In regions of complex topography, local flows are difficult to forecast on a routine basis, especially in stable conditions, due to the coarse resolution of operational models. The Cadarache valley (southeastern France) features this sort of complex topography. The Weather Research and Forecasting (WRF) model is run daily to forecast the weather in this region with a horizontal resolution of 3 km. Such a resolution cannot resolve all topography details of the small Cadarache valley, and therefore its local wind patterns. Other variables, however, that are less dependent on the subgrid topography, are satisfactorily forecasted, and used as inputs to an artificial neural network (ANN) designed to reproduce wind observations inside the valley from WRF forecasts. A variable selection procedure identified 5 key input variables that best drive the ANN. With respect to the WRF output, the ANN significantly improves forecasted low-level winds, both for speed and direction. This study demonstrates the potential for the ANN technique to be used as a correcting tool to forecast weather conditions at the local scale when numerical modeling is performed at a resolution too coarse to take into account the effect of local topography.

中文翻译：

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地方尺度的山谷风：使用人工神经网络校正常规天气预报

在地形复杂的地区，由于运行模型的分辨率较差，因此难以常规地预测局部流量，尤其是在稳定条件下。Cadarache山谷（法国东南部）具有这种复杂的地形。每天运行“天气研究和预报（WRF）”模型，以3 km的水平分辨率预报该地区的天气。这样的分辨率无法解析小卡达拉奇山谷的所有地形细节，因此也无法解析其局部风型。但是，令人满意地预测了其他变量，这些变量较少依赖于子网格的地形，并用作人工神经网络（ANN）的输入，该人工神经网络用于从WRF预报中再现山谷内部的风向观测。变量选择过程确定了最能驱动ANN的5个关键输入变量。关于WRF的输出，ANN在速度和方向上都显着改善了预测的低层风。这项研究证明了当以太粗糙的分辨率执行数值建模而无法考虑局部地形影响时，将ANN技术用作预测局部尺度天气状况的校正工具的潜力。