Advances in Atmospheric Predictability of Heavy Rain and Severe Convection
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摘要: 大气可预报性研究是开展天气、气候预测的基础科学问题。全球变暖背景下，近年暴雨和强对流等中小尺度灾害性天气频发，如何深入认识其可预报性问题成为了天气领域研究热点，也是制约数值天气预报模式能力提升的重要因素。本文在简要回顾国内外大气可预报性研究历程的基础上，重点对近二十年（1999～2018）国际上关于暴雨和强对流可预报性方面的最新研究进展进行了系统的综述和归纳。主要包括：中小尺度可预报性研究的主要方法和评估手段及其与传统大尺度天气可预报性研究的差异，初始误差增长机制的几种主要观点及其争论（误差升尺度、误差降尺度、升降尺度并存），数值模式误差和对流环境误差对实际预报性的影响，以及最近的中尺度可预报性科学观测试验进展等。最后，对暴雨、强对流可预报性研究存在的问题、未来发展方向进行了简要的讨论和展望。Abstract: Atmospheric predictability research is the basis for weather and climate prediction. Under the background of global warming, meso/micro-scale extreme weather events such as heavy rain and severe convection have occurred more frequently in recent years, and their predictability has attracted wide attention. After briefly reviewing the history of atmospheric predictability research, this paper systematically reviews the latest advances in the predictability of heavy rain and strong convection over the last 20 years (1999–2018). The main research methods for meso/micro-scale predictability and their differences with traditional large-scale weather predictability methods are first discussed. Then, the primary initial error growth mechanism (error upscaling under deep moist convection) is elaborated in detail, and some arguments (error downscaling, error upscaling, and downscaling coexisting) are discussed. The effects of errors in NWP (Numerical Weather Prediction) models and convective environments on the practical predictability are also highlighted, and some recent mesoscale predictability experiments are reviewed. Finally, this paper briefly discusses the current problems, challenges, and future directions of the predictability research of heavy rain and severe convection.
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Key words:
- Heavy rain /
- Severe convection /
- Error growth /
- Ensemble forecast /
- Predictability
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图 1 大气可预报性研究主要事件进程示意图
Figure 1. Schematic of the major historical events in atmospheric predictability studies
图 2 减少集合离散度来降低初始误差展示（a）实际可预报性（2003年6月9～10日的飑线和弓形回波）和（b）内在可预报性（同等有利条件的理论集合预报）的理想示意图。阴影区：流体状态1（有利飑线形成），斜条纹区：流体状态2（不利飑线形成），黑点：集合预报成员，白点：集合平均，白十字：预报真值。[引自Melhauser and Zhang（2012）的图18]
Figure 2. Idealized schematic illustrating the reduction of initial condition error by reducing the ensemble spread highlighting the (a) practical predictability representative of the 9–10 June 2003 squall line and bow echo and (b) intrinsic predictability representative of a theoretical ensemble forecast with the ensemble forecast having equally favorable solutions. Solid shading: flow regime 1 (favorable for squall line forming); striped pattern: flow regime 2 (not favorable for squall line forming); black dots: ensemble members; white dots: ensemble mean; white cross: forecast truth. [Cited from Fig. 18 of Melhauser and Zhang (2012)]
图 3 区域平均偏差总能量在三个不同特征尺度的演变。纵坐标为区域偏差总能量；S、M、L分别代表小（波长
$\lambda $ <200 km）、中（200 km<$\lambda $ <1000 km）和大尺度（$\lambda $ >1000 km）；紫、绿、橙色区域代表对流尺度增长、升尺度转换、天气尺度增长阶段。[改自Zhang et al.（2007）的图7]Figure 3. Evolution of the domain-integrated difference total energy at three different characteristic scales. y-axis indicates difference total energy; S: smaller scale
$\lambda $ <200 km; M: intermediate scale 200 km<$\lambda $ <1000 km; and L: larger scale$\lambda $ >1000 km; Purple, green, orange regions represent convective growth, upscale transition, synoptic growth, respectively. [Modified from Fig. 7 of Zhang et al. (2007)]
图 4 改进的Lorenz模式中动能谱密度（“误差”）关于波数k的分布：（a）所有尺度的初始误差；（b）仅保留大尺度初始误差（去除波长<400 km的误差）；（c）仅保留小尺度初始误差（去除波长>400 km的误差）。黑色实线代表饱和时动能谱。[引自Durran and Gingrich（2014）的图6和图8]
Figure 4. Spectral density of kinetic energy as a function of wavenumber k for the improved Lorenz model: (a) Initial error at all scales; (b) initial error at large scales (initial error is removed at wavelengths less than 400 km); (c) initial error at small scales (initial error is removed at wavelengths greater than 400 km). The black curves show the saturation kinetic energy spectrum. [Cited from Figs. 6 and 8 of Durran and Gingrich (2014)]
图 5 （a）模式全部区域、（b）中尺度涡旋区、（c）中尺度对流系统区域、（d）非对流区在不同扰动方案下区域平均的误差能量（RMDTE）随时间的变化。TX_control：控制试验；TX_half：初始扰动减少至1/2；TX_third：初始扰动减少至1/3；TX_10th：初始扰动减少至1/10；TX_20th：初始扰动减少至1/20；TX_100th：初始扰动减少至1/100。[引自Nielsen and Schumacher（2016）的图13]
Figure 5. Temporal evolutions of domain-averaged root mean difference total energy (RMDTE) for the (a) full domain, (b) MCV (mesoscale convective vortex) region, (c) MCS (mesoscale convective system) region, and (d) NOCON (no convection) region in TX_control (control experiment), TX_half (the magnitude of initial perturbation was cut in half), TX_third (the magnitude of initial perturbation was cut in one-third), TX_10th (the magnitude of initial perturbation was cut in one-tenth), TX_20th (the magnitude of initial perturbation was cut in one-twentieth), and TX_100th (the magnitude of initial perturbation was cut in one percent). [Cited from Fig. 13 of Nielsen and Schumacher (2016)]
图 6 2015年5月19日08时（当地标准时间，下同）至20日08时（a）锋面暴雨区域（Region1）、（b）暖区暴雨区域（Region2）、（c）无强降水区（Region3）在不同扰动组合下区域平均的误差能量（RMDTE）随时间的变化。GTOP0-GTOP8：初始扰动差异试验；GTOP2-GTOP7：初始扰动差异减少至5/8；GTOP3-GTOP6：初始扰动差异减少至3/8；GTOP4-GTOP5：初始扰动差异减少至1/8。（d）根据降水量（单位：mm）的区域划分。[引自Wu et al.（2020）的图17]
Figure 6. Temporal evolutions of domain-averaged RMDTE for the (a) region of frontal torrential rainfall (Region1), (b) region of warm-sector torrential rainfall (Region2), and (c) region of no heavy rainfall (Region3) from 0800 LST (Local Standard Time) 19 May to 0800 LST 20 May 2015. GTOP0-GTOP8: experiment with initial perturbation difference; GTOP2-GTOP7: experiment with 5/8 initial perturbation difference; GTOP3-GTOP6: experiment with 3/8 initial perturbation difference; GTOP4-GTOP5: experiment with 1/8 initial perturbation difference. (d) The regional division according to the precipitation (units: mm). [Cited from Fig. 17 of Wu et al. (2020)]
图 7 基于1 h预报误差扰动的100个集合成员（a）30分钟预报、（b）60分钟预报、（c）90分钟预报、（d）120分钟预报的反射率等值线（40 dBZ）面条图。粗实线为控制预报；细线为不同成员预报。[引自Cintineo and Stensrud（2013）的图9]
Figure 7. Spaghetti plots of the 40-dBZ-reflectivity contours from all 100 simulations for the 1-h error runs at (a) 30 minutes, (b) 60 minutes, (c) 90 minutes, and (d) 120 minutes forecasts. [Cited from Fig. 9 of Cintineo and Stensrud (2013)]
图 8 （a）区域地形高度（单位：m），绿色框代表暖区暴雨中心区域，两黑色框区分别为山区2米高度处气温敏感区和海洋925-hPa低空风敏感区。2015年5月19日08时到20日08时好坏集合预报成员对（b）山区敏感区区域平均的2米高度处温度、（c）海洋敏感区区域平均的925 hPa风速预报的时间演变。[引自Wu et al.（2020）的图12]
Figure 8. (a) Terrain height (units: m), the green rectangle identifies the warm-sector torrential rainfall center, two black rectangles represent the sensitive area of 2-m temperature over the mountain area and the sensitive area of 925-hPa low-level wind over the sea, repetitively. Temporal evolution of (b) 2-m temperature averaged within the black rectangle region for the mountain area and (c) 925-hPa wind speed averaged within the black rectangle region for the sea area from 0800 LST 19 May to 0800 LST 20 May 2015. [Cited from Fig. 12 of Wu et al., (2020)]
图 9 MPEX试验的（a）早晨的下投作业的关注区域（数字星为下投点，红点为美国气象局业务探空点）和（b）午后前对流环境采样策略的探空位置（圆圈）示例。[引自Weisman et al.（2015）的图3和图5]
Figure 9. (a) Full domain of interest for MPEX (Mesoscale Predictability Experiment) morning dropsonde operations, numbered stars represent the dropsonde sites, and the operational National Weather Service sounding sites are indicated by the red dots. (b) Examples of upsonde locations (circles) for the preconvective environment sampling strategy in the afternoon during MPEX. [Cited from Figs. 3 and 5 of Weisman et al., (2015)]
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