Cloud-Aerosol Lidar With Orthogonal Polarization Research Articles

Retrieved XCO2 Accuracy Improvement by Reducing Aerosol-Induced Bias for China’s Future High-Precision Greenhouse Gases Monitoring Satellite Mission

China is developing the High-precision Greenhouse gases Monitoring Satellite (HGMS), carrying a high-spectral-resolution lidar (HSRL) for aerosol vertical profiles and imaging grating spectrometers for CO2 measurements at the same time. By providing simultaneous evaluation of the aerosol scattering effect, HGMS would reduce the bias of the XCO2 retrievals from the passive sensor. In this work, we propose a method to reduce aerosol-induced bias in XCO2 retrievals for the future HGMS mission based on the correlation analysis among simulated radiance, XCO2 bias, and aerosol optical depth (AOD) ratio. We exercise the method with the Orbiting Carbon Observatory-2 (OCO-2) XCO2 retrievals and AOD ratio inferred from the OCO-2 O2 A-band aerosol parameters at 755 nm and the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) AOD at 532 nm at several Total Carbon Column Observing Network (TCCON) sites in Europe. The results showed that 80% of measurements from OCO-2 were improved, and data from six TCCON sites show an average of 2.6 ppm reduction in mean bias and a 68% improvement in accuracy. We demonstrate the advantage of fused active–passive observation of the HGMS for more accurate global XCO2 measurements in the future.

Arctic spring and summertime aerosol optical depth baseline from long-term observations and model reanalyses – Part 1: Climatology and trend

Abstract. We present an Arctic aerosol optical depth (AOD) climatology and trend analysis for 2003–2019 spring and summertime periods derived from a combination of multi-agency aerosol reanalyses, remote-sensing retrievals, and ground observations. This includes the U.S. Navy Aerosol Analysis and Prediction System ReAnalysis version 1 (NAAPS-RA v1), the NASA Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and the Copernicus Atmosphere Monitoring Service ReAnalysis (CAMSRA). Spaceborne remote-sensing retrievals of AOD are considered from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Multi-angle Imaging SpectroRadiometer (MISR), and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). Ground-based data include sun photometer data from AErosol RObotic NETwork (AERONET) sites and oceanic Maritime Aerosol Network (MAN) measurements. Aerosol reanalysis AODs and spaceborne retrievals show consistent climatological spatial patterns and trends for both spring and summer seasons over the lower Arctic (60–70∘ N). Consistent AOD trends are also found for the high Arctic (north of 70∘ N) from reanalyses. The aerosol reanalyses yield more consistent AOD results than climate models, can be verified well with AERONET, and corroborate complementary climatological and trend analysis. Speciated AODs are more variable than total AOD among the three reanalyses and a little more so for March–May (MAM) than for June–August (JJA). Black carbon (BC) AOD in the Arctic comes predominantly from biomass burning (BB) sources in both MAM and JJA, and BB overwhelms anthropogenic sources in JJA for the study period. AOD exhibits a multi-year negative MAM trend and a positive JJA trend in the Arctic during 2003–2019, due to an overall decrease in sulfate/anthropogenic pollution and a significant JJA increase in BB smoke. Interannual Arctic AOD variability is significantly large, driven by fine-mode and, specifically, BB smoke, with both smoke contribution and interannual variation larger in JJA than in MAM. It is recommended that climate models should account for BB emissions and BB interannual variabilities and trends in Arctic climate change studies.

Development of China’s first space-borne aerosol-cloud high-spectral-resolution lidar: retrieval algorithm and airborne demonstration

Aerosols and clouds greatly affect the Earth’s radiation budget and global climate. Light detection and ranging (lidar) has been recognized as a promising active remote sensing technique for the vertical observations of aerosols and clouds. China launched its first space-borne aerosol-cloud high-spectral-resolution lidar (ACHSRL) on April 16, 2022, which is capable for high accuracy profiling of aerosols and clouds around the globe. This study presents a retrieval algorithm for aerosol and cloud optical properties from ACHSRL which were compared with the end-to-end Monte-Carlo simulations and validated with the data from an airborne flight with the ACHSRL prototype (A2P) instrument. Using imaging denoising, threshold discrimination, and iterative reconstruction methods, this algorithm was developed for calibration, feature detection, and extinction coefficient (EC) retrievals. The simulation results show that 95.4% of the backscatter coefficient (BSC) have an error less than 12% while 95.4% of EC have an error less than 24%. Cirrus and marine and urban aerosols were identified based on the airborne measurements over different surface types. Then, comparisons were made with U.S. Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) profiles, Moderate-resolution Imaging Spectroradiometer (MODIS), and the ground-based sun photometers. High correlations (R > 0.79) were found between BSC (EC) profiles of A2P and CALIOP over forest and town cover, while the correlation coefficients are 0.57 for BSC and 0.58 for EC over ocean cover; the aerosol optical depth retrievals have correlation coefficient of 0.71 with MODIS data and show spatial variations consistent with those from the sun photometers. The algorithm developed for ACHSRL in this study can be directly employed for future space-borne high-spectral-resolution lidar (HSRL) and its data products will also supplement CALIOP data coverage for global observations of aerosol and cloud properties.

Vertical stratification of aerosols over South Asian cities

This study examines vertically resolved aerosol optical properties retrieved from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard CALIPSO satellite over several cities across South Asia from March 2010 to February 2021. Atmospheric layer-specific stratification of aerosols and dominant aerosol sub-types was recognized over each city with their seasonal trends. A contrasting pattern in aerosol vertical distribution over cities across Indo-Gangetic Plain (IGP) was noted compared to non-IGP cities, with considerable dependency on geographic location of the city itself. In all the cases, total extinction decreased with increasing altitude however, with varying degree of slope. A clear intrusion of transported aerosols at higher altitude (>3 km) was also evident. Extinction coefficient of type-separated aerosols indicate robust contribution of smoke aerosols, urban aerosols/polluted dust, and mineral dust below 3 km height. At higher altitude (>3 km), dust and urban aerosols dominate over majority of the stations. Overall, 51% of total columnar aerosols remained within 0–1 km height over South Asian cities, slightly high over the IGP (57%) against non-IGP cities (39%). Such distribution also has a seasonal pattern with higher fraction of aerosols remaining below 1 km during post-monsoon (October–November, 62%) and winter (December–February, 72%) compared to summer months (March–May, 39%). When partitioned against planetary boundary layer (PBL), 41% (59%) of aerosols remained within the PBL (free troposphere) that too exhibiting strong diurnal variations irrespective of seasons. Dominating aerosol types and their contribution to total aerosol loading was explored by comparing type-based aerosol extinction against total aerosol extinction. Dust, smoke and urban aerosols emerged as three predominating aerosol types, while presence of marine aerosol was noted over the coastal cities. Major fraction of smoke and urban aerosols remained within 2 km height from surface. In contrast, efficient transport of dust aerosol above 2 km height was evident particularly over IGP during summer.

Assessment of CALIOP-Derived CCN Concentrations by In Situ Surface Measurements

The satellite-based cloud condensation nuclei (CCN) proxies used to quantify the aerosol-cloud interactions (ACIs) are column integrated and do not guarantee the vertical co-location of aerosols and clouds. This has encouraged the use of height-resolved measurements of spaceborne lidars for ACI studies and led to advancements in lidar-based CCN retrieval algorithms. In this study, we present a comparison between the number concentration of CCN (nCCN) derived from ground-based in situ and spaceborne lidar cloud-aerosol lidar with orthogonal polarization (CALIOP) measurements. On analysing their monthly time series, we found that about 88% of CALIOP nCCN estimates remained within a factor of 1.5 of the in situ measurements. Overall, the CALIOP estimates of monthly nCCN were in good agreement with the in situ measurements with a normalized mean error of 71%, normalized mean bias of 39% and correlation coefficient of 0.68. Based on our comparison results, we point out the necessary measures that should be considered for global nCCN retrieval. Our results show the competence of CALIOP in compiling a global height- and type-resolved nCCN dataset for use in ACI studies.

Dust transport and advection measurement with spaceborne lidars ALADIN and CALIOP and model reanalysis data

Abstract. In this paper, a long-term large-scale Saharan dust transport event which occurred between 14 and 27 June 2020 is tracked with the spaceborne lidars ALADIN (Atmospheric Laser Doppler Instrument) and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) together with ECMWF (European Centre for Medium-Range Forecasts) and HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory model) analysis. We evaluate the performance of ALADIN and CALIOP on the observations of dust optical properties and wind fields and explore the possibility of tracking the dust events and calculating the dust mass advection with the combination of satellite and model data. The dust plumes are identified with the AIRS/Aqua Dust Score Index and with the vertical feature mask product from CALIOP. The emission, dispersion, transport and deposition of the dust event are monitored using the data from AIRS/Aqua, CALIOP and HYSPLIT. With the quasi-synchronized observations by ALADIN and CALIOP, combined with the wind field and relative humidity, the dust advection values are calculated. From this study, it is found that the dust event generated on 14 and 15 June 2020 from the Sahara in North Africa dispersed and moved westward over the Atlantic Ocean, finally being deposited in the western Atlantic Ocean, the Americas and the Caribbean Sea. During the transport and deposition processes, the dust plumes are trapped in the northeasterly trade-wind zone between latitudes of 5∘ and 30∘ N and altitudes of 0 and 6 km. Aeolus provided the observations of the dynamics of this dust transport event in the Saharan Air Layer (SAL). From the measurement results on 19 June 2020, the dust plumes are captured quasi-simultaneously over the emission region (Western Sahara), the transport region (middle Atlantic) and the deposition region (western Atlantic) individually, which indicates that the dust plume area over the Atlantic on the morning of this day is quite enormous and that this dust transport event is massive and extensive. The quasi-synchronization observation results of 15, 16, 19, 24 and 27 June by ALADIN and CALIOP during the entire transport process show good agreement with the Dust Score Index data and the HYSPLIT trajectories, which indicates that the transport process of the same dust event is tracked by ALADIN and CALIOP, verifies that the dust transport spent around 2 weeks from the emission to the deposition and achieved the respective observations of this dust event's emission phase, development phase, transport phase, descent phase and deposition phase. Finally, the advection values for different dust parts and heights on 19 June and on the entire transport routine during transportation are computed. On 19 June, the mean dust advection values are about 1.91±1.21 mg m−2 s−1 over the emission region, 1.38±1.28 mg m−2 s−1 over the transport region and 0.75±0.68mgm-2s-1 over the deposition region. In the whole lifetime of the dust event, the mean dust advection values were about 1.51±1.03mgm-2s-1 on 15 June 2020, 2.19±1.72mgm-2s-1 on 16 June 2020, 1.38±1.28mgm-2s-1 on 19 June 2020, 1.60±1.08mgm-2s-1 on 24 June 2020 and 1.03±0.60mgm-2s-1 on 27 June 2020. During the dust development stage, the mean advection values gradually increased and reached their maximum on 16 June with the enhancement of the dust event. Then, the mean advection values decreased during the transport and the deposition of the dust over the Atlantic Ocean, the Americas and the Caribbean Sea.

Evaluation of aerosol number concentrations from CALIPSO with ATom airborne in situ measurements

Abstract. The present study aims to evaluate the available aerosol number concentration (ANC) retrieval algorithms for spaceborne lidar CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) aboard the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellite with the airborne in situ measurements from the ATom (Atmospheric Tomography Mission) campaign. We used HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory model) to match both the measurements in space and identified 53 cases that were suitable for comparison. Since the ATom data include the dry aerosol extinction coefficient, we used kappa parameterization to adjust the ambient measurements from CALIOP to dry conditions. As both the datasets have a different vertical resolution, we regrid them to uniform height bins of 240 m from the surface to a height of 5 km. On comparing the dry extinction coefficients, we found a reasonable agreement between the CALIOP and ATom measurements with Spearman's correlation coefficient of 0.715. Disagreement was found mostly for retrievals above 3 km altitude. Thus, to compare the ANC, which may vary by orders of magnitude in space and time, we further limit the datasets and only select those height bins for which the CALIOP-derived dry extinction coefficient is within ±50 % of the ATom measurements. This additional filter further increases the probability of comparing the same air parcel. The altitude bins which qualify the extinction coefficient constraint are used to estimate ANC with a dry radius >50 nm (n50,dry) and >250 nm (n250,dry). The POLIPHON (Polarization Lidar Photometer Networking) and OMCAM (Optical Modelling of CALIPSO Aerosol Microphysics) algorithms were used to estimate the n50,dry and n250,dry. The POLIPHON estimates of n50,dry and n250,dry were found to be in good agreement with the in situ measurements, with a correlation coefficient of 0.829 and 0.47, root mean square error (RMSE) of 234 and 13 cm−3, and bias of −97 and 4 cm−3, respectively. The OMCAM estimates of n50,dry and n250,dry were also in reasonable agreement with the in situ measurements, with a correlation coefficient of 0.823 and 0.463, RMSE of 247 and 13 cm−3, and bias of 44 and 4 cm−3, respectively. However, we found that the OMCAM-estimated n50,dry were about an order of magnitude less than the in situ measurements for marine-dominated cases. We propose a modification to the OMCAM algorithm by using an AERONET-based marine model. With the updated OMCAM algorithm, the n50,dry agrees well with the ATom measurements. Such concurrence between the satellite-derived ANC and the independent ATom in situ measurements emboldens the use of CALIOP in studying the aerosol–cloud interactions.

Improving discrimination between clouds and optically thick aerosol plumes in geostationary satellite data

Abstract. Cloud masking is a key initial step in the retrieval of geophysical properties from satellite data. Despite decades of research, problems still exist of over- or underdetection of clouds. High aerosol loadings, in particular from dust storms or fires, are often classified as clouds, and vice versa. In this paper, we present a cloud mask created using machine learning for the Advanced Himawari Imager (AHI) aboard Himawari-8. In order to train the algorithm, a parallax-corrected collocated data set was created from AHI and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar data. Artificial neural networks (ANNs) were trained on the collocated data to identify clouds in AHI scenes. The resulting neural network (NN) cloud masks are validated and compared to cloud masks produced by the Japanese Meteorological Association (JMA) and the Bureau of Meteorology (BoM) for a number of different solar and viewing geometries, surface types and air masses. Here, five case studies covering a range of challenging scenarios for cloud masks are also presented to demonstrate the performance of the masking algorithm. The NN mask shows a lower false positive rate (FPR) for an equivalent true positive rate (TPR) across all categories, with FPRs of 0.160 and 0.259 for the NN and JMA masks, respectively, and 0.363 and 0.506 for the NN and BoM masks, respectively, at equivalent TPR values. This indicates the NN mask accurately identifies 1.13 and 1.29 times as many non-cloud pixels for the equivalent hit rate when compared to the JMA and BoM masks, respectively. The NN mask was shown to be particularly effective in distinguishing thick aerosol plumes from cloud, most likely due to the inclusion of the 0.47 and 0.51 µm bands. The NN cloud mask shows an improvement over current operational cloud masks in most scenarios, and it is suggested that improvements to current operational cloud masks could be made by including the 0.47 and 0.51 µm bands. The collocated data are made available to facilitate future research.

First Observations of Cirrus Clouds Using the UZ Mie Lidar over uMhlathuze City, South Africa

Clouds cover more than two-thirds of the earth’s surface and play a dominant role in the energy and water cycle of our planet. Cirrus clouds are high-level clouds composed mostly of ice crystals and affect the earth’s radiation allocation mainly by absorbing outgoing longwave radiation and by reflecting solar radiation. This study presents the characterization of cirrus clouds observed on 10 and 11 April 2019 using the ground-based University of Zululand (UZ) light detection and ranging (lidar) for the first time. Dense cirrus clouds with an average thickness of ~1.5 km at a height range of 9.5–12 km on 10 and 11 April 2019 were observed by the UZ lidar. The UZ lidar observation on 10 April 2019 agreed with the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) observation.

An aerosol vertical data assimilation system (NAQPMS-PDAF v1.0): development and application

Abstract. Aerosol vertical stratification is important for global climate and planetary boundary layer (PBL) stability, and no single method can obtain spatiotemporally continuous vertical profiles. This paper develops an online data assimilation (DA) framework for the Eulerian atmospheric chemistry-transport model (CTM) Nested Air Quality Prediction Model System (NAQPMS) with the Parallel Data Assimilation Framework (PDAF) as the NAQPMS-PDAF for the first time. Online coupling occurs based on a memory-based way with two-level parallelization, and the arrangement of state vectors during the filter is specifically designed. Scaling tests demonstrate that the NAQPMS-PDAF can make efficient use of parallel computational resources for up to 25 000 processors with a weak scaling efficiency of up to 0.7. The 1-month long aerosol extinction coefficient profiles measured by the ground-based lidar and the concurrent hourly surface PM2.5 are solely and simultaneously assimilated to investigate the performance and application of the DA system. The hourly analysis and subsequent 1 h simulation are validated through lidar and surface PM2.5 measurements assimilated and not assimilated. The results show that lidar DA can significantly improve the underestimation of aerosol loading, especially at a height of approximately 400 m in the free-running (FR) experiment, with the mean bias (BIAS) changing from −0.20 (−0.14) km−1 to −0.02 (−0.01) km−1 and correlation coefficients increasing from 0.33 (0.28) to 0.91 (0.53) averaged over sites with measurements assimilated (not assimilated). Compared with the FR experiment, simultaneously assimilating PM2.5 and lidar can have a more consistent pattern of aerosol vertical profiles with a combination of surface PM2.5 and lidar, independent extinction coefficients from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), and aerosol optical depth (AOD) from the Aerosol Robotic Network (AERONET). Lidar DA has a larger temporal impact than that in PM2.5 DA but has deficiencies in subsequent quantification on the surface PM2.5. The proposed NAQPMS-PDAF has great potential for further research on the impact of aerosol vertical distribution.

Aerosol optical properties calculated from size distributions, filter samples and absorption photometer data at Dome C, Antarctica, and their relationships with seasonal cycles of sources

Abstract. Optical properties of surface aerosols at Dome C, Antarctica, in 2007–2013 and their potential source areas are presented. Scattering coefficients (σsp) were calculated from measured particle number size distributions with a Mie code and from filter samples using mass scattering efficiencies. Absorption coefficients (σap) were determined with a three-wavelength Particle Soot Absorption Photometer (PSAP) and corrected for scattering by using two different algorithms. The scattering coefficients were also compared with σsp measured with a nephelometer at the South Pole Station (SPO). The minimum σap was observed in the austral autumn and the maximum in the austral spring, similar to other Antarctic sites. The darkest aerosol, i.e., the lowest single-scattering albedo ωo≈0.91, was observed in September and October and the highest ωo≈0.99 in February and March. The uncertainty of the absorption Ångström exponent αap is high. The lowest αap monthly medians were observed in March and the highest in August–October. The equivalent black carbon (eBC) mass concentrations were compared with eBC measured at three other Antarctic sites: the SPO and two coastal sites, Neumayer and Syowa. The maximum monthly median eBC concentrations are almost the same (∼3±1 ng m−3) at all these sites in October–November. This suggests that there is no significant difference in eBC concentrations between the coastal and plateau sites. The seasonal cycle of the eBC mass fraction exhibits a minimum f(eBC) ≈0.1 % in February–March and a maximum ∼4 %–5 % in August–October. Source areas were calculated using 50 d FLEXPART footprints. The highest eBC concentrations and the lowest ωo were associated with air masses coming from South America, Australia and Africa. Vertical simulations that take BC particle removal processes into account show that there would be essentially no BC particles arriving at Dome C from north of latitude 10∘ S at altitudes <1600 m. The main biomass-burning regions Africa, Australia and Brazil are more to the south, and their smoke plumes have been observed at higher altitudes than that, so they can get transported to Antarctica. The seasonal cycle of BC emissions from wildfires and agricultural burning and other fires in South America, Africa and Australia was calculated from data downloaded from the Global Fire Emissions Database (GFED). The maximum total emissions were in August–September, but the peak of monthly average eBC concentrations is observed 2–3 months later in November, not only at Dome C, but also at the SPO and the coastal stations. The air-mass residence-time-weighted BC emissions from South America are approximately an order of magnitude larger than from Africa and Oceania, suggesting that South American BC emissions are the largest contributors to eBC at Dome C. At Dome C the maximum and minimum scattering coefficients were observed in austral summer and winter, respectively. At the SPO σsp was similar to that observed at Dome C in the austral summer, but there was a large difference in winter, suggesting that in winter the SPO is more influenced by sea-spray emissions than Dome C. The seasonal cycles of σsp at Dome C and at the SPO were compared with the seasonal cycles of secondary and primary marine aerosol emissions. The σsp measured at the SPO correlated much better with the sea-spray aerosol emission fluxes in the Southern Ocean than σsp at Dome C. The seasonal cycles of biogenic secondary aerosols were estimated from monthly average phytoplankton biomass concentrations obtained from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) satellite sensor data. The analysis suggests that a large fraction of the biogenic scattering aerosol observed at Dome C has been formed in the polar zone, but it may take a month for the aerosol to be formed, be grown and get transported from the sea level to Dome C.

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.

Assessing the benefits of Imaging Infrared Radiometer observations for the CALIOP version 4 cloud and aerosol discrimination algorithm

Abstract. The features detected in monolayer atmospheric columns sounded by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and classified as cloud or aerosol layers by the CALIOP version 4 (V4) cloud and aerosol discrimination (CAD) algorithm are reassessed using perfectly collocated brightness temperatures measured by the Imaging Infrared Radiometer (IIR) aboard the same satellite. Using the IIR's three wavelength measurements of layers that are confidently classified by the CALIOP CAD algorithm, we calculate two-dimensional (2-D) probability distribution functions (PDFs) of IIR brightness temperature differences (BTDs) for different cloud and aerosol types. We then compare these PDFs with 1-D radiative transfer simulations for ice and water clouds and dust and marine aerosols. Using these IIR 2-D BTD signature PDFs, we develop and deploy a new IIR-based CAD algorithm and compare the classifications obtained to the results reported by the CALIOP-only V4 CAD algorithm. IIR observations are shown to be able to identify clouds with a good accuracy. The IIR cloud identifications agree very well with layers classified as confident clouds by the V4 CAD algorithm (88 %). More importantly, simultaneous use of IIR information reduces the ambiguity in a notable fraction of “not confident” V4 cloud classifications. 28 % and 14 % of the ambiguous V4 cloud classifications are reclassified more appropriately as confident cloud layers through the use of the IIR observations in the tropics and in the midlatitudes, respectively. IIR observations are of relatively little help in deriving high-confidence classifications for most aerosols, as the low altitudes and small optical depths of aerosol layers yield IIR signatures that are similar to those of clear skies. However, misclassifications of aerosol layers, such as dense dust or elevated smoke layers, by the V4 CAD algorithm can be corrected to cloud layer classification by including IIR information. 10 %, 16 %, and 6 % of the ambiguous V4 dust, polluted dust, and tropospheric elevated smoke, respectively, are found to be misclassified cloud layers by the IIR measurements.

Five-satellite-sensor study of the rapid decline of wildfire smoke in the stratosphere

Abstract. Smoke from western North American wildfires reached the stratosphere in large amounts in August 2017. Limb-oriented satellite-based sensors are commonly used for studies of wildfire aerosol injected into the stratosphere (OMPS-LP (Ozone Mapping and Profiler Suite Limb Profiler) and SAGE III/ISS (Stratospheric Aerosol and Gas Experiment III on the International Space Station)). We find that these methods are inadequate for studies of the first 1–2 months after such a strong fire event due to event termination (“saturation”). The nadir-viewing lidar CALIOP (Cloud-Aerosol LIdar with Orthogonal Polarization) is less affected due to shorter path in the smoke; furthermore, it provides a means to develop a method to correct for strong attenuation of the signal. After the initial phase, the aerosol optical depth (AOD) from OMPS-LP and CALIOP show very good agreement above the 380 K isentrope, whereas OMPS-LP tends to produce higher AOD than CALIOP in the lowermost stratosphere (LMS), probably due to reduced sensitivity at altitudes below 17 km. Time series from CALIOP of attenuation-corrected stratospheric AOD of wildfire smoke show an exponential decline during the first month after the fire, which coincides with highly significant changes in the wildfire aerosol optical properties. The AOD decline is verified by the evolution of the smoke layer composition, comparing the aerosol scattering ratio (CALIOP) to the water vapor concentration from MLS (Microwave Limb Sounder). Initially the stratospheric wildfire smoke AOD is comparable with the most important volcanic eruptions during the last 25 years. Wildfire aerosol declines much faster, 80 %–90 % of the AOD is removed with a half-life of approximately 10 d. We hypothesize that this dramatic decline is caused by photolytic loss. This process is rarely observed in the atmosphere. However, in the stratosphere this process can be studied with practically no influence from wet deposition, in contrast to the troposphere where this is the main removal path of submicron aerosol particles. Despite the loss, the aerosol particles from wildfire smoke in the stratosphere are relevant for the climate.

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.

CALIOP-Based Quantification of Central Asian Dust Transport

Central Asia is one of the most important sources of mineral saline dust worldwide. A comprehensive understanding of Central Asian dust transport is essential for evaluating its impacts on human health, ecological safety, weather and climate. This study first puts forward an observation-based climatology of Central Asian dust transport flux by using the 3-D dust detection of Cloud-Aerosol LiDAR with Orthogonal Polarization (CALIOP). The seasonal difference of transport flux and downstream contribution are evaluated and compared with those of the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). Central Asian dust can be transported not only southward in summer under the effect of the South Asian summer monsoon, but also eastward in other seasons under the control of the westerly jet. Additionally, the transport of Central Asian dust across the Pamir Plateau to the Tibetan Plateau is also non-negligible, especially during spring (with a transport flux rate of 150 kg m−1 day−1). The annual CALIOP-based downstream contribution of Central Asian dust to South Asian (164.01 Tg) is 2.1 times that to East Asia (78.36 Tg). This can be attributed to the blocking effect of the higher terrain between Central and East Asia. Additionally, the downstream contributions to South and East Asia from MERRA-2 are only 0.36 and 0.84 times that of CALIOP, respectively. This difference implies the overestimation of the wet and dry depositions of the model, especially in the low latitude zone. The quantification of the Central Asian dust transport allows a better understanding of the Central Asian dust cycle, and supports the calibration/validation of aerosol-related modules of regional and global climate models.

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.

Comparison of scattering ratio profiles retrieved from ALADIN/Aeolus and CALIOP/CALIPSO observations and preliminary estimates of cloud fraction profiles

Abstract. The space-borne active sounders have been contributing invaluable vertically resolved information of atmospheric optical properties since the launch of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) in 2006. To build long-term records from space-borne lidars useful for climate studies, one has to understand the differences between successive space lidars operating at different wavelengths, flying on different orbits, and using different viewing geometries, receiving paths, and detectors. In this article, we compare the results of Atmospheric Laser Doppler INstrument (ALADIN) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidars for the period from 28 June to 31 December 2019. First, we build a dataset of ALADIN–CALIOP collocated profiles (Δdist&lt;1∘; Δtime&lt;6 h). Then we convert ALADIN's 355 nm particulate backscatter and extinction profiles into the scattering ratio vertical profiles SR(z) at 532 nm using molecular density profiles from Goddard Earth Observing System Data Assimilation System, version 5 (GEOS-5 DAS). And finally, we build the CALIOP and ALADIN globally gridded cloud fraction profiles CF(z) by applying the same cloud detection threshold to the SR(z) profiles of both lidars at the same spatial resolution. Before comparing the SR(z) and CF(z) profiles retrieved from the two analyzed lidar missions, we performed a numerical experiment to estimate the best achievable cloud detection agreement CDAnorm(z) considering the differences between the instruments. We define CDAnorm(z) in each latitude–altitude bin as the occurrence frequency of cloud layers detected by both lidars, divided by a cloud fraction value for the same latitude–altitude bin. We simulated the SR(z) and CF(z) profiles that would be observed by these two lidars if they were flying over the same atmosphere predicted by a global model. By analyzing these simulations, we show that the theoretical limit for CDAnormtheor(z) for a combination of ALADIN and CALIOP instruments is equal to 0.81±0.07 at all altitudes. In other words, 19 % of the clouds cannot be detected simultaneously by two instruments due to said differences. The analyses of the actual observed CALIOP–ALADIN collocated dataset containing ∼78 000 pairs of nighttime SR(z) profiles revealed the following points: (a) the values of SR(z) agree well up to ∼3 km height. (b) The CF(z) profiles show agreement below ∼3 km, where ∼80 % of the clouds detected by CALIOP are detected by ALADIN as expected from the numerical experiment. (c) Above this height, the CDAnormobs(z) reduces to ∼50 %. (d) On average, better sensitivity to lower clouds skews ALADIN's cloud peak height in pairs of ALADIN–CALIOP profiles by ∼0.5±0.6 km downwards, but this effect does not alter the heights of polar stratospheric clouds and high tropical clouds thanks to their strong backscatter signals. (e) The temporal evolution of the observed CDAnormobs(z) does not reveal any statistically significant change during the considered period. This indicates that the instrument-related issues in ALADIN L0/L1 have been mitigated, at least down to the uncertainties of the following CDAnormobs(z) values: 68±12 %, 55±14 %, 34±14 %, 39±13 %, and 42±14 % estimated at 0.75, 2.25, 6.75, 8.75, and 10.25 km, respectively.

New insights into the Asian dust cycle derived from CALIPSO lidar measurements

A full understanding of the Asian dust cycle can help with evaluation of the profound impact of mineral dust on human health, the ecosystem, the terrestrial and oceanic biogeochemical cycles, and the weather and climate. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)-based 3-D dust detection and routine sampling capability, with the accurate dust mass extinction efficiency from Dust Constraints from joint Observational-Modelling-experimental analysis (DustCOMM) dataset, has made it possible to estimate the climatology of Asian dust mass loading (DML) and its transport flux. This study draws on this to provide new insights into the Asian dust cycle, especially the variability of its mass-weighted dust transport central axis (TCA), the contribution of different desert sources to its downstream effects, and the resulting dust budgets in terrestrial and oceanic regions. Dust aerosols emitted from the East Asian and Central Asian deserts together form a heavy dust transport belt stretching from the Taklimakan Desert (TD) and Gobi Desert (GD) to the Pacific Ocean. South Asian dust from the Thar Desert (ThD) can also affect southern China by crossing the Hengduan Mountains and the Yunnan-Guizhou Plateau. The dust TCA is controlled by the terrain of northwest inland China, but shifts in remote regions in the range of 35-50°N due to the western Pacific subtropical high and Aleutian low, and it trend towards a zonal straight line as the altitude increases. The dust transport contribution of the East Asian deserts to the mainland of China and adjacent sea is about 7 times than that of South Asia, with the annual transport fluxes being 214.28 and 30.43 Tg, respectively. The GD dominates the contribution of Asian deserts to the downstream effects and accounts for about 60% of the dust. This can be attributed to its maximum transport flux being near the surface, while the dust transport of the TD and ThD is above 3 km because of the blocking effect of the surrounding terrain. The deposition of Asian dust in the adjacent seas decreases significantly along the dust TCA, with the annual deposition rates being about 40.12, 20.41, and 4.01 g m-2 in the Yellow Sea, Japan Sea and the Northwest Pacific Ocean, respectively. These new findings and quantification of the Asian dust cycle will help with validation of the simulations provided by global and regional climate models and enable further evaluation of the impact of Asian dust on various related Earth systems.

Retrieving particulate matter concentrations over the contiguous United States using CALIOP observations

Using twelve years (2007–2018) of NASA Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) near-surface 532 nm aerosol extinction retrievals, multi-year mean and trends of particulate matter (PM) concentrations are derived over the contiguous United States (CONUS). Different from past studies that use column integrated aerosol optical thickness, here only near-surface CALIOP aerosol extinction is used for deriving near-surface PM with aerodynamic diameters less than 2.5 μm (PM2.5) concentrations using an innovative, bulk-mass-modeling-based method. Compared against ground based PM2.5 measurements from the U.S. Environmental Protection Agency (EPA), an encouraging relationship between CALIOP-derived PM2.5 and EPA-observed PM2.5 (Deming slope = 0.89; RMSE = 3.42 μg/m3; mean bias = −1.00 μg/m3) is found using combined daytime/nighttime CALIOP data. Also, comparable trends in PM2.5 concentrations from the EPA and daytime and nighttime CALIOP data are found for most of the eastern CONUS and imply that air quality is generally improving over this region for the study period. Over the western CONUS, a seasonal analysis reveals that PM2.5 trends are positive during the more active wildfire season (June through November) but negative for other months. This study suggests that lidar data show promise in their use for obtaining PM2.5 estimates and provides motivation to further explore aerosol extinction-based PM concentration retrievals in anticipation of future space-based lidar missions.