Ebrahim Nabizadeh , Sandro W Lubis , Pedram Hassanzadeh

Forecast skills of numerical weather prediction (NWP) models and intrinsic predictability can be flow-dependent, e.g., different among
weather regimes. Here, we have examined the predictability of distinct Pacific-North American weather regimes in June-September. Four
weather regimes are identified using a self-organizing map analysis of daily 500-hPa geopotential height anomalies, and are shown to have
distinct and coherent links to near-surface temperature and precipitation anomalies over the North American continent. The 4 to 14-day
forecast skills of these 4 regimes are quantified for the ECMWF and the NCEP models (from the TIGGE project) and the Global Ensemble
Forecast System (GEFS). Based on anomaly correlation coefficient, persistence, and transition frequency, the highest forecast skills are
consistently found for regime 3 (Arctic high). In general, the least skillful forecasts are for regime 1 (Pacific trough). The instantaneous local
dimension and persistence of each regime are computed using a dynamical systems-based analysis. The local dimension and persistence
are indicators of intrinsic predictability. This analysis robustly shows that regime 3 has the highest intrinsic predictability. The analysis
also suggests that overall, regime 1 has the lowest intrinsic predictability. These findings are consistent with the high (low) forecast skills
of NWP models for regime 3 (regime 1). Weather regime 1 is associated with above-normal temperature and precipitation anomalies over
western North America and around the Gulf of Mexico region, indicating potentially important implications for the poor predictability of
this regime. The dynamical systems analysis suggests that better estimates of initial conditions might improve the forecasts of this regime.