Recent years have witnessed considerable developments in multiple fields with the potential
to enhance our capability of forecasting pluvial flash floods, one of the most costly …

Applications of weather radar data to complex orography are manifold, as are the problems.
The difficulties start with the choice of suitable locations for the radar sites and their …

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 …

Radar and satellite joint observation data can provide a more efficient way to study
landfalling typhoon precipitation, but rarely does this combination of circumstances occur. In …

Weather radars are commonly used to track the development of convective storms due to
their high resolution and accuracy. However, the coverage of existing weather radar is very …

Abstract The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement
(IMERG) produces high-resolution precipitation estimates and serves as an invaluable data …

Forecasting heavy precipitation accurately is a challenging task for most deep learning (DL)-
based models. To address this, we present a novel DL architecture called “multi-scale …

Merging radar and rain gauge data is an important means of obtaining precipitation products
with high accuracy and high spatial–temporal resolution. However, due to the change of the …

Weather radars commonly suffer from the data-missing problem that limits their data quality
and applications. Traditional methods for the completion of weather radar missing data …

Weather radar serves as a crucial tool for monitoring aeroecology by enabling the
observation of migrating birds and insects. Although dual-polarization weather radar offers …