Mesoscale Feature Detection
Mesoscale Feature Detection is a methodological framework in meteorological analysis that identifies and characterizes atmospheric phenomena of intermediate spatial and temporal scales, such as mesoscale convective systems and fronts, by distinguishing them from synoptic-scale patterns and microscale turbulence through targeted observational and computational techniques.
35
Publications
1.7K
Citations
125
Authors
76
Institutions
Publications per year
2017–2026
13
Parents
35
Karen Nieto, Hervé Demarcq, Sam McClatchie · Remote Sensing of Environment · 2012 · 115 citations
Oceanic Mesoscale Eddy Detection Method Based on Deep Learning
Zijun Duo, Wenke Wang, Huizan Wang · Remote Sensing · 2019 · 90 citations · Full text
1–5 of 35
125
2
h-index
2
h-index
2
h-index
2
h-index
2
h-index
1–5 of 125
76

Centre National de la Recherche Scientifique
Paris, France
3 h-index
7 publications
879 citations

Ocean University of China
Qingdao, China
2 h-index
9 publications
155 citations
Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)
Guangzhou, China
2 h-index
9 publications
216 citations

University of Cincinnati
Cincinnati, United States
2 h-index
6 publications
189 citations
National Marine Environmental Forecasting Center
Beijing, China
2 h-index
3 publications
72 citations
Colorado State University
Fort Collins, United States
2 h-index
4 publications
137 citations

Johns Hopkins University Applied Physics Laboratory
North Laurel, United States
2 h-index
5 publications
79 citations
Goddard Space Flight Center
Greenbelt, United States
2 h-index
5 publications
121 citations
1–8 of 76
4 publications · 219 citations
4
h-index
JGR · 3 publications · 59 citations
3
h-index
2 publications · 183 citations
2
h-index
2 publications · 45 citations
2
h-index
2 publications · 40 citations
2
h-index
1–5