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

Kód výsledku v 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>

Výsledek na webu

<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

—

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

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

—

e-ISSN

neuvedeno

Počet stran výsledku

11

Strana od-do

81-91

Název nakladatele

Springer

Místo vydání

Berlin

Místo konání akce

Marrákeš

Datum konání akce

11. 12. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—