WRF ARW and CHIMERE models for numerical forecasting of surface ozone concentration

Zaripov, R. B.; Konovalov, I. B.; Кузнецова, И. Н.; Беликов, И. Б.; Zvyagintsev, A. M.

doi:10.3103/s1068373911040054

Cited by 11 publications

(5 citation statements)

References 15 publications

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“…MM5 was initialised with NCEP Reanalysis-2 data (http://www.cpc.ncep.noaa.gov/products/wesley/ncep data/). The resolution of MM5 was defined taking into account available computational resources and results of the study by Zaripov et al (2011), in which no serious difference in accuracy of ozone forecasts produced by CHIMERE for the Moscow region was found when resolution of meteorological data decreased from 20 × 20 km 2 to 70 × 70 km 2 . A probable explanation of this result is that the accuracy of higher resolution meteorological simulations for the considered plain region is mainly limited by global data driving mesoscale simulations.…”

Section: Chimere Chemistry Transport Model: General Description and Nmentioning

confidence: 99%

Atmospheric impacts of the 2010 Russian wildfires: integrating modelling and measurements of an extreme air pollution episode in the Moscow region

et al. 2011

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Abstract. Numerous wildfires provoked by an unprecedented intensive heat wave caused continuous episodes of extreme air pollution in several Russian cities and densely populated regions, including the Moscow region. This paper analyzes the evolution of the surface concentrations of CO, PM 10 and ozone over the Moscow region during the 2010 heat wave by integrating available ground based and satellite measurements with results of a mesoscale model. The CHIMERE chemistry transport model is used and modified to include the wildfire emissions of primary pollutants and the shielding effect of smoke aerosols on photolysis. The wildfire emissions are derived from satellite measurements of the fire radiative power and are optimized by assimilating data of ground measurements of carbon monoxide (CO) and particulate matter (PM 10 ) into the model. It is demonstrated that the optimized simulations reproduce independent observations, which were withheld during the optimisation procedure, quite adequately (specifically, the correlation coefficient of daily time series of CO and PM 10 exceeds 0.8) and that inclusion of the fire emissions into the model significantly improves its performance. The model results show that wildfires are the principal factor causing the observed air pollution episode associated with the extremely high levels of daily mean CO and PM 10 concentrations (up to 10 mg m −3 and 700 µg m −3 in the averages over available monitoring sites, respectively), although accumulation of anthropogenic pollution was also favoured by a stagnant meteorological sitCorrespondence to: I. B. Konovalov (konov@appl.sci-nnov.ru) uation. Indeed, ozone concentrations were simulated to be episodically very large (>400 µg m −3 ) even when fire emissions were omitted in the model. It was found that fire emissions increased ozone production by providing precursors for ozone formation (mainly VOC), but also inhibited the photochemistry by absorbing and scattering solar radiation. In contrast, diagnostic model runs indicate that ozone concentrations could reach very high values even without fire emissions which provide "fuel" for ozone formation, but, at the same time, inhibit it as a result of absorption and scattering of solar radiation by smoke aerosols. A comparison of MOPITT CO measurements and corresponding simulations indicates that the observed episodes of extreme air pollution in Moscow were only a part of a very strong perturbation of the atmospheric composition, caused by wildfires, over European Russia. It is estimated that 2010 fires in this region emitted ∼10 Tg CO, thus more than 85 % of the total annual anthropogenic CO emissions. About 30 % of total CO fire emissions in European Russia are identified as emissions from peat fires.

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Section: Chimere Chemistry Transport Model: General Description and Nmentioning

confidence: 99%

Atmospheric impacts of the 2010 Russian wildfires: integrating modelling and measurements of an extreme air pollution episode in the Moscow region

et al. 2011

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“…Such models are especially needed to predict rapid air quality deteriorations [12]. The necessary information obtained prior to the occurrence of extreme situations makes it possible to minimize the consequences adverse for human health and for environmental conditions.…”

Section: Introductionmentioning

confidence: 99%

“…The available studies of simulations of trace gas contents in the Moscow atmosphere include one or two pollutants usually considered for short time intervals (see, for example, [12,15,16]). In this work, we considered the primary and reliably measured gaseous pollutants, namely, CO, NO 2 , NO, and O 3 for two longer periods in winter and summer: 1-31 January and 1-31 July 2014.…”

Section: Introductionmentioning

confidence: 99%

Air Pollution in Moscow Megacity: Data Fusion of the Chemical Transport Model and Observational Network

2021

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Comparisons of observational data obtained at the Moscow Ecological Monitoring network (MEM) with numerical simulations using a chemical transformation and transport model (SILAM—System for Integrated modeLling of Atmospheric coMposition) showed that the errors in determining the gaseous pollutant concentrations in the urban atmosphere have a more complex structure than those assumed under the conventional algorithms of data assimilation. These errors are statistically nonstationary; they show a pronounced diurnal cycle and a significant lifetime. The statistical features of errors in numerical calculations also depend upon the type of pollutants, i.e., the chemical reactions in which they participate. Our analysis showed that the simulation errors are not small: the ratios of calculated and measured concentrations (even for daily averages at all measuring stations) may vary in a wide range. For the chemically active pollutants, the intradiurnal error variations may reach 100%. The diurnal cycle of such errors was found to vary according to seasons (in our case, summer and winter). The analysis of statistical properties of the errors, including their temporal and spatial variability, allows one to correct and adequately forecast the air pollution in the metropolis area at lead times up to three days in advance.

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“…когда произошло разбавление или оно происходит во времени по мере удаления от источников выбросов. Это и целые монографии [4,5], и статьи, в которых довольно часто сравниваются результаты численного эксперимента и прямых измерений [6][7][8][9][10][11]. Существенно меньшее число работ посвящено изучению скоростей атмосферных химиче-ских реакций в стесненных аэродинамических условиях [12][13][14][15][16].…”

Section: Introductionunclassified

Modeling the Kinetics of Small Gas Impurities in Cramped Aerodynamic Conditions of the Urban Atmosphere

Zaytsev¹

2020

Bulletin of St PbSIT(TU)

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The chemical transformations of a number of pollutants in cramped aerodynamic conditions of the urban atmosphere are considered. Those areas are located between buildings; they are with abnormally high concentrations of pollutants, and it leads to high reaction rates, significantly different from similar ones in an unlimited volume. In the course of numerical experiments the obtained stiff system of differential equations, which describes the kinetics of that process, is solved. For that, the Rosenbrock method is used under various initial conditions corresponding to variations in the concentrations of primary pollutants. Their quantitative contribution to the increase in the content of a highly toxic secondary pollutant is analyzed in detail. Moreover, the processes in this chemically active system occur rather quickly, before the substances leave the reaction zone.

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WRF ARW and CHIMERE models for numerical forecasting of surface ozone concentration

Cited by 11 publications

References 15 publications

Atmospheric impacts of the 2010 Russian wildfires: integrating modelling and measurements of an extreme air pollution episode in the Moscow region

Atmospheric impacts of the 2010 Russian wildfires: integrating modelling and measurements of an extreme air pollution episode in the Moscow region

Air Pollution in Moscow Megacity: Data Fusion of the Chemical Transport Model and Observational Network

Modeling the Kinetics of Small Gas Impurities in Cramped Aerodynamic Conditions of the Urban Atmosphere

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