Ocean current forecasting is still in explorative stage of study. In the study, we face some problems that have not been met before. The solving of these problems has become fundamental premise for realizing the ocean...Ocean current forecasting is still in explorative stage of study. In the study, we face some problems that have not been met before. The solving of these problems has become fundamental premise for realizing the ocean current forecasting. In the present paper are discussed in depth the physical essence for such basic problems as the predictability of ocean current, the predictable currents, the dynamical basis for studying respectively the tidal current and circulation, the necessity of boundary model, the models on regions with different scales and their link. The foundations and plans to solve the problems are demonstrated. Finally a set of operational numerical forecasting system for ocean current is proposed.展开更多
A three-dimensional baroclinic numerical forecasting model for anomaly current field is developed forapplication in the Bohai Sea and the upper layer of the Huanghai Sea and the East China Sea. All the dynamical varia...A three-dimensional baroclinic numerical forecasting model for anomaly current field is developed forapplication in the Bohai Sea and the upper layer of the Huanghai Sea and the East China Sea. All the dynamical variables, including temperature and salinity, can be calculated predictively by using the model. The results of the numerical weather prediction are used as input fields,and various dynamic and thermodynamic boundary conditions areadopted. So, the model can be used as an operational numerical forecasting model for current fields. In this paper,the structure of the model is presented in detail, various tests for the performance of the model are made, and thedependence of the model on some parameters is discussed. The results of the numerical simulation using historicaldata and experimental forecasting tests are also presented.展开更多
Eyewall replacement cycles(ERCs)greatly increase the destructive potential of tropical cyclones(TCs)by affecting the maximum wind speed,wind field size,and storm surge severity while simultaneously reducing confidence...Eyewall replacement cycles(ERCs)greatly increase the destructive potential of tropical cyclones(TCs)by affecting the maximum wind speed,wind field size,and storm surge severity while simultaneously reducing confidence in TC forecasts,most prominently in intensity forecasting.Machine learning(ML)presents new opportunities to improve current forecasting and predictive capabilities,and its application will benefit forecasters and ultimately the public.The objective of this project was to create a proof-of-concept ML convolutional neural network(CNN)to predict ERCs using the 89 GHz microwave band for training and testing.The training set was comprised of North Atlantic basin(NATL)storms from 1999 to 2009.The testing set included NATL storms from 2019 to 2022.Twelve models were created,together known as the CNN Ensemble for Predicting Eyewall Replacement Cycles(CE-PERCY),with each individual member achieving at least 80%in-training accuracy.Two versions were created:versions A and B.Using synthetic aperture radar,land-based radar,aircraft reconnaissance,Microwave-based Probability of ERC(M-PERC),National Hurricane Center reports,and microwave imagery,ERC analysis was conducted on the testing set.28 ERCs were identified throughout 14 hurricanes from 2019 to 2022.CE-PERCY performs well for a proof-of-concept,with versions A and B predicting 21 and 23 ERCs,respectively.This project successfully introduces a foundation for using ML CNNs in ERC prediction,demonstrates the viability of the technique,and proves that a large enough dataset of microwave imagery can be used in this specific application.展开更多
文摘Ocean current forecasting is still in explorative stage of study. In the study, we face some problems that have not been met before. The solving of these problems has become fundamental premise for realizing the ocean current forecasting. In the present paper are discussed in depth the physical essence for such basic problems as the predictability of ocean current, the predictable currents, the dynamical basis for studying respectively the tidal current and circulation, the necessity of boundary model, the models on regions with different scales and their link. The foundations and plans to solve the problems are demonstrated. Finally a set of operational numerical forecasting system for ocean current is proposed.
文摘A three-dimensional baroclinic numerical forecasting model for anomaly current field is developed forapplication in the Bohai Sea and the upper layer of the Huanghai Sea and the East China Sea. All the dynamical variables, including temperature and salinity, can be calculated predictively by using the model. The results of the numerical weather prediction are used as input fields,and various dynamic and thermodynamic boundary conditions areadopted. So, the model can be used as an operational numerical forecasting model for current fields. In this paper,the structure of the model is presented in detail, various tests for the performance of the model are made, and thedependence of the model on some parameters is discussed. The results of the numerical simulation using historicaldata and experimental forecasting tests are also presented.
文摘Eyewall replacement cycles(ERCs)greatly increase the destructive potential of tropical cyclones(TCs)by affecting the maximum wind speed,wind field size,and storm surge severity while simultaneously reducing confidence in TC forecasts,most prominently in intensity forecasting.Machine learning(ML)presents new opportunities to improve current forecasting and predictive capabilities,and its application will benefit forecasters and ultimately the public.The objective of this project was to create a proof-of-concept ML convolutional neural network(CNN)to predict ERCs using the 89 GHz microwave band for training and testing.The training set was comprised of North Atlantic basin(NATL)storms from 1999 to 2009.The testing set included NATL storms from 2019 to 2022.Twelve models were created,together known as the CNN Ensemble for Predicting Eyewall Replacement Cycles(CE-PERCY),with each individual member achieving at least 80%in-training accuracy.Two versions were created:versions A and B.Using synthetic aperture radar,land-based radar,aircraft reconnaissance,Microwave-based Probability of ERC(M-PERC),National Hurricane Center reports,and microwave imagery,ERC analysis was conducted on the testing set.28 ERCs were identified throughout 14 hurricanes from 2019 to 2022.CE-PERCY performs well for a proof-of-concept,with versions A and B predicting 21 and 23 ERCs,respectively.This project successfully introduces a foundation for using ML CNNs in ERC prediction,demonstrates the viability of the technique,and proves that a large enough dataset of microwave imagery can be used in this specific application.
