Convolutional LSTM network: A machine learning approach for precipitation nowcasting
The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region
over a relatively short period of time. Very few previous studies have examined this crucial …
over a relatively short period of time. Very few previous studies have examined this crucial …
Deep learning for precipitation nowcasting: A benchmark and a new model
With the goal of making high-resolution forecasts of regional rainfall, precipitation
nowcasting has become an important and fundamental technology underlying various …
nowcasting has become an important and fundamental technology underlying various …
Convcast: An embedded convolutional LSTM based architecture for precipitation nowcasting using satellite data
Nowcasting of precipitation is a difficult spatiotemporal task because of the non-uniform
characterization of meteorological structures over time. Recently, convolutional LSTM has …
characterization of meteorological structures over time. Recently, convolutional LSTM has …
MM-RNN: A multimodal RNN for precipitation nowcasting
Precipitation nowcasting, the high-resolution forecasting of precipitation in a short term, is
essential in various applications in the real world. Previous deep learning methods use …
essential in various applications in the real world. Previous deep learning methods use …
PFST-LSTM: A spatiotemporal LSTM model with pseudoflow prediction for precipitation nowcasting
Precipitation nowcasting is an important task, which can serve numerous applications such
as urban alert and transportation. Previous studies leverage convolutional recurrent neural …
as urban alert and transportation. Previous studies leverage convolutional recurrent neural …
A deep learning‐based methodology for precipitation nowcasting with radar
Nowcasting and early warning of severe convective weather play crucial roles in heavy
rainfall warning, flood mitigation, and water resource management. However, achieving …
rainfall warning, flood mitigation, and water resource management. However, achieving …
NowCasting-Nets: Representation learning to mitigate latency gap of satellite precipitation products using convolutional and recurrent neural networks
Accurate and timely estimation of precipitation is critical for issuing hazard warnings (eg, for
flash floods or landslides). Current remotely sensed precipitation products have a few hours …
flash floods or landslides). Current remotely sensed precipitation products have a few hours …
Rainformer: Features extraction balanced network for radar-based precipitation nowcasting
C Bai, F Sun, J Zhang, Y Song… - IEEE Geoscience and …, 2022 - ieeexplore.ieee.org
Precipitation nowcasting is one of the fundamental challenges in natural hazard research.
High-intensity rainfall, especially the rainstorm, will lead to the enormous loss of people's …
High-intensity rainfall, especially the rainstorm, will lead to the enormous loss of people's …
All convolutional neural networks for radar-based precipitation nowcasting
Today deep learning is taking its rise in hydrometeorological applications, and it is critical to
extensively evaluate its prediction performance and robustness. In our study, we use deep …
extensively evaluate its prediction performance and robustness. In our study, we use deep …
Convective precipitation nowcasting using U-Net model
L Han, H Liang, H Chen, W Zhang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Convective precipitation nowcasting remains challenging due to the fast change in
convective weather. Radar images are the most important data source in nowcasting …
convective weather. Radar images are the most important data source in nowcasting …