Spatial prediction of monthly wind speeds in complex terrain with adaptive general regression neural networks
Spatial prediction of monthly wind speeds in complex terrain with adaptive general regression neural networks
复制标题
自适应广义回归神经网络对复杂地形月风速的空间预测
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
M. Kanevski
中科院分区：
文献类型：
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作者：
Sylvain Robert;L. Foresti;M. Kanevski
本文提出了一种用于复杂高山地形月风速插值的非线性回归方法--广义回归神经网络(GRNN)。GRNN使用来自瑞士气象网的数据进行训练，以学习地形特征和风速之间的统计关系。通过使用专用卷积滤波从不同空间尺度的数字高程模型中提取特征来考虑地形的凸度、坡度和曝光度。然后，在预测模式下应用GRNN构建了1968-2008年的网格月风速数据库。这项研究表明，在GRNN中使用地形特征作为输入大大减少了对于仅结合地理坐标和地形高度来进行风速内插的低维模型的交叉验证误差。由于更复杂和更弱的风-地形关系，夏季风速的空间可预报性比冬季低。使用GRNN算法的自适应版本来研究这些关系的相关性，该算法允许通过消除噪声来选择有用的地形特征。该研究通过在多个空间尺度上集成考虑地形条件的附加特征，为将低维内插模型扩展到高维空间提供了一个框架。版权所有©2012皇家气象学会
This paper presents the general regression neural networks (GRNN) as a nonlinear regression method for the interpolation of monthly wind speeds in complex Alpine orography. GRNN is trained using data coming from Swiss meteorological networks to learn the statistical relationship between topographic features and wind speed. The terrain convexity, slope and exposure are considered by extracting features from the digital elevation model at different spatial scales using specialised convolution filters. A database of gridded monthly wind speeds is then constructed by applying GRNN in prediction mode during the period 1968–2008. This study demonstrates that using topographic features as inputs in GRNN significantly reduces cross‐validation errors with respect to low‐dimensional models integrating only geographical coordinates and terrain height for the interpolation of wind speed. The spatial predictability of wind speed is found to be lower in summer than in winter due to more complex and weaker wind‐topography relationships. The relevance of these relationships is studied using an adaptive version of the GRNN algorithm which allows to select the useful terrain features by eliminating the noisy ones. This research provides a framework for extending the low‐dimensional interpolation models to high‐dimensional spaces by integrating additional features accounting for the topographic conditions at multiple spatial scales. Copyright © 2012 Royal Meteorological Society