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1.
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
2.
Current status of multimodel superensemble and operational NWP forecast of the Indian summer monsoon 总被引：1，自引：0，他引：1
In the last thirty years great strides have been made by large-scale operational numerical weather prediction models towards
improving skills for the medium range time-scale of 7 days. This paper illustrates the use of these current forecasts towards
the construction of a consensus multimodel forecast product called the superensemble. This procedure utilizes 120 of the recent-past
forecasts from these models to arrive at the training phase statistics. These statistics are described by roughly 107 weights. Use of these weights provides the possibility for real-time medium range forecasts with the superensemble. We show
the recent status of this procedure towards real-time forecasts for the Asian summer monsoon. The member models of our suite
include ECMWF, NCEP/EMC, JMA, NOGAPS (US Navy), BMRC, RPN (Canada) and an FSU global spectral forecast model. We show in this
paper the skill scores for day 1 through day 6 of forecasts from standard variables such as winds, temperature, 500 hPa geopotential
height, sea level pressure and precipitation. In all cases we noted that the superensemble carries a higher skill compared
to each of the member models and their ensemble mean. The skill matrices we use include the RMS errors, the anomaly correlations
and equitable threat scores. For many of these forecasts the improvements of skill for the superensemble over the best model
was found to be quite substantial. This real-time product is being provided to many interested research groups. The FSU multimodel
superensemble, in real-time, stands out for providing the least errors among all of the operational large scale models. 相似文献
3.
This study assesses retrospective decadal prediction skill of Sea Surface Temperature (SST) variability in initialized climate prediction experiments (INT) with the Beijing Climate Center Climate System Model (BCC_CSM1.1). Ensemble forecasts were evaluated using observations, and compared to an ensemble of uninitialized simulations (NoINT). The results show as follows: ①The warming trend of global mean SST simulated by the INT runs is closer to the observation than that in the NoINT runs.②The INT runs show high SST prediction skills over broad regions of tropical Atlantic, western tropical Pacific and tropical Indian Oceans. ③ In the North Pacific and the east-central tropical Pacific Ocean, the prediction skills are very weak, and there are few improvements coming from the initialization in the INT runs. ④ In the southern Indian Ocean, the prediction skills of the INT runs are significantly larger than that of the NoINT runs, with the maximum skill at the 3~6 and 4~7 years lead time. The above-mentioned conclusions are similar to the results of other climate models. However, the prediction skill in the North Atlantic Ocean is much lower than that of other models, especially in the subpolar region. The low skills in the Atlantic Ocean may be attributed to the misrepresentation of the lead-lag relationship between the Atlantic meridional heat transport and the SST in the BCC_CSM1.1. 相似文献
4.
针对两个最新换代的季度集合预测系统对中国季度降水预测中存在的系统缺陷,应用改进的贝叶斯联合概率模型（BJP）加以订正。对订正后的单一模式概率预测应用一种混合模型贝叶斯模型平均（BMA）方法加以集成,以综合各模式的优势来提高中国季度降水预测技巧。结果表明:BJP模型可有效地消除集合模式预测的系统偏差,同时大幅提高了概率预测的可靠性。经过订正的欧洲中尺度天气预报中心的 System4预测在许多季度在中国的很大区域范围内都显示出了一定的预测技巧;而澳洲气象局的POAMA2.4预测只在个别季度局部范围内具有技巧。使用BMA对订正后的单一模式预测进行集成可显著提高对中国季度降水预测的精度,相比单一模式预测,技巧得分为正值的网格百分率分别提高了13.3%和20.0%。 相似文献
5.
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
6.
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
7.
Someshwar Das Raghavendra Ashrit Gopal Raman Iyengar Saji Mohandas M. Das Gupta John P. George E. N. Rajagopal Surya Kanti Dutta 《Journal of Earth System Science》2008,117(5):603-620
Performance of four mesoscale models namely, the MM5, ETA, RSM and WRF, run at NCMRWF for short range weather forecasting has been examined during monsoon-2006. Evaluation is carried out based upon comparisons between observations and day-1 and day-3 forecasts of wind, temperature, specific humidity, geopotential height, rainfall, systematic errors, root mean square errors and specific events like the monsoon depressions.It is very difficult to address the question of which model performs best over the Indian region? An honest answer is ‘none’. Perhaps an ensemble approach would be the best. However, if we must make a final verdict, it can be stated that in general, (i) the WRF is able to produce best All India rainfall prediction compared to observations in the day-1 forecast and, the MM5 is able to produce best All India rainfall forecasts in day-3, but ETA and RSM are able to depict the best distribution of rainfall maxima along the west coast of India, (ii) the MM5 is able to produce least RMSE of wind and geopotential fields at most of the time, and (iii) the RSM is able to produce least errors in the day-1 forecasts of the tracks, while the ETA model produces least errors in the day-3 forecasts. 相似文献
8.
Ankita Singh Nachiketa Acharya Uma Charan Mohanty Gopbandhu Mishra 《Comptes Rendus Geoscience》2013,345(2):62-72
The emerging advances in the field of dynamical prediction of monsoon using state-of-the-art General Circulation Models (GCMs) have led to the development of various multi model ensemble techniques (MMEs). In the present study, the concept of Canonical Correlation Analysis is used for making MME (referred as Multi Model Canonical Correlation Analysis or MMCCA) for the prediction of Indian summer monsoon rainfall (ISMR) during June-July-August-September (JJAS). This method has been employed on the rainfall outputs of six different GCMs for the period 1982 to 2008. The prediction skill of ISMR by MMCCA is compared with the simple composite method (SCM) (i.e. arithmetic mean of all GCMs), which is taken as a benchmark. After a rigorous analysis through different skill metrics such as correlation coefficient and index of agreement, the superiority of MMCCA over SCM is illustrated. Performance of both models is also evaluated during six typical monsoon years and the results indicate the potential of MMCCA over SCM in capturing the spatial pattern during extreme years. 相似文献
9.
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
10.
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
11.
Statistical bias correction methods for numerical weather prediction (NWP) forecasts of maximum and minimum temperatures over India in the medium-range time scale (up to 5 days) are proposed in this study. The objective of bias correction is to minimize the systematic error of the next forecast using bias from past errors. The need for bias corrections arises from the many sources of systematic errors in NWP modeling systems. NWP models have shortcomings in the physical parameterization of weather events and have the inability to handle sub-grid phenomena successfully. The statistical algorithms used for minimizing the bias of the next forecast are running-mean (RM) bias correction, best easy systematic estimator, simple linear regression and the nearest neighborhood (NN) weighted mean, as they are suitable for small samples. Bias correction is done for four global NWP model maximum and minimum temperature forecasts. The magnitude of the bias at a grid point depends upon geographical location and season. Validation of the bias correction methodology is carried out using daily observed and bias-corrected model maximum and minimum temperature forecast over India during July–September 2011. The bias-corrected NWP model forecast generally outperforms direct model output (DMO). The spatial distribution of mean absolute error and root-mean squared error for bias-corrected forecast over India indicate that both the RM and NN methods produce the best skill among other bias correction methods. The inter-comparison reveals that statistical bias correction methods improve the DMO forecast in terms of accuracy in forecast and have the potential for operational applications. 相似文献
12.
Performance of a hybrid assimilation system combining 3D Var based NGFS (NCMRWF Global Forecast System) with ETR (Ensemble Transform with Rescaling) based Global Ensemble Forecast (GEFS) of resolution T-190L28 is investigated. The experiment is conducted for a period of one week in June 2013 and forecast skills over different spatial domains are compared with respect to mean analysis state. Rainfall forecast is verified over Indian region against combined observations of IMD and NCMRWF. Hybrid assimilation produced marginal improvements in overall forecast skill in comparison with 3D Var. Hybrid experiment made significant improvement in wind forecasts in all the regions on verification against mean analysis. The verification of forecasts with radiosonde observations also show improvement in wind forecasts with the hybrid assimilation. On verification against observations, hybrid experiment shows more improvement in temperature and wind forecasts at upper levels. Both hybrid and operational 3D Var failed in prediction of extreme rainfall event over Uttarakhand on 17 June, 2013. 相似文献
13.
In this study, the Florida State University Global Spectral Model (FSUGSM), in association with a high-resolution nested regional
spectral model (FSUNRSM), is used for short-range weather forecasts over the Indian domain. Three-day forecasts for each day
of August 1998 were performed using different versions of the FSUGSM and FSUNRSM and were compared with the observed fields
(analysis) obtained from the European Center for Medium Range Weather Forecasts (ECMWF). The impact of physical initialization
(a procedure that assimilates observed rain rates into the model atmosphere through a set of reverse algorithms) on rainfall
forecasts was examined in detail. A very high nowcasting skill for precipitation is obtained through the use of high-resolution
physical initialization applied at the regional model level. Higher skills in wind and precipitation forecasts over the Indian
summer monsoon region are achieved using this version of the regional model with physical initialization.
A relatively new concept, called the ‘multimodel/multianalysis superensemble’ is described in this paper and is applied for
the wind and precipitation forecasts over the Indian subcontinent. Large improvement in forecast skills of wind at 850 hPa
level over the Indian subcontinent is shown possible through the use of the multimodel superensemble. The multianalysis superensemble
approach that uses the latest satellite data from the Tropical Rainfall Measuring Mission (TRMM) and the Defense Meteorological
Satellite Program (DMSP) has shown significant improvement in the skills of precipitation forecasts over the Indian monsoon
region. 相似文献
14.
Vassiliki Kotroni Evangelos Floros Konstantinos Lagouvardos Goran Pejanovic Luka Ilic Momcilo Zivkovic 《Earth Science Informatics》2010,3(4):209-218
Weather forecasting is based on the use of numerical weather prediction (NWP) models that are able to perform the necessary
calculations that describe/predict the major atmospheric processes. One common problem in weather forecasting derives from
the uncertainty related to the chaotic behaviour of the atmosphere. A solution to that problem is to perform in addition to
“deterministic” forecasts, “stochastic” forecasts that provide an estimate of the prediction skill. A computationally feasible
approach towards this aim is to perform “ensemble forecasts”. Indeed, in the frame of SEE-GRID-SCI EU funded project a Regional
scale Multi-model, Multi-analysis ensemble forecasting system (REFS) was built and ported on the Grid infrastructure. REFS
is based on the use of four limited area models (namely BOLAM, MM5, ETA, and NMM) that are run using a multitude of initial
and boundary conditions over the Mediterranean. This paper presents the tools and procedures followed for developing this
application at a production level. 相似文献
15.
A number of physical factors have been introduced to improve limited area model forecasts. The factors include land surface
fluxes, shallow convection and radiation. The model including these additional physical factors (modified physics) is run
for five cases of monsoon depression which made landfall over the Indian coast, and the results are compared with those of
the control run. The forecasts are verified by computing the root mean square and mean errors. The differences in these skill
scores between the two model runs are tested for their statistical significance. It is found that the modified physics has
a statistically significant effect on the model skill with the maximum impact on the mean sea level pressure and the temperature.
Detailed analyses of mean sea level pressure, wind, rainfall and temperature further confirm that the modified physics has
maximum impact on mean sea level pressure and temperature and marginal impact on wind and rainfall. Furthermore, analyses
of some model parameters related to physics at a grid point for one case of depression were done. The results show that the
inclusion of the land surface physics, shallow convection and radiative processes have produced a better precipitation forecast
over the grid point. 相似文献
16.
The continuous ranked probability score (CRPS) is a much used measure of performance for probabilistic forecasts of a scalar observation. It is a quadratic measure of the difference between the forecast cumulative distribution function (CDF) and the empirical CDF of the observation. Analytic formulations of the CRPS can be derived for most classical parametric distributions, and be used to assess the efficiency of different CRPS estimators. When the true forecast CDF is not fully known, but represented as an ensemble of values, the CRPS is estimated with some error. Thus, using the CRPS to compare parametric probabilistic forecasts with ensemble forecasts may be misleading due to the unknown error of the estimated CRPS for the ensemble. With simulated data, the impact of the type of the verified ensemble (a random sample or a set of quantiles) on the CRPS estimation is studied. Based on these simulations, recommendations are issued to choose the most accurate CRPS estimator according to the type of ensemble. The interest of these recommendations is illustrated with real ensemble weather forecasts. Also, relationships between several estimators of the CRPS are demonstrated and used to explain the differences of accuracy between the estimators. 相似文献
17.
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
18.
Quality and reliable drought prediction is essential for mitigation strategies and planning in disaster-stricken regions globally. Prediction models such as empirical or data-driven models play a fundamental role in forecasting drought. However, selecting a suitable prediction model remains a challenge because of the lack of succinct information available on model performance. Therefore, this review evaluated the best model for drought forecasting and determined which differences if any were present in model performance using standardised precipitation index (SPI). In addition, the most effective combination of the SPI with its respective timescale and lead time was investigated. The effectiveness of data-driven models was analysed using meta-regression analysis by applying a linear mixed model to the coefficient of determination and the root mean square error of the validated model results. Wavelet-transformed neural networks had superior performance with the highest correlation and minimum error. Preprocessing data to eliminate non-stationarity performed substantially better than did the regular artificial neural network (ANN) model. Additionally, the best timescale to calculate the SPI was 24 and 12 months and a lead time of 1–3 months provided the most accurate forecasts. Studies from China and Sicily had the most variation based on geographical location as a random effect; while studies from India rendered consistent results overall. Variation in the result can be attributed to geographical differences, seasonal influence, incorporation of climate indices and author bias. Conclusively, this review recommends use of the wavelet-based ANN (WANN) model to forecast drought indices.
相似文献19.
利用西藏珠穆朗玛峰地区5个气象站点1971-2012年逐日降水量资料,采用滑动平均、线性回归、Mann-Kendall非参数检验和Morlet小波分析等方法,分析了珠穆朗玛峰地区极端降水事件的时空变化特征. 结果表明：1971-2012年42 a来,珠穆朗玛峰地区大部分极端降水指数呈现出自东向西逐渐增大的空间分布格局, 连续干旱日数、连续湿日和降水强度表现为增加趋势,其他极端降水指数趋于减少. 其中,强降水量、极强降水量和年降水总量减幅较大,分别为-5.74 mm·（10a）-1、-1.20 mm·（10a）-1和-5.32 mm·（10a）-1,在喜马拉雅山南坡的聂拉木站表现的最为明显. 大部分极端降水指数在21世纪最初的10 a减幅最大,在30 a际尺度上也表现为减少趋势. 除连续干旱日数外,极端降水与年降水总量关系密切. 各项极端降水指数都存在3～4 a显著周期,也存在10 a、12 a和15 a的周期. 在时间转折上,各项极端降水指数均未发生气候突变. 相似文献
20.
One very specific operational requirement of the Tropical Cyclone (TC) Programme of the Regional Specialized Meteorological
Centre, New Delhi is to provide 12-hourly forecasts valid up to 48 h (preferably 72 h) on the intensity of cyclones over the
southern Indian Seas. In this paper, a simple empirical model for predicting the intensity of TCs occurring in the Bay of
Bengal is proposed. The model parameter has been determined from a database assembled on 30 recent cyclones, and the model
itself is based on the assumption that a TC intensifies exponentially. A method for correcting the forecast during subsequent
observation hours (6- or 12-h intervals) is also presented. The results show that the forecast skill for forecasts of up to
48 h is reasonably good. The absolute mean errors are less than 12 knots for 48-h forecasts, with the forecast skill decreasing
with time. With the incorporation of a correction procedure based on the latest observations, some improvement in the forecast
skill can be obtained. The model is expected to be useful to operational forecasters. 相似文献