Improvements for ground-based remote sensing of atmospheric aerosol properties by additional polarimetric measurements

We discuss the improvements in the aerosol properties characterization resulting from the additional multi-wavelength polarization measurements measured by a new CIMEL polarized sun/sky-photometer, CE318-DP. In order to process direct-sun, sky and polarization measurements in a wide spectral range (340–1640 nm), we developed new calibration methods and strategies, e.g. using the Langley plot method to calibrate both direct-sun irradiance and sky radiance, as well as combining laboratory facilities with a vicarious method to calibrate the polarized sky measurements. For studying the impact of new polarimetric measurements on the retrievals of aerosol properties, we have processed an extensive record of field measurements using an updated Dubovik and King retrieval algorithm [Dubovik O, Sinyuk A, Lapyonok T, Holben BN, Mishchenko MI, et al. Application of spheroid models to account for aerosol particle nonsphericity in remote sensing of desert dust. J Geophys Res 2006;111:D11208.]. A preliminary analysis shows that adding polarization in the inversion can reduce possible errors (notably for about 30% of our field cases) in the fine mode size distribution, real part of refractive index and particle shape parameter retrievals, especially for small particles.     

Monotonicity recovering and accuracy preserving optimization methods for postprocessing finite element solutions

We suggest here a least-change correction to available finite element (FE) solution. This postprocessing procedure is aimed at recovering the monotonicity and some other important properties that may not be exhibited by the FE solution. Although our approach is presented for FEs, it admits natural extension to other numerical schemes, such as finite differences and finite volumes. For the postprocessing, a priori information about the monotonicity is assumed to be available, either for the whole domain or for a subdomain where the lost monotonicity is to be recovered. The obvious requirement is that such information is to be obtained without involving the exact solution, e.g. from expected symmetries of this solution.The postprocessing is based on solving a monotonic regression problem with some extra constraints. One of them is a linear equality-type constraint that models the conservativity requirement. The other ones are box-type constraints, and they originate from the discrete maximum principle. The resulting postprocessing problem is a large scale quadratic optimization problem. It is proved that the postprocessed FE solution preserves the accuracy of the discrete FE approximation.We introduce an algorithm for solving the postprocessing problem. It can be viewed as a dual ascent method based on the Lagrangian relaxation of the equality constraint. We justify theoretically its correctness. Its efficiency is demonstrated by the presented results of numerical experiments.     

Ionic liquid matrices for MALDI mass spectrometry of lignin

Analytical and Bioanalytical Chemistry - The use of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry for the study of lignin is still extremely limited due to its low...     

Erratum to: The Ginibre Ensemble of Real Random Matrices and its Scaling Limits

We correct an error in Theorem 12 of the original article concerning the edge scaling limit of the law of real eigenvalues for the real Ginibre ensemble.