Researchers develop hybrid downscaling model for high-resolution daily rainfall estimation

BEIJING -- A group of Chinese researchers has cooperated with global counterparts in proposing a hybrid downscaling model for deriving high-resolution and high-accuracy precipitation data at a daily scale, according to a research article recently published in the journal IEEE Transactions on Geoscience and Remote Sensing.

Current satellite precipitation products are generally limited by their rough spatial resolution, which fails to meet the demand for high spatio-temporal accuracy data in flash flood monitoring, issuing of landslide warnings and precise water resource management.

The researchers, from institutions such as the Institute of Mountain Hazards and Environment of the Chinese Academy of Sciences (CAS) in Chengdu in Sichuan province, Southwest China, and Sun Yat-sen University in South China's Guangdong province, as well as researchers in Spain and Italy, developed the model, which is based on a soil moisture-based precipitation downscaling and merging (SMPD-MERG) method.

It can provide a very detailed depiction of the spatial variability of precipitation under different seasonal conditions, according to the CAS.

Notably, this new method was successfully applied to global precipitation measurements conducted in the central part of the Iberian Peninsula from 2016 to 2018 -- improving its spatial resolution from 10 to 1 km.

Different from previous empirical downscaling methods, the proposed method not only considers the physical dynamics of the precipitation process but also achieves sound integration of the advantages of multi-source data, the research article noted, while adding that this method shows good potential for producing high-quality precipitation data with high spatio-temporal resolution.