The effects of imputing missing data on ensemble temperature forecasts

The analysis is performed on 48-hour temperature forecasts for ten locations in the Pacific Northwest. The methods are evaluated according to their effect on the forecast performance of two ensemble post-processing forecasting methods, specifically an equal-weight consensus forecast and a ten day performance-weighted window. The methods are also assessed using rank histograms to determine if they preserve the calibration of the ensembles.

For both postprocessing techniques all imputation methods, with the exception of the ensemble mean substitution, produce mean absolute errors not significantly different from the cases when all ensemble members are available. However, the three day mean deviation and ANN have rank histograms similar to that for the baseline of the non-imputed cases (i.e. the ensembles are appropriately calibrated) for all locations, while persistence, ensemble mean, and Fourier substitution do not consistently produce appropriately calibrated ensembles.

The three day mean deviation has the advantage of being computationally efficient in a real-time forecasting environment.