A stochastic operational forecasting system of the Black Sea: Technique and validation

In this article, we present the latest version of an ensemble forecasting system of the hydrodynamics of the Black Sea, based on the GHER model. The system includes the Weakly Constrained Ensembles algorithm to generate random, but physically balanced perturbations to initialize members of the ensemble. On top of initial conditions, the ensemble accounts also for uncertainty on the atmospheric forcing fields, and on some scalar parameters such as river flows or model diffusion coefficients. The forecasting system also includes the Ocean Assimilation Kit, a sequential data assimilation package implementing the SEEK and Ensemble Kalman filters. A novel aspect of the forecasting system is that not only our best estimate of the future ocean state is provided, but also the associated error estimated from the ensemble of models. The primary goal of this paper is to quantitatively show that the ensemble variability is a good estimation of the model error, regardless of the magnitude of the forecast errors themselves. In order for this estimation to be meaningful, the model itself should also be well validated. Therefore, we describe the model validation against general circulation patterns. Some particular aspects critical for the Black Sea circulation are validated as well: the mixed layer depth and the shelfopen sea exchanges. The model forecasts are also compared with observed sea surface temperature, and errors are compared to those of another operational model as well.     

Macroecological study of Centropages typicus in the North Atlantic Ocean

Centropages typicus is a temperate neritic-coastal species of the North Atlantic Oceans, generally found between the latitudes of the Mediterranean and the Norwegian Sea. Therefore, the species experiences a large number of environments and adjusts its life cycle in response to changes in key abiotic parameters such as temperature. Using data from the Continuous Plankton Recorder (CPR) survey, we review the macroecology of C. typicus and factors that influence its spatial distribution, phenology and year-to-year to decadal variability. The ecological preferences are identified and quantified. Mechanisms that allow the species to occur in such different environments are discussed and hypotheses are proposed as to how the species adapts to its environment. We show that temperature and both quantity and quality of phytoplankton are important factors explaining the space and time variability of C. typicus. These results show that C. typicus will not respond only to temperature increase in the region but also to changes in phytoplankton abundance, structure and composition and timing of occurrence. Methods such as a decision tree can help to forecast expected changes in the distribution of this species with hydro-climatic forcing.     

Using a mesoscale ensemble to predict forecast error and perform targeted observation

Using NCEP short range ensemble forecast（SREF） system,demonstrated two fundamental on-going evolutions in numerical weather prediction（NWP） are through ensemble methodology.One evolution is the shift from traditional single-value deterministic forecast to flow-dependent（not statistical） probabilistic forecast to address forecast uncertainty.Another is from a one-way observation-prediction system shifting to an interactive two-way observation-prediction system to increase predictability of a weather system.In the first part,how ensemble spread from NCEP SREF predicting ensemble-mean forecast error was evaluated over a period of about a month.The result shows that the current capability of predicting forecast error by the 21-member NCEP SREF has reached to a similar or even higher level than that of current state-of-the-art NWP models in predicting precipitation,e.g.,the spatial correlation between ensemble spread and absolute forecast error has reached 0.5 or higher at 87 h（3.5 d） lead time on average for some meteorological variables.This demonstrates that the current operational ensemble system has already had preliminary capability of predicting the forecast error with usable skill,which is a remarkable achievement as of today.Given the good spread-skill relation,the probability derived from the ensemble was also statistically reliable,which is the most important feature a useful probabilistic forecast should have.The second part of this research tested an ensemble-based interactive targeting（E-BIT） method.Unlike other mathematically-calculated objective approaches,this method is subjective or human interactive based on information from an ensemble of forecasts.A numerical simulation study was performed to eight real atmospheric cases with a 10-member,bred vector-based mesoscale ensemble using the NCEP regional spectral model（RSM,a sub-component of NCEP SREF） to prove the concept of this E-BIT method.The method seems to work most effective for basic atmospheric state variables,moderately effective for convective instabilities and least effective for precipitations.Precipitation is a complex result of many factors and,therefore,a more challenging field to be improved by targeted observation.     

黄、渤海冷空气海浪场的集合预报试验

利用欧洲集合天气预报系统51个预报风场驱动SWAN海浪模式,对黄、渤海2013年12月-2014年2月期间受冷空气影响的海浪场进行数值模拟试验,并利用浮标观测资料对海浪集合预报结果进行初步检验分析,结果显示:从逐时平均偏差结果可知,24h预报时效内集合平均与控制预报性能相近,48~72h预报时效内,集合平均明显优于控制预报,但均比实况偏小;集合分位值(75、90百分位值和极端值)明显优于集合平均,且预报时效越长,优势越明显,集合预报极端值与实况相当或略偏大;从逐24h平均偏差结果可知,集合分位值(75、90百分位值和极端值)比集合平均和控制预报更接近实况。总的分析表明:集合分位值(75、90百分位值和极端值)对受冷空气影响的海浪场具有较强的分辨能力,可以提高对海浪场的预报水平,且有较好的应用潜力。     

Numerical study of current fields near the Changjiang Estuary and impact of Quick-EnKF assimilation

A 30-d current numerical simulation is running for the Yangshan Port, the Changjiang Estuary, the Hangzhou Bay and their adjacent seas using a finite volume coastal ocean model (FVCOM), with Changjiang River runoff and wind effect being considered. At the open boundary, this model is driven by the water level obtained from prediction including eight main partial tides. After the harmonic analysis, the cotidal chart and the iso-amplitude line as well as the current ellipse distribution map are displayed to illustrate the propagation property of a tidal wave. Horizontal velocity of both the U and V components coincides with the actual measurement, which shows that the model result is credible to describe the hydrodynamic pattern in this sea area. On this basis, real-time current data from high-frequency radar is assimilated with the implementation of quick ensemble Kalman filter, which takes the variation tendency of the state vector to compute the analysis field, instead of integrating the field for N (the number of ensemble) times as it used to in the standard EnKF, aiming at raising the efficiency of computation, reducing the error of prediction and at the same time, improving the forecast effect.     

Examination of targeting methods in a simplified setting

The effectiveness of 2 methods for targeting observations is examined using a T21 L3 QG model in a perfect model context. Target gridpoints are chosen using the pseudo‐inverse (the inverse composed of the first three singular vectors only) and the quasi‐inverse or backward integration (running the tangent equations with a negative time‐step). The effectiveness of a target is measured by setting the analysis error to zero in a region surrounding the target and noting the impact on the forecast error in the verification region. In a post‐time setting, when the targets are based on forecast errors that are known exactly, both methods provide targets that are significantly better than targets chosen at random within a broad region upstream of the verification region. When uncertainty is added to the verifying analysis such that the forecast error is known inexactly, the pseudo‐inverse targets still perform very well, while the backward integration targets are degraded. This degradation due to forecast uncertainty is especially significant when the targets are a function of height as well as horizontal position. When an ensemble‐forecast difference is used in place of the inexact forecast error, the backward integration targets may be improved considerably. However, this significant improvement depends on the characteristics of the initial‐time ensemble perturbation. Pseudo‐inverse targets based on ensemble forecast differences are comparable to pseudo‐inverse targets based on exact forecast errors. Targets based on the largest analysis error are also found to be considerably more effective than random targets. The collocation of the backward integration and pseudo‐inverse targets appears to be a good indicator of target skill.     

黄海近岸海雾的初始场择优集合预报试验:个例研究

选取2014年4月发生的一次黄海近岸海雾个例,利用WRF(Weather Research and Forecasting)模式开展了集合预报试验研究。依据每个集合成员初始场中海平面气压、2 m温度、2 m水汽混合比与2 m相对湿度(relative humidity, RH)4个变量的均方根误差(root mean square error, RMSE)与RMSE集合平均值的相对大小,以剔除高于者而保留低于者的原则,设计了4种不同的初始场集合体择优方案,实施了一系列数值预报试验,比较了不同择优方案的集合预报效果。研究结果表明:(1)蒙特卡罗方法所生成的集合体中存在不少海雾预报效果较差的成员,这会降低集合预报效果,因此初始场择优十分必要;(2)以RH作为择优变量的择优方案(记为RH-RMSE方案),集合预报效果明显优于其他3种方案;(3)对比不择优集合预报,采用RH-RMSE方案的择优集合预报效果不仅节省了50%左右的计算时间,并且公正预兆评分(equitable threat score,ETS)改进率高达36%左右。本研究提出的RH-RMSE方案具有业务化应用前景。     

东亚夏季风降雨的可预报性研究

本文根据CMAP(The Climate Prediction Center Merged Analysis of Precipitation)观测资料,使用相关系数和均方根误差,对CHFP2(Coupled Historical Forecast Project, phase 2 )的2个模式对东亚夏季降雨的季节预报技巧作出评价。在完美模式的理论框架下,分别使用基于信噪比的潜在相关系数和基于信息熵的潜在可预报性指标,对该区域主要针对夏季降雨的可预报性作出评价。通过最可预报分量分析(PrCA),得到季节降雨的最可预报型。将最可预报型投影到海温场,得到了降水最可预报型对应的海温分布。研究发现:相关系数所反映的预报和观测的线性相关程度总体上是低纬度海洋区域比高纬度陆地区域高,而均方根误差反映的则是在海洋区域降雨预报偏离实际值的程度较陆地区域大,预报水平与目前降雨的季节预报水平相符。潜在可预报性估计表明,潜在可预报率存在空间上的变化,从低纬度向高纬度、从海洋到内陆,呈衰减趋势。同时,信号和噪音的分析表明,信号成分占主导作用,形成了潜在可预报率的空间分布格局,暗示了海洋外强迫的重要作用;中国大陆缺少像海洋区域那样明显的外强迫,因此降水季节预报技巧相比热带海洋区域非常有限。海温投影的分析表明海洋的外强迫是东亚降雨季节预报的重要来源。尽管厄尔尼诺本身的复杂性,它对东亚夏季风的重要影响及其与东亚降雨预报之间的遥相关揭示了它们内在的联系。     

A numerical model of online forecasting Black Sea currents

A numerical three-dimensional nonlinear model of the hydrophysical fields of the Black Sea is presented. The properties of model discrete equations are described. The results of test experiments on the choice of model finite-difference approximations and parameters (as applied to the online forecasting of currents) are given. The results of prognostic calculations of the hydrophysical fields of the Black Sea are given for the period of March 31, 2005, to September 26, 2006. These results show that this numerical model with consideration for real atmospheric forcing can yield a satisfactory forecast of the parameters of the upper layers of the sea for 18 months of model time.     

A two-stage inflation method in parameter estimation to com-pensate for constant parameter evolution in Community Earth System Model

Parameter estimation is defined as the process to adjust or optimize the model parameter using observations. A long-term problem in ensemble-based parameter estimation methods is that the parameters are assumed to be constant during model integration. This assumption will cause underestimation of parameter ensemble spread,such that the parameter ensemble tends to collapse before an optimal solution is found. In this work, a two-stage inflation method is developed for parameter estimation, which ...     

在Zebiak-Cane模式中观测MJO强迫对ENSO可预报性的影响, 第1部分:对最大预报误差的影响

With the observational wind data and the Zebiak-Cane model, the impact of Madden-Julian Oscillation (MJO) as external forcing on El Ni(n)o–Southern Oscillation (ENSO) predictability is studied. The obs...     

Corrections to ocean surface forcing in the California Current System using 4D variational data assimilation

The option for surface forcing correction, recently developed in the 4D-variational (4DVAR) data assimilation systems of the Regional Ocean Model System (ROMS), is presented. Assimilation of remotely-sensed (satellite sea surface height anomaly and sea surface temperature) and in situ (from mechanical and expendable bathythermographs, Argo floats and CTD profiles) oceanic observations has been applied in a realistic, high resolution configuration of the California Current System (CCS) to sequentially correct model initial conditions and surface forcing, using the Incremental Strong constraint version of ROMS-4DVAR (ROMS-IS4DVAR). Results from both twin and real data experiments are presented where it is demonstrated that ROMS-IS4DVAR always reduces the difference between the model and the observations that are assimilated. However, without corrections to the surface forcing, the assimilation of surface data can degrade the temperature structure at depth. When using surface forcing adjustment in ROMS-IS4DVAR the system does not degrade the temperature structure at depth, because differences between the model and surface observations can be reduced through corrections to surface forcing rather than to temperature at depth. However, corrections to surface forcing can generate abnormal spatial and temporal variability in the structure of the wind stress or surface heat flux fields if not properly constrained. This behavior can be partially controlled via the choice of decorrelation length scales that are assumed for the forcing errors. Abnormal forcing corrections may also arise due to the effects of model error which are not accounted for in IS4DVAR. In particular, data assimilation tends to weaken the alongshore wind stress in an attempt to reduce the rate of coastal upwelling, which seems to be too strong due to other sources of error. However, corrections to wind stress and surface heat flux improve systematically the ocean state analyses. Trends in the correction of surface heat fluxes indicate that, given the ocean model used and its potential limitations, the heat flux data from the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) used to impose surface conditions in the model are generally too low except in spring-summer, in the upwelling region, where they are too high. Comparisons with independent data provide confidence in the resulting forecast ocean circulation on timescales ～14 days, with less than 1.5 °C, 0.3 psu, and 9 cm RMS error in temperature, salinity and sea surface height anomaly, respectively, compared to observations.     

1988-2002年黄海和渤海风浪后报

本文对黄海和渤海风浪开展长期后报实验,时间范围覆盖1988至2002年,并分析相应的区域波候特征。首先,模式输出的月平均有效波高和卫星数据比对一致。其次,我们讨论了气候态月平均有效波高和平均波周期的时空分布特征。有效波高和平均波周期的气候态空间分布都呈现出西北-东南、或由近岸向深水区增加的趋势,这种空间的分布特征和局地的风强迫和水深密切相关。同时,海浪参数的季节变化也较显著。进一步,我们统计分析了风场和有效波高的极值,给出并揭示了黄海和渤海多年一遇有效波高的空间结构,并讨论了有效波高极值和风强迫极值之间的联系。     

不同 AMDAR资料同化时间窗对台风“摩羯”预报影响研究

2018年第14号台风“摩羯”对山东造成了大范围暴雨和大风天气,基于WRF（Weather Research and Forecasting）模式及其Hybrid-3DVAR混合同化预报系统,对Hybrid-3DVAR不同集合协方差比例和不同航空气象数据转发（aircraft meteorological data relay,以下简称AMDAR）资料同化时间窗对台风“摩羯”预报的影响进行了数值研究。结果表明：加大集合协方差比例对台风“摩羯”路径预报有较大影响和改进;当全部取来自集合体的流依赖误差协方差时,预报的台风路径最好,降水预报也最接近实况;AMDAR资料同化对于台风路径和降水预报也有正的改进作用,但加大集合协方差比例到100%时对台风路径预报影响更大;不同资料同化时间窗会影响同化的AMDAR资料数量,从而影响台风降水精细化预报;45 min同化时间窗的要素预报误差最小,对台风造成的强降水精细特征预报最接近实况;不同资料同化时间窗主要影响台风降水预报落区分布,对台风路径预报影响相对较小。     

Assimilation of surface currents into a regionalmodel over Qingdao coastal waters of China

Surface currents measured by high frequency （HF） radar arrays are assimilated into a regional ocean model over Qingdao coastal waters based on Kalman filter method. A series of numerical experiments are per- formed to evaluate the performance of the data assimilation schemes. In order to optimize the analysis pro- cedure in the traditional ensemble Kalman filter （ENKF）, a different analysis scheme called quasiensemble Kaman filter （QENKF） is proposed. The comparisons between the ENKF and the QENKF suggest that both them can improve the simulated error and the spatial structure. The estimations of the background error covariance （BEC） are also assessed by comparing three different methods： Monte Carlo method; Canadian quick covariance （CQC） method and data uncertainty engine （DUE） method. A significant reduction of the root-mean-square （RMS） errors between model results and the observations shows that the CQC method is able to better reproduce the error statistics for this coastal ocean model and the corresponding external forcing. In addition, the sensibility of the data assimilation system to the ensemble size is also analyzed by means of different scales of the ensemble size used in the experiments. It is found that given the balance of the computational cost and the forecasting accuracy, the ensemble size of 50 will be an appropriate choice in the Qingdao coastal waters.     

Efficient identification of ocean thermodynamics in a physical/biogeochemical ocean model with an iterative Importance Sampling method

Efficient identification of parameters in numerical models remains a computationally demanding problem. Here we present an iterative Importance Sampling approach and demonstrate its application to estimating parameters that control the heat uptake efficiency of a physical/biogeochemical ocean model coupled to a simple atmosphere. The algorithm has similarities to a previously-developed ensemble Kalman filtering (EnKF) method applied to similar problems, but is more flexible and powerful in the case of nonlinear models and non-Gaussian uncertainties. The method is somewhat more computationally demanding than the EnKF but may be preferred in cases where the approximations that the EnKF relies upon are unsound. Our results suggest that the three-dimensional structure of ocean tracer fields may act as a useful constraint on ocean mixing and consequently the heat uptake of the climate system under anthropogenic forcing.     

Estimation of the uncertainty of future changes in atmospheric carbon dioxide concentration and its radiative forcing

An ensemble experiment with the IAP RAS CM was performed to estimate future changes in the atmospheric concentration of carbon dioxide, its radiative forcing, and characteristics of the climate-carbon cycle feedback. Different ensemble members were obtained by varying the governing parameters of the terrestrial carbon cycle of the model. For 1860–2100, anthropogenic CO₂ emissions due to fossil-fuel burning and land use were prescribed from observational estimates for the 19th and 20th centuries. For the 21st century, emissions were taken from the SRES A2 scenario. The ensemble of numerical experiments was analyzed via Bayesian statistics, which made the uncertainty range of estimates much narrower. To distinguish between realistic and unrealistic ensemble members, the observational characteristics of the carbon cycle for the 20th century were used as a criterion. For the given emission scenario, the carbon dioxide concentration expected by the end of the 21st century falls into the range 818 ± 46 ppm (an average plus or minus standard deviation). The corresponding global instantaneous radiative forcing at the top of the atmosphere (relative to the preindustrial state) lies in the uncertainty range 6.8 ± 0.4 W m^?2. The uncertainty range of the strength of the climate-carbon cycle feedback by the end of the 21st century reaches 59 ± 98 ppm in terms of the atmospheric carbon dioxide concentration and 0.4 ± 0.7 W m^?2 in terms of the radiative forcing.     

Modeling the evolution of the world ocean ice cover in the 20th and 21st centuries

The current state of the simulation of sea ice cover as a component of new-generation global climate models is considered. Results from the model ensemble simulation of the observed world ocean ice cover, including its evolution in the 20th century, are analyzed, and projection of possible changes in the 21st century for three scenarios of anthropogenic forcing of the climate system are described. Unresolved problems and priorities for sea ice modeling are discussed.     

Predicting the East Australian Current

Results are presented from an ensemble prediction study (EPS) of the East Australian Current (EAC) with a specific focus on the examination of the role of dynamical instabilities and flow dependent growing errors. The region where the EAC separates from the coast, is characterized by significant mesoscale eddy variability, meandering and is dominated by nonlinear dynamics thereby representing a severe challenge for operational forecasting. Using analyses from OceanMAPS, the Australian operational ocean forecast system, we explore the structures of flow dependent forecast errors over 7 days and examine the role of dynamical instabilities. Forecast ensemble perturbations are generated using the method of bred vectors allowing the identification of those perturbations to a given initial state that grow most rapidly. We consider a 6 month period spanning the Austral summer that corresponds to the season of maximum eddy variability. We find that the bred vector (BV) structures occur in areas of instability where forecast errors are large and in particular in regions associated with the Tasman Front and EAC extension. We also find that very few BVs are required to identify these regions of large forecast error and on that basis we expect that even a small BV ensemble would prove useful for adaptive sampling and targeted observations. The results presented also suggest that it may be beneficial to supplement the static background error covariances typically used in operational ocean data assimilation systems with flow dependent background errors calculated using a relatively cheap EPS.     

Comparison of EnOI and EnKF regional ocean reanalysis systems

This study compares two regional eddy resolving ocean reanalysis systems, based on the ensemble Kalman filter (EnKF) and ensemble optimal interpolation (EnOI), focusing on data assimilation aspects. Both systems are configured for the Tasman Sea using the same ocean model with 0.1° resolution and commonly available observations of satellite altimetry, sea surface temperature and subsurface temperature and salinity. The primary goals are to quantify the difference in performance of the EnKF and EnOI and investigate how important this difference might be from an oceanographic perspective. We find that both systems generally constrain mesoscale circulation in the region, with some exceptions for the East Australian Current separation region, the most energetic and chaotic part of the domain. Overall, the EnKF is found to consistently outperform the EnOI, producing on average 9–21% smaller innovations. The EnKF also has better forecast skill relative to the persisted analysis than the EnOI. For SST the EnKF forecast outperforms persisted analysis by about 17%, which indicates that the surface circulation is mainly constrained. The EnKF and EnOI are shown to produce qualitatively different increments of unobserved or sparsely observed variables; however, we find only moderate improvements of the EnKF over EnOI in subsurface temperature fields when compared against withheld XBT observations. We attribute this lack of a major improvement in subsurface reconstruction to the inability of the EnKF to linearly constrain the system due to initialisation shock, model error caused by open boundaries, and possibly insufficient observations.