TY - JOUR
T1 - Trends in the predictive performance of raw ensemble weather forecasts
AU - Hemri, S.
AU - Scheuerer, M.
AU - Pappenberger, F.
AU - Bogner, K.
AU - Haiden, T.
PY - 2014/1/1
Y1 - 2014/1/1
N2 - This study applies statistical postprocessing to ensemble forecasts of near-surface temperature, 24 h precipitation totals, and near-surface wind speed from the global model of the European Centre for Medium-Range Weather Forecasts (ECMWF). The main objective is to evaluate the evolution of the difference in skill between the raw ensemble and the postprocessed forecasts. Reliability and sharpness, and hence skill, of the former is expected to improve over time. Thus, the gain by postprocessing is expected to decrease. Based on ECMWF forecasts from January 2002 to March 2014 and corresponding observations from globally distributed stations, we generate postprocessed forecasts by ensemble model output statistics (EMOS) for each station and variable. Given the higher average skill of the postprocessed forecasts, we analyze the evolution of the difference in skill between raw ensemble and EMOS. This skill gap remains almost constant over time indicating that postprocessing will keep adding skill in the foreseeable future. Key Points Evolution of raw ensemble forecast skillFuture benefits from statistical postprocessingGlobal distribution of forecast skill development
AB - This study applies statistical postprocessing to ensemble forecasts of near-surface temperature, 24 h precipitation totals, and near-surface wind speed from the global model of the European Centre for Medium-Range Weather Forecasts (ECMWF). The main objective is to evaluate the evolution of the difference in skill between the raw ensemble and the postprocessed forecasts. Reliability and sharpness, and hence skill, of the former is expected to improve over time. Thus, the gain by postprocessing is expected to decrease. Based on ECMWF forecasts from January 2002 to March 2014 and corresponding observations from globally distributed stations, we generate postprocessed forecasts by ensemble model output statistics (EMOS) for each station and variable. Given the higher average skill of the postprocessed forecasts, we analyze the evolution of the difference in skill between raw ensemble and EMOS. This skill gap remains almost constant over time indicating that postprocessing will keep adding skill in the foreseeable future. Key Points Evolution of raw ensemble forecast skillFuture benefits from statistical postprocessingGlobal distribution of forecast skill development
KW - EMOS
KW - ensemble weather forecasts
KW - model verification
KW - statistical postprocessing
UR - http://www.scopus.com/inward/record.url?scp=84921769223&partnerID=8YFLogxK
U2 - 10.1002/2014GL062472
DO - 10.1002/2014GL062472
M3 - Article (Academic Journal)
AN - SCOPUS:84921769223
VL - 41
SP - 9197
EP - 9205
JO - Geophysical Research Letters
JF - Geophysical Research Letters
SN - 0094-8276
IS - 24
ER -