This paper explores the evolution of geoscientific inquiry, tracing the progression from
traditional physics-based models to modern data-driven approaches facilitated by significant …

Climate science has been revolutionized by the combined effects of an exponential growth
in computing power, which has enabled more sophisticated and higher-resolution …

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) …

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 …

As artificial intelligence (AI) continues to rapidly evolve, the realm of Earth and atmospheric
sciences is increasingly adopting data-driven models, powered by progressive …

Deep learning foundation models are revolutionizing many facets of science by leveraging
vast amounts of data to learn general-purpose representations that can be adapted to tackle …

There has been huge recent interest in the potential of making operational weather forecasts
using machine learning techniques. As they become a part of the weather forecasting …

Machine learning and deep learning methods have been widely explored in understanding
the chaotic behavior of the atmosphere and furthering weather forecasting. There has been …

The generation of initial conditions via accurate data assimilation is crucial for reliable
weather forecasting and climate modeling. We propose the DiffDA as a machine learning …

The rapid advancement of artificial intelligence technologies, particularly in recent years,
has led to the emergence of several large parameter artificial intelligence weather forecast …