共查询到20条相似文献，搜索用时 31 毫秒
1.
利用全球谱模式T106L19和增长模繁殖法,分别在气候海温和预测海温强迫下,进行了动力延伸集合预报试验.基于方差分析思想,利用集合预报结果,定义和计算了初值影响指数、海温强迫影响指数、潜在可预报性指数以及波动活动指数.通过分析四个指数,揭示了初值和海温强迫对延伸期可预报性时空分布以及潜在可预报性的影响,并探讨了其影响机理.结果表明：初值影响指数分布具有地域和季节的差异,初值的影响在中高纬度地区大于热带地区;相同季节,海温强迫影响指数分布与初值影响指数分布相似;潜在可预报性指数呈带状分布,大值集中在热带地区,且在低纬度地区,高层的潜在可预报性大于低层;初值和海温强迫对延伸期可预报性时空分布的影响,依赖于大气环流形势,初值和海温强迫影响的显著区正是大气长波的活跃区和西风急流区,急流区的强风切变为长波活动提供了斜压不稳定能量,而长波的发展调控着初值和海温强迫的影响,这说明延伸期的可预报性具有明显的流依赖性,大气外强迫的作用也与大气内部的动力过程密切相关. 相似文献
2.
从理论上探讨如何提高天气预报和气候预测的技巧.气候包括以小时为基本单位的昼夜循环、以日为基本单位的年(季节)循环、年代际循环和世纪循环等时间尺度的变化.这些气候变化存在确定的外强迫,是可以被认识和预报的.相对气候昼夜循环和年(季节)循环的偏差是天气尺度扰动.天气尺度的瞬变大气扰动可引发极端天气事件.有技巧的天气预报正是要通过天气尺度大气扰动信号,提前几天甚至十几天,预报出极端天气事件的发生.相对气候年代际和世纪循环的偏差是气候异常,有技巧的气候预测正是要预报出这种异常.距平天气图会大大提高短期和中期—延伸期天气预报的技巧,距平数值预报模式的研制也会加快提高中期—延伸期天气预报和气候预测的技巧. 相似文献
3.
Short‐term flood inundation prediction using hydrologic‐hydraulic models forced with downscaled rainfall from global NWP 
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下载免费PDF全文 A short‐term flood inundation prediction model has been formulated based on the combination of the super‐tank model, forced with downscaled rainfall from a global numerical weather prediction model, and a one‐dimensional (1D) hydraulic model. Different statistical methods for downscaled rainfall have been explored, taking into account the availability of historical data. It has been found that the full implementation of a statistical downscaling model considering physically‐based corrections to the numerical weather prediction model output for rainfall prediction performs better compared with an altitudinal correction method. The integration of the super‐tank model into the 1D hydraulic model demonstrates a minimal requirement for the calibration of rainfall–runoff and flood propagation models. Updating the model with antecedent rainfall and regular forecast renewal has enhanced the model's capabilities as a result of the data assimilation processes of the runoff and numerical weather prediction models. The results show that the predicted water levels demonstrate acceptable agreement with those measured by stream gauges and comparable to those reproduced using the actual rainfall. Moreover, the predicted flood inundation depth and extent exhibit reasonably similar tendencies to those observed in the field. However, large uncertainties are observed in the prediction results in lower, flat portions of the river basin where the hydraulic conditions are not properly analysed by the 1D flood propagation model. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
4.
Ana J. Abascal Sonia Castanedo Paula Núñez Adam Mellor Annika Clements Beatriz Pérez Mar Cárdenas Helios Chiri Raúl Medina 《Marine pollution bulletin》2017,114(1):302-314
This paper presents a high-resolution operational forecast system for providing support to oil spill response in Belfast Lough. The system comprises an operational oceanographic module coupled to an oil spill forecast module that is integrated in a user-friendly web application. The oceanographic module is based on Delft3D model which uses daily boundary conditions and meteorological forcing obtained from COPERNICUS and from the UK Meteorological Office. Downscaled currents and meteorological forecasts are used to provide short-term oil spill fate and trajectory predictions at local scales. Both components of the system are calibrated and validated with observational data, including ADCP data, sea level, temperature and salinity measurements and drifting buoys released in the study area. The transport model is calibrated using a novel methodology to obtain the model coefficients that optimize the numerical simulations. The results obtained show the good performance of the system and its capability for oil spill forecast. 相似文献
5.
准确、及时的入库洪水预报,对三峡水库综合效益的发挥和长江流域水旱灾害防御、水资源利用、流域综合管理等具有重要作用。基于预报误差的最优分布估计和分布函数动态参数假定,提出了一种三峡水库入库洪水概率预报方法,并进行了洪水概率预报业务试验。结果表明:本文所提方法科学可行,计算快捷,使用方便,便于在实时作业预报中应用推广;概率预报结果较确定性预报结果,在水量预报、预警效果等方面均有所改善,1～5 d预见期预报的确定性系数提高0.1%～3.4%,水量误差减少0.1%～4.8%,可为三峡水库实时调度提供更可靠的预报信息;所提出的三峡水库入库洪水概率预报业务化产品,可提供更多风险信息,为三峡水库的科学调度,尤其是洪水资源化利用提供更好的优化决策支撑。 相似文献
6.
A nonlinear forecasting method was used to predict the behavior of a cloud coverage time series several hours in advance. The method is based on the reconstruction of a chaotic strange attractor using four years of cloud absorption data obtained from half-hourly Meteosat infrared images from Northwestern Spain. An exhaustive nonlinear analysis of the time series was carried out to reconstruct the phase space of the underlying chaotic attractor. The forecast values are used by a non-hydrostatic meteorological model ARPS for daily weather prediction and their results compared with surface temperature measurements from a meteorological station and a vertical sounding. The effect of noise in the time series is analyzed in terms of the prediction results. 相似文献
7.
针对西太平洋副热带高压中长期预报不准确的问题,基于动力系统反演思想和改进自忆性原理等途径建立了副高脊线指数的动力预报模型.本文创新性地引入了最大李雅普诺夫指数改进了传统的自忆性函数,使其对副热带高压之类的混沌非线性系统更加具有针对性,较好地克服了预报初值单一性问题;并根据实际观测资料重构的动力系统作为其动力核,克服了传统自忆性方程动力核设置较为简单的问题.用建立的副热带高压脊线指数动力预报模型实现了副高南北位置的中长期预报,通过了副高异常年份和正常年份的多次实验,可以发现模型在25天以内的预报效果很好,相关系数能达到0.80左右,相对误差控制在8%以下,证明了改进的模型具有较好的中长期预报效果.另外还将此模型推广到对副热带高压的面积指数和西脊点指数的预报,也取得了较好的预报效果,证明此方法适合于副热带高压的整体预报.鉴于西太副高发生发展机理的复杂性和预报的困难性,本文为副高等复杂天气系统的预报探索了新的方法思路. 相似文献
8.
Artificial neural networks as routine for error correction with an application in Singapore regional model 总被引：1，自引：1，他引：0
This research presents an error correction scheme based on artificial neural networks, and demonstrates its application on
water level forecast for the Singapore water. The error correction scheme combines the numerical model outputs with the in
situ measurements on a two-step basis: (1) predicting the model errors at the measurement stations and (2) distributing the
predicted errors to the nonmeasurement stations. Artificial neural networks are used in both error prediction and error distribution
as the mapping function approximators. The efficiency of this scheme is tested on six water level stations in the Singapore
regional model domain with four prediction horizons. The results show that this error correction scheme produces high-precision
forecasts, and improves the forecast accuracy at both measurement and nonmeasurement stations. 相似文献
9.
This paper presents the development of an adaptive, non-parametric forecast model for the direct prediction of the spatial distribution of the Modified Mercalli Intensity (MMI) corresponding to an earthquake scenario. The model is based on recent advances in neural networks computation, and is constructed through supervised learning using historical earthquake and regional geological data as training sets. A MMI forecast model for moderate earthquakes with magnitudes between 6 and 7 was developed based on data from the Loma Prieta, Coalinga and Morgan Hill earthquakes. For these data sets, the neural networks forecast model is shown to have excellent data synthesis capability; multiple sets of data can be encapsulated by a relatively simple network architecture. Limited comparison of forecasts made by the neural networks model and conventional models demonstrates that improved accuracy can be achieved. Implementation and operational advantages of the neural networks approach such as general input features, minimum preconceived knowledge of the data sets, the ability to learn and to adapt incrementally and the autonomous and automatic synthesis of the structure underlying the data sets, have been illustrated. 相似文献
10.
Strategy and methodology of dynamical analogue prediction 总被引：8，自引：0，他引：8
In order to effectively improve numerical prediction level by using current models and data, the strategy and methodology of dynamical analogue prediction (DAP) is deeply studied in the present paper. A new idea to predict the prediction errors of dynamical model on the basis of historical analogue information is put forward so as to transform the dynamical prediction problem into the estimation problem of prediction errors. In terms of such an idea, a new prediction method of final analogue correction of errors (FACE) is developed. Furthermore, the FACE is applied to extra-seasonal prediction experiments on an operational atmosphere-ocean coupled general circulation model. Prediction results of summer mean circulation and total precipitation show that the FACE can to some extent reduce prediction errors, recover prediction variances, and improve prediction skills. Besides, sensitive experiments also show that predictions based on the FACE are evidently influenced by the number of analogues, analogue-selected variables and analogy metric. 相似文献
11.
The influence of the uncertainties of intra-seasonal wind stress forcing on Spring Predictability Barrier (SPB) in El Niño–Southern Oscillation (ENSO) prediction is studied with the Zebiak–Cane model and observational wind data which are analyzed with Continuous Wavelet Transform (CWT) and utilized to extract intra-seasonal wind stress signals as external forcing. The observational intra-seasonal wind stress forcing are joined into Zebiak–Cane model to get the Zebiak–Cane-add model and subsequently with the Conditional Nonlinear Optimal Perturbation (CNOP) method, the evolutions of the optimal initial errors (i.e., CNOPs), model errors caused by intra-seasonal wind stress uncertainties, and their joint errors based on ENSO events are calculated. By investigating their error growth rates and prediction errors of Niño-3 indices, the effect of observational intra-seasonal wind stress forcing on seasonal error growth of ENSO is explored and the impact of initial error and model error on ENSO predictability is compared quantitatively. The results show that the model errors led by observational intra-seasonal wind stress forcing could scarcely cause a significant SPB whereas the initial errors and their joint errors can do; hence, the initial errors are most likely the main error source of SPB. In fact, this result emphasizes the primary influence of initial errors on ENSO predictability and lays the basis of adaptive data assimilation for ENSO forecast. 相似文献
12.
Z. Sokol 《Studia Geophysica et Geodaetica》2006,50(1):105-120
A statistical post-processing methodology for application to numerical weather prediction (NWP) model outputs for precipitation
forecast is proposed. The post-processing is based on the model output statistics approach. The statistical relationships
are described by the multiple linear regression model, which is complemented by an iteration procedure to further correct
the regression outputs. Prognostic fields of the ALADIN/LACE (Aire Limitée Adaptation Dynamique Développement InterNational/Limited
Area Modelling in Central Europe) NWP model are used for the forecast of 6-hourly areal precipitation amounts at 15 river
basins. The NWP model integration starts at 00UTC and forecasts are calculated for lead times of +12, +18, +24 and +30 hours.
The post-processing models are developed separately for each lead time and for separate warm (April to September) and cool
(October to March) seasons. The forecasts are focused on large precipitation amounts. Using all the combinations, data from
four years (1999–2002) are divided into calibration data (3 years), where the models are developed, and verification data.
The models are evaluated by examining the root-mean-square error (RMSE), bias, and correlation coefficient (CC) on the verification
data samples.
The results show that the additional iteration procedure increases the forecast accuracy for a given range of precipitation
amounts and simultaneously does not deteriorate the bias, a situation which can arise when negative regression outputs are
set to zero. The post-processing method improves the forecast of the NWP model in terms of RMSE and CC. For large precipitation
amounts during the summer season, the decrease of RMSE reaches 10% to 20% depending upon the applied method of verification.
For the cool season, the decrease is somewhat smaller (7% to 15%). 相似文献
13.
《中国科学:地球科学(英文版)》2017,(3)
Based on the theory of information entropy concerning nonlinear errors,the growth rules for the nonlinear errors of the Lorenz system and its predictable components are studied.The results show that the impact of the uncertainties,both in the initial error and in the system itself,needs to be considered in a quantitative estimation of the system predictability.The nonlinear error growth is related to the magnitude of the initial error,and to the spatial distribution of the initial error vectors.Even if these initial errors have the same magnitude but different directions,there are also differences in the nonlinear error growth.The predictability of nonlinear error growth is related to the error component,but not related to the ratio of these components.The component with the highest/lowest rate of contribution does not necessarily have the greatest/least predictability.The different components have different predictabilities,and in different time periods,the different predictable components also have different predictabilities. 相似文献
14.
Accurate sonar performance prediction modelling depends on a good knowledge of the local environment, including bathymetry,
oceanography and seabed properties. The function of rapid environmental assessment (REA) is to obtain relevant environmental
data in a tactically relevant time frame, with REA methods categorized by the nature and immediacy of their application, from
historical databases through remotely sensed data to in situ acquisition. However, each REA approach is subject to its own
set of uncertainties, which are in turn transferred to uncertainty in sonar performance prediction. An approach to quantify
and manage this uncertainty has been developed through the definition of sensitivity metrics and Monte Carlo simulations of
acoustic propagation using multiple realizations of the marine environment. This approach can be simplified by using a linearized
two-point sensitivity measure based on the statistics of the environmental parameters used by acoustic propagation models.
The statistical properties of the environmental parameters may be obtained from compilations of historical data, forecast
conditions or in situ measurements. During a field trial off the coast of Nova Scotia, a set of environmental data, including
oceanographic and geoacoustic parameters, were collected together with acoustic transmission loss data. At the same time,
several numerical models to forecast the oceanographic conditions were run for the area, including 5- and 1-day forecasts
as well as nowcasts. Data from the model runs are compared to each other and to in situ environmental sampling, and estimates
of the environmental uncertainties are calculated. The forecast and in situ data are used with historical geoacoustic databases
and geoacoustic parameters collected using REA techniques, respectively, to perform acoustic transmission loss predictions,
which are then compared to measured transmission loss. The progression of uncertainties in the marine environment, within
and between different REA categories, and the consequences on acoustic propagation are examined. 相似文献
15.
ABSTRACTIn this paper, a mid- to long-term runoff forecast model is developed using an ideal point fuzzy neural network–Markov (NFNN-MKV) hybrid algorithm to improve the forecasting precision. Combining the advantages of the new fuzzy neural network and the Markov prediction model, this model can solve the problem of stationary or volatile strong random processes. Defined error statistics algorithms are used to evaluate the performance of models. A runoff prediction for the Si Quan Reservoir is made by utilizing the modelling method and the historical runoff data, with a comprehensive consideration of various runoff-impacting factors such as rainfall. Compared with the traditional fuzzy neural networks and Markov prediction models, the results show that the NFNN-MKV hybrid algorithm has good performance in faster convergence, better forecasting accuracy and significant improvement of neural network generalization. The absolute percentage error of the NFNN-MKV hybrid algorithm is less than 7.0%, MSE is less than 3.9, and qualification rate reaches 100%. For further comparison of the proposed model, the NFNN-MKV model is employed to estimate (training and testing for 120-month-ahead prediction) and predict river discharge for 156 months at Weijiabao on the Weihe River in China. Comparisons among the results of the NFNN-MKV model, the WNN model and the SVR model indicate that the NFNN-MKV model is able to significantly increase prediction accuracy.
Editor D. Koutsoyiannis; Associate editor Y. Gyasi-Agyei 相似文献
16.
《Advances in water resources》2004,27(7):761-772
The classical deterministic approach to tidal prediction is based on barotropic or baroclinic models with prescribed boundary conditions from a global model or measurements. The prediction by the deterministic model is limited by the precision of the prescribed initial and boundary conditions. Improvement to the knowledge of model formulation would only marginally increase the prediction accuracy without the correct driving forces. This study describes an improvement in the forecasting capability of the tidal model by combining the best of a deterministic model and a stochastic model. The latter is overlaid on the numerical model predictions to improve the forecast accuracy. The tidal prediction is carried out using a three-dimensional baroclinic model and, error correction is instigated using a stochastic model based on a local linear approximation. Embedding theorem based on the time lagged embedded vectors is the basis for the stochastic model. The combined model could achieve an efficiency of 80% for 1 day tidal forecast and 73% for a 7 day tidal forecast as compared to the deterministic model estimation. 相似文献
17.
Christina Thoma Werner Schneider Matthieu Masbou Andreas Bott 《Pure and Applied Geophysics》2012,169(5-6):881-893
The numerical prediction of fog requires a very high vertical resolution of the atmosphere. Owing to a prohibitive computational effort of high resolution three dimensional models, operational fog forecast is usually done by means of one dimensional fog models. An important condition for a successful fog forecast with one dimensional models consists of the proper integration of observational data into the numerical simulations. The goal of the present study is to introduce new methods for the consideration of these data in the one dimensional radiation fog model PAFOG. First, it will be shown how PAFOG may be initialized with observed visibilities. Second, a nudging scheme will be presented for the inclusion of measured temperature and humidity profiles in the PAFOG simulations. The new features of PAFOG have been tested by comparing the model results with observations of the German Meteorological Service. A case study will be presented that reveals the importance of including local observations in the model calculations. Numerical results obtained with the modified PAFOG model show a distinct improvement of fog forecasts regarding the times of fog formation, dissipation as well as the vertical extent of the investigated fog events. However, model results also reveal that a further improvement of PAFOG might be possible if several empirical model parameters are optimized. This tuning can only be realized by comprehensive comparisons of model simulations with corresponding fog observations. 相似文献
18.
Using predictions for the sea surface temperature anomaly(SSTA) generated by an intermediate coupled model(ICM)ensemble prediction system(EPS), we first explore the "spring predictability barrier"(SPB) problem for the 2015/16 strong El Nio event from the perspective of error growth. By analyzing the growth tendency of the prediction errors for ensemble forecast members, we conclude that the prediction errors for the 2015/16 El Nio event tended to show a distinct season-dependent evolution, with prominent growth in spring and/or the beginning of the summer. This finding indicates that the predictions for the 2015/16 El Nio occurred a significant SPB phenomenon. We show that the SPB occurred in the 2015/16 El Nio predictions did not arise because of the uncertainties in the initial conditions but because of model errors. As such, the mean of ensemble forecast members filtered the effect of model errors and weakened the effect of the SPB, ultimately reducing the prediction errors for the 2015/16 El Nio event. By investigating the model errors represented by the tendency errors for the SSTA component,we demonstrate the prominent features of the tendency errors that often cause an SPB for the 2015/16 El Nio event and explain why the 2015/16 El Nio was under-predicted by the ICM EPS. Moreover, we reveal the typical feature of the tendency errors that cause not only a significant SPB but also an aggressively large prediction error. The feature is that the tendency errors present a zonal dipolar pattern with the west poles of positive anomalies in the equatorial western Pacific and the east poles of negative anomalies in the equatorial eastern Pacific. This tendency error bears great similarities with that of the most sensitive nonlinear forcing singular vector(NFSV)-tendency errors reported by Duan et al. and demonstrates the existence of an NFSV tendency error in realistic predictions. For other strong El Nio events, such as those that occurred in 1982/83 and 1997/98, we obtain the tendency errors of the NFSV structure, which cause a significant SPB and yield a much larger prediction error. These results suggest that the forecast skill of the ICM EPS for strong El Nio events could be greatly enhanced by using the NFSV-like tendency error to correct the model. 相似文献
19.
Local extreme rain usually resulted in disasters such as flash floods and landslides. Upon today, it is still one of the most difficult tasks for operational weather forecast centers to predict those events accurately. In this paper, we simulate an extreme precipitation event with ensemble Kalman filter (EnKF) assimilation of Doppler radial-velocity observations, and analyze the uncertainties of the assimilation. The results demonstrate that, without assimilation radar data, neither a single initialization of deterministic forecast nor an ensemble forecast with adding perturbations or multiple physical parameterizations can predict the location of strong precipitation. However, forecast was significantly improved with assimilation of radar data, especially the location of the precipitation. The direct cause of the improvement is the buildup of a deep mesoscale convection system with EnKF assimilation of radar data. Under a large scale background favorable for mesoscale convection, efficient perturbations of upstream mid-low level meridional wind and moisture are key factors for the assimilation and forecast. Uncertainty still exists for the forecast of this case due to its limited predictability. Both the difference of large scale initial fields and the difference of analysis obtained from EnKF assimilation due to small amplitude of initial perturbations could have critical influences to the event's prediction. Forecast could be improved through more cycles of EnKF assimilation. Sensitivity tests also support that more accurate forecasts are expected through improving numerical models and observations. 相似文献
20.
The regional verification of soil moisture is a vital step in evaluating and improving numerical model performance and utilizing forecast results. Currently, even with improved spatial and temporal resolutions of numerical model, verification methods for soil moisture data still rely on the traditional intensity verification parameters, such as mean error (ME) and root-mean-squared error (RMSE). Those methods provide only incomplete and sometimes inaccurate messages and thus hinder a proper evaluation of a forecast model. The SAL method is an object-based regional verification method with respect to precipitation forecasts. Based on the SAL method, a novel object-based method (SAL-DN) is proposed here, which can be used to test regional soil moisture. Both the ideal experiment and real experiment show that the SAL-DN method can reveal the differences between the observed and forecast soil moisture in three aspects: structure, amplitude, and location, and the results can reflect the actual situation. Furthermore, compared with the SAL method, the SAL-DN method is also capable of verifying physical quantities with high-value and low-value centers like temperature. Therefore, the SAL-DN method enhances verification accuracy and can be applied widely. 相似文献