Microgrids can be used to restore critical load after a natural disaster, enhancing resilience of a distribution network. To deal with the stochastic nature of intermittent energy resources, such as wind turbines (WTs) and photovoltaics (PVs), forecast information is usually required. However, some microgrids may not be equipped with power forecasting tools. To fill this gap, a risk-limiting strategy based on measurements is proposed. A Gaussian mixture model is used to represent a prior joint probability density function of power outputs of WTs and PVs over multiple periods. As time rolls forward, the probability distribution of WT/PV generation is recursively updated using the latest measurement data. The updated distribution is used as an input of the risk-limiting load restoration problem, enabling an equivalent transformation of the original chance constrained problem into a mixed integer linear programming. Simulations on a distribution system with three microgrids demonstrate the effectiveness of the proposed method. Results indicate that networked microgrids can perform better in uncertainty management relative to stand-alone microgrids.