?Aladin? weather model local revisions using the differential polynomial neural network

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F14%3A86090322" target="_blank" >RIV/61989100:27740/14:86090322 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.14311/NNW.2014.24.009" target="_blank" >http://dx.doi.org/10.14311/NNW.2014.24.009</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.14311/NNW.2014.24.009" target="_blank" >10.14311/NNW.2014.24.009</a>

Result language
angličtina
Original language name
?Aladin? weather model local revisions using the differential polynomial neural network
Original language description
The 48-hour ?Aladin? forecast model can predict significant meteorological quantities in a middle scale area. Neural networks could try to replace some statistical techniques designed to adapt a global meteorological numerical forecast model for local conditions, described with real data surface observations. They succeed commonly a cut above problem solutions with a predefined testing data set, which provides bearing inputs for a trained model. Time-series predictions of the very complex and dynamic weather system are sophisticated and not any time faithful using simple neural network models entered only some few variables of their own next-time step estimations. Predicted values of a global meteorological forecast might instead enter a neural networklocally trained model, for refine it. Differential polynomial neural network is a new neural network type developed by the author; it constructs and substitutes for an unknown general sum partial differential equation of a system descrip
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
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Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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