Séminaire intelligence artificielle le 5 avril 2018 à 11h00 Bât 141 salle André Berthelot
Jeudi 05/04/2018
New Dynamical Systems Tools to study Atmospheric flows
Davide Faranda (LSCE)
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 system can assume or approach again and again, as opposed to those that it will ultimately avoid". The average dimension DD of the attractor corresponds to the number of degrees of freedom sufficient to describe the atmospheric circulation. However, obtaining reliable estimates of DD has proved challenging . Moreover, DD does not provide information on transient atmospheric motions, which lead to weather extremes . Using recent developments in dynamical systems theory , we show that such motions can be classified through instantaneous rather than average properties of the attractor. The instantaneous properties are uniquely determined by instantaneous dimension and stability. Their extreme values correspond to specific atmospheric patterns, and match extreme weather occurrences. We further show the existence of a significant correlation between the time series of instantaneous stability and dimension and the mean spread of sea-level pressure fields in an operational ensemble weather forecast at steps of over two weeks. We believe this method provides an efficient and practical way of evaluating and informing operational weather forecasts 3764 (4 Ko)
Davide Faranda (LSCE)
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 system can assume or approach again and again, as opposed to those that it will ultimately avoid". The average dimension DD of the attractor corresponds to the number of degrees of freedom sufficient to describe the atmospheric circulation. However, obtaining reliable estimates of DD has proved challenging . Moreover, DD does not provide information on transient atmospheric motions, which lead to weather extremes . Using recent developments in dynamical systems theory , we show that such motions can be classified through instantaneous rather than average properties of the attractor. The instantaneous properties are uniquely determined by instantaneous dimension and stability. Their extreme values correspond to specific atmospheric patterns, and match extreme weather occurrences. We further show the existence of a significant correlation between the time series of instantaneous stability and dimension and the mean spread of sea-level pressure fields in an operational ensemble weather forecast at steps of over two weeks. We believe this method provides an efficient and practical way of evaluating and informing operational weather forecasts 3764 (4 Ko)
V. Gautard, dépêche du 30/03/2018
