Results 111 to 120 of about 1,490 (205)

A Systematic Modular Approach for the Coupling of Deep-Learning-Based Models to Forecast Urban Flooding Maps in Early Warning Systems

Hydrology
Deep learning (DL) approaches to forecast precipitation and inundation areas in the short-term forecast horizon have up until now been treated as independent research problems from the model development perspective. However, for the urban hydrology area,
Juliana Koltermann da Silva, Benjamin Burrichter, Andre Niemann, Markus Quirmbach   +3 more
doaj   +1 more source

Rectifying Distribution Shift in Cascaded Precipitation Nowcasting

CoRR
Precipitation nowcasting, which aims to provide high spatio-temporal resolution precipitation forecasts by leveraging current radar observations, is a core task in regional weather forecasting. Recently, the cascaded architecture has emerged as the mainstream paradigm for deep learning-based precipitation nowcasting.
Fanbo Ju, Haiyuan Shi, Qingjian Ni
openaire   +2 more sources

All convolutional neural networks for radar-based precipitation nowcasting

, 2018
Today deep learning is taking its rise in hydrometeorological applications, and it is critical to extensively evaluate its prediction performance and robustness.
Lukyanova, Olga, Nikitin, Oleg, Heistermann, Maik, Ayzel, Georgy, Sorokin, Alexey   +4 more
core   +1 more source

Precipitation Nowcasting using a Generative Adversarial Network

Nowcasting high-intensity precipitation is crucial for emergency services and municipalities when making weather-dependent decisions. This research implements and trains a deep generative model for nowcasting using a cleaned precipitation radar composite
van Os, Sven (author)
core  

Precipitation retrieval and nowcasting from satellite within project “NOWCASTING-PROSA”of the Italian Space Agency

, 2010
Three years ago, the Italian SpaceAgency (ASI), in agreement with the Italian Department for Civil Protection (DPC), funded a project finalized to the nowcasting of the intense storms that produce floods over the Italian peninsula. A goal of this project
S. Dietrich, M. L. Tampellini, D. Casella, F. S. Marzano, S. Laviola, F. Di Paola, PORCU', Federico, A. Mugnai, M. Formenton, F. Prodi, G. P. Marra, D. Capacci, M. De Rosa, P. Sanò, M. Vimercati, MILANI, Lisa, E. Cattani, V. Levizzani   +17 more
core  

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-
core  

Precipitation Nowcasting Using Diffusion Transformer With Causal Attention

IEEE Transactions on Geoscience and Remote Sensing
Short-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, Xudong Ling, Yilan Xue, Wenjie Luo, Lihong Zhu, Fengqing Qin, Yaodong Zhou, Yuanyuan Huang 0007   +7 more
openaire   +2 more sources

CasCast: Skillful High-resolution Precipitation Nowcasting via Cascaded Modelling

Precipitation nowcasting based on radar data plays a crucial role in extreme weather prediction and has broad implications for disaster management. Despite progresses have been made based on deep learning, two key challenges of precipitation nowcasting ...
Bai, Lei, Ye, Peng, Yang, Xiaokang, Liu, Na, Ouyang, Wanli, Xu, Wanghan, Gong, Junchao, Dai, Jianhua   +7 more
core  

Explorations into Machine Learning Techniques for Precipitation Nowcasting

, 2017
Recent advances in cloud-based big-data technologies now makes data driven solutions feasible for increasing numbers of scientific computing applications.
Nagarajan, Aditya
core   +1 more source

Precipitation estimation and nowcasting at IMGW-PIB (SEiNO system)

, 2018
A System for the Estimation and Nowcasting of Precipitation (SEiNO) is being developed at the Institute of Meteorology and Water Management – National Research Institute.
Giszterowicz, M., Ośródka, K., Jurczyk, A., Wyszogrodzki, A., Szturc, J.   +4 more
core   +1 more source