A scattering-based over-land rainfall retrieval algorithm for South Korea using GCOM-W1/AMSR-2 data View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-08

AUTHORS

Young-Joo Kwon, Hayan Shin, Hyunju Ban, Yang-Won Lee, Kyung-Ae Park, Jaeil Cho, No-Wook Park, Sungwook Hong

ABSTRACT

Heavy summer rainfall is a primary natural disaster affecting lives and properties in the Korean Peninsula. This study presents a satellite-based rainfall rate retrieval algorithm for the South Korea combining polarization-corrected temperature (PCT) and scattering index (SI) data from the 36.5 and 89.0 GHz channels of the Advanced microwave Scanning Radiometer 2 (AMSR-2) onboard the Global Change Observation Mission (GCOM)-W1 satellite. The coefficients for the algorithm were obtained from spatial and temporal collocation data from the AMSR-2 and groundbased automatic weather station rain gauges from 1 July - 30 August during the years, 2012-2015. There were time delays of about 25 minutes between the AMSR-2 observations and the ground raingauge measurements. A new linearly-combined rainfall retrieval algorithm focused on heavy rain for the PCT and SI was validated using ground-based rainfall observations for the South Korea from 1 July - 30 August, 2016. The validation presented PCT and SI methods showed slightly improved results for rainfall > 5 mm h-1 compared to the current ASMR-2 level 2 data. The best bias and root mean square error (RMSE) for the PCT method at AMSR-2 36.5 GHz were 2.09 mm h-1 and 7.29 mm h-1, respectively, while the current official AMSR-2 rainfall rates show a larger bias and RMSE (4.80 mm h-1 and 9.35 mm h-1, respectively). This study provides a scatteringbased over-land rainfall retrieval algorithm for South Korea affected by stationary front rain and typhoons with the advantages of the previous PCT and SI methods to be applied to a variety of spaceborne passive microwave radiometers. More... »

PAGES

385-392

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s13143-017-0041-4

DOI

http://dx.doi.org/10.1007/s13143-017-0041-4

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https://app.dimensions.ai/details/publication/pub.1091416421


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