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Analyzing the influence of large wind farms over rainfall pattern using radar data.

机译:使用雷达数据分析大型风电场对降雨模式的影响。

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摘要

Watershed run-off models were mostly developed using point rainfall observations in the past. Water quality model use watershed run-off models. The water quality modeling can be done in a better manner if radar rainfall data is used in the modeling process. However, radar rainfall is not directly available for this purpose. Radar data is available through public domain as radar signals, which are converted to rainfall rates using a Z-R relationship (Pathak and Teegavarapu (2010)). Rendon et al. (2011) proposed a new approach using the mean field bias to derive radar specific Z-R relationship for hydrologic operations. In this study, a neural network approach is attempted to find the daily rainfall magnitude from radar signals. Daily observations from 109 rain gauge stations were used in this analysis. Literature reported ZR parameters "a" and "b" values for stratiform storm was used initially to calculate the rainfall rate. Subsequently, "a" and "b" values were revised using the procedure recommend by Rendon et al. (2011). Two more models were developed using a neural network. The results of conventional ZR using literature reported "a" and "b" values, modified ZR method proposed by Rendon et al. (2011), the fuzzy ANN model and the ANN model results were compared using indices: mean square error and mean related error. ANN models performed better.;Large wind farms are being built in the feasible locations of the United States as this energy production is replenishable and environmentally friendly. This research examines the changes in the precipitation pattern due to the large wind farms using a case study. Large wind farms were built recently in the Benton, White, and Warren Counties of Indiana. This region was considered for this case study. NEXRAD radar rainfall data was used in this analysis. Storms that occurred before and after wind farms installations were compared using radar data to identify the changes in precipitation patterns. Chicago NEXRAD radar data, which covers the study region, was used. To understand the changes in precipitation pattern caused by wind farm, two adjacent regional windows involving three counties each were considered. The results indicate few changes in the rainfall pattern in this region.
机译:在过去,流域径流模型主要是利用点降雨观测来开发的。水质模型使用流域径流模型。如果在建模过程中使用雷达降雨数据,则可以更好的方式进行水质建模。但是,雷达降雨量不能直接用于此目的。雷达数据可通过公共领域以雷达信号的形式获得,并通过Z-R关系转换为降雨率(Pathak和Teegavarapu(2010))。 Rendon等。 (2011年)提出了一种使用平均场偏差推导水文作业雷达特定Z-R关系的新方法。在这项研究中,尝试使用神经网络方法从雷达信号中找到每日的降雨量。该分析使用了来自109个雨量计站的每日观测值。文献报道,最初使用层状风暴的ZR参数“ a”和“ b”值来计算降雨率。随后,使用Rendon等人推荐的方法修改“ a”和“ b”值。 (2011)。使用神经网络开发了另外两个模型。使用文献的常规ZR结果报告了“ a”和“ b”值,这是Rendon等人提出的改进ZR方法。 (2011年),使用指标:均方误差和均值相关误差比较了模糊ANN模型和ANN模型的结果。人工神经网络模型的效果更好。;由于这种能源生产是可补充的且对环境友好的,因此在美国可行的地点正在建造大型风电场。这项研究使用案例研究来考察由于大型风电场导致的降水模式变化。最近在印第安纳州的本顿县,怀特县和沃伦县建立了大型风电场。本案例研究考虑了该区域。 NEXRAD雷达降雨数据用于此分析。使用雷达数据比较了风电场安装前后发生的风暴,以识别降水模式的变化。使用了覆盖研究区域的芝加哥NEXRAD雷达数据。为了了解由风电场引起的降水模式的变化,考虑了两个相邻的区域窗口,每个窗口涉及三个县。结果表明该地区的降雨模式变化很小。

著录项

  • 作者

    Zeng, Le.;

  • 作者单位

    Purdue University.;

  • 授予单位 Purdue University.;
  • 学科 Engineering Civil.
  • 学位 M.S.E.
  • 年度 2012
  • 页码 77 p.
  • 总页数 77
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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