Geostatistical downscaling of AMSR2 precipitation with COMS infrared observations

Pak, No-uk; Hong, Sungwook; Kyriakidis, Phaedon C.; Lee, Woojoo; Lyu, Sang-Jin

Geostatistical downscaling of AMSR2 precipitation with COMS infrared observations

2016

Pak, No-uk | Hong, Sungwook | Kyriakidis, Phaedon C. | Lee, Woojoo | Lyu, Sang-Jin

This article presents a geostatistical approach for downscaling precipitation products from passive microwave satellites with geostationary Meteorological Satellite observations. More precisely, the Advanced Microwave Scanning Radiometer 2 (AMSR2) precipitation (daily level 3 product) with 0.25° spatial resolution and the Communication, Ocean and Meteorological Satellite (COMS) infrared (IR) data with 5 km spatial resolution were used for the downscaling experiment over the Korean peninsula. Brightness temperature data observed at COMS IR 1 and water vapour channels were incorporated for downscaling via area-to-point residual Kriging with non-linear regression. The evaluation results with densely sampled Automatic Weather Station data revealed that incorporating the COMS IR observations with the AMSR2 precipitation showed similar error statistics to those of the AMSR2 precipitation because of the limitations of IR observations themselves and the inherent errors of the AMSR2 precipitation product over land. However, the area-based evaluation using information entropy indicated that incorporating the COMS observations resulted in more detailed spatial variation in the final product than direct downscaling of the AMSR2 precipitation. In addition, local precipitation patterns could be captured when there were regions with missing precipitation values in the AMSR2 product. Consequently, the downscaling result is useful for understanding the local precipitation patterns with an accuracy similar to that provided by microwave satellite observations. It is also suggested that the spatial variability in the downscaling result and errors in input low-resolution data should be considered when downscaling coarse resolution data using fine resolution auxiliary variables.

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