Results 71 to 80 of about 847 (167)

NOAA ACSPO Himawari-8 SST Product

, 2016
Presentation given at the 17th GHRSST science meeting (XVII), Washington DC, USA, June 6 - 10, 2016.
openaire   +1 more source

Machine Learning‐Based Turbulence Intensity Estimation Near Convective Clouds in East Asia Using GK‐2A Satellite Observations

Geophysical Research Letters, Volume 52, Issue 23, 16 December 2025.
Abstract The detection of atmospheric turbulence, particularly near deep convection, are essential for flight safety and efficiency. Geostationary satellites provide continuous coverage where radar is unavailable, but their lack of vertical information limits detection to upper levels.
Yoonjin Lee, Dan‐Bi Lee, Jung‐Hoon Kim   +2 more
wiley   +1 more source

Wide and Deep Learning Model for Satellite-Based Real-Time Aerosol Retrievals in China

Atmosphere
Machine learning methods have been recognized as rapid methods for satellite-based aerosol retrievals but have not been widely applied in geostationary satellites. In this study, we developed a wide and deep learning model to retrieve the aerosol optical
Nana Luo, Junxiao Zou, Zhou Zang, Tianyi Chen, Xing Yan   +4 more
doaj   +1 more source

Generation and Evaluation of LAI and FPAR Products from Himawari-8 Advanced Himawari Imager (AHI) Data

Remote Sensing, 2019
Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) absorbed by vegetation are two of the essential biophysical variables used in most global models of climate, hydrology, biogeochemistry, and ecology.
Yepei Chen, Kaimin Sun, Chi Chen, Ting Bai, Taejin Park, Weile Wang, Ramakrishna R. Nemani, Ranga B. Myneni   +7 more
doaj   +1 more source

Ship‐Based Lidar Evaluation of Southern Ocean Low Clouds in the Storm‐Resolving General Circulation Model ICON and the ERA5 and MERRA‐2 Reanalyses

Journal of Geophysical Research: Atmospheres, Volume 130, Issue 22, 28 November 2025.
Abstract Global storm resolving models (GSRMs) represent the next generation of global climate models. One of them is a 5‐km Icosahedral Nonhydrostatic Weather and Climate Model (ICON). Its high resolution means that parameterizations of convection and clouds, including subgrid‐scale clouds, are omitted, relying on explicit simulation but necessarily ...
Peter Kuma, Frida A.‐M. Bender, Adrian J. McDonald, Simon P. Alexander, Greg M. McFarquhar, John J. Cassano, Graeme E. Plank, Sean Hartery, Simon Parsons, Sally Garrett, Alex J. Schuddeboom, Anna Possner   +11 more
wiley   +1 more source

Introduction to the NJIAS Himawari-8/9 Cloud Feature Dataset for climate and typhoon research [PDF]

Earth System Science Data
The use of remote sensing methods to accurately measure cloud properties and their spatiotemporal changes has been widely welcomed in many fields of atmospheric research.
X. Zhuge, X. Zou, L. Yu, X. Li, M. Zeng, Y. Chen, B. Zhang, B. Yao, F. Tang, F. Chen, W. Kan   +10 more
doaj   +1 more source

NOAA JPSS and Himawari-8 SST Products

, 2015
Presentation given at the Satellite Oceanography Users Workshop, Melbourne, Australia, 9 - 11 Nov 2015.
openaire   +1 more source

Full Coverage Hourly PM_2.5 Concentrations' Estimation Using Himawari-8 and MERRA-2 AODs in China. [PDF]

Int J Environ Res Public Health, 2023
Liu Z, Xiao Q, Li R.
europepmc   +1 more source

Early Stage Forest Fire Detection from Himawari-8 AHI Images Using a Modified MOD14 Algorithm Combined with Machine Learning. [PDF]

Sensors (Basel), 2022
Maeda N, Tonooka H.
europepmc   +1 more source

Icing Detection over East Asia from Geostationary Satellite Data Using Machine Learning Approaches

Remote Sensing, 2018
Even though deicing or airframe coating technologies continue to develop, aircraft icing is still one of the critical threats to aviation. While the detection of potential icing clouds has been conducted using geostationary satellite data in the US and ...
Seongmun Sim, Jungho Im, Sumin Park, Haemi Park, Myoung Hwan Ahn, Pak-wai Chan   +5 more
doaj   +1 more source