Microphysical particle properties derived from inversion algorithms developed in the framework of EARLINET

D. Mueller, C. Boeckmann, Alexei Kolgotin, L. Schneidenbach, Eduard V. Chemyakin, J. Rosemann, P. Znak, A. Romanov

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)
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We present a summary on the current status of two inversion algorithms that are used in EARLINET for the inversion of data collected with EARLINET multiwavelength Raman lidars. These instruments measure backscatter coecients at 355, 532, and 1064 nm, and extinction coecients at 355 and 532 nm. Development of these two algorithms started in 2000 when EARLINET was founded. The algorithms are based on manually controlled inversion of optical data which allows for detailed sensitivity studies and thus provides us with comparably high quality of the derived data products. The algorithms allow us to derive particle eective radius, and volume and surface-area concentration with comparably high confidence. The retrieval of the real and imaginary parts of the complex refractive index still is a challenge in view of the accuracy required for these parameters in climate change studies in which light-absorption needs to be known with high accuracy. Single-scattering albedo can be computed from the retrieved microphysical parameters and allows us to categorize aerosols into high and low absorbing aerosols. We discuss the current status of these manually operated algorithms, the potentially achievable accuracy of data products, and the goals for future work on the basis of a few exemplary simulations with synthetic optical data. The optical data used in our study cover a range of Ångström exponents and extinction-to-backscatter (lidar) ratios that are found from lidar measurements of various aerosol types.We also tested aerosol scenarios that are considered highly unlikely, e.g., the lidar ratios fall outside the commonly accepted range of values measured with Raman lidar, even though the underlying microphysical particle properties are not uncommon. The goal of this part of the study is to test robustness of the algorithms toward their ability to identify aerosol types that have not been measured so far, but cannot be ruled out based on our current knowledge of aerosol physics. We computed the optical data from monomodal logarithmic particle size distributions, i.e., we explicitly excluded the more complicated case of bimodal particle size distributions which is a topic of ongoing research work. Another constraint is that we only considered particles of spherical shape in our simulations. We considered particle
radii as large as 7–10 μm in our simulations. That particle size does not only cover the size range of particles in the fine-mode fraction of naturally occurring particle size distributions but also covers a considerable part of the coarse-mode fraction of particle size distributions. We considered optical-data errors of 15% in the simulation studies. We target 50% uncertainty as a reasonable threshold for our data products, though we attempt to obtain data products with less uncertainty in future work.
Original languageEnglish
Pages (from-to)5007–5035
Number of pages29
JournalAtmospheric Measurement Techniques
Issue number10
Publication statusPublished - 12 Oct 2016


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