  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.
