시공간통계를 이용한 천리안 위성산출물의 품질관리방안
Quality Management of COMS Products using Spatio-Temporal Statistical Methods
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초록
The CMDPS(COMS Meteorological Data Processing System) provides 16 meteorological products by processing satellite data obtained from 1 visible and 4 infrared channels. It is expected that the COMS(Communication, Ocean and Meteorological Satellite) products will contribute to numerical weather prediction and climate change monitoring. For more active utilization of the products, we first need a scheme for the quality management based on outlier detection. This paper describes a method for detecting spatial and temporal outliers from the COMS products using Moran scatterplot and wavelet transform. The applicability of our method was made sure through the tests for seven products such as sea surface temperature, land surface temperature, aerosol optical depth, total precipitable water, upper tropospheric humidity, cloud top height, and cloud top temperature. Their quality was estimated very good in terms of our spatio-temporal statistical approach. For future work, we need more comprehensive study on the outlier detection, particularity focusing on the threshold setting in accordance with physical characteristics of each product.
Abstract
The CMDPS(COMS Meteorological Data Processing System) provides 16 meteorological products by processing satellite data obtained from 1 visible and 4 infrared channels. It is expected that the COMS(Communication, Ocean and Meteorological Satellite) products will contribute to numerical weather prediction and climate change monitoring. For more active utilization of the products, we first need a scheme for the quality management based on outlier detection. This paper describes a method for detecting spatial and temporal outliers from the COMS products using Moran scatterplot and wavelet transform. The applicability of our method was made sure through the tests for seven products such as sea surface temperature, land surface temperature, aerosol optical depth, total precipitable water, upper tropospheric humidity, cloud top height, and cloud top temperature. Their quality was estimated very good in terms of our spatio-temporal statistical approach. For future work, we need more comprehensive study on the outlier detection, particularity focusing on the threshold setting in accordance with physical characteristics of each product.
참고문헌 (9)
- 이재현, "웨이블릿을 이용한 주기 신호 데이터의 이상 탐지에 관한 연구", 한국시뮬레이션학회 논문지, 16(4), pp.13~22 (2007), 한국시뮬레이션학회
- 이충헌, "비정규성 데이터에서의 이상치 검출을 위한 통계적인 방법", 석사, 멀티미디어공학, pp.2008
- Alvera-Azcárate, A., "Outlier detection in satellite data using spatial coherence", Remote Sensing of Environment, 119(16), pp.84~91 (2012)
- Burrus, C. S., "Introduction to Wavelets and Wavelet Transforms: A Primer" (1998), Prentice-Hall
- Byun, Y., "Revision of Moran scatterplot approach for more effective forest fire detections", Proceedings of the 5th WSEAS International Conference on Applied Computer Science, pp.952~956 (2006)
- Hofstra, N., "Spatial variability in correlation decay distance and influence on angular-distance weighting interpolation of daily precipitation over Europe", International Journal of Climatology, 29(12), pp.1872~1880 (2009)
- Kern, M., "Outlier detection algorithms and their performance in GOCE gravity field processing", Journal of Geodesy, 78(9), pp.509~519 (2005)
- Lu, C., "Detecting and tracking regional outliers in meteorological data", Information Sciences, 177(7), pp.1609~1632 (2007)
- Lu, X., "Removal of noise by wavelet method to generate high quality temporal data of terrestrial MODIS products", Photogrammetric Engineering & Remote Sensing, 73(10), pp.1129~1139 (2007)