High Frequency(HF) radar current data is assimilated into a shelf sea circulation model based on optimal interpolation(OI) method. The purpose of this work is to develop a real-time computationally highly efficient as...High Frequency(HF) radar current data is assimilated into a shelf sea circulation model based on optimal interpolation(OI) method. The purpose of this work is to develop a real-time computationally highly efficient assimilation method to improve the forecast of shelf current. Since the true state of the ocean is not known, the specification of background error covariance is arduous. Usually, it is assumed or calculated from an ensemble of model states and is kept in constant. In our method, the spatial covariances of model forecast errors are derived from differences between the adjacent model forecast fields, which serve as the forecast tendencies. The assumption behind this is that forecast errors can resemble forecast tendencies, since variances are large when fields change quickly and small when fields change slowly. The implementation of HF radar data assimilation is found to yield good information for analyses. After assimilation, the root-mean-square error of model decreases significantly. Besides, three assimilation runs with variational observation density are implemented. The comparison of them indicates that the pattern described by observations is much more important than the amount of observations. It is more useful to expand the scope of observations than to increase the spatial interval. From our tests, the spatial interval of observation can be 5 times bigger than that of model grid.展开更多
Surface currents measured by high frequency （HF） radar arrays are assimilated into a regional ocean model over Qingdao coastal waters based on Kalman filter method. A series of numerical experiments are per- formed ...Surface currents measured by high frequency （HF） radar arrays are assimilated into a regional ocean model over Qingdao coastal waters based on Kalman filter method. A series of numerical experiments are per- formed to evaluate the performance of the data assimilation schemes. In order to optimize the analysis pro- cedure in the traditional ensemble Kalman filter （ENKF）, a different analysis scheme called quasiensemble Kaman filter （QENKF） is proposed. The comparisons between the ENKF and the QENKF suggest that both them can improve the simulated error and the spatial structure. The estimations of the background error covariance （BEC） are also assessed by comparing three different methods： Monte Carlo method; Canadian quick covariance （CQC） method and data uncertainty engine （DUE） method. A significant reduction of the root-mean-square （RMS） errors between model results and the observations shows that the CQC method is able to better reproduce the error statistics for this coastal ocean model and the corresponding external forcing. In addition, the sensibility of the data assimilation system to the ensemble size is also analyzed by means of different scales of the ensemble size used in the experiments. It is found that given the balance of the computational cost and the forecasting accuracy, the ensemble size of 50 will be an appropriate choice in the Qingdao coastal waters.展开更多
It is of vital importance to reduce injuries and economic losses by accurate forecasts of typhoon tracks. A huge amount of typhoon observations have been accumulated by the meteorological department, however, they are...It is of vital importance to reduce injuries and economic losses by accurate forecasts of typhoon tracks. A huge amount of typhoon observations have been accumulated by the meteorological department, however, they are yet to be adequately utilized. It is an effective method to employ machine learning to perform forecasts. A long short term memory（LSTM） neural network is trained based on the typhoon observations during 1949–2011 in China＇s Mainland, combined with big data and data mining technologies, and a forecast model based on machine learning for the prediction of typhoon tracks is developed. The results show that the employed algorithm produces desirable 6–24 h nowcasting of typhoon tracks with an improved precision.展开更多
An operational ocean circulation-surface wave coupled forecasting system for the seas off China and adjacent areas（OCFS-C） is developed based on parallelized circulation and wave models. It has been in operation sin...An operational ocean circulation-surface wave coupled forecasting system for the seas off China and adjacent areas（OCFS-C） is developed based on parallelized circulation and wave models. It has been in operation since November 1, 2007. In this paper we comprehensively present the simulation and verification of the system, whose distinguishing feature is that the wave-induced mixing is coupled in the circulation model. In particular, with nested technique the resolution in the China＇s seas has been updated to（1/24）° from the global model with（1/2）°resolution. Besides, daily remote sensing sea surface temperature（SST） data have been assimilated into the model to generate a hot restart field for OCFS-C. Moreover, inter-comparisons between forecasting and independent observational data are performed to evaluate the effectiveness of OCFS-C in upper-ocean quantities predictions, including SST, mixed layer depth（MLD） and subsurface temperature. Except in conventional statistical metrics, non-dimensional skill scores（SS） is also used to evaluate forecast skill. Observations from buoys and Argo profiles are used for lead time and real time validations, which give a large SS value（more than 0.90）. Besides, prediction skill for the seasonal variation of SST is confirmed. Comparisons of subsurface temperatures with Argo profiles data indicate that OCFS-C has low skill in predicting subsurface temperatures between 100 m and 150 m. Nevertheless, inter-comparisons of MLD reveal that the MLD from model is shallower than that from Argo profiles by about 12 m, i.e., OCFS-C is successful and steady in MLD predictions. Validation of 1-d, 2-d and 3-d forecasting SST shows that our operational ocean circulation-surface wave coupled forecasting model has reasonable accuracy in the upper ocean.展开更多
基金supported by the State Oceanic Administration Young Marine Science Foundation (No. 2013201)the Shandong Provincial Key Laboratory of Marine Ecology and Environment & Disaster Prevention and Mitigation Foundation (No. 2012007)+1 种基金the Marine Public Foundation (No. 201005018)the North China Sea Branch Scientific Foundation (No. 2014B10)
文摘High Frequency(HF) radar current data is assimilated into a shelf sea circulation model based on optimal interpolation(OI) method. The purpose of this work is to develop a real-time computationally highly efficient assimilation method to improve the forecast of shelf current. Since the true state of the ocean is not known, the specification of background error covariance is arduous. Usually, it is assumed or calculated from an ensemble of model states and is kept in constant. In our method, the spatial covariances of model forecast errors are derived from differences between the adjacent model forecast fields, which serve as the forecast tendencies. The assumption behind this is that forecast errors can resemble forecast tendencies, since variances are large when fields change quickly and small when fields change slowly. The implementation of HF radar data assimilation is found to yield good information for analyses. After assimilation, the root-mean-square error of model decreases significantly. Besides, three assimilation runs with variational observation density are implemented. The comparison of them indicates that the pattern described by observations is much more important than the amount of observations. It is more useful to expand the scope of observations than to increase the spatial interval. From our tests, the spatial interval of observation can be 5 times bigger than that of model grid.
基金The National High Technology Research and Development Program of China under contract No.2007AA09Z117the Science and Technology Project of the North China Sea Brach of SOA under contract No.2012A01the Joint BMBF-WTZ Project of China under contract No. CHN 09/031
文摘Surface currents measured by high frequency （HF） radar arrays are assimilated into a regional ocean model over Qingdao coastal waters based on Kalman filter method. A series of numerical experiments are per- formed to evaluate the performance of the data assimilation schemes. In order to optimize the analysis pro- cedure in the traditional ensemble Kalman filter （ENKF）, a different analysis scheme called quasiensemble Kaman filter （QENKF） is proposed. The comparisons between the ENKF and the QENKF suggest that both them can improve the simulated error and the spatial structure. The estimations of the background error covariance （BEC） are also assessed by comparing three different methods： Monte Carlo method; Canadian quick covariance （CQC） method and data uncertainty engine （DUE） method. A significant reduction of the root-mean-square （RMS） errors between model results and the observations shows that the CQC method is able to better reproduce the error statistics for this coastal ocean model and the corresponding external forcing. In addition, the sensibility of the data assimilation system to the ensemble size is also analyzed by means of different scales of the ensemble size used in the experiments. It is found that given the balance of the computational cost and the forecasting accuracy, the ensemble size of 50 will be an appropriate choice in the Qingdao coastal waters.
基金The National Natural Science Foundation of China under contract Nos 61273245 and 41306028the Beijing Natural Science Foundation under contract No.4152031+2 种基金the National Special Research Fund for Non-Profit Marine Sector under contract Nos201405022-3 and 2013418026-4the Ocean Science and Technology Program of North China Sea Branch of State Oceanic Administration under contract No.2017A01the Operational Marine Forecasting Program of State Oceanic Administration
文摘It is of vital importance to reduce injuries and economic losses by accurate forecasts of typhoon tracks. A huge amount of typhoon observations have been accumulated by the meteorological department, however, they are yet to be adequately utilized. It is an effective method to employ machine learning to perform forecasts. A long short term memory（LSTM） neural network is trained based on the typhoon observations during 1949–2011 in China＇s Mainland, combined with big data and data mining technologies, and a forecast model based on machine learning for the prediction of typhoon tracks is developed. The results show that the employed algorithm produces desirable 6–24 h nowcasting of typhoon tracks with an improved precision.
基金China-Korea Cooperation Project on the development of oceanic monitoring and prediction system on nuclear safetythe Project of the National Programme on Global Change and Air-sea Interaction under contract No.GASI-03-IPOVAI-05
文摘An operational ocean circulation-surface wave coupled forecasting system for the seas off China and adjacent areas（OCFS-C） is developed based on parallelized circulation and wave models. It has been in operation since November 1, 2007. In this paper we comprehensively present the simulation and verification of the system, whose distinguishing feature is that the wave-induced mixing is coupled in the circulation model. In particular, with nested technique the resolution in the China＇s seas has been updated to（1/24）° from the global model with（1/2）°resolution. Besides, daily remote sensing sea surface temperature（SST） data have been assimilated into the model to generate a hot restart field for OCFS-C. Moreover, inter-comparisons between forecasting and independent observational data are performed to evaluate the effectiveness of OCFS-C in upper-ocean quantities predictions, including SST, mixed layer depth（MLD） and subsurface temperature. Except in conventional statistical metrics, non-dimensional skill scores（SS） is also used to evaluate forecast skill. Observations from buoys and Argo profiles are used for lead time and real time validations, which give a large SS value（more than 0.90）. Besides, prediction skill for the seasonal variation of SST is confirmed. Comparisons of subsurface temperatures with Argo profiles data indicate that OCFS-C has low skill in predicting subsurface temperatures between 100 m and 150 m. Nevertheless, inter-comparisons of MLD reveal that the MLD from model is shallower than that from Argo profiles by about 12 m, i.e., OCFS-C is successful and steady in MLD predictions. Validation of 1-d, 2-d and 3-d forecasting SST shows that our operational ocean circulation-surface wave coupled forecasting model has reasonable accuracy in the upper ocean.
