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The improvement of quantitative precipitation forecasting (QPF) is still one of the major challenges in numerical weather prediction (NWP). Despite the constant increase of computer power resources, which has allowed the development of more and more sophisticated and resolved NWP models, accurate forecasts of extreme weather conditions, especially when related to intense and localised precipitation structures, are still difficult beyond day 2 (Mullen and Buizza, 2001) and, in rare and selected cases, even at 24 hours. This limitation is due, among other reasons, to the inherently low degree of predictability typical of the relevant physical phenomena. The probabilistic approach has been recently increasingly explored to try to come to terms with the chaotic behaviour of the atmosphere and to help forecasting phenomena with low deterministic predictability.
In addition to this, almost twenty years ago Henk Tennekes, at the time member of the ECMWF (European Centre for Medium-Range Weather Forecasts) Scientific Advisory Committee, raised the question of the opportunity of producing a-priori estimates of forecast skill stating that ‘no forecast is complete without a forecast of the forecast skill’. It is not an overstatement to say that his bold assertion contributed greatly to the development, at least at ECMWF, of forecast skill studies, estimates and prediction techniques (e.g. Palmer and Tibaldi, 1988) and to the related development of statistical-dynamical prediction methods like ensemble forecasting.
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