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...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 = aR<sup>b</sup>), 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 = 200R<sup>1.6</sup> (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 = 300R<sup>1.4</sup> (WSR-88D Convective) and Z = 250R<sup>1.2</sup> (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.展开更多
The regular occurrence of flash floods over the region of Jeddah, Saudi Arabia in the past decade has highlighted the serious need for the development of early warning systems. Radar stations have been installed in Je...The regular occurrence of flash floods over the region of Jeddah, Saudi Arabia in the past decade has highlighted the serious need for the development of early warning systems. Radar stations have been installed in Jeddah in the last decade whose active radius covers the Middle Western area of the country. Therefore, radar information and the associated the rainfall estimates are potentially useful components of an effective early warning system. Weather radar can potentially provide high-resolution spatial and temporal rainfall estimates that bring more accuracy to flood warnings as well as having applications in areas with insufficient rainfall stations coverage. Weather radar does not measure rainfall depth directly. An empirical relationship between reflectivity (Z) and rainfall rate (R), called the Z-R relationship (Z = ARb), is generally used to assess the rainfall depth. In this study, the rainfall events during August-September 2007 were analyzed to develop a Z-R relationship using the Spatial Probability Technique (SPT). This technique is based on a basic GIS function and the probability matching method. Using this technique, the Z-R pairs can be analyzed for both linear and empirical power relationships. It is found that the empirical power function is more appropriate to describe Z-R relationship than a linear function for the studied area. The method is applied with some success to the flooding event of November 25, 2009. However, the investigation of the Z-R relationship is only one step in the development of a warning system;further study of other parameters relevant to rainfall and flash flood occurrence is needed.展开更多
This study evaluates the improvement of the radar Quantitative Precipitation Estimation (QPE) by involving microphysical processes in the determination of </span><i><span style="font-family:Verdana...This study evaluates the improvement of the radar Quantitative Precipitation Estimation (QPE) by involving microphysical processes in the determination of </span><i><span style="font-family:Verdana;">Z</span></i><span style="font-family:Verdana;">-</span><i><span style="font-family:Verdana;">R</span></i><span style="font-family:Verdana;"> algorithms. Within the framework of the AMMA campaign, measurements of an X-band radar (Xport), a vertical pointing Micro Rain Radar (MRR) to investigate microphysical processes and a dense network of rain </span><span style="font-family:Verdana;">gauges deployed in Northern Benin (West Africa) in 2006 and 2007 were</span><span style="font-family:Verdana;"> used as support to establish such estimators and evaluate their performance compared to other estimators in the literature. By carefully considering and correcting MRR attenuation and calibration issues, the </span><i><span style="font-family:Verdana;">Z</span></i><span style="font-family:Verdana;">-</span><i><span style="font-family:Verdana;">R</span></i><span style="font-family:Verdana;"> estimator developed </span><span style="font-family:Verdana;">with the contribution of microphysical processes and non-linear least</span></span><span style="font-family:Verdana;">-</span><span style="font-family:""><span style="font-family:Verdana;">squares adjustment proves to be more efficient for quantitative rainfall estimation and produces the best statistic scores than other optimal </span><i><span style="font-family:Verdana;">Z</span></i><span style="font-family:Verdana;">-</span><i><span style="font-family:Verdana;">R</span></i><span style="font-family:Verdana;"> algorithms in the literature. We also find that it gives results comparable to some polarimetric algorithms including microphysical information through DSD integrated parameter retrievals.展开更多
文摘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 = aR<sup>b</sup>), 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 = 200R<sup>1.6</sup> (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 = 300R<sup>1.4</sup> (WSR-88D Convective) and Z = 250R<sup>1.2</sup> (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.
文摘The regular occurrence of flash floods over the region of Jeddah, Saudi Arabia in the past decade has highlighted the serious need for the development of early warning systems. Radar stations have been installed in Jeddah in the last decade whose active radius covers the Middle Western area of the country. Therefore, radar information and the associated the rainfall estimates are potentially useful components of an effective early warning system. Weather radar can potentially provide high-resolution spatial and temporal rainfall estimates that bring more accuracy to flood warnings as well as having applications in areas with insufficient rainfall stations coverage. Weather radar does not measure rainfall depth directly. An empirical relationship between reflectivity (Z) and rainfall rate (R), called the Z-R relationship (Z = ARb), is generally used to assess the rainfall depth. In this study, the rainfall events during August-September 2007 were analyzed to develop a Z-R relationship using the Spatial Probability Technique (SPT). This technique is based on a basic GIS function and the probability matching method. Using this technique, the Z-R pairs can be analyzed for both linear and empirical power relationships. It is found that the empirical power function is more appropriate to describe Z-R relationship than a linear function for the studied area. The method is applied with some success to the flooding event of November 25, 2009. However, the investigation of the Z-R relationship is only one step in the development of a warning system;further study of other parameters relevant to rainfall and flash flood occurrence is needed.
文摘This study evaluates the improvement of the radar Quantitative Precipitation Estimation (QPE) by involving microphysical processes in the determination of </span><i><span style="font-family:Verdana;">Z</span></i><span style="font-family:Verdana;">-</span><i><span style="font-family:Verdana;">R</span></i><span style="font-family:Verdana;"> algorithms. Within the framework of the AMMA campaign, measurements of an X-band radar (Xport), a vertical pointing Micro Rain Radar (MRR) to investigate microphysical processes and a dense network of rain </span><span style="font-family:Verdana;">gauges deployed in Northern Benin (West Africa) in 2006 and 2007 were</span><span style="font-family:Verdana;"> used as support to establish such estimators and evaluate their performance compared to other estimators in the literature. By carefully considering and correcting MRR attenuation and calibration issues, the </span><i><span style="font-family:Verdana;">Z</span></i><span style="font-family:Verdana;">-</span><i><span style="font-family:Verdana;">R</span></i><span style="font-family:Verdana;"> estimator developed </span><span style="font-family:Verdana;">with the contribution of microphysical processes and non-linear least</span></span><span style="font-family:Verdana;">-</span><span style="font-family:""><span style="font-family:Verdana;">squares adjustment proves to be more efficient for quantitative rainfall estimation and produces the best statistic scores than other optimal </span><i><span style="font-family:Verdana;">Z</span></i><span style="font-family:Verdana;">-</span><i><span style="font-family:Verdana;">R</span></i><span style="font-family:Verdana;"> algorithms in the literature. We also find that it gives results comparable to some polarimetric algorithms including microphysical information through DSD integrated parameter retrievals.
