Post-processing rainfall in a high-resolution simulation of the 1994 Piedmont flood
Post-processing rainfall in a high-resolution simulation of the 1994 Piedmont flood
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DOI：
10.1007/s42865-020-00028-z
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Scott Meech;S. Alessandrini;W. Chapman;L. Delle Monache
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文献类型：
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作者：
Scott Meech;S. Alessandrini;W. Chapman;L. Delle Monache
1994年11月，意大利西北部的皮埃蒙特地区发生了一场灾难性的洪水事件，持续了大约3天。与此事件相关的大时间和空间尺度促使进行了许多再分析研究，以评估模式的预测能力。本文研究了另一种使用天气研究与预报（WRF）模型与后处理技术相结合的预测技术：模拟集合（AnEn）和卷积神经网络（CNN）。该地区复杂的地形对数值天气预报（NWP）模式提出了挑战，特别是对于诸如此类的事件，地形对决定降水的分布和数量至关重要。通过将这些后处理技术应用于WRF模型输出，两种技术在洪水事件期间的累积降水场中都观察到显著的改善，尽管使用CNN的改进是以低估最高降水量为代价的。
In November 1994, a catastrophic flooding event occurred in the Piedmont region in Northwestern Italy over a period of about 3 days. The large time and spatial scales associated with this event prompted a number of reanalysis studies to assess the forecast skill of the models. This paper investigates another forecasting technique using the Weather Research and Forecasting (WRF) model coupled with post-processing techniques: the analog ensemble (AnEn) and the convolutional neural network (CNN). The complex topography found in this region presents a challenge for numerical weather prediction (NWP) models especially for events such as these, where the orography is crucial in determining the distribution and amount of precipitation. By applying these post-processing techniques to WRF model output, significant improvements were observed in the accumulated precipitation fields during the flooding event in both techniques, although improvements using the CNN were at the expense of underestimating the highest precipitation.