On the importance of typhoon size in storm surge forecasting

2020 ◽  
Author(s):  
Md Rezuanul Islam ◽  
Hiroshi Takagi
Keyword(s):  
Storm Surge ◽  
Storm Surge Forecasting

scholarly journals Adjusting the Wind Stress Drag Coefficient in Storm Surge Forecasting Using an Adjoint Technique

2013 ◽  
Vol 30 (3) ◽  
pp. 590-608 ◽  
Author(s):  
Shiqiu Peng ◽  
Yineng Li ◽  
Lian Xie
Keyword(s):  
Data Assimilation ◽  
Drag Coefficient ◽  
Storm Surge ◽  
Wind Stress ◽  
Weather Prediction ◽  
Ocean Model ◽  
Error Sources ◽  
Empirical Parameter ◽  
Forecasting Errors ◽  
Storm Surge Forecasting

Abstract A three-dimensional ocean model and its adjoint model are used to adjust the drag coefficient in the calculation of wind stress for storm surge forecasting. A number of identical twin experiments (ITEs) with different error sources imposed are designed and performed. The results indicate that when the errors come from the wind speed, the drag coefficient is adjusted to an “optimal value” to compensate for the wind errors, resulting in significant improvements of the specific storm surge forecasting. In practice, the “true” drag coefficient is unknown and the wind field, which is usually calculated by an empirical parameter model or a numerical weather prediction model, may contain large errors. In addition, forecasting errors may also come from imperfect model physics and numerics, such as insufficient resolution and inaccurate physical parameterizations. The results demonstrate that storm surge forecasting errors can be reduced through data assimilation by adjusting the drag coefficient regardless of the error sources. Therefore, although data assimilation may not fix model imperfection, it is effective in improving storm surge forecasting by adjusting the wind stress drag coefficient using the adjoint technique.

Experimental Storm Surge Forecasting in the Bay of Bengal

2010 ◽  
pp. 15-25 ◽  
Author(s):  
Hassan S. Mashriqui ◽  
G. Paul Kemp ◽  
Ivor van Heerden ◽  
Ahmet Binselam ◽  
Young S. Yang ◽  
...  
Keyword(s):  
Storm Surge ◽  
Bay Of Bengal ◽  
Storm Surge Forecasting

STORM SURGE FORECAST MODEL USING GENETIC PROGRAMMING

2020 ◽  
pp. 75-89
Author(s):  
Quyen
Keyword(s):  
Genetic Programming ◽  
Storm Surge ◽  
Storm Surges ◽  
Forecast Model ◽  
Data Sets ◽  
Model Learning ◽  
Forecasting Models ◽  
Accurate Forecast ◽  
Evolving Models ◽  
Storm Surge Forecasting

Stormsurge is a typical genuine fiasco coming from the ocean. Therefore, an accurate forecast of surges is a vital assignment to dodge property misfortunes and decrease the chance of tropical storm surges. Genetic Programming (GP) is an evolution-based model learning technique that can simultaneously find the functional form and the numeric coefficients for the model. Moreover, GP has been widely applied to build models for predictive problems. However, GP has seldom been applied to the problem of storm surge forecasting. In this paper, a new method to use GP for evolving models for storm surge forecasting is proposed. Experimental results on data-sets collected from the Tottori coast of Japan show that GP can become more accurate storm surge forecasting models than other standard machine learning methods. Moreover, GP can automatically select relevant features when evolving storm surge forecasting models, and the models developed by GP are interpretable.

Genetic Programming for storm surge forecasting

2020 ◽  
Vol 215 ◽  
pp. 107812 ◽  
Author(s):  
Nguyen Thi Hien ◽  
Cao Truong Tran ◽  
Xuan Hoai Nguyen ◽  
Sooyoul Kim ◽  
Vu Dinh Phai ◽  
...  
Keyword(s):  
Genetic Programming ◽  
Storm Surge ◽  
Storm Surge Forecasting

Relocatable Tide Prediction and Storm Surge Forecasting

2018 ◽  
Author(s):  
Thomas Prime
Keyword(s):  
Sea Level ◽  
Storm Surge ◽  
Marine Environment ◽  
Agricultural Land ◽  
Extreme Event ◽  
Negative Impact ◽  
Early Warning Systems ◽  
Coastal Communities ◽  
Prevention Measures ◽  
Storm Surge Forecasting

The marine environment represents a large and important resource for communities around the world. However, the marine environment increasingly presents hazards that can have a large negative impact. One important marine hazard results from storms and their accompanying surges. This can lead to coastal flooding, particularly when surge and astronomical high tides align, with resultant impacts such as destruction of property, saline degradation of agricultural land and coastal erosion. Where tide and storm surge information are provided and accessed in a timely, accurate and understandable way, the data can provide: 1. Evidence for planning: Statistics of past conditions such as the probability of extreme event occurrence can be used to help plan improvements to coastal infrastructure that are able to withstand and mitigate the hazard from a given extreme event. 2. Early warning systems: Short term forecasts of storm surge allow provide early warnings to coastal communities enabling them to take actions to allow them to withstand extreme events, e.g. deploy flood prevention measures or mobilise emergency response measures. Data regarding sea level height can be provided from various in-situ observations such as tide gauges and remote observations such as satellite altimetry. However, to provide a forecast at high spatial and temporal resolution a dynamic ocean model is used. Over recent decades the National Oceanography Centre has been a world leading in developing coastal ocean models. This paper will present our progress on a current project to develop an information system for the Madagascan Met Office. The project, C-RISC, being executed in partnership with Sea Level Research Ltd, is translating the current modelling capability of NOC in storm surge forecasting and tidal prediction into a system that will provide information that can be easily transferred to other regions and is scalable to include other hazard types The outcome, an operational high-resolution storm surge warning system that is easy to relocate, will directly benefit coastal communities, giving them information they need to make effective decisions before and during extreme storm surge events.

scholarly journals Improving Short-Range Ensemble Kalman Storm Surge Forecasting Using Robust Adaptive Inflation

2013 ◽  
Vol 141 (8) ◽  
pp. 2705-2720 ◽  
Author(s):  
M. U. Altaf ◽  
T. Butler ◽  
X. Luo ◽  
C. Dawson ◽  
T. Mayo ◽  
...  
Keyword(s):  
Storm Surge ◽  
Short Range ◽  
Estimation Error ◽  
Test Cases ◽  
Finite Growth ◽  
The Common ◽  
Robust Adaptive ◽  
Ensemble Filtering ◽  
Reported Data ◽  
Storm Surge Forecasting

Abstract This paper presents a robust ensemble filtering methodology for storm surge forecasting based on the singular evolutive interpolated Kalman (SEIK) filter, which has been implemented in the framework of the H∞ filter. By design, an H∞ filter is more robust than the common Kalman filter in the sense that the estimation error in the H∞ filter has, in general, a finite growth rate with respect to the uncertainties in assimilation. The computational hydrodynamical model used in this study is the Advanced Circulation (ADCIRC) model. The authors assimilate data obtained from Hurricanes Katrina and Ike as test cases. The results clearly show that the H∞-based SEIK filter provides more accurate short-range forecasts of storm surge compared to recently reported data assimilation results resulting from the standard SEIK filter.

scholarly journals Urgent Computing for Operational Storm Surge Forecasting in Saint-Petersburg

2012 ◽  
Vol 9 ◽  
pp. 1704-1712 ◽  
Author(s):  
Alexander V. Boukhanovsky ◽  
Sergey V. Ivanov
Keyword(s):  
Storm Surge ◽  
Saint Petersburg ◽  
Urgent Computing ◽  
Storm Surge Forecasting

scholarly journals Development and Verification of NEMO based Regional Storm Surge Forecasting System

2020 ◽  
Vol 32 (6) ◽  
pp. 373-383
Author(s):  
Nary La ◽  
Byoung Woong An ◽  
KiRyong Kang ◽  
Pil-Hun Chang
Keyword(s):  
Storm Surge ◽  
Forecasting System ◽  
Storm Surge Forecasting
Sign in / Sign up

Export Citation Format

Share Document