Machine Learning-Based Front Detection in Central Europe
| Autor: | Danuta Kubacka, Agnieszka Wypych, Zbigniew Ustrnul, Bogdan Bochenek |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Atmospheric Science
Meteorological reanalysis Computer science business.industry weather fronts Front (oceanography) Training (meteorology) Environmental Science (miscellaneous) Machine learning computer.software_genre Extreme weather machine learning Meteorology. Climatology Thunderstorm Weather front ERA5 Artificial intelligence QC851-999 Tornado business computer random forest Air mass |
| Zdroj: | Atmosphere, Vol 12, Iss 1312, p 1312 (2021) Atmosphere Volume 12 Issue 10 |
| ISSN: | 2073-4433 |
| DOI: | 10.3390/atmos12101312 |
| Popis: | Extreme weather phenomena such as wind gusts, heavy precipitation, hail, thunderstorms, tornadoes, and many others usually occur when there is a change in air mass and the passing of a weather front over a certain region. The climatology of weather fronts is difficult, since they are usually drawn onto maps manually by forecasters therefore, the data concerning them are limited and the process itself is very subjective in nature. In this article, we propose an objective method for determining the position of weather fronts based on the random forest machine learning technique, digitized fronts from the DWD database, and ERA5 meteorological reanalysis. Several aspects leading to the improvement of scores are presented, such as adding new fields or dates to the training database or using the gradients of fields. |
| Databáze: | OpenAIRE |
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