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Deep learning for precipitation nowcasting


Deep Learning for Precipitation Nowcasting: A Benchmark and A ...

We propose both a new model and a benchmark for precipitation nowcasting. Specifically, we go beyond ConvLSTM and propose the Trajectory GRU (TrajGRU) model.

Deep Learning for Precipitation Nowcasting: A Benchmark and A ...

First, the deep learning model is only evaluated on a relatively small dataset containing 97 rainy days and only the nowcasting skill score at the 0.5mm/h rain- ...

Deep learning for precipitation nowcasting: A survey from the ... - arXiv

The sub-network of NowcastNet employs a UNet that estimates changes in motion and intensity of radar echoes. These findings suggest that a multi ...

A Deep Learning Model for Precipitation Nowcasting Using Multiple ...

Abstract The optical flow technique has advantages in motion tracking and has long been employed in precipitation nowcasting to track the motion of ...

Deep Learning for Precipitation Nowcasting - Xingjian Shi

Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model. Xingjian Shi, Zhihan Gao, Leonard Lausen, Hao Wang, Dit-Yan Yeung, Wai-kin Wong and ...

Skilful precipitation nowcasting using deep generative models of radar

By training these models on large corpora of radar observations rather than relying on in-built physical assumptions, deep learning methods aim ...

Improvements in deep learning-based precipitation nowcasting ...

In this study, we introduced a deep neural network that can utilize atmospheric factors that play major roles in the development of rainfall systems over the ...

Hzzone/Precipitation-Nowcasting: pytorch implemention of trajGRU.

@inproceedings{xingjian2017deep, title={Deep learning for precipitation nowcasting: a benchmark and a new model}, author={Shi, Xingjian and Gao, Zhihan and ...

[PDF] Deep Learning for Precipitation Nowcasting - Semantic Scholar

719 Citations · Accurate and Clear Precipitation Nowcasting with Consecutive Attention and Rain-map Discrimination · NowCasting-Nets: Representation Learning to ...

A Deep Learning Model for Precipitation Nowcasting Using Multiple ...

A Deep Learning Model for Precipitation Nowcasting Using Multiple Optical. Flow Algorithms. JI-HOON HA a. AND HYESOOK LEE a a National Institute of ...

Deep-Learning-Based Precipitation Nowcasting with Ground ...

Recently, many deep-learning techniques have been applied to various weather-related prediction tasks, including precipitation nowcasting (i.e., ...

A Deep Learning‐Based Methodology for Precipitation Nowcasting ...

A novel deep learning neural network is proposed for precipitation nowcasting Group normalization is shown to be effective in training our ...

Effective training strategies for deep-learning-based precipitation ...

First, we adapt U-Net, a widely-used deep-learning model, for the two problems of interest here: precipitation nowcasting and precipitation estimation from ...

Deep learning for precipitation nowcasting - ACM Digital Library

Recently, the Convolutional LSTM (ConvLSTM) model has been shown to outperform traditional optical flow based methods for precipitation nowcasting, suggesting ...

Deep learning model based on multi-scale feature fusion for ... - GMD

Tan, J., Huang, Q., and Chen, S.: Deep learning model based on multi-scale feature fusion for precipitation nowcasting, Geosci. Model Dev., 17, ...

(PDF) Deep Learning for Precipitation Nowcasting: A Benchmark ...

We propose both a new model and a benchmark for precipitation nowcasting. Specifically, we go beyond ConvLSTM and propose the Trajectory GRU (TrajGRU) model.

Distributed Deep Learning for Precipitation Nowcasting - IEEE Xplore

Distributed Deep Learning for Precipitation Nowcasting. Abstract: Effective training of Deep Neural Networks requires massive amounts of data and compute. As a ...

Precipitation nowcasting using ground radar data and simpler yet ...

Recent QPN studies have actively adopted deep learning (DL) to generate precipitation maps using sequences of ground radar data. Although high ...

Convolutional LSTM Network: A Machine Learning Approach ... - NIPS

The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time.

Key factors for quantitative precipitation nowcasting using ground ...

Recent QPN studies have proposed data-driven models using deep learning (DL) and ground weather radar. Previous studies have primarily focused ...