Computer Science – Performance
Scientific paper
Jul 2005
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2005georl..3214804l&link_type=abstract
Geophysical Research Letters, Volume 32, Issue 14, CiteID L14804
Computer Science
Performance
5
Global Change: Climate Variability (1635, 3305, 3309, 4215, 4513), Atmospheric Processes: Climate Change And Variability (1616, 1635, 3309, 4215, 4513), Atmospheric Processes: General Circulation (1223), Atmospheric Processes: Global Climate Models (1626, 4928), Atmospheric Processes: Ocean/Atmosphere Interactions (0312, 4504)
Scientific paper
A statistical approach to correct ensemble seasonal forecasts is formulated based on the regression of the forecast model's leading forced singular value decomposition (SVD) patterns and the observed 500 hPa geopotential height. This technique is applied to the winter forecasts from two general circulation models (GCMs). The performance of the corrected forecasts is assessed by comparing their cross-validated skill with that of the original GCM ensemble mean forecasts. We are particularly interested in the forecast skill of the Pacific/North American (PNA) and the North Atlantic Oscillation (NAO). In the case of the PNA, the technique significantly improves the skill of the less skillful of the two models, and does not modify significantly that of the other model, which produces very good PNA forecasts even before the correction. For the NAO, the correction significantly improves the forecast skill of both models.
Brunet Gilbert
Derome Jacques
Lin Hainan
No associations
LandOfFree
Correction of atmospheric dynamical seasonal forecasts using the leading ocean-forced spatial patterns does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Correction of atmospheric dynamical seasonal forecasts using the leading ocean-forced spatial patterns, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Correction of atmospheric dynamical seasonal forecasts using the leading ocean-forced spatial patterns will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-1249918