标题： 短时间范围内的局部风速预测，依赖于数值天气预报和周围站点的观测数据 显示英文标题

标题： Local wind speed forecasting at short time horizons relying on both Numerical Weather Prediction and observations from surrounding station

摘要： 本研究提出了一种混合神经网络模型，用于短期（1-6小时 ahead）地表风速预测，结合了数值天气预报（NWP）与地面气象站的观测数据。 它依赖于MeteoNet数据集，该数据集包括法国气象服务的全球（ARPEGE）和区域（AROME）NWP模型的数据，以及法国地中海地区地面站的气象观测数据。 所提出的神经网络架构整合了最近的站点观测数据（过去几小时）以及目标位置周围小网格上的AROME和ARPEGE预测。 该模型旨在提供确定性与概率性预测，后者通过预测适当概率分布的参数，显著允许我们捕捉极端风事件。 我们的结果表明，该混合模型在所有预测时间范围内均显著优于基线方法，包括原始NWP预测、持续性模型和线性回归。 例如，与AROME预测相比，该模型将RMSE降低了高达30%。 概率预测进一步提高了性能，特别是在极端分位数方面，通过估计条件分位数而非仅依赖条件均值。 对特定站点（如科西嘉岛上的站点）进行微调进一步提高了预测精度。 我们的研究强调了整合多种数据源和概率方法在提高短期风速预测中的重要性。 它定义了一种有效的方法，即使在像科西嘉这样地形复杂的地区，局部风变化显著的情况下也是如此。 显示英文摘要

摘要： This study presents a hybrid neural network model for short-term (1-6 hours ahead) surface wind speed forecasting, combining Numerical Weather Prediction (NWP) with observational data from ground weather stations. It relies on the MeteoNet dataset, which includes data from global (ARPEGE) and regional (AROME) NWP models of the French weather service and meteorological observations from ground stations in the French Mediterranean. The proposed neural network architecture integrates recent past station observations (over last few hours) and AROME and ARPEGE predictions on a small subgrid around the target location. The model is designed to provide both deterministic and probabilistic forecasts, with the latter predicting the parameters of a suitable probability distribution that notably allows us to capture extreme wind events. Our results demonstrate that the hybrid model significantly outperforms baseline methods, including raw NWP predictions, persistence models, and linear regression, across all forecast horizons. For instance, the model reduces RMSE by up 30\% compared to AROME predictions. Probabilistic forecasting further enhances performance, particularly for extreme quantiles, by estimating conditional quantiles rather than relying solely on the conditional mean. Fine-tuning the model for specific stations, such as those in the Mediterranean island of Corsica, further improves forecasting accuracy. Our study highlights the importance of integrating multiple data sources and probabilistic approaches to improve short-term wind speed forecasting. It defines an effective approach, even in a complex terrain like Corsica where localized wind variations are significant