Hindcast Results Research Articles

Error-Estimation Ensemble Method in the Forecasting of Tropical Cyclone Tracks

Pan, Y.; Chen, Y.-P.; Yuan, J.-Y.; Liu, Y., and Xu, Z.-S., 2018. Error-Estimation Ensemble method in the forecasting of tropical cyclone tracks. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 771–775. Coconut Creek (Florida), ISSN 0749-0208.Tropical cyclones pose a great threat to coastal areas and accurate forecasting of their tracks is important for reducing and preventing coastal disasters. Multi-mode super-ensemble method was proposed to make full use of the forecasted tracks from different forecasting institutes. This paper proposed a modified multi-mode super-ensemble method, namely Error-Estimation Ensemble method, to overcome the shortcomings of traditional multi-mode super-ensemble method. The hypothesis, methodology and implementation of Error-Estimation Ensemble method are described. The EEE hindcast results of two typical tropical cyclones and all tropical cyclones that affects China coast are compared to those of four forecasting institutes. The comparison show that the EEE method reduces both the mean error and root-mean-square error of tropical cyclone track forecast, indicating that EEE method has better prediction accuracy and reliability. Discussions on present performance of EEE method and future works are present at last.

Forecasting experiments of a dynamical–statistical model of the sea surface temperature anomaly field based on the improved self-memorization principle

Abstract. With the objective of tackling the problem of inaccurate long-term El Niño–Southern Oscillation (ENSO) forecasts, this paper develops a new dynamical–statistical forecast model of the sea surface temperature anomaly (SSTA) field. To avoid single initial prediction values, a self-memorization principle is introduced to improve the dynamical reconstruction model, thus making the model more appropriate for describing such chaotic systems as ENSO events. The improved dynamical–statistical model of the SSTA field is used to predict SSTA in the equatorial eastern Pacific and during El Niño and La Niña events. The long-term step-by-step forecast results and cross-validated retroactive hindcast results of time series T1 and T2 are found to be satisfactory, with a Pearson correlation coefficient of approximately 0.80 and a mean absolute percentage error (MAPE) of less than 15 %. The corresponding forecast SSTA field is accurate in that not only is the forecast shape similar to the actual field but also the contour lines are essentially the same. This model can also be used to forecast the ENSO index. The temporal correlation coefficient is 0.8062, and the MAPE value of 19.55 % is small. The difference between forecast results in spring and those in autumn is not high, indicating that the improved model can overcome the spring predictability barrier to some extent. Compared with six mature models published previously, the present model has an advantage in prediction precision and length, and is a novel exploration of the ENSO forecast method.

Wave energy assessement for Northern Spain from a 33-year hindcast

Abstract The wave energy resource along the Northern Spanish coast is determined, for a period of 33 years (1979–2012) based on hindcast results of WAVEWATCH III for the Atlantic ocean area, coupled with the SWAN model for the coastal areas, and using surface winds from ECMWF's ERA- Interim data base. Results are validated with buoy data, in order to evaluate the model's accuracy. Statistical analysis of the wave parameters and wave power results are presented. The analysis was carried out for a coarse grid, on the North of Spain, and for three local areas with a finer mesh: Northwest Galicia, the area between Cape San Adrian and Cape Ortegal and Santander. The case studies include a more realistic perspective of the amount of energy that can be extracted with a wave energy converter, by adopting examples of a large and a small device. A seasonal variability evaluation is done for the nested areas through the analyzes of the seasonal average wave power resource for the different locations, throughout the 33 year hindcast, and the joint distributions of significant wave height and peak period for the number of occurrences and wave power.

A methodology for the long-term simulation and uncertainty analysis of the operational lifetime performance of wave energy converter arrays

This work presents a new methodology for the long-term simulation and uncertainty analysis of the performance of different alternatives of wave energy converter arrays. With it, we can analyze complete operational lifetime time-series of energy production for any type of wave energy converter. The methodology is based on the application of cutting edge methods, including complex wave climate simulations, downscaling techniques, numerical wave propagation and the application of Monte Carlo techniques for the uncertainty analysis of the results. The methodology was applied to arrays of 9 overtopping wave energy converters for which 9 different geometric alternatives were defined. Results of the mean energy available for the experiments carried out with Monte Carlo techniques indicate that the arrow-shaped array with a distance between devices of 6 times their diameter is the alternative in which more energy is produced. However, the results for some of the 500 experiments indicate that a different alternative is the one with the highest potential production, revealing that the most likely outcome of the experiments can be different from the hindcast results. These results highlight that simulations provide much more information for the decision-makers and that an uncertainty analysis is key towards optimizing energy production.

Inter-comparisons of SWAN hindcasts using boundary conditions from WAM and WWIII for northwest and northeast coasts of India

In this study, we present the inter-comparison of regional SWAN hindcasts using boundary conditions (BC) from the third generation wave models WAM and WWIII implemented globally. Here, SWAN inter-comparison studies with outputs of both global models as well as SWAN results using BC from both WAM and WWIII were carried out for selected months of the year 2000, 2008, 2009 and for the full year 2011 using ERA-40, QuikSCAT/NCEP Blended and OSCAT winds. The results of this experimental simulations/hindcasts suggests that SWAN hindcasts for both the northwest coast as well as northeast coastal model domains in the present model setups were found to be different, and can be considered better than the deep water model outputs such as WAM and WWIII, implemented globally. The comparison between SWAN hindcasts using WAM BC and SWAN hindcasts using WWIII BC revealed high correlations. However, the Percentage Error in the hindcast results of SWAN using WAM BC were found to be generally higher by 5–15% by considering most of the case studies presented in this investigation. Further, the validation results using in-situ and altimeter measurements suggest that SWAN using WAM BC can be an effective means of obtaining more accurate nearshore wave predictions.

Multi-scheme corrected dynamic–analogue prediction of summer precipitation in northeastern China based on BCC_CSM

Based on summer precipitation hindcasts for 1991–2013 produced by the Beijing Climate Center Climate System Model (BCC_CSM), the relationship between precipitation prediction error in northeastern China (NEC) and global sea surface temperature is analyzed, and dynamic–analogue prediction is carried out to improve the summer precipitation prediction skill of BCC_CSM, through taking care of model historical analogue prediction error in the real-time output. Seven correction schemes such as the systematic bias correction, pure statistical correction, dynamic–analogue correction, and so on, are designed and compared. Independent hindcast results show that the 5-yr average anomaly correlation coefficient (ACC) of summer precipitation is respectively improved from –0.13/0.15 to 0.16/0.24 for 2009–13/1991–95 when using the equally weighted dynamic–analogue correction in the BCC_CSM prediction, which takes the arithmetical mean of the correction based on regional average error and that on grid point error. In addition, probabilistic prediction using the results from the multiple correction schemes is also performed and it leads to further improved 5-yr average prediction accuracy.

Effect of Bottom Friction, Wind Drag Coefficient, and Meteorological Forcing in Hindcast of Hurricane Rita Storm Surge Using SWAN + ADCIRC Model

An evaluation of the effect of bottom friction, wind drag coefficient, and meteorological forcing is conducted using a tightly coupled wave and circulation model, SWAN + ADCIRC (i.e., Simulating WAves Nearshore + ADvanced CIRCulation), to hindcast the storm surge of Hurricane Rita (2005). Wind drag coefficient formulations of Powell, Zijlema, and Peng & Li are used to calculate wind stresses. Bottom friction and wind drag coefficients are systematically increased and decreased to quantify their impacts on the hindcast. Different meteorological forcing options are applied to study the effect of wind fields on storm surge development and propagation. Simulated water levels are compared with observed data collected from about 150 locations. It is evident that a lower bottom friction causes higher and faster surge propagation, and earlier arrival of inundation peak at locations far from the land fall. Drag coefficients of Powell, with or without a cap of 0.002, and Zijlema produce similar results, while that of Peng & Li slightly overpredicted the surge. Wind fields may cause overprediction or underprediction of the surge, depending on the choice of the wind model. A good agreement is found between Zijlema’s findings and this study; that simultaneously decreasing or increasing both bottom friction and wind drag essentially provides the same hindcast results.

Long-distance transport of radioactive plume by nocturnal local winds.

Radioactive plumes can spread far and wide depending on wind conditions. The plumes often frequently reached the Tokyo metropolitan area, which is approximately 200 km away from the Fukushima Daiichi nuclear power plant, under spatially heterogeneous wind fields in March 2011. To reduce exposure to radioactive plumes, the behaviour of the plumes must be known. However, the transport mechanism of radioactive plumes is not fully understood. Using a regional climate model, we show that multiple diurnal cycle processes play a key role in the frequent transport of radioactive plumes to the Tokyo metropolitan area. The observed data and hindcast results indicate that the radioactive plume moves along the local winds, which comprise the northeasterly local wind (NELW) associated with the meso-scale low-pressure system (meso-low) and the northerly sea wind (NSW) during the night. The long-term analysis and sensitivity simulations also show the nocturnal processes that the NELW caused by the meso-low and the NSW are formed east of the Tokyo metropolitan area and from Fukushima offshore east of the Tokyo metropolitan area, respectively, when neither winter monsoons nor extra-tropical cyclones are predominant. These findings indicate that the radioactive plumes could reach faraway places frequently via nocturnal local processes.

Hindcasts of Integrated Kinetic Energy in Atlantic Tropical Cyclones: A Neural Network Prediction Scheme

Abstract A new statistical–dynamical scheme is presented for predicting integrated kinetic energy (IKE) in North Atlantic tropical cyclones from a series of environmental input parameters. Predicting IKE is desirable because the metric quantifies the energy across a storm’s entire wind field, allowing it to respond to changes in storm structure and size. As such, IKE is especially useful for quantifying risks in large, low-intensity, high-impact storms such as Sandy in 2012. The prediction scheme, named the Statistical Prediction of Integrated Kinetic Energy, version 2 (SPIKE2), builds upon a previous statistical IKE scheme, by using a series of artificial neural networks instead of more basic linear regression models. By using a more complex statistical scheme, SPIKE2 is able to distinguish nonlinear signals in the environment that could cause fluctuations in IKE. In an effort to evaluate SPIKE2’s performance in a future operational setting, the model is calibrated using archived input parameters from Global Ensemble Forecast System (GEFS) control analyses, and is run in a hindcast mode from 1990 to 2011 using archived GEFS reforecasts. The hindcast results indicate that SPIKE2 performs significantly better than both persistence and climatological benchmarks.

Predictability and prediction of summer rainfall in the arid and semi-arid regions of China

Northwest China (NWC) is an arid and semi-arid region where climate variability and environmental changes are sensitive to precipitation. The present study explores sources and limits of predictability of summer precipitation over NWC using the predictable mode analysis (PMA) of percentage of rainfall anomaly data. Two major modes of NWC summer rainfall variability are identified which are tied to Eurasian continental scale precipitation variations. The first mode features wet northern China corresponding to dry central Siberia and wet Mongolia, which is mainly driven by tropical Pacific sea surface temperature anomalies (SSTA). The second mode features wet western China reflecting wet Central Asia and dry Ural–western Siberia, which strongly links to Indian Ocean SSTA. Anomalous land warming over Eurasia also provides important precursors for the two modes. The cross-validated hindcast results demonstrate these modes can be predicted with significant correlation skills, suggesting that they may be considered as predictable modes. The domain averaged temporal correlation coefficient (TCC) skill during 1979 to 2015 using 0-month (1-month) lead models is 0.39 (0.35), which is considerably higher than dynamical models’ multi-model ensemble mean skill (−0.02). Maximum potential attainable prediction skills are also estimated and discussed. The result illustrates advantage of PMA in predicting rainfall over dry land areas and large room for dynamical model improvement. However, secular changes of predictors need to be detected continuously in order to make practical useful prediction.

Storm Surge Hindcast and Return Period of a Haiyan-Like Super Typhoon

The extremely intense Typhoon Haiyan (local name Yolanda), struck the Philippines in November 2013, catastrophically impacting the region. It is important to understand the storm surge characteristics and occurrence probability of such super typhoons for reconstruction of the devastated area. This study mainly examines two topics. First, the characteristics of Typhoon Haiyan and its related storm surge were analyzed with a series of numerical experiments using an empirical typhoon model and storm surge model. The maximum surge height and inundation area were estimated and validated against measurements of tides and wind speeds obtained from local agencies NAMRIA and PAGASA, as well as against post-event surveys of maximum inundation heights. Based on the analysis of best hindcast results, the maximum storm surge level was about 5 m at Tacloban on Leyte Island. Second, the return period of a Haiyan-like typhoon and associated storm surge was estimated with NOAA's long-term observed IBTrACS typhoon dataset and a stochastic tropical cyclone model. Statistical analysis of typhoons around Tacloban was performed, and the estimated return period of storm surge levels seen during Typhoon Haiyan is 240–360 years.

Estimation of mean and extreme waves in the East China Seas

Accurately estimating the mean and extreme wave statistics and better understanding their directional and seasonal variations are of great importance in the planning and designing of ocean and coastal engineering works. Due to the lack of long-term wave measurement data, the analysis of extreme waves is often based on the numerical wave hind-casting results. In this study, the wave climate in the East China Seas (including the Bohai Sea, the Yellow Sea and the East China Sea) for the past 35 years (1979–2013) is hind-casted using a third generation wave model – WAMC4 (Cycle 4 version of WAM model). Two sets of reanalysis wind data from NCEP (National Centers for Environmental Prediction, USA) and ECMWF (European Centre for Medium-range Weather Forecasts) are used to drive the wave model to generate the long-term wave climate. The hind-casted waves are then analysed to study the mean and extreme wave statistics in the study area. The results show that the mean wave heights decrease from south to north and from sea to land in general. The extreme wave heights with return periods of 50 and 100 years in the summer and autumn seasons are significantly higher than those in the other two seasons, mainly due to the effect of typhoon events. The mean wave heights in the winter season have the highest values, mainly due to the effect of winter monsoon winds. The comparison of extreme wave statistics from both wind fields with the field measurements at several nearshore wave observation stations shows that the extreme waves generated by the ECMWF winds are better than those generated by the NCEP winds. The comparison also shows the extreme waves in deep waters are better reproduced than those in shallow waters, which is partly attributed to the limitations of the wave model used. The results presented in this paper provide useful insight into the wave climate in the area of the East China Seas, as well as the effect of wind data resolution on the simulation of long-term waves.

A validation of an operational wave and surge prediction system for the Dutch coast

Abstract. Knowledge of the actual condition of hydrodynamics in the nearshore and coastal area is essential for coastal monitoring activities. To this end, a coastal operational model system can serve as a tool in providing recent and up-to-date, state-of-the-art hydrodynamics along the coast. In this paper, we apply CoSMoS (Coastal Storm Modeling System), a generic operational wave and tide-surge modelling system applied here to predict waves and water levels along the Dutch coast. The CoSMoS application is not limited to storm impact prediction on the Dutch coast, but can also be applied to other coastal hazards such as rip currents and coastal flooding, in other environments. In this paper, we present the set-up of the CoSMoS system and a validation of the wave and surge model, with deep-water wave buoy data and tidal gauge measurements as ground truth validation material. The evaluation is presented as monthly error measures between computed parameters and observed ones. Hindcast results over the whole year of 2009 show that the simulated wave parameters and surge elevation from the CoSMoS are in good agreement with data, with average root mean square (rms) error over the year of 0.14 m for the surge elevation and 0.24 m for the significant wave height. It is noted that there is a tendency of the wave model to underestimate the height of northerly waves with lower frequencies (swell). Additionally, when a wave separation algorithm is applied on the overall spectrum, results show consistent underestimation of the swell component by the model, which for the Dutch coast will mainly come from the north, where the North Sea is open to the Atlantic Ocean. In the proposed model system, the swell boundary can have a significant effect on the simulated wave results, suggesting room for improvement for the swell boundary conditions to the north and the swell propagation within the Dutch Continental Shelf Model. Finally, we show that in forecast mode, CoSMoS can provide reasonably good wave and surge prediction.

Hindcast of extreme sea states in North Atlantic extratropical storms

This study examines the variability of freak wave parameters around the eye of northern hemisphere extratropical cyclones. The data was obtained from a hindcast performed with the WAve Model (WAM) model forced by the wind fields of the Climate Forecast System Reanalysis (CFSR). The hindcast results were validated against the wave buoys and satellite altimetry data showing a good correlation. The variability of different wave parameters was assessed by applying the empirical orthogonal functions (EOF) technique on the hindcast data. From the EOF analysis, it can be concluded that the first empirical orthogonal function (V1) accounts for greater share of variability of significant wave height (Hs), peak period (Tp), directional spreading (SPR) and Benjamin-Feir index (BFI). The share of variance in V1 varies for cyclone and variable: for the 2nd storm and Hs V1 contains 96 % of variance while for the 3rd storm and BFI V1 accounts only for 26 % of variance. The spatial patterns of V1 show that the variables are distributed around the cyclones centres mainly in a lobular fashion.

Air quality forecast of PM<sub>10</sub> in Beijing with Community Multi-scale Air Quality Modeling (CMAQ) system: emission and improvement

Abstract. The MM5-SMOKE-CMAQ model system, which was developed by the United States Environmental Protection Agency (US EPA) as the MODELS-3 system, has been used for daily air quality forecasts in the Beijing Municipal Environmental Monitoring Center (Beijing MEMC), as a part of the Ensemble air quality Modeling forecast System for Beijing (EMS-Beijing) since the 2008 Olympic Games. According to the daily forecast results for the entire duration of 2010, the model shows good performance in the PM10 forecast on most days but clearly underestimates PM10 concentration during some air pollution episodes. A typical air pollution episode from 11–20 January 2010 was chosen, in which the observed air pollution index of particulate matter (PM10-API) reached 180 while the forecast PM10-API was about 100. In this study, three numerical methods are used for model improvement: first, by enhancing the inner domain with 3 km resolution grids, and expanding the coverage from only Beijing to an area including Beijing and its surrounding cities; second, by adding more regional point source emissions located at Baoding, Landfang and Tangshan, to the south and east of Beijing; third, by updating the area source emissions, including the regional area source emissions in Baoding and Tangshan and the local village/town-level area source emissions in Beijing. The last two methods are combined as the updated emissions method. According to the model sensitivity testing results by the CMAQ model, the updated emissions method and expanded model domain method can both improve the model performance separately. But the expanded model domain method has better ability to capture the peak values of PM10 than the updated emissions method due to better reproduction of the pollution transport process in this episode. As a result, the hindcast results ("New(CMAQ)"), which are driven by the updated emissions in the expanded model domain, show a much better model performance in the national standard station-averaged PM10-API. The daily hindcast PM10-API reaches 180 and is much closer to the observed value, and has a high correlation coefficient of 0.93. The correlation coefficient of the PM10-API in all Beijing MEMC stations between the hindcast and observation is 0.82, clearly higher than the forecast 0.54. The FAC2 increases from 56% in the forecast to 84% in the hindcast, and the NMSE decreases from 0.886 to 0.196. The hindcast also has better model performance in PM10 hourly concentrations during the typical air pollution episode. The updated emissions method accompanied by a suitable domain in this study improved the model performance for the Beijing area significantly.

Extreme wave parameters under North Atlantic extratropical cyclones

A characterization of extreme wave parameters during extratropical cyclones in the Northern hemisphere is made from WAM wave model hindcasts. In February 2007 two extratropical storms were observed in the North Atlantic and the wave fields associated with them are modeled in this paper. Wave buoy and satellite altimetry data were used to validate the WAM hindcast results. The distribution of the Benjamin–Feir index (BFI), kurtosis and the ratio of maximum wave height to significant wave height (abnormality index) around the eye of the two extratropical cyclones is studied. It is found that under these conditions the BFI and kurtosis are significantly larger mainly in the fourth quadrant and also when the wind direction is aligned with the wave propagation direction. In these regions the probability of occurrence of abnormal waves is higher.

Local amplification of storm surge by Super Typhoon Haiyan in Leyte Gulf.

Typhoon Haiyan, which struck the Philippines in November 2013, was an extremely intense tropical cyclone that had a catastrophic impact. The minimum central pressure of Typhoon Haiyan was 895 hPa, making it the strongest typhoon to make landfall on a major island in the western North Pacific Ocean. The characteristics of Typhoon Haiyan and its related storm surge are estimated by numerical experiments using numerical weather prediction models and a storm surge model. Based on the analysis of best hindcast results, the storm surge level was 5–6 m and local amplification of water surface elevation due to seiche was found to be significant inside Leyte Gulf. The numerical experiments show the coherent structure of the storm surge profile due to the specific bathymetry of Leyte Gulf and the Philippines Trench as a major contributor to the disaster in Tacloban. The numerical results also indicated the sensitivity of storm surge forecast.

Reconstructing historical habitat data with predictive models

Historical vegetation data are important to ecological studies, as many structuring processes operate at long time scales, from decades to centuries. Capturing the pattern of variability within a system (enough to declare a significant change from past to present) relies on correct assumptions about the temporal scale of the processes involved. Sufficient long-term data are often lacking, and current techniques have their weaknesses. To address this concern, we constructed multistate and artificial neural network models (ANN) to provide fore- and hindcast vegetation communities considered critical foraging habitat for an endangered bird, the Florida Snail Kite (Rostrhamus sociabilis). Multistate models were not able to hindcast due to our data not satisfying a detailed balance requirement for time reversibility in Markovian dynamics. Multistate models were useful for forecasting and providing environmental variables for the ANN. Results from our ANN hindcast closely mirrored the population collapse of the Snail Kite population using only environmental data to inform the model. The parallel between the two gives us confidence in the hindcasting results and their use in future demographic models.

Analysis and hindcast simulations of an extreme rainfall event in the Mediterranean area: The Genoa 2011 case

The city of Genoa, which places between the Tyrrhenian Sea and the Apennine mountains (Liguria, Italy) was rocked by severe flash floods on the 4th of November, 2011. Nearly 500mm of rain, a third of the average annual rainfall, fell in six hours. Six people perished and millions of Euros in damages occurred. The synoptic-scale meteorological system moved across the Atlantic Ocean and into the Mediterranean generating floods that killed 5 people in Southern France, before moving over the Ligurian Sea and Genoa producing the extreme event studied here.Cloud-permitting simulations (1km) of the finger-like convective system responsible for the torrential event over Genoa have been performed using Advanced Research Weather and Forecasting Model (ARW-WRF, version 3.3).Two different microphysics (WSM6 and Thompson) as well as three different convection closures (explicit, Kain–Fritsch, and Betts–Miller–Janjic) were evaluated to gain a deeper understanding of the physical processes underlying the observed heavy rain event and the model's capability to predict, in hindcast mode, its structure and evolution. The impact of forecast initialization and of model vertical discretization on hindcast results is also examined. Comparison between model hindcasts and observed fields provided by raingauge data, satellite data, and radar data show that this particular event is strongly sensitive to the details of the mesoscale initialization despite being evolved from a relatively large scale weather system. Only meso-γ details of the event were not well captured by the best setting of the ARW-WRF model and so peak hourly rainfalls were not exceptionally well reproduced. The results also show that specification of microphysical parameters suitable to these events have a positive impact on the prediction of heavy precipitation intensity values.

Prediction of wave parameters by using fuzzy inference system and the parametric models along the south coasts of the Black Sea

Forecasting of sea-state characteristics has a great importance in coastal and ocean engineering studies. Therefore, the purpose of this study was to investigate performances of Adaptive-Network-Based Fuzzy Inference System (ANFIS) and several parametric methods in the Black Sea. For this purpose, different fuzzy models with different input combinations were developed for two different wind data sources (TSMS and ECMWF) at two offshore buoy stations. It also aimed to apply several approaches to event-based data sets for wave predictions. Generally, in literature the tendency is to use time series data for wave predictions. In this kind of prediction approach, lagged time series data are taken as inputs and current or future variables are taken as output. In this study, event-based data for each independent storm were extracted from time series data. Simultaneous or concurrent data of wind speed, blowing duration, fetch length and wave characteristics were detected for each single storm. These event data were then used to set up models. The hindcast results were validated with significant wave height and mean wave period data recorded in Hopa and Sinop buoy stations. The performance of developed fuzzy models were also compared with that of four different parametric methods (Wilson, SPM, Jonswap, and CEM methods) applied for two wind data sources at both buoy stations. Finally, it was determined that in the prediction of both wave parameters (Hs and Tz) the ANFIS models (R = 0.66, squared correlation coefficient, and MAE = 0.37 m, mean absolute error, for the best model in prediction of Hs) were more accurate than the parametric methods (R = 0.63 and MAE = 0.75 m for the best model in prediction of Hs).