A point process approach for spatial stochastic modeling of thunderstorm cells

• Autorzy: Kriesche B. , Hess R. , Schmidt V.

Identyfikatory

DOI

10.19195/0208-4147.37.2.14

• Języki publikacji: EN

Abstrakty

EN

In this paper we consider two different approaches for spatial stochastic modeling of thunderstorms. Thunderstorm cells are represented using germ-grain models from stochastic geometry, which are based on Cox or doubly-stochastic cluster processes. We present methods for the operational fitting of model parameters based on available point probabilities and thunderstorm records of past periods. Furthermore, we derive formulas for the computation of point and area probabilities according to the proposed germ-grain models. We also introduce a conditional simulation algorithm in order to increase the model’s ability to precisely predict thunderstorm events. A systematic comparison of area probabilities, which are estimated from the proposed models, and thunderstorm records conclude the paper.

Słowa kluczowe

EN

stochastic modeling; Cox process; cluster process; germ-grain model; Monte Carlo simulation; thunderstorm cell

• Wydawca: Wrocław University of Science and Technology
• Czasopismo: Probability and Mathematical Statistics
• Rocznik: 2017
• Tom: Vol. 37, Fasc. 2
• Strony: 471--496
• Opis fizyczny: Bibliogr. 22 poz., rys., wykr.

Twórcy

autor

Kriesche B.

• Ulm University, Institute of Stochastics, Helmholtzstr. 18, 89069 Ulm, Germany

autor

Hess R.

• Deutscher Wetterdienst, Research and Development, Frankfurter Str. 135, 63067 Offenbach, Germany

autor

Schmidt V.

• Ulm University, Institute of Stochastics, Helmholtzstr. 18, 89069 Ulm, Germany

Bibliografia

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).