On the computation of area probabilities based on a spatial stochastic model for precipitation cells and precipitation amounts

Kriesche, Björn; Koubek, Antonín; Pawlas, Zbyněk; Beneš, Viktor; Hess, Reinhold; Schmidt, Volker

doi:10.1007/s00477-016-1321-8

Cited by 2 publications

(4 citation statements)

References 16 publications

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“…As a first step towards this need, Kriesche et al. () generalized the stochastic model considered here for precipitation amounts and for probabilities that given thresholds of precipitation rates are exceeded.…”

Section: Discussionmentioning

confidence: 99%

“…The proposed methods of stochastic geometry are suitable for further applications in operational weather forecasting where, in particular, forecasts of strong precipitation events are of great interest for warning management at DWD. As a first step towards this need, Kriesche et al (2017b) generalized the stochastic model considered here for precipitation amounts and for probabilities that given thresholds of precipitation rates are exceeded.…”

Section: Discussionmentioning

confidence: 99%

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Area precipitation probabilities derived from point forecasts for operational weather and warning service applications

Hess

Kriesche

Schaumann

et al. 2018

Quart J Royal Meteoro Soc

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Probabilistic precipitation forecasts from numerical models are often calibrated using synoptic observations. The resulting probabilities of precipitation refer to the observation system and thus provide the likelihood that precipitation occurs exactly at the spot of the rain gauge. When probabilistic forecasts are required for larger areas, such as rural districts or catchment areas of rivers, it is not possible to interpolate the point probabilities. Instead area probabilities e.g. increase with the size of the area. In this paper we describe a general method to derive area probabilities from point forecasts based on models and methods of stochastic geometry. The method can be applied over arbitrary areas and can be used for operational applications, since it runs fully automatically without human interaction. The basic idea is to model precipitation patterns by circular precipitation cells using a germ–grain model driven by a spatial Poisson point process in a way that the point forecasts are fitted. Area probabilities can then be estimated statistically as relative frequencies based on repeated Monte Carlo simulations. As the area probabilities significantly depend on the sizes of the modelled precipitation cells, suitable cell radii are estimated based on the spatial correlation structure of given point probabilities. Verification with independent radar precipitation and comparison with area probabilities derived from the raw ensemble system COSMO‐DE‐EPS of DWD is provided and reveals essential advantages of the stochastic model in terms of bias and Brier skill score.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Area precipitation probabilities derived from point forecasts for operational weather and warning service applications

Hess

Kriesche

Schaumann

et al. 2018

Quart J Royal Meteoro Soc

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“…Outline. To overcome the limitations mentioned above, a new spatial stochastic model for precipitation cells has been developed recently, see [13] and [14], which better fulfills the requirements of operational weather prediction. In that approach, precipitation cells occurring in a one-hour forecast period are represented by a germ-grain model with circular grains, which is based on a nonstationary Cox point process.…”

Section: Previous Developmentmentioning

confidence: 99%

“…In a first attempt, we propose a model for thunderstorm cells based on spatial Cox processes. A similar approach has been applied successfully to the modeling of precipitation cells, see [13] and [14]. One major requirement for the application in operational weather prediction is spatial non-stationarity to account for geographical differences as well as locally varying weather conditions in the considered forecast period T .…”

Section: Modeling Of Thunderstorm Cellsmentioning

confidence: 99%

A point process approach for spatial stochastic modeling of thunderstorm cells

Kriesche¹,

Hess²,

Schmidt³

2018

pms

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A POINT PROCESS APPROACH FOR SPATIAL STOCHASTIC MODELING OF THUNDERSTORM CELLSIn 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 Coxor doubly-stochastic cluster processes. We present methods for the operationa lfitting of model parameters based on available point probabilities and thunderstorm records of past periods. Furthermore, we derive formulas forthe 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.

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On the computation of area probabilities based on a spatial stochastic model for precipitation cells and precipitation amounts

Cited by 2 publications

References 16 publications

Area precipitation probabilities derived from point forecasts for operational weather and warning service applications

Area precipitation probabilities derived from point forecasts for operational weather and warning service applications

A point process approach for spatial stochastic modeling of thunderstorm cells

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