Geostatistical merging of rain gauge and radar data for high temporal resolutions and various station density scenarios
Geostatistical merging of rain gauge and radar data for high temporal resolutions and various station density scenarios
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针对高时间分辨率和各种站点密度场景的雨量计和雷达数据的地统计合并
DOI：
10.1016/j.jhydrol.2013.10.028
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发表时间：
2014
影响因子：
6.4
通讯作者：
Haberlandt
中科院分区：
文献类型：
--
作者：
Berndt;Rabiei;Haberlandt
本文研究了不同时间分辨率和雨量站网密度下雷达和雨量站数据融合的性能，并通过交叉验证对三种不同的地统计插值技术：带外漂的克里格、带外漂的指示克里格和条件融合进行了比较和评价。在不使用雷达数据的情况下，普通克里金被认为是参考方法。研究区域位于德国的下萨克森，覆盖了汉诺威雷达站的测量范围。本研究中使用的数据包括2008年至2010年期间90个雨量计和位于汉诺威附近的天气雷达的连续时间序列。七个不同的时间分辨率从10分钟到6小时和5个不同的雨量计网络密度的情况下进行了调查，每种方法的插值性能。此外，还分析了几种时间和空间平滑技术对雷达数据的影响，并分析了雷达数据质量对每种方法插值性能的影响，发现网格化雷达数据的平滑处理显著提高了雨量计和雷达数据的融合性能。条件合并优于克里格与外部漂移和指示克里格与外部漂移的所有组合的站点密度和时间分辨率，而克里格与外部漂移表现同样良好的低站点密度和相当粗糙的时间分辨率。具有外部漂移的指示克立格的结果几乎达到了非常高的时间分辨率的条件合并。克立格与外部漂移似乎是更敏感的雷达数据质量方面比其他两种方法。即使是10分钟的时间分辨率，条件合并表现优于普通克里格没有雷达信息。这说明了合并雨量计和雷达数据的好处，即使是非常高的时间分辨率。
This study investigates the performance of merging radar and rain gauge data for different high temporal resolutions and rain gauge network densities.Three different geostatistical interpolation techniques: Kriging with external drift, indicator kriging with external drift and conditional merging were compared and evaluated by cross validation. Ordinary kriging was considered as the reference method without using radar data. The study area is located in Lower Saxony, Germany, and covers the measuring range of the radar station Hanover. The data used in this study comprise continuous time series from 90 rain gauges and the weather radar that is located near Hanover over the period from 2008 until 2010. Seven different temporal resolutions from 10 min to 6 h and five different rain gauge network density scenarios were investigated regarding the interpolation performance of each method. Additionally, the influence of several temporal and spatial smoothing-techniques on radar data was evaluated and the effect of radar data quality on the interpolation performance was analyzed for each method.It was observed that smoothing of the gridded radar data improves the performance in merging rain gauge and radar data significantly. Conditional merging outperformed kriging with an external drift and indicator kriging with an external drift for all combinations of station density and temporal resolution, whereas kriging with an external drift performed similarly well for low station densities and rather coarse temporal resolutions. The results of indicator kriging with an external drift almost reached those of conditional merging for very high temporal resolutions. Kriging with an external drift appeared to be more sensitive in regard to radar data quality than the other two methods. Even for 10 min temporal resolutions, conditional merging performed better than ordinary kriging without radar information. This illustrates the benefit of merging rain gauge and radar data even for very high temporal resolutions.
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