Impact of Data Assimilation in Sensitive Features on the Predictability of the 2012 Great Arctic Cyclone

Abstract

The Great Arctic Cyclone 2012 (AC12) is used to understand the role of initial condition errors in the predictability at 2–3-day forecast range of a high-impact summer Arctic Cyclone (AC). Ensemble sensitivity analysis (ESA) is first performed to identify potentially sensitive regions of the cyclone evolution using an ensemble baseline forecast with conventional in situ observations assimilated. A pseudo-observation method is then introduced to investigate impacts of hypothetical observations in these sensitive but unobserved regions. In the baseline experiments with in situ observations assimilated, the forecasted AC12 reaches its peak intensity 18 hr earlier than in the verifying Global Forecast System Analysis (GFS-ANL) and the cyclone track is biased toward the southwest. Using ESA, the time of peak intensity and the cyclone track error are identified to be sensitive to the upstream trough, downstream ridge, and the tropopause polar vortex (TPV) to the northeast (NE TPV) of the AC12. These features were not observed by the in situ observation networks. To examine the impact of the observation gaps, pseudo-observations drawn from GFS-ANL are assimilated. Pseudo-observations sample the three features separately to study the impact of the initial condition error on the predictability of AC12. The cyclone peak intensity timing error and track error are greatly reduced when the initial condition error is reduced near the NE TPV. A southward expansion of the NE TPV and the corresponding southward shifting low-level front lead the forecasted AC12 to progress to the east, which better agrees with the verifying GFS-ANL.