Observation-Guided Meteorological Field Downscaling at Station Scale: A
  Benchmark and a New Method
Zili Liu Hao Chen Lei Bai Wenyuan Li Keyan Chen Zhengyi Wang Wanli Ouyang Zhengxia Zou Zhenwei Shi
Downscaling (DS) of meteorological variables involves obtaining
high-resolution states from low-resolution meteorological fields and is an
important task in weather forecasting. Previous methods based on deep learning
treat downscaling as a super-resolution task in computer vision and utilize
high-resolution gridded meteorological fields as supervision to improve
resolution at specific grid scales. However, this approach has struggled to
align with the continuous distribution characteristics of meteorological
fields, leading to an inherent systematic bias between the downscaled results
and the actual observations at meteorological stations. In this paper, we
extend meteorological downscaling to arbitrary scattered station scales,
establish a brand new benchmark and dataset, and retrieve meteorological states
at any given station location from a coarse-resolution meteorological field.
Inspired by data assimilation techniques, we integrate observational data into
the downscaling process, providing multi-scale observational priors. Building
on this foundation, we propose a new downscaling model based on hypernetwork
architecture, namely HyperDS, which efficiently integrates different
observational information into the model training, achieving continuous scale
modeling of the meteorological field. 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 to other methods.
We will release the dataset and code subsequently.