The first Chinese microwave ocean environment satellite HY-2A was launched successfully in August, 201 I. This study presents a quality assessment of HY-2A scatterometer （HYSCAT） data based on comparison with ocean ...The first Chinese microwave ocean environment satellite HY-2A was launched successfully in August, 201 I. This study presents a quality assessment of HY-2A scatterometer （HYSCAT） data based on comparison with ocean buoy data, the Advanced Scatterometer （ASCAT） data, and numerical model data from the National Centers for Environmental Prediction （NCEP）. The in-situ observations include those from buoy arrays operated by the National Data Buoy Center （NDBC） and Tropical Atmosphere Ocean （TAO） project. Only buoys located offshore and in deep water were analyzed. The temporal and spatial collocation windows between HYSCAT data and buoy observations were 30 min and 25 km, respectively. The comparisons showed that the wind speeds and directions observed by HYSCAT agree well with the buoy data. The root-mean-squared errors （RMSEs） of wind speed and direction for the HYSCAT standard wind products are 1.90 m/s and 22.80°, respectively. For the HYSCAT-ASCAT comparison, the temporal and spatial differences were limited to 1 h and 25 km, respectively. This comparison yielded RMSEs of 1.68 m/s for wind speed and 19.1° for wind direction. We also compared HYSCAT winds with reanalysis data from NCEP. The results show that the RMSEs of wind speed and direction are 2.6 m/s and 26°, respectively. The global distribution of wind speed residuals （HYSCAT-NCEP） is also presented here for evaluation of the HYSCAT-retrieved wind field globally. Considering the large temporal and spatial differences of the collocated data, it is concluded that the HYSCAT-retrieved wind speed and direction met the mission requirements, which were 2 rn/s and 20° for wind speeds in the range 2-24 m/s. These encouraging assessment results show that the wind data obtained from HYSCAT will be useful for the scientific community.展开更多
The network coverage is a big problem in ocean communication, and there is no low-cost solution in the short term. Based on the knowledge of Mobile Delay Tolerant Network(MDTN), the mobility of vessels can create the ...The network coverage is a big problem in ocean communication, and there is no low-cost solution in the short term. Based on the knowledge of Mobile Delay Tolerant Network(MDTN), the mobility of vessels can create the chances of end-to-end communication. The mobility pattern of vessel is one of the key metrics on ocean MDTN network. Because of the high cost, few experiments have focused on research of vessel mobility pattern for the moment. In this paper, we study the traces of more than 4000 fishing and freight vessels. Firstly, to solve the data noise and sparsity problem, we design two algorithms to filter the noise and complement the missing data based on the vessel's turning feature. Secondly, after studying the traces of vessels, we observe that the vessel's traces are confined by invisible boundary. Thirdly, through defining the distance between traces, we design MR-Similarity algorithm to find the mobility pattern of vessels. Finally, we realize our algorithm on cluster and evaluate the performance and accuracy. Our results can provide the guidelines on design of data routing protocols on ocean MDTN.展开更多
Correlation analysis is an effective mechanism for studying patterns in data and making predictions.Many interesting discoveries have been made by formulating correlations in seemingly unrelated data. We propose an al...Correlation analysis is an effective mechanism for studying patterns in data and making predictions.Many interesting discoveries have been made by formulating correlations in seemingly unrelated data. We propose an algorithm to quantify the theory of correlations and to give an intuitive, more accurate correlation coefficient.We propose a predictive metric to calculate correlations between paired values, known as the general rank-based correlation coefficient. It fulfills the five basic criteria of a predictive metric： independence from sample size,value between-1 and 1, measuring the degree of monotonicity, insensitivity to outliers, and intuitive demonstration.Furthermore, the metric has been validated by performing experiments using a real-time dataset and random number simulations. Mathematical derivations of the proposed equations have also been provided. We have compared it to Spearman＇s rank correlation coefficient. The comparison results show that the proposed metric fares better than the existing metric on all the predictive metric criteria.展开更多
基金Supported by the National High Technology Research and Development Program of China(863 Program)(No.2013AA09A505)the National Natural Science Foundation of China(No.40906091)the Open Project of School of Marine Sciences,Nanjing University of Information Science and Technology(No.KHYS1304)
文摘The first Chinese microwave ocean environment satellite HY-2A was launched successfully in August, 201 I. This study presents a quality assessment of HY-2A scatterometer （HYSCAT） data based on comparison with ocean buoy data, the Advanced Scatterometer （ASCAT） data, and numerical model data from the National Centers for Environmental Prediction （NCEP）. The in-situ observations include those from buoy arrays operated by the National Data Buoy Center （NDBC） and Tropical Atmosphere Ocean （TAO） project. Only buoys located offshore and in deep water were analyzed. The temporal and spatial collocation windows between HYSCAT data and buoy observations were 30 min and 25 km, respectively. The comparisons showed that the wind speeds and directions observed by HYSCAT agree well with the buoy data. The root-mean-squared errors （RMSEs） of wind speed and direction for the HYSCAT standard wind products are 1.90 m/s and 22.80°, respectively. For the HYSCAT-ASCAT comparison, the temporal and spatial differences were limited to 1 h and 25 km, respectively. This comparison yielded RMSEs of 1.68 m/s for wind speed and 19.1° for wind direction. We also compared HYSCAT winds with reanalysis data from NCEP. The results show that the RMSEs of wind speed and direction are 2.6 m/s and 26°, respectively. The global distribution of wind speed residuals （HYSCAT-NCEP） is also presented here for evaluation of the HYSCAT-retrieved wind field globally. Considering the large temporal and spatial differences of the collocated data, it is concluded that the HYSCAT-retrieved wind speed and direction met the mission requirements, which were 2 rn/s and 20° for wind speeds in the range 2-24 m/s. These encouraging assessment results show that the wind data obtained from HYSCAT will be useful for the scientific community.
基金supported by the National Key R&D Program (No. 2016YFC1401900)the China Postdoctoral Science Foundation (No. 2017M620293)+3 种基金the National Natural Science Foundation of China (Nos. 61379127, 61379128, 61572448)the Fundamental Research Funds for the Central Universities (No. 201713016)Qingdao National Labor for Marine Science and Technology Open Research Project (No. QNLM2016ORP0405)Natural Science Foundation of Shandong (No. ZR2018BF006)
文摘The network coverage is a big problem in ocean communication, and there is no low-cost solution in the short term. Based on the knowledge of Mobile Delay Tolerant Network(MDTN), the mobility of vessels can create the chances of end-to-end communication. The mobility pattern of vessel is one of the key metrics on ocean MDTN network. Because of the high cost, few experiments have focused on research of vessel mobility pattern for the moment. In this paper, we study the traces of more than 4000 fishing and freight vessels. Firstly, to solve the data noise and sparsity problem, we design two algorithms to filter the noise and complement the missing data based on the vessel's turning feature. Secondly, after studying the traces of vessels, we observe that the vessel's traces are confined by invisible boundary. Thirdly, through defining the distance between traces, we design MR-Similarity algorithm to find the mobility pattern of vessels. Finally, we realize our algorithm on cluster and evaluate the performance and accuracy. Our results can provide the guidelines on design of data routing protocols on ocean MDTN.
文摘Correlation analysis is an effective mechanism for studying patterns in data and making predictions.Many interesting discoveries have been made by formulating correlations in seemingly unrelated data. We propose an algorithm to quantify the theory of correlations and to give an intuitive, more accurate correlation coefficient.We propose a predictive metric to calculate correlations between paired values, known as the general rank-based correlation coefficient. It fulfills the five basic criteria of a predictive metric： independence from sample size,value between-1 and 1, measuring the degree of monotonicity, insensitivity to outliers, and intuitive demonstration.Furthermore, the metric has been validated by performing experiments using a real-time dataset and random number simulations. Mathematical derivations of the proposed equations have also been provided. We have compared it to Spearman＇s rank correlation coefficient. The comparison results show that the proposed metric fares better than the existing metric on all the predictive metric criteria.
