AI 中文总结
本研究将带导向核的超分辨率高斯过程回归（SRGP-SK）用于雷达/雨量计分析降水，通过SSIM和PSD评估，发现其在重建精细降水结构上优于双三次插值，还比较了核函数，首次证明SRGP-SK在气象学中的有用性，向有物理可解释性的超分辨率迈进。
AI 中文摘要
超分辨率是从低分辨率图像估计高分辨率图像，已用于气象观测的降尺度和分辨率增强。带导向核的超分辨率高斯过程回归（SRGP-SK）能生成比普通超分辨率高斯过程回归更准确的高分辨率图像，但尚未应用于气象学。本研究将SRGP-SK应用于对流和层状降水案例的雷达/雨量计分析降水，并使用结构相似性指数（SSIM）和径向平均功率谱密度（PSD）评估结果。SRGP-SK在重建更精细降水结构时达到了与双三次插值相当的SSIM：它能重建至6公里波长的变化，而双三次插值只能重建至8公里。还比较了几种核函数，发现SSIM最优的核与几何平均PSD比最优的核不同，反映了两种度量量化的不同属性。这是首次证明SRGP-SK在气象学中的有用性的研究，朝着具有物理可解释性的超分辨率迈出了一步。
英文摘要
Super-resolution (SR), a technique for estimating a high-resolution (HR) image from a low-resolution image, has been used in meteorology for downscaling and resolution enhancement of observations. SR Gaussian process regression with a steering kernel (SRGP-SK) generates more accurate HR images than the original SR Gaussian process regression (SRGP), but it has not yet been applied to meteorological data. This study applied SRGP-SK to radar/rain gauge-analyzed precipitation for convective and stratiform cases and evaluated the results using the structural similarity index (SSIM) and radially averaged power spectral density (PSD). SRGP-SK achieved SSIM values comparable to those of bicubic interpolation and higher than those of SRGP while reconstructing finer precipitation structures; it recovered variations down to a wavelength of 6 km, compared with 8 km for bicubic interpolation. This difference may correspond to an approximately threefold increase in the number of convective cells. Among the kernel functions compared, the Matern 5/2 kernel yielded the highest geometric mean PSD ratio. Further investigation of this point could identify the statistical scaling characteristics of the precipitation field. This study evaluates only two precipitation cases; examining more cases is necessary before SRGP-SK can be applied more broadly.
