【作者】 刘忠；

【导师】 张秋文；

【作者基本信息】 华中科技大学 ， 水利水电工程， 2005， 硕士

【摘要】 降雨量不仅是水文和水资源研究中的一个重要参数,而且对于流域的防洪减灾和水利水电工程建设也具有重要的意义。但是,由于常规降雨监测手段存在监测范围小、成本高和只能点监测等不足,因此,如何准确、廉价、快速地获取流域内任意一个更小单元内的降雨量越来越受到人们的关注。随着科技的进步,卫星遥感雨量监测技术具有的监测范围大、不受地形限制等优势逐步体现出来,基于卫星数据的雨量估算技术和方法越来越受到重视。MODIS 是新一代“图谱合一”的卫星传感器,它具有丰富的与降雨相关的数据产品,如果将其应用于雨量估算必将提高雨量估算的精度,促进雨量估算技术的发展。本文以MODIS 传感器的卫星遥感数据为基础,分析从MODIS 数据产品获取的气象因子和降雨的关系,建立针对流域的分布式的降雨量估算模型。首先通过分析云降雨过程和云层降雨的物理机理,选取与降雨有直接关系的云、大气参数作为降雨估算参数因子,然后在分析MODIS 数据处理流程的基础上,从MODIS 的云产品数据中读取选定的参数因子数据集,并与地面实测降雨数据组成历史样本,最后采用人工神经网络的建模方法,用历史样本训练网络模型,建立针对流域的分布式的降雨量估算模型。为了对所建立的模型进行训练和验证,需要大量的历史数据,本文中的降雨数据采用湖北省清江流域2004 年上半年的雨量观测数据,卫星数据采用华中科技大学数字流域工程中心MODIS 地面站接收并处理的MODIS 卫星数据产品。本文的神经网络采用BP 网络。通过对模型检验发现,此模型的估算能力较好,虽然有一定偏差,但是基本能够反映实际的降雨情况。本文的研究说明MODIS 的高光谱分辨率和丰富的数据产品应用于卫星雨量估算具有实际意义,拓展了MODIS 数据的应用领域。而人工神经网络的自适应学习和非线性映射的优良性能,对提高降雨估算的准确度很有意义。更多还原

【Abstract】 Precipitation is not only an essential parameter in hydrology and in the research of water resources, but also important for the flood control of the valley and the preparation against it, as well as the construction of hydraulic and hydropower engineering. However, the conventional monitoring approaches of precipitation involve many disadvantages, such as limited observing range, high cost and only-one-point precipitation observation. Consequently, how to get the precipitation of any part of the valley attracts more and more attention. With the progress of the science and technology, the satellite based precipitation observation, which can cover a wide range, and is not restricted by terrain, displays its merit in this field. Growing concern has been devoted into the observation system of this approach. MODIS is a new generation satellite sensor, which combines image with spectrum. It can provide substantial data on the rainfall. If it is applied to estimate precipitation, the accuracy of the estimation must be improved, and the development of the precipitation estimating technology would be promoted too. This thesis set up a regional scale estimation model of precipitation, and analyzed the relation between the data of MODIS with rainfall, based on the data of satellite, obtained with the MODIS sensor. The precipitation of any unit of the valley can be acquired through this model. Firstly, the process and the physical mechanics of rainfall are analyzed. Then parameters of cloud and atmosphere, which are directly involved with rainfall, are chosen as the estimating factors. On the basis of analyzing the data processing flow of MODIS, a data set of parameter factors is hereby established by reading the cloud product data of MODIS, which, together with data obtained by surface measurement of rainfall, compose the original sample. At last, the modeling method of artificial neural networks is adopted; and the network model is tested with the original sample. Thus a regional scale precipitation estimating model has been established. In order to train and test the model, large amount of raw data are required. The precipitation data in this thesis is borrowed from observation data of rainfall in the Qingjiang valley, Hubei Province, in the first half of the year, 2004. The MODIS satellite data product, received and processed by the MODIS ground station of Digital Valley Engineering Center of Huazhong University of Sci. & Tech., are adopted for the satellite data in this thesis. For the artificial neural network, BP network is utilized. Through examining the model, it is found that its estimating ability is good, which can reflect the actual rainfall in spite of some deviation. The study of this thesis reveals that it bears practical significance to apply the high spectral resolution and rich data products of MODIS to satellite estimation of precipitation, which can develop the application field of MODIS. Meanwhile, the study also shows the excellent capability of adaptive study and nonlinear mapping of artificial neural networks, which is significant for improving the accuracy of precipitation estimation.更多还原