Lidar data assimilation method based on CRTM and WRF-Chem models and its application in PM2.5 forecasts in Beijing

Cheng, Xiao; Liu, Yuelin; Xu, Xiangde; You, Wei; Zang, Zengliang; Gao, Lijie; Chen, Yubao; Su, Debin; Yan, Peng

doi:10.1016/j.scitotenv.2019.05.186

Cited by 52 publications

(34 citation statements)

References 41 publications

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“…Although it is straightforward to assimilate lidar water vapor, temperature, and wind measurements by adjusting corresponding model variables, fully using high temporal resolution observations requires further investigations. Assimilating lidar aerosol measurements can improve PM2.5 forecast (Cheng et al, 2019;El Amraoui et al, 2020). Using PBL structure information contained within aerosol vertical structures to refine model PBL representation, however, needs further explorations.…”

Section: Lidar Data Assimilations To Improve Weather and Air Quality mentioning

confidence: 99%

Challenges and Opportunities in Lidar Remote Sensing

Wang

Menenti

2021

Front. Remote Sens.

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Section: Lidar Data Assimilations To Improve Weather and Air Quality mentioning

confidence: 99%

Challenges and Opportunities in Lidar Remote Sensing

Wang

Menenti

2021

Front. Remote Sens.

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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%

“…Data assimilation (DA) blends the information from observations with a priori background fields from deterministic models to obtain an optimal analysis (Wang et al, 2001;Bannister, 2017). With lagged emission inventories and unsatisfactory model chemistry mechanisms, there are notable discrepancies between model aerosols and observed levels (He et al, 2017;Chen L. et al, 2019). 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.…”

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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“…Finally, the multi-dimensional evolutionary characteristics of PM2.5 at the surface and in the vertical layer, as well as the 3-D distribution, were analyzed in detail. Although data assimilation has been applied in China using surface observation network data (Gao et al, 2017b), AOD (Liu et al, 2011;Saide et al, 2013;Saide et al, 2014;Schwartz et al, 2012), and lidar data (Cheng et al, 2019), to our knowledge, this is the first attempt in China to apply lidar network data to assimilation technology, from which the high-precision 3-D distribution of pollutants can be provided, thus supplying effective data support for clarifying the formation mechanism of pollutants (Zheng et al, 2017…”

Section: Introductionmentioning

confidence: 99%

Comment on acp-2020-1273

2021

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China has made great efforts to monitor and control air pollution in the past decade.Comprehensive characterization and understanding of pollutants in three-dimension (3-D) are, however, still lacking. Here, we used data from an observation network consisting of 13 aerosol lidars and more than 1000 ground observation stations, combined with a data assimilation technique, to conduct a comprehensive analysis of an extreme heavy aerosol pollution (HAP) over the North China Plain (NCP) from November-December 2017. During the studied period, the maximum hourly mass concentration of surface PM2.5 reached ~390 μg•m -3 . After assimilation, the correlation between model results and the independent observation subdataset was ~50% higher than the that without the assimilation, and the root mean square error was reduced by ~40%. From pollution development to dissipation, we divided the HAP in the NCP (especially in Beijing) into four phases-an early phase (EP), a transport phase (TP), an accumulation phase (AP), and a removal phase (RP). We then analyzed the evolutionary characteristics of PM2.5 concentration during different phases on the surface and in 3-D space.We found that the particles were mainly transported from south to north at a height of 1-2 km (during EP and RP) and near the surface (during TP and AP). The amounts of PM2.5 advected into Beijing with the maximum transport flux intensity (TFI) were through the pathways in the relative order of the southwest > southeast > east pathways. The dissipation of PM2.5 in the RP stage (with negative TFI) was mainly from north to south, with an average transport height of ~1 km above the surface. Our results quantified the multi-dimensional distribution and evolution of PM2.5 concentration over the NCP, which may help policymakers develop efficient air pollution control strategies.

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Lidar data assimilation method based on CRTM and WRF-Chem models and its application in PM2.5 forecasts in Beijing

Cited by 52 publications

References 41 publications

Challenges and Opportunities in Lidar Remote Sensing

Challenges and Opportunities in Lidar Remote Sensing

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

Comment on acp-2020-1273

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