Comparing and Combining Deterministic Surface Temperature Postprocessing Methods over the United States

Abstract

AbstractCommon methods for the postprocessing of deterministic 2-m temperature (T2m) forecasts over the United States were evaluated from +12- to +120-h lead. Forecast data were extracted from the Global Ensemble Forecast System (GEFS) v12 reforecast dataset and thinned to a ½° grid. Analyzed data from the European Centre/Copernicus reanalysis (ERA5) were used for training and validation. Data from the 2000–18 period were used for training, and 2019 forecasts were validated. The postprocessing methods compared were the raw forecast guidance, a decaying-average bias correction (DAV), quantile mapping (QM), a univariate model output statistics (uMOS) algorithm, and a multivariate (mvMOS) algorithm. The mvMOS algorithm used the raw forecast temperature, the DAV adjustment, and the QM adjustment as predictors. Forecasts from all the postprocessing methods reduced the root-mean-square error (RMSE) and bias relative to the raw guidance. QM produced forecasts with slightly higher error than DAV. DAV estimates were the most consistent from day to day. The uMOS and mvMOS algorithms produced statistically significant lower RMSEs than DAV at forecast leads longer than 1 day, with mvMOS exhibiting the lowest error. Taylor diagrams showed that the MOS methods reduced the variability of the forecasts while improving forecast-analyzed correlations. QM and DAV modified the distribution of forecasts to more closely exhibit those of the analyzed data. A main conclusion is that the judicious statistical combination of guidance from multiple postprocessing methods is capable of producing forecasts with improved error statistics relative to any one individual technique. As each method applied here is algorithmically relatively simple, this suggests that operational deterministic postprocessing combining multiple correction methods could produce improved T2m guidance.