Estimation of precipitation fields from 1-minute rain gauge time series – comparison of spatial and spatio-temporal interpolation methods
Estimation of precipitation fields from 1-minute rain gauge time series – comparison of spatial and spatio-temporal interpolation methods
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DOI：
10.1080/13658816.2015.1040022
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发表时间：
2015-09
影响因子：
5.7
通讯作者：
D. Fitzner;Monika Sester
中科院分区：
文献类型：
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作者：
D. Fitzner;Monika Sester
在许多应用中，对未采样位置的时空场的准确估计是很重要的。通常，时空场是平流的，这意味着空间中特定点处的场值随时间的变化在很大程度上源于或多或少恒定的空间场的运动。对于这样的动态场，包括场运动行为信息的内插方法是纯空间(快照)和对称时空方法的有前途的扩展。本文比较了不同确定性和地统计插值法对空间分布雨量计1分钟时间序列进行降水估计的性能。重点放在时空方法上，这些方法包括从天气雷达使用光流估计的雨场运动行为的信息。介绍了不同的内插方法，并使用15天的雨量计测量和交叉验证对其进行了评估。结果表明，在均方根误差方面，包含运动行为的信息显著提高了插补质量。
Accurate estimations of spatio-temporal fields at unsampled locations are important in a number of applications. Often, spatio-temporal fields are advected, which means the change in field values over time at a particular point in space stems to a large extent from motion of a more or less constant spatial field. For such dynamic fields, interpolation methods including information on the motion behaviour of the field are promising extensions of solely spatial (snapshot) and symmetric spatio-temporal methods. In this paper, the performance of different deterministic and geostatistical interpolation methods is compared for precipitation estimation from 1-minute time series of spatially distributed rain gauges. The focus is on spatio-temporal methods that include information on the motion behaviour of the rainfield, estimated from weather radar using optical flow. The different interpolation methods are introduced and evaluated using rain gauge measurements of a 15-day period and cross-validation. The results show that including information on the motion behaviour significantly improves interpolation quality in terms of RMSE.