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Improvements in air quality and Earth’s climate predictions require improvements of the aerosol speciation in chemical transport models, using observational constraints. Aerosol speciation (e.g., organic aerosols, black carbon, sulfate, nitrate, ammonium, dust or sea salt) is typically determined using in situ instrumentation. Continuous, routine surface network aerosol composition measurements are not uniformly widespread over the globe. Satellites, on the other hand, can provide a maximum coverage of the horizontal and vertical atmosphere but observe aerosol optical properties (and not aerosol speciation) based on remote sensing instrumentation. Combinations of satellite-derived aerosol optical properties can inform on air mass aerosol types (AMTs e.g., clean marine, dust, polluted continental). However, these AMTs are subjectively defined, might often be misclassified and are hard to relate to the critical parameters that need to be refined in models. In this paper, we derive AMTs that are more directly related to sources and hence to speciation. They are defined, characterized, and derived using simultaneous in situ gas-phase, chemical and optical instruments on the same aircraft during the Study of Emissions and Atmospheric Composition, Clouds, and Climate Coupling by Regional Surveys (SEAC4RS, US, summer of 2013). First, we prescribe well-informed AMTs that display distinct aerosol chemical and optical signatures to act as a training AMT dataset. These in situ observations reduce the errors and ambiguities in the selection of the AMT training dataset. We also investigate the relative skill of various combinations of aerosol optical properties to define AMTs and how much these optical properties can capture dominant aerosol speciation. We find distinct optical signatures for biomass burning (from agricultural or wildfires), biogenic and dust-influence AMTs. Useful aerosol optical properties to characterize these signatures are the extinction angstrom exponent (EAE), the single scattering albedo, the difference of single scattering albedo in two wavelengths, the absorption coefficient, the absorption angstrom exponent (AAE), and the real part of the refractive index (RRI). We find that all four AMTs studied when prescribed using mostly airborne in situ gas measurements, can be successfully extracted from at least three combinations of airborne in situ aerosol optical properties (e.g., EAE, AAE and RRI) over the US during SEAC4RS. However, we find that the optically based classifications for BB from agricultural fires and polluted dust include a large percentage of misclassifications that limit the usefulness of results relating to those classes. The technique and results presented in this study are suitable to develop a representative, robust and diverse source-based AMT database. This database could then be used for widespread retrievals of AMTs using existing and future remote sensing suborbital instruments/networks. Ultimately, it has the potential to provide a much broader observational aerosol data set to evaluate chemical transport and air quality models than is currently available by direct in situ measurements. This study illustrates how essential it is to explore existing airborne datasets to bridge chemical and optical signatures of different AMTs, before the implementation of future spaceborne missions (e.g., the next generation of Earth Observing System (EOS) satellites addressing Aerosol, Cloud, Convection and Precipitation (ACCP) designated observables).

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.

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.

Monitoring and modeling aerosol particle life cycle in Southeast Asia (SEA) is challenged by high cloud cover, complex meteorology, and the wide range of aerosol species, sources, and transformations found throughout the region. Satellite observations are limited, and there are few in situ observations of aerosol extinction profiles, aerosol properties, and environmental conditions. Therefore, accurate aerosol model outputs are crucial for the region. This work evaluates the Navy Aerosol Analysis and Prediction System Reanalysis (NAAPS-RA) aerosol optical thickness (AOT) and light extinction products using airborne aerosol and meteorological measurements from the Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP2Ex) conducted in 2019 during the SEA southwest monsoon biomass burning season. Modeled AOTs and extinction coefficients are compared to those retrieved with a high spectral resolution lidar (HSRL-2). Agreement between simulated and retrieved AOT (R2= 0.78, relative bias =-5 %, normalized root mean square error (NRMSE) = 48 %) and aerosol extinction coefficients (R2= 0.80, 0.81, and 0.42; relative bias = 3 %, −6 %, and −7 %; NRMSE = 47 %, 53 %, and 118 % for altitudes between 40–500, 500–1500, and >1500 m, respectively) is quite good considering the challenging environment and few opportunities for assimilations of AOT from satellites during the campaign. Modeled relative humidities (RHs) are negatively biased at all altitudes (absolute bias =-5 %, −8 %, and −3 % for altitudes <500 500–1500 and >1500 m, respectively), motivating interest in the role of RH errors in AOT and extinction simulations. Interestingly, NAAPS-RA AOT and extinction agreement with the HSRL-2 does not change significantly (i.e., NRMSE values do not all decrease) when RHs from dropsondes are substituted into the model, yet biases all move in a positive direction. Further exploration suggests changes in modeled extinction are more sensitive to the actual magnitude of both the extinction coefficients and the dropsonde RHs being substituted into the model as opposed to the absolute differences between simulated and measured RHs. Finally, four case studies examine how model errors in RH and the hygroscopic growth parameter, γ, affect simulations of extinction in the mixed layer (ML). We find NAAPS-RA overestimates the hygroscopicity of (i) smoke particles from biomass burning in the Maritime Continent (MC) and (ii) anthropogenic emissions transported from East Asia. This work mainly provides insight into the relationship between errors in modeled RH and simulations of AOT and extinction in a humid and tropical environment influenced by a myriad of meteorological conditions and particle types. These results can be interpreted and addressed by the modeling community as part of the effort to better understand, quantify, and forecast atmospheric conditions in SEA.

This study presents an investigation of aerosol microphysical retrievals from high spectral resolution lidar (HSRL) measurements. Firstly, retrievals are presented for synthetically generated lidar measurements, followed by an application of the retrieval algorithm to real lidar measurements. Here, we perform the investigation for an aerosol state vector that is typically used in multi-angle polarimeter (MAP) retrievals, so that the results can be interpreted in relation to a potential combination of lidar and MAP measurements. These state vectors correspond to a bimodal size distribution, where column number, effective radius, and effective variance of both modes are treated as fit parameters, alongside the complex refractive index and particle shape. The focus is primarily on a lidar configuration based on that of the High Spectral Resolution Lidar-2 (HSRL-2), which participated in the ACEPOL (Aerosol Characterization from Polarimeter and Lidar) campaign, a combined project between NASA and SRON (Netherlands Institute for Space Research). The measurement campaign took place between October and November 2017, over the western region of the USA. Six different instruments were mounted on the aeroplane: four MAPs and two lidar instruments, HSRL-2 and the Cloud Physics Lidar (CPL). Most of the flights were carried out over land, passing over scenes with a low aerosol load. One of the flights passed over a prescribed forest fire in Arizona on 9 November, with a relatively higher aerosol optical depth (AOD), and it is the data from this flight that are focussed on in this study. A retrieval of the aerosol microphysical properties of the smoke plume mixture was attempted with the data from HSRL-2 and compared with a retrieval from the MAPs carried out in previous work pertaining to the ACEPOL data. The synthetic HSRL-2 retrievals resulted for the fine mode in a mean absolute error (MAE) of 0.038 (0.025) µm for the effective radius (with a mean truth value of 0.195 µm), 0.052 (0.037) for the real refractive index, 0.010 (7.20×10-3) for the imaginary part of the refractive index, 0.109 (0.071) for the spherical fraction, and 0.054 (0.039) for the AOD at 532 nm, where the retrievals inside brackets indicate the MAE for noise-free retrievals. For the coarse mode, we find the MAE is 0.459 (0.254) µm for the effective radius (with a mean truth value of 1.970 µm), 0.085 (0.075) for the real refractive index, 2.06×10-4 (1.90×10-4) for the imaginary component, 0.120 (0.090) for the spherical fraction, and 0.051 (0.039) for the AOD. A study of the sensitivity of retrievals to the choice of prior and first guess showed that, on average, the retrieval errors increase when the prior deviates too much from the truth value. These experiments revealed that the measurements primarily contain information on the size and shape of the aerosol, along with the column number. Some information on the real component of the refractive index is also present, with the measurements providing little on absorption or on the effective variance of the aerosol distribution, as both of these were shown to depend heavily on the choice of prior. Retrievals using the HSRL-2 smoke-plume data yielded, for the fine mode, an effective radius of 0.107 µm, a real refractive index of 1.561, an imaginary component of refractive index of 0.010, a spherical fraction of 0.719, and an AOD at 532 nm of 0.505. Additionally, the single-scattering albedo (SSA) from the HSRL-2 retrievals was 0.940. Overall, these results are in good agreement with those from the Spectropolarimeter for Planetary Exploration (SPEX) and Research Scanning Polarimeter (RSP) retrievals.

Shortwave-absorbing aerosols seasonally overlay extensive low-level stratocumulus clouds over the southeast Atlantic. While much attention has focused on the interactions between the low-level clouds and the overlying aerosols, few studies have focused on the mid-level clouds that also occur over the region. The presence of mid-level clouds over the region complicates the space-based remote-sensing retrievals of cloud properties and the evaluation of cloud radiation budgets. Here we characterize the mid-level clouds over the southeast Atlantic using lidar- and radar-based satellite cloud retrievals and observations collected in September 2016 during the ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) field campaign. We find that mid-level clouds over the southeast Atlantic are relatively common, with the majority of the clouds occurring between altitudes of 5 and 7 km and at temperatures between 0 and −20 ∘C. The mid-level clouds occur at the top of a moist mid-tropospheric smoke-aerosol layer, most frequently between August and October, and closer to the southern African coast than farther offshore. They occur more frequently during the night than during the day. Between July and October, approximately 64 % of the mid-level clouds had a geometric cloud thickness less than 1 km, corresponding to a cloud optical depth of less than 4. A lidar-based depolarization–backscatter relationship for September 2016 indicates that the mid-level clouds are liquid-only clouds with no evidence of the existence of ice. In addition, a polarimeter-derived cloud droplet size distribution indicates that approximately 85 % of the September 2016 mid-level clouds had an effective radius less than 7 µm, which could further discourage the ability of the clouds to glaciate. These clouds are mostly associated with synoptically modulated mid-tropospheric moisture outflow that can be linked to the detrainment from the continental-based clouds. Overall, the supercooled mid-level clouds reduce the radiative cooling rates of the underlying low-altitude cloud tops by approximately 10 K d−1, thus influencing the regional cloud radiative budget.

In this paper, we present aerosol retrieval results from the ACEPOL (Aerosol Characterization from Polarimeter and Lidar) campaign, which was a joint initiative between NASA and SRON – the Netherlands Institute for Space Research. The campaign took place in October–November 2017 over the western part of the United States. During ACEPOL six different instruments were deployed on the NASA ER-2 high-altitude aircraft, including four multi-angle polarimeters (MAPs): SPEX airborne, the Airborne Hyper Angular Rainbow Polarimeter (AirHARP), the Airborne Multi-angle SpectroPolarimetric Imager (AirMSPI), and the Research Scanning Polarimeter (RSP). Also, two lidars participated: the High Spectral Resolution Lidar-2 (HSRL-2) and the Cloud Physics Lidar (CPL). Flights were conducted mainly for scenes with low aerosol load over land, but some cases with higher AOD were also observed. We perform aerosol retrievals from SPEX airborne, RSP (410–865 nm range only), and AirMSPI using the SRON aerosol retrieval algorithm and compare the results against AERONET (AErosol RObotic NETwork) and HSRL-2 measurements (for SPEX airborne and RSP). All three MAPs compare well against AERONET for the aerosol optical depth (AOD), with a mean absolute error (MAE) between 0.014 and 0.024 at 440 nm. For the fine-mode effective radius the MAE ranges between 0.021 and 0.028 µm. For the comparison with HSRL-2 we focus on a day with low AOD (0.02–0.14 at 532 nm) over the California Central Valley, Arizona, and Nevada (26 October) as well as a flight with high AOD (including measurements with AOD>1.0 at 532 nm) over a prescribed forest fire in Arizona (9 November). For the day with low AOD the MAEs in AOD (at 532 nm) with HSRL-2 are 0.014 and 0.022 for SPEX and RSP, respectively, showing the capability of MAPs to provide accurate AOD retrievals for the challenging case of low AOD over land. For the retrievals over the smoke plume a reasonable agreement in AOD between the MAPs and HSRL-2 was also found (MAE 0.088 and 0.079 for SPEX and RSP, respectively), despite the fact that the comparison is hampered by large spatial variability in AOD throughout the smoke plume. A good comparison is also found between the MAPs and HSRL-2 for the aerosol depolarization ratio (a measure of particle sphericity), with an MAE of 0.023 and 0.016 for SPEX and RSP, respectively. Finally, SPEX and RSP agree very well for the retrieved microphysical and optical properties of the smoke plume.

The San Joaquin Valley (SJV) of California has one of the nation's most severe wintertime PM2.5 pollution problems. The DISCOVER-AQ (Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality) field campaign took place in the SJV from January 16 to February 6, 2013. It captured two PM2.5 pollution episodes with peak 24-h concentrations approaching 70 μg/m3. Using meteorological fields generated from WRFv3.6, CMAQv5.0.2 was applied to simulate PM2.5 formation in the SJV from January 10 through February 10, 2013. Overall, the model was able to capture the observed accumulation of PM2.5 within the simulation period. The model was able to produce increased concentrations of ammonium nitrate and organic carbon, which are two major components of wintertime PM2.5 in the SJV. Comparison to measurements made by aircraft showed that there was general agreement between observed and modeled daytime vertical distributions of selected gas and particulate species, reflecting the adequacy of modeled daytime mixing layer heights. Excess ammonia predicted by the model implied that ammonium nitrate formation was limited by the availability of nitric acid, consistent with observations. Evaluation of the ammonium nitrate diurnal profile revealed that the observed morning increase of ammonium nitrate was also evident from the model. This paper demonstrates that the CMAQ model is able to simulate elevated wintertime PM2.5 formation observed in the SJV during the DISCOVER-AQ 2013 period, which featured both climatic (i.e., 2011–2014 California Drought) and emissions differences compared to a previous large air quality field campaign in the SJV during 1999–2000. Keywords: CMAQ, DISCOVER-AQ, PM2.5 modeling, San joaquin valley

The typical multiwavelength aerosol lidar data set for inversion of optical to microphysical parameters is composed of three backscatter coefficients (β) at 355, 532, and 1064 nm and two extinction coefficients (α) at 355 and 532 nm. This data combination is referred to as a 3β+2α or 3+2 data set. This set of data is sufficient for retrieving some important microphysical particle parameters if the particles have spherical shape. Here, we investigate the effect of including the particle linear depolarization ratio (δ) as a third input parameter for the inversion of lidar data. The inversion algorithm is generally not used if measurements show values of δ that exceed 0.10 at 532 nm, i.e. in the presence of non-spherical particles such as desert dust, volcanic ash, and, under special circumstances, biomass-burning smoke. We use experimental data collected with instruments that are capable of measuring δ at all three lidar wavelengths with an inversion routine that applies the spheroidal light-scattering model of Dubovik et al. (2006) with a fixed axis-ratio distribution to replicate scattering properties of non-spherical particles. The inversion gives the fraction of spheroids required to replicate the optical data as an additional output parameter. This is the first systematic test of the effect of using all theoretically possible combinations of δ taken at 355, 532, and 1064 nm as input in the lidar data inversion. We find that depolarization information of at least one wavelength already provides useful information for the inversion of optical data that have been collected in the presence of non-spherical mineral dust particles. However, any choice of δλ will give lower values of the single-scattering albedo than the traditional 3+2 data set. We find that input data sets that include δ355 give a spheroid fraction that closely resembles the dust ratio we obtain from using β532 and δ532 in a methodology applied in aerosol-type separation. The use of δ355 in data sets of two or three δλ reduces the spheroid fraction that is retrieved when using δ532 and δ1064. Use of the latter two parameters without accounting for δ355 generally leads to high spheroid fractions that we consider not trustworthy. The use of three δλ instead of two δλ, including the constraint that one of these is measured at 355 nm does not provide any advantage over using 3+2+δ355 for the observations with varying contributions of mineral dust considered here. However, additional measurements at wavelengths different from 355 nm would be desirable for application to a wider range of aerosol scenarios that may include non-spherical smoke particles, which can have values of δ355 that are indistinguishable from those found for mineral dust. We therefore conclude that – depending on measurement capability – the future standard input for inversion of lidar data taken in the presence of mineral dust particles and using the spheroid model of Dubovik et al. (2006) might be 3+2+δ355 or 3+2+δ355+δ532.

There is considerable interest in retrieving profiles of aerosol effective radius, total number concentration, and complex refractive index from lidar measurements of extinction and backscatter at several wavelengths. The combination of three backscatter channels plus two extinction channels (3β + 2α) is particularly important since it is believed to be the minimum configuration necessary for the retrieval of aerosol microphysical properties and because the technological readiness of lidar systems permits this configuration on both an airborne and future spaceborne instrument. The second-generation NASA Langley airborne High Spectral Resolution Lidar (HSRL-2) has been making 3β + 2α measurements since 2012. The planned NASA Aerosol/Clouds/Ecosystems (ACE) satellite mission also recommends the 3β + 2α combination.Here we develop a deeper understanding of the information content and sensitivities of the 3β + 2α system in terms of aerosol microphysical parameters of interest. We use a retrieval-free methodology to determine the basic sensitivities of the measurements independent of retrieval assumptions and constraints. We calculate information content and uncertainty metrics using tools borrowed from the optimal estimation methodology based on Bayes' theorem, using a simplified forward model look-up table, with no explicit inversion. The forward model is simplified to represent spherical particles, monomodal log-normal size distributions, and wavelength-independent refractive indices. Since we only use the forward model with no retrieval, the given simplified aerosol scenario is applicable as a best case for all existing retrievals in the absence of additional constraints. Retrieval-dependent errors due to mismatch between retrieval assumptions and true atmospheric aerosols are not included in this sensitivity study, and neither are retrieval errors that may be introduced in the inversion process. The choice of a simplified model adds clarity to the understanding of the uncertainties in such retrievals, since it allows for separately assessing the sensitivities and uncertainties of the measurements alone that cannot be corrected by any potential or theoretical improvements to retrieval methodology but must instead be addressed by adding information content.The sensitivity metrics allow for identifying (1) information content of the measurements vs. a priori information; (2) error bars on the retrieved parameters; and (3) potential sources of cross-talk or "compensating" errors wherein different retrieval parameters are not independently captured by the measurements. The results suggest that the 3β + 2α measurement system is underdetermined with respect to the full suite of microphysical parameters considered in this study and that additional information is required, in the form of additional coincident measurements (e.g., sun-photometer or polarimeter) or a priori retrieval constraints. A specific recommendation is given for addressing cross-talk between effective radius and total number concentration.

Radiometric calibration of space-based elastic backscatter lidars is accomplished by comparing the measured backscatter signals to theoretically expected signals computed for some well-characterized calibration target. For any given system and wavelength, the choice of calibration target is dictated by several considerations, including signal-to-noise ratios (SNR) and target availability. This paper describes the newly implemented procedures used to calibrate the 1064 nm measurements acquired by CALIOP (i.e., the Cloud Aerosol Lidar with Orthogonal Polarization), the two-wavelength (532 nm and 1064 nm) elastic backscatter lidar currently flying on the Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission. CALIOP’s 532 nm channel is accurately calibrated by normalizing the molecular backscatter from the uppermost aerosol-free altitudes of the CALIOP measurement region to molecular model data obtained from NASA’s Global Modeling and Assimilation Office. However, because CALIOP’s SNR for molecular backscatter measurements is prohibitively lower at 1064 nm than at 532 nm, the direct high altitude molecular normalization method is not a viable option at 1064 nm. Instead, CALIOP’s 1064 nm channel is calibrated relative to the 532 nm channel using the backscatter from a carefully selected subset of cirrus cloud measurements. In this paper we deliver a full account of the revised 1064 nm calibration algorithms implemented for the version 4.1 (V4) release of the CALIPSO lidar data products, with particular emphases on the physical basis for the selection of calibration quality cirrus clouds and on the new averaging scheme required to characterize intra-orbit calibration variability. The V4 procedures introduce latitudinally varying changes in the 1064 nm calibration coefficients of 25 % or more relative to previous data releases and are shown to substantially improve the accuracy of the V4 1064 nm attenuated backscatter coefficients. By evaluating calibration coefficients derived using both water clouds and ocean surfaces as alternate calibration targets, and through comparisons to independent, collocated measurements made by airborne high spectral resolution lidar, we conclude that the CALIOP V4 1064 nm calibration coefficients are accurate to within 3 %.

Estimates of the direct radiative effect (DRE) from absorbing smoke aerosols over the southeast Atlantic Ocean (SAO) require simulation of the microphysical and optical properties of stratocumulus clouds as well as of the altitude and shortwave (SW) optical properties of biomass burning aerosols (BBAs). In this study, we take advantage of the large number of observations acquired during the ObseRvations of Aerosols above Clouds and their intEractionS (ORACLES-2016) and Layered Atlantic Smoke Interactions with Clouds (LASIC) projects during September 2016 and compare them with datasets from the ALADIN-Climate (Aire Limitée Adaptation dynamique Développement InterNational) regional model. The model provides a good representation of the liquid water path but the low cloud fraction is underestimated compared to satellite data. The modeled total-column smoke aerosol optical depth (AOD) and above-cloud AOD are consistent (∼0.7 over continental sources and ∼0.3 over the SAO at 550 nm) with the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), Ozone Monitoring Instrument (OMI) or Moderate Resolution Imaging Spectroradiometer (MODIS) data. The simulations indicate smoke transport over the SAO occurs mainly between 2 and 4 km, consistent with surface and aircraft lidar observations. The BBA single scattering albedo is slightly overestimated compared to the Aerosol Robotic Network (AERONET) and more significantly when compared to Ascension Island surface observations. The difference could be due to the absence of internal mixing treatment in the ALADIN-Climate model. The SSA overestimate leads to an underestimation of the simulated SW radiative heating compared to ORACLES data. ALADIN-Climate simulates a positive (monthly mean) SW DRE of about +6 W m−2 over the SAO (20∘ S–10∘ N and 10∘ W–20∘ E) at the top of the atmosphere and in all-sky conditions. Over the continent, the presence of BBA is shown to significantly decrease the net surface SW flux, through direct and semi-direct effects, which is compensated by a decrease (monthly mean) in sensible heat fluxes (−25 W m−2) and surface land temperature (−1.5 ∘C) over Angola, Zambia and the Democratic Republic of the Congo, notably. The surface cooling and the lower tropospheric heating decrease the continental planetary boundary layer height by about ∼200 m.

The Two-Column Aerosol Project (TCAP), conducted from June 2012 through June 2013, was a unique study designed to provide a comprehensive data set that can be used to investigate a number of important climate science questions, including those related to aerosol mixing state and aerosol radiative forcing. The study was designed to sample the atmosphere between and within two atmospheric columns; one fixed near the coast of North America (over Cape Cod, MA) and a second moveable column over the Atlantic Ocean several hundred kilometers from the coast. The U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF) was deployed at the base of the Cape Cod column, and the ARM Aerial Facility was utilized for the summer and winter intensive observation periods. One important finding from TCAP is that four of six nearly cloud-free flight days had aerosol layers aloft in both the Cape Cod and maritime columns that were detected using the nadir pointing second-generation NASA high-spectral resolution lidar (HSRL-2). These layers contributed up to 60% of the total observed aerosol optical depth (AOD). Many of these layers were also intercepted by the aircraft configured for in situ sampling, and the aerosol in the layers was found to have increased amounts of biomass burning material and nitrate compared to aerosol found near the surface. In addition, while there was a great deal of spatial and day-to-day variability in the aerosol chemical composition and optical properties, no systematic differences between the two columns were observed.

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.

Current remote sensing methods can identify aerosol types within an atmospheric column, presenting an opportunity to incrementally bridge the gap between remote sensing and models. Here a new algorithm was designed for Creating Aerosol Types from CHemistry (CATCH). CATCH-derived aerosol types—dusty mix, maritime, urban, smoke, and fresh smoke—are based on first-generation airborne High Spectral Resolution Lidar (HSRL-1) retrievals during the Ship-Aircraft Bio-Optical Research (SABOR) campaign, July/August 2014. CATCH is designed to derive aerosol types from model output of chemical composition. CATCH-derived aerosol types are determined by multivariate clustering of model-calculated variables that have been trained using retrievals of aerosol types from HSRL-1. CATCH-derived aerosol types (with the exception of smoke) compare well with HSRL-1 retrievals during SABOR with an average difference in aerosol optical depth (AOD)

The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument onboard the Cloud Aerosol Lidar and Pathfinder Satellite Observations (CALIPSO) spacecraft has provided over 8 years of nearly continuous vertical profiling of Earth's atmosphere. In this paper we investigate the CALIOP 532 nm aerosol layer optical depth (AOD) product, the AOD of individual layers, and the column AOD product, the sum AOD of the complete column, using an extensive database of coincident measurements. The CALIOP AOD measurements and AOD uncertainty estimates are compared with collocated AOD measurements collected with the NASA High Spectral Resolution Lidar (HSRL) in the North American and Caribbean regions. In addition, the CALIOP aerosol lidar ratios are investigated using the HSRL measurements. In general, compared with the HSRL values, the CALIOP layer AOD are biased high by less than 50% for AOD < 0.3 with higher errors for higher AOD. Less than 60% of the HSRL AOD measurements are encompassed within the CALIOP layer one-standard-deviation uncertainty range (around the CALIOP layer AOD), so an error estimate is created to encompass 68% of the HSRL data. Using this new metric, the CALIOP layer AOD error is estimated using the HSRL layer AOD as ± 0.035 ± 0.05 · (HSRL layer AOD) at night and ±0.05 ± 0.05 · (HSRL layer AOD) during the daytime. Furthermore, the CALIOP layer AOD error is found to correlate with aerosol loading as well as aerosol subtype, with the AODs in marine and dust layers agreeing most closely with the HSRL values. The lidar ratios used by CALIOP for polluted dust, polluted continental, and biomass burning layers are larger than the values measured by the HSRL in the CALIOP layers, and, therefore, the AODs for these types retrieved by CALIOP were generally too large. We estimated the CALIOP column AOD error can be expressed as ± 0.05 ± 0.07 · (HSRL column AOD) at night and ± 0.08 ± 0.1 · (HSRL column AOD) during the daytime. Multiple sources of error contribute to both positive and negative errors in the CALIOP column AOD, including multiple layers in the column of different aerosol types, lidar ratio errors, cloud misclassification, and undetected aerosol layers. The undetected layers were further investigated and we found that the layer detection algorithm works well at night, although undetected aerosols in the free troposphere introduce a mean underestimate of 0.02 in the column AOD in the dataset examined. The decreased SNR during the daytime led to poorer performance of the layer detection. This caused the daytime CALIOP column AOD to be less accurate than during the nighttime because CALIOP frequently does not detect optically thin aerosol layers with AOD < 0.1. Given that the median vertical extent of aerosol detected within any column was 1.6 km during the nighttime and 1.5 km during the daytime we can estimate the minimum extinction detection threshold to be 0.012 km−1 at night and 0.067 km−1 during the daytime in a layer median sense. This extensive validation of level 2 CALIOP aerosol layer optical depth products extends previous validation studies to nighttime lighting conditions and provides independent measurements of the lidar ratio, thus allowing the assessment of the effect on the CALIOP AOD of using inappropriate lidar ratio values in the extinction retrieval.

We present measurements acquired by the world's first airborne 3 backscatter (β) + 2 extinction (α) High Spectral Resolution Lidar (HSRL-2). HSRL-2 measures particle backscatter coefficients at 355, 532, and 1064 nm, and particle extinction coefficients at 355 and 532 nm. The instrument has been developed by the NASA Langley Research Center. The instrument was operated during Phase 1 of the Department of Energy (DOE) Two-Column Aerosol Project (TCAP) in July 2012. We observed pollution outflow from the northeastern coast of the US out over the western Atlantic Ocean. Lidar ratios were 50–60 sr at 355 nm and 60–70 sr at 532 nm. Extinction-related Ångström exponents were on average 1.2–1.7, indicating comparably small particles. Our novel automated, unsupervised data inversion algorithm retrieved particle effective radii of approximately 0.2 μm, which is in agreement with the large Ångström exponents. We find good agreement with particle size parameters obtained from coincident in situ measurements carried out with the DOE Gulfstream-1 aircraft.

Satellite data of atmospheric pollutants are becoming more widely used in the decision-making and environmental management activities of public, private sector and non-profit organizations. They are employed for estimating emissions, tracking pollutant plumes, supporting air quality forecasting activities, providing evidence for “exceptional event” declarations, monitoring regional long-term trends, and evaluating air quality model output. However, many air quality managers are not taking full advantage of the data for these applications nor has the full potential of satellite data for air quality applications been realized. A key barrier is the inherent difficulties associated with accessing, processing, and properly interpreting observational data. A degree of technical skill is required on the part of the data end-user, which is often problematic for air quality agencies with limited resources. Therefore, we 1) review the primary uses of satellite data for air quality applications, 2) provide some background information on satellite capabilities for measuring pollutants, 3) discuss the many resources available to the end-user for accessing, processing, and visualizing the data, and 4) provide answers to common questions in plain language.

Classifying observed aerosols into types (e.g., urban-industrial, biomass burning, mineral dust, maritime) helps to understand aerosol sources, transformations, effects, and feedback mechanisms; to improve accuracy of satellite retrievals; and to quantify aerosol radiative impacts on climate. The number of aerosol parameters retrieved from spaceborne sensors has been growing, from the initial aerosol optical depth (AOD) at one or a few wavelengths to a list that now includes AOD, complex refractive index, single scattering albedo (SSA), and depolarization of backscatter, each at several wavelengths, plus several particle size and shape parameters. Making optimal use of these varied data products requires objective, multidimensional analysis methods. We describe such a method, which makes explicit use of uncertainties in input parameters. It treats an N-parameter retrieved data point and its N-dimensional uncertainty as an extended data point, E. It then uses a modified Mahalanobis distance, DEC, to assign an observation to the class (cluster) C that has minimum DEC from the point. We use parameters retrieved from the Aerosol Robotic Network (AERONET) to define seven prespecified clusters (pure dust, polluted dust, urban-industrial/developed economy, urban-industrial/developing economy, dark biomass smoke, light biomass smoke, and pure marine), and we demonstrate application of the method to a 5 year record of retrievals from the spaceborne Polarization and Directionality of the Earth's Reflectances 3 (POLDER 3) polarimeter over the island of Crete, Greece. Results show changes of aerosol type at this location in the eastern Mediterranean Sea, which is influenced by a wide variety of aerosol sources.

The Mexico City Metropolitan Area (MCMA) experiences high loadings of atmospheric aerosols from anthropogenic sources, biomass burning and wind-blown dust. This paper uses a combination of measurements and numerical simulations to identify different plumes affecting the basin and to characterize transformation inside the plumes. The High Spectral Resolution Lidar on board the NASA LaRC B-200 King Air aircraft measured extinction coefficients and extinction to backscatter ratio at 532 nm, and backscatter coefficients and depolarization ratios at 532 and 1064 nm. These can be used to identify aerosol types. The measurement curtains are compared with particle trajectory simulations using WRF-Flexpart for different source groups. The good correspondence between measurements and simulations suggests that the aerosol transport is sufficiently well characterized by the models to estimate aerosol types and ages. Plumes in the basin undergo complex transport, and are frequently mixed together. Urban aerosols are readily identifiable by their low depolarization ratios and high lidar ratios, and dust by the opposite properties. Fresh biomass burning plumes have very low depolarization ratios which increase rapidly with age. This rapid transformation is consistent with the presence of atmospheric tar balls in the fresh plumes.

As part of the international project entitled "African Monsoon Multidisciplinary Analysis (AMMA)", NAMMA (NASA AMMA) aimed to gain a better understanding of the relationship between the African Easterly Waves (AEWs), the Sahara Air Layer (SAL), and tropical cyclogenesis. The NAMMA airborne field campaign was based out of the Cape Verde Islands during the peak of the hurricane season, i.e., August and September 2006. Multiple Sahara dust layers were sampled during 62 encounters in the eastern portion of the hurricane main development region, covering both the eastern North Atlantic Ocean and the western Saharan desert (i.e., 5–22° N and 10–35° W). The centers of these layers were located at altitudes between 1.5 and 3.3 km and the layer thickness ranged from 0.5 to 3 km. Detailed dust microphysical and optical properties were characterized using a suite of in-situ instruments aboard the NASA DC-8 that included a particle counter, an Ultra-High Sensitivity Aerosol Spectrometer, an Aerodynamic Particle Sizer, a nephelometer, and a Particle Soot Absorption Photometer. The NAAMA sampling inlet has a size cut (i.e., 50% transmission efficiency size) of approximately 4 μm in diameter for dust particles, which limits the representativeness of the NAMMA observational findings. The NAMMA dust observations showed relatively low particle number densities, ranging from 268 to 461 cm−3, but highly elevated volume density with an average at 45 μm3 cm−3. NAMMA dust particle size distributions can be well represented by tri-modal lognormal regressions. The estimated volume median diameter (VMD) is averaged at 2.1 μm with a small range of variation regardless of the vertical and geographical sampling locations. The Ångström Exponent assessments exhibited strong wavelength dependence for absorption but a weak one for scattering. The single scattering albedo was estimated at 0.97 ± 0.02. The imaginary part of the refractive index for Sahara dust was estimated at 0.0022, with a range from 0.0015 to 0.0044. Closure analysis showed that observed scattering coefficients are highly correlated with those calculated from spherical Mie-Theory and observed dust particle size distributions. These values are generally consistent with literature values reported from studies with similar particle sampling size range.

The Lidar Atmospheric Sensing Experiment (LASE) on board the NASA DC-8 measured high-resolution profiles of water vapor and aerosols, and cloud distributions in 14 flights over the eastern North Atlantic during the NASA African Monsoon Multidisciplinary Analyses (NAMMA) field experiment. These measurements were used to study African easterly waves (AEWs), tropical cyclones (TCs), and the Saharan air layer (SAL). These LASE measurements represent the first simultaneous water vapor and aerosol lidar measurements to study the SAL and its interactions with AEWs and TCs. Three case studies were selected for detailed analysis: (i) a stratified SAL, with fine structure and layering (unlike a well-mixed SAL), (ii) a SAL with high relative humidity (RH), and (iii) an AEW surrounded by SAL dry air intrusions. Profile measurements of aerosol scattering ratios, aerosol extinction coefficients, aerosol optical thickness, water vapor mixing ratios, RH, and temperature are presented to illustrate their characteristics in the SAL, convection, and clear air regions. LASE extinction-to-backscatter ratios for the dust layers varied from 35 ± 5 to 45 ± 5 sr, well within the range of values determined by other lidar systems. LASE aerosol extinction and water vapor profiles are validated by comparison with onboard in situ aerosol measurements and GPS dropsonde water vapor soundings, respectively. An analysis of LASE data suggests that the SAL suppresses low-altitude convection. Midlevel convection associated with the AEW and transport are likely responsible for high water vapor content observed in the southern regions of the SAL on 20 August 2008. This interaction is responsible for the transfer of about 7 × 1015 J (or 8 × 103 J m−2) latent heat energy within a day to the SAL. Initial modeling studies that used LASE water vapor profiles show sensitivity to and improvements in model forecasts of an AEW.

Solar occultation has proven to be a reliable technique for the measurement of atmospheric constituents in the stratosphere. NASA's Stratospheric Aerosol and Gas Experiments (SAGE, SAGE II, and SAGE III) together have provided over 25 years of quality solar occultation data, a data record which has been an important resource for the scientific exploration of atmospheric composition and climate change. Herein, we describe an improvement to the processing of SAGE data that corrects for a previously uncorrected short-term time-dependence in the calibration function. The variability relates to the apparent rotation of the scanning track with respect to the face of the sun due to the motion of the satellite. Correcting for this effect results in a decrease in the measurement noise in the Level 1 line-of-sight optical depth measurements of approximately 40% in the middle and upper stratospheric SAGE II and III observations where it has been applied. The technique is potentially useful for any scanning solar occultation instrument and suggests further improvement for future occultation measurements if a full disk imaging system can be included.

The NASA Langley Research Center (LaRC) airborne High Spectral Resolution Lidar (HSRL) measures vertical profiles of aerosol extinction, backscatter, and depolarization at both 532 nm and 1064 nm. In March of 2006 the HSRL participated in the Megacity Initiative: Local and Global Research Observations (MILAGRO) campaign along with several other suites of instruments deployed on both aircraft and ground based platforms. This paper presents high spatial and vertical resolution HSRL measurements of aerosol extinction and optical depth from MILAGRO and comparisons of those measurements with similar measurements from other sensors and model predictions. HSRL measurements coincident with airborne in situ aerosol scattering and absorption measurements from two different instrument suites on the C-130 and G-1 aircraft, airborne aerosol optical depth (AOD) and extinction measurements from an airborne tracking sunphotometer on the J-31 aircraft, and AOD from a network of ground based Aerosol Robotic Network (AERONET) sun photometers are presented as a validation of the HSRL aerosol extinction and optical depth products. Regarding the extinction validation, we find bias differences between HSRL and these instruments to be less than 3% (0.01 km−1) at 532 nm, the wavelength at which the HSRL technique is employed. The rms differences at 532 nm were less than 50% (0.015 km−1). To our knowledge this is the most comprehensive validation of the HSRL measurement of aerosol extinction and optical depth to date. The observed bias differences in ambient aerosol extinction between HSRL and other measurements is within 15–20% at visible wavelengths, found by previous studies to be the differences observed with current state-of-the-art instrumentation (Schmid et al., 2006).

Since 2000, stratospheric aerosol levels have been relatively stable and at the lowest levels observed in the historical record. Given the challenges of making satellite measurements of aerosol properties at these levels, we have performed a study of the sensitivity of the product to the major components of the processing algorithm used in the production of SAGE II aerosol extinction measurements and the retrieval process that produces the operational surface area density (SAD) product. We find that the aerosol extinction measurements, particularly at 1020 nm, remain robust and reliable at the observed aerosol levels. On the other hand, during background periods, the SAD operational product has an uncertainty of at least a factor of 2 due to the lack of sensitivity to particles with radii less than 100 nm.

Linear particle depolarization ratio is presented for three case studies from the NASA Langley airborne High Spectral Resolution Lidar-2 HSRL-2). Particle depolarization ratio from lidar is an indicator of non-spherical particles and is sensitive to the fraction of non-spherical particles and their size. The HSRL-2 instrument measures depolarization at three wavelengths: 355, 532, and 1064 nm. The three measurement cases presented here include two cases of dust-dominated aerosol and one case of smoke aerosol. These cases have partial analogs in earlier HSRL-1 depolarization measurements at 532 and 1064 nm and in literature, but the availability of three wavelengths gives additional insight into different scenarios for non-spherical particles in the atmosphere. A case of transported Saharan dust has a spectral dependence with a peak of 0.30 at 532 nm with smaller particle depolarization ratios of 0.27 and 0.25 at 1064 and 355 nm, respectively. A case of aerosol containing locally generated wind-blown North American dust has a maximum of 0.38 at 1064 nm, decreasing to 0.37 and 0.24 at 532 and 355 nm, respectively. The cause of the maximum at 1064 nm is inferred to be very large particles that have not settled out of the dust layer. The smoke layer has the opposite spectral dependence, with the peak of 0.24 at 355 nm, decreasing to 0.09 and 0.02 at 532 and 1064 nm, respectively. The depolarization in the smoke case may be explained by the presence of coated soot aggregates. We note that in these specific case studies, the linear particle depolarization ratio for smoke and dust-dominated aerosol are more similar at 355 nm than at 532 nm, having possible implications for using the particle depolarization ratio at a single wavelength for aerosol typing.

Using Multi-angle Imaging SpectroRadiometer (MISR) and sub-orbital measurements from the 2006 INTEX-B/MILAGRO field campaign, in this study we explore MISR's ability to map different aerosol air mass types over the Mexico City metropolitan area. The aerosol air mass distinctions are based on shape, size and single scattering albedo retrievals from the MISR Research Aerosol Retrieval algorithm. In this region, the research algorithm identifies dust-dominated aerosol mixtures based on non-spherical particle shape, whereas spherical biomass burning and urban pollution particles are distinguished by particle size. Two distinct aerosol air mass types based on retrieved particle microphysical properties, and four spatially distributed aerosol air masses, are identified in the MISR data on 6 March 2006. The aerosol air mass type identification results are supported by coincident, airborne high-spectral-resolution lidar (HSRL) measurements. Aerosol optical depth (AOD) gradients are also consistent between the MISR and sub-orbital measurements, but particles having single-scattering albedo of &approx;0.7 at 558 nm must be included in the retrieval algorithm to produce good absolute AOD comparisons over pollution-dominated aerosol air masses. The MISR standard V22 AOD product, at 17.6 km resolution, captures the observed AOD gradients qualitatively, but retrievals at this coarse spatial scale and with limited spherical absorbing particle options underestimate AOD and do not retrieve particle properties adequately over this complex urban region. However, we demonstrate how AOD and aerosol type mapping can be accomplished with MISR data over complex urban regions, provided the retrieval is performed at sufficiently high spatial resolution, and with a rich enough set of aerosol components and mixtures.

Aerosol classification products from the NASA Langley Research Center (LaRC) airborne High Spectral Resolution Lidar (HSRL-1) on the NASA B200 aircraft are compared with coincident V3.01 aerosol classification products from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument on the CALIPSO satellite. For CALIOP, aerosol classification is a key input to the aerosol retrieval, and must be inferred using aerosol loading-dependent observations and location information. In contrast, HSRL-1 makes direct measurements of aerosol intensive properties, including the lidar ratio, that provide information on aerosol type. In this study, comparisons are made for 109 underflights of the CALIOP orbit track. We find that 62% of the CALIOP marine layers and 54% of the polluted continental layers agree with HSRL-1 classification results. In addition, 80% of the CALIOP desert dust layers are classified as either dust or dusty mix byHSRL-1. However, agreement is less for CALIOP smoke (13%) and polluted dust (35%) layers. Specific case studies are examined, giving insight into the performance of the CALIOP aerosol type algorithm. In particular, we find that the CALIOP polluted dust type is overused due to an attenuation-related depolarization bias. Furthermore, the polluted dust type frequently includes mixtures of dust plus marine aerosol. Finally, we find that CALIOP's identification of internal boundaries between different aerosol types in contact with each other frequently do not reflect the actual transitions between aerosol types accurately. Based on these findings, we give recommendations which may help to improve the CALIOP aerosol type algorithms.

The NASA Langley Research Center (LaRC) airborne High Spectral Resolution Lidar (HSRL) on the NASA B200 aircraft has acquired extensive datasets of aerosol extinction (532 nm), aerosol optical thickness (AOT) (532 nm), backscatter (532 and 1064 nm), and depolarization (532 and 1064 nm) profiles during 18 field missions that have been conducted over North America since 2006. The lidar measurements of aerosol intensive parameters (lidar ratio, depolarization, backscatter color ratio, and spectral depolarization ratio) are shown to vary with location and aerosol type. A methodology based on observations of known aerosol types is used to qualitatively classify the extensive set of HSRL aerosol measurements into eight separate types. Several examples are presented showing how the aerosol intensive parameters vary with aerosol type and how these aerosols are classified according to this new methodology. The HSRL-based classification reveals vertical variability of aerosol types during the NASA ARCTAS field experiment conducted over Alaska and northwest Canada during 2008. In two examples derived from flights conducted during ARCTAS, the HSRL classification of biomass burning smoke is shown to be consistent with aerosol types derived from coincident airborne in situ measurements of particle size and composition. The HSRL retrievals of AOT and inferences of aerosol types are used to apportion AOT to aerosol type; results of this analysis are shown for several experiments.

In recent years, substantial effort has been expended toward understanding the impact of tropospheric aerosols on Arctic climate and chemistry. A significant part of this effort has been the collection and documentation of extensive aerosol physical and optical property data sets. However, the data sets present significant interpretive challenges because of the diverse nature of these measurements. Among the longest continuous records is that by the spaceborne Stratospheric Aerosol and Gas Experiment (SAGE) II. Although SAGE tropospheric measurements are restricted to the middle and upper troposphere, they may be able to provide significant insight into the nature and variability of tropospheric aerosol, particularly when combined with ground and airborne observations. This paper demonstrates the capacity of aerosol products from SAGE II and its follow-on experiment SAGE III to describe the temporal and vertical variations of Arctic aerosol characteristics. We find that the measurements from both instruments are consistent enough to be combined. Using this combined data set, we detect a clear annual cycle in the aerosol extinction for the middle and upper Arctic troposphere.

In the paper “Hyperspectral aerosol optical depths from TCAP flights” by Y. Shinozuka et al. (Journal of Geophysical Research: Atmospheres, 118, doi:10.1002/2013JD020596, 2013), Tables 1 and 2 were published with the column heads out of order. Tables 1 and 2 are published correctly here. The publisher regrets the error.

[1] The 4STAR (Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research), a hyperspectral airborne Sun photometer, acquired aerosol optical depths (AOD) at 1 Hz during all July 2012 flights of the Two-Column Aerosol Project. Root-mean-square differences from Aerosol Robotic Network ground-based observations were 0.01 at wavelengths between 500–1020 nm, 0.02 at 380 and 1640 nm, and 0.03 at 440 nm in four clear-sky fly-over events, and similar in ground side-by-side comparisons. Changes in the above-aircraft AOD across 3 km deep spirals were typically consistent with integrals of coincident in situ (on Department of Energy Gulfstream 1 with 4STAR) and lidar (on NASA B200) extinction measurements within 0.01, 0.03, 0.01, 0.02, 0.02, and 0.02 at 355, 450, 532, 550, 700, and 1064 nm, respectively, despite atmospheric variations and combined measurement uncertainties. Finer vertical differentials of the 4STAR measurements matched the in situ ambient extinction profile within 14% for one homogeneous column. For the AOD observed between 350 and 1660 nm, excluding strong water vapor and oxygen absorption bands, estimated uncertainties were ~0.01 and dominated by (then) unpredictable throughput changes, up to ±0.8%, of the fiber optic rotary joint. The favorable intercomparisons herald 4STAR's spatially resolved high-frequency hyperspectral products as a reliable tool for climate studies and satellite validation.

[1] The Stratospheric Aerosol and Gas Experiment (SAGE) II water vapor retrieval process has been updated to reflect a new understanding of the instrument performance. Primarily, this is reflected in a shifted spectral response for the primary water channel near 935 nm for the period after January 1986. In addition, the water vapor and ozone spectroscopy, aerosol clearing process, and error estimation have been updated. The end result is that the measurement bias observed in version 6.1 and earlier versions has been effectively eliminated. The sensitivity to aerosol has been reduced, so that the recommended upper limit for usable water vapor is now 3 × 10−4 km−1 in 1020-nm aerosol extinction. The comparable value for version 6.1 was ∼2 × 10−5 km−1, or 10 times more sensitive than in the new version. The impact of the channel drift on the retrieved water vapor relative to the simple model employed in this version will be difficult to separate from geophysical change, and therefore caution is recommended in evaluating trends derived from this data set.

[1] The Stratospheric Aerosol and Gas Experiment (SAGE) II is a spaceborne solar occultation instrument that makes long-term stable measurements of atmospheric transmission at seven wavelengths between the ultraviolet and the near infrared. It provides near-global coverage (from about 75°S to 75°N latitude) and a data record spanning over 18 years starting in late 1984. Recently, a self-consistent molecular density retrieval was developed which uses SAGE II version 6.10 transmission data above 40 km altitude. Since at this altitude attenuation of solar radiation due to aerosols can be neglected, there is enough independent information in five SAGE II channels (386–600 nm) to separate the absorption due to ozone and nitrogen dioxide from Rayleigh scattering. Vertical inversion to obtain profiles of these quantities from 40 to 60 km proceeds as in the standard SAGE II algorithm, using a matrix of refracted path lengths. The density gradient information for averaged profiles is converted to temperature. The data are sorted into 20° latitude bands from 70°S to 70°N latitude, and the time series are fitted to provide a mean climatology and long-term trend estimates. The climatology agrees well with published climatologies for the northern midlatitudes and includes results for the other latitude bands as well. Analysis of long-term variation finds no statistically significant effect from the solar cycle or quasi-biennial oscillation and no statistically significant trend. An upper bound on a possible cooling trend of 3 or 4 K/decade is derived for high and middle latitudes between 40 and 45 km altitude.