Numerical Weather Prediction Revisions using the Locally Trained Differential Polynomial Network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86095963" target="_blank" >RIV/61989100:27240/16:86095963 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27730/16:86095963

Výsledek na webu

<a href="http://www.sciencedirect.com/science/article/pii/S0957417415006247" target="_blank" >http://www.sciencedirect.com/science/article/pii/S0957417415006247</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.eswa.2015.08.057" target="_blank" >10.1016/j.eswa.2015.08.057</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Numerical Weather Prediction Revisions using the Locally Trained Differential Polynomial Network

Popis výsledku v původním jazyce

Meso-scale forecasts result from global numerical weather prediction models, which are based upon the differential equations for atmospheric dynamics that do not perfectly determine weather conditions near the ground. Statistical corrections can combine complex numerical models, based on the physics of the atmosphere to forecast the large-scale weather patterns, and regression in post-processing to clarify surface weather details according to local observations and climatological conditions. Neural networks trained with local relevant weather observations of fluctuant data relations in current conditions, entered by numerical model outcomes of the same data types, may revise its one target short-term prognosis (e.g. relative humidity or temperature) to stand for these methods. Polynomial neural networks can compose general partial differential equations, which allow model more complicated real system functions from discrete time-series observations than using standard soft-computing methods. This new neural network technique generates convergent series of substitution relative derivative terms, which combination sum can define and solve an unknown general partial differential equation, able to describe dynamic processes of the weather system in a local area, analogous to the differential equation systems of numerical models. The trained network model revises hourly-series of numerical prognosis of one target variable in sequence, applying the general differential equation solution of the correction multi-variable function to corresponding output variables of the 24-hour numerical forecast. The experimental results proved this polynomial network type can successfully revise some numerical weather prognoses after this manner.

Název v anglickém jazyce

Numerical Weather Prediction Revisions using the Locally Trained Differential Polynomial Network

Popis výsledku anglicky

Meso-scale forecasts result from global numerical weather prediction models, which are based upon the differential equations for atmospheric dynamics that do not perfectly determine weather conditions near the ground. Statistical corrections can combine complex numerical models, based on the physics of the atmosphere to forecast the large-scale weather patterns, and regression in post-processing to clarify surface weather details according to local observations and climatological conditions. Neural networks trained with local relevant weather observations of fluctuant data relations in current conditions, entered by numerical model outcomes of the same data types, may revise its one target short-term prognosis (e.g. relative humidity or temperature) to stand for these methods. Polynomial neural networks can compose general partial differential equations, which allow model more complicated real system functions from discrete time-series observations than using standard soft-computing methods. This new neural network technique generates convergent series of substitution relative derivative terms, which combination sum can define and solve an unknown general partial differential equation, able to describe dynamic processes of the weather system in a local area, analogous to the differential equation systems of numerical models. The trained network model revises hourly-series of numerical prognosis of one target variable in sequence, applying the general differential equation solution of the correction multi-variable function to corresponding output variables of the 24-hour numerical forecast. The experimental results proved this polynomial network type can successfully revise some numerical weather prognoses after this manner.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: Centrum excelence IT4Innovations</a><br>

Návaznosti

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

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název periodika

Expert Systems with Applications

ISSN

0957-4174

e-ISSN

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Svazek periodika

44

Číslo periodika v rámci svazku

jaro

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

10

Strana od-do

"265-274"

Kód UT WoS článku

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EID výsledku v databázi Scopus

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