简单插值法在气候数据重建中表现优于复杂模型。
Performance and Efficiency of Climate In-Situ Data Reconstruction: Why Optimized IDW Outperforms kriging and Implicit Neural Representation
- 用优化的反距离加权法重建稀疏气候数据
- 均方根误差低至3.00，决定系数达0.68
- 适合追求高效准确的气象研究者
本研究评估了三种稀疏气候数据重建方法：简单的反距离加权（IDW）、基于统计的普通克里金法（OK）以及先进的隐式神经表示模型（MMGN架构）。所有方法均通过验证集进行超参数调优。在来自ECA&D数据库的100个随机采样的稀疏数据集上进行了广泛实验与综合统计分析。结果表明，简单IDW方法在重建精度和计算效率上均优于其他参考方法。IDW取得最低均方根误差（RMSE: 3.00 ± 1.93）、平均绝对误差（MAE: 1.32 ± 0.77）和最大偏差（ΔMAX: 24.06 ± 17.15），同时获得最高决定系数（R²: 0.68 ± 0.16），各项指标差异具有统计显著性且效应量中等至较大。邓恩事后检验进一步确认了IDW在所有评价指标上的持续优势。
原文摘要 · Abstract (English)
This study evaluates three reconstruction methods for sparse climate data: the simple inverse distance weighting (IDW), the statistically grounded ordinary kriging (OK), and the advanced implicit neural representation model (MMGN architecture). All methods were optimized through hyper-parameter tuning using validation splits. An extensive set of experiments was conducted, followed by a comprehensive statistical analysis. The results demonstrate the superiority of the simple IDW method over the other reference methods in terms of both reconstruction accuracy and computational efficiency. IDW achieved the lowest RMSE ($3.00 \pm 1.93$), MAE ($1.32 \pm 0.77$), and $Δ_{MAX}$ ($24.06 \pm 17.15$), as well as the highest $R^2$ ($0.68 \pm 0.16$), across 100 randomly sampled sparse datasets from the ECA\&D database. Differences in RMSE, MAE, and $R^2$ were statistically significant and exhibited moderate to large effect sizes. The Dunn post-hoc test further confirmed the consistent superiority of IDW across all evaluated quality measures [...]
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