Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Artificial neural network (1)
- Cold pool (1)
- Convolutional neural network (1)
- Data models (1)
- Deep learning (DL) (1)
-
- Diagonal squall line (1)
- Dual-polarimetric weather radar (1)
- Early warning (1)
- Hydrometeorological hazard (1)
- Input variables (1)
- Long short-term memory (LSTM) network (1)
- Mesoscale convective system (1)
- Meteorological radar (1)
- Predictive models (1)
- Radar (1)
- Rain (1)
- Rainfall nowcasting (1)
- Recurrent neural network (RNN) (1)
- Remote sensing (1)
- Storm (1)
- Storm-relative winds (1)
- Storms (1)
- Vertical wind shear (1)
Articles 1 - 3 of 3
Full-Text Articles in Remote Sensing
Kinematic And Dynamic Structure Of The 18 May 2020 Squall Line Over South Korea, Wishnu Agum Swastiko, Chia-Lun Tsai, Seung Hee Kim, Gyuwon Lee
Kinematic And Dynamic Structure Of The 18 May 2020 Squall Line Over South Korea, Wishnu Agum Swastiko, Chia-Lun Tsai, Seung Hee Kim, Gyuwon Lee
Institute for ECHO Articles and Research
The diagonal squall line that passed through the Korean Peninsula on the 18 May 2020 was examined using wind data retrieved from multiple Doppler radar synthesis focusing on its kinematic and dynamic aspects. The low-level jet, along with warm and moist air in the lower level, served as the primary source of moisture supply during the initiation and formation process. The presence of a cold pool accompanying the squall line played a role in retaining moisture at the surface. As the squall line approached the Korean Peninsula, the convective bands in the northern segment (NS) and southern segment (SS) of …
Nowcasting Heavy Rainfall With Convolutional Long Short-Term Memory Networks: A Pixelwise Modeling Approach, Yi Victor Wang, Seung Hee Kim, Geunsu Lyu, Choeng-Lyong Lee, Soorok Ryu, Gyuwon Lee, Ki-Hong Min, Menas C. Kafatos
Nowcasting Heavy Rainfall With Convolutional Long Short-Term Memory Networks: A Pixelwise Modeling Approach, Yi Victor Wang, Seung Hee Kim, Geunsu Lyu, Choeng-Lyong Lee, Soorok Ryu, Gyuwon Lee, Ki-Hong Min, Menas C. Kafatos
Institute for ECHO Articles and Research
The recent decades have seen an increasing academic interest in leveraging machine learning approaches to nowcast, or forecast in a highly short-term manner, precipitation at a high resolution, given the limitations of the traditional numerical weather prediction models on this task. To capture the spatiotemporal associations of data on input variables, a deep learning (DL) architecture with the combination of a convolutional neural network and a recurrent neural network can be an ideal design for nowcasting rainfall. In this study, a long short-term memory (LSTM) modeling structure is proposed with convolutional operations on input variables. To resolve the issue of …
Relative Importance Of Radar Variables For Nowcasting Heavy Rainfall: A Machine Learning Approach, Yi Victor Wang, Seung Hee Kim, Geunsu Lyu, Choeng-Lyong Lee, Gyuwon Lee, Ki-Hong Min, Menas C. Kafatos
Relative Importance Of Radar Variables For Nowcasting Heavy Rainfall: A Machine Learning Approach, Yi Victor Wang, Seung Hee Kim, Geunsu Lyu, Choeng-Lyong Lee, Gyuwon Lee, Ki-Hong Min, Menas C. Kafatos
Institute for ECHO Articles and Research
Highly short-term forecasting, or nowcasting, of heavy rainfall due to rapidly evolving mesoscale convective systems (MCSs) is particularly challenging for traditional numerical weather prediction models. To overcome such a challenge, a growing number of studies have shown significant advantages of using machine learning (ML) modeling techniques with remote sensing data, especially weather radar data, for high-resolution rainfall nowcasting. To improve ML model performance, it is essential first and foremost to quantify the importance of radar variables and identify pertinent predictors of rainfall that can also be associated with domain knowledge. In this study, a set of MCS types consisting of …