[HTML][HTML] The evolving role of humans in weather prediction and communication
NA Stuart, G Hartfield, DM Schultz…�- Bulletin of the�…, 2022 - journals.ametsoc.org
The Evolving Role of Humans in Weather Prediction and Communication in: Bulletin of the
American Meteorological Society Volume 103 Issue 8 (2022) Jump to Content Logo Logo Logo�…
American Meteorological Society Volume 103 Issue 8 (2022) Jump to Content Logo Logo Logo�…
A review of radar-based nowcasting of precipitation and applicable machine learning techniques
A'nowcast'is a type of weather forecast which makes predictions in the very short term,
typically less than two hours-a period in which traditional numerical weather prediction can�…
typically less than two hours-a period in which traditional numerical weather prediction can�…
Deep learning and the weather forecasting problem: Precipitation nowcasting
Precipitation nowcasting refers to the prediction of rainfall with high spatiotemporal
resolutions in a timely and accurate manner for the next 6 hours. The skillful and high�…
resolutions in a timely and accurate manner for the next 6 hours. The skillful and high�…
[HTML][HTML] 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�…
Deep-learning-based precipitation nowcasting with ground weather station data and radar data
Recently, many deep-learning techniques have been applied to various weather-related
prediction tasks, including precipitation nowcasting (ie, predicting precipitation levels and�…
prediction tasks, including precipitation nowcasting (ie, predicting precipitation levels and�…
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�…
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�…
[PDF][PDF] Deep learning framework for precipitation retrievals from communication satellites
It is well known that estimation of rainfall, while taking into account its spatio-temporal
variability, is essential for several applications in earth sciences (Krajewski and Smith, 2002;�…
variability, is essential for several applications in earth sciences (Krajewski and Smith, 2002;�…
Nowcasting-Nets: Deep neural network structures for precipitation nowcasting using IMERG
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�…
[HTML][HTML] RAIN-F+: the data-driven precipitation prediction model for integrated weather observations
Quantitative precipitation prediction is essential for managing water-related disasters,
including floods, landslides, tsunamis, and droughts. Recent advances in data-driven�…
including floods, landslides, tsunamis, and droughts. Recent advances in data-driven�…