To better evaluate the three-dimensional bay health and predict the dynamic bay health conditions, a concept of numerical bay health was introduced and a method of numerical bay health evaluation（NBHE） was developed...To better evaluate the three-dimensional bay health and predict the dynamic bay health conditions, a concept of numerical bay health was introduced and a method of numerical bay health evaluation（NBHE） was developed.To support the NBHE method, a numerical bay health index（NBHI） system was constructed, which assess the natural and socio-economic effects on the entire bay. Five index groups are combined to formulate the NBHI,including geometry, hydrodynamics and sediment dynamics, bio-ecology, water quality and socio-economy.Each group has different number of indices selected and weighted using AHP method according to their importance. Data were mainly synthesized from a variety of numerical models together with monitoring programs, which provide superior to other approaches in discriminating data integrity and predicting data in future. The NBHE method using NBHI system was applied in the Yueqing Bay during spring tide in April 2007.According to the NBHE results, Sta. A, at the surface level of the estuarine mouth, has a healthy geometry condition, sub-healthy hydrodynamic and sediment dynamic condition, and unhealthy water quality and bioecology conditions. The integrated healthy score at Sta. A indicates its sub-healthy condition.展开更多
The Pearl River Estuary(PRE)is one of China’s busiest shipping hubs and fishery production centers,as well as a region with abundant island tourism and wind energy resources,which calls for accurate short-term wind f...The Pearl River Estuary(PRE)is one of China’s busiest shipping hubs and fishery production centers,as well as a region with abundant island tourism and wind energy resources,which calls for accurate short-term wind forecasts.First,this study evaluated three operational numerical models,i.e.,ECMWF-EC,NCEP-GFS,and CMA-GD,for their ability to predict short-term wind speed over the PRE against in-situ observations during 2018-2021.Overall,ECMWF-EC out-performs other models with an average RMSE of 2.24 m s^(-1)and R of 0.57,but the NCEP-GFS performs better in the case of strong winds.Then,various bias correction and multi-model ensemble(MME)methods are used to perform the deterministic post-processing using a local and lead-specific scheme.Two-factor model output statistics(MOS2)is the optimal bias correction method for reducing(increasing)the overall RMSE(R)to 1.62(0.70)m s^(-1),demonstrating the benefits of considering both initial and lead-specific information.Intercomparison of MME results reveals that Multiple linear regression(MLR)presents superior skills,followed by random forest(RF),but it is slightly inferior to MOS2,particularly for the first few forecasting hours.Furthermore,the incorporation of additional features in MLR reduces the overall RMSE to 1.53 m s^(-1)and increases R to 0.74.Similarly,RF presents comparable results,and both outperform MOS2 in terms of correcting their deficiencies at the first few lead hours and limiting the error growth rate.Despite the satisfactory skill of deterministic post-processing techniques,they are unable to achieve a balanced performance between mean and extreme statistics.This highlights the necessity for further development of probabilistic forecasts.展开更多
基金The Key National Project under contract No.009zx07424-001Doctoral Fund of Ministry of Education of China under contract No.2012101110108+2 种基金MEL Visiting Fellowship Programthe Fundamental Research Funds for the Central UniversitiesZhejiang Provincial Natural Science Foundation of China under contract No.LQ16D060002
文摘To better evaluate the three-dimensional bay health and predict the dynamic bay health conditions, a concept of numerical bay health was introduced and a method of numerical bay health evaluation（NBHE） was developed.To support the NBHE method, a numerical bay health index（NBHI） system was constructed, which assess the natural and socio-economic effects on the entire bay. Five index groups are combined to formulate the NBHI,including geometry, hydrodynamics and sediment dynamics, bio-ecology, water quality and socio-economy.Each group has different number of indices selected and weighted using AHP method according to their importance. Data were mainly synthesized from a variety of numerical models together with monitoring programs, which provide superior to other approaches in discriminating data integrity and predicting data in future. The NBHE method using NBHI system was applied in the Yueqing Bay during spring tide in April 2007.According to the NBHE results, Sta. A, at the surface level of the estuarine mouth, has a healthy geometry condition, sub-healthy hydrodynamic and sediment dynamic condition, and unhealthy water quality and bioecology conditions. The integrated healthy score at Sta. A indicates its sub-healthy condition.
基金Science and Technology Research Project of Guangdong Meteorological Service(GRMC2021M19,GRMC2022Q16,GRMC2023M29)。
文摘The Pearl River Estuary(PRE)is one of China’s busiest shipping hubs and fishery production centers,as well as a region with abundant island tourism and wind energy resources,which calls for accurate short-term wind forecasts.First,this study evaluated three operational numerical models,i.e.,ECMWF-EC,NCEP-GFS,and CMA-GD,for their ability to predict short-term wind speed over the PRE against in-situ observations during 2018-2021.Overall,ECMWF-EC out-performs other models with an average RMSE of 2.24 m s^(-1)and R of 0.57,but the NCEP-GFS performs better in the case of strong winds.Then,various bias correction and multi-model ensemble(MME)methods are used to perform the deterministic post-processing using a local and lead-specific scheme.Two-factor model output statistics(MOS2)is the optimal bias correction method for reducing(increasing)the overall RMSE(R)to 1.62(0.70)m s^(-1),demonstrating the benefits of considering both initial and lead-specific information.Intercomparison of MME results reveals that Multiple linear regression(MLR)presents superior skills,followed by random forest(RF),but it is slightly inferior to MOS2,particularly for the first few forecasting hours.Furthermore,the incorporation of additional features in MLR reduces the overall RMSE to 1.53 m s^(-1)and increases R to 0.74.Similarly,RF presents comparable results,and both outperform MOS2 in terms of correcting their deficiencies at the first few lead hours and limiting the error growth rate.Despite the satisfactory skill of deterministic post-processing techniques,they are unable to achieve a balanced performance between mean and extreme statistics.This highlights the necessity for further development of probabilistic forecasts.
