Browse > Article
http://dx.doi.org/10.14191/Atmos.2011.21.3.273

Characteristics of Satellite Brightness Temperature and Rainfall Intensity over the Life Cycle of Convective Cells-Case Study  

Kim, Deok Rae (Climate Change Research Division, Climate and Air Quality Research Department, National Institute of Environmental Research)
Kwon, Tae Yong (Department of Atmospheric and Environmental Sciences, Gangneung-Wonju National University)
Publication Information
Atmosphere / v.21, no.3, 2011 , pp. 273-284 More about this Journal
Abstract
This study investigates the characteristics of satellite brightness temperature (TB) and rainfall intensity over the life cycle of convective cells. The convective cells in the three event cases are detected and tracked from the growth stage to the dissipation stage using the half-hourly infrared (IR) images. For each IR images the values of minimum, mean, and variance for the convective cell's TBs and the sizes of convective cells are calculated and also the relationship between TB and rainfall intensity are investigated, which is obtained using the pixel values of satellite TB and the ground rainfall intensity measured by AWS (Automatic Weather Station). At the growth stage of the convective cells, the TB's variance and cloud size consistently increased, whereas TB's minimum and mean consistently decreased. At this stage the empirical relationships between TB and rainfall intensity are statistically significant and their slopes (intercepts) in absolute values are relatively large (small) compared to those at the dissipation stage. At the dissipation stage of the convective cells, the variability of TB distributions shows the opposite trend. The statistical significance of the empirical relationships are relatively weak, but their slopes (intercepts) vary over life cycle. These results indicate that satellite IR images can provide valuable information in identifying the convective cell's maturity stage and in the growth stage, they may be used in providing considerably accurate rainfall estimates.
Keywords
Brightness temperature (TB); rainfall intensity; infrared (IR) images; convective cell; life cycle;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 권태영, 2001: GMS-5 IR1 밝기온도와 AWS 강우량의 관계성; 1998년 8월 중서부지역 집중호우 사례. 대한원격탐사학회지, 17(1), 15-31.
2 나득균, 곽종흠, 서명석, 홍윤, 2005: 종관적 특징에 따른 남한강수 특성 분석: 30년(1973-2002) 기후 통계. 한국지구과학회지, 26(7), 723-743.
3 이광재, 허기영, 서애숙, 박종서, 하경자, 2010: 호우사례 분석을 위한 개념모델 구성에 위성영상과 위성자료의 활용 연구. 대기, 20(2), 131-151.
4 Barrett, E. C., and D. W. Martin, 1981: The use of satellite data in rainfall monitoring, Academic Press, 340pp.
5 Delgado, G., L. A. T. Machado, C. F. Angelis, M. J. Bottino, A. Redao, J. Lorente, L. Gimeno, and R. Nieto, 2007: Basis for a rainfall estimation technique using IR-VIS cloud classification and parameters over the life cycle of mesoscale convective systems. J. Appl. Meteor. Climal., 47, 1500-1517.
6 Hong, Y., 2004: Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system. J. Appl. Meteor., 43, 1834-1852.   DOI   ScienceOn
7 Kidd, C., D. R. Kniveton, M. C. Todd, and T. J. Bellerby, 2003: Satellite rainfall estimation using combined passive microwaves and infrared algorithms. J. Hydrol., 4, 1088-1104.
8 Lee, T.-Y. and Y.-H. Kim, 2007: Heavy precipitation systems over the Korean peninsula and their classification. J. Korean Meteor. Soc., 43(4), 367-396.
9 Levizzani, V., 1999: Convective rain from a satellite prospect: Achievements and challenges. SAF Training Workshop-Nowcasting and Very Short Forecasting, Madrid, 9-11 Dec., EUM P. 25, 75-84.
10 Mecikalski, J. R., and K. M. Bedka, 2006: Forecasting convective initiation by monitoring the evolution of moving cumulus in daytime GOES imagery. Mon. Wea. Rev., 134, 49-78.   DOI   ScienceOn
11 Orlanski, I., 1975: A rational subdivision of scales for atmospheric processes. Bull. Amer. Meteor. Soc., 56, 527-530.
12 Scofield, R. A., 1987: The NESDIS operational convective precipitation estimation technique. Mon. Wea. Rev., 115, 1773-1792.   DOI
13 Suh, M. S, J. R. Lee and C. H. Kwak, 2004: Evaluation of NOAA/NESDIS Auto-estimator for heavy rainfall events over Korean peninsula, J. Korean Meteor. Soc., 40(6), 685-696.
14 Tadesse, A., and E. N. Anagnostou, 2009: The effect of storm life cycle on satellite rainfall estimation error. J. Atmos. and Ocean. Tech., 26, 769-777.   DOI   ScienceOn
15 Turk, F. J., E. F. Ebert, H.-J. Oh, B.-J. Shon, V. Levizzani, E. A. Smith and R. R. Ferraro, 2003: Validation of an operational global precipitation analysis at short time scales. Prepr. 12th Conf. on Satellite Meteor. and Oceanography, Long Beach, CA, 9-13 Feb., paper 1.2, 21.
16 Uddstrom, M. J., and W. R. Gray, 1996: Satellite cloud classification and rain-rate estimation using multispectral radiances and measures of spatial texture. J. Appl. Meteor., 35, 839-858.   DOI   ScienceOn
17 Vincente, G. A., R. A. Scofield, and W. P. Menzel, 1998: The operational GOES infrared rainfall estimation technique. Bull. Amer. Meteor. Soc., 79, 1883-1898.   DOI   ScienceOn
18 Weckwerth, T. M. and Coauthors, 2004: An overview of the International $H_{2}O$ Project (IHOP_2002) and some preliminary. Bull. Amer. Meteor. Soc., 85, 253-277.   DOI   ScienceOn
19 Weng, F., L. Zhao, R. R. Ferraro, G. Poe, X. Li, and N. C. Grody, 2003: Advanced Microwave Sounding Unit cloud and precipitation algorithms. Radio Sci., 38, 8068-8079.