  Real-time forecasting is important to the society. It uses continuous data
streams to update forecasts for sustained accuracy. But the data source is
vulnerable to attacks or accidents and the dynamics of forecasting failure and
recovery due to data gaps is poorly understood. As the first systematic study,
a Lorenz model-based forecasting system was disrupted with data gaps of various
lengths and timing. The restart time of data assimilation is found to be the
most important factor. The forecasting accuracy is found not returning to the
original even long after the data assimilation recovery.
