• Corpus ID: 247292777

Reducing the Dynamic State Index to its main information using Principal Component Analysis

@inproceedings{Muller2022ReducingTD,
  title={Reducing the Dynamic State Index to its main information using Principal Component Analysis},
  author={Annette Muller and Nikolai Spaeth and Johanna Bollow and Iris Eder and Alina Fischer and Jana Frenzel and Annika Gornt and Kunyan Hao and Johanne Ilchmann and Isabel Kasner and Corinna Langwald and Steffen Laube and Flavio Maggioni and Beatrice Neiszt-Pllana and Daniel A. Redant and Lisa Rogge and Susan Sama and David Scheer and Inga Spreen and Patrick W. Varchmin and Vanessa R. Zabel and Wei Zhang and Anika Zibell},
  year={2022}
}

• Annette Muller, Nikolai Spaeth, +20 authors Anika Zibell
• Published 6 March 2022
• Environmental Science

The Dynamic State Index is a scalar quantity designed to identify atmospheric developments such as fronts, hurricanes or specific weather pattern. The DSI is defined as Jacobian-determinant of three constitutive quantities that characterize three-dimensional fluid flows: the Bernoulli stream function, the potential vorticity (PV) and the potential temperature. Here, we tackle the questions (i) if the mathematical formulation of the DSI can be reduced, while keeping the main information, and (ii) does the reduction of the DSI depend on the spatial scale? Applying principle component analysis we find that three of six DSI terms that sum up to the Jacobi-determinant are sufficient for future DSI calculations.