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A Weather Forecast Model Accuracy Analysis and ECMWF Enhancement Proposal by Neural Network

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’s everyday activities. Numerical weather models require real measur...

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Detalles Bibliográficos
Autores principales: Frnda, Jaroslav, Durica, Marek, Nedoma, Jan, Zabka, Stanislav, Martinek, Radek, Kostelansky, Michal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6928600/
https://www.ncbi.nlm.nih.gov/pubmed/31771275
http://dx.doi.org/10.3390/s19235144
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author Frnda, Jaroslav
Durica, Marek
Nedoma, Jan
Zabka, Stanislav
Martinek, Radek
Kostelansky, Michal
author_facet Frnda, Jaroslav
Durica, Marek
Nedoma, Jan
Zabka, Stanislav
Martinek, Radek
Kostelansky, Michal
author_sort Frnda, Jaroslav
collection PubMed
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’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.
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spelling pubmed-69286002019-12-26 A Weather Forecast Model Accuracy Analysis and ECMWF Enhancement Proposal by Neural Network Frnda, Jaroslav Durica, Marek Nedoma, Jan Zabka, Stanislav Martinek, Radek Kostelansky, Michal Sensors (Basel) Article 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’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. MDPI 2019-11-24 /pmc/articles/PMC6928600/ /pubmed/31771275 http://dx.doi.org/10.3390/s19235144 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Frnda, Jaroslav
Durica, Marek
Nedoma, Jan
Zabka, Stanislav
Martinek, Radek
Kostelansky, Michal
A Weather Forecast Model Accuracy Analysis and ECMWF Enhancement Proposal by Neural Network
title A Weather Forecast Model Accuracy Analysis and ECMWF Enhancement Proposal by Neural Network
title_full A Weather Forecast Model Accuracy Analysis and ECMWF Enhancement Proposal by Neural Network
title_fullStr A Weather Forecast Model Accuracy Analysis and ECMWF Enhancement Proposal by Neural Network
title_full_unstemmed A Weather Forecast Model Accuracy Analysis and ECMWF Enhancement Proposal by Neural Network
title_short A Weather Forecast Model Accuracy Analysis and ECMWF Enhancement Proposal by Neural Network
title_sort weather forecast model accuracy analysis and ecmwf enhancement proposal by neural network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6928600/
https://www.ncbi.nlm.nih.gov/pubmed/31771275
http://dx.doi.org/10.3390/s19235144
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