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Article

Impact of Fengyun-4A Atmospheric Motion Vector Data Assimilation on PM2.5 Simulation

by
Kaiqiang Gu
,
Jinyan Wang
*,
Shixiang Su
,
Jiangtao Zhu
,
Yu Zhang
,
Feifan Bian
and
Yi Yang
Laboratory of Climate Resource Development and Disaster Prevention in Gansu Province, Center for Weather Forecasting and Climate Prediction of Lanzhou University, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(11), 1952; https://doi.org/10.3390/rs17111952
Submission received: 13 March 2025 / Revised: 29 May 2025 / Accepted: 2 June 2025 / Published: 5 June 2025
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Abstract

:
PM2.5 pollution poses significant risks to human health and the environment, underscoring the importance of accurate PM2.5 simulation. This study simulated a representative PM2.5 pollution event using the Weather Research and Forecasting model coupled with chemistry (WRF-Chem), incorporating the assimilation of infrared atmospheric motion vector (AMV) data from the Fengyun-4A (FY-4A) satellite. A comprehensive analysis was conducted to examine the meteorological characteristics of the event and their influence on PM2.5 concentration simulations. The results demonstrate that the assimilation of FY-4A infrared AMV data significantly enhanced the simulation performance of meteorological variables, particularly improving the wind field and capturing local and small-scale wind variations. Moreover, PM2.5 concentrations simulated with AMV assimilation showed improved spatial and temporal agreement with ground-based observations, reducing the root mean square error (RMSE) by 8.2% and the mean bias (MB) by 15.2 µg/m3 relative to the control (CTL) experiment. In addition to regional improvements, the assimilation notably enhanced PM2.5 simulation accuracy in severely polluted cities, such as Tangshan and Tianjin. Mechanistic analysis revealed that low wind speeds and weak atmospheric divergence restricted pollutant dispersion, resulting in higher near-surface concentrations. This was exacerbated by cooler nighttime temperatures and a lower planetary boundary layer height (PBLH). These findings underscore the utility of assimilating satellite-derived wind products to enhance regional air quality modeling and forecasting accuracy. This study highlights the potential of FY-4A infrared AMV data in improving regional pollution simulations, offering scientific support for the application of next-generation Chinese geostationary satellite data in numerical air quality forecasting.

1. Introduction

The Beijing–Tianjin–Hebei region and its surrounding areas are among the most severely polluted regions in China. These regions frequently experience air pollution episodes dominated by fine particulate matter (PM2.5), which pose significant challenges to daily life and public health [1,2,3,4,5,6]. With advances in atmospheric environmental research, numerical simulations have become indispensable tools for investigating air pollution. Among these, the Weather Research and Forecasting model coupled with chemistry (WRF-Chem), an “online” regional chemistry transport model jointly developed by the National Center for Atmospheric Research (NCAR), the National Oceanic and Atmospheric Administration (NOAA), and the Pacific Northwest National Laboratory (PNNL), is widely applied to study the causes and transport mechanisms of regional air pollution [7,8,9]. WRF-Chem effectively simulates the processes of transport, diffusion, transformation, and deposition of air pollutants. Grell et al. [8] compared WRF-Chem with MM5-Chem and reported that WRF-Chem outperformed MM5-Chem in the prediction of O3 and PM2.5 concentrations. Kumar et al. [10] demonstrated the effectiveness of WRF-Chem in simulating the PM10 concentration, accurately capturing both spatial distribution and temporal variation. Similarly, Zhang et al. [11] further demonstrated that WRF-Chem could effectively simulate the regional transport process of PM2.5 during the severe pollution event in the Beijing–Tianjin–Hebei region in November 2015, highlighting the model’s strong ability to represent regional pollution dynamics and the spatiotemporal distribution of PM2.5. Moreover, Qiao et al. [12] conducted regional air pollution predictions in Guangzhou using multi-model cross-validation approaches, further confirming the advantages of WRF-Chem in regional air quality forecasting and model evaluation under complex meteorological conditions.
Currently, meteorological reanalysis data are commonly used in atmospheric chemistry models to simulate pollution processes. Meteorological conditions play a critical role in the distribution and evolution of regional air pollution by directly influencing pollutant transport and diffusion, as well as indirectly impacting the intensity and rates of chemical reactions [13,14,15,16]. Therefore, accurate meteorological fields are essential for reliable pollution event simulations. The WRF-Chem model fully couples meteorological and chemical processes across temporal and spatial scales, enabling real-time feedback between meteorological elements and chemical models during simulations [8]. Previous studies have demonstrated that assimilating meteorological observations can greatly improve pollutant simulations. Specifically, the assimilation of temperature-related data can influence the planetary boundary layer height (PBLH), thereby changing the spatiotemporal distribution of pollutants, while the assimilation of wind field-related data affects the regional transport and diffusion of pollutants [17,18,19]. Bei et al. [20] noted that uncertainties in meteorological conditions substantially impact aerosol composition simulations over the Beijing–Tianjin–Hebei region. Kim et al. [21] reported that data assimilation markedly improved the forecast accuracy under stable weather and low wind speed conditions, while its effects were limited under strong winds and high temperatures. Gao et al. [22] used three-dimensional variational (3DVAR) technology to assimilate Global Positioning System Zenith Total Delay (GPS-ZTD) data and reported improved performance of the WRF model in simulating surface and upper-air meteorological variables, PM2.5 concentration, and visibility. Similarly, Liu et al. [23] demonstrated that multisource meteorological assimilation enhances temperature and humidity forecasts in the lower atmosphere, consequently improving PM2.5 simulations. These findings confirm the feasibility and effectiveness of using WRFDA in conjunction with WRF-Chem to assimilate meteorological data for improved air quality simulations.
High-resolution unconventional meteorological observation methods, such as satellites and radar, have been widely used in numerical forecasts. The assimilation of these high-resolution data significantly improves the initial meteorological fields and model forecast accuracy [24,25,26,27]. As a representative Chinese meteorological satellite, Fengyun-4A (FY-4A) provides extensive atmospheric and surface measurement data, including infrared cloud imagery, water vapor charts, radiometer data, and atmospheric motion vector (AMV) [28]. Hong et al. [29] demonstrated that assimilating FY-4A satellite-retrieved PM2.5 data, in combination with ground-based measurements, significantly improved PM2.5 simulation performance across China using WRF-Chem. Their results highlight the great potential of FY-4A observational data in enhancing regional air quality forecasting through data assimilation. Among the multiple types of FY-4A observations, AMV data derived from infrared cloud tracking offer wide spatial coverage, high temporal resolution, and strong resilience to surface interference [30]. The assimilation of AMV data refines the model’s initial wind, temperature, and humidity fields, bringing them closer to real atmospheric states [31]. Chen et al. [32] reported that the FY-4A AMV provides high precision, high resolution, and real-time accuracy, accurately capturing atmospheric motion trends. The assimilation of high-level AMV improves wind fields in the upper layer, while the assimilation of infrared-channel AMV with good data quality in the lower layer is particularly effective in improving near-surface wind field. Through the assimilation of FY-4A AMV data, WRF-Chem can more effectively capture local wind field variations. However, most previous studies have primarily focused on assimilating FY-4A aerosol products to improve regional air pollution forecasts, while relatively few have investigated the use of FY-4A meteorological data (such as AMV data) for enhancing pollutant prediction accuracy.
This study utilized the WRF-Chem model coupled with the assimilation of infrared AMV data from the FY-4A satellite to simulate surface wind speed and PM2.5 concentrations in the Beijing–Tianjin–Hebei region from 7 to 10 October 2020. The assimilation improved the accuracy of the initial wind fields, thereby enhancing the performance of PM2.5 simulations. By analyzing the effects of improved initial fields on the PM2.5 concentration distribution and spatiotemporal variations, this study explored how meteorological elements influence pollutant diffusion and accumulation in the lower atmosphere. Hourly PM2.5 monitoring data were used to evaluate the reliability of the simulations. The findings validate the application of high-resolution satellite meteorological data in regional pollution simulations and offer a method for enhancing numerical air pollution predictions. These results provide a robust scientific foundation for improving air quality forecasts and developing pollution mitigation strategies.

2. Methods and Data

2.1. Data

The meteorological driving field data for this study were sourced from National Centers for Environmental Prediction (NCEP) FNL reanalysis data, with a temporal resolution of 6 h and a spatial resolution of 0.25° × 0.25°. The emission inventory was based on the 2017 version of the Multiscale Emissions Inventory Model (MEIC), derived from the “Multiscale Emissions Source Inventory Model for China”. This inventory primarily addresses anthropogenic emissions across mainland China and is provided at a horizontal spatial resolution of 0.25° × 0.25° [33]. For data assimilation, AMV observations were sourced from the infrared AMV products of China’s FY-4A geostationary meteorological satellite. The PM2.5 concentration data for comparison with the simulation results were obtained from the hourly monitoring dataset provided by the China National Environmental Monitoring Center. The distribution of air quality stations is presented in Figure 1.

2.2. Assessment Methods

To evaluate the performance of PM2.5 simulation, this study employed four statistical metrics: the mean bias (MB), root mean square error (RMSE), mean fractional bias (MFB), and mean fractional error (MFE). These metrics were used to validate the simulation results and quantitatively assess their accuracy.
M B = 1 N ∑ i = 1 N P i − O i
R M S E = 1 N ∑ i = 1 N P i − O i 2 1 2
M F B = 1 N ∑ i = 1 N P i − O i P i + O i / 2
M F E = 1 N ∑ i = 1 N P i − O i P i + O i / 2
In these formulas, P i represents the simulated PM2.5 (model grid point values interpolated to observation sites), O i represents the observed PM2.5, and N denotes the sample size. MB measures the average deviation between the simulated and observed values, with higher absolute MB indicating greater simulation inaccuracies in the simulation. The RMSE quantifies the overall deviation between the simulated and observed values, where a lower RMSE indicates a more accurate simulation. MFB and MFE, which are commonly applied in regional particulate matter forecasts, are particularly suitable for evaluating PM2.5 simulations. Lower values of MFB and MFE reflect improved simulation accuracy, with results considered satisfactory when MFB < ±0.3 and MFE < 0.5 [34].

3. Model and Experimental Design

3.1. Atmospheric Chemistry Models

This study simulated the PM2.5 concentration in the Beijing–Tianjin–Hebei region with WRF-Chem (V4.2). This model is an atmospheric chemistry model that couples a regional atmospheric dynamics–chemistry model based on the WRF meteorological model [35]. WRF-Chem integrates atmospheric physical and chemical processes through online coupling, employing consistent spatial coordinates, physical parameterization schemes, and time steps to reduce information loss and enhance simulation accuracy [8]. The model incorporates the feedback of chemical processes into physical processes, allowing it to capture the effects of meteorological changes on chemical reactions and the immediate feedback of chemical processes on physical dynamics with greater accuracy [36]. Compared with offline models, the WRF-Chem model demonstrate superior performance in simulating air pollution and providing detailed, high-accuracy forecasts.

3.2. Data Assimilation System

In this study, infrared AMV data from the FY-4A geostationary satellite were assimilated into the WRF-Chem model using the 3DVAR system within WRFDA (V4.2). The AMV data were derived by tracking the displacement of cloud features in successive infrared satellite images, which provided high-resolution horizontal wind vectors at various pressure levels. These vectors represented the motion of air masses at cloud-tracked altitudes and offered extensive coverage across the study region, particularly in areas lacking conventional wind observations.
The AMV data were assimilated to optimize the initial field of the simulation. Within the 3DVAR framework, the assimilation process aims to optimize the initial condition of the model by minimizing a cost function that combines background (model) information with observational data [37]. The background error covariance matrix allows for observation increments (in this case, wind vector differences from AMV) to be spread not only horizontally and vertically, but also across variables, meaning that wind information from AMV can also indirectly adjust temperature and pressure fields via multivariate correlations [32].
The improvements in the initial wind field play a critical role in determining the transport and dispersion of PM2.5. Enhanced low-level wind structures enhance horizontal advection and facilitate pollutant transport across urban areas. Meanwhile, improved upper-level wind divergence and convergence patterns affect vertical mixing and boundary layer development. These factors directly influence PM2.5 concentrations, especially under stagnant meteorological conditions, such as weak wind speeds and temperature inversions. By correcting wind field structures with AMV data, the model captures more realistic atmospheric dynamics, leading to more accurate simulation of pollutant accumulation and removal processes.

3.3. Experimental Design

This study investigated the effects of assimilating FY-4A infrared AMV data to simulate the PM2.5 concentration, focusing on an air pollution event that occurred in the Beijing–Tianjin–Hebei region and its surrounding areas from 7 to 10 October 2020. All times in this study are in China Standard Time (CST). To evaluate the assimilation effects, the control (CTL) and the data assimilation (DA_FY) experiments were conducted for comparison. In the CTL experiment, the model was run from 08:00 on 6 October 2020 to 08:00 on 7 October 2020 as a spin-up time and then continued to run for 96 h on the basis of this initial state to simulate PM2.5 concentration changes. In contrast, the DA_FY experiment incorporated FY-4A infrared AMV observation data at 08:00 on 7 October 2020, updating the initial field to improve the PM2.5 forecast over the following four days.
The simulation domain employed a double-nesting approach (Figure 1), with grid resolutions of 9 km for the outer layer and 3 km for the inner layer, covering grid dimensions of 261 × 221 and 250 × 277, respectively. Centered geographically at 39.0°N and 116.0°E and the model utilized a 33-layer vertical structure. To enhance model accuracy, various advanced parameterization schemes were applied: the Lin scheme for microphysics [38], the RRTM scheme for longwave radiation [39], the Goddard scheme for shortwave radiation [40], and the Noah scheme for land surface processes [41]. Additionally, the Monin–Obukhov scheme was used for the near-surface layer [42], the YSU scheme for the boundary layer [43], the CBMZ scheme for chemical reactions [44], the MOSAIC_4bin scheme for aerosol modeling [45], and the fast-J scheme for photolysis [46].
To further clarify the overall workflow of this study, a flowchart is presented to illustrate the integration of multiple data sources and the structure of the simulation system (Figure 2). The framework included surface PM2.5 observations, FY-4A AMV satellite data, FNL reanalysis data, and MEIC anthropogenic emission inventory. These components underwent specific preprocessing steps. FY-4A AMV satellite data and FNL reanalysis data were assimilated using the WRF-3DVAR module to generate optimized initial meteorological fields for the WRF-Chem model. Meanwhile, the MEIC provided chemical initial and boundary conditions for WRF-Chem. The simulated PM2.5 concentrations were subsequently post-processed and evaluated against observational data.

4. Results

4.1. Analysis of the Severe PM2.5 Pollution Event

From 7 to 10 October 2020, a severe PM2.5 pollution event occurred in the Beijing–Tianjin–Hebei and surrounding areas. This event, typical of the autumn and winter, was characterized by strong temperature inversions and low wind speeds, which led to poor atmospheric diffusion. Therefore, the study of this pollution event is highly important for understanding the atmospheric environment and pollutant distribution patterns.
Figure 3 illustrates the distribution of the observed PM2.5 concentration during this pollution event. At 10:00 on 7 October (Figure 3a), the air quality across the Beijing–Tianjin–Hebei area was generally good, with PM2.5 concentrations below 80 µg/m3. By 10:00 on 8 October (Figure 3b), atmospheric diffusion conditions had begun to deteriorate, leading to a slight increase in the PM2.5 concentration. High PM2.5 concentration centers were observed near Tianjin, Tangshan, Beijing, Baoding, and Shijiazhuang, with concentrations exceeding 110 µg/m3 in Tianjin and Baoding. On 9 October at 10:00 (Figure 3c), the atmospheric diffusion conditions continued to worsen, resulting in a further regional increase in the PM2.5 concentration. A high-concentration band of PM2.5 emerged, stretching across Beijing, Baoding, and Shijiazhuang to Xingtai, Taiyuan, and Handan, with values reaching 120 µg/m3. However, the PM2.5 concentrations in Tianjin and Tangshan declined slightly compared with those on 8 October. By 10:00 on October 10 (Figure 3d), the diffusion conditions had deteriorated further, with PM2.5 concentrations exceeding 160 µg/m3 in Beijing, Baoding, Tianjin, and Tangshan.

4.2. Effect of Assimilation on the Initial Wind Field

The assimilation of FY-4A infrared AMV data significantly enhances the representation of wind fields, allowing for more precise characterization of local variations and small-scale characteristics, thereby improving the simulation accuracy of the initial wind field. Meanwhile, an accurate initial wind field also contributes to better timeliness in model forecasting, allowing the model to respond more sensitively to changes in wind trends more rapidly.
To further investigate the effects of the assimilated FY-4A infrared AMV data on the initial wind field, we analyzed the increments of the horizontal wind and temperature fields. The assimilation of FY-4A infrared AMV data provided the model with extensive information on upper atmospheric movement, aiding in the construction of a more accurate initial field. At the 500 hPa level (Figure 4a1), a divergent increment field was observed near Beijing, while a convergent increment field appeared near Hengshui. The dynamic state of the upper atmosphere influenced the lower atmosphere, transmitting this dynamic signal downward through processes such as vertical motion and turbulent diffusion, thus adjusting lower-level winds to better match actual atmospheric conditions with actual atmospheric dynamic processes. At the 850 hPa level (Figure 4a2), a relatively weak divergence increment field was evident near Beijing, indicating a modest outward flow. This condition may have inhibited the upward transport of pollutants, such as PM2.5, from the near-surface layer, leading to their accumulation in the lower atmosphere and restricting vertical dispersion. On the other hand, the convergence increment field near Hengshui indicates inward atmospheric contraction at this level, forcing air to ascend, which may have alleviated surface-layer pollution to some extent.
Notably, the assimilated infrared AMV data did not directly provide temperature information. However, owing to the physical and spatial correlations among model variables, temperature variations arose from multivariate correlations within the background error covariance matrix, resulting in minor temperature changes [32]. As shown in Figure 4b1,b2, a temperature decrease was observed near Beijing at the 500 hPa level, while an increase occurred at the 850 hPa level. The temperature rise at the 850 hPa level induced atmospheric instability, promoting vertical convection. This updraft facilitated the upward transport of pollutants, such as PM2.5, reducing their concentrations in the near-surface layer. In contrast, near Hengshui, a temperature increase at the 500 hPa level combined with a decrease at the 850 hPa level created relative atmospheric stability, increasing density and inhibiting vertical air movement, particularly updrafts. Under these conditions, pollutants such as PM2.5 struggled to ascend from the near-surface layer to higher altitudes, leading to accumulation in the near-surface layer. This accumulation hindered the vertical dispersion of pollutants and exacerbated air pollution in the near-surface layer.
After assimilating the FY-4A infrared AMV data, the model effectively captured the characteristics of upper atmospheric movements. Strong divergent or convergent motion at upper levels impacted the wind fields at lower levels through vertical motion and turbulent diffusion processes, and then the initial wind field was adjusted and optimized to better reflect the actual atmospheric state. Additionally, through multivariate correlations in the background error covariance matrix, the temperature field within the initial field was also refined.

4.3. Spatial Variation Characteristics of PM2.5

The forecast fields of the two experimental groups were analyzed to investigate the effects of assimilating FY-4A infrared AMV data on the spatial variation characteristics of PM2.5 and to evaluate the simulation effects.
Figure 5 illustrates the distributions of the PM2.5 concentration and surface wind field in the Beijing–Tianjin–Hebei region, as simulated by the CTL and DA_FY from 8 to 10 October 2020. The dots represent ground-based PM2.5 observations. In the CTL, at 10:00 on 8 October (Figure 5a1), the southern Beijing–Tianjin–Hebei region was influenced by southerly airflow, which facilitated the northeastward transport of pollutants along the Taihang Mountains. However, these pollutants were subsequently blocked by the Taihang and Yanshan Mountains, resulting in accumulations in Tianjin, Beijing, Baoding, Shijiazhuang, Xingtai, and Handan. The local wind speed was relatively weak, hindering dispersion and leading to the formation of a high PM2.5 concentration belt (above 100 µg/m3). On 9 October at 10:00 (Figure 5a2), southerly airflow persisted, but the overall PM2.5 concentrations decreased slightly. By 10 October at 10:00 (Figure 5a3), the weakening southerly airflow had reduced the transport capacity of pollutants to the north. This resulted in the high-concentration PM2.5 area shifting south from Beijing to Langfang, forming a high PM2.5 concentration belt (greater than 150 µg/m3) in Langfang and Baoding. Compared with the observations, the CTL accurately simulated the high-concentration PM2.5 zone in Beijing–Baoding–Shijiazhuang on October 8. However, by 9 October, the PM2.5 concentrations were generally underestimated, and on October 10, the high-concentration area of PM2.5 in Beijing had moved southward.
In DA_FY, southerly airflow also influenced the region on 8 October at 10:00 (Figure 5b1), leading to PM2.5 accumulation in Beijing, Tianjin, Baoding, and Shijiazhuang, forming an area with high PM2.5 concentrations (above 100 µg/m3). On 9 October at 10:00 (Figure 5b2), the weakened southerly airflow caused the high-concentration PM2.5 area to extend southward to Hengshui, Xingtai, and Handan. By 10 October at 10:00 (Figure 5b3), the PM2.5 concentration significantly increased in Langfang, Baoding, and Shijiazhuang, exceeding 150 µg/m3. Compared with the observations, DA_FY provided a more accurate simulation of the spatial distribution of PM2.5 on 8 and 9 October. On 10 October, the high-concentration PM2.5 area in Beijing also shifted southward on the 10th, with relatively high PM2.5 concentrations appeared in Shijiazhuang, Xingtai, and Handan.
When comparing the two experiments, the DA_FY simulation depicted weaker southerly airflow on October 8 (Figure 5c1), reducing the outward diffusion capacity of pollutants near Hengshui and Cangzhou. This resulted in lower PM2.5 concentrations in Xingtai and Handan and higher concentrations near Hengshui and Cangzhou, which better aligned with observations. On 9 October (Figure 5c2), DA_FY predicted higher PM2.5 concentrations than CTL, particularly in the high-concentration area. By 10 October (Figure 5c3), the weaker wind speeds at DA_FY resulted in elevated PM2.5 concentrations, an expanded high-concentration area, and a diminished degree of southward migration of the high-concentration PM2.5 area in Beijing, closely matching the observations. In summary, DA_FY produced a simulation of the spatial distribution of PM2.5 that was closer to the actual observation results than CTL.
After examining the spatial variation characteristics of PM2.5 between the two groups of experiments, statistical analyses were performed to quantitatively evaluate and compare their simulation performance. Figure 6 presents the statistical metrics, namely MB, RMSE, MFB, and MFE, for both CTL and DA_FY during the study period. In the CTL, most stations exhibited negative MB and MFB values, with site averages of −17.7 µg/m3 and −0.47, respectively, indicating that the CTL simulations generally underestimated the PM2.5 concentrations compared with the observed values. In contrast, DA_FY produced positive MB and MFB across most stations in the Beijing–Tianjin–Hebei region, except for northern Beijing and Tianjin. The averages for DA_FY were 2.47 µg/m3 and −0.1, respectively, indicating a trend of underestimation in northern Beijing and Tianjin and overestimation in other parts of the region. Compared with CTL, the absolute values of MB and MFB in DA_FY were reduced by 2.45 µg/m3 and 0.23, respectively, suggesting that the DA_FY simulations were closer to the observed values overall. However, the PM2.5 simulations in DA_FY were elevated in southern Beijing and Handan, with DA_FY overestimating the PM2.5 concentration, resulting in larger deviations from the observations than CTL. The site average RMSE and MFE values for CTL were 41.86 µg/m3 and 0.64, respectively, highlighting significant differences between the CTL simulations and the observed values, particularly in high-concentration areas, such as Beijing, Tianjin, Baoding, Tangshan, and Shijiazhuang. For DA_FY, the site average RMSE and MFE values were 38.43 µg/m3 and 0.45, respectively, representing improvements of 3.43 µg/m3 and 0.19, respectively, relative to CTL. These results indicate that the DA_FY simulations were generally closer to the observed values, with particularly notable improvements in heavily polluted areas.
In summary, the assimilation of FY-4A infrared AMV data in DA_FY significantly enhanced the spatial distribution accuracy of the PM2.5 concentration. This improvement was especially apparent in areas with high pollution, where the DA_FY simulation more closely matched observations, thus enhancing the overall prediction accuracy.

4.4. Temporal Variation Characteristics of PM2.5

After the spatial distribution characteristics of PM2.5 concentrations were assessed and improvements were achieved through the assimilation of FY-4A infrared AMV data, we further analyzed the temporal variation characteristics to gain deeper insights into the spatiotemporal evolution of pollutants in the Beijing–Tianjin–Hebei region. This analysis offers a more comprehensive scientific foundation for enhancing pollution prediction and informing prevention and control strategies.
Figure 7 illustrates the temporal variation in the PM2.5 concentration as simulated by CTL and DA_FY from 7 to 10 October 2020. Both experimental groups effectively captured the overall increasing trend in the PM2.5 concentration during this period, demonstrating their sensitivity to concentration changes and strong simulation capabilities. The study period was divided into eight 12 h intervals. The PM2.5 concentrations decreased during the day (0–12 h, 24–36 h, 48–60 h, and 72–84 h) and increased at night (12–24 h, 36–48 h, 60–72 h, and 84–96 h). Compared with the observed average concentration of 69.53 µg/m3, the CTL simulations had a mean of 46.54 µg/m3, indicating significant underestimation. Conversely, DA_FY produced a mean of 70.75 µg/m3, which was a slight overestimation but much closer to the observed values, highlighting its superior ability to capture temporal variations and fluctuations in pollution concentrations.
To further evaluate the performance of the two experimental groups in simulating the temporal distribution of PM2.5, Figure 8 highlights that the DA_FY simulations aligned more closely with the observed temporal variations. DA_FY also more accurately reflected day–night PM2.5 concentration fluctuations. Specifically, the MB and MFB of CTL were generally negative at −17.70 µg/m3 and −0.47, respectively, while DA_FY produced MB and MFB values of 2.47 µg/m3 and −0.1, respectively, indicating improved overall accuracy. Both experiments tended to underestimate the PM2.5 concentration during the day and overestimate it at night; however, DA_FY showed smaller deviations, resulting in daytime values closer to the observations. Although DA_FY slightly overestimated the PM2.5 concentration at night, its overall performance was superior to that of CTL. Additionally, compared with CTL, DA_FY demonstrated lower RMSE and MFE values, indicating enhanced accuracy and reliability. Overall, DA_FY not only captured the general temporal trend more effectively but also more accurately reflected the observed day–night variation in the PM2.5 concentration. These findings confirm that assimilating FY-4A infrared AMV data significantly improves the accuracy and reliability of PM2.5 simulations.

4.5. Simulation of PM2.5 in Severely Polluted Cities

Further analysis of PM2.5 simulations for severely polluted cities provides valuable insights into the variation in pollutant concentrations across different urban areas. The PM2.5 concentrations were examined in four heavily polluted cities: Beijing, Tianjin, Shijiazhuang, and Tangshan. The simulation performances of DA_FY and CTL differed across the four cities (Table 1). The CTL experiment exhibited negative MB values across all four cities, indicating systematic underestimation of PM2.5 concentrations, with the most pronounced underestimation observed in Beijing and relatively smaller deviations in Shijiazhuang. In contrast, DA_FY produced positive MB values in most cities, suggesting a tendency to overestimate PM2.5 levels. The overestimation was moderate in Tangshan, but more significant in Shijiazhuang. In terms of the RMSE, DA_FY demonstrated substantial improvements over CTL. Specifically, DA_FY reduced the RMSE by 7.76, 20.72, 0.47, and 20.59 in Beijing, Tianjin, Shijiazhuang, and Tangshan, respectively, with the most notable increase in simulation accuracy observed in Tianjin. On the basis of the PM model performance criteria proposed by Boylan and Russell [34], where MFB < ±0.3 and MFE < 0.5 represent the optimal achievable accuracy for the model, DA_FY met these benchmarks in four cities. Across all cities, the MFB of DA_FY was less than ±0.25, and the MFE was less than 0.45, indicating that DA_FY has a high simulation accuracy.
Overall, DA_FY effectively reduced RMSE and improved simulation performance compared to CTL, particularly in Tangshan and Tianjin, where the enhancements in PM2.5 simulation accuracy were the most significant.

4.6. Mechanistic Analysis

A detailed analysis of the spatial and temporal variation characteristics of simulated PM2.5 concentrations across heavily polluted cities is essential for understanding the mechanisms underlying pollutant accumulation and dispersion. In particular, examining the effects of assimilating FY-4A infrared AMV data on meteorological fields offers important insights into pollutant transport dynamics and provides a theoretical basis for model improvement.
To illustrate these mechanisms, the region surrounding Hengshui and Cangzhou was selected for case analysis. Vertical cross-sections of PM2.5 concentration, temperature, divergence, and vertical wind fields were analyzed along line AB (Figure 5b2 and Figure 9a1,a2). The results reveal that near-surface wind speeds in the DA_FY experiment were generally lower than those in CTL, accompanied by a weak downdraft near 115.85°E. These lower wind speeds inhibited horizontal dispersion, leading to localized PM2.5 accumulation. The presence of the downdraft further limited vertical mixing, stabilizing the boundary layer and impeding the upward transport of pollutants. In the CTL simulation, a relatively high temperature decrease rate created an unstable atmospheric layer, which was conducive to vertical pollutant diffusion. In contrast, the lower temperature decrement rate in DA_FY promoted the stabilization of the atmosphere, which inhibited vertical pollutant diffusion and resulted in higher PM2.5 concentrations. The temperature difference analysis (Figure 9b) confirms that reduced atmospheric instability in DA_FY contributed to increased accumulation of PM2.5 near the surface. Figure 9c1–c3 illustrates that, in CTL, a strong low-level divergence field under unstable conditions enhanced vertical diffusion, reducing near-surface pollutant concentrations. This vertical diffusion, combined with robust horizontal diffusion, transported pollutants outward, leading to a decrease in local PM2.5 concentrations. Conversely, in DA_FY, the low-level convergence field promoted horizontal inward transport in the near-surface layer, leading to pollutant accumulation in the lower atmosphere. While some vertical diffusion occurred, limited horizontal diffusion restricted dispersal, causing pollutants to concentrate near the surface. Furthermore, weak divergence at upper levels in DA_FY restricted vertical mixing, hindering upward pollutant diffusion and prolonging the presence of pollutants in the lower atmosphere. This vertical stagnation prevented pollutants from rising, further limiting the diffusion of surface-level pollutants and resulting in elevated PM2.5 concentrations near the surface.
Following the analysis of the mechanisms behind the higher PM2.5 concentrations simulated by DA_FY than by CTL, a further examination of the diurnal variation in the PM2.5 concentration provides insights into the pollutant distribution patterns and underlying causes of pollution formation across different time periods. Analyzing the daytime and nighttime variations in meteorological elements allows for a clearer understanding of how DA_FY and CTL differ in their simulations of pollutant diffusion and accumulation mechanisms, thereby informing future model optimizations.
The simulation results from both experiments were compared across four severely polluted cities. As shown in Figure 10, the surface temperatures in all cities exhibited a diurnal pattern, with higher temperatures during the day and lower temperatures at night. DA_FY generally simulated slightly lower surface temperatures than CTL. This reduced surface temperature led to a lower PBLH in the DA_FY simulations, restricting the vertical diffusion and dilution of pollutants and thus increasing the accumulation of pollutants near the surface. This factor partially explains why the simulated PM2.5 concentrations in DA_FY were higher than those in CTL. Notably, DA_FY significantly overestimated the PM2.5 concentration at night, with larger discrepancies between DA_FY and CTL. This difference may stem from the fact that the surface temperature in DA_FY was significantly lower than that in CTL at night, while the PBLH in DA_FY was lower and contributed to the development of a stable and stagnant boundary layer, thereby inhibiting vertical pollutant diffusion.

5. Conclusions

This study presents a comprehensive analysis of the PM2.5 pollution event that occurred in the Beijing–Tianjin–Hebei region and surrounding areas from 7 to 10 October 2020. The pollution during this period exhibited typical autumn–winter characteristics, marked by unfavorable atmospheric diffusion conditions, such as strong temperature inversions and weak wind speeds. Understanding the formation mechanisms of this pollution event is essential for analyzing air quality and pollutant distribution patterns in this region during autumn and winter.
By assimilating FY-4A infrared AMV data into the WRF-Chem model using the WRF-3DVAR system, the accuracy of the initial meteorological fields (particularly the wind field) was substantially enhanced. The analysis of the initial wind field revealed that upper atmospheric movement substantially impacted the surface wind field and the vertical diffusion of PM2.5. Evaluation of the PM2.5 simulations showed that DA_FY outperformed the CTL experiment in both the spatial and temporal dimensions. DA_FY achieved notable improvements in spatial distribution accuracy, as well as reductions in RMSE and MFE by 3.43 µg/m3 and 0.19, respectively. Temporally, although both experiments captured the general trends, the CTL experiment exhibited systematic underestimations with MB of −17.70 µg/m³ and MFB of −0.47, along with higher RMSE and MFE values (41.86 µg/m3 and 0.64). In contrast, the temporal variations and fluctuations in the PM2.5 concentrations simulated by DA_FY were more accurate, especially at night. These findings underscore the effectiveness of assimilating FY-4A AMV data in improving the accuracy of meteorological fields and enhancing PM2.5 forecast performance.
City-level evaluations further demonstrated that DA_FY significantly improved the simulation accuracy of PM2.5 concentrations in severely polluted cities, with the most notable enhancements observed in Tangshan and Tianjin, demonstrating the applicability and potential of assimilating FY-4A infrared AMV data for urban-scale air quality modeling. Moreover, the results highlight that unfavorable meteorological conditions, namely low wind speeds, weak divergence, nocturnal cooling, and a lower PBLH were key contributors to the heavy pollution episode over the Beijing–Tianjin–Hebei region. This study demonstrates the value of incorporating high-resolution satellite-derived wind products in regional air quality modeling and provides new insights into pollution formation under stable atmospheric conditions.
The findings validate the effectiveness of assimilated FY-4A infrared AMV data in simulating PM2.5 concentrations in the Beijing–Tianjin–Hebei region, providing a valuable meteorological foundation for future research. This study serves as a case analysis for the Beijing–Tianjin–Hebei region, focusing on a single pollution event from 7 to 10 October 2020; therefore, the conclusions drawn are specific to this case. Future research will aim to repeat the experiment in other regions and expand the number of pollution episodes to further verify the robustness and generalizability of the proposed assimilation approach. However, this study still faces certain limitations, particularly in terms of model parameter optimization and data availability. For example, the simulated PBLH was low at night, which led to a significant overestimation of the PM2.5 concentration. Therefore, future studies will focus on refining the model parameterization scheme and exploring the effects of other meteorological factors on PM2.5 simulations. In addition, future studies could benefit from coupling the WRF-DA simulations with Lagrangian trajectory models, such as HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) to more robustly analyze transport and dispersion mechanisms under different meteorological regimes. This integration would help independently validate pollutant transport pathways and enhance the understanding of pollutant accumulation processes under complex atmospheric conditions.

Author Contributions

Conceptualization, K.G. and J.W.; data curation, K.G.; formal analysis, K.G., J.W., S.S. and Y.Y.; funding acquisition, J.W.; investigation, K.G.; methodology, K.G.; supervision, J.W. and Y.Y.; visualization, K.G. and J.Z.; writing—original draft, K.G.; writing—review and editing, K.G., J.W., S.S., J.Z., Y.Z., F.B. and Y.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Key Research and Development Program of China (grant number 2020YFA0608402) and the Joint Funds of the National Natural Science Foundation of China (grant number U2342205).

Data Availability Statement

Data will be made available upon request.

Acknowledgments

The authors gratefully acknowledge the support from the Supercomputing Center of Lanzhou University. The authors express their gratitude to NCEP for providing the FNL reanalysis data (https://rda.ucar.edu/datasets/d083003/dataaccess/ (accessed on 16 February 2025)). The authors extend thanks to the China National Environmental Monitoring Center for the PM2.5 observation data (https://air.cnemc.cn:18007/ (accessed on 20 February 2025)) and to Tsinghua University for the MEIC emission inventory (http://www.meicmodel.org (accessed on 16 February 2025)). The authors also acknowledge the National Satellite Meteorological Center for supplying the FY-4A infrared AMV data (https://data.nsmc.org.cn/PortalSite/Data/DataView.aspx (accessed on 19 February 2025)). In addition, the authors sincerely appreciate the guidance and constructive feedback from the editors and reviewers.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Model domain and distribution of ambient air quality monitoring stations. The symbol × represents ambient air quality monitoring stations, while the symbol ✫ denotes key focus cities. The two curves represent the Yanshan and Taihang Mountains, respectively.
Figure 1. Model domain and distribution of ambient air quality monitoring stations. The symbol × represents ambient air quality monitoring stations, while the symbol ✫ denotes key focus cities. The two curves represent the Yanshan and Taihang Mountains, respectively.
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Figure 2. Flowchart of the PM2.5 simulation system incorporating data assimilation.
Figure 2. Flowchart of the PM2.5 simulation system incorporating data assimilation.
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Figure 3. Near-surface PM2.5 mass concentration (µg/m3) in the Beijing–Tianjin–Hebei region from 7 to 10 October 2020. (a–d) correspond to 7, 8, 9, and 10 October 2020, respectively. Major cities mentioned in the article, including Beijing, Tianjin, Shijiazhuang, Tangshan, Baoding, Xintai, Handan, Langfang, Taiyuan, and Hengshui, are marked on the map.
Figure 3. Near-surface PM2.5 mass concentration (µg/m3) in the Beijing–Tianjin–Hebei region from 7 to 10 October 2020. (a–d) correspond to 7, 8, 9, and 10 October 2020, respectively. Major cities mentioned in the article, including Beijing, Tianjin, Shijiazhuang, Tangshan, Baoding, Xintai, Handan, Langfang, Taiyuan, and Hengshui, are marked on the map.
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Figure 4. Horizontal wind increment field (m/s) (a1,a2) and temperature increment field (°C) (b1,b2) at 8:00 CST on 7 October 2020. Arrows direction in (a1,a2) indicate wind direction, with both color and length representing wind speed. Divergence and convergence affect pollutant distribution through horizontal advection and vertical motion, regulated by meteorological conditions, terrain, and vertical circulation structures.
Figure 4. Horizontal wind increment field (m/s) (a1,a2) and temperature increment field (°C) (b1,b2) at 8:00 CST on 7 October 2020. Arrows direction in (a1,a2) indicate wind direction, with both color and length representing wind speed. Divergence and convergence affect pollutant distribution through horizontal advection and vertical motion, regulated by meteorological conditions, terrain, and vertical circulation structures.
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Figure 5. PM2.5 mass concentration (µg/m3) and surface wind field (m/s) simulated by the CTL and DA_FY from 7 to 10 October 2020. (a1–a3), (b1–b3), and (c1–c3) correspond to CTL, DA_FY, and DA_FY−CTL. The numerals 1–3 indicate 8, 9, and 10 October 2020. Arrows indicate wind direction, with their length and color representing wind speed. The dots represent the observed ground PM2.5 mass concentration. The line segment AB marks the location of the vertical cross-section analysis.
Figure 5. PM2.5 mass concentration (µg/m3) and surface wind field (m/s) simulated by the CTL and DA_FY from 7 to 10 October 2020. (a1–a3), (b1–b3), and (c1–c3) correspond to CTL, DA_FY, and DA_FY−CTL. The numerals 1–3 indicate 8, 9, and 10 October 2020. Arrows indicate wind direction, with their length and color representing wind speed. The dots represent the observed ground PM2.5 mass concentration. The line segment AB marks the location of the vertical cross-section analysis.
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Figure 6. Statistics of the simulation results for each site of CTL and DA_FY from 7 to 10 October 2020. |DA_FY|−|CTL| represents the difference between the absolute value of DA_FY and the absolute value of CTL.
Figure 6. Statistics of the simulation results for each site of CTL and DA_FY from 7 to 10 October 2020. |DA_FY|−|CTL| represents the difference between the absolute value of DA_FY and the absolute value of CTL.
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Figure 7. Time series of PM2.5 concentrations (µg/m3) simulated by observations and CTL and DA_FY from 7 to 10 October 2020.
Figure 7. Time series of PM2.5 concentrations (µg/m3) simulated by observations and CTL and DA_FY from 7 to 10 October 2020.
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Figure 8. Statistical time series of the simulation results of CTL and DA_FY from 7 to 10 October 2020. (a) MB, (b) RMSE, (c) MFB, and (d) MFE. Red and blue lines represent the DA_FY and CTL experiments. The average values for each metric are also annotated in the panels.
Figure 8. Statistical time series of the simulation results of CTL and DA_FY from 7 to 10 October 2020. (a) MB, (b) RMSE, (c) MFB, and (d) MFE. Red and blue lines represent the DA_FY and CTL experiments. The average values for each metric are also annotated in the panels.
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Figure 9. Vertical cross-sections of CTL and DA_FY at 10:00 on 9 October 2020, along line segment AB (Figure 4b2). (a1,a2) The PM2.5 concentrations (µg/m3) overlaid with the temperature profile (°C) and vertical wind field (m/s); (b) temperature difference (°C) between CTL and DA_FY, overlaid with the vertical wind field difference (m/s); (c1,c2) divergence field (10−5 s−1); (c3) divergence difference between CTL and DA_FY (10−5 s−1). The arrow direction represents vertical wind motion, while the arrow length indicates the magnitude of the wind speed.
Figure 9. Vertical cross-sections of CTL and DA_FY at 10:00 on 9 October 2020, along line segment AB (Figure 4b2). (a1,a2) The PM2.5 concentrations (µg/m3) overlaid with the temperature profile (°C) and vertical wind field (m/s); (b) temperature difference (°C) between CTL and DA_FY, overlaid with the vertical wind field difference (m/s); (c1,c2) divergence field (10−5 s−1); (c3) divergence difference between CTL and DA_FY (10−5 s−1). The arrow direction represents vertical wind motion, while the arrow length indicates the magnitude of the wind speed.
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Figure 10. Time series of surface temperature (°C) and PBLH (m) in severely polluted cities from 7 to 10 October 2020.
Figure 10. Time series of surface temperature (°C) and PBLH (m) in severely polluted cities from 7 to 10 October 2020.
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Table 1. Comparison of the PM2.5 site simulation results between DA_FY and CTL.
Table 1. Comparison of the PM2.5 site simulation results between DA_FY and CTL.
SiteMBRMSEMFBMFE
CTLDA_FYCTLDA_FYCTLDA_FYCTLDA_FY
Beijing−22.8615.9652.3644.60−0.410.070.590.33
Tianjin−42.59−10.5558.0137.29−0.80−0.220.880.37
Shijiazhuang−7.0817.2054.4453.97−0.340.080.640.42
Tangshan−32.120.4964.7841.19−0.61−0.030.820.34
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Gu, K.; Wang, J.; Su, S.; Zhu, J.; Zhang, Y.; Bian, F.; Yang, Y. Impact of Fengyun-4A Atmospheric Motion Vector Data Assimilation on PM2.5 Simulation. Remote Sens. 2025, 17, 1952. https://doi.org/10.3390/rs17111952

AMA Style

Gu K, Wang J, Su S, Zhu J, Zhang Y, Bian F, Yang Y. Impact of Fengyun-4A Atmospheric Motion Vector Data Assimilation on PM2.5 Simulation. Remote Sensing. 2025; 17(11):1952. https://doi.org/10.3390/rs17111952

Chicago/Turabian Style

Gu, Kaiqiang, Jinyan Wang, Shixiang Su, Jiangtao Zhu, Yu Zhang, Feifan Bian, and Yi Yang. 2025. "Impact of Fengyun-4A Atmospheric Motion Vector Data Assimilation on PM2.5 Simulation" Remote Sensing 17, no. 11: 1952. https://doi.org/10.3390/rs17111952

APA Style

Gu, K., Wang, J., Su, S., Zhu, J., Zhang, Y., Bian, F., & Yang, Y. (2025). Impact of Fengyun-4A Atmospheric Motion Vector Data Assimilation on PM2.5 Simulation. Remote Sensing, 17(11), 1952. https://doi.org/10.3390/rs17111952

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