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.

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.

Global deterministic numerical weather predictions (DNWPs) initialized from the ensemble mean analysis of four-dimensional ensemble-variational (4DEnVar) data assimilation are usually worse than those initialized by four-dimensional variational (4DVar) data assimilation. As the average of ensemble forecasts initialized from 4DEnVar analyses generally outperforms the 4DVar-initialized DNWP in anomaly correlation and anomaly root mean square error, a new approach was proposed to improve the 4DEnVar-initialized DNWP with only doubling forecasting computational resources. This method approximately supplements the diffusion term existing in the ensemble mean forecast, which is missed in the DNWP initialized from ensemble mean analysis. The diffusion filters noise from initial uncertainty. Experiments show higher skill in the new 4DEnVar-initialized DNWP than in those initialized from 4DEnVar ensemble mean analysis using purely ensemble covariances and 4DVar analysis with a purely climatological covariance. This approach provides an effective way to expand applications of 4DEnVar and possibly other ensemble-based methods to operational forecasting.

Pangu-Weather (PGW), trained with deep learning-based methods (DL-based model), shows significant potential for global medium-range weather forecasting. However, the interpretability and trustworthiness of global medium-range DL-based models raise many concerns. This study uses the singular vector (SV) initial condition (IC) perturbations of the China Meteorological Administration’s Global Ensemble Prediction System (CMA-GEPS) as inputs of PGW for global ensemble prediction (PGW-GEPS) to investigate the ensemble forecast sensitivity of DL-based models to the IC errors. Meanwhile, the CMA-GEPS forecasts serve as benchmarks for comparison and verification. The spatial structures and prediction performance of PGW-GEPS are discussed and compared to CMA-GEPS based on seasonal ensemble experiments. The results show that the ensemble mean and dispersion of PGW-GEPS are similar to those of CMA-GEPS in the medium range but with smoother forecasts. Meanwhile, PGW-GEPS is sensitive to the SV IC perturbations. Specifically, PGW-GEPS can generate realistic ensemble spread beyond the sub-synoptic scale (wavenumbers ⩽ 64) with SV IC perturbations. However, PGW’s kinetic energy is significantly reduced at the sub-synoptic scale, leading to error growth behavior inconsistent with CMA-GEPS at that scale. Thus, this behavior indicates that the effective resolution of PGW-GEPS is beyond the sub-synoptic scale and is limited to predicting mesoscale atmospheric motions. In terms of the global medium-range ensemble prediction performance, the probability prediction skill of PGW-GEPS is comparable to CMA-GEPS in the extratropic when they use the same IC perturbations. That means that PGW has a general ability to provide skillful global medium-range forecasts with different ICs from numerical weather prediction.

The China Meteorological Administration convection-permitting ensemble prediction system (CMA-CPEPS) adopted a singular vector (SV) downscaling from the global ensemble prediction system because of its simplicity and applicability. The key to this method is reasonably describing the initial perturbation structures and growth characteristics, with targeted areas being crucial factors. However, previous studies using fixed targeted areas failed to capture perturbation structures at low latitudes and those associated with convection, leading to mismatches with convective weather development, insufficient ensemble spreads, and low precipitation predictability. To address these issues, we constructed convective adjustment targeted areas using composite parameters of convective available potential energy and shear. The results show that (a) the energy norms of SV for experiments with targeted areas across China exhibit a two-peaked structure, effectively capturing perturbation structures at low and middle levels, similar to the largest areas for experiments with convective adjustment targeted areas. Both experiments have larger potential energy norms than kinetic energy norms at the initial time. (b) The new method largely improves the spread-skill relationships and slightly enhances the probabilistic forecasting abilities for both precipitation and nonprecipitation variables, effectively describing uncertainties associated with heavy precipitation. (c) The new method yields more broadly distributed initial perturbation structures and larger perturbation magnitudes, particularly those associated with moist convection, thereby enhancing ensemble prediction skills in the CMA-CPEPS. Moist convection is the primary physical mechanism driving the perturbation growth. Overall, these findings provide a foundation for improving the SV dynamical downscaling method in CMA-CPEPS.

How to construct appropriate perturbations for convection-permitting ensemble prediction systems (CPEPSs) is a critical issue awaiting urgent solutions. As two common perturbations, initial perturbations (IPs) and lateral boundary perturbations (BPs) interact with each other, affecting the model error growth, especially in mesoscale models. Using the China Meteorological Administration (CMA)-CPEPS, this study tries to elucidate how BPs interact with matched and mismatched IPs under varied large-scale weather conditions/forcings. Seven groups of experiments were conducted for strong-forcing and weak-forcing weather regimes over southern China: three with single IPs, one with single BPs, and three with combined perturbations. It is found that the perturbation magnitudes were dominated by meso-α-scale components, and IPs under weak forcing exhibited more pronounced effects than under strong forcing; whereas BPs exerted more pronounced effects under strong forcing than weak forcing regimes. Furthermore, it lasts longer for high-level variables when the perturbation energy from BPs is higher than that from IPs, compared to low-level variables. Moreover, for precipitation and dynamic variables, IPs and BPs can mutually reinforce. The source of these perturbations, and their specific vertical levels, do not alter the extent of their interactions. Nevertheless, the weather regime and the scales of the perturbations influence the strength of their mutual reinforcement. In particular, the weak-forcing regimes exhibit a more pronounced reinforcing effect, and meso-α-scale perturbations are more conducive to fostering interactions compared to meso-β-scale ones. Ultimately, it is the perturbation magnitude inherent in the initial perturbation itself that determines the interactions between IPs and BPs.

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.

Given the chaotic nature of the atmosphere and inevitable initial condition errors, constructing effective initial perturbations (IPs) is crucial for the performance of a convection-allowing ensemble prediction system (CAEPS). The IP growth in the CAEPS is scale- and magnitude-dependent, necessitating the investigation of the impacts of IP scales and magnitudes on CAEPS. Five comparative experiments were conducted by using the China Meteorological Administration Mesoscale Numerical Weather Prediction System (CMA-MESO) 3-km model for 13 heavy rainfall events over eastern China: smaller-scale IPs with doubled magnitudes, larger-, meso-, and smaller-scale IPs; and a chaos seeding experiment as a baseline. First, the constructed IPs outperform unphysical chaos seeding in perturbation growth and ensemble performance. Second, the daily variation of smaller-scale perturbations is more sensitive to convective activity because smaller-scale perturbations during forecasts reach saturation faster than meso- and larger-scale perturbations. Additionally, rapid downscaling cascade that saturates the smallest-scale perturbation within 6 h for larger- and meso-scale IPs is stronger in the lower troposphere and near-surface. After 9-12 h, the disturbance development of large-scale IPs is the largest in each layer on various scales. Moreover, thermodynamic perturbations, concentrated in the lower troposphere and near-surface with meso- and smaller-scale components being dominant, are smaller and more responsive to convective activity than kinematic perturbations, which are concentrated on the middle-upper troposphere and predominantly consist of larger- and meso-scale components. Furthermore, the increasing magnitude of smaller-scale IPs enables only their smaller-scale perturbations in the first 9 h to exceed those of larger- and meso-scale IPs. Third, for forecast of upper-air and surface variables, larger-scale IPs warrant a more reliable and skillful CAEPS. Finally, for precipitation, larger-scale IPs perform best for light rain at all forecast times, whereas meso-scale IPs are optimal for moderate and heavy rains at 6-h forecast time. Increasing magnitude of smaller-scale IPs improves the probability forecast skills for heavy rains during the first 3-6 h.

Using ERA5 reanalysis and numerical forecast productions from the China Meteorological Administration (ECMWF-HRES), and NCEP-GFS, this study investigated the performance of multiple models on the track and intensity forecasts of Doksuri. The results indicate that ECMWF-HRES has the highest forecast skill in the track forecast, followed by CMA-GFS. Meanwhile, CMA-TYM and NCEP-GFS have significant errors in longer forecast lead times with high similarity. Except for ECMWF-HRES, the track forecasts from other models have large errors in the forecast of the early stages of typhoon generation and development, which is mainly due to the forecasts of the eastward receding trend of the western Pacific subtropical high at 500 hPa being faster, and steering flows are more northward compared with the real situation. Thus, the forecasts of the moving direction are biased to the right side of the real-time track. In the landfall position forecast, there are large uncertainties in the initial forecast, and that of CMA-TYM tends to be stable after 24 July. As for the landing time, CMA-GFS exhibits a high degree of reliability, while other models forecast a landfall earlier than 0200 UTC 28 July in terms of fast-moving speed in the initial forecast. In intensity forecast, CMA-TYM has the best forecast capability and also indicates a high indication of rapid intensification and its peak intensity. The other global models are significantly weaker in the forecast of rapid intensification and have limited ability to forecast the peak intensity of Doksuri.

Ensemble prediction is widely used to represent the uncertainty of single deterministic Numerical Weather Prediction (NWP) caused by errors in initial conditions (ICs). The traditional Singular Vector (SV) initial perturbation method tends only to capture synoptic scale initial uncertainty rather than mesoscale uncertainty in global ensemble prediction. To address this issue, a multiscale SV initial perturbation method based on the China Meteorological Administration Global Ensemble Prediction System (CMA-GEPS) is proposed to quantify multiscale initial uncertainty. The multiscale SV initial perturbation approach entails calculating multiscale SVs at different resolutions with multiple linearized physical processes to capture fast-growing perturbations from mesoscale to synoptic scale in target areas and combining these SVs by using a Gaussian sampling method with amplitude coefficients to generate initial perturbations. Following that, the energy norm, energy spectrum, and structure of multiscale SVs and their impact on GEPS are analyzed based on a batch experiment in different seasons. The results show that the multiscale SV initial perturbations can possess more energy and capture more mesoscale uncertainties than the traditional single-SV method. Meanwhile, multiscale SV initial perturbations can reflect the strongest dynamical instability in target areas. Their performances in global ensemble prediction when compared to single-scale SVs are shown to (i) improve the relationship between the ensemble spread and the root-mean-square error and (ii) provide a better probability forecast skill for atmospheric circulation during the late forecast period and for short- to medium-range precipitation. This study provides scientific evidence and application foundations for the design and development of a multiscale SV initial perturbation method for the GEPS.

Many ensemble-based data assimilation (DA) methods use observation space localization to mitigate the sampling errors due to the insufficient ensemble members. Observation space localization is simpler and more timesaving than model space localization in implementation, but more difficult to directly assimilate satellite radiance observations, a kind of non-local observations. The vertical locations of radiance observations are undetermined and the transmission of observational information is thereby obstructed. To determine the vertical coordinates of radiance observations, a weighted average hypsometry is proposed. Using this hypsometry, AMSU-A radiance observations are directly assimilated with an ensemble four-dimensional variational (En4DVar) DA system. It consists of a four-dimensional ensemble-variational (4DEnVar) system providing ensemble covariance and a 4DVar system. Observing system simulation experiments show that the hypsometry alleviates the degradations in the late period of medium-range forecast in the Northern Extratropics that occur in the traditional peak-based hypsometry. It obviously improves the analysis qualities and forecast skills of the En4DVar system and its two components, especially in the Southern Extratropics, when incorporating AMSU-A radiance observations. The improvement in the En4DVar-initialized forecast is comparable to that in the 4DVar-initialized forecast in the Southern Extratropics and Tropics. It indicates that a proper hypsometry enables efficient extraction of useful information from AMSU-A radiance observations by 4DEnVar with observation space localization. Therefore, the 4DEnVar provides high-quality ensemble covariances for En4DVar.

In this study, moist singular vector (MSV) was developed based on GRAPES-GEPS (Global/Regional Assimilation and Prediction System - Global Ensemble Prediction System), the adjoint model of large-scale condensation and cumulus deep convection in GRAPES-4DVar (Four-dimensional variational assimilation). Five consecutive days of numerical experiments were performed for a preliminary evaluation of MSV. The singular values, horizontal distribution structure, spread of MSVs perturbation and its influence on the ensemble prediction were compared for each group of tests. The results showed that in the middle and high latitudes of the northern and southern hemispheres, the addition of both linearized moist physical processes increased the spread of the mid- and low-level SVs, but the linearized large-scale condensation (LC) process plays a leading role in the structure of MSV. The analysis of ensemble forecast shows the inclusion of moist linearized physical processes led to a greater effect of MSV on the rainfall levels of 10 and 25 mm and a slight improvement in anomaly correlation coefficient (ACC) of the atmospheric circulation field, and more obvious improvement due to linearized large-scale condensation. In the future, continuous multi-year testing and tropical-specific analyses are required for operation.

In this paper, based on the regional ensemble CMA-REPS V3.1 system, the algorithm for the neighborhood ensemble probability method of precipitation is optimized. The daily 24-hour accumulated precipitation data from May to July 2021 are selected to calculate the neighborhood probability of precipitation. The grid precipitation product combined from three sources developed by the National Meteorological Information Center is selected as the observational data. The optimized method is evaluated using the area scoring method with the relative operating characteristic curve, and is compared with the scoring results of original neighborhood ensemble probability method and ensemble mean neighborhood probability method. At the same time, a typical precipitation case is selected to evaluate these three methods. It is found that the optimized method has the highest score, and its predicted information of precipitation is more consistent with the observations. In this paper, the three precipitation neighborhood probability prediction results are also used to calculate the FSS (Fractions Skill Score) of ensemble precipitation. It is found that the FSS score based on the optimized method is higher than that of ensemble mean neighborhood probability method. Both the optimized and original methods have some advantages in terms of the FSS score. The former one has better scoring for small precipitation, especially for light rain and moderate rain, and the latter one for large precipitation, especially, the scores of rainstorm is better. FSS score based on the optimized method is relatively more objective.

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.

To compare the roles of two kinds of initial perturbations in a convection-permitting ensemble prediction system (CPEPS) and reveal the effects of the differences in large-scale/small-scale perturbation components on the CPEPS, three initial perturbation schemes are introduced, including a dynamical downscaling (DOWN) scheme originating from a coarse-resolution model, a multiscale ensemble transform Kalman filter (ETKF) scheme, and a filtered ETKF (ETKF_LARGE) scheme. First, the comparisons between the DOWN and ETKF schemes reveal that they behave differently in many ways. Specifically, the ensemble spread and forecast error for precipitation in the DOWN scheme are larger than those in the ETKF; the probabilistic forecasting skill for precipitation in the DOWN scheme is better than that in the ETKF at small neighborhood radii, whereas the advantages of the ETKF begin to appear as the neighborhood radius increases; DOWN possesses better spread–skill relationships than ETKF and has comparable probabilistic forecasting skills for nonprecipitation. Second, the comparisons between DOWN and ETKF_LARGE indicate that the differences in the large-scale initial perturbation components are key to the differences between DOWN and ETKF. Third, the comparisons between ETKF and ETKF_LARGE demonstrate that the small-scale initial perturbations are important since they can increase the precipitation spread in the early times and decrease the forecast errors while simultaneously improving the probabilistic forecasting skill for precipitation. Given the advantages of the DOWN and ETKF schemes and the importance of both large-scale and small-scale initial perturbations, multiscale initial perturbations should be constructed in future research.

The traditional model perturbation method of ensemble prediction is usually used to describe random errors of physical processes, but the model inevitably has systematic bias. Therefore, in order to reduce the impact of systematic bias on ensemble prediction, the CMA-GEPS is employed to obtain systematic bias tendency using the empirical orthogonal function (EOF) method. In the integration process, the systematic bias correction method and the traditional Stochastically Perturbed Parameterization Tendency (SPPT) are combined to build a model perturbation method (Bias correction of bias tendency based on SPPT, SPPT-B) that combines systematic bias and random errors of ensemble forecast. Ensemble forecasting experiments are designed and carried out to explore the impact of SPPT-B on global ensemble forecasting. The conclusions are as follow: (1) The first EOF mode of the systematic bias can reflect the main characteristics of the systematic bias well. It shows that basically the systematic bias in the upper troposphere is larger than that in the middle and lower troposphere and increases linearly with forecast lead time. (2) The systematic bias correction method and SPPT-B can effectively reduce the systematic bias in upper and lower levels in the southern and northern Hemispheres and in the tropics, and SPPT-B can significantly improve Spread in the tropics. (3) The effect of the two schemes on the improvement of ensemble prediction skill in the upper troposphere is better than that in the lower troposphere. The above results indicate that the model perturbation method that considers both systematic bias and random errors can effectively improve global ensemble forecasting skill, and can provide a scientific basis for the development of global ensemble forecasting model perturbation method considering both systematic bias and random errors.

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.

This study developed an ensemble four‐dimensional variational (En4DVar) hybrid data assimilation system. Different from most of the available En4DVar systems that adopted ensemble Kalman Filter class or ensemble data assimilation approaches to produce ensemble covariances for their hybrid background error covariances (BECs), it used a four‐dimensional ensemble variational (4DEnVar) system to obtain the ensemble covariance. The localization scheme for 4DEnVar applied orthogonal functions to decompose the correlation matrix so that it was implemented easily and rapidly. In terms of analysis quality and forecast skill, the En4DVar system was evaluated in the single‐point observation experiments and observing system simulation experiments (OSSEs) with sounding and cloud‐derived wind observations, using its standalone four‐dimensional variational (4DVar) and 4DEnVar components as references. The single‐point observation experiments visually verified the explicit flow‐dependent characteristic of the BEC due to the introduction of the ensemble covariance from the 4DEnVar system. The OSSE‐based sensitivity experiments revealed different contributions of the weight for the ensemble covariance in the En4DVar system to the forecasts in the Northern and Southern Extratropics and Tropics. A much higher weight for the ensemble covariance in a properly inflated hybrid covariance helped En4DVar produce the most reasonable analysis. The forecast initialized by En4DVar is overall better than by 4DVar and 4DEnVar, although the quality of En4DVar analysis is between those of 4DVar and 4DEnVar ensemble mean analyses. It indicates that the flow‐dependent ensemble covariance provided by 4DEnVar dominantly contributes to the improvements in the En4DVar‐initialized forecast, with certain but necessary constraint from the balanced climatological covariance.

This paper describes the GRAPES Evaluation Tools based on Python (GetPy), a community verification and diagnostic tool for the evaluation of numerical models. The traditional statistical verification with confidence level test, the comprehensive scorecard, the precipitation skill score such as TS, ETS, diagnostic score SEEPS and the spatial verification techniques are used as verification modules. The Error tracing techniques conducted on the performance with different scales by wavelet analysis. The diurnal cycle of precipitation can also be calculated by Precipitation frequency-intensity method. Based on simple script architecture GetPy also includes a revised and simplified installation procedure and interactive display system. Users can easily access graphic products and carry out evaluation applications.