华南区域高分辨率数值模式前汛期预报初步评估

基于华南地区自动站逐小时观测资料, 采用传统站点评分、邻域法等评估华南区域高分辨率数值模式(包括GRAPES_GZ_R 1 km模式和GRAPES_GZ 3 km模式)对降水、地面温度和风场等要素的预报能力。结果表明: GRAPES_GZ_R 1 km模式的降水预报技巧优于GRAPES_GZ 3 km模式, 模式预报以正偏差为主。对于不同起报时间的预报, 00时(世界时, 下同)起报的预报效果优于12时。GRAPES_GZ_R 1 km模式的TS评分是GRAPES_GZ 3 km模式的两倍以上, 对不同降水阈值的评分均较高。分数技巧评分(FSS)显示GRAPES_GZ_R 1 km模式6 h累计降水预报在0.1 mm、1 mm及5 mm以上的降水均可达到最低预报技巧尺度, 对所检验降水对象的空间位置把握能力更好。2 m气温和10 m风速检验结果表明两个模式均能较好把握广东省温度的分布特征, GRAPES_GZ_R 1 km模式对2 m气温预报结果优于GRAPES_GZ 3 km模式, 预报绝对误差更小; 两个模式对风速的预报整体偏强, 预报偏差在1~4 m/s之间, 但相比之下GRAPES_GZ 3 km模式在风场预报上表现更好。GRAPES_GZ_R 1 km模式的2 m气温和10 m风速预报偏差随降水过程存在明显波动, 强降水过后温度预报整体偏低, 风速预报偏强, 在模式产品订正、使用等需要考虑模式对主要天气系统的预报情况。总的来说, GRAPES_GZ_R 1 km模式的预报产品具有较好的参考价值。      

Comparison of different land-surface perturbation methods in short-range ensemble forecasts

为了比较不同陆面扰动方法对短时集合预报的影响,本研究设计了陆面模式扰动实验(LSMPE),初始土壤湿度扰动实验(ISMPE),陆面-大气耦合系数扰动实验(LCCPE)以及大气扰动对照实验(GEFSPE).结果表明,在三组陆面扰动实验中,LSMPE能代表最大的不确定性且误差最小;ISMPE的离散度要比LCCPE稍大,但是耦合系数的减弱能有效降低区域平均降水量.然而,所有陆面扰动实验产生的离散度均小于GEFSPE,这表明未来有必要将陆而扰动与大气扰动相结合,以使得模式能代表更多的不确定性.     

A CRPS-Based Spatial Technique for the Verification of Ensemble Precipitation Forecasts

Traditional precipitation skill scores are affected by the well-known“double penalty”problem caused by the slight spatial or temporal mismatches between forecasts and observations. The fuzzy (neighborhood) method has been proposed for deterministic simulations and shown some ability to solve this problem. The increasing resolution of ensemble forecasts of precipitation means that they now have similar problems as deterministic forecasts. We developed an ensemble precipitation verification skill score, i.e., the Spatial Continuous Ranked Probability Score (SCRPS), and used it to extend spatial verification from deterministic into ensemble forecasts. The SCRPS is a spatial technique based on the Continuous Ranked Probability Score (CRPS) and the fuzzy method. A fast binomial random variation generator was used to obtain random indexes based on the climatological mean observed frequency, which were then used in the reference score to calculate the skill score of the SCRPS. The verification results obtained using daily forecast products from the ECMWF ensemble forecasts and quantitative precipitation estimation products from the OPERA datasets during June-August 2018 shows that the spatial score is not affected by the number of ensemble forecast members and that a consistent assessment can be obtained. The score can reflect the performance of ensemble forecasts in modeling precipitation and thus can be widely used.     

基于卡尔曼滤波的中国区域气温和降水的多模式集成预报

利用TIGGE资料集下欧洲中期天气预报中心(ECMWF)、日本气象厅(JMA)、美国国家环境预报中心(NCEP)、中国气象局(CMA)和英国气象局(UKMO)5个模式预报的结果,对基于卡尔曼滤波的气温和降水的多模式集成预报进行研究。结果表明,卡尔曼滤波方法的预报效果优于消除偏差集合平均(BREM)和单模式的预报,但是对于地面气温和降水,其预报效果也存在一定的差异。在中国区域2 m气温的预报中,卡尔曼滤波的预报结果最优。而对于24 h累积降水预报,尽管卡尔曼滤波在所有量级下的TS评分均优于BREM,但随着预报时效增加,其在大雨及以上量级的TS评分跟最佳单模式UKMO预报相当,改进效果不明显。卡尔曼滤波在地面气温和24 h累积降水每个预报时效下的均方根误差均最优,预报效果更佳且稳定。     

CMIP5多模式集合对江苏省气候变化模拟评估及情景预估

利用中国气象局所属的2 400余个台站观测资料制作的分辨率为0.25°×0.25°数据集中的气温、降水量资料评估了CMIP5中17个模式对于1961—2004年江苏省气温和降水量空间分布特征的模拟能力,筛选出了5个对江苏省气候特征模拟较好的模式。之后基于5个优选模式集合平均的结果预估了3种典型浓度路径(Representative Concentration Pathways,RCPs)下江苏省2006—2100年的气温和降水量变化趋势。结果表明:(1)全球耦合气候模式对江苏省的气温和降水量空间分布特征具有一定的模拟能力,并且模式集合平均的气温和降水量与观测资料的空间相关系数分别为0.85和0.93;(2)在低浓度路径(RCP2.6)、中浓度路径(RCP4.5)和高浓度路径(RCP8.5)3种温室气体排放情景下,江苏省2006—2100年的地表温度均呈现明显的增温趋势,并且苏北的增温幅度要高于苏南;(3)3种温室气体排放情景下,江苏省未来百年降水量均呈现出北方增多南方减少的趋势;(4)未来百年江苏省降水量随气温变化的趋势并不稳定,RCP2.6和RCP4.5情景下降水量随气温的升高而增加,而RCP8.5情景下降水量随气温的增加而减少。     

The Relationship between Deterministic and Ensemble Mean Forecast Errors Revealed by Global and Local Attractor Radii

It has been demonstrated that ensemble mean forecasts, in the context of the sample mean, have higher forecasting skill than deterministic(or single) forecasts. However, few studies have focused on quantifying the relationship between their forecast errors, especially in individual prediction cases. Clarification of the characteristics of deterministic and ensemble mean forecasts from the perspective of attractors of dynamical systems has also rarely been involved. In this paper, two attractor statistics—namely, the global and local attractor radii(GAR and LAR, respectively)—are applied to reveal the relationship between deterministic and ensemble mean forecast errors. The practical forecast experiments are implemented in a perfect model scenario with the Lorenz96 model as the numerical results for verification. The sample mean errors of deterministic and ensemble mean forecasts can be expressed by GAR and LAR, respectively, and their ratio is found to approach2~(1/2) with lead time. Meanwhile, the LAR can provide the expected ratio of the ensemble mean and deterministic forecast errors in individual cases.     

Sub-seasonal to Seasonal Hindcasts of Stratospheric Sudden Warming by BCC_CSM1.1(m):A Comparison with ECMWF

This study focuses on model predictive skill with respect to stratospheric sudden warming(SSW) events by comparing the hindcast results of BCC_CSM1.1(m) with those of the ECMWF's model under the sub-seasonal to seasonal prediction project of the World Weather Research Program and World Climate Research Program. When the hindcasts are initiated less than two weeks before SSW onset, BCC_CSM and ECMWF show comparable predictive skill in terms of the temporal evolution of the stratospheric circumpolar westerlies and polar temperature up to 30 days after SSW onset. However, with earlier hindcast initialization, the predictive skill of BCC_CSM gradually decreases, and the reproduced maximum circulation anomalies in the hindcasts initiated four weeks before SSW onset replicate only 10% of the circulation anomaly intensities in observations. The earliest successful prediction of the breakdown of the stratospheric polar vortex accompanying SSW onset for BCC_CSM(ECMWF) is the hindcast initiated two(three) weeks earlier. The predictive skills of both models during SSW winters are always higher than that during non-SSW winters, in relation to the successfully captured tropospheric precursors and the associated upward propagation of planetary waves by the model initializations. To narrow the gap in SSW predictive skill between BCC_CSM and ECMWF, ensemble forecasts and error corrections are performed with BCC_CSM. The SSW predictive skill in the ensemble hindcasts and the error corrections are improved compared with the previous control forecasts.     

GRAPES全球集合预报系统模式扰动随机动能补偿方案初步探究

为了体现次网格尺度能量升尺度转换过程中存在的不确定性, 文中将随机动能补偿(Stochastic Kinetic Energy Backscatter, SKEB)方案应用于GRAPES(Global/Regional Assimilation and Prediction System)全球集合预报系统(GRAPES-GEPS), 以更好地表征模式误差并且增大集合离散度。使用的SKEB方案基于具有一定时、空相关特征的随机型以及由数值扩散导致的局地动能耗散率来构造随机流函数强迫。并根据流函数与水平风速旋转分量的关系, 将SKEB方案中的流函数强迫转化为适用于GRAPES全球模式的水平风速扰动。结果表明, SKEB方案的使用一方面能够提高GRAPES对大气动能谱的模拟能力; 另一方面能够改善GRAPES-GEPS的集合离散度与集合平均误差的关系, 增加了集合离散度, 并在一定程度上减小了集合平均误差, 尤其是在热带地区这种改进更为显著。而且该方案使得热带地区连续分级概率评分(CRPS评分)显著减小。就降水预报而言, 从Brier评分与相对作用特征面积(AROC, Area under the Relative Operating Characteristics)的结果来看, SKEB方案有助于改善中国地区小雨[0.1 mm, 10 mm)、中雨[10 mm, 25 mm)与大雨[25 mm, 50 mm)量级降水的概率预报技巧, 而对暴雨[50 mm, ∞)量级降水预报技巧影响很小(24 h降水量)。总体上, 模式扰动随机动能补偿方案提高了GRAPES-GEPS的概率预报技巧。      

中尺度降水集合预报随机参数扰动方法敏感性试验

中尺度降水模式预报具有很大的不确定性,为更好地描述与模式降水预报密切相关的物理过程关键参数的不确定性,基于中国气象局GRAPES（Global/Regional Assimilation and Prediction System）中尺度区域集合预报模式,从对模式降水预报不确定性有较大影响的积云对流、云微物理、边界层及近地面层等4个参数化方案中选取了18个关键参数,设计了一种随机参数扰动方案（Stochastically Perturbed Parameterization,SPP）,并通过2015年6—7月总计10 d的随机扰动集合预报试验,对比分析了SPP方案对不同物理过程参数扰动敏感性、随机场时、空尺度敏感性、能量变化特征及其集合预报效果。结果显示,对所选择的任一物理过程参数化方案增加SPP扰动后,降水及等压面要素的概率预报技巧优于无SPP扰动的预报,而扰动积云对流和边界层过程中的参数较扰动云微物理过程中的参数影响更显著,且同时扰动积云对流、云微物理、边界层及近地面层参数化方案中的18个参数的集合预报效果优于扰动任何单一物理过程中的部分参数,表明SPP方案能够有效地提高中尺度降水概率预报技巧;从能量变化特征可知,不同物理过程的参数扰动对动能、内能和总能量的影响层次和特征有所不同,但总体而言,扰动前后各项能量基本相同;随机场时、空尺度敏感性试验发现,SPP扰动随机场时间、空间相关尺度对集合预报效果有明显影响,当扰动随机场选用12 h抗相关时间及截断波数20时,集合预报结果最优。上述结果表明,SPP随机参数扰动方案不仅能够有效提高集合概率预报效果,还能够提高集合降水概率预报技巧,具有良好的业务应用与发展前景。      

我国无缝隙精细化网格天气预报技术进展与挑战

本文总结了2014年以来我国无缝隙精细化网格天气预报业务的技术进展,讨论了未来发展所面临的关键技术难点。无缝隙精细化网格预报技术的发展,得益于综合气象观测数据和多源资料融合分析网格实况产品的支撑,更依赖于多尺度数值预报模式和实时快速更新同化预报系统的快速发展。经过近5年的探索和努力,我国已经初步建立了针对不同预报时效的无缝隙精细化网格预报技术体系。对于0～4 h预报时效,主要基于全国雷达拼图和GRAPES-Meso模式预报,发展临近分钟级滚动外推预报技术;对于4 h到30 d预报时效,主要通过对区域或全球不同时空分辨率模式预报进行偏差订正、客观解释应用以及降尺度分析,提高预报的准确度和精细度。与此同时,研发了自动化、智能化的交互式预报制作平台,以满足客观高效制作与预报员对极端或高影响天气主观预报优势相结合的需求。发展了以格点实况分析场为参照的空间分析检验方法,初步实现了对高分辨率网格预报的质量跟踪和性能评估。未来的网格预报技术体系,需要吸纳前沿的技术研究成果,包括人工智能应用技术、高级多模式统计后处理技术和协调一致性关键技术等,并且建立统一完整的技术架构和开发标准等。