Improving PM&amp;lt;sub&amp;gt;2. 5&amp;lt;/sub&amp;gt; forecast over China by the joint  adjustment of initial conditions and source emissions  with an ensemble Kalman filter

Peng, Zhen; Liu, Zhiquan; Chen, D.; Ban, Jie

doi:10.5194/acp-17-4837-2017

Cited by 80 publications

(101 citation statements)

References 72 publications

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“…However, when performing "top-down" emission adjustments, previous studies generally assimilate observations of only one or two species and adjust only the corresponding emission species. For example, Arellano et al (2006) and Yumimoto and Uno (2006) used carbon monoxide CO concentration observations to constrain the CO emissions, and Sekiyama et al (2010), Mao et al (2014), and Peng et al (2017) used aerosol observations to constrain the associated aerosol emissions. Similar strategies for constraining emissions can be found for nitrogen oxide (NO x ) emissions (Kurokawa et al, 2009) and sulfur dioxide (SO 2 ) emissions (Vira & Sofiev, 2012).…”

Section: Introductionmentioning

confidence: 99%

Multiconstituent Data Assimilation With WRF‐Chem/DART: Potential for Adjusting Anthropogenic Emissions and Improving Air Quality Forecasts Over Eastern China

Wang

Mizzi

et al. 2019

JGR Atmospheres

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We use the Weather Research and Forecasting Model with the chemistry/Data Assimilation Research Testbed (WRF‐Chem/DART) chemical weather forecasting/data assimilation system with multiconstituent data assimilation to investigate the improvement of air quality forecasts over eastern China. We assimilate surface in situ observations of sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), carbon monoxide (CO), particulate matter with diameters less than 2.5 μm (PM2.5) and 10 μm (PM10), and satellite aerosol optical depth to adjust the related anthropogenic emissions as well as the chemical initial conditions. We validate our forecast results out to 72 hr by comparison with the in situ observations. Results show that updated emissions improve the model performance between 10% and 65% root mean square error reduction for the assimilated species except particulate matter with a diameter between 2.5 and 10 μm (PM2.5‐10), which is slightly improved due to the limited anthropogenic contribution to it. In a sensitivity experiment with a different update interval, the CO improvement is found to be sensitive to the cycling time used to update the CO emissions. In another sensitivity experiment when NO2 observations are not assimilated and nitrogen oxides (NOx) emission are adjusted by only O3, NO2 forecasts show similar root mean square error improvement but have lower spatial correlation, indicating the value and limitation of the O3‐NOx cross‐variable relationship.

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Section: Introductionmentioning

confidence: 99%

Multiconstituent Data Assimilation With WRF‐Chem/DART: Potential for Adjusting Anthropogenic Emissions and Improving Air Quality Forecasts Over Eastern China

Wang

Mizzi

et al. 2019

JGR Atmospheres

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“…Penner et al, 2001;Bellouin et al, 2003;Liao and Seinfeld, 2005;Wu et al, 2012;Wang et al, 2015); however, large uncertainties remain. Forster et al (2007) noted that the global mean DRF varied from +0.04 to −0.63 W m −2 for total aerosols and from +0.1 to +0.3 W m −2 for BC.…”

Section: Introductionmentioning

confidence: 99%

The optical properties, physical properties and direct radiative forcing of urban columnar aerosols in the Yangtze River Delta, China

et al. 2018

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Abstract. The optical and physical properties as well as the direct radiative forcings (DRFs) of fractionated aerosols in the urban area of the western Yangtze River Delta (YRD) are investigated with measurements from a Cimel sun photometer combined with a radiation transfer model. Ground-based observations of aerosols have much higher temporal resolutions than satellite retrievals. An initial analysis reveals the characteristics of the optical properties of different types of fractionated aerosols in the western YRD. The total aerosols, mostly composed of scattering components (93.8 %), have mean optical depths of 0.65 at 550 nm and refractive index of 1.44 + 0.0084i at 440 nm. The fine aerosols are approximately four times more abundant and have very different compositions from coarse aerosols. The absorbing components account for only ∼ 4.6 % of fine aerosols and 15.5 % of coarse aerosols and have smaller sizes than the scattering aerosols within the same mode. Therefore, fine particles have stronger scattering than coarse ones, simultaneously reflecting the different size distributions between the absorbing and scattering aerosols. The relationships among the optical properties quantify the aerosol mixing and imply that approximately 15 and 27.5 % of the total occurrences result in dustand black-carbon-dominating mixing aerosols, respectively, in the western YRD. Unlike the optical properties, the size distributions of aerosols in the western YRD are similar to those found at other sites over eastern China on a climatological scale, peaking at radii of 0.148 and 2.94 µm. However, further analysis reveals that the coarse-dominated particles can also lead to severe haze pollution over the YRD. Observation-based estimations indicate that both fine and coarse aerosols in the western YRD exert negative DRFs, and this is especially true for fine aerosols (−11.17 W m −2 at the top of atmosphere, TOA). A higher absorption fraction leads directly to the negative DRF being further offset for coarse aerosols (−0.33 W m −2 ) at the TOA. Similarly, the coarsemode DRF contributes to only 13.3 % of the total scattering aerosols but > 33.7 % to the total absorbing aerosols. A sensitivity analysis states that aerosol DRFs are not highly sensitive to their profiles in clear-sky conditions. Most of the aerosol properties and DRFs have substantial seasonality in the western YRD. The results further reveal the contributions of each component of the different size particles to the total aerosol optical depths (AODs) and DRFs. Additionally, these results can be used to improve aerosol modelling performance and the modelling of aerosol effects in the eastern regions of China.

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“…The 4D-LETKF was also implemented to assimilate MODIS Dark Target and Deep Blue AOTs to improve dust analyses and forecasts with assimilations at four time slots (every 6 hr) for a 24-hr assimilation window (Di Tomaso et al, 2017). In addition to the assimilation of routine satellite-based and ground-based AOTs, sparse surface or aircraft observations of aerosol mass concentrations, such as PM 2.5 and PM 10 , were also successfully assimilated to correct model simulations with variational or ensemble-based methods (Li et al, 2013;Pagowski et al, 2014;Pagowski & Grell, 2012;Peng et al, 2017;Zang et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Hourly Aerosol Assimilation of Himawari‐8 AOT Using the Four‐Dimensional Local Ensemble Transform Kalman Filter

Dai

Cheng

Suzuki

et al. 2019

J Adv Model Earth Syst

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The next‐generation geostationary satellite Himawari‐8 has a much higher observation frequency of the aerosol field than polar‐orbiting satellites. Aerosol analyses with a geostationary satellite can advance our understanding of the rapid spatiotemporal evolution of aerosols, which is especially critical for studies of air pollution and its mechanisms. We present a one‐monthlong hourly aerosol analysis using an aerosol data assimilation based on the local ensemble Kalman filter (LETKF), Himawari‐8‐retrieved hourly aerosol optical thicknesses (AOTs), and a global model named Non‐hydrostatic Icosahedral Atmospheric Model coupled with an aerosol model named Spectral Radiation Transport Model for Aerosol Species (NICAM‐SPRINTARS). To assimilate asynchronous observations and avoid frequent switching between the assimilation and ensemble aerosol forecasts, the LETKF is also extended to the four‐dimensional LETKF (4D‐LETKF). The hourly aerosol analyses are evaluated with both the assimilated Himawari‐8 AOTs and independent Moderate Resolution Imaging Spectroradiometer (MODIS)‐ and AErosol RObotic NETwork (AERONET)‐retrieved AOTs. All evaluations show that the assimilations positively affect the model performances and produce simulated AOTs that are closer to the observations. The analyses correctly reduce the significantly positive biases and root‐mean‐square errors of the control experiment, especially over East China and Australia. Our results also show that hourly aerosol analyses with more frequent Himawari‐8 observations are superior to those using the polar satellite MODIS observations. The performances among the LETKF and 4D‐LETKF experiments are generally not so different, but the computational load of the 4D‐LETKF is much lighter than that of the LETKF.

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Improving PM<sub>2. 5</sub> forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter

Cited by 80 publications

References 72 publications

Multiconstituent Data Assimilation With WRF‐Chem/DART: Potential for Adjusting Anthropogenic Emissions and Improving Air Quality Forecasts Over Eastern China

Multiconstituent Data Assimilation With WRF‐Chem/DART: Potential for Adjusting Anthropogenic Emissions and Improving Air Quality Forecasts Over Eastern China

The optical properties, physical properties and direct radiative forcing of urban columnar aerosols in the Yangtze River Delta, China

Hourly Aerosol Assimilation of Himawari‐8 AOT Using the Four‐Dimensional Local Ensemble Transform Kalman Filter

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