论文标题
研究两种超分辨率降水方法：Esrgan和CAR
Investigating two super-resolution methods for downscaling precipitation: ESRGAN and CAR
论文作者
论文摘要
为了为下游建模系统提供最佳的输入（例如，模拟湖泊的水循环的流体动力学模型），我们特此努力将从天气模型的降水场分辨率提高到9倍。我们测试了两个超分辨率模型：2017年提出的增强的超分辨率生成对抗网络（ESRGAN），以及2020年提出的内容自适应重新采样器（CAR）。这两种模型都超过了ESRGAN的简单简单的Bicubic Interpolation，而Esrgan对准确性的预期超出了预期。我们提出了一些扩展工作的建议，以确保它可以成为量化气候变化对当地生态系统的影响的有用工具，同时消除了对能源密集型，高分辨率高分辨率的天气模型仿真的依赖。
In an effort to provide optimal inputs to downstream modeling systems (e.g., a hydrodynamics model that simulates the water circulation of a lake), we hereby strive to enhance resolution of precipitation fields from a weather model by up to 9x. We test two super-resolution models: the enhanced super-resolution generative adversarial networks (ESRGAN) proposed in 2017, and the content adaptive resampler (CAR) proposed in 2020. Both models outperform simple bicubic interpolation, with the ESRGAN exceeding expectations for accuracy. We make several proposals for extending the work to ensure it can be a useful tool for quantifying the impact of climate change on local ecosystems while removing reliance on energy-intensive, high-resolution weather model simulations.
