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.     

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.     

Field campaign for the comparison of SOUSY radar wind measurements with rawinsonde and model data

A field campaign was carried out from 26 October to 7 November 1992, using the SOUSY-VHF radar and a mobile rawinsonde system installed and operated nearby to produce vertical wind profiles. The purpose of this campaign was to compare the two types of wind measurements with one another and with results from forecast models. Numerical algorithms were developed and applied to the radar data in order to eliminate random errors, correct for velocity aliasing, and calculate the effective zenith angle of the off-vertical beams. Differences between wind profiler data and rawinsonde or model results depend not only upon the errors of the different systems, but also on temporal and spatial variations of the wind field. Therefore, methods for the comparison of radar and rawinsonde data were developed which take into consideration these variations. The practical potential of these methods is demonstrated by comparisons of rawinsonde and radar wind profiles. The comparison of radar data and model output shows excellent agreement in the direction and in the speed of the wind at virtually all altitudes. An evaluation of the quality of wind profiler measurements is possible using the estimation of variance and variability of wind components.     

Improved statistical prediction of surface currents based on historic HF-radar observations

Accurate short-term prediction of surface currents can improve the efficiency of search-and-rescue operations, oil-spill response, and marine operations. We developed a linear statistical model for predicting surface currents (up to 48?h in the future) based on a short time history of past HF-radar observations (past 48?h) and an optional forecast of surface winds. Our model used empirical orthogonal functions (EOFs) to capture spatial correlations in the HF-radar data and used a linear autoregression model to predict the temporal dynamics of the EOF coefficients. We tested the developed statistical model using historical observations of surface currents in Monterey Bay, California. The predicted particle trajectories separated from particles advected with HF-radar data at a rate of 4.4?km/day. The developed model was more accurate than an existing statistical model (drifter separation of 5.5?km/day) and a circulation model (drifter separation of 8.9?km/day). When the wind forecast was not available, the accuracy of our model degraded slightly (drifter separation of 4.9?km/day), but was still better than existing models. We found that the minimal length of the HF-radar data required to train an accurate statistical model was between 1 and 2?years, depending on the accuracy desired. Our evaluation showed that the developed model is accurate, is easier to implement and maintain than existing statistical and circulation models, and can be relocated to other coastal systems of similar complexity that have a sufficient history of HF-radar observations.     

A high-resolution operational forecast system for oil spill response in Belfast Lough

This paper presents a high-resolution operational forecast system for providing support to oil spill response in Belfast Lough. The system comprises an operational oceanographic module coupled to an oil spill forecast module that is integrated in a user-friendly web application. The oceanographic module is based on Delft3D model which uses daily boundary conditions and meteorological forcing obtained from COPERNICUS and from the UK Meteorological Office. Downscaled currents and meteorological forecasts are used to provide short-term oil spill fate and trajectory predictions at local scales. Both components of the system are calibrated and validated with observational data, including ADCP data, sea level, temperature and salinity measurements and drifting buoys released in the study area. The transport model is calibrated using a novel methodology to obtain the model coefficients that optimize the numerical simulations. The results obtained show the good performance of the system and its capability for oil spill forecast.     

Assimilation of Doppler Weather Radar Data in WRF Model for Simulation of Tropical Cyclone Aila

For the accurate and effective forecasting of a cyclone, it is critical to have accurate initial structure of the cyclone in numerical models. In this study, Kolkata Doppler weather radar (DWR) data were assimilated for the numerical simulation of a land-falling Tropical Cyclone Aila (2009) in the Bay of Bengal. To study the impact of radar data on very short-range forecasting of a cyclone's path, intensity and precipitation, both reflectivity and radial velocity were assimilated into the weather research and forecasting (WRF) model through the ARPS data assimilation system (ADAS) and cloud analysis procedure. Numerical experiment results indicated that radar data assimilation significantly improved the simulated structure of Cyclone Aila. Strong influences on hydrometeor structures of the initial vortex and precipitation pattern were observed when radar reflectivity data was assimilated, but a relatively small impact was observed on the wind fields at all height levels. The assimilation of radar wind data significantly improved the prediction of divergence/convergence conditions over the cyclone's inner-core area, as well as its wind field in the low-to-middle troposphere (600–900 hPa), but relatively less impact was observed on analyzed moisture field. Maximum surface wind speed produced from DWR–Vr and DWR–ZVr data assimilation experiments were very close to real-time values. The impact of radar data, after final analysis, on minimum sea level pressure was relatively less because the ADAS system does not adjust for pressure due to the lack of pressure observations, and from not using a 3DVAR balance condition that includes pressure. The greatest impact of radar data on forecasting was realized when both reflectivity and wind data (DWR–ZVr and DWR–ZVr00 experiment) were assimilated. It is concluded that after final analysis, the center of the cyclone was relocated very close to the observed position, and simulated cyclone maintained its intensity for a longer duration. Using this analysis, different stages of the cyclone are better captured, and cyclone structure, intensification, direction of movement, speed and location are significantly improved when both radar reflectivity and wind data are assimilated. As compared to other experiments, the maximum reduction in track error was noticed in the DWR–ZVr and DWR–ZVr00 experiments, and the predicted track in these experiments was very close to the observed track. In the DWR–ZVr and DWR–ZVr00 experiments, rainfall pattern and amount of rainfall forecasts were remarkably improved and were similar to the observation over West Bengal, Orissa and Jharkhand; however, the rainfall over Meghalaya and Bangladesh was missed in all the experiments. The influence of radar data reduces beyond a 12-h forecast, due to the dominance of the flow from large-scale, global forecast system models. This study also demonstrates successful coupling of the data assimilation package ADAS with the WRF model for Indian DWR data.     

Surface currents and winds at the Delaware Bay mouth

Knowledge of the circulation of estuaries and adjacent shelf waters has relied on hydrographic measurements, moorings, and local wind observations usually removed from the region of interest. Although these observations are certainly sufficient to identify major characteristics, they lack both spatial resolution and temporal coverage. High-resolution synoptic observations are required to identify important coastal processes at smaller scales. Long observation periods are needed to properly sample low-frequency processes that may also be important. The introduction of high-frequency (HF) radar measurements and regional wind models for coastal studies is changing this situation. Here we analyze synoptic, high-resolution surface winds and currents in the Delaware Bay mouth over an 8-month period (October 2007 through May 2008). The surface currents were measured by two HF radars while the surface winds were extracted from a data-assimilating regional wind model. To illustrate the utility of these monitoring tools we focus on two 45-day periods which previously were shown to present contrasting pictures of the circulation. One, the low-outflow period is from 1 October through 14 November 2007; the other is the high-outflow period from 3 March through 16 April 2008. The large-scale characteristics noted by previous workers are clearly corroborated. Specifically the M2 tide dominates the surface currents, and the Delaware Bay outflow plume is clearly evident in the low frequency currents. Several new aspects of the surface circulation were also identified. These include a map of the spatial variability of the M2 tide (validating an earlier model study), persistent low-frequency cross-mouth flow, and a rapid response of the surface currents to a changing wind field. However, strong wind episodes did not persist long enough to set up a sustained Ekman response.     

SAR observation and model tracking of an oil spill event in coastal waters

Oil spills are a major contributor to marine pollution. The objective of this work is to simulate the oil spill trajectory of oil released from a pipeline leaking in the Gulf of Mexico with the GNOME (General NOAA Operational Modeling Environment) model. The model was developed by NOAA (National Oceanic and Atmospheric Administration) to investigate the effects of different pollutants and environmental conditions on trajectory results. Also, a Texture-Classifying Neural Network Algorithm (TCNNA) was used to delineate ocean oil slicks from synthetic aperture radar (SAR) observations. During the simulation, ocean currents from NCOM (Navy Coastal Ocean Model) outputs and surface wind data measured by an NDBC (National Data Buoy Center) buoy are used to drive the GNOME model. The results show good agreement between the simulated trajectory of the oil spill and synchronous observations from the European ENVISAT ASAR (Advanced Synthetic Aperture Radar) and the Japanese ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array L-band Synthetic Aperture Radar) images. Based on experience with past marine oil spills, about 63.0% of the oil will float and 18.5% of the oil will evaporate and disperse. In addition, the effects from uncertainty of ocean currents and the diffusion coefficient on the trajectory results are also studied.     

北京MST雷达探测中间层-低热层观测结果初步分析

北京MST雷达是子午工程建设的国内仅有的两部MST雷达之一,为研究其在中间层-低热层MLT区域的探测能力以及数据可靠性,本文应用北京MST雷达2012、2013两年高模式数据,从数据获取率、与廊坊流星雷达测风对比以及风场时空分布特征三个方面进行初步分析.结果是:(1)数据获取率日变化特征为:白天65~100km均可获取数据,数据获取率的高值区主要集中在70~80km,最大值可达80%;夜间主要集中在80~100km,数据获取率在30%及以下.表明该MST雷达白天可以探测到电离层D层和E层低层,夜间D层消失,只探测到E层低层.季节变化特征为:夏季白天可获取数据的时间和高度区间都比较大,春季次之,冬季最小.夏季白天以及日落后1h内可探测到120km.(2)对北京MST雷达与廊坊流星雷达2012年5月份、80~100km高度区间测量的水平风进行对比分析,二者测风结果在时空分布上有很好的一致性,表明MST雷达探测数据是可靠的.(3)2012年和2013年相应月份平均的纬向风、经向风时空分布特征有较高的一致性,并与HWM07模式结果也基本一致.上述初步分析结果表明,北京MST雷达对中间层-低热层60~120km高度区域已具备较强的探测能力,所得结果将可用于MLT过程揭示与驱动因子研究,并可与该高度上其他探测手段作综合研究.     

Assimilation of Doppler Weather Radar Radial Velocity and Reflectivity Observations in WRF-3DVAR System for Short-Range Forecasting of Convective Storms

In this paper the impact of Doppler weather radar (DWR) reflectivity and radial velocity observations for the short range forecasting of a tropical storm and associated rainfall event have been examined. Doppler radar observations of a tropical storm case that occurred during 29–30 October 2006 from SHARDWR (13.6° N, 80.2° E) are assimilated in the WRF 3DVAR system. The observation operator for radar reflectivity and radial velocity is included within latest version of WRF 3DVAR system. Keeping all model physics the same, three experiments were conducted at a horizontal resolution of 30?km. In the control experiment (CTRL), NCEP Final Analysis (FNL) interpolated to the model grid was used as the initial condition for 48-h free forecast. In the second experiment (NODWR), 6-h assimilation cycles have been carried out using all conventional (radiosonde and surface data) and non-conventional (satellite) observations from the Global Telecommunication System (GTS). The third experiment (DWR) is the same as the second, except Doppler radar radial velocity and reflectivity observations are also used in the assimilation cycle. Continuous 6-h assimilation cycle employed in the WRF-3DVAR system shows positive impact on the rainfall forecast. Assimilation of DWR data creates several small scale features near the storm centre. Additional sensitivity experiments were conducted to study the individual impact of reflectivity and radial velocity in the assimilation cycle. Radar data assimilation with reflectivity alone produced large analysis response on both thermodynamical and dynamical fields. However, radial velocity assimilation impacted only on dynamical fields. Analysis increments with radar reflectivity and radial velocity produce adjustments in both dynamical and thermodynamical fields. Verification of QPF skill shows that radar data assimilation has a considerable impact on the short range precipitation forecast. Improvement of the QPF skill with radar data assimilation is more clearly seen in the heavy rainfall (for thresholds >7?mm) event than light rainfall (for thresholds of 1 and 3?mm). The spatial pattern of rainfall is well simulated by the DWR experiment and is comparable to TRMM observations.     

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.     

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.     

Correction of inertial oscillations by assimilation of HF radar data in a model of the Ligurian Sea

This article aims at analyzing if high-frequency radar observations of surface currents allow to improve model forecasts in the Ligurian Sea, where inertial oscillations are a dominant feature. An ensemble of ROMS models covering the Ligurian Sea, and nested in the Mediterranean Forecasting System, is coupled with two WERA high-frequency radars. A sensitivity study allows to determine optimal parameters for the ensemble filter. By assimilating observations in a single point, the obtained correction shows that the forecast error covariance matrix represents the inertial oscillations, as well as large- and meso-scale processes. Furthermore, it is shown that the velocity observations can correct the phase and amplitude of the inertial oscillations. Observations are shown to have a strong effect during approximately half a day, which confirms the importance of using a high temporal observation frequency. In general, data assimilation of HF radar observations leads to a skill score of about 30% for the forecasts of surface velocity.     

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.     

Mapping radar-derived sea surface currents with a variational method

High-frequency radars measure projections of surface velocity vectors on the directions of the radar beams. A variational method for reconstruction of the 2d velocity field from such observations is proposed. The interpolation problem is regularized by penalizing high-frequency variability of the surface vorticity and divergence fields. Twin-data experiments are used to assess the method's skill and compare it with two well-known approaches to HFR data processing: conventional local interpolation and more sophisticated non-local scheme known as open-boundary modal analysis (OMA). It is shown that the variational method and OMA have a significant advantage over local interpolation because of their ability to reconstruct the velocity field within the gaps in data coverage, near the coastlines and in the areas covered only by one radar. Compared to OMA, the proposed variational method appears to be more flexible in processing gappy observations and more accurate at noise levels below 30%.     

Numerical reconstruction of trajectory of small-size surface drifter in the Mediterranean sea

In this study, we addressed the effects of wind-induced drift on Lagrangian trajectories of surface sea objects using high-resolution ocean forecast and atmospheric data. Application of stochastic Leeway model for prediction of trajectories drift was considered for the numerical reconstruction of the Elba accident that occurred during the period 21.06.2009–22.06.2009: a person on an inflatable raft was lost in the vicinity of the Elba Island coast; from the initial position, the person on a raft drifted southwards in the open sea and later he was found on a partially deflated raft during rescue operation. For geophysical forcing, we used high-resolution currents from the Mediterranean Forecasting System and atmospheric wind from the European Centre for Medium-Range Weather Forecasts. To investigate the effect of wind on trajectory behavior, numerical simulations were performed using different categories of drifter-like particles similar to a person on an inflatable raft. An algorithm of spatial clustering was used to differentiate the most probable search areas with a high density of particles. Our results showed that the simulation scenarios using particles with characteristics of draft-limited sea drifters provided better prediction of an observed trajectory in terms of the probability density of particles.     

Wind-speed inversion from HF radar first-order backscatter signal

Land-based high-frequency (HF) radars have the unique capability of continuously monitoring ocean surface environments at ranges up to 200 km off the coast. They provide reliable data on ocean surface currents and under slightly stricter conditions can also give information on ocean waves. Although extraction of wind direction is possible, estimation of wind speed poses a challenge. Existing methods estimate wind speed indirectly from the radar derived ocean wave spectrum, which is estimated from the second-order sidebands of the radar Doppler spectrum. The latter is extracted at shorter ranges compared with the first-order signal, thus limiting the method to short distances. Given this limitation, we explore the possibility of deriving wind speed from radar first-order backscatter signal. Two new methods are developed and presented that explore the relationship between wind speed and wave generation at the Bragg frequency matching that of the radar. One of the methods utilizes the absolute energy level of the radar first-order peaks while the second method uses the directional spreading of the wind generated waves at the Bragg frequency. For both methods, artificial neural network analysis is performed to derive the interdependence of the relevant parameters with wind speed. The first method is suitable for application only at single locations where in situ data are available and the network has been trained for while the second method can also be used outside of the training location on any point within the radar coverage area. Both methods require two or more radar sites and information on the radio beam direction. The methods are verified with data collected in Fedje, Norway, and the Ligurian Sea, Italy using beam forming HF WEllen RAdar (WERA) systems operated at 27.68 and 12.5 MHz, respectively. The results show that application of either method requires wind speeds above a minimum value (lower limit). This limit is radar frequency dependent and is 2.5 and 4.0 m/s for 27.68 and 12.5 MHz, respectively. In addition, an upper limit is identified which is caused by wave energy saturation at the Bragg wave frequency. Estimation of this limit took place through an evaluation of a year long database of ocean spectra generated by a numerical model (third generation WAM). It was found to be at 9.0 and 11.0 m/s for 27.68 and 12.5 MHz, respectively. Above this saturation limit, conventional second-order methods have to be applied, which at this range of wind speed no longer suffer from low signal-to-noise ratios. For use in operational systems, a hybrid of first- and second-order methods is recommended.     

Tidal and wind-induced circulation within the Southeastern limit of the Bay of Biscay: Pasaia Bay,Basque Coast

The Basque coastal area, in the southeastern Bay of Biscay, can be characterised as being more influenced by land climate and inputs, than other typically ‘open sea’ areas. The influence of coastal processes, together with the presence of irregular and steep topography, complicate greatly the water circulation patterns. Water movement along the Basque coastal area is not well understood; observations are scarce and long-term current records are lacking. The knowledge available is confined to the surface currents: the surface water circulation is controlled mainly by wind forcing, with tidal and density currents being weak. However, there is a lack of knowledge available on currents within the lower levels of the water column; likewise, on the main time-scales involved in the water circulation. This study quantifies the contribution of the tidal and wind-induced currents, to the overall water circulation; it identifies the main time-scales involved within the tidal and wind-induced flows, investigating difference in such currents, throughout the water column, within Pasaia Bay (Basque coast). Within this context, extensive oceanographic and meteorological data have been obtained, in order to describe the circulation. The present investigation reveals that the circulation, within the surface and the sub-surface waters, is controlled mainly by wind forcing fluctuations, over a wide range of meteorological frequencies: third-diurnal, semidiurnal and diurnal land–sea breezes; synoptic variability; frequencies, near fortnightly periods; and seasonal. At the lower levels of the water column, the main contribution to the water circulation arises from residual currents, followed by wind-induced currents on synoptic time-scales. In contrast, tidal currents contribute minimally to the overall circulation throughout the water column.     

Turbulent behaviour within a coastal boundary layer,observations and modelling at the Isola del Giglio

The hydrodynamics of coastal areas is characterized by the interaction among phenomena occurring at different spatial and temporal scales, such as the interaction of a large-scale ocean current with the local bathymetry and coastline, and local forcing conditions. In order to take into account all relevant phenomena, the study of the hydrodynamics of coastal zones requires a high-spatial and temporal resolution for both observations and simulation of local currents. This resolution can be obtained by using X-band radar, which allows simultaneous measurement of waves and currents in a range of 1–3 miles from the coastline, as well as high-resolution numerical models implemented in the area and configured through multiple nesting techniques in order to reach resolutions comparable to such coastal observations. Such an integrated monitoring system was implemented at the Isola del Giglio in 2012, after the accident of the Costa Concordia ship. Results can be used as a cross-validation of data produced independently by radar observations and numerical models. In addition, results give some important insights on the dynamics of the coastal boundary layer, both for what concerns the attenuation in the profile of the depth-averaged velocities which typically occur in turbulent boundary layers, as well as for the production, detachment and evolution of vorticity produced by the interaction of large-scale ocean currents with the coastline and the subsequent time evolution of such boundary layer. This transition between large-scale regional currents and the coastal boundary layer is often neglected in regional forecasting systems, but it has an important role in the ocean turbulence processes.     

Prediction and predictability of a catastrophic local extreme precipitation event through cloud-resolving ensemble analysis and forecasting with Doppler radar observations

Local extreme rain usually resulted in disasters such as flash floods and landslides. Upon today, it is still one of the most difficult tasks for operational weather forecast centers to predict those events accurately. In this paper, we simulate an extreme precipitation event with ensemble Kalman filter (EnKF) assimilation of Doppler radial-velocity observations, and analyze the uncertainties of the assimilation. The results demonstrate that, without assimilation radar data, neither a single initialization of deterministic forecast nor an ensemble forecast with adding perturbations or multiple physical parameterizations can predict the location of strong precipitation. However, forecast was significantly improved with assimilation of radar data, especially the location of the precipitation. The direct cause of the improvement is the buildup of a deep mesoscale convection system with EnKF assimilation of radar data. Under a large scale background favorable for mesoscale convection, efficient perturbations of upstream mid-low level meridional wind and moisture are key factors for the assimilation and forecast. Uncertainty still exists for the forecast of this case due to its limited predictability. Both the difference of large scale initial fields and the difference of analysis obtained from EnKF assimilation due to small amplitude of initial perturbations could have critical influences to the event's prediction. Forecast could be improved through more cycles of EnKF assimilation. Sensitivity tests also support that more accurate forecasts are expected through improving numerical models and observations.