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Improved global high resolution precipitation estimation using multi-satellite multi-spectral information.

机译：使用多卫星多光谱信息改进的全球高分辨率降水估算。

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摘要

In respond to the community demands, combining microwave (MW) and infrared (IR) estimates of precipitation has been an active area of research since past two decades. The anticipated launching of NASA's Global Precipitation Measurement (GPM) mission and the increasing number of spectral bands in recently launched geostationary platforms will provide greater opportunities for investigating new approaches to combine multi-source information towards improved global high resolution precipitation retrievals. After years of the communities' efforts the limitations of the existing techniques are: (1) Drawbacks of IR-only techniques to capture warm rainfall and screen out no-rain thin cirrus clouds; (2) Grid-box- only dependency of many algorithms with not much effort to capture the cloud textures whether in local or cloud patch scale; (3) Assumption of indirect relationship between rain rate and cloud-top temperature that force high intensity precipitation to any cold cloud; (4) Neglecting the dynamics and evolution of cloud in time; (5) Inconsistent combination of MW and IR-based precipitation estimations due to the combination strategies and as a result of above described shortcomings.;This PhD dissertation attempts to improve the combination of data from Geostationary Earth Orbit (GEO) and Low-Earth Orbit (LEO) satellites in manners that will allow consistent high resolution integration of the more accurate precipitation estimates, directly observed through LEO's PMW sensors, into the short-term cloud evolution process, which can be inferred from GEO images. A set of novel approaches are introduced to cope with the listed limitations and is consist of the following four consecutive components: (1) starting with the GEO part and by using an artificial-neural network based method it is demonstrated that inclusion of multi-spectral data can ameliorate existing problems associated with IR-only precipitating retrievals; (2) through development of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network -- Multi-Spectral Analysis (PERSIANN-MSA) the effectiveness of using multi-spectral data for precipitation estimation are examined. In comparison to the use of a single thermal infrared channel, using multi-spectral data has a potential to significantly improve rain detection and estimation skills; (3) a method proposed to integrate the previously developed cloud classification system (PERSIANNCCS) with PERSIANN-MSA. Through the integration, PERSIANN-MSA benefits from both cloud-patch classification capability as well as multi-spectral information to culminate the GEO-based precipitation estimation techniques; (4) finally, a new combination technique that incorporates multi-sensor information is developed. The technique is called REFAME, short for Rain Estimation using Forward Adjustedadvection of Microwave Estimates. REFAME allows more consistent integration of MWVIS/ IR information through hybrid advection and adjustment of MW precipitation rate along cloud motion streamlines obtained from a 2D cloud tracking algorithm using successive GEO/IR images. Evaluated over a range of spatial and temporal scales it is demonstrated that REFAME is a robust technique for real-time high resolution precipitation estimation using multi-satellite information.
机译：为了响应社区的需求，自过去二十年来，结合微波（MW）和红外（IR）估计的降水量一直是研究的活跃领域。预期NASA的全球降水测量（GPM）任务的发射以及最近发射的地球静止平台中光谱带数量的增加，将为研究将多源信息结合起来以改善全球高分辨率降水检索的新方法提供更大的机会。经过社区的多年努力，现有技术的局限性是：（1）仅使用红外技术的缺点是无法捕获温暖的降雨并屏蔽无雨的薄卷云； （2）许多算法仅依赖于网格盒，而无需花费很多精力来捕获本地或云补丁规模的云纹理； （3）假定降雨率与云顶温度之间存在间接关系，从而迫使高强度降水形成任何冷云； （4）忽略云的动态变化； （5）由于上述结合策略和上述缺点导致的基于兆瓦和基于红外的降水估计的结合不一致；本博士论文试图改进对地静止地球轨道（GEO）和近地轨道的数据结合（LEO）卫星将以一致的高分辨率将通过LEO的PMW传感器直接观测到的更准确的降水估计值进行高分辨率整合，并整合到短期云演化过程中，这可以从GEO图像中推断出来。引入了一套新颖的方法来应对所列的局限性，它由以下四个连续的组成部分组成：（1）从GEO部分开始，并通过使用基于人工神经网络的方法，证明了包含多光谱数据可以改善与仅IR沉淀检索相关的现有问题； （2）通过使用人工神经网络-多光谱分析（PERSIANN-MSA）开发来自遥感信息的降水估算，研究了使用多光谱数据进行降水估算的有效性。与使用单个热红外通道相比，使用多光谱数据有可能显着提高降雨的检测和估算能力； （3）提出了一种将先前开发的云分类系统（PERSIANNCCS）与PERSIANN-MSA集成的方法。通过整合，PERSIANN-MSA既受益于云补丁分类功能，又受益于多光谱信息，最终达到了基于GEO的降水估算技术； （4）最后，开发了一种结合多传感器信息的新组合技术。该技术称为REFAME，是使用微波估计值的正向调整对流进行雨量估计的缩写。 REFAME允许通过混合平流和沿着从使用连续GEO / IR图像的2D云跟踪算法获得的云运动流线调整MW降水速率，更一致地整合MWVIS / IR信息。在一系列时空尺度上进行评估，结果表明REFAME是使用多卫星信息进行实时高分辨率降水估算的可靠技术。

著录项

  • 作者

    Behrangi, Ali.;

  • 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 2009
  • 页码 204 p.
  • 总页数 204
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:38:26

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