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The atmospheric scientific community is nowadays facing the ambitious challenge of providing useful forecasts of atmospheric events that produce high societal impact. The low level of social resilience to false alarms creates tremendous pressure on forecasting offices to issue accurate, timely and reliable warnings.
Currently, no operational numerical forecasting system is able to respond to the societal demand for high-resolution (in time and space) predictions in the 12-72h time span. The main reasons for such deficiencies are the lack of adequate observations and the high non-linearity of the numerical models that are currently used. The whole weather forecasting problem is intrinsically probabilistic and current methods aim at coping with the various sources of uncertainties and the error propagation throughout the forecasting system. Ensemble prediction systems have proven efficient for the mid-term (3-15 days) forecasts at the synoptic scales. However, the challenges that emerge when the highly non-linear mesoscale and convective-scale dynamics are abided for the short-range high-impact weather forecasts demand, new methods to explore the space of uncertainties must be explored. The dynamical properties of the current short-range numerical forecasting models will be discussed and experimental methods to improve the performance of such models by targeting a collection of quasi-most unstable modes will be presented. |
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