MA-SARNet: A one-shot nowcasting framework for SAR image prediction with physical driving forces
Remote sensing imagery is one of the most widely used data sources for large-scale Earth
observations with consistent spatial and temporal quality. However, the current usage …
observations with consistent spatial and temporal quality. However, the current usage …
Precipitation nowcasting using ground radar data and simpler yet better video prediction deep learning
Skillful quantitative precipitation nowcasting (QPN) is important for predicting precipitation in
the upcoming few hours and thus avoiding significant socioeconomic damage. Recent QPN …
the upcoming few hours and thus avoiding significant socioeconomic damage. Recent QPN …
Evaluation of deep-learning-based very short-term rainfall forecasts in South Korea
This study evaluates the performance of a deep learning model, Deep-learning-based Rain
Nowcasting and Estimation (DEEPRANE), for very short-term (1–6 h) rainfall forecasts in …
Nowcasting and Estimation (DEEPRANE), for very short-term (1–6 h) rainfall forecasts in …
A Forecast-Refinement Neural Network Based on DyConvGRU and U-Net for Radar Echo Extrapolation
J Yao, F Xu, Z Qian, Z Cai - IEEE Access, 2023 - ieeexplore.ieee.org
Precipitation nowcasting is very important for the sectors which critically depend on timely
and accurate weather information. One of the challenges of precipitation nowcasting is radar …
and accurate weather information. One of the challenges of precipitation nowcasting is radar …
[HTML][HTML] Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning
Quantitative precipitation nowcasting (QPN) can help to reduce the enormous
socioeconomic damage caused by extreme weather. The QPN has been a challenging topic …
socioeconomic damage caused by extreme weather. The QPN has been a challenging topic …
[HTML][HTML] Deep learning model for heavy rainfall nowcasting in South Korea
Accurate nowcasting is critical for preemptive action in response to heavy rainfall events
(HREs). However, operational numerical weather prediction models have difficulty …
(HREs). However, operational numerical weather prediction models have difficulty …
Deep-learning-based precipitation nowcasting with ground weather station data and radar data
Recently, many deep-learning techniques have been applied to various weather-related
prediction tasks, including precipitation nowcasting (ie, predicting precipitation levels and …
prediction tasks, including precipitation nowcasting (ie, predicting precipitation levels and …
Region-conditioned orthogonal 3d u-net for weather4cast competition
The Weather4Cast competition (hosted by NeurIPS 2022) required competitors to predict
super-resolution rain movies in various regions of Europe when low-resolution satellite …
super-resolution rain movies in various regions of Europe when low-resolution satellite …
基于深度学习的雷达降雨临近预报及洪水预报
李建柱， 李磊菁， 冯平， 唐若宜 - 水科学进展, 2023 - skxjz.nhri.cn
为探究深度学习的雷达降雨临近预报在流域洪水预报中的适用性, 采用U-Net, 嵌入注意力门的
Attention-Unet 和添加转换器的多级注意力TransAtt-Unet 开展雷达降雨临近预报 …
Attention-Unet 和添加转换器的多级注意力TransAtt-Unet 开展雷达降雨临近预报 …
[HTML][HTML] Improvements in deep learning-based precipitation nowcasting using major atmospheric factors with radar rain rate
Recently, deep learning-based precipitation nowcasting has been investigated and its
usefulness has been recognized. However, existing approaches have treated precipitation …
usefulness has been recognized. However, existing approaches have treated precipitation …