AI 中文总结
该研究提出TMI框架，发现卫星降水的机器学习校正性能受机制纯度而非算法复杂度支配，经跨区域验证可作为适用性筛选工具。
AI 中文摘要
IMERG等卫星降水产品存在随地形、季节和降水类型变化的偏差，使得机器学习校正的适用边界尚不明确。本研究提出以机制纯度为核心的Terrain-Moisture-Intensity（TMI，地形-湿度-强度）框架，将校正问题从单纯的算法优化扩展至物理一致性诊断。本研究在湖南省开展概念验证，采用IMERG V07、SRTM DEM和ERA5变量（tcwv、u10、v10）。消融实验结果表明，在本研究条件下，地形-湿度关系主要呈加性：RF-Full相比LR-Full仅使R²提升0.001，但偏差升至1.282 mm·d⁻¹；MAE降低约14%，反映出尾部拟合改善与均值偏移间的权衡。SHAP诊断识别出三类边界：空间上，尽管变量激活强烈，湖南中部仍出现显著退化（R²=0.133），与混合地形导致的机制碎片化一致；时间上，u10在春季至夏季发生方向反转（+0.096至-0.156），呈现“静默失效”；极端降水（≥50 mm·d⁻¹）近似机制饱和边界而非孤立的分布外样本，DEM在SHAP紊乱中显示最大相对放大（+150%）。结果表明，机器学习校正性能主要受机制纯度限制。预先注册的跨区域测试（湖南、广西、广东）在样本外验证了该筛选能力：先验一致性代理以2.6个百分点的平均绝对误差预测校正效率，而迁移与再训练的对比区分出机制不匹配（沿海广东）与可迁移性（广西），确立该框架为经验证的适用性筛选工具。
英文摘要
Satellite precipitation products such as IMERG exhibit biases that vary with terrain, season, and precipitation regime, leaving the applicability boundaries of machine learning correction unclear. This study proposes the Terrain-Moisture-Intensity (TMI) framework, centered on mechanism purity, extending the correction problem from purely algorithmic optimization to physical consistency diagnosis. A proof-of-concept study in Hunan Province employs IMERG V07, SRTM DEM, and ERA5 variables (tcwv, u10, v10). Ablation results indicate that, under the conditions of this study, terrain-moisture relationships are predominantly additive: RF-Full yields merely +0.001 R^2 gain over LR-Full, while bias rises to 1.282 mm d^-1; MAE decreases by approximately 14%, reflecting a trade-off between tail-fitting improvement and mean shift. SHAP diagnostics identify three categories of boundaries. Spatially, Central Hunan exhibits significant degradation (R^2=0.133) despite strong variable activation, consistent with mechanism fragmentation induced by mixed terrain. Temporally, u10 undergoes directional reversal between summer and spring (+0.096 to -0.156), presenting "silent failure." Extreme precipitation (>=50 mm d^-1) approximates a mechanism saturation frontier rather than isolated out-of-distribution samples, with DEM showing the largest relative amplification in SHAP disorder (+150%). The results demonstrate that machine learning correction performance is primarily constrained by mechanism purity. A pre-registered cross-regional test (Hunan, Guangxi, Guangdong) confirms this screening capability out of sample: a priori coherence proxies predict correction efficiency with a mean absolute error of 2.6 percentage points, while the transfer-versus-retraining contrast separates mechanism mismatch (coastal Guangdong) from portability (Guangxi), establishing the framework as a validated applicability screen.
Comments44 pages, 8 figures, 5 tables; supplementary material included (8 tables, 2 figures)
