Classification of aerosols using particle linear depolarization ratio (PLDR) over seven urban locations of Asia

Active lidar remote sensing has been used to obtain detailed and quantitative information about the properties of aerosols. We have analyzed the spatio-temporal classification of aerosols using the parameters of particle linear depolarization ratio and single scattering albedo from Aerosol Robotic Network (AERONET) over seven megacities of Asia namely; Lahore, Karachi, Kanpur, Pune, Beijing, Osaka, and Bandung. We find that pollution aerosols dominate during the winter season in all the megacities. The concentrations, however, vary concerning the locations, i.e., 70-80% pollution aerosols are present over Lahore, 40-50% over Karachi, 90-95% over Kanpur and Pune, 60-70% and over Beijing and Osaka. Pure Dust (PD), Pollution Dominated Mixture (PDM), and Dust Dominated Mixture (DDM) are found to be dominant during spring and summer seasons.This proposes that dust over Asia normally exists as a mixture with pollution aerosols instead of pure form. We also find that black carbon (BC) dominated pollution aerosols.

Variations and drivers of aerosol vertical characterization after clean air policy in China based on 7-years consecutive observations

Understanding the aerosol vertical characterization is of great importance to both climate and atmospheric environment. This study investigated the variations of aerosol profiles over eight regions of interest in China after clean air policy (2013-2019) and discussed the drivers of the vertical aerosol structure, using observations from active satellite measurements (CALIPSO). From the annual variation, the amplitude of extinction coefficient profiles showed a decreasing trend with fluctuations, and the maximum was 0.21 km^-1 in Beijing-Tianjin-Hebei (JJJ). For regions suffered from air pollution, the variation was greatest below 0.45 km, while it was between 1-1.5 km for Sichuan Basin. The correlation coefficient between the relative humidity (RH) and the extinction coefficient indicated that the increase of RH inhibited the decrease of the extinction coefficient in the Yangtze River Delta. In most regions, the main aerosol subtypes were polluted dust and polluted continental, but they were coarser in JJJ and North West. The frequency of concurrency of dust and polluted dust aerosols decreased in JJJ, but polluted continental aerosols occurred more frequently. Further, the aerosol extinction coefficient profiles under different pollution conditions showed that it changed most during heavy pollution periods in JJJ, especially in 2017, with a significant aerosol loading between ∼700 and 1200 m. The atmospheric reanalysis data revealed that the weak convergence at low level and the divergence at high level supported the upward transport of aerosols in 2017. Overall, the differences in divergence allocation, RH, and wind filed were the main meteorological drivers.

Method for retrieving range-resolved aerosol microphysical properties from polarization lidar measurements

Aerosol microphysical properties, such as volume concentration (VC) and effective radius (ER), are of great importance to evaluate their radiative forcing and impacts on climate change. However, range-resolved aerosol VC and ER still cannot be obtained by remote sensing currently except for the column-integrated one from sun-photometer observation. In this study, a retrieval method of range-resolved aerosol VC and ER is firstly proposed based on the partial least squares regression (PLSR) and deep neural networks (DNN), combining polarization lidar and collocated AERONET (AErosol RObotic NETwork) sun-photometer observations. The results show that the measurement of widely-used polarization lidar can be reasonably used to derive the aerosol VC and ER, with the determination coefficient (R²) of 0.89 (0.77) for VC (ER) by use of the DNN method. Moreover, it is proven that the lidar-based height-resolved VC and ER at near-surface are well consistent with independent observations of collocated Aerodynamic Particle Sizer (APS). Additionally, we found that there are significant diurnal and seasonal variations of aerosol VC and ER in the atmosphere at Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL). Compared with columnar ones from the sun-photometer observation, this study provides a reliable and practical way to obtain full-day range-resolved aerosol VC and ER from widely-used polarization lidar observation, even under cloud conditions. Moreover, this study also can be applied to long-term observations by current ground-based lidar networks and spaceborne CALIPSO lidar, aiming to further evaluate aerosol climatic effects more accurately.

Passive versus Active Transport of Saharan Dust Aerosols by African Easterly Waves

Theory and modeling are combined to reveal the physical and dynamical processes that control Saharan dust transport by amplifying African easterly waves (AEWs). Two cases are examined: active transport, in which the dust is radiatively coupled to the circulation; passive transport, in which the dust is radiatively decoupled from the circulation. The theory is built around a dust conservation equation for dust-coupled AEWs in zonal-mean African easterly jets. The theory predicts that, for both the passive and active cases, the dust transports will be largest where the zonal-mean dust gradients are maximized on an AEW critical surface. Whether the dust transports are largest for the radiatively passive or radiatively active case depends on the growth rate of the AEWs, which is modulated by the dust heating. The theoretical predictions are confirmed via experiments carried out with the Weather Research and Forecasting model, which is coupled to a dust conservation equation. The experiments show that the meridional dust transports dominate in the passive case, while the vertical dust transports dominate in the active case.

Improved representation of the global dust cycle using observational constraints on dust properties and abundance

Even though desert dust is the most abundant aerosol by mass in Earth's atmosphere, atmospheric models struggle to accurately represent its spatial and temporal distribution. These model errors are partially caused by fundamental difficulties in simulating dust emission in coarse-resolution models and in accurately representing dust microphysical properties. Here we mitigate these problems by developing a new methodology that yields an improved representation of the global dust cycle. We present an analytical framework that uses inverse modeling to integrate an ensemble of global model simulations with observational constraints on the dust size distribution, extinction efficiency, and regional dust aerosol optical depth. We then compare the inverse model results against independent measurements of dust surface concentration and deposition flux and find that errors are reduced by approximately a factor of two relative to current model simulations of the Northern Hemisphere dust cycle. The inverse model results show smaller improvements in the less dusty Southern Hemisphere, most likely because both the model simulations and the observational constraints used in the inverse model are less accurate. On a global basis, we find that the emission flux of dust with geometric diameter up to 20 μm (PM20) is approximately 5,000 Tg/year, which is greater than most models account for. This larger PM20 dust flux is needed to match observational constraints showing a large atmospheric loading of coarse dust. We obtain gridded data sets of dust emission, vertically integrated loading, dust aerosol optical depth, (surface) concentration, and wet and dry deposition fluxes that are resolved by season and particle size. As our results indicate that this data set is more accurate than current model simulations and the MERRA-2 dust reanalysis product, it can be used to improve quantifications of dust impacts on the Earth system.

Aerosol and Cloud Detection Using Machine Learning Algorithms and Space-Based Lidar Data

Clouds and aerosols play a significant role in determining the overall atmospheric radiation budget, yet remain a key uncertainty in understanding and predicting the future climate system. In addition to their impact on the Earth’s climate system, aerosols from volcanic eruptions, wildfires, man-made pollution events and dust storms are hazardous to aviation safety and human health. Space-based lidar systems provide critical information about the vertical distributions of clouds and aerosols that greatly improve our understanding of the climate system. However, daytime data from backscatter lidars, such as the Cloud-Aerosol Transport System (CATS) on the International Space Station (ISS), must be averaged during science processing at the expense of spatial resolution to obtain sufficient signal-to-noise ratio (SNR) for accurately detecting atmospheric features. For example, 50% of all atmospheric features reported in daytime operational CATS data products require averaging to 60 km for detection. Furthermore, the single-wavelength nature of the CATS primary operation mode makes accurately typing these features challenging in complex scenes. This paper presents machine learning (ML) techniques that, when applied to CATS data, (1) increased the 1064 nm SNR by 75%, (2) increased the number of layers detected (any resolution) by 30%, and (3) enabled detection of 40% more atmospheric features during daytime operations at a horizontal resolution of 5 km compared to the 60 km horizontal resolution often required for daytime CATS operational data products. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime.

Improving the estimating accuracy of extinction coefficient of surface aerosol with a new layer-resolved model in China

The aerosol extinction coefficient was an important factor for air quality. To estimate the aerosol extinction levels, widely used pure statistical models are generally not based on aerosol vertical structures. In this study, we estimated large-scale aerosol extinction coefficients by developing a new layer-resolved model with explicit inference for aerosol vertical distribution. The CALIOP aerosol profile, MODIS AOD and reanalysis boundary layer height data are used. The layer-resolved model was formulated by developing an explicit, steady and straightforward relationship between aerosol within boundary layer and corresponding AOD values. The estimated surface extinction coefficient from this model was compared against the values derived from station visibility observations in China in 2016. The results revealed that our model had outperformed the traditional one-layer model and the simplified two-layer model. Specifically, the numbers of ground stations with an NME value < 0.4 are enhanced by a percentage > 100%, with the NME values significantly decreased from 46%, 48% to 36% and RMSE values from 0.27, 0.25 to 0.21 km^-1. Our model is easy for operational implementation thanks to its clear structure and input, and also informative to understand aerosol vertical distributions. Furthermore, this work will also be beneficial to air quality modeling studies to improve accuracy estimating ground-level PM_2.5 concentrations.

Vertical distribution of the Asian tropopause aerosols detected by CALIPSO

Characterizing the vertical distribution of aerosol optical properties is crucial to reduce the uncertainty in quantifying the radiative forcing and climate effects of aerosols. The analysis of four-year (2007-2010) Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar measurements revealed the existence of tropospheric aerosol layers associated with the Asian summer monsoon. The measurements of five typical aerosol optical and microphysical parameters were used to explore the properties, spatial/vertical distributions, annual evolution of tropopause aerosols over the South Asia region. Results extracted from various latitude-height and longitude-height cross sections of aerosol extinction coefficient at 532 and 1064 nm, backscatter coefficient at 532 nm, and depolarization ratio at 532 nm demonstrated that a large amount of aerosols vertically extended up to the tropopause (12 km) during the monsoon season over the north Arabian Sea, India, north Bay of Bengal, and equatorial Indian Ocean, finally reaching the southeast of the Tibetan Plateau. Convective transport associated with Asian summer monsoon is an important factor controlling the vertical distribution of tropopause aerosols. The evolution of aerosol scattering ratio at 532 nm indicated that from equatorial Indian Ocean to South Asia, there exists an upward tilting and ascending structure of the aerosols layer during the monsoon season, which typically indicates enhanced aerosols over the Asian monsoon region. Information on aerosol size distribution and detailed composition are needed for better understanding the nature and origin of this aerosol layer. Enhancement of the tropopause aerosols should be considered in the future studies in evaluating the regional or global climate systems. Further satellite observations of aerosols and in-situ observations are also urgently needed to diagnose this aerosol layer, which likely originate from anthropogenic emissions.

Saharan Dust Transport during the Incipient Growth Phase of African Easterly Waves

An analytical analysis is combined with numerical modeling simulations in order to expose the physical and dynamical processes that control the zonal-mean transport of Saharan mineral dust aerosols during the incipient growth phase of African easterly waves. The analytical analysis provides the theoretical basis for understanding and predicting how the waves and background flow combine to affect the zonal-mean eddy transports of dust. The analytically derived transport equations―which are valid for any wave field, irrespective of its spatial or temporal scale―predict that the eddy transports of dust are largest where the maximum in the background dust gradients coincide with a critical surface, i.e., where the Doppler-shifted frequency of the wave field vanishes. Linear simulations of the eddy dust transports are conducted using a mechanistic version of the Weather Research and Forecasting (WRF) model coupled to an interactive dust model. The simulations show that the eddy dust transports are directed down the background dust gradients and that the meridional transports of dust dominate over the vertical transports. The numerical simulations confirm the theoretical predictions. The predictions are used to explain recent statistical analyses of reanalysis data for dust-coupled African easterly waves.

Modeling Dust in East Asia by CESM and Sources of Biases

East Asian dust has a significant impact on regional and global climate. In this study, we evaluate the spatial distributions and temporal variations of dust extinction profiles and dust optical depth (DOD) over East Asia simulated from the Community Earth System Model (CESM) with satellite retrievals from Luo et al. (2015a, 2015b) (L15), Yu et al. (2015) (Y15), and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) level 3 (CAL-L3) products. Both L15 and Y15 are based on CALIPSO products but use different algorithms to separate dust from non-dust aerosols. We find high model biases of dust extinction in the upper troposphere over the Taklamakan Desert, Gobi Desert, and Tibetan Plateau, especially in the summer (June-July-August, JJA). CESM with dust emission scheme of Kok et al. (2014a, 2014b) has the best agreement with dust extinction profiles and DOD from L15 in the Taklamakan Desert and Tibetan Plateau. CESM with the default dust emission scheme of Zender et al. (2003a) underpredicts DOD in the Tibetan Plateau compared with observations from L15 due to the underestimation of local dust emission. Large uncertainties exist in observations from L15, Y15, and CAL-L3 and have significant impacts on the model evaluation of dust spatial distributions. We also assess dust surface concentrations and 10 m wind speed with meteorological records from weather stations in the Taklamakan and Gobi Deserts during dust events. CESM underestimates dust surface concentrations at most weather stations due to the inability of CESM to capture strong surface wind events.

Estimates of African Dust Deposition Along the Trans-Atlantic Transit Using the Decade-long Record of Aerosol Measurements from CALIOP, MODIS, MISR, and IASI

Deposition of mineral dust into ocean fertilizes ecosystems and influences biogeochemical cycles and climate. In-situ observations of dust deposition are scarce, and model simulations depend on the highly parameterized representations of dust processes with few constraints. By taking advantage of satellites' routine sampling on global and decadal scales, we estimate African dust deposition flux and loss frequency (LF, a ratio of deposition flux to mass loading) along the trans-Atlantic transit using the three-dimensional distributions of aerosol retrieved by spaceborne lidar (CALIOP) and radiometers (MODIS, MISR, and IASI). On the basis of a ten-year (2007-2016) and basin scale average, the amount of dust deposition into the tropical Atlantic Ocean is estimated at 136 - 222 Tg yr-1. The 65-83% of satellite-based estimates agree with the in-situ climatology within a factor of 2. The magnitudes of dust deposition are highest in boreal summer and lowest in fall, whereas the interannual variability as measured by the normalized standard deviation with mean is largest in spring (28-41%) and smallest (7-15%) in summer. The dust deposition displays high spatial heterogeneity, revealing that the meridional shifts of major dust deposition belts are modulated by the seasonal migration of the intertropical convergence zone (ITCZ). On the basis of the annual and basin mean, the dust LF derived from the satellite observations ranges from 0.078 to 0.100 d-1, which is lower than model simulations by up to factors of 2 to 5. The most efficient loss of dust occurs in winter, consistent with the higher possibility of low-altitude transported dust in southern trajectories being intercepted by rainfall associated with the ITCZ. The satellite-based estimates of dust deposition can be used to fill the geographical gaps and extend time span of in-situ measurements, study the dust-ocean interactions, and evaluate model simulations of dust processes.

Identification of pre-microRNAs by characterizing their sequence order evolution information and secondary structure graphs

BACKGROUND

Distinction between pre-microRNAs (precursor microRNAs) and length-similar pseudo pre-microRNAs can reveal more about the regulatory mechanism of RNA biological processes. Machine learning techniques have been widely applied to deal with this challenging problem. However, most of them mainly focus on secondary structure information of pre-microRNAs, while ignoring sequence-order information and sequence evolution information.

RESULTS

We use new features for the machine learning algorithms to improve the classification performance by characterizing both sequence order evolution information and secondary structure graphs. We developed three steps to extract these features of pre-microRNAs. We first extract features from PSI-BLAST profiles and Hilbert-Huang transforms, which contain rich sequence evolution information and sequence-order information respectively. We then obtain properties of small molecular networks of pre-microRNAs, which contain refined secondary structure information. These structural features are carefully generated so that they can depict both global and local characteristics of pre-microRNAs. In total, our feature space covers 591 features. The maximum relevance and minimum redundancy (mRMR) feature selection method is adopted before support vector machine (SVM) is applied as our classifier. The constructed classification model is named MicroRNA -NHPred. The performance of MicroRNA -NHPred is high and stable, which is better than that of those state-of-the-art methods, achieving an accuracy of up to 94.83% on same benchmark datasets.

CONCLUSIONS

The high prediction accuracy achieved by our proposed method is attributed to the design of a comprehensive feature set on the sequences and secondary structures, which are capable of characterizing the sequence evolution information and sequence-order information, and global and local information of pre-microRNAs secondary structures. MicroRNA -NHPred is a valuable method for pre-microRNAs identification. The source codes of our method can be downloaded from https://github.com/myl446/MicroRNA-NHPred .

Intra-annual variations of regional aerosol optical depth, vertical distribution, and particle types from multiple satellite and ground-based observational datasets

The climatic and health effects of aerosols are strongly dependent on the intra-annual variations in their loading and properties. While the seasonal variations of regional aerosol optical depth (AOD) have been extensively studied, understanding the temporal variations in aerosol vertical distribution and particle types is also important for an accurate estimate of aerosol climatic effects. In this paper, we combine the observations from four satellite-borne sensors and several ground-based networks to investigate the seasonal variations of aerosol column loading, vertical distribution, and particle types over three populous regions: the Eastern United States (EUS), Western Europe (WEU), and Eastern and Central China (ECC). In all three regions, column AOD, as well as AOD at heights above 800 m, peaks in summer/spring, probably due to accelerated formation of secondary aerosols and hygroscopic growth. In contrast, AOD below 800m peaks in winter over WEU and ECC regions because more aerosols are confined to lower heights due to the weaker vertical mixing. In the EUS region, AOD below 800m shows two maximums, one in summer and the other in winter. The temporal trends in low-level AOD are consistent with those in surface fine particle (PM2.5) concentrations. AOD due to fine particles (< 0.7 μm diameter) is much larger in spring/summer than in winter over all three regions. However, the coarse mode AOD (> 1.4 μm diameter), generally shows small variability, except that a peak occurs in spring in the ECC region due to the prevalence of airborne dust during this season. When aerosols are classified according to sources, the dominant type is associated with anthropogenic air pollution, which has a similar seasonal pattern as total AOD. Dust and sea-spray aerosols in the WEU region peak in summer and winter, respectively, but do not show an obvious seasonal pattern in the EUS region. Smoke aerosols, as well as absorbing aerosols, present an obvious unimodal distribution with a maximum occurring in summer over the EUS and WEU regions, whereas they follow a bimodal distribution with peaks in August and March (due to crop residue burning) over the ECC region.

Comparison of AOD from CALIPSO, MODIS, and Sun Photometer under Different Conditions over Central China

Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) provides three-dimensional information on aerosol optical properties across the globe. However, the performance of CALIPSO aerosol optical depth (AOD) products under different air quality conditions remains unclear. In this research, three years of CALIPSO level 2 AOD data (November 2013 to December 2017) were employed to compare with the Moderate Resolution Imaging Spectroradiometer (MODIS) level 2 columnar AOD products and ground-based sun photometer measurements for the same time period. To investigate the effect of air quality on AODs retrieved from CALIPSO, the AODs obtained from CALIPSO, MODIS, and sun photometer were inter-compared under different air quality conditions over Wuhan and Dengfeng. The average absolute bias of AOD between CALIPSO and sun photometer was 0.22 ± 0.21, 0.11 ± 0.07, and 0.14 ± 0.13 under clean, moderate, and polluted weather, respectively. The result indicates that the CALIPSO AOD were more reliable under moderate and polluted days. Moreover, the deviation of AOD between CALIPSO and sun photometer was largest (0.23 ± 0.21) in the autumn season, and lowest (0.13 ± 0.12) in the winter season. The results show that CALIPSO AOD products were more applicable to regions and seasons with high aerosol concentrations.

CALIPSO lidar level 3 aerosol profile product: version 3 algorithm design

The CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) level 3 aerosol profile product reports globally gridded, quality-screened, monthly mean aerosol extinction profiles retrieved by CALIOP (the Cloud-Aerosol Lidar with Orthogonal Polarization). This paper describes the quality screening and averaging methods used to generate the version 3 product. The fundamental input data are CALIOP level 2 aerosol extinction profiles and layer classification information (aerosol, cloud, and clear-air). Prior to aggregation, the extinction profiles are quality-screened by a series of filters to reduce the impact of layer detection errors, layer classification errors, extinction retrieval errors, and biases due to an intermittent signal anomaly at the surface. The relative influence of these filters are compared in terms of sample rejection frequency, mean extinction, and mean aerosol optical depth (AOD). The "extinction QC flag" filter is the most influential in preventing high-biases in level 3 mean extinction, while the "misclassified cirrus fringe" filter is most aggressive at rejecting cirrus misclassified as aerosol. The impact of quality screening on monthly mean aerosol extinction is investigated globally and regionally. After applying quality filters, the level 3 algorithm calculates monthly mean AOD by vertically integrating the monthly mean quality-screened aerosol extinction profile. Calculating monthly mean AOD by integrating the monthly mean extinction profile prevents a low bias that would result from alternately integrating the set of extinction profiles first and then averaging the resultant AOD values together. Ultimately, the quality filters reduce level 3 mean AOD by -24 and -31% for global ocean and global land, respectively, indicating the importance of quality screening.

Trends of absorption, scattering and total aerosol optical depths over India and surrounding oceanic regions from satellite observations: role of local production, transport and atmospheric dynamics

The study examines trends of scattering, absorption and total aerosol optical depths (SAOD, AAOD and AOD) over India and surrounding oceanic regions and explores role of local production, long-range transport and atmospheric dynamics on observed trends. Long-term satellite observations are used to estimate trends and assess their statistical significance. Significant spatial and seasonal changes are observed in trends of SAOD, AAOD and AOD. AOD is observed to be increasing during post monsoon and winter over most of the land mass and surrounding oceanic regions, whereas decreasing trends over land and increasing trends over oceanic regions are observed in pre-monsoon and summer months. In general, SAOD and AAOD show similar trends (if there is any) as that of AOD over most of the regions in most of the months. Strongest positive trends over land regions are observed in November with trend of AOD greater than 0.01 year^-1, especially over Indo-Gangetic Plain (IGP). Increase of AOD over IGP in post monsoon is contributed significantly by absorbing aerosols with rate of increase ~ 0.005 AAOD year^-1. AAODs are observed to be increasing over Arabian Sea and Bay of Bengal (BoB) in December also, with rate ~ 0.003 AAOD year^-1. Strongest positive trends over Arabian Sea and BoB are observed in June with rate of increase greater than 0.02 AOD year^-1, whereas strong negative trends are observed over north-west India in the same period with rate of decrease greater than 0.02 AOD year^-1. Over IGP, AOD, AAOD and SAOD show contrasting trends in winter and summer seasons. AAOD exhibits strongest decreasing trend over IGP during April-June. Positive trends of AOD over Arabian Sea and BoB are favoured significantly by changes in circulation dynamics. Atmospheric convergence is observed to be strengthening over these regions in April and June, leading to more accumulation and hence positive trends of AOD. Aerosol transport over to the Arabian Sea is observed to be enhancing and contributing significantly to AOD increase over the Arabian Sea in pre-monsoon and summer months. Enhancement in aerosol transport over to the Arabian Sea is observed in pre-monsoon at higher altitudes above 3 km, whereas it is observed in summer at lower levels. However, decreasing trends of AOD over north-west India and IGP during pre-monsoon and summer are observed to be due to decrease in aerosol transport from the continental regions at the west.

Ambient PM2.5 exposure and expected premature mortality to 2100 in India under climate change scenarios

Premature mortality from current ambient fine particulate (PM_2.5) exposure in India is large, but the trend under climate change is unclear. Here we estimate ambient PM_2.5 exposure up to 2100 by applying the relative changes in PM_2.5 from baseline period (2001-2005) derived from Coupled Model Inter-comparison Project 5 (CMIP5) models to the satellite-derived baseline PM_2.5. We then project the mortality burden using socioeconomic and demographic projections in the Shared Socioeconomic Pathway (SSP) scenarios. Ambient PM_2.5 exposure is expected to peak in 2030 under the RCP4.5 and in 2040 under the RCP8.5 scenario. Premature mortality burden is expected to be 2.4-4 and 28.5-38.8% higher under RCP8.5 scenario relative to the RCP4.5 scenario in 2031-2040 and 2091-2100, respectively. Improved health conditions due to economic growth are expected to compensate for the impact of changes in population and age distribution, leading to a reduction in per capita health burden from PM_2.5 for all scenarios except the combination of RCP8.5 exposure and SSP3.

Characteristics of air pollution in different zones of Sichuan Basin, China

Sichuan Basin, located in southwest China, has been ranked as the fourth of heavily air polluted regions in China partly due to its deep mountain-basin topography. However, spatial-temporal distribution of air pollution over the basin is still unclear due to the lack of monitoring data and poor knowledge. Since January 2015, six criteria air pollutants began to be monitored in 20 cities across the basin. The measured data enable us to analyze the basin-wide spatial-temporal distribution characteristics of these air pollutants. Results revealed heavy air pollution in the bottom zone, medium in the slope zone, and light pollution in the edge zone of the Basin in terms of the altitudes of air quality monitoring stations across the Basin. The average concentrations of PM_2.5 and PM₁₀ were 55.87μg/m³ and 86.49μg/m³ in the bottom, 33.76μg/m³ and 63.33μg/m³ in the slope, and 19.71μg/m³ and 35.06μg/m³ in the edge, respectively. In the bottom and slope of the basin, high PM_2.5 concentration events occurred most frequently in winter_. While in summer, ozone became primary pollutant. Among the six air pollutants, concentrations of PM_2.5 and PM₁₀ decrease dramatically with increasing altitude which was fitted by a nonlinear relationship between particulate matter (PM) concentrations and altitude. This relationship was validated by extinction coefficient profiles from CALIPSO observations and EV-lidar data, and hence used to reflect vertical distribution of air PM concentrations. It has been found that the thickness of higher PM concentrations is less than 500m in the basin. In the bottom of the basin, PM concentrations exhibited stronger horizontal homogeneities as compared with those in the North China Plain and Yangtze River Delta. However, gaseous pollutants seemed not to show clear relationships between their concentrations and altitudes in the basin. Their horizontal homogeneities were less significant compared to PM.

The CALIPSO Version 4 Automated Aerosol Classification and Lidar Ratio Selection Algorithm

The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) version 4.10 (V4) level 2 aerosol data products, released in November 2016, include substantial improvements to the aerosol subtyping and lidar ratio selection algorithms. These improvements are described along with resulting changes in aerosol optical depth (AOD). The most fundamental change in V4 level 2 aerosol products is a new algorithm to identify aerosol subtypes in the stratosphere. Four aerosol subtypes are introduced for the stratospheric aerosols: polar stratospheric aerosol (PSA), volcanic ash, sulfate/other, and smoke. The tropospheric aerosol subtyping algorithm was also improved by adding the following enhancements: (1) all aerosol subtypes are now allowed over polar regions, whereas the version 3 (V3) algorithm allowed only clean continental and polluted continental aerosols; (2) a new "dusty marine" aerosol subtype is introduced, representing mixtures of dust and marine aerosols near the ocean surface; and (3) the "polluted continental" and "smoke" subtypes have been renamed "polluted continental/smoke" and "elevated smoke", respectively. V4 also revises the lidar ratios for clean marine, dust, clean continental, and elevated smoke subtypes. As a consequence of the V4 updates, the mean 532 nm AOD retrieved by CALIOP has increased by 0.044 (0.036) or 52 % (40 %) for nighttime (daytime). Lidar ratio revisions are the most influential factor for AOD changes from V3 to V4, especially for cloud-free skies. Preliminary validation studies show that the AOD discrepancies between CALIOP and AERONET/MODIS (ocean) are reduced in V4 compared to V3.

The CALIPSO Version 4 Automated Aerosol Classification and Lidar Ratio Selection Algorithm

The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) version 4.10 (V4) level 2 aerosol data products, released in November 2016, include substantial improvements to the aerosol subtyping and lidar ratio selection algorithms. These improvements are described along with resulting changes in aerosol optical depth (AOD). The most fundamental change in V4 level 2 aerosol products is a new algorithm to identify aerosol subtypes in the stratosphere. Four aerosol subtypes are introduced for the stratospheric aerosols: polar stratospheric aerosol (PSA), volcanic ash, sulfate/other, and smoke. The tropospheric aerosol subtyping algorithm was also improved by adding the following enhancements: (1) all aerosol subtypes are now allowed over polar regions, whereas the version 3 (V3) algorithm allowed only clean continental and polluted continental aerosols; (2) a new "dusty marine" aerosol subtype is introduced, representing mixtures of dust and marine aerosols near the ocean surface; and (3) the "polluted continental" and "smoke" subtypes have been renamed "polluted continental/smoke" and "elevated smoke", respectively. V4 also revises the lidar ratios for clean marine, dust, clean continental, and elevated smoke subtypes. As a consequence of the V4 updates, the mean 532 nm AOD retrieved by CALIOP has increased by 0.044 (0.036) or 52 % (40 %) for nighttime (daytime). Lidar ratio revisions are the most influential factor for AOD changes from V3 to V4, especially for cloud-free skies. Preliminary validation studies show that the AOD discrepancies between CALIOP and AERONET/MODIS (ocean) are reduced in V4 compared to V3.

Minimum aerosol layer detection sensitivities and their subsequent impacts on aerosol optical thickness retrievals in CALIPSO level 2 data products

Due to instrument sensitivities and algorithm detection limits, level 2 (L2) Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) 532nm aerosol extinction profile retrievals are often populated with retrieval fill values (RFVs), which indicate the absence of detectable levels of aerosol within the profile. In this study, using 4 years (2007-2008 and 2010-2011) of CALIOP version 3 L2 aerosol data, the occurrence frequency of daytime CALIOP profiles containing all RFVs (all-RFV profiles) is studied. In the CALIOP data products, the aerosol optical thickness (AOT) of any all-RFV profile is reported as being zero, which may introduce a bias in CALIOP-based AOT climatologies. For this study, we derive revised estimates of AOT for all-RFV profiles using collocated Moderate Resolution Imaging Spectroradiometer (MODIS) Dark Target (DT) and, where available, AErosol RObotic NEtwork (AERONET) data. Globally, all-RFV profiles comprise roughly 71% of all daytime CALIOP L2 aerosol profiles (i.e., including completely attenuated profiles), accounting for nearly half (45 %) of all daytime cloud-free L2 aerosol profiles. The mean collocated MODIS DT (AERONET) 550 nm AOT is found to be near 0.06 (0.08) for CALIOP all-RFV profiles. We further estimate a global mean aerosol extinction profile, a so-called "noise floor", for CALIOP all-RFV profiles. The global mean CALIOP AOT is then recomputed by replacing RFV values with the derived noise-floor values for both all-RFV and non-all-RFV profiles. This process yields an improvement in the agreement of CALIOP and MODIS over-ocean AOT.

Passive remote sensing of altitude and optical depth of dust plumes using the oxygen A and B bands: first results from EPIC/DSCOVR at Lagrange-1 point

We presented an algorithm for inferring aerosol layer height (ALH) and optical depth (AOD) over ocean surface from radiances in oxygen A and B bands measured by the Earth Polychromatic Imaging Camera (EPIC) on the Deep Space Climate Observatory orbiting at Lagrangian-1 point. The algorithm was applied to EPIC imagery of a two-day dust outbreak over the North Atlantic Ocean. Retrieved ALHs and AODs were evaluated against counterparts observed by Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Moderate Resolution Imaging Spectroradiometer (MODIS), and Aerosol Robotic Network. The comparisons showed 71.5% of EPIC-retrieved ALHs were within ±0.5 km of those determined from CALIOP and 74.4% of EPIC AOD retrievals fell within a ±(0.1+10%) envelope of MODIS retrievals. This study demonstrates the potential of EPIC measurements for retrieving global aerosol height multiple times daily, which are essential for evaluating aerosol profile simulated in climate models and for better estimating aerosol radiative effects.

Evaluation of the aerosol vertical distribution in global aerosol models through comparison against CALIOP measurements: AeroCom phase II results

The ability of 11 models in simulating the aerosol vertical distribution from regional to global scales, as part of the second phase of the AeroCom model intercomparison initiative (AeroCom II), is assessed and compared to results of the first phase. The evaluation is performed using a global monthly gridded data set of aerosol extinction profiles built for this purpose from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) Layer Product 3.01. Results over 12 subcontinental regions show that five models improved, whereas three degraded in reproducing the interregional variability in Z _{α0-6 km}, the mean extinction height diagnostic, as computed from the CALIOP aerosol profiles over the 0-6 km altitude range for each studied region and season. While the models' performance remains highly variable, the simulation of the timing of the Z _{α0-6 km} peak season has also improved for all but two models from AeroCom Phase I to Phase II. The biases in Z _{α0-6 km} are smaller in all regions except Central Atlantic, East Asia, and North and South Africa. Most of the models now underestimate Z _{α0-6 km} over land, notably in the dust and biomass burning regions in Asia and Africa. At global scale, the AeroCom II models better reproduce the Z _{α0-6 km} latitudinal variability over ocean than over land. Hypotheses for the performance and evolution of the individual models and for the intermodel diversity are discussed. We also provide an analysis of the CALIOP limitations and uncertainties contributing to the differences between the simulations and observations.

Multifractal analysis of weighted networks by a modified sandbox algorithm

Complex networks have attracted growing attention in many fields. As a generalization of fractal analysis, multifractal analysis (MFA) is a useful way to systematically describe the spatial heterogeneity of both theoretical and experimental fractal patterns. Some algorithms for MFA of unweighted complex networks have been proposed in the past a few years, including the sandbox (SB) algorithm recently employed by our group. In this paper, a modified SB algorithm (we call it SBw algorithm) is proposed for MFA of weighted networks. First, we use the SBw algorithm to study the multifractal property of two families of weighted fractal networks (WFNs): "Sierpinski" WFNs and "Cantor dust" WFNs. We also discuss how the fractal dimension and generalized fractal dimensions change with the edge-weights of the WFN. From the comparison between the theoretical and numerical fractal dimensions of these networks, we can find that the proposed SBw algorithm is efficient and feasible for MFA of weighted networks. Then, we apply the SBw algorithm to study multifractal properties of some real weighted networks - collaboration networks. It is found that the multifractality exists in these weighted networks, and is affected by their edge-weights.

Interactions between biomass-burning aerosols and clouds over Southeast Asia: current status, challenges, and perspectives

The interactions between aerosols, clouds, and precipitation remain among the largest sources of uncertainty in the Earth's energy budget. Biomass-burning aerosols are a key feature of the global aerosol system, with significant annually-repeating fires in several parts of the world, including Southeast Asia (SEA). SEA in particular provides a "natural laboratory" for these studies, as smoke travels from source regions downwind in which it is coupled to persistent stratocumulus decks. However, SEA has been under-exploited for these studies. This review summarizes previous related field campaigns in SEA, with a focus on the ongoing Seven South East Asian Studies (7-SEAS) and results from the most recent BASELInE deployment. Progress from remote sensing and modeling studies, along with the challenges faced for these studies, are also discussed. We suggest that improvements to our knowledge of these aerosol/cloud effects require the synergistic use of field measurements with remote sensing and modeling tools.