gas Phase Chemistry ◽  
Multiphase Reactions ◽  
Benzene Toluene ◽  
Acidic Conditions ◽  
Phase Chemistry ◽  
The Impact

Abstract. The impact of multiphase reactions involving nitrogen dioxide (NO2) and aromatic compounds was simulated in this study. A mechanism (CAPRAM 2.4, MODAC Mechanism) was applied for the aqueous phase reactions, whereas RACM was applied for the gas phase chemistry. Liquid droplets were considered as monodispersed with a mean radius of 0.1 µm and a liquid content (LC) of 50 µg m-3. The multiphase mechanism has been further extended to the chemistry of aromatics, i.e. reactions involving benzene, toluene, xylene, phenol and cresol have been added. In addition, reaction of NO2 with dissociated hydroxyl substituted aromatic compounds has also been implemented. These reactions proceed through charge exchange leading to nitrite ions and therefore to nitrous acid formation. The strength of this source was explored under urban polluted conditions. It was shown that it may increase gas phase HONO levels under some conditions and that the extent of this effect is strongly pH dependent. Especially under moderate acidic conditions (i.e. pH above 4) this source may represent more than 75% of the total HONO/NO2 - production rate, but this contribution drops down close to zero in acidic droplets (as those often encountered in urban environments).

scholarly journals Formation of secondary aerosols: impact of the gas-phase chemical mechanism

2010 ◽  
Vol 10 (8) ◽  
pp. 20625-20672
Author(s):  
Y. Kim ◽  
K. Sartelet ◽  
C. Seigneur
Keyword(s):  
Gas Phase ◽  
Transport Model ◽  
Model Performance ◽  
Organic Aerosols ◽  
Chemical Kinetic ◽  
Chemical Mechanism ◽  
Chemical Components ◽  
The Impact ◽  
Phase Chemical ◽  
Inorganic And Organic

Abstract. The impact of two recent gas-phase chemical kinetic mechanisms (CB05 and RACM2) on the formation of secondary inorganic and organic aerosols is compared for simulations of PM2.5 over Europe between 15 July and 15 August 2001. The host chemistry transport model is Polair3D of the Polyphemus air-quality platform. Particulate matter is modeled with SIREAM, which is coupled to the thermodynamic model ISORROPIA and to the secondary organic aerosol module MAEC. Model performance is satisfactory with both mechanisms for speciated PM2.5. The monthly-mean difference of the concentration of PM2.5 is less than 1 μg/m3 (6%) over the entire domain. Secondary chemical components of PM2.5 include sulfate, nitrate, ammonium and organic aerosols, and the chemical composition of PM2.5 is not significantly different between the two mechanisms. Monthly-mean concentrations of inorganic aerosol are higher with RACM2 than with CB05 (+16% for sulfate, +11% for nitrate, and +12% for ammonium), whereas the concentrations of organic aerosols are slightly higher with CB05 than with RACM2 (+26% for anthropogenic SOA and +1% for biogenic SOA). Differences in the inorganic and organic aerosols result primarily from differences in oxidant concentrations (OH, O3 and NO3). Nitrate formation tends to be HNO3-limited over land and differences in the concentrations of nitrate are due to differences in concentration of HNO3. Differences in aerosols formed from aromatics SVOC are due to different aromatics oxidation between CB05 and RACM2. The aromatics oxidation in CB05 leads to more cresol formation, which then leads to more SOA. Differences in the aromatics aerosols would be significantly reduced with the recent CB05-TU mechanism for toluene oxidation. Differences in the biogenic aerosols are due to different oxidant concentrations (monoterpenes) and different particulate organic mass concentrations affecting the gas-particle partitioning of SOA (isoprene).

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