Abstract
The constant development of science and technology in weather radar results in high-resolution spatial and temporal rainfall estimates and improved early warnings of meteorological phenomena such as flood [1]. Weather radars do not measure the rainfall amount directly, so a relationship between the reflectivity (Z) and rainfall rate (R), called the Z-R relationship (Z = aRb), where a and b are empirical constants, can be used to estimate the rainfall amount. In this research, mathematical techniques were used to find the best climatological Z-R relationships for the Low Coastal Plain of Guyana. The reflectivity data from the S-Band Doppler Weather Radar for February 17 and 21, 2011 and May 8, 2012 together with the daily rainfall depths at 29 rainfall stations located within a 150 km radius were investigated. A climatological Z-R relationship type Z = 200R1.6 (Marshall-Palmer) configured by default into the radar system was used to investigate the correlation between the radar reflectivity and the rainfall by gauges. The same data sets were used with two distinct experimental Z-R relationships, Z = 300R1.4 (WSR-88D Convective) and Z = 250R1.2 (Rosenfeld Tropical) to determine if any could be applicable for area of study. By comprehensive regression analysis, New Z-R and R-Z relationships for each of the three events aforementioned were developed. In addition, a combination of all the samples for all three events were used to produce another relationship called “All in One”. Statistical measures were then applied to detect BIAS and Error STD in order to produce more evidence-based results. It is proven that different Z-R relationships could be calibrated into the radar system to provide more accurate rainfall estimation.
Highlights
High accuracy of rainfall estimation is required for better decisions by policy makers and researchers
The same data sets were used with two distinct experimental Z-R relationships, Z = 300R1.4 (WSR-88D Convective) and Z = 250R1.2 (Rosenfeld Tropical) to determine if any could be applicable for area of study
With respect to regression analysis, results show good correlation between rain gauge observation and radar estimations for all relationships, R2 is considered the best according to both BIAS and Error STD
Summary
High accuracy of rainfall estimation is required for better decisions by policy makers and researchers. The rain gauge has been the standardized instrument for collecting and measuring surface rainfall and is assumed to be “ground truth” because of its long service and widespread use [2]. Rain gauges produce point measurements that are commonly assumed to represent a much wider surface area. Rain gauges are reliable instruments which meteorologists and hydrologists can rely on at least for point measurement [3], but rainfall can vary both in space and time which is not really captured by the rain gauges. The fundamental feature of weather radars is that they do not measure rainfall directly but rather the back scattered energy from precipitation particles from elevated volumes and an algorithm should be developed and calibrated against the rain gauge network. The received energy from the precipitation particles is given by: Pr
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