The Forecast Skills and Predictability Sources of Marine Heatwaves in the NUIST-CFS1.0 Hindcasts

Using monthly observations and ensemble hindcasts of the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS1.0) for the period 1983–2020, this study investigates the forecast skill of marine heatwaves(MHWs) over the globe and the predictability sources of the MHWs over the tropical oceans. The MHW forecasts are demonstrated to be skillful on seasonal-annual time scales, particularly in tropical oceans. The forecast skill of the MHWs over the tropical Pacific Ocean(TPO) remains high at lead times of 1–24 months, indicating a forecast better than random chance for up to two years. The forecast skill is subject to the spring predictability barrier of El Nino-Southern Oscillation(ENSO). The forecast skills for the MHWs over the tropical Indian Ocean(TIO), tropical Atlantic Ocean(TAO), and tropical Northwest Pacific(NWP) are lower than that in the TPO. A reliable forecast at lead times of up to two years is shown over the TIO, while a shorter reliable forecast window(less than 17 months) occurs for the TAO and NWP.Additionally, the forecast skills for the TIO, TAO, and NWP are seasonally dependent. Higher skills for the TIO and TAO appear in boreal spring, while a greater skill for the NWP emerges in late summer-early autumn. Further analyses suggest that ENSO serves as a critical source of predictability for MHWs over the TIO and TAO in spring and MHWs over the NWP in summer.

Evaluation of the subseasonal forecast skill of surface soil moisture in the S2S database

Based on the reforecasts from ve models of the Subseasonal to Seasonal(S2S)Prediction project,the S2S prediction skill of surface soil moisture(SM)over East Asia during May September is evaluated against ERA-Interim.Results show that good prediction skill of SM is generally 510 forecast days prior over southern and northeastern China in the majority of models.Over the Tibetan Plateau and northwestern China,only the ECMWF model has good prediction skill 20 days in advance.Generally,better prediction skill tends to appear over wet regions rather than dry regions.In terms of the seasonal variation of SM prediction skill,some diffierences are noticed among the models,but most of them show good prediction skill during September.Furthermore,the significant positive correlation between the prediction skill of SM and ENSO index indicates modulation by ENSO of the S2S prediction of SM.When there is an El Nino(a La Nina)event,the SM prediction skill over eastern China tends to be high(low).Through evaluation of the S2S prediction skill of SM in these models,it is found that the prediction skill of SM is lower than that of most atmospheric variables in S2S forecasts.Therefore,more attention needs to be given to the S2S forecasting of land processes.

Factors Limiting the Forecast Skill of the Boreal Summer Intraseasonal Oscillation in a Subseasonal-to-Seasonal Model

In this study,we evaluate the forecast skill of the subseasonal-to-seasonal(S2S)prediction model of the Beijing Climate Center(BCC)for the boreal summer intraseasonal oscillation(BSISO).We also discuss the key factors that inhibit the BSISO forecast skill in this model.Based on the bivariate anomaly correlation coefficient(ACC)of the BSISO index,defined by the first two EOF modes of outgoing longwave radiation and 850-hPa zonal wind anomalies over the Asian monsoon region,we found that the hindcast skill degraded as the lead time increased.The ACC dropped to below 0.5for lead times of 11 days and longer when the predicted BSISO showed weakened strength and insignificant northward propagation.To identify what causes the weakened forecast skill of BSISO at the forecast lead time of 11 days,we diagnosed the main mechanisms responsible for the BSISO northward propagation.The same analysis was also carried out using the observations and the outputs of the four-day forecast lead that successfully predicted the observed northward-propagating BSISO.We found that the lack of northward propagation at the 11-day forecast lead was due to insufficient increases in low-level cyclonic vorticity,moistening and warm temperature anomalies to the north of the convection,which were induced by the interaction between background mean flows and BSISO-related anomalous fields.The BCC S2S model can predict the background monsoon circulations,such as the low-level southerly and the northerly and easterly vertical shears,but has limited capability in forecasting the distributions of circulation and moisture anomalies.

A New Way to Predict Forecast Skill

Forecast skill (Anomaly Correlated Coefficient, ACC) is a quantity to show the forecast quality of the products of numerical weather forecasting models. Predicting forecast skill, which is the foundation of ensemble forecasting, means submitting products to predict their forecast quality before they are used. Checking the reason is to understand the predictability for the real cases. This kind of forecasting service has been put into operational use by statistical methods previously at the National Meteorological Center (NMC), USA (now called the National Center for Environmental Prediction (NCEP)) and European Center for Medium-range Weather Forecast (ECMWF). However, this kind of service is far from satisfactory because only a single variable is used with the statistical method. In this paper, a new way based on the Grey Control Theory with multiple predictors to predict forecast skill of forecast products of the T42L9 of the NMC, China Meteorological Administration (CMA) is introduced. The results show: (1) The correlation coefficients between 'forecasted' and real forecast skill range from 0.56 to 0.7 at different seasons during the two-year period. (2) The grey forecasting model GM(1,8) forecasts successfully the high peaks, the increasing or decreasing tendency, and the turning points of the change of forecast skill of cases from 5 January 1990 to 29 February 1992.

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.

Applications of Bias-removed Ensemble Mean in the Gale Forecasts over the Yellow Sea and the Bohai Sea

Based on the daily sea surface wind field prediction data of Japan Meteorological Agency(JMA) forecast model,National Centers for Environmental Prediction(NCEP GFS) model and U.S.Navy Operational Global Atmospheric Prediction System(NOGAPS) model at 12:00 UTC from June 28 to August 10 in 2009,the bias-removed ensemble mean(BRE) was used to do the forecast test on the sea surface wind fields,and the root-mean-square error(RMSE) was used to test and evaluate the forecast results.The results showed that the BRE considerably reduced the RMSEs of 24 and 48 h sea surface wind field forecasts,and the forecast skill was superior to that of the single model forecast.The RMSE decreases in the south of central Bohai Sea and the middle of the Yellow Sea were the most obvious.In addition,the BRE forecast improved evidently the forecast skill of the gale process which occurred during July 13-14 and August 7 in 2009.The forecast accuracy of the wind speed and the gale location was also improved.

CFSv2对浙江省延伸期逐日降水预报性能评估及解释应用初探

利用CFSv2长序列回报资料和近6 a实时预报数据,系统分析了模式对浙江短中期(Short-medium Range,SMR,1~10 d)和延伸期(Extended Range,ER,11~30 d)逐日降水的预测性能,并基于系统误差订正(Systematic Bias Correction,SBC)技术开展了解释应用。结果表明:(1)模式回报技巧在SMR时段快速衰减,而在ER时段的衰减明显趋缓;SMR时段的相关系数远高于ER时段,但这两个时段的均方根误差较为接近;(2)从季节演变来看,模式技巧在秋冬季较高而在春夏季相对较低;(3)模式回报结果表现出显著的系统性偏差,这一偏差在各个起报日(提前1~30 d起报)中稳定存在,采用SBC技术开展解释应用,发现订正后模式的回报技巧在ER时段显著提升;(4)进一步将SBC技术应用于实时预报,发现实时预报的技巧也得到了一定的改善,且ER时段的改进效果更为显著。

Performance of the Seasonal Forecasting of the Asian Summer Monsoon by BCC_CSM1.1(m)

This paper provides a comprehensive assessment of Asian summer monsoon prediction skill as a function of lead time and its relationship to sea surface temperature prediction using the seasonal hindcasts of the Beijing Climate Center Climate System Model, BCC_CSM1. l（m）. For the South and Southeast Asian summer monsoon, reasonable skill is found in the model＇s forecasting of certain aspects of monsoon climatology and spatiotemporal variability. Nevertheless, deficiencies such as significant forecast errors over the tropical western North Pacific and the eastern equatorial Indian Ocean are also found. In particular, overestimation of the connections of some dynamical monsoon indices with large-scale circulation and precipitation patterns exists in most ensemble mean forecasts, even for short lead-time forecasts. Variations of SST, measured by the first mode over the tropical Pacific and Indian oceans, as well as the spatiotemporal features over the Nifio3.4 region, are overall well predicted. However, this does not necessarily translate into successful forecasts of the Asian summer monsoon by the model. Diagnostics of the relationships between monsoon and SST show that difficulties in predicting the South Asian monsoon can be mainly attributed to the limited regional response of monsoon in observations but the extensive and exaggerated response in predictions due partially to the application of ensemble average forecasting methods. In contrast, in spite of a similar deficiency, the Southeast Asian monsoon can still be forecasted reasonably, probably because of its closer relationship with large-scale circulation patterns and E1 Nifio-Southern Oscillation.

MULTIMODEL CONSENSUS FORECASTING OF LOW TEMPERATURE AND ICY WEATHER OVER CENTRAL AND SOUTHERN CHINA IN EARLY 2008

Based on the daily mean temperature and 24-h accumulated total precipitation over central and southern China, the features and the possible causes of the extreme weather events with low temperature and icing conditions,which occurred in the southern part of China during early 2008, are investigated in this study. In addition, multimodel consensus forecasting experiments are conducted by using the ensemble forecasts of ECMWF, JMA, NCEP and CMA taken from the TIGGE archives. Results show that more than a third of the stations in the southern part of China were covered by the extremely abundant precipitation with a 50-a return period, and extremely low temperature with a 50-a return period occurred in the Guizhou and western Hunan province as well. For the 24- to 216-h surface temperature forecasts, the bias-removed multimodel ensemble mean with running training period(R-BREM) has the highest forecast skill of all individual models and multimodel consensus techniques. Taking the RMSEs of the ECMWF 96-h forecasts as the criterion, the forecast time of the surface temperature may be prolonged to 192 h over the southeastern coast of China by using the R-BREM technique. For the sprinkle forecasts over central and southern China, the R-BREM technique has the best performance in terms of threat scores(TS) for the 24- to 192-h forecasts except for the 72-h forecasts among all individual models and multimodel consensus techniques. For the moderate rain, the forecast skill of the R-BREM technique is superior to those of individual models and multimodel ensemble mean.

A Timescale Decomposed Threshold Regression Downscaling Approach to Forecasting South China Early Summer Rainfall

A timescale decomposed threshold regression （TSDTR） downscaling approach to forecasting South China early summer rainfall （SCESR） is described by using long-term observed station rainfall data and NOAA ERSST data. It makes use of two distinct regression downscaling models corresponding to the interannual and interdecadal rainfall variability of SCESR. The two models are developed based on the partial least squares （PLS） regression technique, linking SCESR to SST modes in preceding months on both interannual and interdecadal timescales. Specifically, using the datasets in the calibration period 1915-84, the variability of SCESR and SST are decomposed into interannual and interdecadal components. On the interannual timescale, a threshold PLS regression model is fitted to interannual components of SCESR and March SST patterns by taking account of the modulation of negative and positive phases of the Pacific Decadal Oscillation （PDO）. On the interdecadal timescale, a standard PLS regression model is fitted to the relationship between SCESR and preceding November SST patterns. The total rainfall prediction is obtained by the sum of the outputs from both the interannual and interdecadal models. Results show that the TSDTR downscaling approach achieves reasonable skill in predicting the observed rainfall in the validation period 1985-2006, compared to other simpler approaches. This study suggests that the TSDTR approach, considering different interannual SCESR-SST relationships under the modulation of PDO phases, as well as the interdecadal variability of SCESR associated with SST patterns, may provide a new perspective to improve climate predictions.

Ice concentration assimilation in a regional ice-ocean coupled model and its application in sea ice forecasting

A reasonable initial state of ice concentration is essential for accurate short-term forecasts of sea ice using ice-ocean coupled models. In this study, sea ice concentration data are assimilated into an operational ice forecast system based on a com- bined optimal interpolation and nudging scheme. The scheme produces a modeled sea ice concentration at every time step, based on the difference between observational and forecast data and on the ratio of observational error to modeled error. The impact and the effectiveness of data assimilation are investigated. Significant improvements to predictions of sea ice extent were obtained through the assimilation of ice concentration, and minor improvements through the adjustment of the upper ocean properties. The assimilation of ice thickness data did not significantly improve predictions. Forecast experiments show that the forecast accuracy is higher in summer, and that the errors on five-day forecasts occur mainly around the marginal ice zone.

Monthly Forecast of Indian Southwest Monsoon Rainfall Based on NCEP’s Coupled Forecast System

The monthly forecast of Indian monsoon rainfall during June to September is investigated by using the hindcast data sets of the National Centre for Environmental Prediction (NCEP)’s operational coupled model (known as the Climate Forecast System) for 25 years from 1981 to 2005 with 15 ensemble members each. The ensemble mean monthly rainfall over land region of India from CFS with one month lead forecast is underestimated during June to September. With respect to the inter-annual variability of monthly rainfall it is seen that the only significant correlation coefficients (CCs) are found to be for June forecast with May initial condition and September rainfall with August initial conditions. The CFS has got lowest skill for the month of August followed by that of July. Considering the lower skill of monthly forecast based on the ensemble mean, all 15 ensemble members are used separately for the preparation of probability forecast and different probability scores like Brier Score (BS), Brier Skill Score (BSS), Accuracy, Probability of Detection (POD), False Alarm Ratio (FAR), Threat Score (TS) and Heidke Skill Score (HSS) for all the three categories of forecasts (above normal, below normal and normal) have been calculated. In terms of the BS and BSS the skill of the monthly probability forecast in all the three categories are better than the climatology forecasts with positive BSS values except in case of normal forecast of June and July. The “TS”, “HSS” and other scores also provide useful probability forecast in case of CFS except the normal category of July forecast. Thus, it is seen that the monthly probability forecast based on NCEP CFS coupled model during the southwest monsoon season is very encouraging and is found to be very useful.

CMA全球集合预报系统误差增长及预报性能的尺度依赖特征诊断分析

利用CMA全球集合预报(CMA-GEPS)业务系统2020年6月1日至2021年5月31日一整年的500 hPa位势高度场(H_(500))预报数据,诊断评估了CMA-GEPS在北半球地区误差增长及预报性能的尺度依赖特征。使用谱滤波方法实现H_(500)不同尺度(包括行星尺度、天气尺度与次天气尺度)分量的分离。从集合平均均方根误差(简称集合平均误差)-离散度关系来看,在预报前期(108 h之前),CMA-GEPS集合平均误差小于集合离散度,存在过度发散的问题,主要是由天气尺度分量离散度过大导致;在预报后期(108 h之后),CMA-GEPS集合平均误差大于集合离散度,离散度偏小,是由行星尺度与天气尺度分量离散度不足共同引起。采用Dalcher等1987年修订的误差增长模型对H_(500)集合平均预报误差增长特征进行诊断分析,发现CMA-GEPS误差增长过程合理,初始误差在次天气尺度上增长最快,行星尺度上增长最慢;就绝对(相对)误差而言,模式误差对预报误差的影响随空间尺度的增大而增大(减小)。此外,将使用1989至2018年共计30 a的ERA-Interim再分析逐日数据得到的气候态分布作为参考预报,通过连续分级概率预报技巧评分(Continuously Ranked Probability Skill Score,CRPSS)检验了CMA-GEPS H_(500)及其不同尺度分量的概率预报技巧。结果表明,行星尺度分量概率预报技巧最高,次天气尺度分量最小,未经滤波的H_(500)预报技巧位于行星尺度与天气尺度分量预报技巧之间。上述诊断结果可为CMA-GEPS改进方向提供一定的客观依据。

Skills Needs and Upskilling Addressing the European Metal AM Industry

As Europe seeks to retain its leading position in industrial competitiveness,there is an urgent need to establish a platform for Additive Manufacturing(AM)skills at European,National and Regional levels.AM processes enable economic component production through the efficient use of materials and increased design freedom as compared to conventional manufacturing.AM also raises the level of digital literacy among workers,and it contributes to the digitisation of European Industry.In face of this increasing growth of Metal AM technology,and consequent requirement of the definition of new professional profiles and skills and knowledge for personnel working in this sector,the European Federation for Welding,Joining and Cutting(EWF)recently launched the first International Additive Manufacturing Qualification System(IAMQS).For creating and managing the IAMQS,EWF relies on its experience of more than 25 years in managing a European/International Training/Educational System for qualification and certification of welding and joining personnel,covering different levels(from Operator to Engineer),assessing knowledge and skills(examination)and providing a Quality Assurance System that ensures that the same qualification is recognised in all countries that share the system,is recognised by the Industry and is accepted by Enterprises,professionals,training institutions and certification bodies.IAMQS is also based on the work currently being developed in the scope of three European Funded projects in the field of AM,in which EWF is actively involved,together with the respective partners from eight EU countries.In collaboration with SAM,CLLAIM and ADMIRE projects’partners,EWF has conducted market searches/surveys to collect information on market needs and possible solutions for future workers and professionals already involved in AM sector,carried out validation workshops with experts from the Industry and Education,and developed European qualification pathways.This systemic approach that encourages a close collaboration with major European AM companies and organisations to collect inputs from different sources in the creation of AM Qualification System,ensures Professional Profiles’quality and transparency.As a result of the skills gap analysis carried out by EWF during the last 2 years,7 Qualifications for Additive Manufacturing,ranging from Operator to Engineer levels,were developed,while others are being finalised and validated.

Development of a skill assessment tool for the Korea operational oceanographic system

A standard skill assessment （SA） tool was developed and implemented to evaluate the performance of op- erational forecast models in the Korea operational oceanographic system. The SA tool provided a robust way to assess model skill in the system by comparing predictions and observations, and involved the com- putation of multiple skill metrics including correlation and error skills. User- and system-based acceptance criteria of skill metrics were applied to determine whether predictions were acceptable for the system. To achieve this, the tool produced a time series comparison plot, a skill score table, and an advanced sum- marized diagram to effectively demonstrate the multiple skill scores. Moreover, the SA was conducted to evaluate both atmospheric and hydrodynamic forecast variables. For the atmospheric variables, acceptable error criteria were preferable to acceptable correlation criteria over short timescales, since the mean square error overwhelmed the observation variance. Conversely, for the hydrodynamic variables, acceptable root mean square percentage error （e.g., perms） criteria were preferable to acceptable error （e.g., erms） criteria owing to the spatially variable tidal intensity around the Korean Peninsula. Furthermore, the SA indicated that predetermined acceptance error criteria were appropriate to satisfy a target central frequency （fc） for which errors fell within the specified limits （i.e., the .fc equals 70%）.

Guidance on the Choice of Threshold for Binary Forecast Modeling

This paper proposes useful guidance on the choice of threshold for binary forecasts. In weather forecast systems, the probabilistic forecast cannot be used directly when estimated too smoothly. In this case, the binary forecast, whether a meteorological event will occur or not, is preferable to the probabilistic forecast. A threshold is needed to generate a binary forecast, and the guidance in this paper encompasses the use of skill scores for the choice of threshold according to the forecast pattern. The forecast pattern consists of distribution modes of estimated probabilities, occurrence rates of observations, and variation modes. This study is performed via Monte-Carlo simulation, with 48 forecast patterns considered. Estimated probabilities are generated by random variate sampling from five distributions separately. Varying the threshold from 0 to 1, binary forecasts are generated by threshold. For the assessment of binary forecast models, a 2×2 contingency table is used and four skill scores （Heidke skill score, hit rate, true skill statistic, and threat score） are compared for each forecast pattern. As a result, guidance on the choice of skill score to find the optimal threshold is proposed.

NCEP CFSv2模式对川渝夏季降水次季节预测技巧评估及预报偏差分析

利用NCEP的第二代气候预测系统(CFSv2)提供的2000-2009年降水场历史回报试验资料以及川渝182个测站的降水实况资料。采用时间相关系数、均方根误差、距平相关系数、距平符号一致率以及PS评分等方法,对模式在川渝地区夏季降水以及夏季降水异常的次季节尺度预测技巧进行检验,并进一步分析了模式在概率密度和降水频次方面的预报偏差特征。结果表明:该模式对川渝夏季降水的可用预报时效为3候左右,能够较好地模拟出夏季降水的高值中心,但量级偏大。预报技巧高值区主要位于四川盆地西北部及渝东北地区,对攀西地区南部及川西高原部分地区也有一定的预报技巧。该模式也能够较好地把握川渝地区夏季降水异常偏少的趋势,有效预报技巧为2候以内。模式各时效预报与观测的降水概率密度主要集中在10 mm以下量级;模式预报各量级降水频次与实况相比均偏高得较为明显,且随着预报时效延长,偏差越大,其中偏高最为明显的是小雨频次。

欧盟技能预测实践概览:主体、内容与方法

文章追溯了欧盟的技能预测实践并解析其技能预测的主体构成、内容和方法。欧洲职业培训发展中心负责提供欧盟职业教育与培训政策的基础数据,制定和实施欧盟职业培训政策,监测劳动力市场发展趋势。技能预测工作涵盖顶层设计、统计信息收集和预测结果使用三个环节,不同部门或主体负责不同环节的工作。技能预测内容聚焦于职业结构变迁预测和资格水平变迁预测。欧洲技能指数监测从技能开发、技能激活到技能匹配这一技能全周期的技能错配状况,并据此预判欧盟的技能发展趋势。欧盟常用的技能预测方法为在线职位空缺与技能分析法和欧洲技能和就业调查,其中在线职位空缺与技能分析法是欧洲职业培训发展中心的职位空缺分析系统,负责提供欧盟的技能供需数据;欧洲技能和就业调查定期收集欧盟各国劳动力市场的技能要求、技能错配以及初始和继续教育等信息为制定职业教育与培训政策提供建议。

WQSRTP方法在甘肃省高低温客观预报中的应用

基于ECMWF细网格数值预报产品和国家级考核站气温观测数据,采用加权准对称滑动训练期方法(WQSRTP)生成甘肃省智能网格最高(低)客观产品,检验该产品的预报效果,将其与中国气象局智能网格指导预报产品(SCMOC)、甘肃省城镇网格预报产品(SPCC)进行对比。结果表明:WQSRTP订正方法能够显著改善ECMWF细网格数值模式24 h最高(低)气温的预报能力,24 h最高、最低气温预报准确率分别提升了32.16%、15.48%;WQSRTP订正产品相对于SCMOC、SPCC和ECMWF最高(低)气温产品均为正订正技巧,且最高气温订正能力优于最低气温订正能力。空间误差检验来看,WQSRTP订正方法可有效提升祁连山区和甘岷山区等地形复杂地区的最高(低)气温预报准确率,显著降低了平均绝对误差。

基于Local-BGM法的冷涡暴雨集合预报试验及评估检验

基于传统增长模繁殖法(Breeding Growing Mode,BGM)和局地增长模繁殖法(Local Breeding Growing Mode,Local-BGM)生成初始扰动成员,对一次冷涡暴雨过程进行集合预报试验,从多方面比较两种方案的预报效果,并且在邻域概率法(Neighborhood Probability,NP)中引入时间邻域,评估概率预报结果。结果表明,引入局地化思想的Local-BGM方案能够生成比传统BGM方案更合理的初始扰动,具有很明显的局地特征。对于扰动变量的预报,Local-BGM方案在均方根误差和离散度等方面均表现更好,同时能够提高各量级降水的预报技巧。邻域集合概率法能够综合各个集合成员预报的降水信息得到优于集合平均的概率预报,分数技巧评分更高。并且在考虑时间不确定性后,无论是控制预报、集合平均还是邻域集合概率法,分数技巧评分均有很大改善,并且降水阈值越大改善效果越明显,能够为极端强降水天气提供较为客观的概率预报信息。