Heat waves (HWs) can cause large socioeconomic and environmental impacts. The
observed increases in their frequency, intensity and duration are projected to continue with …

Forecast combinations have flourished remarkably in the forecasting community and, in
recent years, have become part of mainstream forecasting research and activities …

Most state-of-the-art approaches for weather and climate modeling are based on physics-
informed numerical models of the atmosphere. These approaches aim to model the non …

WeatherBench 2 is an update to the global, medium‐range (1–14 days) weather forecasting
benchmark proposed by (Rasp et al., 2020, https://doi. org/10.1029/2020ms002203) …

Over the past few years, the rapid development of machine learning (ML) models for
weather forecasting has led to state-of-the-art ML models that have superior performance …

Machine learning (ML) provides novel and powerful ways of accurately and efficiently
recognizing complex patterns, emulating nonlinear dynamics, and predicting the spatio …

The recent hype about artificial intelligence has sparked renewed interest in applying the
successful deep learning (DL) methods for image recognition, speech recognition, robotics …

Weather forecasting is a fundamental problem for anticipating and mitigating the impacts of
climate change. Recently, data-driven approaches for weather forecasting based on deep …

Data‐driven approaches, most prominently deep learning, have become powerful tools for
prediction in many domains. A natural question to ask is whether data‐driven methods could …

We present a significantly improved data‐driven global weather forecasting framework using
a deep convolutional neural network (CNN) to forecast several basic atmospheric variables …