Forecasting the Daily Maximal and Minimal Temperatures from Radiosonde Measurements Using Neural Networks
| Autor: | Gregor Skok, Žiga Zaplotnik, Doruntina Hoxha |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Technology
klimatologija Forecast error Meteorology neural network QH301-705.5 QC1-999 nevronske mreže law.invention explainable AI Troposphere maximum temperature temperatura zraka law Range (statistics) General Materials Science weather forecasting Biology (General) Instrumentation QD1-999 Fluid Flow and Transfer Processes Maximum temperature Artificial neural network Process Chemistry and Technology Physics General Engineering Ranging climatology meritve z radiosondo prediction neural networks Engineering (General). Civil engineering (General) Computer Science Applications strojno učenje air temperature Chemistry machine learning radiosonde measurements udc:551.509 napovedovanje vremena Radiosonde Environmental science TA1-2040 Lead time minimum temperature |
| Zdroj: | Applied Sciences, Vol 11, Iss 10852, p 10852 (2021) Applied sciences, vol. 11, no. 22, 10852, 2021. Applied Sciences Volume 11 Issue 22 |
| ISSN: | 2076-3417 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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