Improving multi-model ensemble probabilistic prediction of Yangtze River valley summer rainfall

Seasonal prediction of summer rainfall over the Yangtze River valley(YRV) is valuable for agricultural and industrial production and freshwater resource management in China, but remains a major challenge. Earlier multi-model ensemble(MME) prediction schemes for summer rainfall over China focus on single-value prediction, which cannot provide the necessary uncertainty information, while commonly-used ensemble schemes for probability density function(PDF) prediction are not adapted to YRV summer rainfall prediction. In the present study, an MME PDF prediction scheme is proposed based on the ENSEMBLES hindcasts. It is similar to the earlier Bayesian ensemble prediction scheme, but with optimization of ensemble members and a revision of the variance modeling of the likelihood function. The optimized ensemble members are regressed YRV summer rainfall with factors selected from model outputs of synchronous 500-h Pa geopotential height as predictors. The revised variance modeling of the likelihood function is a simple linear regression with ensemble spread as the predictor. The cross-validation skill of 1960–2002 YRV summer rainfall prediction shows that the new scheme produces a skillful PDF prediction, and is much better-calibrated, sharper, and more accurate than the earlier Bayesian ensemble and raw ensemble.     

Probabilistic Seasonal Prediction of Summer Rainfall over East China Based on Multi-Model Ensemble Schemes

The skill of probability density function (PDF) prediction of summer rainfall over East China using optimal ensemble schemes is evaluated based on the precipitation data from ˉve coupled atmosphere-ocean general circulation models that participate in the ENSEMBLES project. The optimal ensemble scheme in each region is the scheme with the highest skill among the four commonly-used ones: the equally-weighted ensemble (EE), EE for calibrated model-simulations (Cali-EE), the ensemble scheme based on multiple linear regression analysis (MLR), and the Bayesian ensemble scheme (Bayes). The results show that the optimal ensemble scheme is the Bayes in the southern part of East China; the Cali-EE in the Yangtze River valley, the Yangtze-Huaihe River basin, and the central part of northern China; and the MLR in the eastern part of northern China. Their PDF predictions are well calibrated, and are sharper than or have approximately equal interval-width to the climatology prediction. In all regions, these optimal ensemble schemes outperform the climatology prediction, indicating that current commonly-used multi-model ensemble schemes are able to produce skillful PDF prediction of summer rainfall over East China, even though more information for other model variables is not derived.     

Assessing the Seasonal Predictability of Summer Precipitation over the Huaihe River Basin with Multiple APCC Models

Seasonal rainfall predictability over the Huaihe River basin is evaluated in this paper on the basis of 23-year(1981-2003) retrospective forecasts by 10 climate models from the Asia-Pacific Economic Cooperation(APEC) Climate Center(APCC) multi-model ensemble(MME) prediction system.It is found that the summer rainfall variance in this basin is largely internal,which leads to lower rainfall predictability for most individual climate models.By dividing the 10 models into three categories according to their sea surface temperature(SST) boundary conditions including observed,predicted,and persistent SSTs,the MME deterministic predictive skill of summer rainfall over Huaihe River basin is investigated.It is shown that the MME is effective for increasing the current seasonal forecast skill.Further analysis shows that the MME averaged over predicted SST models has the highest rainfall prediction skill,which is closely related to model’s capability in reproducing the observed dominant modes of the summer rainfall anomalies in Huaihe River basin.This result can be further ascribed to the fact that the predicted SST MME is the most effective model ensemble for capturing the relationship between the summer rainfall anomalies over Huaihe River basin and the SST anomalies(SSTAs) in equatorial oceans.     

用一种新的同化方法同化降水量资料

Observations of accumulated precipitation are extremely valuable for effectively improving rainfall analysis and forecast. It is, however, difficult to use such observations directly through sequential assimilation methods, such as three-dimensional variational data assimilation or an Ensemble Kalman Filter. In this study, the authors illustrate a new approach that makes effective use of precipitation data to improve rainfall forecast. The new method directly obtains an optimal solution in a reduced space by fitting observations with historical time series generated by the model; it also avoids the implementation of tangent linear model and its adjoint. A lot of historical samples are produced as the ensemble of precipitation observations with the fully nonlinear forecast model. The results show that the new approach is capable of extracting information from precipitation observations to improve the analysis and forecast. This method provides comparable performance with the standard four- dimensional variational data assimilation at a much lower computational cost.     

A Statistical-Dynamical Scheme for the Extraseasonal Prediction of Summer Rainfall for 160 Observation Stations across China

The purpose of this study was to design and test a statistical-dynamical scheme for the extraseasonal(one season in advance) prediction of summer rainfall at 160 observation stations across China.The scheme combined both valuable information from the preceding observations and dynamical information from synchronous numerical predictions of atmospheric circulation factors produced by an atmospheric general circulation model.First,the key preceding climatic signals and synchronous atmospheric circulation factors that were not only closely related to summer rainfall but also numerically predictable were identified as the potential predictors.Second,the extraseasonal prediction models of summer rainfall were constructed using a multivariate linear regression analysis for 15 subregions and then 160 stations across China.Cross-validation analyses performed for the period 1983-2008 revealed that the performance of the prediction models was not only high in terms of interannual variation,trend,and sign but also was stable during the whole period.Furthermore,the performance of the scheme was confirmed by the accuracy of the real-time prediction of summer rainfall during 2009 and 2010.     

Intermodel Diversity in the Zonal Location of the Climatological East Asian Westerly Jet Core in Summer and Association with Rainfall over East Asia in CMIP5 Models

The East Asian westerly jet(EAJ), an important midlatitude circulation of the East Asian summer monsoon system,plays a crucial role in affecting summer rainfall over East Asia. The multimodel ensemble of current coupled models can generally capture the intensity and location of the climatological summer EAJ. However, individual models still exhibit large discrepancies. This study investigates the intermodel diversity in the longitudinal location of the simulated summer EAJ climatology in the present-day climate and its implications for rainfall over East Asia based on 20 CMIP5 models. The results show that the zonal location of the simulated EAJ core is located over either the midlatitude Asian continent or the western North Pacific(WNP) in different models. The zonal shift of the EAJ core depicts a major intermodel diversity of the simulated EAJ climatology. The westward retreat of the EAJ core is related to a warmer mid–upper tropospheric temperature in the midlatitudes, with a southwest–northeast tilt extending from Southwest Asia to Northeast Asia and the northern North Pacific, induced partially by the simulated stronger rainfall climatology over South Asia. The zonal shift of the EAJ core has some implications for the summer rainfall climatology, with stronger rainfall over the East Asian continent and weaker rainfall over the subtropical WNP in relation to the westward-located EAJ core.     

Ensemble mean forecast skill and applications with the T213 ensemble prediction system

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.     

Statistical Downscaling for Multi-Model Ensemble Prediction of Summer Monsoon Rainfall in the Asia-Pacific Region Using Geopotential Height Field

The 21-yr ensemble predictions of model precipitation and circulation in the East Asian and western North Pacific （Asia-Pacific） summer monsoon region （0°-50°N, 100° 150°E） were evaluated in nine different AGCM, used in the Asia-Pacific Economic Cooperation Climate Center （APCC） multi-model ensemble seasonal prediction system. The analysis indicates that the precipitation anomaly patterns of model ensemble predictions are substantially different from the observed counterparts in this region, but the summer monsoon circulations are reasonably predicted. For example, all models can well produce the interannual variability of the western North Pacific monsoon index （WNPMI） defined by 850 hPa winds, but they failed to predict the relationship between WNPMI and precipitation anomalies. The interannual variability of the 500 hPa geopotential height （GPH） can be well predicted by the models in contrast to precipitation anomalies. On the basis of such model performances and the relationship between the interannual variations of 500 hPa GPH and precipitation anomalies, we developed a statistical scheme used to downscale the summer monsoon precipitation anomaly on the basis of EOF and singular value decomposition （SVD）. In this scheme, the three leading EOF modes of 500 hPa GPH anomaly fields predicted by the models are firstly corrected by the linear regression between the principal components in each model and observation, respectively. Then, the corrected model GPH is chosen as the predictor to downscale the precipitation anomaly field, which is assembled by the forecasted expansion coefficients of model 500 hPa GPH and the three leading SVD modes of observed precipitation anomaly corresponding to the prediction of model 500 hPa GPH during a 19-year training period. The cross-validated forecasts suggest that this downscaling scheme may have a potential to improve the forecast skill of the precipitation anomaly in the South China Sea, western North Pacific and the East Asia Pacific regions, wh     

Sensitivity Analysis of Ensemble Simulations on a Torrential Rainfall Case over South China Using Multiple PBL and SL Parameterizations

A good representation of the interaction between the planetary boundary layer(PBL) and the surface layer(SL) in numerical models is of great importance for the prediction of the initiation and development of convection. This study examined an ensemble that consists of the available suites of PBL and SL parameterizations based on a torrential rainfall event over south China. The sensitivity of the simulations was investigated against objective measurements using multiple PBL and SL parameterization schemes. The main causes of the bias from different parameterization schemes were further analysed by comparing the good and bad ensemble members. The results showed that good members tended to underestimate the rainfall amount but presented a decent evolution of mesoscale convective systems that were responsible for the torrential rainfall. Using the total energy mass flux(TEMF) scheme, the bad members overestimated the amount and spatial coverage of rainfall. The failure of the bad member was due to a spurious convection initiation(CI) resulting from the overestimated high-θe elevated air. The spurious CI developed and expanded rapidly, causing intensive and extensive rainfall over south China. Consistent with previous studies, the TEMF scheme tends to produce a warmer and moister PBL environment. The detailed sensitivity analysis of this case may provide reference for the operational forecast of rainfall over south China using multiple PBL and SL parameterizations.     

INTERCOMPARISON OF THE INTERDECADAL VARIATIONS OF SUMMER PRECIPITATION IN CHINA SIMULATED BY AOGCMS FROM THE IPCC-DDC

Simulations of the interdecadal variations of summer rainfall over China are assessed from 5 coupled AOGCMs from the Data Distribution Center (DDC) of the Intergovernmental Panel in Climate Change (IPCC) under the IPCC-Special Report in Emission Scenarios (SRES) A2 and B2 scenario. We examined their ability in simulating the interdecadal variations of summer precipitation over China from 1951 to 1990. The difference before and after the mid-1960’s and the late 1970’s is given respectively to check the capability of the models, especially in reproducing the rainfall jump in North China. We also investigated the interdecadal variations simulated by the models in the 1990’s and the average of 2001-2020 in the future under the scenario A2 and B2. The analysis shows that the current AOGCMs is not good enough in simulating the interdecadal variations of summer precipitation in China. The interdecadal variations of summer rainfall simulated by most of the models cannot reproduce the observation in North China. Higher resolution models are suggested to well simulate the interdecadal variability in regional scale.     

基于多模式集合方案的中国东部夏季降水概率季度预测

借助ENSEMBLES计划提供的5个海-气耦合模式(CGCM)的多初值后报降水资料,采用常用的4种多模式集合方案,即等权集合(EE)、对单个集合成员先订正再等权集合(Cali-EE)、基于多元线性回归的集合方案(MLR)、基于贝叶斯统计学的集合方案(Bayes),制作1960—2005年中国东部夏季降水概率密度函数(PDF)季度预测。在此基础上,比较最优(技巧最高)集合方案与气候学预测(衡量概率密度函数预测是否有技巧的基准)的技巧,初步评估目前基于多模式集合方案的、中国东部夏季降水的概率密度函数季度预测能力。结果表明,Bayes方案在华南最优,Cali-EE在长江流域、江淮流域以及中国北方的中部最优,MLR在中国北方的东部最优;基于这些最优集合方案的概率密度函数预测产品均具有高校准度,且其锐度高于或接近气候学预测;并且,对于所有区域,最优集合方案的预测技巧总是高于气候学预测,这暗示即使不提取模式其他变量中所包含的预测信息,对于中国东部夏季降水季度预测,常用的多模式集合方案也已具备制作有技巧的概率密度函数预测产品的能力。     

Probabilistic seasonal prediction of summer rainfall over East China based on multi-model ensemble schemes

The skill of probability density function (PDF) prediction of summer rainfall over East China using optimal ensemble schemes is evaluated based on the precipitation data from five coupled atmosphere-ocean general circulation models that participate in the ENSEMBLES project. The optimal ensemble scheme in each region is the scheme with the highest skill among the four commonly-used ones: the equally-weighted ensemble (EE), EE for calibrated model-simulations (Cali-EE), the ensemble scheme based on multiple linear regression analysis (MLR), and the Bayesian ensemble scheme (Bayes). The results show that the optimal ensemble scheme is the Bayes in the southern part of East China; the Cali-EE in the Yangtze River valley, the Yangtze-Huaihe River basin, and the central part of northern China; and the MLR in the eastern part of northern China. Their PDF predictions are well calibrated, and are sharper than or have approximately equal interval-width to the climatology prediction. In all regions, these optimal ensemble schemes outperform the climatology prediction, indicating that current commonly-used multi-model ensemble schemes are able to produce skillful PDF prediction of summer rainfall over East China, even though more information for other model variables is not derived.     

Probabilistic prediction of Indian summer monsoon rainfall using global climate models

Probabilistic seasonal predictions of rainfall that incorporate proper uncertainties are essential for climate risk management. In this study, three different multi-model ensemble (MME) approaches are used to generate probabilistic seasonal hindcasts of the Indian summer monsoon rainfall based on a set of eight global climate models for the 1982–2009 period. The three MME approaches differ in their calculation of spread of the forecast distribution, treated as a Gaussian, while all three use the simple multi-model subdivision average to define the mean of the forecast distribution. The first two approaches use the within-ensemble spread and error residuals of ensemble mean hindcasts, respectively, to compute the variance of the forecast distribution. The third approach makes use of the correlation between the ensemble mean hindcasts and the observations to define the spread using a signal-to-noise ratio. Hindcasts are verified against high-resolution gridded rainfall data from India Meteorological Department in terms of meteorological subdivision spatial averages. The use of correlation for calculating the spread provides better skill than the other two methods in terms of rank probability skill score. In order to further improve the skill, an additional method has been used to generate multi-model probabilistic predictions based on simple averaging of tercile category probabilities from individual models. It is also noted that when such a method is used, skill of probabilistic forecasts is improved as compared with using the multi-model ensemble mean to define the mean of the forecast distribution and then probabilities are estimated. However, skill of the probabilistic predictions of the Indian monsoon rainfall is too low.     

Forecasting quantitative rainfall over India using multi-model ensemble technique

A new approach to ensemble forecasting of rainfall over India based on daily outputs of four operational numerical weather prediction (NWP) models in the medium-range timescale (up to 5 days) is proposed in this study. Four global models, namely ECMWF, JMA, GFS and UKMO available on real-time basis at India Meteorological Department, New Delhi, are used simultaneously with adequate weights to obtain a multi-model ensemble (MME) technique. In this technique, weights for each NWP model at each grid point are assigned on the basis of unbiased mean absolute error between the bias-corrected forecast and observed rainfall time series of 366 daily data of 3 consecutive southwest monsoon periods (JJAS) of 2008, 2009 and 2010. Apart from MME, a simple ensemble mean (ENSM) forecast is also generated and experimented. The prediction skill of MME is examined against observed and corresponding outputs of each constituent model during monsoon 2011. The inter-comparison reveals that MME is able to provide more realistic forecast of rainfall over Indian monsoon region by taking the strength of each constituent model. It has been further found that the weighted MME technique has higher skill in predicting daily rainfall compared to ENSM and individual member models. RMSE is found to be lowest in MME forecasts both in magnitude and area coverage. This indicates that fluctuations of day-to-day errors are relatively less in the MME forecast. The inter-comparison of domain-averaged skill scores for different rainfall thresholds further clearly demonstrates that the MME algorithm improves slightly above the ENSM and member models.     

IAP AGCM 4.1对淮河流域夏季降水的预报技巧评估

基于中国科学院大气物理研究所新一代大气环流模式IAP AGCM 4.1共30 a(1981—2010年)的集合回报试验结果,评估了模式对淮河流域夏季降水的预报技巧。分析结果表明,模式总体上可以较好地再现出淮河流域夏季平均降水南多北少的空间分布特征,其中模式模拟的6月降水量与观测值的空间相关可达0.93。但降水强度与观测相比具有系统性的偏差,且模式模拟的降水年际变率显著偏弱。基于降水距平相关系数的确定性预报技巧分析表明,模式对流域西南部夏季降水的预测技巧较高,达到0.2以上,且模式对6月降水异常的预测能力相对最好,7月次之。针对淮河不同子流域的预报技巧分析表明,IAP AGCM 4. 1对蚌埠、鲁台子、王家坝水文控制站以上集水面积的夏季面雨量异常具有一定的预报技巧,30 a集合回报的时间相关系数分别为0. 11、0. 13、0. 16。基于降水等级的概率预报技巧评估表明,模式对7月淮河流域南部少雨事件具有很好的预报能力,同时对6月流域中部多雨事件的预报技巧也较高。     

Indian summer monsoon rainfall variability in global coupled ocean-atmospheric models

Performance of seven fully coupled models in simulating Indian summer monsoon climatology as well as the inter-annual variability was assessed using multi member 1 month lead hindcasts made by several European climate groups as part of the program called Development of a European multi-model ensemble system for seasonal-to-inter-annual prediction (DEMETER). Dependency of the model simulated Indian summer monsoon rainfall and global sea surface temperatures on model formulation and initial conditions have been studied in detail using the nine ensemble member simulations of the seven different coupled ocean–atmosphere models participated in the DEMETER program. It was found that the skills of the monsoon predictions in these hindcasts are generally positive though they are very modest. Model simulations of India summer monsoon rainfall for the earlier period (1959–1979) are closer to the ‘perfect model’ (attainable) score but, large differences are observed between ‘actual’ skill and ‘perfect model’ skill in the recent period (1980–2001). Spread among the ensemble members are found to be large in simulations of India summer monsoon rainfall (ISMR) and Indian ocean dipole mode (IODM), indicating strong dependency of model simulated Indian summer monsoon on initial conditions. Multi-model ensemble performs better than the individual models in simulating ENSO indices, but does not perform better than the individual models in simulating ISMR and IODM. Decreased skill of multi-model ensemble over the region indicates amplification of errors due to existence of similar errors in the individual models. It appears that large biases in predicted SSTs over Indian Ocean region and the not so perfect ENSO-monsoon (IODM-monsoon) tele-connections are some of the possible reasons for such lower than expected skills in the recent period. The low skill of multi-model ensemble, large spread among the ensemble members of individual models and the not so perfect monsoon tele-connection with global SSTs points towards the importance of improving individual models for better simulation of the Indian monsoon.     

Evaluation of summer temperature and precipitation predictions from NCEP CFSv2 retrospective forecast over China

National Centers for Environmental Prediction recently upgraded its operational seasonal forecast system to the fully coupled climate modeling system referred to as CFSv2. CFSv2 has been used to make seasonal climate forecast retrospectively between 1982 and 2009 before it became operational. In this study, we evaluate the model’s ability to predict the summer temperature and precipitation over China using the 120 9-month reforecast runs initialized between January 1 and May 26 during each year of the reforecast period. These 120 reforecast runs are evaluated as an ensemble forecast using both deterministic and probabilistic metrics. The overall forecast skill for summer temperature is high while that for summer precipitation is much lower. The ensemble mean reforecasts have reduced spatial variability of the climatology. For temperature, the reforecast bias is lead time-dependent, i.e., reforecast JJA temperature become warmer when lead time is shorter. The lead time dependent bias suggests that the initial condition of temperature is somehow biased towards a warmer condition. CFSv2 is able to predict the summer temperature anomaly in China, although there is an obvious upward trend in both the observation and the reforecast. Forecasts of summer precipitation with dynamical models like CFSv2 at the seasonal time scale and a catchment scale still remain challenge, so it is necessary to improve the model physics and parameterizations for better prediction of Asian monsoon rainfall. The probabilistic skills of temperature and precipitation are quite limited. Only the spatially averaged quantities such as averaged summer temperature over the Northeast China of CFSv2 show higher forecast skill, of which is able to discriminate between event and non-event for three categorical forecasts. The potential forecast skill shows that the above and below normal events can be better forecasted than normal events. Although the shorter the forecast lead time is, the higher deterministic prediction skill appears, the probabilistic prediction skill does not increase with decreased lead time. The ensemble size does not play a significant role in affecting the overall probabilistic forecast skill although adding more members improves the probabilistic forecast skill slightly.     

GRAPES区域集合预报对2019年中国汛期降水预报评估

2019年,数值预报中心开发了以GRAPES全球模式为驱动场,集合变换卡尔曼滤波为初值扰动方法,随机物理过程倾向项为模式扰动方法的10km水平分辨率GRAPES-REPS V3.0区域集合预报模式,并投入业务运行.基于该模式,作者开展了2019年7～9月夏季降水不确定性的集合预报实时试验,并从统计检验和个例分析角度,与...     

A convection-allowing ensemble forecast based on the breeding growth mode and associated optimization of precipitation forecast

A convection-allowing ensemble forecast experiment on a squall line was conducted based on the breeding growth mode (BGM). Meanwhile, the probability matched mean (PMM) and neighborhood ensemble probability (NEP) methods were used to optimize the associated precipitation forecast. The ensemble forecast predicted the precipitation tendency accurately, which was closer to the observation than in the control forecast. For heavy rainfall, the precipitation center produced by the ensemble forecast was also better. The Fractions Skill Score (FSS) results indicated that the ensemble mean was skillful in light rainfall, while the PMM produced better probability distribution of precipitation for heavy rainfall. Preliminary results demonstrated that convection-allowing ensemble forecast could improve precipitation forecast skill through providing valuable probability forecasts. It is necessary to employ new methods, such as the PMM and NEP, to generate precipitation probability forecasts. Nonetheless, the lack of spread and the overprediction of precipitation by the ensemble members are still problems that need to be solved.