Investigating the Impact of Rapid Cycling in a Global Data Assimilation System

Abstract Most operational weather prediction centers cycle their global data assimilation systems at a cadence of two to four times per day for global weather prediction. Increases in computing power, observation availability, and horizontal model resolution motivate the development of a more rapidly updated global assimilation system. Experiments using hybrid four-dimensional ensemble-variational data assimilation (4DEnVar) with 1-h assimilation windows are performed in a version of the NOAA Global Data Assimilation System, and results are compared to those from traditional 6-hourly cycling. Skill is evaluated via comparisons with in situ observations, satellite winds, and regional analyses. Ensemble mean forecasts initialized from the hourly cycled fields show significantly better skill for some variables and regions than those initialized from traditional 6-hourly cycled fields, though they are informed by the same observations. These benefits appear only when dense, accurate observations are assimilated (e.g., aircraft wind). When aircraft data are withheld from assimilation, hourly cycling degrades the fit to in situ observations. Investigations into kinetic energy spectra demonstrate that fields from the hourly cycling system retain more energy at synoptic and subsynoptic scales than fields from the 6-hourly cycling system. These results suggest that a global hourly cycling assimilation system can use temporally and spatially dense observations more effectively than a 6-hourly cycling system. Significance Statement Global weather forecasts in the United States are currently initialized by assimilating in situ and satellite observations every 6 h. This work aims to determine the impact of shortening this assimilation window to one hour and increasing the frequency of updates. Results suggest that the shortened 1-h update cadence can improve the performance of the data assimilation by using dense observations, particularly of wind from aircraft and satellites, more effectively. While computational constraints remain a challenge, this suggests that modifying the operational data assimilation cadence could provide better global and regional weather forecasts.