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Predictive skill of an NWP system in the southern lower stratosphere
Author(s) -
Waugh D. W.,
Sisson J. M.,
Karoly D. J.
Publication year - 1998
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.49712455102
Subject(s) - stratosphere , polar vortex , forecast skill , climatology , troposphere , anomaly (physics) , data assimilation , meteorology , environmental science , numerical weather prediction , atmospheric sciences , vortex , geopotential height , statistics , mathematics , geology , physics , precipitation , condensed matter physics
The predictive skill of the Australian Bureau of Meteorology's Global Assimilation and Prediction (GASP) system in the southern lower stratosphere is examined using two different sets of diagnostics: (i) conventional verification statistics used in numerical weather‐prediction studies (namely, root‐mean‐square (RMS) error, anomaly correlation, and bias), and (ii) elliptical diagnostics of the polar vortex (defined using potential vorticity on isen‐tropic surfaces). Both sets of diagnostics indicate the same variation in predictive skill for forecasts during October 1994. The stratospheric forecasts are a large improvement over persistence even at seven days, with the performance at seven days being comparable to that in the troposphere of three‐day forecasts. There is large daily variability in the forecast scores for seven‐day forecasts, and the days with below‐average scores occur when the flow (vortex) is rapidly changing. Examination of the differences in the elliptical diagnostics show that the forecast vortex is weaker, less disturbed (i.e. closer to the pole and less elongated), and rotates faster than the analysed vortex. Consistent with a weaker forecast vortex, the minimum polar temperature and maximum zonal wind are underpredicted in the forecasts. The verification statistics in the stratosphere have a large seasonal variation, although the variation is different for different statistics. The GASP RMS errors are largest (smallest) in late‐spring (summer) whereas both the ratio of GASP to persistence RMS error and the anomaly correlation indicate that the performance relative to persistence is best (worst) in late‐spring (summer).