Combining Local and Global Weather Data to Improve Forecast Accuracy for Agriculture

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F24%3A00380559" target="_blank" >RIV/68407700:21240/24:00380559 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.15439/2024F5990" target="_blank" >https://doi.org/10.15439/2024F5990</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.15439/2024F5990" target="_blank" >10.15439/2024F5990</a>

Result language

angličtina

Original language name

Combining Local and Global Weather Data to Improve Forecast Accuracy for Agriculture

Original language description

Accurate local weather forecasting is vital for farmers to optimize crop yields and manage resources effectively, but existing forecasts often lack the precision required locally. This study explores the potential of combining data from local weather stations with global forecasts and reanalysis data to improve the accuracy of local weather predictions. We propose integrating the HadISD data set, which contains data from 27 stations in the Czech Republic, with the Global Forecast System predictions and ERA5-Land reanalysis data. Our goal is to improve 24-hour weather forecasts using Multilayer Perceptrons, CatBoost, and Long Short-Term Memory neural networks. The findings demonstrate that combining local weather station data with global forecasts and incorporating ERA5-Land reanalysis data improves the accuracy of weather predictions in specific locations. Notably, using deep learning to estimate ERA5-Land data and including this estimation in the final model reduced the forecasting error by 59%. This advancement holds promise in optimizing agricultural practices and mitigating weather-related risks in the region.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

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

Article name in the collection

Communication Papers of the 19th Conference on Computer Science and Intelligence Systems

ISBN

978-83-973291-0-2

ISSN

2300-5963

e-ISSN

2300-5963

Number of pages

6

Pages from-to

77-82

Publisher name

Institute of Electrical and Electronics Engineers Inc.

Place of publication

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Event location

Belgrade

Event date

Sep 8, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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