A weather ensemble forecasting tool based on statistical and probabilistic methods

By Meriem Krouma

A weather ensemble forecasting tool based on statistical and probabilistic methods

Ensemble weather forecasts can help to anticipate the risks and probabilities of extreme weather events. Nevertheless, weather forecasting is a complex task due to the chaotic behavior of the atmosphere [1]. This represents a major source of uncertainties in particular for the sub-seasonal lead times (from a few days to one month) [2]. To overcome those uncertainties, a large number of numerical simulations is necessary.

In this study, we propose a weather ensemble forecasting tool based on statistical and probabilistic methods to generate ensemble weather forecasts [3]. The Analog-SWG is designed to mimic the behavior of climate variables [4] by assuming the relationship between past and future weather and using similarities in atmospheric circulation patterns [5]. We have tested the Analog-SWG to forecast European precipitation at a local scale ( i.e. station level: Berlin, Madrid, Paris and Toulouse) [3]. We assessed the performance of our forecasts against other forecasts from meteorological centers [3]. We found good performance in different regions of Europe for up to 10 days. We confirmed the importance of atmospheric circulation in driving the meteorological parameters. We also identified the influence of high and low pressure on good and bad forecasts [3].

References

[1] Faranda, D., Messori, G., and Yiou, P.: Dynamical proxies of North Atlantic predictability and extremes. Scientific Reports (2017).

[2] Vitart, F., et al.: The Subseasonal to Seasonal (S2S) Prediction Project Database, Bulletin of the American Meteorological Society. (2017).

[3] Krouma, M., Yiou, P., Déandreis, C., and Thao, S.: Assessment of stochastic weather forecast of precipitation near European cities, based on analogs of circulation, Geosci. Model Dev. (2022).

[4] Ailliot, P., Allard, D., Monbet, V., and Naveau, P.: Stochastic weather generators: an overview of weather type models, Journal de la Société Française de Statistique. (2015)

[5] Yiou, P. and Déandréis, C.: Stochastic ensemble climate forecast with an analogue model, Geoscientific Model Development. (2019).

[6] Krouma, M., Silini, R., and Yiou, P.: Ensemble forecast of an index of the Madden Julian Oscillation using a stochastic weather generator based on circulation analogs, EGUsphere [preprint] (2022)

More Resources

Get to know: Meriem Krouma (Aria Technologies, Paris, France) Ensemble weather forecast with a stochastic weather generator and analogs of the atmospheric circ…