Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11765/14865
Event selection for dynamical downscaling: a neural network approach for physically-constrained precipitation events
Title: | Event selection for dynamical downscaling: a neural network approach for physically-constrained precipitation events |
Authors: | Gómez Navarro, Juan José
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Keywords: | Dynamical downscaling; Extreme events; Statistical downscaling; Precipitation extremes |
Issue Date: | 2022 |
Publisher: | Springer |
Citation: | Climate Dynamics. 2022, 58, p. 2863–2879 |
Publisher version: | https://doi.org/10.1007/s00382-019-04818-w |
Abstract: | This study presents a new dynamical downscaling strategy for extreme events. It is based on a combination of statistical downscaling of coarsely resolved global model simulations and dynamical downscaling of specific extreme events constrained by the statistical downscaling part. The method is applied to precipitation extremes over the upper Aare catchment, an area in Switzerland which is characterized by complex terrain. The statistical downscaling part consists of an Artificial Neural Network (ANN) framework trained in a reference period. Thereby, dynamically downscaled precipitation over the target area serve as predictands and large-scale variables, received from the global model simulation, as predictors. Applying the ANN to long term global simulations produces a precipitation series that acts as a surrogate of the dynamically downscaled precipitation for a longer climate period, and therefore are used in the selection of events. These events are then dynamically downscaled with a regional climate model to 2 km. The results show that this strategy is suitable to constraint extreme precipitation events, although some limitations remain, e.g., the method has lower efficiency in identifying extreme events in summer and the sensitivity of extreme events to climate change is underestimated. |
URI: | http://hdl.handle.net/20.500.11765/14865 |
ISSN: | 0930-7575 1432-0894 |
Appears in Collections: | Artículos científicos 2019-2022 |
Files in This Item:
File | Description | Size | Format | ||
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![]() | CD_Gomez_2019.pdf | 1,19 MB | Adobe PDF | ![]() View/Open |
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