Data Assimilation (DA) is an important mathematical method for predicting turbulent flows for weather forecasting. However, the origins of the critical length scale, a crucial parameter in this method, and its dependence on the Reynolds number are not well understood. Now, researchers have developed a novel theoretical framework that treats DA as a stability problem to explain this parameter. This framework can contribute significantly to turbulence research and inspire novel data-driven methods to predict turbulence.