An Implementation of Full Cycle Strategy Using Dynamic Blending for Rapid Refresh Short-range Weather Forecasting in China

The partial cycle (PC) strategy has been used in many rapid refresh cycle systems (RRC) for regional short-range weather forecasting. Since the strategy periodically reinitializes the regional model (RM) from the global model (GM) forecasts to correct the large-scale drift, it has replaced the traditional full cycle (FC) strategy in many RRC systems. However, the extra spin-up in the PC strategy increases the computer burden on RRC and generates discontinuous small-scale systems among cycles. This study returns to the FC strategy but with initial fields generated by dynamic blending (DB) and data assimilation (DA). The DB ingests the time-varied large-scale information from the GM to the RM to generate less-biased background fields. Then the DA is performed. We applied the new FC strategy in a series of 7-day batch forecasts with the 3-hour cycle in July 2018, and February, April, and October 2019 over China using a Weather Research and Forecast (WRF) model-based RRC. A comparison shows that the new FC strategy results in less model bias than the PC strategy in most state variables and improves the forecast skills for moderate and light precipitation. The new FC strategy also allows the model to reach a balanced state earlier and gives favorable forecast continuity between adjacent cycles. Hence, this new FC strategy has potential to be applied in RRC forecast systems to replace the currently used PC strategy.