Results 71 to 80 of about 1,490 (205)

Distribution‐Guided Ensemble Postprocessing for S2S Precipitation Forecasts: A Seamless Pathway Using Deep Generative Models

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 3, June 2026.
Abstract Atmosphere‐ocean‐land coupled forecasting systems, despite their comprehensiveness, face substantial challenges in the “predictability desert” at subseasonal to seasonal (S2S) timescales, particularly for precipitation—a variable crucial for socioeconomic activities yet of stunning spatiotemporal variance. Post‐processing methods developed for
Wen Shi, Baoxiang Pan, Jianbin Huang, Tingfeng Dou, Jie Feng, Huihui Yuan, Anran Wang, Xin Xia, Lei Huang, Yong Luo   +9 more
wiley   +1 more source

Prediction of Radar Echo Space-Time Sequence Based on Improving TrajGRU Deep-Learning Model

Remote Sensing, 2022
Nowcasting of severe convective precipitation is of great importance in meteorological disaster prevention. Radar echo extrapolation is an effective method for short-term precipitation nowcasting. The traditional radar echo extrapolation methods lack the
Qiangyu Zeng, Haoran Li, Tao Zhang, Jianxin He, Fugui Zhang, Hao Wang, Zhipeng Qing, Qiu Yu, Bangyue Shen   +8 more
doaj   +1 more source

Self‐Supervised Radar Nowcasting of Typhoon Cloud Evolution

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 3, June 2026.
Abstract Self‐supervised learning (SSL) provides an innovative paradigm for pretraining without involving any new data or labels. However, its potential has not yet been evaluated in the field of typhoon raincloud nowcasting. Therefore, we explore two different SSL approaches: autoencoder (AE) and masked autoencoder (MAE).
Hongyi Yao, Ji Chen, Yi Lu, Xunlai Chen, Wai‐kin Wong, Chao Tan, Hongxu Zhu, Xianqi Jiang   +7 more
wiley   +1 more source

Advancing very short-term rainfall prediction with blended U-Net and partial differential approaches

Frontiers in Earth Science
Accurate and timely prediction of short-term rainfall is crucial for reducing the damages caused by heavy rainfall events. Therefore, various precipitation nowcasting models have been proposed.
Ji-Hoon Ha, Junsang Park
doaj   +1 more source

On the Role of Electron Precipitation in Excess Radiation Doses Measured at Aviation Altitudes

Space Weather, Volume 24, Issue 6, June 2026.
Abstract Radiation from space in the form of galactic cosmic rays (GCRs) generates a persistent background of ionizing radiation in Earth's atmosphere. The dose rate of ionizing radiation due to GCRs increases from sea level to aviation altitudes.
Julia Luna Claxton, Robert Marshall
wiley   +1 more source

EasyRain: A User-Friendly Platform for Comparing Precipitation Nowcasting Models

, 2019
Precipitation nowcasting, which predicts rainfall intensity in the near future, has been studied by meteorologists for decades. Currently, computer vision techniques, especially optical flow based methods, are widely adopted by observatories since they ...
Cheng, Ji, Da Yan, Wilfred Ng, Yan, Da, Hao, Xiaotian, Guimu Guo, Ng, Wilfred, Ji Cheng, Guo, Guimu, Xiaotian Hao   +9 more
core   +1 more source

Do Storm Characteristics Affect Urban Flood and Its Control? Assessment of Urban Drainage System Considering Rainfall Spatial Variability

Journal of Flood Risk Management, Volume 19, Issue 2, June 2026.
ABSTRACT The interaction between rainfall spatial–temporal variability and watershed response has been extensively studied in recent decades. Due to the influence of spatiotemporal non‐uniformity and variability in urban rainfall processes, the urban drainage system can exhibit different capabilities of handling flood risk.
Wenqi Wang, Zongzhi Wang, Lingling Ni, Ying Bai, Kun Wang, Wenhua Wan, Yuwei Tao   +6 more
wiley   +1 more source

Extreme precipitation nowcasting using deep generative model

, 2022
Extreme precipitation can often cause serious hazards such as flooding and landslide. Both pose a threat to human lives and lead to substantial economic loss.
Bi, Haoran (author)
core  

Fully differentiable Lagrangian convolutional neural network for physics-informed precipitation nowcasting

Applied Computing and Geosciences
This paper presents a convolutional neural network model for precipitation nowcasting that combines data-driven learning with physics-informed domain knowledge.
Peter Pavlík, Martin Výboh, Anna Bou Ezzeddine, Viera Rozinajová   +3 more
doaj   +1 more source

GRENet: GNSS‐Enhanced Radar Extrapolation Network for Precipitation Nowcasting

Geophysical Research Letters, Volume 53, Issue 10, 28 May 2026.
Abstract Accurate precipitation nowcasting is one of the most challenging tasks in atmospheric sciences. The current methods of nowcasting primarily rely on inferring precipitation from radar reflectivity, which inevitably leads to uncertainties in forecasts due to the limitations of single radar data in capturing the detailed initial conditions of ...
Cuixian Lu, Quanfei Wang, Yuxin Zheng, Xiaohong Zhang, Jiafeng Li, Xuanzhen Zhang, Galina Dick, Jonathan Jones   +7 more
wiley   +1 more source