Development and evaluation of a storm surge warning system in Taiwan

An operational storm surge forecasting system aimed at providing warning information for storm surges has been developed and evaluated using four typhoon events. The warning system triggered by typhoon forecasts from Taiwan Cooperative Precipitation Ensemble Forecast Experiment (TAPEX) has been executed with two storm surge forecasting scenarios with and without tides. Three numerical experiments applying different meteorological inputs have been designed to assess the impact of typhoon forcing on storm surges. One uses synthetic wind fields, and the others use realistic wind fields with and without adjustments to the initial wind fields for the background circulation. Local observations from Central Weather Bureau (CWB) weather stations and tide gauge stations are used to evaluate the wind fields and storm surges from our numerical experiments. The comparison results show that the accuracy of the storm surge forecast is dominated by the track, the intensity, and the driving flow of a typhoon. When the structure of a typhoon is disturbed by Taiwan’s topography, using meteorological inputs from real wind fields can result in a better typhoon simulation than using inputs from synthetic wind fields. The driving flow also determines the impact of topography on typhoon movement. For quickly moving typhoons, storm forcing from TAPEX is reliable when a typhoon is strong enough to be relatively unaffected by environmental flows; otherwise, storm forcing from a sophisticated typhoon initialization scheme that better simulates the typhoon and environmental flows results in a more accurate prediction of storm surges. Therefore, when a typhoon moves slowly and interacts more with the topography and environmental flows, incorporating realistic wind fields with adjustments to the initial wind fields for the background circulation in the warning system will obtain better predictions for a typhoon and its resultant storm surges.     

Simultaneous perturbation stochastic approximation for tidal models

The Dutch continental shelf model (DCSM) is a shallow sea model of entire continental shelf which is used operationally in the Netherlands to forecast the storm surges in the North Sea. The forecasts are necessary to support the decision of the timely closure of the moveable storm surge barriers to protect the land. In this study, an automated model calibration method, simultaneous perturbation stochastic approximation (SPSA) is implemented for tidal calibration of the DCSM. The method uses objective function evaluations to obtain the gradient approximations. The gradient approximation for the central difference method uses only two objective function evaluation independent of the number of parameters being optimized. The calibration parameter in this study is the model bathymetry. A number of calibration experiments is performed. The effectiveness of the algorithm is evaluated in terms of the accuracy of the final results as well as the computational costs required to produce these results. In doing so, comparison is made with a traditional steepest descent method and also with a newly developed proper orthogonal decomposition-based calibration method. The main findings are: (1) The SPSA method gives comparable results to steepest descent method with little computational cost. (2) The SPSA method with little computational cost can be used to estimate large number of parameters.     

Comparison of storm surge/tide predictions between a 2-D operational forecast system, the regional tide/storm surge model (RTSM), and the 3-D regional ocean modeling system (ROMS)

In this study, we compare simulated storm surges run on the two-dimensional operational storm surge/tide forecast system (regional tide/storm surge model (RTSM), based on Princeton ocean model) of the Korean Meteorological Administration and the three-dimensional regional ocean modeling system (ROMS), using observational data from 30 coastal tidal stations of three typhoons that struck Korea in 2007. A maximum positive bias of 6.8 cm was found for Typhoon Manyi predicted by ROMS, while a maximum negative bias of −7.4 cm was shown for Typhoon Nari predicted by RTSM. For all three typhoons, the total averaged root mean square error was 10 cm for the two models. Although the statistical results for the storm surge comparison between the observations and RTSM predictions were better than those for ROMS, with the exception of Typhoon Nari, the spatial and temporal variations of ROMS were larger than those of RTSM.     

An investigation of ensemble-based assimilation of satellite altimetry and tide gauge data in storm surge prediction

Cyclogenesis and long-fetched winds along the southeastern coast of South America may lead to floods in populated areas, as the Buenos Aires Province, with important economic and social impacts. A numerical model (SMARA) has already been implemented in the region to forecast storm surges. The propagation time of the surge in such extensive and shallow area allows the detection of anomalies based on observations from several hours up to the order of a day prior to the event. Here, we investigate the impact and potential benefit of storm surge level data assimilation into the SMARA model, with the objective of improving the forecast. In the experiments, the surface wind stress from an ensemble prediction system drives a storm surge model ensemble, based on the operational 2-D depth-averaged SMARA model. A 4-D Local Ensemble Transform Kalman Filter (4D-LETKF) initializes the ensemble in a 6-h cycle, assimilating the very few tide gauge observations available along the northern coast and satellite altimeter data. The sparse coverage of the altimeters is a challenge to data assimilation; however, the 4D-LETKF evolving covariance of the ensemble perturbations provides realistic cross-track analysis increments. Improvements on the forecast ensemble mean show the potential of an effective use of the sparse satellite altimeter and tidal gauges observations in the data assimilation prototype. Furthermore, the effects of the localization scale and of the observational errors of coastal altimetry and tidal gauges in the data assimilation approach are assessed.     

Influence of non-linear effects upon surge elevations along the west coast of Britain

A two-dimensional vertically integrated hydrodynamic finite-element model of the west coast of Britain is used to examine the response of the region to extreme meteorological forcing. The extent to which tide–surge interaction modifies the computed surge elevation and current distributions is examined in detail. The nature of the finite-element model with its ability to refine the mesh in nearshore regions is ideal for examining the influence of non-linear effects upon surges in these regions. Calculations using spatially uniform orthogonal wind stresses show that the surge elevation and current in shallow water are particularly sensitive to the method used to remove the tide as a result of the highly non-linear nature of the tide–surge interaction in these regions. The most accurate means of de-tiding the solution is by subtracting a tide derived by harmonic analysis of the tide and surge time series at the time of the surge. Subtracting a tide-only solution (the usual approach) leads to tidal energy leaking into the surge solution. Calculations show that this arises because the surge modifies the tidal amplitude and phase in shallow-water regions to such an extent that they are appreciably different to those found in the tide-only calculation. Results suggest that this problem becomes more important, as nearshore meshes are refined in an attempt to improve surge prediction. This suggests that in the future, highly accurate fine-mesh models will be required to compute total water levels without the present linear separation into tidal and surge signal used in operational surge prediction.     

Effects of tide-surge interactions on storm surges along the coast of the Bohai Sea,Yellow Sea,and East China Sea

A two-dimensional coupled tide-surge model was used to investigate the effects of tide-surge interactions on storm surges along the coast of the Bohai Sea, Yellow Sea, and East China Sea. In order to estimate the impacts of tide-surge interactions on storm surge elevations, Typhoon 7203 was assumed to arrive at 12 different times, with all other conditions remaining constant. This allowed simulation of tide and total water levels for 12 separate cases. Numerical simulation results for Yingkou, Huludao, Shijiusuo, and Lianyungang tidal stations were analyzed. Model results showed wide variations in storm surge elevations across the 12 cases. The largest difference between 12 extreme storm surge elevation values was of up to 58 cm and occurred at Yingkou tidal station. The results indicate that the effects of tide-surge interactions on storm surge elevations are very significant. It is therefore essential that these are taken into account when predicting storm surge elevations.     

A variational method for correcting non-systematic errors in numerical weather prediction

A variational method based on previous numerical forecasts is developed to estimate and correct non-systematic component of numerical weather forecast error. In the method, it is assumed that the error is linearly dependent on some combination of the forecast fields, and three types of forecast combination are applied to identifying the forecasting error: 1) the forecasts at the ending time, 2) the combination of initial fields and the forecasts at the ending time, and 3) the combination of the forecasts at...     

A two-way nested coupled tide-surge model for the Taiwan Strait

A two-way nested coupled tide-surge prediction model was established and applied in the Taiwan Strait and adjacent sea area in this study. This two-dimensional (2D) model had a fine horizontal resolution and took into account the interaction between storm surges and astronomical tides, which made it suitable for depicting the complicated physical properties of storm surges in the Taiwan Strait. A two-way nesting technique and an open boundary condition developed from Flather's radiation condition and Røed and Smedstad's local mode idea, were successfully implemented in the model. A simulation experiment showed that the open boundary condition could be used in the coupled tide-surge model and that the performance of the two-way nested model was slightly superior in accuracy to that of the one-way nested one.The fluctuations of storm surge residuals with tidal period at Sansha and Pingtan tide stations during the period of typhoon Dan in 1999 were well reproduced by the model, with the coupling effect between storm surges and tides indicating that the effect of astronomical tides upon typhoon surges should be considered in a storm-surge prediction model for the Taiwan Strait. The forecast experiment during typhoon Talim in 2005 showed that the storm surge prediction outputs by the model were better in the early 20 h of the forecast period of each model run than those in the later period due to the prediction accuracy of the typhoon track, maximum winds, and central air pressures.     

Short-term optimal operation of water systems using ensemble forecasts

Short-term water system operation can be realized using Model Predictive Control (MPC). MPC is a method for operational management of complex dynamic systems. Applied to open water systems, MPC provides integrated, optimal, and proactive management, when forecasts are available. Notwithstanding these properties, if forecast uncertainty is not properly taken into account, the system performance can critically deteriorate.Ensemble forecast is a way to represent short-term forecast uncertainty. An ensemble forecast is a set of possible future trajectories of a meteorological or hydrological system. The growing ensemble forecasts’ availability and accuracy raises the question on how to use them for operational management.The theoretical innovation presented here is the use of ensemble forecasts for optimal operation. Specifically, we introduce a tree based approach. We called the new method Tree-Based Model Predictive Control (TB-MPC). In TB-MPC, a tree is used to set up a Multistage Stochastic Programming, which finds a different optimal strategy for each branch and enhances the adaptivity to forecast uncertainty. Adaptivity reduces the sensitivity to wrong forecasts and improves the operational performance.TB-MPC is applied to the operational management of Salto Grande reservoir, located at the border between Argentina and Uruguay, and compared to other methods.     

Ensemble prediction of regional droughts using climate inputs and the SVM–copula approach

In this study, the climate teleconnections with meteorological droughts are analysed and used to develop ensemble drought prediction models using a support vector machine (SVM)–copula approach over Western Rajasthan (India). The meteorological droughts are identified using the Standardized Precipitation Index (SPI). In the analysis of large‐scale climate forcing represented by climate indices such as El Niño Southern Oscillation, Indian Ocean Dipole Mode and Atlantic Multidecadal Oscillation on regional droughts, it is found that regional droughts exhibits interannual as well as interdecadal variability. On the basis of potential teleconnections between regional droughts and climate indices, SPI‐based drought forecasting models are developed with up to 3 months' lead time. As traditional statistical forecast models are unable to capture nonlinearity and nonstationarity associated with drought forecasts, a machine learning technique, namely, support vector regression (SVR), is adopted to forecast the drought index, and the copula method is used to model the joint distribution of observed and predicted drought index. The copula‐based conditional distribution of an observed drought index conditioned on predicted drought index is utilized to simulate ensembles of drought forecasts. Two variants of drought forecast models are developed, namely a single model for all the periods in a year and separate models for each of the four seasons in a year. The performance of developed models is validated for predicting drought time series for 10 years' data. Improvement in ensemble prediction of drought indices is observed for combined seasonal model over the single model without seasonal partitions. The results show that the proposed SVM–copula approach improves the drought prediction capability and provides estimation of uncertainty associated with drought predictions. Copyright © 2013 John Wiley & Sons, Ltd.     

Forecast-skill-based simulation of streamflow forecasts

Streamflow forecasts are updated periodically in real time, thereby facilitating forecast evolution. This study proposes a forecast-skill-based model of forecast evolution that is able to simulate dynamically updated streamflow forecasts. The proposed model applies stochastic models that deal with streamflow variability to generate streamflow scenarios, which represent cases without forecast skill of future streamflow. The model then employs a coefficient of prediction to determine forecast skill and to quantify the streamflow variability ratio explained by the forecast. By updating the coefficients of prediction periodically, the model efficiently captures the evolution of streamflow forecast. Simulated forecast uncertainty increases with increasing lead time; and simulated uncertainty during a specific future period decreases over time. We combine the statistical model with an optimization model and design a hypothetical case study of reservoir operation. The results indicate the significance of forecast skill in forecast-based reservoir operation. Shortage index reduces as forecast skill increases and ensemble forecast outperforms deterministic forecast at a similar forecast skill level. Moreover, an effective forecast horizon exists beyond which more forecast information does not contribute to reservoir operation and higher forecast skill results in longer effective forecast horizon. The results illustrate that the statistical model is efficient in simulating forecast evolution and facilitates analysis of forecast-based decision making.     

Assessment of meteorological climate models as inputs for coastal studies

Modeling studies of future changes in coastal hydrodynamics, in terms of storm surges and wave climate, need appropriate wind and atmospheric forcings, a necessary requirement for the realistic reproduction of the statistics and the resolution of small scale features. This work compares meteorological results from different climate models in the Mediterranean area, with a focus on the Adriatic Sea, in order to assess their capability to reproduce coastal meteorological features and their possibility to be used as forcings for hydrodynamic simulations. Five meteorological datasets are considered. They are obtained from two regional climate models, implemented with different spatial resolutions and setups and are downscaled from two different global climate models. Wind and atmospheric pressure fields are compared with measurements at four stations along the Italian Adriatic coast. The analysis is carried out both on simulations of the control period 1960–1990 and on the A1B Intergovernmental Panel for Climate Change scenario projections (2070–2100), highlighting the ability of each model in reproducing the statistical coastal meteorological behavior and possible changes. The importance of simulated global- and regional-scale meteorological processes, in terms of correct spatial resolution of the phenomena, is also discussed. Within the Adriatic Sea, the meteorological climate is influenced by the local orography that controls the strengthening of north-eastern katabatic winds like Bora. Results show indeed that the increase in spatial resolution provides a more realistic wind forcing for the hydrodynamic simulations. Moreover, the chosen setup and the global climate models that drive the regional downscalings appear to play an important role in reproducing correct atmospheric pressure fields. The comparison between scenario and control simulations shows a small increase in the mean atmospheric pressure values, while a decrease in mean wind speed and in extreme wind events is observed, particularly for the datasets with higher spatial resolution. Finally, results suggest that an ensemble of downscaled climate models is likely to provide the most suitable climatic forcings (wind and atmospheric pressure fields) for coastal hydrodynamic modeling.     

A One-dimensional Ensemble Forecast and Assimilation System for Fog Prediction

A probabilistic fog forecast system was designed based on two high resolution numerical 1-D models called COBEL and PAFOG. The 1-D models are coupled to several 3-D numerical weather prediction models and thus are able to consider the effects of advection. To deal with the large uncertainty inherent to fog forecasts, a whole ensemble of 1-D runs is computed using the two different numerical models and a set of different initial conditions in combination with distinct boundary conditions. Initial conditions are obtained from variational data assimilation, which optimally combines observations with a first guess taken from operational 3-D models. The design of the ensemble scheme computes members that should fairly well represent the uncertainty of the current meteorological regime. Verification for an entire fog season reveals the importance of advection in complex terrain. The skill of 1-D fog forecasts is significantly improved if advection is considered. Thus the probabilistic forecast system has the potential to support the forecaster and therefore to provide more accurate fog forecasts.     

Storm surges and coastal impacts at Mar del Plata,Argentina

Positive storm surges (PSS) lasting for several days can raise the water level producing significant differences between the observed level and the astronomical tide. These storm events can be more severe if they coincide with a high tide or if they bracket several tidal cycles, particularly in the case of the highest astronomical tide. Besides, the abnormal sea-level elevation near the coast can cause the highest waves generated to attack the upper beach. This combination of factors can produce severe erosion, threatening sectors located along the coastline. These effects would be more serious if the storm surge height and duration increase as a result of a climatic change. The Mar del Plata (Argentina) coastline and adjacent areas are exposed to such effects. A statistical characterization of PSS based on their intensity, duration and frequency, including a surge event classification, was performed utilizing tide-gauge records over the period 1956–2005. A storm erosion potential index (SEPI) was calculated from observed levels based on hourly water level measurements. The index was related to beach profile responses to storm events. Also, a return period for extreme SEPI values was calculated. Results show an increase in the average number of positive storm surge events per decade. Considering all the events, the last decade (1996–2005) exhibits an average 7% increase compared to each one of the previous decades. A similar behavior was found for the decadal average of the heights of maximum annual positive storm surges. In this case the average height of the last two decades exceeds that of the previous decades by approximately 8 cm. The decadal average of maximum annual duration of these meteorological events shows an increase of 2 h in the last three decades. A possible explanation of the changes in frequency, height and duration of positive storm surges at Mar del Plata would seem to lie in the relative mean sea-level rise.     

The predictability of near-coastal currents using a baroclinic unstructured grid model

A limited domain, coastal ocean forecast system consisting of an unstructured grid model, a meteorological model, a regional ocean model, and a global tidal database is designed to be globally relocatable. For such a system to be viable, the predictability of coastal currents must be well understood with error sources clearly identified. To this end, the coastal forecast system is applied at the mouth of Chesapeake Bay in response to a Navy exercise. Two-day forecasts are produced for a 10-day period from 4 to 14 June 2010 and compared to real-time observations. Interplay between the temporal frequency of the regional model boundary forcing and the application of external tides to the coastal model impacts the tidal characteristics of the coastal current, even contributing a small phase error. Frequencies of at least 3 h are needed to resolve the tidal signal within the regional model; otherwise, externally applied tides from a database are needed to capture the tidal variability. Spatial resolution of the regional model (3 vs 1 km) does not impact skill of the current prediction. Tidal response of the system indicates excellent representation of the dominant M ₂ tide for water level and currents. Diurnal tides, especially K ₁, are amplified unrealistically with the application of coarse 27-km winds. Higher-resolution winds reduce current forecast error with the exception of wind originating from the SSW, SSE, and E. These winds run shore parallel and are subject to strong interaction with the shoreline that is poorly represented even by the 3-km wind fields. The vertical distribution of currents is also well predicted by the coastal model. Spatial and temporal resolution of the wind forcing including areas close to the shoreline is the most critical component for accurate current forecasts. Additionally, it is demonstrated that wind resolution plays a large role in establishing realistic thermal and density structures in upwelling prone regions.     

The use of scale recursive estimation for short term quantitative precipitation forecast

Scale recursive estimation (SRE) is adopted for short term quantitative precipitation forecast (QPF). The precipitation field is modelled using a lognormal random cascade, well suited to properly represent the scaling properties of rainfall fields. To estimate the random cascade parameters, scale recursive maximum likelihood estimation (MLE) is carried out by the iterative expectation maximization (EM) algorithm. To illustrate the potentiality of the SRE, forecast of a synthetically generated rainfall time series is shown. Adaptive estimation of the process parameters is carried out and precipitation forecasts are issued. The forecasts from the SRE are compared with those from standard ARMA models, showing a good performance. The SRE is then adopted for forecasting of an observed half hourly precipitation series for a two day storm event in northern Italy. The SRE provides good performance and it can therefore be adopted as a tool for short term QPF.     

Towards improved post‐processing of hydrologic forecast ensembles

Forecast ensembles of hydrological and hydrometeorologial variables are prone to various uncertainties arising from climatology, model structure and parameters, and initial conditions at the forecast date. Post‐processing methods are usually applied to adjust the mean and variance of the ensemble without any knowledge about the uncertainty sources. This study initially addresses the drawbacks of a commonly used statistical technique, quantile mapping (QM), in bias correction of hydrologic forecasts. Then, an auxiliary variable, the failure index (γ), is proposed to estimate the ineffectiveness of the post‐processing method based on the agreement of adjusted forecasts with corresponding observations during an analysis period prior to the forecast date. An alternative post‐processor based on copula functions is then introduced such that marginal distributions of observations and model simulations are combined to create a multivariate joint distribution. A set of 2500 hypothetical forecast ensembles with parametric marginal distributions of simulated and observed variables are post‐processed with both QM and the proposed multivariate post‐processor. Deterministic forecast skills show that the proposed copula‐based post‐processing is more effective than the QM method in improving the forecasts. It is found that the performance of QM is highly correlated with the failure index, unlike the multivariate post‐processor. In probabilistic metrics, the proposed multivariate post‐processor generally outperforms QM. Further evaluation of techniques is conducted for river flow forecast of Sprague River basin in southern Oregon. Results show that the multivariate post‐processor performs better than the QM technique; it reduces the ensemble spread and is a more reliable approach for improving the forecast. Copyright © 2012 John Wiley & Sons, Ltd.     

A data based mechanistic approach to nonlinear flood routing and adaptive flood level forecasting

Operational flood forecasting requires accurate forecasts with a suitable lead time, in order to be able to issue appropriate warnings and take appropriate emergency actions. Recent improvements in both flood plain characterization and computational capabilities have made the use of distributed flood inundation models more common. However, problems remain with the application of such models. There are still uncertainties associated with the identifiability of parameters; with the computational burden of calculating distributed estimates of predictive uncertainty; and with the adaptive use of such models for operational, real-time flood inundation forecasting. Moreover, the application of distributed models is complex, costly and requires high degrees of skill. This paper presents an alternative to distributed inundation models for real-time flood forecasting that provides fast and accurate, medium to short-term forecasts. The Data Based Mechanistic (DBM) methodology exploits a State Dependent Parameter (SDP) modelling approach to derive a nonlinear dependence between the water levels measured at gauging stations along the river. The transformation of water levels depends on the relative geometry of the channel cross-sections, without the need to apply rating curve transformations to the discharge. The relationship obtained is used to transform water levels as an input to a linear, on-line, real-time and adaptive stochastic DBM model. The approach provides an estimate of the prediction uncertainties, including allowing for heterescadasticity of the multi-step-ahead forecasting errors. The approach is illustrated using an 80 km reach of the River Severn, in the UK.