1 citations · 1 across the 12 of their papers we have counts for
12 papers
UniGIO: Unified Generative Global In-situ Weather Modeling from Spatiotemporal Incomplete Observations
Songru Yang, Zili Liu, Tao Han +7
Global In-situ Observation (GIO) provides fine-scale, direct records of the global weather system from sparse point stations, making it an indispensable source for capturing locali…
TSSM: Triaxial State Space Model for Global Station Weather Forecasting with Temporal-Variable-Historical Modeling
Songru Yang, Zili Liu, Tao Han +7
Global Station Weather Forecasting (GSWF) is pivotal for localized and extreme weather prediction over key regions. Despite efforts to exploit look-back windows, existing methods s…
STCast: Adaptive Boundary Alignment for Global and Regional Weather Forecasting
Hao Chen, Tao Han, Jie Zhang +2
To gain finer regional forecasts, many works have explored the regional integration from the global atmosphere, e.g., by solving boundary equations in physics-based methods or crop…
Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction
Sijie Zhao, Feng Liu, Enzhuo Zhang +5
The proliferation of multi-source remote sensing data has propelled the development of deep learning for dense prediction, yet significant challenges in data and task unification p…
Satellite Observations Guided Diffusion Model for Accurate Meteorological States at Arbitrary Resolution
Siwei Tu, Ben Fei, Weidong Yang +7
Accurate acquisition of surface meteorological conditions at arbitrary locations holds significant importance for weather forecasting and climate simulation. Due to the fact that m…
Kolmogorov Arnold Neural Interpolator for Downscaling and Correcting Meteorological Fields from In-Situ Observations
Zili Liu, Hao Chen, Lei Bai +3
Obtaining accurate weather forecasts at station locations is a critical challenge due to systematic biases arising from the mismatch between multi-scale, continuous atmospheric cha…
LWFNet: Coherent Doppler Wind Lidar-Based Network for Wind Field Retrieval
Ran Tao, Chong Wang, Hao Chen +9
Accurate detection of wind fields within the troposphere is essential for atmospheric dynamics research and plays a crucial role in extreme weather forecasting. Coherent Doppler wi…
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…
MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modeling
Zili Liu, Hao Chen, Lei Bai +4
In an era of frequent extreme weather and global warming, obtaining precise, fine-grained near-surface weather forecasts is increasingly essential for human activities. Downscaling…
Fast Information Streaming Handler (FisH): A Unified Seismic Neural Network for Single Station Real-Time Earthquake Early Warning
Tianning Zhang, Feng Liu, Yuming Yuan +3
Existing EEW approaches often treat phase picking, location estimation, and magnitude estimation as separate tasks, lacking a unified framework. Additionally, most deep learning mo…
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…