Winter Storm Tracks and Related Weather in the NCEP Climate Forecast System Weeks 3-4 Reforecasts for North America

Variability in time ranges from 2 weeks to 2 months falls in between the fields of weather and climate prediction and is not well defined by either category. This subseasonal-to-seasonal (S2S) range represents a yet unresolved major gap in operational forecasting. The need to address this gap and advance S2S prediction has become a high priority for disaster mitigation and resource management decisions. This work examines whether storm track behavior can contribute to the weeks 3-4 prediction of winter weather in North America. The weeks 3-4 time frame is our chosen sub-monthly forecast period of analysis, as it represents the S2S gap in predictability. It is known that the evolution of individual storms within the storm tracks follows the typical behavior of wave packets with centers decaying upstream as new ones grow downstream. It is argued that since wave packets have a lower frequency than the individual storms, it is feasible that storm tracks will contain S2S signals. The characterization of storm tracks in long-term forecasts could advance S2S prediction by providing relevant information that may not be found in standard wind and precipitation forecasts. The hypothesis is tested using the NCEP Climate Forecast System Reanalysis (CFSR) and its companion reforecast dataset (CFSRR). CFSRR consists of 9-mo forecasts taking initial conditions from CFSR.

The storm tracks are computed by objectively tracking isentropic potential vorticity (PV) anomalies for two periods (base, 1983–2002; validation, 2003–2010) in CFSR and CFSRR. The base period is employed to diagnose storm track properties, their relation to hazardous weather, and to evaluate the biases in the reforecasts. The validation period is used to evaluate the skill of the reforecasts in reproducing those same storm track properties in a more realistic prediction mode. Statistically significant positive PV biases are found in the storm track reforecasts. The removal of systematic errors is found to improve general storm track features.

An assessment of the near-surface winds and precipitation patterns associated with the storm tracks shows that CFSRR reproduces well the observed intensity and spatial distributions of storm track-related near-surface winds, with small yet significant biases found in the storm track regions. The spatial distributions of the reforecast precipitation correspond well with the reanalysis, although significant positive biases are found across the contiguous United States. Removal of the corresponding mean biases reduces the low-level wind error on average by 12% and the precipitation error by about 25%. The bias-corrected fields better depict the observed variability and exhibit additional improvements in the representation of winter weather associated with strong-storm tracks (the storms with more intense PV). Lastly, the reforecasts reproduce the characteristic intensity and frequency of hazardous strong-storm winds. It is concluded that CFSRR contains useful S2S storm track-related information that can contribute to the advancement of S2S prediction of hazardous weather in North America.