Statistical Downscaling of ERA-Interim Forecast Precipitation Data in Complex Terrain Using LASSO Algorithm
Joint Authors
Gao, Lu
Bernhardt, Matthias
Schulz, Karsten
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-07-10
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Abstract EN
Precipitation is an essential input parameter for land surface models because it controls a large variety of environmental processes.
However, the commonly sparse meteorological networks in complex terrains are unable to provide the information needed for many applications.
Therefore, downscaling local precipitation is necessary.
To this end, a new machine learning method, LASSO algorithm (least absolute shrinkage and selection operator), is used to address the disparity between ERA-Interim forecast precipitation data (0.25° grid) and point-scale meteorological observations.
LASSO was tested and validated against other three downscaling methods, local intensity scaling (LOCI), quantile-mapping (QM), and stepwise regression (Stepwise) at 50 meteorological stations, located in the high mountainous region of the central Alps.
The downscaling procedure is implemented in two steps.
Firstly, the dry or wet days are classified and the precipitation amounts conditional on the occurrence of wet days are modeled subsequently.
Compared to other three downscaling methods, LASSO shows the best performances in precipitation occurrence and precipitation amount prediction on average.
Furthermore, LASSO could reduce the error for certain sites, where no improvement could be seen when LOCI and QM were used.
This study proves that LASSO is a reasonable alternative to other statistical methods with respect to the downscaling of precipitation data.
American Psychological Association (APA)
Gao, Lu& Schulz, Karsten& Bernhardt, Matthias. 2014. Statistical Downscaling of ERA-Interim Forecast Precipitation Data in Complex Terrain Using LASSO Algorithm. Advances in Meteorology،Vol. 2014, no. 2014, pp.1-16.
https://search.emarefa.net/detail/BIM-474259
Modern Language Association (MLA)
Gao, Lu…[et al.]. Statistical Downscaling of ERA-Interim Forecast Precipitation Data in Complex Terrain Using LASSO Algorithm. Advances in Meteorology No. 2014 (2014), pp.1-16.
https://search.emarefa.net/detail/BIM-474259
American Medical Association (AMA)
Gao, Lu& Schulz, Karsten& Bernhardt, Matthias. Statistical Downscaling of ERA-Interim Forecast Precipitation Data in Complex Terrain Using LASSO Algorithm. Advances in Meteorology. 2014. Vol. 2014, no. 2014, pp.1-16.
https://search.emarefa.net/detail/BIM-474259
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-474259