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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F17%3A10365860" target="_blank" >RIV/00216208:11320/17:10365860 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/s00477-016-1321-8" target="_blank" >http://dx.doi.org/10.1007/s00477-016-1321-8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00477-016-1321-8" target="_blank" >10.1007/s00477-016-1321-8</a>

Result language

angličtina

Original language name

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

Original language description

A main task of weather services is the issuing of warnings for potentially harmful weather events. Automated warning guidances can be derived, e.g., from statistical post-processing of numerical weather prediction using meteorological observations. These statistical methods commonly estimate the probability of an event (e.g. precipitation) occurring at a fixed location (a point probability). However, there are no operationally applicable techniques for estimating the probability of precipitation occurring anywhere in a geographical region (an area probability). We present an approach to the estimation of area probabilities for the occurrence of precipitation exceeding given thresholds. This approach is based on a spatial stochastic model for precipitation cells and precipitation amounts. The basic modeling component is a non-stationary germ-grain model with circular grains for the representation of precipitation cells. Then, we assign a randomly scaled response function to each precipitation cell and sum these functions up to obtain precipitation amounts. We derive formulas for expectations and variances of point precipitation amounts and use these formulas to compute further model characteristics based on available sequences of point probabilities. Area probabilities for arbitrary areas and thresholds can be estimated by repeated Monte Carlo simulation of the fitted precipitation model. Finally, we verify the proposed model by comparing the generated area probabilities with independent rain gauge adjusted radar data. The novelty of the presented approach is that, for the first time, a widely applicable estimation of area probabilities is possible, which is based solely on predicted point probabilities (i.e., neither precipitation observations nor further input of the forecaster are necessary). Therefore, this method can be applied for operational weather predictions.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10103 - Statistics and probability

Project

<a href="/en/project/7AMB14DE006" target="_blank" >7AMB14DE006: Mathematical analysis, modelling and simulation of random phenomena observed on complex spatial domains, with a special emphasis on random marked sets</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Stochastic Environmental Research and Risk Assessment

ISSN

1436-3240

e-ISSN

—

Volume of the periodical

31

Issue of the periodical within the volume

10

Country of publishing house

DE - GERMANY

Number of pages

16

Pages from-to

2659-2674

UT code for WoS article

000415137900013

EID of the result in the Scopus database

2-s2.0-84988692196