We address a two-stage newsvendor model in which the mean demand -- but not the actual demand -- at first random itself, gets revealed in midstream, in time to place a second order, albeit that the unit cost price of this second order is higher than that of the original one. The two-stage process is most relevant to many retail organizations, where the retailer has access to two supply options: one with a relatively long lead time where orders need to be placed with much uncertainty about even the mean demand for the season, and a second more expensive option with a much smaller lead time that can be exercised after a signal is revealed, which provides the decision maker with an update of the mean demand. To this end, we propose a forecast evolution model which describes how the initial forecast for mean demand varies in response to the signal. We show that the optimal first-stage order can be found by solving a simple ordinary differential equation, while the second-stage order is analytically available. We characterize the asymptotic behavior of the first-stage order, as well as the controllable costs, as the mean forecasted demand grows. We also characterize the benefits of an optimal two-stage replenishment policy, compared to the classical newsvendor strategy, both theoretically and numerically. Furthermore, we derive the optimality gaps for several heuristics. One of these heuristics follows directly from the asymptotic analysis and is shown to be asymptotically optimal. We derive an estimation procedure for the parameters of our forecast evolution model, and apply it to data from a national retail chain. Extensions to a distribution-free approach as well as capacitated systems are derived as well.