High Spectral Resolution Lidar Research Articles

Aerosol Vertical Turbulent Mass Flux Retrievals Through Novel Remote Sensing Algorithm

AbstractIntegrated measurements of aerosol, radiation, cloud, and turbulent transport in the planetary boundary layer (PBL) are essential for understanding and modeling climate and air quality. Here, we developed a new technique for the identification of convective turbulent regions and deriving the vertical distribution of aerosol turbulent mass fluxes within PBL. The algorithm uses retrievals from coherent Doppler lidars and a high spectral resolution lidar. The technique was applied to study particle mass fluxes over 2 months (November–December 2020) during the campaign conducted at the DOE Atmospheric Radiation Measurement Southern Great Plains (SGP) site in Lamont, Oklahoma. The algorithm developed here is capable of continuously deriving vertically resolved (curtains) aerosol mass fluxes. Our data analysis shows that at the site, the 30‐min averaged fluxes at 135 m above the surface were mainly positive (upward) at ∼1 μg m−2 s−1, suggesting that the surface is the primary source of the particle mass supplied to the boundary layer at the SGP site. Analyses of the individual case studies have revealed that not all the derived fluxes can be linked to surface emissions. Both positive and negative values in a range of ±5 μg m−2 s−1 can be caused by convective thermals interacting between the residual layer and the mixed layer and by rotation of the horizontal wind with the height. Large erroneous negative fluxes can also be caused by drizzling/precipitating clouds. We anticipate that the application of the current technique will lead to a more realistic representation of aerosol mass budgets and bidirectional mixing rates.

High Spectral Resolution Lidar – generation 2 (HSRL-2) retrievals of ocean surface wind speed: methodology and evaluation

Abstract. Ocean surface wind speed (i.e., wind speed 10 m above sea level) is a critical parameter used by atmospheric models to estimate the state of the marine atmospheric boundary layer (MABL). Accurate surface wind speed measurements in diverse locations are required to improve characterization of MABL dynamics and assess how models simulate large-scale phenomena related to climate change and global weather patterns. To provide these measurements, this study introduces and evaluates a new surface wind speed data product from the NASA Langley Research Center nadir-viewing High Spectral Resolution Lidar – generation 2 (HSRL-2) using data collected as part of the NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) mission. The HSRL-2 can directly measure vertically resolved aerosol backscatter and extinction profiles without additional constraints or assumptions, enabling the instrument to accurately derive atmospheric attenuation and directly determine surface reflectance (i.e., surface backscatter). Also, the high horizontal spatial resolution of the HSRL-2 retrievals (0.5 s or ∼ 75 m along track) allows the instrument to probe the fine-scale spatial variability in surface wind speeds over time along the flight track and over breaks in broken cloud fields. A rigorous evaluation of these retrievals is performed by comparing coincident HSRL-2 and National Center for Atmospheric Research (NCAR) Airborne Vertical Atmosphere Profiling System (AVAPS) dropsonde data, owing to the joint deployment of these two instruments on the ACTIVATE King Air aircraft. These comparisons show correlations of 0.89, slopes of 1.04 and 1.17, and y intercepts of −0.13 and −1.05 m s−1 for linear and bisector regressions, respectively, and the overall accuracy is calculated to be 0.15 ± 1.80 m s−1. It is also shown that the dropsonde surface wind speed data most closely follow the HSRL-2 distribution of wave slope variance using the distribution proposed by Hu et al. (2008) rather than the ones proposed by Cox and Munk (1954) and Wu (1990) for surface wind speeds below 7 m s−1, with this category comprising most of the ACTIVATE data set. The retrievals are then evaluated separately for surface wind speeds below 7 m s−1 and between 7 and 13.3 m s−1 and show that the HSRL-2 retrieves surface wind speeds with a bias of ∼ 0.5 m s−1 and an error of ∼ 1.5 m s−1, a finding not apparent in the cumulative comparisons. Also, it is shown that the HSRL-2 retrievals are more accurate in the summer (−0.18 ± 1.52 m s−1) than in the winter (0.63 ± 2.07 m s−1), but the HSRL-2 is still able to make numerous (N=236) accurate retrievals in the winter. Overall, this study highlights the abilities and assesses the performance of the HSRL-2 surface wind speed retrievals, and it is hoped that further evaluation of these retrievals will be performed using other airborne and satellite data sets.

Boundary Layer Structures Over the Northwest Atlantic Derived From Airborne High Spectral Resolution Lidar and Dropsonde Measurements During the ACTIVATE Campaign

AbstractThe Planetary Boundary Layer height (PBLH) is essential for studying PBL and ocean‐atmosphere interactions. Marine PBL is usually defined to include a mixed layer (ML) and a capping inversion layer. The ML height (MLH) estimated from the measurements of aerosol backscatter by a lidar was usually compared with PBLH determined from radiosondes/dropsondes in the past, as the PBLH is usually similar to MLH in nature. However, PBLH can be much greater than MLH for decoupled PBL. Here we evaluate the retrieved MLH from an airborne lidar (HSRL‐2) by utilizing 506 co‐located dropsondes during the ACTIVATE field campaign over the Northwest Atlantic from 2020 to 2022. First, we define and determine the MLH and PBLH from the temperature and humidity profiles of each dropsonde, and find that the MLH values from HSRL‐2 and dropsondes agree well with each other, with a coefficient of determination of 0.66 and median difference of 18 m. In contrast, the HSRL‐2 MLH data do not correspond to dropsonde‐derived PBLH, with a median difference of −47 m. Therefore, we modify the current operational and automated HSRL‐2 wavelet‐based algorithm for PBLH retrieval, decreasing the median difference significantly to −8 m. Further data analysis indicates that these conclusions remain the same for cases with higher or lower cloud fractions, and for decoupled PBLs. These results demonstrate the potential of using HSRL‐2 aerosol backscatter data to estimate both marine MLH and PBLH and suggest that lidar‐derived MLH should be compared with radiosonde/dropsonde‐determined MLH (not PBLH) in general.

Retrievals of aerosol optical depth over the western North Atlantic Ocean during ACTIVATE

Abstract. Aerosol optical depth was retrieved from two airborne remote sensing instruments, the Research Scanning Polarimeter (RSP) and Second Generation High Spectral Resolution Lidar (HSRL-2), during the National Aeronautics and Space Administration (NASA) Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE). The field campaign offers a unique opportunity to evaluate an extensive 3-year dataset under a wide range of meteorological conditions from two instruments on the same platform. However, a long-standing issue in atmospheric field studies is that there is a lack of reference datasets for properly validating field measurements and estimating their uncertainties. Here we address this issue by using the triple collocation method, in which a third collocated satellite dataset from the Moderate Resolution Imaging Spectroradiometer (MODIS) is introduced for comparison. HSRL-2 is found to provide a more accurate retrieval than RSP over the study region. The error standard deviation of HSRL-2 with respect to the ground truth is 0.027. Moreover, this approach enables us to develop a simple, yet efficient, quality control criterion for RSP data. The physical reasons for the differences in two retrievals are determined to be cloud contamination, aerosols near the surface, multiple aerosol layers, absorbing aerosols, non-spherical aerosols, and simplified retrieval assumptions. These results demonstrate the pathway for optimal aerosol retrievals by combining information from both lidars and polarimeters for future airborne and satellite missions.

Characterization of dust aerosols from ALADIN and CALIOP measurements

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

Aerosol and cloud data processing and optical property retrieval algorithms for the spaceborne ACDL/DQ-1

Abstract. The new-generation atmospheric environment monitoring satellite DQ-1, launched successfully in April 2022, carries the Aerosol and Carbon Detection Lidar (ACDL), which is capable of globally profiling aerosol and cloud optical properties with high accuracy. The ACDL/DQ-1 is a high-spectral-resolution lidar (HSRL) that separates molecular backscatter signals using an iodine filter and has 532 nm polarization detection and dual-wavelength detection at 532 and 1064 nm, which can be utilized to derive aerosol optical properties. The methods have been specifically developed for data processing and optical property retrieval according to the specific characteristics of the ACDL system and are introduced in detail in this paper. Considering the different signal characteristics and different background noise behaviors of each channel during daytime and nighttime, the procedures of data pre-processing, denoising process and quality control are applied to the original measurement signals. The aerosol and cloud optical property products of the ACDL/DQ-1, including the total depolarization ratio, backscatter coefficient, extinction coefficient, lidar ratio and color ratio, can be calculated by the retrieval algorithms presented in this paper. Two measurement cases with use of the ACDL/DQ-1 on 27 June 2022 and the global averaged aerosol optical depth (AOD) from 1 June to 4 August 2022 are provided and analyzed, demonstrating the measurement capability of the ACDL/DQ-1.

Validation of initial observation from the first spaceborne high-spectral-resolution lidar with a ground-based lidar network

Abstract. On 16 April 2022, China successfully launched the world's first spaceborne high-spectral-resolution lidar (HSRL), which is called the Aerosol and Carbon Detection Lidar (ACDL), on board the Atmospheric Environment Monitoring Satellite known as Daqi-1 (DQ-1). The ACDL is expected to precisely detect the three-dimensional distribution of aerosol and cloud globally with high spatial–temporal resolutions. To assess the performance of the newly launched satellite lidar, the ACDL-retrieved observations were compared with ground-based lidar measurements of atmospheric aerosol and cloud over northwest China from May to July 2022 using the Belt and Road lidar network (BR-lidarnet) initiated by Lanzhou University in China and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar observations. A total of six cases in the daytime and nighttime, including clear days, dust events, and cloudy conditions, were selected for further analysis. Moreover, profiles of the total attenuated backscatter coefficient (TABC) and the volume depolarization ratio (VDR) at 532 nm measured by the ACDL, the CALIPSO lidar, and ground-based lidar are compared in detail. Comparison is made between the 532 nm extinction coefficient and lidar ratio obtained from ACDL HSRL retrieval and the Raman retrieval results obtained from BR-lidarnet. The achieved results revealed that the ACDL observations were in good agreement with the ground-based lidar measurements during dust events with a relative deviation of about −10.5 ± 25.4 % for the TABC and −6.0 ± 38.5 % for the VDR. Additionally, the heights of the cloud top and bottom from these two measurements were well matched and comparable. Compared with the observation of CALIPSO, the ACDL also shows high consistency. This study proves that the ACDL provides reliable observations of aerosol and cloud in the presence of various climatic conditions, which helps to further evaluate the impacts of aerosol on climate and the environment, as well as on the ecosystem in the future.

Performance analysis of high-spectral-resolution lidar with/without laser seeding technique for measuring aerosol optical properties

High-spectral-resolution lidar (HSRL) is a powerful tool for aerosol measurements. With/without laser seeding technique in the transmitted laser, the HSRL can be distinguished as the single-longitudinal-mode (SLM) HSRL or the multi-longitudinal-mode (MLM) HSRL, and the Mach-Zehnder interferometer (MZI) with periodic transmittance function can be used as the spectral discriminator in both the SLM HSRL and MLM HSRL. To in-depth knowledge of the respective advantages of the SLM HSRL and MLM HSRL for measuring aerosol optical properties, the working principle, optimal parameter setting, and detection performance of the SLM HSRL and MLM HSRL are analyzed and discussed in detail, respectively. The working principle of the SLM HSRL and MLM HSRL indicate that the effective transmittance of MZI is the important parameter of data retrieval, the main source of retrieval uncertainties, and the key factor of MZI optical path difference (OPD) settings. To ensure that the MZI can achieve the preferable separation for aerosol Mie scattering signals and molecular Rayleigh scattering signals, the optimal OPDs of MZI are set at 165 mm and 1000 mm in the SLM HSRL and MLM HSRL from the aspects of the effective transmittance of MZI and the spectral discrimination ratio (SDR). Besides, to analyze the influence of frequency difference and divergence angle for the detection performance of HSRL, the effective transmittance of MZI and SDR are simulated and the results show that the MLM HSRL has higher requirements for the environmental parameters and the echo beam collimation than the SLM HSRL. Moreover, the HSRLs with SLM and MLM transmitted lasers are constructed in Xi'an for measuring aerosol optical properties. The preliminary measurement results show that the range square corrected signal (RSCS) of Rayleigh channel is smaller than that of Mie channel in both the SLM HSRL and MLM HSRL, while the difference between RSCS of Rayleigh channel and RSCS of Mie channel in the SLM HSRL is larger than that in the MLM HSRL, and the detection range of the SLM HSRL is lower than that of the MLM HSRL.

Assessing the utility of high spectral resolution lidar for measuring particulate backscatter in the ocean and evaluating satellite ocean color retrievals

Airborne high spectral resolution lidar (HSRL) measurements of ocean particulate backscatter (bbp) offer dramatic improvements in spatiotemporal coverage over in situ techniques, filling observational “blind spots” that limit our ability to study ocean processes. However, the technique has been assessed in only a few limited cases, and uncertainties remain regarding its applicability across a diversity of optical domains. In this study, we present the first comprehensive comparison of bbp derived from airborne HSRL, satellite ocean color, and in situ measurements to increase confidence in HSRL bbp retrievals and to demonstrate the value of airborne HSRL for assessing/improving satellite ocean color algorithms. Retrievals of bbp performed using the NASA Langley HSRL-1 instrument agreed with in situ measurements performed across a diversity of optical and ecological domains. Comparisons across multiple campaigns revealed regional and seasonal dependencies in the ocean color retrievals that likely resulted from applying a single configuration of the ocean color retrieval across multiple distinct domains. In two case studies, atmospheric measurements from HSRL-1 and systematic differences in bbp between HSRL and the 2018 NASA ocean color distribution provided evidence of insufficient atmospheric correction of the ocean color retrieval. These differences in bbp were absent from comparisons against the 2022 ocean color distribution, suggesting that changes made to the algorithm resulted in improved retrievals. These cases highlight the advantages of airborne HSRL for assessing and improving ocean color retrievals, namely its ability to provide simultaneous, independent profiles of atmosphere and ocean optical properties, and improvements in the spatiotemporal coverage of satellite matchups.

The generation of EarthCARE L1 test data sets using atmospheric model data sets

Abstract. The Earth Cloud, Aerosol and Radiation Explorer mission (EarthCARE) is a multi-instrument cloud–aerosol–radiation process study mission embarking a high spectral resolution lidar, a cloud profiling radar, a multi-spectral imager, and a three-view broadband radiometer. An important aspect of the EarthCARE mission is its focus on instrument synergy. Many L2 products are the result of L1 inputs from one or more instruments. Since no existing complete observational proxy data sets comprised of co-located and co-temporal “EarthCARE-like” data exists, it has been necessary to create synthetic data sets for the testing and development of various retrieval algorithms and the data processing chain. Given the synergistic nature of the processing chain, it is important that the test data are physically consistent across the various instruments. Within the EarthCARE project, a version of the EarthCARE simulator multi-instrument framework (ECSIM) has been used to create unified realistic test data frames. These simulations have been driven using high-resolution atmospheric model data (described in a companion paper). In this paper, the methods used to create the test data scenes are described. In addition, the simulated L1 data corresponding to each scene are presented and discussed.

Evaluating WRF-GC v2.0 predictions of boundary layer height and vertical ozone profile during the 2021 TRACER-AQ campaign in Houston, Texas

Abstract. The TRacking Aerosol Convection ExpeRiment – Air Quality (TRACER-AQ) campaign probed Houston air quality with a comprehensive suite of ground-based and airborne remote sensing measurements during the intensive operating period in September 2021. Two post-frontal high-ozone episodes (6–11 and 23–26 September) were recorded during the aforementioned period. In this study, we evaluated the simulation of the planetary boundary layer (PBL) height and the vertical ozone profile by a high-resolution (1.33 km) 3-D photochemical model, the Weather Research and Forecasting (WRF)-driven GEOS-Chem (WRF-GC). We evaluated the PBL heights with a ceilometer at the coastal site La Porte and the airborne High Spectral Resolution Lidar 2 (HSRL-2) flying over urban Houston and adjacent waters. Compared with the ceilometer at La Porte, the model captures the diurnal variations in the PBL heights with a very strong temporal correlation (R>0.7) and ±20 % biases. Compared with the airborne HSRL-2, the model exhibits a moderate to strong spatial correlation (R=0.26–0.68), with ±20 % biases during the noon and afternoon hours during ozone episodes. For land–water differences in PBL heights, the water has shallower PBL heights compared to land. The model predicts larger land–water differences than the observations because the model consistently underestimates the PBL heights over land compared to water. We evaluated vertical ozone distributions by comparing the model against vertical measurements from the TROPospheric OZone lidar (TROPOZ), the HSRL-2, and ozonesondes, as well as surface measurements at La Porte from a model 49i ozone analyzer and one Continuous Ambient Monitoring Station (CAMS). The model underestimates free-tropospheric ozone (2–3 km aloft) by 9 %–22 % but overestimates near-ground ozone (<50 m aloft) by 6 %-39 % during the two ozone episodes. Boundary layer ozone (0.5–1 km aloft) is underestimated by 1 %–11 % during 8–11 September but overestimated by 0 %–7 % during 23–26 September. Based on these evaluations, we identified two model limitations, namely the single-layer PBL representation and the free-tropospheric ozone underestimation. These limitations have implications for the predictivity of ozone's vertical mixing and distribution in other models.

Multi-campaign ship and aircraft observations of marine cloud condensation nuclei and droplet concentrations

In-situ marine cloud droplet number concentrations (CDNCs), cloud condensation nuclei (CCN), and CCN proxies, based on particle sizes and optical properties, are accumulated from seven field campaigns: ACTIVATE; NAAMES; CAMP2EX; ORACLES; SOCRATES; MARCUS; and CAPRICORN2. Each campaign involves aircraft measurements, ship-based measurements, or both. Measurements collected over the North and Central Atlantic, Indo-Pacific, and Southern Oceans, represent a range of clean to polluted conditions in various climate regimes. With the extensive range of environmental conditions sampled, this data collection is ideal for testing satellite remote detection methods of CDNC and CCN in marine environments. Remote measurement methods are vital to expanding the available data in these difficult-to-reach regions of the Earth and improving our understanding of aerosol-cloud interactions. The data collection includes particle composition and continental tracers to identify potential contributing CCN sources. Several of these campaigns include High Spectral Resolution Lidar (HSRL) and polarimetric imaging measurements and retrievals that will be the basis for the next generation of space-based remote sensors and, thus, can be utilized as satellite surrogates.

Particle Microphysical Parameters and the Complex Refractive Index from 3β + 2α HSRL/Raman Lidar Measurements: Conditions of Accurate Retrieval, Retrieval Uncertainties and Constraints to Suppress the Uncertainties

We study retrieval methods in regard to their potential to accurately retrieve particle microphysical parameters (PMP) from 3β + 2α HSRL/Raman lidar measurements. PMPs estimated with these methods are number, surface-area and volume concentrations, the effective radius, and complex refractive index of the investigated particle size distribution (PSD). The 3β + 2α optical data are particle backscatter coefficients at 355, 532 and 1064 nm and extinction coefficients at 355 and 532 nm. We present results that are fundamental for our understanding of how uncertainties of the optical data convert into uncertainties of PMPs. PMPs can only be retrieved with preset accuracy if the input optical data are accurate to at least eight significant digits, i.e., 10−6%. Such measurement accuracy cannot be achieved by currently existing lidar measurement techniques and the fact that atmospheric conditions are not static during lidar observations. Our analysis of the results derived with the novel approach shows that (a) the uncertainty of the retrieved surface-area concentration increases proportionally to the measurement uncertainty of the extinction coefficient at 355 nm, (b) the uncertainty of the effective radius is inversely proportional to the measurement uncertainty of the extinction-related Ångström exponent, (c) the uncertainty of volume concentration is close to the one of the effective radius, and (d) the uncertainty of number concentration is proportional to the inverse of the square value of the uncertainty of the effective radius. The complex refractive index (CRI) cannot be estimated without introducing extra constraints, even if measurement uncertainties of the optical data are as low as 1−3%. We tested constraints and their impact on the solution space, and in how far these constraints could allow us to restrict the retrieval uncertainties. For example, we used information about relative humidity that can be measured with Raman lidar. Relative humidity is an important piece of information that allows for more accurate aerosol typing and thus plays a vital role in any kind of aerosol characterization. The measurement example we used in this study shows that such a constraint can reduce the retrieval uncertainty of single scattering albedo (SSA) to as low as ±0.01–±0.025 (at 532 nm), on the condition that the uncertainty of the input optical data stays below 15%. The results will be used for uncertainty analysis of data products provided by future versions of the Tikhonov Advanced Regularization Algorithm (TiARA). This algorithm has evolved into a standard tool for the derivation of microphysical particle properties from multiwavelength High-Spectral-Resolution Lidar (HSRL)/Raman lidar operated in Europe, East Asia, and the US.

Optical Properties of Boundary Layer Aerosols From High Spectral Resolution Lidar Measurements in a Polluted Urban Environment (Seoul, Korea)

AbstractContinuous measurements of aerosol extinction coefficient (σext) from the High Spectral Resolution Lidar (HSRL) in Seoul from 2016 to 2018 were used to investigate the diurnal variation of the aerosol optical depth (AOD). Nighttime AOD displayed a larger mean and standard deviation (0.45 ± 0.47) than daytime (0.40 ± 0.29). Hygroscopic growth of aerosols under humid conditions was a key factor in the relative enhancement of nighttime AOD. Taking advantage of the HSRL's vertically resolved measurements, the contribution of aerosols within the boundary layer (BL) and free troposphere (FT) to AOD and its temporal variation were investigated. Unlike the diurnal AOD variation, AOD within the BL (AODBL) showed similar diurnal variations with the mixing layer height (MLH), displaying lower nighttime values with a peak around 14–15 local standard time. However, the low correlation between MLH and AODBL (R2 = 0.06) implied that MLH was not the sole deterministic factor of AODBL. A larger AOD of FT aerosols (AODFT) was observed during spring due to frequent elevated dust layers. Using mean σext within the BL and surface PM10 concentrations, the mass extinction efficiency (MEE) of aerosols in Seoul was estimated. The PM10 MEE showed a mean of 5.40 g m−2 and displayed significant variability by PM2.5 to PM10 ratio, season, and ambient relative humidity. The uncertainty of estimated surface PM10 concentrations was minimized when the seasonality and humidity factor in MEE were taken into account (the normalized mean bias decreased from 10.6%, using a single MEE value, to 6.2%).

Aeolus winds impact on volcanic ash early warning systems for aviation

Forecasting volcanic ash atmospheric pathways is of utmost importance for aviation. Volcanic ash can interfere with aircraft navigational instruments and can damage engine parts. Early warning systems, activated after volcanic eruptions can alleviate the impacts on aviation by providing forecasts of the volcanic ash plume dispersion. The quality of these short-term forecasts is subject to the accuracy of the meteorological wind fields used for the initialization of regional models. Here, we use wind profiling data from the first high spectral resolution lidar in space, Aeolus, to examine the impact of measured wind fields on regional NWP and subsequent volcanic ash dispersion forecasts, focusing on the case of Etna’s eruption on March 2021. The results from this case study demonstrate a significant improvement of the volcanic ash simulation when using Aeolus-assimilated meteorological fields, with differences in wind speed reaching up to 8 m/s when compared to the control run. When comparing the volcanic ash forecast profiles with downwind surface-based aerosol lidar observations, the modeled field is consistent with the measurements only when Aeolus winds are assimilated. This result clearly demonstrates the potential of Aeolus and highlights the necessity of future wind profiling satellite missions for improving volcanic ash forecasting and hence aviation safety.

Use of lidar aerosol extinction and backscatter coefficients to estimate cloud condensation nuclei (CCN) concentrations in the southeast Atlantic

Abstract. Accurately capturing cloud condensation nuclei (CCN) concentrations is key to understanding the aerosol–cloud interactions that continue to feature the highest uncertainty amongst numerous climate forcings. In situ CCN observations are sparse, and most non-polarimetric passive remote sensing techniques are limited to providing column-effective CCN proxies such as total aerosol optical depth (AOD). Lidar measurements, on the other hand, resolve profiles of aerosol extinction and/or backscatter coefficients that are better suited for constraining vertically resolved aerosol optical and microphysical properties. Here we present relationships between aerosol backscatter and extinction coefficients measured by the airborne High Spectral Resolution Lidar 2 (HSRL-2) and in situ measurements of CCN concentrations. The data were obtained during three deployments in the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) project, which took place over the southeast Atlantic (SEA) during September 2016, August 2017, and September–October 2018. Our analysis of spatiotemporally collocated in situ CCN concentrations and HSRL-2 measurements indicates strong linear relationships between both data sets. The correlation is strongest for supersaturations (S) greater than 0.25 % and dry ambient conditions above the stratocumulus deck, where relative humidity (RH) is less than 50 %. We find CCN–HSRL-2 Pearson correlation coefficients between 0.95–0.97 for different parts of the seasonal burning cycle that suggest fundamental similarities in biomass burning aerosol (BBA) microphysical properties. We find that ORACLES campaign-average values of in situ CCN and in situ extinction coefficients are qualitatively similar to those from other regions and aerosol types, demonstrating overall representativeness of our data set. We compute CCN–backscatter and CCN–extinction regressions that can be used to resolve vertical CCN concentrations across entire above-cloud lidar curtains. These lidar-derived CCN concentrations can be used to evaluate model performance, which we illustrate using an example CCN concentration curtain from the Weather Research and Forecasting Model coupled with physics packages from the Community Atmosphere Model version 5 (WRF-CAM5). These results demonstrate the utility of deriving vertically resolved CCN concentrations from lidar observations to expand the spatiotemporal coverage of limited or unavailable in situ observations.

On the differences in the vertical distribution of modeled aerosol optical depth over the southeastern Atlantic

Abstract. The southeastern Atlantic is home to an expansive smoke aerosol plume overlying a large cloud deck for approximately a third of the year. The aerosol plume is mainly attributed to the extensive biomass burning activities that occur in southern Africa. Current Earth system models (ESMs) reveal significant differences in their estimates of regional aerosol radiative effects over this region. Such large differences partially stem from uncertainties in the vertical distribution of aerosols in the troposphere. These uncertainties translate into different aerosol optical depths (AODs) in the planetary boundary layer (PBL) and the free troposphere (FT). This study examines differences of AOD fraction in the FT and AOD differences among ESMs (WRF-CAM5, WRF-FINN, GEOS-Chem, EAM-E3SM, ALADIN, GEOS-FP, and MERRA-2) and aircraft-based measurements from the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) field campaign. Models frequently define the PBL as the well-mixed surface-based layer, but this definition misses the upper parts of decoupled PBLs, in which most low-level clouds occur. To account for the presence of decoupled boundary layers in the models, the height of maximum vertical gradient of specific humidity profiles from each model is used to define PBL heights. Results indicate that the monthly mean contribution of AOD in the FT to the total-column AOD ranges from 44 % to 74 % in September 2016 and from 54 % to 71 % in August 2017 within the region bounded by 25∘ S–0∘ N–S and 15∘ W–15∘ E (excluding land) among the ESMs. ALADIN and GEOS-Chem show similar aerosol plume patterns to a derived above-cloud aerosol product from the Moderate Resolution Imaging Spectroradiometer (MODIS) during September 2016, but none of the models show a similar above-cloud plume pattern to MODIS in August 2017. Using the second-generation High Spectral Resolution Lidar (HSRL-2) to derive an aircraft-based constraint on the AOD and the fractional AOD, we found that WRF-CAM5 produces 40 % less AOD than those from the HSRL-2 measurements, but it performs well at separating AOD fraction between the FT and the PBL. AOD fractions in the FT for GEOS-Chem and EAM-E3SM are, respectively, 10 % and 15 % lower than the AOD fractions from the HSRL-2. Their similar mean AODs reflect a cancellation of high and low AOD biases. Compared with aircraft-based observations, GEOS-FP, MERRA-2, and ALADIN produce 24 %–36 % less AOD and tend to misplace more aerosols in the PBL. The models generally underestimate AODs for measured AODs that are above 0.8, indicating their limitations at reproducing high AODs. The differences in the absolute AOD, FT AOD, and the vertical apportioning of AOD in different models highlight the need to continue improving the accuracy of modeled AOD distributions. These differences affect the sign and magnitude of the net aerosol radiative forcing, especially when aerosols are in contact with clouds.

Airborne HSRL-2 measurements of elevated aerosol depolarization associated with non-spherical sea salt

Airborne NASA Langley Research Center (LaRC) High Spectral Resolution Lidar-2 (HSRL-2) measurements acquired during the recent NASA Earth Venture Suborbital-3 (EVS-3) Aerosol Cloud Meteorology Interactions over the Western Atlantic Experiment (ACTIVATE) revealed elevated particulate linear depolarization associated with aerosols within the marine boundary layer. These observations were acquired off the east coast of the United States during both winter and summer 2020 and 2021 when the HSRL-2 was deployed on the NASA LaRC King Air aircraft. During 20 of 63 total flight days, particularly on days with cold air outbreaks, linear particulate depolarization at 532 nm exceeded 0.15–0.20 within the lowest several hundred meters of the atmosphere, indicating that these particles were non-spherical. Higher values of linear depolarization typically were measured at 355 nm and lower values were measured at 1,064 nm. Several lines of evidence suggest that these non-spherical particles were sea salt including aerosol extinction/backscatter ratio (“lidar ratio”) values of 20–25 sr measured at both 355 and 532 nm by the HSRL-2, higher values of particulate depolarization measured at low (< 60%) relative humidity, coincident airbornein situsize and composition measurements, and aerosol transport simulations. The elevated aerosol depolarization values were not correlated with wind speed but were correlated with salt mass fraction and effective radius of the aerosol when the relative humidity was below 60%. HSRL-2 measured median particulate extinction values of about 20 Mm−1 at 532 nm associated with these non-spherical sea salt particles and found that the aerosol optical depth (AOD) contributed by these particles remained small (0.03–0.04) but represented on average about 30%–40% of the total column AOD. Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) spaceborne lidar aerosol measurements during several cold air outbreaks and CALIOP retrievals of column aerosol lidar ratio using column AOD constraints suggest that CALIOP operational aerosol algorithms can misclassify these aerosols as dusty marine rather than marine aerosols. Such misclassification leads to ∼40–50% overestimates in the assumed lidar ratio and in subsequent retrievals of aerosol optical depth and aerosol extinction.

Application of DIAL/HSRL and CATCH algorithm-based methodologies for surface PM2.5 concentrations during the KORUS-AQ campaign

Particulate matter with an aerodynamic diameter of equal to or less than 2.5 μm (PM2.5) has been found to have a serious adverse effect on human health and the environment. While the importance of measuring PM2.5 has been demonstrated, doing so remotely remains challenging. In this study, methodologies for the assessment of aerosol PM2.5 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 PM2.5 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 PM2.5. 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 PM2.5. 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 PM2.5 concentration and derived chemical composition over South Korea when HSRL data are available.

First assessment of Aeolus Standard Correct Algorithm particle backscatter coefficient retrievals in the eastern Mediterranean

Abstract. Since 2018, the Aeolus satellite of the European Space Agency (ESA) has acquired wind HLOS (horizontal line-of-sight) profiles throughout the troposphere and up to the lower stratosphere, filling a critical gap in the Global Observing System (GOS). Aeolus, carrying ALADIN (Atmospheric LAser Doppler INstrument), the first UV HSRL (High Spectral Resolution Lidar) Doppler lidar ever placed in space, provides also vertically resolved optical properties of particulates (aerosols and clouds). The present study focuses on the assessment of Aeolus L2A particulate backscatter coefficient (baseline 2A11), retrieved by the Standard Correct Algorithm (SCA), in the eastern Mediterranean, a region hosting a variety of aerosol species. Ground-based retrievals acquired by lidar instruments operating in Athens (central Greece), Thessaloniki (northern Greece) and Antikythera (southwestern Greece) serve as reference. All lidar stations provide routine measurements to the PANACEA (PANhellenic infrastructure for Atmospheric Composition and climatE chAnge) network. A set of ancillary data, including sun-photometric observations (AERONET), reanalysis products (CAMS and MERRA-2), satellite observations (MSG-SEVIRI and MODIS Aqua) and backward-trajectories modelling (FLEXPART), is utilized towards an optimum characterization of the probed atmospheric conditions under the absence of a classification scheme in Aeolus SCA profiles. First, emphasis is given on the assessment of Aeolus SCA backscatter coefficient under specific aerosol scenarios over Antikythera island. Due to the misdetection of the cross-polar component of the backscattered lidar signal, Aeolus underestimates the aerosol backscatter coefficient by up to 33 % when non-spherical mineral particles are recorded (10 July 2019). A good performance is revealed on 3 July 2019, when horizontally homogeneous loads of fine spherical particles are confined below 4 km. For other two cases (8 July 2020 and 5 August 2020), due to noise issues, the SCA performance degrades in terms of depicting the stratification of aerosol layers composed of particles of different origin. According to the statistical assessment analysis of 43 identified cases, a poor-to-moderate performance is revealed for the unfiltered (aerosols plus clouds) SCA profiles, which improves substantially when cloud-contaminated profiles are excluded from the collocated sample. This improvement is evident at both Aeolus vertical scales (regular scales have 24 bins and mid-bin scales have 23 bins), and it is justified by the drastic reduction in the bias (from 0.45 to 0.27 Mm−1 sr−1 for SCA and from 0.69 to 0.37 Mm−1 sr−1 for SCA mid-bin) and root mean square error (from 2.00 to 1.65 Mm−1 sr−1 for SCA and from 1.88 to 1.00 Mm−1 sr−1 for SCA mid-bin) scores. In the vertical, the SCA performance degrades at the lowermost bins due to either the contamination from surface signals or the increased noise levels for the aerosol retrievals. Among the three PANACEA stations, the best agreement is found at the remote site of Antikythera with respect to the urban sites of Athens and Thessaloniki. Finally, all key Cal/Val (calibration and validation) aspects necessary for future relevant studies, the recommendations for a possible Aeolus follow-on mission and an overview of the ongoing related activities are thoroughly discussed.