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http://dx.doi.org/10.5467/JKESS.2018.39.2.131

Improvement of Non-linear Estimation Equation of Rainfall Intensity over the Korean Peninsula by using the Brightness Temperature of Satellite and Radar Reflectivity Data  

Choi, Haklim (Department of Astronomy and Atmospheric Sciences, KyungPook National University)
Seo, Jong-Jin (Department of Astronomy and Atmospheric Sciences, KyungPook National University)
Bae, Juyeon (Department of Astronomy and Atmospheric Sciences, KyungPook National University)
Kim, Sujin (Department of Astronomy and Atmospheric Sciences, KyungPook National University)
Lee, Kwang-Mog (Department of Astronomy and Atmospheric Sciences, KyungPook National University)
Publication Information
Journal of the Korean earth science society / v.39, no.2, 2018 , pp. 131-138 More about this Journal
Abstract
The purpose of this study is to improve the quantitative precipitation estimation method based on satellite brightness temperature. The non-linear equation for rainfall estimation is improved by analysing precipitation cases around the Korean peninsula in summer. Radar reflectivity is adopted the CAPPI 1.5 and CMAX composite fields that provided by the Korea Meteorological Agency (KMA). In addition, the satellite data are used infrared, water vapor and visible channel measured from meteorological imager sensor mounted on the Chollian satellite. The improved algorithm is compared with the results of the A-E method and CRR analytic function. POD, FAR and CSI are 0.67, 0.76 and 0.21, respectively. The MAE and RMSE are 2.49 and 6.18 mm/h. As the quantitative error was reduced in comparison to A-E and qualitative accuracy increased in compare with CRR, the disadvantage of both algorithms are complemented. The method of estimating precipitation through a relational expression can be used for short-term forecasting because of allowing precipitation estimation in a short time without going through complicated algorithms.
Keywords
satellite; radar; rainfall intensity; non-linear equation; COMS;
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1 Bedka, K., Brunner, J., Dworak, R., Feltz, W., Otkin, J., and Greenwald, T., 2010, Objective satellite-based detection of overshooting tops using infrared window channel brightness temperature gradients. Journal of applied meteorology and climatology, 49(2), 181-202.   DOI
2 Borneman, R. 1988, Satellite rainfall estimating program of the NOAA/NESDIS Synoptic Analysis Branch. Natl. Wea. Dig, 13(2), 7-15.
3 Dixon, M. and Wiener, G., 1993, TITAN: Thunderstorm Identification, Tracking, Analysis, and Nowcasting-A radar-based methodology. Journal of Atmospheric and Oceanic Technology, 10, 785-797.   DOI
4 Hong, K.-O., M.-S. Shu, and D.-K. Rha, 2006, Temporal and Spatial Variations of Precipitation in South Korea for REcent 30 Years (1976-2005) and Geographic Environments. J. Korean Earth Sci. Soc., 27, 433-449.
5 In, S. R., Han, S. O., Im, E. S., Kim, K. H., and Shim, J., 2014, Study on Temporal and Spatial Characteristics of Summertime Precipitation over Korean Peninsula. Atmosphere, 24.
6 Inoue, T., 1987, A cloud type classification with NOAA 7 split-window measurements. Journal of Geophysical Research: Atmospheres, 92(D4), 3991-4000.   DOI
7 Johnson, J.T., 1998, The storm cell identification and tracking algorithm: An enhanced WSR-88D algorithm. Weather and Forecasting, 13, 263-276.   DOI
8 Joss, J. and A. Waldvogel, 1990, Precipitation measurements and hydrology. Radar in Meteorology, Boston, Amer, Meteor. Soc., 577-606.
9 Jung, S. H., Lee, G., Kim, H. W., and Kuk, B, 2011, Development of convective cell identification and tracking algorithm using 3-dimensional radar reflectivity fields. Atmosphere, 21(3), 243-256.x
10 Jung, S. H., and Lee. G., 2015, Radar-based cell tracking with fuzzy logic approach. Meteorol. Appl. 22, 716-730   DOI
11 Lovejoy, S. and Austin, G. L.,1979, The delineation of rain areas from visible and IR satellite data for GATE and mid-latitudes. Atmosphere-ocean, 17(1), 77-92.   DOI
12 Kidd, C., 2001, Satellite rainfall climatology: a review. International Journal of Climatology, 21(9), 1041-1066.   DOI
13 Kurino, T., 1997, A rainfall estimation with the GMS-5 infrared split-window and water vapour measurements. Meteorol Center Tech Note, Japan Meteorol Agency, 33, 91-101.
14 Lee, S.H., 2016, Improvement of convective rainfall rate based on COMS over the Korean Peninsula. Kongju National University, Korea, 53 p.
15 Marshall, J. S. and Palmer, W. M., 1948, The distribution of raindrops with size. J. meteor., 5, 154-166.
16 Lu, G. Y. and Wong, D. W., 2008, An adaptive inverse-distance weighting spatial interpolation technique. Computers & Geosciences, 34(9), 1044-1055.   DOI
17 Luque, A., Gomez, I. and Manso, M., 2006, Convective rainfall rate multi-channel algorithm for Meteosat-7 and radar derived calibration matrices. Atmosfera, 19(3), 145-168.
18 Marquardt, D. W., 1963, An algorithm for least-squares estimation of nonlinear parameters. Journal of the society for Industrial and Applied Mathematics, 11(2), 431-441.   DOI
19 Moon, Y. S. and Lee, K. Y., 2016, Improvement and Validation of Convective Rainfall Rate Retrieved from Visible and Infrared Image Bands of the COMS Satellite. J. Korean Earth Sci. Soc., v. 37, no. 6, p. 420-433   DOI
20 NMSC., 2012, COMS Rainfall Intensity Algorithm Theoretical Basis Documents (RI ATBD), NMSC/SCI/ATBD/RI, 22 p.
21 Olander, T. L. and Velden, C. S., 2009, Tropical cyclone convection and intensity analysis using differenced infrared and water vapor imagery. Weather and Forecasting, 24(6), 1558-1572.   DOI
22 Vicente, G. A., Scofield R. A., and Menzel W. P., 1998, Operational GOES infrared rainfall estimation technique. Bull. Amer. Meteor. Soc. 79, 1883-1897.   DOI
23 Rodriguez, A. and Marcos, C., 2013, Algorithm theoretical basis document for "convective rainfall rate" (CRR-PGE05 v4.0). SAF/NWC/CDOP2/INM/SCI/ATBD/05, 36 pp. [Available online at http://www.nwcsaf.org/scidocs/Documentation/SAF-NWC-CDOP2-INM-SCI-ATBD-05_v4.0.pdf.].
24 Scofield, R. A., 1987, The ENSDIS operational convective precipitation technique [J]. Monthly Weather Review, 1773-1792.
25 Scofield, R. A., 2001, Comments on "A quantitative assessment of the NESDIS Auto-Estimator." Wea. Forecasting, 16, 277-278.   DOI
26 Yoon S.M., 2013, statistical of extreme rainfall events and applications of radar rainfall estimates for reducing flood risk in Gyeongnam area. Kyungsang University, Korea, 183 p.
27 Vicente, J. C. Davenport and R. A. Scofield., 2002, The role of orographic and paralax corrections on real time high resolution satellite rainfall rate distribution. International Journal of Remote Sensing, Vol. 23, 2, 221-230.   DOI