Nowadays, meteorological data plays a crucial role in various fields such as remote sensing,
weather forecasting, climate change, and agriculture. The regional and local studies call for …

Despite continuous improvements, precipitation forecasts are still not as accurate and
reliable as those of other meteorological variables. A major contributing factor to this is that …

In the last 40 years, remote sensing technology has evolved, significantly advancing ocean
observation and catapulting its data into the big data era. How to efficiently and accurately …

Effective selection of climate predictors is a fundamental aspect of climate modeling
research. Predictor Screening (PS) plays a crucial role in identifying regional climate drivers …

Deep Learning (DL) based downscaling has recently become a popular tool in earth
sciences. Multiple DL methods are routinely used to downscale coarse-scale precipitation …

Flooding, driven in part by intense rainfall, is the leading cause of mortality and damages
from the most intense tropical cyclones (TCs). With rainfall from TCs set to increase under …

Systematic biases and coarse resolutions are major limitations of current precipitation
datasets. Many deep learning (DL) based studies have been conducted for precipitation …

Downscaling methods are critical in efficiently generating high‐resolution atmospheric data.
However, state‐of‐the‐art statistical or dynamical downscaling techniques either suffer from …

This paper explores the application of emerging machine learning methods from image
super resolution (SR) to the task of statistical downscaling. We specifically focus on …

The prediction of surface ozone is essential attributing to its impact on human and
environmental health. Volatile organic compounds (VOCs) are crucial in driving ozone …