Hamill, T. M., J. S. Whitaker, and X. Wei, 2004: Ensemble reforecasting: Improving medium-range forecast skill using retrospective forecasts. Mon. Wea. Rev., 132, 1434-1447.


ABSTRACT

The value of the model output statistics (MOS) approach to improving 610-day and week 2 probabilistic forecasts of surface temperature and precipitation is demonstrated. Retrospective 2-week ensemble °»reforecasts°… were computed using a version of the NCEP medium-range forecast model with physics operational during 1998. An NCEPNCAR reanalysis initial condition and bred modes were used to initialize the 15-member ensemble. Probabilistic forecasts of precipitation and temperature were generated by a logistic regression technique with the ensemble mean (precipitation) or ensemble mean anomaly (temperature) as the only predictor. Forecasts were computed and evaluated during 23 winter seasons from 1979 to 2001.

Evaluated over the 23 winters, these MOS-based probabilistic forecasts were skillful and highly reliable. When compared against operational NCEP forecasts for a subset of 100 days from the 20012002 winters, the MOS-based forecasts were comparatively much more skillful and reliable. For example, the MOS-based week 2 forecasts were more skillful than operational 610-day forecasts. Most of the benefit of the MOS approach could be achieved with 10 years of training data, and since sequential sample days provided correlated training data, the costs of reforecasts could also be reduced by skipping days between forecast samples.

MOS approaches will still require a large dataset of retrospective forecasts in order to achieve their full benefit. This forecast model must remain unchanged until reforecasts have been computed for the next model version, a penalty that will slow down the implementation of model updates. Given the substantial improvements noted here, it is argued that reforecast-based MOS techniques should become an integral part of the medium-range forecast process despite this cost. Techniques for computing reforecasts while minimizing the impact to operational weather prediction facilities and model development are discussed.