A multimodel short-range ensemble forecasting system created as part of a National Oceanic and Atmospheric Administration pilot program on temperature and air quality forecasting over New England during the summer of 2002 is evaluated. A simple 7-day running mean bias correction is applied individually to each of the 23 ensemble members. Various measures of accuracy are used to compare these bias-corrected ensemble predictions of 2-m temperature and dewpoint temperature with those available from the nested grid model (NGM) model output statistics (MOS). Results indicate that the bias-corrected ensemble mean prediction is as accurate as the NGM MOS for temperature predictions, and is more accurate than the NGM MOS for dewpoint temperature predictions, for the 48 days studied during the warm season. When the additional probabilistic information from the ensemble is examined, results indicate that the ensemble clearly provides value above that of NGM MOS for both variables, especially as the events become more unlikely. Results also indicate that the ensemble has some ability to predict forecast skill for temperature with a correlation between ensemble spread and the error of the ensemble mean of greater than 0.7 for some forecast periods. The use of a multimodel ensemble clearly helps to improve the spread-skill relationship.
|Original language||English (US)|
|Number of pages||15|
|Journal||Monthly Weather Review|
|State||Published - Oct 2003|
All Science Journal Classification (ASJC) codes
- Atmospheric Science