用随机发生器减少全球风系综的存储
Reducing Storage of Global Wind Ensembles with Stochastic Generators
摘要：
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，旨在再现数据生成机制的合奏运行通过一个随机发生器（SG）的全球年度风数据。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。即使对模拟输出应用了有效的有损数据压缩算法，拟议的SG从风生成替代集合成员所需的存储空间也比从气候模型创建额外风场少几个数量级。
英文摘要：
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.
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