用证据神经网络提升美国东北部阵风预测的可靠性与可信度
Uncertainty Quantification of Wind Gust Predictions in the Northeast United States: An Evidential Neural Network and Explainable Artificial Intelligence Approach
- 引入证据神经网络量化阵风预测不确定性，无需集成方法
- 相比WRF模型，均方根误差降低47%，95%观测阵风被覆盖
- 揭示风暴强度和阵风梯度是影响不确定性的关键因素
利用美国东北部61场温带气旋的数据，本文提出一种新型证据神经网络（ENN）用于阵风预测的不确定性量化，基于天气研究与预报（WRF）模型的气象变量。解释性AI分析表明，关键预测特征导致更高不确定性，且与风暴强度和空间阵风梯度强相关。相较于WRF模型，ENN将均方根误差降低47%，在266个站点中的179个成功构建出包含至少95%观测阵风的预测区间，无需依赖集成方法。从实际应用角度，提供带不确定性的阵风预报可增强利益相关方对极端阵风事件风险评估与应对规划的信心。
原文摘要 · Abstract (English)
Machine learning algorithms have shown promise in reducing bias in wind gust predictions, while still underpredicting high gusts. Uncertainty quantification (UQ) supports this issue by identifying when predictions are reliable or need cautious interpretation. Using data from 61 extratropical storms in the Northeastern USA, we introduce evidential neural network (ENN) as a novel approach for UQ in gust predictions, leveraging atmospheric variables from the Weather Research and Forecasting (WRF) model. Explainable AI techniques suggested that key predictive features contributed to higher uncertainty, which correlated strongly with storm intensity and spatial gust gradients. Compared to WRF, ENN demonstrated a 47% reduction in RMSE and allowed the construction of gust prediction intervals without an ensemble, successfully capturing at least 95% of observed gusts at 179 out of 266 stations. From an operational perspective, providing gust forecasts with quantified uncertainty enhances stakeholders' confidence in risk assessment and response planning for extreme gust events.
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