As high-performance computing (HPC) is being scaled up to exascale to accommodate new modeling and simulation needs, I/O has continued to be a major bottleneck in the end-to-end scientific processes. Nevertheless, prior work in this area mostly aimed to maximize the average performance, and there has been a lack of study and solutions that can manage I/O performance variation on HPC systems. This work aims to take advantage of the storage characteristics and explore application level solutions that are interference-aware. In particular, we monitor the performance of data analytics and estimate the state of shared storage resources using discrete fourier transform (DFT). If heavy I/O interference is predicted to occur at a given timestep, data analytics can dynamically adapt to the environment by lowering the accuracy and performing partial or no augmentation from the shared storage, dictated by an augmentation-bandwidth plot. We evaluate three data analytics, XGC, GenASiS, and Jet, on Chameleon, and quantitatively demonstrate that both the average and variation of I/O performance can be vastly improved using our dynamic augmentation, with the mean and variance improved by as much as 67% and 96%, respectively, while maintaining acceptable outcome of data analysis.