Forecast verification of a 3D model of the Mediterranean Sea. The use of discrete wavelet transforms and EOFs in the skill assessment of spatial forecasts

The quality assessment of a nested model system of the Mediterranean Sea is realised. The model has two zooms in the Provençal Basin and in the Ligurian Sea, realised with a two-way nesting approach. The experiment lasts for nine weeks, and at each week sea surface temperature (SST) and sea level anomaly are assimilated. The quality assessment of the surface temperature is done in a spatio-temporal approach, to take into account the high complexity of the SST distribution. We focus on the multi-scale nature of oceanic processes using two powerful tools for spatio-temporal analysis, wavelets and Empirical Orthogonal Functions (EOFs). We apply two-dimensional wavelets to decompose the high-resolution model and observed SST into different spatial scales. The Ligurian Sea model results are compared to observations at each of those spatial scales, with special attention on how the assimilation affects the model behaviour. We also use EOFs to assess the similarities between the Mediterranean Sea model and the observed SST. The results show that the assimilation mainly affects the model large-scale features, whereas the small scales show little or no improvement and sometimes, even a decrease in their skill. The multiresolution analysis reveals the connection between large- and small-scale errors, and how the choice of the maximum correlation length of the assimilation scheme affects the distribution of the model error among the different spatial scales.     

Multigrid state vector for data assimilation in a two-way nested model of the Ligurian Sea

A system of two nested models composed by a coarse resolution model of the Mediterranean Sea, an intermediate resolution model of the Provençal Basin and a high resolution model of the Ligurian Sea is coupled with a Kalman-filter based assimilation method. The state vector for the data assimilation is composed by the temperature, salinity and elevation of the three models. The forecast error is estimated by an ensemble run of 200 members by perturbing initial condition and atmospheric forcings. The 50 dominant empirical orthogonal functions (EOF) are taken as the error covariance of the model forecast. This error covariance is assumed to be constant in time. Sea surface temperature (SST) and sea surface height (SSH) are assimilated in this system.     

Assessment of sea surface temperature observational networks in the Baltic Sea and North Sea

The satellite and in situ Sea Surface Temperature (SST) observational networks in the Baltic Sea and North Sea are evaluated based on the quality of the gridded SST products generated from the networks. A multi-indicator approach is applied in the assessment. It includes evaluation of data quality, effective data coverage, field reconstruction error and model nowcast error. The results show that the best available full-coverage SST product is generated by assimilating the SST observations to obtain a yearly mean model bias of 0.07 °C and RMSE of 0.64 °C. The effective data coverage rate is 31% by using AVHRR (Advanced Very High Resolution Radiometer) data from NOAA (National Ocean and Atmosphere Administration) satellites 12, 14 and 16. The data redundancy increases rapidly with the number of infrared sensors. Using either NOAA satellite 12 or all 3 satellites makes a small difference with regard to derived effective coverage and the ocean model nowcast error. The influence of using the in situ SST observations in the SST field reconstruction is negligibly small. Instead, the major role of in situ SST observations is in calibrating the satellite observations. To study the relative importance of data quality and data coverage, an assessment is done for two satellite products: one product is based entirely on NOAA 12 data and has larger coverage but lower quality. The other product is a subset of the SAF products (derived from NOAA 14 and 16) and has lower coverage but higher quality. Based on current monitoring, modelling and assimilation technology, the results suggest that the data quality is an important factor in further improving the quality of the gridded SST products. Recommendations are made for possible further improvements of the existing SST observational networks.     

Preoperational ocean forecasting in the southeastern Mediterranean Sea: Implementation and evaluation of the models and selection of the atmospheric forcing

Within the framework of several local and international programs, a quasi-operational ocean-forecasting system for the Southeastern Mediterranean Sea has been established and evaluated through a series of preoperational tests. The Princeton Ocean Model (POM) is used for simulating and predicting the hydrodynamics while the Wave Model (WAM) is used for predicting surface waves. Both models were set up to allow varying resolution and multiple nesting. In addition, POM was set up to be easily relocatable to allow rapid deployment of the model for any region of interest within the Mediterranean Sea. A common requirement for both models is the need for atmospheric forcing. Both models require time varying wind or wind stress. In addition, the hydrodynamic model requires initial conditions as well as time dependent surface heat fluxes, fresh water flux, and lateral boundary conditions at the open boundaries. Several sources of atmospheric forcing have been assessed based on their availability and their impact on the quality of the ocean models' forecasts. The various sources include operational forecast centers, other research centers, as well as running an in-house regional atmospheric model. For surface waves, higher spatial and temporal resolution of the winds plays a central role in improving the forecasts in terms of significant wave height and the timing of various high wave events. For the hydrodynamics, using the predicted wind stress and heat fluxes directly from an atmospheric model can potentially produce short range ocean forecasts that are nearly as good as hindcasts forced with gridded atmospheric analyses. Finally, a high-resolution, nested version of the model has shown to be stable under a variety of forcing conditions and time scales, thus indicating the robustness of the selected nesting strategy. For the southeastern corner of the Mediterranean, at forecast lead times of up to 4 days the high-resolution model shows improved skill over the coarser resolution driving model when compared to satellite derived sea surface temperatures. Most of the error appears to be due to the analysis error inherent in the initial conditions.     

Validation of a hybrid optimal interpolation and Kalman filter scheme for sea surface temperature assimilation

A hybrid data assimilation scheme designed for operational assimilation of satellite sea surface temperatures (SST) into an ocean model has been developed and validated against in-situ observations. The scheme consists of an optimal interpolation (OI) part and a greatly simplified Kalman filter (KF) part.The OI is performed only in the longitudinal and latitudinal directions. A climatological field is used as a background field for the interpolation. It is constructed by fitting daily averages of satellite SST to the annual mean, annual, and semiannual harmonics in a 20 km by 20 km grid. The background error covariance is approximated by a spatially varying two-dimensional exponential covariance model. The parameters of the covariance model are fitted to the deviations of the satellite data from the background field using data from a full year.The simplified KF uses ocean model forecasts as a background field. It is based on the assumption that it is possible to neglect horizontal SST covariances in the filter and that the typical time scale for vertical mixing in the mixed layer is much shorter than the average time between observations. We therefore assume that the error variance in a column of water is evenly spread out throughout the mixed layer. The result of these simplifications is a computationally very efficient KF.A one year validation of the scheme is performed for year 2001 using an operational eddy resolving ocean model covering the North Sea and the Baltic Sea. It is found that assimilation of sea surface temperature data reduces the model root mean square error from 1.13 °C to 0.70 °C. The hybrid scheme is found to reduce the root mean square error slightly more than the simplified KF without OI to 0.66 °C. The inclusion of spatially varying satellite error variances does not improve the performance of the scheme significantly.     

A study of the Alboran sea mesoscale system by means of empirical orthogonal function decomposition of satellite data

This paper presents the results of a combined empirical orthogonal function (EOF) analysis of Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature (SST) data and sea-viewing wide field-of-view sensor (SeaWiFS) chlorophyll concentration data over the Alboran Sea (Western Mediterranean), covering a period of 1 year (November 1997–October 1998). The aim of this study is to go beyond the limited temporal extent of available in situ measurements by inferring the temporal and spatial variability of the Alboran Gyre system from long temporal series of satellite observations, in order to gain insight on the interactions between the circulation and the biological activity in the system. In this context, EOF decomposition permits concise and synoptic representation of the effects of physical and biological phenomena traced by SST and chlorophyll concentration. Thus, it is possible to focus the analysis on the most significant phenomena and to understand better the complex interactions between physics and biology at the mesoscale. The results of the EOF analysis of AVHRR-SST and SeaWiFS-chlorophyll concentration data are presented and discussed in detail. These improve and complement the knowledge acquired during the in situ observational campaigns of the MAST-III Observations and Modelling of Eddy scale Geostrophic and Ageostrophic motion (OMEGA) Project.     

1/32° real-time global ocean prediction and value-added over 1/16° resolution

A 1/32° global ocean nowcast/forecast system has been developed by the Naval Research Laboratory at the Stennis Space Center. It started running at the Naval Oceanographic Office in near real-time on 1 Nov. 2003 and has been running daily in real-time since 1 Mar. 2005. It became an operational system on 6 March 2006, replacing the existing 1/16° system which ceased operation on 12 March 2006. Both systems use the NRL Layered Ocean Model (NLOM) with assimilation of sea surface height from satellite altimeters and sea surface temperature from multi-channel satellite infrared radiometers. Real-time and archived results are available online at http://www.ocean.nrlssc.navy.mil/global_nlom. The 1/32° system has improvements over the earlier system that can be grouped into two categories: (1) better resolution and representation of dynamical processes and (2) design modifications. The design modifications are the result of accrued knowledge since the development of the earlier 1/16° system. The improved horizontal resolution of the 1/32° system has significant dynamical benefits which increase the ability of the model to accurately nowcast and skillfully forecast. At the finer resolution, current pathways and their transports become more accurate, the sea surface height (SSH) variability increases and becomes more realistic and even the global ocean circulation experiences some changes (including inter-basin exchange). These improvements make the 1/32° system a better dynamical interpolator of assimilated satellite altimeter track data, using a one-day model forecast as the first guess. The result is quantitatively more accurate nowcasts, as is illustrated by several model-data comparisons. Based on comparisons with ocean color imagery in the northwestern Arabian Sea and the Gulf of Oman, the 1/32° system has even demonstrated the ability to map small eddies, 25–75 km in diameter, with 70% reliability and a median eddy center location error of 22.5 km, a surprising and unanticipated result from assimilation of altimeter track data. For all of the eddies (50% small eddies), the reliability was 80% and the median eddy center location error was 29 km. The 1/32° system also exhibits improved forecast skill in relation to the 1/16° system. This is due to (a) a more accurate initial condition for the forecast and (b) better resolution and representation of critical dynamical processes (such as upper ocean – topographic coupling via mesoscale flow instabilities) which allow the model to more accurately evolve these features in time while running in forecast mode (forecast atmospheric forcing for the first 5 days, then gradually reverting toward climatology for the remainder of the 30-day forecast period). At 1/32° resolution, forecast SSH generally compares better with unassimilated observations and the anomaly correlation of the forecast SSH exceeds that from persistence by a larger amount than found in the 1/16° system.     

Assimilation of sea surface temperature in the POL Coastal Ocean Modelling System

A one-dimensional scheme is used to assimilate satellite Sea Surface Temperature data into the Proudman Oceanographic Laboratory Coastal Ocean Modelling System, set up in the Irish Sea with a fine resolution ( 1.8 km). The capabilities of the assimilation scheme are investigated using two different sets of satellite data, of lower and similar resolution to that of the model respectively. Comparison of results with independent data show that assimilation improves the modelled Sea Surface Temperature, but does not address model representation of the temperature vertical structure. It is concluded that for the Irish Sea and at the scales resolved by the model, the assimilation problem cannot be approached in a one-dimensional framework. It is also pointed out that forecast error needs to account explicitly for errors in the representation of the vertical structure of the thermal field.Three-dimensional methods that are suited for coastal systems are then suggested.     

海流要素辅助航行决策的区划技术研究

由于很难得到对任何海区满足需要的时空尺度、精度的海洋环境要素预报结果,因此,了解某海洋环境要素的区域特性也是很有参考价值的.为了给船舶航行提供有效的信息支持,文章以对船舶的航速和航向影响较大的海流为例,分析了海流预报技术在满足应用需求上的不足,提出了利用主因子分析技术对海流样本数据进行研究的方案,并基于此方案给出了东海、南海海域海流要素信息区划结果,为船舶航行决策提供信息指导.     

基于加权最小二乘法的武器装备批量生产成本费用研究

加权最小二乘法考虑了现实情况下普遍存在的异方差性,在经济预测中有着更为广泛的应用。在系统研究我国武器装备批量生产规律和状况的基础上,结合计量经济的相关理论,提出基于加权最小二乘法的费用预测模型,并辅以实例进行分析,结果表明,该方法移植性好,由于消除了异方差性的影响,模型的拟合和预测性能较一般最小二乘法更佳。     

Operational surface drift prediction using linear and non-linear hyper-ensemble statistics on atmospheric and ocean models

Multimodel super-ensemble forecasts, which exploit the power of an optimal local combination of individual models usually show superior forecasting skills when compared to individual models because they allow for local correction and/or bias removal. Deterministic approaches to the problem of surface drift are often limited by strong assumptions on the underlying physics. A new approach based on linear and non-linear optimization is proposed, using hyper-ensemble deduced statistics to forecast at short time scale Lagrangian drifts from combined atmospheric and ocean operational models and local observations that were made available during the MREA04 field experiment along the West coast of Portugal. Optimization methods are based on a training/forecast cycle. The performance and the limitations of the hyper-ensembles and the individual models are discussed. Results suggest that our statistical methods reduce the position errors significantly for 12 to 48 h forecasts and hence compete with pure deterministic approaches.     

海洋环境要素区划技术研究

将中国近海现有海洋环境要素的观测数据有机地融合起来进行区域划分,对实现传统导航信息与海洋环境辅助信息合理地结合,提高船舶航行的安全性有重要的意义。以对船舶航行影响最大的海流要素为例,采用主因子分析的方法将24个量测指标综合成4个主因子,既简化运算又不失真地反映真实海况。通过方差最大正交旋转对荷载矩阵进行处理使其列向量两极分化,以分析影响各个主因子的主要指标。最后,利用4个主因子在各采样点的得分数将中国东海、南海划分为六个区域。此结果弥补了物理海洋学方法对海洋环境要素小尺度预报精度的不足,为船舶航行提供全域背景和信息决策指导。     

Multivariate assimilation in MERCATOR project: New statistical parameters from forecast error estimation

The new operational prototype of Mercator (french Global Ocean Data Assimilation Experiment contribution) is composed of a North Atlantic primitive equation ocean model OPA (Ocean Parallel Algorithm between 20°S and 70°N, [Madec, G., P. Delecluse, M. Imbard and C. Lévy (1998). OPA8.1 ocean general circulation model reference manuel. Notes du pôle de modélisation IPSL. n°11: 91p]) and of a multivariate and multidata assimilation scheme [De Mey, P. and M. Benkiran (2002). “A multivariate reduced-order optimal interpolation method and its application in Mediterranean basin-scale circulation.” Ocean Forecasting : Conceptual basis and application, Pinardi, N., Springer Verlag.] This system has already given some significant improvements from previous Mercator configurations (M. Benkiran, personal communication). However some biases on ocean state still remain in the tropics where the reduced-order optimal interpolation scheme is suspected to be ill-parameted in the model forecast error. Indeed the guess error covariance matrix is decomposed into an error variance value and a spatio-temporal correlation function which are assumed to have some “good” properties (spatial homogeneity of the correlation function, constant ratio between signal and error variance). This study shows how we can use ensemble methods to validate these assumptions. We can see that the correlation function can reach negative values locally, mostly in regions of high variability contradictory with the homogeneous hypothesis. The reduced space used in the operational configuration is based on the signal seasonal Empirical Orthogonal Functions (EOFs). An empirical relationship between signal and error variance has been set and the correlation function is the same on every dimension of the reduced space. By projection of the estimated guess error variance onto the reduced space, we find a repartition of this quantity quite different to what was set in the system. The error statistics is found to be inhomogeneous compared to hypothesis made in the assimilation scheme. These two new parameters tested separately in the assimilation scheme gives significant improvements of the forecast and analysis results. This is particularly obvious in the tropics. But relationship between signal and error statistics (as assumed in the optimal interpolation) is found to be complex.     

Forecasting wind with neural networks

Wind forecasts over a varying period of time are needed for a variety of applications in the coastal and ocean region, like planning of construction and operation-related works as well as prediction of power output from wind turbines located in coastal areas. Such forecasting is currently done by adopting complex atmospheric models or by using statistical time-series analysis. Because occurrence of wind in nature is extremely uncertain no single technique can be entirely satisfactory. This leaves scope for alternative approaches. The present work employs the technique of neural networks in order to forecast daily, weekly as well as monthly wind speeds at two coastal locations in India. Both feed forward as well as recurrent networks are used. They are trained based on past data in an auto-regressive manner using back-propagation and cascade correlation algorithms. A generally satisfactory forecasting as reflected in its higher correlation and lower deviations with actual observations is noted. The neural network forecasting is also found to be more accurate than traditional statistical time-series analysis.     

考古船阻力计算中湍流模型与格式研究

为了研究不同湍流模型在船舶水动力性能预报中的适用性,在求解RANS方程的数值计算方法过程中,通过采用S-A、k-Omega、SST、EASM、DES等湍流模型,对考古船的阻力进行了预报,并将计算结果与试验结果进行了对比分析。由5种湍流模型的预报结果与试验值的对比得出：S-A、k-Omega模型对阻力性能的预报存在明显的缺陷;SST模型会有改进,计算误差能控制在工程应用的范围内,能够很好地应用于船模的阻力计算。     

黄渤海风浪气候统计分析

了解掌握黄渤海风浪气候规律和特征,对于海上运输、海域工程施工及港口建设、石油钻井开采、军事和海上生产活动等,均有着重要意义;同时,加强海洋气候的科学研究,提高海浪预报准确率,对于减少海洋气象灾害也有着十分显著的经济和社会效益。     

Seasonal variability in sea surface oceanographic conditions in the Aegean Sea (Eastern Mediterranean): an overview

Seasonal variability and the spatial distribution of sea surface temperatures (SST) and salinities (SSS) are reviewed, in relation to the prevailing climatological conditions, heat fluxes, water budget and general water circulation patterns. Within this context, consideration is given to: sea surface temperatures; air temperatures; precipitation; evaporation; wind speeds and directions; freshwater (mainly riverine) discharges throughout the Aegean; and the exchange of water masses with the Black Sea and eastern Mediterranean Sea. The investigation of satellite images, covering a 6-yr period (1988–1994), has enabled a synthesis of the monthly sea surface thermal distribution to be established.The climate of the Aegean Sea is characterised by annual air temperatures of 16–19.5°C, precipitation of about 500 mm yr⁻¹ and evaporation of some 4 mm d⁻¹. The Aegean has a negative heat budget (approximately −25 W m⁻²) and positive water balance (+ 1.0 m yr⁻¹), when inflow from the Black Sea is considered. During the summer, the (northerly) Etesians are the dominant winds over the Sea.Mean monthly sea surface temperatures (SST) vary from 8°C in the north during winter, up to 26°C in the south during summer. SST depends mainly upon air temperature; there is a month's delay between the former and latter maxima. The sea surface salinity (SSS) varies also spatially and seasonally, ranging from less than 31 psu, in the north, to more than 39 psu, in the southeast; lower values (< 25 psu) occur adjacent to the river mouths. SSSs present their maximum differences during summer, whilst during winter and autumn the distribution of SSS is more uniform. The overall spatial SST and SSS distribution pattern is controlled by: distribution of the (colder) Black Sea Waters; advection of the (warmer) Levantine Waters, from the southeastern part of the Aegean; upwelling and downwelling; and, to a lesser extent, but locally important, freshwater riverine inflows.     

Particle fluxes in the Almeria-Oran Front: control by coastal upwelling and sea surface circulation

Particle flux data were obtained from one instrumented array moored under the direct influence of the Almeria-Oran Front (AOF) in the Eastern Alboran Sea, Western Mediterranean Sea, within the frame of the “Mediterranean Targeted Project II-MAss Transfer and Ecosystem Response” (MTPII-MATER) EU-funded research project. The mooring line was deployed from July 1997 to May 1998, and was equipped with three sequential sampling sediment trap-current meter pairs at 645, 1170 and 2210 m (30 m above the seafloor). The settling material was analysed to obtain total mass, organic carbon, opal, calcium carbonate and lithogenic fluxes. Qualitative analyses of SST and SeaWiFS images allowed monitoring the location and development of the Western and Eastern Alboran Sea gyres and associated frontal systems to determine their influence on particle fluxes.Particle flux time series obtained at the three depths showed a downward decrease of the time-weighed total mass flux annual means, thus illustrating the role of pelagic particle settling. The total mass flux was dominated by the lithogenic fraction followed by calcium carbonate, opal and organic carbon. The time series at the various depths were rather similar, with two strong synchronous biogenic peaks (up to 98 mg m⁻² day⁻¹ of organic carbon and 156 mg m⁻² day⁻¹ of opal) recorded in July 1997 and May 1998. Through comparing the fluctuations of the lithogenic and calcium carbonate-rich fluxes with the biogenic flux, we observed that the non-biogenic fluxes remained roughly constant, while the biogenic flux responded strongly to seasonal variations throughout the water column.Overall, the temporal variability of particle fluxes appeared to be linked to the evolution of several tens of kilometres in length sea surface hydrological structures and circulation of the Alboran Sea. Periodic southeastward advective displacements of waters from upwelling events off the southern Spanish coast were observed on SST and SeaWiFS images. In between these periods, widespread phytoplankton blooms were observed. The influence of the varying surface structures resulted in changes in the biogenic particle flux. For example, we observed an opal pulse in April 1998 that resulted from a diatom-rich highly productive frontal surface situation above the mooring line.Estimation of the annual organic carbon export and calculation of a seasonality index indicate that the overall dynamics of the carbon reservoir within the Eastern Alboran Sea appears to be strongly influenced by the sea surface hydrological structures.     

A Mean Dynamic Topography of the Mediterranean Sea computed from altimetric data, in-situ measurements and a general circulation model

In the Mediterranean Sea, where the mean circulation is largely unknown and characterized by smaller scales and less intensity than in the open ocean, the interpretation of altimetric Sea Level Anomalies (SLA) is rather difficult. In the context of operational systems such as MFS (Mediterranean Forecasting System) or MERCATOR, that assimilate the altimetric information, the estimation of a realistic Mean Dynamic Topography (MDT) consistent with altimetric SLA to be used to reconstruct absolute sea level is a crucial issue. A method is developed here to estimate the required MDT combining oceanic observations as altimetric and in-situ measurements and outputs from an ocean general circulation model (OGCM).In a first step, the average over the 1993–1999 period of dynamic topography outputs from MFS OGCM provides a first guess for the computation of the MDT. Then, in a second step, drifting buoy velocities and altimetric data are combined using a synthetic method to obtain local estimates of the mean geostrophic circulation which are then used to improve the first guess through an inverse technique and map the MDT field (hereafter the Synthetic Mean Dynamic Topography or SMDT) on a 1/8° resolution grid.Many interesting current patterns and cyclonic/anticyclonic structures are visible on the SMDT obtained. The main Mediterranean coastal currents are well marked (as the Algerian Current or the Liguro–Provenço–Catalan Current). East of the Sicily channel, the Atlantic Ionian Stream divides into several main branches crossing the Ionian Sea at various latitudes before joining at 19°E into a unique Mid-Mediterranean Jet. Also, strong signatures of the main Mediterranean eddies are obtained (as for instance the Alboran gyre, the Pelops, Ierapetra, Mersa-Matruh or Shikmona anticyclones and the Cretan, Rhodes or West Cyprius cyclones). Independent in-situ measurements from Sea Campaigns NORBAL in the North Balearic Sea and the North Tyrrhenian Sea and SYMPLEX in the Sicily channel are used to validate locally the SMDT: deduced absolute altimetric dynamic topography compares well with in-situ observations. Finally, the SMDT is used to compute absolute altimetric maps in the Alboran Sea and the Algerian Current. The use of absolute altimetric signal allows to accurately follow the formation and propagation of cyclonic and anticyclonic eddies in both areas.     

Application of the Grey topological method to predict the effects of ship pitching

Ship motion, with six degrees of freedom, is a complex stochastic process. Sea wind and waves are the primary influencing factors. Prediction of ship motion is significant for ship navigation. To eliminate errors, a path prediction model incorporating ship pitching was developed using the Gray topological method, after analyzing ship pitching motions. With the help of simple introduction to Gray system theory, we selected a group of threshold values. Based on an analysis of ship pitch angle sequences over 40 second intervals, a Grey metabolism GM（1,1） model was established according to the time-series which every threshold corresponded to. Forecasting future ship motion with the GM （1,1） model allowed drawing of the forecast curve with effective forecasting points. The precision of the test results show that the model is accurate, and the forecast results are reliable.