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A Hybrid Framework for Verification of Satellite Precipitation Products.

机译：验证卫星降水产品的混合框架。

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摘要

Advances in satellite technology have led to the development of many remote-sensing algorithms to estimate precipitation at quasi-global scales. A number of satellite precipitation products are provided at high spatial and temporal resolutions that are suitable for short-term hydrologic applications. Several coordinated validation activities have been established to evaluate the accuracy of satellite precipitation. Traditional verification measures summarize pixel-to-pixel differences between observation and estimates. Object-based verification methods, however, extend pixel based validation to address errors related to spatial patterns and storm structure, such as the shape, volume, and distribution of precipitation rain-objects.;In this study, an image processing approach known as watershed transformation, being capable of detect the local/individual storm systems, is adopted in the object-based validation framework. After image segmentation, several key attributes of the segmented storm systems are selected and membership scores of those attributes are estimated based on the distance measurement of the estimated and reference images. An overall membership score is estimated from all the selected attributes and their membership values.;The proposed object-based validation framework was used to evaluate PERSIANN, PERSIANN-CCS, CMORPH, 3B42RT against NOAA stage IV MPE multi-sensor composite rain analysis. All estimates are evaluated at 0.25° by 0.25° on a daily-scale in the summer of 2008 and winter of 2010 over the contiguous United States (CONUS). The results show that CMORPH outperforms the other three satellite products in both seasons. Different satellite products present different characteristics of precipitation. For example, the sizes of storm objects acquired from PERSIANN-CCS are smaller, while storm objects obtained from PERSIANN typically cover larger area. Furthermore, the satellite-based precipitation products perform differently in different seasons, and the seasonal variability can be captured by the framework for each product. It is concluded that the discrepancies between various satellite precipitation estimates can be identified through the proposed verification framework.
机译：卫星技术的进步导致了许多遥感算法的发展，以估计准全球尺度的降水。提供了高时空分辨率的许多卫星降水产品，适用于短期水文应用。已经建立了一些协调的验证活动，以评估卫星降水的准确性。传统的验证措施总结了观察值和估计值之间的像素间差异。但是，基于对象的验证方法将基于像素的验证扩展到解决与空间模式和暴风雨结构有关的错误，例如降雨雨对象的形状，体积和分布。在本研究中，一种称为分水岭的图像处理方法基于对象的验证框架采用了能够检测本地/单个风暴系统的转换。在图像分割之后，选择分割的暴风雨系统的几个关键属性，并基于估计图像和参考图像的距离测量来估计这些属性的隶属度分数。从所有选定的属性及其成员值估计总体成员评分。拟议的基于对象的验证框架用于针对NOAA IV级MPE多传感器复合降雨分析评估PERSIANN，PERSIANN-CCS，CMORPH，3B42RT。在连续的美国（CONUS）上，所有估算值在2008年夏季和2010年冬季的日尺度上以0.25°x 0.25°进行评估。结果表明，CMORPH在两个季节中均优于其他三个卫星产品。不同的卫星产品具有不同的降水特征。例如，从PERSIANN-CCS获取的风暴对象的大小较小，而从PERSIANN-CCS获取的风暴对象通常覆盖较大的区域。此外，基于卫星的降水产品在不同季节的表现也有所不同，每个产品的框架都可以捕获季节性变化。结论是，可以通过提议的验证框架确定各种卫星降水估计之间的差异。

著录项

  • 作者

    Li, Jingjing.;

  • 作者单位

    University of California, Irvine.;

  • 授予单位 University of California, Irvine.;
  • 学科 Hydrology.;Remote Sensing.
  • 学位 Ph.D.
  • 年度 2012
  • 页码 122 p.
  • 总页数 122
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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