利用荣成、海阳两站的自建浮标海温观测数据以及区域大气模式WRF(Weather Research and Forecasting)的气象数值预报数据,基于主成分分析(Principal Component Analysis,PCA)法和长短时记忆(Long Short-Term Memory,LSTM)神经网络,提出...利用荣成、海阳两站的自建浮标海温观测数据以及区域大气模式WRF(Weather Research and Forecasting)的气象数值预报数据,基于主成分分析(Principal Component Analysis,PCA)法和长短时记忆(Long Short-Term Memory,LSTM)神经网络,提出了适用于单站海表温度预报的PCALSTM海温预报模型。该模型可以提供24~120 h预报时效的海温预报,预测效果比数值模型和统计模型明显提高。展开更多
Numerical modeling is an important tool to study and predict the transport of oil spills. However, the accu- racy of numerical models is not always good enough to provide reliable information for oil spill transport. ...Numerical modeling is an important tool to study and predict the transport of oil spills. However, the accu- racy of numerical models is not always good enough to provide reliable information for oil spill transport. It is necessary to analyze and identify major error sources for the models. A case study was conducted to analyze error sources of a three-dimensional oil spill model that was used operationally for oil spill forecast- ing in the National Marine Environmental Forecasting Center （NMEFC）, the State Oceanic Administration, China. On June 4, 2011, oil from sea bed spilled into seawater in Penglai 19-3 region, the largest offshore oil field of China, and polluted an area of thousands of square kilometers in the Bohai Sea. Satellite remote sensing images were collected to locate oil slicks. By performing a series of model sensitivity experiments with different wind and current forcings and comparing the model results with the satellite images, it was identified that the major errors of the long-term simulation for oil spill transport were from the wind fields, and the wind-induced surface currents. An inverse model was developed to estimate the temporal variabil- ity of emission intensity at the oil spill source, which revealed the importance of the accuracy in oil spill source emission time function.展开更多
This study investigated the linkage between winter temperature in the Yellow Sea(YS), China, and atmospheric indices and established this linkage through statistical models. The water temperature was obtained through ...This study investigated the linkage between winter temperature in the Yellow Sea(YS), China, and atmospheric indices and established this linkage through statistical models. The water temperature was obtained through hindcast simulation using a global–regional nested ocean model for the period of 1958–2007. The interannual variations of the simulated temperature were validated using satellite and in-situ observations. In the YS, the winter sea surface temperature(SST) had obvious interannual variations, with the maximum SST exceeding 2℃, and a significant shift from the cold to warm phase during 1988–1989. Based on the mechanism study, statistical models for the variations of water temperature in the YS were established using suitable atmospheric indices as predictors. For the northern YS(NYS) and the coastal region of the southern YS(SYS), statistical models of SST were established using linear regression based on the December–January–February mean Arctic oscillation index(AOI), representing the dominant large-scale atmospheric variability in boreal winter. For the YS warm current(YSWC) region, statistical models were established using both the AOI and the first principal component of the local wind stress curl(PC1-Curl), derived from the empirical orthogonal functions analysis. The PC1-Curl represents the influence of the local wind stress curl on the west-to-east shifts of the YSWC pathway. The applications proved that the models presented in this study have the ability to estimate winter temperatures in the YS within the recent years.展开更多
文摘利用荣成、海阳两站的自建浮标海温观测数据以及区域大气模式WRF(Weather Research and Forecasting)的气象数值预报数据,基于主成分分析(Principal Component Analysis,PCA)法和长短时记忆(Long Short-Term Memory,LSTM)神经网络,提出了适用于单站海表温度预报的PCALSTM海温预报模型。该模型可以提供24~120 h预报时效的海温预报,预测效果比数值模型和统计模型明显提高。
基金supported by Marine Industry Scientific Research Special Funds for Public Welfare Project "The development and application of fine-scale high precision comprehensive forecast system on the key protection coastal area",under contact No.201305031 and "The modular construction and application of marine forecasting operational system",under contact No.201205017
文摘Numerical modeling is an important tool to study and predict the transport of oil spills. However, the accu- racy of numerical models is not always good enough to provide reliable information for oil spill transport. It is necessary to analyze and identify major error sources for the models. A case study was conducted to analyze error sources of a three-dimensional oil spill model that was used operationally for oil spill forecast- ing in the National Marine Environmental Forecasting Center （NMEFC）, the State Oceanic Administration, China. On June 4, 2011, oil from sea bed spilled into seawater in Penglai 19-3 region, the largest offshore oil field of China, and polluted an area of thousands of square kilometers in the Bohai Sea. Satellite remote sensing images were collected to locate oil slicks. By performing a series of model sensitivity experiments with different wind and current forcings and comparing the model results with the satellite images, it was identified that the major errors of the long-term simulation for oil spill transport were from the wind fields, and the wind-induced surface currents. An inverse model was developed to estimate the temporal variabil- ity of emission intensity at the oil spill source, which revealed the importance of the accuracy in oil spill source emission time function.
基金support of the National Basic Research Program of China (973 Program) (No. 2011CB403606)the Tianjin Science and Technology Program (No. 14JCQNJC09900)the National Natural Science Foundation of China (Nos. 41606028, 41376112)
文摘This study investigated the linkage between winter temperature in the Yellow Sea(YS), China, and atmospheric indices and established this linkage through statistical models. The water temperature was obtained through hindcast simulation using a global–regional nested ocean model for the period of 1958–2007. The interannual variations of the simulated temperature were validated using satellite and in-situ observations. In the YS, the winter sea surface temperature(SST) had obvious interannual variations, with the maximum SST exceeding 2℃, and a significant shift from the cold to warm phase during 1988–1989. Based on the mechanism study, statistical models for the variations of water temperature in the YS were established using suitable atmospheric indices as predictors. For the northern YS(NYS) and the coastal region of the southern YS(SYS), statistical models of SST were established using linear regression based on the December–January–February mean Arctic oscillation index(AOI), representing the dominant large-scale atmospheric variability in boreal winter. For the YS warm current(YSWC) region, statistical models were established using both the AOI and the first principal component of the local wind stress curl(PC1-Curl), derived from the empirical orthogonal functions analysis. The PC1-Curl represents the influence of the local wind stress curl on the west-to-east shifts of the YSWC pathway. The applications proved that the models presented in this study have the ability to estimate winter temperatures in the YS within the recent years.
