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.     

Temporal evolution of innovation and residual statistics in the ECMWF variational data assimilation systems

The temporal evolution of innovation and residual statistics of the ECMWF 3D‐ and 4D‐Var data assimilation systems have been studied. First, the observational method is applied on an hourly basis to the innovation sequences in order to partition the perceived forecast error covariance into contributions from observation and background errors. The 4D‐Var background turns out to be ignificantly more accurate than the background in the 3D‐Var. The estimated forecast error variance associated with the 4D‐Var background trajectory increases over the assimilation window. There is also a marked broadening of the horizontal error covariance length scale over the assimilation window. Second, the standard deviation of the residuals, i.e., the fit of observations to the analysis is studied on an hourly basis over the assimilation window. This fit should, in theory, reveal the effect of model error in a strong constraint variational problem. A weakly convex curve is found for this fit implying that the perfect model assumption of 4D‐Var may be violated with as short an assimilation window as six hours. For improving the optimality of variational data assimilation systems, a sequence of retunes are needed, until the specified and diagnosed error covariances agree.     

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.     

Targeted ensemble prediction for northern Europe and parts of the north Atlantic Ocean

The targeting procedure developed at ECMWF is used to make ensembles specially designed for northern Europe and parts of the north Atlantic Ocean. A total of 35 ensembles are integrated, consisting of 20 winter cases and 15 summer cases in 1997, each consisting of 20 members plus one control forecast. The ensembles are run up to day 10, and the ensemble spread inside the target area continues to increase all through the 10 days. Two distinct regimes of increase can be found, the first increase is consistent with the perturbations moving in and through the target area, it is hypothesised that the latter increase in ensemble spread around forecast day 5‐7 is connected with increasing non‐linearity. The performance of the experimental ensembles is compared to the operational ensemble prediction system (EPS) at ECMWF, both with all 50 members and with only 20 members. The spread increases when the number of members in the ensemble prediction system is increased, and the spread increases inside the target area when targeting is applied. We find that the increase in spread when going from EPS with 50 members to the targeted ensembles is larger than when going from 20 to 50 ensemble members of the operational sets. Clearly targeting must be an option when predicting for a sub‐domain of the hemisphere. Looking at other measures, such as the Brier skill score (BSS), relative operating characteristic (ROC) curves and cost/loss analyses, the impact of the targeting is modest for the winter cases, but the impact for the summer cases is evident. For the winter cases a large part of the operational perturbations were located in the same area as the targeted perturbations, and the differences for the two sets are small. For the summer cases the operational perturbations were mostly split between two locations and hence the targeting will give results differing more from the operational.     

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.     

Low‐dimensional representation of error covariance

Ensemble and reduced‐rank approaches to prediction and assimilation rely on low‐dimensional approximations of the estimation error covariances. Here stability properties of the forecast/analysis cycle for linear, time‐independent systems are used to identify factors that cause the steady‐state analysis error covariance to admit a low‐dimensional representation. A useful measure of forecast/analysis cycle stability is the bound matrix , a function of the dynamics, observation operator and assimilation method. Upper and lower estimates for the steady‐state analysis error covariance matrix eigenvalues are derived from the bound matrix. The estimates generalize to time‐dependent systems. If much of the steady‐state analysis error variance is due to a few dominant modes, the leading eigenvectors of the bound matrix approximate those of the steady‐state analysis error covariance matrix. The analytical results are illustrated in two numerical examples where the Kalman filter is carried to steady state. The first example uses the dynamics of a generalized advection equation exhibiting non‐modal transient growth. Failure to observe growing modes leads to increased steady‐state analysis error variances. Leading eigenvectors of the steady‐state analysis error covariance matrix are well approximated by leading eigenvectors of the bound matrix. The second example uses the dynamics of a damped baroclinic wave model. The leading eigenvectors of a lowest‐order approximation of the bound matrix are shown to approximate well the leading eigenvectors of the steady‐state analysis error covariance matrix.     

Reducing systematic errors by empirically correcting model errors

A methodology for the correction of systematic errors in a simplified atmospheric general‐circulation model is proposed. First, a method for estimating initial tendency model errors is developed, based on a 4‐dimensional variational assimilation of a long‐analysed dataset of observations in a simple quasi‐geostrophic baroclinic model. Then, a time variable potential vorticity source term is added as a forcing to the same model, in order to parameterize subgrid‐scale processes and unrepresented physical phenomena. This forcing term consists in a (large‐scale) flow dependent parametrization of the initial tendency model error computed by the variational assimilation. The flow dependency is given by an analogues technique which relies on the analysis dataset. Such empirical driving causes a substantial improvement of the model climatology, reducing its systematic error and improving its high frequency variability. Low‐frequency variability is also more realistic and the model shows a better reproduction of Euro‐Atlantic weather regimes. A link between the large‐scale flow and the model error is found only in the Euro‐Atlantic sector, other mechanisms being probably the origin of model error in other areas of the globe.     

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...     

台风“梅花”(2212)的主要特点和路径预报难点分析

对2022年第12号台风“梅花”的主要特点、路径预报难点问题和路径预报误差特征进行分析,研究主要结论显示：(1)“梅花”登陆次数多、登陆强度强,是首个4次登陆不同省(市)的台风,也是2022年最强登陆台风,造成华东与东北地区长时间、大范围的风雨影响。(2)台风生成初期的中长期时效路径预报是路径预报难点之一,模式对台风主要影响系统的长时效预报存在明显偏差,针对模式的及时检验和订正对预报调整非常重要。(3)双台风或多涡旋情景下,集合预报发散度大,“梅花”陆上路径预报偏西偏慢,其东侧“南玛都”的强度和位置差异对其路径有明显影响。(4)台风变性过程中的移速误差是路径预报极大误差的来源,关注台风是否处于变性过程可作为调整台风移速预报的参考。未来开展多模式交叉实时检验,基于集合预报研发针对转向变性台风路径预报订正技术可为主观预报提供支撑。     

Sea surface temperature predictability in the North Pacific from multi-model seasonal forecast

Sea surface temperature (SST) prediction based on the multi-model seasonal forecast with numerous ensemble members have more useful skills to estimate the possibility of climate events than individual models. Hence, we assessed SST predictability in the North Pacific (NP) from multi-model seasonal forecasts. We used 23 years of hindcast data from three seasonal forecasting systems in the Copernicus Climate Change Service to estimate the prediction skill based on temporal correlation. We evaluated the predictability of the SST from the ensemble members' width spread, and co-variability between the ensemble mean and observation. Our analysis revealed that areas with low prediction skills were related to either the large spread of ensemble members or the ensemble members not capturing the observation within their spread. The large spread of ensemble members reflected the high forecast uncertainty, as exemplified in the Kuroshio–Oyashio Extension region in July. The ensemble members not capturing the observation indicates the model bias; thus, there is room for improvements in model prediction. On the other hand, the high prediction skills of the multi-model were related to the small spread of ensemble members that captures the observation, as in the central NP in January. Such high predictability is linked to El Niño Southern Oscillation (ENSO) via teleconnection.

     

基于MASNUM海浪预报系统的北印度洋波浪特征模拟与预报分析

基于MASNUM海浪数值预报系统的全球10 a后报数据库资料,分析了北印度洋区域波浪分布特征.由于该地区受季风控制显著,夏季波浪大于冬季;在空间分布上,西部比东部风大、浪大,在亚丁湾、索马里外海波浪最大.基于Janson-1卫星高度计有效波高观测资料,对MASNUM海浪预报系统的预报性能进行了检验,检验结果表明,预报波高均方根误差在0.5 m左右,短期的24 h预报效果好于48 h和72 h,冬季好于夏季.另外,对预报误差进行了相应的概率分布分析.     

台风风暴潮异模式集合数值预报技术研究及应用

台风风暴潮数值预报的准确性在很大程度上取决于台风路径预报和强度预报的精度以及风暴潮预报模型的计算精度。目前,国际上24/48 h台风路径预报平均误差分别约为120/210 km左右[1],对于走向异常的台风误差更大;更有,根据单一的台风路径和单族的风暴潮数值预报模式并不能保证获得可靠的风暴潮预报结果。考虑多重网格法原理具有在疏密不同的网格层上进行迭代以达到平滑不同频率的误差分量,使得计算快速收敛,精度提高的特性。在前期研究基础上基于业务化高分辨率(结构网格/有限差分算法)和精细化(非结构网格/有限元算法)台风风暴潮集合数值预报模型构建多模型台风风暴潮集合数值预报系统。采用"非同族"模型进行集合预报很大程度上降低了误差相似遗传的可能性。应用该方法对典型台风风暴潮过程进行了试应用,试报结果表明:该方法对风暴潮增、减水预报效果高于单一集合预报,具有一定的应用前景。     

Semisupervised heterogeneous ensemble for ship target discrimination in synthetic aperture radar images

Ship detection using synthetic aperture radar (SAR) plays an important role in marine applications. The existing methods are capable of quickly obtaining many candidate targets, but numerous non-ship objects may be wrongly detected in complex backgrounds. These non-ship false alarms can be excluded by training discriminators, and the desired accuracy is obtained with enough verified samples. However, the reliable verification of targets in large-scene SAR images still inevitably requires manual interpretation, which is difficult and time consuming. To address this issue, a semisupervised heterogeneous ensemble ship target discrimination method based on a tri-training scheme is proposed to take advantage of the plentiful candidate targets. Specifically, various features commonly used in SAR image target discrimination are extracted, and several acknowledged classification models and their classic variants are investigated. Multiple discriminators are constructed by dividing these features into different groups and pairing them with each model. Then, the performance of all the discriminators is tested, and better discriminators are selected for implementing the semisupervised training process. These strategies enhance the diversity and reliability of the discriminators, and their heterogeneous ensemble makes more correct judgments on candidate targets, which facilitates further positive training. Experimental results demonstrate that the proposed method outperforms traditional tri-training.     

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.     

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。     

Analysis of solutions to the inverse problem on the retrieval of the microstructure of stratospheric aerosol from satellite measurements

A statistical ensemble of microphysical parameters of the background stratospheric aerosol at altitudes of 15 to 30 km is modeled on the basis of experimental data. The aerosol attenuation coefficients (AACs) in the wavelength range 0.38–16.3 μm are calculated for all realizations of the ensemble by algorithms of the Mie theory. Analysis of correlations between the AACs and the microphysical parameters indicate that the AAC correlates most strongly with the total volume V and area S of all particles. The errors of determining the microphysical parameters from AAC measurements are analyzed via the method of linear regression. It is shown that, if the AAC is measured with an error of 5%, the errors of determining both the particle size distribution (PSD) for particles with sizes of 0.4 to 4 μm and the parameter S are an order of magnitude smaller than the prior uncertainty, whereas the error of determining V is two orders of magnitude smaller than the prior uncertainty. Schemes of AAC measurements with the SAGE III, ISAMS, CLAES, HALOE instruments and an IR interferometer in the visible and IR regions are discussed. It is shown that combining the schemes makes it possible to extend the range of particle sizes for which the PSD is retrieved with a satisfactory accuracy and to increase the accuracy of determining S and V substantially and the accuracy of determining the total number of particles N _opt to a lesser extent. Examples of interpreting AAC measurements carried out simultaneously with the SAGE III and HALOE instruments within the same spatial region are presented. A systematic discrepancy between vertical profiles of S and V obtained from SAGE III and HALOE measurements is revealed.     

Towards a true 4‐dimensional data assimilation algorithm: application of a cycling representer algorithm to a simple transport problem

3‐dimensional variational algorithms are widely used for atmospheric data assimilation at the present time, particularly on the synoptic and global scales. However, mesoscale and convective scale phenomena are considerably more chaotic and intermittent and it is clear that true 4‐dimensional data assimilation algorithms will be required to properly analyze these phenomena. In its most general form, the data assimilation problem can be posed as the minimization of a 4‐dimensional cost function with the forecast model as a weak constraint. This is a much more difficult problem than the widely discussed 4DVAR algorithm where the model is a strong constraint. Bennett and collaborators have considered a method of solution to the weak constraint problem, based on representer theory. However, their method is not suitable for the numerical weather prediction problem, because it does not cycle in time. In this paper, the representer method is modified to permit cycling in time, in a manner which is entirely internally consistent. The method was applied to a simple 1‐dimensional constituent transport problem where the signal was sampled (perfectly and imperfectly) with various sparse observation network configurations. The cycling representer algorithm discussed here successfully extracted the signal from the noisy, sparse observations     

Role of the metric in forecast error growth: how chaotic is the weather?

The atmosphere is often cited as an archetypal example of a chaotic system, where prediction is limited by the model's sensitivity to initial conditions. Experiments have indeed shown that forecast errors, as measured in 500 hPa heights, can double in 1.5 d or less. Recent work, however, has shown that, when errors are measured in total energy, model error is the primary contributor to forecast inaccuracy. In this paper we attempt to reconcile these apparently conflicting sets of results by examining the role of the chosen metric. Using a simple medium-dimensional model for illustration, it is found that the metric has a strong effect, not just on apparent error growth, but on the perceived causes of error. If an insufficiently global metric is used, then it may appear that error is due to sensitivity to initial condition, when in fact it is caused by sensitivity to error in the other variables. If the goal is to diagnose the causes of error, a sufficiently global metric must be used. The simple model is used to predict the internal rate of growth of the ECMWF operational model, and preliminary results compared. It is found that both 500 hPa and total energy results are consistent with high model error and a relatively low internal rate of growth. Experiments are suggested to further verify the results for weather models.     

基于多源数据的台风风暴潮概率预报研究:台风集合的构建

建立了一套用于台风风暴潮集合预报的台风集合构建方案.首先基于中国中央气象台、中国香港天文台、中国台湾中央气象局、美国联合台风预警中心、日本气象厅和韩国气象台6家预报中心的预报数据,构建一个误差更小的24 h、48h和72 h预报时效的台风分析数据;然后基于分析数据构建9个路径样本(1条分析路径+2个概率圆上的8条概率路...     

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。