We propose the use of physics-informed neural networks for solving the shallow-water
equations on the sphere in the meteorological context. Physics-informed neural networks …

Global climate models (GCMs) are commonly downscaled to understand future local climate
change. The high computational cost of regional climate models (RCMs) limits how many …

We use a conditional deep convolutional generative adversarial network to predict the
geopotential height of the 500 hPa pressure level, the two-meter temperature and the total …

Many countries are suffering from water resource constraints due to rising demands from
different water-consuming sectors and a changing climate. In some countries, such as …

Temperature changes have widespread impacts on the environment, economy, and
municipal planning. Generating accurate climate prediction at finer spatial resolution …

We compare three machine learning models—artificial neural network (ANN), random forest
(RF), and convolutional neural network (CNN)—for spatial downscaling of temperature at 2 …

High-resolution gridded datasets of meteorological variables are needed in order to resolve
fine-scale hydrological gradients in complex mountainous terrain. Across the United States …

In an effort to provide optimal inputs to downstream modeling systems (eg, a hydrodynamics
model that simulates the water circulation of a lake), we hereby strive to enhance resolution …

Sea surface temperature (SST) can be measured from space using infrared sensors on
Earth-observing satellites. However, the tradeoff between spatial resolution and swath size …

One of the most important open challenges in climate science is downscaling. It is a
procedure that allows making predictions at local scales, starting from climatic field …