Real-Time Forecasting System of Winds, Waves and Surge in Tropical Cyclones

2004 ◽  
Author(s):  
Hans C. Graber ◽  
Mark A. Donelan ◽  
Michael G. Brown ◽  
Donald N. Slinn ◽  
Scott C. Hagen ◽  
...  
2003 ◽  
Author(s):  
Hans C. Graber ◽  
Mark A. Donelan ◽  
Michael G. Brown ◽  
Donald N. Slinn ◽  
Scott C. Hagen ◽  
...  

2015 ◽  
Vol 30 (2) ◽  
pp. 471-485 ◽  
Author(s):  
Shiqiu Peng ◽  
Yineng Li ◽  
Xiangqian Gu ◽  
Shumin Chen ◽  
Dongxiao Wang ◽  
...  

Abstract A real-time regional forecasting system for the South China Sea (SCS), called the Experimental Platform of Marine Environment Forecasting (EPMEF), is introduced in this paper. EPMEF consists of a regional atmosphere model, a regional ocean model, and a wave model, and performs a real-time run four times a day. Output from the Global Forecast System (GFS) from the National Centers for Environmental Prediction (NCEP) is used as the initial and boundary conditions of two nested domains of the atmosphere model, which can exert a constraint on the development of small- and mesoscale atmospheric perturbations through dynamical downscaling. The forecasted winds at 10-m height from the atmosphere model are used to drive the ocean and wave models. As an initial evaluation, a census on the track predictions of 44 tropical cyclones (TCs) during 2011–13 indicates that the performance of EPMEF is very encouraging and comparable to those of other official agencies worldwide. In particular, EPMEF successfully predicted several abnormal typhoon tracks including the sharp recurving of Megi (2010) and the looping of Roke (2011). Further analysis reveals that the dynamically downscaled GFS forecasts from the most updated forecast cycle and the optimal combination of different microphysics and PBL schemes primarily contribute to the good performance of EPMEF in TC track forecasting. EPMEF, established primarily for research purposes with the potential to be implemented into operations, provides valuable information not only to the operational forecasters of local marine/meteorological agencies or international TC forecast centers, but also to other stakeholders such as the fishing industry and insurance companies.


2006 ◽  
Author(s):  
Hans C. Graber ◽  
Mark A. Donelan ◽  
Michael G. Brown ◽  
Donald N. Slinn ◽  
Scott C. Hagen ◽  
...  

2007 ◽  
Author(s):  
Hans C. Graber ◽  
Mark A. Donelan ◽  
Michael G. Brown ◽  
Donald N. Slinn ◽  
Scott C. Hagen ◽  
...  

2002 ◽  
Author(s):  
Hans C. Graber ◽  
Mark A. Donelan ◽  
Michael G. Brown ◽  
Donald N. Slinn ◽  
Scott C. Hagen ◽  
...  

2001 ◽  
Author(s):  
Joo Heon Lee ◽  
Do Hun Lee ◽  
Sang Man Jeong ◽  
Eun Tae Lee

2017 ◽  
Author(s):  
James A. Coller ◽  
Andrew Silver ◽  
Okey Nwogu ◽  
Benjamin S.H. Connell

The US Nav has developed a real-time multi-ship ship motion forecasting system which combines forecast wave conditions with ship motion simulations to produce a prediction of the relative motions between two ships operating in a skin-to-skin configuration. The system utilizes two different simulation methods for predicting ship motions: MotionSim and Reduced Order Model (ROM) based on AEGIR. MotionSim is a fast three-dimensional panel method that is used to estimate the Response Amplitude Operators (RAOs) necessary for multi-ship motion predictions. The ROM works to maximize the accuracy of high fidelity ship motion prediction methods while maintaining the computational speed required for real-time forecasting. A model scale experiment was performed in 2015 on two Navy ships conventionally moored together. The predicted relative ship motions from MotionSim and ROM were compared to the model data using three different metrics: RMS (root mean square) ratio, correlation coefficient, and average angle measurement (AAM).This paper provides an overview of the two methods for predicting the multi-ship motions, a description of the model test, challenges faced during testing, and a discussion on the methodology of the evaluation and the results of each code correlation.


2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Chao Zhao ◽  
Jinyan Yang

The standard boxplot is one of the most popular nonparametric tools for detecting outliers in univariate datasets. For Gaussian or symmetric distributions, the chance of data occurring outside of the standard boxplot fence is only 0.7%. However, for skewed data, such as telemetric rain observations in a real-time flood forecasting system, the probability is significantly higher. To overcome this problem, a medcouple (MC) that is robust to resisting outliers and sensitive to detecting skewness was introduced to construct a new robust skewed boxplot fence. Three types of boxplot fences related to MC were analyzed and compared, and the exponential function boxplot fence was selected. Operating on uncontaminated as well as simulated contaminated data, the results showed that the proposed method could produce a lower swamping rate and higher accuracy than the standard boxplot and semi-interquartile range boxplot. The outcomes of this study demonstrated that it is reasonable to use the new robust skewed boxplot method to detect outliers in skewed rain distributions.


Sign in / Sign up

Export Citation Format

Share Document