Deriving Accurate Surface Meteorological States at Arbitrary Locations via Observation-Guided Continuous Neural Field Modeling

Accurately retrieving surface meteorological states at arbitrary locations is of great application significance in weather forecasting and climate modeling. Since meteorological variables are typically provided as coarse-resolution gridded fields, common methods that obtain the states at a specific location directly through spatial interpolation can lead to significant accuracy deviations compared to actual observations. Traditional downscaling, the process of obtaining fixed-scale high-resolution meteorological fields from low-resolution inputs, has been proposed as a way to indirectly improve the accuracy of retrieving states at arbitrary locations by providing more detailed subgrid-scale information. However, for arbitrary locations at the station scale, their states are influenced by subgrid information, resulting in systematic biases between the downscaled results after interpolation and the actual observations at specific station locations. To address this issue, in this article, we propose a new task called station-scale downscaling, which aims to directly derive accurate meteorological states at any given station location from a coarse-resolution meteorological field. To achieve this, we propose a new downscaling model based on hypernetwork architecture, namely, HyperDS, which efficiently integrates the multiscale observational information to guide the continuous neural field modeling of the meteorological variables, enabling accurate sampling of the states at any target location. Through extensive experiments, our proposed method outperforms other specially designed baseline models on multiple surface variables. Notably, the mean squared error (mse) for wind speed and surface pressure improved by 67% and 19.5% compared with other methods, respectively.