Overlapping Windows In A Global Hourly Data Assimilation System

Abstract

The US operational global data assimilation system provides updated analysis and forecast fields every six hours, which is not frequent enough to handle the rapid error growth associated with hurricanes or other storms. This motivates development of an hourly-updating global data assimilation system, but observational data latency can be a barrier. Two methods are presented to overcome this challenge: “catch-up cycles”, in which a 1-hourly system is reinitialized from a 6-hourly system that has assimilated high-latency observations; and “overlapping assimilation windows”, in which the system is updated hourly with new observations valid in the past three hours. The performance of these methods is assessed in a near-operational setup using the Global Forecast System by comparing forecasts to in-situ observations. At short forecast leads, the overlapping windows method performs comparably to the 6-hourly control in a simplified configuration and outperforms the control in a full-input configuration. In the full-input experiment, the catch-up cycle method performs similarly to the 6-hourly control; reinitializing from the 6-hourly control does not appear to provide a significant benefit. Results suggest that the overlapping windows method performs well in part because of the hourly-update cadence, but also because hourly cycling systems can make better use of available observations. The impact of the hourly update relative to the 6-hourly update is most significant during the first forecast day, while impacts on longer range forecasts were found to be mixed and mostly insignificant. Further effort towards an operational global hourly-updating system should be pursued.