packages.bib

@Book{xie2015,
  title = {Dynamic Documents with {R} and knitr},
  author = {Yihui Xie},
  publisher = {Chapman and Hall/CRC},
  address = {Boca Raton, Florida},
  year = {2015},
  edition = {2nd},
  note = {ISBN 978-1498716963},
  url = {http://yihui.name/knitr/},
}

@book{xie2018r,
  title={R Markdown: The Definitive Guide},
  author={Xie, Yihui and Allaire, JJ and Grolemund, Garrett},
  year={2018},
  publisher={CRC Press},
  url = {https://bookdown.org/yihui/rmarkdown/},
}

@book{xie2016bookdown,
  title={Bookdown: Authoring Books and Technical Documents with R Markdown},
  author={Xie, Yihui},
  year={2016},
  publisher={Chapman and Hall/CRC}
}

@Manual{R-base,
  title = {R: A Language and Environment for Statistical Computing},
  author = {{R Core Team}},
  organization = {R Foundation for Statistical Computing},
  address = {Vienna, Austria},
  year = {2018},
  url = {https://www.R-project.org/},
}
@Manual{R-bookdown,
  title = {bookdown: Authoring Books and Technical Documents with R Markdown},
  author = {Yihui Xie},
  year = {2018},
  note = {R package version 0.7},
  url = {https://CRAN.R-project.org/package=bookdown},
}
@Manual{R-knitr,
  title = {knitr: A General-Purpose Package for Dynamic Report Generation in R},
  author = {Yihui Xie},
  year = {2018},
  note = {R package version 1.20},
  url = {https://CRAN.R-project.org/package=knitr},
}
@Manual{R-rmarkdown,
  title = {rmarkdown: Dynamic Documents for R},
  author = {JJ Allaire and Yihui Xie and Jonathan McPherson and Javier Luraschi and Kevin Ushey and Aron Atkins and Hadley Wickham and Joe Cheng and Winston Chang},
  year = {2018},
  note = {R package version 1.10},
  url = {https://CRAN.R-project.org/package=rmarkdown},
}

@article{CHAO2018275,
title = {Geographically weighted regression based methods for merging satellite and gauge precipitation},
journal = {Journal of Hydrology},
volume = {558},
pages = {275-289},
year = {2018},
issn = {0022-1694},
doi = {https://doi.org/10.1016/j.jhydrol.2018.01.042},
url = {https://www.sciencedirect.com/science/article/pii/S0022169418300490},
author = {Lijun Chao and Ke Zhang and Zhijia Li and Yuelong Zhu and Jingfeng Wang and Zhongbo Yu},
keywords = {Satellite precipitation, Precipitation data fusion, Precipitation downscaling, Geographically weighted regression, Mixed geographically weighted regression},
abstract = {Real-time precipitation data with high spatiotemporal resolutions are crucial for accurate hydrological forecasting. To improve the spatial resolution and quality of satellite precipitation, a three-step satellite and gauge precipitation merging method was formulated in this study: (1) bilinear interpolation is first applied to downscale coarser satellite precipitation to a finer resolution (PS); (2) the (mixed) geographically weighted regression methods coupled with a weighting function are then used to estimate biases of PS as functions of gauge observations (PO) and PS; and (3) biases of PS are finally corrected to produce a merged precipitation product. Based on the above framework, eight algorithms, a combination of two geographically weighted regression methods and four weighting functions, are developed to merge CMORPH (CPC MORPHing technique) precipitation with station observations on a daily scale in the Ziwuhe Basin of China. The geographical variables (elevation, slope, aspect, surface roughness, and distance to the coastline) and a meteorological variable (wind speed) were used for merging precipitation to avoid the artificial spatial autocorrelation resulting from traditional interpolation methods. The results show that the combination of the MGWR and BI-square function (MGWR-BI) has the best performance (R = 0.863 and RMSE = 7.273 mm/day) among the eight algorithms. The MGWR-BI algorithm was then applied to produce hourly merged precipitation product. Compared to the original CMORPH product (R = 0.208 and RMSE = 1.208 mm/hr), the quality of the merged data is significantly higher (R = 0.724 and RMSE = 0.706 mm/hr). The developed merging method not only improves the spatial resolution and quality of the satellite product but also is easy to implement, which is valuable for hydrological modeling and other applications.}
}