Satellite Monitoring of the Dust Storm over Northern China on 15 March 2021

Jingning, Luo; Huang, Fuxiang; Gao, Song; Song, Liu; Liu, Ruixia; Devasthale, Abhay

doi:10.3390/atmos13020157

Cited by 11 publications

(7 citation statements)

References 15 publications

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“…These results (relationships) are statistically significant at 95% confidence interval and are less likely to be affected by meteorological factors, as revealed from partial correlation coefficient values. In the summer season, Asian dusts are negligibly present in the atmosphere, although they are known to have profound aerosol indirect effects (Y. Liu et al., 2019; Luo et al., 2023). The effects of dust aerosols on cloud properties modifications can be prominent during spring season in our study regions because westerly winds easily transport dust aerosols emitted in the source regions (Bi et al., 2014; Y. Liu et al., 2011) to downwind regions, including East China, East China Sea, Korea, and Japan (Khatri et al., 2010, 2014).…”

Section: Resultsmentioning

confidence: 99%

Aerosol Effects on Water Cloud Properties in Different Atmospheric Regimes

Khatri,

Yoshida,

Hayasaka

2023

JGR Atmospheres

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Aerosol‐cloud interaction remains one of the least understood processes in climate science arena, despite its profound impacts in radiation and water budget perturbations. The aerosol effects on clouds largely depend on aerosol characteristics. Here, we implemented 17‐year (2003–2020) data set of aerosol, cloud, and meteorological factors collected over East Asia—a highly polluted region with recent decreasing trend of air pollution due to control measures—to elucidate atmospheric regime‐dependent aerosol effects on water cloud properties by simultaneously accessing the response of air pollution control measures in cloud field. The study found a very close relationship between aerosol loading and cloud properties modifications in the continental region of East Asia with a significant response of air pollution control measures in the cloud field. The study further revealed that aerosols of the polluted continental atmosphere affected cloud micro‐ and macro‐physics differently than aerosols of the clean maritime atmosphere: in the former, increased aerosol loading increased the stability under cloud base and then enhanced cloud droplet collision‐coalescence process, resulting to increase cloud droplet size by decreasing cloud top height; whereas in the latter, increased aerosol loading decreased cloud droplet size without notable influence in the atmosphere thermodynamics and cloud top height. This study further showed a complex aerosol‐cloud interaction process in the polluted maritime atmosphere due to the mixed effect of polluted continental and clean maritime atmospheres. In all atmospheric regimes, cloud fraction was found to increase with the increase of aerosol loading.

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Section: Resultsmentioning

confidence: 99%

Aerosol Effects on Water Cloud Properties in Different Atmospheric Regimes

Khatri,

Yoshida,

Hayasaka

2023

JGR Atmospheres

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“…FY-4A was launched on 11 December 2016, and it is currently positioned above the equator at 104.7 • E [26]. Its successful launch opened a new era for China's geostationary meteorological satellite [27].…”

Section: Fy-4a/agrimentioning

confidence: 99%

“…The data used for different purposes are shown in Table 2. The L1 product data of Aqua-MODIS (MYD021KM) are the cross-calibration reference data, and the L1 product data of FY-4A/AGRI (FY-4A/AGRI L1) are the pending calibration data [26]. The synchro-…”

Section: Datamentioning

confidence: 99%

Cross-Radiometric Calibration and NDVI Application Comparison of FY-4A/AGRI Based on Aqua-MODIS

He,

Li,

Zhou

et al. 2023

Remote Sensing

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To enhance the accuracy and stability of FY-4A/AGRI detection data, the MODIS, with highly accurate onboard calibration, is selected as the reference sensor for cross-radiation calibration calculations. The following are the data selection conditions: full considered time, observation geometries, field angles, cloud cover, etc. FY-4A/AGRI and Aqua-MODIS image data are selected as matching sample region locations, where the time difference between the observations for the same ground object is less than 15 min, the satellite zenith angle is less than 30°, and the field angle difference is less than 0.01. The 245 collected reflectance spectral curves are convolved with the spectral response functions of the two sensors, and the spectral band adjustment factors of the corresponding bands are calculated for spectral correction purposes. The cross-calibration coefficients for the red and near-infrared bands are calculated by linearly fitting the simulated top of the atmosphere reflectance values and digital number values from the AGRI sensor in a homogeneous area. In this paper, 16 cross-calibration calculations are performed on FY-4A/AGRI image data from August 2018 to September 2020, and the results are compared with the original calibration coefficients to test the feasibility of the proposed method. Additionally, 31 cross-calibration calculations are performed on image data from October 2020 to December 2022 to study the resulting AGRI sensor quality and performance changes. The NDVI of the FY-4A/AGRI image data was calculated before and after the cross-radiometric calibration using the maximum synthesis method. Additionally, the NDVI of the MODIS image data was compared and analyzed from three aspects: time, space, and the change trend. The results show that the spectral band adjustment factor calculated using the reflectance spectral curves of the ground objects in this paper can effectively correct for the spectral differences between the two sensors. Sixteen cross-calibration coefficients are less than 5.2% different from the original calibration coefficients, which fully proves the feasibility of the method used in this paper. All of the cross-calibration results show that the AGRI sensors have a certain degree of attenuation in the red and near-infrared bands, and the annual attenuation rates are approximately 1.37% and 2.55%, respectively. Cross-radiometric calibration has further improved the quality of the NDVI in FY-4A/AGRI imagery, enhancing the precision of its data application.

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“…It can be seen that sandstorm weather is still one of the atmospheric environmental problems that cannot be ignored in the 21st century [28]. Several studies have already provided information of this severe dust storm event in March 2021 from ground-based sensors, satellites and atmospheric models [29][30][31][32][33][34][35][36]. Filonchyk et al [29] stated that the cause of the dust storm was a cyclone with a significant drop in pressure leading to high winds and dry components of the soil over parts of the Gobi Desert becoming airborne.…”

Section: Introductionmentioning

confidence: 99%

“…They also studied the dust transport routes, emission source regions and vertical characteristics of aerosols in the dust storm. Luo et al [30] carried out a day-and-night continuous monitoring of the dust transport path using multi-spectral data from the FY-4A satellite combined with the Himawary-8 from visible to near-infrared, mid-infrared and far-infrared bands. Moreover, they deduced that western Mongolia and arid and semi-arid regions of the northwest of China are the primary sources of this dust storm affecting Beijing from Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) backward tracking results.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Methods’ Integration for Remote Sensing Monitoring and Process Analysis of Dust Storm Based on Multi-Source Data

et al. 2022

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Dust storms are of great importance to climate change, air quality, and human health. In this study, a complete application frame of integrating hybrid methods based on multi-source data is proposed for remote sensing monitoring and process analysis of dust storms. In the frame, horizontal spatial distribution of dust intensity can be mapped by optical remote sensing products such as aerosol optical depth (AOD) from MODIS; the vertical spatial distribution of dust intensity by LIDAR satellite remote sensing products such as AOD profile from CALIPSO; geostationary satellite remote sensing products such as Chinese Fengyun or Japanese Himawari can achieve high-frequency temporal distribution information of dust storms. More detailed process analysis of dust storms includes air quality analysis supported by particulate matter (PM) data from ground stations and the dust emission trace and transport pathways from HYSPLIT back trajectory driven by meteorological data from the Global Data Assimilation System (GDAS). The dust storm outbreak condition of the source location can be proved by precipitation data from the WMO and soil moisture data from remote sensing products, which can be used to verify the deduced emission trace from HYSPLIT. The proposed application frame of integrating hybrid methods was applied to monitor and analyze a very heavy dust storm that occurred in northern China from 14–18 March 2021, which was one of the most severe dust storms in recent decades. Results showed that the dust storm event could be well monitored and analyzed dynamically. It was found that the dust originated in western Mongolia and northwestern China and was then transmitted along the northwest–southeast direction, consequently affected the air quality of most cities of northern China. The results are consistent with the prior research and showed the excellent potential of the integration of the hybrid methods in monitoring dust storms.

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Satellite Monitoring of the Dust Storm over Northern China on 15 March 2021

Cited by 11 publications

References 15 publications

Aerosol Effects on Water Cloud Properties in Different Atmospheric Regimes

Aerosol Effects on Water Cloud Properties in Different Atmospheric Regimes

Cross-Radiometric Calibration and NDVI Application Comparison of FY-4A/AGRI Based on Aqua-MODIS

Hybrid Methods’ Integration for Remote Sensing Monitoring and Process Analysis of Dust Storm Based on Multi-Source Data

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