Efficient single-scattering look-up table for lidar and polarimeter water cloud studies.

Combined lidar and polarimeter retrievals of aerosol, cloud, and ocean microphysical properties involve single-scattering cloud calculations that are time consuming. We create a look-up table to speed up these calculations for water droplets in the atmosphere. In our new Lorenz-Mie look-up table we tabulate the light scattering by an ensemble of homogeneous isotropic spheres at wavelengths starting from 0.35 µm. The look-up table covers liquid water cloud particles with radii in the range of 0.001-500 µm while gaining an increase of up to 104 in computational speed. The covered complex refractive indices range from 1.25 to 1.36 for the real part and from 0 to 0.001 for the imaginary part. We show that we can precisely compute inherent optical properties for the particle size distributions ranging up to 100 µm for the effective radius and up to 0.6 for the effective variance. We test wavelengths from 0.35 to 2.3 µm and find that the elements of the normalized scattering matrix as well as the asymmetry parameter, the absorption, backscatter, extinction, and scattering coefficients are precise to within 1% for 96.7%-100% of cases depending on the inherent optical property. We also provide an example of using the look-up table with in situ measurements to determine agreement with remote sensing. The table together with C++, Fortran, MATLAB, and Python codes to interpolate the complex refractive index and apply different particle size distributions are freely available online.

Improving Estimates of PM2.5 Concentration and Chemical Composition by Application of High Spectral Resolution Lidar (HSRL) and Creating Aerosol Types from Chemistry (CATCH) Algorithm.

Improved characterization of ambient PM2.5 mass concentration and chemical speciation is a topic of interest in air quality and climate sciences. Over the past decades, considerable efforts have been made to improve ground-level PM2.5 using remotely sensed data. Here we present two new approaches for estimating atmospheric PM2.5 and chemical composition based on the High Spectral Resolution Lidar (HSRL)-retrieved aerosol extinction values and types and Creating Aerosol Types from Chemistry (CATCH)-derived aerosol chemical composition. The first methodology (CMAQ-HSRL-CH) improves EPA's Community Multiscale Air Quality (CMAQ) predictions by applying variable scaling factors derived using remotely-sensed information about aerosol vertical distribution and types and the CATCH algorithm. The second methodology (HSRL-CH) does not require regional model runs and can provide atmospheric PM2.5 mass concentration and chemical speciation using only the remotely sensed data and the CATCH algorithm. The resulting PM2.5 concentrations and chemical speciation derived for NASA DISCOVER-AQ (Deriving Information on Surface Conditions from COlumn and VERtically Resolved Observations Relevant to Air Quality) Baltimore-Washington, D.C. Corridor (BWC) Campaign (2011) are compared to surface measurements from EPA's Air Quality Systems (AQS) network. The analysis shows that the CMAQ-HSRL-CH method leads to considerable improvement of CMAQ's predicted PM2.5 concentrations (R2 value increased from 0.37 to 0.63, the root mean square error (RMSE) was reduced from 11.9 to 7.2 µg m-3, and the normalized mean bias (NMB) was lowered from -46.0 to 4.6%). The HSRL-CH method showed statistics (R2=0.75, RMSE=8.6 µgm-3, and NMB=24.0%), which were better than the CMAQ prediction of PM2.5 alone and analogous to CMAQ-HSRL-CH. In addition to mass concentration, HSRL-CH can also provide aerosol chemical composition without specific model simulations. We expect that the HSRL-CH method will be able to make reliable estimates of PM2.5 concentration and chemical composition where HSRL data are available.

Intercomparison of airborne multi-angle polarimeter observations from the Polarimeter Definition Experiment.

In early 2013, three airborne polarimeters were flown on the high altitude NASA ER-2 aircraft in California for the Polarimeter Definition Experiment (PODEX). PODEX supported the pre-formulation NASA Aerosol-Cloud-Ecosystem (ACE) mission, which calls for an imaging polarimeter in polar orbit (among other instruments) for the remote sensing of aerosols, oceans, and clouds. Several polarimeter concepts exist as airborne prototypes, some of which were deployed during PODEX as a capabilities test. Two of those instruments to date have successfully produced Level 1 (georegistered, calibrated radiance and polarization) data from that campaign: the Airborne Multiangle Spectropolarimetric Imager (AirMSPI) and the Research Scanning Polarimeter (RSP). We compared georegistered observations of a variety of scene types by these instruments to test whether Level 1 products agreed within stated uncertainties. Initial comparisons found radiometric agreement, but polarimetric biases beyond measurement uncertainties. After subsequent updates to calibration, georegistration, and the measurement uncertainty models, observations from the instruments now largely agree within stated uncertainties. However, the 470 nm reflectance channels have a roughly +6% bias of AirMSPI relative to RSP, beyond expected measurement uncertainties. We also find that observations of dark (ocean) scenes, where polarimetric uncertainty is expected to be largest, do not agree within stated polarimetric uncertainties. Otherwise, AirMSPI and RSP observations are consistent within measurement uncertainty expectations, providing credibility for the subsequent creation of Level 2 (geophysical product) data from these instruments, and comparison thereof. The techniques used in this work can also form a methodological basis for other intercomparisons, for example, of the data gathered during the recent Aerosol Characterization from Polarimeter and Lidar (ACEPOL) field campaign, carried out in October and November of 2017 with four polarimeters (including AirMSPI and RSP).

SPEX airborne spectropolarimeter calibration and performance.

To improve our understanding of the complex role of aerosols in the climate system and on air quality, measurements are needed of optical and microphysical aerosol. From many studies, it has become evident that a satellite-based multiangle, multiwavelength polarimeter will be essential to provide such measurements. Here, high accuracy (∼0.003) on the degree of linear polarization (DoLP) measurements is important to retrieve aerosol properties with an accuracy needed to advance our understanding of the aerosol effect on climate. SPEX airborne, a multiangle hyperspectral polarimeter, has been developed for observing and characterizing aerosols from NASA's high-altitude research aircraft ER-2. It delivers measurements of radiance and DoLP at visual wavelengths with a spectral resolution of 3 and 7-30 nm, respectively, for radiance and polarization, at nine fixed equidistant viewing angles from -56° to +56° oriented along the ground track, and a swath of 7° oriented across-track. SPEX airborne uses spectral polarization modulation to determine the state of linear polarization of scattered sunlight. This technique has been developed in the Netherlands and has been demonstrated with ground-based instruments. SPEX airborne serves as a demonstrator for a family of space-based SPEX instruments that have the ability to measure and characterize atmospheric aerosol by multiangle hyperspectral polarimetric imaging remotely from a satellite platform. SPEX airborne was calibrated radiometrically and polarimetrically using Jet Propulsion Laboratory (JPL) facilities including the Polarization Stage Generator-2 (PSG-2), which is designed for polarimetric calibration and validation of the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI). Using the PSG-2, the accuracy of the SPEX airborne DoLP measurements in the laboratory setup is found to be 0.002-0.004. Radiometric calibration is realized with an estimated accuracy of 4%. In 2017, SPEX airborne took part in the "Aerosol Characterization from Polarimeters and Lidar" campaign on the ER-2 that included four polarimeters and two lidars. Polarization measurements of SPEX airborne and the coflying Research Scanning Polarimeter (RSP), recorded during the campaign, were compared and display root-mean-square (RMS) differences ranging from 0.004 (at 555 nm) up to 0.02 (at 410 nm). For radiance measurements, excellent agreement between SPEX airborne and RSP is obtained with an RMS difference of ∼4%. The lab- and flight-performance values for polarization are similar to those recently published for AirMSPI, where also an intercomparison with RSP was made using data from field campaigns in 2013. The intercomparison of radiometric and polarimetric data both display negligible bias. The in-flight comparison results provide verification of SPEX airborne's capability to deliver high-quality data.

Retrieval of aerosol parameters from multiwavelength lidar: investigation of the underlying inverse mathematical problem.

We present an investigation of some important mathematical and numerical features related to the retrieval of microphysical parameters [complex refractive index, single-scattering albedo, effective radius, total number, surface area, and volume concentrations] of ambient aerosol particles using multiwavelength Raman or high-spectral-resolution lidar. Using simple examples, we prove the non-uniqueness of an inverse solution to be the major source of the retrieval difficulties. Some theoretically possible ways of partially compensating for these difficulties are offered. For instance, an increase in the variety of input data via combination of lidar and certain passive remote sensing instruments will be helpful to reduce the error of estimation of the complex refractive index. We also demonstrate a significant interference between Aitken and accumulation aerosol modes in our inversion algorithm, and confirm that the solutions can be better constrained by limiting the particle radii. Applying a combination of an analytical approach and numerical simulations, we explain the statistical behavior of the microphysical size parameters. We reveal and clarify why the total surface area concentration is consistent even in the presence of non-unique solution sets and is on average the most stable parameter to be estimated, as long as at least one extinction optical coefficient is employed. We find that for selected particle size distributions, the total surface area and volume concentrations can be quickly retrieved with fair precision using only single extinction coefficients in a simple arithmetical relationship.

Vertically resolved separation of dust and other aerosol types by a new lidar depolarization method.

This paper developed a new retrieval framework of external mixing of the dust and non-dust aerosol to predict the lidar ratio of the external mixing aerosols and to separate the contributions of non-spherical aerosols by using different depolarization ratios among dust, sea salt, smoke, and polluted aerosols. The detailed sensitivity tests and case study with the new method showed that reliable dust information could be retrieved even without prior information about the non-dust aerosol types. This new method is suitable for global dust retrievals with satellite observations, which is critical for better understanding global dust transportation and for model improvements.

Arrange and average algorithm for the retrieval of aerosol parameters from multiwavelength high-spectral-resolution lidar/Raman lidar data.

We present the results of a feasibility study in which a simple, automated, and unsupervised algorithm, which we call the arrange and average algorithm, is used to infer microphysical parameters (complex refractive index, effective radius, total number, surface area, and volume concentrations) of atmospheric aerosol particles. The algorithm uses backscatter coefficients at 355, 532, and 1064 nm and extinction coefficients at 355 and 532 nm as input information. Testing of the algorithm is based on synthetic optical data that are computed from prescribed monomodal particle size distributions and complex refractive indices that describe spherical, primarily fine mode pollution particles. We tested the performance of the algorithm for the "3 backscatter (ß)+2 extinction (α)" configuration of a multiwavelength aerosol high-spectral-resolution lidar (HSRL) or Raman lidar. We investigated the degree to which the microphysical results retrieved by this algorithm depends on the number of input backscatter and extinction coefficients. For example, we tested "3ß+1α," "2ß+1α," and "3ß" lidar configurations. This arrange and average algorithm can be used in two ways. First, it can be applied for quick data processing of experimental data acquired with lidar. Fast automated retrievals of microphysical particle properties are needed in view of the enormous amount of data that can be acquired by the NASA Langley Research Center's airborne "3ß+2α" High-Spectral-Resolution Lidar (HSRL-2). It would prove useful for the growing number of ground-based multiwavelength lidar networks, and it would provide an option for analyzing the vast amount of optical data acquired with a future spaceborne multiwavelength lidar. The second potential application is to improve the microphysical particle characterization with our existing inversion algorithm that uses Tikhonov's inversion with regularization. This advanced algorithm has recently undergone development to allow automated and unsupervised processing; the arrange and average algorithm can be used as a preclassifier to further improve its speed and precision. First tests of the performance of arrange and average algorithm are encouraging. We used a set of 48 different monomodal particle size distributions, 4 real parts and 15 imaginary parts of the complex refractive index. All in all we tested 2880 different optical data sets for 0%, 10%, and 20% Gaussian measurement noise (one-standard deviation). In the case of the "3ß+2α" configuration with 10% measurement noise, we retrieve the particle effective radius to within 27% for 1964 (68.2%) of the test optical data sets. The number concentration is obtained to 76%, the surface area concentration to 16%, and the volume concentration to 30% precision. The "3ß" configuration performs significantly poorer. The performance of the "3ß+1α" and "2ß+1α" configurations is intermediate between the "3ß+2α" and the "3ß."

Atmospheric Research Over the Western North Atlantic Ocean Region and North American East Coast: A Review of Past Work and Challenges Ahead.

Decades of atmospheric research have focused on the Western North Atlantic Ocean (WNAO) region because of its unique location that offers accessibility for airborne and ship measurements, gradients in important atmospheric parameters, and a range of meteorological regimes leading to diverse conditions that are poorly understood. This work reviews these scientific investigations for the WNAO region, including the East Coast of North America and the island of Bermuda. Over 50 field campaigns and long-term monitoring programs, in addition to 715 peer-reviewed publications between 1946 and 2019 have provided a firm foundation of knowledge for these areas. Of particular importance in this region has been extensive work at the island of Bermuda that is host to important time series records of oceanic and atmospheric variables. Our review categorizes WNAO atmospheric research into eight major categories, with some studies fitting into multiple categories (relative %): Aerosols (25%), Gases (24%), Development/Validation of Techniques, Models, and Retrievals (18%), Meteorology and Transport (9%), Air-Sea Interactions (8%), Clouds/Storms (8%), Atmospheric Deposition (7%), and Aerosol-Cloud Interactions (2%). Recommendations for future research are provided in the categories highlighted above.

Simultaneous analog and photon counting detection for Raman lidar.

The Atmospheric Radiation Measurement program Raman lidar was upgraded in 2004 with a new data system that provides simultaneous measurements of both the photomultiplier analog output voltage and photon counts. We describe recent improvements to the algorithm used to merge these two signals into a single signal with improved dynamic range. The effect of modifications to the algorithm are evaluated by comparing profiles of water vapor mixing ratio from the lidar with radiosonde measurements over a six month period. The modifications that were implemented resulted in a reduction of the mean bias in the daytime water vapor mixing ratio from a 3% dry bias to well within 1%. This improvement was obtained by ignoring the temporal variation of the glue coefficients and using only the nighttime average glue coefficients throughout the entire diurnal cycle.

Quantifying the direct radiative effect of absorbing aerosols for numerical weather prediction: a case study.

We conceptualize aerosol radiative transfer processes arising from the hypothetical coupling of a global aerosol transport model and a global numerical weather prediction model by applying the US Naval Research Laboratory Navy Aerosol Analysis and Prediction System (NAAPS) and the Navy Global Environmental Model (NAVGEM) meteorological and surface reflectance fields. A unique experimental design during the 2013 NASA Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) field mission allowed for collocated airborne sampling by the high spectral resolution Lidar (HSRL), the Airborne Multi-angle SpectroPolarimetric Imager (AirMSPI), up/down shortwave (SW) and infrared (IR) broadband radiometers, as well as NASA A-Train support from the Moderate Resolution Imaging Spectroradiometer (MODIS), to attempt direct aerosol forcing closure. The results demonstrate the sensitivity of modeled fields to aerosol radiative fluxes and heating rates, specifically in the SW, as induced in this event from transported smoke and regional urban aerosols. Limitations are identified with respect to aerosol attribution, vertical distribution, and the choice of optical and surface polarimetric properties, which are discussed within the context of their influence on numerical weather prediction output that is particularly important as the community propels forward towards inline aerosol modeling within global forecast systems.

Airborne high spectral resolution lidar for profiling aerosol optical properties.

A compact, highly robust airborne High Spectral Resolution Lidar (HSRL) that provides measurements of aerosol backscatter and extinction coefficients and aerosol depolarization at two wavelengths has been developed, tested, and deployed on nine field experiments (over 650 flight hours). A unique and advantageous design element of the HSRL system is the ability to radiometrically calibrate the instrument internally, eliminating any reliance on vicarious calibration from atmospheric targets for which aerosol loading must be estimated. This paper discusses the design of the airborne HSRL, the internal calibration and accuracy of the instrument, data products produced, and observations and calibration data from the first two field missions: the Joint Intercontinental Chemical Transport Experiment--Phase B (INTEX-B)/Megacity Aerosol Experiment--Mexico City (MAX-Mex)/Megacities Impacts on Regional and Global Environment (MILAGRO) field mission (hereafter MILAGRO) and the Gulf of Mexico Atmospheric Composition and Climate Study/Texas Air Quality Study II (hereafter GoMACCS/TexAQS II).

SAM-CAAM: A Concept for Acquiring Systematic Aircraft Measurements to Characterize Aerosol Air Masses.

A modest operational program of systematic aircraft measurements can resolve key satellite-aerosol-data-record limitations. Satellite observations provide frequent, global aerosol-amount maps, but offer only loose aerosol property constraints needed for climate and air quality applications. We define and illustrate the feasibility of flying an aircraft payload to measure key aerosol optical, microphysical, and chemical properties in situ. The flight program could characterize major aerosol air-mass types statistically, at a level-of-detail unobtainable from space. It would: (1) enhance satellite aerosol retrieval products with better climatology assumptions, and (2) improve translation between satellite-retrieved optical properties and species-specific aerosol mass and size simulated in climate models to assess aerosol forcing, its anthropogenic components, and other environmental impacts. As such, Systematic Aircraft Measurements to Characterize Aerosol Air Masses (SAM-CAAM) could add value to data records representing several decades of aerosol observations from space, improve aerosol constraints on climate modeling, help interrelate remote-sensing, in situ, and modeling aerosol-type definitions, and contribute to future satellite aerosol missions. Fifteen Required Variables are identified, and four Payload Options of increasing ambition are defined, to constrain these quantities. "Option C" could meet all the SAM-CAAM objectives with about 20 instruments, most of which have flown before, but never routinely several times per week, and never as a group. Aircraft integration, and approaches to data handling, payload support, and logistical considerations for a long-term, operational mission are discussed. SAM-CAAM is feasible because, for most aerosol sources and specified seasons, particle properties tend to be repeatable, even if aerosol loading varies.

Observing atmospheric formaldehyde (HCHO) from space: validation and intercomparison of six retrievals from four satellites (OMI, GOME2A, GOME2B, OMPS) with SEAC4RS aircraft observations over the Southeast US.

Formaldehyde (HCHO) column data from satellites are widely used as a proxy for emissions of volatile organic compounds (VOCs) but validation of the data has been extremely limited. Here we use highly accurate HCHO aircraft observations from the NASA SEAC4RS campaign over the Southeast US in August-September 2013 to validate and intercompare six retrievals of HCHO columns from four different satellite instruments (OMI, GOME2A, GOME2B and OMPS) and three different research groups. The GEOS-Chem chemical transport model is used as a common intercomparison platform. All retrievals feature a HCHO maximum over Arkansas and Louisiana, consistent with the aircraft observations and reflecting high emissions of biogenic isoprene. The retrievals are also interconsistent in their spatial variability over the Southeast US (r=0.4-0.8 on a 0.5°×0.5° grid) and in their day-to-day variability (r=0.5-0.8). However, all retrievals are biased low in the mean by 20-51%, which would lead to corresponding bias in estimates of isoprene emissions from the satellite data. The smallest bias is for OMI-BIRA, which has high corrected slant columns relative to the other retrievals and low scattering weights in its air mass factor (AMF) calculation. OMI-BIRA has systematic error in its assumed vertical HCHO shape profiles for the AMF calculation and correcting this would eliminate its bias relative to the SEAC4RS data. Our results support the use of satellite HCHO data as a quantitative proxy for isoprene emission after correction of the low mean bias. There is no evident pattern in the bias, suggesting that a uniform correction factor may be applied to the data until better understanding is achieved.