doi: 10.1007/s00376-018-8075-9
    Aerosol Data Assimilation Using Data from Fengyun-3A and MODIS: Application to a Dust Storm over East Asia in 2011
    Xiaoli XIA, Jinzhong MIN*,, Feifei SHEN, Yuanbing WANG, Chun YANG


    Aerosol optical depth (AOD) is the most basic parameter that describes the optical properties of atmospheric aerosols, and it can be used to indicate aerosol content. In this study, we assimilated AOD data from the Fengyun-3A (FY-3A) and MODIS meteorological satellite using the Gridpoint Statistical Interpolation three-dimensional variational data assimilation system. Experiments were conducted for a dust storm over East Asia in April 2011. Each 0600 UTC analysis initialized a 24-h Weather Research and Forecasting with Chemistry model forecast. The results generally showed that the assimilation of satellite AOD observational data can significantly improve model aerosol mass prediction skills. The AOD distribution of the analysis field was closer to the observations of the satellite after assimilation of satellite AOD data. In addition, the analysis resulting from the experiment assimilating both FY-3A/MERSI (Medium-resolution Spectral Imager) AOD data and MODIS AOD data had closer agreement with the ground-based values than the individual assimilation of the two datasets for the dust storm over East Asia. These results suggest that the Chinese FY-3A satellite aerosol products can be effectively applied to numerical models and dust weather analysis.

    Key words: Fengyun-3A satellite; aerosol optical depth; data assimilation; dust storm;
    摘要: 气溶胶光学厚度(Aerosol Optical Depth,AOD)是表征大气气溶胶光学特征的最基本量,并且可以用来推算大气气溶胶含量。本研究使用GSI三维变分同化系统,同化了风云3A(FY-3A)和MODIS两种卫星的气溶胶数据,并应用在2011年4月东亚一次沙尘过程中。研究中将每天06时(UTC)作为同化时刻,用WRF-Chem(Weather Research and Forecasting with Chemistry model)模式向后积分24小时。结果表明,同化卫星观测数据能够显著提高模式预报能力。同化试验后,分析场的AOD分布更接近卫星观测场。此外,在本次沙尘个例研究中,通过试验对比发现,同时同化FY-3A与MODIS两种卫星数据的试验结果比单独同化时更接近地面观测数据。以上结果表明,我国FY-3A卫星气溶胶数据产品在数值模式及空气质量预报中具有广泛的应用前景,对于我国风云系列卫星气溶胶产品的应用和推广具有一定的积极指示意义。
    关键词: 风云3A 卫星 ; 气溶胶光学厚度 ; 资料同化 ; 沙尘暴

    1. Introduction

    Atmospheric aerosols have a significant impact on the climate and environment. In particular, increasingly severe particulate matter pollution events have threatened public health and ecosystems (Lin et al., 2014). In recent years, there has been a growing recognition that the tiny aerosol particles suspended in the atmosphere have effects at different geographical scales, such as visibility, acid deposition, cloud and precipitation, atmospheric chemical processes in the stratosphere and troposphere, and atmospheric radiation balance (Lai et al., 2016).

    Aerosol optical depth (AOD) can be used to test calibration of satellite retrieval data, and AOD is the key factor to determine the aerosol climate effect (Kaufman et al., 2002). Currently, AOD can be obtained from ground-based equipment [e.g., the Aerosol Robotic Network (AERONET) (Holben et al., 1998), airborne AOD observations (Shinozuka et al., 2011), and satellite sensors (Kaufman et al., 2002)]. However, there is little data from ground-based observations of aerosols in East Asia (Che et al., 2014), so it is important to improve the accuracy of atmospheric chemical model predictions by combining satellite observations.

    Data assimilation (DA) can integrate observational information and background fields, and it can give the optimal estimation of the atmospheric state (Guan et al., 2011). In the 1970s, DA was introduced into the field of air quality prediction and became a new direction for atmospheric environmental science research (Bai et al., 2007). However, the assimilation of aerosol data was conducted in the 1990s (Jacobson, 2001; Otte et al., 2005). With the enrichment of observational data and model improvement, efforts have also focused on satellite AOD DA. Atmospheric chemical models contain both aerosol particulate matter and gaseous chemical quantities, and current assimilation studies have focused on gaseous chemical quantities, such as ozone, methane, carbon dioxide, and sulfur dioxide (Zhang et al., 2008; Kukkonen et al., 2011). However, there are some limitations in the assimilation of aerosol particulate matter data. Firstly, the observational data are limited, and data reliability is still improving. Secondly, there is uncertainty in pollutant emission estimations, and the related chemical reaction processes in the model are imperfect. Compared with weather forecast models, atmospheric chemical prediction models have many chemical variables, and the aerosol particles are divided into different particle size segments, which increases the computational cost.

    At present, there are several kinds of AOD DA technology, including optimal interpolation (OI), two-dimensional variational DA, three-dimensional variational (3D-Var) DA, the Ensemble Kalman filter, and four-dimensional variational DA. (Niu et al., 2008) assimilated Fengyun-2C satellite-derived dust and sand concentration data into the Global/ Regional Assimilation and Prediction System-Chinese Unified Atmospheric Chemistry Environment for Dust by using the 3D-Var method. They found that the 3D-Var method could effectively improve short-term sand and dust weather forecasting. (Adhikary et al., 2008) used the OI technique with a regional chemical transport model, while (Collins et al., 2001) used Model of Atmospheric Transport and Chemistry to simulate aerosol distributions; (Schroedter-Homscheidt et al., 2010) and (Nieradzik and Elbern, 2006) used variational approaches with European Acid Deposition models. (Liu et al., 2011) used the Gridpoint Statistical Interpolation (GSI) system to assimilate Moderate Resolution Imaging Spectroradiometer (MODIS) AOD data and compared with AOD observations from the Aerosol Robotic Network (AERONET) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. More recently, (Yumimoto et al., 2016) showed the first application of aerosol optical properties derived from Himawari-8 data for aerosol DA. Their results showed that AOD data can improve air quality prediction.

    The Fengyun-3A (FY-3A) meteorological satellite, a Chinese second-generation polar-orbiting meteorological satellite, has been widely used in meteorology, environmental protection, and other national sectors. It has ultraviolet, visible, infrared, and microwave multispectral instruments, and it has accumulated aerosol optical thickness inversion data since 2008 (Yang et al., 2009). In this study, we performed a DA experiment with AOD data derived from the Chinese FY-3A Medium-resolution Spectral Imager (MERSI) targeting transboundary pollutants and dust outflows over East Asia in April 2011.

    2. Data and methods
    2.1. AOD data

    The FY-3A satellite, launched on 27 May 2008, is the first satellite of the second-generation polar orbital series in China, and it passes over the equator between 1000 and 1020 LST (Yang et al., 2009). In this study, the assimilated AOD data were obtained from FY-3/MERSI (Li et al., 2008). The FY-3A aerosol product includes AODs at three wavebands: 470 nm, 550 nm, and 650 nm. We used AOD data at the 550 nm wavelength, which has a spatial resolution of 0.01°. The assimilation time window was set to 3 h, to obtain maximum coverage.

    AOD data with a spatial resolution of 10 km from MODIS sensors onboard the Terra and Aqua satellites were used in our study. We used AOD retrievals derived from the Dark Target and Deep Blue products according to the inversion algorithm (Hsu et al., 2004; Hsu et al., 2006; Remer et al., 2005). In this study, we used collection 051 of level 2 total AOD retrievals from both Terra and Aqua. MODIS aerosol inversion products provide seven wavelengths of AOD data: 470 nm, 550 nm, 660 nm, 870 nm, 1240 nm, 1630 nm, and 2130 nm. In this study, we used AOD at 550 nm, to compare with the assimilation results of the FY-3 aerosol product.

    An observation error specification for MODIS AOD data was suggested by (Remer et al., 2005), and we used the MODIS AOD retrieval uncertainty of 5% for AOD over oceans and 15% for AOD over land. We estimated FY-3A observation errors to be the retrieval uncertainty attached to the FY-3 AOD data plus a standard deviation calculated as the representative error in the regridding (Zhang et al., 2008). The FY-3 retrieval uncertainty ranged from 0.0001 to 0.93, with an average of 0.05. We only assimilated the highest quality AOD retrievals and thinned to the same resolution as the model grid. To reduce cloud contamination and noise in the data, pixels adjacent to missing values were discarded and only AOD values below 2.5 were used (Saide et al., 2014). In addition, ground-based AOD data acquired by AERONET (Holben et al., 1998) and the China Aerosol Remote Sensing Network (CARSNET) (Che et al., 2015) were used to evaluate the assimilation results. AERONET uses the CE318-type solar photometer for aerosol ground-based observations, and it has more than 500 sites worldwide. It provides the AOD Ångstr\"om index, inversion parameter products, and precipitation data for different aerosol types in the world (Holben et al., 1998). Due to its high precision (error from 0.01-0.02), the AERONET data play an important role in the validation of aerosol satellite remote sensing and model products. In this study, we used the level-2 AOD data acquired by AERONET in Beijing, Lahore, Jaipur, and Taihu (Fig. 1) from 28 April to 3 May 2011. CARSNET is a ground-based network for monitoring aerosol optical properties, and it uses

    Fig. 1 The experimental domain. Blue labeling indicates the ground-based AOD data acquired by the AERONET sites, and red the locations of the CARSNET sites, used in this study.

    the same types of instruments as AERONET (Che et al., 2009). It has 60 stations that are now operated by the China Meteorological Administration and local meteorological administrations, institutes, and universities throughout China (Che et al., 2015). It has become a national resource for studying aerosol optical properties for different regions in China, and it is used for validating satellite retrievals and aerosol numerical models (Xie et al., 2011; Zhao et al., 2013; Che et al., 2014; Lin et al., 2014). To evaluate the dust storm in mainland China, we used the ground-based AOD data acquired by CARSNET in Dunhuang, Datong, Xi'an, and Lanzhou from 28 April to 3 May 2011.

    2.2. Aerosol DA system

    In this study, we used a GSI 3D-Var meteorological DA system to assimilate FY-3 AOD data. The work in this study is based on Liu's (2011) work, and we added new interface to the FY-3A/MERSI AOD data. The 3D-Var assimilation method (Lorenc, 1986) is the process of minimizing the objective function, which can be expressed as: \begin{equation} \label{eq1} J({\textbf{x}})=\frac{1}{2}\{(x-x_{\rm o})^{\rm T}B^{-1}(x-x_{\rm b})+[H({\textbf{x}})-y_{\rm o}]^{\rm T}O^{-1}[H(x)-y_{\rm o}]\}, \ \ (1)\end{equation} where x o is the state vector composed of the model variables to be analyzed at every grid point of the 3-D model computational grid; x b is the background state vector; y o is usually provided by a previous forecast; y b is the vector of observations; J(x) is the cost function; H is an observation operator that establishes the relationship between model variables and observations with specific maps from the space of the model state vector to the space of the observation vector; and B and O are the background and the observation error covariance, respectively. By adjusting the weight between the background and observational data, the analysis fields achieved the best fit; the analysis fields were produced when the objective function reached the minimum value.

    2.3. Experimental design

    A dust storm that started in Gansu blasted Beijing on 29 April 2011 and covered large areas of China in the following days. Our experiments were performed for the period of 28 April 2011 to 3 May 2011, during which a sand-dust storm affected China and surrounding areas. This storm influenced visibility, air quality, and human health in the eastern Tibetan Plateau, the southern Xinjiang Basin, the eastern part of Northwest China, mid-western Inner Mongolia, and North and Northeastern China. On 28 April, poor visibility of less than 100 m appeared in Jiuquan, while the Air Pollution Index measurements reached 500 in Lanzhou and Jinchang. The dust was lofted by strong winds accompanying a cold front that crossed China on 30 April. The winds passed over the regions of Mongolia, Xining, and Yan'an. From 2 May, the dust storm arrived in the Yangtze River Delta region, and then severe air pollution occurred in Shanghai, Nantong, Ningbo, Suzhou, Nanjing, and other cities.

    We performed four experiments to evaluate the impacts of FY-3 AOD DA on aerosol analysis and forecasting over China. The first experiment was a control and it had no DA (CNT). The other experiments had DA: the second and third experiments employed the FY-3 AOD DA (FY3 DA) and the MODIS AOD DA (MODIS DA), respectively. As we knew the FY-3 overpass time was similar to the Terra satellite, we hypothesized that the combined MODIS and FY-3 data would have a better fit to the observational data than the MODIS AOD data. Therefore, in the last experiment, both FY-3 AOD DA and MODIS AOD DA were assimilated (FY3 + MODIS DA).

    Each experiment initialized the Weather Research and Forecasting with Chemistry (WRF/Chem) forecasting every 24 h, from 0600 UTC 27 April to 0600 UTC 3 May, using NCEP FNL data as the initial condition and then making 24-h forecasts. This group of experiments cycled for six days from 0600 UTC 28 April to 0600 UTC 3 May 2011. The assimilation frequency was 24 h. So, six assimilation cases were tested. In addition, the initial aerosol fields were produced by a three-day forecast. In other words, the spin-up period was three days, which was needed to overcome the unrealistic initial fields of the WRF/Chem forecasting.

    The WRF/Chem model grid settings were set at 200 grids in the longitudinal direction and 150 grids in the latitudinal direction, with a grid interval of 40 km, and 40 layers in the vertical direction with a 50 hPa top level. The initial condition and lateral meteorological boundary conditions were obtained from the NCEP's global 11 reanalysis data, and the initial lateral chemical boundary conditions were obtained from 6-h simulation data from the NCAR's MOZART-4 model (Pang, 2012). The Goddard Chemistry Aerosol Radiation and Transport (GOCART) aerosol scheme (Chin et al., 2000) and the Regional Atmospheric Chemistry Mechanism-Kinetic Preprocessor gas chemistry scheme (Stockwell et al., 1997) were used as the aerosol and chemical process parameterization schemes for the model. In the WRF/Chem model, the following physical parameterizations (Stockwell et al., 1997, and references therein) were used: the Madronich photochemical process option, the Goddard shortwave radiation scheme, the RRTM longwave radiation scheme, the Lin microphysical scheme, the Noah land surface parameterization scheme, the Kain-Fritsch convective parameterization scheme, the Yonsei University planetary boundary layer scheme, and the Monin-Obukhov scheme for meteorological processes in the near-surface layer.

    Table 1. Statistical analysis of the simulated and observed AOD in the experiments.
    Date B b B a R b R a B b B a R b R a B b B a R b R a
    28 April -0.483 -0.117 0.496 0.249 -0.372 -0.187 0.396 0.258 -0.543 0.037 0.466 0.145
    29 April -0.689 -0.150 0.583 0.220 -0.469 -0.177 0.464 0.243 -0.589 -0.025 0.563 0.136
    30 April -0.599 -0.124 0.510 0.205 -0.507 -0.205 0.516 0.259 -0.597 -0.006 0.520 0.116
    01 May -0.434 0.026 0.685 0.309 -0.486 -0.130 0.579 0.263 -0.464 0.107 0.628 0.174
    02 May -0.467 0.014 0.659 0.188 -0.495 -0.122 0.528 0.220 -0.472 0.116 0.606 0.197
    03 May -0.655 -0.102 0.427 0.109 -0.579 -0.241 0.397 0.127 -0.539 -0.109 0.407 0.092

    Notes: B b and B a denote the mean bias before and after assimilation, respectively; R b and R a denote the RMSE before and after assimilation for the experiments (MODIS DA; FY3 DA; FY3 + MODIS DA) over all six times.

    Table 1. Statistical analysis of the simulated and observed AOD in the experiments.

    2.4. Background error covariance

    In our AOD DA system, the background field was obtained from 14 aerosol species: hydrophobic black carbon (BC1) and hydrophilic black carbon (BC2) (particle size: 0.036 μm); hydrophobic organic carbon (OC1) and hydrophilic organic carbon (OC2) (particle size: 0.087 μm); four sizes of sea-salt particles (sea-salt 1-4; particle sizes: 0.3 μm, 1.0 μm, 3.25 μm and 7.5 μm, respectively); five sizes of dust particles (dust 1-5; particle sizes: 0.5 μm, 1.4 μm, 2.4 μm, 4.5 μm and 8.0 μm, respectively); and sulfate particles (particle size: 0.242 μm). The background error covariance was obtained by using the National Meteorology Center (NMC) method (Parrish and Derber, 1992). The NMC method uses the forecasts from two different time periods at a common time to estimate the background error covariance. We used differences of 24-h and 12-h WRF/Chem forecasts of the aerosol species valid at the same time for 62 valid pairs at either 0000 or 1200 UTC from 14 April 2011 to 28 April 2011 to compute the aerosol background error covariance.

    In this study, we used WRF data assimilation system's GEN_BE package (Barker et al., 2012) to calculate the NMC statistics. Only standard deviation and horizontal and vertical length scales of the background error for GSI analysis variables were needed to apply recursive filters both horizontally and vertically (Liu et al., 2011). Therefore, Figs. 2 and 3 show the regional average vertical profile with standard deviation and the regional average horizontal correlation length scale of background error covariances for 14 aerosol species, respectively. The standard deviations were closely related to the aerosol particle species at different levels, which reflected the uncertainty of the model predictions. The shape of the regional average vertical profile with standard deviation was consistent with (Benedetti and Fisher, 2007) and (Liu et al., 2011). In addition, the regional average horizontal correlation length scale, which represented the range of influence between the deviations of the observations and the background, differed among the 14 aerosol species. The values of the horizontal correlation length scale were 1-2.5 times the grid interval, which also matched the conclusions of (Kahnert, 2008).

    Fig. 2 Regional average vertical profiles with standard deviations (units: μg kg-1) of the background error covariances for BC1, BC2, OC1, OC2, sea-salt 1-4, dust 1-5, and sulfate with different effective diameters.

    Fig. 3 Regional average horizontal correlation length-scale (units: km) of the background error covariance for BC1, BC2, OC1, OC2, sea-salt 1-4, dust 1-5, and sulfate with different effective diameters.

    3. Results
    3.1 Comparison to FY-3 AOD and MODIS AOD

    To examine the impacts of AOD DA on the aerosol analysis, we plotted the bias (Figs. 4a-c) and root-mean-square error (RMSE) (Figs. 4d-f) of the simulated AOD when using the FY-3 and MODIS observation data as reference data. The DA experiments were performed at 0600 UTC, when both the FY-3 and the MODISAOD data were present, from 28 April to 3 May 2011. As shown in Fig. 4 and Table 1, it is clear that the model underpredicted AOD (biases of -0.3 to -0.7). After DA, the model low bias was substantially reduced, with bias (RMSE) values consistently near -0.1 (0.2) in FY3 DA, while bias (RMSE) values were consistently near -0.2 (0.2) in MODIS DA and 0.1 (0.1) in FY3+MODIS DA. Overall, the bias and RMSE were reduced by an average of 30% compared to the CNT experiment during the dust storm period, which verified the positive effects of AOD DA systems. In addition, assimilation using different satellite data showed different effects: the bias and RMSE values calculated based on the FY3 DA experiment were reduced more than the values from the MODIS DA experiment. Furthermore, in the FY3+MODIS DA experiment, the bias and RMSE values were reduced more than the two individual data systems.

    We plotted the distributions of AOD observations and model simulations from the FY3 DA and MODIS DA experiments in Fig. 5. AOD represents contributions from all aerosol types, so we also plotted the distributions of the dust field (Fig. 6), which gives a direct indication of the dust storm event. Both the AOD and dust field give the same indication regarding the performance of the experiments. It is clear that the CNT experiment only simulated a large AOD distribution in central and southeastern Asia, an area with a tropical monsoon climate where fires on farmland and forest land occur frequently during the spring, leading to air pollution. However, the CNT experiment contained very little information on the dust weather in northern China. The FY-3 AOD data (Fig. 5a) contained more information than the MODIS AOD data (Fig. 5b) over East Asia. In particular, more information was captured about the dust outflows over most parts of China, while the MODIS AOD data (Fig. 5b) possessed information on the eastern regions. After assimilating satellite AOD data, the main region of the dust storm was added to the analysis fields (Figs. 5c and d). The FY-3 analysis field (Fig. 5c) showed that AOD data were distributed in northwestern Xinjiang, northeastern and southern Mongolia, southern Gansu, Qinghai, and most parts of Tibet. The MODIS analysis field (Fig. 5d) presented the distribution of AOD data in northeastern India, northern Burma, southeastern China, and Bohai Bay. After adjustment of the DA, the observational information in the analysis fields increased and the aerosol distribution of the analysis fields were closer to the satellite observation values. The results showed that there were good adjustment effects to the background field in our DA systems, and the FY-3 analysis field captured more information from the dust storm.

    Fig. 4 Time series of the (a-c) bias and (d-f) RMSE of the simulated AOD. The DA systems involved (a, d) FY-3 AOD data, (b, e) MODIS AOD data, and (c, f) both FY-3 AOD and MODIS AOD data.

    Fig. 5 Observations of 3-h DA windows at 550 nm from (a) FY-3 and (b) MODIS. (c-f) Distributions of simulated AOD based on DA analysis fields for the (c) FY-3 DA system and (d) MODIS DA system, and the background fields from the DA experiments for the (e) FY-3 DA system and the (f) MODIS DA system, on 1 May 2011 (0600 UTC).

    Fig. 6 (a) Observations of 3-h ADO DA windows at 550 nm from the FY3. (b) Background dust fields from CNT experiments. (c) Distributions of simulated dust field based on FY3 DA analysis, and (d) the background dust fields from FY3 DA experiments, on 1 May 2011 (0600 UTC).

    Fig. 7 Observations at 550 nm from (a) FY-3 and (b) MODIS. True-color imagery, which is formed via a weighted combination of red, green, and blue (RGB) spectral information from (c) FY3 and (d) MODIS. The area in the rectangle represents the upstream area of the dust storm.

    Table 2 summarizes the number of satellite observations in the MODIS DA and FY3 DA experiments statistics over all six times. As shown in Table 2, the number of satellite observations in MODIS DA was more than that in the FY3 DA experiment. In addition, as shown in Fig. 6 and Fig. 7, we plotted true color images from both MERSI and MODIS on 1 May 2011 (0600 UTC). Both the true color images from MERSI and MODIS captured the dust storm over the northwestern areas as framed in Fig. 7, while FY-3A had more retrievals than MODIS. (Deng et al., 2016) and (Zhang et al., 2016) studied the quality of FY-3 AOD products in Guangdong and Shenyang, respectively. Overall, most of the areas covered by FY3 were dust-source areas, and also located upstream of the environmental field; although the MODIS satellite covers more in the east, it is neither the source of dust nor the upstream area of the environment. These two factors worked together to yield the benefits in the FY3 DA experiment.

    Table 2. Number of satellite observations in the MODIS DA and FY3 DA experiments.
    Date MODIS DA FY3 DA
    28 April 1428 1134
    29 April 1654 1230
    30 April 1568 1446
    01 May 1281 1057
    02 May 1309 1199
    03 May 1334 1163

    Table 2. Number of satellite observations in the MODIS DA and FY3 DA experiments.

    3.2. Comparison with AERONET AOD

    As shown in Fig. 8, under normal conditions without pollution, the impacts of DA were not significant. For example, when the AOD calculated in the CNT experiment was approximately 0.3, the AOD calculated in the assimilation experiment ranged from 0.4-0.5 (Figs. 8b and c). However, during the dust storm, the DA significantly improved the AOD simulation, resulting in much closer agreement between the analysis and ground-based AERONET AOD data at all stations. After assimilation, the AOD values in the experiment assimilating both FY-3/MERSI AOD data and MODIS AOD data were closer to the ground-based observations than the individual assimilation systems. However, all experiments did not adequately capture the observed AOD peaks. The emissions used for this study were suggested by (Zhang et al., 2009), who used the 2006 Asia emissions inventory. GOCART prognoses a global distribution of sulfate and its precursors, organic carbon, black carbon, mineral dusts and sea salt. AOD is determined from the dry mass concentrations and the mass extinction coefficients, which are functions of the size distributions, refractive indices, and RH-dependent hygroscopic growth of individual aerosol types (Chin et al., 2000). The aerosol species experience the processes of emission, advection, convection, diffusion, dry deposition and wet deposition. The dust was modeled in the WRF/Chem-GOCART by running the code of module_gocart_dust.F. Clearly, the larger AOD values from the DA experiment agreed more closely with the AERONET observations.

    3.3. Comparison with CARSNET AOD

    As shown in Fig. 9, after assimilation of the AOD data, the trend of the analysis fields was generally more consistent with ground-based measurements than the control experiment. The control experiment underestimated AOD values, which is consistent with the results of (Zhang and Reid, 2006) and (Liu et al., 2011).

    Table 3. Summary of the statistics for the modeled, AERONET and CARSNET observed AOD comparisons.
    Site name M o B b B a R b R a
    Beijing 0.612 -0.631 -0.377 0.459 0.234
    Lahore 0.971 -0.563 -0.205 0.516 0.192
    Jaipur 0.528 -0.542 -0.196 0.430 0.183
    Taihu 0.834 -0.628 -0.470 0.532 0.267
    Dunhuang 0.649 -0.592 -0.291 0.621 0.314
    Datong 0.462 -0.493 -0.406 0.525 0.310
    Xi'an 0.716 -0.509 -0.378 0.491 0.302
    Lanzhou 0.961 -0.582 -0.291 0.439 0.205

    Notes: M o denote the modeled AOD; B b and B a denote the mean bias before and after assimilation, respectively; R b and R a denote the RMSE before and after assimilation for the experiments (MODIS DA; FY3 DA; FY3 + MODIS DA) at 8 sites (Beijing; Lahore; Jaipur; Taihu; Dunhuang; Datong; Xi'an; Lanzhou).

    Table 3. Summary of the statistics for the modeled, AERONET and CARSNET observed AOD comparisons.

    China is the most populated and largest developing country in the world, and it has become one of the largest global sources for aerosol particles (Huebert et al., 2003; Seinfeld et al., 2004). The AOD values in the MODIS DA analysis also underestimated the dust storm, likely due to less coverage of the MODIS AOD data (e.g., Fig. 3b). The analysis results in the experiment assimilating both FY-3/MERSI AOD data and MODIS AOD data were more consistent with the ground-based values.

    Fig. 8 Comparisons between AERONET retrievals and modeled results in the four experiments from 28 April to 3 May 2011, at the AERONET sites of (a) Beijing, (b) Lahore, (c) Jaipur, and (d) Taihu.

    Fig. 9 Comparisons between CARSNET retrievals and modeled results in the four experiments from 28 April to 3 May 2011, at the CARSNET sites of (a) Dunhuang, (b) Datong, (c) Xi'an, and (d) Lanzhou.

    A summary of the statistics for the modeled, AERONET and CARSNET observed AOD comparisons is shown in Table 3. In general, the results showed good assimilation efficiency to improve the capability of the model to simulate the AOD over eastern Asia. The assimilation achieved improvements at all the eight sites as measured by the correlation coefficient and the bias between the model and the observation. Greater improvements were found over the sites that had more available assimilated observations, such as the surrounding area of Lanzhou. Interestingly, the assimilation yielded larger improvements at sites where both FY3 and MODIS had retrievals (Lahore and Jaipur).

    4. Summary and discussion

    In this study, we adopted the method of 3D-Var DA in the GSI system to develop AOD DA systems. Each 0600 UTC analysis initialized a 24-h WRF/Chem model forecast. The impacts of FY-3A and MODIS AOD DA were evaluated for a dust storm over East Asia in April 2011. By using the NMC method to simulate the background error covariance of the various aerosol variables over East Asia, the vertical characteristics of each aerosol variable were well reflected. The domain average standard deviation closely related to the aerosol particle species and the model level; the regional average horizontal correlation length scale, which represents the range of influence between the deviation of the observation and the background, differed among the 14 aerosol species.

    We conducted assimilation experiments of FY-3AOD and MODIS AOD data, and the results showed that the assimilation of AOD satellite observation data had a good adjustment effect on the background field. After assimilation, the AOD distribution of the analysis field was closer to the empirical satellite observations. In addition, due to the abundant observational information from mainland China, the FY-3 DA analysis results agreed better with the ground-based values than the MODIS DA analysis results. Furthermore, the analysis results in the experiment assimilating both FY-3/MERSI AOD data and MODIS AOD data agreed better with the ground-based values than the other experiments.

    Comparison of the DA of the MODIS and FY-3 aerosol products for the dust storm over East Asia indicated that the Chinese FY series satellite aerosol products are widely applicable in numerical models and dust weather analysis, which may increase the application and popularity of the Chinese FY series satellite aerosol products. Future work may be needed to assimilate and analyze multispectral, multi-sensor aerosol-related data. For example, the Earth Observation System satellite, the Advanced Very High Resolution Radiometer, the Visible Light Infrared Imaging Radiometer Suite, and the Multiangle Imaging Spectroradiometer are all capable of detecting and retrieving AOD. The Chinese FY series satellites FY-3A and FY-3B provide different wavelengths of land aerosols, marine aerosols, and dust detection, with daily and monthly products, which can also be used for further investigation.

    Acknowledgements. This research was primarily supported by the National Key Research and Development Program of China (Grant Nos. 2017YFC1502100 and 2016YFA0602302), the Natural Science Foundation of Jiangsu Province (Grant Nos. BK20160954 and BK20170940), the Beijige Funding from Jiangsu Research Institute of Meteorological Science (Grant Nos. BJG201510 and BJG201604), the Startup Foundation for Introducing Talent of NUIST (Grant Nos. 2016r27, 2016r043 and 2017r058), a project for data application of Fengyun3 meteorological satellite [FY-3(02)-UDS-1.1.2], and the Priority Academic Program Development of Jiangsu Higher Education Institutions.


    [1] Adhikary B., Coauthors, 2008: A regional scale chemical transport modeling of Asian aerosols with data assimilation of AOD observations using optimal interpolation technique. Atmos. Environ., 42, 8600-8615,
    URL     [Cited within:1]
    [2] Bai X. P.,H. Li, D. Fang, F. Hu, F. Costabile, and F. J. Wang, 2007: Applications of data assimilation in air quality prediction. Advances in Earth Science, 22, 66-73,
    URL     [Cited within:1]
    [3] Barker D., Coauthors, 2012: The weather research and forecasting model's community variational/ensemble data assimilation system: WRFDA. Bull. Amer. Meteor. Soc., 93, 831-843,
    URL     [Cited within:1]
    [4] Benedetti A.,M. Fisher, 2007: Background error statistics for aerosols. Quart. J. Roy. Meteor. Soc., 133, 391-405,
    URL     [Cited within:1]
    [5] Che H., Coauthors, 2014: Column aerosol optical properties and aerosol radiative forcing during a serious haze-fog month over North China Plain in 2013 based on ground-based sunphotometer measurements. Atmospheric Chemistry and Physics, 14, 2125-2138,
    URL     [Cited within:2]
    [6] Che H., Coauthors, 2015: Ground-based aerosol climatology of China: Aerosol optical depths from the China Aerosol Remote Sensing Network (CARSNET) 2002-2013. Atmospheric Chemistry and Physics, 15, 7619-7652,
    URL     [Cited within:2]
    [7] Che, H. Z.,Coauthors, 2009: Instrument calibration and aerosol optical depth validation of the China Aerosol Remote Sensing Network. J. Geophys. Res., 114, D03206,
    URL     [Cited within:1]
    [8] Chin M.,R. B. Rood, S. J. Lin, J. F. Müller, and A. M. Thompson, 2000: Atmospheric sulfur cycle simulated in the global model GOCART: Model description and global properties. J. Geophys. Res., 105, 24 671-24 687,
    URL     [Cited within:2]
    [9] Collins W. D.,P. J. Rasch, B. E. Eaton, B. V. Khattatov, J. F. Lamarque, and C. S. Zender, 2001: Simulating aerosols using a chemical transport model with assimilation of satellite aerosol retrievals: Methodology for INDOEX. J. Geophys. Res., 106, 7313-7336,
    URL     [Cited within:1]
    [10] Deng Y. J.,M. Hu, C. Y. Lin, and J. Cao, 2016: Analysis of aerosol optical depth in Guangdong based on FY3A/MERSI data. Meteorological Monthly, 42, 61-66,
    URL     [Cited within:1]
    [11] Guan Y. H.,G. Q. Zhou, and W. S. Lu, 2011: The effect of the initial conditions after data assimilation on short-term climate prediction. Journal of Nanjing University of Information Science and Technology: Natural Science Edition, 3, 331-340,
    URL     [Cited within:1]
    [12] Holben B. N.,Coauthors, 1998: AERONET-A federated instrument network and data archive for aerosol characterization. Remote Sensing of Environment, 66, 1-16,
    URL     [Cited within:3]
    [13] Hsu N. C.,S. C. Tsay, M. D. King, and J. R. Herman, 2004: Aerosol properties over bright-reflecting source regions. IEEE Trans. Geosci. Remote Sens., 42, 557-569,
    URL     [Cited within:1]
    [14] Hsu N. C.,S. C. Tsay, M. D. King, and J. R. Herman, 2006: Deep blue retrievals of Asian aerosol properties during ACE-Asia. IEEE Trans. Geosci. Remote Sens., 44, 3180-3195,
    URL     [Cited within:1]
    [15] Huebert B. J.,T. Bates, P. B. Russell, G. Y. Shi, Y. J. Kim, K. Kawamura, G. Carmichael, and T. Nakajima, 2003: An overview of ACE-Asia: Strategies for quantifying the relationships between Asian aerosols and their climatic impacts. J. Geophys. Res., 108, 8633,
    URL     [Cited within:1]
    [16] Jacobson M. Z.,2001: GATOR-GCMM: A global- through urban-scale air pollution and weather forecast model: 1. Model design and treatment of subgrid soil, vegetation, roads, rooftops, water, sea ice, and snow. J. Geophys. Res., 106, 5385-5401,
    URL     [Cited within:1]
    [17] Kahnert M.,2008: Variational data analysis of aerosol species in a regional CTM: Background error covariance constraint and aerosol optical observation operators. Tellus B: Chemical and Physical Meteorology, 60, 753-770,
    URL     [Cited within:1]
    [18] Kaufman Y. J.,D. Tanré, and O. Boucher, 2002: A satellite view of aerosols in the climate system. Nature, 419, 215-223,
    URL     [Cited within:2]
    [19] Kukkonen J., Coauthors, 2011: A review of operational, regional-scale, chemical weather forecasting models in Europe. Atmospheric Chemistry and Physics, 12, 1-87,
    URL     [Cited within:1]
    [20] Lai X.,F. M. Yang, and K. B. He, 2016: The impacts of atmospheric aerosols on weather and climate. Ecology and Environmental Monitoring of Three Gorges, 1, 2-8,
    URL     [Cited within:1]
    [21] Li X. J.,P. Zhang, H. Qiu, and C. L. Fu, 2008: Introduction of MERSI/FY-3A land aerosol products. China Meteorological Society, Beijing. (in Chinese), 1.
    [Cited within:1]
    [22] Lin J. T.,A. Van Donkelaar, J. Y. Xin, H. Z. Che, and Y. S. Wang, 2014: Clear-sky aerosol optical depth over East China estimated from visibility measurements and chemical transport modeling. Atmos. Environ., 95, 258-267,
    URL     [Cited within:2]
    [23] Liu Z. Q.,Q. L. Liu, H. C. Lin, C. S. Schwartz, Y. H. Lee, and T. J. Wang, 2011: Three-dimensional variational assimilation of MODIS aerosol optical depth: Implementation and application to a dust storm over East Asia. J. Geophys. Res., 116, D23206,
    URL     [Cited within:4]
    [24] Lorenc A. C.,1986: Analysis method for numerical weather prediction, Q. Meteorol. Soc., 112, 1177-1194,
    URL     [Cited within:1]
    [25] Nieradzik L. P.,H. Elbern, 2006: Variational assimilation of combined satellite retrieved and in situ aerosol data in an advanced chemistry transport model. Proceedings of the ESA Atmospheric Science Conference, ESA, Frascati.
    [Cited within:1]
    [26] Niu T.,S. L. Gong, G. F. Zhu, H. L. Liu, X. Q. Hu, C. H. Zhou, and Y. Q. Wang, 2008: Data assimilation of dust aerosol observations for the CUACE/dust forecasting system. Atmospheric Chemistry and Physics, 8, 3473-3482,
    URL     [Cited within:1]
    [27] Otte, T. L.,Coauthors, 2005: Linking the Eta Model with the Community Multiscale Air Quality (CMAQ) modeling system to build a national air quality forecasting system. Wea. Forecasting, 20, 367-384,
    URL     [Cited within:1]
    [28] Pang Y.,2012: Distribution and evolution of atmospheric pollutants over Beijing-Tianjing-Hebei region. M.S. thesis, Nanjing University of Information Science & Technology. 1- 51.
    [Cited within:1]
    [29] Parrish D. F.,J. C. Derber, 1992: The national meteorological center's spectral statistical-interpolation analysis system. Mon. Wea. Rev., 120, 1747-1763,
    URL     [Cited within:1]
    [30] Remer, L. A.,Coauthors, 2005: The MODIS aerosol algorithm, products, and validation. J. Atmos. Sci., 62, 947-973,
    URL     [Cited within:2]
    [31] Saide P. E.,J. Kim, C. H. Song, M. Choi, Y. Cheng, and G. R. Carmichael, 2014: Assimilation of next generation geostationary aerosol optical depth retrievals to improve air quality simulations, Geophys. Res. Lett., 41, 9188-9196,
    URL     [Cited within:1]
    [32] Schroedter-Homscheidt M.,H. Elbern, and T. Holzer-Popp, 2010: Observation operator for the assimilation of aerosol type resolving satellite measurements into a chemical transport model. Atmospheric Chemistry and Physics, 10, 10 435-10 452,
    URL     [Cited within:1]
    [33] Seinfeld, J. H.,Coauthors, 2004: ACE-ASIA: Regional climatic and atmospheric chemical effects of Asian dust and pollution. Bull. Amer. Meteor. Soc., 85, 367-380,
    URL     [Cited within:1]
    [34] Shinozuka Y., Coauthors, 2011: Airborne observation of aerosol optical depth during ARCTAS: Vertical profiles, inter-comparison and fine-mode fraction. Atmospheric Chemistry and Physic, 11, 3673-3688,
    URL     [Cited within:1]
    [35] Stockwell W. R.,F. Kirchner, M. Kuhn, and S. Seefeld, 1997: A new mechanism for regional atmospheric chemistry modeling. J. Geophys. Res., 102, 25 847-25 879,
    URL     [Cited within:2]
    [36] Xie Y.,Y. Zhang, X. X. Xiong, J. J. Qu, and H. Z. Che, 2011: Validation of MODIS aerosol optical depth product over China using CARSNET measurements. Atmos. Environ., 45, 5970-5978,
    URL     [Cited within:1]
    [37] Yang J.,C. H. Dong, N. M. Lu, Z. D. Yang, J. M. Shi, P. Zhang, Y. J. Liu, and B. Cai, 2009: FY-3A: The new generation polar-orbiting meteorological satellite of China. Acta Meteorologica Sinica, 67, 501-509,
    URL     [Cited within:2]
    [38] Yumimoto K., Coauthors, 2016: Aerosol data assimilation using data from Himawari-8, a next-generation geostationary meteorological satellite. Geophys. Res. Lett., 43, 5886-5894,
    URL     [Cited within:1]
    [39] Zhang J.,H. Y. Liu, J. Y. Xin, W. Y. Zhang, G. J. Xiao, X. Y. Feng, and L. L. Wang, 2016: The comparison of MODIS and MERSI aerosol products in Shenyang. Journal of Remote Sensing, 20, 549-560,
    URL     [Cited within:1]
    [40] Zhang J, J. S. Reid, 2006: MODIS aerosol product analysis for data assimilation: Assessment of over-ocean level 2 aerosol optical thickness retrievals. Journal of Geophysical Research Atmospheres, 111, D22207,
    URL     [Cited within:1]
    [41] Zhang J. L.,J. S. Reid, D. L. Westphal, N. L. Baker, and E. J. Hyer, 2008: A system for operational aerosol optical depth data assimilation over global oceans. J. Geophys. Res., 113, D10208,
    URL     [Cited within:2]
    [42] Zhang Q, D. G. Streets, G. R. Carmichael, K. He. H. Huo, A. Kannari, Z. Klimont, I. Park, S. Reddy, J. S. Fu, D. Chen, L. Duan, Y. Lei, L. Wang, Z. Yao, 2009: Asian emissions in 2006 for the NASA INTEX-B mission. Atmospheric Chemistry & Physics Discussions, 9, 5131- 5153.
    [Cited within:1]
    [43] Zhao H. J.,H. Z. Che, X. Y. Zhang, Y. J. Ma, Y. F. Wang, H. Wang, and Y. Q. Wang, 2013: Characteristics of visibility and particulate matter (PM) in an urban area of Northeast China. Atmospheric Pollution Research, 4, 427-434,
    URL     [Cited within:1]
    RichHTML Viewed    
    Abstract Viewed    


    Related articles:
    Key words
    Fengyun-3A satellite
    aerosol optical depth
    data assimilation
    dust storm

    Xiaoli XIA
    Jinzhong MIN
    Feifei SHEN
    Yuanbing WANG
    Chun YANG