Easy-to-use spatial random-forest-based downscaling-calibration method for producing precipitation data with high resolution and high accuracy

Chen, Chuanfa; Hu, Baojian; Li, Yanyan

doi:https://doi.org/10.5194/hess-25-5667-2021

Articles | Volume 25, issue 11

https://doi.org/10.5194/hess-25-5667-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Experiments in Hydrology and Hydraulics

https://doi.org/10.5194/hess-25-5667-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 25, issue 11

Research article

|

03 Nov 2021

Research article |

| 03 Nov 2021

Easy-to-use spatial random-forest-based downscaling-calibration method for producing precipitation data with high resolution and high accuracy

Chuanfa Chen, Baojian Hu, and Yanyan Li

Data sets

Gauge data China Meteorological Data Service Center http://data.cma.cn/data/detail/dataCode/SURF_CLI_CHN_MUL_DAY_CES_V3.0.html

Global Precipitation Measurement NASA https://doi.org/10.5067/GPM/IMERG/3B-MONTH/06

MOD13A3 data NASA Earth Data https://doi.org/10.5067/MODIS/mod13a3.006

MOD11A2 data NASA Earth Data https://doi.org/10.5067/MODIS/MOD11A2.006

SRTM Data CGIAR https://doi.org/10.5066/F7PR7TFT

Short summary

This study proposes an easy-to-use downscaling-calibration method based on a spatial random forest with the incorporation of high-resolution variables. The proposed method is general, robust, accurate and easy to use as it shows more accurate results than the classical methods in the study area with heterogeneous terrain morphology and precipitation. It can be easily applied to other regions where precipitation data with high resolution and high accuracy are urgently required.