Can I read Comparison of Bias-corrected Multisatellite Precipitation Products by Deep Learning Framework on EtoBox?

Comparison of Bias-corrected Multisatellite Precipitation Products by Deep Learning Framework by Xuan-Hien Le; Linh Nguyen Van; Duc Hai Nguyen; Giang V. Nguyen; Sungho Jung; Giha Lee is a Environmental Science article available to read on EtoBox.

What is Comparison of Bias-corrected Multisatellite Precipitation Products by Deep Learning Framework about?

Despite satellite-based precipitation products (SPPs) providing a worldwide span with a high spatial and temporal 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 efficacy by successfully reducing the spatial–temporal gap between the four SPPs and APHRODITE. In addition, the ADJ-TRMM p

Who reads Comparison of Bias-corrected Multisatellite Precipitation Products by Deep Learning Framework?

Author

Xuan-Hien Le; Linh Nguyen Van; Duc Hai Nguyen; Giang V. Nguyen; Sungho Jung; Giha Lee

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Environmental Science (Physical Sciences)