Extreme Precipitation Nowcasting using Transformer-based Generative models [PDF]
Extreme precipitation, like floods and landslides, poses major risks to safety and the economy, underscoring the need for sophisticated weather forecasting to predict these events accurately, enhancing readiness and resilience.
Roy, Ankush (author)
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PostCast: Generalizable Postprocessing for Precipitation Nowcasting via Unsupervised Blurriness Modeling [PDF]
Precipitation nowcasting plays a pivotal role in socioeconomic sectors, especially in severe convective weather warnings. Although notable progress has been achieved by approaches mining the spatiotemporal correlations with deep learning, these methods ...
Bai, Lei +8 more
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Self-supervised Spatial-Temporal Learner for Precipitation Nowcasting [PDF]
Nowcasting, the short-term prediction of weather, is essential for making timely and weather-dependent decisions. Specifically, precipitation nowcasting aims to predict precipitation at a local level within a 6-hour time frame. This task can be framed as
Li, Haotian +2 more
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Short-term precipitation nowcasting for composite radar rainfall fields [PDF]
, 2003 Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2003.Includes bibliographical references (p. 75-80).This electronic version was submitted by the student author. The certified thesis is available in the
Van Horne, Matthew P. (Matthew Philip), 1980-
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Precipitation Nowcasting Using Diffusion Transformer With Causal Attention
IEEE Transactions on Geoscience and Remote SensingShort-term precipitation forecasting remains challenging due to the difficulty in capturing long-term spatiotemporal dependencies. Current deep learning methods fall short in establishing effective dependencies between conditions and forecast results, while also lacking interpretability.
Chaorong Li +7 more
openaire +2 more sources
Precipitation Analysis from AMSU (Nowcasting SAF) [PDF]
, 1999 We describe a method to remotely sense precipitation and classify its intensity over water, coast, and land surfaces. This method is intended to be used in a nowcasting environment.
Bennartz, Ralf +3 more
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Skillful Precipitation Nowcasting Using Physical‐Driven Diffusion Networks [PDF]
Accurate and timely precipitation nowcasting is essential for numerous applications including emergency services, infrastructure management, and agriculture. Recently, deep learning (DL) techniques have shown promise in enhancing nowcasting capabilities.
Alexis K. H. Lau +5 more
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TAASRAD19, a high-resolution weather radar reflectivity dataset for precipitation nowcasting. [PDF]
Sci Data, 2020 Franch G +5 more
europepmc +1 more source
Global Precipitation Nowcasting of Integrated Multi-satellitE Retrievals for GPM: A U-Net Convolutional LSTM Architecture [PDF]
This paper presents a deep learning architecture for nowcasting of precipitation almost globally every 30 min with a 4-hour lead time. The architecture fuses a U-Net and a convolutional long short-term memory (LSTM) neural network and is trained using ...
Ebtehaj, Ardeshir +5 more
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Multi-Source Temporal Attention Network for Precipitation Nowcasting [PDF]
Precipitation nowcasting is crucial across various industries and plays a significant role in mitigating and adapting to climate change. We introduce an efficient deep learning model for precipitation nowcasting, capable of predicting rainfall up to 8 ...
Sjørup, Jeppe Liborius +5 more
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