Bias Correction and Forecast Skill of NCEP GFS Ensemble Week-1 and Week-2 Precipitation, 2-m Surface Air Temperature, and Soil Moisture Forecasts

Abstract

Abstract A simple bias correction method was used to correct daily operational ensemble week-1 and week-2 precipitation and 2-m surface air temperature forecasts from the NCEP Global Forecast System (GFS). The study shows some unexpected and striking features of the forecast errors or biases of both precipitation and 2-m surface air temperature from the GFS. They are dominated by relatively large-scale spatial patterns and low-frequency variations that resemble the annual cycle. A large portion of these forecast errors is removable, but the effectiveness is time and space dependent. The bias-corrected week-1 and week-2 ensemble precipitation and 2-m surface air temperature forecasts indicate some improvements over their raw counterparts. However, the overall levels of week-1 and week-2 forecast skill in terms of spatial anomaly correlation and root-mean-square error are still only modest. The dynamical soil moisture forecasts (i.e., land surface hydrological model forced with bias-corrected precipitation and 2-m surface air temperature integrated forward for up to 2 weeks) have very high skill, but hardly beat persistence over the United States. The inability to outperform persistence mainly relates to the skill of the current GFS week-1 and week-2 precipitation forecasts not being above a threshold (i.e., anomaly correlation > 0.5 is required).