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Monitoring cloud droplet effective radius (re) is of great significance for studying aerosol-cloud interactions (ACI). Passive satellite retrieval, e.g., MODIS (Moderate Resolution Imaging Spectroradiometer), requires sunlight. This requirement prompted developing re retrieval using active sensors, e.g., CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization). Given the highest sensitivity of vertically homogeneous clouds to aerosols that feed to cloud base, here CALIOP profile measurements were used for the first time to quantify cloud vertical homogeneity and estimate cloud re during both day and night. Comparison using simultaneous Aqua-MODIS measurements demonstrates that CALIOP retrieval has the highest accuracy for vertically homogeneous clouds, with R2 (MAE, RMSE) of 0.72 (1.75 µm, 2.25 µm), while the accuracy is lowest for non-homogeneous clouds, with R2 (MAE, RMSE) of 0.60 (2.90 µm, 3.70 µm). The improved re retrieval in vertically homogeneous clouds provides a basis for possible breakthrough insights in ACI by CALIOP since re in such clouds reflects most directly aerosol effects on cloud properties. Global day-night maps of cloud vertical homogeneity and respective re are presented.
Lin Zang; Daniel Rosenfeld; Feiyue Mao; Zengxin Pan; Yannian Zhu; Wei Gong; Zemin Wang. CALIOP retrieval of droplet effective radius accounting for cloud vertical homogeneity. Optics Express 2021, 29, 21921 -21935.
AMA StyleLin Zang, Daniel Rosenfeld, Feiyue Mao, Zengxin Pan, Yannian Zhu, Wei Gong, Zemin Wang. CALIOP retrieval of droplet effective radius accounting for cloud vertical homogeneity. Optics Express. 2021; 29 (14):21921-21935.
Chicago/Turabian StyleLin Zang; Daniel Rosenfeld; Feiyue Mao; Zengxin Pan; Yannian Zhu; Wei Gong; Zemin Wang. 2021. "CALIOP retrieval of droplet effective radius accounting for cloud vertical homogeneity." Optics Express 29, no. 14: 21921-21935.
Aerosols affect cloud microstructure, dynamics, and precipitation by acting as cloud condensation nuclei (CCN) and ice nuclei with a large uncertainty for deep convective clouds (DCCs). Here, we quantify the relationships between aerosols and DCC properties after isolating aerosol impacts from meteorology based on the METEOSAT geostationary satellite and Modern‐Era Retrospective Analysis for Research and Application Version 2 (MERRA‐2) reanalysis data. Results show that fine aerosols (radius <1 µm), which serve as the best proxy for CCN from MERRA‐2, exhibit the strongest aerosol invigoration for DCC compared with aerosol optical depth and coarse aerosols. Overall, added fine aerosols result in colder cloud top temperatures (CTTs), longer lifetime, and more rainfall amounts, especially over land. As CTT decreases monotonically with added aerosols, cloud lifetime and rainfall amount reach a maximum at aerosol loading of 5 and 1.5 µg/m3 over land and ocean, respectively. Added precipitable water (PW) vapor and convective available potential energy (CAPE) are conducive to the development of more vigorous DCC. For fixed PW and CAPE, CTT decreases by up to −12.2°C ± 0.5°C with fine aerosol concentration over land and up to −4.4°C ± 1.0°C over ocean. The DCC lifetime is lengthened by a factor of 1.3 ± 0.1 from clean condition to optimal aerosol loading over land. A respective enhancement in rainfall amounts over land is indicated by a factor of 2.6 ± 0.4. The decreases in lifetime and rainfall beyond the optimal aerosol concentration are likely due to less aerosol wet scavenging from smaller and less rainy DCCs. The increases in the lifetime and rainfall amounts over ocean are much weaker.
Zengxin Pan; Daniel Rosenfeld; Yannian Zhu; Feiyue Mao; Wei Gong; Lin Zang; Xin Lu. Observational Quantification of Aerosol Invigoration for Deep Convective Cloud Lifecycle Properties Based on Geostationary Satellite. Journal of Geophysical Research: Atmospheres 2021, 126, 1 .
AMA StyleZengxin Pan, Daniel Rosenfeld, Yannian Zhu, Feiyue Mao, Wei Gong, Lin Zang, Xin Lu. Observational Quantification of Aerosol Invigoration for Deep Convective Cloud Lifecycle Properties Based on Geostationary Satellite. Journal of Geophysical Research: Atmospheres. 2021; 126 (9):1.
Chicago/Turabian StyleZengxin Pan; Daniel Rosenfeld; Yannian Zhu; Feiyue Mao; Wei Gong; Lin Zang; Xin Lu. 2021. "Observational Quantification of Aerosol Invigoration for Deep Convective Cloud Lifecycle Properties Based on Geostationary Satellite." Journal of Geophysical Research: Atmospheres 126, no. 9: 1.
Marine low clouds of the busy shipping lane in the southeast Atlantic during the springs of 2003‐2015 were analyzed to study the dependence of their properties and radiative forcing on the background cloud drop concentrations (Nd‐bg). The overall average cloud radiative effect within the shipping lane was larger by only ‐1 Wm‐2 compared to the adjacent clouds. However, this near‐zero averaged effect was composed of large negative cloud radiative forcing (CRF) for the cleanest (13%) of the cases with Nd‐bg < 50 cm‐3, which was almost neutralized by a positive CRF for the 40% of the cases with Nd‐bg > 70 cm‐3. A possible explanation for this positive forcing is the cloud burning effect of the black carbon from ship emissions. The negative forcing was composed of 45% for the cloud fraction (Cf) effect and 55% for the albedo effects, which included a small contribution of the liquid water path (LWP) effect. Positive Cf susceptibility to Nd‐core was at maximum for the lowest Nd‐bg and disappeared near 60 cm‐3. The albedo susceptibility to Nd‐core reached its theoretical limit of 1/3 for constant LWP when Nd‐bg <40 cm‐3, and diminishes to 0.2 when Nd‐bg =60 cm‐3. These susceptibilities represent cause and effect relationships because the differences of aerosols caused by the ship emissions vary at a much smaller scale than meteorology. Globally, under similar environmental conditions, nearly half of the area has Nd < 50 cm‐3, thus possessing the indicated large susceptibility of negative radiative forcing to anthropogenic aerosols. This article is protected by copyright. All rights reserved.
Shuang Hu; Yannian Zhu; Daniel Rosenfeld; Feiyue Mao; Xin Lu; Zengxin Pan; Lin Zang; Wei Gong. The dependence of ship‐polluted marine cloud properties and radiative forcing on background drop concentrations. Journal of Geophysical Research: Atmospheres 2021, 1 .
AMA StyleShuang Hu, Yannian Zhu, Daniel Rosenfeld, Feiyue Mao, Xin Lu, Zengxin Pan, Lin Zang, Wei Gong. The dependence of ship‐polluted marine cloud properties and radiative forcing on background drop concentrations. Journal of Geophysical Research: Atmospheres. 2021; ():1.
Chicago/Turabian StyleShuang Hu; Yannian Zhu; Daniel Rosenfeld; Feiyue Mao; Xin Lu; Zengxin Pan; Lin Zang; Wei Gong. 2021. "The dependence of ship‐polluted marine cloud properties and radiative forcing on background drop concentrations." Journal of Geophysical Research: Atmospheres , no. : 1.
Previous studies have shown that it is feasible to retrieve multiple cloud properties simultaneously based on the space-borne hyperspectral observation in the oxygen A-band, such as cloud optical depth, cloud-top height, and cloud geometrical thickness. However, hyperspectral remote sensing is time-consuming if based on the precise radiative transfer solution that counts multiple scatterings of light. To speed up the radiation transfer solution in cloud scenarios for nadir space-borne observations, we developed a physical parameterization of hyperspectral reflectance in the oxygen A-band for single-layer water clouds. The parameterization takes into account the influences of cloud droplet forward-scattering and nonlinear oxygen absorption on hyperspectral reflectance, which are improvements over the previous studies. The performance of the parameterization is estimated through comparison with DISORT (Discrete Ordinates Radiative Transfer Program Multi-Layered Plane-Parallel Medium) on the cases with solar zenith angle θ, the cloud optical depth τc, and the single-scattering albedo ω in the range of 0 ≤ θ ≤ 75, 5 ≤ τc ≤ 50, 0.5 ≤ ω ≤ 1. The relative error of the cloud reflectance is within 5% for most cases, even for clouds with optical depths around five or at strong absorption wavelengths. We integrate the parameterization with a slit function and a simplified atmosphere to evaluate its performance in simulating the observed cloud reflection at the top of the atmosphere by OCO-2 (Orbiting Carbon Observatory-2). To better visualize the possible errors from the new parameterization, gas molecular scattering, aerosol scattering, and reflection from the underlying surface are ignored. The relative error of the out-of-band radiance is less than 4% and the relative error of the intra-band radiance ratio is less than 4%. The radiance ratio is the ratio of simulated observations with and without in-cloud absorption and is used to assess the accuracy of the parameterization in quantifying the in-cloud absorption. The parameterization is a preparation for rapid hyperspectral remote sensing in the oxygen A-band. It would help to improve retrieval efficiency and provide cloud geometric thickness products.
Jie Yang; Siwei Li; Feiyue Mao; Qilong Min; Wei Gong; Lei Zhang; Sheng Liu. Physical Parameterization of Hyperspectral Reflectance in the Oxygen A-Band for Single-Layer Water Clouds. Remote Sensing 2020, 12, 2252 .
AMA StyleJie Yang, Siwei Li, Feiyue Mao, Qilong Min, Wei Gong, Lei Zhang, Sheng Liu. Physical Parameterization of Hyperspectral Reflectance in the Oxygen A-Band for Single-Layer Water Clouds. Remote Sensing. 2020; 12 (14):2252.
Chicago/Turabian StyleJie Yang; Siwei Li; Feiyue Mao; Qilong Min; Wei Gong; Lei Zhang; Sheng Liu. 2020. "Physical Parameterization of Hyperspectral Reflectance in the Oxygen A-Band for Single-Layer Water Clouds." Remote Sensing 12, no. 14: 2252.
Reliable aerosol optical depth (AOD) data with high spatial and temporal resolutions are needed for research on air pollution in China. AOD products from the Advanced Himawari Imager (AHI) onboard the geostationary Himawari-8 satellite and reanalysis datasets make it possible to capture diurnal variations of aerosol loadings. However, due to the different retrieval methods, their applicability may vary with different space and time. Thus, in this study, taking the measured AOD at the Aerosol Robotic NETwork (AERONET) stations as the gold standard, the performance of the latest AHI hourly AOD product (i.e., L3 AOD) was evaluated and then compared with that of two reanalysis AOD datasets offered by Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Copernicus Atmosphere Monitoring Service (CAMS), respectively, covering from July 2015 to December 2017 over China. For all the matchups, AHI AOD shows the highest robustness with a high correlation (R) of 0.82, low root-mean-square error (RMSE) of 0.23, and moderate mean absolute relative error (MARE) of 0.56. Although MERRA-2 and CAMS products both have lower R values (0.74, 0.72) and higher RMSE (0.28, 0.26), the former is slightly better than the latter. Accuracy of AOD products could be mainly affected by the pollution level and less affected by particle size distribution. Comparisons among these AOD products imply that AHI AOD is more reliable in regions with high pollution levels, such as central and eastern China, while in the northern and western part, MERRA-2 AOD seems more satisfying. The performance of all the three AOD products presents a significant diurnal variety, as indicated by the highest accuracy in the morning for AHI and at noon for reanalysis data. Moreover, due to various pollution distribution patterns and meteorological conditions, there are distinct seasonal characteristics in the performance of AOD products for different regions.
Taixin Zhang; Lin Zang; Feiyue Mao; Youchuan Wan; Yannian Zhu. Evaluation of Himawari-8/AHI, MERRA-2, and CAMS Aerosol Products over China. Remote Sensing 2020, 12, 1684 .
AMA StyleTaixin Zhang, Lin Zang, Feiyue Mao, Youchuan Wan, Yannian Zhu. Evaluation of Himawari-8/AHI, MERRA-2, and CAMS Aerosol Products over China. Remote Sensing. 2020; 12 (10):1684.
Chicago/Turabian StyleTaixin Zhang; Lin Zang; Feiyue Mao; Youchuan Wan; Yannian Zhu. 2020. "Evaluation of Himawari-8/AHI, MERRA-2, and CAMS Aerosol Products over China." Remote Sensing 12, no. 10: 1684.
The new-generation geostationary satellites feature higher radiometric, spectral, and spatial resolutions, thereby making richer data available for the improvement of PM2.5 predictions. Various aerosol optical depth (AOD) data assimilation methods have been developed, but the accurate representation of the AOD-PM2.5 relationship remains challenging. Empirical statistical methods are effective in retrieving ground-level PM2.5, but few have been evaluated in terms of whether and to what extent they can help improve PM2.5 predictions. Therefore, an empirical and statistics-based scheme was developed for optimizing the estimation of the initial conditions (ICs) of aerosol in WRF-Chem (Weather Research and Forecasting/Chemistry) and for improving the PM2.5 predictions by integrating Himawari-8 data and ground observations. The proposed method was evaluated via two one-year experiments that were conducted in parallel over eastern China. The contribution of the satellite data to the model performance was evaluated via a 2-week control experiment. The results demonstrate that the proposed method improved the PM2.5 predictions throughout the year and mitigated the underestimation during pollution episodes. Spatially, the performance was highly correlated with the amount of valid data.
Jia Hong; Feiyue Mao; Qilong Min; Zengxin Pan; Wei Wang; Tianhao Zhang; Wei Gong. Improved PM2.5 predictions of WRF-Chem via the integration of Himawari-8 satellite data and ground observations. Environmental Pollution 2020, 263, 114451 .
AMA StyleJia Hong, Feiyue Mao, Qilong Min, Zengxin Pan, Wei Wang, Tianhao Zhang, Wei Gong. Improved PM2.5 predictions of WRF-Chem via the integration of Himawari-8 satellite data and ground observations. Environmental Pollution. 2020; 263 ():114451.
Chicago/Turabian StyleJia Hong; Feiyue Mao; Qilong Min; Zengxin Pan; Wei Wang; Tianhao Zhang; Wei Gong. 2020. "Improved PM2.5 predictions of WRF-Chem via the integration of Himawari-8 satellite data and ground observations." Environmental Pollution 263, no. : 114451.
Data assimilation (DA) is a promising approach to improve meteorological and PM2.5 forecasts, but to what extent and by what process the DA of meteorological fields helps improve PM2.5 forecast still call for more discussion. By utilizing WRFDA and WRF-Chem models, we have assimilated AMSU-A, MHS radiances and conventional observations, and studied the influences of the meteorological DA on meteorological and PM2.5 forecasts over Central China through a series of experiments. The results show that multi-source meteorological DA helps improve temperature and relative humidity forecasts in the lower atmosphere, and the improved meteorological fields further improve PM2.5 forecast with a reduction of bias and RMSE by 7.4% and 4.1% over the study area, especially during PM2.5 episode. This study also helps understand how DA improve the PM2.5 forecasts over Central China.
Jian Liu; Jia Hong; Feiyue Mao; Wei Gong; Longjiao Shen; Shengwen Liang; Jiangping Chen. Impact of assimilating multi-source observations on meteorological and PM2.5 forecast over Central China. Atmospheric Research 2020, 241, 104945 .
AMA StyleJian Liu, Jia Hong, Feiyue Mao, Wei Gong, Longjiao Shen, Shengwen Liang, Jiangping Chen. Impact of assimilating multi-source observations on meteorological and PM2.5 forecast over Central China. Atmospheric Research. 2020; 241 ():104945.
Chicago/Turabian StyleJian Liu; Jia Hong; Feiyue Mao; Wei Gong; Longjiao Shen; Shengwen Liang; Jiangping Chen. 2020. "Impact of assimilating multi-source observations on meteorological and PM2.5 forecast over Central China." Atmospheric Research 241, no. : 104945.
Ambient PM1 (particulate matter with aerodynamic diameter ≤1 μm) is an important contribution of PM2.5 mass. However, little is known worldwide regarding the PM1-associated health effects due to a wide lack of ground-based PM1 measurements from air monitoring stations. We collected daily records of hospital admission for respiratory diseases and station-based measurements of air pollution and weather conditions in Shenzhen, China, 2015–2016. Time-stratified case-crossover design and conditional logistic regression models were adopted to estimate hospitalization risks associated with short-term exposures to PM1 and PM2.5. PM1 and PM2.5 showed significant adverse effects on respiratory disease hospitalizations, while no evident associations with PM1–2.5 were identified. Admission risks for total respiratory diseases were 1.09 (95% confidence interval: 1.04 to 1.14) and 1.06 (1.02 to 1.10), corresponding to per 10 μg/m3 rise in exposure to PM1 and PM2.5 at lag 0–2 days, respectively. Both PM1 and PM2.5 were strongly associated with increased admission for pneumonia and chronic obstructive pulmonary diseases, but exhibited no effects on asthma and upper respiratory tract infection. Largely comparable risk estimates were observed between male and female patients. Groups aged 0–14 years and 45–74 years were significantly affected by PM1- and PM2.5-associated risks. PM-hospitalization associations exhibited a clear seasonal pattern, with significantly larger risks in cold season than those in warm season among some subgroups. Our study suggested that PM1 rather than PM1–2.5 contributed to PM2.5-induced risks of hospitalization for respiratory diseases and effects of PM1 and PM2.5 mainly occurred in cold season.
Yunquan Zhang; Zan Ding; Qianqian Xiang; Wei Wang; Li Huang; Feiyue Mao. Short-term effects of ambient PM1 and PM2.5 air pollution on hospital admission for respiratory diseases: Case-crossover evidence from Shenzhen, China. International Journal of Hygiene and Environmental Health 2019, 224, 113418 .
AMA StyleYunquan Zhang, Zan Ding, Qianqian Xiang, Wei Wang, Li Huang, Feiyue Mao. Short-term effects of ambient PM1 and PM2.5 air pollution on hospital admission for respiratory diseases: Case-crossover evidence from Shenzhen, China. International Journal of Hygiene and Environmental Health. 2019; 224 ():113418.
Chicago/Turabian StyleYunquan Zhang; Zan Ding; Qianqian Xiang; Wei Wang; Li Huang; Feiyue Mao. 2019. "Short-term effects of ambient PM1 and PM2.5 air pollution on hospital admission for respiratory diseases: Case-crossover evidence from Shenzhen, China." International Journal of Hygiene and Environmental Health 224, no. : 113418.
The Advanced Himawari Imager (AHI), the primary sensor aboard the Japanese Himawari‐8 geostationary satellite, measures regional aerosol observations with high temporal‐spatial resolution. To improve product quality and scientific applications, we performed a comprehensive evaluation of AHI aerosol products (version 1.0). We compared nearly 2 years (July 15, 2015 to June 31, 2017) of AHI aerosol optical depth at 500 nm (AOD500) with AODs from the Aerosol Robotic Network (AERONET) and the Maritime Aerosol Network (MAN). Results showed that, over land, AHI retrievals exhibit a large overall bias of –0.062, with an R of 0.78; over ocean, average bias measured 0.036 (0.051 for MAN), with an R of 0.89 (0.95 for MAN). AHI retrievals collocated with AERONET AODs (τA) showed the following expected AHI AOD errors: (–0.66 × τA + 0.02, –0.34 × τA + 0.16) over land and (–0.24 × τA + 0.03, 0.10 × τA + 0.11) over ocean. AHI retrievals with degraded performance correlated to different regions, angles, aerosol types, and surface types, suggesting that the AHI aerosol algorithm can be improved by changing aerosol optical models, using better cloud filters, and combining multiple methods to estimate ground reflectance. Collocated comparisons of AHI‐MODIS‐AERONET demonstrate that, over land, AHI daytime AODs clearly improve when retrievals with a large viewing zenith angle and small scattering angle are excluded.
Wei Wang; Feiyue Mao; Zengxin Pan; Wei Gong; Mayumi Yoshida; Bin Zou; Huiyun Ma. Evaluating Aerosol Optical Depth From Himawari‐8 With Sun Photometer Network. Journal of Geophysical Research: Atmospheres 2019, 124, 5516 -5538.
AMA StyleWei Wang, Feiyue Mao, Zengxin Pan, Wei Gong, Mayumi Yoshida, Bin Zou, Huiyun Ma. Evaluating Aerosol Optical Depth From Himawari‐8 With Sun Photometer Network. Journal of Geophysical Research: Atmospheres. 2019; 124 (10):5516-5538.
Chicago/Turabian StyleWei Wang; Feiyue Mao; Zengxin Pan; Wei Gong; Mayumi Yoshida; Bin Zou; Huiyun Ma. 2019. "Evaluating Aerosol Optical Depth From Himawari‐8 With Sun Photometer Network." Journal of Geophysical Research: Atmospheres 124, no. 10: 5516-5538.
Widespread and severe PM1.0 (particulate matter ≤1.0 μm) pollution in China has a significant negative influence on human health. However, knowledge of the large-scale distribution and variability of PM1.0 has been hindered by sparsely distributed PM1.0 concentration data. In this study, a two-stage model called linear mixed effect–bagged tree model was developed to estimate hourly PM1.0 pollution levels from July 2015 to June 2017 in China at 0.1° resolution by using Himawari-8 aerosol products and coincident geographic data, meteorology, and site-based PM1.0 concentrations from ground monitoring network. The cross-validation for the developed model displayed R2 and mean absolute error value of 0.80 and 9.3 μg/m3, respectively. Validation demonstrated that the model accurately estimated PM1.0 concentrations with high R2 of 0.63–0.85 and low bias of 8.7–10.1 μg/m3 at the hourly levels. Analysis of the estimated PM1.0 concentrations on daily scale showed peaks with PM1.0 of 36.9 ± 8.4 μg/m3 at rush hours during daytime. Seasonal variations displayed that summer was the cleanest season with an average PM1.0 of 20.9 ± 6.8 μg/m3 and winter was the most polluted season with an average PM1.0 of 45.6 ± 16.8 μg/m3. These results indicated that the proposed satellite-based model can estimate reliable spatial distribution of PM1.0 concentrations at the national scale.
Wei Wang; Feiyue Mao; Bin Zou; Jianping Guo; Lixin Wu; Zengxin Pan; Lin Zang. Two-stage model for estimating the spatiotemporal distribution of hourly PM1.0 concentrations over central and east China. Science of The Total Environment 2019, 675, 658 -666.
AMA StyleWei Wang, Feiyue Mao, Bin Zou, Jianping Guo, Lixin Wu, Zengxin Pan, Lin Zang. Two-stage model for estimating the spatiotemporal distribution of hourly PM1.0 concentrations over central and east China. Science of The Total Environment. 2019; 675 ():658-666.
Chicago/Turabian StyleWei Wang; Feiyue Mao; Bin Zou; Jianping Guo; Lixin Wu; Zengxin Pan; Lin Zang. 2019. "Two-stage model for estimating the spatiotemporal distribution of hourly PM1.0 concentrations over central and east China." Science of The Total Environment 675, no. : 658-666.
The accuracy of the Atmospheric Infrared Sounder (AIRS) water‐vapor product in China is as yet unknown due to the lack of collocated in situ sounding observations. Based on high‐resolution soundings at 1400 Beijing time from 113 radiosonde sites across China, along with the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Global Positioning System (GPS) datasets, a preliminary assessment has been conducted of AIRS water‐vapor mixing ratio (q) and precipitable water vapor (PWV) products in June 2013 and June 2014. Comparison between AIRS and radiosonde data suggests that the correlation coefficient (R) and mean bias of these two q products in China exhibit a distinct geographical dependence (with the highest R values in north‐west China). This suggests that the AIRS q product tends to be underestimated in south‐east China where cloud cover prevails, but overestimated in northwest China where cloud cover is sparse. With regard to the height‐resolved distribution, the q products from both AIRS and radiosondes tend to decrease with increasing altitude, irrespective of the particular region. The spatial distribution of AIRS PWV is consistent with that from radiosonde‐derived PWV, except in south China where the AIRS PWV dataset is considerably underestimated. The accuracy of the AIRS water‐vapor product tends to be impaired under highly cloudy conditions, corroborating the notion of clouds affecting the retrieval of AIRS PWV. Our findings highlight the importance of afternoon sounding measurements in validating AIRS data, and call for the improved understanding of the role of water vapor in the context of global climate change.
Zhaoliang Zeng; Feiyue Mao; Zemin Wang; Jianping Guo; Ke Gui; Jiachun An; Steve Hung Lam Yim; YuanJian Yang; Baojun Zhang; Hu Jiang. Preliminary Evaluation of the Atmospheric Infrared Sounder Water Vapor Over China Against High‐Resolution Radiosonde Measurements. Journal of Geophysical Research: Atmospheres 2019, 124, 3871 -3888.
AMA StyleZhaoliang Zeng, Feiyue Mao, Zemin Wang, Jianping Guo, Ke Gui, Jiachun An, Steve Hung Lam Yim, YuanJian Yang, Baojun Zhang, Hu Jiang. Preliminary Evaluation of the Atmospheric Infrared Sounder Water Vapor Over China Against High‐Resolution Radiosonde Measurements. Journal of Geophysical Research: Atmospheres. 2019; 124 (7):3871-3888.
Chicago/Turabian StyleZhaoliang Zeng; Feiyue Mao; Zemin Wang; Jianping Guo; Ke Gui; Jiachun An; Steve Hung Lam Yim; YuanJian Yang; Baojun Zhang; Hu Jiang. 2019. "Preliminary Evaluation of the Atmospheric Infrared Sounder Water Vapor Over China Against High‐Resolution Radiosonde Measurements." Journal of Geophysical Research: Atmospheres 124, no. 7: 3871-3888.
Particulates smaller than 1.0 μm (PM1.0) have strong associations with public health and environment, and considerable exposure data should be obtained to understand the actual environmental burden. This study presented a PM1.0 estimation strategy based on the generalised regression neural network model. The proposed strategy combined ground-based observations of PM2.5 and satellite-derived aerosol optical depth (AOD) to estimate PM1.0 concentrations in China from July 2015 to June 2017. Results indicated that the PM1.0 estimates agreed well with the ground-based measurements with an R2 of 0.74, root mean square error of 19.0 μg/m3 and mean absolute error of 11.4 μg/m3 as calculated with the tenfold cross-validation method. The diurnal estimation performance displayed remarkable single-peak variation with the highest R2 of 0.80 at noon, and the seasonal estimation performance showed that the proposed method could effectively capture high-pollution events of PM1.0 in winter. Spatially, the most polluted areas were clustered in the North China Plain, where the average estimates presented a bimodal distribution during daytime. In addition, the quality of satellite-derived AOD, the robustness of the interpolation algorithm and the proportion of PM1.0 in PM2.5 were confirmed to affect the estimation accuracy of the proposed model.
Lin Zang; Feiyue Mao; Jianping Guo; Wei Wang; Zengxin Pan; Huanfeng Shen; Bo Zhu; Zemin Wang. Estimation of spatiotemporal PM1.0 distributions in China by combining PM2.5 observations with satellite aerosol optical depth. Science of The Total Environment 2018, 658, 1256 -1264.
AMA StyleLin Zang, Feiyue Mao, Jianping Guo, Wei Wang, Zengxin Pan, Huanfeng Shen, Bo Zhu, Zemin Wang. Estimation of spatiotemporal PM1.0 distributions in China by combining PM2.5 observations with satellite aerosol optical depth. Science of The Total Environment. 2018; 658 ():1256-1264.
Chicago/Turabian StyleLin Zang; Feiyue Mao; Jianping Guo; Wei Wang; Zengxin Pan; Huanfeng Shen; Bo Zhu; Zemin Wang. 2018. "Estimation of spatiotemporal PM1.0 distributions in China by combining PM2.5 observations with satellite aerosol optical depth." Science of The Total Environment 658, no. : 1256-1264.
Changes in anthropogenic aerosol loading affect cloud albedo and the Earth's radiative balance with a low level of scientific understanding. Aerosol–cloud interaction and its effects on climate are mainly evaluated using passive observations in a global scale. Here, this study estimated the intrinsic response of clouds to aerosols by combining active and passive satellite observations from July 2006 to February 2011 in South Asia. We evaluate the average radiative forcing by the intrinsic aerosol‐cloud interaction for warm liquid clouds as 0.63 ± 0.19, −0.34 ± 0.40 and 1.11 ± 0.08 W m−2 during the annual, monsoon and non‐monsoon periods in South Asia, respectively. Relationships derived among liquid water path (LWP), cloud droplet number concentration (CDNC) and consequent cloud albedo are assessed as a function of aerosol concentration. The intensity of the aerosol‐cloud interaction gradually weakens with increasing cloud base height above ground level in South Asia. Moreover, distinct regional and seasonal variations in the aerosol‐cloud interaction are observed for LWP, CDNC and the resulting cloud albedo in South Asia. These variations are associated with water vapour and aerosol absorption levels. Results contribute to the understanding and modelling of aerosol‐cloud interactions and determining their effects on radiative forcing and climate in South Asia.
Zengxin Pan; Feiyue Mao; Wei Wang; Timothy Logan; Jia Hong. Examining Intrinsic Aerosol-Cloud Interactions in South Asia Through Multiple Satellite Observations. Journal of Geophysical Research: Atmospheres 2018, 123, 11,210 -11,224.
AMA StyleZengxin Pan, Feiyue Mao, Wei Wang, Timothy Logan, Jia Hong. Examining Intrinsic Aerosol-Cloud Interactions in South Asia Through Multiple Satellite Observations. Journal of Geophysical Research: Atmospheres. 2018; 123 (19):11,210-11,224.
Chicago/Turabian StyleZengxin Pan; Feiyue Mao; Wei Wang; Timothy Logan; Jia Hong. 2018. "Examining Intrinsic Aerosol-Cloud Interactions in South Asia Through Multiple Satellite Observations." Journal of Geophysical Research: Atmospheres 123, no. 19: 11,210-11,224.
The intensity of a lidar signal decreases with transmittance and the square of detection range. Consequently, the effective measure range and retrieval accuracy are severely affected. A method for denoising and retrieval of lidar data is proposed in this study by combining dual ensemble Kalman filter (DEnKF) and Fernald methods to avoid the abovementioned issue. Compared with ensemble Kalman filter (EnKF) method, the DEnKF method provides a feedback function in the iteration; thus, the DEnKF method provides a generally improved accuracy of denoising and retrieval. We select an ensemble size of 60 and determine the covariance δ on the basis of the defined performance function. The DEnKF, EnKF and standard Fernald methods are tested using complex simulated and real signals. Results show that an aerosol backscatter coefficient retrieved through the DEnKF method demonstrates lower uncertainty in the far range (above 4 km) than the coefficients obtained through the two other methods and fits the results retrieved through the two other methods in the near range (below 4 km). In addition, the results indicate that the retrieval results are better through the DEnKF method than through the 64 min averaged signals, which can divide the standard error thrice (i.e. averaging 64 replications). Overall, the results demonstrate that the DEnKF method is effective and useful for retrieving signals with low signal-to-noise ratios, such as the far-range signals of a ground lidar and full-range signals of a space lidar.
Feiyue Mao; Jian Liu; Lei Wang; Shihua Chen; Chen Li. Denoising and retrieval algorithm based on a dual ensemble Kalman filter for elastic lidar data. Optics Communications 2018, 433, 137 -143.
AMA StyleFeiyue Mao, Jian Liu, Lei Wang, Shihua Chen, Chen Li. Denoising and retrieval algorithm based on a dual ensemble Kalman filter for elastic lidar data. Optics Communications. 2018; 433 ():137-143.
Chicago/Turabian StyleFeiyue Mao; Jian Liu; Lei Wang; Shihua Chen; Chen Li. 2018. "Denoising and retrieval algorithm based on a dual ensemble Kalman filter for elastic lidar data." Optics Communications 433, no. : 137-143.
Particulate matter with diameter less than 1 μm (PM) has been found to be closely associated with air quality, climate changes, and even adverse human health. However, a large gap in our knowledge concerning the large-scale distribution and variability of PM remains, which is expected to be bridged with advanced remote-sensing techniques. In this study, a hybrid model called principal component analysis-general regression neural network (PCA-GRNN) is developed to estimate hourly PM concentrations from Himawari-8 aerosol optical depth in combination with coincident ground-based PM measurements in China. Results indicate that the hourly estimated PM concentrations from satellite agree well with the measured values at national scale, with R of 0.65, root-mean-square error (RMSE) of 22.0 μg/m and mean absolute error (MAE) of 13.8 μg/m. On daily and monthly time scales, R increases to 0.70 and 0.81, respectively. Spatially, highly polluted regions of PM are largely located in the North China Plain and Northeast China, in accordance with the distribution of industrialisation and urbanisation. In terms of diurnal variability, PM concentration tends to peak in rush hours during the daytime. PM exhibits distinct seasonality with winter having the largest concentration (31.5±3.5 μg/m), largely due to peak combustion emissions. We further attempt to estimate PM and PM with the proposed method and find that the accuracies of the proposed model for PM and PM estimation are significantly higher than that of PM. Our findings suggest that geostationary data is one of the promising data to estimate fine particle concentration on large spatial scale.
Lin Zang; Feiyue Mao; Jianping Guo; Wei Gong; Wei Wang; Zengxin Pan. Estimating hourly PM1 concentrations from Himawari-8 aerosol optical depth in China. Environmental Pollution 2018, 241, 654 -663.
AMA StyleLin Zang, Feiyue Mao, Jianping Guo, Wei Gong, Wei Wang, Zengxin Pan. Estimating hourly PM1 concentrations from Himawari-8 aerosol optical depth in China. Environmental Pollution. 2018; 241 ():654-663.
Chicago/Turabian StyleLin Zang; Feiyue Mao; Jianping Guo; Wei Gong; Wei Wang; Zengxin Pan. 2018. "Estimating hourly PM1 concentrations from Himawari-8 aerosol optical depth in China." Environmental Pollution 241, no. : 654-663.
South Asia is experiencing a levelling-off trend in solar radiation and even a transition from dimming to brightening. Any change in incident solar radiation, which is the only significant energy source of the global ecosystem, profoundly affects our habitats. Here, we use multiple observations of the A-Train constellation to evaluate the impacts of three-dimensional (3D) aerosol, cloud, and water vapor variations on the changes in surface solar radiation during the monsoon season (June–September) in South Asia from 2006 to 2015. Results show that surface shortwave radiation (SSR) has possibly increased by 16.2 W m−2 during this period. However, an increase in aerosol loading is inconsistent with the SSR variations. Instead, clouds are generally reduced and thinned by approximately 8.8% and 280 m, respectively, with a decrease in both cloud water path (by 34.7 g m−2) and particle number concentration under cloudy conditions. Consequently, the shortwave cloud radiative effect decreases by approximately 45.5 W m−2 at the surface. Moreover, precipitable water in clear-sky conditions decreases by 2.8 mm (mainly below 2 km), and related solar brightening increases by 2.5 W m−2. Overall, the decreases in 3D water vapor and clouds distinctly result in increased absorption of SSR and subsequent surface brightening.
Zengxin Pan; Feiyue Mao; Wei Wang; Bo Zhu; Xin Lu; Wei Gong. Impacts of 3D Aerosol, Cloud, and Water Vapor Variations on the Recent Brightening during the South Asian Monsoon Season. Remote Sensing 2018, 10, 651 .
AMA StyleZengxin Pan, Feiyue Mao, Wei Wang, Bo Zhu, Xin Lu, Wei Gong. Impacts of 3D Aerosol, Cloud, and Water Vapor Variations on the Recent Brightening during the South Asian Monsoon Season. Remote Sensing. 2018; 10 (4):651.
Chicago/Turabian StyleZengxin Pan; Feiyue Mao; Wei Wang; Bo Zhu; Xin Lu; Wei Gong. 2018. "Impacts of 3D Aerosol, Cloud, and Water Vapor Variations on the Recent Brightening during the South Asian Monsoon Season." Remote Sensing 10, no. 4: 651.
Aerosols greatly influence global and regional atmospheric systems, and human life. However, a comprehensive understanding of the source regions and three-dimensional (3D) characteristics of aerosol transport over central China is yet to be achieved. Thus, we investigate the 3D macroscopic, optical, physical, and transport properties of the aerosols over central China based on the March 2007 to February 2016 data obtained from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission and the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model. Our results showed that approximately 60% of the aerosols distributed over central China originated from local areas, whereas non-locally produced aerosols constituted approximately 40%. Anthropogenic aerosols constituted the majority of the aerosol pollutants (69%) that mainly distributed less than 2.0 km above mean sea level. Natural aerosols, which are mainly composed of dust, accounted for 31% of the total aerosols, and usually existed at an altitude higher than that of anthropogenic aerosols. Aerosol particles distributed in the near surface were smaller and more spherical than those distributed above 2.0 km. Aerosol optical depth (AOD) and the particulate depolarization ratio displayed decreasing trends, with a total decrease of 0.11 and 0.016 from March 2007 to February 2016, respectively. These phenomena indicate that during the study period, the extinction properties of aerosols decreased, and the degree of sphericity in aerosol particles increased. Moreover, the annual anthropogenic and natural AOD demonstrated decreasing trends, with a total decrease of 0.07 and 0.04, respectively. This study may benefit the evaluation of the effects of the 3D properties of aerosols on regional climates.
Xin Lu; Feiyue Mao; Zengxin Pan; Wei Gong; Wei Wang; Liqiao Tian; Shenghui Fang. Three-Dimensional Physical and Optical Characteristics of Aerosols over Central China from Long-Term CALIPSO and HYSPLIT Data. Remote Sensing 2018, 10, 314 .
AMA StyleXin Lu, Feiyue Mao, Zengxin Pan, Wei Gong, Wei Wang, Liqiao Tian, Shenghui Fang. Three-Dimensional Physical and Optical Characteristics of Aerosols over Central China from Long-Term CALIPSO and HYSPLIT Data. Remote Sensing. 2018; 10 (2):314.
Chicago/Turabian StyleXin Lu; Feiyue Mao; Zengxin Pan; Wei Gong; Wei Wang; Liqiao Tian; Shenghui Fang. 2018. "Three-Dimensional Physical and Optical Characteristics of Aerosols over Central China from Long-Term CALIPSO and HYSPLIT Data." Remote Sensing 10, no. 2: 314.
The Visible Infrared Imaging Radiometer Suite (VIIRS) is a next-generation polar-orbiting operational environmental sensor with a capability for global aerosol observations. Identifying land aerosol types is important because aerosol types are a basic input in retrieving aerosol optical properties for VIIRS. The VIIRS algorithm can automatically select the optimal land aerosol model by minimizing the residual between the derived and expected spectral surface reflectance. In this study, these selected VIIRS aerosol types are evaluated using collocated aerosol types obtained from the Aerosol Robotic Network (AERONET) level 1.5 from 23 January 2013 to 28 February 2017. The spatial distribution of VIIRS aerosol types and the aerosol optical depth bias (VIIRS minus AERONET) demonstrate that misidentifying VIIRS aerosol types may lead to VIIRS retrieval being overestimated over the Eastern United States and the developed regions of East Asia, as well as underestimated over Southern Africa, India, and Northeastern China. Approximately 22.33% of VIIRS aerosol types are coincident with that of AERONET. The agreements between VIIRS and AERONET for fine non-absorbing and absorbing aerosol types are approximately 36% and 57%, respectively. However, the agreement between VIIRS and AERONET is extremely low (only 3.51%). The low agreement for coarse absorbing dust may contribute to the poor performance of VIIRS retrieval under the aerosol model (R = 0.61). Results also show that an appropriate aerosol model can improve the retrieval performance of VIIRS over land, particularly for dust type (R increases from 0.61 to 0.72).
Wei Wang; Zengxin Pan; Feiyue Mao; Wei Gong; Longjiao Shen. Evaluation of VIIRS Land Aerosol Model Selection with AERONET Measurements. International Journal of Environmental Research and Public Health 2017, 14, 1016 .
AMA StyleWei Wang, Zengxin Pan, Feiyue Mao, Wei Gong, Longjiao Shen. Evaluation of VIIRS Land Aerosol Model Selection with AERONET Measurements. International Journal of Environmental Research and Public Health. 2017; 14 (9):1016.
Chicago/Turabian StyleWei Wang; Zengxin Pan; Feiyue Mao; Wei Gong; Longjiao Shen. 2017. "Evaluation of VIIRS Land Aerosol Model Selection with AERONET Measurements." International Journal of Environmental Research and Public Health 14, no. 9: 1016.
Monitoring fine particulate matter with diameters of less than 2.5 μm (PM2.5) is a critical endeavor in the Beijing–Tianjin–Hebei (BTH) region, which is one of the most polluted areas in China. Polar orbit satellites are limited by observation frequency, which is insufficient for understanding PM2.5 evolution. As a geostationary satellite, Himawari-8 can obtain hourly optical depths (AODs) and overcome the estimated PM2.5 concentrations with low time resolution. In this study, the evaluation of Himawari-8 AODs by comparing with Aerosol Robotic Network (AERONET) measurements showed Himawari-8 retrievals (Level 3) with a mild underestimate of about −0.06 and approximately 57% of AODs falling within the expected error established by the Moderate-resolution Imaging Spectroradiometer (MODIS) (±(0.05 + 0.15AOD)). Furthermore, the improved linear mixed-effect model was proposed to derive the surface hourly PM2.5 from Himawari-8 AODs from July 2015 to March 2017. The estimated hourly PM2.5 concentrations agreed well with the surface PM2.5 measurements with high R2 (0.86) and low RMSE (24.5 μg/m3). The average estimated PM2.5 in the BTH region during the study time range was about 55 μg/m3. The estimated hourly PM2.5 concentrations ranged extensively from 35.2 ± 26.9 μg/m3 (1600 local time) to 65.5 ± 54.6 μg/m3 (1100 local time) at different hours.
Wei Wang; Feiyue Mao; Lin Du; Zengxin Pan; Wei Gong; Shenghui Fang. Deriving Hourly PM2.5 Concentrations from Himawari-8 AODs over Beijing–Tianjin–Hebei in China. Remote Sensing 2017, 9, 858 .
AMA StyleWei Wang, Feiyue Mao, Lin Du, Zengxin Pan, Wei Gong, Shenghui Fang. Deriving Hourly PM2.5 Concentrations from Himawari-8 AODs over Beijing–Tianjin–Hebei in China. Remote Sensing. 2017; 9 (8):858.
Chicago/Turabian StyleWei Wang; Feiyue Mao; Lin Du; Zengxin Pan; Wei Gong; Shenghui Fang. 2017. "Deriving Hourly PM2.5 Concentrations from Himawari-8 AODs over Beijing–Tianjin–Hebei in China." Remote Sensing 9, no. 8: 858.
Visible Infrared Imaging Radiometer Suite (VIIRS) is a next-generation polar-orbiting operational environmental sensor with a capability for global aerosol observations. A comprehensive validation of VIIRS products is significant for improving product quality, assessing environment quality for human life, and studying regional climate change. In this study, three-year (from 1 January 2014 to 31 December 2016) records of VIIRS Intermediate Product (IP) data and Moderate Resolution Imaging Spectroradiometer (MODIS) retrievals on aerosol optical depth (AOD) at 550 nm were evaluated by comparing them to ground sun photometer measurements over Wuhan. Results indicated that VIIRS IP retrievals were underestimated by 5% for the city. A comparison of VIIRS IP retrievals and ground sun photometer measurements showed a lower R2 of 0.55 (0.79 for Terra-MODIS and 0.76 for Aqua-MODIS), with only 52% of retrievals falling within the expected error range established by MODIS over land (i.e., ±(0.05 + 0.15AOD)). Bias analyses with different Ångström exponents (AE) demonstrated that land aerosol model selection of the VIIRS retrieval over Wuhan was appropriate. However, the larger standard deviations (i.e., uncertainty) of VIIRS AODs than MODIS AODs could be attributed to the less robust retrieval algorithm. Monthly variations displayed largely underestimated AODs of VIIRS in winter, which could be caused by a large positive bias in surface reflectance estimation due to the sparse vegetation and greater surface brightness of Wuhan in this season. The spatial distribution of VIIRS and MODIS AOD observations revealed that the VIIRS IP AODs over high-pollution areas (AOD > 0.8) with sparse vegetation were underestimated by more than 20% in Wuhan, and 40% in several regions. Analysis of several clear rural areas (AOD < 0.2) with native vegetation indicated an overestimation of about 20% in the northeastern region of the city. These findings showed that the VIIRS IP AOD at 550 nm can provide a solid dataset with a high resolution (750 m) for quantitative scientific investigations and environmental monitoring over Wuhan. However, the performance of dark target algorithms in VIIRS was associated with aerosol types and ground vegetation conditions.
Wei Wang; Feiyue Mao; Zengxin Pan; Lin Du; Wei Gong. Validation of VIIRS AOD through a Comparison with a Sun Photometer and MODIS AODs over Wuhan. Remote Sensing 2017, 9, 403 .
AMA StyleWei Wang, Feiyue Mao, Zengxin Pan, Lin Du, Wei Gong. Validation of VIIRS AOD through a Comparison with a Sun Photometer and MODIS AODs over Wuhan. Remote Sensing. 2017; 9 (5):403.
Chicago/Turabian StyleWei Wang; Feiyue Mao; Zengxin Pan; Lin Du; Wei Gong. 2017. "Validation of VIIRS AOD through a Comparison with a Sun Photometer and MODIS AODs over Wuhan." Remote Sensing 9, no. 5: 403.