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.

scholarly journals Assimilation Research of Wind Stress Drag Coefficient Based on the Linear Expression

2021 ◽  
Vol 9 (10) ◽  
pp. 1135
Author(s):  
Junli Xu ◽  
Yuling Nie ◽  
Kai Ma ◽  
Wenqi Shi ◽  
Xianqing Lv
Keyword(s):  
Data Assimilation ◽  
Drag Coefficient ◽  
Storm Surge ◽  
Wind Stress ◽  
Bohai Sea ◽  
Storm Surges ◽  
Drag Coefficients ◽  
Initial Values ◽  
Linear Expression ◽  
Better Than

The wind stress drag coefficient plays an important role in storm surge models. This study reveals the influences of wind stress drag coefficients, which are given in form of formulas and inverted by the data assimilation method, on the storm surge levels in the Bohai Sea, Yellow Sea, and East China Sea during Typhoon 7008. In the process of data assimilation, the drag coefficient is based on the linear expression Cd = (a + b × U10) × 10−3 (generally speaking, a and b are empirical parameters determined by observed data). The results showed that the performance of the data assimilation method was far superior to those of drag coefficient formulas. Additionally, the simulated storm surge levels obviously changed in the neighborhood of typhoon eye. Furthermore, the effect of initial values of a and b in the Cd expression on the storm surge levels was also investigated when employing the data assimilation method. The results indicated that the simulation of storm surge level was the closest to the observation when a and b were simultaneously equal to zero, whereas the simulations had slight differences when the initial values of a and b were separately equal to the drag coefficients from the work of Smith, Wu, and Geernaert et al. Therefore, we should choose appropriate initial values for a and b by using the data assimilation method. As a whole, the data assimilation method is much better than drag coefficient parameterization formulas in the simulation of storm surges.

scholarly journals On the Impact of Unmanned Aerial System Observations on Numerical Weather Prediction in the Coastal Zone

2018 ◽  
Vol 146 (2) ◽  
pp. 599-622 ◽  
Author(s):  
David D. Flagg ◽  
James D. Doyle ◽  
Teddy R. Holt ◽  
Daniel P. Tyndall ◽  
Clark M. Amerault ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Research Laboratory ◽  
Weather Prediction ◽  
Ocean Model ◽  
Naval Research ◽  
Unmanned Aerial System ◽  
Numerical Weather ◽  
The Impact

Abstract The Trident Warrior observational field campaign conducted off the U.S. mid-Atlantic coast in July 2013 included the deployment of an unmanned aerial system (UAS) with several payloads on board for atmospheric and oceanic observation. These UAS observations, spanning seven flights over 5 days in the lowest 1550 m above mean sea level, were assimilated into a three-dimensional variational data assimilation (DA) system [the Naval Research Laboratory Atmospheric Variational Data Assimilation System (NAVDAS)] used to generate analyses for a numerical weather prediction model [the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS)] with a coupled ocean model [the Naval Research Laboratory Navy Coastal Ocean Model (NCOM)]. The impact of the assimilated UAS observations on short-term atmospheric prediction performance is evaluated and quantified. Observations collected from 50 radiosonde launches during the campaign adjacent to the UAS flight paths serve as model forecast verification. Experiments reveal a substantial reduction of model bias in forecast temperature and moisture profiles consistently throughout the campaign period due to the assimilation of UAS observations. The model error reduction is most substantial in the vicinity of the inversion at the top of the model-estimated boundary layer. Investigations reveal a consistent improvement to prediction of the vertical position, strength, and depth of the boundary layer inversion. The relative impact of UAS observations is explored further with experiments of systematic denial of data streams from the NAVDAS DA system and removal of individual measurement sources on the UAS platform.

Evaluation of a NOAH-MP Land Surface Model Ensemble over the Mississippi Basin

2020 ◽  
Author(s):  
Jonas Rothermel ◽  
Maike Schumacher
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Land Surface ◽  
Hydrological Cycle ◽  
Weather Prediction ◽  
Surface Model ◽  
Empirical Parameter ◽  
Numerical Weather ◽  
Parameter Values

<p><span>Physical-based Land Surface Models (LSMs) have deepened the understanding of the hydrological cycle and serve as the lower boundary layer in atmospheric models for numerical weather prediction. As any numerical model, they are subject to various sources of uncertainty, including simplified model physics, unknown empirical parameter values and forcing errors, particularly precipitation. Quantifying these uncertainties is important for assessing the predictive power of the model, especially in applications for environmental hazard warning. Data assimilation systems also benefit from realistic model error estimates.</span></p><p><span><span>In this study, the LSM NOAH-MP is evaluated over the Mississippi basin by running a large ensemble of model configurations with suitably perturbed forcing data and parameter values. For this, sensible parameter distributions are obtained by performing a thorough sensitivity analysis, identifying the most informative parameters beforehand by a screening approach. The ensemble of model outputs is compared against various hydrologic and atmospheric feedback observations, including SCAN soil moisture data, GRACE TWS anomaly data and AmeriFlux evapotranspiration measurements. The long-term aim of this study is to improve land-surface states via data assimilation and to investigate their influence on short- to midterm numerical weather prediction. Thus, the uncertainty of the simulated model states, such as snow, soil moisture in various layers, and groundwater are thoroughly studied to estimate the relative impact of possible hydrologic data sets in the assimilation.</span></span></p>

scholarly journals Research of the Influential Factors on the Simulation of Storm Surge in the Bohai Sea

2014 ◽  
Vol 8 (1) ◽  
pp. 151-156 ◽  
Author(s):  
Yumei Ding ◽  
Hao Wei
Keyword(s):  
Drag Coefficient ◽  
Storm Surge ◽  
Bohai Sea ◽  
Three Dimensional ◽  
Ocean Model ◽  
Influential Factors ◽  
Influential Factor ◽  
Data Set ◽  
The Bohai Sea ◽  
Sensitivity Studies

A hindcast of typical extratropical storm surge occurring in the Bohai Sea in Oct. 2003 is performed using a three-dimensional storm surge model system based on Finite-Volume Coastal Ocean Model (FVCOM). The surface winds are obtained from the WRF data set. Some preliminary sensitivity studies of the influential factors affecting the storm surge simulation in the Bohai Sea are conducted with the high revolution numerical model of storm surge. The factors of tide-surge interaction, the wind stress, the water depth, the bottom drag coefficient and the critical depth in the model are studied. After considering the tide-wind interaction and the severe wind, the most important influential factor affecting the storm surge in the Bohai Sea is the bottom drag coefficient. These sensitivity studies indicate that the storm surge simulations depend critically on the parameterizations. Hence additional experimental guidance is required on the bottom drag coefficient. This study is useful for the storm surge simulation in order to select the proper parameter to make possible a good conservation behavior of the storm surge model.

scholarly journals Storm surge and wave simulations in the Gulf of Mexico using a consistent drag relation for atmospheric and storm surge models

2012 ◽  
Vol 12 (7) ◽  
pp. 2399-2410 ◽  
Author(s):  
D. Vatvani ◽  
N. C. Zweers ◽  
M. van Ormondt ◽  
A. J. Smale ◽  
H. de Vries ◽  
...  
Keyword(s):  
Wind Speed ◽  
Gulf Of Mexico ◽  
Drag Coefficient ◽  
Storm Surge ◽  
Weather Prediction ◽  
Surface Wind ◽  
Wave Model ◽  
Water Levels ◽  
Observation Data ◽  
Wave Effects

Abstract. To simulate winds and water levels, numerical weather prediction (NWP) and storm surge models generally use the traditional bulk relation for wind stress, which is characterized by a wind drag coefficient. A still commonly used drag coefficient in those models, some of them were developed in the past, is based on a relation, according to which the magnitude of the coefficient is either constant or increases monotonically with increasing surface wind speed (Bender, 2007; Kim et al., 2008; Kohno and Higaki, 2006). The NWP and surge models are often tuned independently from each other in order to obtain good results. Observations have indicated that the magnitude of the drag coefficient levels off at a wind speed of about 30 m s−1, and then decreases with further increase of the wind speed. Above a wind speed of approximately 30 m s−1, the stress above the air-sea interface starts to saturate. To represent the reducing and levelling off of the drag coefficient, the original Charnock drag formulation has been extended with a correction term. In line with the above, the Delft3D storm surge model is tested using both Charnock's and improved Makin's wind drag parameterization to evaluate the improvements on the storm surge model results, with and without inclusion of the wave effects. The effect of waves on storm surge is included by simultaneously simulating waves with the SWAN model on identical model grids in a coupled mode. However, the results presented here will focus on the storm surge results that include the wave effects. The runs were carried out in the Gulf of Mexico for Katrina and Ivan hurricane events. The storm surge model was initially forced with H*wind data (Powell et al., 2010) to test the effect of the Makin's wind drag parameterization on the storm surge model separately. The computed wind, water levels and waves are subsequently compared with observation data. Based on the good results obtained, we conclude that, for a good reproduction of the storm surges under hurricane conditions, Makin's new drag parameterization is favourable above the traditional Charnock relation. Furthermore, we are encouraged by these results to continue the studies and establish the effect of improved Makin's wind drag parameterization in the wave model. The results from this study will be used to evaluate the relevance of extending the present towards implementation of a similar wind drag parameterization in the SWAN wave model, in line with our aim to apply a consistent wind drag formulation throughout the entire storm surge modelling approach.

scholarly journals Inversion of Wind-Stress Drag Coefficient in Simulating Storm Surges by Means of Regularization Technique

2019 ◽  
Vol 16 (19) ◽  
pp. 3591
Author(s):  
Junli Xu ◽  
Yuhong Zhang ◽  
Xianqing Lv ◽  
Qiang Liu
Keyword(s):  
Drag Coefficient ◽  
Storm Surge ◽  
Wind Stress ◽  
Yellow Sea ◽  
Bohai Sea ◽  
Water Levels ◽  
The Yellow Sea ◽  
Regularization Technique ◽  
The Bohai Sea ◽  
Comparison Results

In this study, water levels observed at tide stations in the Bohai Sea, Yellow Sea, and East China Sea during Typhoons 7203 and 8509 were assimilated into a numerical assimilation storm surge model combined with regularization technique to study the wind-stress drag coefficient. The Tikhonov regularization technique with different regularization parameters was tested during the assimilation. Using the regularization technique, the storm surge elevations were successfully simulated in the whole sea areas during Typhoons 7203 and 8509. The storm surge elevations calculated with the regularization technique and the elevations calculated with independent point method were separately compared with the observed data. Comparison results demonstrated that the former was closer to the observed data. The regularization technique had the best performance when the regularization parameter was 100. The spatial distribution of the inverted drag coefficient, storm surge elevations, and the wind fields during both typhoons were presented. Simulated results indicated that the change of drag coefficient is more significant in the coastal regions of the Bohai Sea and north of the Yellow Sea. Further analysis showed that the rising water elevation in the Bohai Sea is mostly attributed to the influence of onshore winds, and the negative storm surge in the South Yellow Sea is mainly caused by offshore winds.

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