长江流域资源与环境 >> 2013, Vol. 22 >> Issue (06): 786-.

• 区域可持续发展 • 上一篇    下一篇

基于FY2C卫星数据的藏北高原降水估算研究

夏 双|阮仁宗|周 义|王玉强   

  1. (1.河海大学地球科学与工程学院|江苏 南京 210098; 2.南京大学国际地球系统科学研究所|江苏 南京 210093 
  • 出版日期:2013-06-20

PRECIPITATION ESTIMATION IN THE NORTHERN TIBETAN PLATEAU USING FY2C SATELLITE DATA

XIA Shuang1|RUAN Renzong1, ZHOU Yi2, WANG Yuqiang1   

  1. (1.School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China;2.International Institute for Earth System Science, Nanjing University, Nanjing 210093, China
  • Online:2013-06-20

摘要:

如何确切的掌握降水的时空分布,对区域气候、水文和生态应用等至关重要。以藏北高原典型区为研究区,在大量地面实况降水观测数据与对长时间序列FY2C 影像光谱特征和云图特征分析的基础上,获取卫星降水模拟参数特征集以刻画云降水的发生与发展过程,选用最值归一化方法对不同量纲云图特征参数进行归一化处理。构建基于三层前向型反向传播神经网络的卫星降水估算模型,用于该地域降水估算,并采用多指标体系分析模型的降水模拟精度。结果表明:静止气象卫星红外波段能较精确地揭示云的降水机理,较高时间分辨率遥感图像可以监测云图的变化细节,并获取能够反映云图降水特征的降水模拟参数;人工神经网络能较好地刻画该地域卫星降水特征的非线性规律;三层前向型反向传播神经网络卫星降水估算模型的估算结果与雨量计实测值间的相关性可以达到0.57。模型估算结果系统性的低估偏小,预示着对该地域弱降水强度将有较好的指示性

Abstract:

It is crucial to the regional climate and hydrological and ecological applications that how to get a better understanding of the spatiotemporal distribution of precipitation. With the rapid development of satellite remote sensing technology and applications, the estimation of precipitation make it possible for the precipitation forecast by using remote sensing complements the data from ground gauge observation in the areas where the number of observation stations is not enough.Due to the artificial neural network characterized by a description of the nonlinear relationship,it can be used to describe the nonlinear information of precipitation and its regional differences and thus fit the nonlinear relationship between values of precipitation estimation and gauge observation. The artificial neural network is powerful due to its capability in processing data by using parallel,selfadaptive and selflearning methodology and mapping the nonlinear patterns. Thus, artificial neural network technology can make a new and valuable method for the estimates of the precipitation information.In this paper,the typical northern Tibetan Plateau is taken as the study area.The data mainly includes gauge observation data and a long time series of FY2C imagery.Based on the analysis of observation data,the multispectral characteristics and features of clouds,the parameters for the description of the process of precipitation in the study area were acquired and normalized.Then,the parameters for precipitation simulation and the intensity of precipitation were used as the input and the output of the neural network for the estimation of precipitation by using satellite remote sensing data.Finally,a combination of qualitative and quantitative indicator evaluation system was employed to test the accuracy of model estimation.Multiindicator system was developed for analyzing the accuracy of precipitation simulation.The results show that the infrared band could be used to effectively indicate precipitation of clouds.By using high temporal resolution satellite remote sensing imagery,the subtle changes of clouds could be captured and the parameters of precipitation simulation,which reflect precipitation characteristics,could be acquired.The nonlinear features of precipitation in this area could be well described by using artificial neural network.The precipitation estimation model used in the paper was stable,efficient,and globally representative.The model could better disclose the precipitation characteristics of the study area in the northern Tibet Plateau.The results also demonstrate that correlation coefficient between values of modeling estimation and gauge observation data was 0.57.Moreover,the systematic bias of the estimates was small,indicating that the model could predict the precipitation of weak intensity more accurately in the site.The values of precipitation estimation were less than that of gauge observation data,which had a better indication of the weak intensity of precipitation.The precipitation estimation results could provide valuable parameters for various hydrologic researches.Due to the significant variation in terrain of the north Tibetan Plateau,whether or not the precipitation simulation parameters and the model by using FY2C data can be used in other parts of the Tibetan Plateau is to be answered by further researches

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