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Estimation of Tropical Cyclone Intensity via Deep Learning Techniques from Satellite Cloud Images., , , , , и . Remote. Sens., 15 (17): 4188 (сентября 2023)Impact of Aerosol Mixing State and Hygroscopicity on the Lidar Ratio., , , , , , и . Remote. Sens., 14 (7): 1554 (2022)AHI/Himawari-8 Yonsei Aerosol Retrieval (YAER): Algorithm, Validation and Merged Products., , , , и . Remote. Sens., 10 (5): 699 (2018)Diagnosis and Classification of Typhoon-Associated Low-Altitude Turbulence Using HKO-TDWR Radar Observations and Machine Learning., , , , и . IEEE Trans. Geosci. Remote. Sens., 57 (6): 3633-3648 (2019)A Deep Learning-Based Wind Field Nowcasting Method With Extra Attention on Highly Variable Events., , , , , , и . IEEE Geosci. Remote. Sens. Lett., (2022)A Hybrid Method for Fine-Scale Wind Field Retrieval Based on Machine Learning and Data Assimilation., , , , и . IEEE Trans. Geosci. Remote. Sens., (2022)A Review of Progress and Applications of Pulsed Doppler Wind LiDARs., , , , , , , и . Remote. Sens., 11 (21): 2522 (2019)LIDAR and Chaotic Oscillatory-based Neural Network for Wind Shear Forecasting., , и . FCS, стр. 22-28. CSREA Press, (2010)High-Order Taylor Expansion for Wind Field Retrieval Based on Ground-Based Scanning Lidar., , , , , , и . IEEE Trans. Geosci. Remote. Sens., (2022)A Spatio-Temporal Neural Network for Fine-Scale Wind Field Nowcasting Based on Lidar Observation., , , , , , , и . IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., (2022)