Korean Journal of Remote Sensing, Vol.22, No.6, 2006, pp.485~493 Estimation of Quantitative Precipitation Rate Using an Optimal Weighting Method with RADAR Estimated Rainrate and AWS Rainrate Hyun-Mi Oh, Kyung-Ja Ha, and Ki-Young Heo Division of Earth Environmental System, Pusan National University Abstract : This study is to combine precipitation data with different spatial-temporal characteristics using an optimal weighting method. This optimal weighting method is designed for combination of AWS rain gage data and S-band RADAR-estimated rain data with weighting function in inverse proportion to own mean square error for the previous time step. To decide the optimal weight coefficient for optimized precipitation according to different training time, the method has been performed on Changma case with a long spell of rainy hour for the training time from 1 hour to 10 hours. Horizontal field of optimized precipitation tends to be smoothed after 2 hours training time, and then optimized precipitation has a good agreement with synoptic station rainfall assumed as true value. This result suggests that this optimal weighting method can be used for production of high-resolution quantitative precipitation rate using various data sets. Key Words : Optimal weighting method, optimal weight, RADAR-estimated rain, AWS rain gage. kjha@pusan.ac.kr 485
Korean Journal of Remote Sensing, Vol.22, No.6, 2006 e e 2 i < (Y _ Y i ) 2 >, i = R (RADAR) or i = G (gages) (1) e G 2 a R = (2) e 2 2 G + e R e R 2 a G = (3) e 2 2 G + e R Y A Y A = a R Y R + a G Y G (4) 1 = a R + a G (5) 486
Estimation of Quantitative Precipitation Rate Using an Optimal Weighting Method with RADAR Estimated Rainrate and AWS Rainrate (a) (b) (c) Fig. 1. Schematic diagram of (a) Synoptic, (b) AWS and (c) RADAR observations. D Fig. 2. The position of Jindo radar (center of circle) and the verification area (rectangle area). The black dots denote the synoptoc stations and the white dots denote the AWS stations. 487
Korean Journal of Remote Sensing, Vol.22, No.6, 2006 Table 1. Altitude of Jindo RADAR PPI (0.19 ). distance from 0 50 100 150 200 240 RADAR (km) 0 50 100 150 200 240 altitude (km) 0.49 0.72 1.23 2.05 3.13 4.26 Fig. 3. Time series of synoptic rainrates summation in case region from 0010LST 23 to 2300LST 24 June 2002. 488
Estimation of Quantitative Precipitation Rate Using an Optimal Weighting Method with RADAR Estimated Rainrate and AWS Rainrate Table 2. Average of mean of ag and ar, standard deviation of ag and ar from 22LST 23 to 11LST 24 June 2002. mean stan. dev. EXP1 EXP2 EXP1 EXP3 EXP2 EXP4 EXP3 EXP5 EXP4 EXP6 EXP5 EXP7 EXP6 EXP8 EXP7 EXP9 EXP8 EXP10 EXP9 EXP10 a G 0.60 0.60 0.60 0.60 0.58 0.58 0.58 0.58 0.59 0.59 a R 0.40 0.40 0.40 0.40 0.42 0.42 0.42 0.42 0.41 0.41 a G 0.30 0.24 0.21 0.20 0.19 0.18 0.17 0.17 0.17 0.16 a R 0.30 0.24 0.21 0.20 0.19 0.18 0.17 0.17 0.17 0.16 (a) (b) Fig. 4. (a) synoptic rainfall, (b) AWS rainfall and (c) RADAR estimated rainfall at 0500LST 24 June 2002. (c) 489
Korean Journal of Remote Sensing, Vol.22, No.6, 2006 (a) (b) (c) (d) Fig. 5. Optimal weight of AWS (a G ) of (a) EXP1, (b) EXP2, (c) EXP3 and (d) EXP10 (0500LST 24 June 2002). 490
Estimation of Quantitative Precipitation Rate Using an Optimal Weighting Method with RADAR Estimated Rainrate and AWS Rainrate (a) (b) (c) (d) Fig. 6. Optimized precipitation of (a) EXP1, (b) EXP2, (c) EXP3 and (d) EXP10 at 0500LST 24 June 2002. (a) (b) (c) (d) Fig. 7. Scattered diagram of synoptic rain and (a) AWS rain, (b) RADAR rain, (c) optimized precipitation of EXP1 and (d) optimized precipitation of EXP10. 491
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