Accurate medium-range global weather forecasting with 3D neural networks
Weather forecasting is important for science and society. At present, the most accurate
forecast system is the numerical weather prediction (NWP) method, which represents …
forecast system is the numerical weather prediction (NWP) method, which represents …
Skilful precipitation nowcasting using deep generative models of radar
Precipitation nowcasting, the high-resolution forecasting of precipitation up to two hours
ahead, supports the real-world socioeconomic needs of many sectors reliant on weather …
ahead, supports the real-world socioeconomic needs of many sectors reliant on weather …
Pangu-weather: A 3d high-resolution model for fast and accurate global weather forecast
In this paper, we present Pangu-Weather, a deep learning based system for fast and
accurate global weather forecast. For this purpose, we establish a data-driven environment …
accurate global weather forecast. For this purpose, we establish a data-driven environment …
Metnet: A neural weather model for precipitation forecasting
Weather forecasting is a long standing scientific challenge with direct social and economic
impact. The task is suitable for deep neural networks due to vast amounts of continuously …
impact. The task is suitable for deep neural networks due to vast amounts of continuously …
Machine learning for precipitation nowcasting from radar images
S Agrawal, L Barrington, C Bromberg, J Burge… - arxiv preprint arxiv …, 2019 - arxiv.org
High-resolution nowcasting is an essential tool needed for effective adaptation to climate
change, particularly for extreme weather. As Deep Learning (DL) techniques have shown …
change, particularly for extreme weather. As Deep Learning (DL) techniques have shown …
A deep learning method for bias correction of ECMWF 24–240 h forecasts
L Han, M Chen, K Chen, H Chen, Y Zhang, B Lu… - … in Atmospheric Sciences, 2021 - Springer
Correcting the forecast bias of numerical weather prediction models is important for severe
weather warnings. The refined grid forecast requires direct correction on gridded forecast …
weather warnings. The refined grid forecast requires direct correction on gridded forecast …
[BOOK][B] Deep learning for the Earth Sciences: A comprehensive approach to remote sensing, climate science and geosciences
DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep
learning in the field of earth sciences, from four leading voices Deep learning is a …
learning in the field of earth sciences, from four leading voices Deep learning is a …
[HTML][HTML] Broad-UNet: Multi-scale feature learning for nowcasting tasks
JG Fernández, S Mehrkanoon - Neural Networks, 2021 - Elsevier
Weather nowcasting consists of predicting meteorological components in the short term at
high spatial resolutions. Due to its influence in many human activities, accurate nowcasting …
high spatial resolutions. Due to its influence in many human activities, accurate nowcasting …
NowCasting-Nets: Representation learning to mitigate latency gap of satellite precipitation products using convolutional and recurrent neural networks
Accurate and timely estimation of precipitation is critical for issuing hazard warnings (eg, for
flash floods or landslides). Current remotely sensed precipitation products have a few hours …
flash floods or landslides). Current remotely sensed precipitation products have a few hours …
Spatiotemporal inference network for precipitation nowcasting with multimodal fusion
Q **, X Zhang, X **ao, Y Wang, G Meng… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
Precipitation plays a significant role in global water and energy cycles, largely affecting
many aspects of human life, such as transportation and agriculture. Recently, meteorologists …
many aspects of human life, such as transportation and agriculture. Recently, meteorologists …