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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378289%3A_____%2F18%3A00490102" target="_blank" >RIV/68378289:_____/18:00490102 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1002/qj.3247" target="_blank" >http://dx.doi.org/10.1002/qj.3247</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/qj.3247" target="_blank" >10.1002/qj.3247</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10509 - Meteorology and atmospheric sciences

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2018

Confidentiality

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

Name of the periodical

Quarterly Journal of the Royal Meteorological Society

ISSN

0035-9009

e-ISSN

—

Volume of the periodical

144

Issue of the periodical within the volume

712

Country of publishing house

GB - UNITED KINGDOM

Number of pages

12

Pages from-to

780-791

UT code for WoS article

000443007800012

EID of the result in the Scopus database

2-s2.0-85052512078