The digital revolution of Earth-system science
Computational science is crucial for delivering reliable weather and climate predictions.
However, despite decades of high-performance computing experience, there is serious …
However, despite decades of high-performance computing experience, there is serious …
Opportunities and challenges for machine learning in weather and climate modelling: hard, medium and soft AI
In September 2019, a workshop was held to highlight the growing area of applying machine
learning techniques to improve weather and climate prediction. In this introductory piece, we …
learning techniques to improve weather and climate prediction. In this introductory piece, we …
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 …
[HTML][HTML] Deep learning for twelve hour precipitation forecasts
Existing weather forecasting models are based on physics and use supercomputers to
evolve the atmosphere into the future. Better physics-based forecasts require improved …
evolve the atmosphere into the future. Better physics-based forecasts require improved …
Climatelearn: Benchmarking machine learning for weather and climate modeling
Modeling weather and climate is an essential endeavor to understand the near-and long-
term impacts of climate change, as well as to inform technology and policymaking for …
term impacts of climate change, as well as to inform technology and policymaking for …
Short-term rainfall forecasting using cumulative precipitation fields from station data: a probabilistic machine learning approach
Rainfall nowcasting supports emergency decision-making in hydrological, agricultural, and
economical sectors. However, short-term prediction is challenging because meteorological …
economical sectors. However, short-term prediction is challenging because meteorological …
A spatiotemporal deep learning model ST-LSTM-SA for hourly rainfall forecasting using radar echo images
Accurate and timely short-term forecasting services of precipitation variable are significant
for people's lives and property security. The data-driven approaches demonstrate promising …
for people's lives and property security. The data-driven approaches demonstrate promising …
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 …
TSRC: a deep learning model for precipitation short-term forecasting over China using radar echo data
Currently, most deep learning (DL)-based models for precipitation forecasting face two
conspicuous issues: the smoothing effect in the precipitation field and the degenerate effect …
conspicuous issues: the smoothing effect in the precipitation field and the degenerate effect …
Development and application of a real-time flood forecasting system (RTFlood System) in a tropical urban area: A case study of Ramkhamhaeng Polder, Bangkok …
In urban areas of Thailand, and especially in Bangkok, recent flash floods have caused
severe damage and prompted a renewed focus to manage their impacts. The development …
severe damage and prompted a renewed focus to manage their impacts. The development …