Forecasting the daily maximal and minimal temperatures from radiosonde measurements using neural networks

Skok, Gregor (avtor), Hoxha, Doruntina (avtor), Zaplotnik, Žiga (avtor)

This study investigates the potential of direct prediction of daily extremes of temperature at 2 m from a vertical profile measurement using neural networks (NNs). The analysis is based on 3800 daily profiles measured in the period 2004–2019. Various setups of dense sequential NNs are trained to predict the daily extremes at different lead times ranging from 0 to 500 days into the future. The short- to medium-range forecasts rely mainly on the profile data from the lowest layer—mostly on the temperature in the lowest 1 km. For the long-range forecasts (e.g., 100 days), the NN relies on the data from the whole troposphere. The error increases with forecast lead time, but at the same time, it exhibits periodic behavior for long lead times. The NN forecast beats the persistence forecast but becomes worse than the climatological forecast on day two or three. The forecast slightly improves when the previous-day measurements of temperature extremes are added as a predictor. The best forecast is obtained when the climatological value is added as well, with the biggest improvement in the long-term range where the error is constrained to the climatological forecast error.

• Jezik: Angleški jezik
• Ključne besede: machine learning, neural networks, weather forecasting, air temperature, climatology, radiosonde measurements, prediction, maximum temperature, minimum temperature, explainable AI
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FMF - Fakulteta za matematiko in fiziko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2021
• Št. strani: 17 str.
• Številčenje: Vol. 11, iss. 22, art. 10852
• PID: 20.500.12556/RUL-136369
• UDK: 551.509
• ISSN pri članku: 2076-3417
• DOI: 10.3390/app112210852
• COBISS.SI-ID: 85351683
• Datum objave v RUL: 26.04.2022

SKOK, Gregor, HOXHA, Doruntina in ZAPLOTNIK, Žiga, 2021, Forecasting the daily maximal and minimal temperatures from radiosonde measurements using neural networks. Applied sciences [na spletu]. 2021. Vol. 11, no. 22,  10852. [Dostopano 14 april 2025]. DOI 10.3390/app112210852. Pridobljeno s: https://repozitorij.uni-lj.si/IzpisGradiva.php?lang=slv&id=136369

Gradivo je del revije

• Naslov: Applied sciences
• Skrajšan naslov: Appl. sci.
• Založnik: MDPI
• ISSN: 2076-3417
• COBISS.SI-ID: 522979353

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
• Začetek licenciranja: 17.11.2021

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: strojno učenje, nevronske mreže, napovedovanje vremena, temperatura zraka, klimatologija, meritve z radiosondo

Projekti

• Financer: ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
• Številka projekta: P1-0188
• Naslov: Astrofizika in fizika atmosfere

• Financer: ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
• Številka projekta: J1-9431
• Naslov: Prispevek Rossbyjevih in inercijsko-težnostnih valov k vertikalni hitrosti in pretoku gibalne količine v ozračju