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Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Cloud Classification System.

机译：使用人工神经网络-云分类系统从遥感信息中进行降水估计。

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摘要

Precipitation estimation from satellite information (VISIBLE , IR, or microwave) is becoming increasingly imperative because of its high spatial/temporal resolution and board coverage unparalleled by ground-based data. After decades' efforts of rainfall estimation using IR imagery as basis, it has been explored and concluded that the limitations/uncertainty of the existing techniques are: (1) pixel-based local-scale feature extraction; (2) IR temperature threshold to define rain/no-rain clouds; (3) indirect relationship between rain rate and cloud-top temperature; (4) lumped techniques to model high variability of cloud-precipitation processes; (5) coarse scales of rainfall products.; As continuing studies, a new version of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN), called Cloud Classification System (CCS), has been developed to cope with these limitations in this dissertation. CCS includes three consecutive components: (1)  a hybrid segmentation algorithm, namely Hierarchically Topographical Thresholding and Stepwise Seeded Region Growing (HTH-SSRG), to segment satellite IR images into separated cloud patches; (2) a 3D feature extraction procedure to retrieve both pixel-based local-scale and patch-based large-scale features of cloud patch at various heights; (3) an ANN model, Self-Organizing Nonlinear Output (SONO) network, to classify cloud patches into similarity-based clusters, using Self-Organizing Feature Map (SOFM), and then calibrate hundreds of multi-parameter nonlinear functions to identify the relationship between every cloud types and their underneath precipitation characteristics using Probability Matching Method and Multi-Start Downhill Simplex optimization techniques.; The model was calibrated over the Southwest of United States (100°–130°W and 25°–45°N) first and then adaptively adjusted to the study region of North America Monsoon Experiment (65°–135°W and 10°–50°N) using observations from Geostationary Operational Environmental Satellite (GOES) IR imagery, Next Generation Radar (NEXRAD) rainfall network, and Tropical Rainfall Measurement Mission (TRMM) microwave rain rate estimates. CCS functions as a distributed model that first identifies cloud patches and then dispatches different but the best matching cloud-precipitation function for each cloud patch to estimate instantaneous rain rate at high spatial resolution (4km) and full temporal resolution of GOES IR images (every 30-minute).; Evaluated over a range of spatial and temporal scales, the performance of CCS compared favorably with GOES Precipitation Index (GPI), Universal Adjusted GPI (UAGPI), PERSIANN, and Auto-Estimator (AE) algorithms, consistently. Particularly, the large number of nonlinear functions and optimum IR-rain rate thresholds of CCS model are highly variable, reflecting the complexity of dominant cloud-precipitation processes from cloud patch to cloud patch over various regions. As a result, CCS can more successfully capture variability in rain rate at small scales than existing algorithms and potentially provides rainfall product from GOES IR-NEXARD-TRMM TMI (SSM/I) at 0.12° x 0.12° and 3-hour resolution with relative low standard error (∼=3.0mm/hr) and high correlation coefficient (∼=0.65).
机译：由于卫星信息（ VISIBLE ， IR 或微波）的降水估计变得势在必行，因为它具有很高的时空分辨率和无与伦比的电路板覆盖率根据地面数据。经过数十年的以红外图像为基础的降雨估算工作，已经探索并得出结论，现有技术的局限性/不确定性是：（1）基于像素的局部尺度特征提取; （2）红外温度阈值以定义雨/无雨云; （3）降雨率与云顶温度之间的间接关系; （4）集总技术来模拟云降水过程的高可变性; （5）降雨产品的粗尺度;作为继续研究，开发了一种新版本的使用人工神经网络（PERSIANN）从遥感信息中进行降水估算的方法，即云分类系统（CCS），以解决本文中的这些局限性。 CCS包含三个连续的组件：（1）混合分割算法，即分层地形阈值和逐步播种区域生长（HTH-SSRG），用于将卫星IR图像分割为单独的云块; （2） 3D特征提取程序，可同时检索不同高度的基于像素的局部和基于补丁的大规模云特征； （3） ANN模型，即自组织非线性输出（SONO）网络，使用自组织特征图（SOFM）将云补丁分类为基于相似度的群集，然后校准数百个参数非线性函数，使用概率匹配方法和多起点下坡单纯形优化技术来识别每种云类型与其下层降水特征之间的关系。首先在美国西南部（100°–130°W和25°–45°N）对模型进行校准，然后针对北美季风实验的研究区域（65°–135°W和10°– 50°N），使用对地静止作战环境卫星（GOES）红外图像，下一代雷达（NEXRAD）降雨网络和热带降雨测量任务（TRMM）微波降雨率估算值进行观测。 CCS充当分布式模型，该模型首先识别云斑块，然后为每个云斑块分配不同但最匹配的云降水函数，以估计高空间分辨率（4km）和GOES IR图像的全时分辨率（每30个）的瞬时降雨率-分钟）。;在一系列时空尺度上进行评估，CCS的性能始终优于GOES降水指数（GPI），通用调整GPI（UAGPI），PERSIANN和自动估计器（AE）算法。特别是，CCS模型的大量非线性函数和最佳IR雨率阈值是高度可变的，反映了在各个区域中从云斑到云斑的主要云降水过程的复杂性。结果，与现有算法相比，CCS可以更成功地在小范围内捕获降雨率的变化，并可能以0.12°x 0.12°和3小时的分辨率从GOES IR-NEXARD-TRMM TMI（SSM / I）提供降雨产品低标准误差（〜= 3.0mm / hr）和高相关系数（〜= 0.65）。

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