Nowcasting Heavy Rainfall With Convolutional Long Short-Term Memory Networks: A Pixelwise Modeling Approach-Reference-Cited by-同舟云学术

Nowcasting Heavy Rainfall With Convolutional Long Short-Term Memory Networks: A Pixelwise Modeling Approach

Published:2024 Issue: Volume:17 Page:8424-8433
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Wang Yi Victor¹^ORCID,Kim Seung Hee²^ORCID,Lyu Geunsu³^ORCID,Lee Choeng-Lyong³,Ryu Soorok³,Lee Gyuwon⁴,Min Ki-Hong⁴^ORCID,Kafatos Menas C.²

Affiliation:

1. Department of Emergency Management, Massachusetts Maritime Academy, Buzzards Bay, MA, USA

2. Institute for Earth, Computing, Human and Observing, Chapman University, Orange, CA, USA

3. Center for Atmospheric REmote sensing (CARE), Kyungpook National University, Daegu, Republic of Korea

4. BK21 Weather Extremes Education & Research Team, Department of Atmospheric Sciences, Center for Atmospheric Remote sensing (CARE), Kyungpook National University, Daegu, Republic of Korea

Funder

Korea Meteorological Administration Research and Development Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/4609443/10330207/10487101.pdf?arnumber=10487101

Reference42 articles.

1. Relative Importance of Radar Variables for Nowcasting Heavy Rainfall: A Machine Learning Approach

2. Comparing nowcastings of three severe convective events by statistical and NWP models

3. Reducing the spin‐up of a regional NWP system without data assimilation

4. Modernization of Atmospheric Physics Parameterization in Canadian NWP

5. The Storm Cell Identification and Tracking Algorithm: An Enhanced WSR-88D Algorithm