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71.
海洋生态预报的复杂性与研究方法的讨论 总被引:1,自引:1,他引:0
海洋生态动态预报预测研究已经成为海洋科学乃至地球系统科学领域中的热点问题。比较深入地分析了海洋生态预报的复杂性、不确定性和实况监测资料严重不足等问题,为促进海洋生态预报研究的快速发展,借鉴中期数值天气预报的一些有效方法,提出以下建议:①加深理解海洋生态系统的非线性动力学特征,深入开展随机-动力耦合的海洋生态系统模型研究;②加强海洋生态集合(ensemble)预报和综合预报方法研究;③大力促进卫星遥感信息的海洋生态应用研究,加强资料同化研究和反问题研究方法的应用,努力发掘各种信息资料的预报应用。 相似文献
72.
春季海温对中国夏季降水影响的诊断研究和预测试验 总被引:9,自引:2,他引:9
文中利用季降水异常集合的典型相关预测模式 ,以全球春季 (3~ 5月 )海温场作为因子场 ,对中国夏季降水场进行了诊断研究 ,并对 1998,1999及 2 0 0 0年这几个典型的中国夏季降水进行了回报试验。结果表明 ,春季海温与中国夏季降水之间存在较好的关系 ,春季海温在较大程度上决定了中国夏季降水雨带及其分布类型。考虑面积因子的集合典型相关预测方案对中国夏季降水具有较强的回报能力 ,此模式不仅能诊断出降水场和海温场中一些比较典型的空间模态和时间变化规律 ,而且可以再现 1998和 2 0 0 0年中国大部分地区的旱涝灾害。揭示了全球春季海温的异常变化在中国夏季 (6~ 8月 )降水异常中的作用。 相似文献
73.
Qiang Shu Mariush W. Kemblowski Mac McKee 《Stochastic Environmental Research and Risk Assessment (SERRA)》2005,19(5):361-374
Data assimilation method provides a framework to decrease the uncertainty of hydrological modeling by sequentially incorporating
observations into numerical model. Such a process involves estimating statistical moments of different order based on the
evolution of conditional probability distribution function. Because of the nonlinearity of many hydrological dynamics, explicit
and analytical solutions for moments of state distribution are often impossible. Evensen [J Geophys Res 99(c5): 10143–10162
(1994)] introduced Ensemble Kalman Filtering (EnKF) method to address such problems. We test and evaluate the performance
of EnKF in fusing model predictions and observations for a saturated–unsaturated integral-balance subsurface model. We find
EnKF improve the model predictions, and also we conclude a good estimate of state variance is essential for the success of
EnKF. 相似文献
74.
Quantifying uncertainty in changes in extreme event frequency in response to doubled CO2 using a large ensemble of GCM simulations 总被引:2,自引:1,他引:2
David N. Barnett Simon J. Brown James M. Murphy David M. H. Sexton Mark J. Webb 《Climate Dynamics》2006,26(5):489-511
We discuss equilibrium changes in daily extreme surface air temperature and precipitation events in response to doubled atmospheric
CO2, simulated in an ensemble of 53 versions of HadSM3, consisting of the HadAM3 atmospheric general circulation model (GCM)
coupled to a mixed layer ocean. By virtue of its size and design, the ensemble, which samples uncertainty arising from the
parameterisation of atmospheric physical processes and the effects of natural variability, provides a first opportunity to
quantify the robustness of predictions of changes in extremes obtained from GCM simulations. Changes in extremes are quantified
by calculating the frequency of exceedance of a fixed threshold in the 2 × CO2 simulation relative to the 1 × CO2 simulation. The ensemble-mean value of this relative frequency provides a best estimate of the expected change while the
range of values across the ensemble provides a measure of the associated uncertainty. For example, when the extreme threshold
is defined as the 99th percentile of the 1 × CO2 distribution, the global-mean ensemble-mean relative frequency of extremely warm days is found to be 20 in January, and 28
in July, implying that events occurring on one day per hundred under present day conditions would typically occur on 20–30 days
per hundred under 2 × CO2 conditons. However the ensemble range in the relative frequency is of similar magnitude to the ensemble-mean value, indicating
considerable uncertainty in the magnitude of the increase. The relative frequencies in response to doubled CO2 become smaller as the threshold used to define the extreme event is reduced. For one variable (July maximum daily temperature)
we investigate this simulated variation with threshold, showing that it can be quite well reproduced by assuming the response
to doubling CO2 to be characterised simply as a uniform shift of a Gaussian distribution. Nevertheless, doubling CO2 does lead to changes in the shape of the daily distributions for both temperature and precipitation, but the effect of these
changes on the relative frequency of extreme events is generally larger for precipitation. For example, around one-fifth of
the globe exhibits ensemble-mean decreases in time-averaged precipitation accompanied by increases in the frequency of extremely
wet days. The ensemble range of changes in precipitation extremes (relative to the ensemble mean of the changes) is typically
larger than for temperature extremes, indicating greater uncertainty in the precipitation changes. In the global average,
extremely wet days are predicted to become twice as common under 2 × CO2 conditions. We also consider changes in extreme seasons, finding that simulated increases in the frequency of extremely warm
or wet seasons under 2 × CO2 are almost everywhere greater than the corresponding increase in daily extremes. The smaller increases in the frequency of
daily extremes is explained by the influence of day-to-day weather variability which inflates the variance of daily distributions
compared to their seasonal counterparts. 相似文献
75.
The paper presents a novel approach to the setup of a Kalman filter by using an automatic calibration framework for estimation of the covariance matrices. The calibration consists of two sequential steps: (1) Automatic calibration of a set of covariance parameters to optimize the performance of the system and (2) adjustment of the model and observation variance to provide an uncertainty analysis relying on the data instead of ad-hoc covariance values. The method is applied to a twin-test experiment with a groundwater model and a colored noise Kalman filter. The filter is implemented in an ensemble framework. It is demonstrated that lattice sampling is preferable to the usual Monte Carlo simulation because its ability to preserve the theoretical mean reduces the size of the ensemble needed. The resulting Kalman filter proves to be efficient in correcting dynamic error and bias over the whole domain studied. The uncertainty analysis provides a reliable estimate of the error in the neighborhood of assimilation points but the simplicity of the covariance models leads to underestimation of the errors far from assimilation points. 相似文献
76.
神威集合数值天气预报系统是以国产巨型机“神威”为平台的实时业务系统, 检验子系统是其重要的组成部分。文章介绍了神威集合数值天气预报系统中检验资料提取的并行化实现方法及并行效率。 相似文献
77.
78.
文章讨论了基于神威巨型机的并行化集合数值天气预报系统中实现的各种并行算法, 性能分析结果表明并行方案最大限度的利用了神威机的处理器资源, 设计的并行算法效率较高, 满足了实时业务运行的时效要求。 相似文献
79.
根据神威集合数值天气预报的运行特点, 针对其运行过程中可能遇到的问题, 介绍了自主研制的可视化实时监控系统的解决方案。 相似文献
80.
The representer method is applied to a one-dimensional two-phase flow model in porous media; capillary pressure and gravity
are neglected. The Euler–Lagrange equations must be linearized, and one such linearization is presented here. The representer
method is applied to the linear system iteratively until convergence, though a rigorous proof of convergence is out of reach.
The linearization chosen is easy to calculate but does not converge for certain weights; however, a simple damping restores
convergence at the cost of extra iterations. Numerical experiments are performed that illustrate the method, and quick comparison
to the ensemble Kalman smoother is made.
This research was supported by NSF grant EIA-0121523. 相似文献