Local Improvements in Numerical Forecasts of Relative Humidity Using Polynomial Solutions of General Differential Equations

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27730%2F18%3A10237630" target="_blank" >RIV/61989100:27730/18:10237630 - isvavai.cz</a>

Výsledek na webu

<a href="https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/qj.3247" target="_blank" >https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/qj.3247</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Local Improvements in Numerical Forecasts of Relative Humidity Using Polynomial Solutions of General Differential Equations

Popis výsledku v původním jazyce

Large-scale forecast models are based on the numerical integration of differential equation systems, which can describe atmospheric processes in light of global meteorological observations. Meso-scale forecast systems need to define the initial and lateral boundary conditions, which may be carried out with robust global numerical models. Their overall solutions are able to describe the dynamic weather system on the earth scale using a large number of complete globe 3D matrix variables in several atmospheric layers. Post-processing methods using local measurements were developed in order to clarify surface weather details and adapt numerical weather prediction model outputs for local conditions. Differential polynomial network is a new type of neural network that can model local weather using spatial data observations to process forecasts of the input variables and revise the target 24-hour prognosis. It defines and solves general partial differential equations, being able to describe unknown dynamic systems. The proposed forecast correction method uses differential network to estimate the optimal numbers of training days and form derivative prediction models. It can improve final numerical forecasts, processed with additional data analysis and statistical techniques, in the majority of cases. The presented 2-stage procedure is analogous to the perfect-prog method using real observations to derive a model, which is applied to the forecasts of the predictors to calculate output predictions.

Název v anglickém jazyce

Local Improvements in Numerical Forecasts of Relative Humidity Using Polynomial Solutions of General Differential Equations

Popis výsledku anglicky

Large-scale forecast models are based on the numerical integration of differential equation systems, which can describe atmospheric processes in light of global meteorological observations. Meso-scale forecast systems need to define the initial and lateral boundary conditions, which may be carried out with robust global numerical models. Their overall solutions are able to describe the dynamic weather system on the earth scale using a large number of complete globe 3D matrix variables in several atmospheric layers. Post-processing methods using local measurements were developed in order to clarify surface weather details and adapt numerical weather prediction model outputs for local conditions. Differential polynomial network is a new type of neural network that can model local weather using spatial data observations to process forecasts of the input variables and revise the target 24-hour prognosis. It defines and solves general partial differential equations, being able to describe unknown dynamic systems. The proposed forecast correction method uses differential network to estimate the optimal numbers of training days and form derivative prediction models. It can improve final numerical forecasts, processed with additional data analysis and statistical techniques, in the majority of cases. The presented 2-stage procedure is analogous to the perfect-prog method using real observations to derive a model, which is applied to the forecasts of the predictors to calculate output predictions.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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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)

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

Quarterly Journal of the Royal Meteorological Society

ISSN

1477-870X

e-ISSN

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Svazek periodika

144

Číslo periodika v rámci svazku

712

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

780-791

Kód UT WoS článku

000443007800012

EID výsledku v databázi Scopus

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