A weather forecast model accuracy analysis and ecmwf enhancement proposal by neural network

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F19%3A10243677" target="_blank" >RIV/61989100:27240/19:10243677 - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/record/display.uri?eid=2-s2.0-85075463131&origin=resultslist&sort=plf-f&src=s&sid=b366385fcdf681dad681d1b71bcb30e8&sot=autdocs&sdt=autdocs&sl=18&s=AU-ID%2857014879400%29&relpos=1&citeCnt=0&searchTerm=" target="_blank" >https://www.scopus.com/record/display.uri?eid=2-s2.0-85075463131&origin=resultslist&sort=plf-f&src=s&sid=b366385fcdf681dad681d1b71bcb30e8&sot=autdocs&sdt=autdocs&sl=18&s=AU-ID%2857014879400%29&relpos=1&citeCnt=0&searchTerm=</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/s19235144" target="_blank" >10.3390/s19235144</a>

Result language

angličtina

Original language name

A weather forecast model accuracy analysis and ecmwf enhancement proposal by neural network

Original language description

This paper presents a neural network approach for weather forecast improvement. Predicted parameters, such as air temperature or precipitation, play a crucial role not only in the transportation sector but they also influence people&apos;s everyday activities. Numerical weather models require real measured data for the correct forecast run. This data is obtained from automatic weather stations by intelligent sensors. Sensor data collection and its processing is a necessity for finding the optimal weather conditions estimation. The European Centre for Medium-Range Weather Forecasts (ECMWF) model serves as the main base for medium-range predictions among the European countries. This model is capable of providing forecast up to 10 days with horizontal resolution of 9 km. Although ECMWF is currently the global weather system with the highest horizontal resolution, this resolution is still two times worse than the one offered by limited area (regional) numeric models (e.g., ALADIN that is used in many European and north African countries). They use global forecasting model and sensor-based weather monitoring network as the input parameters (global atmospheric situation at regional model geographic boundaries, description of atmospheric condition in numerical form), and because the analysed area is much smaller (typically one country), computing power allows them to use even higher resolution for key meteorological parameters prediction. However, the forecast data obtained from regional models are available only for a specific country, and end-users cannot find them all in one place. Furthermore, not all members provide open access to these data. Since the ECMWF model is commercial, several web services offer it free of charge. Additionally, because this model delivers forecast prediction for the whole of Europe (and for the whole world, too), this attitude is more user-friendly and attractive for potential customers. Therefore, the proposed novel hybrid method based on machine learning is capable of increasing ECMWF forecast outputs accuracy to the same level as limited area models provide, and it can deliver a more accurate forecast in real-time.

Czech name

—

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

—

OECD FORD branch

20201 - Electrical and electronic engineering

Project

<a href="/en/project/EF17_049%2F0008425" target="_blank" >EF17_049/0008425: A Research Platform focused on Industry 4.0 and Robotics in Ostrava Agglomeration</a><br>

Continuities

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

Publication year

2019

Confidentiality

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

Name of the periodical

Sensors

ISSN

1424-3210

e-ISSN

—

Volume of the periodical

19

Issue of the periodical within the volume

23

Country of publishing house

CH - SWITZERLAND

Number of pages

15

Pages from-to

1-15

UT code for WoS article

000507606200087

EID of the result in the Scopus database

2-s2.0-85075463131