No abstract

Abstract. There is limited understanding of the role of aerosols in the formation and modification of clouds, partly due to inadequate data on such systems. Aircraft-based aerosol measurements in the presence of cloud particles have proven to be challenging because of the problem of cloud droplet/ice particle shatter and the generation of secondary artifact particles that contaminate aerosol samples. Recently, the design of a new aircraft inlet, called the Blunt-body Aerosol Sampler (BASE), which enables sampling of interstitial aerosol particles, was introduced. Numerical modeling results and laboratory test data suggested that the BASE inlet should sample interstitial particles with minimal shatter particle contamination. Here, the sampling performance of the inlet is established from aircraft-based measurements. Initial aircraft test results obtained during the PLOWS (Profiling of Winter Storms) campaign indicated two problems with the original BASE design: separated flows around the BASE at high altitudes and a significant shatter problem when sampling in drizzle. The test data were used to improve the accuracy of flow and particle trajectory modeling around the inlet, and the results from the improved flow model were used to guide design modifications of the BASE to overcome the problems identified in its initial deployment. The performance of the modified BASE was tested during the ICE-T (Ice in Clouds Experiment -Tropics) campaign, and the inlet was seen to provide near shatter-free measurements in a wide range of cloud conditions. The initial aircraft test results, design modifications, and the performance characteristics of the BASE relative to another interstitial inlet, the submicron aerosol inlet (SMAI), are presented.

No abstract

Abstract. There is limited understanding of aerosol role in the formation and modification of clouds partly due to inadequate data on such systems. Aircraft-based aerosol measurements in the presence of cloud particles has proven to be challenging because of the problem of cloud-droplet/ice-particle shatter and the generation of secondary artifact particles that contaminate aerosol samples. Recently, design of a new aircraft inlet, called the blunt-body aerosol sampler (BASE), which enables sampling of interstitial aerosol particles, was introduced. Numerical modeling results and laboratory test data suggested that the BASE inlet should sample interstitial particles with minimal shatter particle contamination. Here, the sampling performance of the inlet is established from aircraft-based measurements. Initial aircraft test results obtained during the PLOWS campaign indicated two problems with the original BASE design: separated flows around the BASE at high altitudes; and a significant shatter problem when sampling in drizzle. The test data was used to improve the accuracy of flow and particle trajectory modeling around the inlet, and the results from the improved flow model informed several design modifications of BASE to overcome the problems identified from its initial deployment. The performance of the modified BASE was tested during the ICE-T campaign and the inlet was seen to provide near shatter-free measurements in a wide range of cloud conditions. The initial aircraft test results, design modifications, and the performance characteristics of BASE relative to another interstitial inlet, the sub-micron aerosol inlet (SMAI), are presented.

The accuracy of particle size distributions obtained from scanning electrical mobility spectrometer (SEMS) measurements is strongly dependent on the accurate consideration of the instrument characteristics in the formulation of the SEMS problem and the effective inversion of the resulting SEMS equation. The estimation of size distributions from SEMS measurements requires a solution of the discretized form of the Fredholm integral equation of the first kind. The often ill-conditioned nature of the linear inverse problem coupled with the possible presence of measurement noise complicates these calculations. The use of standard inversion approaches, such as nonnegative least squares (NNLS) or regularization-based algorithms, requires SEMS measurements with significant signal-to-noise ratio or some a priori knowledge of the shape of the sampled size distribution. These severe constraints for SEMS measurements can be relaxed with the new multiscale expectation-maximization SEMS inversion method introduced here. Performance testing with a broad range of sample size distributions and SEMS operating conditions suggests that the multiscale approach is generally more accurate than both the NNLS and regularization approaches.