Nueva base de datos de precipitaciones mensuales de la república Argentina (PMRAv1), 2000-2022

Juan J. Gaitan; Lucio Biancari

doi:10.24215/1850468Xe032

Authors

Juan J. Gaitan Universidad Nacional de Luján, Argentina
Lucio Biancari Universidad de Buenos Aires, Argentina

DOI:

https://doi.org/10.24215/1850468Xe032

Keywords:

regression random forest, rain gauges, precipitation estimation

Abstract

The accurate representation of spatio-temporal precipitation patterns is an essential input for numerous environmental applications. However, the estimation of precipitation patterns derived solely from rain gauges is subject to large uncertainties, especially in data-scarce regions. We present a new database of Monthly Precipitation of the Argentine Republic (PMRAv1) for the period 2000-2022, with 5 km of spatial resolution. PMRAv1 employs a Regression Random Forest-based methodology to combine monthly data from ground measurements (ranging from 142 to 227 rain gauges in each month) and four global precipitation products derived from satellite-estimated precipitation, global atmospheric circulation models or interpolation of ground-measured data (TERRACLIMATE, ERA5-LAND, GPMv6 y PERSIANN-CDR) with the aim of improving the estimation of monthly precipitation in Argentina. The developed methodology enhanced the spatio-temporal representation of precipitation by allowing the fusion of multiple sources of satellite information and ground measurements. Validation performed using 30% of the rain gauge data showed that PMRAv1 significantly improves the parameters RMSE, MAE, EM and R2 compared to the four global precipitation products. Furthermore, the model was more stable in predicting the observed monthly values, presenting a lower standard deviation in the three adjustment parameters. The PMRAv1 database is available to users in several formats. The term ‘v1’ (version 1) refers to the fact that this product will have successive future versions in the future that will update and refine the precision of the estimates. Likewise, the method presented could also be used to improve the estimation of other climatological variables when ground-based data are available.

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