| Two years of operational prediction of forecast skill at NMC - :11436 | National Weather Service (NWS)
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
Two years of operational prediction of forecast skill at NMC
Filetype[PDF-2.47 MB]

  • Description:
    At the National Meteorological Center we have been running in real time since 1990 a system to predict the forecast skill of the global spectral model, using as predictors the agreement of an ensemble of operational forecasts from various centers, the persistence in the forecast, and the amplitude of the anomalies. These are used in a multiple regression scheme with a 60-day training period, and we predict the regional anomaly correlation of the 00Z NMC global forecast from days 1 to 6. The most important predictor of skill is the agreement between the NMC global forecast started at 00Z, out to 6 days, and four other 12 hour "older" forecasts (JMA, UKMO, and ECMWF, as well as the average of the NMC forecast at OOZ with the previous day's forecast), so that this is like a "poor person's" Monte Carlo ensemble forecast. The other predictors have been selected to add to the predictive capability of the agreement alone, and together they quantify the factors that forecasters use subjectively when evaluating the available forecasts. These predictions are available to NMC forecasters on workstations and to outside users through Internet. The predictive ability of this system, compares favorably with recent theoretical and experimental studies. The correlation between predicted and observed forecast skill seems to be best in regions where forecast skill varies significantly, and the seasonal variation in predicting the skill is small except in the tropics. The overall performance shows that these predictors include enough information about forecast skill to justify further development of skill predictions based on larger forecast ensembles and on more sophisticated statistical techniques.

  • Document Type:
  • Place as Subject:
  • Main Document Checksum:
  • Supporting Files:
    No Additional Files
No Related Documents.
You May Also Like: