2024-03-29T13:58:48Zhttp://digital.csic.es/dspace-oai/requestoai:digital.csic.es:10261/1471942020-06-02T09:19:03Zcom_10261_75com_10261_6col_10261_328
Serrano-Notivoli, Roberto
Luis, Martín de
Beguería, Santiago
2017-03-23T09:35:46Z
2017-03-23T09:35:46Z
2017-03
Serrano-Notivoli R, Luis M, Beguería S. An R package for daily precipitation climate series reconstruction. Environmental Modelling & Software 89: 190–195 (2017)
1364-8152
http://hdl.handle.net/10261/147194
10.1016/j.envsoft.2016.11.005
http://dx.doi.org/10.13039/501100003329
http://dx.doi.org/10.13039/501100000780
http://dx.doi.org/10.13039/501100010067
Daily precipitation datasets are usually large, bulky and hard to handle, but they are of key importance in many environmental studies. We developed a tool to create custom datasets from observed daily precipitation records. Reference values (RV) are computed for each day and location using multivariate logistic regression with altitude, latitude and longitude as covariates. The operations were compiled in an Open Source R package called reddPrec. The reddPrec package consists of a set of functions used to: i) apply a comprehensive quality control over original daily precipitation datasets, flagging suspect data based on five predefined criteria; ii) fill missing values in original data series by estimating precipitation values using the 10 nearest observations for each day; and iii) create new series and gridded datasets in locations where no data were recorded.
eng
http://creativecommons.org/licenses/by-nc-nd/4.0/
openAccess
reddPrec
Daily precipitation
Quality control
Missing values
Grid
An R package for daily precipitation climate series reconstruction
artículo