Integrating Ground-based Observations and Radar Data Into Gridding Sub-daily Precipitation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F20%3A00531913" target="_blank" >RIV/67985807:_____/20:00531913 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/s11269-020-02622-4" target="_blank" >http://dx.doi.org/10.1007/s11269-020-02622-4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11269-020-02622-4" target="_blank" >10.1007/s11269-020-02622-4</a>

Result language

angličtina

Original language name

Integrating Ground-based Observations and Radar Data Into Gridding Sub-daily Precipitation

Original language description

A new and general approach is proposed for interpolating 6-h precipitation series over large spatial areas. The outputs are useful for distributed hydrological modelling and studies of flooding. We apply our approach to large-scale data, measured between 2014 and 2016 at 159 weather stations network of Meteo Romania, using weather radar information and local topography as ancillary data. Novelty of our approach is in systematic development of a statistical model underlying the interpolation. Seven methods have been tested for the interpolation of the 6-h precipitation measurements: four regression methods (linear regression via ordinary least squares (OLS), with and without logarithmic transformation, and two models of generalized additive model (GAM) class, with logarithmic and identity links), and three regression-kriging models (one uses semivariogram fitted separately every 6-h, based on the residuals of the GAM with identity links models, and other two with pooled semivariograms, based on the OLS and GAM with identity links models). The prediction accuracy of the spatial interpolation methods was evaluated on a part of the dataset not used in the model-fitting stage. Due to the good results in interpolating sub-daily precipitation, normal general additive model with identity link followed with kriging of residuals with kriging parameters estimated from pooled semivariograms was applied to construct the final 6-h precipitation maps (PRK-NGAM). The final results of this work are the 6-h precipitation gridded datasets available in high spatial resolution (1000 m × 1000 m), together with their estimated accuracy.

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

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2020

Confidentiality

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

Name of the periodical

Water Resources Management

ISSN

0920-4741

e-ISSN

—

Volume of the periodical

34

Issue of the periodical within the volume

11

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

19

Pages from-to

3479-3497

UT code for WoS article

000563174700004

EID of the result in the Scopus database

2-s2.0-85089369361