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首页> 外文学位 >Evaluation of ordinary cokriging and artificial neural networks for optimizing rainfall estimate using stage III NEXRAD precipitation surfaces and rain gauge measurements.
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Evaluation of ordinary cokriging and artificial neural networks for optimizing rainfall estimate using stage III NEXRAD precipitation surfaces and rain gauge measurements.

机译：使用第三阶段NEXRAD降水面和雨量计测量评估普通协同克里格法和人工神经网络以优化降雨估计。

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The deployment of the National Weather Service Weather Surveillance Radar-1988 Doppler (WSR-88D) has provided an improved tool for monitoring real-time areal mean precipitation spatial distribution (4-km resolution) for hydrometeorological modeling. Unfortunately, a number of factors introduce discrepancies between radar precipitation estimates and actual precipitation at the Earth's surface. In this project, a pilot study was performed by making two types of statistical analyses to describe the correlation between SEMCOG rain gage values and stage III NEXRAD: (1) the agreement of occurrence of precipitation between the two sources and the magnitude of error in precipitation when there is disagreement of occurrence, and (2) the error in magnitude between rain gage measurement and NEXRAD estimate when they both register a precipitation amount. These analyses provided a basis of justification for using models to improve the correlation between the two sources of precipitation measurements. Twenty-two daily precipitation events (partial and full rainfall coverage) during the months of May through September in 1999 and 2000 were selected to estimate the precipitation using Stage III NEXRAD data and SEMCOG rain gage measurements. Artificial neural network and ordinary cokriging models were evaluated by the performances of improved precipitation estimates. The best performing model, the ANN model, significantly improved the accuracy of the radar-derived precipitation surfaces (Average correlation coefficient was improved from 0.61 to 0.76).; The ANN model was applied to improve the precipitation estimate of the entire state of Michigan. The 16-km NEXRAD grid size was used for improving the Stage III NEXRAD data for the entire state of Michigan. Six daily precipitation events (partial and full rainfall coverage) were selected to optimally estimate the precipitation by combining Stage III NEXRAD data with forty-two National Weather Service Fisher & Porter rain gages distributed around the Michigan. The results showed that the Stage III NEXRAD precipitation surfaces were fairly improved (Average correlation coefficient was improved from 0.72 to 0.87).
机译：国家气象局气象监视雷达1988多普勒（WSR-88D）的部署提供了一种改进的工具，可用于监测实时平均降水空间分布（4公里分辨率），以进行水文气象建模。不幸的是，许多因素导致了雷达降水估计与地球表面实际降水之间的差异。在该项目中，通过进行两种统计分析来描述SEMCOG雨量计值与III NEXRAD阶段之间的相关性进行了一项试点研究：（1）两种来源之间发生降水的协议和降水误差的大小（2）雨量计测量值和NEXRAD估计值两者都记录有降水量时的幅度误差。这些分析为使用模型改善两种降水测量来源之间的相关性提供了依据。利用III期NEXRAD数据和SEMCOG雨量计测量，选择了1999年和2000年5月至9月的22个日降水量事件（部分和全部降雨），以估算降水量。人工神经网络和普通cokriging模型通过改进的降水估计的性能进行了评估。表现最好的模型，即ANN模型，显着提高了雷达衍生的降水面的精度（平均相关系数从0.61提高到0.76）。应用ANN模型来改善整个密歇根州的降水估算。 NEXRAD的16公里网格大小用于改善整个密歇根州的III期NEXRAD数据。通过将III期NEXRAD数据与密歇根州周围分布的42个国家气象局Fisher和Porter雨量计相结合，选择了6个每日降水事件（部分和全部降雨覆盖）来最佳估算降水量。结果表明，Ⅲ期NEXRAD降水面得到了相当大的改善（平均相关系数从0.72提高到0.87）。

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