Forecasting the daily maximal and minimal temperatures from radiosonde measurements using neural networks
ID Skok, Gregor (Author), ID Hoxha, Doruntina (Author), ID Zaplotnik, Žiga (Author)

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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.

Keywords:machine learning, neural networks, weather forecasting, air temperature, climatology, radiosonde measurements, prediction, maximum temperature, minimum temperature, explainable AI
Work type:Article (dk_c)
Typology:1.01 - Original Scientific Article
Organization:FMF - Faculty of Mathematics and Physics
Publication status in journal:Published
Article version:Publisher's version of article
Number of pages:17 str.
Numbering:Vol. 11, iss. 22, art. 10852
PID:20.500.12556/RUL-136369 This link opens in a new window
ISSN on article:2076-3417
DOI:10.3390/app112210852 This link opens in a new window
COBISS.SI-ID:85351683 This link opens in a new window
Publication date in RUL:26.04.2022
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Record is a part of a journal

Title:Applied sciences
Shortened title:Appl. sci.
COBISS.SI-ID:522979353 This link opens in a new window


License:CC BY 4.0, Creative Commons Attribution 4.0 International
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:17.11.2021

Secondary language

Keywords:strojno učenje, nevronske mreže, napovedovanje vremena, temperatura zraka, klimatologija, meritve z radiosondo


Funder:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Project number:P1-0188
Name:Astrofizika in fizika atmosfere

Funder:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Project number:J1-9431
Name:Prispevek Rossbyjevih in inercijsko-težnostnih valov k vertikalni hitrosti in pretoku gibalne količine v ozračju

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