论文标题
数据同化的高频观察的随机近似
Stochastic Approximation for High-frequency Observations in Data Assimilation
论文作者
论文摘要
随着在许多生物和物理系统中高频传感器的渗透不断提高，所得观察结果的丰度为下游估计的更高统计准确性提供了机会，但是它们的频率导致了大量数据同化任务中的计算问题。传统上，通过使用诸如积累，平均和抽样等数据修改策略来处理这些观察结果的高频。但是，这些数据修改策略将降低估计值的质量，这对于许多系统可能站不住脚。因此，为了确保高质量的估计，我们适应了随机近似方法，以应对数据同化中高频观察的独特挑战。结果，我们能够以避免上述计算问题的方式产生所有观察结果的估计值，并保留估计值的统计准确性。
With the increasing penetration of high-frequency sensors across a number of biological and physical systems, the abundance of the resulting observations offers opportunities for higher statistical accuracy of down-stream estimates, but their frequency results in a plethora of computational problems in data assimilation tasks. The high-frequency of these observations has been traditionally dealt with by using data modification strategies such as accumulation, averaging, and sampling. However, these data modification strategies will reduce the quality of the estimates, which may be untenable for many systems. Therefore, to ensure high-quality estimates, we adapt stochastic approximation methods to address the unique challenges of high-frequency observations in data assimilation. As a result, we are able to produce estimates that leverage all of the observations in a manner that avoids the aforementioned computational problems and preserves the statistical accuracy of the estimates.
