As our planet is entering into the “global boiling” era, understanding regional climate change
becomes imperative. Effective downscaling methods that provide localized insights are …

Weather forecasting, which is challenging due to the complex atmospheric correlation,
focuses on providing explicit meteorological estimations as accurate as possible. Recently …

High-resolution wind analysis plays an essential role in pollutant dispersion and renewable
energy utilization. This article focuses on spatial wind downscaling. Specifically, a novel …

Image-based quantified visibility estimation is an important task for both atmospheric
science and computer vision. Traditional methods rely largely on meteorological observation …

Radar reflectivity plays a crucial role in detecting heavy rainfall and is an important tool for
meteorological analysis. However, the coverage of a single radar is limited, leading to the …

Reconstructing fine‐grained, detailed spatial structures from time‐evolving coarse‐scale
geophysical fields has been a long‐standing challenge. Current deep learning approaches …

Climate data with high spatial and temporal resolution were of great significance for regional
environmental management, such as for early response to possible predicted local climate …

Statistical post-processing is a pivotal approach in enhancing the statistical accuracy and
applicability of precipitation forecasts from numerical weather prediction models. The …

In the field of numerical weather prediction, fine-grained precipitation fields play a crucial
role in forecasting and analyzing the spatial distribution and intensity of the precipitation …

High temperature is one of the most severe disasters in the world, which causes the death of
millions of people each year. Accurate temperature forecasting, as a key member of weather …