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1.
The orthogonal conditional nonlinear optimal perturbations (CNOPs) method, orthogonal singular vectors (SVs)method and CNOP+SVs method, which is similar to the orthogonal SVs method but replaces the leading SV (LSV) with the first CNOP, are adopted in both the Lorenz-96 model and Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) Fifth-Generation Mesoscale Model (MM5) for ensemble forecasts. Using the MM5, typhoon track ensemble forecasting experiments are conducted for strong Typhoon Matsa in 2005. The results of the Lorenz-96 model show that the CNOP+SVs method has a higher ensemble forecast skill than the orthogonal SVs method, but ensemble forecasts using the orthogonal CNOPs method have the highest forecast skill. The results from the MM5 show that orthogonal CNOPs have a wider horizontal distribution and better describe the forecast uncertainties compared with SVs. When generating the ensemble mean forecast, equally averaging the ensemble members in addition to the anomalously perturbed forecast members may contribute to a higher forecast skill than equally averaging all of the ensemble members. Furthermore, for given initial perturbation amplitudes, the CNOP+SVs method may not have an ensemble forecast skill greater than that of the orthogonal SVs method, but the orthogonal CNOPs method is likely to have the highest forecast skill. Compared with SVs, orthogonal CNOPs fully consider the influence of nonlinear physical processes on the forecast results; therefore, considering the influence of nonlinearity may be important when generating fast-growing initial ensemble perturbations. All of the results show that the orthogonal CNOP method may be a potential new approach for ensemble forecasting. 相似文献
2.
Conditional nonlinear optimal perturbation: Applications to stability,sensitivity, and predictability 总被引：4，自引：0，他引：4
Conditional nonlinear optimal perturbation (CNOP) is a nonlinear generalization of linear singular vector (LSV) and features
the largest nonlinear evolution at prediction time for the initial perturbations in a given constraint. It was proposed initially
for predicting the limitation of predictability of weather or climate. Then CNOP has been applied to the studies of the problems
related to predictability for weather and climate. In this paper, we focus on reviewing the recent advances of CNOP’s applications,
which involves the ones of CNOP in problems of ENSO amplitude asymmetry, block onset, and the sensitivity analysis of ecosystem
and ocean’s circulations, etc. Especially, CNOP has been primarily used to construct the initial perturbation fields of ensemble
forecasting, and to determine the sensitive area of target observation for precipitations. These works extend CNOP’s applications
to investigating the nonlinear dynamical behaviors of atmospheric or oceanic systems, even a coupled system, and studying
the problem of the transition between the equilibrium states. These contributions not only attack the particular physical
problems, but also show the superiority of CNOP to LSV in revealing the effect of nonlinear physical processes. Consequently,
CNOP represents the optimal precursors for a weather or climate event; in predictability studies, CNOP stands for the initial
error that has the largest negative effect on prediction; and in sensitivity analysis, CNOP is the most unstable (sensitive)
mode. In multi-equilibrium state regime, CNOP is the initial perturbation that induces the transition between equilibriums
most probably. Furthermore, CNOP has been used to construct ensemble perturbation fields in ensemble forecast studies and
to identify sensitive area of target observation. CNOP theory has become more and more substantial. It is expected that CNOP
also serves to improve the predictability of the realistic predictions for weather and climate events plays an increasingly
important role in exploring the nonlinear dynamics of atmospheric, oceanic and coupled atmosphere-ocean system.
Supported by National Basic Research Program of China (Grant Nos. 2006CB403606, 2007CB411800), National Natural Science Foundation
of China (Grant Nos. 40830955, 40675030, 40505013), Institute of Atmospheric Physics, Chinese Academy of Sciences (Grant No.
IAP07202), and LASG State Key Laboratory Special Fund 相似文献
3.
The proper orthogonal decomposition (POD) method is used to construct a set of basis functions for spanning the ensemble of
data in a certain least squares optimal sense. Compared with the singular value decomposition (SVD), the POD basis functions
can capture more energy in the forecast ensemble space and can represent its spatial structure and temporal evolution more
effectively. After the analysis variables are expressed by a truncated expansion of the POD basis vectors in the ensemble
space, the control variables appear explicitly in the cost function, so that the adjoint model, which is used to derive the
gradient of the cost function with respect to the control variables, is no longer needed. The application of this new technique
significantly simplifies the data assimilation process. Several assimilation experiments show that this POD-based explicit
four-dimensional variational data assimilation method performs much better than the usual ensemble Kalman filter method on
both enhancing the assimilation precision and reducing the computation cost. It is also better than the SVD-based explicit
four-dimensional assimilation method, especially when the forecast model is not perfect and the forecast error comes from
both the noise of the initial filed and the uncertainty of the forecast model.
Supported by the National Natural Science Foundation of China (Grant No. 40705035), National High Technology Research and
Development Program of China (Grant No. 2007AA12Z144), Knowledge Innovation Project of Chinese Academy of Sciences (Grant
Nos. KZCX2-YW-217 and KZCX2-YW-126-2), and National Basic Research Program of China (Grant No. 2005CB321704) 相似文献
4.
《中国科学:地球科学(英文版)》2015,(3)
Conditional nonlinear optimal perturbation(CNOP) is an extension of the linear singular vector technique in the nonlinear regime.It represents the initial perturbation that is subjected to a given physical constraint,and results in the largest nonlinear evolution at the prediction time.CNOP-type errors play an important role in the predictability of weather and climate.Generally,when calculating CNOP in a complicated numerical model,we need the gradient of the objective function with respect to the initial perturbations to provide the descent direction for searching the phase space.The adjoint technique is widely used to calculate the gradient of the objective function.However,it is difficult and cumbersome to construct the adjoint model of a complicated numerical model,which imposes a limitation on the application of CNOP.Based on previous research,this study proposes a new ensemble projection algorithm based on singular vector decomposition(SVD).The new algorithm avoids the localization procedure of previous ensemble projection algorithms,and overcomes the uncertainty caused by choosing the localization radius empirically.The new algorithm is applied to calculate the CNOP in an intermediate forecasting model.The results show that the CNOP obtained by the new ensemble-based algorithm can effectively approximate that calculated by the adjoint algorithm,and retains the general spatial characteristics of the latter.Hence,the new SVD-based ensemble projection algorithm proposed in this study is an effective method of approximating the CNOP. 相似文献
5.
Weather radar has a potential to provide accurate short‐term (0–3 h) forecasts of rainfall (i.e. radar nowcasts), which are of great importance in warnings and risk management for hydro‐meteorological events. However, radar nowcasts are affected by large uncertainties, which are not only linked to limitations in the forecast method but also because of errors in the radar rainfall measurement. The probabilistic quantitative precipitation nowcasting approach attempts to quantify these uncertainties by delivering the forecasts in a probabilistic form. This study implements two forms of probabilistic quantitative precipitation nowcasting for a hilly area in the south of Manchester, namely, the theoretically based scheme [ensemble rainfall forecasts (ERF)‐TN] and the empirically based scheme (ERF‐EM), and explores which one exhibits higher predictive skill. The ERF‐TN scheme generates ensemble forecasts of rainfall in which each ensemble member is determined by the stochastic realisation of a theoretical noise component. The so‐called ERF‐EM scheme proposed and applied for the first time in this study, aims to use an empirically based error model to measure and quantify the combined effect of all the error sources in the radar rainfall forecasts. The essence of the error model is formulated into an empirical relation between the radar rainfall forecasts and the corresponding ‘ground truth’ represented by the rainfall field from rain gauges measurements. The ensemble members generated by the two schemes have been compared with the rain gauge rainfall. The hit rate and the false alarm rate statistics have been computed and combined into relative operating characteristic curves. The comparison of the performance scores for the two schemes shows that the ERF‐EM achieves better performance than the ERF‐TN at 1‐h lead time. The predictive skills of both schemes are almost identical when the lead time increases to 2 h. In addition, the relation between uncertainty in the radar rainfall forecasts and lead time is also investigated by computing the dispersion of the generated ensemble members. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
6.
High-resolution models and realistic boundary conditions are necessary to reproduce the mesoscale dynamics of the Gulf of Mexico (GOM). In order to achieve this, we use a nested configuration of the Hybrid Coordinate Ocean Model (HYCOM), where the Atlantic TOPAZ system provides lateral boundary conditions to a high-resolution (5 km) model of the GOM . However, such models cannot provide accurate forecasts of mesoscale variability, such as eddy shedding event, without data assimilation. Eddy shedding events involve the rapid growth of nonlinear instabilities that are difficult to forecast. The known sources of error are the initial state, the atmospheric condition, and the lateral boundary condition. We present here the benefit of using a small ensemble forecast (10 members) for providing confidence indices for the prediction, while using a data assimilation scheme based on optimal interpolation. Our set of initial states is provided by using different values of a data assimilation parameter, while the atmospheric and lateral boundary conditions are perturbed randomly. Changes in the data assimilation parameter appear to control the main position of the large features of the GOM in the initial state, whereas changes in the boundary conditions (lateral and atmospheric) appears to control the propagation of cyclonic eddies at their boundary. The ensemble forecast is tested for the shedding of Eddy Yankee (2006). The Loop Current and eddy fronts observed from ocean color and altimetry are almost always within the estimated positions from the ensemble forecast. The ensemble spread is correlated both in space and time to the forecast error, which implies that confidence indices can be provided in addition to the forecast. Finally, the ensemble forecast permits the optimization of a data assimilation parameter for best performance at a given forecast horizon. 相似文献
7.
This study examines the roles of the multi-physics approach in accounting for model errors for typhoon forecasts with the local ensemble transform Kalman filter (LETKF). Experiments with forecasts of Typhoon Conson (2010) using the weather research and forecasting (WRF) model show that use of the WRF’s multiple physical parameterization schemes to represent the model uncertainties can help the LETKF provide better forecasts of Typhoon Conson in terms of the forecast errors, the ensemble spread, the root mean square errors, the cross-correlation between mass and wind field as well as the coherent structure of the ensemble spread along the storm center. Sensitivity experiments with the WRF model show that the optimum number of the multi-physics ensemble is roughly equal to the number of combinations of different physics schemes assigned in the multi-physics ensemble. Additional idealized experiments with the Lorenz 40-variable model to isolate the dual roles of the multi-physics ensemble in correcting model errors and expanding the local ensemble space show that the multi-physics approach appears to be more essential in augmenting the local rank representation of the LETKF algorithm rather than directly accounting for model errors during the early cycles. The results in this study suggest that the multi-physics approach is a good option for short-range forecast applications with full physics models in which the spinup of the ensemble Kalman filter may take too long for the ensemble spread to capture efficiently model errors and cross-correlations among model variables. 相似文献
8.
Zhisheng An Sumin Wang Xihao Wu Mingyang Chen Donghuai Sun Xiuming Liu Fubao Wang Li Li Youbin Sun Weijian Zhou Jie Zhou Xiaodong Liu Huayu Lu Yunxiang Zhang Guangrong Dong Xiaoke Qiang 《中国科学D辑(英文版)》1999,42(3):258-271
On the basis of a newly-constructed record of magnetic susceptibility (SUS) and the depositional rate change of eolian loess-red
clay sequences in the last 7.2 Ma BP from the hea Plateau, together with a cornperison of a record of °18O values from the equatorial East Pacific Ocean and eolian Quartz flux variations fmm the North Pacific Ocean, the evolutiomuy
process of the Late Cenozoic Great Glaciation in the Northern Hemisphere can be divided into three stages: the arrival stage
around 7.2–3.4 Ma BP, the initial stage at about 3.4—2.6 Ma BP, and the Great Ice Age since 2.6 Ma BP. The evolution of the
East Asian monsoon is characterized by paid winter and summer monsoons, and it is basically composed of the initial stage
of weak winter and summer monsoons, the transitional stage of simultaneous increase in intensity of winter and summer monsoons,
and the prevailing stage of strong winter and week summer monsoons, or weak winter and strong summer monsoons. The Late Cenowic
global tectonic uplift, paaicdarly the Qinghai-Xizang Plateau uplift and the associated CO2 concentration variation, controls the dng processes of the onset of Great Glaciation and the long-term changes of East Asian
monsoom climate in the Northern Hemisphere to a large extent. The accelerating uplift of the Qinghai-Xizang Plateau between
3.4 and 2.6 Ma BP provided an important driving force to global climiatic change.
Project supported by the foundation of Chinese Academy of Sciences (Grant No. KZ951-A1-402), the State Science and Technology
Committee (Grant No. 95-pre-40)and the Chinese Nature Science Foundation (Grant No. 49672140) 相似文献
9.
Skillful low visibility forecasts are essential for air-traffic managers to effectively regulate traffic and to optimize air-traffic
control at international airports. For this purpose, the COBEL-ISBA local numerical forecast system has been implemented at
Paris CDG international airport. This local approach is robust owing to the assimilation of detailed local observations. However,
even with dedicated observations and initialization, uncertainties remain in both initial conditions and mesoscale forcings.
The goal of the research presented here is to address the sensitivity of COBEL-ISBA forecast to initial conditions and mesoscale
forcings during the winter season 2002–2003. The main sources of uncertainty of COBEL-ISBA input parameters have been estimated
and the evaluation of parameter uncertainty on the forecasts has been studied. A budget strategy is applied during the winter
season to quantify COBEL-ISBA sensitivity. This study is the first step toward building a local ensemble prediction system
based on COBEL-ISBA. The conclusions of this work point out the potential for COBEL-ISBA ensemble forecasting and quantify
sources of uncertainty that lead to dispersion. 相似文献
10.
Forecasts of tropical cyclones(TCs) of the western North Pacific basin during the period of July to August 2018,especially of Rumbia(2018), Ampil(2018) and Jongdari(2018) that made landfall over Shanghai, have opposed great challenges for numerical models and forecasters. The predictive skill of these TCs are analyzed based on ensemble forecasts of ECMWF and NCEP. Results of the overall performance show that ensemble forecasts of ECMWF generally have higher predictive skill of track and intensity forecasts than those of NCEP. Specifically, ensemble forecasts of ECMWF have higher predictive skill of intensity forecasts for Rumbia(2018) and Ampil(2018) than those of NCEP, and both have low predictive skill of intensity forecasts for Jongdari(2018) at peak intensity. To improve the predictive skill of ensemble forecasts for TCs, a method that estimates adaptive weights for members of an ensemble forecast is proposed. The adaptive weights are estimated based on the fit of ensemble priors and posteriors to observations. The performances of ensemble forecasts of ECMWF and NCEP using the adaptive weights are generally improved for track and intensity forecasts. The advantages of the adaptive weights are more prominent for ensemble forecasts of ECMWF than for those of NCEP. 相似文献
11.
BMA集合预报在淮河流域应用及参数规律初探 总被引：1，自引：1，他引：0
以淮河流域吴家渡水文站作为试验站点,采用基于贝叶斯平均法(BMA)的集合预报模型处理来源于马斯京根法、一维水动力学方法、BPNN(Back Propagation Neural Network)的预报流量序列,通过分析BMA的参数以及其预报结果,对各方法在淮河典型站点流量预报中的适用性进行验证与分析.经2003—2016年19场洪水模拟检验可知,BMA模型能够有效避免模型选择带来的洪水预报误差放大效应,可以提供高精度、鲁棒性强的洪水预报结果.通过进一步比较各模型统计最优的频率与BMA权重值之间的相关性,发现权重值不适用于对单场洪水预报精度评定,而适用于描述多场洪水预报中,模型为最优的统计频率;基于大量先验信息,提前获取BMA的权重等参数,将是指导模型选择、降低洪水预报不确定性、改进洪水预报技术的有效手段. 相似文献
12.
Streamflow forecasts are updated periodically in real time, thereby facilitating forecast evolution. This study proposes a forecast-skill-based model of forecast evolution that is able to simulate dynamically updated streamflow forecasts. The proposed model applies stochastic models that deal with streamflow variability to generate streamflow scenarios, which represent cases without forecast skill of future streamflow. The model then employs a coefficient of prediction to determine forecast skill and to quantify the streamflow variability ratio explained by the forecast. By updating the coefficients of prediction periodically, the model efficiently captures the evolution of streamflow forecast. Simulated forecast uncertainty increases with increasing lead time; and simulated uncertainty during a specific future period decreases over time. We combine the statistical model with an optimization model and design a hypothetical case study of reservoir operation. The results indicate the significance of forecast skill in forecast-based reservoir operation. Shortage index reduces as forecast skill increases and ensemble forecast outperforms deterministic forecast at a similar forecast skill level. Moreover, an effective forecast horizon exists beyond which more forecast information does not contribute to reservoir operation and higher forecast skill results in longer effective forecast horizon. The results illustrate that the statistical model is efficient in simulating forecast evolution and facilitates analysis of forecast-based decision making. 相似文献
13.
Correction of inertial oscillations by assimilation of HF radar data in a model of the Ligurian Sea 总被引：1，自引：1，他引：0
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. 相似文献
14.
Estimates of surface drifter trajectories in the equatorial Atlantic: a multi-model ensemble approach 总被引：3，自引：3，他引：0
Robert Bruce Scott Nicolas Ferry Marie Drévillon Charlie N. Barron Nicolas C. Jourdain Jean-Michel Lellouche Edward Joseph Metzger Marie-Hélène Rio Ole Martin Smedstad 《Ocean Dynamics》2012,62(7):1091-1109
We compared the estimates of surface drifter trajectories from 1 to 7?days in the equatorial Atlantic over an 18-month period with five eddying ocean general circulation model (OGCM) reanalyses and one observational product. The cumulative distribution of trajectory error was estimated using over 7,000?days of drifter trajectories. The observational product had smaller errors than any of the individual OGCM reanalyses. Three strategies for improving trajectory estimates using the ensemble of five operational ocean analysis and forecasting products were explored: two methods using a multi-model ensemble estimate and also spatial low-pass filtering. The results were insensitive to the method used to create the ensemble estimates, and by most measures, the results were better than the observational product. Comparison of relative skill of the various OGCM reanalyses suggested promising avenues for exploration for further improvements: forcing with higher frequency wind stress and quality control of input data. One of the lowest horizontal resolution OGCMs, with 1/4° longitude horizontal resolution, made the best trajectory estimates. The individual OGCMs were dominated by errors at spatial scales smaller than about 100 to 200?km, i.e., less than the local deformation radius. But buried in those errors were valuable signals that could be retrieved by combining all the OGCM velocity fields to produce a multi-model ensemble-based estimate. This estimate had skill down to spatial scales about 75?km. Results from this study are consistent with previous work showing that ensemble-mean forecast skill is superior to individual forecasts. 相似文献
15.
Short-term water system operation can be realized using Model Predictive Control (MPC). MPC is a method for operational management of complex dynamic systems. Applied to open water systems, MPC provides integrated, optimal, and proactive management, when forecasts are available. Notwithstanding these properties, if forecast uncertainty is not properly taken into account, the system performance can critically deteriorate.Ensemble forecast is a way to represent short-term forecast uncertainty. An ensemble forecast is a set of possible future trajectories of a meteorological or hydrological system. The growing ensemble forecasts’ availability and accuracy raises the question on how to use them for operational management.The theoretical innovation presented here is the use of ensemble forecasts for optimal operation. Specifically, we introduce a tree based approach. We called the new method Tree-Based Model Predictive Control (TB-MPC). In TB-MPC, a tree is used to set up a Multistage Stochastic Programming, which finds a different optimal strategy for each branch and enhances the adaptivity to forecast uncertainty. Adaptivity reduces the sensitivity to wrong forecasts and improves the operational performance.TB-MPC is applied to the operational management of Salto Grande reservoir, located at the border between Argentina and Uruguay, and compared to other methods. 相似文献
16.
A variational method based on previous numerical forecasts is developed to estimate and correct non-systematic component of numerical weather forecast error. In the method, it is assumed that the error is linearly dependent on some combination of the forecast fields, and three types of forecast combination are applied to identifying the forecasting error: 1) the forecasts at the ending time, 2) the combination of initial fields and the forecasts at the ending time, and 3) the combination of the forecasts at... 相似文献
17.
18.
Forecast ensembles of hydrological and hydrometeorologial variables are prone to various uncertainties arising from climatology, model structure and parameters, and initial conditions at the forecast date. Post‐processing methods are usually applied to adjust the mean and variance of the ensemble without any knowledge about the uncertainty sources. This study initially addresses the drawbacks of a commonly used statistical technique, quantile mapping (QM), in bias correction of hydrologic forecasts. Then, an auxiliary variable, the failure index (γ), is proposed to estimate the ineffectiveness of the post‐processing method based on the agreement of adjusted forecasts with corresponding observations during an analysis period prior to the forecast date. An alternative post‐processor based on copula functions is then introduced such that marginal distributions of observations and model simulations are combined to create a multivariate joint distribution. A set of 2500 hypothetical forecast ensembles with parametric marginal distributions of simulated and observed variables are post‐processed with both QM and the proposed multivariate post‐processor. Deterministic forecast skills show that the proposed copula‐based post‐processing is more effective than the QM method in improving the forecasts. It is found that the performance of QM is highly correlated with the failure index, unlike the multivariate post‐processor. In probabilistic metrics, the proposed multivariate post‐processor generally outperforms QM. Further evaluation of techniques is conducted for river flow forecast of Sprague River basin in southern Oregon. Results show that the multivariate post‐processor performs better than the QM technique; it reduces the ensemble spread and is a more reliable approach for improving the forecast. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
19.
Teddy R. Holt James A. Cummings Craig H. Bishop James D. Doyle Xiaodong Hong Sue Chen Yi Jin 《Ocean Dynamics》2011,61(11):1937-1954
A coupled ocean–atmosphere mesoscale ensemble prediction system has been developed by the Naval Research Laboratory. This paper describes the components and implementation of the system and presents baseline results from coupled ensemble simulations for two tropical cyclones. The system is designed to take into account major sources of uncertainty in: (1) non-deterministic dynamics, (2) model error, and (3) initial states. The purpose of the system is to provide mesoscale ensemble forecasts for use in probabilistic products, such as reliability and frequency of occurrence, and in risk management applications. The system components include COAMPS® (Coupled Ocean/Atmosphere Mesoscale Prediction System) and NCOM (Navy Coastal Ocean Model) for atmosphere and ocean forecasting and NAVDAS (NRL Atmospheric Variational Data Assimilation System) and NCODA (Navy Coupled Ocean Data Assimilation) for atmosphere and ocean data assimilation. NAVDAS and NCODA are 3D-variational (3DVAR) analysis schemes. The ensembles are generated using separate applications of the Ensemble Transform (ET) technique in both the atmosphere (for moving or non-moving nests) and the ocean. The atmospheric ET is computed using wind, temperature, and moisture variables, while the oceanographic ET is derived from ocean current, temperature, and salinity variables. Estimates of analysis error covariance, which is used as a constraint in the ET, are provided by the ocean and atmosphere 3DVAR assimilation systems. The newly developed system has been successfully tested for a variety of configurations, including differing model resolution, number of members, forecast length, and moving and fixed nest options. Results from relatively coarse resolution (～27-km) ensemble simulations of Hurricanes Hanna and Ike demonstrate that the ensemble can provide valuable uncertainty information about the storm track and intensity, though the ensemble mean provides only a small amount of improved predictive skill compared to the deterministic control member. 相似文献
20.
AbstractData-based mechanistic (DBM) models can offer a parsimonious representation of catchment dynamics. They have been shown to provide reliable accurate flood forecasts in many hydrological situations. In this work, the DBM methodology is applied to forecast flash floods in a small Alpine catchment. Compared to previous DBM modelling studies, the catchment response is rapid. The use of novel radar-derived ensemble quantitative precipitation forecasts based on analogues to drive the DBM model allows the forecast horizon to be increased to a level useful for emergency response. The characterization of the predictive uncertainty in the resulting hydrological forecasts is discussed and a framework for its representation illustrated.
Editor Z.W. Kundzewicz; Guest editor R.J. Moore 相似文献