arXiv：cs.LG（机器学习，全量分类）· Loys Masquelier, Etienne Le Naour·· 14 小时前AI 评分31
超越逐点误差：空间气候降尺度的多指标评估
Beyond Pointwise Error: A Multi-Metric Evaluation of Spatial Climate Downscaling
AI 导读
一项多指标基准在 ERA5 温度、风场和降水数据上对比了五种空间降尺度方法，采用逐点误差、结构相似性、分布误差、谱误差和梯度误差五项标准评估。结果显示空间保真度与细尺度变异性之间存在系统性权衡：部分方法在逐点和空间对齐指标上表现最佳却丢失高频内容，另一些方法保留更多谱变异性但局部结构定位不准。方法排名随指标和变量变化，不存在单一最优降尺度方法。
正文
Abstract:Climate downscaling aims to reconstruct fine scale spatial fields from coarse resolution inputs. Evaluating the quality of these reconstructions is challenging: low pointwise error can come at the cost of fine scale variability, while realistic spatial variability can be achieved with inaccurate local structures. The evaluation metric can therefore change which method appears to perform best. This work presents a multi metric benchmark comparing five spatial downscaling methods on ERA5 temperature, wind, and precipitation fields. Five criteria assess complementary properties: pointwise error, structural similarity, distribution error, spectral error, and gradient error. The results reveal a systematic trade off between spatial fidelity and fine scale variability. Some methods perform best on pointwise and spatially aligned metrics, but lose high frequency content, while others preserve substantially more spectral variability at the cost of less accurately positioned local structures. Consequently, method rankings change across metrics and variables. These results show that there is no single best downscaling method. Multi metric evaluation is therefore essential for assessing which properties of a climate field are preserved.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.01579 [cs.LG] |
| (or arXiv:2610.01579v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.01579 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Loys Masquelier [view email]
[v1]
Thu, 1 Oct 2026 12:35:39 UTC (1,287 KB)
来源：arXiv：cs.LG（机器学习，全量分类） · arxiv.org