Is considering (in)consistency between runs so useless for weather forecasting?
La prise en compte de l'(in)cohérence entre les séries est-elle si inutile pour les prévisions météorologiques ?
Marchal, Hugo ; Bouttier, François ; Nuissier, Olivier
This paper addresses the issue of forecasting the weather using consecutive runs of one given numerical weather prediction (NWP) system. In the literature, considering how forecasts evolve from one run to another has never proven relevant to predicting the upcoming weather. That is why the usual approach to deal with this consists of blending all together the successive runs, which leads to the well-known "lagged" ensemble. However, some aspects of this approach are questionable, and if the relationship between changes in forecasts and predictability has so far been considered weak, this does not mean that the door is closed. In this paper, we intend to further explore this relationship by focusing on a particular aspect of ensemble prediction systems, the persistence of a given weather scenario over consecutive runs. The idea is that the more a phenomenon persists over successive runs, the more it is likely to occur, but its likelihood is not necessarily estimated as it should be by the latest run alone. Using the regional ensemble of Météo-France, AROME-EPS, and forecasting the probability of certain (warning) precipitation amounts being exceeded in 24 h, we have found that reliability, an important aspect of probabilistic forecasts, is highly sensitive to that persistence. The present study also shows that this dependence can be exploited to improve reliability for individual runs as well as lagged ensembles. From these results, some recommendations for forecasters are made, and the use of new predictors for statistical postprocessing based on consecutive runs is encouraged. The reason for such sensitivity is also discussed, leading to a new insight into weather forecasting using consecutive ensemble runs.</p>
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