Forecast Performance of the Pre-operational CMA-TRAMS (EPS) in South China During April - September 2020

The mesoscale ensemble prediction system based on the Tropical Regional Atmosphere Model for the South China Sea (CMA-TRAMS (EPS)) has been pre-operational since April 2020 at South China Regional Meteorological Center (SCRMC), which was developed by the Guangzhou Institute of Tropical and Marine Meteorology (GITMM). To better understand the performance of the CMA-TRAMS (EPS) and provide guidance to forecasters, we assess the performance of this system on both deterministic and probabilistic forecasts from April to September 2020 in this study through objective verification. Compared with the control (deterministic) forecasts, the ensemble mean of the CMATRAMS (EPS) shows advantages in most non-precipitation variables. In addition, the threat score indicates that the CMA-TRAMS (EPS) obviously improves light and heavy rainfall forecasts in terms of the probability-matched mean. Compared with the European Center for Medium-range Weather Forecasts operational ensemble prediction system (ECMWF-EPS), the CMA-TRAMS (EPS) improves the probabilistic forecasts of light rainfall in terms of accuracy, reliability and discrimination, and this system also improves the heavy rainfall forecasts in terms of discrimination. Moreover, two typical heavy rainfall cases in south China during the pre-summer rainy season are investigated to visually demonstrate the deterministic and probabilistic forecasts, and the results of these two cases indicate the differences and advantages (deficiencies) of the two ensemble systems.     

Characteristics of tropical Pacific SST predictability in coupled GCM forecasts using the NCEP CFS

The limits of predictability of El Niño and the Southern Oscillation (ENSO) in coupled models are investigated based on retrospective forecasts of sea surface temperature (SST) made with the National Centers for Environmental Prediction (NCEP) coupled forecast system (CFS). The influence of initial uncertainties and model errors associated with coupled ENSO dynamics on forecast error growth are discussed. The total forecast error has maximum values in the equatorial Pacific and its growth is a strong function of season irrespective of lead time. The largest growth of systematic error of SST occurs mainly over the equatorial central and eastern Pacific and near the southeastern coast of the Americas associated with ENSO events. After subtracting the systematic error, the root-mean-square error of the retrospective forecast SST anomaly also shows a clear seasonal dependency associated with what is called spring barrier. The predictability with respect to ENSO phase shows that the phase locking of ENSO to the mean annual cycle has an influence on the seasonal dependence of skill, since the growth phase of ENSO events is more predictable than the decay phase. The overall characteristics of predictability in the coupled system are assessed by comparing the forecast error growth and the error growth between two model forecasts whose initial conditions are 1 month apart. For the ensemble mean, there is fast growth of error associated with initial uncertainties, becoming saturated within 2 months. The subsequent error growth follows the slow coupled mode related the model’s incorrect ENSO dynamics. As a result, the Lorenz curve of the ensemble mean NINO3 index does not grow, because the systematic error is identical to the same target month. In contrast, the errors of individual members grow as fast as forecast error due to the large instability of the coupled system. Because the model errors are so systematic, their influence on the forecast skill is investigated by analyzing the erroneous features in a long simulation. For the ENSO forecasts in CFS, a constant phase shift with respect to lead month is clear, using monthly forecast composite data. This feature is related to the typical ENSO behavior produced by the model that, unlike the observations, has a long life cycle with a JJA peak. Therefore, the systematic errors in the long run are reflected in the forecast skill as a major factor limiting predictability after the impact of initial uncertainties fades out.     

Testing a four-dimensional variational data assimilation method using an improved intermediate coupled model for ENSO analysis and prediction

A four-dimensional variational (4D-Var) data assimilation method is implemented in an improved intermediate coupled model (ICM) of the tropical Pacific. A twin experiment is designed to evaluate the impact of the 4D-Var data assimilation algorithm on ENSO analysis and prediction based on the ICM. The model error is assumed to arise only from the parameter uncertainty. The “observation” of the SST anomaly, which is sampled from a “truth” model simulation that takes default parameter values and has Gaussian noise added, is directly assimilated into the assimilation model with its parameters set erroneously. Results show that 4D-Var effectively reduces the error of ENSO analysis and therefore improves the prediction skill of ENSO events compared with the non-assimilation case. These results provide a promising way for the ICM to achieve better real-time ENSO prediction.     

多模式集成的概率天气预报和气候预测研究进展

基于大气的混沌特性,单一的确定性预报逐步向多值的不确定性概率预报转化已成为一种趋势。本文系统地评述了概率天气预报产生的背景,介绍了概率预报的相关概念及国内外的研究状况,着重讨论了多模式集成的概率预报的两种集成方法,即贝叶斯模式平均(Bayesian model averaging,BMA)和多元高斯集合核拟合法(Gaussian ensemble kernel dressing,GEKD),并给出了两个例子的概率预报试验结果。利用BMA方法制作的概率预报的方差较小,减小了预报的不确定性,因此预报结果更接近大气的真实值。作为另一种多模式集成方法,多元高斯集合核拟合法回报的地面气温距平均值及趋势的概率预测结果与实测结果基本一致。利用此方法建立了地面气温年代际变化的概率多模式集合预测模型,并从中提取年代际气候变化特征,对东亚季风区年代际预测具有重要应用价值。     

Seasonal prediction of African precipitation with ECHAM4–T42 ensemble simulations using a multivariate MOS re-calibration scheme

A 15 member ensemble of 20th century simulations using the ECHAM4–T42 atmospheric GCM is utilized to investigate the potential predictability of interannual variations of seasonal rainfall over Africa. Common boundary conditions are the global sea surface temperatures (SST) and sea ice extent. A canonical correlation analysis (CCA) between observed and ensemble mean ECHAM4 precipitation over Africa is applied in order to identify the most predictable anomaly patterns of precipitation and the related SST anomalies. The CCA is then used to formulate a re-calibration approach similar to model output statistics (MOS) and to derive precipitation forecasts over Africa. Predictand is the climate research unit (CRU) gridded precipitation over Africa. As predictor we use observed SST anomalies, ensemble mean precipitation over Africa and a combined vector of mean sea level pressure, streamfunction and velocity potential at 850 hPa. The different forecast approaches are compared. Most skill for African precipitation forecasts is provided by tropical Atlantic (Gulf of Guinea) SST anomalies which mainly affect rainfall over the Guinean coast and Sahel. The El Niño/Southern Oscillation (ENSO) influences southern and East Africa, however with a lower skill. Indian Ocean SST anomalies, partly independent from ENSO, have an impact particularly on East Africa. As suggested by the large agreement between the simulated and observed precipitation, the ECHAM4 rainfall provides a skillful predictor for CRU precipitation over Africa. However, MOS re-calibration is needed in order to provide skillful forecasts. Forecasts using MOS re-calibrated model precipitation are at least as skillful as forecast using dynamical variables from the model or instantaneous SST. In many cases, MOS re-calibrated precipitation forecasts provide more skill. However, differences are not systematic for all regions and seasons, and often small.     

Interdecadal change of interannual variability and predictability of two types of ENSO

A significant interdecadal climate shift of interannual variability and predictability of two types of the El Niño-Southern Oscillation (ENSO), namely the canonical or eastern Pacific (EP)-type and Modoki or central Pacific (CP) type, are investigated. Using the retrospective forecasts of six-state-of-the-art coupled models and their multi-model ensemble (MME) for December–January–February during the period of 1972–2005 along with corresponding observed and reanalyzed data, we examine the climate regime shift that occurred in the winter of 1988/1989 and how the shift affected interannual variability and predictability of two types of ENSO for the two periods of 1972–1988 (hereafter PRE) and 1989–2005 (hereafter POST). The result first shows substantial interdecadal changes of observed sea surface temperature (SST) in mean state and variability over the western and central Pacific attributable to the significant warming trend in the POST period. In the POST period, the SST variability increased (decreased) significantly over the western (eastern) Pacific. The MME realistically reproduces the observed interdecadal changes with 1- and 4-month forecast lead time. It is found that the CP-type ENSO was more prominent and predictable during the POST than the PRE period while there was no apparent difference in the variability and predictability of the EP-type ENSO between two periods. Note that the second empirical orthogonal function mode of the Pacific SST during the POST period represents the CP-type ENSO but that during the PRE period captures the ENSO transition phase. The MME better predicts the former than the latter. We also investigate distinctive regional impacts associated with the two types of ENSO during the two periods.     

Prediction and monitoring of monsoon intraseasonal oscillations over Indian monsoon region in an ensemble prediction system using CFSv2

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.     

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.     

区域集合预报系统2 m温度预报的校准技术

采用非齐次高斯回归 (NGR) 技术对国家气象中心区域集合预报系统的2 m温度预报结果开展了一阶偏差和二阶离散度的校准研究。对预报结果比较详尽的检验分析表明:校准后的2 m温度预报可靠性和预报技巧均显著提高,表现为校准后集合预报成员的均方根误差与离散度更为接近;原Talagrand直方图中的“L”形分布现象得到有效改善;Brier评分、最小连续分级概率评分 (CRPS) 明显减小,相对作用特征 (ROC) 面积增大,说明校准后的2 m温度预报表现出更好的预报技能。此外,NGR技术与自适应误差订正技术的对比试验表明,NGR在消除集合平均偏差和提高集合离散度两个方面均有优势。     

An assessment of the ENSO forecast skill of GEOS-5 system

The seasonal forecast skill of the NASA Global Modeling and Assimilation Office atmosphere–ocean coupled global climate model (AOGCM) is evaluated based on an ensemble of 9-month lead forecasts for the period 1993 to 2010. The results from the current version (V2) of the AOGCM consisting of the GEOS-5 AGCM coupled to the MOM4 ocean model are compared with those from an earlier version (V1) in which the AGCM (the NSIPP model) was coupled to the Poseidon Ocean Model. It was found that the correlation skill of the Sea Surface Temperature (SST) forecasts is generally better in V2, especially over the sub-tropical and tropical central and eastern Pacific, Atlantic, and Indian Ocean. Furthermore, the improvement in skill in V2 mainly comes from better forecasts of the developing phase of ENSO from boreal spring to summer. The skill of ENSO forecasts initiated during the boreal winter season, however, shows no improvement in terms of correlation skill, and is in fact slightly worse in terms of root mean square error (RMSE). The degradation of skill is found to be due to an excessive ENSO amplitude. For V1, the ENSO amplitude is too strong in forecasts starting in boreal spring and summer, which causes large RMSE in the forecast. For V2, the ENSO amplitude is slightly stronger than that in observations and V1 for forecasts starting in boreal winter season. An analysis of the terms in the SST tendency equation, shows that this is mainly due to an excessive zonal advective feedback. In addition, V2 forecasts that are initiated during boreal winter season, exhibit a slower phase transition of El Nino, which is consistent with larger amplitude of ENSO after the ENSO peak season. It is found that this is due to weak discharge of equatorial Warm Water Volume (WWV). In both observations and V1, the discharge of equatorial WWV leads the equatorial geostrophic easterly current so as to damp the El Nino starting in January. This process is delayed by about 2 months in V2 due to the slower phase transition of the equatorial zonal current from westerly to easterly.     

Current status of ENSO prediction skill in coupled ocean–atmosphere models

The overall skill of ENSO prediction in retrospective forecasts made with ten different coupled GCMs is investigated. The coupled GCM datasets of the APCC/CliPAS and DEMETER projects are used for four seasons in the common 22 years from 1980 to 2001. As a baseline, a dynamic-statistical SST forecast and persistence are compared. Our study focuses on the tropical Pacific SST, especially by analyzing the NINO34 index. In coupled models, the accuracy of the simulated variability is related to the accuracy of the simulated mean state. Almost all models have problems in simulating the mean and mean annual cycle of SST, in spite of the positive influence of realistic initial conditions. As a result, the simulation of the interannual SST variability is also far from perfect in most coupled models. With increasing lead time, this discrepancy gets worse. As one measure of forecast skill, the tier-1 multi-model ensemble (MME) forecasts of NINO3.4 SST have an anomaly correlation coefficient of 0.86 at the month 6. This is higher than that of any individual model as well as both forecasts based on persistence and those made with the dynamic-statistical model. The forecast skill of individual models and the MME depends strongly on season, ENSO phase, and ENSO intensity. A stronger El Niño is better predicted. The growth phases of both the warm and cold events are better predicted than the corresponding decaying phases. ENSO-neutral periods are far worse predicted than warm or cold events. The skill of forecasts that start in February or May drops faster than that of forecasts that start in August or November. This behavior, often termed the spring predictability barrier, is in part because predictions starting from February or May contain more events in the decaying phase of ENSO.     

夏季亚欧中高纬度环流的集合预报效果检验

使用NCEP集合预报资料, 对亚洲中高纬地区2003年6—8月500 hPa高度场的集合预报效果进行了检验。环流预报效果检验结果表明:预报时效大于5 d时, 集合平均预报明显优于单一预报; 使用相同模式分辨率时, 集合平均能将可用预报时效延长12 h以上, 达到7.5 d; 通过集合预报可获得真正意义的概率预报结果, 取得较单一高分辨率预报好的预报效果。阻塞过程的个例分析也表明集合平均的预报效果明显优于单一确定性预报; 特征等值线可反映集合成员的不一致信息和少数集合成员的异常表现, 以此为基础, 可估计分析对象出现与否的概率, 达到提高预报效果的目的。     

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.     

集合数值预报在洪水预报中的应用进展

水文集合预报是近几年正在形成和发展的水文预报分支,其发展大致可分为两个阶段:第1阶段是1970年至20世纪末进行的长期径流预报,第2阶段从21世纪开始,主要学习气象数值预报中集合预报的概念在短期水文集合预报中的应用。目前,除了单一预报中心的集合预报系统在水文集合预报中应用外,多个预报中心的集合预报大集合也逐渐被应用于流域水文预报,甚至一些小流域的洪水预报。如利用TIGGE(THORPEX Interactive Grand Global Ensemble)集合预报驱动形成的大气-水文-水力的串联系统进行早期的洪水预警研究,将全球集合预报作为洪水模型输入的有限区域模式的初始条件和侧边界条件的研究。这些均表明,基于水文集合预报的洪水预报增加了预报附加值,并能够延长预警提前时间。以欧洲中期天气预报中心的欧洲洪水预警系统(EFAS)和美国NOAA的先进水文预报系统(AHPS)为代表,实现了集合预报在洪水中的实时业务预报,但仍存在数据处理和计算量大,以及如何基于集合水文预报做决策等问题。对于水文集合预报的前处理和后处理的各种技术已处于探索和验证阶段,如何更好地理解基于概率预报的洪水预警决策仍存在许多困难和挑战。     

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.     

An assessment of the predictability of the East Asian Subtropical Westerly Jet based on TIGGE data

The predictability of the position, spatial coverage and intensity of the East Asian subtropical westerly jet(EASWJ) in the summers of 2010 to 2012 was examined for ensemble prediction systems(EPSs) from four representative TIGGE centers,including the ECMWF, the NCEP, the CMA, and the JMA. Results showed that each EPS predicted all EASWJ properties well, while the levels of skill of all EPSs declined as the lead time extended. Overall, improvements from the control to the ensemble mean forecasts for predicting the EASWJ were apparent. For the deterministic forecasts of all EPSs, the prediction of the average axis was better than the prediction of the spatial coverage and intensity of the EASWJ. ECMWF performed best, with a lead of approximately 0.5–1 day in predictability over the second-best EPS for all EASWJ properties throughout the forecast range. For probabilistic forecasts, differences in skills among the different EPSs were more evident in the earlier part of the forecast for the EASWJ axis and spatial coverage, while they departed obviously throughout the forecast range for the intensity. ECMWF led JMA by about 0.5–1 day for the EASWJ axis, and by about 1–2 days for the spatial coverage and intensity at almost all lead times. The largest lead of ECMWF over the relatively worse EPSs, such as NCEP and CMA, was approximately 3–4 days for all EASWJ properties. In summary, ECMWF showed the highest level of skill for predicting the EASWJ, followed by JMA.     

Dynamical seasonal prediction of Southern African summer precipitation

Prediction skill for southern African (16°–33°E, 22°–35°S) summer precipitation in the Scale Interaction Experiment-Frontier coupled model is assessed for the period of 1982–2008. Using three different observation datasets, deterministic forecasts are evaluated by anomaly correlation coefficients, whereas scores of relative operating characteristic and relative operating level are used to evaluate probabilistic forecasts. We have found that these scores for December–February precipitation forecasts initialized on October 1st are significant at 95 % confidence level. On a local scale, the level of prediction skill in the northwestern and central parts of southern Africa is higher than that in northeastern South Africa. El Niño/Southern Oscillation (ENSO) provides the major source of predictability, but the relationship with ENSO is too strong in the model. The Benguela Niño, the basin mode in the tropical Indian Ocean, the subtropical dipole modes in the South Atlantic and the southern Indian Oceans and ENSO Modoki may provide additional sources of predictability. Within the wet season from October to the following April, the precipitation anomalies in December-February are the most predictable. This study presents promising results for seasonal prediction of precipitation anomaly in the extratropics, where seasonal prediction has been considered a difficult task.     

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.     

Incorporating a semi-stochastic model of ocean-modulated westerly wind bursts into an ENSO prediction model

Prediction models of the El Niño-Southern Oscillation (ENSO) phenomenon often represent westerly wind bursts (WWBs), a significant player in ENSO dynamics, as stochastic forcing. A recent paper developed an observationally motivated semi-stochastic statistical model that quantifies the dependence of WWBs on large-scale sea-surface temperature. This WWB model is added here to a hybrid coupled model, thus activating a two-way SST-WWB feedback. The WWB model represents both the deterministic and stochastic elements of WWBs and thus is especially appropriate for ensemble ENSO prediction experiments. An ensemble of retrospective forecasts is performed for the years 1979–2002. Overall statistical measures of predictability are neither degraded nor improved relative to the hybrid, coupled general circulation model, perhaps because of the limitations of the hybrid coupled model and the initialization procedure used. While the present work is meant as a proof-of-concept, it is found that the addition of the WWB model does improve the prediction of the onset and the development of the large 1997 warm event, pointing to the potential for ENSO prediction skill improvement using this approach.     

基于TIGGE多模式集合的24小时气温BMA 概率预报

利用TIGGE(THORPEX Interactive Grand Global Ensemble)单中心集合预报系统(ECMWF、United Kingdom Meteorological Office、China Meteorological Administration和NCEP)以及由此所构成的多中心模式超级集合预报系统24小时地面日均气温预报,结合淮河流域地面观测率定贝叶斯模型平均(Bayesian model averaging,BMA)参数,从而建立地面日均气温BMA概率预报模型.由此针对淮河流域进行地面日均气温BMA概率预报及其检验与评估,结果表明BMA模型比原始集合预报效果好;单中心的BMA概率预报都有较好的预报效果,其中ECMWF最好.多中心模式超级集合比单中心BMA概率预报效果更好,采用可替换原则比普通的多中心模式超级集合BMA模型计算量小,且在上述BMA集合预报系统中效果最好.它与原始集合预报相比其平均绝对误差减少近7%,其连续等级概率评分提高近10%.基于采用可替换原则的多中心模式超级集合BMA概率预报,针对研究区域提出了极端高温预警方案,这对防范高温天气有着重要意义.