Downscaling satellite-derived daily precipitation products with an integrated framework

TitleDownscaling satellite-derived daily precipitation products with an integrated framework
Publication TypeJournal Article
Year of Publication2018
AuthorsChen, F, Gao, Y, Wang, Y, Qin, F, Li, X
JournalInternational Journal of Climatology
Keywordsdaily precipitation, downscaling, fusion, IMERG, integrated framework
Abstract

Spatially downscaling satellite precipitation products have been performed on annual and monthly precipitation. Accurate downscaling on daily precipitation remains a challenge due to the limitation of the downscaling assumption, the large spatial discontinuity of daily precipitation, and the relatively poor quality of satellite-derived daily precipitation product. In this study, an integrated downscaling-fusion framework was proposed and used to downscale satellite-derived daily precipitation. First, a spatio-temporal downscaling scheme is applied to produce preliminary downscaled daily precipitation. The accuracy of the derived preliminary results is then boosted by merging with daily gauge observations using an ensemble fusion method. The performance of the proposed framework was tested and evaluated by downscaling the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) daily precipitation data from 0.1 to 0.01° over eastern and central China for the period of 2015–2016. The results showed that (a) the downscaling scheme accurately mapped the spatio-temporal variation in daily precipitation, and the preliminary downscaled results perfectly maintained the accuracy of the original IMERG data; (b) the fused results were much more accurate than the original IMERG data, decreasing the root-mean-square errors (RMSEs) by 22, 10, and 18% at daily, monthly, and annual timescales, respectively, for the whole period; and (c) the fused daily precipitation data considerably strengthened the detection of rain/no rain area compared with the original IMERG daily precipitation data, with a 17% reduction in the inconsistency index.

URLhttps://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.5879
DOI10.1002/joc.5879
Author Address

Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education/Collaborative Innovation Center of Yellow River Civilization, Henan University, Kaifeng, China

Nansen Environmental and Remote Sensing Center/Bjerknes Center for Climate Research, Bergen, Norway

Nansen-Zhu International Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China