09 Mar 2022
09 Mar 2022
Status: a revised version of this preprint is currently under review for the journal NHESS.

Challenges assessing the effect of AMVs to improve the predictability of a medicane weather event using the EnKF. Storm-scale analysis and short-range forecast

Diego Saúl Carrió1,2 Diego Saúl Carrió
  • 1School of Geography, Earth and Atmospheric Sciences, The University of Melbourne, Parkville, Victoria, Australia
  • 2ARC Centre of Excellence for Climate Extremes, Australia

Abstract. Coastal population in the Western Mediterranean basin is frequently affected by high-impact weather events that produce loss of life and property for an invaluable amount. Among the wide spectrum of maritime severe weather events, tropical-like Mediterranean cyclones (a.k.a. medicanes) draw particular attention, specially due to their poor predictability. The accurate prediction of this kind of events still remains a key challenge to our community, mainly because of (i) the errors in the initial conditions, (ii) the lack of accuracy modeling micro-scale physic processes and (iii) the chaotic behavior inherent to current numerical weather prediction models. In particular, the 7th October 2014 Qendresa medicane, that took place over the Sicilian channel, affecting the Islands of Lampedusa, Pantelleria and Malta was selected for this study because of its extremely low predictability behavior in terms of its track and intensity. To enhance the prediction of Qendresa, a high-resolution (4-km) ensemble-based data assimilation technique, known as Ensemble Kalman Filter (EnKF) is used. In this study both in-situ conventional and satellite-derived observations are assimilated with the main objective of improving Qendresa's model initial conditions and thus, its subsequent forecast. The performance of the EnKF system and its impact on the Qendresa forecast is quantitatively assessed using different deterministic and probabilistic verification methods. A discussion in terms of the relevant physical mechanisms adjusted by the EnKF is also provided. Results reveal that the assimilation of both conventional and satellite-derived observations improves the short-range forecasts of the trajectory and intensity of Qendresa. In this context, it is shown the relevance of assimilating satellite-derived observations to improve the pre-convective estimation of Qendresa's upper-level dynamics, which is key to obtain a realistic track and intensity forecast of this event.

Diego Saúl Carrió

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-58', Anonymous Referee #1, 11 Apr 2022
    • AC1: 'Reply on RC1', Diego Saul Carrio Carrio, 26 Apr 2022
  • RC2: 'Comment on nhess-2022-58', Anonymous Referee #1, 27 Apr 2022
    • AC3: 'Reply on RC2', Diego Saul Carrio Carrio, 23 Sep 2022
  • RC3: 'Comment on nhess-2022-58', Anonymous Referee #2, 16 Aug 2022
    • AC2: 'Reply on RC3', Diego Saul Carrio Carrio, 23 Sep 2022

Diego Saúl Carrió

Diego Saúl Carrió


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Short summary
The accurate prediction of medicanes still remains a key challenge in the scientific community because of their poor predictability. In this study we assimilate different observations to improve the trajectory and intensity forecasts of the Qendresa's medicane. Results show the importance of using data assimilation techniques to improve the estimate of the atmospheric flow in the upper level atmosphere, which has been showed key to improve the prediction of Qendresa.