共查询到18条相似文献，搜索用时 328 毫秒
1.
以福建金溪池潭水库流域为例,采用TIGGE数据中心的ECMWF、UKMO、NCEP等7种模式控制预报产品驱动新安江模型,开展径流集合预报。通过集合挑选、多模式集成前处理以及基于BMA模型的后处理等过程,探讨不同处理方案和初始集合质量对气象水文耦合径流预报精度及不确定性的影响。结果表明,不同的处理方案均能有效提高径流预报的精度和稳定性,同时进行前处理和后处理能从降低误差输入和控制误差输出两方面减小预报误差,相对于其他方案表现更好。初始集合质量对气象水文耦合径流集合预报有一定影响,但前处理或后处理对预报误差的有效控制使得该影响并不显著。总体而言,前处理和后处理过程是提高气象水文耦合径流预报准确性和可靠性必不可少的环节,应予以重视。 相似文献
2.
数值天气预报检验方法研究进展 总被引：10，自引：1，他引：9
数值天气预报检验是改进及应用数值模式的重要环节。近年来,模式检验中的观念不断更新,适用于不同预报产品及不同用户需求的模式检验方法也不断涌现。首先简单回顾了以列联表为基础的传统的模式检验方法。其次重点总结了伴随高分辨率数值预报而出现的空间诊断检验技术,按照检验目的的不同,诊断方法可以归纳为:①基于滤波技术的分辨模式在不同时空尺度上预报能力的邻域法、尺度分离法;②利用位移偏差诊断模式预报位置、面积、方位、轴角等与观测差异的属性判别法、变形评估法。然后阐述了集合样本成员的概率分布函数(PDF)、集合预报与观测概率分布函数相似程度、事件发生的概率预报等集合预报检验方法。最后论述了空间诊断技术、集合预报检验方法的适用领域,并讨论了模式检验中存在的一些问题及未来的发展方向。 相似文献
3.
为了考虑预见期内降水预报的不确定性对洪水预报的影响,采用中国气象局、美国环境预测中心和欧洲中期天气预报中心的TIGGE(THORPEX Interactive Grand Global Ensemble)降水预报数据驱动GR4J水文模型,开展三峡入库洪水集合概率预报,分析比较BMA、Copula-BMA、EMOS、M-BMA 4种统计后处理方法的有效性。结果表明:4种统计后处理方法均能提供一个合理可靠的预报置信区间;其期望值预报精度相较于确定性预报有所提高,尤其是水量误差显著减小;M-BMA方法概率预报效果最佳,它能够考虑预报分布的异方差性,不需要进行正态变换,结构简单,应用灵活。 相似文献
4.
随着全球气候变化、自然变迁及陆表生境改变，极端天气频发且呈现出多尺度时空变异特征，对其进行预报和预警一直是气象水文领域关注的焦点。临近预报可较准确地预报未来短时间天气显著变化，是当前预报强降水等极端事件的主要手段。从基于天气雷达0~3 h外推临近预报、融合数值模式0~6 h临近预报的发展历程梳理了临近预报的研究进展，阐述了雷达外推算法的发展进程、雷达外推预报与数值模式预报融合技术进展，指出"取长补短"的0~6 h融合预报在提高降水预报精度、延长降水预见期等多方面有较大的发展潜力，进一步探寻及提升融合技术是未来融合预报发展的核心。将临近预报以气象水文耦合的方式引入水文预报是从源头提高水文预报精度、保障水文预报效果的主要途径，总结了现阶段主流耦合方式、空间尺度匹配技术、水文模型不确定等陆气耦合中的关键问题，阐述了外推临近预报、融合临近预报作为水文预报输入的研究进展，明确了融合临近预报在延长洪水预见期、提高洪水预报精度中存在优势，并讨论了未来的研究重点及发展方向。 相似文献
5.
为研究洮河流域径流预报,根据河流集合预报方法,利用红旗水文站实时监测资料,基于中国洪水预报系统开发的三水源蓄满产流、滞后演算和马斯京根模块构建全流域洪水预报模型,并应用该模型分析预测红旗水文站径流及其概率分布统计。结果表明:径流拟合过程拟合较好,取得了较好的预报效果,模型满足河流集合预报应用的条件,结论可为洮河流域水资源开发利用、防汛保安提供参考依据。 相似文献
6.
海洋可预报性和集合预报研究综述 总被引：1，自引：0，他引：1
海洋是高度复杂的非线性动力系统,由于海洋初值和数值模式本身存在无法避免的误差,海洋数值预报具有不确定性。通过理解和认识海洋不同时空尺度运动的特征和规律,定量估计和预测海洋动力系统的可预报性,研究预报误差产生的原因及其增长和传播机制,探讨减小预报误差的方法和延长可预报时限的途径,对于改进海洋预报系统、提高预报技巧,具有重要意义。系统回顾了海洋可预报性及其应用的研究进展,论述了海洋可预报性的概念、分类以及国内外的研究现状,其中重点介绍了常用的奇异向量法、李亚普诺夫指数法和繁殖向量法等3种动力学方法以及海洋集合预报研究现状,最后对海洋可预报性的未来发展方向和应用前景给以展望。 相似文献
7.
全球气象模型及新兴人工智能模型为流域水文预报提供了日、次季节、季节等不同时间尺度的海量气象预报数据。与此同时,基于气象预报开展水文预报,涉及到数据获取、模型构建、评估检验等技术问题。本文以全球气象预报相关的研究计划为切入点,调研现有的1 d至2周小时尺度中短期天气预报、1～60 d次季节尺度气象预报、1～12个月季节尺度气象预报以及新兴的人工智能气象预报;梳理气象预报驱动下流域水文预报模型方法,阐述气象预报订正、水文模型设置和预报评估检验等技术环节。基于全球气象预报生成实时和回顾性流域水文预报,定量检验不同预见期下预报精度以评估相关模型方法的预报性能,为水利工程预报-调度实践应用打下坚实的基础。 相似文献
8.
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
9.
基于TIGGE数据的五个单中心集合预报结果(CMA、CMC、ECMWF、NCEP、UKMO)构成的多中心超级集合预报系统的降水量预报,以及相应时段的实测降水量值,应用贝叶斯模式平均法(Bayesian Model Averaging,BMA)建立大渡河流域的BMA概率预报模型。通过CRPS、MAE、BS三种评价指标,对大渡河流域的BMA降水概率预报模型进行评价与检验,三种指标均显示BMA降水概率预报比原始集合预报具有更高的准确性,其中BMA模型的CRPS和MAE指标均值分别相比原始集合预报减少了31.6%和23.9%;分析模型权重参数,得出ECMWF对大渡河流域BMA降水预报贡献最大,即ECMWF对研究区域降水预报效果最好;模型对大渡河流域极端降水预报效果较差,常低估极端降水量。 相似文献
10.
基于全球多模式集合预报(TIGGE)资料,以柴河流域为研究区域,采用TS评分、Brier评分和Talagrand分布等方法对欧洲中期天气预报中心(ECMWF)、美国国家环境中心(NCEP)和英国气象局(UKMO)三个中心集合预报的6h、12h、24h短期降雨量进行定量评估和对比,并分别以实测降雨和NCEP预报降雨驱动新安江模型模拟洪水过程,据此探讨了集合降雨预报的可利用性。得到两个重要结论:ECMWF、NCEP和UKMO对低雨量级降雨的预报效果较好,各雨量级的预报效果有随预见期增长而增加的趋势,且普遍存在空报率较高、漏报率偏低的情况;集合降雨预报信息可应用于新安江模型进行洪水预报,并能够有效的延长洪水预报的预见期。研究成果可在适当条件下推广应用至其它流域的洪水预报作业中。 相似文献
11.
Progress in Researches on Ensemble Forecasting of Extreme Weather Based on Numerical Models 总被引：1，自引：1，他引：0
Under the background of climate change, extreme weather events (e.g., heavy rainfall, heat wave, and cold damage) in China have been occurring more frequently with an increasing trend of induced meteorological disasters. Therefore, it is of great importance to carry out research on forecasting of extreme weather. This paper systematically reviewed the primary methodology of extreme weather forecast, current status in development of ensemble weather forecasting based on numerical models and their applications to forecast of extreme weather, as well as progress in approaches for correcting ensemble probabilistic forecast. Nowadays, the forecasting of extreme weather has been generally dominated by methodology using dynamical models. That is to say, the dynamical forecasting methods based on ensemble probabilistic forecast information have become prevailing in current operational extreme weather forecast worldwide. It can be clearly found that the current major directions of research and development in this field are the application of ensemble forecasts based on numerical models to forecasting of extreme weather, and its improvement through bias correction of ensemble probabilistic forecast. Based on a relatively comprehensive review in this paper, some suggestions with respect to development of extreme weather forecast in future were further given in terms of the issues of how to propose effective approaches on improving level of identification and forecasting of extreme events. 相似文献
12.
In recent decades, population growth associated with unplanned urban occupation has increased the vulnerability of the Brazilian population to natural disasters. In susceptible regions, early flood forecasting is essential for risk management. Still, in Brazil, most flood forecast and warning systems are based either on simplified models of flood wave propagation through the drainage network or on stochastic models. This paper presents a methodology for flood forecasting aiming to an operational warning system that proposes to increase the lead time of a warning through the use of an ensemble of meteorological forecasts. The chosen configuration was chosen so it would be feasible for an operational flood forecast and risk management. The methodology was applied to the flood forecast for the Itajaí-Açu River basin, a region which comprises a drainage area of approximately 15,500 km2 in the state of Santa Catarina, Brazil, historically affected by floods. Ensemble weather forecasts were used as input to the MHD-INPE hydrological model, and the performance of the methodology was assessed through statistical indicators. Results suggest that flood warnings can be issued up to 48 h in advance, with a low rate of false warnings. Streamflow forecasting through the use of hydrological ensemble prediction systems is still scarce in Brazil. To the best of our knowledge, this is the first time this methodology aiming to an operational flood risk management system has been tested in Brazil. 相似文献
13.
气候变化条件下青藏铁路沿线多年冻土概率预报(I):活动层厚度与地温 总被引：3，自引：3，他引：0
对1961-2100年IPCC气候模拟与预测结果进行降尺度处理,得到铁路沿线空间分辨率为1km、时间分辨率为1h的大气边界条件.对铁路和公路沿线钻孔资料在垂直和水平两个方向进行空间差值处理,得到水平1 km、垂直0.1m分辨率的沿线地下含水（冰）量的二维分布,作为初始条件.考虑气候模型预测误差和空间格网内地形的变化,以... 相似文献
14.
为利用水文现象相似性和极限学习机(ELM)集成学习提高洪水预报精度,提出了一种基于相似度匹配的集成ELM洪水预报方法(SM-ELM)。方法首先从多个ELM模型中,为每一个训练样本找到最优的ELM模型,然后从训练集中,为测试样本匹配出最相似的前k个训练样本,最后利用这k个训练样本分别对应的最优ELM模型,对测试样本采用加权平均法进行集成预报。为证明提出方法的可行性和有效性,以昌化流域的历史洪水为例进行了验证。结果表明,相对于单个ELM,集成ELM模型能有效地提高预测精度。从均方根误差上看,集成ELM模型性能比单个ELM模型提升了10%~15%。在三种集成方法中,SM-ELM能够以较少的模型数量获得较高且稳定的预报精度。 相似文献
15.
Probabilistic prediction has the ability to convey the intrinsic uncertainty of forecast that helps the decision makers to manage the climate risk more efficiently than deterministic forecasts. In recent times, probabilistic predictions obtained from the products from General Circulation Models (GCMs) have gained considerable attention. The probabilistic forecast can be generated in parametric (assuming Gaussian distribution) as well as non-parametric (counting method) ways. The present study deals with the non-parametric approach that requires no assumption about the form of the forecast distribution for the prediction of Indian summer monsoon rainfall (ISMR) based on the hindcast run of seven general circulation models from 1982 to 2008. Probabilistic prediction from each of the GCM products has been generated by non-parametric methods for tercile categories (viz. below normal (BN), near-normal (NN), and above normal (AN)) and evaluation of their skill is assessed against observed data. Five different types of PMME schemes have been used for combining probabilities from each GCM to improve the forecast skill as compared to the individual GCMs. These schemes are different in nature of assigning the weights for combining probabilities. After a rigorous analysis through Rank Probability Skill Score (RPSS) and relative operating characteristic (ROC) curve, the superiority of PMME has been established over climatological probability. It is also found that, the performances of PMME1 and PMME3 are better than all the other methods whereas PMME3 has showed more improvement over PMME1. 相似文献
16.
Experimental real-time multi-model ensemble (MME) prediction of rainfall during monsoon 2008: Large-scale medium-range aspects 总被引：1，自引：0，他引：1
A K MITRA G R IYENGAR V R DURAI J SANJAY T N KRISHNAMURTI A MISHRA D R SIKKA 《Journal of Earth System Science》2011,120(1):27-52
Realistic simulation/prediction of the Asian summer monsoon rainfall on various space–time scales is a challenging scientific
task. Compared to mid-latitudes, a proportional skill improvement in the prediction of monsoon rainfall in the medium range
has not happened in recent years. Global models and data assimilation techniques are being improved for monsoon/tropics. However,
multi-model ensemble (MME) forecasting is gaining popularity, as it has the potential to provide more information for practical
forecasting in terms of making a consensus forecast and handling model uncertainties. As major centers are exchanging model
output in near real-time, MME is a viable inexpensive way of enhancing the forecasting skill and information content. During
monsoon 2008, on an experimental basis, an MME forecasting of large-scale monsoon precipitation in the medium range was carried
out in real-time at National Centre for Medium Range Weather Forecasting (NCMRWF), India. Simple ensemble mean (EMN) giving
equal weight to member models, bias-corrected ensemble mean (BCEMn) and MME forecast, where different weights are given to
member models, are the products of the algorithm tested here. In general, the aforementioned products from the multi-model
ensemble forecast system have a higher skill than individual model forecasts. The skill score for the Indian domain and other
sub-regions indicates that the BCEMn produces the best result, compared to EMN and MME. Giving weights to different models
to obtain an MME product helps to improve individual member models only marginally. It is noted that for higher rainfall values,
the skill of the global model rainfall forecast decreases rapidly beyond day-3, and hence for day-4 and day-5, the MME products
could not bring much improvement over member models. However, up to day-3, the MME products were always better than individual
member models. 相似文献
17.
Due to the limitations of model performances, the predictive skills of current climate models for the Asian-Australian summer monsoon precipitation are still poor. The prediction based on the combination of statistical and dynamic approaches is an effective way to improve the predictive skills. We used such method to identify the predictable modes of the Asian-Australian summer monsoon precipitation with clear physical interpretation from the historical observational data. Then we combined the principal components time series of these modes predicted by the coupled models, which is derived from the seasonal prediction experiments in the ENSEMBLES project, and the corresponding spatial patterns derived from the above observational analysis to reconstruct the precipitation field. These formed a statistical-dynamic seasonal prediction model for the Asian-Australian summer monsoon precipitation. We analyzed the predictive skills of the model at 1-, 4-and 7-month leads. The result shows that the forecast skills of the statistical-dynamic prediction model are higher than those of the simple dynamic predictions. In addition, the predictive skills of the Multi-Model Ensemble (MME) mean are superior to those of any individual models. Therefore, it is very necessary to implement multi-model ensemble prediction for the monsoon precipitation. 相似文献
18.
Ocean is a highly complex and nonlinear dynamical system. The inevitable errors in both data and numerical models lead to uncertainties in ocean numerical prediction. By understanding features and properties in the ocean on multiple scales, it is important to quantify and estimate the predictability of the ocean, and analyze the reasons and mechanism of error growth. The efforts focus on investigating the method to reduce the uncertainties and errors in forecasting and increase the time limit of ocean predictability. The advances will result in improved marine forecasting models and forecasting skill. Understanding limitations and identifying the research needed to increase accuracy will lead to fundamental progress in ocean forecast, which is of great significance. The present study described and illustrated the mechanics and computations involved in modeling and predicting uncertainties for ocean prediction and its modern applications. Firstly, it discussed the fundamental concept and classification of the ocean predictability. The research status of ocean predictability is introduced including the dynamics methodologies and the ocean ensemble prediction. Three of the dynamical computational methodologies including the singular vector, Lyapunov exponent and bred vector method were introduced. Three ocean ensemble prediction methods including initial condition ensemble, multi-model ensemble and atmospheric forcing ensemble were described and illustrated. Finally, this paper gave a future prospective of ocean predictability and its application. 相似文献