Statistical Postprocessing of Different Variables for Airports in Spain Using Machine Learning
Joint Authors
Quintero Plaza, David
García-Moya Zapata, José Antonio
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-04-30
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
The results of a deterministic calibration for the nonhydrostatic convection-permitting LAM-EPS AEMET-γSREPS are shown.
LAM-EPS AEMET-γSREPS is a multiboundary condition, multimodel ensemble forecast system developed for Spain.
Machine learning tools are used to calibrate the members of the ensemble.
Machine learning (hereafter ML) has been considerably successful in many problems, and recent research suggests that meteorology and climatology are not an exception.
These machine learning tools range from classical statistical methods to contemporary successful and powerful methods such as kernels and neural networks.
The calibration has been done for airports located in many regions of Spain, representing different climatic conditions.
The variables to be calibrated are the 2-meter temperature, the 10-meter wind speed, and the precipitation in 24 hours.
Classical statistical methods perform very well with the temperature and the wind speed; the precipitation is a subtler case: it seems there is not a general rule, and for each point, a decision has to be taken of what method (if any) improves the direct output of the model, but even recognizing this, a slight improvement can be shown with ML methods for the precipitation.
American Psychological Association (APA)
Quintero Plaza, David& García-Moya Zapata, José Antonio. 2019. Statistical Postprocessing of Different Variables for Airports in Spain Using Machine Learning. Advances in Meteorology،Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1118603
Modern Language Association (MLA)
Quintero Plaza, David& García-Moya Zapata, José Antonio. Statistical Postprocessing of Different Variables for Airports in Spain Using Machine Learning. Advances in Meteorology No. 2019 (2019), pp.1-14.
https://search.emarefa.net/detail/BIM-1118603
American Medical Association (AMA)
Quintero Plaza, David& García-Moya Zapata, José Antonio. Statistical Postprocessing of Different Variables for Airports in Spain Using Machine Learning. Advances in Meteorology. 2019. Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1118603
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1118603