Spatio-temporal variability of remotely sensed precipitation data from COMS and TRMM: Case study of Korean peninsula in East Asia. Issue 6 (15th September 2015)

• Record Type: Journal Article
• Authors: Baik, Jongjin; Choi, Minha
• Is Part Of: Advances in space research. Volume 56:Issue 6(2015)
• Page Start: 1125
• Page End: 1138
• Publication Date: 2015-09-15
• Subjects: Precipitation -- Communication -- Ocean and Meteorological Satellite (COMS) -- Tropical Rainfall Measuring Mission (TRMM) -- Automatic Weather Station (AWS); Space sciences -- Periodicals; Astronautics -- Periodicals; Geophysics -- Periodicals; 500.505
• DOI: 10.1016/j.asr.2015.06.015
• Languages: English
• ISSNs: 0273-1177

Abstract

Highlights: Evaluation of brand new satellite, COMS for rainfall intensity. Comparison of rainfall intensity using COMS and TRMM products. Over- and underestimation of RI in pre- and monsoon season from COMS and TRMM. Abstract: While a large amount of quantitative ground-based precipitation data is currently available, many limitations remain when striving to provide data on spatial distribution of precipitation. As satellite-based precipitation data are continuously observed across the globe, they may serve as input data for hydrological models when ground station data is unavailable. The goal of this study was to validate the precipitation data for the Korean peninsula in East Asia at three different time scales (1-h, 3-h, and daily) using several Automatic Weather Stations (AWS) and two satellite based precipitation datasets: the Communication, Ocean and Meteorological Satellite (COMS) which is a brand new geostationary satellite, and the Tropical Rainfall Measuring Mission (TRMM). The Index of Agreement (IOA) for daily precipitation between the AWS and both the COMS or TRMM averaged 0.65 (ranged from 0.49 to 0.75) and 0.80 (ranged from 0.68 to 0.89), respectively. Bias and RMSE from the COMS (Bias ranged from −2.5 to 3.98 mm, RMSE ranged from 16.78 to 38.2 mm) and TRMM (Bias ranged from −3.37 to 1.84 mm, RMSE ranged from 14.63 to 32.0 mm) also indicated that precipitation data obtained from satellite and AWS showed similar tend on a daily time scale, while the majority of the satellite based datasets exhibited over- or underestimation patterns during pre- or monsoon seasons, respectively. The spatial distribution of data from the TRMM and COMS showed favorable agreement with that of accumulated precipitation at AWS sites. However, TRMM underestimated the precipitation amounts in mountainous areas. Based on these results, COMS data would be helpful for understanding hydrological modeling and spatial–temporal precipitation variability. To improve the discrepancies between the satellite- and ground-based datasets, further validation of satellite algorithms using various climatic and environmental conditions may be required.