Dataset Description: This dataset supports our research presented in the paper "A deep learning-based framework for multi-source precipitation fusion" by Gavahi et al., (2023) published in Remote Sensing of Environment. The study introduces a novel deep learning architecture for merging and downscaling multiple precipitation products, aiming to enhance quantitative precipitation estimation (QPE) accuracy. The developed model, the Precipitation Data Fusion Network (PDFN), integrates 3D-CNN and ConvLSTM layers to capture the inherent spatiotemporal dependencies of precipitation data. The results indicate significant improvements in error statistics.

The dataset includes merged daily precipitation estimations using the PDFN model. The data cover the Continental United States (CONUS) and are provided at a spatial resolution of 0.05 degrees. Temporal coverage spans from January 1, 2015, to April 30, 2024. The coordinate reference system used is WGS1984.

Note: Since the PERSIANN-CDR dataset is only available until the end of 2023, in this dataset, we used PDIR-Now instead to ensure the dataset's continuity and completeness. In the original paper, we used PERSIANN-CDR, but here we used PDIR-Now to extend the dataset to cover the period until April 30, 2024.

Usage Notes: This dataset is intended for use in applications such as land surface modeling, flood forecasting, drought monitoring and prediction. Users are requested to cite the associated paper when utilizing the dataset for academic or research purposes.

Related Publications: For further details on the methodology and applications of this dataset, refer to the paper "Gavahi, K., E. Foroumandi, and H. Moradkhani (2023), A deep learning-based framework for multi-source precipitation fusion, Remote Sensing of Environment, doi:10.1016/j.rse.2023.113723"

The dataset can be accessed at: https://zenodo.org/records/12518776.