Evaluation of the analog method for daily precipitation simulation in a complex orography region
DOI:
https://doi.org/10.24215/1850468Xe031Keywords:
ERA-Interim, Central Andes, Argentina, Chile, statistical downscalingAbstract
Global Climate Models (GCMs) are the main tool available to predict future climate scenarios, nevertheless their performance of GCMs might be hindered by their limited spatial resolution in complex orography regions, such as the Central Andes. In this paper, possible added value via statistical downscaling modeling for daily precipitation in Central Andes region was explored. Data from 83 rain gauge stations in the period 1981-2015 was used in order to calibrate an analog method employing ERA-Interim reanalysis. Statistical downscaling models proved to be more accurate than reanalysis precipitation raw data, particularly for mean precipitation values and variables built on a daily basis. Models based on information from local atmospheric predictors showed better performances than those built on large scale data simplified via principal component analysis. Performance of downscaling models was not even throughout the domain, showing more accurate results in Southern Chilean stations. This was probably due to synoptic forcing being well captured by downscaling models. Precipitation variability at different time scales (intraannual, interannual and long-term tendencies) was successfully replicated by statistical models.
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