Study on the influence of ground and satellite observations on the numerical air-quality for PM10 over Romanian territory

Dumitrache, Rodica; Iriza, Amalia; Maco, Bogdan Alexandru; Barbu, Cristian Marian; Hirtl, Marcus; Mantovani, Simone; Nicola, Oana; Irimescu, Anișoara; Crǎciunescu, Vasile; Ristea, Alina; Diamandi, Andrei

doi:10.1016/j.atmosenv.2016.08.063

Cited by 11 publications

(6 citation statements)

References 33 publications

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“…Although the concentration in the HK region had been improved, it was still underestimated, possibly due to the uncertainty of the emission inventory. Comparing the assimilation results for the three seasons, the benefits in winter (January) were more significant than in summer (July), which was consistent with a previous study (Dumitrache et al., 2016). This phenomenon's primary driver is that the north‐easterly wind brings the air pollutants along the coastal line to the PRD water to lift the domain‐wide assimilated air pollutants.…”

Section: Resultssupporting

confidence: 92%

“…For the nested numerical modeling runs, assimilating PM 2.5 in the outermost domain brought substantial simulation improvements for the innermost domain. Comparing the assimilation results of a winter month (January), summer month (July), and domestic‐heating month (November), the benefits of assimilation were greater in winter than summer, which was consistent with a previous study (Dumitrache et al., 2016). The primary driver of this phenomenon is that the north‐easterly wind brings the air pollutants along the coastal line to the PRD water to lift the domain‐wide assimilated air pollutants.…”

Section: Discussionsupporting

confidence: 91%

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Improved Modeling of Spatiotemporal Variations of Fine Particulate Matter Using a Three‐Dimensional Variational Data Fusion Method

et al. 2021

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Section: Resultssupporting

confidence: 92%

Section: Discussionsupporting

confidence: 91%

Improved Modeling of Spatiotemporal Variations of Fine Particulate Matter Using a Three‐Dimensional Variational Data Fusion Method

et al. 2021

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“…It is similar to previous studies in that the influence of assimilation can continue for 6–24 h and will gradually approach the control experiment (Cheng et al. 2019; Dumitrache et al., 2016; Jiang et al. 2013).…”

Section: Conclusion and Discussionsupporting

confidence: 87%

Development of Three‐Dimensional Variational Data Assimilation Method of Aerosol for the CMAQ Model: An Application for PM_2.5 and PM₁₀ Forecasts in the Sichuan Basin

Zhang

Zang

Cheng

et al. 2021

Earth and Space Science

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Air quality models, such as the Community Multiscale Air Quality (CMAQ), Weather Research and Forecasting-Chemistry (WRF-chem), the Comprehensive Air Quality Model with Extensions (CAMx), and the Nested Air Quality Prediction Modeling System (NAQPMS) models, are effective tools for atmospheric pollution analysis. They are widely used in the prediction and analysis of the temporal and spatial distribution of atmospheric pollutants. However, there are uncertainties in terms of emission sources, initial conditions (ICs), boundary conditions (BCs), and chemical processes that affect the accuracy of these models (Bouttier & Courtier, 1999;Hanna et al., 1998;Moore & Londergan, 2001). In particular, the ICs input the atmospheric state at a specified time into the model, playing an important role in air quality prediction. Data assimilation (DA), which can incorporate observations into the model, has been shown to be an effective method to improve the ICs.Since the assimilation of the initial field is significant for building an accurate analysis field, which is crucial for improving the short-term forecast and analyzing the vertical diffusion and extinction of aerosol, many studies and multiple DA methods have been conducted and have obtained significant assimilation effects (

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“…DA is used for the integration of available observations into models in order to produce aerosol concentration fields, which are then used to provide model initial conditions to improve forecasts. Previous publications have shown that initial conditions play an important role in chemical forecasting [12][13][14][15][16][17][18]. A review of the current status of data assimilation in atmospheric chemistry models has been presented by Bocquet et al [19].…”

Section: Introductionmentioning

confidence: 99%

Can Data Assimilation of Surface PM2.5 and Satellite AOD Improve WRF-Chem Forecasting? A Case Study for Two Scenarios of Particulate Air Pollution Episodes in Poland

2019

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Based on the Weather Research and Forecasting model with Chemistry (WRF-Chem) model and Gridpoint Statistical Interpolation (GSI) assimilation tool, a forecasting system was used for two selected episodes (winter and summer) over Eastern Europe. During the winter episode, very high particular matter (PM2.5, diameter less than 2.5 µm) concentrations, related to low air temperatures and increased emission from residential heating, were measured at many stations in Poland. During the summer episode, elevated aerosol optical depth (AOD), likely related to the transport of pollution from biomass fires, was observed in Southern Poland. Our aim is to verify if there is a relevant positive impact of surface and satellite data assimilation (DA) on modeled PM2.5 concentrations, and to assess whether there are significant differences in the DA’s impact on concentrations between the two seasons. The results show a significant difference in the impact of surface and satellite DA on the model results between the summer and winter episode, which to a large degree is related to the availability of the satellite data. For example, the application of satellite DA raises the factor of two statistic from 0.18 to 0.78 for the summer episode, whereas this statistic remains unchanged (0.71) for the winter. The study suggests that severe winter air pollution episodes in Poland and Eastern Europe in general, often related to the dense cover of low clouds, will benefit from the assimilation of surface observations rather than satellite data, which can be very sparse in such meteorological situations. In contrast, the assimilation of satellite data can have a greater positive impact on the model results during summer than the assimilation of surface data for the same period.

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Study on the influence of ground and satellite observations on the numerical air-quality for PM10 over Romanian territory

Cited by 11 publications

References 33 publications

Improved Modeling of Spatiotemporal Variations of Fine Particulate Matter Using a Three‐Dimensional Variational Data Fusion Method

Improved Modeling of Spatiotemporal Variations of Fine Particulate Matter Using a Three‐Dimensional Variational Data Fusion Method

Development of Three‐Dimensional Variational Data Assimilation Method of Aerosol for the CMAQ Model: An Application for PM_2.5 and PM₁₀ Forecasts in the Sichuan Basin

Can Data Assimilation of Surface PM2.5 and Satellite AOD Improve WRF-Chem Forecasting? A Case Study for Two Scenarios of Particulate Air Pollution Episodes in Poland

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Study on the influence of ground and satellite observations on the numerical air-quality for PM10 over Romanian territory

Cited by 11 publications

References 33 publications

Improved Modeling of Spatiotemporal Variations of Fine Particulate Matter Using a Three‐Dimensional Variational Data Fusion Method

Improved Modeling of Spatiotemporal Variations of Fine Particulate Matter Using a Three‐Dimensional Variational Data Fusion Method

Development of Three‐Dimensional Variational Data Assimilation Method of Aerosol for the CMAQ Model: An Application for PM2.5 and PM10 Forecasts in the Sichuan Basin

Can Data Assimilation of Surface PM2.5 and Satellite AOD Improve WRF-Chem Forecasting? A Case Study for Two Scenarios of Particulate Air Pollution Episodes in Poland

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Development of Three‐Dimensional Variational Data Assimilation Method of Aerosol for the CMAQ Model: An Application for PM_2.5 and PM₁₀ Forecasts in the Sichuan Basin