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Influence of small scale rainfall variability on standard comparison tools between radar and rain gauge data

机译:小范围降雨变化对雷达和雨量计数据之间标准比较工具的影响

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Rain gauges and weather radars do not measure rainfall at the same scale; roughly 20 cm for the former and 1 km for the latter. This significant scale gap is not taken into account by standard comparison tools (e.g. cumulative depth curves, normalized bias, RMSE) despite the fact that rainfall is recognized to exhibit extreme variability at all scales. In this paper we suggest to revisit the debate of the representativeness of point measurement by explicitly modelling small scale rainfall variability with the help of Universal Multifractals. First the downscaling process is validated with the help of a dense networks of 16 disdrometers (in Lausanne, Switzerland), and one of 16 rain gauges (Bradford, United Kingdom) both located within a 1 km~2 area. Second this downscaling process is used to evaluate the impact of small scale (i.e. sub-radar pixel) rainfall variability on the standard indicators. This is done with rainfall data from the Seine-Saint-Denis County (France). Although not explaining all the observed differences, it appears that this impact is significant which suggests changing some usual practice.
机译:雨量计和天气雷达无法测量相同规模的降雨;前者大约20厘米,后者大约1公里。尽管公认降雨在所有尺度上都表现出极大的变化性,但是标准比较工具(例如,累积深度曲线,归一化偏差,RMSE)并未考虑到这一巨大的尺度差异。在本文中,我们建议通过借助通用多重分形对小规模降雨变化率进行显式建模来重新讨论点测量的代表性。首先,缩小规模的过程在16 km的密集仪表网络(位于瑞士洛桑)和16个雨量计之一(英国Bradford)的帮助下进行了验证,两者均位于1 km〜2范围内。其次,该缩小规模的过程用于评估小规模(即子雷达像素以下)降雨变化对标准指标的影响。这是通过塞纳-圣但尼县(法国)的降雨数据完成的。尽管没有解释所有观察到的差异,但这种影响似乎很明显,这表明需要改变一些常规做法。

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