Cloud-Aerosol Lidar With Orthogonal Polarization Research Articles

Influence of the transported Canadian wildfire smoke on the ozone and particle pollution over the Mid-Atlantic United States

The integration of observations and models can improve air quality forecasts (in particular ozone (O3) and particulate matter (PM)) for extreme events (e.g., wildfires). We present our work on the Canadian wildfire event on 6–12 June 2015 that impacted the air quality in the Mid-Atlantic region in the U.S. We use the Weather Research and Forecasting model coupled with Chemistry package (WRF-Chem), and various measurements from both ground-based and spaceborne observations, including the U.S. Environmental Protection Agency (EPA) AirNow data, the National Aeronautics and Space Administration (NASA) operated TROPospheric OZone lidar (TROPOZ), wind radar profiler, ceilometer, Moderate Resolution Imaging Spectroradiometer (MODIS), Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The objective is to understand the physics of the Planetary Boundary Layer (PBL) and its role on the O3 and PM forecast. The findings show that the model captured the O3 diurnal variation and PM spatial distribution both horizontally and vertically by comparing with EPA AirNow and MODIS/CALIOP observations, respectively. Wildfire smoke was transported from central Canada through Lake Michigan, passing the Ohio River Valley and down to the Baltimore-Washington D.C. metropolis. The night-time O3 mixing ratio reached 30 ppbv, while the daytime O3 mixing ratio approached larger than 100 ppbv near AirNow stations in Maryland, due to the mixing of the transported smoke into the PBL. The novel NASA TROPOZ lidar at Beltsville resolved the O3 vertical profile and the ceilometer identified the smoke intrusion at altitudes above 3.5 km, but later mixed down into the PBL and surface which was also resolved by the model. Thus, integrating both model and observations from different platforms confirms the Canadian wildfire source and transport pathway and improves the understanding of the air quality forecast during the extreme wildfire event.

Assimilating spaceborne lidar dust extinction can improve dust forecasts

Abstract. Atmospheric mineral dust has a rich tri-dimensional spatial and temporal structure that is poorly constrained in forecasts and analyses when only column-integrated aerosol optical depth (AOD) is assimilated. At present, this is the case of most operational global aerosol assimilation products. Aerosol vertical distributions obtained from spaceborne lidars can be assimilated in aerosol models, but questions about the extent of their benefit upon analyses and forecasts along with their consistency with AOD assimilation remain unresolved. Our study thoroughly explores the added value of assimilating spaceborne vertical dust profiles, with and without the joint assimilation of dust optical depth (DOD). We also discuss the consistency in the assimilation of both sources of information and analyse the role of the smaller footprint of the spaceborne lidar profiles in the results. To that end, we have performed data assimilation experiments using dedicated dust observations for a period of 2 months over northern Africa, the Middle East, and Europe. We assimilate DOD derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board Suomi National Polar-Orbiting Partnership (SUOMI-NPP) Deep Blue and for the first time Cloud-Aerosol Lidar with Orthogonal Polarisation (CALIOP)-based LIdar climatology of Vertical Aerosol Structure for space-based lidar simulation studies (LIVAS) pure-dust extinction coefficient profiles on an aerosol model. The evaluation is performed against independent ground-based DOD derived from AErosol RObotic NETwork (AERONET) Sun photometers and ground-based lidar dust extinction profiles from the Cyprus Clouds Aerosol and Rain Experiment (CyCARE) and PREparatory: does dust TriboElectrification affect our ClimaTe (Pre-TECT) field campaigns. Jointly assimilating LIVAS and Deep Blue data reduces the root mean square error (RMSE) in the DOD by 39 % and in the dust extinction coefficient by 65 % compared to a control simulation that excludes assimilation. We show that the assimilation of dust extinction coefficient profiles provides a strong added value to the analyses and forecasts. When only Deep Blue data are assimilated, the RMSE in the DOD is reduced further, by 42 %. However, when only LIVAS data are assimilated, the RMSE in the dust extinction coefficient decreases by 72 %, the largest improvement across experiments. We also show that the assimilation of dust extinction profiles yields better skill scores than the assimilation of DOD under an equivalent sensor footprint. Our results demonstrate the strong potential of future lidar space missions to improve desert dust forecasts, particularly if they foresee a depolarization lidar channel to allow discrimination of desert dust from other aerosol types.

Cloud, Atmospheric Radiation and Renewal Energy Application (CARE) Version 1.0 Cloud Top Property Product From Himawari-8/AHI: Algorithm Development and Preliminary Validation

Investigations of the effects of clouds on Earth’s radiation budget demand accurate representations of cloud top parameters, which can be efficiently obtained by large-scale satellite remote sensing approaches. However, the insufficient utilization of multiband information is one of the major sources of uncertainty in cloud top products derived from geostationary satellites. In this study, we developed a new algorithm to estimate Cloud, Atmospheric Radiation and renewal Energy application (CARE) version 1.0 cloud top properties (cloud top height (CTH), cloud top pressure (CTP), and cloud top temperature (CTT)). The algorithm is constructed from ten thermal spectral measurements in Himawari-8 observations by using the random forests method to comprehensively consider the contribution of each band to the cloud top parameters. We chose the highly accurate Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) products in 2018 as the true values. The sensitivity analysis demonstrated that the products can be fully reproduced by using multiple Himawari-8 channels with the addition of the digital elevation model (DEM) data. The validation results of the 2019 CALIOP data confirm that the new algorithm shows an effective performance, with correlation coefficients (R) of 0.89, 0.89, and 0.90 for CTH, CTP, and CTT, respectively. Moreover, a significant improvement in the ice cloud estimation is achieved, wherein the CTT R value increased from 0.46 to 0.70, as well as an improvement in the sea area, where the CTT R value increased from 0.71 to 0.84 compared with the Himawari-8 products of the Japan Aerospace Exploration Agency (JAXA) P-tree system. The further analyses performed herein capture the diurnal cycle of cloud top parameters well in different temporal scales over the Asia-Pacific region.

Combining CALIPSO and AERONET Data to Classify Aerosols Globally

Angstrom exponent (AE) and aerosol optical depth (AOD) obtained from the aerosol robotic network (AERONET) and volume depolarization ratio (VDR) obtained from the cloud-aerosol Lidar with orthogonal polarization (CALIOP) from March 2018 to February 2019 were used in our study. Data used in this study are direct observation, avoiding the limitations and uncertainties from the inversion process, and providing accurate information about the aerosol properties. Both instruments were within colocation criteria of a 40-km radius and ±2 h were defined as coincident cases. Six aerosol types were differentiated using the threshold method based on the AE, AOD, and VDR data. Discussion of the aerosol classification yielded the following results: 1) clean marine aerosols were the most abundant and widely distributed aerosols, followed by other types of aerosol (33.2%), polluted dust aerosols (26.8%), natural dust aerosols (2.3%), biomass burning aerosols (1.8%), and clean continental aerosols (1.1%); 2) clean marine aerosols were mainly distributed in North America and Europe, and polluted dust aerosols frequently appeared on the edges or downwind of deserts; and 3) the aerosols controlled by natural conditions (e.g., natural dust aerosols) were sensitive to seasonal variations, whereas those controlled by anthropogenic activities (e.g., polluted dust aerosols) were not. This study provides a new method for the collaborative observation of aerosol types with ground-based and satellite data. It is rare to provide annual global distribution of aerosol types and their seasonal variations; these results provide a reference for understanding the global aerosol distribution status.

Cloud Detection and Classification Algorithms for Himawari-8 Imager Measurements Based on Deep Learning

A deep-learning-based cloud detection and classification algorithm for advanced Himawari imager (AHI) measurements from the geostationary satellite Himawari-8 has been developed. It is found that a combination of observed radiances and simulated clear-sky radiances can substantially improve cloud phase discrimination, especially for optically thin clouds. Therefore, cloud detection, cloud phase classification, and multilayer cloud detection are obtained simultaneously from multispectral observed radiances and simulated clear-sky radiances using deep neural networks (DNNs). Two DNN models are established for all-day and daytime-only applications, respectively, using active Cloud Profiling Radar (CPR) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) merged cloud products from 2016 as reference labels. The independent dataset from 2017 is used to validate the DNN models. It is shown that both the DNN models outperform the official Moderate Resolution Imaging Spectroradiometer (MODIS) and AHI products in cloud detection and phase discrimination, and the enhancement is more significant over land than over water surface. For multilayer cloud detection, the probability of detecting multilayer clouds reaches ~60% for the all-day model and is increased to ~70% for the daytime model, which is substantially better than MODIS and AHI products. In practical cases, multilayer cloud detection by DNN models is more consistent with CPR/CALIOP than two official products. In addition, the DNN models have superior capability in detecting the optically thin cirrus, which is omitted by MODIS and AHI products. Specifically, the cases also demonstrate that the DNN models can provide effective mixed-phase cloud identification. This deep-learning-based algorithm has the potential for measurements from other similar instruments.

The thermal infrared optical depth of mineral dust retrieved from integrated CALIOP and IIR observations

Recent studies have suggested that global climate models tend to underestimate dust particle size in particular, the very coarse mode, leading to an underestimated direct radiative effect (DRE) of dust in the longwave (LW) thermal infrared (TIR) region. However, the magnitude of LW DRE remains highly uncertain, because of limited observations of dust optical depth at the TIR (DAODTIR). This study presents a simple approach to retrieve the DAODTIR over the oceans during nighttime through synergistic use of observations from the Infrared Imaging Radiometer (IIR) and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), both onboard of the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission. For each cloud-free dust-laden profile identified by the IIR-CALIOP observation, a Lookup-Table (LUT) of the 10.6 μm IIR band brightness temperatures (BT) difference (dBT) under different DAODTIR with respect to their dust-free BTs is constructed based on the CALIOP retrieved dust vertical profile and a priori dust scattering properties using a fast radiative transfer model. Then the DAODTIR is retrieved by projecting the IIR-observed dBT on the LUT. Sensitivity studies show that the DAODTIR retrieval at 10.6 μm is more susceptible to the dust particle size distribution (PSD) assumption than dust refractive indices. To estimate the uncertainty caused by PSD assumption, two DAODTIR retrieval products, one based on the dust PSD from the AERONET at Cape Verde and the other on an in situ measured PSD from the recent Fennec campaign, are provided. The retrieval uncertainty is mainly contributed by the BT difference between the observation and simulation using auxiliary atmospheric data. The climatology of the retrieval from 2013 to 2019 shows reasonable spatiotemporal variations of DAODTIR with the global-averaged value of 0.008 and 0.013 based on different pre-assumed dust PSDs. Climatological results agree reasonably well with two independent DAODTIR retrieval products based on the Infrared Atmospheric Sounding Interferometer (IASI) over the active dust transport regions, such as North and Tropical Atlantic (r = 0.9 and r = 0.8) and Indian Ocean (r = 0.8). The seasonal and interannual variation is also well-compared (r = 0.76) with AERONET coarse-mode AOD at 97 selected sites. The synergic CALIOP observation allows the retrieved DAODTIR to directly compare with the extrapolated DAODTIR from DAOD in the visible (i.e., 532 nm), which helps evaluate the observational constraints on DAODTIR. Our study clearly reveals that the retrieved DAODTIR from IIR is much less susceptible to various uncertainties than converted results from CALIOP. It also offers a unique prospect of collocated active lidar and passive IR observations for retrieving dust DAODTIR.

A scSE-LinkNet Deep Learning Model for Daytime Sea Fog Detection

Sea fog is a precarious weather disaster affecting transportation on the sea. The accuracy of the threshold method for sea fog detection is limited by time and region. In comparison, the deep learning method learns features of objects through different network layers and can therefore accurately extract fog data and is less affected by temporal and spatial factors. This study proposes a scSE-LinkNet model for daytime sea fog detection that leverages residual blocks to encoder feature maps and attention module to learn the features of sea fog data by considering spectral and spatial information of nodes. With the help of satellite radar data from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), a ground sample database was extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) L1B data. The scSE-LinkNet was trained on the training set, and quantitative evaluation was performed on the test set. Results showed the probability of detection (POD), false alarm rate (FAR), critical success index (CSI), and Heidke skill scores (HSS) were 0.924, 0.143, 0.800, and 0.864, respectively. Compared with other neural networks (FCN, U-Net, and LinkNet), the CSI of scSE-LinkNet was improved, with a maximum increase of nearly 8%. Moreover, the sea fog detection results were consistent with the measured data and CALIOP products.

Cloud and Aerosol Distributions From SAGE III/ISS Observations

AbstractWe describe our Solar Aerosol and Gas Experiment (SAGE) III/ISS cloud detection algorithm and observations. We identify three types of clouds: visible cirrus (extinction coefficient > 3 x 10−2 km−1), subvisible cirrus (extinction coefficient between 3 x 10−2 km−1 and 10−3 km−1), and very low extinction cloud‐aerosol mixtures (extinction coefficient between 10−3 km−1and 10−4 km−1). Visible cirrus cannot be quantitatively measured by SAGE because of its high extinction, but we infer the presence of cirrus through the termination of the SAGE vertical scan. We then assume that cirrus layers extend 0.5 km below the scan termination height. SAGE cirrus cloud fraction estimated in this way is in qualitative agreement with Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) measurements. Analyzing 2018–2020 SAGE III/ISS data, we find that visible cirrus and subvisible cirrus have nearly equal abundance in the tropical upper troposphere and the average cloud fraction is about 25%. At 16 km, the highest concentration of visible cirrus and subvisible cirrus is over the Tropical West Pacific, central Africa, and central South America during boreal winter. Latitudinal gaps in zonal mean cloud fraction are aligned with descending branch of the residual mean circulation. Using tropopause relative coordinates, we show that maximum cloud‐top heights are consistently restricted to a narrow region near the tropopause. The aerosol extinction fields also show the latitudinal gaps coincident with the descending residual circulation. We identify four anomalous aerosol extinction periods that can be tentatively assigned to significant volcanic or fire events.

Algorithm for Improved Stereoscopic Cloud-Top Height Retrieval Based on Visible and Infrared Bands for Himawari-8 and FY-4A

Stereoscopic cloud-top height (CTH) retrieval from two geostationary (GEO) satellites is usually realized through a visible (VIS) band with a high horizontal resolution. A stereoscopic-based CTH retrieval algorithm (prototype dual-GEO CTH algorithm) proposed in our previous study also adopts this approach. Although this approach can retrieve accurate stereoscopic CTHs, the heights of optically thin upper clouds overlying the lower clouds are challenging to retrieve because the parallax difference between two GEOs is determined by the lower clouds owing to the low reflectance from the upper clouds. To address this problem, this paper proposes an improved stereoscopic CTH retrieval algorithm, named the improved dual-GEO CTH algorithm, for Himawari-8 and FengYun (FY)-4A GEOs. The proposed algorithm employs an infrared (IR) band in addition to a VIS band. A seamless image cloning technique is adopted to blend the VIS and IR images, which are then used to retrieve the stereoscopic CTHs. The retrieved CTHs are compared with the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and Cloud Profiling Radar (CPR) CTHs for three occasions involving upper clouds overlying lower clouds. Results show that the proposed algorithm outperforms the prototype dual-GEO CTH algorithm in the case of upper clouds overlying lower clouds. Notably, although the proposed algorithm is intended for Himawari-8 and FY-4A GEOs, it can be easily extended to any combination of two GEOs.

Day-to-day variability of upper troposphere and lower stratosphere temperature in response to Taal volcanic eruption inferred from COSMIC-2 RO measurements

For the first time after 43 years of its previous eruption in 1977, the Taal volcano in the Philippines (14°N, 120°.59E) erupted in the afternoon of 12 January 2020. Due to the strong anticyclonic circulation in the upper troposphere (~150 hPa), the resultant volcanic ash and sulfur dioxide (SO2) were transported to the downwind tropical central Pacific Ocean within a few days after the initial eruption. In this study, day-to-day variability of upper troposphere lower stratosphere (UTLS) temperature in response to the Taal volcanic eruption event over the source region (±5o latitude and longitude around the volcano) and the downwind Pacific region (10-20oN, 160-180°E) is delineated by using the recently launched Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC)-2 radio occultation (RO) data. The daily mean temperature over the source and downwind regions were compared with the background long-term January mean temperature (obtained from 2007 to 2019 COSMIC-1 RO data) over the respective regions. Significant positive temperature anomalies in the lower stratosphere are evident over the source (between 12 and 14 January) and downwind (between 14 and 19 January) regions after the eruption. These positive anomalies may be due to the association of absorption and local radiative heating by volcanic aerosols. The Ozone Monitoring Instrument (OMI) measurements clearly show the presence of SO2 in the UTLS region. The presence of sulfate aerosols in the UTLS region a few days after eruption is also supported by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) images. Furthermore, we also describe the temperature structure near the volcano and away from the volcano and found a quite different structure in the UTLS region. A significant temperature inversion at ~15 km altitude is evident near the volcano, whereas it is noticed at ~16 km away from the volcano. In conclusion, in line with the previous studies, the present study provides further insights to the UTLS temperature in response to volcanic eruptions.

CALIPSO Aerosol-Typing Scheme Misclassified Stratospheric Fire Smoke: Case Study From the 2019 Siberian Wildfire Season

In August 2019, a 4-km thick wildfire smoke layer was observed in the lower stratosphere over Leipzig, Germany, with a ground-based multiwavelength Raman lidar. The smoke was identified by the smoke-specific spectral dependence of the extinction-to-backscatter ratio (lidar ratio) measured with the Raman lidar. The spaceborne CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) lidar CALIOP (Cloud–Aerosol Lidar with Orthogonal Polarization) detected the smoke and classified it as sulfate aerosol layer (originating from the Raikoke volcanic eruption). In this article, we discuss the reason for this misclassification. Two major sources for stratospheric air pollution were active in the summer of 2019 and complicated the CALIPSO aerosol typing effort. Besides intense forest fires at mid and high northern latitudes, the Raikoke volcano erupted in the Kuril Islands. We present two cases observed at Leipzig, one from July 2019 and one from August 2019. In July, pure volcanic sulfate aerosol layers were found in the lower stratosphere, while in August, wildfire smoke dominated in the height range up to 4–5 km above the local tropopause. In both cases, the CALIPSO aerosol typing scheme classified the layers as sulfate aerosol layers. The aerosol identification algorithm assumes non-spherical smoke particles in the stratosphere as consequence of fast lifting by pyrocumulonimbus convection. However, we hypothesize (based on presented simulations) that the smoke ascended as a results of self-lifting and reached the tropopause within 2–7 days after emission and finally entered the lower stratosphere as aged spherical smoke particles. These sphercial particles were then classified as liquid sulfate particles by the CALIPSO data analysis scheme. We also present a successful case of smoke identification by the CALIPSO retrieval method.

Methodology to obtain highly resolved SO<sub>2</sub> vertical profiles for representation of volcanic emissions in climate models

Abstract. In this study we describe a methodology to create high-vertical-resolution SO2 profiles from volcanic emissions. We demonstrate the method's performance for the volcanic clouds following the eruption of Sarychev in June 2009. The resulting profiles are based on a combination of satellite SO2 and aerosol retrievals together with trajectory modelling. We use satellite-based measurements, namely lidar backscattering profiles from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) satellite instrument, to create vertical profiles for SO2 swaths from the Atmospheric Infrared Sounder (AIRS) aboard the Aqua satellite. Vertical profiles are created by transporting the air containing volcanic aerosol seen in CALIOP observations using the FLEXible PARTicle dispersion model (FLEXPART) while preserving the high vertical resolution using the potential temperatures from the MERRA-2 (Modern-Era Retrospective analysis for Research and Application) meteorological data for the original CALIOP swaths. For the Sarychev eruption, air tracers from 75 CALIOP swaths within 9 d after the eruption are transported forwards and backwards and then combined at a point in time when AIRS swaths cover the complete volcanic SO2 cloud. Our method creates vertical distributions for column density observations of SO2 for individual AIRS swaths, using height information from multiple CALIOP swaths. The resulting dataset gives insight into the height distribution in the different sub-clouds of SO2 within the stratosphere. We have compiled a gridded high-vertical-resolution SO2 inventory that can be used in Earth system models, with a vertical resolution of 1 K in potential temperature, 61 ± 56 m, or 1.8 ± 2.9 mbar.

Hourly Mapping of the Layer Height of Thick Smoke Plumes Over the Western U.S. in 2020 Severe Fire Season

A series of huge smoke plume events from the largest wildfire season recorded in California’s modern history has occurred in 2020. Here, a research algorithm was modified to retrieve the aerosol optical centroid height (AOCH) and aerosol optical depth (AOD) from Earth Polychromatic Imaging Camera (EPIC) measurements. The research focus is to gain insights of the algorithm’s feasibility in heavy smoke conditions to study the diurnal variation of AOCH; this is only made possible via EPIC due to its unique position at Lagrange-1 point and its equipment of O2 B-band at which the vegetated surface reflectance is low. Vicarious calibration is applied to the EPIC 443, 680 and 688 nm channels based on the Tropospheric Monitoring Instrument (TROPOMI) observation. This new calibration leads to a better agreement of AOCH values between EPIC retrievals and the counterparts derived from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aerosol extinction vertical profile. The hourly variation of AOCH up to 0.45 km on September 7 is shown to have important implications for estimating hourly change of surface PM2.5, although more quantitative studies are needed in the future.

Nighttime smoke aerosol optical depth over U.S. rural areas: First retrieval from VIIRS moonlight observations

An algorithm for retrieving nighttime aerosol optical depth (AOD) from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band (DNB) observations of reflected moonlight is presented for rural areas during the western U.S. fire seasons. The algorithm uses the UNified and Linearized Vector Radiative Transfer Model (UNL-VRTM) with newly developed capabilities for considering lunar illuminations. Cloud and fire pixels are screened out by utilizing the radiance from the VIIRS Moderate-resolution Bands (M-Band) and the DNB. Rural and city pixels are classified based on a pre-calculated city light database. The surface spectral reflectance for DNB ranging from 342 to 1107 nm is estimated by a random forest approach, which is trained using the surface spectral reflectance from the existing spectral libraries. For the fire seasons of 2017 and 2020, the nighttime AOD retrieval is shown to play an indispensable role in describing the nonlinear diurnal movement of smoke transport and discerning the source of smoke plumes heretofore observable only in the daytime. The retrieved AOD values show good agreement with spatiotemporally collocated Aerosol Robotic NETwork (AERONET) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) AOD values, with linear correlation coefficient values of ~0.96/0.95 and ~86%/69% of the AOD pairs falling in an uncertainty envelope of ±(0.085 + 0.10AOD), which is superior to AOD reanalysis from Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2). These results affirm the significant potential of nighttime AOD to improve the analysis and forecast of regional to global biomass-burning aerosol distributions, filling a critical gap in the diurnal description of a key element of Earth's climate system.

Three-dimensional climatology, trends, and meteorological drivers of global and regional tropospheric type-dependent aerosols: insights from 13 years (2007–2019) of CALIOP observations

Abstract. Globally gridded aerosol extinction data from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) during 2007–2019 are utilized to investigate the three-dimensional (3D) climatological distribution of tropospheric type-dependent aerosols and to identify the trends in column aerosol optical depth (AOD), partitioned within different altitude regimes, and their meteorological drivers. Using detection samples of layer aerosols, we also yield a 3D distribution of the frequency of occurrence (FoO) of aerosol subtypes classified by CALIOP. The results show that the aerosol extinction coefficient (AEC) shows contrasting vertical distribution patterns over land and ocean, with the former possessing significant geographical dependence, while the enhancement of AEC in the latter is mainly located below 1 km. The vertical structures of the type-dependent AECs, however, are strongly dependent on altitude. When the total AOD (TAOD) is partitioned into the planetary boundary layer (PBL) and the free troposphere (FT), results demonstrate that the PBL and FT contribute 62.08 % and 37.92 %, respectively, of the global tropospheric TAOD averaged over daytime and nighttime. Yet this CALIOP-based partitioning of the different aerosol subtypes in the PBL and FT varies significantly. Among all 12 typical regions of interest analyzed, more than 50 % of TAOD is located in the lower troposphere (0–2 km), while the contribution is less than 2 % above 6 km. In global average terms, we found the aerosol FoO averaged over all layers is 4.45 %, with the largest contribution from “clean marine” (1.79 %) and the smallest from “clean continental” (0.05 %). Overall, the FoO vertical structures of the aerosol layer exhibit a distribution pattern similar to that of AEC. The resulting trend analyses show that CALIOP accurately captures significant regional anomalies in TAOD, as observed in other satellite measurements and aerosol reanalysis. Our correlation analysis between meteorological factors and TAOD suggests the interannual variability of TAOD is related to the variability of precipitation (PPT), volumetric soil moisture (VSM), and wind speed (WS) in the particular regions. For instance, the positive TAOD trend over the equatorial central Pacific is mainly attributable to the increased PPT and decreased WS. In contrast, in dry convective regions dominated by dust and smoke, the interannual variability/trend in TAOD is largely modified by the VSM driven by the PPT. Additionally, we further found that these significant regional correlations are more robust within the PBL and significantly weakened or even reversed within the FT. This highlights the superiority of using the TAOD partitioned within the PBL as a proxy variable for the widely applied TAOD to explore the relationships between atmospheric pollution and meteorology.

The Asian tropopause aerosol layer within the 2017 monsoon anticyclone: microphysical properties derived from aircraft-borne in situ measurements

Abstract. The Asian summer monsoon is an effective pathway for aerosol particles and precursors from the planetary boundary layer over Central, South, and East Asia into the upper troposphere and lower stratosphere. An enhancement of aerosol particles within the Asian monsoon anticyclone (AMA), called the Asian tropopause aerosol layer (ATAL), has been observed by satellites. We discuss airborne in situ and remote sensing observations of aerosol microphysical properties conducted during the 2017 StratoClim field campaign within the AMA region. The aerosol particle measurements aboard the high-altitude research aircraft M55 Geophysica (maximum altitude reached of ∼20.5 km) were conducted with a modified ultra-high-sensitivity aerosol spectrometer – airborne (UHSAS-A; particle diameter detection range of 65 nm to 1 µm), the COndensation PArticle counting System (COPAS, detecting total concentrations of submicrometer-sized particles), and the New Ice eXpEriment – Cloud and Aerosol Spectrometer with Detection of POLarization (NIXE-CAS-DPOL). In the COPAS and UHSAS-A vertical particle mixing ratio (PMR) profiles and the size distribution profiles (for number, surface area, and volume concentration), the ATAL is evident as a distinct layer between ∼370 and 420 K potential temperature (Θ). Within the ATAL, the maximum detected PMRs (from the median profiles) were ∼700 mg−1 for particle diameters between 65 nm and 1 µm (UHSAS-A) and higher than 2500 mg−1 for diameters larger than 10 nm (COPAS). These values are up to 2 times higher than those previously found at similar altitudes in other tropical locations. The difference between the PMR profiles measured by the UHSAS-A and the COPAS indicate that the region below the ATAL at Θ levels from 350 to 370 K is influenced by the nucleation of aerosol particles (diameter <65 nm). We provide detailed analyses of the vertical distribution of the aerosol particle size distributions and the PMR and compare these with previous tropical and extratropical measurements. The backscatter ratio (BR) was calculated based on the aerosol particle size distributions measured in situ. The resulting data set was compared with the vertical profiles of the BR detected by the multiwavelength aerosol scatterometer (MAS) and an airborne miniature aerosol lidar (MAL) aboard the M55 Geophysica and by the satellite-borne Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The data of all four methods largely agree with one another, showing enhanced BR values in the altitude range of the ATAL (between ∼15 and 18.5 km) with a maximum at 17.5 km altitude. By means of the AMA-centered equivalent latitude calculated from meteorological reanalysis data, it is shown that such enhanced values of the BR larger than 1.1 could only be observed within the confinement of the AMA.

New Ocean Subsurface Optical Properties From Space Lidars: CALIOP/CALIPSO and ATLAS/ICESat‐2

AbstractRemote sensing from Earth‐observing satellites is now providing valuable information about ocean phytoplankton distributions. This paper presents the new ocean subsurface optical properties obtained from two space‐based lidars: the Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations satellite and the Advanced Topographic Laser Altimeter System (ATLAS) aboard the Ice, Cloud, and land Elevation Satellite‐2 satellite. Obtaining reliable estimates of subsurface biomass necessitates removing instrument artifacts peculiar to each sensor, that is, polarization crosstalk artifacts in the CALIOP signals and after pulsing effects arising from the ATLAS photodetectors. We validate the optical properties derived from the corrected lidar backscatter signals using MODerate‐resolution Imaging Spectroradiometer ocean color measurements and autonomous biogeochemical Argo float profiles. Our results support the continued use of present and future spaceborne lidars to study the global plankton system and characterize its vertical structures in the upper ocean.

Obtaining vertical distribution of PM2.5 from CALIOP data and machine learning algorithms

Aerosol optical depth (AOD) has been widely used to estimate the near-surface PM2.5 (fine particulate matter with particle size less than 2.5 μm). However, the total-column AOD obtained by passive remote sensing instruments can neither distinguish the contribution of AOD in various altitude layers nor obtain the vertical PM2.5 concentration. In this study, we compared several AOD-PM2.5 models including Extra Trees (ET), Random Forest (RF), Deep Neural Network (DNN), and Gradient Boosting Regression Tree (GBRT), and analyzed the corresponding results using AOD of different altitudes and auxiliary data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The results indicate that the ET model performs best in terms of the model effectiveness and feature interpretation on the training dataset. We conclude that the feature importance of the bottom layer AOD is higher than that of the upper and total column AOD. The results showed that regional differences existed in the optimal height of the AOD-PM2.5 correlation in study area. The results of cross-validation indicate that ET manages the most appealing overall performance with an R2 (RMSE) of 0.85 (17.77 μg/m3). Regarding the 729 sites involved in this study, 73% had R2 > 0.7, and the region or season with higher AOD feature importance achieves better model performance. The results of the AOD-PM2.5 model in each layer were corrected using the AOD weight, to obtained the PM2.5 vertical concentrations from 2015 to 2019. The results highlight that the high PM2.5 concentration area is primarily near the ground and decreases with height. Additionally, the PM2.5 vertical concentration in Beijing-Tianjin-Hebei (−1.80 μg/m3, P < 0.001), Central China (−1.62 μg/m3, P < 0.001), and Pearl River Delta (−0.66 μg/m3, P < 0.001) show an apparent downward trend. We believe that the vertical distribution analysis of PM2.5 can provide meaningful information for studying air pollution.

Global dust optical depth climatology derived from CALIOP and MODIS aerosol retrievals on decadal timescales: regional and interannual variability

Abstract. We derived two observation-based global monthly mean dust aerosol optical depth (DAOD) climatological datasets from 2007 to 2019 with a 2∘ (latitude) × 5∘ (longitude) spatial resolution, one based on Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and the other on Moderate Resolution Imaging Spectroradiometer (MODIS) observations. In addition, the CALIOP climatological dataset also includes dust vertical extinction profiles. Dust is distinguished from non-dust aerosols based on particle shape information (e.g., lidar depolarization ratio) for CALIOP and on dust size and absorption information (e.g., fine-mode fraction, Ångström exponent, and single-scattering albedo) for MODIS, respectively. The two datasets compare reasonably well with the results reported in previous studies and the collocated Aerosol Robotic Network (AERONET) coarse-mode AOD. Based on these two datasets, we carried out a comprehensive comparative study of the spatial and temporal climatology of dust. On a multi-year average basis, the global (60∘ S–60∘ N) annual mean DAOD is 0.032 and 0.067 according to CALIOP and MODIS retrievals, respectively. In most dust-active regions, CALIOP DAOD generally correlates well (correlation coefficient R&gt;0.6) with the MODIS DAOD, although the CALIOP value is significantly smaller. The CALIOP DAOD is 18 %, 34 %, 54 %, and 31 % smaller than MODIS DAOD over the Sahara, the tropical Atlantic Ocean, the Caribbean Sea, and the Arabian Sea, respectively. Applying a regional specific lidar ratio (LR) of 58 sr instead of the 44 sr used in the CALIOP operational retrieval reduces the difference from 18 % to 8 % over the Sahara and from 34 % to 12 % over the tropical Atlantic Ocean. However, over eastern Asia and the northwestern Pacific Ocean (NWP), the two datasets show weak correlation. Despite these discrepancies, CALIOP and MODIS show similar seasonal and interannual variations in regional DAOD. For dust aerosol over the NWP, both CALIOP and MODIS show a declining trend of DAOD at a rate of about 2 % yr−1. This decreasing trend is consistent with the observed declining trend of DAOD in the southern Gobi Desert at a rate of 3 % yr−1 and 5 % yr−1 according to CALIOP and MODIS, respectively. The decreasing trend of DAOD in the southern Gobi Desert is in turn found to be significantly correlated with increasing vegetation and decreasing surface wind speed in the area.

Investigating aerosol vertical distribution using CALIPSO time series over the Middle East and North Africa (MENA), Europe, and India: A BFAST-based gradual and abrupt change detection

Monitoring spatio-temporal changes of aerosols is necessary to better understand atmospheric processes. Here, the vertical distribution of aerosols and how it has changed from 2006 to 2017 is studied using time series data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument on board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite. Aerosol centroid altitude, a variable representing the aerosol vertical extent, is estimated using the aerosol flags of the Vertical Feature Mask (VFM) product of CALIOP. The case study includes four overpasses, with various aerosol types and densities, covering the Middle East and North Africa (MENA), and India regions characterized by frequent dust storms and smoke emissions, respectively, and Europe, a pioneer in establishing air quality policies. The values of aerosol centroid altitude are examined to determine the effects of climate (space) and season (time) on aerosol vertical distribution. For this purpose, 10-year mean values of aerosol centroid altitude were calculated over climate regions extracted from the Food and Agriculture Organization (FAO) climate map as well as over each season, and the resultant values were then statistically analyzed. The results indicate that environmental heat, steam from climate or season, leads to aerosols increasing in altitude. The time series was imported in a version of the Breaks For Additive Season and Trend (BFAST) algorithm, so-called BFAST01, allowing the magnitude of gradual changes along with the time and type of the major abrupt change to be determined. The strongest positive trends in aerosol centroid altitude were recorded in the desert regions. Positive trends were also observed in the mid-latitudes steppes and the North Sea. India was another region with a relatively positive trend which could be mainly caused by the increasing anthropogenic activities and smoke emission. The strongest negative trend detected in Europe is a result of strict environmental regulation policies and efforts for air quality improvement. The results show that a small fraction of granules (12 out of 4036) mainly in the northern part of overpass A, over Europe, the mid part of overpasses B and C, over the Middle East, and southern part of overpass D, over India, experienced structural changes. The reversal changes type 8 (50% of the abrupt changes) and interruption type 6 (33%) are the dominant types, mostly occurred around 2014 and 2012, respectively. Changing environmental factors such as temperature, wind speed, and anthropogenic activities are the main drivers of these changes. The study demonstrates the applicability of BFAST01 and the usability of CALIOP VFM time series data to monitor the vertical structure of the atmosphere.