ECMWF short-term prediction accuracy improvement by deep learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10249860" target="_blank" >RIV/61989100:27240/22:10249860 - isvavai.cz</a>

Result on the web

<a href="https://www.nature.com/articles/s41598-022-11936-9" target="_blank" >https://www.nature.com/articles/s41598-022-11936-9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41598-022-11936-9" target="_blank" >10.1038/s41598-022-11936-9</a>

Result language

angličtina

Original language name

ECMWF short-term prediction accuracy improvement by deep learning

Original language description

This paper aims to describe and evaluate the proposed calibration model based on a neural network for post-processing of two essential meteorological parameters, namely near-surface air temperature (2 m) and 24 h accumulated precipitation. The main idea behind this work is to improve short-term (up to 3 days) forecasts delivered by a global numerical weather prediction (NWP) model called ECMWF (European Centre for Medium-Range Weather Forecasts). In comparison to the existing local weather models that typically provide weather forecasts for limited geographic areas (e.g., within one country but they are more accurate), ECMWF offers a prediction of the weather phenomena across the world. Another significant benefit of this global NWP model includes the fact, that by using it in several well-known online applications, forecasts are freely available while local models outputs are often paid. Our proposed ECMWF-enhancing model uses a combination of raw ECMWF data and additional input parameters we have identified as useful for ECMWF error estimation and its subsequent correction. The ground truth data used for the training phase of our model consists of real observations from weather stations located in 10 cities across two European countries. The results obtained from cross-validation indicate that our parametric model outperforms the accuracy of a standard ECMWF prediction and gets closer to the forecast precision of the local NWP models.

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

20200 - Electrical engineering, Electronic engineering, Information engineering

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

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

Name of the periodical

Scientific Reports

ISSN

2045-2322

e-ISSN

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Volume of the periodical

12

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

11

Pages from-to

1-11

UT code for WoS article

000795163100128

EID of the result in the Scopus database

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