NWP Model Revisions using Polynomial Similarity Solutions of the General Partial Differential Equation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27730%2F17%3A10237633" target="_blank" >RIV/61989100:27730/17:10237633 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007/978-3-319-76354-5_8" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-319-76354-5_8</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

NWP Model Revisions using Polynomial Similarity Solutions of the General Partial Differential Equation

Original language description

Global weather models solve systems of differential equations to forecast large-scale weather patterns, which do not perfectly represent atmospheric processes near the ground. Statistical corrections were developed to adapt numerical weath-er prognoses for specific local conditions. These techniques combine complex long-term forecasts, based on the physics of the atmosphere, with surface obser-vations using regression in post-processing to clarify surface weather details. Differential polynomial neural network is a new neural network type, which gen-erates series of relative derivative terms to substitute for the general linear partial differential equation, being able to describe the local weather dynamics. The gen-eral derivative formula is expanded by means of the network backward structure into a convergent sum combination of selected composite polynomial fraction terms. Their equality derivative changes can model actual relations of local weath-er data, which are too complex to be represented by standard computing tech-niques. The derivative models can process numerical forecasts of the trained data variables to refine the target 24-hour prognosis of relative humidity or tempera-ture and improve the statistical corrections. Overnight weather changes break the similarity of trained and forecast patterns so that the models are improper and fail in actual revisions but these intermittent days only follow a sort of settled longer periods.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

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)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2017

Confidentiality

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

Article name in the collection

8th International Conference on Innovations in Bio-Inspired Computing and Application (IBICA&apos;17) : proceedings : December 11-13, 2017, Marrakech, Morocco

ISBN

978-3-319-76353-8

ISSN

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e-ISSN

neuvedeno

Number of pages

11

Pages from-to

81-91

Publisher name

Springer

Place of publication

Berlin

Event location

Marrákeš

Event date

Dec 11, 2017

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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