TY  - JOUR
AU  - Legasa Ríos, Mikel Nestor
AU  - García Manzanas, Rodrigo
AU  - Calviño Martínez, Aída
AU  - Gutiérrez Llorente, José Manuel
PY  - 2022
DO  - 10.1029/2021WR030272
SN  - 0043-1397
UR  - https://hdl.handle.net/20.500.14352/92170
T2  - Water Resources Research
AB  - This work presents a comprehensive assessment of the suitability of random forests, a well-known machine learning technique, for the statistical downscaling of precipitation. Building on the experimental and validation framework proposed in the...
AB  - Statistical downscaling methods aim to improve the limited spatial resolution of current climate models by linking a set of key large-scale predictor variables (e.g., geopotential, winds, etc.) to the predictand of interest (e.g., precipitation)....
LA  - eng
M2  - 1
PB  - Washington American Geophysical Union
KW  - Random forest
KW  - Stochastic precipitation
KW  - Rainfall
KW  - Predictive power
TI  - A posteriori random forests for stochastic downscaling of precipitation by predicting probability distributions
TY  - journal article
VL  - 58
ER  -