BAKTRAK: backtracking drifting objects using an iterative algorithm with a forward trajectory model

The task of determining the origin of a drifting object after it has been located is highly complex due to the uncertainties in drift properties and environmental forcing (wind, waves, and surface currents). Usually, the origin is inferred by running a trajectory model (stochastic or deterministic) in reverse. However, this approach has some severe drawbacks, most notably the fact that many drifting objects go through nonlinear state changes underway (e.g., evaporating oil or a capsizing lifeboat). This makes it difficult to naively construct a reverse-time trajectory model which realistically predicts the earliest possible time the object may have started drifting. We propose instead a different approach where the original (forward) trajectory model is kept unaltered while an iterative seeding and selection process allows us to retain only those particles that end up within a certain time–space radius of the observation. An iterative refinement process named BAKTRAK is employed where those trajectories that do not make it to the goal are rejected, and new trajectories are spawned from successful trajectories. This allows the model to be run in the forward direction to determine the point of origin of a drifting object. The method is demonstrated using the leeway stochastic trajectory model for drifting objects due to its relative simplicity and the practical importance of being able to identify the origin of drifting objects. However, the methodology is general and even more applicable to oil drift trajectories, drifting ships, and hazardous material that exhibit nonlinear state changes such as evaporation, chemical weathering, capsizing, or swamping. The backtracking method is tested against the drift trajectory of a life raft and is shown to predict closely the initial release position of the raft and its subsequent trajectory.     

Stochastic Lagrangian trajectory model for drifting objects in the ocean

The prediction of drifting object trajectories in the ocean is a complex problem plagued with uncertainties. This problem is usually solved simulating the possible trajectories based on wind and advective numerical and/or instrumental data in real time, which are incorporated into Lagrangian trajectory models. However, both data and Lagrangian models are approximations of reality and when comparing trajectory data collected from drifter exercises with respect to Lagrangian models results, they differ considerably. This paper introduces a stochastic Lagrangian trajectory model that allows quantifying the uncertainties related to: (i) the wind and currents numerical and/or instrumental data, and (ii) the Lagrangian trajectory model. These uncertainties are accounted for within the model through random model parameters. The quantification of these uncertainties consists in an estimation problem, where the parameters of the probability distribution functions of the random variables are estimated based on drifter exercise data. Particularly, it is assumed that estimated parameters maximize the likelihood of our model to reproduce the trajectories from the exercise. Once the probability distribution parameters are estimated, they can be used to simulate different trajectories, obtaining location probability density functions at different times. The advantage of this method is that it allows: (i) site specific calibration, and (ii) comparing uncertainties related to different wind and currents predictive tools. The proposed method is applied to data collected during the DRIFTER Project (eranet AMPERA, VI Programa Marco), showing very good predictive skills.     

Observation and simulation of a floe drift near the North Pole

The drift trajectory of a floe near the North Pole (87° N, 175° W) was observed during 8–19 August, 2010 based on the fourth Chinese National Arctic Research Expedition. The trajectory of the floe showed circular motions superimposed on straight drift. Each cycle had a period of about 12?h. The circular motion is inertial oscillation. The largest amplitude of inertial oscillation speed can reach 20?cm/s. After removing the inertial oscillation, the floe drift direction is about 40° on average to the right of the observed 10-m wind which is much larger than previous reports on the angle between sea-ice velocity and the geostrophic wind, and floe drift moves with a speed of about 1.4?% of the observed 10-m wind speed throughout the whole observation period. A simple dynamic sea ice-ocean coupled model and a three-dimensional sea ice-ocean coupled model are employed to simulate the floe drift. Both numerical models are with the widely used quadratic water-drag formulation, i.e., the stress is proportional to the square of the ice velocity relative to the ocean surface current. The inertial oscillation of the floe is successfully simulated by the simple passive drag model, while the floe drift amplitudes simulated from the three-dimensional model are relatively small.     

Backtracking drifting objects using surface currents from high-frequency (HF) radar technology

In this work, the benefits of high-frequency (HF) radar ocean observation technology for backtracking drifting objects are analysed. The HF radar performance is evaluated by comparison of trajectories between drifter buoys versus numerical simulations using a Lagrangian trajectory model. High-resolution currents measured by a coastal HF radar network combined with atmospheric fields provided by numerical models are used to backtrack the trajectory of two dataset of surface-drifting buoys: group I (with drogue) and group II (without drogue). A methodology based on optimization methods is applied to estimate the uncertainty in the trajectory simulations and to optimize the search area of the backtracked positions. The results show that, to backtrack the trajectory of the buoys in group II, both currents and wind fields were required. However, wind fields could be practically discarded when simulating the trajectories of group I. In this case, the optimal backtracked trajectories were obtained using only HF radar currents as forcing. Based on the radar availability data, two periods ranging between 8 and 10?h were selected to backtrack the buoy trajectories. The root mean squared error (RMSE) was found to be 1.01?km for group I and 0.82?km for group II. Taking into account these values, a search area was calculated using circles of RMSE radii, obtaining 3.2 and 2.11?km² for groups I and II, respectively. These results show the positive contribution of HF radar currents for backtracking drifting objects and demonstrate that these data combined with atmospheric models are of value to perform backtracking analysis of drifting objects.     

Application of HF radar currents to oil spill modelling

In this work, the benefits of high-frequency (HF) radar currents for oil spill modeling and trajectory analysis of floating objects are analyzed. The HF radar performance is evaluated by means of comparison between a drifter buoy trajectory and the one simulated using a Lagrangian trajectory model. A methodology to optimize the transport model performance and to calculate the search area of the predicted positions is proposed. This method is applied to data collected during the Galicia HF Radar Experience. This experiment was carried out to explore the capabilities of this technology for operational monitoring along the Spanish coast. Two long-range HF radar stations were installed and operated between November 2005 and February 2006 on the Galician coast. In addition, a drifter buoy was released inside the coverage area of the radar. The HF radar currents, as well as numerical wind data were used to simulate the buoy trajectory using the TESEO oil spill transport model. In order to evaluate the contribution of HF radar currents to trajectory analysis, two simulation alternatives were carried out. In the first one, wind data were used to simulate the motion of the buoy. In the second alternative, surface currents from the HF radar were also taken into account. For each alternative, the model was calibrated by means of the global optimization algorithm SCEM-UA (Shuffled Complex Evolution Metropolis) in order to obtain the probability density function of the model parameters. The buoy trajectory was computed for 24 h intervals using a Monte Carlo approach based on the results provided in the calibration process. A bivariate kernel estimator was applied to determine the 95% confidence areas. The analysis performed showed that simulated trajectories integrating HF radar currents are more accurate than those obtained considering only wind numerical data. After a 24 h period, the error in the final simulated position improves using HF radar currents. Averaging the information from all the simulated daily periods, the mean search and rescue area calculated using HF radar currents, is reduced by approximately a 62% in comparison with the search area calculated without these data. These results show the positive contribution of HF radar currents for trajectory analysis, and demonstrate that these data combined with atmospheric forecast models, are of value for trajectory analysis of oil spills or floating objects.     

Observation-based evaluation of surface wave effects on currents and trajectory forecasts

Knowledge of upper ocean currents is needed for trajectory forecasts and is essential for search and rescue operations and oil spill mitigation. This paper addresses effects of surface waves on ocean currents and drifter trajectories using in situ observations. The data set includes colocated measurements of directional wave spectra from a wave rider buoy, ocean currents measured by acoustic Doppler current profilers (ADCPs), as well as data from two types of tracking buoys that sample the currents at two different depths. The ADCP measures the Eulerian current at one point, as modelled by an ocean general circulation model, while the tracking buoys are advected by the Lagrangian current that includes the wave-induced Stokes drift. Based on our observations, we assess the importance of two different wave effects: (a) forcing of the ocean current by wave-induced surface fluxes and the Coriolis–Stokes force, and (b) advection of surface drifters by wave motion, that is the Stokes drift. Recent theoretical developments provide a framework for including these wave effects in ocean model systems. The order of magnitude of the Stokes drift is the same as the Eulerian current judging from the available data. The wave-induced momentum and turbulent kinetic energy fluxes are estimated and shown to be significant. Similarly, the wave-induced Coriolis–Stokes force is significant over time scales related to the inertial period. Surface drifter trajectories were analysed and could be reproduced using the observations of currents, waves and wind. Waves were found to have a significant contribution to the trajectories, and we conclude that adding wave effects in ocean model systems is likely to increase predictability of surface drifter trajectories. The relative importance of the Stokes drift was twice as large as the direct wind drag for the used surface drifter.     

Observation-based evaluation of surface wave effects on currents and trajectory forecasts

Knowledge of upper ocean currents is needed for trajectory forecasts and is essential for search and rescue operations and oil spill mitigation. This paper addresses effects of surface waves on ocean currents and drifter trajectories using in situ observations. The data set includes colocated measurements of directional wave spectra from a wave rider buoy, ocean currents measured by acoustic Doppler current profilers (ADCPs), as well as data from two types of tracking buoys that sample the currents at two different depths. The ADCP measures the Eulerian current at one point, as modelled by an ocean general circulation model, while the tracking buoys are advected by the Lagrangian current that includes the wave-induced Stokes drift. Based on our observations, we assess the importance of two different wave effects: (a) forcing of the ocean current by wave-induced surface fluxes and the Coriolis–Stokes force, and (b) advection of surface drifters by wave motion, that is the Stokes drift. Recent theoretical developments provide a framework for including these wave effects in ocean model systems. The order of magnitude of the Stokes drift is the same as the Eulerian current judging from the available data. The wave-induced momentum and turbulent kinetic energy fluxes are estimated and shown to be significant. Similarly, the wave-induced Coriolis–Stokes force is significant over time scales related to the inertial period. Surface drifter trajectories were analysed and could be reproduced using the observations of currents, waves and wind. Waves were found to have a significant contribution to the trajectories, and we conclude that adding wave effects in ocean model systems is likely to increase predictability of surface drifter trajectories. The relative importance of the Stokes drift was twice as large as the direct wind drag for the used surface drifter.

     

A coastal prediction system as an event response tool: Particle tracking simulation of an anhydrous ammonia spill in Tampa Bay

A coastal prediction system for Tampa Bay, comprised of a numerical circulation model and Lagrangian particle transport model, rapidly produces hindcast/forecast simulations that alert authorities to high impact areas following the introduction of hazardous material into the bay. The effectiveness of the prediction system as an event response tool is evaluated during an anhydrous ammonia spill. A week-long simulation predicts the trajectory of the material due to winds and currents. Physical transport of the model particles alternates from being tidally driven to being driven both by wind action and residual circulation. A forecast simulation showing particle distribution drove field sampling that resulted in the detection of a Pseudo-nitzschia bloom likely initiated from excess ammonium in the bay. An online component of the coastal prediction system is in development to better manage response and mitigation efforts for future hazardous material spills in Tampa Bay.     

An Experimental High-Resolution Forecast System During the Vancouver 2010 Winter Olympic and Paralympic Games

Environment Canada ran an experimental numerical weather prediction (NWP) system during the Vancouver 2010 Winter Olympic and Paralympic Games, consisting of nested high-resolution (down to 1-km horizontal grid-spacing) configurations of the GEM–LAM model, with improved geophysical fields, cloud microphysics and radiative transfer schemes, and several new diagnostic products such as density of falling snow, visibility, and peak wind gust strength. The performance of this experimental NWP system has been evaluated in these winter conditions over complex terrain using the enhanced mesoscale observing network in place during the Olympics. As compared to the forecasts from the operational regional 15-km GEM model, objective verification generally indicated significant added value of the higher-resolution models for near-surface meteorological variables (wind speed, air temperature, and dewpoint temperature) with the 1-km model providing the best forecast accuracy. Appreciable errors were noted in all models for the forecasts of wind direction and humidity near the surface. Subjective assessment of several cases also indicated that the experimental Olympic system was skillful at forecasting meteorological phenomena at high-resolution, both spatially and temporally, and provided enhanced guidance to the Olympic forecasters in terms of better timing of precipitation phase change, squall line passage, wind flow channeling, and visibility reduction due to fog and snow.     

A strategy for producing refined currents in the Equatorial Atlantic in the context of the search of the AF447 wreckage

On the night of 1st June 2009, a Rio-Paris Air France flight (AF447) disappeared in a highly variable and poorly observed part of the western tropical Atlantic Ocean. The first debris was located 5 days after the accident. Several reverse drift computations were conducted in order to define the likely position of the wreckage. Unfortunately, the performance of the operational ocean analyses available in the region of interest ranges from 80 to 100 km of positioning error after 5 days of inverse drift computation. In preparation of the third phase of research of the wreckage at sea, a series of numerical experiments was performed at Météo-France and Mercator Océan in an attempt to better compute the surface currents in the region and for the period of the accident of the AF447 (May and June 2009). Tailored high-resolution atmosphere and ocean reanalyses were first produced respectively at Météo-France and Mercator Océan. Several nested experiments were then performed with a small and flexible ocean model limited to the region of interest. The date of the initial conditions and the type of atmospheric forcing fields were varied in order to produce a small ensemble from which information on the sensitivity to these changes could be derived. Probabilistic and statistical combinations between model and observations were tested and a solution was finally selected by means of a comparison of drift computations with independent surface drift observations.     

大气变率对北极地区近期海冰变化趋势影响数值模拟研究

为研究近期21年(1989—2009年)北极地区海冰变化原因,本文利用欧洲中期天气预报中心ERA-Interim数据集资料和美国麻省理工学院MITgcm全球海冰-海洋耦合模式开展了不同大气强迫条件下海冰变化的数值模拟研究.研究工作中共设计了6个数值试验,除1个试验全部采用1989—2009年每日4个时次的大气强迫场外,其余5个试验各有一种大气强迫(地表气温、地表大气比湿、向下短波辐射通量、向下长波辐射通量和地表风)采用1989年月平均结果.分析了各模拟试验结果中3月和9月北极地区海冰面积的年际变化特征及最小二乘拟合意义下的线性变化趋势,并以ERA-Interim结果为参照标准对各模拟试验结果进行了对比和检验,以说明不同大气强迫量变率对海冰变化的作用.结果表明:地表气温变率和向下长波辐射通量变率是造成海冰面积减少的主要原因;向下短波辐射通量变率对海冰面积变化影响几乎可以忽略;地表大气比湿变率对海冰面积线性变化趋势影响较小,但对海冰面积年际变化特征有调制作用;地表风变率对海冰季节变化、海冰面积线性变化趋势及年际变化特征均有明显影响,说明提高大气风应力精度是改善海冰数值模拟结果的重要手段.     

High-frequency and meso-scale winter sea-ice variability in the Southern Ocean in a high-resolution global ocean model

This study is on high-frequency temporal variability (HFV) and meso-scale spatial variability (MSV) of winter sea-ice drift in the Southern Ocean simulated with a global high-resolution (0.1°) sea ice-ocean model. Hourly model output is used to distinguish MSV characteristics via patterns of mean kinetic energy (MKE) and turbulent kinetic energy (TKE) of ice drift, surface currents, and wind stress, and HFV characteristics via time series of raw variables and correlations. We find that (1) along the ice edge, the MSV of ice drift coincides with that of surface currents, in particular such due to ocean eddies; (2) along the coast, the MKE of ice drift is substantially larger than its TKE and coincides with the MKE of wind stress; (3) in the interior of the ice pack, the TKE of ice drift is larger than its MKE, mostly following the TKE pattern of wind stress; (4) the HFV of ice drift is dominated by weather events, and, in the absence of tidal currents, locally and to a much smaller degree by inertial oscillations; (5) along the ice edge, the curl of the ice drift is highly correlated with that of surface currents, mostly reflecting the impact of ocean eddies. Where ocean eddies occur and the ice is relatively thin, ice velocity is characterized by enhanced relative vorticity, largely matching that of surface currents. Along the ice edge, ocean eddies produce distinct ice filaments, the realism of which is largely confirmed by high-resolution satellite passive-microwave data.     

Coupled assimilation for an intermediated coupled ENSO prediction model

The value of coupled assimilation is discussed using an intermediate coupled model in which the wind stress is the only atmospheric state which is slavery to model sea surface temperature (SST). In the coupled assimilation analysis, based on the coupled wind–ocean state covariance calculated from the coupled state ensemble, the ocean state is adjusted by assimilating wind data using the ensemble Kalman filter. As revealed by a series of assimilation experiments using simulated observations, the coupled assimilation of wind observations yields better results than the assimilation of SST observations. Specifically, the coupled assimilation of wind observations can help to improve the accuracy of the surface and subsurface currents because the correlation between the wind and ocean currents is stronger than that between SST and ocean currents in the equatorial Pacific. Thus, the coupled assimilation of wind data can decrease the initial condition errors in the surface/subsurface currents that can significantly contribute to SST forecast errors. The value of the coupled assimilation of wind observations is further demonstrated by comparing the prediction skills of three 12-year (1997–2008) hindcast experiments initialized by the ocean-only assimilation scheme that assimilates SST observations, the coupled assimilation scheme that assimilates wind observations, and a nudging scheme that nudges the observed wind stress data, respectively. The prediction skills of two assimilation schemes are significantly better than those of the nudging scheme. The prediction skills of assimilating wind observations are better than assimilating SST observations. Assimilating wind observations for the 2007/2008 La Niña event triggers better predictions, while assimilating SST observations fails to provide an early warning for that event.     

Impact of Doppler Radar Wind in Simulating the Intensity and Propagation of Rainbands Associated with Mesoscale Convective Complexes Using MM5-3DVAR System

Pre-monsoon rainfall around Kolkata (northeastern part of India) is mostly of convective origin as 80% of the seasonal rainfall is produced by Mesoscale Convective Systems (MCS). Accurate prediction of the intensity and structure of these convective cloud clusters becomes challenging, mostly because the convective clouds within these clusters are short lived and the inaccuracy in the models initial state to represent the mesoscale details of the true atmospheric state. Besides the role in observing the internal structure of the precipitating systems, Doppler Weather Radar (DWR) provides an important data source for mesoscale and microscale weather analysis and forecasting. An attempt has been made to initialize the storm-scale numerical model using retrieved wind fields from single Doppler radar. In the present study, Doppler wind velocities from the Kolkata Doppler weather radar are assimilated into a mesoscale model, MM5 model using the three-dimensional variational data assimilation (3DVAR) system for the prediction of intense convective events that occurred during 0600 UTC on 5 May and 0000 UTC on 7 May, 2005. In order to evaluate the impact of the DWR wind data in simulating these severe storms, three experiments were carried out. The results show that assimilation of Doppler radar wind data has a positive impact on the prediction of intensity, organization and propagation of rain bands associated with these mesoscale convective systems. The assimilation system has to be modified further to incorporate the radar reflectivity data so that simulation of the microphysical and thermodynamic structure of these convective storms can be improved.     

The effect of density stratification on the prediction of global storm surges

With the long-term goal of developing an operational forecast system for total water level, we conduct a hindcast study of global storm surges for Fall 2014 using a baroclinic ocean model based on the NEMO framework. The model has 19 vertical levels, a horizontal resolution of 1/12°, and is forced by hourly forecasts of atmospheric wind and air pressure. Our first objective is to evaluate the model’s ability to predict hourly sea levels recorded by a global array of 257 tide gauges. It is shown that the model can provide reasonable predictions of surges for the whole test period at tide gauges with relatively large tidal residuals (i.e., gauges where the standard deviation of observed sea level, after removal of the tide, exceeds 5 cm). Our second objective is to quantify the effect of density stratification on the prediction of global surges. It is found that the inclusion of density stratification increases the overall predictive skill at almost all tide gauges. The increase in skill for the instantaneous peak surge is smaller. The location for which the increase in overall skill is largest (east coast of South Africa) is discussed in detail and physical reasons for the improvement are given.     

Predictability of storm wave heights in the ice-free Beaufort Sea

A predictability study on wave forecast of the Arctic Ocean is necessary to help identify hazardous areas and ensure sustainable shipping along the trans-Arctic routes. To assist with validation of the Arctic Ocean wave model, two drifting wave buoys were deployed off Point Barrow, Alaska for two months in September 2016. Both buoys measured significant wave heights exceeding 4 m during two different storm events on 19 September and 22 October. The NOAA-WAVEWATCH III^? model with 16-km resolution was forced using wind and sea ice reanalysis data and obtained general agreement with the observation. The September storm was reproduced well; however, model accuracy deteriorated in October with a negative wave height bias of around 1 m during the October storm. Utilising reanalysis data, including the most up-to-date ERA5, this study investigated the cause: grid resolution, wind and ice forcing, and in situ sea level pressure observations assimilated for reanalysis. The analysis has found that there is a 20% reduction of in situ SLP observations in the area of interest, presumably due to fewer ships and deployment options during the sea ice advance period. The 63-member atmospheric ensemble reanalysis, ALERA2, has shown that this led to a larger ensemble spread in the October monthly mean wind field compared to September. Since atmospheric physics is complex during sea ice advance, it is speculated that the elevated uncertainty of synoptic-scale wind caused the negative wave model bias. This has implications for wave hindcasts and forecasts in the Arctic Ocean.     

On the contributions of atmospheric pressure and wind to daily sea level in the northern Adriatic Sea

Daily sea level variability in the Adriatic Sea is studied from different data sets using Empirical Orthogonal Functions, in connection with atmospheric pressure and wind stress. The first mode explains 56–69% of total variance and consists of uniform sea level variability all over the basin, correlated with atmospheric pressure through the inverse barometer effect. The second mode explains 13–16% of variance and accounts for an along-basin sea level gradient, which is correlated with the meridional wind stress component. The first two Principal Components are used as proxies to pressure- and wind-induced components of storm surges in the northern Adriatic. The analysis of the frequency of the most intense events in the 1957–2005 period shows that the wind contribution to storm surges has decreased, while no significant trends are found in the contribution of atmospheric pressure.     

The Prestige crisis: operational oceanography applied to oil recovery, by the Basque fishing fleet

On 19th November 2002, the oil tanker Prestige (containing 77,000 tonnes of heavy fuel no. 2 (M100)) sank in 3500 m of water, off the coast of northwestern Spain. Intermittent discharge of oil from the stricken tanker, combined with large-scale sea surface dispersion, created a tracking and recovery problem. Initially, conventional oil recovery approaches were adopted, close to the wreck. With time and distance from the source, the oil dispersed dramatically and became less viscous. Consequently, a unique monitoring, prediction and data dissemination system was established, based upon the principles of 'operational oceanography'; this utilised in situ tracked buoys and numerical (spill trajectory) modelling outputs, in combination with remote sensing (satellite sensors and visual observation). Overall, wind effects on the surface waters were found to be the most important mechanism controlling the smaller oil slick movements. The recovery operation involved up to 180 fishing boats, 9-30 m in length. Such labour-intensive recovery of the oil (21,000 tonnes, representing an unprecedented ratio of 6.6 tonnes at sea, per tonne recovered on land) continued over a 10 month period. The overall recovery at sea, by the fishing vessels, represented 63% of the total oil recovered at sea; this compares to only 37% recovered by specialised 'counter- pollution' vessels.     

Difference between the Lagrangian trajectories and Eulerian residual velocity fields in the southwestern Yellow Sea

The responses to tidal and/or wind forces of Lagrangian trajectories and Eulerian residual velocity in the southwestern Yellow Sea are investigated using a high-resolution circulation model. The simulated tidal harmonic constants agree well with observations and existing studies. The numerical experiment reproduces the long-range southeastward Eulerian residual current over the sloping bottom around the Yangtze Bank also shown in previous studies. However, the modeled drifters deployed at the northeastern flank of the Yangtze Bank in the simulation move northeastward, crossing over this strong southeastward Eulerian residual current rather than following it. Additional sensitivity experiments reveal that the influence of the Eulerian tidal residual currents on Lagrangian trajectories is relatively weaker than that of the wind driven currents. This result is consistent with the northeastward movement of ARGOS surface drifters actually released in the southwestern Yellow Sea. Further experiments suggest that the quadratic nature of the bottom friction is the crucial factor, in the southwestern Yellow Sea, for the weaker influence of the Eulerian tidal residual currents on the Lagrangian trajectories. This study demonstrates that the Lagrangian trajectories do not follow the Eulerian residual velocity fields in the shallow coastal regions of the southwestern Yellow Sea.     

The comparison of ensemble or deterministic dispersion modeling on global dispersion during Fukushima Dai-ichi nuclear accident

Ensemble forcasting,originally developed for weather prediction,is lately being extended to atmospheric dispersion applications,which is a new,effective methodology for improving the atmospheric dispersion numerical modeling.In March 2011,due to the massive 9.0 earthquakes and ensuing tsunami that struck off the northern coast of the island of Honshu,the Fukushima Nuclear Plant I had the substantial leak of radioactive materials into surrounding environment and atmosphere.To aim at the global dispersion modeling of atmospheric radionuclides from Fukushima Nuclear Accident,this paper presents two approaches of atmospheric dispersion forecasting:ensemble dispersion modeling(EDM) and deterministic dispersion modeling(DDM),conducts the globally dispersion modeling cases for Fukushima nuclear accident,and analyzes and evaluates the simulation results using observation data.In this paper,EDM includes three different perturbation methods:meteorological perturbation method,turbulence perturbation method,and physical parameterization ensemble forecasting method.The simulation results show that the trajectories from EDM have a better performance,which is in better agreement with the atmospheric circulation and observation data; the spread from DDM is slower and not as far as EDM.Additionally,the results from EDM display a better performance in the modeling of transport from Japan to China East Sea on April 4.The reasons for these results are:the techniques of MET and TUR are performed by adding perturbations on mean wind and turbulent velocity,respectively; the various different flow fields will result in far spreading in horizontal and the simulation results closer to observation; PHY is performed by using different diffusion physical parameterizations and produces the perturbations on vertical wind,which results the spreading in smaller range and discontinuous in horizontal.Finally,the comparative analysis between modeling results and observation data shows that all cases results are in good agreement with trends of observed radionuclides surface concentration; however,the modeling surface concentration is smaller than observation,especially in DDM and PHY.Furthermore,the EDM results show that MET and TUR are of more evolutionary advantage than PHY in modeling of average and maximum concentration.Therefore,this study can serve as a reference to atmospheric dispersion and environmental emergency response(EER).