周口市中短期寒潮预报

利用ECMWF1986～1998年历史实况资料,采用相似方法,找出寒潮天气预报指标,制作1～6天寒潮天气预报.     

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.     

不同模式扰动方案在风暴尺度集合预报中的对比试验研究

由于适用于中长期集合预报的模式扰动技术在风暴尺度集合预报系统中的影响并不明确,为探究不同模式扰动方案在风暴尺度集合预报中的效果,基于WRF模式设计了3组模式扰动方案:多物理扰动（MP）方案、随机物理倾向扰动（Stochastically Perturbed Parameterization Tendencies,SPPT）方案以及由MP方案与SPPT方案组合构建的一种新混合扰动（SPMP）方案。对2013年7月5—6日发生在江淮流域的一次强对流天气过程进行了数值模拟。结果表明:MP方案在积分前期的降水概率评分较高,对高层大气的扰动效果更为合理;SPPT方案主要作用于积分中后期,对大气低层及近地面的扰动效果最为理想,尤其是对于地面水汽场的模拟;SPMP方案能显著提高大气中高层各预报变量的离散度,降低均方根误差,提升集合成员的可信度,有效弥补降水预报评分在单独使用MP方案和SPPT方案不同积分时段的不足。在扰动水平传播方向上,SPMP方案的扰动形态主要受MP方案主导;垂直方向上,SPMP方案在低层的扰动形态与SPPT方案一致,在高层受MP方案控制。波谱能量分析表明3组方案的扰动能量随积分时间均有向大尺度传播的趋势,SPMP方案能有效补偿两种方案能量在各尺度的耗散。      

Using the fractions skill score to assess the relationship between an ensemble QPF spread and skill

The numerical weather prediction model LM COSMO was employed to study the regional ensemble forecast of convective precipitation. The relationship between ensemble spread and ensemble skill and the possibility of estimating ensemble skill on the basis of ensemble spread were investigated. Five convective events that produced heavy local rainfall in the Czech Republic were studied. The LM COSMO was run with a horizontal resolution of 2.8 km and an ensemble of 13 forecasts was created by modifying the initial and boundary conditions. Forecasts were verified by gauge-adjusted radar-based rainfalls. Ensemble skill and ensemble spread were determined using the Fractions Skill Score (FSS), which depended on the scale of the elementary area and on a precipitation threshold. The spread represents the differences between the control forecast and the forecasts provided by each ensemble member, while the skill evaluates the difference between the precipitation forecast and radar-based rainfalls. In this study, the ensemble skill is estimated on the basis of the ensemble spread. The numerical experiments used the FSS-based skill and spread values related to four events to estimate the skill–spread relationship. The relationship was applied to a fifth event to estimate the QPF ensemble skill given the ensemble FSS-based spread. The evaluation was performed separately for 1, 3, and 6 h rainfalls using various threshold values and scales. The absolute frequencies of the differences between diagnostic and prognostic FSS-based skill show that all of the distributions have means and medians close to zero and that the interquartile ranges are between 0.10 and 0.30. The results indicate that 67% of all the fitted FSS-skill values were within 0.15 of the true values. One of five events showed a marked overestimation of the prognostic FSS-skill so that only 39% of skill values were fitted. At the other four events, the 75% of predicted FSS-skill values were in the range of 0.15 of the diagnosed FSS-skill. The results appear to be encouraging; however, tests with more extended data are needed to confirm the potential of the technique.     

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

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

Comparison of intra-seasonal forecast of Indian summer monsoon between two versions of NCEP coupled models

The real-time forecasting of monsoon activity over India on extended range time scale (about 3 weeks) is analyzed for the monsoon season of 2012 during June to September (JJAS) by using the outputs from latest (CFSv2 [Climate Forecast System version 2]) and previous version (CFSv1 [Climate Forecast System version 1]) of NCEP coupled modeling system. The skill of monsoon rainfall forecast is found to be much better in CFSv2 than CFSv1. For the country as a whole the correlation coefficient (CC) between weekly observed and forecast rainfall departure was found to be statistically significant (99 % level) at least for 2 weeks (up to 18 days) and also having positive CC during week 3 (days 19–25) in CFSv2. The other skill scores like the mean absolute error (MAE) and the root mean square error (RMSE) also had better performance in CFSv2 compared to that of CFSv1. Over the four homogeneous regions of India the forecast skill is found to be better in CFSv2 with almost all four regions with CC significant at 95 % level up to 2 weeks, whereas the CFSv1 forecast had significant CC only over northwest India during week 1 (days 5–11) forecast. The improvement in CFSv2 was very prominent over central India and northwest India compared to other two regions. On the meteorological subdivision level (India is divided into 36 meteorological subdivisions) the percentage of correct category forecast was found to be much higher than the climatology normal forecast in CFSv2 as well as in CFSv1, with CFSv2 being 8–10 % higher in the category of correct to partially correct (one category out) forecast compared to that in CFSv1. Thus, it is concluded that the latest version of CFS coupled model has higher skill in predicting Indian monsoon rainfall on extended range time scale up to about 25 days.     

Diagnosing southeast tropical Atlantic SST and ocean circulation biases in the CMIP5 ensemble

Warm sea-surface temperature (SST) biases in the southeastern tropical Atlantic (SETA), which is defined by a region from 5°E to the west coast of southern Africa and from 10°S to 30°S, are a common problem in many current and previous generation climate models. The Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble provides a useful framework to tackle the complex issues concerning causes of the SST bias. In this study, we tested a number of previously proposed mechanisms responsible for the SETA SST bias and found the following results. First, the multi-model ensemble mean shows a positive shortwave radiation bias of ~20 W m^?2, consistent with models’ deficiency in simulating low-level clouds. This shortwave radiation error, however, is overwhelmed by larger errors in the simulated surface turbulent heat and longwave radiation fluxes, resulting in excessive heat loss from the ocean. The result holds for atmosphere-only model simulations from the same multi-model ensemble, where the effect of SST biases on surface heat fluxes is removed, and is not sensitive to whether the analysis region is chosen to coincide with the maximum warm SST bias along the coast or with the main SETA stratocumulus deck away from the coast. This combined with the fact that there is no statistically significant relationship between simulated SST biases and surface heat flux biases among CMIP5 models suggests that the shortwave radiation bias caused by poorly simulated low-level clouds is not the leading cause of the warm SST bias. Second, the majority of CMIP5 models underestimate upwelling strength along the Benguela coast, which is linked to the unrealistically weak alongshore wind stress simulated by the models. However, a correlation analysis between the model simulated vertical velocities and SST biases does not reveal a statistically significant relationship between the two, suggesting that the deficient coastal upwelling in the models is not simply related to the warm SST bias via vertical heat advection. Third, SETA SST biases in CMIP5 models are correlated with surface and subsurface ocean temperature biases in the equatorial region, suggesting that the equatorial temperature bias remotely contributes to the SETA SST bias. Finally, we found that all CMIP5 models simulate a southward displaced Angola–Benguela front (ABF), which in many models is more than 10° south of its observed location. Furthermore, SETA SST biases are most significantly correlated with ABF latitude, which suggests that the inability of CMIP5 models to accurately simulate the ABF is a leading cause of the SETA SST bias. This is supported by simulations with the oceanic component of one of the CMIP5 models, which is forced with observationally derived surface fluxes. The results show that even with the observationally derived surface atmospheric forcing, the ocean model generates a significant warm SST bias near the ABF, underlining the important role of ocean dynamics in SETA SST bias problem. Further model simulations were conducted to address the impact of the SETA SST biases. The results indicate a significant remote influence of the SETA SST bias on global model simulations of tropical climate, underscoring the importance and urgency to reduce the SETA SST bias in global climate models.     

NCEP集合预报系统在亚欧和北美区域的预报效果对比

使用NCEP集合预报系统（EPS）输出的500hPa位势高度场预报资料和相应的NCEP／NCAR再分析资料,针对集合平均预报和概率预报,采用多种预报效果检验评价方法,对该系统在亚欧和北美区域的预报效果进行全面的分析比较。总体而言,NCEP—EPS对亚欧区域的环流集合预报效果不亚于其对北关区域的预报效果。1）ACC检验表明,亚欧区域的集合平均预报效果在除冬季外的三个季节都明显优于北美区域,可用预报的时效相差达0．6～1d,且夏季的差别最大。RMSE检验表明,亚欧区域的预报效果在四个季节里均优于北美区域。2）集合概率预报可靠性的季节差别不明显,均为预报时效较短（长）时,北关（亚欧）区域的可靠性更好。系统对亚欧区域的事件识别范围相对较小,但其预报可靠性较高,北美区域则正好相反。3）夏季亚欧区域的集合概率预报效果明显优于北美区域,秋季和冬季北关区域的预报效果较好,春季在预报时效小于5d时北美区域占优,而其后则是亚欧区域的预报分辨能力更好。     

Verification of short- and medium-range precipitation forecasts from the ensemble modeling system of the hydrometcenter of Russia

Precipitation forecasts from short- and medium-range ensemble prediction system of the Hydrometeorological Research Center of the Russian Federation (Hydrometcenter of Russia) are verified. The verification system includes probabilistic and deterministic scores. The precipitation forecast quality is analyzed for different seasons and large-scale circulation types. Further development of ensemble modeling and verification at the Hydrometcenter of Russia is discussed.     

Application of a serial extended forecast experiment using the ECMWF model to interpret the predictive skill of tropical intraseasonal variability

The extended-range forecast skill of the ECMWF operational forecast model is evaluated during tropical intraseasonal oscillation (ISO) events in the Indo-West Pacific warm pool. The experiment consists of ensemble extended serial forecasts including winter and summer ISO cases. The forecasts are compared with the ERA-40 analyses. The analysis focuses on understanding the origin of forecast errors by studying the vertical structure of relevant dynamical and moist convective features associated with the ISO. The useful forecast time scale for circulation anomalies is in average 13 days during winter compared to 7–8 days during summer. The forecast skill is not stationary and presents evidence of a flow-dependent nature, with states of the coupled system corresponding to long-lived convective envelopes associated with the ISO for which the skill is always low regardless of the starting date of the forecast. The model is not able to forecast skillfully the generation of specific humidity anomalies and results indicate that the convective processes in the model are associated with the erosion of the ISO forecast skill in the model. Circulation-associated anomalies are forecast better than moist convective associated anomalies. The model tends to generate a more stable atmosphere, limiting the model’s capability to reproduce deep convective events, resulting in smaller humidity and circulation anomalies in the forecasts compared to those in ERA-40.     

A convection-allowing ensemble forecast based on the breeding growth mode and associated optimization of precipitation forecast

A convection-allowing ensemble forecast experiment on a squall line was conducted based on the breeding growth mode (BGM). Meanwhile, the probability matched mean (PMM) and neighborhood ensemble probability (NEP) methods were used to optimize the associated precipitation forecast. The ensemble forecast predicted the precipitation tendency accurately, which was closer to the observation than in the control forecast. For heavy rainfall, the precipitation center produced by the ensemble forecast was also better. The Fractions Skill Score (FSS) results indicated that the ensemble mean was skillful in light rainfall, while the PMM produced better probability distribution of precipitation for heavy rainfall. Preliminary results demonstrated that convection-allowing ensemble forecast could improve precipitation forecast skill through providing valuable probability forecasts. It is necessary to employ new methods, such as the PMM and NEP, to generate precipitation probability forecasts. Nonetheless, the lack of spread and the overprediction of precipitation by the ensemble members are still problems that need to be solved.     

基于GRAPES_Meso的集合预报扰动方案设计与比较

基于GRAPES_Meso区域集合预报系统,设计了三种集合预报扰动方案,即多初值、多初值多物理、多初值多物理多边值,并针对三种方案进行了连续一个月的批量试验,重点分析了2008年7月23日江淮暴雨过程.结果表明,对于降水预报,三种集合扰动方案均相对于控制预报均有所改善,多初值多物理与多初值多物理多边值方案对小雨、中雨预报改进效果显著,对暴雨预报略有改进;多初值方案仅能产生有限的集合离散度且难以增长,引入物理参数方案扰动及边界条件扰动能显著提高集合离散度,改善各物理量场的预报效果;通过比较,多初值多物理多边值为最优方案.该批量试验表明,模式物理过程及边界条件是影响GRAPES _Meso区域集合预报不确定性的不可忽视因素.     

NCEP、ECMWF及CMC全球集合预报业务系统发展综述

总结了目前最具代表性的3个全球集合预报系统（global ensemble forecast system,GEFS）——美国国家环境预报中心（National Centers for Environmental Prediction,NCEP）、欧洲中期天气预报中心（European Centre for Medium-Range Weather Forecasts,ECMWF）和加拿大气象中心（Canadian Meteoro-logical Centre,CMC）建成至今的发展概况。由于计算资源的不断扩展,各中心集合预报系统的模式分辨率、集合成员数也随之增加。同时各中心都在不断地致力于发展和完善初始和模式扰动方法,来更好地估计与初值和模式有关的不确定性,促进预报技巧的提高。其中初始扰动方法从最初的奇异向量法（ECMWF）、增殖向量法（NCEP）和观测扰动法（CMC）更新为现在的集合资料同化—奇异向量法（ECMWF）、重新尺度化集合转换法（NCEP）和集合卡尔曼滤波（CMC）。在估计模式不确定性方面,ECMWF和CMC都修订了各自的随机参数化方案和多参数化方案,NCEP最近也在模式中加入了随机全倾向扰动。为提高全球高影响天气预报的准确率,TIGGE计划（the THORPEX interactive grand global ensemble）的提出增进了国际间对多模式、多中心集合预报的合作研究,北美集合预报系统（North American ensemble forecast system,NAEFS）为建立全球多模式集合预报系统提供了业务框架,这都将有助于未来全球交互式业务预报系统的构建     

Subseasonal Prediction of Early-summer Northeast Asian Cut-off Lows by BCC-CSM2-HR and GloSea5

Northeast Asian cut-off lows are crucial cyclonic systems that can bring temperature and precipitation extremes over large areas. Skillful subseasonal forecasting of Northeast Asian cut-off lows is of great importance. Using two dynamical forecasting systems, one from the Beijing Climate Center(BCC-CSM2-HR) and the other from the Met Office(GloSea5),this study assesses simulation ability and subseasonal prediction skill for early-summer Northeast Asian cut-off lows. Both models are shown to have...     

一次特大暴雨过程高分辨率集合预报试验的检验和评估

针对2018年4月22日发生在湖北西部山地的一次特大暴雨过程,采用降尺度方案和显式对流参数化方案模式,开展了高分辨率对流许可尺度(3 km)的集合预报试验,并对全球集合预报(GEFS)和对流尺度集合预报(SSEF)的降水预报进行了对比评估,结果表明:(1)SSEF集合平均的雨量和落区预报均优于GEFS。(2)SSEF各成员的降水离散度分布更合理,因而具有更优的降水区间预报,其“离散度-误差关系”更优,能更好地给出预报误差的分布及其可能的大小。(3)SSEF的概率预报在所有空间尺度上均优于GEFS,且在短历时强降水上的优势更加明显。由此可见,针对此类山地暴雨过程,对流尺度集合预报相对于全球集合预报具有巨大的改进潜力。     

初始场对暴雨数值预报的影响及集合预报试验

以2003年7月3日至4日发生在淮河流域的暴雨过程为例,利用AREM模式,分析了初始场对暴雨预报的影响,提出了暴雨预报中初始场不确定性包含的两层含义,一是被常规观测遗漏的中小尺度信息误差;另一个则是随着环流变化造成的信息误差的不确定性。并针对着初始场的不确定性,设计了一种初值集合预报的方法,它包含了经典的集合预报方法MCF、LAF、BGM的思想。用这种方法进行了集合预报试验,结果表明:集合平均预报的预报技巧高于24 h控制预报,集合预报还可给出降水概率预报、离散度等产品为暴雨可预报性的评估提供参考。     

基于CMA区域集合预报的海河流域面雨量预报评估与应用

面雨量是流域防汛抗洪调度的重要依据,单模式的确定性预报无法反映面雨量的多种可能性,为了解决面雨量预报存在不确定性的问题,本文基于中国气象局区域集合预报系统(China Meteorological Administration-Regional Ensemble Prediction System,CMA-REPS)降水结果对海河流域集合预报面雨量开展了适用性评估和分析,结合高分辨率格点实况资料对其2020—2022年5—8月面雨量进行检验,并依检验结果开展了集合预报面雨量产品及跨度预报产品研发。检验表明CMA-REPS集合预报对海河流域面雨量预报有改进:1)海河流域集合平均面雨量预报的绝对误差明显优于控制预报,在空间分布上表现为南部河系预报误差较大,中部次之,北部最小。2)模糊评分表明小雨和中雨量级集合平均预报更贴近实况,而暴雨量级则需进一步参考集合成员的极端性。3) TS及Bias评分表明集合平均在小雨量级预报效果较好,中雨及以上量级在预报后期改进更加明显。4)概率预报评分发现CMA-REPS对海河流域中部河系中雨以下面雨量有较好的表现,而南部河系的评分则低于中部河系。开发集合预报面雨量产品并对海河流域两次致洪暴雨过程检验发现:1)集合成员对24 h大暴雨等级面雨量的预报有较高的概率,与实况量级相当,对极端降水有较好的提示。2)24 h内集合平均具有较好的评分,而24～48 h,75％分位数产品对于强降水过程预报效果更好;本文依据检验结果开发的集合预报混合百分位产品及跨度预报产品对于强降水有较好的参考性。3)依据概率预报随时间的变化曲线对面雨量进行平滑拟合,可以得到更贴近于实况的逐小时面雨量预报产品。     

基于时空不确定性的对流尺度集合预报效果评估检验

针对对流尺度天气系统的高度非线性特征和高分辨率模式预报结果存在时、空不确定性现象,以及当前邻域概率法主要考虑高分辨率预报结果的空间位移误差,而不能有效解决预报结果存在时间超前与滞后问题,将时间因素引入到邻域概率法中,结合一次强飑线过程进行对流尺度集合预报试验,并基于改进后的新型邻域概率法与分数技巧评分,对降水预报进行了不同时、空尺度的效果评估检验。结果表明:（1）邻域集合概率法和概率匹配平均法在极端降水的分数技巧评分远高于传统集合平均,弥补了集合平均对极端降水预报能力偏低的缺陷。（2）对于此类飑线过程的对流尺度天气系统而言,邻域半径为15—45 km的空间尺度能够改善降水位移误差的空间不确定性,并使其预报效果达到最优,其中15—30 km的邻域半径对于尺度更小的大量级降水事件预报能力更强。（3）对流尺度降水预报考虑时间尺度与降水强度存在着对应关系,不同时间尺度可以捕获到不同量级降水的时间不确定性。同时,时间尺度与空间尺度对于降水预报效果的影响是相互关联的。（4）改进的邻域概率法能够同时体现高分辨率模式预报结果在对流尺度降水事件上存在的时、空不确定性,实现了对流尺度降水在时、空尺度上的综合评估,并能为不同量级降水提供与其时、空尺度相匹配的概率预报结果。      

A further study on an extended nonlinear ocean—atmosphere coupled hydrodynamic characteristic system and the abrupt feature of ENSO events

In this paper, the decade data of meteorological satellite and surface meteorological observation of China have been analysed. The relationship between cloud and radiation has been studied. A set of empirical formulae of the ralationships between the albedo and cloud amount, the outgoing longwave radiation and cloud amount in Chinese different districts and different seasons has been deduced. They express simply the response of both planet reflectivity and earth-atmosphere outgoing longwave radiation to the change of cloud amount. So that the sensitivity of net radiation of the earth-atmosphere system to the change of cloud amount and the ratio of cloud reflective effect to greenhouse effect can be estimated. In this paper, the radiative process of the earth-atmosphere system, cloud and radiative balance and its effect on climate have been synthetically studied. ¹The project is supported by National Natural Sciences Fundation of China (NNSFC).