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Boming Liu
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China

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Journal article
Published: 13 August 2021 in IEEE Transactions on Geoscience and Remote Sensing
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The vertical distribution of fine particles with a diameter <2.5 μm (PM $_{2.5}$ ) plays an important role in understanding the transport of air pollution and in making decisions regarding the prevention and control of regional air pollution. However, the studies of the vertical distribution of PM $_{2.5}$ were limited by the lack of monitoring data obtained with vertical sampling strategies. The lidar system can obtain the aerosol profile, which provides the possibility to measure PM $_{2.5}$ profile. Here, the vertical distributions of PM $_{2.5}$ concentrations were investigated on the basis of lidar data from January 2014 to October 2015. Linear regression, improved linear regression, and random forest (RF) models were used to retrieve the PM $_{2.5}$ concentration profile from lidar data. The models were built based on the relationship among extinction coefficient (EC), temperature (T), relative humidity (RH), and surface PM $_{2.5}$ mass concentration. Comparison of the estimated and observed PM $_{2.5}$ showed that the RF model exhibited the best inversion effect. The correlation coefficient reached 0.75, and the root mean absolute error (RMAE) and root mean square error (RMSE) were 3.94 and 21.1 μg/m³, respectively. Error analysis indicated that the estimated PM $_{2.5}$ retrieved using the linear and improved linear models (ILMs) was smaller than the observed PM $_{2.5}$ when EC was less than 0.7 km⁻¹, whereas PM $_{2.5}$ was evidently overestimated during winter pollution days. The reason might be that the effects of T and RH were inaccurately considered. Finally, the seasonal variation of the PM $_{2.5}$ profiles was investigated. Results indicated that the mass concentration of PM $_{2.5}$ was relatively large within 0.5-1.5 km, with a maximum of 60 μg/m³. The findings obtained here provide guidance for PM $_{2.5}$ vertical observation and regional pollutant transport.

ACS Style

Yang Zhu; Yingying Ma; Boming Liu; Xin Xu; Shikuan Jin; Wei Gong. Retrieving the Vertical Distribution of PM $_{2.5}$ Mass Concentration From Lidar Via a Random Forest Model. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -9.

AMA Style

Yang Zhu, Yingying Ma, Boming Liu, Xin Xu, Shikuan Jin, Wei Gong. Retrieving the Vertical Distribution of PM $_{2.5}$ Mass Concentration From Lidar Via a Random Forest Model. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-9.

Chicago/Turabian Style

Yang Zhu; Yingying Ma; Boming Liu; Xin Xu; Shikuan Jin; Wei Gong. 2021. "Retrieving the Vertical Distribution of PM $_{2.5}$ Mass Concentration From Lidar Via a Random Forest Model." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-9.

Technical note
Published: 26 February 2021 in Atmospheric Chemistry and Physics
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Aeolus is the first satellite mission to directly observe wind profile information on a global scale. After implementing a set of bias corrections, the Aeolus data products went public on 12 May 2020. However, Aeolus wind products over China have thus far not been evaluated extensively by ground-based remote sensing measurements. In this study, the Mie-cloudy and Rayleigh-clear wind products from Aeolus measurements are validated against wind observations from the radar wind profiler (RWP) network in China. Based on the position of each RWP site relative to the closest Aeolus ground tracks, three matchup categories are proposed, and comparisons between Aeolus wind products and RWP wind observations are performed for each category separately. The performance of Mie-cloudy wind products does not change much between the three matchup categories. On the other hand, for Rayleigh-clear and RWP wind products, categories 1 and 2 are found to have much smaller differences compared with category 3. This could be due to the RWP site being sufficiently approximate to the Aeolus ground track for categories 1 and 2. In the vertical, the Aeolus wind products are similar to the RWP wind observations, except for the Rayleigh-clear winds in the height range of 0–1 km. The mean absolute normalized differences between the Mie-cloudy (Rayleigh-clear) and the RWP wind components are 3.06 (5.45), 2.79 (4.81), and 3.32 (5.72) m/s at all orbit times and ascending and descending Aeolus orbit times, respectively. This indicates that the wind products for ascending orbits are slightly superior to those for descending orbits, and the observation time has a minor effect on the comparison. From the perspective of spatial differences, the Aeolus Mie-cloudy winds are consistent with RWP winds in most of east China, except in coastal areas where the Aeolus Rayleigh-clear winds are more reliable. Overall, the correlation coefficient R between the Mie-cloudy (Rayleigh-clear) wind and RWP wind component observation is 0.94 (0.81), suggesting that Aeolus wind products are in good agreement with wind observations from the RWP network in China. The findings give us sufficient confidence in assimilating the newly released Aeolus wind products in operational weather forecasting in China.

ACS Style

Jianping Guo; Boming Liu; Wei Gong; Lijuan Shi; Yong Zhang; Yingying Ma; Jian Zhang; Tianmeng Chen; Kaixu Bai; Ad Stoffelen; Gerrit de Leeuw; Xiaofeng Xu. Technical note: First comparison of wind observations from ESA's satellite mission Aeolus and ground-based radar wind profiler network of China. Atmospheric Chemistry and Physics 2021, 21, 2945 -2958.

AMA Style

Jianping Guo, Boming Liu, Wei Gong, Lijuan Shi, Yong Zhang, Yingying Ma, Jian Zhang, Tianmeng Chen, Kaixu Bai, Ad Stoffelen, Gerrit de Leeuw, Xiaofeng Xu. Technical note: First comparison of wind observations from ESA's satellite mission Aeolus and ground-based radar wind profiler network of China. Atmospheric Chemistry and Physics. 2021; 21 (4):2945-2958.

Chicago/Turabian Style

Jianping Guo; Boming Liu; Wei Gong; Lijuan Shi; Yong Zhang; Yingying Ma; Jian Zhang; Tianmeng Chen; Kaixu Bai; Ad Stoffelen; Gerrit de Leeuw; Xiaofeng Xu. 2021. "Technical note: First comparison of wind observations from ESA's satellite mission Aeolus and ground-based radar wind profiler network of China." Atmospheric Chemistry and Physics 21, no. 4: 2945-2958.

Journal article
Published: 28 January 2021 in IEEE Transactions on Geoscience and Remote Sensing
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Satellite-derived aerosol optical depth (AOD) is an important parameter for studies related to atmospheric environment, climate change, and biogeochemical cycle. Unfortunately, the relatively high data missing ratio of satellite-derived AOD limits the atmosphere-related research and applications to a certain extent. Accordingly, numerous AOD fusion algorithms have been proposed in recent years. However, most of these algorithms focused on merging AOD products from multiple passive sensors, which cannot complementarily recover the AOD missing values due to cloud obscuration and the misidentification between optically thin cloud and aerosols. In order to address these issues, a spatiotemporal AOD fusion framework combining active and passive remote sensing based on Bayesian maximum entropy methodology (AP-BME) is developed to provide satellite-derived AOD data sets with high spatial coverage and good accuracy in large scale. The results demonstrate that AP-BME fusion significantly improves the spatial coverage of AOD, from an averaged spatial completeness of 27.9%-92.8% in the study areas, in which the spatial coverage improves from 91.1% to 92.8% when introducing Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) AOD data sets into the fusion process. Meanwhile, the accuracy of recovered AOD nearly maintains that of the original satellite AOD products, based on evaluation against ground-based Aerosol Robotic Network (AERONET) AOD. Moreover, the efficacy of the active sensor in AOD fusion is discussed through overall accuracy comparison and two case analyses, which shows that the provision of key aerosol information by the active sensor on haze condition or under thin cloud is important for not only restoring the real haze situations but also avoiding AOD overestimation caused by cloud optical depth (COD) contamination in AOD fusion results.

ACS Style

Xinghui Xia; Bin Zhao; Tianhao Zhang; Luyao Wang; Yu Gu; Kuo-Nan Liou; Feiyue Mao; Boming Liu; Yanchen Bo; Yusi Huang; Jiadan Dong; Wei Gong; Zhongmin Zhu. Satellite-Derived Aerosol Optical Depth Fusion Combining Active and Passive Remote Sensing Based on Bayesian Maximum Entropy. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -13.

AMA Style

Xinghui Xia, Bin Zhao, Tianhao Zhang, Luyao Wang, Yu Gu, Kuo-Nan Liou, Feiyue Mao, Boming Liu, Yanchen Bo, Yusi Huang, Jiadan Dong, Wei Gong, Zhongmin Zhu. Satellite-Derived Aerosol Optical Depth Fusion Combining Active and Passive Remote Sensing Based on Bayesian Maximum Entropy. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-13.

Chicago/Turabian Style

Xinghui Xia; Bin Zhao; Tianhao Zhang; Luyao Wang; Yu Gu; Kuo-Nan Liou; Feiyue Mao; Boming Liu; Yanchen Bo; Yusi Huang; Jiadan Dong; Wei Gong; Zhongmin Zhu. 2021. "Satellite-Derived Aerosol Optical Depth Fusion Combining Active and Passive Remote Sensing Based on Bayesian Maximum Entropy." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-13.

Journal article
Published: 08 January 2021 in IEEE Transactions on Geoscience and Remote Sensing
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Satellite observation is an effective way of obtaining global aerosol information. The study focuses on developing a new scheme to apply the traditional dark target (DT) method to the advanced Medium Resolution Spectral Imager (MERSI II), which is a part of the Chinese Fengyun-3-D satellite. Compared with the Moderate Resolution Imaging Spectroradiometer (MODIS), MERSI II shows higher ratios between red (0.65 μm) and near-infrared (2.13 μm) bands in surface reflectance estimation and the green band (0.55 μm) that is more sensitive to cloud screening. Aerosol optical depth (AOD) is retrieved from earlier MERSI II observations by following the adapted DT method over land in Asia in 2018. Overall, AOD from MERSI II has a good performance compared with ground-based measurements with an expected error (EE%) of 66.38% and R² of 0.834, which is close to the MODIS EE% of 70.59% and R² of 0.829. Both sensors slightly overestimate the AOD over heavy aerosol loading regions, but MERSI-II has larger retrieval area covering a wider swath than MODIS in heavy hazy areas. On a spatial scale, the MERSI II effectively reflects the AOD distribution pattern but tends to overestimate and underestimate AOD at low and high latitudes, respectively, when compared with MODIS. The MERSI II sensor shows good aerosol detection potential, and the DT algorithm can be applied. MERSI II will provide important observation data on climate change and atmospheric pollution for the investigations in the future.

ACS Style

Shikuan Jin; Ming Zhang; Yingying Ma; Wei Gong; Cheng Chen; Leiku Yang; Xiuqing Hu; Boming Liu; Nan Chen; Bo Du; Yifan Shi. Adapting the Dark Target Algorithm to Advanced MERSI Sensor on the FengYun-3-D Satellite: Retrieval and Validation of Aerosol Optical Depth Over Land. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -17.

AMA Style

Shikuan Jin, Ming Zhang, Yingying Ma, Wei Gong, Cheng Chen, Leiku Yang, Xiuqing Hu, Boming Liu, Nan Chen, Bo Du, Yifan Shi. Adapting the Dark Target Algorithm to Advanced MERSI Sensor on the FengYun-3-D Satellite: Retrieval and Validation of Aerosol Optical Depth Over Land. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-17.

Chicago/Turabian Style

Shikuan Jin; Ming Zhang; Yingying Ma; Wei Gong; Cheng Chen; Leiku Yang; Xiuqing Hu; Boming Liu; Nan Chen; Bo Du; Yifan Shi. 2021. "Adapting the Dark Target Algorithm to Advanced MERSI Sensor on the FengYun-3-D Satellite: Retrieval and Validation of Aerosol Optical Depth Over Land." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-17.

Journal article
Published: 07 August 2020 in Atmospheric Environment
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Aerosol loading within the surface layer is an important aspect in studying air quality. However, a comprehensive understanding of the characteristics and effects of aerosol in the residual layer (RL) over China is yet to be achieved. In this study, the characteristics of aerosol in the RL and its effects on the surface PM2.5 over China are investigated using ten-year Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data from January 2008 to December 2017. Our results show that the high aerosol optical depth in the RL (RAOD) is generally found in the north-central and southwestern parts, whereas low values are observed in the northeastern and northwestern areas. The RAOD accounts for >50% of the total columnar aerosols in most regions, and the main components of aerosols in RL are polluted continental aerosol and polluted dust. A decreasing trend of RAOD was observed in the Yellow River Delta (YRD), North China Plain (NCP), Central China (CC) and Sichuan Basin (SCB), which are related to the decreasing emission of aerosols. Moreover, the effects of meteorological parameters on the RAOD were investigated. The relative humidity and the latent heat flux has a positive correlation with the RAOD in most areas, while the wind speed and the sensible heat flux has a negative correlation with the RAOD. The relationship between the RAOD and the following daytime surface particulate matters (PM2.5) were assessed. The correlation coefficients between RAOD and following daytime surface PM2.5 in YRD, NCP, CC and SCB were 0.29, 0.34, 0.4 and 0.39, respectively. The results proved that a high RAOD can promote the concentration of surface PM2.5. These findings are significant to the improvement of our understanding of the effects of aerosols in the RL on air quality.

ACS Style

Yifan Shi; Boming Liu; Shihua Chen; Wei Gong; Yingying Ma; Ming Zhang; Shikuan Jin; Yinbao Jin. Characteristics of aerosol within the nocturnal residual layer and its effects on surface PM2.5 over China. Atmospheric Environment 2020, 241, 117841 .

AMA Style

Yifan Shi, Boming Liu, Shihua Chen, Wei Gong, Yingying Ma, Ming Zhang, Shikuan Jin, Yinbao Jin. Characteristics of aerosol within the nocturnal residual layer and its effects on surface PM2.5 over China. Atmospheric Environment. 2020; 241 ():117841.

Chicago/Turabian Style

Yifan Shi; Boming Liu; Shihua Chen; Wei Gong; Yingying Ma; Ming Zhang; Shikuan Jin; Yinbao Jin. 2020. "Characteristics of aerosol within the nocturnal residual layer and its effects on surface PM2.5 over China." Atmospheric Environment 241, no. : 117841.

Journal article
Published: 21 May 2020 in Remote Sensing
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The turbulent mixing and dispersion of air pollutants is strongly dependent on the vertical structure of the wind, which constitutes one of the major challenges affecting the determination of boundary layer height (BLH). Here, an adaptive method is proposed to estimate BLH from measurements of radar wind profilers (RWPs) in Beijing (BJ), Nanjing (NJ), Chongqing (CQ), and Wulumuqi (WQ), China, during the summer of 2019. Validation against simultaneous BLH estimates from radiosondes (RSs) yielded a correlation coefficient of 0.66, indicating that the method can be used to derive BLH from RWPs. Diurnal variations of BLH and the ventilation coefficient (VC) at four sites were then examined. A distinct diurnal cycle of BLH was observed over all four cities; BLH gradually increased from sunset, reached a maximum in the afternoon, and then dropped sharply after sunset. The maximum hourly average BLH (1.426 ± 0.46 km) occurred in WQ, consistent with the maximum hourly mean VC larger than 5000 m2/s observed there. By comparison, the diurnal variation of VC was not strong, with values ranging between 2000 and 3000 m2/s, likely owing to the high-humidity environment. Furthermore, surface sensible heat flux, latent heat flux, and dry mass of particulate matter with aerodynamic diameter ≤2.5 µm (PM2.5) concentrations were found to somehow affect the vertical structure of wind and thermodynamic features, leading to a difference between RS and RWP BLH estimates. This indicates that the atmospheric environment can affect BLH estimates using RWP data. The BLH results from RWPs were better in some specific cases. These findings show great potential of RWP measurements in air quality research, and will provide key data references for policy-making toward emission reductions.

ACS Style

Boming Liu; Jianping Guo; Wei Gong; Yifan Shi; Shikuan Jin. Boundary Layer Height as Estimated from Radar Wind Profilers in Four Cities in China: Relative Contributions from Aerosols and Surface Features. Remote Sensing 2020, 12, 1657 .

AMA Style

Boming Liu, Jianping Guo, Wei Gong, Yifan Shi, Shikuan Jin. Boundary Layer Height as Estimated from Radar Wind Profilers in Four Cities in China: Relative Contributions from Aerosols and Surface Features. Remote Sensing. 2020; 12 (10):1657.

Chicago/Turabian Style

Boming Liu; Jianping Guo; Wei Gong; Yifan Shi; Shikuan Jin. 2020. "Boundary Layer Height as Estimated from Radar Wind Profilers in Four Cities in China: Relative Contributions from Aerosols and Surface Features." Remote Sensing 12, no. 10: 1657.

Journal article
Published: 26 September 2019 in Atmospheric Pollution Research
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Visible Infrared Imaging Radiometer Suite (VIIRS) is a next-generation polar-orbiting operational environmental sensor, and its aerosol optical depth (AOD) data has been widely applied to ground-level PM2.5 prediction and air pollution research. However, VIIRS performance affects the accuracy of prediction. In this study, three types of VIIRS AOD products were collected from January 2014 to December 2016 to evaluate their performance under different air quality conditions. Near-surface PM2.5 concentrations were determined for the same period to categorize air quality as clean, moderate, or heavily polluted. The performance of three VIIRS AOD products was evaluated from the successful retrieval rate and AOD accuracy aspects. For clean weather days, the AODs obtained from VIIRS intermediate products (IP) had the lowest average absolute bias (0.15 ± 0.15). For the moderate and heavy pollution days, the average absolute deviations of VIIRS environmental data record (EDR) AOD (Quality Flags = 3 and > 1) products were lowest at 0.14 ± 0.14 and 0.2 ± 0.1 respectively. These results suggest that the EDR AOD, Quality Flags > 1 and =3, products were more suitable for heavy and moderate air pollution, respectively. During clean weather, performance of _IP AODs was found to be best. Moreover, seasonal analysis indicated that the EDR AOD (Quality Flags = 3) products were more suitable for spring and autumn and the IP AOD for summer. However, the performance of the EDR AOD products (Quality flags > 1) were best during winter. To ensure accuracy of PM2.5 predictions over central China, researchers should carefully balance the needs for successful retrieval rate and accuracy of VIIRS AOD products.

ACS Style

Yingying Ma; Boming Liu; Wei Gong; Yifan Shi; Shikuan Jin. Impact of environmental pollution on the retrieval of AOD products from Visible Infrared Imaging Radiometer Suite (VIIRS) over wuhan. Atmospheric Pollution Research 2019, 10, 2063 -2071.

AMA Style

Yingying Ma, Boming Liu, Wei Gong, Yifan Shi, Shikuan Jin. Impact of environmental pollution on the retrieval of AOD products from Visible Infrared Imaging Radiometer Suite (VIIRS) over wuhan. Atmospheric Pollution Research. 2019; 10 (6):2063-2071.

Chicago/Turabian Style

Yingying Ma; Boming Liu; Wei Gong; Yifan Shi; Shikuan Jin. 2019. "Impact of environmental pollution on the retrieval of AOD products from Visible Infrared Imaging Radiometer Suite (VIIRS) over wuhan." Atmospheric Pollution Research 10, no. 6: 2063-2071.

Letter
Published: 23 September 2019 in Remote Sensing
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Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products are used worldwide for their reliable accuracy. However, the aerosol optical depth (AOD) usually retrieved by the operational dark target (DT) algorithm of MODIS has been missing for most of the urban regions in Central China. This was due to a high surface reflectance and heavy aerosol loading, especially in winter, when a high cloud cover fraction and the frequent occurrence of haze events reduce the number of effective satellite observations. The retrieval of the AOD from limited satellite data is much needed and important for further aerosol investigations. In this paper, we propose an improved AOD retrieval method for 500 m MODIS data, which is based on an extended surface reflectance estimation scheme and dynamic aerosol models derived from ground-based sun-photometric observations. This improved method was applied to retrieve AOD during heavy aerosol loading and effectively complements the scarcity of AOD in correspondence with urban surface of a higher spatial resolution. The validation results showed that the retrieved AOD was consistent with MODIS DT AOD (R = ~0.87; RMSE = ~0.11) and ground measurements (R = ~0.89; RMSE = ~0.15) from both the Terra and the Aqua satellite. The method can be easily applied to different urban environments affected by air pollution and contributes to the research on aerosol.

ACS Style

Shikuan Jin; Yingying Ma; Ming Zhang; Wei Gong; Oleg Dubovik; Boming Liu; Yifan Shi; Changlan Yang. Retrieval of 500 m Aerosol Optical Depths from MODIS Measurements over Urban Surfaces under Heavy Aerosol Loading Conditions in Winter. Remote Sensing 2019, 11, 2218 .

AMA Style

Shikuan Jin, Yingying Ma, Ming Zhang, Wei Gong, Oleg Dubovik, Boming Liu, Yifan Shi, Changlan Yang. Retrieval of 500 m Aerosol Optical Depths from MODIS Measurements over Urban Surfaces under Heavy Aerosol Loading Conditions in Winter. Remote Sensing. 2019; 11 (19):2218.

Chicago/Turabian Style

Shikuan Jin; Yingying Ma; Ming Zhang; Wei Gong; Oleg Dubovik; Boming Liu; Yifan Shi; Changlan Yang. 2019. "Retrieval of 500 m Aerosol Optical Depths from MODIS Measurements over Urban Surfaces under Heavy Aerosol Loading Conditions in Winter." Remote Sensing 11, no. 19: 2218.

Journal article
Published: 26 June 2019 in IEEE Transactions on Geoscience and Remote Sensing
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The vertical structure of wind is a key factor in modulating air quality, from which the determination of boundary layer height (BLH) remains a major challenge. In this paper, we developed an improved threshold method to determine the BLH from radar wind profiler (RWP) measurements. The normalized signal-to-noise ratio (SNR) profiles were used instead of the original SNR profiles to avoid instrumental inconsistencies. Additionally, a peak filter was designed to indicate the BLH based on the maximum SNR by taking into account the multiple peaks in the SNR profile. This algorithm was then applied to the RWP measurements taken in the summer (June-July-August) of 2018 in Beijing to obtain the BLHs. Validation analyses suggested that the BLH retrievals from RWP exhibited high consistency with those from radiosondes, with an average correlation coefficient of 0.69 (0.66) and a root mean squared error of 0.39 (0.41) in the daytime (nighttime). Additionally, the major features of summertime BLHs in Beijing were examined. In particular, a distinct diurnal variation in BLH was observed with a peak (1630 ± 510 m) occurring at 0600 universal time coordinated (UTC) and a minimum (587 ± 343 m) at 2300 UTC. Therefore, the algorithm presented here has great potential to be applied to other regions to obtain reliable BLHs. The findings obtained here highlight the importance of vertical wind structure in air quality studies.

ACS Style

Boming Liu; Yingying Ma; Jianping Guo; Wei Gong; Yong Zhang; Feiyue Mao; Jian Li; Xiaoran Guo; Yifan Shi. Boundary Layer Heights as Derived From Ground-Based Radar Wind Profiler in Beijing. IEEE Transactions on Geoscience and Remote Sensing 2019, 57, 8095 -8104.

AMA Style

Boming Liu, Yingying Ma, Jianping Guo, Wei Gong, Yong Zhang, Feiyue Mao, Jian Li, Xiaoran Guo, Yifan Shi. Boundary Layer Heights as Derived From Ground-Based Radar Wind Profiler in Beijing. IEEE Transactions on Geoscience and Remote Sensing. 2019; 57 (10):8095-8104.

Chicago/Turabian Style

Boming Liu; Yingying Ma; Jianping Guo; Wei Gong; Yong Zhang; Feiyue Mao; Jian Li; Xiaoran Guo; Yifan Shi. 2019. "Boundary Layer Heights as Derived From Ground-Based Radar Wind Profiler in Beijing." IEEE Transactions on Geoscience and Remote Sensing 57, no. 10: 8095-8104.

Journal article
Published: 07 September 2018 in Atmospheric Measurement Techniques
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The atmospheric boundary layer is an important atmospheric feature that affects environmental health and weather forecasting. In this study, we proposed a graphics algorithm for the derivation of atmospheric boundary layer height (BLH) from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data. Owing to the differences in scattering intensity between molecular and aerosol particles, the total attenuated backscatter coefficient 532 and attenuated backscatter coefficient 1064 were used simultaneously for BLH detection. The proposed algorithm transformed the gradient solution into graphics distribution solution to overcome the effects of large noise and improve the horizontal resolution. This method was then tested with real signals under different horizontal smoothing numbers (1, 3, 15 and 30). Finally, the results of BLH obtained by CALIPSO data were compared with the results retrieved by the ground-based lidar measurements. Under the horizontal smoothing number of 15, 12 and 9, the correlation coefficients between the BLH derived by the proposed algorithm and ground-based lidar were both 0.72. Under the horizontal smoothing number of 6, 3 and 1, the correlation coefficients between the BLH derived by graphics distribution method (GDM) algorithm and ground-based lidar were 0.47, 0.14 and 0.12, respectively. When the horizontal smoothing number was large (15, 12 and 9), the CALIPSO BLH derived by the proposed method demonstrated a good correlation with ground-based lidar. The algorithm provided a reliable result when the horizontal smoothing number was greater than 9. This finding indicated that the proposed algorithm can be applied to the CALIPSO satellite data with 3 and 5 km horizontal resolution.

ACS Style

Boming Liu; Yingying Ma; Jiqiao Liu; Wei Gong; Wei Wang; Ming Zhang. Graphics algorithm for deriving atmospheric boundary layer heights from CALIPSO data. Atmospheric Measurement Techniques 2018, 11, 5075 -5085.

AMA Style

Boming Liu, Yingying Ma, Jiqiao Liu, Wei Gong, Wei Wang, Ming Zhang. Graphics algorithm for deriving atmospheric boundary layer heights from CALIPSO data. Atmospheric Measurement Techniques. 2018; 11 (9):5075-5085.

Chicago/Turabian Style

Boming Liu; Yingying Ma; Jiqiao Liu; Wei Gong; Wei Wang; Ming Zhang. 2018. "Graphics algorithm for deriving atmospheric boundary layer heights from CALIPSO data." Atmospheric Measurement Techniques 11, no. 9: 5075-5085.

Preprint content
Published: 05 June 2018
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The atmospheric boundary layer is an important atmospheric feature that affects environmental health and weather forecasting. In this study, we proposed a graphics algorithm for the derivation of atmospheric boundary layer height (BLH) from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data. Owing to the differences in scattering intensity between molecular and aerosol particles, the total attenuated backscatter coefficient 532 and attenuated backscatter coefficient 1064 were used simultaneously for BLH detection. The proposed algorithm transformed the gradient solution into graphics distribution solution to overcome the effects of large noise and improve the horizontal resolution. This method was then tested with real signals under different horizontal smoothing numbers (1, 3, 15 and 30). The algorithm provided a reliable result when the horizontal smoothing number was greater than 5. Finally, the results of BLH obtained by CALIPSO data were compared with the results retrieved by the ground-based Lidar and radiosonde (RS) measurements. Under the horizontal smoothing number of 15, 9 and 3, the correlation coefficients between the BLH derived by the proposed algorithm and ground-based Lidar were 0.72, 0.72 and 0.14, respectively, and those between the BLH derived by the proposed algorithm and radiosonde measurements were 0.59, 0.59 and 0.07. When the horizontal smoothing number was 15 and 9, the CALIPSO BLH derived by the proposed method demonstrated a good correlation with ground-based Lidar and RS. This finding indicated that the proposed algorithm can be applied to the CALIPSO satellite data with 3 and 5 km horizontal resolution.

ACS Style

Boming Liu; Yingying Ma; Jiqiao Liu; Wei Gong; Wei Wang; Ming Zhang. Graphics Algorithm for Deriving Atmospheric Boundary Layer Heights from CALIPSO Data. 2018, 11, 5075 -5085.

AMA Style

Boming Liu, Yingying Ma, Jiqiao Liu, Wei Gong, Wei Wang, Ming Zhang. Graphics Algorithm for Deriving Atmospheric Boundary Layer Heights from CALIPSO Data. . 2018; 11 (9):5075-5085.

Chicago/Turabian Style

Boming Liu; Yingying Ma; Jiqiao Liu; Wei Gong; Wei Wang; Ming Zhang. 2018. "Graphics Algorithm for Deriving Atmospheric Boundary Layer Heights from CALIPSO Data." 11, no. 9: 5075-5085.

Journal article
Published: 01 February 2018 in Journal of Quantitative Spectroscopy and Radiative Transfer
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ACS Style

Boming Liu; Yingying Ma; Wei Gong; Yang Jian; Zhang Ming. Two-wavelength Lidar inversion algorithm for determining planetary boundary layer height. Journal of Quantitative Spectroscopy and Radiative Transfer 2018, 206, 117 -124.

AMA Style

Boming Liu, Yingying Ma, Wei Gong, Yang Jian, Zhang Ming. Two-wavelength Lidar inversion algorithm for determining planetary boundary layer height. Journal of Quantitative Spectroscopy and Radiative Transfer. 2018; 206 ():117-124.

Chicago/Turabian Style

Boming Liu; Yingying Ma; Wei Gong; Yang Jian; Zhang Ming. 2018. "Two-wavelength Lidar inversion algorithm for determining planetary boundary layer height." Journal of Quantitative Spectroscopy and Radiative Transfer 206, no. : 117-124.

Journal article
Published: 01 January 2018 in Atmospheric Pollution Research
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ACS Style

Boming Liu; Yingying Ma; Wei Gong; Ming Zhang; Jian Yang. Study of continuous air pollution in winter over Wuhan based on ground-based and satellite observations. Atmospheric Pollution Research 2018, 9, 156 -165.

AMA Style

Boming Liu, Yingying Ma, Wei Gong, Ming Zhang, Jian Yang. Study of continuous air pollution in winter over Wuhan based on ground-based and satellite observations. Atmospheric Pollution Research. 2018; 9 (1):156-165.

Chicago/Turabian Style

Boming Liu; Yingying Ma; Wei Gong; Ming Zhang; Jian Yang. 2018. "Study of continuous air pollution in winter over Wuhan based on ground-based and satellite observations." Atmospheric Pollution Research 9, no. 1: 156-165.

Journal article
Published: 18 May 2016 in International Journal of Environmental Research and Public Health
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We comprehensively evaluated particle lidar ratios (i.e., particle extinction to backscatter ratio) at 532 nm over Wuhan in Central China by using a Raman lidar from July 2013 to May 2015. We utilized the Raman lidar data to obtain homogeneous aerosol lidar ratios near the surface through the Raman method during no-rain nights. The lidar ratios were approximately 57 ± 7 sr, 50 ± 5 sr, and 22 ± 4 sr under the three cases with obviously different pollution levels. The haze layer below 1.8 km has a large particle extinction coefficient (from 5.4e-4 m−1 to 1.6e-4 m−1) and particle backscatter coefficient (between 1.1e-05 m−1sr−1 and 1.7e-06 m−1sr−1) in the heavily polluted case. Furthermore, the particle lidar ratios varied according to season, especially between winter (57 ± 13 sr) and summer (33 ± 10 sr). The seasonal variation in lidar ratios at Wuhan suggests that the East Asian monsoon significantly affects the primary aerosol types and aerosol optical properties in this region. The relationships between particle lidar ratios and wind indicate that large lidar ratio values correspond well with weak winds and strong northerly winds, whereas significantly low lidar ratio values are associated with prevailing southwesterly and southerly wind.

ACS Style

Wei Wang; Wei Gong; Feiyue Mao; Zengxin Pan; Boming Liu. Measurement and Study of Lidar Ratio by Using a Raman Lidar in Central China. International Journal of Environmental Research and Public Health 2016, 13, 508 .

AMA Style

Wei Wang, Wei Gong, Feiyue Mao, Zengxin Pan, Boming Liu. Measurement and Study of Lidar Ratio by Using a Raman Lidar in Central China. International Journal of Environmental Research and Public Health. 2016; 13 (5):508.

Chicago/Turabian Style

Wei Wang; Wei Gong; Feiyue Mao; Zengxin Pan; Boming Liu. 2016. "Measurement and Study of Lidar Ratio by Using a Raman Lidar in Central China." International Journal of Environmental Research and Public Health 13, no. 5: 508.

Journal article
Published: 07 August 2015 in Atmosphere
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Wuhan is a rapidly developing large city in central China. To analyze the aerosol characteristics over Wuhan, the optical properties of the nocturnal aerosol layers in the lower troposphere were observed using a ground-based LIDAR(Light Detection And Ranging) located in the Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS) from Wuhan University, China (114°21′E, 30°32′N) in January 2013–January 2015. The vertical distribution and temporal variation of tropospheric aerosols over Wuhan were summarized. The atmospheric boundary layer height (ABLH) was mainly at an altitude of 1.5–2 km (~33.1% probability), with an annual average of 1.66 km. The ABLH was higher in spring–summer (~2 km) and lower in autumn–winter (~1.2 km). The aerosol optical depth (AOD) was higher in spring–autumn than in summer–winter. The highest AOD was about 0.79 in October and the lowest was about 0.08 in January. The annual average was about 0.3. To study the relationship between the AOD and the particulate matter ≤2.5 µm in the aerodynamic diameter (PM2.5) in the lower troposphere, a typical haze event from 9–14 October 2014 was investigated. The results showed a correlation coefficient of 0.5165 between these two variables.

ACS Style

Wei Gong; Boming Liu; Yingying Ma; Miao Zhang. Mie LIDAR Observations of Tropospheric Aerosol over Wuhan. Atmosphere 2015, 6, 1129 -1140.

AMA Style

Wei Gong, Boming Liu, Yingying Ma, Miao Zhang. Mie LIDAR Observations of Tropospheric Aerosol over Wuhan. Atmosphere. 2015; 6 (8):1129-1140.

Chicago/Turabian Style

Wei Gong; Boming Liu; Yingying Ma; Miao Zhang. 2015. "Mie LIDAR Observations of Tropospheric Aerosol over Wuhan." Atmosphere 6, no. 8: 1129-1140.