Process-based evaluation of the VALUE perfect predictor experiment of statistical downscaling methods
Statistical downscaling methods (SDMs) are techniques used to downscale and/or bias-correct climate model results to regional or local scales. The European network VALUE developed a framework to evaluate and inter-compare SDMs. One of VALUE's experiments is the perfect predictor experiment that...
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ftcsic:oai:digital.csic.es:10261/213545 2024-02-11T10:06:39+01:00 Process-based evaluation of the VALUE perfect predictor experiment of statistical downscaling methods Soares, Pedro M. M. Maraun, Douglas Brands, Swen Jury, Martin Gutiérrez, José M. San-Martín, Daniel Hertig, Elke Belušić Vozila, A. Cardoso, Rita M. Kotlarski, Sven Drobinski, Philippe Obermann-Hellhund, A. 2019 http://hdl.handle.net/10261/213545 https://doi.org/10.1002/joc.5911 unknown John Wiley & Sons https://doi.org/10.1002/joc.5911 Sí doi:10.1002/joc.5911 e-issn: 1097-0088 issn: 0899-8418 International Journal of Climatology 39(9): 3868-3893 (2019) http://hdl.handle.net/10261/213545 none Bias adjustment Climate change Downscaling Model output statistics Perfectprognosis Regime-oriented artículo http://purl.org/coar/resource_type/c_6501 2019 ftcsic https://doi.org/10.1002/joc.5911 2024-01-16T10:54:57Z Statistical downscaling methods (SDMs) are techniques used to downscale and/or bias-correct climate model results to regional or local scales. The European network VALUE developed a framework to evaluate and inter-compare SDMs. One of VALUE's experiments is the perfect predictor experiment that uses reanalysis predictors to isolate downscaling skill. Most evaluation papers for SDMs employ simple statistical diagnostics and do not follow a process-based rationale. Thus, in this paper, a process-based evaluation has been conducted for the more than 40 participating model output statistics (MOS, mostly bias correction) and perfect prognosis (PP) methods, for temperature and precipitation at 86 weather stations across Europe. The SDMs are analysed following the so-called “regime-oriented” technique, focussing on relevant features of the atmospheric circulation at large to local scales. These features comprise the North Atlantic Oscillation, blocking and selected Lamb weather types and at local scales the bora wind and the western Iberian coastal-low level jet. The representation of the local weather response to the selected features depends strongly on the method class. As expected, MOS is unable to generate process sensitivity when it is not simulated by the predictors (ERA-Interim). Moreover, MOS often suffers from an inflation effect when a predictor is used for more than one station. The PP performance is very diverse and depends strongly on the implementation. Although conditioned on predictors that typically describe the large-scale circulation, PP often fails in capturing the process sensitivity correctly. Stochastic generalized linear models supported by well-chosen predictors show improved skill to represent the sensitivities. Article in Journal/Newspaper North Atlantic North Atlantic oscillation Digital.CSIC (Spanish National Research Council) International Journal of Climatology 39 9 3868 3893 |
institution |
Open Polar |
collection |
Digital.CSIC (Spanish National Research Council) |
op_collection_id |
ftcsic |
language |
unknown |
topic |
Bias adjustment Climate change Downscaling Model output statistics Perfectprognosis Regime-oriented |
spellingShingle |
Bias adjustment Climate change Downscaling Model output statistics Perfectprognosis Regime-oriented Soares, Pedro M. M. Maraun, Douglas Brands, Swen Jury, Martin Gutiérrez, José M. San-Martín, Daniel Hertig, Elke Belušić Vozila, A. Cardoso, Rita M. Kotlarski, Sven Drobinski, Philippe Obermann-Hellhund, A. Process-based evaluation of the VALUE perfect predictor experiment of statistical downscaling methods |
topic_facet |
Bias adjustment Climate change Downscaling Model output statistics Perfectprognosis Regime-oriented |
description |
Statistical downscaling methods (SDMs) are techniques used to downscale and/or bias-correct climate model results to regional or local scales. The European network VALUE developed a framework to evaluate and inter-compare SDMs. One of VALUE's experiments is the perfect predictor experiment that uses reanalysis predictors to isolate downscaling skill. Most evaluation papers for SDMs employ simple statistical diagnostics and do not follow a process-based rationale. Thus, in this paper, a process-based evaluation has been conducted for the more than 40 participating model output statistics (MOS, mostly bias correction) and perfect prognosis (PP) methods, for temperature and precipitation at 86 weather stations across Europe. The SDMs are analysed following the so-called “regime-oriented” technique, focussing on relevant features of the atmospheric circulation at large to local scales. These features comprise the North Atlantic Oscillation, blocking and selected Lamb weather types and at local scales the bora wind and the western Iberian coastal-low level jet. The representation of the local weather response to the selected features depends strongly on the method class. As expected, MOS is unable to generate process sensitivity when it is not simulated by the predictors (ERA-Interim). Moreover, MOS often suffers from an inflation effect when a predictor is used for more than one station. The PP performance is very diverse and depends strongly on the implementation. Although conditioned on predictors that typically describe the large-scale circulation, PP often fails in capturing the process sensitivity correctly. Stochastic generalized linear models supported by well-chosen predictors show improved skill to represent the sensitivities. |
format |
Article in Journal/Newspaper |
author |
Soares, Pedro M. M. Maraun, Douglas Brands, Swen Jury, Martin Gutiérrez, José M. San-Martín, Daniel Hertig, Elke Belušić Vozila, A. Cardoso, Rita M. Kotlarski, Sven Drobinski, Philippe Obermann-Hellhund, A. |
author_facet |
Soares, Pedro M. M. Maraun, Douglas Brands, Swen Jury, Martin Gutiérrez, José M. San-Martín, Daniel Hertig, Elke Belušić Vozila, A. Cardoso, Rita M. Kotlarski, Sven Drobinski, Philippe Obermann-Hellhund, A. |
author_sort |
Soares, Pedro M. M. |
title |
Process-based evaluation of the VALUE perfect predictor experiment of statistical downscaling methods |
title_short |
Process-based evaluation of the VALUE perfect predictor experiment of statistical downscaling methods |
title_full |
Process-based evaluation of the VALUE perfect predictor experiment of statistical downscaling methods |
title_fullStr |
Process-based evaluation of the VALUE perfect predictor experiment of statistical downscaling methods |
title_full_unstemmed |
Process-based evaluation of the VALUE perfect predictor experiment of statistical downscaling methods |
title_sort |
process-based evaluation of the value perfect predictor experiment of statistical downscaling methods |
publisher |
John Wiley & Sons |
publishDate |
2019 |
url |
http://hdl.handle.net/10261/213545 https://doi.org/10.1002/joc.5911 |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_relation |
https://doi.org/10.1002/joc.5911 Sí doi:10.1002/joc.5911 e-issn: 1097-0088 issn: 0899-8418 International Journal of Climatology 39(9): 3868-3893 (2019) http://hdl.handle.net/10261/213545 |
op_rights |
none |
op_doi |
https://doi.org/10.1002/joc.5911 |
container_title |
International Journal of Climatology |
container_volume |
39 |
container_issue |
9 |
container_start_page |
3868 |
op_container_end_page |
3893 |
_version_ |
1790604502622011392 |