A Modified Inverse Distance Weighting Method for Finding Hourly Bias Adjustment to Increase the Accuracy of Omkoi radar rainfall Estimation
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Abstract
The effect of the Earth’s curvature, various geographical features and the variability in each rainfall event results in different rainfall drop size distributions depending on timing. Therefore, using climatological Z-R relationship to estimate radar rainfall can lead to bias in its estimate seeing from the unequal amount of air rainfall and ground rainfall. This study uses rainfall data collected by 2 sources, first is Omkoi rainfall radar that is set at 1.5° elevation angle measuring under the radar’s umbrella that covers 240 kilometers radius, and the other is 122 automatic rain gauge networks. The data had been recorded since January 2015 until October 2017. The collection of 126 selected rainfall events, 100 calibration events (80%) and 26 verification events (20%) had been taken into account to find hourly bias adjustment that is able to reduce the bias in grid cells in size 1x1 kilometer (1 km2) of Omkoi radar rainfall estimation. This research applies the Inverse Distance Weighting method that requires previous rainfall accumulating of various duration: 1 hour, 2 hours, 3 hours, 6 hours, 12 hours and 24 hours from both sources that locate no further than 20 kilometers from the targeted grid. The result of this study indicates that the rainfall estimate process with the application of the equation Z=42R1.6 and the Inverse Distance Weighting method together with the previous rainfall accumulating of 1 hour from both sources, is the most suitable method for Omkoi radar rainfall estimate. This method gives the closest RMSE (Root Mean Square Error), MSE (Mean Square Error) and MAE (Mean Absolute Error) to 0, and closest R (Correlation coefficient) to 1 between estimation of radar rainfall after adjusted and rainfall at automatic rain gauge networks in both calibration and verification events. Comparing to initial radar rainfall estimating with the sole use of the equation Z=42R1.6, this method can increase accuracy in RMSE evaluation to 11.42% (4.39%). In the meantime, other assessments those are MSE, MAE and R, also show that this method can increase the accuracy to 21.83% (8.60%), 3.82% (4.46%) and 22.95% (18.03%) in calibration and verification events, in order.
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