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The Application of Data Fusion Method in the Analysis of Ocean and Meteorology Observation Data

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The Application of Data Fusion Method in the Analysis of Ocean and Meteorology Observation Data International Journal of Hydrology Research Article Open Access The application of data fusion method in the analysis of ocean and meteorology observation data Abstract Volume 3 Issue 3 - 2019 Data fusion is a new technology of data processing for multi - source ocean meteorology 1 2 3 data. In order to clearly understand how to apply this technology in the data processing Wang Wei, Liu Zhi-jie, Zhao Yue, Gong 1 of ocean meteorology observation, the author first divides the fusion methods into three Xiao-qing categories, i.e., pixel-level fusion, feature-level fusion and decision-making level fusion. 1Tianjin Institute of Meteorological Science, China Then, the corresponding methods of each data fusion are discussed in detail. Afterward, the 2Meteorology Administrative of Xiqing District, China SST fusion data from China Meteorological administrative is analyzed in order to explain 3Meteorology Administrative of Jinnan District, China that the fusion data is applied in numerical forecast model on the China’s Bohai sea region. Finally, the authors believe that the data fusion technology of multi-source observation data Correspondence: Wang Wei, Tianjin Institute of has a good application value when the observation data is processed into grid data of ocean Meteorological Science, Tianjin, China, Tel 086-022-23358783, meteorology, which can be used as the grid truth-value of numerical model inspection. Email In addition, the fusion technology at decision-making level can also develop with the intelligent technology, and deeply mine the application of data fusion technology to the Received: May 13, 2019 | Published: June 03, 2019 ocean meteorology observation data. Keyword: data fusion, ocean meteorology, observation data, data processing Introduction evaluation tasks. Although the concept of data fusion started in the early 1970s, the actual progress and development of the technology Recently, data fusion technology emerges in China as a new data happened in the 1980s, especially in recent years, which has attracted processing method. Because of the maturity and fast development of worldwide attention.4,5 Data fusion technology is often applied in new observation technology, researchers can obtain more and more image processing to merge the crude and fine resolution images into high-resolution real-time observation data to study the interaction one image which is used to improve the crude image texture and between ocean and atmosphere. The technology of data fusion can remain the large image coverage.6,7 help researchers quickly extract rich information of meso-scale weather from multi-source observation data. Moreover, the analysis At present, data fusion technology has also been applied to the field of integrating various observation data has significant advantages grid data processing of ocean meteorological observation. The basic of analyzing ocean weather situation, precipitation analysis, physical principle of data fusion method in ocean meteorological observation quantity diagnosis and so on, and is much more objective than the data is to estimate variable X on the basis of two independent and unbiased of the single atmospheric sounding because the fusion data contains estimates of variables X1 and X2, so as to obtain a new weighted comprehensive information of various atmospheric observation.1,2 The estimate of X. The accuracy of X1 and X2 may not be equal, and fusion information of abundant meso-scale observation has a good the variables are allowed to have different mean square deviations so application prospect in improving the spatial- temporal resolution of that the estimate of merged X should have a minimum mean square observation data, cloud information and precipitation prediction.3 The deviation. Data fusion methods of ocean meteorological observation fusion data can also represent the meso-scale characteristics of local can be divided into three categories: pixel-level fusion, feature-level weather system, and describe meso-scale 3d structure and evolution fusion and decision-making level fusion. in detail, which presents fine variation characteristic of meso-scale Data fusion method of pixel level weather factor so that researchers could discover some new meso- scale weather information. Pixel-level fusion is performed directly on the collected raw data level, and data synthesis and analysis are carried out before the This paper mainly discusses the feasibility of data fusion and the original measurements of various sensors are deeply preprocessed. application of data fusion methods in ocean meteorology. At present, Pixel-level fusion usually uses the centralized fusion system to more and more researcher pay attention on the data fusion technology complete the fusion process. This data fusion is a low-level fusion, of ocean meteorology observation, but few researchers systematically for example, an imaging of sensor generating validates the target discuss the data fusion theory of ocean meteorology. The author’s attributes by processing fuzzy image of several pixels. The pixel-level comprehensive discussion can facilitate the application of data fusion fusion methods include Poisson method8 and Kalman method.9,10 technology in ocean meteorological observation. i. The data fusion of Poisson equation. Data fusion method uses the Data fusion method of two dimension ocean Poisson equation in which S is the anomaly of the observation meteorology data variable and the symbol Φ is the anomaly of fusion data. The Φ is Data fusion technology is a kind of information processing calculated as follow: 2 technology that can automatically analyze and synthesize several ∇Φ=ρ (1) observation information acquired by computer in time sequence under certain criteria, so as to complete the required decision-making and Submit Manuscript | http://medcraveonline.com Int J Hydro. 2019;3(3):205‒208. 205 ©2019 Wei et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and build upon your work non-commercially. Copyright: The application of data fusion method in the analysis of ocean and meteorology observation data ©2019 Wei et al. 206 The ρ in the above equation is defined as follow: the decision analysis. Feature-level fusion usually adopts distributed 2 or centralized fusion system. For example, data fusion of multi-scale ρ = ∇ S (2) grid is often used in ocean meteorological data fusion. This algorithm In the application, the area of grid data is usually greater than the is based on the sequential variation method of the multi-scale grid observation data, so the boundary values are set to neighbor value, algorithm. All the temporal-spatial information is utilized by the multi- and cloud coverage is set to missing value. scale grid method. The target functional on each grid is as follows: ii. The data fusion of Kalman method. Kalman method is an effective data fusion method. If the fusion data are sampled at ()n11 () nTn () () nn () () nTn ()1− () nn () () n J= X X +−( HX Y)( O HX − Y) ( n = 1, 2, 3, , N) discrete points with time sequence, data fusion of kalman method 22 is assumed that the n-dimensional linear dynamic system and the (9) m-dimensional linear observation system could be described by Where, Y=Y -HX , X=X -X . O is the error covariance matrix of the following different equation : obs b a b the observation field; bX is background field vector; aX is the analysis X=Φ+ XW (3) field vector; Y is the observed constant vector; H is the bilinear k kk,1−− k 1 k − 1 obs interpolation operator from the model grid to the observation point; Y= HX + V (4) X represents the correction vector of model vector which is calculated k kk from the variable assimilation system. Y is the difference between the Where, X is n-dimensional vector which represents the system k observation field and the model field. n represents the nth mesh, and N state at moment k. Φ is n-by-n matrix called the state transition k,k-1 is the scale number of mesh. matrix of the system, which reflects the transformation of the system (1) obs (1) b from the state k-1 to the state k at sampling time. W is n-dimensional Y= Y−= HX( n 1) (10) k vector that represents the random disturbance impacting on the system (n ) ( n− 1) ( nn −− 1) ( 1) at moment k, which is called model noise. {W } is generally assumed Y= Y−= HX( n 2, 3, , N ) (11) k to be a sequence of gaussian white noise with a known zero mean and The multi-scale analysis starts at the crude level. When n=1, Y(1) is covariance matrix Qk. Yk is the m-dimensional observation vector. Hk is m-by-n observation matrix which represents the transition from the the deviation of mapping the observation and model background field to the observation position, and X(n-1) is the solution or approximate state variable Xk to the observed variable Yk. Vk is m-dimensional observation noise vector which is assumed that {V } is a sequence of solution of J(n-1). After solving J(n-1), X(n-1) is interpolated into a k th th gaussian white noise with known zero mean and covariance matrix finer mesh at the n layer. Y(n) can be calculated by formula 11 , and R . X(n) can be solved by J(n) minimization. The final analysis result is k the superposition of the analysis results of multi-scale grid. After derivation, the recursive formula of filtering can be obtained N ab b as follows: X=+=+ XX X∑ X()n (12) L n=1 X=Φ X + GYH −Φ X (5) k kk,1−− k 1 k k k kk,1−− k 1 −1 Data fusion method of decision-making level T T G= PH Hkk PH k+ R k (6) k kk The data fusion of decision-making level integrates different types of sensors observing the same target, and each sensor completes T P=ΦΦ+ CQ (7) k++1 kkkkk 1, + 1, k basic data processing locally.
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