Atmospheric flows are characterized by chaotic dynamics and recurring large-scale patterns. These two characteristics point to the existence of an atmospheric attractor defined by Lorenz as: “the collection of all states that the systemcan assume or approach again and again, as opposed to those that it will ultimately avoid”. While this dynamical systems perspective can seem horribly abstract, it has immediate applications to the study of large-scale atmospheric patterns and extreme weather events. I will first show that we can compute measures of the stability and complexity (dimension) of instantaneous atmospheric fields in a (relatively) easy way. Next, I hope to convince you that these two quantities are actually useful! Their extreme values correspond to specific large-scale atmospheric patterns, and match extreme weather occurrences. They can also be used to identify "maximum predictability" states of the atmosphere, where the flow at positive lags of up to one week is particularly stable and with a small number of degrees of freedom. Finally, there is a significant correlation between the time series of instantaneous stability and complexity of an atmospheric field and the mean spread at lead times of over two weeks of an operational ensemble weather forecast initialized from that state.
|Veranstalter:||Max Planck Institute für Meteorologie|
|Veranstaltungsort:||Seminar Room 022/023
|Beginn:||13.06.2018 00:30 Uhr|
|Ende:||20.06.2018 15:00 Uhr|