This is FAST: multivariate Full-permutAtion based Stochastic foresT method—improving the retrieval of fine-mode aerosol microphysical properties with multi-wavelength lidar

Remote Sensing of Environment - Tập 280 - Trang 113226 - 2022
Nanchao Wang1, Da Xiao1, Igor Veselovskii2, Yuan Wang3, Lynn M. Russell4, Chuanfeng Zhao5, Jianping Guo6, Chengcai Li5, Silke Gross7, Xu Liu8, Xueqi Ni8, Lizhou Tan8, Yuxuan Liu1, Kai Zhang1, Yicheng Tong1, Lingyun Wu1, Feitong Chen1, Binyu Wang1, Chong Liu1,9,10,11, Weibiao Chen12
1State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310000, China
2Prokhorov General Physics Institute of the Russian Academy of Sciences, Moscow, Russia
3Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, IN 47907, USA
4Scripps Institution of Oceanography, University of California, San Diego, CA 92093, USA
5Department of Atmospheric and Oceanic Sciences, Laboratory for Climate and Ocean-Atmosphere Studies, School of Physics, Peking University, Beijing, 100871, China
6State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
7Institut für Physik der Atmosphäre, Deutsches Zentrum für Luft und Raumfahrt, Oberpfaffenhofen, Germany
8Department of Physics, Zhejiang University, Hangzhou 310000, China
9International Research Center for Advanced Photonics, Zhejiang University, Hangzhou 310000, China
10Jiaxing Key Laboratory of Photonic Sensing & Intelligent Imaging, Jiaxing 314000, China
11Intelligent Optics & Photonics Research Center, Jiaxing Research Institute Zhejiang University, Jiaxing 314000, China
12Key Laboratory of Space Laser Communication and Detection Technology, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China

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