资料同化中高频观测值的随机逼近
Stochastic Approximation for High-frequency Observations in Data Assimilation
摘要：
随着高频传感器在许多生物和物理系统中的日益渗透，大量的观测结果为下游估计的更高统计准确性提供了机会，但它们的频率导致了数据同化任务中的大量计算问题。这些观测的高频率传统上是通过使用数据修改策略来处理的，例如累积、平均和采样。然而，这些数据修改策略将降低估计的质量，这对于许多系统来说可能是站不住脚的。因此，为了确保高质量的估计，我们采用随机逼近方法来解决数据同化中高频观测的独特挑战。因此，我们能够以避免上述计算问题并保持估计的统计准确性的方式产生利用所有观察结果的估计。
英文摘要：
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.
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