Abstract
Numerical modelling is a continuously developing discipline in meteorology, which provides meteorological forecasts and climate change projections based on the numerical solutions of the set of equations describing the processes in the atmosphere and the related spheres. The progress in numerical weather prediction (NWP) and climate modelling has been enormous in the last few decades thanks to the improved theoretical understanding of the meteorological processes, the growing number of observations and the increasing available computer power. In spite of the steady progress, meteorological forecasts cannot be fully perfect due to the intrinsic characteristics of the atmosphere and the climate system. Weather forecast uncertainties exist in initial conditions and in the model formulations themselves and evolve rapidly with lead time. In climate change projections the initial conditions have negligible role, but the internal climate variability and the unknown future evolution of the anthropogenic activity are additional sources of uncertainties. Since they cannot be avoided (just minimized), their representation and quantification are essential tasks both in numerical weather prediction and climate research. Currently the only feasible way to challenge this problem is the ensemble approach, which delivers probabilistic information and attributes uncertainty information to the numerical weather forecasts and climate projections. This additional uncertainty estimation is a valuable bonus for the users and can be efficiently applied in decision-making.
Published Version
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