Based on experiment data of the Sino-German comprehensive investigations in the Bohai Sea in 1998 and 1999, a simple coupled pelagic-benthic ecosystem multi-box model is used to simulate the ecosystem seasonal variati...Based on experiment data of the Sino-German comprehensive investigations in the Bohai Sea in 1998 and 1999, a simple coupled pelagic-benthic ecosystem multi-box model is used to simulate the ecosystem seasonal variation. The pelagic sub-model consists of seven state variables： phytoplankton, zooplankton, TIN, TIP, DOC, POC and dissolved oxygen （DO）. The benthic sub-model includes macro-benthos, meiobenthos, bacteria, detritus, TIN and TIP in the sediment. Besides the effects of solar radiation, water temperature and the nutrient from sea bottom exudation, land-based inputs are considered. The impact of the advection terms between the boxes is also considered. Meanwhile, the effects of the micro- bial-loop are introduced with a simple parameterization. The seasonal variations and the horizontal distributions of the ecosystem state variables of the Bohai Sea are simulated. Compared with the observations, the results of the multi-box model are reasonable. The modeled results show that about 13% of the photosynthesis primary production goes to the main food loop, 20% transfers to the benthic domain, 44% is consumed by the respiration of phytoplankton, and the rest goes to DOC. Model results also show the importance of the microbial food loop in the ecosystem of the Bohai Sea, and its contribution to the annual zooplankton production can be 60%-64%.展开更多
In this paper, a new bias estimation method is proposed and applied in a regional ensemble Kalman filter （EnKF） based on the Weather Research and Forecasting （WRF） Model. The method is based on a homogeneous linea...In this paper, a new bias estimation method is proposed and applied in a regional ensemble Kalman filter （EnKF） based on the Weather Research and Forecasting （WRF） Model. The method is based on a homogeneous linear bias model, and the model bias is estimated using statistics at each assimilation cycle, which is different from the state augmentation methods proposed in pre- vious literatures. The new method provides a good estimation for the model bias of some specific variables, such as sea level pres- sure （SLP）. A series of numerical experiments with EnKF are performed to examine the new method under a severe weather condi- tion. Results show the positive effect of the method on the forecasting of circulation pattern and meso-scale systems, and the reduc- tion of analysis errors. The background error covarianee structures of surface variables and the effects of model system bias on EnKF are also studied under the error covariance structures and a new concept ＇correlation scale＇ is introduced. However, the new method needs further evaluation with more cases of assimilation.展开更多
基金supported by the National Natural Science Foundation of China(Nos.G49790010 and 40476045).
文摘Based on experiment data of the Sino-German comprehensive investigations in the Bohai Sea in 1998 and 1999, a simple coupled pelagic-benthic ecosystem multi-box model is used to simulate the ecosystem seasonal variation. The pelagic sub-model consists of seven state variables： phytoplankton, zooplankton, TIN, TIP, DOC, POC and dissolved oxygen （DO）. The benthic sub-model includes macro-benthos, meiobenthos, bacteria, detritus, TIN and TIP in the sediment. Besides the effects of solar radiation, water temperature and the nutrient from sea bottom exudation, land-based inputs are considered. The impact of the advection terms between the boxes is also considered. Meanwhile, the effects of the micro- bial-loop are introduced with a simple parameterization. The seasonal variations and the horizontal distributions of the ecosystem state variables of the Bohai Sea are simulated. Compared with the observations, the results of the multi-box model are reasonable. The modeled results show that about 13% of the photosynthesis primary production goes to the main food loop, 20% transfers to the benthic domain, 44% is consumed by the respiration of phytoplankton, and the rest goes to DOC. Model results also show the importance of the microbial food loop in the ecosystem of the Bohai Sea, and its contribution to the annual zooplankton production can be 60%-64%.
基金supported by the Provincial Science and Technology Development Program of Shandong under Grant No.2008GG10008001Key Technology Integration and Application Program of China Meteorological Administration,under Grant No.CMAGJ2011M32+1 种基金Forecaster Research Program of China Meteorological Administration,under Grant No.CMAYBY2012-031Science and Technology Research Programs of Shandong Provincial Meteorological Bureau,under Grant Nos.2012sdqxz03,2012sdqxz01,2010sdqxz01
文摘In this paper, a new bias estimation method is proposed and applied in a regional ensemble Kalman filter （EnKF） based on the Weather Research and Forecasting （WRF） Model. The method is based on a homogeneous linear bias model, and the model bias is estimated using statistics at each assimilation cycle, which is different from the state augmentation methods proposed in pre- vious literatures. The new method provides a good estimation for the model bias of some specific variables, such as sea level pres- sure （SLP）. A series of numerical experiments with EnKF are performed to examine the new method under a severe weather condi- tion. Results show the positive effect of the method on the forecasting of circulation pattern and meso-scale systems, and the reduc- tion of analysis errors. The background error covarianee structures of surface variables and the effects of model system bias on EnKF are also studied under the error covariance structures and a new concept ＇correlation scale＇ is introduced. However, the new method needs further evaluation with more cases of assimilation.
