Systematic biases in numerical weather prediction commonly require post-processing correction.Ensemble Model Output Statistics(EMOS)is a post-processing method for ensemble forecasts.In recent years,two other variations of EMOS(gEMOS and SAMOS)have been proposed to improve EMOS.This paper aims to evaluate their performance.A comparative study has been conducted for 2 m temperature,relative humidity,10 m wind speed,and 3 h cumulative precipitation in North China using five numerical models,i.e.,the Global Ensemble Prediction System(GEPS),the Global Forecast System(GFS),the Regional Ensemble Prediction System(REPS),and two mesoscale weather numerical forecasts(MESO-10 km,MESO-3 km)with different spatial resolutions from the China Meteorological Administration(CMA).Results show that all the three post-processing methods can reduce forecast errors of the CMA models across these variables.Specifically,(1)the EMOS method,which independently calculates parameters for each station,retains the unique characteristics of individual stations,resulting in optimal performance;(2)gEMOS underperforms EMOS due to its neglect of inter-station independence;(3)for variables such as temperature,humidity,and wind speed,which accurately simulate climatological distribution,SAMOS performance is comparable to that of EMOS.For precipitation,SAMOS's performance is constrained by climatological precipitation distribution simulation;the forecast error of SAMOS is larger than that of EMOS,yet it is still smaller than that of gEMOS.

The "spin-up" problem—where convection-permitting models require hours to develop realistic clouds from large-scale initial fields—critically limits short-term severe weather forecasting. Cloud analysis offers a potential solution by directly incorporating hydrome-teor information from remote sensing observations. In this study, we leverage multi-source remote sensing data, including three-dimensional mosaic radar reflectivity, hourly aver-aged FY-2G satellite black-body temperature (TBB), and FY-2G total cloud water products, within a stepwise cloud-analysis initialization scheme. The scheme is implemented in a convective-scale ensemble forecasting system (CMA-Meso, 3 km resolution) for a heavy rainfall event. For each ensemble member, three-dimensional hydrometeor increments are independently generated from these remote sensing retrievals and gradually introduced over the first ten time steps, ensuring smooth coordination with the model's dynam-ic-thermal framework. Results demonstrate that the remote sensing-driven cloud analysis substantially enhances ensemble system performance across multiple dimensions: (i) spin-up time is significant-ly reduced, with precipitation forecasts exhibiting reasonable structure from the initial forecast hour; (ii) deterministic forecast accuracy improves systematically, with reduced RMSE for geopotential height, temperature, and wind fields across all levels; (iii) proba-bilistic forecasting skill is enhanced, evidenced by improved CRPS and AROC for surface elements and precipitation thresholds; (iv) ensemble reliability is optimized, with spread better matching forecast errors. Mechanistic analysis reveals that these improvements stem from physically coordinated hydrometeor-latent heat initial perturbations and sub-sequent cloud-radiation feedbacks that continuously regulate thermal-dynamic structures. This study establishes that assimilating diverse remote sensing data via cloud analysis is an effective approach for addressing spin-up challenges in convective-scale ensemble prediction.

The “spin-up” problem, in which convection-permitting models require hours to develop realistic clouds from large-scale initial fields, critically limits short-term severe weather forecasting. Cloud analysis can serve as a feasible approach to directly assimilate hydrometeor information from remote sensing retrievals. In this study, we leverage multi-source remote sensing data, including three-dimensional mosaic radar reflectivity, hourly averaged FY-2G satellite brightness temperature (black-body temperature, TBB), and FY-2G total cloud water products, within a stepwise cloud analysis initialization scheme. The scheme is implemented in a convective-scale ensemble forecasting system (CMA-Meso, 3 km resolution) for a heavy rainfall event. For each ensemble member, three-dimensional hydrometeor increments are independently generated from these remote sensing retrievals and gradually introduced over the first ten time steps, ensuring smooth coordination with the model’s dynamic thermal framework. Quantitatively, the scheme reduces near-surface Continuous Rank Probability Score (CRPS) errors, improves the overall predictive skill by 2.6–7.9% (maximum at the 12 h spin-up period), and increases ensemble spread by 2–5.8%, mitigating under-dispersion. Probabilistic precipitation forecasts show uniform area under the relative operating characteristic curve (AROC) improvements across all thresholds, 1.16–5.77% for light rain, 3.03–8.97% for moderate rain, and 6.00–12.07% for heavy rain, with these maxima consistently occurring at the 12 h spin-up time. Although Brier scores are marginally larger, these AROC gains confirm the enhanced discrimination of convective rainfall. At 500 hPa, CRPS reductions of 7.1–15.6% emerge after 24 h (largest 15.6% for geopotential height at 24 h), zonal wind CRPS is reduced by 2.2% at 12 h, and ensemble spread increases by 3.1–7.0% for all three variables. These improvements, particularly the pronounced benefits during the initial 12 h, demonstrate that the remote sensing-driven cloud analysis effectively shortens spin-up. Mechanistically, the gains arise from physically coordinated hydrometeor-latent heat perturbations and subsequent cloud radiation feedback that continuously regulate thermal-dynamic structures. This study establishes that assimilating diverse remote sensing data via cloud analysis is an effective approach for overcoming spin-up challenges in convective-scale ensembles.

The ongoing rise in greenhouse gas emissions is leading to a sharp increase in global surface temperatures and more frequent extreme weather events, which has intensified the fluctuation range of daily extreme temperatures and increased the difficulty of prediction. Research on forecasting changes in daily extreme temperature can provide reliable scientific data for assessing future disaster risks and support decision-making. Due to limitations in the performance and sensitivity, current global climate models (GCM) exhibit considerable uncertainty in predicting extreme temperatures, increasing the difficulty of predicting future trends. It is necessary to correct the direct prediction results of GCMs to obtain more reliable prediction results. Therefore, Siberian sea level pressure and the sea surface temperature of the Indian Ocean, both of which have significant impacts on the daily extreme temperature changes in China, are selected as physical factors for correction. Two methods, emergent constraints and Pareto optimal ensemble, are employed to correct GCM' predictions of daily extreme temperature changes in China under the SSP1-2.6 scenario for the middle of the 21st century. A comparison of results before and after correction reveals that both methods could effectively reduce the inter-model uncertainty of future daily extreme temperature changes. Among them, Pareto optimal ensemble scheme, which integrates three-variable factors-daily extreme temperature in China, Siberian sea level pressure, and the Indian Ocean sea surface temperature, proves most effective in minimizing inter-model uncertainty. The range of multi-model predictions of daily maximum (minimum) temperature changes in China for the mid-21st century, as corrected by the three-variable Pareto optimal ensemble scheme, is narrowed to 1.26 ℃ to 2.10 ℃ (1.12℃ to 2.06 ℃). The uncertainty range is reduced by approximately 36.8% (32.9%) compared to the uncorrected results. Moreover, the signal-to-noise ratio of the predicted daily extreme temperature changes increase in most areas of China, rising from below 1 without correction to above 1. At the same time, corrected results based on three-variable Pareto optimal ensemble scheme show significant regional differences, adjusting the magnitude of warming differentially over the Qinghai-Xizang Plateau, Northwest China, and Sichuan Basin. Overall, employing physical constraint derived from selected constraint factors to correct predictions of future daily extreme temperature changes in China is shown to be useful and feasible.

ABSTRACT Accurate representation of forecast uncertainty is essential for effective ensemble forecasting. To quantify the strengths and weaknesses of the China Meteorological Administration (CMA) convection‐permitting ensemble prediction system (CPEPS) in representing spread–skill relationships and to identify avenues for improvement, this study applied multidimensional diagnostic metrics—temporal evolution, spatial distribution, spread–skill of perturbations applied at different scales, and point‐to‐point distributions of the spread–skill relationships between the ensemble mean root mean square error (RMSE) and ensemble spread—to operational CMA‐CPEPS data for the period January–June 2025. The results revealed that the CMA‐CPEPS is under‐dispersive for most variables except 500‐hPa geopotential height. Temperature exhibits a poorer spread–skill relationship than that of wind variables, and near‐surface variables show a poorer relationship than that of variables at mid‐ and low‐tropospheric levels. Spread–skill relationships vary regionally, with larger spreads and higher spread–skill ratios over China north of 30° N but smaller spreads and under‐dispersion over China south of 30° N. However, the higher spread–skill ratio over northern China does not yield uniformly larger correlation coefficients between the RMSE and ensemble spread. The point‐to‐point distributions effectively explain the observed six‐month averaged spread–skill relationships. Moreover, contributions of perturbations at different scales on the relationships differ across variables and forecast lead times. Based on these diagnostics, we have identified potential areas for improvements to enhance the spread–skill relationships in the CMA‐CPEPS. The diagnostic methods can be extended to evaluations of other ensemble prediction systems, providing a foundation for continuously improving EPSs.

The SMART2022 project (Sciences of Meteorology and Artificial Intelligence in Re-search and Technology for the Beijing 2022 Olympic and Paralympic Winter Games) was initiated to enhance weather forecasting and services for the Beijing 2022 Olympic and Paralympic Winter Games. The focus of the project was to carry out intensive meteorological field experiments and develop advanced high-precision weather forecasting methods to support the games. The field experiments fully considered the advantages and limitations of various types of instruments and provided detailed firsthand information that enabled forecasters to understand the small-scale weather phenomena in the mountain competition venues. Novel methods were developed, enhanced, and used during the games, such as integration-based, 10-min-updating forecasts with 100-m horizontal grid spacing for 0-24-h lead times; large-eddy-simulation-based nu-merical model forecasts with 67-m horizontal grid spacing for 24-240-h lead times; and artificial intelligence-based, site-specific, postprocessed seamless forecasts with 0-240-h lead times. These innovations increased the precision of the weather forecasting and allowed effective correction of kilometer-scale numerical model forecast errors, with a specific emphasis on ultrahigh-resolution predictions of wind gusts and temperature in the complex mountainous competition areas affected mainly by the continental winter monsoon. Utilization of the SMART2022 achievements had a key role in supporting weather forecasts and services for the official training and competition events, scheduling, competition safety window selection, and other aspects related to both the 2022 Winter Olympics and Paralympics. Ultimately, these achievements contributed to the successful completion of all the snow-based events and the safety of athletes during the 2022 Games. SIGNIFICANCE STATEMENT: The successful hosting of the modern Winter Olympics and Paralympics is closely linked to the weather conditions. This paper introduces the SMART2022 project, which aimed to provide essential weather forecasting support for the Beijing 2022 Olympic and Paralympic Winter Games. The project involved meteorological field experiments in two mountainous competition zones over four consecutive winters, using comprehensive arrays of advanced meteorological observation instruments. It also included the development of novel, high-precision weather forecasting systems and methods by utilizing enhanced meteorological observations, advanced numerical weather prediction models, and artificial intelligence techniques. The outcomes of the project in supporting the 2022 Games are evaluated. The project can serve as a demonstration of high-spatiotemporal-resolution field experiments and forecasts for winter mountain weather in the midlatitudes.

This paper reviews the development of ensemble weather forecast and the primary techniques employed in themain ensemble prediction systems (EPSs) designed by China and other countries. Here, the emphasis is placed on theadvancements in the China Meteorological Administration (CMA) global and regional ensemble prediction systems (i.e., CMA-GEPS and CMA-REPS), with particular attention to operational technologies such as initial and modelperturbation methods and the applications of ensemble forecast. Through comparative verification with EPSs fromother leading international numerical weather prediction (NWP) centers, CMA's EPSs demonstrate forecast skillscomparable to its global counterparts. As EPSs progress to convective scales and coupled systems between sea, land,air, and ice, the paper addresses some key challenges in ensemble forecast technologies across the aspects of opera-tion, science, integration of artificial intelligence (AI), merging of weather and climate models, and challenging userrequirements. Finally, a summary of conclusions and future perspectives on ensemble forecast are provided.

From 2 to 6 October 2021， Shanxi experienced the most intense continuous precipitation in autumn in meteorological records.Based on the precipitation observation data of automatic meteorological stations， ERA5 reanalysis data and NCEP GDAS reanalysis data， this study analyzed the circulation situation， low-level jet and water vapor transportation characteristics and water vapor sources of the extreme precipitation.The results indicated that the continuous extreme precipitation process had obvious phased characteristics.In the first stage （from 2nd to 3rd October）， the precipitation occurred in the warm area in front of the cold front and mainly in southern Shanxi.There were obvious convective precipitation characteristics which showed high and strongly fluctuant precipitation intensity.The precipitation of the second stage （from 4th to 6th October） was stable one behind the front which showed low precipitation intensity and long duration， resulting in continuous rainstorm in central and southern Shanxi.The circulation analysis revealed that the abnormally north-located and strong Western Pacific subtropical high and the abnormally south-located and strong West Siberian cut-off vortex constituted a favorable circulation background.During the whole precipitation process， compared with other rainstorm process， the abnormally north-located and strong low-level jet at 700 hPa remained stable which played an important role in the strengthening of low-level convergence， the intense development of vertical upward movement and the continuous water vapor supply.And it was the key to the formation of extreme precipitation.The low-level jet at 850 hPa existed a shorter time but played an important role in the transportation of warm wet air and the construction of unstable layer in southeast Shanxi in the first stage.The main water vapor input level of the heavy rainfall area increased gradually during the precipitation， with the first stage mainly around 850 hPa and the second stage mainly around 700 hPa， which was corresponding to the evolution of the weather situation.The sources of the water vapor in this extreme precipitation were mainly from the South China Sea， the East China Sea and the Yellow Sea， and the transportation path was southeast path， obviously different from other heavy rainfalls which main water vapor sources were mostly from the Bay of Bengal， the Arabian Sea and the South China Sea and the transportation path was southwest path.

This paper reviews the development of ensemble weather forecast and the primary techniques employed in the main ensemble prediction systems (EPSs) designed by China and other countries. Here, the emphasis is placed on the advancements in the China Meteorological Administration (CMA) global and regional ensemble prediction systems (i.e., CMA-GEPS and CMA-REPS), with particular attention to operational technologies such as initial and model perturbation methods and the applications of ensemble forecast. Through comparative verification with EPSs from other leading international numerical weather prediction (NWP) centers, CMA’s EPSs demonstrate forecast skills comparable to its global counterparts. As EPSs progress to convective scales and coupled systems between sea, land, air, and ice, the paper addresses some key challenges in ensemble forecast technologies across the aspects of operation, science, integration of artificial intelligence (AI), merging of weather and climate models, and challenging user requirements. Finally, a summary of conclusions and future perspectives on ensemble forecast are provided.

Atmospheric motions are inherently nonlinear, and high-impact weather events are often associated with the rapid amplification of initial perturbations within such nonlinear flows. Ensemble prediction systems rely heavily on the initial perturbations to characterize future uncertainties, but traditional linear methods such as singular vector (SV) are inadequate in capturing the nonlinear evolution of meso- and convective-scale systems. Conditional nonlinear optimal perturbation (CNOP) effectively addresses this limitation, yet its practical application is hindered by the high complexity of adjoint model construction. This study applies an adjoint-free orthogonal CNOP solution scheme by an ensemble projection algorithm in the convection-allowing ensemble prediction system (CAEPS) based on the CMA-MESO model. Multi-scale blending SVs from CMA-MESO are used to construct initial perturbations and corresponding forecast increments, establishing an approximate mapping model between them. This method effectively retains crucial meso- and microscale information from the background field, generating perturbations with more dynamical relevance. Key findings show that: (1) The orthogonal CNOP-I in CAEPS based on CMA-MESO can be effectively achieved by the ensemble projection algorithm. (2) The perturbated energy of CNOP is more concentrated in key dynamic–thermal zones, including upper-level troughs, mid-to-lower-level vortices, and the prospective development zones of these weather systems. (3) Experimental CNOP results more accurately reproduce the observed extreme precipitation characteristics in both location and intensity, whereas most SV-driven members systematically underestimate rainfall. The ratio of the ensemble spread to RMSE of the CNOP experiments is generally closer to the value 1 than that of the SVs.

To meet the demands for seamless medium- and short-range weather forecasting during the Beijing Winter Olympics (2022), the Winter Olympics research team at the Earth System Modeling and Prediction Centre (CEMC) of the China Meteorological Administration (CMA) developed an integrated global and regional numerical weather prediction (NWP) model system. In support of the Winter Olympics, the system focuses on key short- and medium-range deterministic and ensemble forecast technologies for complex terrain. By introducing a three-dimensional reference atmosphere and a predictor-corrector iterative algorithm into the regional model’s dynamical framework, the team enhanced the spatial accuracy and temporal integration stability of the high-resolution regional model. The team also developed data assimilation techniques for dense surface automatic weather stations and high spatiotemporal resolution imagery from China’s Fengyun satellites, improving the monitoring and application capability of unconventional observations for the Winter Olympics. Furthermore, they established a 3 km high-resolution regional ensemble prediction system by advancing multiscale hybrid initial perturbation techniques and stochastic perturbation methods for physical processes with spatiotemporal correlations, suitable for complex terrain. To enhance deterministic and probabilistic forecasts at grid and station scales over complex terrain, the team studied bias correction techniques across different resolutions and developed methods for rapidly and effectively extracting key forecast information from large volumes of model output. In particular, machine learning-based approaches were employed to process and fuse massive forecast products containing probabilistic information. These efforts led to the development of a seamless Winter Olympics meteorological forecasting system covering a lead time of 0–15 days and the entire competition zone, featuring forecast updates every hour within 24 h, every 3 h within 24–72 h, and every 12 h within 72–360 h. These products were applied comprehensively in real-time operations during the winter training, test events, and the Olympic and Paralympic Games, representing the highest level of China’s independently developed NWP systems in meteorological support for major events. The integrated technological achievements have since been incorporated into the national operational NWP system, and they continue to play a vital role in daily forecasting services, disaster prevention and mitigation, and support for major events.

This research constructed an air quality ensemble forecasting model consisting of fifteen members using the China Meteorological Administration regional ensemble forecasting system (CMA_REPS) and Comprehensive Air Quality Model Extensions (CAMx) models to investigate the influence of atmospheric field uncertainty on air quality simulations. Focusing on the Beijing Winter Olympics in February 2022, this study examines the effects of both ground-level and vertical meteorological conditions on PM2.5 concentration distributions. The simulation accuracy of the model was validated, and its performance was analyzed. Results revealed that the ensemble mean simulations exhibit high correlation coefficients with observations for temperature (0.95), wind speed (0.80), relative humidity (0.83), and pressure (0.99). Both the control forecast and the ensemble mean for PM2.5 concentration aligned well with observations, with the ensemble mean demonstrating a strong correlation between the root mean square error and ensemble spread. In terms of reducing the false alarm rate (FAR) and improving the Bias Score (BS), the ensemble mean outperformed the control forecast. The control forecast for PM2.5 concentration was found to be more accurate at and around pollutant concentration inflection points, which may be attributed to simulation deviations in temperature and pressure that introduce uncertainty in atmospheric stability simulations. The correlation between PM2.5 and various meteorological elements varied during different periods. The vertical distribution of meteorological factors also significantly affected simulation outcomes, particularly uncertainties in simulating wind speed and inversion temperature processes, which further contributed to the uncertainty in pollutant simulations.

Since the Beijing 2022 Winter Olympics was the first Winter Olympics in history held in continental winter monsoon climate conditions across complex terrain areas, there is a deficiency of relevant research, operational techniques, and experience. This made providing meteorological services for this event particularly challenging. The China Meteorological Administration (CMA) Earth System Modeling and Prediction Centre, achieved breakthroughs in research on short- and medium-term deterministic and ensemble numerical predictions. Several key technologies crucial for precise winter weather services during the Winter Olympics were developed. A comprehensive framework, known as the Operational System for High-Precision Weather Forecasting for the Winter Olympics, was established. Some of these advancements represent the highest level of capabilities currently available in China. The meteorological service provided to the Beijing 2022 Games also exceeded previous Winter Olympic Games in both variety and quality. This included achievements such as the “100-meter level, minute level” downscaled spatiotemporal resolution and forecasts spanning 1 to 15 days. Around 30 new technologies and over 60 kinds of products that align with the requirements of the Winter Olympics Organizing Committee were developed, and many of these techniques have since been integrated into the CMA’s operational national forecasting systems. These accomplishments were facilitated by a dedicated weather forecasting and research initiative, in conjunction with the preexisting real-time operational forecasting systems of the CMA. This program represents one of the five subprograms of the WMO’s high-impact weather forecasting demonstration project (SMART2022), and is also a part of their Regional Association (RA) II Research Development Project (Hangzhou RDP). Therefore, the research accomplishments and meteorological service experiences from this program will be carried forward into forthcoming high-impact weather forecasting activities. This article provides an overview and assessment of this program and the operational national forecasting systems.

To avoid selection of filtered scales in multiscale initial perturbations and to evaluate the roles of multiscale initial perturbations on the China Meteorological Administration-convection-permitting ensemble prediction system (CMA-CPEPS), we proposed a new scale-blending technique and then constructed a blended initial perturbation scheme (BLEND) based on the ensemble transform Kalman filter (ETKF) and dynamical downscaling (DOWN) schemes. First, the results revealed that the BLEND scheme can increase the small-scale (largescale) perturbations of the DOWN (ETKF) scheme, with the multiscale characteristics. Second, for dynamical variables, the BLEND scheme can improve the under-dispersion of the ETKF scheme at all forecast hours, and also improve the over-dispersion of the DOWN scheme at the initial lead time. Additionally, the probabilistic forecasting skill of the BLEND scheme is similar to that of the DOWN and ETKF schemes. Third, for precipitation, the BLEND scheme can increase the ensemble spread and reduce the forecast error of the ETKF scheme at all forecast hours, and at 15-21 and 30-36 h, respectively. And the BLEND scheme can reduce the forecast error of the DOWN scheme for most forecast hours and neighborhood radii. Furthermore, the BLEND scheme can completely improve the Brier scores of the ETKF scheme, and improve those of the DOWN scheme for large neighborhood radii and the final 6-12 h. Therefore, it is desirable to construct multiscale initial perturbations in CPEPSs.

对2020年7月22日山东半岛一次极端暴雨天气过程开展观测分析,并利用中尺度模式WRF对此次局地降水过程进行了高分辨率数值模拟,对暴雨过程进行了天气背景和中尺度降雨的诊断.WRF模式较好地再现了此次极端暴雨过程,结果表明:此次极端暴雨过程短时降水强度大且局地性强,在时空上具有明显中尺度特征.降水发生在北抬副热带高压与华北低涡底部之间的西南气流中,强低涡与低空急流是影响此次降水的重要天气系统.西南急流为本次暴雨过程极端水汽的主要输送载体;在弱高空辐散场下,从地表延伸至500 hPa高空的深厚低涡是造成本次暴雨的主要影响因子,其时空演变特征与中尺度云团变化一致,与暴雨的发生直接相关.低涡、低空急流和副高之间的相互作用使低涡加强发展,低涡南部有暖湿气流入流,北部有干冷气流流入,比湿梯度基本呈现为自南向北递减分布,是典型的伴有低空急流的中尺度低涡流场分布;低涡辐合及其与副热带高压边缘强风速带的共同作用,导致强垂直运动发展并维持,是造成本次山东半岛极端暴雨的重要原因.

数值天气预报的发展状况和预报水平是当今世界用来衡量气象现代化水平和国际先进性的重要标志,是服务国家防灾减灾不可替代的技术手段之一.一直以来,数值预报的不确定性是影响数值预报准确率的世界难题,估计数值模式预报不确定性是世界各国数值预报业务焦点问题之一.

Using a 3-km regional ensemble prediction system (EPS), this study tested a three-dimensional (3D) rescaling mask for initial condition (IC) perturbation. Whether the 3D mask-based EPS improves ensemble forecasts over current two-dimensional (2D) mask-based EPS has been evaluated in three aspects: ensemble mean, spread, and probability. The forecasts of wind, temperature, geopotential height, sea level pressure, and precipitation were examined for a summer month (1-28 July 2018) and a winter month (1-27 February 2019) over a region in North China. The EPS was run twice per day (initiated at 0000 and 1200 UTC) to 36 h in forecast length, providing 56 warm-season forecast cases and 54 cold-season cases for verification. The warm and cold seasons are verified separately for comparison. The study found the following: 1) The vertical profile of IC perturbation becomes closer to that of analysis uncertainty with the 3D rescaling mask. 2) Ensemble performance is significantly improved in all three aspects. The biggest improvement is in the ensemble spread, followed by the probabilistic forecast, and the least improvement is in the ensemble mean forecast. Larger improvements are seen in the warm season than in the cold season. 3) More improvement is in the shorter time range (<24 h) than in the longer range. 4) Surface and lower-level variables are improved more than upper-level ones. 5) The underlying mechanism for the improvement has been investigated. Convective instability is found to be responsible for the spread increment and, thus, overall ensemble forecast improvement. Therefore, using a 3D rescaling mask is recommended for an EPS to increase its utility especially for shorter time range and surface weather elements. Significant StatementA weather prediction model is a complex system that consists of nonlinear differential equations. Small errors in either its inputs or model itself will grow with time during model integration, which will contaminate a forecast. To quantify such contamination ("uncertainty") of a forecast, the ensemble forecasting technique is used. An ensemble of forecasts is a multiple of model runs at the same time but with slightly "perturbed" inputs or model versions. These small perturbations are supposed to represent true "uncertainty" in inputs or model representation. This study proposed a technique that makes a perturbation's vertical structure more resemble real uncertainty (intrinsic error) in input data and confirmed that it can significantly improve ensemble forecast quality especially for a shorter time range and lower-level weather elements. It is found that convective instability is responsible for the improvement.

冰雪运动项目与气象条件关系密切,气象条件是冬奥会赛事顺利进行的关键因素之一.中国气象局地球系统数值预报中心根据2022年北京冬奥会比赛气象保障需求,基于多尺度混合初值扰动方法和侧边界扰动方法,初步建立了高分辨率区域集合预报试验系统,针对北京冬奥会比赛同期时段开展了连续试验.初步试验统计结果表明:主要预报变量高、中、低层等压面要素集合平均值的均方根误差基本小于等于控制预报误差,体现了集合平均相对于单一确定性预报的优势;地面要素风和降水预报效果较好,但温度24 h预报偏差高于2℃,距离精准冬奥气象保障还有一定差距.针对试验期间两次寒潮大风过程开展了高分辨率区域集合预报,天气学分析的检验结果表明,集合预报产品可以比较准确地描述地面温度主要分布特征、寒潮移动过程和降水预报,为预报员提供寒潮标准24 h变温预报、大风预报等有价值的概率预测信息.基于诊断方法开发了能见度、大风、降水相态等对冬奥赛事运行和运动员表现有重要影响的天气要素集合预报产品,初步试验结果表明不同集合成员的取舍对能见度预报反应敏感,具有一定预报能力,但预报范围偏大,数值偏低,需进一步改进;阵风预报与实况大值区分布比较一致,降水相态预报与观测分布吻合,雨雪分界线,降雨、雨夹雪、雪、冰粒落区范围合理,进一步提升了北京冬奥会气象的保障能力.

模式中常应用水平扩散项以抑制非线性计算不稳定或阻尼虚假短波,但这会导致数值模式在截断尺度附近出现小尺度动能过度耗散.为了将被过度耗散的小尺度动能补偿回模式,将随机动能后向散射扰动方法(stochastic kinetic energy backscatter,SKEB)引入CMA-REPS区域集合预报系统.首先基于由一阶自回归随机过程在水平方向上进行球谐函数展开得到的随机型,然后计算由数值扩散方案引起的局地动能耗散率,进而构造随机流函数强迫,并将其转化为水平风速扰动,对耗散的动能进行随机补偿.开展了2018年9月、10月(选取1日、7日、13日、19日、25日)的10 d集合预报随机型时间及空间尺度敏感性试验,并对试验结果进行评估.获得如下结论:在CMA-REPS区域集合预报中应用SKEB方案,可在一定程度上补偿过度耗散的小尺度动能,进而改善了模式对实际大气动能谱的模拟能力.就集合预报技巧改进而言,SKEB方案可以显著改善区域模式水平风场U、V的离散度,同时水平风场、温度等要素连续分级概率评分(CRPS)和离群值评分均获得改善.对SKEB方案开展的6个时间尺度(失相关时间尺度τ选取1、3、6、9、12、15 h)和6个空间相关尺度(最大截断波数Lmax选取80、100、120、160、200、240)敏感性试验结果表明,12 h失相关时间尺度和最大截断波数为240空间相关尺度的集合概率预报技巧更优.结论证明SKEB方案可以补偿在截断尺度耗散的小尺度动能,有效提高集合预报技巧.

A forecast from a numerical weather prediction (NWP) model can be decomposed into model climate and anomaly. Each part contributes to forecast error. To avoid errors from model climate, an anomaly, rather than a full field, should be used in a model. Model climate is replaced by the observed climate to reconstruct a new forecast for application. Using a Lorenz model, which has similar error characteristics to an NWP model, the following results were obtained. (a) The new anomaly-based method can significantly and steadily increase forecast accuracy throughout the entire forecast period (28 model days). On average, the total forecast error was reduced ~25%, and the correlation was increased by ~100–200%. The correlation improvement increases with the increasing of forecast length. (b) The method has different impacts on different types of error. Bias error was almost eliminated (over 90% in reduction). However, the change in flow-dependent error was mixed: a slight reduction (~5%) for model day 1–14 forecasts and increase (~15%) for model day 15–28 forecasts on average. The larger anomaly forecast error leads to the worsening of flow-dependent error. (c) Bias error stems mainly from model climate prediction, while flow-dependent error is largely associated with anomaly forecast. The method works more effectively for a forecast that has larger bias and smaller flow-dependent error. (d) A more accurate anomaly forecast needs to be constructed relative to model climate rather than observed climate by taking advantage of cancelling model systematic error (i.e., perfect-model assumption). In principle, this approach can be applicable to any model-based prediction.