Institute for Atmospheric and Earth System Research (INAR/Physics), Faculty of Science, University of
Helsinki, 00014 Helsinki, Finland
Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric
Sciences, Nanjing University, Nanjing, 210023, China
Institute for Atmospheric and Earth System Research (INAR/Physics), Faculty of Science, University of
Helsinki, 00014 Helsinki, Finland
Institute for Atmospheric and Earth System Research (INAR/Forest Sciences), Faculty of Agriculture and Forestry, University of Helsinki, 00014 Helsinki, Finland
Institute for Atmospheric and Earth System Research (INAR/Physics), Faculty of Science, University of
Helsinki, 00014 Helsinki, Finland
Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric
Sciences, Nanjing University, Nanjing, 210023, China
Institute for Atmospheric and Earth System Research (INAR/Physics), Faculty of Science, University of
Helsinki, 00014 Helsinki, Finland
Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric
Sciences, Nanjing University, Nanjing, 210023, China
Petri Pellikka
Department of Geosciences and Geography, University of Helsinki,
00014 Helsinki, Finland
Institute for Atmospheric and Earth System Research (INAR/Physics), Faculty of Science, University of
Helsinki, 00014 Helsinki, Finland
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We regarded the banana shapes in the surface plots as a special kind of object (similar to cats) and applied an instance segmentation technique to automatically identify the new particle formation (NPF) events (especially the strongest ones), in addition to their growth rates, start times, and end times. The automatic method generalized well on datasets collected in different sites, which is useful for long-term data series analysis and obtaining statistical properties of NPF events.
We regarded the banana shapes in the surface plots as a special kind of object (similar to cats)...
