Comparison of Aerosol Classification Results from Airborne High Spectral Resolution Lidar (hsrl) Measurements and the Calipso Vertical Feature Mask

The NASA Langley Research Center (LaRC) airborne High Spectral Resolution Lidar (HSRL) on the NASA B200 aircraft has acquired large datasets of aerosol extinction (532nm), backscatter (532 and 1064nm), and depolarization (532 and 1064nm) profiles during 18 field missions across North America since 2006. The lidar measurements include scale-invariant aerosol parameters that vary with aerosol type but not concentration. These have been used to qualitatively classify HSRL aerosol measurements into eight separate composition types. The classification methodology uses models formed from “training cases” with known aerosol type. The remaining measurements are then compared with these models using the Mahalanobis distance. Aerosol products from the CALIPSO satellite include aerosol type information as well, which must be inferred using aerosol loadingdependent observations and location information as input to the aerosol retrieval. The HSRL instrument regularly flies over the CALIPSO satellite ground track, presenting the opportunity for comparisons between the HSRL aerosol typing and the CALIPSO Vertical Feature Mask product, giving insight into the performance of the CALIPSO aerosol type algorithm. INTRODUCTION An aerosol classification scheme was introduced in [1] for airborne High Spectral Resolution Lidar (HSRL) measurements from the NASA Langley HSRL instrument. The ability to accurately characterize and discriminate aerosol type can improve both measurement retrievals and modeling, on both a regional and global scale. Since 2006, the NASA Langley HSRL has routinely participated in chemistry and radiation-focused field missions throughout North America, where its high accuracy, high resolution, vertically resolved measurements of aerosol provide vertical context for ground-based, in situ, and satellite observations of aerosols and clouds. The HSRL also routinely provides data for validating the CloudAerosol Lidar with Orthogonal Polarization (CALIOP) lidar instrument aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite [2]. Furthermore, the HSRL serves as a testbed for advanced satellite lidar instruments, and the advanced retrievals required for those measurements may benefit from the aerosol classification described here. In this work, we describe the HSRL aerosol classification methodology and do a detailed comparison with the aerosol types that are used in the CALIPSO retrieval [3] for 100 flights of the HSRL along the CALIPSO ground track. INSTRUMENT High Spectral Resolution Lidar instruments have the key advantage over backscatter lidar that it measures aerosol extinction and backscatter coefficients independently, without the need to assume or infer aerosol type. The LaRC airborne HSRL [4] uses the HSRL technique to independently retrieve aerosol and tenuous cloud extinction and backscatter without a priori assumptions on aerosol type or extinction-tobackscatter ratio. Ref. [4] describes the instrument and measurement technique in detail. The HSRL technique is employed at 532 nm and the standard backscatter technique is used at 1064 nm. The instrument also measures depolarization at both wavelengths. Therefore, the HSRL provides vertically resolved measurements of both “extensive” properties that depend on aerosol loading and “intensive” or bulk properties. The latter are the lidar ratio (i.e., the ratio of extinction and backscatter), aerosol depolarization ratio, backscatter color ratio, and spectral depolarization ratio (i.e., the ratio of aerosol depolarization at the two wavelengths). The intensive parameters provide information about the aerosol physical properties which are combined to infer aerosol type. CLASSIFICATION METHODOLOGY The HSRL aerosol classification methodology is presented in [1]. The HSRL aerosol classification is performed in two parts. First, specific samples of https://ntrs.nasa.gov/search.jsp?R=20120016655 2017-10-11T06:42:18+00:00Z