Extreme precipitation events inflict detrimental socio-economic impacts in the Eastern Mediterranean. These are mainly associated with Mediterranean cyclones or the 'Wet' Red Sea Trough (WRST). The region's weather forecasters consider the second challenging to forecast, even just a few days in advance. Here, we study the dynamic and thermodynamic factors influencing the intrinsic predictability of WRST events. With this aim, we combine insights from traditional atmospheric analysis techniques, Lagrangian air-parcel backward trajectories, and dynamical systems theory. The latter describes atmospheric states via their local dimension (d) and inverse persistence (?), which inform us of the intrinsic predictability of the atmosphere in phase space. We compare WRST events of low (upper decile of d and ?) with high (lower decile of d and ?) predictability. We argue that low-predictability events display a significantly different atmospheric pattern. Moreover, the low-predictability events show significantly higher daily precipitation rates, more extensive spatial spread, and greater precipitation variability among events than more predictable ones. On average, low predictability events are initiated by two distinct moisture sources with different water vapor content. We conclude that the dynamical systems framework may become a valuable tool to improve the forecast of extreme precipitation events associated with the WRST by providing a priori information on their intrinsic predictability. We foresee successfully implementing such a framework for other extreme weather events and regions.
The source of extreme precipitation predictability; the case of the 'Wet' Red Sea Trough
F Marra;
2023
Abstract
Extreme precipitation events inflict detrimental socio-economic impacts in the Eastern Mediterranean. These are mainly associated with Mediterranean cyclones or the 'Wet' Red Sea Trough (WRST). The region's weather forecasters consider the second challenging to forecast, even just a few days in advance. Here, we study the dynamic and thermodynamic factors influencing the intrinsic predictability of WRST events. With this aim, we combine insights from traditional atmospheric analysis techniques, Lagrangian air-parcel backward trajectories, and dynamical systems theory. The latter describes atmospheric states via their local dimension (d) and inverse persistence (?), which inform us of the intrinsic predictability of the atmosphere in phase space. We compare WRST events of low (upper decile of d and ?) with high (lower decile of d and ?) predictability. We argue that low-predictability events display a significantly different atmospheric pattern. Moreover, the low-predictability events show significantly higher daily precipitation rates, more extensive spatial spread, and greater precipitation variability among events than more predictable ones. On average, low predictability events are initiated by two distinct moisture sources with different water vapor content. We conclude that the dynamical systems framework may become a valuable tool to improve the forecast of extreme precipitation events associated with the WRST by providing a priori information on their intrinsic predictability. We foresee successfully implementing such a framework for other extreme weather events and regions.File | Dimensione | Formato | |
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