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1.
The approach of getting useful information of monthly dynamical prediction from ensemble forecasts is studied. The extended
range ensemble forecasts (8 members, the initial perturbations of the lagged average forecast (LAF)(0000, 0600, 1200 and 1800
GMT in two consecutive days) of the 500 hPa height field with the global spectral model (T63L16) from January to May 1997
are provided by the National Climate Center of China. The relationship between the spread of ensemble measured by root–mean–square
deviation of ensemble member from ensemble mean and forecast skill (the anomaly correlation or the root–mean–square distance
between the ensemble mean forecast and the observation) is significant. The spread of ensemble can evaluate the useful forecast
days N for the best estimate of 30 days mean. Thus, a weighted mean approach based on ensemble spread is put forward for monthly
dynamical prediction. The anomaly correlation of the weighted monthly mean by the ensemble spread is higher than that of both
the arithmetic mean and the linear weighted mean. Better results of the monthly mean circulation and anomaly are obtained
from the ensemble spread weighted mean.
Supported by the Excellent National State Key Laboratory Project (49823002), the National Key Project ‘Study on Chinese Short-Term
Climate Forecast System’ (96-908-02) and IAP Innovation Foundation (8-1308).
The data were provided through the National Climate Center of China. The authors wish to thank Ms. Chen Lijuan for her assistance. 相似文献
2.
K. K. W. Cheung 《Meteorology and Atmospheric Physics》2001,78(1-2):23-34
Summary
Random perturbations (RPs) and a modified version for breeding of growing modes are used with a regional baroclinic mesoscale
model to perform ensemble forecasting of tropical cyclone motion. Based on a sample of six cases, similar conclusions are
found as in previous barotropic modeling studies. Even after introducing a larger spatial correlation into the RPs using a
multi-quadric analysis scheme, the skill of this ensemble mean track prediction is almost always lower than that of the control
forecast in the cases considered. The track prediction performance of the ensemble using regional bred modes (RBMs) as perturbations
has a higher average skill. At nearly all forecast intervals except less than 24 h when the initial position error still dominates,
the ensemble mean tracks in all six cases are improved over the control forecast. In the 6 h–24 h range, the success rate
(ratio of the cases with a forecast improvement to the total number of cases) has a value of 10/24. In the 30 h–48 h range,
the success rate increases to 20/24, but drops to 18/24 in the 54 h–72 h range. A relative skill score (RSS) is used to compare
the skills of the two perturbation methodologies. It is found that the average RSSs of using RBMs are significantly higher
than the corresponding ones of RPs at the 99% confidence level in all three 24-h periods. Note that the above conclusion is
only based on ensemble mean forecasts. All of the possibilities from an ensemble-based probabilistic track distribution are
not explored in this paper. The ensemble spreads in these RBM ensembles are large enough to include the verifying tracks in
all the cases considered. It is also found that the ensemble spread is well correlated with the average error in an ensemble
when using RBMs, but not with the ensemble mean forecast error in both methodologies.
Received February 7, 2001/Revised April 18, 2001 相似文献
3.
The influence of ocean–atmosphere coupling on the simulation and prediction of the boreal winter Madden–Julian Oscillation
(MJO) is examined using the Seoul National University coupled general circulation model (CGCM) and atmospheric—only model
(AGCM). The AGCM is forced with daily SSTs interpolated from pentad mean CGCM SSTs. Forecast skill is examined using serial
extended simulations spanning 26 different winter seasons with 30-day forecasts commencing every 5 days providing a total
of 598 30-day simulations. By comparing both sets of experiments, which share the same atmospheric components, the influence
of coupled ocean–atmosphere processes on the simulation and prediction of MJO can be studied. The mean MJO intensity possesses
more realistic amplitude in the CGCM than in AGCM. In general, the ocean–atmosphere coupling acts to improve the simulation
of the spatio-temporal evolution of the eastward propagating MJO and the phase relationship between convection (OLR) and SST
over the equatorial Indian Ocean and the western Pacific. Both the CGCM and observations exhibit a near-quadrature relationship
between OLR and SST, with the former lagging by about two pentads. However, the AGCM shows a less realistic phase relationship.
As the initial conditions are the same in both models, the additional forcing by SST anomalies in the CGCM extends the prediction
skill beyond that of the AGCM. To test the applicability of the CGCM to real-time prediction, we compute the Real-time Multivariate
MJO (RMM) index and compared it with the index computed from observations. RMM1 (RMM2) falls away rapidly to 0.5 after 17–18
(15–16) days in the AGCM and 18–19 (16–17) days in the CGCM. The prediction skill is phase dependent in both the CGCM and
AGCM. 相似文献
4.
Forecast skill as a function of time lead and time averaging is examined in two 6-member ensembles of seasonal hindcasts.
One ensemble is produced with the second generation general circulation model of the Canadian Centre for Climate Modelling
and Analysis (GCM2) and the other with a reduced resolution version of the numerical weather prediction model of the Canadian
Meteorological Centre (SEF). The integrations are initiated from the NCEP/NCAR reanalyzed data. Monthly sea surface temperature
anomalies observed prior to the forecast period are maintained throughout the forecast season. A statistical forecast improvement
technique, based on the singular value decomposition of forecast and reanalyzed fields, is discussed and evaluated. A simple
analogue of the hindcast integrations is used to examine the behavior of two common skill scores, the correlation skill score
and the explained variance skill score. The maximal skill score and the corresponding optimal forecast in this analogue are
identified. The total skill of the optimal forecast is a sum of two terms, one associated with the initial conditions and
the other with the lower boundary forcing. The two sources of skill operate on different time scales, with initial conditions
being more important in the first one-two weeks and the atmospheric response to the boundary forcing becoming more dominant
for longer time leads and time averages. This suggests that these sources of skill should be considered separately in forecast
optimization. The statistical technique is moderately successful in improving the skill of monthly to seasonal forecasts of
500 hPa height (Z
500) and 700 hPa temperature (T
700) in the Northern Hemisphere and in the North Pacific/North America sector. The improvement is better when the forecasts for
the first week and for the rest of the season are optimized separately. The SEF model produces better Z
500 and T
700 forecasts than GCM2 in the first one-two weeks whereas GCM2 performs slightly better at longer time leads. The skill of zero
time lead forecast decays rapidly with averaging interval for time averages up to about 30–45 days and stabilizes, or even
rises, for longer time averages. Excluding the first week from seasonal forecasts results in substantial degradation of predictive
skill.
Received: 1 November 1999 / Accepted: 24 May 2000 相似文献
5.
集合方法在月动力预报信息提取中的应用 总被引:1,自引:0,他引:1
本工作将集合方法应用于提取月动力预报有用信息。利用中国气象局国家气候中心T63L16全球谱模式的500百帕高度场月集合预报产品(集合成员数为8个,初始场的选取采用滞后方法(LAF),即相邻两天的0000,0600,1200和1800GMT的初始化资料),就1997年1月至5月共15次预报,分析了集合预报成员间的离散度与预报评分(距平相关系数和均方根误差)的关系,研究了用集合各成员预报离散度作为各个成员逐日预报的权重对月预报效果的影响。结果表明集合预报成员的离散度与预报评分有显著的相关,是有效预报长度N的一个很好估计;用离散度作为权重平均的月预报高度距平相关系数明显高于算术平均和线性权重,此外个例分析表明月平均环流及其异常的预报得到明显的提高。 相似文献
6.
The extended-range forecast skill of the ECMWF operational forecast model is evaluated during tropical intraseasonal oscillation
(ISO) events in the Indo-West Pacific warm pool. The experiment consists of ensemble extended serial forecasts including winter
and summer ISO cases. The forecasts are compared with the ERA-40 analyses. The analysis focuses on understanding the origin
of forecast errors by studying the vertical structure of relevant dynamical and moist convective features associated with
the ISO. The useful forecast time scale for circulation anomalies is in average 13 days during winter compared to 7–8 days
during summer. The forecast skill is not stationary and presents evidence of a flow-dependent nature, with states of the coupled
system corresponding to long-lived convective envelopes associated with the ISO for which the skill is always low regardless
of the starting date of the forecast. The model is not able to forecast skillfully the generation of specific humidity anomalies
and results indicate that the convective processes in the model are associated with the erosion of the ISO forecast skill
in the model. Circulation-associated anomalies are forecast better than moist convective associated anomalies. The model tends
to generate a more stable atmosphere, limiting the model’s capability to reproduce deep convective events, resulting in smaller
humidity and circulation anomalies in the forecasts compared to those in ERA-40. 相似文献
7.
Summary A revised 25-point Shuman-Shapiro Spatial Filter (RSSSF) has been applied to six atmospheric circulation models and multi-model
ensemble (MME) predictions, and its effect on the improvement of model forecast skill scores of the Asian summer precipitation
anomaly is discussed in this paper. On the basis of 21-yr model ensemble predictions, the RSSSF can remove the unpredictable
‘noise’ with respect to the 2-grid wavelength in the model precipitation anomaly fields and maintain the large-scale counterpart,
which is related to the response of the model to large-scale boundary forcing. Therefore, this could possibly enhance the
forecast skill of the Asian summer rainfall anomaly in the models and the MME. The potential improvement of model forecasting
skill is found in the Asian summer monsoon region, where the anomaly correlation coefficient (ACC) has been improved by 7–40%,
corresponding to the decreased root mean square error (RMSE) in the model and the MME precipitation anomaly forecasts. 相似文献
8.
目前中国气象局全球集合预报系统(China Meteorological Administration Global Ensemble Prediction System,CMA-GEPS)利用CMA全球数值预报系统分析场计算奇异向量(ANSV),欧洲中期天气预报中心采用同化背景场计算奇异向量(FCSV),在业务流程上先于计算ANSV,可优化集合预报系统运行时间。为此,在CMA-GEPS中探索采用FCSV进行集合预报的可行性,分析ANSV和FCSV的空间分布及相似指数,进而针对夏秋季节10个个例开展采用ANSV和FCSV的全球集合预报试验,从等压面要素集合预报技巧、中国地区24 h累积降水概率预报技巧、台风路径集合预报技巧、台风中心最低海平面气压预报技巧等方面对比二者结果。结果表明:ANSV和FCSV的主要结构特征相似,两组集合预报结果相当,表明在CMA-GEPS中使用FCSV可行,可作为未来高分辨率CMA-GEPS业务系统建设的选项。 相似文献
9.
Forecast skill as a function of the ensemble size is examined in a 24-member ensemble of northern winter (DJF) hindcasts
produced with the second generation general circulation model of the Canadian Centre for Climate Modelling and Analysis. These
integrations are initialized from the NCEP reanalyses at 6 h intervals prior to the forecast season. The sea surface temperatures
that are applied as lower boundary conditions are predicted by persisting the monthly mean anomaly observed prior to the forecast
period. The potential predictability that is attributed to lower boundary forced variability is estimated. In lagged-average
forecasting, the forecast skill in the first two weeks, which originates predominately from the initial conditions, is greatest
for relatively small ensemble sizes. The forecast skill increases monotonically with the ensemble size in the rest of the
season. The skill of DJF 500 hPa geopotential height hindcasts in the Northern Hemisphere and in the Pacific/North America
sector improves substantially when the ensemble size increases from 6 to 24. A statistical skill improvement technique based
on the singular value decomposition method is also more successful for larger ensembles.
Received: 22 February 2000 / Accepted: 6 December 2000 相似文献
10.
This study investigates multi-model ensemble forecasts of track and intensity of tropical cyclones over the western Pacific, based on forecast outputs from the China Meteorological Administration, European Centre for Medium-Range Weather Forecasts, Japan Meteorological Agency and National Centers for Environmental Prediction in the THORPEX Interactive Grand Global Ensemble (TIGGE) datasets. The multi-model ensemble schemes, namely the bias-removed ensemble mean (BREM) and superensemble (SUP), are compared with the ensemble mean (EMN) and single-model forecasts. Moreover, a new model bias estimation scheme is investigated and applied to the BREM and SUP schemes. The results showed that, compared with single-model forecasts and EMN, the multi-model ensembles of the BREM and SUP schemes can have smaller errors in most cases. However, there were also circumstances where BREM was less skillful than EMN, indicating that using a time-averaged error as model bias is not optimal. A new model bias estimation scheme of the biweight mean is introduced. Through minimizing the negative influence of singular errors, this scheme can obtain a more accurate model bias estimation and improve the BREM forecast skill. The application of the biweight mean in the bias calculation of SUP also resulted in improved skill. The results indicate that the modification of multi-model ensemble schemes through this bias estimation method is feasible. 相似文献
11.
A. Yu. Bundel V. N. Kryzhov Young-Mi Min V. M. Khan R. M. Vilfand V. A. Tishchenko 《Russian Meteorology and Hydrology》2011,36(3):145-154
The probability multimodel forecast system based on the Asia-Pacific Economic Cooperation Climate Center (APCC) model data is verified. The winter and summer seasonal mean fields T 850 and precipitation seasonal totals are estimated. To combine the models into a multimodel ensemble, the probability forecast is calculated for each of single models first, and then these forecasts are combined using the total probability formula. It is shown that the multimodel forecast is considerably more skilful than the single-model forecasts. The forecast quality is higher in the tropics compared to the mid- and high latitudes. The multimodel ensemble temperature forecasts outperform the random and climate forecasts for Northern Eurasia in the above- and below-normal categories. Precipitation forecast is less successful. For winter, the combination of single-model ensembles provides the precipitation forecast skill exceeding that of the random forecast for both Northern Eurasia and European Russia. 相似文献
12.
A Comparison of Three Kinds of Multimodel Ensemble Forecast Techniques Based on the TIGGE Data 总被引:13,自引:0,他引:13 下载免费PDF全文
Based on the ensemble mean outputs of the ensemble forecasts from the ECMWF (European Centre for Medium-Range Weather Forecasts),JMA (Japan Meteorological Agency),NCEP (National Centers for Environment... 相似文献
13.
Prediction and monitoring of monsoon intraseasonal oscillations over Indian monsoon region in an ensemble prediction system using CFSv2 总被引:1,自引:0,他引:1
S. Abhilash A. K. Sahai N. Borah R. Chattopadhyay S. Joseph S. Sharmila S. De B. N. Goswami Arun Kumar 《Climate Dynamics》2014,42(9-10):2801-2815
An ensemble prediction system (EPS) is devised for the extended range prediction (ERP) of monsoon intraseasonal oscillations (MISO) of Indian summer monsoon (ISM) using National Centers for Environmental Prediction Climate Forecast System model version 2 at T126 horizontal resolution. The EPS is formulated by generating 11 member ensembles through the perturbation of atmospheric initial conditions. The hindcast experiments were conducted at every 5-day interval for 45 days lead time starting from 16th May to 28th September during 2001–2012. The general simulation of ISM characteristics and the ERP skill of the proposed EPS at pentad mean scale are evaluated in the present study. Though the EPS underestimates both the mean and variability of ISM rainfall, it simulates the northward propagation of MISO reasonably well. It is found that the signal-to-noise ratio of the forecasted rainfall becomes unity by about 18 days. The potential predictability error of the forecasted rainfall saturates by about 25 days. Though useful deterministic forecasts could be generated up to 2nd pentad lead, significant correlations are found even up to 4th pentad lead. The skill in predicting large-scale MISO, which is assessed by comparing the predicted and observed MISO indices, is found to be ~17 days. It is noted that the prediction skill of actual rainfall is closely related to the prediction of large-scale MISO amplitude as well as the initial conditions related to the different phases of MISO. An analysis of categorical prediction skills reveals that break is more skillfully predicted, followed by active and then normal. The categorical probability skill scores suggest that useful probabilistic forecasts could be generated even up to 4th pentad lead. 相似文献
14.
R. S. Ross A. Chakraborty A. Chen L. Stefanova S. Sirdas T. N. Krishnamurti 《Meteorology and Atmospheric Physics》2007,98(3-4):137-174
Summary Climate variations in the Caribbean, largely manifest in rainfall activity, have important consequences for the large-scale
water budget, natural vegetation, and land use in the region. The wet and dry seasons will be defined, and the important roles
played by the El Ni?o-Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO) in modulating the rainfall during
these seasons will be discussed.
The seasonal climate forecasts in this paper are made by 13 state of the art coupled atmosphere-ocean general circulation
models (CGCMs) and by the Florida State University Synthetic Superensemble (FSUSSE), whose forecasts are obtained by a weighted
combination of the individual CGCM forecasts based on a training period. The success of the models in simulating the observed
1989–2001 climatology of the various forecast parameters will be examined and linked to the models’ success in predicting
the seasonal climate for individual years. Seasonal forecasts will be examined for precipitation, sea-surface temperature (SST), 2-meter air temperature, and
850 hPa u- and v-wind components during the period 1989–2001. Evaluation metrics include root mean square (RMS) error and Brier skill score.
It will be shown that the FSUSSE is superior to the individual CGCMs and their ensemble mean both in simulating the 1989–2001
climatology for the various parameters and in predicting the seasonal climate of the various parameters for individual years.
The seasonal climate forecasts of the FSUSSE and of the ensemble mean of the 13 state of the art CGCMs will be evaluated for
years (during the period 1989–2001) that have particular ENSO and NAO signals that are known to influence Caribbean weather,
particularly the rainfall. It will be shown that the FSUSSE provides superior forecasts of rainfall, SST, 2-meter air temperature,
and 850 hPa u- and v-wind components during dry summers that are modulated by negative SOI and/or positive NAO indices. Such summers have become
a feature of a twenty-year pattern of drought in the Caribbean region. The results presented in this paper will show that
the FSUSSE is a valuable tool for forecasting rainfall and other atmospheric and oceanic variables during such periods of
drought. 相似文献
15.
Jeffrey Shaman Jonathan F. Day Marc Stieglitz Stephen Zebiak Mark Cane 《Climatic change》2006,75(4):495-511
We present a method for the ensemble seasonal prediction of human St. Louis encephalitis (SLE) incidence and SLE virus transmission
in Florida. We combine empirical relationships between modeled land surface wetness and the incidence of human clinical cases
of SLE and modeled land surface wetness and the occurrence of SLE virus transmission throughout south Florida with a previously
developed method for generating ensemble, seasonal hydrologic forecasts. Retrospective seasonal forecasts of human SLE incidence
are made for Indian River County, Florida, and forecast skill is demonstrated for 2–4 months. A sample seasonal forecast of
human SLE incidence is presented. This study establishes the skill of a potential component of an operational SLE forecast
system in south Florida, one that provides information well in advance of transmission and may enable early interventions
that reduce transmission. Future development of this method and operational application of these forecasts are discussed.
The methodology also will be applied to West Nile virus monitoring and forecasting. 相似文献
16.
New Models for Long Range Forecasts of Summer Monsoon Rainfall over North West and Peninsular India 总被引:1,自引:0,他引:1
M. Rajeevan Pulak Guhathakurta V. Thapliyal 《Meteorology and Atmospheric Physics》2000,73(3-4):211-225
Summary New models based on (a) Multivariate Principal Component Regression (PCR) (b) Neural Network (NN) and (c) Linear Discriminant
Analysis (LDA) techniques were developed for long-range forecasts of summer monsoon (June–September) rainfall over two homogeneous
regions of India, viz., North West India and Peninsular India. The PCR and NN models were developed with two different data
sets. One set consisted 42 years (1958–1999) of data with 8 predictors and the other, 49 years (1951–1999) of data with 6
predictors. The predictors were subjected to the Principal Component Analysis (PCA) before model development. Two different
neural networks were designed with 2 and 3 hidden neurons. To avoid the nonlinear instability, 20 ensemble runs were made
while training the network and the ensemble mean results are discussed. The LDA model was developed with 42 years of data
(1958–1999) for classifying three rainfall intervals with equal prior probability of 0.33. Both the PCR and NN models showed
useful forecast skill for NW India and Peninsular India. Models with 8 predictors performed better than the models with only
6 predictors. The NN model with 3 hidden neurons performed better than model with 2 hidden neurons. For NW India, the NN model
performed better than the PCR model. The RMSE of the NN model and PCR model with 8 predictors for NW India (Peninsular India)
during the independent period 1984–99 was 12.5% (12.2%) and 12.6% (11.5%), respectively. Corresponding figures for the models
with 6 predictors are 15.0% (13.0%) and 13.9% (11.4%) respectively. During the independent period, model errors were large
in 1991, 1994, 1997 and 1999. However all the models showed deteriorating predictive skill after 1988, both for NW India and
Peninsular India. The LDA model correctly classified 62% of grouped cases for NW India and Peninsular India. The LDA model
showed better skill in classifying deficient rainfall (< − 8%) over NW India and excess rainfall (> 3%) over Peninsular India.
Received October 2, 1999 Revised December 28, 1999 相似文献
17.
The possible changes in the frequency of extreme temperature events in Hong Kong in the 21st century were investigated by statistically downscaling 26 sets of the daily global climate model projections (a combination of 11 models and 3 greenhouse gas emission scenarios, namely A2, A1B, and B1) of the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. The models’ performance in simulating the past climate during 1971–2000 has also been verified and discussed. The verification revealed that the models in general have an acceptable skill in reproducing past statistics of extreme temperature events. Moreover, the models are more skillful in simulating the past climate of the hot nights and cold days than that of the very hot days. The projection results suggested that, in the 21st century, the frequency of occurrence of extremely high temperature events in Hong Kong would increase significantly while that of the extremely low temperature events is expected to drop significantly. Based on the multi-model scenario ensemble mean, the average annual numbers of very hot days and hot nights in Hong Kong are expected to increase significantly from 9 days and 16 nights in 1980–1999 to 89 days and 137 nights respectively in 2090–2099. On the other hand, the average annual number of cold days will drop from 17 days in 1980–1999 to about 1 day in 2090–2099. About 65 percent of the model-scenario combinations indicate that there will be on average less than one cold day in 2090–2099. While all the model-emission scenarios in general have projected consistent trends in the change of temperature extremes in the 21st century, there is a large divergence in the projections between difierent model/emission scenarios. This reflects that there are still large uncertainties in the model simulation of the future climate of extreme temperature events. 相似文献
18.
利用多模式超级集合预报法,以欧洲中期天气预报中心、日本气象厅、德国气象局、中国气象局和中国空军气象中心共5个决定性7 d预报产品为集合成员,对2010年8月500 hPa高度场和850 hPa温度场分别进行固定训练期和滑动训练期超级集合预报。采用均方根误差和相关系数对超级集合预报、单一模式预报和简单集合平均预报进行对比检验,同时对各预报结果的均方根误差空间分布进行对比分析。结果表明:超级集合预报在所有预报结果中最佳,且滑动集合预报对8月后期时段预报要略好于固定集合预报,两者预报效果均好于参与集合预报的各模式,也好于集合平均预报。但随着预报时效的延长,集合平均预报的优势也随之提升。从预报结果均方根误差的空间分布可知,多模式超级集合预报相比于单一模式预报效果提高的区域,500 hPa位势高度场主要位于印度半岛、印度洋、青藏高原及以西地区,而850 hPa温度场则主要位于蒙古、青藏高原、中国新疆及以西地区。 相似文献
19.
Ensemble mean forecast skill and applications with the T213 ensemble prediction system 总被引:1,自引:0,他引:1
Ensemble forecasting has become the prevailing method in current operational weather forecasting. Although ensemble mean forecast skill has been studied for many ensemble prediction systems(EPSs) and different cases, theoretical analysis regarding ensemble mean forecast skill has rarely been investigated, especially quantitative analysis without any assumptions of ensemble members. This paper investigates fundamental questions about the ensemble mean, such as the advantage of the ensemble mean over individual members, the potential skill of the ensemble mean, and the skill gain of the ensemble mean with increasing ensemble size. The average error coefficient between each pair of ensemble members is the most important factor in ensemble mean forecast skill, which determines the mean-square error of ensemble mean forecasts and the skill gain with increasing ensemble size. More members are useful if the errors of the members have lower correlations with each other, and vice versa. The theoretical investigation in this study is verified by application with the T213 EPS. A typical EPS has an average error coefficient of between 0.5 and 0.8; the 15-member T213 EPS used here reaches a saturation degree of 95%(i.e., maximum 5% skill gain by adding new members with similar skill to the existing members) for 1–10-day lead time predictions, as far as the mean-square error is concerned. 相似文献
20.
基于TIGGE资料, 采用均方根误差分别对欧洲中期天气预报中心、日本气象厅、美国国家环境预报中心和英国气象局4个中心集合预报的地面气温场集合平均结果进行检验评估, 比较各中心地面气温的预报效果。并利用超级集合、多模式集合平均和消除偏差集合平均3种方法对4个中心的地面气温预报进行集成, 同时对预报结果进行分析。结果表明: 2007年夏季日本气象厅与欧洲中期天气预报中心在北半球大部分地区预报效果最好, 各中心在不同地区预报效果不同。超级集合与消除偏差集合平均降低了预报误差, 预报效果优于最好的单个中心预报和多模式集合平均。对于较长的预报时效, 消除偏差集合平均表现出了更好的预报性能。 相似文献