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

• Published in: Atmospheric measurement techniques Vol. 9; no. 10; pp. 5007 - 5035
• Main Authors: Müller, Detlef, Böckmann, Christine, Kolgotin, Alexei, Schneidenbach, Lars, Chemyakin, Eduard, Rosemann, Julia, Znak, Pavel, Romanov, Anton
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 12-10-2016; Copernicus Publications
• Subjects: Accuracy; Aerosol research; Aerosols; Albedo; Albedo (solar); Algorithms; Backscatter; Backscattering; Climate change; Climate studies; Coefficients; Computation; Computer simulation; Data; Data analysis; Data errors; Electromagnetic absorption; Instruments; Inversion; Lidar; Lidar measurements; Light absorption; Manual control; Optical properties; Optical radar; Parameters; Particle size; Physics; Properties; Ratios; Refractive index; Refractivity; Regularization methods; Remote sensing; Software; Uncertainty; Europe; Germany
• ISSN: 1867-8548; 1867-1381; 1867-8548
• DOI: 10.5194/amt-9-5007-2016

We present a summary on the current status of two inversion algorithms that are used in EARLINET (European Aerosol Research Lidar Network) for the inversion of data collected with EARLINET multiwavelength Raman lidars. These instruments measure backscatter coefficients at 355, 532, and 1064 nm, and extinction coefficients at 355 and 532 nm. Development of these two algorithms started in 2000 when EARLINET was founded. The algorithms are based on a manually controlled inversion of optical data which allows for detailed sensitivity studies. The algorithms allow us to derive particle effective radius as well as 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. It is an extreme challenge to retrieve the real part with an accuracy better than 0.05 and the imaginary part with accuracy better than 0.005–0.1 or ±50 %. Single-scattering albedo can be computed from the retrieved microphysical parameters and allows us to categorize aerosols into high- and low-absorbing aerosols. On the basis of a few exemplary simulations with synthetic optical data we discuss the current status of these manually operated algorithms, the potentially achievable accuracy of data products, and the goals for future work. One algorithm was used with the purpose of testing how well microphysical parameters can be derived if the real part of the complex refractive index is known to at least 0.05 or 0.1. The other algorithm was used to find out how well microphysical parameters can be derived if this constraint for the real part is not applied. 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 the robustness of the algorithms towards 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 where the Potsdam algorithm is limited to the lower value. 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.