Reducing Storage of Global Wind Ensembles with Stochastic Generators
Reducing Storage of Global Wind Ensembles with Stochastic Generators
复制标题
使用随机发电机减少全球风群的存储
DOI：
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发表时间：
2017
期刊：
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通讯作者：
M. Genton
中科院分区：
文献类型：
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作者：
J. Jeong;S. Castruccio;P. Crippa;M. Genton
风能有可能对未来的能源做出重大贡献。然而，在全球范围内定位这种可再生能源的来源极具挑战性，因为很难存储由现代计算机模型生成的非常大的数据集。我们提出了一个统计模型，其目的是通过全球年度风数据的随机发生器（SG）再现运行集合的数据生成机制。我们引入了一个进化谱的方法，空间变化的参数的基础上大规模的地理描述符，如海拔高度，以更好地考虑不同的制度，在地球的地形。我们考虑一个多步骤的条件似然方法来估计的参数，明确占非平稳特征，同时也平衡内存存储和分布式计算。我们将所提出的模型应用于超过1800万个全球年风速点。拟议的SG需要的数量级少的存储从风生成代理合奏成员比创建额外的风场从气候模型，即使一个有效的有损数据压缩算法应用到模拟输出。
Wind has the potential to make a significant contribution to future energy resources. Locating the sources of this renewable energy on a global scale is however extremely challenging, given the difficulty to store very large data sets generated by modern computer models. We propose a statistical model that aims at reproducing the data-generating mechanism of an ensemble of runs via a Stochastic Generator (SG) of global annual wind data. We introduce an evolutionary spectrum approach with spatially varying parameters based on large-scale geographical descriptors such as altitude to better account for different regimes across the Earth's orography. We consider a multi-step conditional likelihood approach to estimate the parameters that explicitly accounts for nonstationary features while also balancing memory storage and distributed computation. We apply the proposed model to more than 18 million points of yearly global wind speed. The proposed SG requires orders of magnitude less storage for generating surrogate ensemble members from wind than does creating additional wind fields from the climate model, even if an effective lossy data compression algorithm is applied to the simulation output.