The heavy rain that has occurred recently in Korea due to climate change and long-term rainfall variability and the resulting damage to property and human life reveal the importance of analyzing the characteristics of rainfall variability. Satellite retrieval of precipitation information from COMS is important to understanding the hydrological cycle from the regional to the global scale. Accurate measurement of precipitation is also important for heavy rain in order to reduce the damages caused by heavy precipitation, it is in the interest of meteorology and climate change to dedicate efforts to the quantitative prediction of precipitation. Rainfall is a highly discontinuous process, in terms of both space and time. Accurate and reliable measurements of rainfall over extensive areas of ocean presents a formidable challenge to meteorologists. Even with our best efforts, ground-based measurements only cover a small fraction of the globe. In addition to large uncertainties in the derived estimates, there are problems related to nonuniformities in coverage, quality, and logistics of operations. The observation and monitoring of clouds from space using remote sensing techniques has the potential for providing rainfall information at the desired time and spatial scales.

The assessment of precipitation contributes to improving weather forecasting, at small and large spatial scales, and studying rainfall leads to better understanding of climate variability. We used the COMS satellite and TRMM 3B42 combined passive microwave satellites, including the Microwave Imager (TMI) on TRMM, the Special Sensor Microwave Imager (SSM/I) on the Defense Meteorological Satellite Program (DMSP) satellites, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) on Aqua, and the Advanced Microwave Sounding Unit-B (AMSU-B) on the National Oceanic and Atmospheric Administration (NOAA) satellite series. Satellite-based precipitation retrievals for applications has been studied at the National Environmental Satellite Data and Information Service (NEDIS) of the National Oceanic and Atmospheric Administration (NOAA) and data from the Geostationary Operational Environmental Satellite (GOES) have been used within the Interactive Flash Flood Analyzer (IFFA) system (Scofied 1987). Iguchi et al., (1994) and Kozu et al., (1996) developed a retrieval algorithm for precipitation using the TRMM precipitation radar. Kemmerow et al. (1996) and Olson et al. (1999) estimate precipitation using TRMM with sensor-specific versions of the Goddard Profiling Algorithm (GPROF). The associated vertical profiles of hydrometeors are then used to provide an estimated surface precipitation rate. We also made use of additional satellite data: the TRMM combined estimate, which employs data from both TMI and the TRMM precipitation radar (PR) as a source of calibration (TRMM product 2b31; Haddad et al. 1997a,b). Huffman et al. (1997,2007) developed 3-hourly precipitation using passive microwave data. Combined with a multi-satellite data assimilation system is needed to develop timely monitoring capability for unexpected precipitation in the Asia-Pacific region. The purpose of this report is to evaluate the performance of retrieved precipitation of heavy rainfall events to establish a baseline of performance and directions for improvement at daily and monthly time scales at a spatial resolution of 4km latitude/longitude. We used reference data from a relatively dense station network (ASOS data from KMA) of about 61 rain gauges over South Korea. The COMS-based precipitation estimates were evaluated from April 2011 to –April 2012 using 30-min mean rainfall and 8-min mean rainfall, along with ground measurements. Five methods were explored to evaluate the results comparing COMS rainfall and ASOS rainfall, specifically statistical methods, such as Root Mean Square Error (RMSE), ; Mean Absolute Error (MAE), Mean Error (ME), BIAS, and efficiency (EFF). The evaluation showed that the 30-min mean rainfall from COMS is underestimated in comparison with ASOS data. However, the 8-min mean rainfall is overestimated without a classification of rain/no rain. Finally, we applied the methodology to case studies, including landslides at Woomeon Mountain, Typhoon Bolaven, and the crash of Lao Airlines Flight 3013.

These results can be applied to the development of data services and monitoring systems using the Korea COMS satellites for the Korean peninsula and the Asia Pacific region and, perhaps, a new data assimilation system, combining NASA with COMS. It may also be applied to understanding the nature of the energy and hydrological cycles, forecasting climate change in the Asia-Pacific region, and supporting data for short-term climate forecasts.

1. 서론

2. 연구 자료 및 방법

2.1 연구자료

3. 연구내용

3.1 분석 방법

3.2 분석결과

3.3 사례적용

4. 결론 및 토론