Climate variability and weather phenomena can cause extremes and pose significant risk to
society and ecosystems, making continued advances in our physical understanding of such …

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 …

Downscaling is a critical step to bridge the gap between large‐scale climate information and
local‐scale impact assessment. This study presents a novel deep learning approach: Super …

Over the past decade the use of machine learning in meteorology has grown rapidly.
Specifically neural networks and deep learning have been used at an unprecedented rate …

U-Net, a deep-learning convolutional neural network, is used to downscale coarse
meteorological data. Based on 19 models from the Coupled Model Intercomparison Project …

An accurate spatially continuous air temperature data set is crucial for multiple applications
in the environmental and ecological sciences. Existing spatial interpolation methods have …

As the largest cotton-growing region in China, Xinjiang has contributed more than 80% of
the total national cotton production in recent years. Timely and accurate estimation of cotton …

This paper describes the development of U-net++ models, a type of neural network that
performs deep learning, to emulate the shortwave Rapid Radiative Transfer Model (RRTM) …

Statistical downscaling (SD) derives localized information from larger-scale numerical
models. Convolutional neural networks (CNNs) have learning and generalization abilities …

Predicting the timing and location of thunderstorms (“convection”) allows for preventive
actions that can save both lives and property. We have applied U-nets, a deep-learning …