Recognizing Sources of Forecast Uncertainty of Extreme Low-temperature Events: The C-NFSVs Method

The C-NFSVs combines initial and model perturbations and accounts for initial and model uncertainties in ensemble forecasts through Nonlinear Forcing Singular Vector (NFSV; also referred to as CNOP-F) approach. We apply C-NFSVs to Weather Research and Forecasting (WRF) Model and investigate the forecast uncertainty of the 2m temperature over southern China during four major sequential periods of the 2008 extreme cold event. Results show that the C-NFSVs can provide more reliable ensemble forecasts than the scenarios considering only initial or model perturbations. Furthermore, the C-NFSVs reveal that the 2m temperature forecast uncertainties are predominantly sensitive to the uncertainties of the upstream circulation system, while the sensitivity to initial and model uncertainty varies across different periods in the cold event. It is shown that the early period of the extreme cold event tends to propagate the forecast uncertainty represented by the ensemble spread provided by the C-NFSVs by moving itself from the upstream circulation to southern China following the background circulation; however, the forecasts of later periods present spread-characterized uncertainty that persists in the upstream circulation while staying in touch with the background circulation and continuously propagating its effect downstream to southern China. This mechanism indicates that the forecast uncertainties are dominated by initial uncertainties in the forecast of the former period, while model uncertainties play a much significant role in the forecasts of the latter periods. These findings highlight the potential of the C-NFSVs method in identifying the source of forecast uncertainty and delivering skillful forecasts for extreme cold events.