Probabilistic Forecast Calibration Using ECMWF and GFS Ensemble Reforecasts. Part I: Two-Meter Temperatures

Abstract

Abstract Recently, the European Centre for Medium-Range Weather Forecasts (ECMWF) produced a reforecast dataset for a 2005 version of their ensemble forecast system. The dataset consisted of 15-member reforecasts conducted for the 20-yr period 1982–2001, with reforecasts computed once weekly from 1 September to 1 December. This dataset was less robust than the daily reforecast dataset produced for the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS), but it utilized a much higher-resolution, more recent model. This manuscript considers the calibration of 2-m temperature forecasts using these reforecast datasets as well as samples of the last 30 days of training data. Nonhomogeneous Gaussian regression was used to calibrate forecasts at stations distributed across much of North America. Significant observations included the following: (i) although the “raw” GFS forecasts (probabilities estimated from ensemble relative frequency) were commonly unskillful as measured in continuous ranked probability skill score (CRPSS), after calibration with a 20-yr set of weekly reforecasts their skill exceeded that of the raw ECMWF forecasts; (ii) statistical calibration using the 20-yr weekly ECMWF reforecast dataset produced a large improvement relative to the raw ECMWF forecasts, such that the ∼4–5-day calibrated reforecast-based product had a CRPSS as large as a 1-day raw forecast; (iii) a calibrated multimodel GFS/ECMWF forecast trained on 20-yr weekly reforecasts was slightly more skillful than either the individual calibrated GFS or ECMWF reforecast products; (iv) approximately 60%–80% of the improvement from calibration resulted from the simple correction of time-averaged bias; (v) improvements were generally larger at locations where the forecast skill was originally lower, and these locations were commonly found in regions of complex terrain; (vi) the past 30 days of forecasts were adequate as a training dataset for short-lead forecasts, but longer-lead forecasts benefited from more training data; and (vii) a small but consistent improvement was produced by calibrating GFS forecasts using the full 25-yr, daily reforecast training dataset versus the subsampled, 20-yr weekly training dataset.