Results 11 to 20 of about 1,500 (207)

Precipitation Nowcasting Based on Deep Learning over Guizhou, China [PDF]

Atmosphere, 2023
Accurate precipitation nowcasting (lead time: 0–2 h), which requires high spatiotemporal resolution data, is of great relevance in many weather-dependent social and operational activities.
Yuntao Tian, Xiefei Zhi, Dexuan Kong
exaly   +4 more sources

Skilful Precipitation Nowcasting Using NowcastNet [PDF]

CoRR, 2023
Designing early warning system for precipitation requires accurate short-term forecasting system. Climate change has led to an increase in frequency of extreme weather events, and hence such systems can prevent disasters and loss of life. Managing such events remain a challenge for both public and private institutions. Precipitation nowcasting can help
Kumar, Ajitabh
openaire   +3 more sources

Reliable precipitation nowcasting using probabilistic diffusion models [PDF]

Environmental Research Letters
Precipitation nowcasting is a crucial element in current weather service systems. Data-driven methods have proven highly advantageous, due to their flexibility in utilizing detailed initial hydrometeor observations, and their capability to approximate ...
Congyi Nai, Baoxiang Pan, Xi Chen, Qiuhong Tang, Guangheng Ni, Qingyun Duan, Bo Lu, Ziniu Xiao, Xingcai Liu   +8 more
doaj   +2 more sources

Joint Intensity and Spatio-Temporal Representation Learning for Extreme Precipitation Nowcasting [PDF]

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
As a result of global warming, the intensity and frequency of extreme precipitation events have increased, posing significant threats to human life and property. Currently, precipitation nowcasting methods based on long short-term memory (LSTM) or Vision
Zefeng Pan, Renlong Hang, Qingshan Liu, Chunxiang Shi, Zhiqiang Xu, Xiao-Tong Yuan   +5 more
doaj   +2 more sources

Mutual Information Boosted Precipitation Nowcasting from Radar Images [PDF]

Remote Sensing, 2023
Precipitation nowcasting has long been a challenging problem in meteorology. While recent studies have introduced deep neural networks into this area and achieved promising results, these models still struggle with the rapid evolution of rainfall and ...
Yuan Cao, Danchen Zhang, Xin Zheng, Hongming Shan, Junping Zhang   +4 more
doaj   +2 more sources

Physical‐Dynamic‐Driven AI‐Synthetic Precipitation Nowcasting Using Task‐Segmented Generative Model [PDF]

Geophysical Research Letters, 2023
Precise and timely rainfall nowcasting plays a critical role in ensuring public safety amid disasters triggered by heavy precipitation. While deep‐learning models have exhibited superior performance over traditional nowcasting methods in recent years ...
Rui Wang, Jimmy C. H. Fung, Alexis K. H. Lau   +2 more
doaj   +2 more sources

Two-Stage UA-GAN for Precipitation Nowcasting

Remote Sensing, 2022
Short-term rainfall prediction by radar echo map extrapolation has been a very hot area of research in recent years, which is also an area worth studying owing to its importance for precipitation disaster prevention.
Liujia Xu, Dan Niu, Tianbao Zhang, Pengju Chen, Xunlai Chen, Yinghao Li   +5 more
doaj   +3 more sources

A Convolutional Neural Network‐Based Model for Precipitation Nowcasting Leveraging Data From Gauge Stations

Journal of Geophysical Research: Machine Learning and Computation
Rainfall nowcasting, the short‐term prediction of precipitation, is a vital component of early warning systems aimed at mitigating the effects of extreme weather events.
Fereshteh Taromideh, Giovanni Francesco Santonastaso, Mehdi Masoodi, Andrea Cominola, Roberto Greco   +4 more
doaj   +2 more sources

Skilful nowcasting of extreme precipitation with NowcastNet. [PDF]

Nature, 2023
AbstractExtreme precipitation is a considerable contributor to meteorological disasters and there is a great need to mitigate its socioeconomic effects through skilful nowcasting that has high resolution, long lead times and local details1–3. Current methods are subject to blur, dissipation, intensity or location errors, with physics-based numerical ...
Zhang Y, Long M, Chen K, Xing L, Jin R, Jordan MI, Wang J.   +6 more
europepmc   +3 more sources

Enhancing Rainfall Nowcasting Using Generative Deep Learning Model with Multi-Temporal Optical Flow

Remote Sensing, 2023
Precipitation nowcasting is critical for preventing damage to human life and the economy. Radar echo tracking methods such as optical flow algorithms have been widely employed for precipitation nowcasting because they can track precipitation motions well.
Ji-Hoon Ha, Hyesook Lee
doaj   +1 more source