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Aerosols significantly affect the Earth–atmosphere energy balance and climate change by acting as cloud condensation nuclei. Specifically, the susceptibility of cloud and precipitation to aerosols is stronger when aerosols are faint but tends to be saturated in polluted conditions. However, previous methodologies generally miss these faint aerosols based on instantaneous observations because they are too optically thin to be detected and are therefore usually unretrieved. This result in a large underestimation when quantifying aerosol climate impacts. Here, we focus on retrieving and verifying the instantaneous extinction of undetected faint aerosol by the CALIPSO layer detection algorithm on a global scale. Using the observations during the Stratospheric Aerosol and Gas Experiment III on the International Space Station (SAGE III/ISS) as constraints, the lidar ratios of undetected faint aerosol are estimated with a global median of 42.2 and 24.5 sr at the stratosphere and the troposphere, respectively. The retrieved extinction of undetected aerosol during night-time shows good agreement with the independent 12-month SAGE III/ISS product on a 1∘ average. The corresponding correlation coefficient and averaged normalized root-mean-square error are 0.66 % and 100.6 %, respectively. The minimum retrieved extinction coefficients can be extended to 10−3 and 10−4 km−1 with an uncertainty of 35 % and 125 % during night-time, respectively. The CALIPSO retrieval during daytime has a positive bias and relatively low agreement with SAGE III/ISS due to the low signal-to-noise ratio caused by sunlight. This study has great potential for improving the understanding of aerosol variations and the quantification of aerosol impacts on global climate change.

Aerosols significantly affect the Earth-atmosphere energy balance and climate change by acting as cloud condensation nuclei. Particularly, the susceptibility of clouds to aerosols is more pronounced when the aerosols are faint. However, previous methodologies generally miss these faint aerosols and their climate effect based on instantaneous observations because they are too optically thin to be detected. Here, we focus on retrieving faint aerosol extinction based on instantaneous observations from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). Results show a good agreement between faint aerosol extinction retrieval of CALIPSO and Stratospheric Aerosol and Gas Experiment III on the International Space Station (SAGE III-ISS) product over June 2017 to 2019 during nighttime, with correlation coefficients (R) and root mean square error (RMSE) of 0.58 in logarithmic scale and 0.0008, respectively. The lower bound of retrieved aerosol extinction extended to 0.0001 km−1 product (0.01 km−1, much lower than the CALIPSO Level 2 Extinction). The CALIPSO retrieval during daytime has a positive bias and low agreement with SAGE III-ISS with R and RMSE of 0.16 and 0.0034, respectively, due to the low signal-to-noise ratio caused by sunlight. Additionally, the retrieval at 20 km resolution successfully capture the enhanced faint aerosol from Siberian fires in 2019 instantaneously, which are also shown by CALIPSO monthly-averaged aerosol product at much lower temporal-spatial resolution. It indicates a significant potential for improving the quantification of aerosol impacts on climate change through retrieving instantaneous faint aerosol.

Aerosols can modify cloud properties by affecting the concentration and size of cloud droplets and the strength of cloud convection. Understanding the effects of aerosol on cloud remains challenging because of the different effects of aerosol on various conditions and regions. In this study, we survey the effect of aerosol on cloud base height (CBH) through four-year ground-based observations in Wuhan, China. A robust positive correlation between aerosol loading and CBH can be found via annual and correlation analyses, which suggest the systematic invigoration of clouds through aerosol loading. The statistical analysis of collocated measurements of PM10 concentrations, meteorological factors, and CBHs suggest that temperature, relative humidity and aerosol particles collectively influence CBH variations. The present finding also demonstrate that the influence of aerosol particles on promoting CBH is prominent under the conditions of relatively high humidity and low temperature. This study provides a new perspective for understanding the response of aerosol-cloud interactions in an urban area, which will play important roles in regional climate change and human life.

Improving the understanding of cloud–radiation–monsoon interactions is difficult due to the limited knowledge regarding the impacts of vertical cloud radiative forcing on monsoon circulation. Here,...

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.

Changes in aerosol loading affect cloud albedo and emission and Earth's radiative balance with a low level of scientific understanding. In this study, we investigate the vertical response of ice clouds to aerosols within the Indian subcontinent during monsoon season (2006–2010) based on multiple satellite observations. As a function of aerosol loading, we find that the cloud optical depth, cloud geometrical depth and ice water path decrease by 0.23 (from 0.39 to 0.16), 0.8 km (from 2.6 to 1.8 km), 5.1 g/m2 (from 7.9 to 2.8 g/m2), respectively, and that ice particles possibly decrease in size and become more spherical in shape as aerosol optical depth (AOD) increases from 0.1 to 1; these changes tend to plateau as AOD increases beyond 1. The absolute negative response between ice clouds and aerosols under moist and unstable atmospheric conditions is stronger than that under drier and stable atmospheric conditions, and vice versa. Moreover, the negative impact of smoke on ice clouds is stronger than dust and polluted dust, which is likely related to the strong absorption properties and poor ice nucleation efficiency of smoke. Aerosol impacts on ice clouds lead to a decrease in the net cloud radiative effect of 7.3 W/m2 (from 18.5 to 11.2 W/m2) as AOD increases from 0.1 to 1. This change in ice cloud properties mainly results in the decrease in downwelling LW radiation to the surface and consequently weakened radiative forcing of ice clouds during the Indian summer monsoon season.

Space lidar is a unique tool for detecting the 3D properties of cloud and aerosol layers. Layer boundaries and lidar ratio are significant preconditions for retrieving the layer optical properties of a space lidar. The CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) team adopts the threshold method to accurately detect a layer top. However, the layer base detection continues to rely on the slope method, which causes considerable uncertainty. Furthermore, a fixed lidar ratio is mainly used for retrieving optical properties of layers of the same layer type, which also contains large uncertainty. To reduce the above uncertainties, we propose an iterative method to determine layer base and lidar ratio of a permeable layer of a space lidar on the basis of lidar ratio and transmission relationship. The simulated and measured experimental results demonstrate that the proposed iterative method simultaneously retrieves layer boundaries and lidar ratio accurately. The proposed iterative method has the potential to become a significant part of data retrieval of a space lidar in the future.

Particulate matter with diameter less than 1 μm (PM1) 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 PM1 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 PM1 concentrations from Himawari-8 aerosol optical depth in combination with coincident ground-based PM1 measurements in China. Results indicate that the hourly estimated PM1 concentrations from satellite agree well with the measured values at national scale, with R2 of 0.65, root-mean-square error (RMSE) of 22.0 μg/m3 and mean absolute error (MAE) of 13.8 μg/m3. On daily and monthly time scales, R2 increases to 0.70 and 0.81, respectively. Spatially, highly polluted regions of PM1 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, PM1 concentration tends to peak in rush hours during the daytime. PM1 exhibits distinct seasonality with winter having the largest concentration (31.5±3.5 μg/m3), largely due to peak combustion emissions. We further attempt to estimate PM2.5 and PM10 with the proposed method and find that the accuracies of the proposed model for PM1 and PM2.5 estimation are significantly higher than that of PM10. Our findings suggest that geostationary data is one of the promising data to estimate fine particle concentration on large spatial scale.

The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) is unique in its ability to perform profiling measurements of aerosol and cloud layers globally. Detecting the layer boundaries of aerosols and clouds is a crucial step in CALIPSO data retrieval. The CALIPSO team uses the selective iterated boundary location (SIBYL) algorithm based on threshold arrays to find aerosol and cloud layers at different horizontal resolutions. However, threshold arrays could obstruct the detection of optically tenuous layers at a high resolution and may cause overestimation when averaging signals of layer and clear air at a low resolution. Here, a multiscale algorithm using a series of sliding window sizes without threshold setting is proposed based on a pre-defined probability. The results over land and marine areas show that the multiscale algorithm detected 37.41% and 16.36% more layer area than the SIBYL at 1–80 km resolutions at daytime and 1–5 km resolutions at night time, respectively. This indicates that the multiscale algorithm does not need a threshold array, allowing more tenuous layers to be detected, especially at low signal to noise ratios (SNRs). In contrast, the SIBYL detects 4.40% more layer area than the multiscale algorithm at 1–80 km resolutions at nighttime, mainly caused by the large proportion of layer area detected by SIBYL at 20 and 80 km resolutions. This implies possible noteworthy overestimation by the SIBYL at low resolutions. Additionally, the evaluation using the depolarization ratio of ice clouds shows that the extra detected layers by the multiscale algorithm are reliable. Besides, simulation tests show that the multiscale and SIBYL algorithms achieve a 100% true detection rate when SNR is approximately 2 and 4, respectively. The new multiscale algorithm could upgrade the resolution and accuracy of the layer detection of space lidars and reduce the underestimation of layer optical depth due to missing layers.

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.

Overlap factor is an instrumental phenomenon caused by the incomplete overlay of the transmitting and receiving systems of a light detection and ranging (lidar) system. Conventional methods of overlap calculation for Raman lidar by combining Mie and N2-Raman signals is based on a user-assumed lidar ratio, assumption of which may introduce larger uncertainties when the characters of an aerosol loading is unknown. In this study, a physical constraint method is proposed to obtain an appropriate lidar ratio for overlap profile calculation of Raman lidar. The experiment of six representative cases verified that the correction of the overlap profile obtained by our method is practical and feasible. The signal of the experiment was derived from the Raman lidar at the Southern Great Plains site (SGPRL) of Atmospheric Radiation Measurement Program. The particle extinction coefficient of Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation was used as a reference value. The mean absolute errors of the particle extinction coefficient derived based on the proposed method is small (7.0–22.9 Mm−1) for 0–2 km by comparing the reference value. Additionally, the large bias below 0.8 km between the particle extinction coefficient corrected by the SGPRL-released overlap profile and the reference value suggest that the overlap profile applied in SGPRL still has larger room to be improved.

The Tibetan Plateau (TP) has experienced evident warming in recent decades, but the exact reasons for this warming remain unclear. In this study, we investigated the possible effect of the three-dimensional (3D) variations of cloud during daytime on climate warming over the TP from 2007 to 2015 based on CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations. We found that the 3D changes occur mostly to low clouds, whose fraction and geometrical depth decrease by approximately 4.2% and 130 m, respectively. These changes result in the increase of the surface shortwave radiation (SSR) by approximately 29.7 W/m2, which is one magnitude larger than that of anthropogenic CO2. The increase in SSR leads to the increase in direct solar radiation absorption in the surface, which significantly enhances the warming of the TP.

The new-generation geostationary satellites feature higher radiometric, spectral, and spatial resolutions, thereby making richer data available for the improvement of PM

In this work, a cross-linked porous polyimide (PI) network, fabricated from an “elongated” dianhydride and melamine, was synthesized by polycondensation and thermal dehydration. The as-prepared PI revealed an excellent adsorption performance and removal for acid orange 10 (AO-10) from aqueous solution. The adsorption quantity can reach 599 mg/g when the initial concentration is 500 mg/L with the temperature 298 K and pH value of 3. The maximum desorption ratio can be 38.9%. It is expected that the adsorption quantity and desorption ratio can be optimally improved by changing the length of PI monomer, indicating the cross-linked porous PI network is not only served effectively in sewage treatment but also recycled. Pseudo-2nd-order kinetic model and Langmuir isotherm are better fitted with the kinetic data and the adsorption equilibrium data respectively.

Knowledge of cloud geometrical thickness (H) is of importance for the study of radiative balance and cloud microphysics. However, the retrieval of H remains challenging, especially for passive instruments. In this work, we derive a semi-analytical algorithm for retrieving H of single-layer liquid cloud based on oxygen A-band hyperspectral measurements from NASA's Orbiting Carbon Observatory-2 (OCO-2). In this algorithm, a high-order correction is introduced to the approximation formula to accurately calculate oxygen A-band hyperspectral cloud reflectance. The algorithm can retrieve H in real time as it does not require the use of the time-consuming radiative transfer model for radiation calculation during each retrieval. In addition, the algorithm currently requires cloud optical depth, cloud top height, and aerosol properties measured by other instruments as input. In idealized simulations using ten thousand A-band spectra spanning a range of cloud cases, the root mean squared error (RMSE) of the retrieval is approximately 2.0 hPa (for low clouds, 1 hPa is about 10 m). We also retrieve H based on millions of real OCO-2 observations and compare the retrieval results with the cloud product from CloudSat/CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations). After abnormal samples are removed, the correlation coefficient is 0.716, the average bias is −15.6 hPa, and the RMSE is 27.4 hPa. The statistical results show that the absolute bias increases systematically with the reference cloud geometrical thickness, which may be caused by the unrealistic vertical homogeneous cloud assumption. The similar phenomenon was also found in comparison with OCO2CLD-LIDAR-AUX, a joint retrieval product based on OCO-2, CALIPSO, and accurate radiative transfer model. The fast algorithm shows a similar distribution of retrieved cloud bottom pressure and a smaller bias on cloud geometrical thickness retrieval compared to the OCO2CLD-LIDAR-AUX products.

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.

Meteorological conditions are largely driven by synoptic circulations, affecting pollutant transport, dispersion, and even emissions. Partly due to the lack of systematic investigations of meteorological impact at large- and local-scale, aerosol pollution in Central China is not yet fully understood. This study conducted synoptic classification based on the obliquely rotated principal component analysis in T-mode, and investigated the relationship between aerosol pollution and the prevailing synoptic patterns over Wuhan during wintertime from 2014 to 2018. Among five identified patterns, three driven by the northerly high-pressure systems, accompanied by weak surface winds, low boundary layer height, and high relative humidity, were found to be closely related to heavy pollution. Notably, the winter aerosol pollution in Wuhan showed a declining trend in recent years, which also slightly benefited from the interannual variation of synoptic patterns besides emission control. However, with the increase in humidity, pollution caused by secondary particles cannot be ignored. The oxidation rate of sulfur and nitrogen in the atmosphere was about 0.1 higher in 2018 than in 2014. This study is significant for pollution control in Wuhan and its surrounding areas of Central China.

Particulates smaller than 1.0 μm (PM

Particulate matter with diameter less than 1 μm (PM

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.

The atmospheric boundary layer (ABL), an atmospheric region near the Earth’s surface, is affected by surface forcing and is important for studying air quality, climate, and weather forecasts. In this study, long-term urban nocturnal boundary layers (NBLs) were estimated by an elastic backscatter light detection and ranging (LiDAR) with various methods in Wuhan (30.5° N, 114.4° E), a city in Central China. This study aims to explore two ABL research topics: (1) the relationship between NBL height (NBLH) and near-surface parameters (e.g., sensible heat flux, temperature, wind speed, and relative humidity) to elucidate meteorological processes governing NBL variability; and (2) the influence of NBLH variations in surface particulate matter (PM) in Wuhan. We analyzed the nocturnal ABL-dilution/ABL-accumulation effect on surface particle concentration by using a typical case. A long-term analysis was then performed from 5 December 2012–17 June 2016. Results reveal that the seasonal averages of nocturnal (from 20:00 to 05:00 next day, Chinese standard time) NBLHs are 386 ± 161 m in spring, 473 ± 154 m in summer, 383 ± 137 m in autumn, and 309 ± 94 m in winter. The seasonal variations in NBLH, AOD, and PM2.5 display a deep (shallow) seasonal mean NBL, consistent with a small (larger) seasonal mean PM2.5 near the surface. Seasonal variability of NBLH is partly linearly correlated with sensible heat flux at the surface (R = 0.72). Linear regression analyses between NBLH and other parameters show the following: (1) the positive correlation (R = 0.68) between NBLH and surface temperature indicates high (low) NBLH corresponding to warm (cool) conditions; (2) the slight positive correlation (R = 0.52) between NBLH and surface relative humidity in Wuhan; and (3) the weak positive correlation (R = 0.38) between NBLH and wind speed inside the NBL may imply that the latter is not an important direct driver that governs the seasonal variability of NBLH.

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(-1)sr(-1) and 1.7e-06 m(-1)sr(-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.

The cloud detection algorithm for passive sensors is usually based on a fuzzy logic system with thresholds determined from previous observations. In recent years, haze and high aerosol concentrations with high aerosol optical depth (AOD) occur frequently in China and may critically impact the accuracy of the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud detection. Thus, we comprehensively explore this impact by comparing the results from MODIS/Aqua (passive sensor), Cloud-Aerosol Lidar with Orthogonal Polarization/CALIPSO (lidar sensor), and Cloud Profiling Radar/CloudSat (microwave sensor) of the A-Train suite of instruments using an averaged AOD as an index for an aerosol concentration value. Case studies concerning the comparison of the three sensors indicate that MODIS cloud detection is reduced during haze events. In addition, statistical studies show that an increase in AOD creates an increase in the percentage of uncertain flags and a decrease in hit rate, a consistency index between consecutive sets of cloud retrievals. On average, AOD values lower than 0.1 give hit rate values up to 80.0% and uncertainty values lower than 16.8%, while AOD values greater than 1.0 reduce the hit rate below to 66.6% and increase the percentage of uncertain flags up to 46.6%. Therefore, we can conclude that the ability of MODIS cloud detection is weakened by large concentrations of aerosols. This suggests that use of the MODIS cloud mask, and derived higher-level products, in situations with haze requires caution. Further improvement of this retrieval algorithm is desired as haze studies based on MODIS products are of great interest in a number of related fields.

The signal-to-noise ratio (SNR) of an atmospheric lidar decreases rapidly as range increases, so that maintaining high accuracy when retrieving lidar data at the far end is difficult. To avoid this problem, many de-noising algorithms have been developed; in particular, an effective de-noising algorithm has been proposed to simultaneously retrieve lidar data and obtain a de-noised signal by combining the ensemble Kalman filter (EnKF) and the Fernald method. This algorithm enhances the retrieval accuracy and effective measure range of a lidar based on the Fernald method, but sometimes leads to a shift (bias) in the near range as a result of the over-smoothing caused by the EnKF. This study proposes a new scheme that avoids this phenomenon using a particle filter (PF) instead of the EnKF in the de-noising algorithm. Synthetic experiments show that the PF performs better than the EnKF and Fernald methods. The root mean square error of PF are 52.55% and 38.14% of that of the Fernald and EnKF methods, and PF increases the SNR by 44.36% and 11.57% of that of the Fernald and EnKF methods, respectively. For experiments with real signals, the relative bias of the EnKF is 5.72%, which is reduced to 2.15% by the PF in the near range. Furthermore, the suppression impact on the random noise in the far range is also made significant via the PF. An extensive application of the PF method can be useful in determining the local and global properties of aerosols.

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.

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.

The macrophysical and optical properties of clouds over East Asia (18°N–54°N, 73°E–145°E) from 1 March 2007 to 28 February 2015 are investigated using Cloud-Aerosol Lidar with Orthogonal Polarization data. Data analysis determines the macrophysical properties, such as cloud fraction, cloud vertical structure, cloud top height (CTH), cloud base height, and cloud geometrical depth (CGD), as well as the optical properties of clouds. Statistical analysis shows that the annual cloud fractions of single-layer (SL), multilayer (ML), and total clouds over East Asia are 41.4 ± 0.7%, 25.1 ± 0.9%, and 66.5 ± 1.6%, respectively, with a slight interannual variation. The maximum annual cloud fraction that appeared over the Sichuan Basin is mainly attributed to unique occlusive topographic features. Moreover, the annual vertical distribution of cloud occurrence frequency over East Asia presents a multipeak structure. Furthermore, at a height below 2 km, cloud frequency distribution exhibits a large peak over the south, north, northeast, eastern sea, and East Asia, a small peak over the northwest, and the smallest peak over Tibet, which is mainly ascribed to terrain topographies. For the average uppermost CTH and cloud fraction, the same seasonal characteristic is demonstrated; that is, CTH and cloud fraction are highest in summer and lowest in winter, except in the northwest. This seasonal characteristic mainly results from the East Asian summer monsoon circulation. Overall, the annual cloud optical depths (CODs) of SL, ML, and total cloud over East Asia are 0.98 ± 0.02, 0.83 ± 0.09, and 1.81 ± 0.12, respectively. Moreover, the COD of each layer is mainly below 0.5 (52.3%), and the second peak of probability (10.4%) exists from 2.5 to 3.0. The two crests of probability are caused by clouds of different types. Overall, the annual cloud layer over East Asia mainly consists of cirrus (44.4%), which indicates that cirrus clouds play a leading role. Most geometrically thick clouds (CGD > 2 km) are cirrus and deep convective clouds. In general, annual CGD decreases with the increase in the number of ML cloud system layers, and CGD increases with the increase in altitude, whereas the COD of each layer exhibits a reverse trend.

Clouds’ macrophysical characteristics play an important role in the climate system and dramatically vary because of the diverse climatic and geographic factors in China. We analyze cloud macrophysical characteristics and the differences between subregions in China (18°–54°N, 73°–135°E) from March 2012 to February 2015 based on Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations, including cloud fractions, cloud vertical distribution, and cloud geometrical properties with the perspective of daytime and nighttime. We found that annual single layer, multilayer (ML), and total cloud fractions are 40.4±1.1%, 22.4±0.4%, and 62.8±1.5%, respectively, and clouds are generally located between 6 and 12 km. The cloud fractions in daytime are less than that in nighttime over the south while that of Tibet shows the reverse trend. In the vertical direction, except for Tibet, the clouds in nighttime have larger spatial coverage and are higher in altitude than that in daytime. The regional average values of cloud macrophysical characteristics in the south are highest, followed successively by Tibet, north, and northwest. Cloud geometrical depth and spacing show a gradually declining trend with the increase in layers and decrease of altitude in ML cloud system.