利用浅层神经网络从再分析数据估计风强度数据

Dionisio Rodríguez-Esparragón, J. Marcello, N. M. Betancort, C. Gonzalo-Martín
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引用次数: 0

摘要

全球变化是当今的突出问题之一。这就是为什么对监测气候变量的关注和经济资源有所增加的原因。风资料是决定当地气候的关键因素之一。本文对浅层神经网络(SNN)的性能进行了测试,以模拟来自附近位置再分析数据的遥感风强度数据。因此,可以获得具有更高空间分辨率的风数据序列,从而可以在局部尺度上获得更多数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimation of wind intensity data from reanalysis data using a shallow neural network
Global change is one of the outstanding problems nowadays. This is the reason why considerable attention, and economic resources to monitor climate variables have increased. Wind data constitute one of the key elements that determine the local climate. In this paper, the performance of a shallow neural net (SNN) is tested to simulate remote sensing wind intensity data from reanalysis data from nearby location. As a result, a sequence of wind data with more spatial resolution can be achieved, allowing the availability of more data at the local scale.
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