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Aerosol vertical distributions impact both the direct and indirect radiative effects of aerosols. High Spectra Resolution Lidar (HSRL) separates between atmospheric molecular signals and aerosol particle signals and therefore can provide reliable measurements of aerosol properties. Six years of HSRL measurements between 2014 and 2019 from the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) North Slope of Alaska (NSA) atmospheric observatory at Utqiaġvik are used to statistically analyze Arctic aerosol vertical distributions. The annual cycle of aerosol vertical distributions in terms of aerosol particulate backscatter coefficient ( β p ), lidar scattering ratio (SR), and aerosol particulate depolarization ratio ( δ p ) profiles at the wavelength of 532 nm shows that Arctic Haze events are prevalent in later winter and spring at the NSA site. Mineral dust is frequently presented in strong aerosol layers in the spring, fall, and winter seasons. Over the summer season, the NSA site has large aerosol loadings that are dominated by small spherical aerosol particles. [ABSTRACT FROM AUTHOR]

The backscattering optical properties of an ensemble of randomly oriented dust particles at a wavelength of 355 nm were comprehensively studied by examining the invariant imbedding T‐matrix results of the super‐spheroid dust model. In particular, we focused on the lidar ratio (S $S$) and depolarization ratio (δ $\delta $) relations of dust aerosols to aid interpretation of data from the Atmospheric Lidar (ATLID) instrument that will be onboard the Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) satellite. Super‐spheroid models with various aspect ratios (α $\alpha $), roundness parameters (n) $(n)$, and refractive indices were investigated over a wide range of particle sizes and compared to the observation data of the National Aeronautics and Space Administration (NASA) Langley 355‐nm airborne high spectral resolution lidar. We found that super‐spheroid dust particles with different sets of n $n$ and α $\alpha $ could be used to model almost the entire range of the observed joint distributions of S $S$ and δ $\delta $. The S−δ $S-\delta $ relation could effectively discriminate among dust particle types. The observed S $S$ and δ $\delta $ values with the largest population density were best covered by models with n > 2, especially by those with n $n$ varying from 2.4 to 3.0. Plain Language Summary: Dust aerosols are irregularly shaped. Dust shape has a significant impact on the modeling of lidar observations. In this work, super‐spheroid models (the conventional spheroid model is a special case) were used to study the impacts of nonsphericity and irregularity on lidar observations. In particular, we were interested in the lidar and depolarization ratios, because these ratios have proven useful for distinguishing among aerosol types in the atmosphere and will be measurable by the future Earth Cloud, Aerosol, and Radiation Explorer (EarthCARE) satellite. Because the lidar and depolarization ratios can be affected by multiple factors, including dust shape, refractive indices, and particle sizes, systematic theoretical studies related to these factors were conducted. Furthermore, we compared the theoretical simulations with NASA Langley airborne high spectral resolution lidar data, and found that super‐spheroid models could be used to explain the wide variation of dust optical properties found in the lidar data. The present study confirmed that dust particles represented by super‐spheroid models can be useful in lidar remote sensing applications. Based on this, the optimal parameters of super‐spheroid models can be used to parameterize dust shapes. Key Points: The backscattering optical properties of dust aerosols were modeled systematically using homogeneous super‐spheroidsA comprehensive survey of the lidar ratio‐depolarization ratio relations produced by super‐spheroids at 355 nm wavelength was conductedSuper‐spheroids could be used to explain the wide variation of dust optical properties found in the high spectral resolution lidar data [ABSTRACT FROM AUTHOR]

Biomass burning (BB) aerosol events were characterized over the U.S. East Coast and Bermuda over the western North Atlantic Ocean (WNAO) between 2005 and 2018 using a combination of ground‐based observations, satellite data, and model outputs. Days with BB influence in an atmospheric column (BB days) were identified using criteria biased toward larger fire events based on anomalously high AERONET aerosol optical depth (AOD) and MERRA‐2 black carbon (BC) column density. BB days are present year‐round with more in June–August (JJA) over the northern part of the East Coast, in contrast to more frequent events in March–May (MAM) over the southeast U.S. and Bermuda. BB source regions in MAM are southern Mexico and by the Yucatan, Central America, and the southeast U.S. JJA source regions are western parts of North America. Less than half of the BB days coincide with anomalously high PM2.5 levels in the surface layer, according to data from 14 IMPROVE sites over the East Coast. Profiles of aerosol extinction suggest that BB particles can be found in the boundary layer and into the upper troposphere with the potential to interact with clouds. Higher cloud drop number concentration and lower drop effective radius are observed during BB days. In addition, lower liquid water path is found during these days, especially when BB particles are present in the boundary layer. While patterns are suggestive of cloud‐BB aerosol interactions over the East Coast and the WNAO, additional studies are needed for confirmation. Key Points: Biomass burning (BB) particles over U.S. East Coast and Bermuda are common year‐round with varying sources and at altitudes impacting cloudsSmoke‐cloud interactions are likely based on higher cloud drop number concentration and lower drop effective radius on BB daysA significant reduction in cloud liquid water path was noted on days with enhanced columnar and surface smoke over the study region [ABSTRACT FROM AUTHOR]

Aerosol vertical distributions impact both the direct and indirect radiative effects of aerosols. High Spectra Resolution Lidar (HSRL) separates between atmospheric molecular signals and aerosol particle signals and therefore can provide reliable measurements of aerosol properties. Six years of HSRL measurements between 2014 and 2019 from the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) North Slope of Alaska (NSA) atmospheric observatory at Utqiaġvik are used to statistically analyze Arctic aerosol vertical distributions. The annual cycle of aerosol vertical distributions in terms of aerosol particulate backscatter coefficient (βp), lidar scattering ratio (SR), and aerosol particulate depolarization ratio (δp) profiles at the wavelength of 532 nm shows that Arctic Haze events are prevalent in later winter and spring at the NSA site. Mineral dust is frequently presented in strong aerosol layers in the spring, fall, and winter seasons. Over the summer season, the NSA site has large aerosol loadings that are dominated by small spherical aerosol particles.

Changes in airborne high spectral resolution lidar (HSRL) measurements of scattering, depolarization, and attenuation coincided with a shift in phytoplankton community composition across an anticyclonic eddy in the North Atlantic. We normalized the total depolarization ratio (δ) by the particulate backscattering coefficient (bbp) to account for the covariance in δ and bbp that has been attributed to multiple scattering. A 15% increase in δ/bbp inside the eddy coincided with decreased phytoplankton biomass and a shift to smaller and more elongated phytoplankton cells. Taxonomic changes (reduced dinoflagellate relative abundance inside the eddy) were also observed. The δ signal is thus potentially most sensitive to changes in phytoplankton shape because neither the observed change in the particle size distribution (PSD) nor refractive index (assuming average refractive indices) are consistent with previous theoretical modeling results. We additionally calculated chlorophyll-a (Chl) concentrations from measurements of the diffuse light attenuation coefficient (Kd) and divided by bbp to evaluate another optical metric of phytoplankton community composition (Chl:bbp), which decreased by more than a factor of two inside the eddy. This case study demonstrates that the HSRL is able to detect changes in phytoplankton community composition. High spectral resolution lidar measurements reveal complex structures in both the vertical and horizontal distribution of phytoplankton in the mixed layer providing a valuable new tool to support other remote sensing techniques for studying mixed layer dynamics. Our results identify fronts at the periphery of mesoscale eddies as locations of abrupt changes in near-surface optical properties.

For a given cloud, whether the cloud top is predominately made up of ice crystals or supercooled liquid droplets plays a large role in the clouds overall radiative effects. This study uses collocated airborne radar, lidar, and thermodynamic data from 12 high‐altitude flight legs during the Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) to characterize Southern Ocean (SO) cold sector cloud top phase (i.e., within 96 m of top) as a function of cloud top temperature (CTT). A training data set was developed to create probabilistic phase classifications based on High Spectral Resolution Lidar data and Cloud Radar data. These classifications were then used to identify dominant cloud top phase. Case studies are presented illustrating examples of supercooled liquid water at cloud top at different CTT ranges over the SO (−3°C < CTTs < −28°C). During SOCRATES, 67.4% of sampled cloud top had CTTs less than 0°C. Of the subfreezing cloud tops sampled, 91.7% had supercooled liquid water present in the top 96 m and 74.9% were classified entirely as liquid‐bearing. Liquid‐bearing cloud tops were found at CTTs as cold as −30°C. Horizontal cloud extent was also determined as a function of median cloud top height. Plain Language Summary: Low‐level clouds over the Southern Ocean have a large effect on the region's radiation budget. The radiation budget is strongly influenced by the phase (liquid or ice) of cloud tops, which is where most solar radiation is reflected, and most infrared radiation is radiated to space. For this reason, identifying the phase of cloud tops is important. In this study, airborne radar, lidar, and temperature data from 12 high‐altitude flight legs during the Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) are used to characterize Southern Ocean cloud top phase as a function of cloud top temperature. The results show that liquid is the dominant phase present in clouds over the Southern Ocean, with liquid present at cloud top temperatures as cold as −30°C. Key Points: Airborne radar, lidar, and thermodynamic data are used to determine Southern Ocean cloud top phase as a function of cloud top temperatureConsidering only clouds with subfreezing cloud top temperatures, the dominant phase at cloud top in 74.9% of clouds was liquidLiquid‐bearing cloud tops were even found at temperatures as cold as −30°C [ABSTRACT FROM AUTHOR]

Lidar retrievals of aerosol extinction and collocated relative humidity (RH) were acquired during the Department of Energy Combined High Spectral Resolution Lidar (HSRL) and Raman Measurement Study campaign in the summer of 2015 at the Southern Great Plains site in northern Oklahoma. Measurements of the hygroscopic properties of aerosols are crucial for accurately representing their relationship with clouds, which can be a significant source of uncertainty in assessing direct and indirect radiative effects. The ability for lidar to retrieve measurements of the vertically resolved f (RH), that is, the aerosol extinction at some wet RH normalized by the aerosol extinction at a dry reference RH, is investigated here and compared with nephelometer‐measured f (RH) at the surface. We introduce a modified approach to fitting the lidar measurements of aerosol extinction and our comparisons reveal that lidar and nephelometer measurements of f (RH) are consistent, both with each other and with reported values in the literature. The implications for this work present a path forward for global‐scale retrievals of remotely sensed aerosol hygroscopic properties. Most importantly, the efforts in this study could lead to closing the gap on uncertainties associated with the aerosol indirect radiative effect when combined with inversion retrievals of aerosol microphysical properties. Plain Language Summary: Atmospheric aerosols, or tiny particles suspended in the atmosphere, are an important component of the climate system. These particles can sometimes undergo humidification processes that allow them to grow in size and scatter more solar radiation. When certain conditions are met, aerosols can grow to become cloud droplets and modify cloud properties like brightness or reflectivity. Remote sensing systems can observe optical properties of aerosols on a large scale and in a timely fashion. However, some essential aspects of these aerosols, like their humidification properties, remain a challenge for remote sensing instruments to obtain. In this study, we demonstrate the capability of a unique lidar system to expand the current set of observations that are regularly retrieved by remote sensing instruments. Specifically, we make simultaneous retrievals of the atmospheric relative humidity and aerosol light extinction coefficients by combining Raman and High Spectral Resolution Lidar. We show that this system can accurately retrieve humidification properties of aerosols within the atmospheric mixed layer. These results reach a considerable milestone for the future advancement of remote sensing lidar and represent a path forward in reducing climate forcing uncertainty. Key Points: Data from combined Raman and HSRL lidars can retrieve aerosol humidification factors within the mixed layer in the Southern Great PlainsLidar humidification factors retrieved near the mixed layer top are comparable to surface in situ nephelometer and composition measurementsThis technique leverages the real atmospheric humidity environment to retrieve aerosol hygroscopicity over the realistic dynamic range [ABSTRACT FROM AUTHOR]

Abstract: Liquid layers in clouds affect their microphysical processes, as well as the surface energy budget. Studies focusing on these and other areas of research are often in need of skillful estimation of liquid‐bearing cloud layer boundaries. The bases of these layers are predominantly determined by ground‐based lidar instruments. Most studies requiring liquid cloud base height (LCBH) information use either fixed lidar parameter (depolarization and/or backscatter cross section) thresholds or cloud base height data products that do not distinguish between ice and liquid, all of which might introduce inconsistencies and errors in the resolved LCBH. In this paper, two explicit LCBH detection algorithms are presented. The first algorithm uses the high spectral resolution lidar (HSRL) data. Examination of this algorithm in multiple cases and scenarios during numerous days and first‐order comparison with microwave‐radiometer data show satisfactory results. The second algorithm incorporates widely available micropulse lidar (MPL) data for the LCBH detection. A 1‐year long comparison of data gathered at Barrow, Alaska, and McMurdo Station, Antarctica, which includes other cloud base detection methodologies (ceilometer, MPL value‐added product cloud base height, and LCBHs detected using a fixed MPL depolarization threshold), emphasizes the merits of the presented MPL algorithm. Examination of several unusual LCBH configurations suggests that the current practice of operating lidar at a tilting angle of 4° off zenith may not be sufficient to avoid specular reflection from oriented ice crystals. Data collected at Madison Wisconsin are used to show that specular reflection may impact measurements even at 4°. [ABSTRACT FROM AUTHOR]

Particulate matter with an aerodynamic diameter of equal to or less than 2.5 μm (PM 2.5) has been found to have a serious adverse effect on human health and the environment. While the importance of measuring PM 2.5 has been demonstrated, doing so remotely remains challenging. In this study, methodologies for the assessment of aerosol PM 2.5 and chemical composition based on a combination of regional and global models and active remote sensing were evaluated against surface observations from the KORUS-AQ campaign. The model outputs from the Community Multiscale Air Quality (CMAQ) and GEOS-Chem were used and were available at the KORUS-AQ campaign data archive. For remote sensing, aerosol extinction and derived aerosol types available from NASA Langley Airborne Differential Absorption Lidar (DIAL)/High Spectral Resolution Lidar (HSRL) flying onboard DC-8 aircraft were used. A revised version of the algorithm, which incorporates size-specific aerosol dry mass extinction efficiencies for sulfate, nitrate, and ammonia as well as organic matter, is also presented. The PM 2.5 concentration estimates were compared with measurements taken at the ground stations. The estimated mean absolute error between the ground station measurements and the remote-sensing-based methodologies was significantly lower compared to the models. The data analysis has shown that uncertainties in relative humidity values, the presence of particles larger than 2.5 μm in diameter, and the abundance of black carbon and organic matter in Asian aerosol were unlikely to explain the differences between measured and predicted surface PM 2.5. Local meteorology was found to play a key role influencing the spatiotemporal variability of aerosols and the most important factor determining the agreement between the estimated and ground site-measured PM 2.5. The lowest mean absolute error was found for the May 1–16 period, when aerosols were well mixed within the mixing layer and homogeneous across the temporal (1 h) and spatial (8 km) scales used in this study. Under these conditions, the methodologies presented here could give reasonable estimates of PM 2.5 concentration and derived chemical composition over South Korea when HSRL data are available. • New methods for estimating surface PM 2.5 are applied to Asian aerosols. • PM 2.5 and speciation are derived using HSRL-retrieved extinction and aerosol type. • The methods improve regional and global model-predicted surface PM 2.5 [ABSTRACT FROM AUTHOR]

The first field campaign of DISCOVER-AQ (Deriving Information on Surface conditions from COlumn and VERtically resolved observations relevant to Air Quality) took place in July 2011 over Baltimore–Washington Corridor (BWC). A suite of airborne remote sensing and in-situ sensors was deployed along with ground networks for mapping vertical and horizontal distribution of aerosols. Previous researches were based on a single lidar station because of the lack of regional coverage. This study uses the unique airborne HSRL (High Spectral Resolution Lidar) data to baseline PM 2.5 (particulate matter of aerodynamic diameter less than 2.5 μm) estimates and applies to regional air quality with satellite AOD (Aerosol Optical Depth) retrievals over BWC (∼6500 km 2 ). The linear approximation takes into account aerosols aloft above AML (Aerosol Mixing Layer) by normalizing AOD with haze layer height (i.e., AOD/HLH). The estimated PM 2.5 mass concentrations by HSRL AOD/HLH are shown within 2 RMSE (Root Mean Square Error ∼9.6 μg/m 3 ) with correlation ∼0.88 with the observed over BWC. Similar statistics are shown when applying HLH data from a single location over the distance of 100 km. In other words, a single lidar is feasible to cover the range of 100 km with expected uncertainties. The employment of MPLNET–AERONET (MicroPulse Lidar NETwork – AErosol RObotic NETwork) measurements at NASA GSFC produces similar statistics of PM 2.5 estimates as those derived by HSRL. The synergy of active and passive remote sensing aerosol measurements provides the foundation for satellite application of air quality on a daily basis. For the optimal range of 10 km, the MODIS-estimated PM 2.5 values are found satisfactory at 27 (out of 36) sunphotometer locations with mean RMSE of 1.6–3.3 μg/m 3 relative to PM 2.5 estimated by sunphotometers. The remaining 6 of 8 marginal sites are found in the coastal zone, for which associated large RMSE values ∼4.5–7.8 μg/m 3 are most likely due to overestimated AOD because of water-contaminated pixels. [ABSTRACT FROM AUTHOR]

Abstract: Prognostic meteorological models such as Mesoscale Model (MM5) and Weather Research and Forecasting (WRF) are often used to supply inputs for retrospective air quality modeling done to support ozone and PM2.5 emission control demonstrations. In this study, multiple configurations of the WRF model are applied at 4 km grid resolution and compared to routine meteorological measurements and special study measurements taken in California during May–June 2010. One configuration is routinely used by US EPA to generate meteorological inputs for regulatory air quality modeling and another that is used by research scientists for evaluating meteorology and air quality. Mixing layer heights estimated from airborne High Spectral Resolution Lidar (HSRL) measurements of aerosol backscatter are compared with WRF modeled planetary boundary layer (PBL) height estimates. Both WRF configurations generally capture the variability in HSRL mixing height between days, hour-to-hour, and between surface features such as terrain and land–water interfaces. Fractional bias over all flights and both model configurations range from −38% to 32% and fractional error ranges from 22% to 58%. Surface and upper level measurements of temperature, water mixing ratio, and winds are generally well characterized by both WRF model configurations, often more closely matching surface observations than the input analysis data (12-NAM). The WRF model generally captures orographic and mesoscale meteorological features in the central Valley (bifurcation of wind flow from the San Francisco bay into the Sacramento and San Joaquin valleys) and Los Angeles air basin (ocean-land flows) during this summer period. [Copyright &y& Elsevier]

Improved characterization of ambient PM 2.5 mass concentration and chemical speciation is a topic of interest in air quality and climate sciences. Over the past decades, considerable efforts have been made to improve ground-level PM 2.5 using remotely sensed data. Here we present two new approaches for estimating atmospheric PM 2.5 and chemical composition based on the High Spectral Resolution Lidar (HSRL)-retrieved aerosol extinction values and types and Creating Aerosol Types from Chemistry (CATCH)-derived aerosol chemical composition. The first methodology (CMAQ-HSRL-CH) improves EPA's Community Multiscale Air Quality (CMAQ) predictions by applying variable scaling factors derived using remotely-sensed information about aerosol vertical distribution and types and the CATCH algorithm. The second methodology (HSRL-CH) does not require regional model runs and can provide atmospheric PM 2.5 mass concentration and chemical speciation using only the remotely sensed data and the CATCH algorithm. The resulting PM 2.5 concentrations and chemical speciation derived for NASA DISCOVER-AQ (Deriving Information on Surface Conditions from COlumn and VERtically Resolved Observations Relevant to Air Quality) Baltimore-Washington, D.C. Corridor (BWC) Campaign are compared to surface measurements from EPA's Air Quality Systems (AQS) network. The analysis shows that the CMAQ-HSRL-CH method leads to considerable improvement of CMAQ's predicted PM 2.5 concentrations (R2 value increased from 0.37 to 0.63, the root mean square error (RMSE) was reduced from 11.9 to 7.2 μg m−3, and the normalized mean bias (NMB) was lowered from −46.0 to 4.6%). The HSRL-CH method showed statistics (R2 = 0.75, RMSE = 8.6 μgm−3, and NMB = 24.0%), which were better than the CMAQ prediction of PM 2.5 alone and analogous to CMAQ-HSRL-CH. In addition to mass concentration, HSRL-CH can also provide aerosol chemical composition without specific model simulations. We expect that the HSRL-CH method will be able to make reliable estimates of PM 2.5 concentration and chemical composition where HSRL data are available. • New methods for PM 2.5 chemical composition retrievals using HSRL and CATCH. • Improves air quality model predicted PM 2.5 and chemical composition. • Estimates PM 2.5 and chemical composition without air quality model simulations. [ABSTRACT FROM AUTHOR]

A hypothetical model illustrating the mechanism by which Tanshinone IIA down-regulated p-Smad3 signaling to inhibit TGF-β1-mediated fibroblast proliferation via lncRNA-HSRL/SNX9. After HSFs treated with TSIIA, TSIIA got into these cells, inhibited HSRL/SNX9 expression, suppressed phosphorylated Smad3, thereby reduced the translocation of phosphorylated Smad3 into nucleus, then inhibited gene transcription of Fibroblast induced by phosphorylated Smad3. • TSIIA could effectively attenuate TGF-β1-mediated fibroblasts proliferation in a dose-dependent manner. • HSRL knockdown attenuate TGF-β1-mediated fibroblasts proliferation, which was reversed by SNX9 overexpression. • TSIIA inhibited TGF-β1-mediated fibroblasts proliferation by down-regulating SNX9. • HSRL was the upstream regulator for SNX9/p-Smad3 signaling in TSIIA regulating TGF-β1-stimulated fibroblasts proliferation. Tanshinone IIA (TSIIA), an active component of Salvia miltiorrhiza (Danshen), is reported to inhibit cell proliferation in hypertrophic scars (HS). In our previous study, we observed that lncRNA human-specific regulatory loci (HSRL) was up-regulated in HS tissues. However, whether TSIIA serves as an effective treatment for HS through affecting HSRL is still unexplored. TGF-β1-stimulated fibroblast were used as the in vitro HS model. The effects of TSIIA on cell proliferation were evaluated using CCK-8, Edu staining and colony formation assays. By performing loss-of-function and rescue experiments, we explored the role of HSRL and Sorting nexin 9 (SNX9) in TGF-β1-stimulated fibroblast. Employing RNA-protein pull-down assay and Co-immunoprecipitation, we further investigated the mechanisms through which TSIIA attenuated TGF-β1-stimulated fibroblast. Our data demonstrated that TSIIA could effectively attenuate TGF-β1-mediated fibroblast proliferation in a dose-dependent manner. Meanwhile, TSIIA could down-regulate the expression of α-SMA, VEGFA, Collagen 1, HSRL, SNX9 and p-Smad2/3 in TGF-β1-stimulated HSF. In addition, we found that SNX9 overexpression reversed the effects of HSRL knockdown on TGF-β1-stimulated HSF. Furthermore, we confirmed that TSIIA treatment weakens the interaction between p-Smad3 and SNX9 in HS models. Tanshinone IIA down-regulated p-Smad3 signaling to inhibit TGF-β1-mediated fibroblast proliferation via lncRNA-HSRL/SNX9. [ABSTRACT FROM AUTHOR]