Applying Satellite Observations of Tropical Cyclone Internal Structures to Rapid Intensification Forecast With Machine Learning

Tropical cyclone (TC) intensity change is controlled by both environmental conditions and internal storm processes. We show that TC 24‐hr subsequent intensity change (DV24) is linearly correlated with the departures in satellite observations of inner‐core precipitation, ice water content, and outflo...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Geophysical research letters 2020-09, Vol.47 (17), p.n/a
Hauptverfasser:	Su, Hui, Wu, Longtao, Jiang, Jonathan H., Pai, Raksha, Liu, Alex, Zhai, Albert J., Tavallali, Peyman, DeMaria, Mark
Format:	Artikel
Sprache:	eng
Schlagworte:	Amplification convective heating Cyclones Detection Environmental conditions False alarms Hurricanes internal storm processes Learning algorithms Machine learning Mathematical models Moisture content operational forecast Outflow Precipitation Probability theory rapid intensification Satellite observation Satellites Statistical analysis Statistical models Storms Temperature Tropical climate tropical cyclone Tropical cyclone intensities Tropical cyclones Water content
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!