Impact of 3DVAR assimilation of surface PM2.5 observations on PM2.5 forecasts over China during wintertime

Feng, Shuzhuang; Jiang, Fei; Jiang, Zhe; Wang, Hengmao; Cai, Zucong; Lin, Zhang

doi:10.1016/j.atmosenv.2018.05.049

Cited by 45 publications

(37 citation statements)

References 56 publications

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“…Therefore, before the cycling assimilations, a 5‐day spin‐up simulation using the WRF/CMAQ model is conducted to generate a relatively good initial field, and then, the 3DVAR CO DA method within the Grid point Statistical Interpolation (GSI) framework of the National Centers for Environmental Prediction is used to further optimize the CO initial field (Kleist et al, 2009; Wu et al, 2002). The GSI CO 3DVAR framework is basically the same as that used by Feng et al (2018), who constructed a WRF/CMAQ‐3DVAR DA system for the simulation of PM 2.5 using surface measurements. During the cycling assimilations, instead of simultaneously optimizing the CO concentrations and emissions (e.g., Fortems‐Cheiney et al, 2012; Y. Yin et al, 2015), in which the emission and concentration contributions to the model bias are hard to distinguish (Z. Jiang et al, 2017), in this study, we assume that all the biases between the simulations and observations are from the emissions since we have generated a relatively perfect initial field through the spin‐up simulation and 3DVAR DA as mentioned before.…”

Section: Methods and Datamentioning

confidence: 99%

“…Therefore, before the cycling assimilations, a 5-day spin-up simulation using the WRF/CMAQ model is conducted to generate a relatively good initial field, and then, the 3DVAR CO DA method within the Grid point Statistical Interpolation (GSI) framework of the National Centers for Environmental Prediction is used to further optimize the CO initial field (Kleist et al, 2009;Wu et al, 2002). The GSI CO 3DVAR framework is basically the same as that used by Feng et al (2018), who constructed a WRF/CMAQ-3DVAR DA system for the simulation of PM 2.5 using surface measurements. During the cycling assimilations, instead of simultaneously optimizing the CO concentrations and emissions (e.g., Fortems-Cheiney et al, 2012;Y.…”

Section: Methods and Datamentioning

confidence: 99%

“…Many studies have shown that WRF/CMAQ has a good performance in extensive applications (N. Wang et al, 2015). In this study, the WRF/CMAQ settings are very similar to those of Feng et al (2018). WRF simulations are performed across East Asia (169 west-east and 129 south-north cells) with a 36-km horizontal grid spacing ( Figure 2).…”

Section: Wrf/cmaq Modelmentioning

confidence: 99%

See 2 more Smart Citations

CO Emissions Inferred From Surface CO Observations Over China in December 2013 and 2017

Feng

Jiang

Wu³

et al. 2020

JGR Atmospheres

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China has implemented active clean air policies in recent years, and the spatiotemporal patterns of major pollutant emissions have changed substantially. In this study, we construct a regional air pollution data assimilation system based on the WRF/CMAQ model and ensemble Kalman filter algorithm to quantitatively optimize gridded CO emissions using hourly surface CO measurements over China. The Multi‐resolution Emission Inventory of China CO emission inventories in December 2012 and 2016 are treated as prior emissions, and the CO emissions in December 2013 and 2017 are optimized using the CO observations of December of 2013 and 2017, respectively. The results show that in both periods, assimilation of CO observations significantly improves the CO simulations and emission estimates. Assimilation increases the CO emissions in most areas of mainland China, especially in northern China, and the spatial patterns of the increases in the two periods are similar. Overall, the posterior CO emissions in December 2017 are 17% lower than those in December 2013. Large emission decreases are mainly found in most key urban areas and developed regions, and emission increases are mainly located in their surrounding areas and certain central and western regions, which might reflect the emission migration from developed regions or urban areas to developing regions or surrounding areas. These changes are not found in the prior emissions but are basically consistent with the emission control strategies and industrial transformation and upgrade phenomena in recent years, indicating that our CO assimilation system could successfully capture the temporal and spatial variations.

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Section: Methods and Datamentioning

confidence: 99%

Section: Methods and Datamentioning

confidence: 99%

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CO Emissions Inferred From Surface CO Observations Over China in December 2013 and 2017

Feng

Jiang

Wu³

et al. 2020

JGR Atmospheres

Self Cite

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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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“…The DA technology incorporates aerosol measurements into the models to optimize emissions (Peng et al, 2017;Ma et al, 2019), and cyclically updates the background fields in forecasts. This effectively improves the air quality forecasts in China (Bao et al, 2019;Cheng et al, 2019;Feng et al, 2018;Hong et al, 2020;Liu et al, 2011;Pang et al, 2018;Peng et al, 2018;Xia et al, 2019aXia et al, , 2019b.…”

Section: Introductionmentioning

confidence: 99%

Improving the Sectional MOSAIC Aerosol models of WRF-Chem with the revised Gridpoint Statistical Interpolation System and multi-wavelength aerosol optical measurements: DAO-K experiment 2019 at Kashi, near the Taklamakan Desert, northwestern China

Chang

Zhang

et al. 2020

Preprint

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Abstract. The Gridpoint Statistical Interpolation data assimilation (DA) system was developed for the four-size bin sectional Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) aerosol mechanism in the Weather Research and Forecasting-Chemistry (WRF-Chem) model. The forward and adjoint operators for the aerosol optical depth (AOD) analysis were derived from WRF-Chem aerosol optical code. We applied three-dimensional variational DA to assimilate the multi-wavelength AOD, ambient aerosol scattering coefficient, and aerosol absorption coefficient, measured by the sun-sky photometer, nephelometer, and aethalometer, respectively. These were undertaken during a dust observation field campaign at Kashi in northwestern China in April 2019. The results showed that the DA analyses decreased the low biases in the model aerosols; however, it had had some deficiencies. Assimilating the surface particle concentration increased the coarse particles in the dust episodes, but AOD, and the coefficients for aerosol scattering and absorption, were still lower than observed values. Assimilating aerosol scattering coefficient separately from AOD improved the two optical quantities. However, it caused an overestimation of the particle concentrations at the surface. Assimilating the aerosol absorption coefficient yielded the highest positive bias in the surface particle concentration, aerosol scattering coefficient, and AOD. The positive biases in the DA analysis were caused by the forward operator underestimating particle scattering and absorption efficiency. As a compensation, the DA system increased particle concentrations excessively so as to fit the observed optical values. The best overall improvements were obtained from the simultaneous assimilation of the surface particle concentration and AOD. The assimilation did not substantially change the aerosol chemical fractions. After DA, the clear-sky aerosol radiative forcing at Kashi was −10.5 W m−2 at the top of the atmosphere, which was 46 % higher than the background radiative forcing value.

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Impact of 3DVAR assimilation of surface PM2.5 observations on PM2.5 forecasts over China during wintertime

Cited by 45 publications

References 56 publications

CO Emissions Inferred From Surface CO Observations Over China in December 2013 and 2017

CO Emissions Inferred From Surface CO Observations Over China in December 2013 and 2017

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

Improving the Sectional MOSAIC Aerosol models of WRF-Chem with the revised Gridpoint Statistical Interpolation System and multi-wavelength aerosol optical measurements: DAO-K experiment 2019 at Kashi, near the Taklamakan Desert, northwestern China

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