SEEDING CHAOS The Dire Consequences of Numerical Noise in NWP Perturbation Experiments
SEEDING CHAOS The Dire Consequences of Numerical Noise in NWP Perturbation Experiments
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DOI：
10.1175/bams-d-17-0129.1
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发表时间：
2018-03-01
影响因子：
8
通讯作者：
Nauert, Christian J.
中科院分区：
文献类型：
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作者：
Ancell, Brian C.;Bogusz, Allison;Nauert, Christian J.
扰动实验是一种常用的技术，用于研究模型模拟之间的差异如何在混沌系统中演变。这种扰动实验包括对初始条件（包括与数据同化有关的条件）、边界条件和模式参数化的修改。然而，我们已经发现，模型模拟之间的任何差异都会以音速的许多倍的速度在所有预测模型变量中产生非常小的变化的快速传播。快速传播似乎是由于该模型的高阶空间离散方案，允许通信的数值误差在许多网格点与每个时间步长。在天气研究及预报模式中，即使使用数字滤波或数值扩散等技术，这种现象也是不可避免的。这些微小的差异很快就扩散到整个模式域。虽然这些误差最初相对于温度的百万分之一度的数量级，例如，它们可以通过发生潮湿过程的非线性混沌过程快速增长。随后的进化可以在一天之内产生与强降雨地区或旋转超级单体存在等高影响天气事件相当的重大变化。最重要的是，这些不切实际的扰动可能会污染实验结果，给人一种错误的印象，即现实的物理过程发挥了作用。这项研究的特点，这种类型的噪声通过混沌的传播和增长，显示各种扰动策略的例子，并讨论了过去和未来的研究，可能会受到这种现象的重要影响。
Perturbation experiments are a common technique used to study how differences between model simulations evolve within chaotic systems. Such perturbation experiments include modifications to initial conditions (including those involved with data assimilation), boundary conditions, and model parameterizations. We have discovered, however, that any difference between model simulations produces a rapid propagation of very small changes throughout all prognostic model variables at a rate many times the speed of sound. The rapid propagation seems to be due to the model's higher-order spatial discretization schemes, allowing the communication of numerical error across many grid points with each time step. This phenomenon is found to be unavoidable within the Weather Research and Forecasting (WRF) Model even when using techniques such as digital filtering or numerical diffusion.These small differences quickly spread across the entire model domain. While these errors initially are on the order of a millionth of a degree with respect to temperature, for example, they can grow rapidly through nonlinear chaotic processes where moist processes are occurring. Subsequent evolution can produce within a day significant changes comparable in magnitude to high-impact weather events such as regions of heavy rainfall or the existence of rotating supercells. Most importantly, these unrealistic perturbations can contaminate experimental results, giving the false impression that realistic physical processes play a role. This study characterizes the propagation and growth of this type of noise through chaos, shows examples for various perturbation strategies, and discusses the important implications for past and future studies that are likely affected by this phenomenon.