Comparison of bias-corrected multisatellite precipitation products by deep learning framework

Despite satellite-based precipitation products (SPPs) providing a worldwide span with a high spatial and tem-poral resolution, their efficiency in disaster risk forecasting, hydrological, and watershed management remains a challenge due to the significant dependence of rainfall on the spatiotemporal pattern and geographical features of each area. This research proposes an effective deep learning-based solution that combines the convolutional neural network and the benefit of encoder-decoder architecture to eliminate pixel-by-pixel bias to enhance the accuracy of daily SPPs. This work uses five gridded precipitation products, four of which are satellite-based (TRMM, CMORPH, CHIRPS, and PERSIANN-CDR) and one of which is gauge-based (APHRODITE). The Lancang-Mekong River Basin (LMRB), an international basin, was chosen as the research region because of its diverse climate and geographical spread spanning six countries. According to the results of the analyses, the TRMM product exhibits better performance than the other three SPPs. The deep learning model proved its ef-ficacy by successfully reducing the spatial-temporal gap between the four SPPs and APHRODITE. In addition, the ADJ-TRMM product performed the best of the four corrected items, followed by the ADJ-CDR and ADJ-CHIRPS products. This study's findings indicate that each SPP has advantages and disadvantages across LMRB. In the aftermath of the discontinuation of the APHRODITE product in 2015, we believe that the deep learning framework will be a solution for generating a more up-to-date and dependable dataset for LMRB research.

Automated summary

This research proposes an effective deep learning-based solution to enhance the accuracy of daily satellite-based precipitation products (SPPs). The study finds that the TRMM product performs better than the other SPPs when applied to the Lancang-Mekong River Basin. The deep learning framework is believed to be a solution for generating more up-to-date and dependable dataset for LMRB research.