Helicopter downwash impact on ship airwake is addressed from a three-pronged approach: (1) Analysis of one-point statistics of autospectrum and two-point statistics of cross-spectrum and coherence from a Computational Fluid Dynamics database of flow velocities effected by helicopter downwash and shipboard airwake; (2) Development of a mathematical framework for extracting interpretive turbulence models in closed form from these autospectral statistics; and (3) Simulation through white-noise-driven filters for the extracted models. The framework begins with an earlier-exercised perturbation-type series expansion of the autocorrelation for all three velocity components, where the first term of the series has a form of the von Karman longitudinal or lateral correlation function. After transformation into equivalent series of autospectrum, the coefficients in the series are evaluated by satisfying theoretical constraints and fitting a curve on a set of selected autospectral data points generated from the database. The framework represents a sensible combination of series expansion, exploitation of a database, and theoretical constraints to provide a foothold on airwake-downwash phenomenon for engineering analysis. It ensures that the extracted model and the autospectral data points have the same time scale, mean square value, and asymptotic decay according to the Kolmogorov –5/3 Law. The framework's strengths and weaknesses, and its major advancement over the earlier series-expansion schemes are also addressed. Finally it is shown that downwash increases airwake energy (mean square value) by one order of magnitude, and almost all of this airwake-downwash energy is concentrated within the bandwidth (0.16 < f(Hz) < 1.6) that affects flight mechanics.