Welcome to the NOAA Institutional Repository |
Stacks Logo
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.
Clear All Simple Search
Advanced Search
Trends in the predictive performance of raw ensemble weather forecasts
  • Published Date:
  • Source:
    Geophys. Res. Lett., 41(24), 9197-9205.
Filetype[PDF-592.91 KB]

  • Description:
    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.
  • Document Type:
  • Main Document Checksum:
No Related Documents.
You May Also Like: