Stochastic Approximation for High-frequency Observations in Data Assimilation
This addresses computational bottlenecks for researchers and practitioners in fields like biology and physics dealing with high-frequency data, though it is incremental as it builds on existing stochastic approximation techniques.
The paper tackles the computational challenges of high-frequency sensor data in data assimilation by adapting stochastic approximation methods, enabling estimates that use all observations without compromising statistical accuracy.
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.