Cloud-Aerosol Lidar And Infrared Pathfinder Satellite Observations Research Articles

A global view on stratospheric ice clouds: assessment of processes related to their occurrence based on satellite observations

Abstract. Ice clouds play an important role in regulating water vapor and influencing the radiative budget in the atmosphere. This study investigates stratospheric ice clouds (SICs) in the latitude range between ±60∘ based on the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). As polar stratospheric clouds include other particles, they are not discussed in this work. Tropopause temperature, double tropopauses, clouds in the upper troposphere and lower stratosphere (UTLS), gravity waves, and stratospheric aerosols are analyzed to investigate their relationships with the occurrence of and variability in SICs in the tropics and at midlatitudes. We found that SICs with cloud-top heights of 250 m above the first lapse rate tropopause are mainly detected in the tropics. Monthly time series of SICs from 2007 to 2019 show that high occurrence frequencies of SICs follow the Intertropical Convergence Zone (ITCZ) over time in the tropics and that SICs vary interannually at different latitudes. Results show that SICs associated with double tropopauses, which are related to poleward isentropic transport, are mostly found at midlatitudes. More than 80 % of the SICs around 30∘ N/S are associated with double tropopauses. Correlation coefficients of SICs and all the other abovementioned processes confirm that the occurrence of and variability in SICs are mainly associated with the tropopause temperature in the tropics and at midlatitudes. UTLS clouds, which are retrieved from the Atmospheric Infrared Sounder (AIRS) and used as a proxy for deep convection in the tropics and high-altitude ice cloud sources at midlatitudes, have the highest correlations with SICs in the monsoon regions and the central United States. Gravity waves are mostly related to SICs at midlatitudes, especially over Patagonia and the Drake Passage. However, the second-highest correlation coefficients show that the cold tropopause temperature, the occurrence of double tropopauses, high stratospheric aerosol loading, frequent UTLS clouds, and gravity waves are highly correlated with the SICs locally. The long-term anomaly analyses show that interannual anomalies of SICs are correlated with the tropopause temperature and stratospheric aerosols instead of the UTLS clouds and gravity waves. The overlapping and similar correlation coefficients between SICs and all processes mentioned above indicate strong associations between those processes themselves. Due to their high inherent correlations, it is challenging to disentangle and evaluate their contributions to the occurrence of SICs on a global scale. However, the correlation coefficient analyses between SICs and all abovementioned processes (tropopause temperature, double tropopauses, clouds in the upper troposphere and lower stratosphere (UTLS), gravity waves, and stratospheric aerosols) in this study help us better understand the sources of SICs on a global scale.

A characterization of clouds over the Great Barrier Reef and the role of local forcing

AbstractA characterization of cloud properties associated with precipitation in the region around the Great Barrier Reef (GBR) is constructed using decade‐long (2007–2017) satellite observations from Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) combined with CloudSat. The spatial and vertical distributions of low‐level cloud properties over the region are also investigated and discussed. In general, different cloud types are observed over different regions and vary by season: high clouds are dominant over the low latitudes in summer, altocumulus are mainly observed over northeast Queensland and low‐level clouds are dominant over the ocean and coast during the winter months at higher latitudes under a trade wind regime. A strong latitudinal dependence of total precipitation across the Greater GBR region is identified with a significant orographic enhancement near Cairns in the wet tropics. MODIS and CloudSat‐CALIPSO cloud observations show good agreement on significant differences in low‐level cloud microphysical properties between the land and the ocean. The largest land–ocean differences in warm cloud microphysical properties are found over the mid‐latitudes near 18°S, which is strongly associated with orographic forcing, with these enhancements extending further east to the coral reef area. However, the frequency of warm cloud is not enhanced upwind of the mountains in the wet tropics. In addition, no significant differences in warm cloud properties between the actual GBR and the open ocean are identified. These results suggest that low‐level clouds over the GBR do not show any significant response to the reef‐related microphysical perturbations.

Investigation of aerosol optical properties and pollutant transmission mechanisms in Qinhuangdao, North China – A case study during a pollution event

In this study, the aerosol optical properties, vertical distributions, and pollutant transmission characteristics were analyzed using multi-source data during a pollution event that occurred in March 2019 in Qinhuangdao (QHD), China. During this event, the air pollutant (PM, SO2, NO2, and CO) concentrations increased by 100 μgm−3, 10 μgm−3, 40 μgm−3, and 0.7 mgm−3, respectively. The key meteorological factors were investigated and are closely related to the variations in the concentration of the pollutant. The aerosol optical depth at 500 (AOD500) measured using the Cimel Sunphotometer reached 1.6 on polluted days. Also, the water vapor content increased from 0.55 to 1.25 cm. The result of scatter plots between AOD and the Ångström exponent (AE) indicates that the primary pollutants were fine particles and small amounts of dust, showing the characteristics of mixed pollution. Furthermore, the results of the wind field and backward trajectory indicate that the pollutants with high concentrations originated from the central and northern parts of China, including Henan, Hebei, and Shandong provinces. It is revealed from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) that the pollutants were mainly mixed aerosols in the source area, and this is consistent with the results obtained from the ground-based Sunphotometer. The study could provide a reference for understanding the pollution characteristics and pollutants transmission mechanism in Northeast China.

Development and Evaluation of AMSU-A Cloud Detection over the Tibetan Plateau

Advanced Microwave Sounding Unit-A (AMSU-A) and Microwave Humidity Sounder (MHS) data have been widely assimilated in operational forecasting systems. However, effective distinction between cloudy and clear-sky data is still an essential prerequisite for the assimilation of microwave observations. Cloud detection over the Tibetan Plateau has long been a challenge owing to the influence of low temperatures, terrain height, surface vegetation, and inaccurate background fields. Based on the variations in the response characteristics of different channels of AMSU-A to clouds, five AMSU-A window and low-peaking channels (channels 1–4 and 15) are chosen to establish a cloud detection index. Combined with the existing MHS cloud detection index, a cloud detection scheme over the Tibetan Plateau is proposed. Referring to VISSR-II (Stretched Visible and Infrared Spin Scan Radiometer-II) and CALIPSO (The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) cloud classification products, the detection rate of cloudy data and the rejection rate of clear-sky data under different cloud index thresholds are evaluated. Results show that the new cloud detection scheme can identify more than 80% of cloudy data on average, but this decreases to 72% for area with terrain higher than 5 km, and the false deletion rate remains stable at 45%. The detection rates of mixed clouds and cumulonimbus are higher than 90%, but it is lower than 50% for altostratus with an altitude of about 7–8 km. Comparative analysis shows that the new method is more suitable for areas with terrain higher than 700 m. Based on the cloud detection results, the effects of terrain height on the characteristics of observation error and bias are also discussed for AMSU-A channels 5 and 6.

In situ observation of warm atmospheric layer and the heat contribution of suspended dust over the Tarim Basin

Abstract. Based on radiosonde observations from the spring and summer of 2016–2017, we verified the existence of an anomalously warm atmospheric layer and quantified the heat contribution of suspended dust over the Tarim Basin (TB) in northwest China. The atmospheric layer was identified between 300 and 500 hPa over the TB, with 2.53 and 1.39 K average intensities in spring and summer, respectively. The Taklimakan Desert (TD), i.e., the world's second-largest moving desert, is contained in the TB and emits large amounts of dust particles, which remain suspended over the TB. Using Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data, we found that dust can be lifted as high as the upper atmospheric layer, i.e., between 3.0 and 5.5 km above mean sea level, over the TB. Consequently, suspended dust can exert a maximum heating effect of approximately +0.45 and +0.25 K in spring and summer, respectively. The heat contributions of dust to the anomalously warm atmospheric layer over the TB in spring and summer were 13.77 % and 10.25 %, respectively. With regard to topographical feature, the TB is adjacent to the Tibetan Plateau (TP), which acts as an elevated heat source in spring and summer. The warm atmospheric layer over the TB seems a northward extension of Tibet heat source, the concept of which is proposed in this study. Such a northward “extension” of the elevated heating by the Tibetan Plateau may be a controlling factor of the regional climate, especially in the western section of the Silk Road Economic Belt, and therefore requires further investigations.

A novel method of identifying and analysing oil smoke plumes based on MODIS and CALIPSO satellite data

Abstract. Black carbon aerosols are the second largest contributor to global warming while also being linked to respiratory and cardiovascular disease. These particles are generally found in smoke plumes originating from biomass burning and fossil fuel combustion. They are also heavily concentrated in smoke plumes originating from oil fires, exhibiting the largest ratio of black carbon to organic carbon. In this study, we identified and analysed oil smoke plumes derived from 30 major industrial events within a 12-year timeframe. To our knowledge, this is the first study of its kind that utilized a synergetic approach based on satellite remote sensing techniques. Satellite data offer access to these events, which, as seen in this study, are mainly located in war-prone or hazardous areas. This study focuses on the use of MODIS (Moderate Resolution Imaging Spectroradiometer) and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) products regarding these types of aerosol while also highlighting their intrinsic limitations. By using data from both MODIS instruments on board Terra and Aqua satellites, we addressed the temporal evolution of the smoke plume while assessing lidar-specific properties and plume elevation using CALIPSO data. The analysis method in this study was developed to better differentiate between oil smoke aerosols and the local atmospheric scene. We present several aerosol properties in the form of plume-specific averaged values. We believe that MODIS values are a conservative estimation of plume aerosol optical depth (AOD) since MODIS algorithms rely on general aerosol models and various atmospheric conditions within the look-up tables, which do not reflect the highly absorbing nature of these smoke plumes. Based on this study we conclude that the MODIS land algorithms are not yet suited for retrieving aerosol properties for these types of smoke plumes due to the strong absorbing properties of these aerosols. CALIPSO retrievals rely heavily on the type of lidar solutions showing discrepancy between constrained and unconstrained retrievals. Smoke plumes identified within a larger aerosol layer were treated as unconstrained retrievals and resulted in conservative AOD estimates. Conversely, smoke plumes surrounded by clear air were identified as opaque aerosol layers and resulted in higher lidar ratios and AOD values. Measured lidar ratios and particulate depolarization ratios showed values similar to the upper ranges of biomass burning smoke. Results agree with studies that utilized ground-based retrievals, in particular for Ångström exponent (AE) and effective radius (Reff) values. MODIS and CALIPSO retrieval algorithms disagree on AOD ranges, for the most part, due to the extreme light-absorbing nature of these types of aerosols. We believe that these types of studies are a strong indicator for the need of improved aerosol models and retrieval algorithms.

Investigation of June 2020 giant Saharan dust storm using remote sensing observations and model reanalysis

This paper investigates the characteristics and impact of a major Saharan dust storm during June 14th–19th 2020 on atmospheric radiative and thermodynamics properties over the Atlantic Ocean. The event witnessed the highest ever aerosol optical depth for June since 2002. The satellites and high-resolution model reanalysis products well captured the origin and spread of the dust storm. The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) measured total attenuated backscatter and aerosol subtype profiles, lower angstrom exponent values (~ 0.12) from Modern-Era Retrospective Analysis for Research and Application—version 2 (MERRA-2) and higher aerosol index value from Ozone monitoring instrument (> 4) tracked the presence of elevated dust. It was found that the dust AOD was as much as 250–300% higher than their climatology resulting in an atmospheric radiative forcing ~ 200% larger. As a result, elevated warming (8–16%) was observed, followed by a drop in relative humidity (2–4%) in the atmospheric column, as evidenced by both in-situ and satellite measurements. Quantifications such as these for extreme dust events provide significant insights that may help in understanding their climate effects, including improvements to dust simulations using chemistry-climate models.

Assessment of PM2.5 using satellite lidar observations: Effect of bio-mass burning emissions over India

The present study estimates the particulate matter with aerodynamic diameters less than 2.5 μm (PM2.5) over the Indian sub-continent using near-surface retrieval of aerosol extinction coefficient (2007–2021) of Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite. Climatology of wintertime PM2.5 during the last 15 years shows the highest concentration over the middle Indo-Gangetic Plain (IGP) and northwest India with a 3 to 4 fold increase in magnitude compared to the peninsular India. Surface-level PM2.5 mass concentration during winter (December to February) shows statistically significant positive trends over the Indian subcontinent. It increases at a rate of ~3% over the IGP and arid regions of northwest India, and ~4% over peninsular India during the last fifteen years (2006–2020). Interannual variability of average near-surface PM2.5 concentration over the Indian sub-continent during the fog occurring season (December to February) shows a statistically significant correlation with the post-harvest agro-residue burning over the western IGP (Punjab and Haryana) during November. The wintertime near-surface PM2.5 concentration shows a higher correlation with anthropogenic agro-residue burning activity compared to meteorological parameters. The influence of agro-residue burning during November over northern India extends up to peninsular India and might contribute to continental pollution outflow and associated aerosol plumes persisting over the Northern Indian Ocean during the winter season. Sustainable energy recovery solutions to the agro-residue burning need to be implemented to effectively reduce the far-reaching implications of the post-monsoon burning activity over the western IGP.

Investigating the long-range transport of particulate matter in East Asia: Introducing a new Lagrangian diagnostic tool

This research introduces PM-Tracker, a novel diagnostic tool for determining the impact of long-range transport in PM2.5 high-peak occurrences. The module was built and deployed on the Community Multiscale Air Quality (CMAQ) modeling framework using the Trajectory Grid method as Lagrangian advection. To explore the applicability of the established model, we investigate the long-range transport of PM2.5 over East Asia, specifically from the North China Plain region to South Korea, during the early months of 2019. The time series of PM2.5 over the South Korean region combined with spatial distribution plots reveal three high-PM episodes during the simulation period. We also compare modeled Aerosol Optical Depth (AOD) values of the episodes with Himawari-8 AOD retrievals. Moreover, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar observations validate the transport of polluted air mass during the three high-PM episodes. According to our findings, the long-range transport of pollutants leads to high PM concentrations in the downwind region for January 14 and March 5 episodes. The PM-Tracker module reveals clusters of trajectories with high values of PM2.5 (80–150 μg/m3) from the Shandong Peninsula and the Beijing region on both dates. Although PM2.5 concentrations were lower during the March 27 episode (∼60 μg/m3), we still observe some regional transport impact according to the results of PM-Tracker. The PM-Tracker module shows high amounts of nitrate, ammonium, and sulfate in the region and recognizes the advection process in CMAQ as the primary contributor to high PM episodes. The aggregation of clustered trajectories with PM2.5 speciation and physical process contributions institutes the PM-Tracker module as a highly effective tool for the study of both local and long-range transport.

Cloud identification and property retrieval from Himawari-8 infrared measurements via a deep neural network

Clouds constitute a key component of weather and climate systems, whereas the uniform retrieval of cloud properties, such as cloud top height (CTH) and cloud optical thickness (COT), requires accuracy and computational efficiency improvements. In this study, an image-based deep neural network (DNN) model for cloud identification and simultaneous retrieval of CTH and ice-COT is developed for Himawari-8 satellite infrared measurements. The DNN model is trained with brightness temperature data from four months in 2016 as the input, and cloud properties of an active remote sensing product from CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) as the target truth. Supplementary variables, including the vertical temperature profile, the surface elevation, and the geometrical parameters, are added as the input data. DNN model performance is first tested with an independent dataset, and then cases over a CloudSat track and a Himawari-8 granule (85°E–205°E, 60°S–60°N) are selected for further validation of the model by comparing its results with those from two physics-based models. For both the water- and ice-CTH estimates, the DNN model shows high consistency with the target values, with an overall CTH correlation coefficient of 0.90 for high ice clouds with COT ≥0.3. Notably, as an infrared method in nature, the DNN extends the predictable ice-COT to ~200, with relative biases of ~20% for high ice clouds with COT >1. The strong accuracy of the DNN model is primarily derived from its ability to learn from the spatial features imprinted on the input brightness temperature image, and its integration of information from neighboring pixels in a three-dimensional space. A single full disk estimation with the DNN model takes about 20 min using one processor; therefore, near-real-time cloud property retrieval that is uniformly available over 24 h can be obtained for severe weather monitoring and mesoscale cloud-system studies.

First Lidar Campaign in the Industrial Sites of Volta Redonda-RJ and Lorena-SP, Brazil

We report on the first aerosol profiling campaign in the Paraíba valley, a hub connecting the region between the two largest Brazilian metropolitan areas, São Paulo and Rio de Janeiro. São Paulo Sanitation Technology Company (CETESB) air quality and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data show homogeneous behavior of the atmosphere throughout the region. A more detailed description of the particulate material in the local atmosphere, including its temporal dependence, can be obtained by using ground-based lidars. Measurements were carried out with a backscatter lidar system in two industrial cities, Volta Redonda and Lorena. The aerosol backscatter profiles present several peaks at different altitudes, indicating the presence of aerosol in several atmospheric layers. In addition, The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) air-mass back-trajectories indicate a possible detection of long-range aerosol transported from biomass burning areas of South America. The present study emphasizes the importance of investigating and monitoring the emission of particulate matter at this important hub connection between two dense populated regions of Brazil.

Estimate of daytime single-layer cloud base height from advanced baseline imager measurements

Cloud base height (CBH) is an important parameter to describe cloud state and is highly related to the vertical motions in the atmosphere. CBH information is critical for both aviation safety and synoptic analysis. In this study, daytime CBH is estimated directly from Geostationary Operational Environmental Satellite-R Series (GOES-16) Advanced Baseline Imager (ABI) level 1b data and the European Centre for Medium-Range Weather Forecasts' (ECMWF) fifth generation reanalysis (ERA5) data using the Gradient Boosted Regression Trees (GBRT) machine learning technique. The CBH estimate algorithm, which is named as GETCBH, covers the same areal extent as the full disk of the ABI/GOES-16 and only for single-layer clouds. The 2-years of CBH measurements from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite is used as the label (which is the true value/class of the model output for regression/classification problem in machine learning terminology). A quality flag algorithm using another machine learning technique, the Gradient Boosted Decision Trees machine learning technique is developed to provide a confidence level for the CBH estimate. The evaluations show an overall root mean square error (RMSE) of 1.87 km and Pearson's correlation coefficient (Pearson's r) of 0.92 before any quality control. After excluding CBH estimates with low confidence (19.2% of all samples), the RMSE is reduced to 1.14 km, Pearson's r increases to 0.97, and 96% of the estimates are within 2 km of the CALIOP results. By analyzing model bias and feature importance, cloud phase information has the biggest impact on the CBH estimate, although all input features have positive impact on the estimate accuracy. Limited by the penetrability of CALIOP, GETCBH is valid for clouds with COD < 8.5. The CBH estimates have reduced accuracy (Pearson's r of 0.88) for optically thin clouds (clouds with cloud optical depth [COD] < 0.1) where little cloud information is contained in the ABI measurements, as well as for optically thick clouds (clouds with COD ≥ 3) where a larger proportion of opaque clouds is excluded. Furthermore, for the GBTCBH model using 9 months of CloudSat measurements as label, the CBH estimates are improved with an RMSE of 1.41 km and Pearson's r of 0.92. In a case study of Hurricane Dorian, CBHs for most of the single-layer clouds are successfully estimated with small errors and flagged with high confidence, for both high and low clouds. Deep convective clouds and multi-layer clouds, both of which are not included in the training, are reasonably flagged as low confidence with large CBH estimate errors. In this particular case, 65% of cloudy pixels have CBH estimate with high confidence in the scene. Daytime CBH with high spatial (2 km) and temporal (10 min) resolution can be derived from ABI measurements using this methodology.

Machine learning-based retrieval of day and night cloud macrophysical parameters over East Asia using Himawari-8 data

Clouds play a significant role in the climate system, which affects the radiation balance and modulates the global hydrological cycle. However, the existing cloud property products have poor spatiotemporal continuity with only daytime cloud property retrieval results, which makes it challenging for us to carry out researches related to clouds at night. In this study, the effect of parallax error between the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and Himawari-8 data is corrected based on parallax correction algorithm by referring to cloud parameters extracted from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) cloud property product. Due to the different sensitivity of different channels to clouds, this study treats the CALIOP data with cloud optical depth greater than 0.2 as clouds, otherwise as clear sky. Cloud property retrieval model based on the XGBoost machine learning (ML) algorithm is developed for the advanced Himawari imager (AHI) onboard the Himawari-8 satellite. The ML model can achieve unified cloud mask, cloud top temperature (CTT), and cloud top height (CTH) retrieval for both daytime and nighttime with high spatial (0.02°) and temporal (10 min) resolutions using the AHI thermal data. The retrieval results are extensively evaluated over study regions (80° E ~ 135° E, 18° N ~ 55° N) by comparisons with cloud property products of JAXA (Japan Aerospace Exploration Agency) AHI, MODIS, and CALIOP data. The ML algorithm has higher cloud detection accuracy with cloudy sky (clear sky) hit rate (CHR) and false alarm rate (FAR) of 90.79% (88.35%) and 5.74% (4.67%) compared with the JAXA AHI (CHR = 92.81% (60.64%) and FAR = 19.66% (3.61%)) and MODIS (CHR = 88.50% (72.32%) and FAR = 13.10% (6.06%)) cloud products, and the ML algorithm also performs well for thin clouds over bright surfaces. The CTH and CTT retrieved by the ML algorithm (RMSE = 1.83 km and 12.29 K) are in good agreement with the CALIOP cloud property product, and the root mean squared error (RMSE) of CTH and CTT is reduced by 33.70% (18.67%) and 35.28% (16.45%) on average, respectively, compared with the JAXA AHI (MODIS) cloud product with RMSE of 2.76 km (2.25 km) and 18.99 K (14.71 K). In addition, the ML algorithm can obtain higher cloud property retrieval accuracy at night compared with the results during the day.

Evaluation of High Cloud Product of ECMWF Over South China Sea Using CALIOP

AbstractHigh clouds are globally widespread and are crucially important to global radiative processes and to the thermal balance of the Earth. Active remote sensing measurements can provide more reliable detection of optically thin cirrus clouds than passive remote sensing. The reanalysis datasets from European Centre for Medium‐Range Weather Forecasts (ECMWF) have a variety of applications in different branches of atmospheric science. A careful evaluation of these datasets is a prerequisite for their successful use. In this paper, the high cloud product from ECMWF is compared with Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) observations over the South China Sea from December 2006 to February 2019. The annual average of high cloud amount is analyzed and compared between CALIOP and ECMWF day and night, and the monthly comparison is also conducted. The results show that the distributions of high cloudiness in CALIOP and ECMWF are in fairly good agreement over the study area both day and night. The correlation coefficient between the two products is 0.79 during daytime, 0.75 nighttime. 50.8% of high cloud amount from ECMWF is 8.8% lower than 59.6% from CALIOP during daytime. A larger difference of 16.2% occurred at night with 53.9% from ECMWF, and 70.1% from CALIOP. Quantitative analysis of subvisible cirrus clouds (SVC, optical depth &lt;0.03) is given. The results show that the discrepancies between the two data sets are reduced to 2.3% during daytime, and 5.2% at nighttime after screening out SVC. The correlation coefficients also rise to 0.82 both daytime and nighttime. The distribution and occurrence of SVC are presented over the study area.

Study of Mixed Pollution of Haze and Dust in Jinan Based on LiDAR

From 26 March to 30 March 2021, a severe mixed haze and dust pollution process occurred in Jinan, China. Ground light detection and ranging (LiDAR) and satellite radar were used to observe and analyze the mixed pollution. In this study, a variety of data, including the pollutant concentration, meteorological factors, radiosonde data, and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite data, as well as back-trajectory analysis, were employed to analyze the causes of the mixed pollution. The results show that, at the beginning of this weather process, fine particulate matter such as PM2.5 was the main source of pollution, and the low air mass transporting the main pollution originated from nearby cities and polluted industrial areas. After 28 March, due to the influence of a high-altitude Mongolian air mass, the concentration of coarse particulate matter such as PM10 increased significantly. The surface temperature, humidity, and wind speed also affected the accumulation of pollutants, which affected the timely diffusion of the pollution. In this study, ground LiDAR and satellite-based radar were used to detect the pollution. On the basis of a comprehensive analysis of the various meteorological factors, the formation and influence of the pollution were investigated from various perspectives. The extinction coefficient reflected the PM2.5 concentration, verifying the effectiveness of LiDAR in pollution concentration detection. The variation in the boundary layer height also had an important effect on pollution dissipation.

Global estimation of clear-sky shortwave aerosol direct radiative effects based on CALIPSO observations

ABSTRACT The total and individual aerosol direct radiative effects (ADREs) were estimated for clear-sky conditions using Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) observations and radiative transfer model. In this study, a parametric sensitivity analysis was performed for the Santa Barbara DISORT Atmospheric Radiative Transfer model using a global sensitivity analysis method. Uncertainties in the ADREs due to aerosol optical properties and surface albedo errors were evaluated. The single-scattering albedo and asymmetry factor for marine, dust, pollution, and smoke aerosols required for the model calculations were obtained from the classified AErosol RObotic NETwork observations. The estimated average global ADREs and errors at the top-of-atmosphere (TOA) and the surface were −2.36 ± 0.54 and −4.78 ± 2.2 Wm−2, respectively. In regions with higher dust, pollution, and smoke aerosol loading, the ADREs exhibited significant seasonal variability. In the Sahara and Arabian deserts, during the June-July-August season with higher dust aerosol loading, the seasonal average ADREs in the region were −3.23 and −21.37 Wm−2 at the TOA and surface, respectively. In the Indian region, during the March–April–May season with higher pollution aerosol loading, the ADREs were −10.33 and −28.04 Wm−2 at the TOA and surface, respectively. In Southern Africa, the smoke aerosol with a single-scattering albedo of 0.87 caused negative radiative effects at the TOA, and during the September-October-November season with higher smoke aerosol loading, the seasonal average ADREs were −2.34 and −6.36 Wm−2 at the TOA and surface, respectively.

Estimating cloud condensation nuclei concentrations from CALIPSO lidar measurements

Abstract. We present a novel methodology to estimate cloud condensation nuclei (CCN) concentrations from spaceborne CALIPSO (Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations) lidar measurements. The algorithm utilizes (i) the CALIPSO-derived backscatter and extinction coefficient, depolarization ratio, and aerosol subtype information; (ii) the normalized volume size distributions and refractive indices from the CALIPSO aerosol model; and (iii) the MOPSMAP (modelled optical properties of ensembles of aerosol particles) optical modelling package. For each CALIPSO height bin, we first select the aerosol-type specific size distribution and then adjust it to reproduce the extinction coefficient derived from the CALIPSO retrieval. The scaled size distribution is integrated to estimate the aerosol number concentration, which is then used in the CCN parameterizations to calculate CCN concentrations at different supersaturations. To account for the hygroscopicity of continental and marine aerosols, we use the kappa parameterization and correct the size distributions before the scaling step. The sensitivity of the derived CCN concentrations to variations in the initial size distributions is also examined. It is found that the uncertainty associated with the algorithm can range between a factor of 2 and 3. Our results are comparable to results obtained using the POLIPHON (Polarization Lidar Photometer Networking) method for extinction coefficients larger than 0.05 km−1. An initial application to a case with coincident airborne in situ measurements for independent validation shows promising results and illustrates the potential of CALIPSO for constructing a global height-resolved CCN climatology.

The CALIPSO retrieved spatiotemporal and vertical distributions of AOD and extinction coefficient for different aerosol types during 2007–2019: A recent perspective over global and regional scales

In the present study, we investigated the global climatology of the spatiotemporal and vertical distributions of aerosol optical properties for different aerosol types, mainly including total aerosols (All), desert dust (DD), polluted dust (PD), and elevated smoke (ES). The data used for the study is derived from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) for Research and Application. We found that the seasonal distribution of the global mean aerosol optical depth (AOD) of All type in the land area are in the order JJA (0.157) > MAM (0.134) > DJF (0.127) > SON (0.124). This is attributed to the more frequent sand and dust activities over the Taklimakan Desert and the Sahara Desert in MAM and JJA. Owing to the transportation of DD, ES, and PD from the aerosol source, the ocean areas (especially for downwind regions) present the high AOD, and with the same seasonal distribution trend as in the land area. Also, the mean extinction coefficient (EC) for all aerosols decreases with the increase of height in all seasons for both hemispheres. The maximum mean EC for All aerosols is 0.0102 km−1 (0.016 km−1) during the day (night) in DJF (JJA) in the northern hemisphere, while the maximum is 0.006 km−1 (0.008 km−1) during the day (night) in JJA (SON) in the southern hemisphere. Also, the occurrence of frequencies of DD, PD, and ES aerosols are distributed throughout the entire troposphere in all seasons, but the clean marine (CM) and polluted continent (PC) are mostly below 3 km.

Optimization of Aeolus' aerosol optical properties by maximum-likelihood estimation

Abstract. The European Space Agency (ESA) Earth Explorer Mission Aeolus was launched in August 2018, carrying the first Doppler wind lidar in space. Its primary payload, the Atmospheric LAser Doppler INstrument (ALADIN), is an ultraviolet (UV) high-spectral-resolution lidar (HSRL) measuring atmospheric backscatter from air molecules and particles in two separate channels. The primary mission product is globally distributed line-of-sight wind profile observations in the troposphere and lower stratosphere. Atmospheric optical properties are provided as a spin-off product. Being an HSRL, Aeolus is able to independently measure the particle extinction coefficients, co-polarized particle backscatter coefficients and the co-polarized lidar ratio (the cross-polarized return signal is not measured). This way, the retrieval is independent of a priori lidar ratio information. The optical properties are retrieved using the standard correct algorithm (SCA), which is an algebraic inversion scheme and therefore sensitive to measurement noise. In this work, we reformulate the SCA into a physically constrained maximum-likelihood estimation (MLE) problem and demonstrate a predominantly positive impact and considerable noise suppression capabilities. These improvements originate from the use of all available information by the MLE in conjunction with the expected physical bounds concerning positivity and the expected range of the lidar ratio. To consolidate and to illustrate the improvements, the new MLE algorithm is evaluated against the SCA on end-to-end simulations of two homogeneous scenes and for real Aeolus data collocated with measurements by a ground-based lidar and the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. The largest improvements were seen in the retrieval precision of the extinction coefficients and lidar ratio ranging up to 1 order of magnitude or more in some cases due to effective noise dampening. In real data cases, the increased precision of MLE with respect to the SCA is demonstrated by increased horizontal homogeneity and better agreement with the ground truth, though proper uncertainty estimation of MLE results is challenged by the constraints, and the accuracy of MLE and SCA retrievals can depend on calibration errors, which have not been considered.

Study on Vertically Distributed Aerosol Optical Characteristics over Saudi Arabia Using CALIPSO Satellite Data

The optical characteristics of vertically distributed aerosols over Saudi Arabia were investigated using the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data from 2007 to 2019. The study region was divided into three parts (Region I: Tabuk, Makkah, Al Madinah, Asir, Al Bahah, Jizan, Riyadh, Mecca, Medina, the eastern region, Kassim, Hale, Asir, Baha, Tabuk, the northern border region, Jizan, Najilan, and Jufu. Region II: Ar, Al, Ha, Al, and Najran. Region III Al Hudud ash Shamaliyah and Ash Sharqiyah) to understand regional aerosol characteristics by performing interannual and seasonal analysis for nine aerosol types during the day and nighttime. We found that the aerosol optical depth (AOD) estimates were the highest over eastern Saudi Arabia (region III) and were seemingly driven by the presence of an expansive desert in the region. As anticipated, the AOD observations were substantially higher in spring and summer than in autumn and winter owing to the frequent occurrence of dust events during the former. Daytime observations exhibited higher AOD values than those at nighttime, which might be related to higher daytime anthropogenic activities. The estimates of the base height of the lowest aerosol layer (HB1) and the top altitude of the highest aerosol layer (TAH) were altered depending on the topography (the higher the altitude, the higher the annual mean value of HB1 and TAH). The aerosol layers (N) were relatively abundant over region III, seemingly due to the relatively stronger atmospheric convection over this region. The volume depolarization ratio of the lowest aerosol layer (VDR1) was considerable during the night due to deposition at nighttime, and VDR1 was relatively substantial in spring and summer. The color ratio of the lowest aerosol layer (CR1) estimates over regions II and III was higher at night. We report a weak positive correlation between the thickness of the lowest aerosol layer (HTH1) and the AOD of the lowest aerosol layer (AOD1) in the three regions, a strong positive correlation between TAH and N, and a negative correlation between the AOD proportion of the lowest aerosol layer (PAOD1) and N in Saudi Arabia. In this paper, the optical and physical properties of aerosols in Saudi Arabia have been studied for 13 years. Our results could provide references for researchers and the government, and relevant departments with data support on the aerosol layer to help control air pollution in Saudi Arabia.