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.

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.

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.