Assessing the impact of Chinese FY-3/MERSI AOD data assimilation on air quality forecasts: Sand dust events in northeast China

Bao, Yuhai; Zhu, Liuhua; Guan, Qin; Guan, Yuanhong; Lu, Qifeng; Petropoulos, George P.; Che, Huizheng; Ali, Gohar; Yan, Dong; Tang, Zhaokang; Gu, Y.; Tang, Wei; Hou, Yue

doi:10.1016/j.atmosenv.2019.02.026

Cited by 16 publications

(4 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the DA of AOP products has been carried out in many previous studies, such bias inconsistency is seldom noticed because many similar works only validate their results against the same kind of AOP observations assimilated. When validated against the surface PM observation, the Table 4 Similar to Table 2 Journal of Geophysical Research: Atmospheres results of some works (Bao et al, 2019;Mathur, 2008;Schwartz et al, 2012) achieve an overall improvement (although similar overestimations after DA could be found sometimes) because their natural run already underestimates the surface PM concentration in total, which is similar to what occurred in the November case. Mangold et al (2011) and Liu et al (2011) were the first to note this problem.…”

Section: Cause Of the Bias Inconsistencymentioning

confidence: 54%

“…They are usually in the form of the mixing ratio of particle mass (PM) with diameters of less than 10 μm (PM 10 ) (Jiang et al, 2013) and 2.5 μm (PM 2.5 ) (Werner et al, 2019) or the mass concentrations of specific aerosol species (Henze et al, 2009;Li et al, 2013), which mostly originate from surface monitoring sites and sometimes from aircrafts. The other category is aerosol optical properties (AOP), which usually include the aerosol optical depth (AOD) (Bao et al, 2019;Dai et al, 2014;Dai et al, 2019;Rubin et al, 2017;Saide et al, 2014), vertical profile of aerosol extinction coefficient (AEXT) (Uno et al, 2008;Zhang et al, 2011) and backscattering coefficient (Kahnert, 2008;Sekiyama et al, 2010). These observations can be obtained from spaceborne instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar with Orthogonal Polarization or from surface-based instruments such as sun photometers from the Aerosol Robotic Network (AERONET) and ground-based lidar.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Importance of Bias Correction in Data Assimilation of Multiple Observations Over Eastern China Using WRF‐Chem/DART

Wang

Jiang³

et al. 2020

JGR Atmospheres

View full text Add to dashboard Cite

Three types of observations, aerosol optical depth from the Moderate Resolution Imaging Spectroradiometer, surface particulate matter with diameters less than 2.5 (PM 2.5 ) and 10 μm (PM 10 ), and aerosol extinction coefficient (AEXT) profiles from ground-based lidars, were separately and simultaneously assimilated using the Weather Research and Forecasting Model with the Chemistry/Data Assimilation Research Testbed (WRF-Chem/DART). Two cases in June and November 2018 were selected over middle and eastern China. Experiments assimilating single-type and multiple observations were evaluated by cross validating their analysis and forecast against the three observation types. Compared to the experiment without data assimilation (DA), DA of single-type observations is always closer to the type of observations assimilated. However, DA of aerosol optical depth or AEXT sometimes significantly degraded the error performance for PM 2.5 . This problem is caused by the inconsistency of bias tendencies when modeling aerosol optical properties and surface aerosol mass. It is found that WRF-Chem tends to predict dryer air within the boundary layer over eastern China, which may have played a role in the underestimation of AEXT even when PM 2.5 was overestimated. After applying a simple bias correction (BC), the problem was alleviated. DA of multiple observations with BC gives the best overall error performance when validated against all types of observations and even performs better than any DA of single-type observations experiments in reproducing AEXT profiles. The results illustrate that BC is important in DA of multiple observations and that the simultaneous DA of aerosol observations with different vertical information can work synergistically to improve aerosol forecasts.

show abstract

Section: Cause Of the Bias Inconsistencymentioning

confidence: 54%

Section: Introductionmentioning

confidence: 99%

Importance of Bias Correction in Data Assimilation of Multiple Observations Over Eastern China Using WRF‐Chem/DART

Wang

Jiang³

et al. 2020

JGR Atmospheres

View full text Add to dashboard Cite

show abstract

“…For most regions in Western China, surface observation stations are still sparse. In addition, some studies also certified the importance of assimilation of the initial aerosol vertical profile (Bao et al, 2019;Ha et al, 2020;Jiang et al, 2013;. Thus, the development of satellite AOD, Lidar, and other multisource DA methods is necessary in the future.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

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

View full text Add to dashboard Cite

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 (

show abstract

“…Aerosol density is necessitated to compute aerosol optical parameters in the AOD forward operator and construct our tangent linear operator. The Supplement also shows the aerosol density (Table S2) that follows the data in Barnard et al (2010).…”

Section: Aerosol Complex Refractive Indexes In Gsimentioning

confidence: 99%

Improving the sectional Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) aerosols of the Weather Research and Forecasting-Chemistry (WRF-Chem) model with the revised Gridpoint Statistical Interpolation system and multi-wavelength aerosol optical measurements: the dust aerosol observation campaign at Kashi, near the Taklimakan Desert, northwestern China

Chang

Zhang

et al. 2021

Atmos. Chem. Phys.

View full text Add to dashboard Cite

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 tangent linear 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 measurements 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 model aerosols' low biases; however, it 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 those observed. 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 aerosol mass scattering and absorption efficiency. As compensation, the DA system increased particle concentrations excessively 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 −10.4 W m−2 at the top of the atmosphere, which was 55 % higher than the radiative forcing value before DA.

show abstract

Assessing the impact of Chinese FY-3/MERSI AOD data assimilation on air quality forecasts: Sand dust events in northeast China

Cited by 16 publications

References 43 publications

Importance of Bias Correction in Data Assimilation of Multiple Observations Over Eastern China Using WRF‐Chem/DART

Importance of Bias Correction in Data Assimilation of Multiple Observations Over Eastern China Using WRF‐Chem/DART

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

Contact Info

Product

Resources

About

Assessing the impact of Chinese FY-3/MERSI AOD data assimilation on air quality forecasts: Sand dust events in northeast China

Cited by 16 publications

References 43 publications

Importance of Bias Correction in Data Assimilation of Multiple Observations Over Eastern China Using WRF‐Chem/DART

Importance of Bias Correction in Data Assimilation of Multiple Observations Over Eastern China Using WRF‐Chem/DART

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

Contact Info

Product

Resources

About

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