@article{fdi:010090303,
  title = {{D}ownscaling daily satellite-based precipitation estimates using {MODIS} cloud optical and microphysical properties in machine-learning models},
  author = {{M}edrano, {S}. {C}. and {S}atg{\'e}, {F}r{\'e}d{\'e}ric and {M}olina-{C}arpio, {J}. and {Z}ola, {R}. {P}. and {B}onnet, {M}arie-{P}aule},
  editor = {},
  language = {{ENG}},
  abstract = {{T}his study proposes a method for downscaling the spatial resolution of daily satellite-based precipitation estimates ({SPE}s) from 10 km to 1 km. {T}he method deliberates a set of variables that have close relationships with daily precipitation events in a {R}andom {F}orest ({RF}) regression model. {T}he considered variables include cloud optical thickness ({COT}), cloud effective radius ({CER}) an cloud water path ({CWP}), derived from {MODIS}, along with maximum and minimum temperature ({T}x, {T}n), derived from {CHIRTS}. {A}dditionally, topographic features derived from {ALOS}-{DEM} are also investigated to improve the downscaling procedure. {T}he approach consists of two main steps: firstly, the {RF} model training at the native 10 km spatial resolution of the studied {SPE}s (i.e., {IMERG}) using rain gauge observations as targets; secondly, the application of the trained {RF} model at a 1 km spatial resolution to downscale {IMERG} from 10 km to 1 km over a one-year period. {T}o assess the reliability of the method, the {RF} model outcomes were compared with the rain gauge records not considered in the {RF} model training. {B}efore the downscaling process, the {CC}, {MAE} and {RMSE} metrics were 0.32, 1.16 mm and 6.60 mm, respectively, and improved to 0.48, 0.99 mm and 4.68 mm after the downscaling process. {T}his corresponds to improvements of 50%, 15% and 29%, respectively. {T}herefore, the method not only improves the spatial resolution of {IMERG}, but also its accuracy.},
  keywords = {{IMERG} ; downscaling ; random forest model ; {MODIS} cloud optical and microphysical properties ; {CHIRTS} ; {PEROU}},
  booktitle = {},
  journal = {{A}tmosphere},
  volume = {14},
  numero = {9},
  pages = {1349 [17 ]},
  year = {2023},
  DOI = {10.3390/atmos14091349},
  URL = {https://www.documentation.ird.fr/hor/fdi:010090303},
}