Resources

Speaker: Laura Slivinski

The US operational global data assimilation system cycles with a six-hourly cadence, which is not frequent enough to handle the rapid error growth associated with fast-moving 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 short-term forecasts to in-situ observations. Experiments in which the role of data latency is eliminated are also analyzed to further evaluate the impact of cycling cadence on analyses and forecasts.