The value of a variable resolution approach to numerical weather prediction

Title
The value of a variable resolution approach to numerical weather prediction
Technical memorandum
Date Published
10/2009
Secondary Title
ECMWF Technical Memoranda
Number
605
Author
Roberto Buizza
Publisher
ECMWF
Abstract It is shown that a numerical weather prediction system with variable resolution, higher in the early forecast range and lower afterwards, provides more skilful forecasts than a system with a constant resolution system. Results indicate that the advantage can be detected also beyond the time when the resolution is truncated (truncation time). Forecasts generated with a T399 spectral truncation up to forecast day 3 and a T255 truncation from day 3 to day 8 (VAR3), are compared with forecasts generated with a constant T319 truncation. Firstly, forecasts are verified in an Idealized Model Error (IME) scenario against higher resolution, T799 simulations. In this scenario, VAR3 outperforms the T319 system beyond the day-3 truncation time for the entire 8-day forecast range, with differences statistically significant at the 5% level. Secondly, forecasts are verified in a realistic scenario against T799 analyses. In this case, although the advantage of VAR3 can still be detected beyond day-3, it is less evident and not statistically significant. Forecast error spectra indicate that using a higher resolution model during the first forecast days improves the forecasts of the large-scales, thus helping to maintain the advantage of the variable resolution system beyond the truncation time. VAR3 and T319 ensembles are also compared with forecasts with a T255, T399 and T799 constant resolution. The predictability ‘gain' of all ensemble configurations is measured with respect to the reference constant T255 configuration. Results show that, in the realistic scenario, VAR3 gives gains 50-75% higher than T319, and 50-75% lower than T799.
URL https://www.ecmwf.int/en/elibrary/73833-value-variable-resolution-approach-numerical-weather-prediction
DOI 10.21957/uxwm7tqg
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