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.     

Ensemble hindcasts of ENSO events over the past 120 years using a large number of ensembles

Based on an intermediate coupled model (ICM), a probabilistic ensemble prediction system (EPS) has been developed. The ensemble Kalman filter (EnKF) data assimilation approach is used for generating the initial ensemble conditions, and a linear, first-order Markov-Chain SST anomaly error model is embedded into the EPS to provide model-error perturbations. In this study, we perform ENSO retrospective forecasts over the 120 year period 1886–2005 using the EPS with 100 ensemble members and with initial conditi...     

The Relationship between Deterministic and Ensemble Mean Forecast Errors Revealed by Global and Local Attractor Radii

It has been demonstrated that ensemble mean forecasts, in the context of the sample mean, have higher forecasting skill than deterministic(or single) forecasts. However, few studies have focused on quantifying the relationship between their forecast errors, especially in individual prediction cases. Clarification of the characteristics of deterministic and ensemble mean forecasts from the perspective of attractors of dynamical systems has also rarely been involved. In this paper, two attractor statistics—namely, the global and local attractor radii(GAR and LAR, respectively)—are applied to reveal the relationship between deterministic and ensemble mean forecast errors. The practical forecast experiments are implemented in a perfect model scenario with the Lorenz96 model as the numerical results for verification. The sample mean errors of deterministic and ensemble mean forecasts can be expressed by GAR and LAR, respectively, and their ratio is found to approach2~(1/2) with lead time. Meanwhile, the LAR can provide the expected ratio of the ensemble mean and deterministic forecast errors in individual cases.     

BCC二代气候系统模式的季节预测评估和可预报性分析

本文利用国家气候中心（BCC）第二代季节预测模式系统历史回报数据,从确定性预报和概率预报两个方面系统地评估了该模式对气温、降水和大气环流的季节预报性能,并与BCC一代气候预测模式的结果进行了对比,重点分析了二代模式的季节可预报性问题。结果显示,BCC二代模式对全球气温、降水和环流的预报性能整体上优于一代模式,特别在热带中东太平洋、印度洋和海洋大陆地区的温度和降水的预报效果改进尤为明显。这些热带地区降水预报的改进,可以通过激发太平洋—北美型（PNA）、东亚—太平洋型（EAP）等遥相关波列提升该模式在中高纬地区的季节预报技巧。分析表明,厄尔尼诺和南方涛动（ENSO）信号在热带和热带外地区均是模式季节可预报性的重要来源,BCC二代模式能够较好把握全球大气环流对ENSO信号的响应特征,从而通过对ENSO预报技巧的改进有效地提升了模式整体的预报性能。从概率预报来看,BCC二代模式对我国冬季气温和夏季降水具备一定的预报能力,特别是对我国东部大部分地区冬季气温正异常和负异常事件预报的可靠性和辨析度相对较高。因此,进一步提高模式对热带大尺度异常信号和大气主要模态的预报能力、加强概率预报产品释用对提高季节气候预测水平具有重要意义。     

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。     

Skill assessment of seasonal hindcasts from the Canadian historical forecast project

Abstract

The performance of seasonal hindcasts produced with four global atmospheric models in the second phase of the Canadian Historical Forecasting Project is evaluated. Deterministic and probabilistic forecast skill assessments are carried out using common verification measures. Several methods of combining multi‐model output to produce deterministic and probabilistic forecasts of near‐surface air temperature, 500 hPa geopotential height, and 700 hPa temperature for zero‐month and one‐month leads are considered. A variance‐based weighting modestly improves the skill of deterministic and probabilistic hindcasts in some cases. A parametric Gaussian probability estimator is superior to a non‐parametric count‐method estimator for producing multi‐model probability forecasts. Statistical adjustment is beneficial for deterministic and probabilistic hindcasts of near‐surface temperature over the ocean but not always over land. Skill improves with the number of different models used for a given total ensemble size. The four‐model ensemble is shown to be a reasonable multi‐model configuration.     

蒙特卡罗方法在降水评估中的应用研究

预报检验重点关注预报与观测间的综合统计特征用以探讨模式预报性能,而统计显著性检验方法是衡量评估结论的重要指标,是判断预报效果改进与否的有效手段.当前诸多重要检验指标如降水技巧评分等由于不满足正态分布特征均难以采用简单的计算方式获得置信区间以衡量检验指标的误差特征,因此难以正确判断通过统计检验所获得的评估差异是真实反映模式预报效果差异或是由检验样本的不确定性所造成.蒙特卡罗方法可通过样本重构获取正态分布的统计样本从而有效地解决这一问题.采用2015年8月的T639模式及GRAPES全球预报模式24 h降水预报产品,使用中国区域2400站日降水资料作为实况,重点研究蒙特卡罗方法在统计显著性检验中的应用特征,分析不同蒙特卡罗重构次数对检验结果的收敛性.结果表明10 000次蒙特卡罗重构后统计指标可满足正态分布,而通过显著性检验分析后可明显区分预报系统间降水评分差异的统计特征.     

Advance and prospectus of seasonal prediction: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980–2004)

We assessed current status of multi-model ensemble (MME) deterministic and probabilistic seasonal prediction based on 25-year (1980–2004) retrospective forecasts performed by 14 climate model systems (7 one-tier and 7 two-tier systems) that participate in the Climate Prediction and its Application to Society (CliPAS) project sponsored by the Asian-Pacific Economic Cooperation Climate Center (APCC). We also evaluated seven DEMETER models’ MME for the period of 1981–2001 for comparison. Based on the assessment, future direction for improvement of seasonal prediction is discussed. We found that two measures of probabilistic forecast skill, the Brier Skill Score (BSS) and Area under the Relative Operating Characteristic curve (AROC), display similar spatial patterns as those represented by temporal correlation coefficient (TCC) score of deterministic MME forecast. A TCC score of 0.6 corresponds approximately to a BSS of 0.1 and an AROC of 0.7 and beyond these critical threshold values, they are almost linearly correlated. The MME method is demonstrated to be a valuable approach for reducing errors and quantifying forecast uncertainty due to model formulation. The MME prediction skill is substantially better than the averaged skill of all individual models. For instance, the TCC score of CliPAS one-tier MME forecast of Niño 3.4 index at a 6-month lead initiated from 1 May is 0.77, which is significantly higher than the corresponding averaged skill of seven individual coupled models (0.63). The MME made by using 14 coupled models from both DEMETER and CliPAS shows an even higher TCC score of 0.87. Effectiveness of MME depends on the averaged skill of individual models and their mutual independency. For probabilistic forecast the CliPAS MME gains considerable skill from increased forecast reliability as the number of model being used increases; the forecast resolution also increases for 2 m temperature but slightly decreases for precipitation. Equatorial Sea Surface Temperature (SST) anomalies are primary sources of atmospheric climate variability worldwide. The MME 1-month lead hindcast can predict, with high fidelity, the spatial–temporal structures of the first two leading empirical orthogonal modes of the equatorial SST anomalies for both boreal summer (JJA) and winter (DJF), which account for about 80–90% of the total variance. The major bias is a westward shift of SST anomaly between the dateline and 120°E, which may potentially degrade global teleconnection associated with it. The TCC score for SST predictions over the equatorial eastern Indian Ocean reaches about 0.68 with a 6-month lead forecast. However, the TCC score for Indian Ocean Dipole (IOD) index drops below 0.40 at a 3-month lead for both the May and November initial conditions due to the prediction barriers across July, and January, respectively. The MME prediction skills are well correlated with the amplitude of Niño 3.4 SST variation. The forecasts for 2 m air temperature are better in El Niño years than in La Niña years. The precipitation and circulation are predicted better in ENSO-decaying JJA than in ENSO-developing JJA. There is virtually no skill in ENSO-neutral years. Continuing improvement of the one-tier climate model’s slow coupled dynamics in reproducing realistic amplitude, spatial patterns, and temporal evolution of ENSO cycle is a key for long-lead seasonal forecast. Forecast of monsoon precipitation remains a major challenge. The seasonal rainfall predictions over land and during local summer have little skill, especially over tropical Africa. The differences in forecast skills over land areas between the CliPAS and DEMETER MMEs indicate potentials for further improvement of prediction over land. There is an urgent need to assess impacts of land surface initialization on the skill of seasonal and monthly forecast using a multi-model framework.     

邻域空间检验技术在集合降水预报检验中的应用

常规降水检验受空间及时间微小差异所带来的"双重惩罚"影响严重,邻域空间检验FSS(Fraction Skill Score)方法在确定性预报中已体现出弥补这一不足的明显优势.随着集合预报分辨率的不断提高,集合降水预报同样存在与确定性预报相似的问题.本研究将FSS方法拓展至集合预报领域,构建适用于集合预报的降水空间检验指...     

The Meta-Gaussian Bayesian Processor of Forecasts and Associated Preliminary Experiments

Public weather services are trending toward providing users with probabilistic weather forecasts, in place of traditional deterministic forecasts. Probabilistic forecasting techniques are continually being improved to optimize available forecasting information. The Bayesian Processor of Forecast (BPF), a new statistical method for probabilistic forecast, can transform a deterministic forecast into a probabilistic forecast according to the historical statistical relationship between observations and forecasts generated by that forecasting system. This technique accounts for the typical forecasting performance of a deterministic forecasting system in quantifying the forecast uncertainty. The meta-Gaussian likelihood model is suitable for a variety of stochastic dependence structures with monotone likelihood ratios. The meta-Gaussian BPF adopting this kind of likelihood model can therefore be applied across many fields, including meteorology and hydrology. The Bayes theorem with two continuous random variables and the normal-linear BPF are briefly introduced. The meta-Gaussian BPF for a continuous predictand using a single predictor is then presented and discussed. The performance of the meta-Gaussian BPF is tested in a preliminary experiment. Control forecasts of daily surface temperature at 0000 UTC at Changsha and Wuhan stations are used as the deterministic forecast data. These control forecasts are taken from ensemble predictions with a 96-h lead time generated by the National Meteorological Center of the China Meteorological Administration, the European Centre for Medium-Range Weather Forecasts, and the US National Centers for Environmental Prediction during January 2008. The results of the experiment show that the meta-Gaussian BPF can transform a deterministic control forecast of surface temperature from any one of the three ensemble predictions into a useful probabilistic forecast of surface temperature. These probabilistic forecasts quantify the uncertainty of the control forecast; accordingly, the performance of the probabilistic forecasts differs based on the source of the underlying deterministic control forecasts.     

QBO influence on extratropical predictive skill

The quasi-biennial oscillation (QBO) in the zonal wind in the tropical stratosphere is one of the most predictable aspects of the circulation anywhere in the atmosphere and can be accurately forecast for many months in advance. If the stratospheric QBO systematically (and significantly) affects the tropospheric circulation, it potentially provides a predictable signal useful for seasonal forecasting. The stratospheric QBO itself is generally not well represented in current numerical models, however, including those used for seasonal prediction and this potential may not be exploited by current numerical-model based forecast systems. The purpose of the present study is to ascertain if a knowledge of the state of the QBO can contribute to extratropical boreal winter seasonal forecast skill and, if so, to motivate further research in this area. The investigation is in the context of the second Historical Forecasting Project (HFP2), a state-of-the-art multimodel two-tier ensemble seasonal forecasting system. The first tier, consisting of a prediction of sea surface temperature anomalies (SSTAs), is followed by the second tier which is a prediction of the state of the atmosphere and surface using an AGCM initialized from atmospheric analyses and using the predicted SSTs as boundary conditions. The HFP2 forecasts are successful in capturing the extratropical effects of sea surface temperature anomalies in the equatorial Pacific to the extent that a linear statistical correction based on the NINO3.4 index does not provide additional extratropical skill. By contrast, knowledge of the state of the stratospheric QBO can be used statistically to add extratropical skill centred in the region of the North Atlantic Oscillation. Although the additional skill is modest, the result supports the contention that taking account of the QBO could improve extratropical seasonal forecasting skill. This might be done statistically after the fact, by forcing the QBO state into the forecast model as it runs or, preferably, by using models which correctly represent the physical processes and behaviour of the QBO.     

GRAPES-GEPS环流集合预报的分类释用方法研究与检验

集合预报在数值天气预报体系中具有重要地位,因此如何有效提取集合样本信息以提高集合预报技巧一直是一个重要课题.基于中国全球集合预报业务系统(GRAPES-GEPS)的500?hPa高度场集合资料开展对环流集合预报的分类释用方法研究,并对集合聚类预报结果进行了检验分析.通过在传统Ward聚类法中引入动态聚类的"手肘法"方案...     

Comparison of Statistical Post-Processing Methods for Probabilistic Wind Speed Forecasting

In this study, the statistical post-processing methods that include bias-corrected and probabilistic forecasts of wind speed measured in PyeongChang, which is scheduled to host the 2018 Winter Olympics, are compared and analyzed to provide more accurate weather information. The six post-processing methods used in this study are as follows: mean bias-corrected forecast, mean and variance bias-corrected forecast, decaying averaging forecast, mean absolute bias-corrected forecast, and the alternative implementations of ensemble model output statistics (EMOS) and Bayesian model averaging (BMA) models, which are EMOS and BMA exchangeable models by assuming exchangeable ensemble members and simplified version of EMOS and BMA models. Observations for wind speed were obtained from the 26 stations in PyeongChang and 51 ensemble member forecasts derived from the European Centre for Medium-Range Weather Forecasts (ECMWF Directorate, 2012) that were obtained between 1 May 2013 and 18 March 2016. Prior to applying the post-processing methods, reliability analysis was conducted by using rank histograms to identify the statistical consistency of ensemble forecast and corresponding observations. Based on the results of our study, we found that the prediction skills of probabilistic forecasts of EMOS and BMA models were superior to the biascorrected forecasts in terms of deterministic prediction, whereas in probabilistic prediction, BMA models showed better prediction skill than EMOS. Even though the simplified version of BMA model exhibited best prediction skill among the mentioned six methods, the results showed that the differences of prediction skills between the versions of EMOS and BMA were negligible.     

Global seasonal climate predictability in a two tiered forecast system: part I: boreal summer and fall seasons

This paper shows demonstrable improvement in the global seasonal climate predictability of boreal summer (at zero lead) and fall (at one season lead) seasonal mean precipitation and surface temperature from a two-tiered seasonal hindcast forced with forecasted SST relative to two other contemporary operational coupled ocean–atmosphere climate models. The results from an extensive set of seasonal hindcasts are analyzed to come to this conclusion. This improvement is attributed to: (1) The multi-model bias corrected SST used to force the atmospheric model. (2) The global atmospheric model which is run at a relatively high resolution of 50 km grid resolution compared to the two other coupled ocean–atmosphere models. (3) The physics of the atmospheric model, especially that related to the convective parameterization scheme. The results of the seasonal hindcast are analyzed for both deterministic and probabilistic skill. The probabilistic skill analysis shows that significant forecast skill can be harvested from these seasonal hindcasts relative to the deterministic skill analysis. The paper concludes that the coupled ocean–atmosphere seasonal hindcasts have reached a reasonable fidelity to exploit their SST anomaly forecasts to force such relatively higher resolution two tier prediction experiments to glean further boreal summer and fall seasonal prediction skill.     

天气预报技巧和价值的关系

利用一个简单的花费-损失比模型介绍了天气预报系统的技巧和其对用户的价值之间的关系。以欧洲中期天气预报中心的集合预报系统的控制预报和集合预报为例，对确定性预报和概率预报的情况分别进行了说明。结果表明，有技巧的天气预报系统只有在用产的花费-损矢比(C／L)在某一数值区间内时对用户才是有价值的。通过对比分析集合预报系统EPS概率预报和确定性预报的相对经济价值曲线，说明概率预报系统比一个与其质量相当的确定性预报系统具有较大的价值优势，而根据C／L选择最佳概率阈值对于实现其最大预报价值尤为重要。     

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.     

Seasonal Prediction of Summer Precipitation over East Africa Using NUIST-CFS1.0

East Africa is particularly vulnerable to precipitation variability, as the livelihood of much of the population depends on rainfed agriculture. Seasonal forecasts of the precipitation anomalies, when skillful, can therefore improve implementation of coping mechanisms with respect to food security and water management. This study assesses the performance of Nanjing University of Information Science and Technology Climate Forecast System version 1.0(NUISTCFS1.0) on forecasting June–September(JJAS) seasonal precipitation anomalies over East Africa. The skill in predicting the JJAS mean precipitation initiated from 1 May for the period of 1982–2019 is evaluated using both deterministic and probabilistic verification metrics on grid cell and over six distinct clusters. The results show that NUIST-CFS1.0 captures the spatial pattern of observed seasonal precipitation climatology, albeit with dry and wet biases in a few parts of the region. The model has positive skill across a majority of Ethiopia, Kenya, Uganda, and Tanzania, whereas it doesn’t exceed the skill of climatological forecasts in parts of Sudan and southeastern Ethiopia. Positive forecast skill is found over regions where the model shows better performance in reproducing teleconnections related to oceanic SST. The prediction performance of NUIST-CFS1.0 is found to be on a level that is potentially useful over a majority of East Africa.     

Seasonal Forecasts of the Summer 2016 Yangtze River Basin Rainfall

The Yangtze River has been subject to heavy flooding throughout history,and in recent times severe floods such as those in 1998 have resulted in heavy loss of life and livelihoods.Dams along the river help to manage flood waters,and are important sources of electricity for the region.Being able to forecast high-impact events at long lead times therefore has enormous potential benefit.Recent improvements in seasonal forecasting mean that dynamical climate models can start to be used directly for operational services.The teleconnection from El Ni ?no to Yangtze River basin rainfall meant that the strong El Ni ?no in winter 2015/16 provided a valuable opportunity to test the application of a dynamical forecast system.This paper therefore presents a case study of a real-time seasonal forecast for the Yangtze River basin,building on previous work demonstrating the retrospective skill of such a forecast.A simple forecasting methodology is presented,in which the forecast probabilities are derived from the historical relationship between hindcast and observations.Its performance for2016 is discussed.The heavy rainfall in the May–June–July period was correctly forecast well in advance.August saw anomalously low rainfall,and the forecasts for the June–July–August period correctly showed closer to average levels.The forecasts contributed to the confidence of decision-makers across the Yangtze River basin.Trials of climate services such as this help to promote appropriate use of seasonal forecasts,and highlight areas for future improvements.     

Downscaling of the global seasonal forecasts of hydrometcenter of Russia for North Eurasia

Proposed is a method of downscaling of the global ensemble seasonal forecasts of air temperature computed using the SLAV model of the Hydrometcenter of Russia. The method is based on the regression and suggests a probabilistic interpretation of forecasts based on the assessment of uncertainty associated with the regression and model forecast ensemble spread. The verification of the method for 70 weather stations of North Eurasia using the rank probability skill score RPSS showed a significant advantage of downscaled forecasts over the forecasts interpolated from the model grid points. It is concluded that the use of the downscaling method is reasonable for the long-range forecasting of the station air temperature for North Eurasia.     

Multi-model calibration and combination of tropical seasonal sea surface temperature forecasts

Different combination methods based on multiple linear regression are explored to identify the conditions that lead to an improvement of seasonal forecast quality when individual operational dynamical systems and a statistical–empirical system are combined. A calibration of the post-processed output is included. The combination methods have been used to merge the ECMWF System 4, the NCEP CFSv2, the Météo-France System 3, and a simple statistical model based on SST lagged regression. The forecast quality was assessed from a deterministic and probabilistic point of view. SSTs averaged over three different tropical regions have been considered: the Niño3.4, the Subtropical Northern Atlantic and Western Tropical Indian SST indices. The forecast quality of these combinations is compared to the forecast quality of a simple multi-model (SMM) where all single models are equally weighted. The results show a large range of behaviours depending on the start date, target month and the index considered. Outperforming the SMM predictions is a difficult task for linear combination methods with the samples currently available in an operational context. The difficulty in the robust estimation of the weights due to the small samples available is one of the reasons that limit the potential benefit of the combination methods that assign unequal weights. However, these combination methods showed the capability to improve the forecast reliability and accuracy in a large proportion of cases. For example, the Forecast Assimilation method proved to be competitive against the SMM while the other combination methods outperformed the SMM when only a small number of forecast systems have skill. Therefore, the weighting does not outperform the SMM when the SMM is very skilful, but it reduces the risk of low skill situations that are found when several single forecast systems have a low skill.