scholarly journals Approaches for Tracking Clouds from Geostationary Satellite Images: A Literature Survey

2018 ◽  
Vol 180 (36) ◽  
pp. 14-17
Author(s):  
Chitra Merin ◽  
Sreekumar K.
Keyword(s):  
Satellite Images ◽  
Literature Survey ◽  
Geostationary Satellite

Extrapolation of Sequence of Geostationary Satellite Images for Weather Nowcasting

2011 ◽  
Vol 8 (2) ◽  
pp. 216-219 ◽  
Author(s):  
Bipasha Paul Shukla ◽  
Pradip K. Pal ◽  
Prakash C. Joshi
Keyword(s):  
Satellite Images ◽  
Geostationary Satellite

Detection of sea-breeze inland penetration in the coastal-urban region using geostationary satellite images

Urban Climate ◽  
2020 ◽  
Vol 31 ◽  
pp. 100586 ◽  
Author(s):  
Muhammad Rezza Ferdiansyah ◽  
Atsushi Inagaki ◽  
Manabu Kanda
Keyword(s):  
Satellite Images ◽  
Sea Breeze ◽  
Geostationary Satellite ◽  
Urban Region

scholarly journals Monitoring paddy productivity in North Korea employing geostationary satellite images integrated with GRAMI-rice model

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Jong-min Yeom ◽  
Seungtaek Jeong ◽  
Gwanyong Jeong ◽  
Chi Tim Ng ◽  
Ravinesh C. Deo ◽  
...  
Keyword(s):  
North Korea ◽  
Satellite Images ◽  
Geostationary Satellite

scholarly journals Deriving AMVs from Geostationary Satellite Images Using Optical Flow Algorithm Based on Polynomial Expansion

2016 ◽  
Vol 33 (8) ◽  
pp. 1727-1747 ◽  
Author(s):  
Qiong Wu ◽  
Hong-Qing Wang ◽  
Yin-Jing Lin ◽  
Yi-Zhou Zhuang ◽  
Yan Zhang
Keyword(s):  
Optical Flow ◽  
Satellite Images ◽  
Geostationary Satellite ◽  
Polynomial Expansion ◽  
Strong Wind ◽  
Wind Fields ◽  
Brightness Temperatures ◽  
Flow Algorithm ◽  
Vector Difference ◽  
Two Parameters

AbstractAn optical flow algorithm based on polynomial expansion (OFAPE) was used to derive atmospheric motion vectors (AMVs) from geostationary satellite images. In OFAPE, there are two parameters that can affect the AMV results: the sizes of the expansion window and optimization window. They should be determined according to the temporal interval and spatial resolution of satellite images. A helpful experiment was conducted for selecting those sizes. The limitations of window sizes can cause loss of strong wind speed, and an image-pyramid scheme was used to overcome this problem. Determining the heights of AMVs for semitransparent cloud pixels (STCPs) is challenging work in AMV derivation. In this study, two-dimensional histograms (H2Ds) between infrared brightness temperatures (6.7- and 10.8-μm channels) formed from a long time series of cloud images were used to identify the STCPs and to estimate their actual temperatures/heights. The results obtained from H2Ds were contrasted with CloudSat radar reflectivity and CALIPSO cloud-feature mask data. Finally, in order to verify the algorithm adaptability, three-month AMVs (JJA 2013) were calculated and compared with the wind fields of ERA data and the NOAA/ESRL radiosonde observations in three aspects: speed, direction, and vector difference.

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