Cloud-Aerosol Lidar With Orthogonal Polarization Research Articles

Statistics of Smoke Sphericity and Optical Properties Using Spaceborne Lidar Measurements

Smoke particles from biomass burning events are typically assumed to be spherical despite previous observations of non-spherical smoke. As such, large uncertainties exist in some physical and optical parameters used in lidar aerosol retrievals, including depolarization and lidar ratio of non-spherical smoke aerosols. In this analysis, using NASA’s Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data during the biomass burning season over Africa from 2015 to 2017, we studied the frequency and distribution of non-spherical smoke particles to compare with findings of smoke particle non-sphericity from the Cloud-Aerosol Transport System (CATS) lidar. A supplemental smoke aerosol typing algorithm was developed to identify aerosol layers containing non-spherical smoke particles, which might otherwise be misclassified as desert dust, polluted dust, or dusty marine by the CALIOP standard aerosol typing algorithm. Then, the relationships between smoke particle sphericity, lidar ratio, and relative humidity are analyzed for CATS and CALIOP observations over Africa. Approximately 18% of smoke layers observed by CALIOP over Africa are non-spherical (depolarization ratio > 0.075) and agree with spatial distributions of non-spherical smoke found in CATS observations. A dependance of lidar ratio on relative humidity was found for layers of spherical smoke over Africa in both CATS and CALIOP data; however, no such dependance was evident for the depolarization ratio and layer relative humidity. While the supplemental smoke aerosol typing algorithm presented in this analysis was targeted only for specific biomass burning regions during biomass burning seasons and is not meant for global operational use, it presents one potential method for improved backscatter lidar aerosol typing. These results suggest that a dynamic lidar ratio, based on layer-relative humidity for spherical smoke, could be used to reduce uncertainties in smoke aerosol extinction retrievals for future backscatter lidars.

Characterisation of dust aerosols and their absorbing efficiency over South Asia using infrared observations from INSAT-3D Imager

ABSTRACT Radiative impacts of dust aerosols exhibit large regional and temporal heterogeneity, despite the global net cooling effect. Hence, accurate assessment of dust aerosol impacts on weather and climate calls for their regional Characterisation and its proper incorporation in climate models. In this study, dust aerosol distribution and its effects on the absorbing efficiency of aerosol system over South Asia are examined using the Infrared Difference Dust Index (IDDI) derived from the Imager onboard ISRO’s INSAT-3D satellite. IDDI estimated from INSAT-3D agrees well (r ranges from 0.4 to 0.7) with the dust aerosol optical depth (DAOD) from Moderate Resolution Imaging Spectroradiometer (MODIS) and Second Modern-Era Retrospective analysis for Research and Applications (MERRA-2). This shows the potential of this parameter for the Characterisation of dust aerosol and its diurnal and seasonal evolutions. Using IDDI from INSAT-3D, this study further examines the changes in aerosol distribution and characteristics over the Indian landmass and surrounding oceanic regions during severe dust storm episodes. Spatial distribution of dust aerosols and prevailing circulation pattern show long range transport of dust particles from their source regions to faraway locations during the dust events. This is further confirmed by the presence of elevated aerosol layers with high particulate depolarization ratio, observed by Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). An increase in absorbing efficiency of the aerosol system is observed, due to these transported dust particles, over the South Asian region. Dust absorbing efficiency (DAE) is about 20–25K during the dust storm events. This enhancement in radiation absorption due to the wide spread distribution of dust aerosols over a vast domain would significantly alter the circulation dynamics and weather systems over South Asia, especially during pre-monsoon and monsoon seasons.

Total column optical depths retrieved from CALIPSO lidar ocean surface backscatter

Abstract. This paper introduces the Ocean Derived Column Optical Depth (ODCOD) algorithm. ODCOD is now being used to retrieve full-column optical depths from the 532 nm measurements acquired by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) spacecraft. ODCOD uses the lidar integrated attenuated backscatter from the ocean surface, together with collocated wind speed estimates from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), to estimate the full-column optical depths of particulates (i.e., clouds and aerosols) in the Earth's atmosphere. Unlike CALIOP's standard retrievals, which estimate optical depths only when particulate layers are detected, ODCOD retrievals deliver a comprehensive estimate that accounts for attenuation by all particulates present within the lidar profiles. This paper describes the ODCOD algorithm, develops random uncertainty estimates, and characterizes the systematic differences between ODCOD optical depths and those reported by previously validated data sets. This paper presents performance assessments of ODCOD cloud-free profiles to compare the ODCOD aerosol optical depth (AOD) retrievals to collocated measurements made by the airborne High Spectral Resolution Lidar (HSRL) instruments flown by NASA Langley Research Center (LaRC), to daytime estimates derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), and to daytime and nighttime retrievals using the Synergized Optical Depth of Aerosols (SODA) algorithm. ODCOD AODs are biased high relative to LaRC HSRL AODs by 0.009 ± 0.043 (median ± median absolute deviation), with a correlation coefficient of 0.724, and biased low relative to MODIS by 0.009 ± 0.041, with a correlation coefficient of 0.834. Relative to SODA, which derives AOD from a combination of CALIOP and CloudSat ocean surface measurements, ODCOD is biased high in the daytime by 0.004 ± 0.035 and higher at night by 0.027 ± 0.034, with correlation coefficients of 0.887 and 0.891, respectively. Because ODCOD estimates are independent from the standard CALIOP optical depth retrievals, they offer potential for future advances in the CALIPSO data record, both in validating CALIOP's standard estimates and as a potential total column constraint to improve extinction coefficient retrievals.

Contrail altitude estimation using GOES-16 ABI data and deep learning

Abstract. The climate impact of persistent aircraft contrails is currently estimated to be comparable to that due to aviation-emitted CO2. A potential near-term and low-cost mitigation option is contrail avoidance, which involves rerouting aircraft around ice-supersaturated regions, preventing the formation of persistent contrails. Current forecasting methods for these regions of ice supersaturation have been found to be inaccurate when compared to in situ measurements. Further assessment and improvements of the quality of these predictions can be realized by comparison with observations of persistent contrails, such as those found in satellite imagery. In order to further enable comparison between these observations and contrail predictions, we develop a deep learning algorithm to estimate contrail altitudes based on GOES-16 Advanced Baseline Imager (ABI) infrared imagery. This algorithm is trained using a dataset of 3267 contrails found within Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) data and achieves a root mean square error (RMSE) of 570 m. The altitude estimation algorithm outputs probability distributions for the contrail top altitude in order to represent predictive uncertainty. The 95 % confidence intervals constructed using these distributions, which are shown to contain approximately 95 % of the contrail data points, are found to be 2.2 km thick on average. These intervals are found to be 34.1 % smaller than the 95 % confidence intervals constructed using flight altitude information alone, which are 3.3 km thick on average. Furthermore, we show that the contrail altitude estimates are consistent in time and, in combination with contrail detections, can be used to observe the persistence and three-dimensional (3D) evolution of contrail-forming regions from satellite images alone.

PM2.5 modeling based on CALIPSO in bangkok

Air quality has become a severe issue in Bangkok, mainly due to PM2.5 (fine particulate matter with particle size less than 2.5 μm). Aerosol optical depth (AOD) obtained for active satellite data has been widely used to estimate PM2.5 near the ground. Nevertheless, passive satellite data are rarely used to estimate PM2.5 near the ground. In this study, a total AOD in troposphere data achieved from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) was used to determine PM2.5 with climate parameters (Temperature (TEM), relative humidity (RH), wind speed (WS), boundary layer height (BLH), and the normalized difference vegetation index (NDVI) using Linear Mixed Effect Method (LMEM). It was found that the coefficient (R2) increases from model 1 (0.87) to model 6 (0.99), and the root mean square error (RMSE) reduces from 2.65 to 0.00 μg/m3. The best model gives an R2=0.99 (models 5 and 6). PM2.5 patterns between observed and predicted show similar representative patterns. Therefore, our study provides CALIPSO AOD data with a potentially helpful estimation of PM2.5. GRAPHICAL ABSTRACT HIGHLIGHTS PM2.5 concentrations were obtained using CALOPSO satellite data PM2.5 model based on AOD, climate and other parameters R2 of PM2.5 model obtained from the best model more than 0.99

Seasonal Variability in the Relationship between the Volume-Scattering Function at 180° and the Backscattering Coefficient Observed from Spaceborne Lidar and Biogeochemical Argo (BGC-Argo) Floats

The derivation of the particulate-backscattering coefficient (bbp) from Lidar signals is highly influenced by the parameter χp(π), which is defined by χp(π) = bbp/(2πβp(π)). This parameter facilitates the correlation of the particulate-volume-scattering function at 180°, denoted βp(π), with bbp. However, studies exploring the global and seasonal fluctuations of χp(π) remain sparse, largely due to measurement difficulties of βp(π) in the field conditions. This study pioneers the global data collection for χp(π), integrating bbp observations from Biogeochemical Argo (BGC-Argo) floats and βp(π) data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) spaceborne lidar. Our findings indicate that χp(π) experiences significant seasonal differences globally, peaking during summer and nadiring in winter. The global average χp(π) was calculated as 0.40, 0.48, 0.43, and 0.35 during spring, summer, autumn, and winter, respectively. The daytime values of χp(π) slightly exceeded those registered at night. To illuminate the seasonal variations in χp(π) in 26 sea regions worldwide, we deployed passive ocean color data MODIS bbp and active remote sensing data CALIOP βp(π), distinguishing three primary seasonal change patterns—the “summer peak”, the “decline”, and the “autumn pole”—with the “summer peak” typology being the most common. Post recalibration of the CALIOP bbp product considering seasonal χp(π) variations, we observed substantial statistical improvements. Specifically, the coefficient of determination (R2) markedly improved from 0.84 to 0.89, while the root mean square error (RMSE) declined from 4.0 × 10−4 m−1 to 3.0 × 10−4 m−1. Concurrently, the mean absolute percentage error (MAPE) also dropped significantly, from 31.48% to 25.27%.

First atmospheric aerosol-monitoring results from the Geostationary Environment Monitoring Spectrometer (GEMS) over Asia

Abstract. Aerosol optical properties have been provided by the Geostationary Environment Monitoring Spectrometer (GEMS), the world's first geostationary-Earth-orbit (GEO) satellite instrument designed for air quality monitoring. This study describes improvements made to the GEMS aerosol retrieval (AERAOD) algorithm, including spectral binning, surface reflectance estimation, cloud masking, and post-processing, along with validation results. These enhancements aim to provide more accurate and reliable aerosol-monitoring results for Asia. The adoption of spectral binning in the lookup table (LUT) approach reduces random errors and enhances the stability of satellite measurements. In addition, we introduced a new high-resolution database for surface reflectance estimation based on the minimum-reflectance method, which was adapted to the GEMS pixel resolution. Monthly background aerosol optical depth (BAOD) values were used to estimate hourly GEMS surface reflectance consistently. Advanced cloud-removal techniques have been implemented to significantly improve the effectiveness of cloud detection and enhance aerosol retrieval quality. An innovative post-processing correction method based on machine learning has been introduced to address artificial diurnal biases in aerosol optical depth (AOD) observations. In this study, we investigated selected aerosol events, highlighting the capability of GEMS in monitoring and providing insights into hourly aerosol optical properties during various atmospheric events. The performance of the GEMS AERAOD products was validated against the Aerosol Robotic Network (AERONET) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data for the period from November 2021 to October 2022. GEMS AOD at 443 nm demonstrated a strong correlation with AERONET AOD at 443 nm (R = 0.792). However, it exhibited biased patterns, including the underestimation of high AOD values and overestimation of low-AOD conditions. Different aerosol types (highly absorbing fine aerosols, dust aerosols, and non-absorbing aerosols) exhibited distinct validation results. The retrievals of GEMS single-scattering albedo (SSA) at 443 nm agreed well with the AERONET SSA at 440 nm within reasonable error ranges, with variations observed among aerosol types. For GEMS AOD at 443 nm exceeding 0.4 (1.0), 42.76 % (56.61 %) and 67.25 % (85.70 %) of GEMS SSA data points fell within the ±0.03 and ±0.05 error bounds, respectively. Model-enforced post-processing correction improved GEMS AOD and SSA performance, thereby reducing the diurnal variation in the biases. The validation of the retrievals of GEMS aerosol layer height (ALH) against the CALIOP data demonstrates good agreement, with a mean bias of −0.225 km and 55.29 % (71.70 %) of data points falling within ±1 km (1.5 km).

Aircraft engine dust ingestion at global airports

Abstract. Atmospheric mineral dust aerosol constitutes a threat to aircraft engines from deterioration of internal components. Here we fulfil an overdue need to quantify engine dust ingestion at airports worldwide. The vertical distribution of dust is of key importance since ascent/descent rates and engine power both vary with altitude and affect dust ingestion. We use representative jet engine power profile information combined with vertically and seasonally varying dust concentrations to calculate the “dust dose” ingested by an engine over a single ascent or descent. Using the Copernicus Atmosphere Monitoring Service (CAMS) model reanalysis, we calculate climatological and seasonal dust dose at 10 airports for 2003–2019. Dust doses are mostly largest in Northern Hemisphere summer for descent, with the largest at Delhi in June–August (JJA; 6.6 g) followed by Niamey in March–May (MAM; 4.7 g) and Dubai in JJA (4.3 g). Holding patterns at altitudes coincident with peak dust concentrations can lead to substantial quantities of dust ingestion, resulting in a larger dose than the take-off, climb, and taxi phases. We compare dust dose calculated from CAMS to spaceborne lidar observations from two dust datasets derived from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP). In general, seasonal and spatial patterns are similar between CAMS and CALIOP, though large variations in dose magnitude are found, with CAMS producing lower doses by a factor of 1.9 to 2.8, particularly when peak dust concentration is very close to the surface. We show that mitigating action to reduce engine dust damage could be achieved, firstly by moving arrivals and departures to after sunset and secondly by altering the altitude of the holding pattern away from that of the local dust peak altitude, reducing dust dose by up to 44 % and 41 % respectively. We suggest that a likely low bias of dust concentration in the CAMS reanalysis should be considered by aviation stakeholders when estimating dust-induced engine wear.

Lidar–radar synergistic method to retrieve ice, supercooled water and mixed-phase cloud properties

Abstract. Mixed-phase clouds are not well represented in climate and weather forecasting models, due to a lack of the key processes controlling their life cycle. Developing methods to study these clouds is therefore essential, despite the complexity of mixed-phase cloud processes and the difficulty of observing two cloud phases simultaneously. We propose in this paper a new method to retrieve the microphysical properties of mixed-phase clouds, ice clouds and supercooled water clouds using airborne or satellite radar and lidar measurements, called VarPy-mix. This new approach extends an existing variational method developed for ice cloud retrieval using lidar, radar and passive radiometers. We assume that the lidar attenuated backscatter β at 532 nm is more sensitive to particle concentration and is consequently mainly sensitive to the presence of supercooled water. In addition, radar reflectivity Z at 95 GHz is sensitive to the size of hydrometeors and hence more sensitive to the presence of ice particles. Consequently, in the mixed phase the supercooled droplets are retrieved with the lidar signal and the ice particles with the radar signal, meaning that the retrievals rely strongly on a priori and error values. This method retrieves simultaneously the visible extinction for ice αice and liquid αliq particles, the ice and liquid water contents IWC and LWC, the effective radius of ice re,ice and liquid re,liq particles, and the ice and liquid number concentrations Nice and Nliq. Moreover, total extinction αtot, total water content (TWC) and total number concentration Ntot can also be estimated. As the retrieval of ice and liquid is different, it is necessary to correctly identify each phase of the cloud. To this end, a cloud-phase classification is used as input to the algorithm and has been adapted for mixed-phase retrieval. The data used in this study are from DARDAR-MASK v2.23 products, based on the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and Cloud Profiling Radar (CPR) observations from the CALIPSO and CloudSat satellites, respectively, belonging to the A-Train constellation launched in 2006. Airborne in situ measurements performed on 7 April 2007 during the Arctic Study of Tropospheric Aerosol, Clouds and Radiation (ASTAR) campaign and collected under the track of CloudSat–CALIPSO are compared with the retrievals of the new algorithm to validate its performance. Visible extinctions, water contents, effective radii and number concentrations derived from in situ measurements and the retrievals showed similar trends and are globally in good agreement. The mean percent error between the retrievals and in situ measurements is 39 % for αliq, 398 % for αice, 49 % for LWC and 75 % for IWC. It is also important to note that temporal and spatial collocations are not perfect, with a maximum spatial shift of 1.68 km and a maximum temporal shift of about 10 min between the two platforms. In addition, the sensitivity of remote sensing and that of in situ measurements is not the same, and in situ measurement uncertainties are between 25 % and 60 %.

A novel physics-based cloud retrieval algorithm based on neural networks (CRANN) from hyperspectral measurements in the O2-O2 band

Clouds play a crucial role in the Earth's climate system and their properties can be detected by hyperspectral measurements from space. With the increasing spectral resolution, traditional retrieval methods based on look-up tables (LUT) and optimal estimation are limited in both efficiency and accuracy compared with machine learning methods. However, the machine learning techniques used to establish the relationships between spectral measurements and cloud properties often lack physical explainability and universality. Additionally, traditional physical retrieval methods based on oxygen A-band are not applicable to instruments without the O2-A band like the ozone monitoring instrument (OMI). Therefore, we have proposed a novel physics-based deep neural networks (DNN) retrieval method––the cloud retrieval algorithm based on neural networks (CRANN)––which incorporates a deep neural network model with radiative transfer model to retrieve cloud fraction and cloud-top pressure from the oxygen–oxygen collision-induced (O4) absorption band. Validation using simulated test data supported the superior accuracy of CRANN, with the correlation coefficients for cloud fraction and cloud-top pressure are 0.989 and 0.993, respectively, whereas the correlation coefficients for cloud fraction and cloud-top pressure of the LUT method are 0.928 and 0.865, respectively. In comparison with the OMCLDO2 cloud product from the OMI, the CRANN results retrieved from OMI observations exhibit substantial consistency, boasting correlation coefficients surpassing 0.95 for cloud fraction and 0.83 for cloud pressure. As compared with the tropospheric monitoring instrument (TROPOMI) official products, the CRANN retrieval results from TROPOMI exhibit a high level of consistency with correlation coefficients exceeding 0.8 for cloud fraction and 0.73 for cloud pressure. Additionally, a promising agreement is observed between the CRANN retrievals from TROPOMI and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data, yielding RMSEs of 127.3, 134.6 and 106.4 hPa for the validation dataset, respectively.

A Level 3 monthly gridded ice cloud dataset derived from 12 years of CALIOP measurements

Abstract. Clouds play important roles in weather, climate, and the global water cycle. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) spacecraft has measured global vertical profiles of clouds and aerosols in the Earth’s atmosphere since June 2006. CALIOP provides vertically resolved information on cloud occurrence, thermodynamic phase, and properties. We describe version 1.0 of a monthly gridded ice cloud product derived from over 12 years of global, near-continuous CALIOP measurements. The primary contents are monthly vertically resolved histograms of ice cloud extinction coefficient and ice water content (IWC) retrievals. The CALIOP Level 3 Ice Cloud product is built from the CALIOP Version 4.20 Level 2 5 km Cloud Profile product that, relative to previous versions, features substantial improvements due to more accurate lidar backscatter calibration, better extinction coefficient retrievals, and a temperature-sensitive parameterization of IWC. The gridded ice cloud data are reported as histograms, which provides data users with the flexibility to compare CALIOP’s retrieved ice cloud properties with those from other instruments with different measurement sensitivities or retrieval capabilities. It is also convenient to aggregate monthly histograms for seasonal, annual, or decadal trend and climate analyses. This CALIOP gridded ice cloud product provides a unique characterization of the global and regional vertical distributions of optically thin ice clouds and deep convection cloud tops, and it should provide significant value for cloud research and model evaluation. A DOI has been issued for the product: https://doi.org/10.5067/CALIOP/CALIPSO/L3_ICE_CLOUD-STANDARD-V1-00 (Winker et al., 2018).

Polar Stratospheric Cloud Observations at Concordia Station by Remotely Controlled Lidar Observatory

Polar stratospheric clouds (PSCs) form in polar regions, typically between 15 and 25 km above mean sea level, when the local temperature is sufficiently low. PSCs play an important role in the ozone chemistry and the dehydration and denitrification of the stratosphere. Lidars with a depolarization channel may be used to detect and classify different classes of PSCs. The main PSC classes are water ice, nitric acid trihydrate (NAT), and supercooled ternary solutions (STSs), the latter being liquid droplets consisting of water, nitric acid, and sulfuric acid. PSCs have been observed at the lidar observatory at Concordia Station from 2014 onward. The harsh environmental conditions at Concordia during winter render successful lidar operation difficult. To facilitate the operation of the observatory, several measures have been put in place to achieve an almost complete remote control of the system. PSC occurrence is strongly correlated with local temperatures and is affected by dynamics, as the PSC coverage during the observation season shows. PSC observations in 2021 are shown as an example of the capability and functionality of the lidar observatory. A comparison of the observations with the satellite-borne CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) lidar has been made to demonstrate the quality of the data and their representativeness for the Antarctic Plateau.

A near-global multiyear climate data record of the fine-mode and coarse-mode components of atmospheric pure dust

Abstract. A new four-dimensional, multiyear, and near-global climate data record of the fine-mode (submicrometer in terms of diameter) and coarse-mode (supermicrometer in terms of diameter) components of atmospheric pure dust is presented. The separation of the two modes of dust in detected atmospheric dust layers is based on a combination of (1) the total pure-dust product provided by the well-established European Space Agency (ESA) “LIdar climatology of Vertical Aerosol Structure” (LIVAS) database and (2) the coarse-mode component of pure dust provided by the first step of the two-step POlarization LIdar PHOtometer Networking (POLIPHON) technique, developed in the framework of the European Aerosol Research Lidar Network (EARLINET). Accordingly, the fine-mode component of pure dust is extracted as the residual between the LIVAS total pure dust and the coarse-mode component of pure dust. Intermediate steps involve the implementation of regionally dependent lidar-derived lidar ratio values and AErosol RObotic NETwork (AERONET)-based climatological extinction-to-volume conversion factors, facilitating conversion of dust backscatter into extinction and subsequently extinction into mass concentration. The decoupling scheme is applied to observations from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) at 532 nm. The final products consist of the fine mode and coarse mode of atmospheric pure dust, quality-assured profiles of backscatter coefficient at 532 nm, extinction coefficient at 532 nm, and mass concentration for each of the two components. The datasets are established primarily with the original L2 horizontal (5 km) and vertical (60 m) resolution of the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) along the CALIPSO orbit path and secondly in averaged profiles of seasonal–temporal resolution, 1° × 1° spatial resolution, and the original vertical resolution of CALIPSO, focusing on the latitudinal band extending between 70° S and 70° N and covering more than 15 years of Earth observations (June 2006–December 2021). The quality of the CALIPSO-based fine-mode and coarse-mode dust products is assessed through the use of AERONET fine-mode and coarse-mode aerosol optical thickness (AOT) interpolated to 532 nm and the AERosol properties – Dust (AER-D) campaign airborne in situ particle size distributions (PSDs) as reference datasets during atmospheric conditions characterized by dust presence. The near-global fine-mode and coarse-mode pure-dust climate data record is considered unique with respect to a wide range of potential applications, including climatological, time series, and trend analysis over extensive geographical domains and temporal periods, validation of atmospheric dust models and reanalysis datasets, assimilation activities, and investigation of the role of airborne dust in radiation and air quality.

Three-Dimensional Structure and Transport Properties of Dust Aerosols in Central Asia—New Insights from CALIOP Observations, 2007–2022

Central Asia (CA) is one of the major sources of global dust aerosols. They pose a serious threat to regional climate change and environmental health and also make a significant contribution to the global dust load. However, there is still a gap in our understanding of dust transport in this region. Therefore, this study utilizes Cloud–Aerosol LiDAR with Orthogonal Polarization (CALIOP) data from 2007 to 2022 to depict the three-dimensional spatiotemporal distribution of dust aerosols over CA and to analyze their transport processes. In addition, the Tropospheric Monitoring Instrument (TROPOMI) was employed to assist in monitoring the movement of typical dust events, and the trajectory model was utilized to simulate the forward and backward trajectories of a dust incident. Additionally, a random forest (RF) model was employed to rank the contributions of various environmental factors. The findings demonstrate that high extinction values (0.6 km−1) are mostly concentrated within the Tarim Basin of Xinjiang, China, maintaining high values up to 2 km in altitude, with a noticeable decrease as the altitude increases. The frequency of dust occurrences is especially pronounced in the spring and summer seasons, with dust frequencies in the Tarim Basin and the Karakum and Kyzylkum deserts exceeding 80%, indicating significant seasonal and regional differences. The high values of dust optical depth (DOD) in CA are primarily concentrated in the summer, concurrent with the presence of a stable aerosol layer of dust in the atmosphere with a thickness of 0.62 km. Furthermore, dust from CA can traverse the Tianshan mountains via the westerlies, transporting it eastward. Additionally, skin temperature can mitigate regional air pollution. Our results contribute to a deeper understanding of the dynamic processes of dust in CA and provide scientific support for the development of regional climate regulation strategies.

Extensive coverage of ultrathin tropical tropopause layer cirrus clouds revealed by balloon-borne lidar observations

Abstract. Tropical tropopause layer (TTL) clouds have a significant impact on the Earth's radiative budget and regulate the amount of water vapor entering the stratosphere. Estimating the total coverage of tropical cirrus clouds is challenging, since the range of their optical depth spans several orders of magnitude, from thick opaque cirrus detrained from convection to sub-visible clouds just below the stratosphere. During the Strateole-2 observation campaign, three microlidars were flown on board stratospheric superpressure balloons from October 2021 to late January 2022, slowly drifting only a few kilometers above the TTL. These measurements have unprecedented sensitivity to thin cirrus and provide a fine-scale description of cloudy structures both in time and in space. Case studies of collocated observations with the spaceborne Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) show very good agreement between the instruments and highlight the Balloon-borne Cirrus and convective overshOOt Lidar's (BeCOOL) higher detection sensitivity. Indeed, the microlidar is able to detect optically very thin clouds (optical depth τ<2×10-3) that are undetected by CALIOP. Statistics on cloud occurrence show that TTL cirrus appear in about 50 % of the microlidar profiles and have a mean geometrical depth of 1 km. Ultrathin TTL cirrus (τ<2×10-3) have a significant coverage (23 % of the profiles), and their mean geometrical depth is 0.5 km.

An iterative algorithm to simultaneously retrieve aerosol extinction and effective radius profiles using CALIOP

Abstract. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite has been widely used in climate and environment studies to obtain the vertical profiles of atmospheric aerosols. To retrieve the vertical profile of aerosol extinction, the CALIOP algorithm assumes column-averaged lidar ratios based on a clustering of aerosol optical properties measured at surface stations. On one hand, these lidar ratio assumptions may not be appropriate or representative at certain locations. One the other hand, the two-wavelength design of CALIOP has the potential to constrain aerosol size information, which has not been considered in the operational algorithm. In this study, we present a modified inversion algorithm to simultaneously retrieve aerosol extinction and effective radius profiles using two-wavelength elastic lidars such as CALIOP. Specifically, a lookup table is built to relate the lidar ratio with the Ångström exponent calculated using aerosol extinction at the two wavelengths, and the lidar ratio is then determined iteratively without a priori assumptions. The retrieved two-wavelength extinction at each layer is then converted to the particle effective radius assuming a lognormal distribution. The algorithm is tested on synthetic data, Raman lidar measurements and then finally the real CALIOP backscatter measurements. Results show improvements over the CALIPSO operational algorithm by comparing with ground-based Raman lidar profiles.

Characterization of dust aerosols from ALADIN and CALIOP measurements

Abstract. Atmospheric aerosols have pronounced effects on climate at both regional and global scales, but the magnitude of these effects is subject to considerable uncertainties. A major contributor to these uncertainties is an incomplete understanding of the vertical structure of aerosol, largely due to observational limitations. Spaceborne lidars can directly observe the vertical distribution of aerosols globally and are increasingly used in atmospheric aerosol remote sensing. As the first spaceborne high-spectral-resolution lidar (HSRL), the Atmospheric LAser Doppler INstrument (ALADIN) on board the Aeolus satellite was operational from 2018 to 2023. ALADIN data can be used to estimate aerosol extinction and co-polar backscatter coefficients separately without an assumption of the lidar ratio. This study assesses the performance of ALADIN's aerosol retrieval capabilities by comparing them with Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) measurements. A statistical analysis of retrievals from both instruments during the June 2020 Saharan dust event indicates consistency between the observed backscatter and extinction coefficients. During this extreme dust event, CALIOP-derived aerosol optical depth (AOD) exhibited large discrepancies with Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua measurements. Using collocated ALADIN observations to revise the dust lidar ratio to 63.5 sr, AODs retrieved from CALIOP are increased by 46 %, improving the comparison with MODIS data. The combination of measurements from ALADIN and CALIOP can enhance the tracking of aerosols' vertical transport. This study demonstrates the potential for spaceborne HSRL to retrieve aerosol optical properties. It highlights the benefits of spaceborne HSRL in directly obtaining the lidar ratio, significantly reducing uncertainties in extinction retrievals.

Characteristics of Daytime‐And‐Nighttime AOD Differences Over China: A Perspective From CALIOP Satellite Observations and GEOS‐Chem Model Simulations

AbstractWe use the GEOS‐Chem chemistry transport model to quantify the factors in the diel discrepancy of Aerosol Optical Depth (AOD) retrieved from Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) satellite observations over eastern China. The GEOS‐Chem simulation reveals that the AOD below 1 km is 58.5% larger at night than during the daytime, which is comparable to the counterpart of 41.3% from CALIOP (v4.2). Model sensitivity simulations show that the diurnal variation in wind barely impacts the AOD difference between daytime and nighttime, and the increase in AOD at nighttime is primarily caused by the lower temperature at nighttime compared to daytime. Further simulations demonstrate that the low temperature at night increases AOD primarily by increasing relative humidity, and hence particle hygroscopic growth, while the effect of temperature on chemical rate barely influences AOD. CALIOP also observes that the absolute difference in AOD above 1 km between nighttime and daytime is 0.105, while the counterpart in GEOS‐Chem simulations is −0.031. This contrast can be partly explained by the factor that the percentage of valid CALIOP retrievals below 5 km is 15%–20% greater at nighttime than in the daytime due to the CALIOP detection limit. Removing the detection limit impact decreases the difference in the CALIOP AOD above 1 km between nighttime and daytime to 0.073.

Introduction to the NJIAS Himawari-8/9 Cloud Feature Dataset for climate and typhoon research

Abstract. The use of remote sensing methods to accurately measure cloud properties and their spatiotemporal changes has been widely welcomed in many fields of atmospheric research. The Nanjing Joint Institute for Atmospheric Sciences (NJIAS) Himawari-8/9 Cloud Feature Dataset (HCFD) provides a comprehensive description of cloud features over the East Asia and west North Pacific regions for the 7-year period from April 2016 to December 2022. Multiple cloud variables, such as cloud mask, phase/type, top height, optical thickness, and particle effective radius, as well as snow, dust, and haze masks, were generated from the visible and infrared measurements of the Advanced Himawari Imager (AHI) on board the Japanese geostationary satellites Himawari-8 and Himawari-9 using a series of recently developed cloud retrieval algorithms. Verifications with the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) 1 km cloud layer product and the Moderate Resolution Imaging Spectroradiometer (MODIS) Level-2 cloud product (MYD06) demonstrate that the NJIAS HCFD gives higher skill scores than the Japanese Himawari-8/9 operational cloud product for all cloud variables except for cloud particle effective radius. The NJIAS HCFD even outperforms the MYD06 in nighttime cloud detection; cloud-top height, pressure, and temperature estimation; and infrared-only cloud-top phase determination. All evaluations are performed at the nominal 2 km scale, not including the effects of sub-pixel cloudiness or very thin cirrus. Two examples are presented to demonstrate applications of the NJIAS HCFD for climate and typhoon research. The NJIAS HCFD has been published in the Science Data Bank (https://doi.org/10.57760/sciencedb.09950, Zhuge 2023a; https://doi.org/10.57760/sciencedb.09953, Zhuge 2023b; https://doi.org/10.57760/sciencedb.09954, Zhuge 2023c; https://doi.org/10.57760/sciencedb.10158, Zhuge 2023d; https://doi.org/10.57760/sciencedb.09945, Zhuge 2023e).

Accumulation of absorbing aerosols over the Indian Sector of Southern Ocean during the austral summer: Role of thermal inversions

Optical properties of aerosols and their direct radiative effect were analyzed from columnar Aerosol Optical Depth (AOD) and ambient Black Carbon (BC) mass concentration (MB) estimated during Indian Southern Ocean Expeditions (SOEs), conducted in the austral summers of 2016–17 (SOE-IX) and 2017–18 (SOE-X). The aforementioned observations were supplemented with concurrent vertical soundings of the lower atmosphere. Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data aided in investigating the vertical distribution of aerosols up to 10 km altitude. The relative contribution of BC aerosols emanating from fossil fuel combustion and biomass burning showed the former to be predominant, as the latter is larger and preferentially scavenged during long-range transport to remote marine regions. The highest values of AOD at 500 nm (AOD500; 0.156 ± 0.026) and MB (90.78 ± 40.11 ng m−3) were encountered in the region between 50 and 60°S. Consequently, the atmospheric clear-sky Aerosol Direct Radiative Effect (ADRE; +3.10 ± 0.38 W m−2) and heating rate (HR; 0.087 ± 0.011 K day−1) were also observed to be the highest in the region. Multiple atmospheric inversions were detected between 40 and 69°S. Although a much lower AOD500 (0.070 ± 0.017) prevailed between 60 and 69°S, the average clear-sky ADRE (+1.56 ± 0.49 W m−2) and HR (0.044 ± 0.014 K day−1) were fairly high. The mean MB in the region was high (68.81 ± 28.40 ng m−3) for the pristine ice-covered region. Low-lying inversions prevented the vertical ventilation and dispersal of BC therein. Simulations of BC-induced atmospheric heating rates at three locations in the coastal Antarctic waters indicated a ≈ 3–5-fold increase, corresponding to an increase in ambient BC from 50 to 300 ng m−3.