Article on Random Forest for merging satellite-based datasets with gorund observations published in RSE
The article "RF-MEP: A novel Random Forest method for merging gridded precipitation products and ground-based measurements" was accepted for publication in December 2019 and made available online on January 2nd 2020 in the Remote Sensing of Environment journal.
This work presents the Random Forest based MErging Procedure (RF-MEP), which allows to combine information from ground-based measurements, satellite-based precipitation products, and topography-related features to improve the representation of the spatio-temporal distribution of precipitation, especially in data-scarce regions. RF-MEP is applied over Chile for 2000-2016, using daily measurements from 258 rain gauges for model training and 111 stations for validation. Two merged datasets were computed: RF-MEP3P (based on PERSIANN-CDR, ERA-Interim, and CHIRPSv2) and RF-MEP5P (which additionally includes CMORPHv1 and TRMM 3B42v7). Our results suggest that RF-MEP could successfully be applied to other regions and to correct other climatological variables. The RFmerge R package, which implements RF-MEP, is freely available online at https://github.com/hzambran/RFmerge.
My Caption
I am an Associate Professor in the Department of Civil Engineering at the University of La Frontera. I hold a PhD in Environmental Engineering from the University of Trento (Italy) and completed postdoctoral training at the European Commission’s Joint Research Centre. I have more than 20 years of experience in water resources research and have previously served as an Associate Researcher at the Center for Climate and Resilience Research (CR)2 and as a member of the Earth Sciences Assessment Group of the Chilean National Research and Development Agency (ANID).
My research lies at the interface of hydrology, data science, and environmental sciences, with a particular focus on the use of gridded datasets and open-source tools to investigate droughts, extreme events, and water-related impacts of global change.
I work across spatial and temporal scales to improve the understanding of catchment-scale hydrological processes and to translate this knowledge into operational modelling, forecasting, and early-warning systems that support robust environmental decision-making.
Please reach out to collaborate 😃