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rdf:type
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Description
| - Různé interpolační metody byly využity pro získání syntetických meteorologických údajů jako vstupů do různých modelů, vzcházejících z meteorologických dat. (cs)
- Stochastic weather generators (WGs) are used to produce observed-like synthetic weather series, which may serve as an input to various weather-dependent models (e.g. crop models [1,2,3,4,5] and rainfall-runoff models [3]). Typically, WGs are used in assessing sensitivity of the modelled processes to variability and changes in climatic characteristics.To calibrate WG for the ungauged location, WG parameters may be interpolated from the surrounding stations. In our previous experiments, we tested three interpolation techniques: kriging or co-kriging run from ArcGIS, neural networks and nearest neighbours. In this contribution, we focus on the nearest neighbours interpolator which defines the interpolated value as a weighted average from the surrounding stations using a bell-shaped distance-based weight. The performance of the interpolation techniques is examined in two ways: (i) accuracy of interpolation of individual WG parameters is assessed in terms of the Root-Mean-Square Error and Reduct
- Stochastic weather generators (WGs) are used to produce observed-like synthetic weather series, which may serve as an input to various weather-dependent models (e.g. crop models [1,2,3,4,5] and rainfall-runoff models [3]). Typically, WGs are used in assessing sensitivity of the modelled processes to variability and changes in climatic characteristics.To calibrate WG for the ungauged location, WG parameters may be interpolated from the surrounding stations. In our previous experiments, we tested three interpolation techniques: kriging or co-kriging run from ArcGIS, neural networks and nearest neighbours. In this contribution, we focus on the nearest neighbours interpolator which defines the interpolated value as a weighted average from the surrounding stations using a bell-shaped distance-based weight. The performance of the interpolation techniques is examined in two ways: (i) accuracy of interpolation of individual WG parameters is assessed in terms of the Root-Mean-Square Error and Reduct (en)
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Title
| - Reproduction of climatic characteristics by interpolated weather generator
- Reproduction of climatic characteristics by interpolated weather generator (en)
- Reprodukce klimatických charakteristik prostřednictvím interpolací meteorologického generátoru (cs)
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skos:prefLabel
| - Reproduction of climatic characteristics by interpolated weather generator
- Reproduction of climatic characteristics by interpolated weather generator (en)
- Reprodukce klimatických charakteristik prostřednictvím interpolací meteorologického generátoru (cs)
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skos:notation
| - RIV/62156489:43210/06:00104149!RIV07-GA0-43210___
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http://linked.open.../vavai/riv/strany
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http://linked.open...avai/riv/aktivita
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http://linked.open...avai/riv/aktivity
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http://linked.open...iv/cisloPeriodika
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http://linked.open...vai/riv/dodaniDat
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http://linked.open...aciTvurceVysledku
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http://linked.open.../riv/druhVysledku
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http://linked.open...iv/duvernostUdaju
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http://linked.open...titaPredkladatele
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http://linked.open...dnocenehoVysledku
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http://linked.open...ai/riv/idVysledku
| - RIV/62156489:43210/06:00104149
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http://linked.open...riv/jazykVysledku
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http://linked.open.../riv/klicovaSlova
| - kriging; neural networks; weather data (en)
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http://linked.open.../riv/klicoveSlovo
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http://linked.open...odStatuVydavatele
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http://linked.open...ontrolniKodProRIV
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http://linked.open...i/riv/nazevZdroje
| - EMS Annual Meeting Abstracts
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http://linked.open...in/vavai/riv/obor
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http://linked.open...ichTvurcuVysledku
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http://linked.open...cetTvurcuVysledku
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http://linked.open...vavai/riv/projekt
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http://linked.open...UplatneniVysledku
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http://linked.open...v/svazekPeriodika
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http://linked.open...iv/tvurceVysledku
| - Dubrovský, Martin
- Semerádová, Daniela
- Trnka, Miroslav
- Metelka, Ladislav
- Halásová, Olga
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issn
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number of pages
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http://localhost/t...ganizacniJednotka
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