Deep learning in environmental remote sensing: Achievements and challenges
Various forms of machine learning (ML) methods have historically played a valuable role in
environmental remote sensing research. With an increasing amount of “big data” from earth …
environmental remote sensing research. With an increasing amount of “big data” from earth …
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 …
[图书][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 …
Rainfall prediction using machine learning models: literature survey
Research on rainfall prediction contributes to different fields that have a huge impact on our
daily life. With the advancement of computer technology, machine learning has been …
daily life. With the advancement of computer technology, machine learning has been …
PFST-LSTM: A spatiotemporal LSTM model with pseudoflow prediction for precipitation nowcasting
Precipitation nowcasting is an important task, which can serve numerous applications such
as urban alert and transportation. Previous studies leverage convolutional recurrent neural …
as urban alert and transportation. Previous studies leverage convolutional recurrent neural …
Enhancing spatial variability representation of radar nowcasting with generative adversarial networks
Weather radar plays an important role in accurate weather monitoring and modern weather
forecasting, as it can provide timely and refined weather forecasts for the public and for …
forecasting, as it can provide timely and refined weather forecasts for the public and for …
Precipitation nowcasting with orographic enhanced stacked generalization: Improving deep learning predictions on extreme events
One of the most crucial applications of radar-based precipitation nowcasting systems is the
short-term forecast of extreme rainfall events such as flash floods and severe thunderstorms …
short-term forecast of extreme rainfall events such as flash floods and severe thunderstorms …
TempEE: Temporal-Spatial Parallel Transformer for Radar Echo Extrapolation Beyond Auto-Regression
Meteorological radar reflectivity data (ie, radar echo) significantly influences precipitation
prediction. It can facilitate accurate and expeditious forecasting of short-term heavy rainfall …
prediction. It can facilitate accurate and expeditious forecasting of short-term heavy rainfall …