10 citations · 28 across the 15 of their papers we have counts for
7 papers · 1 filter
WeatherGFM: Learning A Weather Generalist Foundation Model via In-context Learning
Xiangyu Zhao, Zhiwang Zhou, Wenlong Zhang +9
The Earth's weather system encompasses intricate weather data modalities and diverse weather understanding tasks, which hold significant value to human life. Existing data-driven m…
DiffSR: Learning Radar Reflectivity Synthesis via Diffusion Model from Satellite Observations
Xuming He, Zhiwang Zhou, Wenlong Zhang +4
Weather radar data synthesis can fill in data for areas where ground observations are missing. Existing methods often employ reconstruction-based approaches with MSE loss to recons…
VegeDiff: Latent Diffusion Model for Geospatial Vegetation Forecasting
Sijie Zhao, Hao Chen, Xueliang Zhang +3
In the context of global climate change and frequent extreme weather events, forecasting future geospatial vegetation states under these conditions is of significant importance. Th…
FNP: Fourier Neural Processes for Arbitrary-Resolution Data Assimilation
Kun Chen, Tao Chen, Peng Ye +5
Data assimilation is a vital component in modern global medium-range weather forecasting systems to obtain the best estimation of the atmospheric state by combining the short-term…
Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling
Wanghan Xu, Fenghua Ling, Wenlong Zhang +4
Data-driven artificial intelligence (AI) models have made significant advancements in weather forecasting, particularly in medium-range and nowcasting. However, most data-driven we…
RS-Mamba for Large Remote Sensing Image Dense Prediction
Sijie Zhao, Hao Chen, Xueliang Zhang +3
Context modeling is critical for remote sensing image dense prediction tasks. Nowadays, the growing size of very-high-resolution (VHR) remote sensing images poses challenges in eff…
ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast
Wanghan Xu, Kang Chen, Tao Han +3
Data-driven weather forecast based on machine learning (ML) has experienced rapid development and demonstrated superior performance in the global medium-range forecast compared to…