气象自动观测站数据处理方法研究
|
摘要
在人类社会不断得以改造的同时,人类赖以生存的生活环境发生了更多的改变：地质灾害频发、温室效应日益明显、台风、龙卷、冰雹、强降水等极端天气象事件日益频繁,给人们带来了巨大的生命威胁和财产损失。受制于当前科技水平的制约,极端天气事件的长期预测还没形成有效的方法和完善的理论体系,气象领域的“准确有效的长期天气预报”也是当今世界的难题之一。然而,对天气系统的实时监测能有效地提高气象预报的准确性,能及时预测龙卷,台风等极端天气事件的发生,为决策部门的防灾减灾提供有力的技术支持。地面气象观测是实现天气系统监测的重要手段,是气象预报与气候分析以及灾害监测的重要途径和数据基础。近些年来,无线传感器技术的飞速发展与应用为地面气象观测系统带来了新的发展机遇。无线传感器与地面气象观测系统相结合形成的自动观测站弥补了传统人工观测频率低、空间分辨率差、数据误差不确定等许多不足,实现了全天候的实时观测,在极端天气系统观测、大型运动会场服务等应用中发挥了重要作用,成为当前气象数字化预报以及地面气象观测现代化新的数据基础,也是当前世界各国研究的热点。然而,天气系统的局部不稳定性、观测环境的复杂性、电子仪器的不确定性以及报文传输的误差等众多因素使得观测数据与实际天气状况有着较大误差。因此,基于气象自动观测站数据质量控制对于极端天气事件的防治、天气预报制作和区域气候分析以及其他基础数据应用均有着重要的作用。根据自动观测站测量序列在不同时间尺度条件下的不同特性,本文基于混沌理论、测量动态数据处理以及GIS技术等方法对不同时间尺度条件下的观测序列提出了气象自动观测站测量数据质量控制方法。主要的创新内容有：
1.基于混沌理论,分析了气象自动观测站地面气象观测多要素如降水、地温、气温、湿度,矢量风等数据序列的混沌特性,提出了基于连续性数据序列滤波方法。
2.根据自动观测站分钟级观测数据的连续性,利用现代测量数据处理技术和非线性时间序列分析技术,针对温度观测序列等非线性系统,利用AR模型和神经网络以及卡尔曼滤波方法,提出了温度序列的二层卡尔曼滤波方法；对降水数据序列等随机性强的观测序列,利用在线向量机方法和基于Unscented变换的卡尔曼滤波技术提出了基于在线向量机和卡尔曼滤波的降水序列数据处理方法。
3.针对观测频率大于10分钟的观测数据序列引入概率转换模型并结合地面气象观测站多源数据特点系统性地提出了相关数据序列奇异值检测方法。在降水序列的有效性检测研究中,通过对分钟级降水序列中记录转换特性分析,利用概率转换模型提出了基于隐马尔可夫模型的降水序列有效性计算方法；在对温度序列的连续性奇异值检测过程中,通过对观测序列的学习,建立了观测数据隐含的“稳定状态”的转换关系并利用该模型对观测数据进行有效性识别。该方法对连续观测异常记录有很高的识别效率,同时能有效地降低了离散奇异值干扰。
4.利用观测数据序列的空间相关性特点,对于有空间相关性的数据序列(如温度,气压等),以温度序列为研究对象提出了基于GIS的温度观测复原方法；对空间相关性较弱的观测序列,基于GIS平台和信息融合理论,提出了基于气象雷达的地面气象观测站降水序列有效性估计方法。
The development of science and technology promotes the advance of human society as well as strengthens the relations between them. The environment of our life has changed greatly as well as the people's exploration. Many disasters happened in human history, such as earthquake, tornados and shower which have made catastrophy to many people. It is well know that the accurate weather forecast is one of the most difficult works in the world and lots of issues with it are still problematic. The monitoring of air on line can made great effort to improve the accuracy of weather forecast and lower greatly the loss of meteorological disaster. Especially in the process of surveying tornados, an accurate and valid data series about weather condition is vital to effectively predicate the bad weather.
Based on the technology of wireless sensor net, automatic weather station becomes an effective way to survey the air condition of some place. During the last decade, more and more data set collected by the Automatic Weather Station (AWS) was employed meteorological service. In addition, the advance of high density monitoring both in temporal and space, unattended operation and normal collection made the AWS becomes a popular tool instead of traditional manual way, which can be seen frequently in the service of sport, agriculture and monitoring of extreme weather condition.
However, the complicated weather system, the unstable ability of electronic instrument and unknown disturb of outer made part of those data series wrong, which become a bottleneck of improving the data validation. As a result, it is significant to monitor the quality of data series collect by AWS in the process of surveying the weather condition in extreme weather and other applications.
The main works done in this dissertation are as follows.
1. Based on the theory of chaos, the characteristics of multi-data source are analyzed and a new method for noise filtering of temperature series is developed;
2. By employing the method of dynamic surveying, nonlinear data series and the AR based Kalman filter method, we providea two stage Kalman filter to denoise the temperature series collected by AWS. Besides that, rainfall series have more stochastic than former, we present an effective method based on the Online Support Vector Machine and Unscented Kalman Filter method to filter a precipitation series. The empirical results reveal that the proposed method outperforms the other two models using traditional Kalman filter and support vector machine respectively.
3. With the theory of Hiden Markov theory, the characteristic of rainfall series is studied and an important rule of a precipitation is obtained. Based on the introduced rule, an adaptive Hidden Markov Module (HMM) is been built to evaluate a session of precipitation series. In addition, with the theory of stochastic theory, we propose a Probability Finite State Automata based Algorithm (PFSAA) for detecting outliers of air temperature series data caused by sensor's errors. The proposed method can be used effectively in other nonlinear system.
4. GIS is good platform to support the analysis the relationship of an adjacent data series in space. We propose a new method for recovering of temperature series with the support of adjacent automatic weather stations (AWS) with a GIS platform. Besides that, by using the platform of GIS, which can be used to build relation between different data type, we provide a new method to value the precipitation series by the conference of synthesis radar, which is a good way to monitor the valid of daily rainfall.
1.基于混沌理论,分析了气象自动观测站地面气象观测多要素如降水、地温、气温、湿度,矢量风等数据序列的混沌特性,提出了基于连续性数据序列滤波方法。
2.根据自动观测站分钟级观测数据的连续性,利用现代测量数据处理技术和非线性时间序列分析技术,针对温度观测序列等非线性系统,利用AR模型和神经网络以及卡尔曼滤波方法,提出了温度序列的二层卡尔曼滤波方法；对降水数据序列等随机性强的观测序列,利用在线向量机方法和基于Unscented变换的卡尔曼滤波技术提出了基于在线向量机和卡尔曼滤波的降水序列数据处理方法。
3.针对观测频率大于10分钟的观测数据序列引入概率转换模型并结合地面气象观测站多源数据特点系统性地提出了相关数据序列奇异值检测方法。在降水序列的有效性检测研究中,通过对分钟级降水序列中记录转换特性分析,利用概率转换模型提出了基于隐马尔可夫模型的降水序列有效性计算方法；在对温度序列的连续性奇异值检测过程中,通过对观测序列的学习,建立了观测数据隐含的“稳定状态”的转换关系并利用该模型对观测数据进行有效性识别。该方法对连续观测异常记录有很高的识别效率,同时能有效地降低了离散奇异值干扰。
4.利用观测数据序列的空间相关性特点,对于有空间相关性的数据序列(如温度,气压等),以温度序列为研究对象提出了基于GIS的温度观测复原方法；对空间相关性较弱的观测序列,基于GIS平台和信息融合理论,提出了基于气象雷达的地面气象观测站降水序列有效性估计方法。
The development of science and technology promotes the advance of human society as well as strengthens the relations between them. The environment of our life has changed greatly as well as the people's exploration. Many disasters happened in human history, such as earthquake, tornados and shower which have made catastrophy to many people. It is well know that the accurate weather forecast is one of the most difficult works in the world and lots of issues with it are still problematic. The monitoring of air on line can made great effort to improve the accuracy of weather forecast and lower greatly the loss of meteorological disaster. Especially in the process of surveying tornados, an accurate and valid data series about weather condition is vital to effectively predicate the bad weather.
Based on the technology of wireless sensor net, automatic weather station becomes an effective way to survey the air condition of some place. During the last decade, more and more data set collected by the Automatic Weather Station (AWS) was employed meteorological service. In addition, the advance of high density monitoring both in temporal and space, unattended operation and normal collection made the AWS becomes a popular tool instead of traditional manual way, which can be seen frequently in the service of sport, agriculture and monitoring of extreme weather condition.
However, the complicated weather system, the unstable ability of electronic instrument and unknown disturb of outer made part of those data series wrong, which become a bottleneck of improving the data validation. As a result, it is significant to monitor the quality of data series collect by AWS in the process of surveying the weather condition in extreme weather and other applications.
The main works done in this dissertation are as follows.
1. Based on the theory of chaos, the characteristics of multi-data source are analyzed and a new method for noise filtering of temperature series is developed;
2. By employing the method of dynamic surveying, nonlinear data series and the AR based Kalman filter method, we providea two stage Kalman filter to denoise the temperature series collected by AWS. Besides that, rainfall series have more stochastic than former, we present an effective method based on the Online Support Vector Machine and Unscented Kalman Filter method to filter a precipitation series. The empirical results reveal that the proposed method outperforms the other two models using traditional Kalman filter and support vector machine respectively.
3. With the theory of Hiden Markov theory, the characteristic of rainfall series is studied and an important rule of a precipitation is obtained. Based on the introduced rule, an adaptive Hidden Markov Module (HMM) is been built to evaluate a session of precipitation series. In addition, with the theory of stochastic theory, we propose a Probability Finite State Automata based Algorithm (PFSAA) for detecting outliers of air temperature series data caused by sensor's errors. The proposed method can be used effectively in other nonlinear system.
4. GIS is good platform to support the analysis the relationship of an adjacent data series in space. We propose a new method for recovering of temperature series with the support of adjacent automatic weather stations (AWS) with a GIS platform. Besides that, by using the platform of GIS, which can be used to build relation between different data type, we provide a new method to value the precipitation series by the conference of synthesis radar, which is a good way to monitor the valid of daily rainfall.
引文
[1]张文煜,袁九毅.大气探测原理与方法[M].北京：气象出版社,2007.
[2]中国气象局监测网络司,综合气象观测研究计划(2009-2015),ed.北京：2009-5
[3]窦莉新.地面观测自动化为气象观测带来质的飞跃.Available: http://www.weather.com.cn/news/1276446.shtml,2011
[4]林红梅.我国高速公路通车总里程超3万公里居世界第二.Available: www.people.com.cn,2004-5-13
[5]窦以文,屈玉贵,陶士伟,胡保昆.北京自动气象站实时数据质量控制应用[J].气象2008,34(8)：77-81.
[6]M. Salvati and E. Bramilia. data qulity control procedures in Alpine Meteorological services M. S. Stefano Serafin, Stefano Mariani,Fulvio Stel, Ed., ed:universita degli studi di Trento; dipartimento di ingegneria civile eAmbientale,2008
[7]I. Zahumensky. Guidelines on Quality Control Procedures for Data from Automatic Weather Stations. Available:http://www.wmo.int/pages/prog/ www/IMOP/publications/IOM-82-TECO_2005/Papers/3(14)_Slovakia_2_Za humensky.pdf,2004.
[8]N. L.Baker, Quality control of meteorological observations for the Fleet Numerical Oceanography Center operational atmospheric database.[C] Naval Research Laboratory,Marine meteorology devision, Monterey 1994.
[9]Slovakia. Guidelines on Quality Control procedures for Data from Automatic Weather Stations [C], presented at the Expert Team onRequirement for Data from Automatic Weather Stations, Gnveva,Switzerland,2004.
[10]A. Boissonnade, et al. Weather data:cleaning and enhancement[M].London: Risk Books,2002.74-93.
[11]G. Hoogenboom and D. Gresham, Automated weather station network[c], 1997:483-486.
[12]N. Wolfson, et al. Automatic Real-Time Quality Control of Surface Synoptic Observations [J]. Journal of Applied Meteorology,1978,17 (2):449-457.
[13]D. Meek and J. Hatfield. Data quality checking for single station meteorological databases[J]. Agricultural and Forest Meteorology,1994,69 (1-2):85-109.
[14]M. Tatarskata, et al. Quality Control of Ground-Based Radiometric Observations of Integrated Moisture Using Surface Meteorological Observations[J]. Journal of Atmospheric and Oceanic Technology,1994,11 (2):599-607.
[15]A. DeGaetano. A quality-control routine for hourly wind observations [J]. Journal of Atmospheric and Oceanic Technology,1997,14 (2):308-317.
[16]H. Jarvinen, et al. Variational assimilation of time sequences of surface observations with serially correlated errors[J].Tellus A,1999,51 (4):469-488.
[17]B. Miller. Observation control for discrete-continuous stochastic systems[J]. IEEE Transactions on Automatic Control,2000,45 (5):993-998.
[18]D. Dee, et al. An adaptive buddy check for observational quality control[J]. Quarterly Journal of the Royal Meteorological Society,2001,127 (577): 2451-2471.
[19]B.David, B.Richard. Automatic Weather Stations and Artificial Neural Networks:Improving the Instrumental Record in West Antarctica [J].Monthly Weather Review.2002,130(2):3037-3053.
[20]J. Wu, et al. Towards integrated and efficient scientific sensor data processing: a database approach[C]. Proceedings of the 12th International Conference on Extending Database Technology:Advances in Database Technology Saint-Petersburg, Russian Federation 2009(6):922-933.
[21]K. Yisak Abdella A GIS tool set for automated processing and analysis of radar precipitation data[J].Computers&Geosciences,2009,36:422-429.
[22]V. Hasu. Automatic Minimum and Maximum Alarm Thresholds for Quality Control[J]. Journal of Atmospheric and Oceanic Technology,2010,28 (1): 74-84.
[23]陆霞,殷明洁.自动站与人工观测降水量差值的成因分析[J].气象研究与应用,2007,28(3)：56-65.
[24]曹晓钟,王强.神经网络在气象观测资料优化中的应用研究[J].高原气象,2002,21(1)：96-101.
[25]胡玉峰.自动与人工观测数据的差异[J].应用气象学报,2004,15(6)：719-726.
[26]熊安元.北欧气象自动观测站数据质量控制[J].气象技术,2003,31(5)：314-320.
[27]王小蕾,林婕.自动气象站温度传感器的辐射误差[J].解放军理工大学学报,2006,7(4)：405-408.
[28]王海军,杨志彪,杨代才,龚贤创.自动站实时资料自动质量控制方法及其应用[J].气象,2007,33(10)：102-110.
[29]王艳,方晓,高杰.小波阈值法在地面气象自动站观测资料审核中的应用[J].气象与环境学报,2007,23(2)：42-44.
[30]A. Tsonis and J. Elsner. Chaos, strange attractors, and weather[J].Bulletin of the American Meteorological Society,1989,70 (1):14-23.
[31]J. Lu, et al., Secure communication based on synchronization of a unified chaotic system, IEEE,2004, (2):212-64
[32]C. z.-m. Lu Zhen-bo, Jiang ke-yu. determination of embedding prarmeters for phase space reconstruction based on improved C-C method[J]. journal of system simulation,2007,19 (Oil):2527-2529.
[33]M. B. Kennel, et al. Determining embedding dimension for phase-space reconstruction using a geometrical construction[J]. Physical Review A,1992, 45 (6):3403.
[34]L. Jiayu, et al. Selection of Proper Time-Delay in Phase Space Reconstruction of Speech Signals [J]. Signal Processing,1999,3 (1): 735-742.
[35]A. Jayawardena, et al. Neighbourhood selection for local modelling and prediction of hydrological time series[J]. Journal of Hydrology,2002,258 (1-4):40-57.
[36]安鸿志,陈敏.非线性时间序列分析[M].上海：上海科技出版社,1998.
[37]中国气象局监测网络司,气象信息网络传输业务手册,vo1.4,中国气象局监测网络司,Ed.,ed.北京：气象出版社,2006,(4)：44-62
[38]D. Schertzer, et al. Discussion of Evidence of chaos in the rainfall-runoff process"[J].Hydrological Sciences Journal,2002,47 (1):139-148.
[39]F. C. Moon. Chaotic vibrations[M]:Wiley New York,1987.
[40]P. Fede and O. Simonin. Numerical study of the subgrid fluid turbulence effects on the statistics of heavy colliding particles[J].Physics of Fluids,2006, 18 (4):45-103.
[41]J. Theiler. Efficient algorithm for estimating the correlation dimension from a set of discrete points[J].Physical Review A,1987,36 (9):4456.
[42]M. Unka evi.Statistical analysis of daily maximum and monthly precipitation at Belgrade[J].Theoretical and Applied Climatology,2000,66 (3):241-249.
[43]E. Gorgucci, et al. Radar and surface measurement of rainfall during CaPE: 26 July 1991 case study[J] Journal of Applied Meteorology,1995,34 (7): 1570-1577.
[44]I. Daniel, Ralph Wright. Weather data Quality Conrol Procedures in the Alberta Agriculture Drought Monitoring Network[C], presented at the The Canadian Society for Bioengineering, Edmonton Alberta,2006.1-13.
[45]N. S. Markos Gouvas, Fotios Xystrakis. The relationship between altitude of meteorological stations and average monthly and annual precipiation[J].studia geophysica&geodaetica,2009,53 (4):557-570.
[46]C. Piani, et al. Statistical bias correction for daily precipitation in regional climate models over Europe[J].Theoretical and Applied Climatology,2010,99 (1):187-192.
[47]D. W. Cash, et al. Scale and cross-scale dynamics:governance and information in a multilevel world[J].Ecology and Society,2006,11 (2):8.
[48]R. E. Kalman. A New Approach to Linear Filtering and Prediction Problems [J].Transactions of the ASME-Journal of Basic Engineering,1960, vol.82 (Series D):35-45.
[49]P. Grassberger and I. Procaccia. Estimation of the Kolmogorov entropy from a chaotic signal[J].Physical review. A, General physics,1983,28 (4): 2591-2593.
[50]杨元喜.动态Kalman滤波模型误差的影响[J].测绘科学,2006,31(1)：17-19.
[51]赵长胜,陶本藻.有色噪声作用下的卡尔曼滤波[J].武汉大学学报(信息科学版),2007,32(10)：880-882.
[52]陶本藻.卡尔曼滤波模型误差的识别[J].地壳形变与振动,1999,19(4)：15-20.
[53]王国富,朱建军.具有随机观测矩阵的线性系统的Kalman滤波[J].测绘通报,2004,1(3)：18-21.
[54]宋迎春,朱建军,陈正阳.动态定位中测量噪声时间相关的Kalman滤波[J].测绘学报,2006,35(4)：328-331.
[55]宋迎春,陈宇波,曾联斌.测量噪声污染模型下的动态定位Bayes方法[J].武汉大学学报.信息科学版,2009,34(6)：736-740.
[56]S. Lototsky.Chaos Approach to Nonlinear Filtering.The Royal swedish academy of sciences, Djursholm,Sweden17 2007.
[57]Bruno otavios Soares Teixeira and Jaganath Chandrasekar. Gain-Constrained Kalman Filtering for Linear and Nonlinear Sy stems [J]. IEEE Transactions On Signal Processing,2008,56 (9):4413-4123.
[58]P. Dewallef and O. L(?)onard, On-line performance monitoring and engine diagnostic using robust Kalman filtering techniques[c]. in Proceedings of GI2003, Georgia,2003:1-9.
[59]S. J. U. Julier, Jeffrey K. New extension of the Kalman filter to nonlinear[J]. Proc. Signal Processing, Sensor Fusion, and Target Recognition Ⅵ, Ivan Kadar Ed.,1997,7 (3068):182-193.
[60]K.-H. Kim. The Stability of the Adatptive Two-stage Extend Kalman Filter [J].International Conference on Control,Automation ans Systems,2008,11 (53):1378-1383.
[61]J. K. U. Simo J.Juleir. Unscented Filtering and Nonlinear Estimation[J]. Proceedings Of Tthe IEEE,2004,3 (92):401-422.
[62]P. Setoodeh, et al. Attitude estimation by divided difference filter-based sensor fusion[J]. Journal of Navigation,2007,60 (1):119-128.
[63]M. M. Andreasen, et al. Non-linear DSGE models, the central difference Kalman filter, and the mean shifted particle filter[M]:CAF, Centre for Analytical Finance,2007.
[64]R. Van Der Merwe. Sigma-point Kalman filters for probabilistic inference in dynamic state-space models,http://www.Citeseer.com, available:2004-6-18.
[65]J. D. Miroslav Simandl, Sigma point Gaussian sum filter design using square root unscented filters[C]. in World Congress, Czech Republic,2005:1-6.
[66]邓自立.自校正滤波理论及其应用[M].哈尔滨：哈尔滨工业大学出版社,2003.
[67]Z. L. D. a. Y.M.Liu. Descriptor Kalman estimator,[J].International Journal of System science,1999,30 (11):1205-1212.
[68]B.Friedland. Treatment of bias in recursive filtering[J]. IEEE Transaction on Automatic Control,1969,14350-367.
[69]J. M. Mendel H. D. Washburn. Multistage Estimation of Bias States in Linear Systems [J]. International Journal of Control,1978,28 (4):511-524.
[70]H. Chao. Self-Memory Spectral Model and its Preliminary [J]Application Scientific Atmospheric Sinica,1998,22.(1):21-27.
[71]P. Y. Lin Chih-Min Adaptive CMAC-Based Supervisory Control for Uncertain Nonlinear System[J]. IEEE Transaction on Systems,2004,34 (2): 1248-1260.
[72]中国气象局,地面气象观测数据文件和记录薄表格式,ed.北京：气象出版社,200516-17
[73]A. Hermoso-Carazo and J.Linares-Perez.Extended and unscented filtering algorithms using one-step randomly delayed observations [J].Applied Mathematics and Computation,2007,190 (2):1375-1393.
[74]Y. S. Moon, et al. Delay-dependent robust stabilization of uncertain state-delayed systems[J]. International Journal of Control,2001,74 (14): 1447-1455.
[75]L. Peng, et al. Improved square root unscented Kalman filter and its application in rendezvous and docking[J]. Electric Machines and Control,2010,11.
[76]J. M. Tao Chen, Elaine Martin. Particle filters for the estimation of a state space model [J].14th European Symposium on Computer Aided Process Engineering,2004,6 (ESCAPE-14):613-618.
[77]催先强,杨元喜,高为广.多种有色噪声自适应算法的比较[J].武汉大学学报(信息科学版),2006,31(8)：731-735.
[78]H. Leung, Zhiwen Zhu,Zhen Ding. A periodic phenomenon of the extended Kalman filter in filtering noisy chaotic signals[J]. Signal Processing, IEEE Transactions 2000,6 (48):1807-1810.
[79]AF.Izmailov.M.V.Solodov. Karush-Kuhn-Tucker systems:regularity conditions, error bounds and a class of Newton-type methods.[J]. Math. Program,2003,3 (A95):631-650.
[80]方千,花向红.基于相空间重构与支持向量机预测滑坡位移的一种新方法[J].武汉大学学报.信息科学版,2009,2(34)：248-251.
[81]X.Luo.I.M.Moroz. Ensemble Kalman filter with the Unscented transform[J]. Physical D,2009,1 (238):549-562.
[82]M. A. F. Shafer, Christopher A.; Arndt, Derek S. Quality Assurance Procedures in the Oklahoma Mesonetwork[J]. Journal of Atmospheric and Oceanic Technology,,2000,4 (17):474-494.
[83]M. Davy, et al. An online support vector machine for abnormal events detection[J]. Signal Processing,2006,86 (8):2009-2025.
[84]吕金虎,陆君安,陈士华.混沌时间序列分析及其应用[M].武汉：武汉大学出版社,2002.
[85]黄细霞,石繁槐,顾伟等.加权支持向量回归在线学习方法[J].上海交通大学学报,2009,6(43)：927-930.
[86]K. Stordahl. The history behind the probability theory and the queuing theory[J].Telektronikk,2007,103 (2):123.
[87]A. Pnueli and L. Zuck. Verification of multiprocess probabilistic protocols[J]. Distributed Computing,1986,1(1):53-72.
[88]M. Kwiatkowska. Model checking for probability and time:from theory to practice[J].2003,351-360.
[89]M. Y. Vardi, Automatic verification of probabilistic concurrent finite state programs[C].1985:327-338.
[90]C. Myers, et al., Modeling chaotic systems with hidden Markov models[c], in Acoustics, Speech, and Signal Processing,1992, New York,2010:565-568
[91]R. Alur and D. L. Dill. A theory of timed automata[J].Theoretical computer science,1994,126 (2):183-235.
[92]N. Dalezios and A. Bartzokas. Phenomenological features of precipitation series in agricultural regions[J]. Extreme Hydrological Events:Precipitation, Floods and Droughts,1988,2 (13):51-57.
[93]E. Mekis and W. Hogg. Rehabilitation and analysis of Canadian daily precipitation time series[J]. Atmosphere-Ocean,1999,37 (1):53-85.
[94]A. Ray. Symbolic dynamic analysis of complex systems for anomaly detection[J]. Signal Process.,2004,84 (7):1115-1130.
[95]B. Edwards.GIS-based Radar Rainfall Verification. Available:. http://nldr.library.ucar.edu/views/?collId=collections&itemId=SOARS-000-0 00-000-027,2011.
[96]J. Borges, M. Levene.A dynamic clustering-based Markov model for web usage mining, Artificial Intelligence 2004(1):1-19.
[97]T. R. Sofia Andersson subspace estimation and prediction methods for hidden markov models [J]. The Annals of Statistics,2009,37 (6B):21.
[98]X. W. Chang, et al. MLAMBDA:A modified LAMBDA method for integer least-squares estimation[J]. Journal of Geodesy,2005,79 (9):552-565.
[99]Y. Ephraim and W. Roberts. An EM algorithm for Markov modulated Markov processes[J]. Signal Processing, IEEE Transactions on,2009,57 (2): 463-470.
[100]M. Hahn, et al. Markov chain Monte Carlo methods for parameter estimation in multidimensional continuous time Markov switching models [J]. Journal of Financial Econometrics,2009,8 (1):88-121.
[101]L. E. Baum, et al. A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains[J].The annals of mathematical statistics,1970,41 (1):164-171.
[102]R. I. A. Davis and B. C. Lovell. Comparing and evaluating HMM ensemble training algorithms using train and test and condition number criteria[J]. Pattern Analysis & Applications,2004,6 (4):327-335.
[103]R. De Maesschalck, et al. The mahalanobis distance[J].Chemometrics and Intelligent Laboratory Systems,2000,50 (1):1-18.
[104]G. Cembrowski, et al. Assessment of Average of Normals" quality control procedures and guidelines for implementation [J].American journal of clinical pathology,1984,81 (4):492-499.
[105]Y. Z. Ji wu, Karl Aberer,Kian-Lee Tan, Towards integrated and efficient scientific sensor data processing:a database approach[C].In Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology, Saint Petersburg, Russia,2009:922-933.
[106]M. Sturza. Navigation system integrity monitoring using redundant measurements [J]. Navigation,1988,35 (4):483-501.
[107]R. Di Francesco. Real-time speech segmentation using pitch and convexity jump models:application to variable rate speech coding[J]. Acoustics, Speech and Signal Processing, IEEE Transactions on,2002,38 (5):741-748.
[108]A. Khatkhate, et al. Symbolic time-series analysis for anomaly detection in mechanical systems[J].Mechatronics, IEEE/ASME Transactions on,2006,11 (4):439-447.
[109]V. Rajagopalan and A. Ray. Symbolic time series analysis via wavelet-based partitioning [J]. Signal Processing,2006,86 (11):3309-3320.
[110]A. L. Goldberger, et al. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals[J]. Circulation,2000,101 (23):215-220.
[111]G. Karypis, et al. Chameleon:Hierarchical clustering using dynamic modeling[J]. Computer,1999,32 (8):68-75.
[112]O. Dain, et al., Irep++, a faster rule learning algorithm[C]. In Proceedings of the Fourth SIAM International Conference on Data Mining, Lake Buena Vista, Florida, USA,2004:138-146.
[113]T. Kanungo, et al. An efficient k-means clustering algorithm:Analysis and implementation[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,2002,24 (7):881-892.
[114]K. J. Hole, et al. Adaptive multidimensional coded modulation over flat fading channels[J].Selected Areas in Communications, IEEE Journal on,2000, 18(7):1153-1158.
[115]D. Heyman and D. Lucantoni. Modeling multiple IP traffic streams with rate limits [J].Networking, IEEE/ACM Transactions on,2004,11 (6):948-958.
[116]Q. L. Zhu Li, Xue Kouying,Zhang Xinyan;, A Novel BP Neural Network Model for Traffic Prediction of Next Generation Network[c], in Natural Computation,2009. ICNC'09. Fifth International Conference tianjin,china, 2009:32-38.
[117]邬伦,刘瑜,张晶等.地理信息系统--原理、方法和应用[M].北京：科学出版社,2001.
[118]S. T. Shipley, et al., GIS applications in climate and meteorology[c],2000.
[119]R. Aspinall and K. Matthews. Climate change impact on distribution and abundance of wildlife species:An analytical approach using GIS[J]. Environmental Pollution,1994,86 (2):217-223.
[120]D. C. Curtis, et al. Comparing Spatial Distributions of Rainfall Derived from Rain Gages and Radar[J].Journal of Floodplain Management. NEXRAIN Corporation Folsom, CA,1999,6 (1):1-7.
[121]D. P. Dee, et al. An adaptive buddy check for observational quality control [J]. Quarterly Journal of the Royal Meteorological Society,2001,127 (577): 2451-2471.
[122]R. Samet and S. Tural. Web based real-time meteorological data analysis and mapping information system[J].WSEAS Transactions on Information Science and Applications,2010,7 (9):1115-1125.
[123]C. Santhi, et al. A GIS-based regional planning tool for irrigation demand assessment and savings using SWAT[J].Transactions of the ASAE,2005,48 (1):137-147.
[124]C. Cardinali. Monitoring the observation impact on the short-range forecast[J].Quarterly Journal of the Royal Meteorological Society,2009,135 (638):239-250.
[125]R. Watkins, et al. Increased temperature and intensification of the urban heat island:implications for human comfort and urban design[J].Built Environment,2007,33 (1):85-96.
[126]A. J. Bell and T. J. Sejnowski. An information-maximization approach to blind separation and blind deconvolution[J].Neural computation,1995,7 (6): 1129-1159.
[127]A. Hyv rinen and E. Oja. Independent component analysis:algorithms and applications[J]. Neural networks,2000,13 (4):411-430.
[128]T. W. Lee, et al. Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources [J].Neural computation,1999,11 (2):417-441.
[129]J. M. Mendel. Tutorial on higher-order statistics (spectra) in signal processing and system theory:Theoretical results and some applications [J]. Proceedings of the IEEE,1991,79 (3):278-305.
[130]C. L. Nikias and J. M. Mendel. Signal processing with higher-order spectra[J]. Signal Processing Magazine, IEEE,1993,10 (3):10-37.
[131]J. Bobin, et al. Morphological component analysis:An adaptive thresholding strategy [J].Image Processing, IEEE Transactions on,2007,16 (11):2675-2681.
[132]J. Bobin, et al. Morphological diversity and sparsity for multichannel data restoration[J].Journal of Mathematical Imaging and Vision,2009,33 (2): 149-168.
[133]F. Abrard and Y. Deville. A time-frequency blind signal separation method applicable to underdetermined mixtures of dependent sources[J]. Signal Processing,2005,85 (7):1389-1403.
[134]Y. Deville and M. Puigt. Temporal and time-frequency correlation-based blind source separation methods. Part Ⅰ:Determined and underdetermined linear instantaneous mixtures [J]. Signal Processing,2007,87 (3):374-407.
[135]沈钟平,张华.影响地面太阳辐射及其谱分布的因子分析[J].太阳能学报,2009,30(10)：1209-1215.
[136]中国气象局.地面气象观测规范.北京：气象出版社,2003：5-8
[137]白树忠,刘琚,孙国霞.基于最小均方误差和稀疏特征的欠定盲源分离[J]. 山东大学学报,2008,38(4)：43-46.
[138]H. Liu and Y. Cheung. A learning framework for blind source separation using generalized eigenvalues[J].Advances in Neural Networks CISNN, 2005,3 (3):472-477.
[139]K. M. Stellman, et al. An examination of radar and rain gauge-derived mean areal precipitation over Georgia watersheds[J].Weather and forecasting,2001, 16(1):133-144.
[140]J. Koistinen and D. B. Michelson. BALTEX weather radar-based precipitation products and their accuracies[J].Boreal environment research,2002,7 (3):253-263.
[141]俞小鼎,姚秀萍,熊廷南,周小刚.多普勒天气雷达原理与业务应用[M].北京：气象出版社,2006.
[142]P. A. Kucera, et al. Radar beam occultation studies using GIS and DEM technology:An example study of Guam [J]. Journal of Atmospheric and Oceanic Technology,2004,21 (7):995-1006.
[143]D. R. Legates. Real Time Calibration of Radar Precipitation Estimates [J]. The Professional Geographer,2000,52 (2):235-246.
[144]E. Morin and M. Gabella. Radar-based quantitative precipitation estimation over Mediterranean and dry climate regimes[J].J. Geophys. Res,2007,112 D20108.
[145]Y. Gorokhovich and G. Villarini. Application of GIS for processing and establishing the correlation between weather radar reflectivity and precipitation data[J].Meteorological Applications,2005,12 (01):91-99.
[146]X. Zhang and R. Srinivasan. GIS-based spatial precipitation estimation using next generation radar and raingauge data [J]. Environmental Modelling & Software,2010,25 (12):1781-1788.
[147]S. Hardegree, et al. Multi-Watershed Evaluation of WSR-88D (NEXRAD) Radar-Precipitation Products [C]. presented at the Interagency Conference on Research in the Watersheds Benson, Arizona,2003.486-491.
[148]D. McLaughlin. An integrated approach to hydrologic data assimilation: interpolation, smoothing, and filtering[J]. Advances in Water Resources,2002, 25(8-12):1275-1286.
[149]A. Karnieli. Application of kriging technique to areal precipitation mapping in Arizona[J].GeoJournal,1990,22 (4):391-398.
[150]S. Gudmundsson, et al. Three-dimensional surface motion maps estimated from combined interferometric synthetic aperture radar and GPS data[J].J. Geophys. Res,2002,107 (B10):1-12.
[151]郭尚,苏鸿根.基于距离倒数加权法的等值面绘制简化算法[J].计算机工程与设计,2008,29(5)：1200-1202.
[152]H. Xie, et al. GIS-based NEXRAD Stage III precipitation database: automated approaches for data processing and visualization[J]. Computers & geosciences,2005,31 (1):65-76.
[153]王飞行,汤广富,贺思三,付强.高重频频率步进雷达强杂波抑制[J].国防科技大学学报,2009,31(4)：52-57.
[154]杜雨洺,杨建宇,熊金涛.线性调频连续波雷达速度模糊消除新方法[J].上海交通大学学报,2006,40(1)：86-89.
[2]中国气象局监测网络司,综合气象观测研究计划(2009-2015),ed.北京：2009-5
[3]窦莉新.地面观测自动化为气象观测带来质的飞跃.Available: http://www.weather.com.cn/news/1276446.shtml,2011
[4]林红梅.我国高速公路通车总里程超3万公里居世界第二.Available: www.people.com.cn,2004-5-13
[5]窦以文,屈玉贵,陶士伟,胡保昆.北京自动气象站实时数据质量控制应用[J].气象2008,34(8)：77-81.
[6]M. Salvati and E. Bramilia. data qulity control procedures in Alpine Meteorological services M. S. Stefano Serafin, Stefano Mariani,Fulvio Stel, Ed., ed:universita degli studi di Trento; dipartimento di ingegneria civile eAmbientale,2008
[7]I. Zahumensky. Guidelines on Quality Control Procedures for Data from Automatic Weather Stations. Available:http://www.wmo.int/pages/prog/ www/IMOP/publications/IOM-82-TECO_2005/Papers/3(14)_Slovakia_2_Za humensky.pdf,2004.
[8]N. L.Baker, Quality control of meteorological observations for the Fleet Numerical Oceanography Center operational atmospheric database.[C] Naval Research Laboratory,Marine meteorology devision, Monterey 1994.
[9]Slovakia. Guidelines on Quality Control procedures for Data from Automatic Weather Stations [C], presented at the Expert Team onRequirement for Data from Automatic Weather Stations, Gnveva,Switzerland,2004.
[10]A. Boissonnade, et al. Weather data:cleaning and enhancement[M].London: Risk Books,2002.74-93.
[11]G. Hoogenboom and D. Gresham, Automated weather station network[c], 1997:483-486.
[12]N. Wolfson, et al. Automatic Real-Time Quality Control of Surface Synoptic Observations [J]. Journal of Applied Meteorology,1978,17 (2):449-457.
[13]D. Meek and J. Hatfield. Data quality checking for single station meteorological databases[J]. Agricultural and Forest Meteorology,1994,69 (1-2):85-109.
[14]M. Tatarskata, et al. Quality Control of Ground-Based Radiometric Observations of Integrated Moisture Using Surface Meteorological Observations[J]. Journal of Atmospheric and Oceanic Technology,1994,11 (2):599-607.
[15]A. DeGaetano. A quality-control routine for hourly wind observations [J]. Journal of Atmospheric and Oceanic Technology,1997,14 (2):308-317.
[16]H. Jarvinen, et al. Variational assimilation of time sequences of surface observations with serially correlated errors[J].Tellus A,1999,51 (4):469-488.
[17]B. Miller. Observation control for discrete-continuous stochastic systems[J]. IEEE Transactions on Automatic Control,2000,45 (5):993-998.
[18]D. Dee, et al. An adaptive buddy check for observational quality control[J]. Quarterly Journal of the Royal Meteorological Society,2001,127 (577): 2451-2471.
[19]B.David, B.Richard. Automatic Weather Stations and Artificial Neural Networks:Improving the Instrumental Record in West Antarctica [J].Monthly Weather Review.2002,130(2):3037-3053.
[20]J. Wu, et al. Towards integrated and efficient scientific sensor data processing: a database approach[C]. Proceedings of the 12th International Conference on Extending Database Technology:Advances in Database Technology Saint-Petersburg, Russian Federation 2009(6):922-933.
[21]K. Yisak Abdella A GIS tool set for automated processing and analysis of radar precipitation data[J].Computers&Geosciences,2009,36:422-429.
[22]V. Hasu. Automatic Minimum and Maximum Alarm Thresholds for Quality Control[J]. Journal of Atmospheric and Oceanic Technology,2010,28 (1): 74-84.
[23]陆霞,殷明洁.自动站与人工观测降水量差值的成因分析[J].气象研究与应用,2007,28(3)：56-65.
[24]曹晓钟,王强.神经网络在气象观测资料优化中的应用研究[J].高原气象,2002,21(1)：96-101.
[25]胡玉峰.自动与人工观测数据的差异[J].应用气象学报,2004,15(6)：719-726.
[26]熊安元.北欧气象自动观测站数据质量控制[J].气象技术,2003,31(5)：314-320.
[27]王小蕾,林婕.自动气象站温度传感器的辐射误差[J].解放军理工大学学报,2006,7(4)：405-408.
[28]王海军,杨志彪,杨代才,龚贤创.自动站实时资料自动质量控制方法及其应用[J].气象,2007,33(10)：102-110.
[29]王艳,方晓,高杰.小波阈值法在地面气象自动站观测资料审核中的应用[J].气象与环境学报,2007,23(2)：42-44.
[30]A. Tsonis and J. Elsner. Chaos, strange attractors, and weather[J].Bulletin of the American Meteorological Society,1989,70 (1):14-23.
[31]J. Lu, et al., Secure communication based on synchronization of a unified chaotic system, IEEE,2004, (2):212-64
[32]C. z.-m. Lu Zhen-bo, Jiang ke-yu. determination of embedding prarmeters for phase space reconstruction based on improved C-C method[J]. journal of system simulation,2007,19 (Oil):2527-2529.
[33]M. B. Kennel, et al. Determining embedding dimension for phase-space reconstruction using a geometrical construction[J]. Physical Review A,1992, 45 (6):3403.
[34]L. Jiayu, et al. Selection of Proper Time-Delay in Phase Space Reconstruction of Speech Signals [J]. Signal Processing,1999,3 (1): 735-742.
[35]A. Jayawardena, et al. Neighbourhood selection for local modelling and prediction of hydrological time series[J]. Journal of Hydrology,2002,258 (1-4):40-57.
[36]安鸿志,陈敏.非线性时间序列分析[M].上海：上海科技出版社,1998.
[37]中国气象局监测网络司,气象信息网络传输业务手册,vo1.4,中国气象局监测网络司,Ed.,ed.北京：气象出版社,2006,(4)：44-62
[38]D. Schertzer, et al. Discussion of Evidence of chaos in the rainfall-runoff process"[J].Hydrological Sciences Journal,2002,47 (1):139-148.
[39]F. C. Moon. Chaotic vibrations[M]:Wiley New York,1987.
[40]P. Fede and O. Simonin. Numerical study of the subgrid fluid turbulence effects on the statistics of heavy colliding particles[J].Physics of Fluids,2006, 18 (4):45-103.
[41]J. Theiler. Efficient algorithm for estimating the correlation dimension from a set of discrete points[J].Physical Review A,1987,36 (9):4456.
[42]M. Unka evi.Statistical analysis of daily maximum and monthly precipitation at Belgrade[J].Theoretical and Applied Climatology,2000,66 (3):241-249.
[43]E. Gorgucci, et al. Radar and surface measurement of rainfall during CaPE: 26 July 1991 case study[J] Journal of Applied Meteorology,1995,34 (7): 1570-1577.
[44]I. Daniel, Ralph Wright. Weather data Quality Conrol Procedures in the Alberta Agriculture Drought Monitoring Network[C], presented at the The Canadian Society for Bioengineering, Edmonton Alberta,2006.1-13.
[45]N. S. Markos Gouvas, Fotios Xystrakis. The relationship between altitude of meteorological stations and average monthly and annual precipiation[J].studia geophysica&geodaetica,2009,53 (4):557-570.
[46]C. Piani, et al. Statistical bias correction for daily precipitation in regional climate models over Europe[J].Theoretical and Applied Climatology,2010,99 (1):187-192.
[47]D. W. Cash, et al. Scale and cross-scale dynamics:governance and information in a multilevel world[J].Ecology and Society,2006,11 (2):8.
[48]R. E. Kalman. A New Approach to Linear Filtering and Prediction Problems [J].Transactions of the ASME-Journal of Basic Engineering,1960, vol.82 (Series D):35-45.
[49]P. Grassberger and I. Procaccia. Estimation of the Kolmogorov entropy from a chaotic signal[J].Physical review. A, General physics,1983,28 (4): 2591-2593.
[50]杨元喜.动态Kalman滤波模型误差的影响[J].测绘科学,2006,31(1)：17-19.
[51]赵长胜,陶本藻.有色噪声作用下的卡尔曼滤波[J].武汉大学学报(信息科学版),2007,32(10)：880-882.
[52]陶本藻.卡尔曼滤波模型误差的识别[J].地壳形变与振动,1999,19(4)：15-20.
[53]王国富,朱建军.具有随机观测矩阵的线性系统的Kalman滤波[J].测绘通报,2004,1(3)：18-21.
[54]宋迎春,朱建军,陈正阳.动态定位中测量噪声时间相关的Kalman滤波[J].测绘学报,2006,35(4)：328-331.
[55]宋迎春,陈宇波,曾联斌.测量噪声污染模型下的动态定位Bayes方法[J].武汉大学学报.信息科学版,2009,34(6)：736-740.
[56]S. Lototsky.Chaos Approach to Nonlinear Filtering.The Royal swedish academy of sciences, Djursholm,Sweden17 2007.
[57]Bruno otavios Soares Teixeira and Jaganath Chandrasekar. Gain-Constrained Kalman Filtering for Linear and Nonlinear Sy stems [J]. IEEE Transactions On Signal Processing,2008,56 (9):4413-4123.
[58]P. Dewallef and O. L(?)onard, On-line performance monitoring and engine diagnostic using robust Kalman filtering techniques[c]. in Proceedings of GI2003, Georgia,2003:1-9.
[59]S. J. U. Julier, Jeffrey K. New extension of the Kalman filter to nonlinear[J]. Proc. Signal Processing, Sensor Fusion, and Target Recognition Ⅵ, Ivan Kadar Ed.,1997,7 (3068):182-193.
[60]K.-H. Kim. The Stability of the Adatptive Two-stage Extend Kalman Filter [J].International Conference on Control,Automation ans Systems,2008,11 (53):1378-1383.
[61]J. K. U. Simo J.Juleir. Unscented Filtering and Nonlinear Estimation[J]. Proceedings Of Tthe IEEE,2004,3 (92):401-422.
[62]P. Setoodeh, et al. Attitude estimation by divided difference filter-based sensor fusion[J]. Journal of Navigation,2007,60 (1):119-128.
[63]M. M. Andreasen, et al. Non-linear DSGE models, the central difference Kalman filter, and the mean shifted particle filter[M]:CAF, Centre for Analytical Finance,2007.
[64]R. Van Der Merwe. Sigma-point Kalman filters for probabilistic inference in dynamic state-space models,http://www.Citeseer.com, available:2004-6-18.
[65]J. D. Miroslav Simandl, Sigma point Gaussian sum filter design using square root unscented filters[C]. in World Congress, Czech Republic,2005:1-6.
[66]邓自立.自校正滤波理论及其应用[M].哈尔滨：哈尔滨工业大学出版社,2003.
[67]Z. L. D. a. Y.M.Liu. Descriptor Kalman estimator,[J].International Journal of System science,1999,30 (11):1205-1212.
[68]B.Friedland. Treatment of bias in recursive filtering[J]. IEEE Transaction on Automatic Control,1969,14350-367.
[69]J. M. Mendel H. D. Washburn. Multistage Estimation of Bias States in Linear Systems [J]. International Journal of Control,1978,28 (4):511-524.
[70]H. Chao. Self-Memory Spectral Model and its Preliminary [J]Application Scientific Atmospheric Sinica,1998,22.(1):21-27.
[71]P. Y. Lin Chih-Min Adaptive CMAC-Based Supervisory Control for Uncertain Nonlinear System[J]. IEEE Transaction on Systems,2004,34 (2): 1248-1260.
[72]中国气象局,地面气象观测数据文件和记录薄表格式,ed.北京：气象出版社,200516-17
[73]A. Hermoso-Carazo and J.Linares-Perez.Extended and unscented filtering algorithms using one-step randomly delayed observations [J].Applied Mathematics and Computation,2007,190 (2):1375-1393.
[74]Y. S. Moon, et al. Delay-dependent robust stabilization of uncertain state-delayed systems[J]. International Journal of Control,2001,74 (14): 1447-1455.
[75]L. Peng, et al. Improved square root unscented Kalman filter and its application in rendezvous and docking[J]. Electric Machines and Control,2010,11.
[76]J. M. Tao Chen, Elaine Martin. Particle filters for the estimation of a state space model [J].14th European Symposium on Computer Aided Process Engineering,2004,6 (ESCAPE-14):613-618.
[77]催先强,杨元喜,高为广.多种有色噪声自适应算法的比较[J].武汉大学学报(信息科学版),2006,31(8)：731-735.
[78]H. Leung, Zhiwen Zhu,Zhen Ding. A periodic phenomenon of the extended Kalman filter in filtering noisy chaotic signals[J]. Signal Processing, IEEE Transactions 2000,6 (48):1807-1810.
[79]AF.Izmailov.M.V.Solodov. Karush-Kuhn-Tucker systems:regularity conditions, error bounds and a class of Newton-type methods.[J]. Math. Program,2003,3 (A95):631-650.
[80]方千,花向红.基于相空间重构与支持向量机预测滑坡位移的一种新方法[J].武汉大学学报.信息科学版,2009,2(34)：248-251.
[81]X.Luo.I.M.Moroz. Ensemble Kalman filter with the Unscented transform[J]. Physical D,2009,1 (238):549-562.
[82]M. A. F. Shafer, Christopher A.; Arndt, Derek S. Quality Assurance Procedures in the Oklahoma Mesonetwork[J]. Journal of Atmospheric and Oceanic Technology,,2000,4 (17):474-494.
[83]M. Davy, et al. An online support vector machine for abnormal events detection[J]. Signal Processing,2006,86 (8):2009-2025.
[84]吕金虎,陆君安,陈士华.混沌时间序列分析及其应用[M].武汉：武汉大学出版社,2002.
[85]黄细霞,石繁槐,顾伟等.加权支持向量回归在线学习方法[J].上海交通大学学报,2009,6(43)：927-930.
[86]K. Stordahl. The history behind the probability theory and the queuing theory[J].Telektronikk,2007,103 (2):123.
[87]A. Pnueli and L. Zuck. Verification of multiprocess probabilistic protocols[J]. Distributed Computing,1986,1(1):53-72.
[88]M. Kwiatkowska. Model checking for probability and time:from theory to practice[J].2003,351-360.
[89]M. Y. Vardi, Automatic verification of probabilistic concurrent finite state programs[C].1985:327-338.
[90]C. Myers, et al., Modeling chaotic systems with hidden Markov models[c], in Acoustics, Speech, and Signal Processing,1992, New York,2010:565-568
[91]R. Alur and D. L. Dill. A theory of timed automata[J].Theoretical computer science,1994,126 (2):183-235.
[92]N. Dalezios and A. Bartzokas. Phenomenological features of precipitation series in agricultural regions[J]. Extreme Hydrological Events:Precipitation, Floods and Droughts,1988,2 (13):51-57.
[93]E. Mekis and W. Hogg. Rehabilitation and analysis of Canadian daily precipitation time series[J]. Atmosphere-Ocean,1999,37 (1):53-85.
[94]A. Ray. Symbolic dynamic analysis of complex systems for anomaly detection[J]. Signal Process.,2004,84 (7):1115-1130.
[95]B. Edwards.GIS-based Radar Rainfall Verification. Available:. http://nldr.library.ucar.edu/views/?collId=collections&itemId=SOARS-000-0 00-000-027,2011.
[96]J. Borges, M. Levene.A dynamic clustering-based Markov model for web usage mining, Artificial Intelligence 2004(1):1-19.
[97]T. R. Sofia Andersson subspace estimation and prediction methods for hidden markov models [J]. The Annals of Statistics,2009,37 (6B):21.
[98]X. W. Chang, et al. MLAMBDA:A modified LAMBDA method for integer least-squares estimation[J]. Journal of Geodesy,2005,79 (9):552-565.
[99]Y. Ephraim and W. Roberts. An EM algorithm for Markov modulated Markov processes[J]. Signal Processing, IEEE Transactions on,2009,57 (2): 463-470.
[100]M. Hahn, et al. Markov chain Monte Carlo methods for parameter estimation in multidimensional continuous time Markov switching models [J]. Journal of Financial Econometrics,2009,8 (1):88-121.
[101]L. E. Baum, et al. A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains[J].The annals of mathematical statistics,1970,41 (1):164-171.
[102]R. I. A. Davis and B. C. Lovell. Comparing and evaluating HMM ensemble training algorithms using train and test and condition number criteria[J]. Pattern Analysis & Applications,2004,6 (4):327-335.
[103]R. De Maesschalck, et al. The mahalanobis distance[J].Chemometrics and Intelligent Laboratory Systems,2000,50 (1):1-18.
[104]G. Cembrowski, et al. Assessment of Average of Normals" quality control procedures and guidelines for implementation [J].American journal of clinical pathology,1984,81 (4):492-499.
[105]Y. Z. Ji wu, Karl Aberer,Kian-Lee Tan, Towards integrated and efficient scientific sensor data processing:a database approach[C].In Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology, Saint Petersburg, Russia,2009:922-933.
[106]M. Sturza. Navigation system integrity monitoring using redundant measurements [J]. Navigation,1988,35 (4):483-501.
[107]R. Di Francesco. Real-time speech segmentation using pitch and convexity jump models:application to variable rate speech coding[J]. Acoustics, Speech and Signal Processing, IEEE Transactions on,2002,38 (5):741-748.
[108]A. Khatkhate, et al. Symbolic time-series analysis for anomaly detection in mechanical systems[J].Mechatronics, IEEE/ASME Transactions on,2006,11 (4):439-447.
[109]V. Rajagopalan and A. Ray. Symbolic time series analysis via wavelet-based partitioning [J]. Signal Processing,2006,86 (11):3309-3320.
[110]A. L. Goldberger, et al. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals[J]. Circulation,2000,101 (23):215-220.
[111]G. Karypis, et al. Chameleon:Hierarchical clustering using dynamic modeling[J]. Computer,1999,32 (8):68-75.
[112]O. Dain, et al., Irep++, a faster rule learning algorithm[C]. In Proceedings of the Fourth SIAM International Conference on Data Mining, Lake Buena Vista, Florida, USA,2004:138-146.
[113]T. Kanungo, et al. An efficient k-means clustering algorithm:Analysis and implementation[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,2002,24 (7):881-892.
[114]K. J. Hole, et al. Adaptive multidimensional coded modulation over flat fading channels[J].Selected Areas in Communications, IEEE Journal on,2000, 18(7):1153-1158.
[115]D. Heyman and D. Lucantoni. Modeling multiple IP traffic streams with rate limits [J].Networking, IEEE/ACM Transactions on,2004,11 (6):948-958.
[116]Q. L. Zhu Li, Xue Kouying,Zhang Xinyan;, A Novel BP Neural Network Model for Traffic Prediction of Next Generation Network[c], in Natural Computation,2009. ICNC'09. Fifth International Conference tianjin,china, 2009:32-38.
[117]邬伦,刘瑜,张晶等.地理信息系统--原理、方法和应用[M].北京：科学出版社,2001.
[118]S. T. Shipley, et al., GIS applications in climate and meteorology[c],2000.
[119]R. Aspinall and K. Matthews. Climate change impact on distribution and abundance of wildlife species:An analytical approach using GIS[J]. Environmental Pollution,1994,86 (2):217-223.
[120]D. C. Curtis, et al. Comparing Spatial Distributions of Rainfall Derived from Rain Gages and Radar[J].Journal of Floodplain Management. NEXRAIN Corporation Folsom, CA,1999,6 (1):1-7.
[121]D. P. Dee, et al. An adaptive buddy check for observational quality control [J]. Quarterly Journal of the Royal Meteorological Society,2001,127 (577): 2451-2471.
[122]R. Samet and S. Tural. Web based real-time meteorological data analysis and mapping information system[J].WSEAS Transactions on Information Science and Applications,2010,7 (9):1115-1125.
[123]C. Santhi, et al. A GIS-based regional planning tool for irrigation demand assessment and savings using SWAT[J].Transactions of the ASAE,2005,48 (1):137-147.
[124]C. Cardinali. Monitoring the observation impact on the short-range forecast[J].Quarterly Journal of the Royal Meteorological Society,2009,135 (638):239-250.
[125]R. Watkins, et al. Increased temperature and intensification of the urban heat island:implications for human comfort and urban design[J].Built Environment,2007,33 (1):85-96.
[126]A. J. Bell and T. J. Sejnowski. An information-maximization approach to blind separation and blind deconvolution[J].Neural computation,1995,7 (6): 1129-1159.
[127]A. Hyv rinen and E. Oja. Independent component analysis:algorithms and applications[J]. Neural networks,2000,13 (4):411-430.
[128]T. W. Lee, et al. Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources [J].Neural computation,1999,11 (2):417-441.
[129]J. M. Mendel. Tutorial on higher-order statistics (spectra) in signal processing and system theory:Theoretical results and some applications [J]. Proceedings of the IEEE,1991,79 (3):278-305.
[130]C. L. Nikias and J. M. Mendel. Signal processing with higher-order spectra[J]. Signal Processing Magazine, IEEE,1993,10 (3):10-37.
[131]J. Bobin, et al. Morphological component analysis:An adaptive thresholding strategy [J].Image Processing, IEEE Transactions on,2007,16 (11):2675-2681.
[132]J. Bobin, et al. Morphological diversity and sparsity for multichannel data restoration[J].Journal of Mathematical Imaging and Vision,2009,33 (2): 149-168.
[133]F. Abrard and Y. Deville. A time-frequency blind signal separation method applicable to underdetermined mixtures of dependent sources[J]. Signal Processing,2005,85 (7):1389-1403.
[134]Y. Deville and M. Puigt. Temporal and time-frequency correlation-based blind source separation methods. Part Ⅰ:Determined and underdetermined linear instantaneous mixtures [J]. Signal Processing,2007,87 (3):374-407.
[135]沈钟平,张华.影响地面太阳辐射及其谱分布的因子分析[J].太阳能学报,2009,30(10)：1209-1215.
[136]中国气象局.地面气象观测规范.北京：气象出版社,2003：5-8
[137]白树忠,刘琚,孙国霞.基于最小均方误差和稀疏特征的欠定盲源分离[J]. 山东大学学报,2008,38(4)：43-46.
[138]H. Liu and Y. Cheung. A learning framework for blind source separation using generalized eigenvalues[J].Advances in Neural Networks CISNN, 2005,3 (3):472-477.
[139]K. M. Stellman, et al. An examination of radar and rain gauge-derived mean areal precipitation over Georgia watersheds[J].Weather and forecasting,2001, 16(1):133-144.
[140]J. Koistinen and D. B. Michelson. BALTEX weather radar-based precipitation products and their accuracies[J].Boreal environment research,2002,7 (3):253-263.
[141]俞小鼎,姚秀萍,熊廷南,周小刚.多普勒天气雷达原理与业务应用[M].北京：气象出版社,2006.
[142]P. A. Kucera, et al. Radar beam occultation studies using GIS and DEM technology:An example study of Guam [J]. Journal of Atmospheric and Oceanic Technology,2004,21 (7):995-1006.
[143]D. R. Legates. Real Time Calibration of Radar Precipitation Estimates [J]. The Professional Geographer,2000,52 (2):235-246.
[144]E. Morin and M. Gabella. Radar-based quantitative precipitation estimation over Mediterranean and dry climate regimes[J].J. Geophys. Res,2007,112 D20108.
[145]Y. Gorokhovich and G. Villarini. Application of GIS for processing and establishing the correlation between weather radar reflectivity and precipitation data[J].Meteorological Applications,2005,12 (01):91-99.
[146]X. Zhang and R. Srinivasan. GIS-based spatial precipitation estimation using next generation radar and raingauge data [J]. Environmental Modelling & Software,2010,25 (12):1781-1788.
[147]S. Hardegree, et al. Multi-Watershed Evaluation of WSR-88D (NEXRAD) Radar-Precipitation Products [C]. presented at the Interagency Conference on Research in the Watersheds Benson, Arizona,2003.486-491.
[148]D. McLaughlin. An integrated approach to hydrologic data assimilation: interpolation, smoothing, and filtering[J]. Advances in Water Resources,2002, 25(8-12):1275-1286.
[149]A. Karnieli. Application of kriging technique to areal precipitation mapping in Arizona[J].GeoJournal,1990,22 (4):391-398.
[150]S. Gudmundsson, et al. Three-dimensional surface motion maps estimated from combined interferometric synthetic aperture radar and GPS data[J].J. Geophys. Res,2002,107 (B10):1-12.
[151]郭尚,苏鸿根.基于距离倒数加权法的等值面绘制简化算法[J].计算机工程与设计,2008,29(5)：1200-1202.
[152]H. Xie, et al. GIS-based NEXRAD Stage III precipitation database: automated approaches for data processing and visualization[J]. Computers & geosciences,2005,31 (1):65-76.
[153]王飞行,汤广富,贺思三,付强.高重频频率步进雷达强杂波抑制[J].国防科技大学学报,2009,31(4)：52-57.
[154]杜雨洺,杨建宇,熊金涛.线性调频连续波雷达速度模糊消除新方法[J].上海交通大学学报,2006,40(1)：86-89.