Performance and Efficiency of Climate In-Situ Data Reconstruction: Why Optimized IDW Outperforms kriging and Implicit Neural Representation

2025-12-03

AI Summary

This study addresses the problem of reconstructing sparse meteorological station data. We systematically evaluate and optimize three interpolation methods—Inverse Distance Weighting (IDW), Ordinary Kriging (OK), and Implicit Neural Representation (MMGN)—with respect to both accuracy and computational efficiency. Using 100 sparse datasets from the ECA&D archive, we conduct rigorous hyperparameter tuning and statistical validation via Dunn’s post-hoc test. Results demonstrate that a lightweight, optimized IDW significantly outperforms both OK and MMGN across all metrics: RMSE = 3.00 ± 1.93, MAE = 1.32 ± 0.77, and R² = 0.68 ± 0.16; all differences are statistically significant with medium-to-large effect sizes. This finding challenges the common assumption that model complexity inherently improves performance, and instead establishes that carefully tuned simple methods can achieve superior accuracy–efficiency trade-offs. The work provides a robust, computationally efficient paradigm for climate data reconstruction in resource-constrained settings.

Technology Category

• Data Mining & Knowledge Management: Data Compression
• Machine Learning: Feature Construction/Reformulation
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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 $Delta_{MAX}$ ($24.06 pm 17.15$), 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 [...]

Problem

• Evaluates sparse climate data reconstruction methods
• Compares IDW, kriging, and neural representation performance
• Demonstrates IDW's superior accuracy and computational efficiency

Innovation

• Optimized IDW method outperforms kriging and neural models
• IDW achieves highest accuracy with lowest error metrics
• Hyper-parameter tuning enhances simple inverse distance weighting