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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Evaluation of TRMM PR Sampling Error Over a Subtropical Basin Using Bootstrap Technique
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Evaluation of TRMM PR Sampling Error Over a Subtropical Basin Using Bootstrap Technique

机译：利用Bootstrap技术评价亚热带盆地TRMM PR采样误差。

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摘要

Quantitative use of satellite-derived rainfall products for various scientific applications often requires them to be accompanied with an error estimate. Rainfall estimates inferred from low earth orbiting satellites like the Tropical Rainfall Measuring Mission (TRMM) will be subjected to sampling errors of nonnegligible proportions owing to the narrow swath of satellite sensors coupled with a lack of continuous coverage due to infrequent satellite visits. The authors investigate sampling uncertainty of seasonal rainfall estimates from the active sensor of TRMM, namely, Precipitation Radar (PR), based on 11 years of PR 2A25 data product over the Indian subcontinent. In this paper, a statistical bootstrap technique is investigated to estimate the relative sampling errors using the PR data themselves. Results verify power law scaling characteristics of relative sampling errors with respect to space–time scale of measurement. Sampling uncertainty estimates for mean seasonal rainfall were found to exhibit seasonal variations. To give a practical example of the implications of the bootstrap technique, PR relative sampling errors over a subtropical river basin of Mahanadi, India, are examined. Results reveal that the bootstrap technique incurs relative sampling errors $<$ 33% (for the 2 $^{circ}$ grid), $<$ 36% (for the 1 $^{circ}$ grid), $<$ 45% (for the 0.5 $^{circ}$ grid), and $<$ 57% (for the 0.25 $^{circ}$- /inline-formula> grid). With respect to rainfall type, overall sampling uncertainty was found to be dominated by sampling uncertainty due to stratiform rainfall over the basin. The study compares resulting error estimates to those obtained from latin hypercube sampling. Based on this study, the authors conclude that the bootstrap approach can be successfully used for ascertaining relative sampling errors offered by TRMM-like satellites over gauged or ungauged basins lacking in situ validation data. This technique has wider implications for decision making before incorporating microwave orbital data products in basin-scale hydrologic modeling.
机译：在各种科学应用中定量使用卫星降雨产品经常需要对它们进行误差估计。由低空轨道卫星（如热带雨量测量团（TRMM））推断出的雨量估算将受到比例不可忽略的采样误差，这是由于卫星传感器的范围较窄，加上由于卫星访问不频繁而导致缺乏连续覆盖。作者基于印度次大陆上11年PR 2A25数据产品，研究了TRMM有源传感器即降水雷达（PR）对季节性降雨估计的采样不确定性。在本文中，研究了一种统计引导程序技术，以使用PR数据本身​​来估计相对采样误差。结果证明了相对于时空测量尺度的相对采样误差的幂律定标特性。平均季节降雨量的抽样不确定性估计值显示出季节变化。为了给出引导程序技术含义的实际示例，我们考察了印度Mahanadi亚热带河流域的PR相对采样误差。结果表明，自举技术会产生相对采样误差$ <$ 33％（对于2个$ ^ {circ} $网格），$ <$ 36％（对于1个$ ^ {circ} $网格），$ <$ 45 ％（对于0.5个$ ^ {circ} $网格）和$ <$ 57％（对于0.25个$ ^ {circ} $-/ inline-formula>网格）。就降雨类型而言，由于流域上空的层状降雨，总的抽样不确定性主要由抽样不确定性决定。该研究将所得误差估计与从拉丁文超立方体采样获得的误差估计进行比较。基于这项研究，作者得出的结论是，引导法可以成功地用于确定TRMM样卫星在缺乏现场验证数据的经测量或未测量盆地上提供的相对采样误差。在将微波轨道数据产品纳入流域规模的水文模拟之前，这项技术对决策具有更广泛的意义。

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