WeatherBench 2: A benchmark for the next generation of data‐driven global weather models
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) …
benchmark proposed by (Rasp et al., 2020, https://doi. org/10.1029/2020ms002203) …
Foundation models for weather and climate data understanding: A comprehensive survey
As artificial intelligence (AI) continues to rapidly evolve, the realm of Earth and atmospheric
sciences is increasingly adopting data-driven models, powered by progressive …
sciences is increasingly adopting data-driven models, powered by progressive …
[HTML][HTML] The EUPPBench postprocessing benchmark dataset v1. 0
Statistical postprocessing of medium-range weather forecasts is an important component of
modern forecasting systems. Since the beginning of modern data science, numerous new …
modern forecasting systems. Since the beginning of modern data science, numerous new …
Digital typhoon: Long-term satellite image dataset for the spatio-temporal modeling of tropical cyclones
This paper presents the official release of the Digital Typhoon dataset, the longest typhoon
satellite image dataset for 40+ years aimed at benchmarking machine learning models for …
satellite image dataset for 40+ years aimed at benchmarking machine learning models for …
[HTML][HTML] Improving medium-range ensemble weather forecasts with hierarchical ensemble transformers
Statistical postprocessing of global ensemble weather forecasts is revisited by leveraging
recent developments in machine learning. Verification of past forecasts is exploited to learn …
recent developments in machine learning. Verification of past forecasts is exploited to learn …
Precipitation nowcasting with generative diffusion models
In recent years traditional numerical methods for accurate weather prediction have been
increasingly challenged by deep learning methods. Numerous historical datasets used for …
increasingly challenged by deep learning methods. Numerous historical datasets used for …
Histgnn: Hierarchical spatio-temporal graph neural network for weather forecasting
Weather forecasting is an attractive yet challenging task due to its significant impacts on
human life and the intricate nature of atmospheric motion. Deep learning-based techniques …
human life and the intricate nature of atmospheric motion. Deep learning-based techniques …
OCEANBENCH: the sea surface height edition
The ocean is a crucial component of the Earth's system. It profoundly influences human
activities and plays a critical role in climate regulation. Our understanding has significantly …
activities and plays a critical role in climate regulation. Our understanding has significantly …
Spatial mixture-of-experts
Many data have an underlying dependence on spatial location; it may be weather on the
Earth, a simulation on a mesh, or a registered image. Yet this feature is rarely taken …
Earth, a simulation on a mesh, or a registered image. Yet this feature is rarely taken …
Diffda: a diffusion model for weather-scale data assimilation
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 …
weather forecasting and climate modeling. We propose the DiffDA as a machine learning …