Satellite Observations Guided Diffusion Model for Accurate Meteorological States at Arbitrary Resolution

• URL: http://arxiv.org/abs/2502.07814v1
• Date: Sun, 09 Feb 2025 02:05:33 GMT
• Title: Satellite Observations Guided Diffusion Model for Accurate Meteorological States at Arbitrary Resolution
• Authors: Siwei Tu, Ben Fei, Weidong Yang, Fenghua Ling, Hao Chen, Zili Liu, Kun Chen, Hang Fan, Wanli Ouyang, Lei Bai,
• Abstract summary: We propose a conditional diffusion model pre-trained on ERA5 reanalysis data with satellite observations (GridSat) as conditions. During the training process, we propose to fuse the information from GridSat satellite observations into ERA5 maps via the attention mechanism. In the sampling, we employed optimizable convolutional kernels to simulate the upscale process.
• Score: 48.34051432429767
• License:
• Abstract: Accurate acquisition of surface meteorological conditions at arbitrary locations holds significant importance for weather forecasting and climate simulation. Due to the fact that meteorological states derived from satellite observations are often provided in the form of low-resolution grid fields, the direct application of spatial interpolation to obtain meteorological states for specific locations often results in significant discrepancies when compared to actual observations. Existing downscaling methods for acquiring meteorological state information at higher resolutions commonly overlook the correlation with satellite observations. To bridge the gap, we propose Satellite-observations Guided Diffusion Model (SGD), a conditional diffusion model pre-trained on ERA5 reanalysis data with satellite observations (GridSat) as conditions, which is employed for sampling downscaled meteorological states through a zero-shot guided sampling strategy and patch-based methods. During the training process, we propose to fuse the information from GridSat satellite observations into ERA5 maps via the attention mechanism, enabling SGD to generate atmospheric states that align more accurately with actual conditions. In the sampling, we employed optimizable convolutional kernels to simulate the upscale process, thereby generating high-resolution ERA5 maps using low-resolution ERA5 maps as well as observations from weather stations as guidance. Moreover, our devised patch-based method promotes SGD to generate meteorological states at arbitrary resolutions. Experiments demonstrate SGD fulfills accurate meteorological states downscaling to 6.25km.

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