Post-processing of numerical forecasts using polynomial networks with the operational calculus PDE substitution

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F18%3A10240154" target="_blank" >RIV/61989100:27240/18:10240154 - isvavai.cz</a>

Alternative codes found

RIV/61989100:27730/18:10240154

Result on the web

<a href="https://link.springer.com/chapter/10.1007/978-3-030-01818-4_42" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-01818-4_42</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-01818-4_42" target="_blank" >10.1007/978-3-030-01818-4_42</a>

Result language

angličtina

Original language name

Post-processing of numerical forecasts using polynomial networks with the operational calculus PDE substitution

Original language description

Large-scale weather forecast models are based on the numerical integration of systems of differential equation which can describe atmospheric processes in light of physical patterns. Meso-scale weather forecast systems need to define the initial and lateral boundary conditions which can be supplied by global numerical models. Their overall solutions, using a large number of data variables in several atmospheric layers, represent the weather dynamics on the earth scale. Post-processing methods using local measurements were developed in order to adapt numerical weather prediction model outputs for local conditions with surface details. The proposed forecasts correction procedure is based on the 2-stage approach of the Perfect Prog method using data observations to derive a model which is applied to the forecasts of input variables to predict 24-h series of the target output. The post-processing model formation requires an additional initial estimation of the optimal number of training days in consideration of the latest test data. Differential polynomial network is a recent machine learning technique using a polynomial PDE substitution of Operational calculus to form the test and prediction models. It decomposes the general PDE into the 2nd order sub-PDEs in its nodes, being able to describe the local weather dynamics in the surface level. The PDE sum models represent the current local data relations in a sort of settled weather which allow improvements in local forecasts corrected with NWP utilities in the majority of days. (C) Springer Nature Switzerland AG 2019.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

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)

Publication year

2018

Confidentiality

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

Article name in the collection

Advances in Intelligent Systems and Computing. Volume 875

ISBN

978-3-030-01820-7

ISSN

2194-5357

e-ISSN

2194-5365

Number of pages

11

Pages from-to

423-433

Publisher name

Springer

Place of publication

Cham

Event location

Soči

Event date

Sep 17, 2018

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

—