Modern deep learning approaches have shown promising results in meteorological
applications like precipitation nowcasting, synthetic radar generation, front detection and …

Development and Interpretation of a Neural-Network-Based Synthetic Radar Reflectivity
Estimator Using GOES-R Satellite Observations in: Journal of Applied Meteorology and …

Convective clouds serve as a primary mechanism for the transfer of thermal energy,
moisture, and momentum through the troposphere. Arguably, satellite observations are the …

Radar reflectivity (RR) greater than 35 dBZ usually indicates the presence of severe
convective weather, which affects a variety of human activities, including aviation. However …

Drought forecasting is very important in reducing the drought damage and optimizing water
resources. This paper focuses on confirming the advantage of wavelet long short-term …

Radar reflectivity data snapshot fine-grained atmospheric variations that cannot be
represented well by numerical weather prediction models or satellites, which poses a limit …

Radar Super Resolution Using a Deep Convolutional Neural Network in: Journal of Atmospheric
and Oceanic Technology Volume 37 Issue 12 (2020) Jump to Content Logo Logo Logo Logo …

Meteorological radar data are essential for meteorological monitoring, forecasting, and
research, and it plays a crucial role in observing and warning of extreme weather risks …

A conventional way to monitor severe convective weather is using the composite reflectivity
of radar as an indicator. For oceanic areas without radar deployment, reconstruction from …

Artificial intelligence (AI) can be used to improve performance across a wide range of Earth
system prediction tasks. As with any application of AI, it is important for AI to be developed in …