Detection of Separation Scenarios in Extreme Weather Events Using Regional Ensemble Prediction Data

To support disaster prevention, it is essential to know in advance when scenarios start to distinguish one from the others, thus requiring the development of early detection methods of such separations. Ensemble prediction systems has been developed to provide scenarios of evolutions via their ensemble members, because the future state of the atmosphere predicted by a single ensemble member is less meaningful than the estimate of the future probability density from all the ensemble members. By construction, the primary function of an ensemble prediction system is to provide forecasters with a degree of uncertainty and level of confidence. For the last couple of years, we have developed different approaches which take advantage of the information provided by different ensemble prediction systems.

Tropical cyclone tracks forecasted by a prediction system sometimes group together into trajectories parting away from each other. An objective method, based on a robust clustering approach, has been created to detect such separation scenarios in the Mesoscale Ensemble Prediction System developed by the Japan Meteorological Agency. At each initialization time, when the number of clusters is greater than 1, local separation scenarios exist. Separations are related to different steering environments predicted by the different ensemble members.

On the same data used for the clustering, we also applied biological concepts such as the Waddington’s epigenetic landscape, and bioinformatics techniques like the graph-Hodge decomposition, to produce “MeteoScape”, an innovative way to characterize the evolution of a tropical cyclone. Particularly, “MeteoScape” can detect the possible paths and their associated probabilities of realization, as well as the regional separatrix, i.e., the spatial boundary between paths/scenarios, regardless of the initialization time (as in the clustering approach).

Using the cases of intense precipitations that occurred in western Japan in July 2018 and August 2021, we introduce a way to detect separation scenarios in a n-dimensional space. A dimensionality reduction technique is applied to the geopotential height at 500 hPa extracted from two different ensemble prediction systems (the Japanese Mesoscale Ensemble Prediction System and the North American National Centers for Environmental Prediction). In the resulting 3-dimensional latent space, trajectories of ensemble members at different initialization times can also part away. We show that these separations are linked to attracting atmospheric trajectories.

Further, the different techniques developed provide information on controllability, particularly where and when a manipulation could be performed.