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The use of Gauge-Adjusted Radar (GAR) for rainfall measurement is a recent innovation in Urban Hydrology in the United States for measuring rainfall between rain gauges. Gauge Adjusted Radar is the result of calibrating a radar image with ground truth rain gauges. This technology is particularly useful for Rainfall Dependent Infiltration Inflow (RDII) programs in which the relationship between rainfall and RDII is critical to defining sewer basin performance and directing rehabilitation programs. The rainfall and RDII relationship is equally dependent on quality of both flow measurement and rainfall measurement.Users of this technology generally have demonstrated its effectiveness in one of four ways; 1) Comparing the results of hydraulic model prediction and RDII calculations using GAR and conventional rain gauges as input, 2) Determining the accuracy of the image calibration by comparing the closeness of rain gauge accumulation to the accumulation of the radar pixel containing the rain gauge, 3) Comparing the closeness of a "test" rain gauge, which was not used in the calibration process, to the radar pixel containing the test rain gauge and 4) Observing the measurement of localized rain events that would have gone undetected by a rain gauge network. This paper evaluates the technology by investigating two projects performed by the author and reviewing results reported by others. Some users consider it almost self evident that the use of Gauge Adjusted Radar will result in better rainfall measurements and consequently yield better wet weather analyses. However, the magnitudes of improvement from the use of GAR reported by authors have varied from no improvement in hydraulic model prediction to the discovery and elimination of an over-prediction of RDII by over 50%. Key variables contributing to the wide variation of results appear to be to differences in temporal and geographic resolution of GAR system being used. Rainfall measurements can vary as much as 14% between geographic resolution of 1 Km 2 and 4 Km 2 .
The use of Gauge-Adjusted Radar (GAR) for rainfall measurement is a recent innovation in Urban Hydrology in the United States for measuring rainfall between rain gauges. Gauge Adjusted Radar is the result of calibrating a radar image with ground truth rain gauges. This technology is particularly useful for Rainfall Dependent Infiltration Inflow (RDII) programs in which the relationship between rainfall and RDII is critical to defining sewer basin performance and directing rehabilitation programs. The rainfall and RDII relationship is equally dependent on quality of both flow measurement and rainfall measurement.Users of this technology generally have demonstrated its effectiveness in one of four ways; 1) Comparing the results of hydraulic model prediction and RDII calculations using GAR and conventional rain gauges as input, 2) Determining the accuracy of the image calibration by comparing the closeness of rain gauge accumulation to the accumulation of the radar pixel containing the rain gauge, 3) Comparing the closeness of a "test" rain gauge, which was not used in the calibration process, to the radar pixel containing the test rain gauge and 4) Observing the measurement of localized rain events that would have gone undetected by a rain gauge network. This paper evaluates the technology by investigating two projects performed by the author and reviewing results reported by others. Some users consider it almost self evident that the use of Gauge Adjusted Radar will result in better rainfall measurements and consequently yield better wet weather analyses. However, the magnitudes of improvement from the use of GAR reported by authors have varied from no improvement in hydraulic model prediction to the discovery and elimination of an over-prediction of RDII by over 50%. Key variables contributing to the wide variation of results appear to be to differences in temporal and geographic resolution of GAR system being used. Rainfall measurements can vary as much as 14% between geographic resolution of 1 Km 2 and 4 Km 2 .
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