Articles | Volume 9, issue 10
https://doi.org/10.5194/amt-9-5007-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/amt-9-5007-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Microphysical particle properties derived from inversion algorithms developed in the framework of EARLINET
School of Physics, Astronomy and Mathematics, University of Hertfordshire, Hatfield, Hertfordshire, UK
Christine Böckmann
Institute of Mathematics, University of Potsdam, Am Neuen Palais 10, 14469 Potsdam, Germany
Alexei Kolgotin
Physics Instrumentation Center, Troitsk, Russia
Lars Schneidenbach
formerly at: Institute for Computer Science, University of Potsdam, Am Neuen Palais 10, 14469 Potsdam, Germany
Eduard Chemyakin
Science Systems and Applications, Inc., NASA Langley Research Center, Hampton VA, USA
Julia Rosemann
Institute of Mathematics, University of Potsdam, Am Neuen Palais 10, 14469 Potsdam, Germany
Pavel Znak
V. A. Fock Institute of Physics, St. Petersburg University, Ulyanovskaya 1, 198504 St. Petersburg, Russia
Anton Romanov
The National University of Science and Technology, Moscow, Russia
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Latest update: 13 Dec 2024
Short summary
We present a comparison study of two data inversion algorithms that are used to derive microphysical properties of atmospheric particle pollution. The algorithms have been developed for the analysis of data collected with advanced light detection and ranging (lidar) instruments from the European EARLINET network. The result of this study shows that two key parameters needed for climate change studies, i.e. particle size and light absorption capacity, can be derived with reasonable accuracy.
We present a comparison study of two data inversion algorithms that are used to derive...
Special issue