Separating mixtures of aerosol types in airborne High Spectral Resolution Lidar data

Abstract

Abstract. Knowledge of aerosol type is important for determining the magnitude and assessing the consequences of aerosol radiative forcing, and can provide useful information for source attribution studies. However, atmospheric aerosol is frequently not a single pure type, but instead occurs as a mixture of types, and this mixing affects the optical and radiative properties of the aerosol. This paper extends the work of earlier researchers by using the aerosol intensive parameters measured by the NASA Langley Research Center airborne High Spectral Resolution Lidar (HSRL-1) to develop a comprehensive and unified set of rules for characterizing the external mixing of several key aerosol intensive parameters: extinction-to-backscatter ratio (i.e., lidar ratio), backscatter color ratio, and depolarization ratio. We present the mixing rules in a particularly simple form that leads easily to mixing rules for the covariance matrices that describe aerosol distributions, rather than just single values of measured parameters. These rules can be applied to infer mixing ratios from the lidar-observed aerosol parameters, even for cases without significant depolarization. We demonstrate our technique with measurement curtains from three HSRL-1 flights which exhibit mixing between two aerosol types, urban pollution plus dust, marine plus dust, and smoke plus marine. For these cases, we infer a time-height cross-section of extinction mixing ratio along the flight track, and partition aerosol extinction into portions attributed to the two pure types.

Observations of the spectral dependence of linear particle depolarization ratio of aerosols using NASA Langley airborne High Spectral Resolution Lidar

“…On the positive side (from the perspective of corresponding CALIPSO and EarthCARE measurements), for aerosols dominated by dust the 355 and 532 nm particle depolarization ratios appear to be fairly consistent even for different particle sizes and may be relatively easily converted. Variation in the 532 and 355 nm particle depolarization ratios for dusty aerosols has been primarily linked to the fraction of dust particles in a sample (Sugimoto et al, 2003); therefore, there is no reason to think that inferences of dust mixing ratios (e.g., Sugimoto and Lee, 2006;Tesche et al, 2009a;Nishizawa et al, 2011;Burton et al, 2014) may not be done with 355 nm measurements. However, in the case of dust-dominated aerosol, the 355 nm signal consistently is significantly both smaller and more difficult to measure accurately than the 532 nm signal, and therefore the signature of dust may be harder to detect from space at 355 nm than at 532 nm for dilute dust mixtures.…”

“…The aerosol particle depolarization ratio from lidar is of key importance for the detection and assessment of dust and volcanic ash since it is a clear indicator of non-spherical particles. The particle depolarization ratio is also used to infer the amount of dust or ash in a mixture (Sugimoto and Lee, 2006;Tesche et al, 2009aTesche et al, , 2011Ansmann et al, 2011Ansmann et al, , 2012David et al, 2013;Burton et al, 2014;Mamouri and Ansmann, 2014). It is also sensitive to the size of the non-spherical particles Sakai et al, 2010;Gasteiger et al, 2011;Gasteiger and Freudenthaler, 2014).…”

Long-term profiling of mineral dust and pollution aerosol with multiwavelength polarization Raman lidar at the Central Asian site of Dushanbe, Tajikistan: case studies

“…Iwasaka et al (2003) measured a depolarization ratio of 0.27 * at 532 nm wavelength in a lofted dust layer in Dunhuang (northern Taklamakan). Note that the values denoted with * are published as aerosol depolarization potentials and are converted to particle linear depolarization ratios (see Burton et al, 2014;Cairo et al, 1999;Gimmestad, 2008).…”

Separation of the optical and mass features of particle components in different aerosol mixtures by using POLIPHON retrievals in synergy with continuous polarized Micro-Pulse Lidar (P-MPL) measurements

“…POLIPHON is also the basis of the retrieval of ice nuclei number concentration in desert dust layers (Mamouri and Ansmann, 2015) and cloud condensation nucleus number concentration (Mamouri and Ansmann, 2016). In addition, a similar method is used for separating aerosol mixtures in HSRL systems (Burton et al, 2012(Burton et al, , 2014.…”