Statistical Downscaling of ERA-Interim Forecast Precipitation Data in Complex Terrain Using LASSO Algorithm
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 lo...
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| Format: | Article |
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Wiley
2014-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2014/472741 |
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| author | Lu Gao Karsten Schulz Matthias Bernhardt |
| author_facet | Lu Gao Karsten Schulz Matthias Bernhardt |
| author_sort | Lu Gao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-682e1da945e449caba11a7fc4c8db56d |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
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| series | Advances in Meteorology |
| spelling | doaj-art-682e1da945e449caba11a7fc4c8db56d2025-02-03T01:13:14ZengWileyAdvances in Meteorology1687-93091687-93172014-01-01201410.1155/2014/472741472741Statistical Downscaling of ERA-Interim Forecast Precipitation Data in Complex Terrain Using LASSO AlgorithmLu Gao0Karsten Schulz1Matthias Bernhardt2College of Geographical Sciences, Fujian Normal University, Fuzhou 350007, ChinaInstitute of Water Management, Hydrology and Hydraulic Engineering, University of Natural Resources and Life Sciences, 1190 Vienna, AustriaDepartment of Geography, Ludwig-Maximilians-Universität München, 80333 Munich, GermanyPrecipitation 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.http://dx.doi.org/10.1155/2014/472741 |
| spellingShingle | Lu Gao Karsten Schulz Matthias Bernhardt Statistical Downscaling of ERA-Interim Forecast Precipitation Data in Complex Terrain Using LASSO Algorithm Advances in Meteorology |
| title | Statistical Downscaling of ERA-Interim Forecast Precipitation Data in Complex Terrain Using LASSO Algorithm |
| title_full | Statistical Downscaling of ERA-Interim Forecast Precipitation Data in Complex Terrain Using LASSO Algorithm |
| title_fullStr | Statistical Downscaling of ERA-Interim Forecast Precipitation Data in Complex Terrain Using LASSO Algorithm |
| title_full_unstemmed | Statistical Downscaling of ERA-Interim Forecast Precipitation Data in Complex Terrain Using LASSO Algorithm |
| title_short | Statistical Downscaling of ERA-Interim Forecast Precipitation Data in Complex Terrain Using LASSO Algorithm |
| title_sort | statistical downscaling of era interim forecast precipitation data in complex terrain using lasso algorithm |
| url | http://dx.doi.org/10.1155/2014/472741 |
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